rocksdb/tools/db_bench_tool.cc

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// Copyright (c) 2011-present, Facebook, Inc. All rights reserved.
// This source code is licensed under both the GPLv2 (found in the
// COPYING file in the root directory) and Apache 2.0 License
// (found in the LICENSE.Apache file in the root directory).
//
// Copyright (c) 2011 The LevelDB Authors. All rights reserved.
// Use of this source code is governed by a BSD-style license that can be
// found in the LICENSE file. See the AUTHORS file for names of contributors.
#ifdef GFLAGS
Adding NUMA support to db_bench tests Summary: Changes: - Adding numa_aware flag to db_bench.cc - Using numa.h library to bind memory and cpu of threads to a fixed NUMA node Result: There seems to be no significant change in the micros/op time with numa_aware enabled. I also tried this with other implementations, including a combination of pthread_setaffinity_np, sched_setaffinity and set_mempolicy methods. It'd be great if someone could point out where I'm going wrong and if we can achieve a better micors/op. Test Plan: Ran db_bench tests using following command: ./db_bench --db=/mnt/tmp --num_levels=6 --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --block_size=4096 --cache_size=17179869184 --cache_numshardbits=6 --compression_type=none --compression_ratio=1 --min_level_to_compress=-1 --disable_seek_compaction=1 --hard_rate_limit=2 --write_buffer_size=134217728 --max_write_buffer_number=2 --level0_file_num_compaction_trigger=8 --target_file_size_base=134217728 --max_bytes_for_level_base=1073741824 --disable_wal=0 --wal_dir=/mnt/tmp --sync=0 --disable_data_sync=1 --verify_checksum=1 --delete_obsolete_files_period_micros=314572800 --max_grandparent_overlap_factor=10 --max_background_compactions=4 --max_background_flushes=0 --level0_slowdown_writes_trigger=16 --level0_stop_writes_trigger=24 --statistics=0 --stats_per_interval=0 --stats_interval=1048576 --histogram=0 --use_plain_table=1 --open_files=-1 --mmap_read=1 --mmap_write=0 --memtablerep=prefix_hash --bloom_bits=10 --bloom_locality=1 --perf_level=0 --duration=300 --benchmarks=readwhilewriting --use_existing_db=1 --num=157286400 --threads=24 --writes_per_second=10240 --numa_aware=[False/True] The tests were run in private devserver with 24 cores and the db was prepopulated using filluniquerandom test. The tests resulted in 0.145 us/op with numa_aware=False and 0.161 us/op with numa_aware=True. Reviewers: sdong, yhchiang, ljin, igor Reviewed By: ljin, igor Subscribers: igor, leveldb Differential Revision: https://reviews.facebook.net/D19353
2014-07-07 19:53:31 +02:00
#ifdef NUMA
#include <numa.h>
#endif
#ifndef OS_WIN
#include <unistd.h>
#endif
#include <fcntl.h>
#include <stdio.h>
#include <stdlib.h>
#include <sys/types.h>
#ifdef __APPLE__
#include <mach/host_info.h>
#include <mach/mach_host.h>
#include <sys/sysctl.h>
#endif
#ifdef __FreeBSD__
#include <sys/sysctl.h>
#endif
#include <atomic>
#include <cinttypes>
#include <condition_variable>
#include <cstddef>
#include <memory>
#include <mutex>
Create fillanddeleteuniquerandom benchmark (db_bench), with new option flags. (#8593) Summary: Introduction of a new `fillanddeleteuniquerandom` benchmark (`db_bench`) with 5 new option flags to simulate a benchmark where the following sequence is repeated multiple times: "A set of keys S1 is inserted ('`disposable entries`'), then after some delay another set of keys S2 is inserted ('`persistent entries`') and the first set of keys S1 is deleted. S2 artificially represents the insertion of hypothetical results from some undefined computation done on the first set of keys S1. The next sequence can start as soon as the last disposable entry in the set S1 of this sequence is inserted, if the `delay` is non negligible." New flags: - `disposable_entries_delete_delay`: minimum delay in microseconds between insertion of the last `disposable` entry, and the start of the insertion of the first `persistent` entry. - `disposable_entries_batch_size`: number of `disposable` entries inserted at the beginning of each sequence. - `disposable_entries_value_size`: size of the random `value` string for the `disposable` entries. - `persistent_entries_batch_size`: number of `persistent` entries inserted at the end of each sequence, right before the deletion of the `disposable` entries starts. - `persistent_entries_value_size`: size of the random value string for the `persistent` entries. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8593 Reviewed By: pdillinger Differential Revision: D29974436 Pulled By: bjlemaire fbshipit-source-id: f578033e5b45e8268ba6fa6f38f4770c2e6e801d
2021-07-29 23:57:03 +02:00
#include <queue>
#include <thread>
#include <unordered_map>
#include "db/db_impl/db_impl.h"
#include "db/malloc_stats.h"
#include "db/version_set.h"
#include "hdfs/env_hdfs.h"
#include "monitoring/histogram.h"
#include "monitoring/statistics.h"
#include "options/cf_options.h"
#include "port/port.h"
#include "port/stack_trace.h"
#include "rocksdb/cache.h"
#include "rocksdb/convenience.h"
#include "rocksdb/db.h"
#include "rocksdb/env.h"
Implement full filter for block based table. Summary: 1. Make filter_block.h a base class. Derive block_based_filter_block and full_filter_block. The previous one is the traditional filter block. The full_filter_block is newly added. It would generate a filter block that contain all the keys in SST file. 2. When querying a key, table would first check if full_filter is available. If not, it would go to the exact data block and check using block_based filter. 3. User could choose to use full_filter or tradional(block_based_filter). They would be stored in SST file with different meta index name. "filter.filter_policy" or "full_filter.filter_policy". Then, Table reader is able to know the fllter block type. 4. Some optimizations have been done for full_filter_block, thus it requires a different interface compared to the original one in filter_policy.h. 5. Actual implementation of filter bits coding/decoding is placed in util/bloom_impl.cc Benchmark: base commit 1d23b5c470844c1208301311f0889eca750431c0 Command: db_bench --db=/dev/shm/rocksdb --num_levels=6 --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --write_buffer_size=134217728 --max_write_buffer_number=2 --target_file_size_base=33554432 --max_bytes_for_level_base=1073741824 --verify_checksum=false --max_background_compactions=4 --use_plain_table=0 --memtablerep=prefix_hash --open_files=-1 --mmap_read=1 --mmap_write=0 --bloom_bits=10 --bloom_locality=1 --memtable_bloom_bits=500000 --compression_type=lz4 --num=393216000 --use_hash_search=1 --block_size=1024 --block_restart_interval=16 --use_existing_db=1 --threads=1 --benchmarks=readrandom —disable_auto_compactions=1 Read QPS increase for about 30% from 2230002 to 2991411. Test Plan: make all check valgrind db_test db_stress --use_block_based_filter = 0 ./auto_sanity_test.sh Reviewers: igor, yhchiang, ljin, sdong Reviewed By: sdong Subscribers: dhruba, leveldb Differential Revision: https://reviews.facebook.net/D20979
2014-09-08 19:37:05 +02:00
#include "rocksdb/filter_policy.h"
#include "rocksdb/memtablerep.h"
#include "rocksdb/options.h"
#include "rocksdb/perf_context.h"
#include "rocksdb/persistent_cache.h"
#include "rocksdb/rate_limiter.h"
#include "rocksdb/secondary_cache.h"
#include "rocksdb/slice.h"
#include "rocksdb/slice_transform.h"
#include "rocksdb/stats_history.h"
#include "rocksdb/utilities/object_registry.h"
#include "rocksdb/utilities/optimistic_transaction_db.h"
#include "rocksdb/utilities/options_type.h"
#include "rocksdb/utilities/options_util.h"
#ifndef ROCKSDB_LITE
#include "rocksdb/utilities/replayer.h"
#endif // ROCKSDB_LITE
add simulator Cache as class SimCache/SimLRUCache(with test) Summary: add class SimCache(base class with instrumentation api) and SimLRUCache(derived class with detailed implementation) which is used as an instrumented block cache that can predict hit rate for different cache size Test Plan: Add a test case in `db_block_cache_test.cc` called `SimCacheTest` to test basic logic of SimCache. Also add option `-simcache_size` in db_bench. if set with a value other than -1, then the benchmark will use this value as the size of the simulator cache and finally output the simulation result. ``` [gzh@dev9927.prn1 ~/local/rocksdb] ./db_bench -benchmarks "fillseq,readrandom" -cache_size 1000000 -simcache_size 1000000 RocksDB: version 4.8 Date: Tue May 17 16:56:16 2016 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Compression: Snappy Memtablerep: skip_list Perf Level: 0 WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ DB path: [/tmp/rocksdbtest-112628/dbbench] fillseq : 6.809 micros/op 146874 ops/sec; 16.2 MB/s DB path: [/tmp/rocksdbtest-112628/dbbench] readrandom : 6.343 micros/op 157665 ops/sec; 17.4 MB/s (1000000 of 1000000 found) SIMULATOR CACHE STATISTICS: SimCache LOOKUPs: 986559 SimCache HITs: 264760 SimCache HITRATE: 26.84% [gzh@dev9927.prn1 ~/local/rocksdb] ./db_bench -benchmarks "fillseq,readrandom" -cache_size 1000000 -simcache_size 10000000 RocksDB: version 4.8 Date: Tue May 17 16:57:10 2016 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Compression: Snappy Memtablerep: skip_list Perf Level: 0 WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ DB path: [/tmp/rocksdbtest-112628/dbbench] fillseq : 5.066 micros/op 197394 ops/sec; 21.8 MB/s DB path: [/tmp/rocksdbtest-112628/dbbench] readrandom : 6.457 micros/op 154870 ops/sec; 17.1 MB/s (1000000 of 1000000 found) SIMULATOR CACHE STATISTICS: SimCache LOOKUPs: 1059764 SimCache HITs: 374501 SimCache HITRATE: 35.34% [gzh@dev9927.prn1 ~/local/rocksdb] ./db_bench -benchmarks "fillseq,readrandom" -cache_size 1000000 -simcache_size 100000000 RocksDB: version 4.8 Date: Tue May 17 16:57:32 2016 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Compression: Snappy Memtablerep: skip_list Perf Level: 0 WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ DB path: [/tmp/rocksdbtest-112628/dbbench] fillseq : 5.632 micros/op 177572 ops/sec; 19.6 MB/s DB path: [/tmp/rocksdbtest-112628/dbbench] readrandom : 6.892 micros/op 145094 ops/sec; 16.1 MB/s (1000000 of 1000000 found) SIMULATOR CACHE STATISTICS: SimCache LOOKUPs: 1150767 SimCache HITs: 1034535 SimCache HITRATE: 89.90% ``` Reviewers: IslamAbdelRahman, andrewkr, sdong Reviewed By: sdong Subscribers: MarkCallaghan, andrewkr, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D57999
2016-05-24 08:35:23 +02:00
#include "rocksdb/utilities/sim_cache.h"
#include "rocksdb/utilities/transaction.h"
#include "rocksdb/utilities/transaction_db.h"
#include "rocksdb/write_batch.h"
#include "test_util/testutil.h"
#include "test_util/transaction_test_util.h"
#include "tools/simulated_hybrid_file_system.h"
#include "util/cast_util.h"
2015-01-09 22:04:06 +01:00
#include "util/compression.h"
#include "util/crc32c.h"
#include "util/gflags_compat.h"
#include "util/mutexlock.h"
#include "util/random.h"
#include "util/stderr_logger.h"
#include "util/string_util.h"
#include "util/xxhash.h"
#include "utilities/blob_db/blob_db.h"
#include "utilities/merge_operators.h"
#include "utilities/merge_operators/bytesxor.h"
New API to get all merge operands for a Key (#5604) Summary: This is a new API added to db.h to allow for fetching all merge operands associated with a Key. The main motivation for this API is to support use cases where doing a full online merge is not necessary as it is performance sensitive. Example use-cases: 1. Update subset of columns and read subset of columns - Imagine a SQL Table, a row is encoded as a K/V pair (as it is done in MyRocks). If there are many columns and users only updated one of them, we can use merge operator to reduce write amplification. While users only read one or two columns in the read query, this feature can avoid a full merging of the whole row, and save some CPU. 2. Updating very few attributes in a value which is a JSON-like document - Updating one attribute can be done efficiently using merge operator, while reading back one attribute can be done more efficiently if we don't need to do a full merge. ---------------------------------------------------------------------------------------------------- API : Status GetMergeOperands( const ReadOptions& options, ColumnFamilyHandle* column_family, const Slice& key, PinnableSlice* merge_operands, GetMergeOperandsOptions* get_merge_operands_options, int* number_of_operands) Example usage : int size = 100; int number_of_operands = 0; std::vector<PinnableSlice> values(size); GetMergeOperandsOptions merge_operands_info; db_->GetMergeOperands(ReadOptions(), db_->DefaultColumnFamily(), "k1", values.data(), merge_operands_info, &number_of_operands); Description : Returns all the merge operands corresponding to the key. If the number of merge operands in DB is greater than merge_operands_options.expected_max_number_of_operands no merge operands are returned and status is Incomplete. Merge operands returned are in the order of insertion. merge_operands-> Points to an array of at-least merge_operands_options.expected_max_number_of_operands and the caller is responsible for allocating it. If the status returned is Incomplete then number_of_operands will contain the total number of merge operands found in DB for key. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5604 Test Plan: Added unit test and perf test in db_bench that can be run using the command: ./db_bench -benchmarks=getmergeoperands --merge_operator=sortlist Differential Revision: D16657366 Pulled By: vjnadimpalli fbshipit-source-id: 0faadd752351745224ee12d4ae9ef3cb529951bf
2019-08-06 23:22:34 +02:00
#include "utilities/merge_operators/sortlist.h"
#include "utilities/persistent_cache/block_cache_tier.h"
Provide an allocator for new memory type to be used with RocksDB block cache (#6214) Summary: New memory technologies are being developed by various hardware vendors (Intel DCPMM is one such technology currently available). These new memory types require different libraries for allocation and management (such as PMDK and memkind). The high capacities available make it possible to provision large caches (up to several TBs in size), beyond what is achievable with DRAM. The new allocator provided in this PR uses the memkind library to allocate memory on different media. **Performance** We tested the new allocator using db_bench. - For each test, we vary the size of the block cache (relative to the size of the uncompressed data in the database). - The database is filled sequentially. Throughput is then measured with a readrandom benchmark. - We use a uniform distribution as a worst-case scenario. The plot shows throughput (ops/s) relative to a configuration with no block cache and default allocator. For all tests, p99 latency is below 500 us. ![image](https://user-images.githubusercontent.com/26400080/71108594-42479100-2178-11ea-8231-8a775bbc92db.png) **Changes** - Add MemkindKmemAllocator - Add --use_cache_memkind_kmem_allocator db_bench option (to create an LRU block cache with the new allocator) - Add detection of memkind library with KMEM DAX support - Add test for MemkindKmemAllocator **Minimum Requirements** - kernel 5.3.12 - ndctl v67 - https://github.com/pmem/ndctl - memkind v1.10.0 - https://github.com/memkind/memkind **Memory Configuration** The allocator uses the MEMKIND_DAX_KMEM memory kind. Follow the instructions on[ memkind’s GitHub page](https://github.com/memkind/memkind) to set up NVDIMM memory accordingly. Note on memory allocation with NVDIMM memory exposed as system memory. - The MemkindKmemAllocator will only allocate from NVDIMM memory (using memkind_malloc with MEMKIND_DAX_KMEM kind). - The default allocator is not restricted to RAM by default. Based on NUMA node latency, the kernel should allocate from local RAM preferentially, but it’s a kernel decision. numactl --preferred/--membind can be used to allocate preferentially/exclusively from the local RAM node. **Usage** When creating an LRU cache, pass a MemkindKmemAllocator object as argument. For example (replace capacity with the desired value in bytes): ``` #include "rocksdb/cache.h" #include "memory/memkind_kmem_allocator.h" NewLRUCache( capacity /*size_t*/, 6 /*cache_numshardbits*/, false /*strict_capacity_limit*/, false /*cache_high_pri_pool_ratio*/, std::make_shared<MemkindKmemAllocator>()); ``` Refer to [RocksDB’s block cache documentation](https://github.com/facebook/rocksdb/wiki/Block-Cache) to assign the LRU cache as block cache for a database. Pull Request resolved: https://github.com/facebook/rocksdb/pull/6214 Reviewed By: cheng-chang Differential Revision: D19292435 fbshipit-source-id: 7202f47b769e7722b539c86c2ffd669f64d7b4e1
2020-04-10 05:45:17 +02:00
#ifdef MEMKIND
#include "memory/memkind_kmem_allocator.h"
#endif
#ifdef OS_WIN
#include <io.h> // open/close
#endif
using GFLAGS_NAMESPACE::ParseCommandLineFlags;
using GFLAGS_NAMESPACE::RegisterFlagValidator;
using GFLAGS_NAMESPACE::SetUsageMessage;
#ifdef ROCKSDB_LITE
#define IF_ROCKSDB_LITE(Then, Else) Then
#else
#define IF_ROCKSDB_LITE(Then, Else) Else
#endif
DEFINE_string(
benchmarks,
"fillseq,"
"fillseqdeterministic,"
"fillsync,"
"fillrandom,"
"filluniquerandomdeterministic,"
"overwrite,"
"readrandom,"
"newiterator,"
"newiteratorwhilewriting,"
"seekrandom,"
"seekrandomwhilewriting,"
"seekrandomwhilemerging,"
"readseq,"
"readreverse,"
"compact,"
"compactall,"
"flush,"
IF_ROCKSDB_LITE("",
"compact0,"
"compact1,"
"waitforcompaction,"
)
"multireadrandom,"
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 19:40:44 +01:00
"mixgraph,"
"readseq,"
"readtorowcache,"
"readtocache,"
"readreverse,"
"readwhilewriting,"
"readwhilemerging,"
"readwhilescanning,"
"readrandomwriterandom,"
"updaterandom,"
"xorupdaterandom,"
For ApproximateSizes, pro-rate table metadata size over data blocks (#6784) Summary: The implementation of GetApproximateSizes was inconsistent in its treatment of the size of non-data blocks of SST files, sometimes including and sometimes now. This was at its worst with large portion of table file used by filters and querying a small range that crossed a table boundary: the size estimate would include large filter size. It's conceivable that someone might want only to know the size in terms of data blocks, but I believe that's unlikely enough to ignore for now. Similarly, there's no evidence the internal function AppoximateOffsetOf is used for anything other than a one-sided ApproximateSize, so I intend to refactor to remove redundancy in a follow-up commit. So to fix this, GetApproximateSizes (and implementation details ApproximateSize and ApproximateOffsetOf) now consistently include in their returned sizes a portion of table file metadata (incl filters and indexes) based on the size portion of the data blocks in range. In other words, if a key range covers data blocks that are X% by size of all the table's data blocks, returned approximate size is X% of the total file size. It would technically be more accurate to attribute metadata based on number of keys, but that's not computationally efficient with data available and rarely a meaningful difference. Also includes miscellaneous comment improvements / clarifications. Also included is a new approximatesizerandom benchmark for db_bench. No significant performance difference seen with this change, whether ~700 ops/sec with cache_index_and_filter_blocks and small cache or ~150k ops/sec without cache_index_and_filter_blocks. Pull Request resolved: https://github.com/facebook/rocksdb/pull/6784 Test Plan: Test added to DBTest.ApproximateSizesFilesWithErrorMargin. Old code running new test... [ RUN ] DBTest.ApproximateSizesFilesWithErrorMargin db/db_test.cc:1562: Failure Expected: (size) <= (11 * 100), actual: 9478 vs 1100 Other tests updated to reflect consistent accounting of metadata. Reviewed By: siying Differential Revision: D21334706 Pulled By: pdillinger fbshipit-source-id: 6f86870e45213334fedbe9c73b4ebb1d8d611185
2020-06-02 21:27:59 +02:00
"approximatesizerandom,"
"randomwithverify,"
"fill100K,"
"crc32c,"
"xxhash,"
"compress,"
"uncompress,"
"acquireload,"
"fillseekseq,"
"randomtransaction,"
"randomreplacekeys,"
New API to get all merge operands for a Key (#5604) Summary: This is a new API added to db.h to allow for fetching all merge operands associated with a Key. The main motivation for this API is to support use cases where doing a full online merge is not necessary as it is performance sensitive. Example use-cases: 1. Update subset of columns and read subset of columns - Imagine a SQL Table, a row is encoded as a K/V pair (as it is done in MyRocks). If there are many columns and users only updated one of them, we can use merge operator to reduce write amplification. While users only read one or two columns in the read query, this feature can avoid a full merging of the whole row, and save some CPU. 2. Updating very few attributes in a value which is a JSON-like document - Updating one attribute can be done efficiently using merge operator, while reading back one attribute can be done more efficiently if we don't need to do a full merge. ---------------------------------------------------------------------------------------------------- API : Status GetMergeOperands( const ReadOptions& options, ColumnFamilyHandle* column_family, const Slice& key, PinnableSlice* merge_operands, GetMergeOperandsOptions* get_merge_operands_options, int* number_of_operands) Example usage : int size = 100; int number_of_operands = 0; std::vector<PinnableSlice> values(size); GetMergeOperandsOptions merge_operands_info; db_->GetMergeOperands(ReadOptions(), db_->DefaultColumnFamily(), "k1", values.data(), merge_operands_info, &number_of_operands); Description : Returns all the merge operands corresponding to the key. If the number of merge operands in DB is greater than merge_operands_options.expected_max_number_of_operands no merge operands are returned and status is Incomplete. Merge operands returned are in the order of insertion. merge_operands-> Points to an array of at-least merge_operands_options.expected_max_number_of_operands and the caller is responsible for allocating it. If the status returned is Incomplete then number_of_operands will contain the total number of merge operands found in DB for key. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5604 Test Plan: Added unit test and perf test in db_bench that can be run using the command: ./db_bench -benchmarks=getmergeoperands --merge_operator=sortlist Differential Revision: D16657366 Pulled By: vjnadimpalli fbshipit-source-id: 0faadd752351745224ee12d4ae9ef3cb529951bf
2019-08-06 23:22:34 +02:00
"timeseries,"
"getmergeoperands",
"Comma-separated list of operations to run in the specified"
" order. Available benchmarks:\n"
"\tfillseq -- write N values in sequential key"
" order in async mode\n"
"\tfillseqdeterministic -- write N values in the specified"
" key order and keep the shape of the LSM tree\n"
"\tfillrandom -- write N values in random key order in async"
" mode\n"
"\tfilluniquerandomdeterministic -- write N values in a random"
" key order and keep the shape of the LSM tree\n"
"\toverwrite -- overwrite N values in random key order in"
" async mode\n"
"\tfillsync -- write N/1000 values in random key order in "
"sync mode\n"
"\tfill100K -- write N/1000 100K values in random order in"
" async mode\n"
"\tdeleteseq -- delete N keys in sequential order\n"
"\tdeleterandom -- delete N keys in random order\n"
"\treadseq -- read N times sequentially\n"
"\treadtocache -- 1 thread reading database sequentially\n"
"\treadreverse -- read N times in reverse order\n"
"\treadrandom -- read N times in random order\n"
"\treadmissing -- read N missing keys in random order\n"
"\treadwhilewriting -- 1 writer, N threads doing random "
"reads\n"
"\treadwhilemerging -- 1 merger, N threads doing random "
"reads\n"
"\treadwhilescanning -- 1 thread doing full table scan, "
"N threads doing random reads\n"
"\treadrandomwriterandom -- N threads doing random-read, "
"random-write\n"
"\tupdaterandom -- N threads doing read-modify-write for random "
"keys\n"
"\txorupdaterandom -- N threads doing read-XOR-write for "
"random keys\n"
"\tappendrandom -- N threads doing read-modify-write with "
"growing values\n"
"\tmergerandom -- same as updaterandom/appendrandom using merge"
" operator. "
"Must be used with merge_operator\n"
"\treadrandommergerandom -- perform N random read-or-merge "
"operations. Must be used with merge_operator\n"
"\tnewiterator -- repeated iterator creation\n"
"\tseekrandom -- N random seeks, call Next seek_nexts times "
"per seek\n"
"\tseekrandomwhilewriting -- seekrandom and 1 thread doing "
"overwrite\n"
"\tseekrandomwhilemerging -- seekrandom and 1 thread doing "
"merge\n"
"\tcrc32c -- repeated crc32c of 4K of data\n"
"\txxhash -- repeated xxHash of 4K of data\n"
"\tacquireload -- load N*1000 times\n"
"\tfillseekseq -- write N values in sequential key, then read "
"them by seeking to each key\n"
"\trandomtransaction -- execute N random transactions and "
"verify correctness\n"
"\trandomreplacekeys -- randomly replaces N keys by deleting "
"the old version and putting the new version\n\n"
"\ttimeseries -- 1 writer generates time series data "
"and multiple readers doing random reads on id\n\n"
"Meta operations:\n"
"\tcompact -- Compact the entire DB; If multiple, randomly choose one\n"
"\tcompactall -- Compact the entire DB\n"
IF_ROCKSDB_LITE("",
"\tcompact0 -- compact L0 into L1\n"
"\tcompact1 -- compact L1 into L2\n"
"\twaitforcompaction - pause until compaction is (probably) done\n"
)
"\tflush - flush the memtable\n"
"\tstats -- Print DB stats\n"
"\tresetstats -- Reset DB stats\n"
"\tlevelstats -- Print the number of files and bytes per level\n"
"\tmemstats -- Print memtable stats\n"
"\tsstables -- Print sstable info\n"
"\theapprofile -- Dump a heap profile (if supported by this port)\n"
#ifndef ROCKSDB_LITE
New API to get all merge operands for a Key (#5604) Summary: This is a new API added to db.h to allow for fetching all merge operands associated with a Key. The main motivation for this API is to support use cases where doing a full online merge is not necessary as it is performance sensitive. Example use-cases: 1. Update subset of columns and read subset of columns - Imagine a SQL Table, a row is encoded as a K/V pair (as it is done in MyRocks). If there are many columns and users only updated one of them, we can use merge operator to reduce write amplification. While users only read one or two columns in the read query, this feature can avoid a full merging of the whole row, and save some CPU. 2. Updating very few attributes in a value which is a JSON-like document - Updating one attribute can be done efficiently using merge operator, while reading back one attribute can be done more efficiently if we don't need to do a full merge. ---------------------------------------------------------------------------------------------------- API : Status GetMergeOperands( const ReadOptions& options, ColumnFamilyHandle* column_family, const Slice& key, PinnableSlice* merge_operands, GetMergeOperandsOptions* get_merge_operands_options, int* number_of_operands) Example usage : int size = 100; int number_of_operands = 0; std::vector<PinnableSlice> values(size); GetMergeOperandsOptions merge_operands_info; db_->GetMergeOperands(ReadOptions(), db_->DefaultColumnFamily(), "k1", values.data(), merge_operands_info, &number_of_operands); Description : Returns all the merge operands corresponding to the key. If the number of merge operands in DB is greater than merge_operands_options.expected_max_number_of_operands no merge operands are returned and status is Incomplete. Merge operands returned are in the order of insertion. merge_operands-> Points to an array of at-least merge_operands_options.expected_max_number_of_operands and the caller is responsible for allocating it. If the status returned is Incomplete then number_of_operands will contain the total number of merge operands found in DB for key. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5604 Test Plan: Added unit test and perf test in db_bench that can be run using the command: ./db_bench -benchmarks=getmergeoperands --merge_operator=sortlist Differential Revision: D16657366 Pulled By: vjnadimpalli fbshipit-source-id: 0faadd752351745224ee12d4ae9ef3cb529951bf
2019-08-06 23:22:34 +02:00
"\treplay -- replay the trace file specified with trace_file\n"
#endif // ROCKSDB_LITE
New API to get all merge operands for a Key (#5604) Summary: This is a new API added to db.h to allow for fetching all merge operands associated with a Key. The main motivation for this API is to support use cases where doing a full online merge is not necessary as it is performance sensitive. Example use-cases: 1. Update subset of columns and read subset of columns - Imagine a SQL Table, a row is encoded as a K/V pair (as it is done in MyRocks). If there are many columns and users only updated one of them, we can use merge operator to reduce write amplification. While users only read one or two columns in the read query, this feature can avoid a full merging of the whole row, and save some CPU. 2. Updating very few attributes in a value which is a JSON-like document - Updating one attribute can be done efficiently using merge operator, while reading back one attribute can be done more efficiently if we don't need to do a full merge. ---------------------------------------------------------------------------------------------------- API : Status GetMergeOperands( const ReadOptions& options, ColumnFamilyHandle* column_family, const Slice& key, PinnableSlice* merge_operands, GetMergeOperandsOptions* get_merge_operands_options, int* number_of_operands) Example usage : int size = 100; int number_of_operands = 0; std::vector<PinnableSlice> values(size); GetMergeOperandsOptions merge_operands_info; db_->GetMergeOperands(ReadOptions(), db_->DefaultColumnFamily(), "k1", values.data(), merge_operands_info, &number_of_operands); Description : Returns all the merge operands corresponding to the key. If the number of merge operands in DB is greater than merge_operands_options.expected_max_number_of_operands no merge operands are returned and status is Incomplete. Merge operands returned are in the order of insertion. merge_operands-> Points to an array of at-least merge_operands_options.expected_max_number_of_operands and the caller is responsible for allocating it. If the status returned is Incomplete then number_of_operands will contain the total number of merge operands found in DB for key. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5604 Test Plan: Added unit test and perf test in db_bench that can be run using the command: ./db_bench -benchmarks=getmergeoperands --merge_operator=sortlist Differential Revision: D16657366 Pulled By: vjnadimpalli fbshipit-source-id: 0faadd752351745224ee12d4ae9ef3cb529951bf
2019-08-06 23:22:34 +02:00
"\tgetmergeoperands -- Insert lots of merge records which are a list of "
"sorted ints for a key and then compare performance of lookup for another "
"key "
"by doing a Get followed by binary searching in the large sorted list vs "
"doing a GetMergeOperands and binary searching in the operands which are"
"sorted sub-lists. The MergeOperator used is sortlist.h\n");
DEFINE_int64(num, 1000000, "Number of key/values to place in database");
DEFINE_int64(numdistinct, 1000,
"Number of distinct keys to use. Used in RandomWithVerify to "
"read/write on fewer keys so that gets are more likely to find the"
" key and puts are more likely to update the same key");
DEFINE_int64(merge_keys, -1,
"Number of distinct keys to use for MergeRandom and "
"ReadRandomMergeRandom. "
"If negative, there will be FLAGS_num keys.");
DEFINE_int32(num_column_families, 1, "Number of Column Families to use.");
DEFINE_int32(
num_hot_column_families, 0,
"Number of Hot Column Families. If more than 0, only write to this "
"number of column families. After finishing all the writes to them, "
"create new set of column families and insert to them. Only used "
"when num_column_families > 1.");
DEFINE_string(column_family_distribution, "",
"Comma-separated list of percentages, where the ith element "
"indicates the probability of an op using the ith column family. "
"The number of elements must be `num_hot_column_families` if "
"specified; otherwise, it must be `num_column_families`. The "
"sum of elements must be 100. E.g., if `num_column_families=4`, "
"and `num_hot_column_families=0`, a valid list could be "
"\"10,20,30,40\".");
DEFINE_int64(reads, -1, "Number of read operations to do. "
"If negative, do FLAGS_num reads.");
DEFINE_int64(deletes, -1, "Number of delete operations to do. "
"If negative, do FLAGS_num deletions.");
DEFINE_int32(bloom_locality, 0, "Control bloom filter probes locality");
DEFINE_int64(seed, 0, "Seed base for random number generators. "
"When 0 it is deterministic.");
DEFINE_int32(threads, 1, "Number of concurrent threads to run.");
DEFINE_int32(duration, 0, "Time in seconds for the random-ops tests to run."
" When 0 then num & reads determine the test duration");
DEFINE_string(value_size_distribution_type, "fixed",
"Value size distribution type: fixed, uniform, normal");
DEFINE_int32(value_size, 100, "Size of each value in fixed distribution");
static unsigned int value_size = 100;
DEFINE_int32(value_size_min, 100, "Min size of random value");
DEFINE_int32(value_size_max, 102400, "Max size of random value");
DEFINE_int32(seek_nexts, 0,
"How many times to call Next() after Seek() in "
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
2015-03-30 20:28:25 +02:00
"fillseekseq, seekrandom, seekrandomwhilewriting and "
"seekrandomwhilemerging");
SkipListRep::LookaheadIterator Summary: This diff introduces the `lookahead` argument to `SkipListFactory()`. This is an optimization for the tailing use case which includes many seeks. E.g. consider the following operations on a skip list iterator: Seek(x), Next(), Next(), Seek(x+2), Next(), Seek(x+3), Next(), Next(), ... If `lookahead` is positive, `SkipListRep` will return an iterator which also keeps track of the previously visited node. Seek() then first does a linear search starting from that node (up to `lookahead` steps). As in the tailing example above, this may require fewer than ~log(n) comparisons as with regular skip list search. Test Plan: Added a new benchmark (`fillseekseq`) which simulates the usage pattern. It first writes N records (with consecutive keys), then measures how much time it takes to read them by calling `Seek()` and `Next()`. $ time ./db_bench -num 10000000 -benchmarks fillseekseq -prefix_size 1 \ -key_size 8 -write_buffer_size $[1024*1024*1024] -value_size 50 \ -seekseq_next 2 -skip_list_lookahead=0 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.389 micros/op 2569047 ops/sec; real 0m21.806s user 0m12.106s sys 0m9.672s $ time ./db_bench [...] -skip_list_lookahead=2 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.153 micros/op 6540684 ops/sec; real 0m19.469s user 0m10.192s sys 0m9.252s Reviewers: ljin, sdong, igor Reviewed By: igor Subscribers: dhruba, leveldb, march, lovro Differential Revision: https://reviews.facebook.net/D23997
2014-09-24 00:52:28 +02:00
DEFINE_bool(reverse_iterator, false,
"When true use Prev rather than Next for iterators that do "
"Seek and then Next");
DEFINE_int64(max_scan_distance, 0,
"Used to define iterate_upper_bound (or iterate_lower_bound "
"if FLAGS_reverse_iterator is set to true) when value is nonzero");
DEFINE_bool(use_uint64_comparator, false, "use Uint64 user comparator");
DEFINE_int64(batch_size, 1, "Batch size");
static bool ValidateKeySize(const char* /*flagname*/, int32_t /*value*/) {
return true;
}
static bool ValidateUint32Range(const char* flagname, uint64_t value) {
if (value > std::numeric_limits<uint32_t>::max()) {
fprintf(stderr, "Invalid value for --%s: %lu, overflow\n", flagname,
(unsigned long)value);
return false;
}
return true;
}
DEFINE_int32(key_size, 16, "size of each key");
DEFINE_int32(user_timestamp_size, 0,
"number of bytes in a user-defined timestamp");
DEFINE_int32(num_multi_db, 0,
"Number of DBs used in the benchmark. 0 means single DB.");
DEFINE_double(compression_ratio, 0.5, "Arrange to generate values that shrink"
" to this fraction of their original size after compression");
Add overwrite_probability for filluniquerandom benchmark in db_bench (#8569) Summary: Add flags `overwrite_probability` and `overwrite_window_size` flag to `db_bench`. Add the possibility of performing a `filluniquerandom` benchmark with an overwrite probability. For each write operation, there is a probability _p_ that the write is an overwrite (_p_=`overwrite_probability`). When an overwrite is decided, the key is randomly chosen from the last _N_ keys previously inserted into the DB (with _N_=`overwrite_window_size`). When a pure write is decided, the key inserted into the DB is unique and therefore will not be an overwrite. The `overwrite_window_size` is used so that the user can decide if the overwrite are mostly targeting recently inserted keys (when `overwrite_window_size` is small compared to the total number of writes), or can also target keys inserted "a long time ago" (when `overwrite_window_size` is comparable to total number of writes). Note that total number of writes = # of unique insertions + # of overwrites. No unit test specifically added. Local testing show the following **throughputs** for `filluniquerandom` with 1M total writes: - bypass the code inserts (no `overwrite_probability` flag specified): ~14.0MB/s - `overwrite_probability=0.99`, `overwrite_window_size=10`: ~17.0MB/s - `overwrite_probability=0.10`, `overwrite_window_size=10`: ~14.0MB/s - `overwrite_probability=0.99`, `overwrite_window_size=1M`: ~14.5MB/s - `overwrite_probability=0.10`, `overwrite_window_size=1M`: ~14.0MB/s Pull Request resolved: https://github.com/facebook/rocksdb/pull/8569 Reviewed By: pdillinger Differential Revision: D29818631 Pulled By: bjlemaire fbshipit-source-id: d472b4ea4e457a4da7c4ee4f14b40cccd6a4587a
2021-07-21 20:32:36 +02:00
DEFINE_double(
overwrite_probability, 0.0,
"Used in 'filluniquerandom' benchmark: for each write operation, "
"we give a probability to perform an overwrite instead. The key used for "
"the overwrite is randomly chosen from the last 'overwrite_window_size' "
"keys "
"previously inserted into the DB. "
"Valid overwrite_probability values: [0.0, 1.0].");
DEFINE_uint32(overwrite_window_size, 1,
"Used in 'filluniquerandom' benchmark. For each write "
"operation, when "
"the overwrite_probability flag is set by the user, the key used "
"to perform "
"an overwrite is randomly chosen from the last "
"'overwrite_window_size' keys "
"previously inserted into the DB. "
"Warning: large values can affect throughput. "
"Valid overwrite_window_size values: [1, kMaxUint32].");
Create fillanddeleteuniquerandom benchmark (db_bench), with new option flags. (#8593) Summary: Introduction of a new `fillanddeleteuniquerandom` benchmark (`db_bench`) with 5 new option flags to simulate a benchmark where the following sequence is repeated multiple times: "A set of keys S1 is inserted ('`disposable entries`'), then after some delay another set of keys S2 is inserted ('`persistent entries`') and the first set of keys S1 is deleted. S2 artificially represents the insertion of hypothetical results from some undefined computation done on the first set of keys S1. The next sequence can start as soon as the last disposable entry in the set S1 of this sequence is inserted, if the `delay` is non negligible." New flags: - `disposable_entries_delete_delay`: minimum delay in microseconds between insertion of the last `disposable` entry, and the start of the insertion of the first `persistent` entry. - `disposable_entries_batch_size`: number of `disposable` entries inserted at the beginning of each sequence. - `disposable_entries_value_size`: size of the random `value` string for the `disposable` entries. - `persistent_entries_batch_size`: number of `persistent` entries inserted at the end of each sequence, right before the deletion of the `disposable` entries starts. - `persistent_entries_value_size`: size of the random value string for the `persistent` entries. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8593 Reviewed By: pdillinger Differential Revision: D29974436 Pulled By: bjlemaire fbshipit-source-id: f578033e5b45e8268ba6fa6f38f4770c2e6e801d
2021-07-29 23:57:03 +02:00
DEFINE_uint64(
disposable_entries_delete_delay, 0,
"Minimum delay in microseconds for the series of Deletes "
"to be issued. When 0 the insertion of the last disposable entry is "
"immediately followed by the issuance of the Deletes. "
"(only compatible with fillanddeleteuniquerandom benchmark).");
DEFINE_uint64(disposable_entries_batch_size, 0,
"Number of consecutively inserted disposable KV entries "
"that will be deleted after 'delete_delay' microseconds. "
"A series of Deletes is always issued once all the "
"disposable KV entries it targets have been inserted "
"into the DB. When 0 no deletes are issued and a "
"regular 'filluniquerandom' benchmark occurs. "
"(only compatible with fillanddeleteuniquerandom benchmark)");
DEFINE_int32(disposable_entries_value_size, 64,
"Size of the values (in bytes) of the entries targeted by "
"selective deletes. "
"(only compatible with fillanddeleteuniquerandom benchmark)");
DEFINE_uint64(
persistent_entries_batch_size, 0,
"Number of KV entries being inserted right before the deletes "
"targeting the disposable KV entries are issued. These "
"persistent keys are not targeted by the deletes, and will always "
"remain valid in the DB. (only compatible with "
"--benchmarks='fillanddeleteuniquerandom' "
"and used when--disposable_entries_batch_size is > 0).");
DEFINE_int32(persistent_entries_value_size, 64,
"Size of the values (in bytes) of the entries not targeted by "
"deletes. (only compatible with "
"--benchmarks='fillanddeleteuniquerandom' "
"and used when--disposable_entries_batch_size is > 0).");
DEFINE_double(read_random_exp_range, 0.0,
"Read random's key will be generated using distribution of "
"num * exp(-r) where r is uniform number from 0 to this value. "
"The larger the number is, the more skewed the reads are. "
"Only used in readrandom and multireadrandom benchmarks.");
DEFINE_bool(histogram, false, "Print histogram of operation timings");
Adding NUMA support to db_bench tests Summary: Changes: - Adding numa_aware flag to db_bench.cc - Using numa.h library to bind memory and cpu of threads to a fixed NUMA node Result: There seems to be no significant change in the micros/op time with numa_aware enabled. I also tried this with other implementations, including a combination of pthread_setaffinity_np, sched_setaffinity and set_mempolicy methods. It'd be great if someone could point out where I'm going wrong and if we can achieve a better micors/op. Test Plan: Ran db_bench tests using following command: ./db_bench --db=/mnt/tmp --num_levels=6 --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --block_size=4096 --cache_size=17179869184 --cache_numshardbits=6 --compression_type=none --compression_ratio=1 --min_level_to_compress=-1 --disable_seek_compaction=1 --hard_rate_limit=2 --write_buffer_size=134217728 --max_write_buffer_number=2 --level0_file_num_compaction_trigger=8 --target_file_size_base=134217728 --max_bytes_for_level_base=1073741824 --disable_wal=0 --wal_dir=/mnt/tmp --sync=0 --disable_data_sync=1 --verify_checksum=1 --delete_obsolete_files_period_micros=314572800 --max_grandparent_overlap_factor=10 --max_background_compactions=4 --max_background_flushes=0 --level0_slowdown_writes_trigger=16 --level0_stop_writes_trigger=24 --statistics=0 --stats_per_interval=0 --stats_interval=1048576 --histogram=0 --use_plain_table=1 --open_files=-1 --mmap_read=1 --mmap_write=0 --memtablerep=prefix_hash --bloom_bits=10 --bloom_locality=1 --perf_level=0 --duration=300 --benchmarks=readwhilewriting --use_existing_db=1 --num=157286400 --threads=24 --writes_per_second=10240 --numa_aware=[False/True] The tests were run in private devserver with 24 cores and the db was prepopulated using filluniquerandom test. The tests resulted in 0.145 us/op with numa_aware=False and 0.161 us/op with numa_aware=True. Reviewers: sdong, yhchiang, ljin, igor Reviewed By: ljin, igor Subscribers: igor, leveldb Differential Revision: https://reviews.facebook.net/D19353
2014-07-07 19:53:31 +02:00
DEFINE_bool(enable_numa, false,
"Make operations aware of NUMA architecture and bind memory "
"and cpus corresponding to nodes together. In NUMA, memory "
"in same node as CPUs are closer when compared to memory in "
"other nodes. Reads can be faster when the process is bound to "
"CPU and memory of same node. Use \"$numactl --hardware\" command "
"to see NUMA memory architecture.");
DEFINE_int64(db_write_buffer_size,
ROCKSDB_NAMESPACE::Options().db_write_buffer_size,
"Number of bytes to buffer in all memtables before compacting");
DEFINE_bool(cost_write_buffer_to_cache, false,
"The usage of memtable is costed to the block cache");
DEFINE_int64(arena_block_size, ROCKSDB_NAMESPACE::Options().arena_block_size,
"The size, in bytes, of one block in arena memory allocation.");
DEFINE_int64(write_buffer_size, ROCKSDB_NAMESPACE::Options().write_buffer_size,
"Number of bytes to buffer in memtable before compacting");
DEFINE_int32(max_write_buffer_number,
ROCKSDB_NAMESPACE::Options().max_write_buffer_number,
"The number of in-memory memtables. Each memtable is of size"
" write_buffer_size bytes.");
DEFINE_int32(min_write_buffer_number_to_merge,
ROCKSDB_NAMESPACE::Options().min_write_buffer_number_to_merge,
"The minimum number of write buffers that will be merged together"
"before writing to storage. This is cheap because it is an"
"in-memory merge. If this feature is not enabled, then all these"
"write buffers are flushed to L0 as separate files and this "
"increases read amplification because a get request has to check"
" in all of these files. Also, an in-memory merge may result in"
" writing less data to storage if there are duplicate records "
" in each of these individual write buffers.");
Support saving history in memtable_list Summary: For transactions, we are using the memtables to validate that there are no write conflicts. But after flushing, we don't have any memtables, and transactions could fail to commit. So we want to someone keep around some extra history to use for conflict checking. In addition, we want to provide a way to increase the size of this history if too many transactions fail to commit. After chatting with people, it seems like everyone prefers just using Memtables to store this history (instead of a separate history structure). It seems like the best place for this is abstracted inside the memtable_list. I decide to create a separate list in MemtableListVersion as using the same list complicated the flush/installalflushresults logic too much. This diff adds a new parameter to control how much memtable history to keep around after flushing. However, it sounds like people aren't too fond of adding new parameters. So I am making the default size of flushed+not-flushed memtables be set to max_write_buffers. This should not change the maximum amount of memory used, but make it more likely we're using closer the the limit. (We are now postponing deleting flushed memtables until the max_write_buffer limit is reached). So while we might use more memory on average, we are still obeying the limit set (and you could argue it's better to go ahead and use up memory now instead of waiting for a write stall to happen to test this limit). However, if people are opposed to this default behavior, we can easily set it to 0 and require this parameter be set in order to use transactions. Test Plan: Added a xfunc test to play around with setting different values of this parameter in all tests. Added testing in memtablelist_test and planning on adding more testing here. Reviewers: sdong, rven, igor Reviewed By: igor Subscribers: dhruba, leveldb Differential Revision: https://reviews.facebook.net/D37443
2015-05-29 01:34:24 +02:00
DEFINE_int32(max_write_buffer_number_to_maintain,
ROCKSDB_NAMESPACE::Options().max_write_buffer_number_to_maintain,
Support saving history in memtable_list Summary: For transactions, we are using the memtables to validate that there are no write conflicts. But after flushing, we don't have any memtables, and transactions could fail to commit. So we want to someone keep around some extra history to use for conflict checking. In addition, we want to provide a way to increase the size of this history if too many transactions fail to commit. After chatting with people, it seems like everyone prefers just using Memtables to store this history (instead of a separate history structure). It seems like the best place for this is abstracted inside the memtable_list. I decide to create a separate list in MemtableListVersion as using the same list complicated the flush/installalflushresults logic too much. This diff adds a new parameter to control how much memtable history to keep around after flushing. However, it sounds like people aren't too fond of adding new parameters. So I am making the default size of flushed+not-flushed memtables be set to max_write_buffers. This should not change the maximum amount of memory used, but make it more likely we're using closer the the limit. (We are now postponing deleting flushed memtables until the max_write_buffer limit is reached). So while we might use more memory on average, we are still obeying the limit set (and you could argue it's better to go ahead and use up memory now instead of waiting for a write stall to happen to test this limit). However, if people are opposed to this default behavior, we can easily set it to 0 and require this parameter be set in order to use transactions. Test Plan: Added a xfunc test to play around with setting different values of this parameter in all tests. Added testing in memtablelist_test and planning on adding more testing here. Reviewers: sdong, rven, igor Reviewed By: igor Subscribers: dhruba, leveldb Differential Revision: https://reviews.facebook.net/D37443
2015-05-29 01:34:24 +02:00
"The total maximum number of write buffers to maintain in memory "
"including copies of buffers that have already been flushed. "
"Unlike max_write_buffer_number, this parameter does not affect "
"flushing. This controls the minimum amount of write history "
"that will be available in memory for conflict checking when "
"Transactions are used. If this value is too low, some "
"transactions may fail at commit time due to not being able to "
"determine whether there were any write conflicts. Setting this "
"value to 0 will cause write buffers to be freed immediately "
"after they are flushed. If this value is set to -1, "
"'max_write_buffer_number' will be used.");
Refactor trimming logic for immutable memtables (#5022) Summary: MyRocks currently sets `max_write_buffer_number_to_maintain` in order to maintain enough history for transaction conflict checking. The effectiveness of this approach depends on the size of memtables. When memtables are small, it may not keep enough history; when memtables are large, this may consume too much memory. We are proposing a new way to configure memtable list history: by limiting the memory usage of immutable memtables. The new option is `max_write_buffer_size_to_maintain` and it will take precedence over the old `max_write_buffer_number_to_maintain` if they are both set to non-zero values. The new option accounts for the total memory usage of flushed immutable memtables and mutable memtable. When the total usage exceeds the limit, RocksDB may start dropping immutable memtables (which is also called trimming history), starting from the oldest one. The semantics of the old option actually works both as an upper bound and lower bound. History trimming will start if number of immutable memtables exceeds the limit, but it will never go below (limit-1) due to history trimming. In order the mimic the behavior with the new option, history trimming will stop if dropping the next immutable memtable causes the total memory usage go below the size limit. For example, assuming the size limit is set to 64MB, and there are 3 immutable memtables with sizes of 20, 30, 30. Although the total memory usage is 80MB > 64MB, dropping the oldest memtable will reduce the memory usage to 60MB < 64MB, so in this case no memtable will be dropped. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5022 Differential Revision: D14394062 Pulled By: miasantreble fbshipit-source-id: 60457a509c6af89d0993f988c9b5c2aa9e45f5c5
2019-08-23 22:54:09 +02:00
DEFINE_int64(max_write_buffer_size_to_maintain,
ROCKSDB_NAMESPACE::Options().max_write_buffer_size_to_maintain,
Refactor trimming logic for immutable memtables (#5022) Summary: MyRocks currently sets `max_write_buffer_number_to_maintain` in order to maintain enough history for transaction conflict checking. The effectiveness of this approach depends on the size of memtables. When memtables are small, it may not keep enough history; when memtables are large, this may consume too much memory. We are proposing a new way to configure memtable list history: by limiting the memory usage of immutable memtables. The new option is `max_write_buffer_size_to_maintain` and it will take precedence over the old `max_write_buffer_number_to_maintain` if they are both set to non-zero values. The new option accounts for the total memory usage of flushed immutable memtables and mutable memtable. When the total usage exceeds the limit, RocksDB may start dropping immutable memtables (which is also called trimming history), starting from the oldest one. The semantics of the old option actually works both as an upper bound and lower bound. History trimming will start if number of immutable memtables exceeds the limit, but it will never go below (limit-1) due to history trimming. In order the mimic the behavior with the new option, history trimming will stop if dropping the next immutable memtable causes the total memory usage go below the size limit. For example, assuming the size limit is set to 64MB, and there are 3 immutable memtables with sizes of 20, 30, 30. Although the total memory usage is 80MB > 64MB, dropping the oldest memtable will reduce the memory usage to 60MB < 64MB, so in this case no memtable will be dropped. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5022 Differential Revision: D14394062 Pulled By: miasantreble fbshipit-source-id: 60457a509c6af89d0993f988c9b5c2aa9e45f5c5
2019-08-23 22:54:09 +02:00
"The total maximum size of write buffers to maintain in memory "
"including copies of buffers that have already been flushed. "
"Unlike max_write_buffer_number, this parameter does not affect "
"flushing. This controls the minimum amount of write history "
"that will be available in memory for conflict checking when "
"Transactions are used. If this value is too low, some "
"transactions may fail at commit time due to not being able to "
"determine whether there were any write conflicts. Setting this "
"value to 0 will cause write buffers to be freed immediately "
"after they are flushed. If this value is set to -1, "
"'max_write_buffer_number' will be used.");
DEFINE_int32(max_background_jobs,
ROCKSDB_NAMESPACE::Options().max_background_jobs,
"The maximum number of concurrent background jobs that can occur "
"in parallel.");
Introduce bottom-pri thread pool for large universal compactions Summary: When we had a single thread pool for compactions, a thread could be busy for a long time (minutes) executing a compaction involving the bottom level. In multi-instance setups, the entire thread pool could be consumed by such bottom-level compactions. Then, top-level compactions (e.g., a few L0 files) would be blocked for a long time ("head-of-line blocking"). Such top-level compactions are critical to prevent compaction stalls as they can quickly reduce number of L0 files / sorted runs. This diff introduces a bottom-priority queue for universal compactions including the bottom level. This alleviates the head-of-line blocking situation for fast, top-level compactions. - Added `Env::Priority::BOTTOM` thread pool. This feature is only enabled if user explicitly configures it to have a positive number of threads. - Changed `ThreadPoolImpl`'s default thread limit from one to zero. This change is invisible to users as we call `IncBackgroundThreadsIfNeeded` on the low-pri/high-pri pools during `DB::Open` with values of at least one. It is necessary, though, for bottom-pri to start with zero threads so the feature is disabled by default. - Separated `ManualCompaction` into two parts in `PrepickedCompaction`. `PrepickedCompaction` is used for any compaction that's picked outside of its execution thread, either manual or automatic. - Forward universal compactions involving last level to the bottom pool (worker thread's entry point is `BGWorkBottomCompaction`). - Track `bg_bottom_compaction_scheduled_` so we can wait for bottom-level compactions to finish. We don't count them against the background jobs limits. So users of this feature will get an extra compaction for free. Closes https://github.com/facebook/rocksdb/pull/2580 Differential Revision: D5422916 Pulled By: ajkr fbshipit-source-id: a74bd11f1ea4933df3739b16808bb21fcd512333
2017-08-04 00:36:28 +02:00
DEFINE_int32(num_bottom_pri_threads, 0,
"The number of threads in the bottom-priority thread pool (used "
"by universal compaction only).");
DEFINE_int32(num_high_pri_threads, 0,
"The maximum number of concurrent background compactions"
" that can occur in parallel.");
DEFINE_int32(num_low_pri_threads, 0,
"The maximum number of concurrent background compactions"
" that can occur in parallel.");
DEFINE_int32(max_background_compactions,
ROCKSDB_NAMESPACE::Options().max_background_compactions,
"The maximum number of concurrent background compactions"
" that can occur in parallel.");
DEFINE_int32(base_background_compactions, -1, "DEPRECATED");
DEFINE_uint64(subcompactions, 1,
"Maximum number of subcompactions to divide L0-L1 compactions "
"into.");
static const bool FLAGS_subcompactions_dummy
__attribute__((__unused__)) = RegisterFlagValidator(&FLAGS_subcompactions,
&ValidateUint32Range);
DEFINE_int32(max_background_flushes,
ROCKSDB_NAMESPACE::Options().max_background_flushes,
"The maximum number of concurrent background flushes"
" that can occur in parallel.");
static ROCKSDB_NAMESPACE::CompactionStyle FLAGS_compaction_style_e;
DEFINE_int32(compaction_style,
(int32_t)ROCKSDB_NAMESPACE::Options().compaction_style,
"style of compaction: level-based, universal and fifo");
static ROCKSDB_NAMESPACE::CompactionPri FLAGS_compaction_pri_e;
DEFINE_int32(compaction_pri,
(int32_t)ROCKSDB_NAMESPACE::Options().compaction_pri,
"priority of files to compaction: by size or by data age");
DEFINE_int32(universal_size_ratio, 0,
"Percentage flexibility while comparing file size"
" (for universal compaction only).");
DEFINE_int32(universal_min_merge_width, 0, "The minimum number of files in a"
" single compaction run (for universal compaction only).");
DEFINE_int32(universal_max_merge_width, 0, "The max number of files to compact"
" in universal style compaction");
DEFINE_int32(universal_max_size_amplification_percent, 0,
"The max size amplification for universal style compaction");
DEFINE_int32(universal_compression_size_percent, -1,
"The percentage of the database to compress for universal "
"compaction. -1 means compress everything.");
DEFINE_bool(universal_allow_trivial_move, false,
"Allow trivial move in universal compaction.");
DEFINE_int64(cache_size, 8 << 20, // 8MB
"Number of bytes to use as a cache of uncompressed data");
DEFINE_int32(cache_numshardbits, 6,
"Number of shards for the block cache"
" is 2 ** cache_numshardbits. Negative means use default settings."
" This is applied only if FLAGS_cache_size is non-negative.");
DEFINE_double(cache_high_pri_pool_ratio, 0.0,
"Ratio of block cache reserve for high pri blocks. "
"If > 0.0, we also enable "
"cache_index_and_filter_blocks_with_high_priority.");
DEFINE_bool(use_clock_cache, false,
"Replace default LRU block cache with clock cache.");
add simulator Cache as class SimCache/SimLRUCache(with test) Summary: add class SimCache(base class with instrumentation api) and SimLRUCache(derived class with detailed implementation) which is used as an instrumented block cache that can predict hit rate for different cache size Test Plan: Add a test case in `db_block_cache_test.cc` called `SimCacheTest` to test basic logic of SimCache. Also add option `-simcache_size` in db_bench. if set with a value other than -1, then the benchmark will use this value as the size of the simulator cache and finally output the simulation result. ``` [gzh@dev9927.prn1 ~/local/rocksdb] ./db_bench -benchmarks "fillseq,readrandom" -cache_size 1000000 -simcache_size 1000000 RocksDB: version 4.8 Date: Tue May 17 16:56:16 2016 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Compression: Snappy Memtablerep: skip_list Perf Level: 0 WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ DB path: [/tmp/rocksdbtest-112628/dbbench] fillseq : 6.809 micros/op 146874 ops/sec; 16.2 MB/s DB path: [/tmp/rocksdbtest-112628/dbbench] readrandom : 6.343 micros/op 157665 ops/sec; 17.4 MB/s (1000000 of 1000000 found) SIMULATOR CACHE STATISTICS: SimCache LOOKUPs: 986559 SimCache HITs: 264760 SimCache HITRATE: 26.84% [gzh@dev9927.prn1 ~/local/rocksdb] ./db_bench -benchmarks "fillseq,readrandom" -cache_size 1000000 -simcache_size 10000000 RocksDB: version 4.8 Date: Tue May 17 16:57:10 2016 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Compression: Snappy Memtablerep: skip_list Perf Level: 0 WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ DB path: [/tmp/rocksdbtest-112628/dbbench] fillseq : 5.066 micros/op 197394 ops/sec; 21.8 MB/s DB path: [/tmp/rocksdbtest-112628/dbbench] readrandom : 6.457 micros/op 154870 ops/sec; 17.1 MB/s (1000000 of 1000000 found) SIMULATOR CACHE STATISTICS: SimCache LOOKUPs: 1059764 SimCache HITs: 374501 SimCache HITRATE: 35.34% [gzh@dev9927.prn1 ~/local/rocksdb] ./db_bench -benchmarks "fillseq,readrandom" -cache_size 1000000 -simcache_size 100000000 RocksDB: version 4.8 Date: Tue May 17 16:57:32 2016 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Compression: Snappy Memtablerep: skip_list Perf Level: 0 WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ DB path: [/tmp/rocksdbtest-112628/dbbench] fillseq : 5.632 micros/op 177572 ops/sec; 19.6 MB/s DB path: [/tmp/rocksdbtest-112628/dbbench] readrandom : 6.892 micros/op 145094 ops/sec; 16.1 MB/s (1000000 of 1000000 found) SIMULATOR CACHE STATISTICS: SimCache LOOKUPs: 1150767 SimCache HITs: 1034535 SimCache HITRATE: 89.90% ``` Reviewers: IslamAbdelRahman, andrewkr, sdong Reviewed By: sdong Subscribers: MarkCallaghan, andrewkr, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D57999
2016-05-24 08:35:23 +02:00
DEFINE_int64(simcache_size, -1,
"Number of bytes to use as a simcache of "
"uncompressed data. Nagative value disables simcache.");
DEFINE_bool(cache_index_and_filter_blocks, false,
"Cache index/filter blocks in block cache.");
Provide an allocator for new memory type to be used with RocksDB block cache (#6214) Summary: New memory technologies are being developed by various hardware vendors (Intel DCPMM is one such technology currently available). These new memory types require different libraries for allocation and management (such as PMDK and memkind). The high capacities available make it possible to provision large caches (up to several TBs in size), beyond what is achievable with DRAM. The new allocator provided in this PR uses the memkind library to allocate memory on different media. **Performance** We tested the new allocator using db_bench. - For each test, we vary the size of the block cache (relative to the size of the uncompressed data in the database). - The database is filled sequentially. Throughput is then measured with a readrandom benchmark. - We use a uniform distribution as a worst-case scenario. The plot shows throughput (ops/s) relative to a configuration with no block cache and default allocator. For all tests, p99 latency is below 500 us. ![image](https://user-images.githubusercontent.com/26400080/71108594-42479100-2178-11ea-8231-8a775bbc92db.png) **Changes** - Add MemkindKmemAllocator - Add --use_cache_memkind_kmem_allocator db_bench option (to create an LRU block cache with the new allocator) - Add detection of memkind library with KMEM DAX support - Add test for MemkindKmemAllocator **Minimum Requirements** - kernel 5.3.12 - ndctl v67 - https://github.com/pmem/ndctl - memkind v1.10.0 - https://github.com/memkind/memkind **Memory Configuration** The allocator uses the MEMKIND_DAX_KMEM memory kind. Follow the instructions on[ memkind’s GitHub page](https://github.com/memkind/memkind) to set up NVDIMM memory accordingly. Note on memory allocation with NVDIMM memory exposed as system memory. - The MemkindKmemAllocator will only allocate from NVDIMM memory (using memkind_malloc with MEMKIND_DAX_KMEM kind). - The default allocator is not restricted to RAM by default. Based on NUMA node latency, the kernel should allocate from local RAM preferentially, but it’s a kernel decision. numactl --preferred/--membind can be used to allocate preferentially/exclusively from the local RAM node. **Usage** When creating an LRU cache, pass a MemkindKmemAllocator object as argument. For example (replace capacity with the desired value in bytes): ``` #include "rocksdb/cache.h" #include "memory/memkind_kmem_allocator.h" NewLRUCache( capacity /*size_t*/, 6 /*cache_numshardbits*/, false /*strict_capacity_limit*/, false /*cache_high_pri_pool_ratio*/, std::make_shared<MemkindKmemAllocator>()); ``` Refer to [RocksDB’s block cache documentation](https://github.com/facebook/rocksdb/wiki/Block-Cache) to assign the LRU cache as block cache for a database. Pull Request resolved: https://github.com/facebook/rocksdb/pull/6214 Reviewed By: cheng-chang Differential Revision: D19292435 fbshipit-source-id: 7202f47b769e7722b539c86c2ffd669f64d7b4e1
2020-04-10 05:45:17 +02:00
DEFINE_bool(use_cache_memkind_kmem_allocator, false,
"Use memkind kmem allocator for block cache.");
DEFINE_bool(partition_index_and_filters, false,
"Partition index and filter blocks.");
DEFINE_bool(partition_index, false, "Partition index blocks");
DEFINE_bool(index_with_first_key, false, "Include first key in the index");
Minimize memory internal fragmentation for Bloom filters (#6427) Summary: New experimental option BBTO::optimize_filters_for_memory builds filters that maximize their use of "usable size" from malloc_usable_size, which is also used to compute block cache charges. Rather than always "rounding up," we track state in the BloomFilterPolicy object to mix essentially "rounding down" and "rounding up" so that the average FP rate of all generated filters is the same as without the option. (YMMV as heavily accessed filters might be unluckily lower accuracy.) Thus, the option near-minimizes what the block cache considers as "memory used" for a given target Bloom filter false positive rate and Bloom filter implementation. There are no forward or backward compatibility issues with this change, though it only works on the format_version=5 Bloom filter. With Jemalloc, we see about 10% reduction in memory footprint (and block cache charge) for Bloom filters, but 1-2% increase in storage footprint, due to encoding efficiency losses (FP rate is non-linear with bits/key). Why not weighted random round up/down rather than state tracking? By only requiring malloc_usable_size, we don't actually know what the next larger and next smaller usable sizes for the allocator are. We pick a requested size, accept and use whatever usable size it has, and use the difference to inform our next choice. This allows us to narrow in on the right balance without tracking/predicting usable sizes. Why not weight history of generated filter false positive rates by number of keys? This could lead to excess skew in small filters after generating a large filter. Results from filter_bench with jemalloc (irrelevant details omitted): (normal keys/filter, but high variance) $ ./filter_bench -quick -impl=2 -average_keys_per_filter=30000 -vary_key_count_ratio=0.9 Build avg ns/key: 29.6278 Number of filters: 5516 Total size (MB): 200.046 Reported total allocated memory (MB): 220.597 Reported internal fragmentation: 10.2732% Bits/key stored: 10.0097 Average FP rate %: 0.965228 $ ./filter_bench -quick -impl=2 -average_keys_per_filter=30000 -vary_key_count_ratio=0.9 -optimize_filters_for_memory Build avg ns/key: 30.5104 Number of filters: 5464 Total size (MB): 200.015 Reported total allocated memory (MB): 200.322 Reported internal fragmentation: 0.153709% Bits/key stored: 10.1011 Average FP rate %: 0.966313 (very few keys / filter, optimization not as effective due to ~59 byte internal fragmentation in blocked Bloom filter representation) $ ./filter_bench -quick -impl=2 -average_keys_per_filter=1000 -vary_key_count_ratio=0.9 Build avg ns/key: 29.5649 Number of filters: 162950 Total size (MB): 200.001 Reported total allocated memory (MB): 224.624 Reported internal fragmentation: 12.3117% Bits/key stored: 10.2951 Average FP rate %: 0.821534 $ ./filter_bench -quick -impl=2 -average_keys_per_filter=1000 -vary_key_count_ratio=0.9 -optimize_filters_for_memory Build avg ns/key: 31.8057 Number of filters: 159849 Total size (MB): 200 Reported total allocated memory (MB): 208.846 Reported internal fragmentation: 4.42297% Bits/key stored: 10.4948 Average FP rate %: 0.811006 (high keys/filter) $ ./filter_bench -quick -impl=2 -average_keys_per_filter=1000000 -vary_key_count_ratio=0.9 Build avg ns/key: 29.7017 Number of filters: 164 Total size (MB): 200.352 Reported total allocated memory (MB): 221.5 Reported internal fragmentation: 10.5552% Bits/key stored: 10.0003 Average FP rate %: 0.969358 $ ./filter_bench -quick -impl=2 -average_keys_per_filter=1000000 -vary_key_count_ratio=0.9 -optimize_filters_for_memory Build avg ns/key: 30.7131 Number of filters: 160 Total size (MB): 200.928 Reported total allocated memory (MB): 200.938 Reported internal fragmentation: 0.00448054% Bits/key stored: 10.1852 Average FP rate %: 0.963387 And from db_bench (block cache) with jemalloc: $ ./db_bench -db=/dev/shm/dbbench.no_optimize -benchmarks=fillrandom -format_version=5 -value_size=90 -bloom_bits=10 -num=2000000 -threads=8 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=false $ ./db_bench -db=/dev/shm/dbbench -benchmarks=fillrandom -format_version=5 -value_size=90 -bloom_bits=10 -num=2000000 -threads=8 -optimize_filters_for_memory -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=false $ (for FILE in /dev/shm/dbbench.no_optimize/*.sst; do ./sst_dump --file=$FILE --show_properties | grep 'filter block' ; done) | awk '{ t += $4; } END { print t; }' 17063835 $ (for FILE in /dev/shm/dbbench/*.sst; do ./sst_dump --file=$FILE --show_properties | grep 'filter block' ; done) | awk '{ t += $4; } END { print t; }' 17430747 $ #^ 2.1% additional filter storage $ ./db_bench -db=/dev/shm/dbbench.no_optimize -use_existing_db -benchmarks=readrandom,stats -statistics -bloom_bits=10 -num=2000000 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=false -duration=10 -cache_index_and_filter_blocks -cache_size=1000000000 rocksdb.block.cache.index.add COUNT : 33 rocksdb.block.cache.index.bytes.insert COUNT : 8440400 rocksdb.block.cache.filter.add COUNT : 33 rocksdb.block.cache.filter.bytes.insert COUNT : 21087528 rocksdb.bloom.filter.useful COUNT : 4963889 rocksdb.bloom.filter.full.positive COUNT : 1214081 rocksdb.bloom.filter.full.true.positive COUNT : 1161999 $ #^ 1.04 % observed FP rate $ ./db_bench -db=/dev/shm/dbbench -use_existing_db -benchmarks=readrandom,stats -statistics -bloom_bits=10 -num=2000000 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=false -optimize_filters_for_memory -duration=10 -cache_index_and_filter_blocks -cache_size=1000000000 rocksdb.block.cache.index.add COUNT : 33 rocksdb.block.cache.index.bytes.insert COUNT : 8448592 rocksdb.block.cache.filter.add COUNT : 33 rocksdb.block.cache.filter.bytes.insert COUNT : 18220328 rocksdb.bloom.filter.useful COUNT : 5360933 rocksdb.bloom.filter.full.positive COUNT : 1321315 rocksdb.bloom.filter.full.true.positive COUNT : 1262999 $ #^ 1.08 % observed FP rate, 13.6% less memory usage for filters (Due to specific key density, this example tends to generate filters that are "worse than average" for internal fragmentation. "Better than average" cases can show little or no improvement.) Pull Request resolved: https://github.com/facebook/rocksdb/pull/6427 Test Plan: unit test added, 'make check' with gcc, clang and valgrind Reviewed By: siying Differential Revision: D22124374 Pulled By: pdillinger fbshipit-source-id: f3e3aa152f9043ddf4fae25799e76341d0d8714e
2020-06-22 22:30:57 +02:00
DEFINE_bool(
optimize_filters_for_memory,
ROCKSDB_NAMESPACE::BlockBasedTableOptions().optimize_filters_for_memory,
"Minimize memory footprint of filters");
DEFINE_int64(
index_shortening_mode, 2,
"mode to shorten index: 0 for no shortening; 1 for only shortening "
"separaters; 2 for shortening shortening and successor");
DEFINE_int64(metadata_block_size,
ROCKSDB_NAMESPACE::BlockBasedTableOptions().metadata_block_size,
"Max partition size when partitioning index/filters");
// The default reduces the overhead of reading time with flash. With HDD, which
// offers much less throughput, however, this number better to be set to 1.
DEFINE_int32(ops_between_duration_checks, 1000,
"Check duration limit every x ops");
DEFINE_bool(pin_l0_filter_and_index_blocks_in_cache, false,
"Pin index/filter blocks of L0 files in block cache.");
DEFINE_bool(
pin_top_level_index_and_filter, false,
"Pin top-level index of partitioned index/filter blocks in block cache.");
DEFINE_int32(block_size,
static_cast<int32_t>(
ROCKSDB_NAMESPACE::BlockBasedTableOptions().block_size),
"Number of bytes in a block.");
DEFINE_int32(format_version,
static_cast<int32_t>(
ROCKSDB_NAMESPACE::BlockBasedTableOptions().format_version),
"Format version of SST files.");
DEFINE_int32(block_restart_interval,
ROCKSDB_NAMESPACE::BlockBasedTableOptions().block_restart_interval,
"Number of keys between restart points "
"for delta encoding of keys in data block.");
DEFINE_int32(
index_block_restart_interval,
ROCKSDB_NAMESPACE::BlockBasedTableOptions().index_block_restart_interval,
"Number of keys between restart points "
"for delta encoding of keys in index block.");
DEFINE_int32(read_amp_bytes_per_bit,
ROCKSDB_NAMESPACE::BlockBasedTableOptions().read_amp_bytes_per_bit,
"Number of bytes per bit to be used in block read-amp bitmap");
DEFINE_bool(
enable_index_compression,
ROCKSDB_NAMESPACE::BlockBasedTableOptions().enable_index_compression,
"Compress the index block");
DEFINE_bool(block_align,
ROCKSDB_NAMESPACE::BlockBasedTableOptions().block_align,
"Align data blocks on page size");
DEFINE_int64(prepopulate_block_cache, 0,
"Pre-populate hot/warm blocks in block cache. 0 to disable and 1 "
"to insert during flush");
DEFINE_bool(use_data_block_hash_index, false,
"if use kDataBlockBinaryAndHash "
"instead of kDataBlockBinarySearch. "
"This is valid if only we use BlockTable");
DEFINE_double(data_block_hash_table_util_ratio, 0.75,
"util ratio for data block hash index table. "
"This is only valid if use_data_block_hash_index is "
"set to true");
DEFINE_int64(compressed_cache_size, -1,
"Number of bytes to use as a cache of compressed data.");
DEFINE_int64(row_cache_size, 0,
"Number of bytes to use as a cache of individual rows"
" (0 = disabled).");
DEFINE_int32(open_files, ROCKSDB_NAMESPACE::Options().max_open_files,
"Maximum number of files to keep open at the same time"
" (use default if == 0)");
DEFINE_int32(file_opening_threads,
ROCKSDB_NAMESPACE::Options().max_file_opening_threads,
"If open_files is set to -1, this option set the number of "
"threads that will be used to open files during DB::Open()");
DEFINE_bool(new_table_reader_for_compaction_inputs, true,
"If true, uses a separate file handle for compaction inputs");
DEFINE_int32(compaction_readahead_size, 0, "Compaction readahead size");
DEFINE_int32(log_readahead_size, 0, "WAL and manifest readahead size");
2015-10-29 19:34:34 +01:00
DEFINE_int32(random_access_max_buffer_size, 1024 * 1024,
"Maximum windows randomaccess buffer size");
DEFINE_int32(writable_file_max_buffer_size, 1024 * 1024,
"Maximum write buffer for Writable File");
DEFINE_int32(bloom_bits, -1,
"Bloom filter bits per key. Negative means use default."
"Zero disables.");
Support optimize_filters_for_memory for Ribbon filter (#7774) Summary: Primarily this change refactors the optimize_filters_for_memory code for Bloom filters, based on malloc_usable_size, to also work for Ribbon filters. This change also replaces the somewhat slow but general BuiltinFilterBitsBuilder::ApproximateNumEntries with implementation-specific versions for Ribbon (new) and Legacy Bloom (based on a recently deleted version). The reason is to emphasize speed in ApproximateNumEntries rather than 100% accuracy. Justification: ApproximateNumEntries (formerly CalculateNumEntry) is only used by RocksDB for range-partitioned filters, called each time we start to construct one. (In theory, it should be possible to reuse the estimate, but the abstractions provided by FilterPolicy don't really make that workable.) But this is only used as a heuristic estimate for hitting a desired partitioned filter size because of alignment to data blocks, which have various numbers of unique keys or prefixes. The two factors lead us to prioritize reasonable speed over 100% accuracy. optimize_filters_for_memory adds extra complication, because precisely calculating num_entries for some allowed number of bytes depends on state with optimize_filters_for_memory enabled. And the allocator-agnostic implementation of optimize_filters_for_memory, using malloc_usable_size, means we would have to actually allocate memory, many times, just to precisely determine how many entries (keys) could be added and stay below some size budget, for the current state. (In a draft, I got this working, and then realized the balance of speed vs. accuracy was all wrong.) So related to that, I have made CalculateSpace, an internal-only API only used for testing, non-authoritative also if optimize_filters_for_memory is enabled. This simplifies some code. Pull Request resolved: https://github.com/facebook/rocksdb/pull/7774 Test Plan: unit test updated, and for FilterSize test, range of tested values is greatly expanded (still super fast) Also tested `db_bench -benchmarks=fillrandom,stats -bloom_bits=10 -num=1000000 -partition_index_and_filters -format_version=5 [-optimize_filters_for_memory] [-use_ribbon_filter]` with temporary debug output of generated filter sizes. Bloom+optimize_filters_for_memory: 1 Filter size: 197 (224 in memory) 134 Filter size: 3525 (3584 in memory) 107 Filter size: 4037 (4096 in memory) Total on disk: 904,506 Total in memory: 918,752 Ribbon+optimize_filters_for_memory: 1 Filter size: 3061 (3072 in memory) 110 Filter size: 3573 (3584 in memory) 58 Filter size: 4085 (4096 in memory) Total on disk: 633,021 (-30.0%) Total in memory: 634,880 (-30.9%) Bloom (no offm): 1 Filter size: 261 (320 in memory) 1 Filter size: 3333 (3584 in memory) 240 Filter size: 3717 (4096 in memory) Total on disk: 895,674 (-1% on disk vs. +offm; known tolerable overhead of offm) Total in memory: 986,944 (+7.4% vs. +offm) Ribbon (no offm): 1 Filter size: 2949 (3072 in memory) 1 Filter size: 3381 (3584 in memory) 167 Filter size: 3701 (4096 in memory) Total on disk: 624,397 (-30.3% vs. Bloom) Total in memory: 690,688 (-30.0% vs. Bloom) Note that optimize_filters_for_memory is even more effective for Ribbon filter than for cache-local Bloom, because it can close the unused memory gap even tighter than Bloom filter, because of 16 byte increments for Ribbon vs. 64 byte increments for Bloom. Reviewed By: jay-zhuang Differential Revision: D25592970 Pulled By: pdillinger fbshipit-source-id: 606fdaa025bb790d7e9c21601e8ea86e10541912
2020-12-18 23:29:48 +01:00
DEFINE_bool(use_ribbon_filter, false, "Use Ribbon instead of Bloom filter");
DEFINE_double(memtable_bloom_size_ratio, 0,
"Ratio of memtable size used for bloom filter. 0 means no bloom "
"filter.");
DEFINE_bool(memtable_whole_key_filtering, false,
"Try to use whole key bloom filter in memtables.");
DEFINE_bool(memtable_use_huge_page, false,
"Try to use huge page in memtables.");
DEFINE_bool(use_existing_db, false, "If true, do not destroy the existing"
" database. If you set this flag and also specify a benchmark that"
" wants a fresh database, that benchmark will fail.");
DEFINE_bool(use_existing_keys, false,
"If true, uses existing keys in the DB, "
"rather than generating new ones. This involves some startup "
"latency to load all keys into memory. It is supported for the "
"same read/overwrite benchmarks as `-use_existing_db=true`, which "
"must also be set for this flag to be enabled. When this flag is "
"set, the value for `-num` will be ignored.");
Add argument --show_table_properties to db_bench Summary: Add argument --show_table_properties to db_bench -show_table_properties (If true, then per-level table properties will be printed on every stats-interval when stats_interval is set and stats_per_interval is on.) type: bool default: false Test Plan: ./db_bench --show_table_properties=1 --stats_interval=100000 --stats_per_interval=1 ./db_bench --show_table_properties=1 --stats_interval=100000 --stats_per_interval=1 --num_column_families=2 Sample Output: Compaction Stats [column_family_name_000001] Level Files Size(MB) Score Read(GB) Rn(GB) Rnp1(GB) Write(GB) Wnew(GB) Moved(GB) W-Amp Rd(MB/s) Wr(MB/s) Comp(sec) Comp(cnt) Avg(sec) Stall(cnt) KeyIn KeyDrop --------------------------------------------------------------------------------------------------------------------------------------------------------------------- L0 3/0 5 0.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 86.3 0 17 0.021 0 0 0 L1 5/0 9 0.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 0 0.000 0 0 0 L2 9/0 16 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 0 0.000 0 0 0 Sum 17/0 31 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 86.3 0 17 0.021 0 0 0 Int 0/0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 83.9 0 2 0.022 0 0 0 Flush(GB): cumulative 0.030, interval 0.004 Stalls(count): 0 level0_slowdown, 0 level0_numfiles, 0 memtable_compaction, 0 leveln_slowdown_soft, 0 leveln_slowdown_hard Level[0]: # data blocks=2571; # entries=84813; raw key size=2035512; raw average key size=24.000000; raw value size=8481300; raw average value size=100.000000; data block size=5690119; index block size=82415; filter block size=0; (estimated) table size=5772534; filter policy name=N/A; Level[1]: # data blocks=4285; # entries=141355; raw key size=3392520; raw average key size=24.000000; raw value size=14135500; raw average value size=100.000000; data block size=9487353; index block size=137377; filter block size=0; (estimated) table size=9624730; filter policy name=N/A; Level[2]: # data blocks=7713; # entries=254439; raw key size=6106536; raw average key size=24.000000; raw value size=25443900; raw average value size=100.000000; data block size=17077893; index block size=247269; filter block size=0; (estimated) table size=17325162; filter policy name=N/A; Level[3]: # data blocks=0; # entries=0; raw key size=0; raw average key size=0.000000; raw value size=0; raw average value size=0.000000; data block size=0; index block size=0; filter block size=0; (estimated) table size=0; filter policy name=N/A; Level[4]: # data blocks=0; # entries=0; raw key size=0; raw average key size=0.000000; raw value size=0; raw average value size=0.000000; data block size=0; index block size=0; filter block size=0; (estimated) table size=0; filter policy name=N/A; Level[5]: # data blocks=0; # entries=0; raw key size=0; raw average key size=0.000000; raw value size=0; raw average value size=0.000000; data block size=0; index block size=0; filter block size=0; (estimated) table size=0; filter policy name=N/A; Level[6]: # data blocks=0; # entries=0; raw key size=0; raw average key size=0.000000; raw value size=0; raw average value size=0.000000; data block size=0; index block size=0; filter block size=0; (estimated) table size=0; filter policy name=N/A; Reviewers: anthony, IslamAbdelRahman, MarkCallaghan, sdong, igor Reviewed By: igor Subscribers: dhruba, leveldb Differential Revision: https://reviews.facebook.net/D45651
2015-08-27 03:27:23 +02:00
DEFINE_bool(show_table_properties, false,
"If true, then per-level table"
" properties will be printed on every stats-interval when"
" stats_interval is set and stats_per_interval is on.");
DEFINE_string(db, "", "Use the db with the following name.");
// Read cache flags
DEFINE_string(read_cache_path, "",
"If not empty string, a read cache will be used in this path");
DEFINE_int64(read_cache_size, 4LL * 1024 * 1024 * 1024,
"Maximum size of the read cache");
DEFINE_bool(read_cache_direct_write, true,
"Whether to use Direct IO for writing to the read cache");
DEFINE_bool(read_cache_direct_read, true,
"Whether to use Direct IO for reading from read cache");
DEFINE_bool(use_keep_filter, false, "Whether to use a noop compaction filter");
static bool ValidateCacheNumshardbits(const char* flagname, int32_t value) {
if (value >= 20) {
fprintf(stderr, "Invalid value for --%s: %d, must be < 20\n",
flagname, value);
return false;
}
return true;
}
DEFINE_bool(verify_checksum, true,
"Verify checksum for every block read"
" from storage");
DEFINE_bool(statistics, false, "Database statistics");
DEFINE_int32(stats_level, ROCKSDB_NAMESPACE::StatsLevel::kExceptDetailedTimers,
"stats level for statistics");
DEFINE_string(statistics_string, "", "Serialized statistics string");
static class std::shared_ptr<ROCKSDB_NAMESPACE::Statistics> dbstats;
DEFINE_int64(writes, -1, "Number of write operations to do. If negative, do"
" --num reads.");
DEFINE_bool(finish_after_writes, false, "Write thread terminates after all writes are finished");
DEFINE_bool(sync, false, "Sync all writes to disk");
DEFINE_bool(use_fsync, false, "If true, issue fsync instead of fdatasync");
DEFINE_bool(disable_wal, false, "If true, do not write WAL for write.");
DEFINE_string(wal_dir, "", "If not empty, use the given dir for WAL");
DEFINE_string(truth_db, "/dev/shm/truth_db/dbbench",
"Truth key/values used when using verify");
DEFINE_int32(num_levels, 7, "The total number of levels");
DEFINE_int64(target_file_size_base,
ROCKSDB_NAMESPACE::Options().target_file_size_base,
"Target file size at level-1");
DEFINE_int32(target_file_size_multiplier,
ROCKSDB_NAMESPACE::Options().target_file_size_multiplier,
"A multiplier to compute target level-N file size (N >= 2)");
DEFINE_uint64(max_bytes_for_level_base,
ROCKSDB_NAMESPACE::Options().max_bytes_for_level_base,
"Max bytes for level-1");
DEFINE_bool(level_compaction_dynamic_level_bytes, false,
"Whether level size base is dynamic");
DEFINE_double(max_bytes_for_level_multiplier, 10,
"A multiplier to compute max bytes for level-N (N >= 2)");
static std::vector<int> FLAGS_max_bytes_for_level_multiplier_additional_v;
DEFINE_string(max_bytes_for_level_multiplier_additional, "",
"A vector that specifies additional fanout per level");
DEFINE_int32(level0_stop_writes_trigger,
ROCKSDB_NAMESPACE::Options().level0_stop_writes_trigger,
"Number of files in level-0"
" that will trigger put stop.");
DEFINE_int32(level0_slowdown_writes_trigger,
ROCKSDB_NAMESPACE::Options().level0_slowdown_writes_trigger,
"Number of files in level-0"
" that will slow down writes.");
DEFINE_int32(level0_file_num_compaction_trigger,
ROCKSDB_NAMESPACE::Options().level0_file_num_compaction_trigger,
"Number of files in level-0"
" when compactions start");
DEFINE_uint64(periodic_compaction_seconds,
ROCKSDB_NAMESPACE::Options().periodic_compaction_seconds,
"Files older than this will be picked up for compaction and"
" rewritten to the same level");
static bool ValidateInt32Percent(const char* flagname, int32_t value) {
if (value <= 0 || value>=100) {
fprintf(stderr, "Invalid value for --%s: %d, 0< pct <100 \n",
flagname, value);
return false;
}
return true;
}
DEFINE_int32(readwritepercent, 90, "Ratio of reads to reads/writes (expressed"
" as percentage) for the ReadRandomWriteRandom workload. The "
"default value 90 means 90% operations out of all reads and writes"
" operations are reads. In other words, 9 gets for every 1 put.");
DEFINE_int32(mergereadpercent, 70, "Ratio of merges to merges&reads (expressed"
" as percentage) for the ReadRandomMergeRandom workload. The"
" default value 70 means 70% out of all read and merge operations"
" are merges. In other words, 7 merges for every 3 gets.");
DEFINE_int32(deletepercent, 2, "Percentage of deletes out of reads/writes/"
"deletes (used in RandomWithVerify only). RandomWithVerify "
"calculates writepercent as (100 - FLAGS_readwritepercent - "
"deletepercent), so deletepercent must be smaller than (100 - "
"FLAGS_readwritepercent)");
DEFINE_bool(optimize_filters_for_hits, false,
"Optimizes bloom filters for workloads for most lookups return "
"a value. For now this doesn't create bloom filters for the max "
"level of the LSM to reduce metadata that should fit in RAM. ");
Speed up FindObsoleteFiles() Summary: There are two versions of FindObsoleteFiles(): * full scan, which is executed every 6 hours (and it's terribly slow) * no full scan, which is executed every time a background process finishes and iterator is deleted This diff is optimizing the second case (no full scan). Here's what we do before the diff: * Get the list of obsolete files (files with ref==0). Some files in obsolete_files set might actually be live. * Get the list of live files to avoid deleting files that are live. * Delete files that are in obsolete_files and not in live_files. After this diff: * The only files with ref==0 that are still live are files that have been part of move compaction. Don't include moved files in obsolete_files. * Get the list of obsolete files (which exclude moved files). * No need to get the list of live files, since all files in obsolete_files need to be deleted. I'll post the benchmark results, but you can get the feel of it here: https://reviews.facebook.net/D30123 This depends on D30123. P.S. We should do full scan only in failure scenarios, not every 6 hours. I'll do this in a follow-up diff. Test Plan: One new unit test. Made sure that unit test fails if we don't have a `if (!f->moved)` safeguard in ~Version. make check Big number of compactions and flushes: ./db_stress --threads=30 --ops_per_thread=20000000 --max_key=10000 --column_families=20 --clear_column_family_one_in=10000000 --verify_before_write=0 --reopen=15 --max_background_compactions=10 --max_background_flushes=10 --db=/fast-rocksdb-tmp/db_stress --prefixpercent=0 --iterpercent=0 --writepercent=75 --db_write_buffer_size=2000000 Reviewers: yhchiang, rven, sdong Reviewed By: sdong Subscribers: dhruba, leveldb Differential Revision: https://reviews.facebook.net/D30249
2014-12-22 12:04:45 +01:00
DEFINE_uint64(delete_obsolete_files_period_micros, 0,
"Ignored. Left here for backward compatibility");
DEFINE_int64(writes_before_delete_range, 0,
"Number of writes before DeleteRange is called regularly.");
DEFINE_int64(writes_per_range_tombstone, 0,
"Number of writes between range tombstones");
DEFINE_int64(range_tombstone_width, 100, "Number of keys in tombstone's range");
DEFINE_int64(max_num_range_tombstones, 0,
"Maximum number of range tombstones "
"to insert.");
DEFINE_bool(expand_range_tombstones, false,
"Expand range tombstone into sequential regular tombstones.");
#ifndef ROCKSDB_LITE
// Transactions Options
DEFINE_bool(optimistic_transaction_db, false,
"Open a OptimisticTransactionDB instance. "
"Required for randomtransaction benchmark.");
DEFINE_bool(transaction_db, false,
"Open a TransactionDB instance. "
"Required for randomtransaction benchmark.");
DEFINE_uint64(transaction_sets, 2,
"Number of keys each transaction will "
"modify (use in RandomTransaction only). Max: 9999");
DEFINE_bool(transaction_set_snapshot, false,
"Setting to true will have each transaction call SetSnapshot()"
" upon creation.");
DEFINE_int32(transaction_sleep, 0,
"Max microseconds to sleep in between "
"reading and writing a value (used in RandomTransaction only). ");
DEFINE_uint64(transaction_lock_timeout, 100,
"If using a transaction_db, specifies the lock wait timeout in"
" milliseconds before failing a transaction waiting on a lock");
DEFINE_string(
options_file, "",
"The path to a RocksDB options file. If specified, then db_bench will "
"run with the RocksDB options in the default column family of the "
"specified options file. "
"Note that with this setting, db_bench will ONLY accept the following "
"RocksDB options related command-line arguments, all other arguments "
"that are related to RocksDB options will be ignored:\n"
"\t--use_existing_db\n"
"\t--use_existing_keys\n"
"\t--statistics\n"
"\t--row_cache_size\n"
"\t--row_cache_numshardbits\n"
"\t--enable_io_prio\n"
"\t--dump_malloc_stats\n"
"\t--num_multi_db\n");
// FIFO Compaction Options
DEFINE_uint64(fifo_compaction_max_table_files_size_mb, 0,
"The limit of total table file sizes to trigger FIFO compaction");
DEFINE_bool(fifo_compaction_allow_compaction, true,
"Allow compaction in FIFO compaction.");
FIFO Compaction with TTL Summary: Introducing FIFO compactions with TTL. FIFO compaction is based on size only which makes it tricky to enable in production as use cases can have organic growth. A user requested an option to drop files based on the time of their creation instead of the total size. To address that request: - Added a new TTL option to FIFO compaction options. - Updated FIFO compaction score to take TTL into consideration. - Added a new table property, creation_time, to keep track of when the SST file is created. - Creation_time is set as below: - On Flush: Set to the time of flush. - On Compaction: Set to the max creation_time of all the files involved in the compaction. - On Repair and Recovery: Set to the time of repair/recovery. - Old files created prior to this code change will have a creation_time of 0. - FIFO compaction with TTL is enabled when ttl > 0. All files older than ttl will be deleted during compaction. i.e. `if (file.creation_time < (current_time - ttl)) then delete(file)`. This will enable cases where you might want to delete all files older than, say, 1 day. - FIFO compaction will fall back to the prior way of deleting files based on size if: - the creation_time of all files involved in compaction is 0. - the total size (of all SST files combined) does not drop below `compaction_options_fifo.max_table_files_size` even if the files older than ttl are deleted. This feature is not supported if max_open_files != -1 or with table formats other than Block-based. **Test Plan:** Added tests. **Benchmark results:** Base: FIFO with max size: 100MB :: ``` svemuri@dev15905 ~/rocksdb (fifo-compaction) $ TEST_TMPDIR=/dev/shm ./db_bench --benchmarks=readwhilewriting --num=5000000 --threads=16 --compaction_style=2 --fifo_compaction_max_table_files_size_mb=100 readwhilewriting : 1.924 micros/op 519858 ops/sec; 13.6 MB/s (1176277 of 5000000 found) ``` With TTL (a low one for testing) :: ``` svemuri@dev15905 ~/rocksdb (fifo-compaction) $ TEST_TMPDIR=/dev/shm ./db_bench --benchmarks=readwhilewriting --num=5000000 --threads=16 --compaction_style=2 --fifo_compaction_max_table_files_size_mb=100 --fifo_compaction_ttl=20 readwhilewriting : 1.902 micros/op 525817 ops/sec; 13.7 MB/s (1185057 of 5000000 found) ``` Example Log lines: ``` 2017/06/26-15:17:24.609249 7fd5a45ff700 (Original Log Time 2017/06/26-15:17:24.609177) [db/compaction_picker.cc:1471] [default] FIFO compaction: picking file 40 with creation time 1498515423 for deletion 2017/06/26-15:17:24.609255 7fd5a45ff700 (Original Log Time 2017/06/26-15:17:24.609234) [db/db_impl_compaction_flush.cc:1541] [default] Deleted 1 files ... 2017/06/26-15:17:25.553185 7fd5a61a5800 [DEBUG] [db/db_impl_files.cc:309] [JOB 0] Delete /dev/shm/dbbench/000040.sst type=2 #40 -- OK 2017/06/26-15:17:25.553205 7fd5a61a5800 EVENT_LOG_v1 {"time_micros": 1498515445553199, "job": 0, "event": "table_file_deletion", "file_number": 40} ``` SST Files remaining in the dbbench dir, after db_bench execution completed: ``` svemuri@dev15905 ~/rocksdb (fifo-compaction) $ ls -l /dev/shm//dbbench/*.sst -rw-r--r--. 1 svemuri users 30749887 Jun 26 15:17 /dev/shm//dbbench/000042.sst -rw-r--r--. 1 svemuri users 30768779 Jun 26 15:17 /dev/shm//dbbench/000044.sst -rw-r--r--. 1 svemuri users 30757481 Jun 26 15:17 /dev/shm//dbbench/000046.sst ``` Closes https://github.com/facebook/rocksdb/pull/2480 Differential Revision: D5305116 Pulled By: sagar0 fbshipit-source-id: 3e5cfcf5dd07ed2211b5b37492eb235b45139174
2017-06-28 02:02:20 +02:00
DEFINE_uint64(fifo_compaction_ttl, 0, "TTL for the SST Files in seconds.");
DEFINE_uint64(fifo_age_for_warm, 0, "age_for_warm for FIFO compaction.");
// Stacked BlobDB Options
DEFINE_bool(use_blob_db, false, "[Stacked BlobDB] Open a BlobDB instance.");
DEFINE_bool(
blob_db_enable_gc,
ROCKSDB_NAMESPACE::blob_db::BlobDBOptions().enable_garbage_collection,
"[Stacked BlobDB] Enable BlobDB garbage collection.");
DEFINE_double(
blob_db_gc_cutoff,
ROCKSDB_NAMESPACE::blob_db::BlobDBOptions().garbage_collection_cutoff,
"[Stacked BlobDB] Cutoff ratio for BlobDB garbage collection.");
DEFINE_bool(blob_db_is_fifo,
ROCKSDB_NAMESPACE::blob_db::BlobDBOptions().is_fifo,
"[Stacked BlobDB] Enable FIFO eviction strategy in BlobDB.");
DEFINE_uint64(blob_db_max_db_size,
ROCKSDB_NAMESPACE::blob_db::BlobDBOptions().max_db_size,
"[Stacked BlobDB] Max size limit of the directory where blob "
"files are stored.");
DEFINE_uint64(blob_db_max_ttl_range, 0,
"[Stacked BlobDB] TTL range to generate BlobDB data (in "
"seconds). 0 means no TTL.");
DEFINE_uint64(
blob_db_ttl_range_secs,
ROCKSDB_NAMESPACE::blob_db::BlobDBOptions().ttl_range_secs,
"[Stacked BlobDB] TTL bucket size to use when creating blob files.");
DEFINE_uint64(
blob_db_min_blob_size,
ROCKSDB_NAMESPACE::blob_db::BlobDBOptions().min_blob_size,
"[Stacked BlobDB] Smallest blob to store in a file. Blobs "
"smaller than this will be inlined with the key in the LSM tree.");
DEFINE_uint64(blob_db_bytes_per_sync,
ROCKSDB_NAMESPACE::blob_db::BlobDBOptions().bytes_per_sync,
"[Stacked BlobDB] Bytes to sync blob file at.");
DEFINE_uint64(blob_db_file_size,
ROCKSDB_NAMESPACE::blob_db::BlobDBOptions().blob_file_size,
"[Stacked BlobDB] Target size of each blob file.");
DEFINE_string(
blob_db_compression_type, "snappy",
"[Stacked BlobDB] Algorithm to use to compress blobs in blob files.");
static enum ROCKSDB_NAMESPACE::CompressionType
FLAGS_blob_db_compression_type_e = ROCKSDB_NAMESPACE::kSnappyCompression;
#endif // ROCKSDB_LITE
// Integrated BlobDB options
DEFINE_bool(
enable_blob_files,
ROCKSDB_NAMESPACE::AdvancedColumnFamilyOptions().enable_blob_files,
"[Integrated BlobDB] Enable writing large values to separate blob files.");
DEFINE_uint64(min_blob_size,
ROCKSDB_NAMESPACE::AdvancedColumnFamilyOptions().min_blob_size,
"[Integrated BlobDB] The size of the smallest value to be stored "
"separately in a blob file.");
DEFINE_uint64(blob_file_size,
ROCKSDB_NAMESPACE::AdvancedColumnFamilyOptions().blob_file_size,
"[Integrated BlobDB] The size limit for blob files.");
DEFINE_string(blob_compression_type, "none",
"[Integrated BlobDB] The compression algorithm to use for large "
"values stored in blob files.");
DEFINE_bool(enable_blob_garbage_collection,
ROCKSDB_NAMESPACE::AdvancedColumnFamilyOptions()
.enable_blob_garbage_collection,
"[Integrated BlobDB] Enable blob garbage collection.");
DEFINE_double(blob_garbage_collection_age_cutoff,
ROCKSDB_NAMESPACE::AdvancedColumnFamilyOptions()
.blob_garbage_collection_age_cutoff,
"[Integrated BlobDB] The cutoff in terms of blob file age for "
"garbage collection.");
#ifndef ROCKSDB_LITE
// Secondary DB instance Options
DEFINE_bool(use_secondary_db, false,
"Open a RocksDB secondary instance. A primary instance can be "
"running in another db_bench process.");
DEFINE_string(secondary_path, "",
"Path to a directory used by the secondary instance to store "
"private files, e.g. info log.");
DEFINE_int32(secondary_update_interval, 5,
"Secondary instance attempts to catch up with the primary every "
"secondary_update_interval seconds.");
#endif // ROCKSDB_LITE
DEFINE_bool(report_bg_io_stats, false,
"Measure times spents on I/Os while in compactions. ");
DEFINE_bool(use_stderr_info_logger, false,
"Write info logs to stderr instead of to LOG file. ");
#ifndef ROCKSDB_LITE
DEFINE_string(trace_file, "", "Trace workload to a file. ");
DEFINE_double(trace_replay_fast_forward, 1.0,
"Fast forward trace replay, must > 0.0.");
DEFINE_int32(block_cache_trace_sampling_frequency, 1,
"Block cache trace sampling frequency, termed s. It uses spatial "
"downsampling and samples accesses to one out of s blocks.");
DEFINE_int64(
block_cache_trace_max_trace_file_size_in_bytes,
uint64_t{64} * 1024 * 1024 * 1024,
"The maximum block cache trace file size in bytes. Block cache accesses "
"will not be logged if the trace file size exceeds this threshold. Default "
"is 64 GB.");
DEFINE_string(block_cache_trace_file, "", "Block cache trace file path.");
DEFINE_int32(trace_replay_threads, 1,
"The number of threads to replay, must >=1.");
DEFINE_bool(io_uring_enabled, true,
"If true, enable the use of IO uring if the platform supports it");
extern "C" bool RocksDbIOUringEnable() { return FLAGS_io_uring_enabled; }
#endif // ROCKSDB_LITE
static enum ROCKSDB_NAMESPACE::CompressionType StringToCompressionType(
const char* ctype) {
assert(ctype);
if (!strcasecmp(ctype, "none"))
return ROCKSDB_NAMESPACE::kNoCompression;
else if (!strcasecmp(ctype, "snappy"))
return ROCKSDB_NAMESPACE::kSnappyCompression;
else if (!strcasecmp(ctype, "zlib"))
return ROCKSDB_NAMESPACE::kZlibCompression;
else if (!strcasecmp(ctype, "bzip2"))
return ROCKSDB_NAMESPACE::kBZip2Compression;
2014-02-08 03:12:30 +01:00
else if (!strcasecmp(ctype, "lz4"))
return ROCKSDB_NAMESPACE::kLZ4Compression;
2014-02-08 03:12:30 +01:00
else if (!strcasecmp(ctype, "lz4hc"))
return ROCKSDB_NAMESPACE::kLZ4HCCompression;
else if (!strcasecmp(ctype, "xpress"))
return ROCKSDB_NAMESPACE::kXpressCompression;
else if (!strcasecmp(ctype, "zstd"))
return ROCKSDB_NAMESPACE::kZSTD;
fprintf(stdout, "Cannot parse compression type '%s'\n", ctype);
return ROCKSDB_NAMESPACE::kSnappyCompression; // default value
}
static std::string ColumnFamilyName(size_t i) {
if (i == 0) {
return ROCKSDB_NAMESPACE::kDefaultColumnFamilyName;
} else {
char name[100];
snprintf(name, sizeof(name), "column_family_name_%06zu", i);
return std::string(name);
}
}
DEFINE_string(compression_type, "snappy",
"Algorithm to use to compress the database");
static enum ROCKSDB_NAMESPACE::CompressionType FLAGS_compression_type_e =
ROCKSDB_NAMESPACE::kSnappyCompression;
DEFINE_int64(sample_for_compression, 0, "Sample every N block for compression");
DEFINE_int32(compression_level, ROCKSDB_NAMESPACE::CompressionOptions().level,
"Compression level. The meaning of this value is library-"
"dependent. If unset, we try to use the default for the library "
"specified in `--compression_type`");
DEFINE_int32(compression_max_dict_bytes,
ROCKSDB_NAMESPACE::CompressionOptions().max_dict_bytes,
"Maximum size of dictionary used to prime the compression "
"library.");
DEFINE_int32(compression_zstd_max_train_bytes,
ROCKSDB_NAMESPACE::CompressionOptions().zstd_max_train_bytes,
"Maximum size of training data passed to zstd's dictionary "
"trainer.");
DEFINE_int32(min_level_to_compress, -1, "If non-negative, compression starts"
" from this level. Levels with number < min_level_to_compress are"
" not compressed. Otherwise, apply compression_type to "
"all levels.");
DEFINE_int32(compression_parallel_threads, 1,
"Number of threads for parallel compression.");
Limit buffering for collecting samples for compression dictionary (#7970) Summary: For dictionary compression, we need to collect some representative samples of the data to be compressed, which we use to either generate or train (when `CompressionOptions::zstd_max_train_bytes > 0`) a dictionary. Previously, the strategy was to buffer all the data blocks during flush, and up to the target file size during compaction. That strategy allowed us to randomly pick samples from as wide a range as possible that'd be guaranteed to land in a single output file. However, some users try to make huge files in memory-constrained environments, where this strategy can cause OOM. This PR introduces an option, `CompressionOptions::max_dict_buffer_bytes`, that limits how much data blocks are buffered before we switch to unbuffered mode (which means creating the per-SST dictionary, writing out the buffered data, and compressing/writing new blocks as soon as they are built). It is not strict as we currently buffer more than just data blocks -- also keys are buffered. But it does make a step towards giving users predictable memory usage. Related changes include: - Changed sampling for dictionary compression to select unique data blocks when there is limited availability of data blocks - Made use of `BlockBuilder::SwapAndReset()` to save an allocation+memcpy when buffering data blocks for building a dictionary - Changed `ParseBoolean()` to accept an input containing characters after the boolean. This is necessary since, with this PR, a value for `CompressionOptions::enabled` is no longer necessarily the final component in the `CompressionOptions` string. Pull Request resolved: https://github.com/facebook/rocksdb/pull/7970 Test Plan: - updated `CompressionOptions` unit tests to verify limit is respected (to the extent expected in the current implementation) in various scenarios of flush/compaction to bottommost/non-bottommost level - looked at jemalloc heap profiles right before and after switching to unbuffered mode during flush/compaction. Verified memory usage in buffering is proportional to the limit set. Reviewed By: pdillinger Differential Revision: D26467994 Pulled By: ajkr fbshipit-source-id: 3da4ef9fba59974e4ef40e40c01611002c861465
2021-02-19 23:06:59 +01:00
DEFINE_uint64(compression_max_dict_buffer_bytes,
ROCKSDB_NAMESPACE::CompressionOptions().max_dict_buffer_bytes,
"Maximum bytes to buffer to collect samples for dictionary.");
static bool ValidateTableCacheNumshardbits(const char* flagname,
int32_t value) {
if (0 >= value || value >= 20) {
fprintf(stderr, "Invalid value for --%s: %d, must be 0 < val < 20\n",
flagname, value);
return false;
}
return true;
}
DEFINE_int32(table_cache_numshardbits, 4, "");
#ifndef ROCKSDB_LITE
DEFINE_string(env_uri, "",
"URI for registry Env lookup. Mutually exclusive"
" with --hdfs and --fs_uri");
DEFINE_string(fs_uri, "",
"URI for registry Filesystem lookup. Mutually exclusive"
" with --hdfs and --env_uri."
" Creates a default environment with the specified filesystem.");
#endif // ROCKSDB_LITE
DEFINE_string(hdfs, "",
"Name of hdfs environment. Mutually exclusive with"
" --env_uri and --fs_uri");
DEFINE_string(simulate_hybrid_fs_file, "",
"File for Store Metadata for Simulate hybrid FS. Empty means "
"disable the feature. Now, if it is set, "
"bottommost_temperature is set to kWarm.");
static std::shared_ptr<ROCKSDB_NAMESPACE::Env> env_guard;
static ROCKSDB_NAMESPACE::Env* FLAGS_env = ROCKSDB_NAMESPACE::Env::Default();
DEFINE_int64(stats_interval, 0, "Stats are reported every N operations when "
"this is greater than zero. When 0 the interval grows over time.");
DEFINE_int64(stats_interval_seconds, 0, "Report stats every N seconds. This "
"overrides stats_interval when both are > 0.");
DEFINE_int32(stats_per_interval, 0, "Reports additional stats per interval when"
" this is greater than 0.");
DEFINE_int64(report_interval_seconds, 0,
"If greater than zero, it will write simple stats in CSV format "
"to --report_file every N seconds");
DEFINE_string(report_file, "report.csv",
"Filename where some simple stats are reported to (if "
"--report_interval_seconds is bigger than 0)");
DEFINE_int32(thread_status_per_interval, 0,
"Takes and report a snapshot of the current status of each thread"
" when this is greater than 0.");
DEFINE_int32(perf_level, ROCKSDB_NAMESPACE::PerfLevel::kDisable,
"Level of perf collection");
static bool ValidateRateLimit(const char* flagname, double value) {
const double EPSILON = 1e-10;
if ( value < -EPSILON ) {
fprintf(stderr, "Invalid value for --%s: %12.6f, must be >= 0.0\n",
flagname, value);
return false;
}
return true;
}
DEFINE_double(soft_rate_limit, 0.0, "DEPRECATED");
DEFINE_double(hard_rate_limit, 0.0, "DEPRECATED");
DEFINE_uint64(soft_pending_compaction_bytes_limit, 64ull * 1024 * 1024 * 1024,
"Slowdown writes if pending compaction bytes exceed this number");
DEFINE_uint64(hard_pending_compaction_bytes_limit, 128ull * 1024 * 1024 * 1024,
"Stop writes if pending compaction bytes exceed this number");
DEFINE_uint64(delayed_write_rate, 8388608u,
"Limited bytes allowed to DB when soft_rate_limit or "
"level0_slowdown_writes_trigger triggers");
DEFINE_bool(enable_pipelined_write, true,
"Allow WAL and memtable writes to be pipelined");
DEFINE_bool(
unordered_write, false,
"Enable the unordered write feature, which provides higher throughput but "
"relaxes the guarantees around atomic reads and immutable snapshots");
Unordered Writes (#5218) Summary: Performing unordered writes in rocksdb when unordered_write option is set to true. When enabled the writes to memtable are done without joining any write thread. This offers much higher write throughput since the upcoming writes would not have to wait for the slowest memtable write to finish. The tradeoff is that the writes visible to a snapshot might change over time. If the application cannot tolerate that, it should implement its own mechanisms to work around that. Using TransactionDB with WRITE_PREPARED write policy is one way to achieve that. Doing so increases the max throughput by 2.2x without however compromising the snapshot guarantees. The patch is prepared based on an original by siying Existing unit tests are extended to include unordered_write option. Benchmark Results: ``` TEST_TMPDIR=/dev/shm/ ./db_bench_unordered --benchmarks=fillrandom --threads=32 --num=10000000 -max_write_buffer_number=16 --max_background_jobs=64 --batch_size=8 --writes=3000000 -level0_file_num_compaction_trigger=99999 --level0_slowdown_writes_trigger=99999 --level0_stop_writes_trigger=99999 -enable_pipelined_write=false -disable_auto_compactions --unordered_write=1 ``` With WAL - Vanilla RocksDB: 78.6 MB/s - WRITER_PREPARED with unordered_write: 177.8 MB/s (2.2x) - unordered_write: 368.9 MB/s (4.7x with relaxed snapshot guarantees) Without WAL - Vanilla RocksDB: 111.3 MB/s - WRITER_PREPARED with unordered_write: 259.3 MB/s MB/s (2.3x) - unordered_write: 645.6 MB/s (5.8x with relaxed snapshot guarantees) - WRITER_PREPARED with unordered_write disable concurrency control: 185.3 MB/s MB/s (2.35x) Limitations: - The feature is not yet extended to `max_successive_merges` > 0. The feature is also incompatible with `enable_pipelined_write` = true as well as with `allow_concurrent_memtable_write` = false. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5218 Differential Revision: D15219029 Pulled By: maysamyabandeh fbshipit-source-id: 38f2abc4af8780148c6128acdba2b3227bc81759
2019-05-14 02:43:47 +02:00
DEFINE_bool(allow_concurrent_memtable_write, true,
support for concurrent adds to memtable Summary: This diff adds support for concurrent adds to the skiplist memtable implementations. Memory allocation is made thread-safe by the addition of a spinlock, with small per-core buffers to avoid contention. Concurrent memtable writes are made via an additional method and don't impose a performance overhead on the non-concurrent case, so parallelism can be selected on a per-batch basis. Write thread synchronization is an increasing bottleneck for higher levels of concurrency, so this diff adds --enable_write_thread_adaptive_yield (default off). This feature causes threads joining a write batch group to spin for a short time (default 100 usec) using sched_yield, rather than going to sleep on a mutex. If the timing of the yield calls indicates that another thread has actually run during the yield then spinning is avoided. This option improves performance for concurrent situations even without parallel adds, although it has the potential to increase CPU usage (and the heuristic adaptation is not yet mature). Parallel writes are not currently compatible with inplace updates, update callbacks, or delete filtering. Enable it with --allow_concurrent_memtable_write (and --enable_write_thread_adaptive_yield). Parallel memtable writes are performance neutral when there is no actual parallelism, and in my experiments (SSD server-class Linux and varying contention and key sizes for fillrandom) they are always a performance win when there is more than one thread. Statistics are updated earlier in the write path, dropping the number of DB mutex acquisitions from 2 to 1 for almost all cases. This diff was motivated and inspired by Yahoo's cLSM work. It is more conservative than cLSM: RocksDB's write batch group leader role is preserved (along with all of the existing flush and write throttling logic) and concurrent writers are blocked until all memtable insertions have completed and the sequence number has been advanced, to preserve linearizability. My test config is "db_bench -benchmarks=fillrandom -threads=$T -batch_size=1 -memtablerep=skip_list -value_size=100 --num=1000000/$T -level0_slowdown_writes_trigger=9999 -level0_stop_writes_trigger=9999 -disable_auto_compactions --max_write_buffer_number=8 -max_background_flushes=8 --disable_wal --write_buffer_size=160000000 --block_size=16384 --allow_concurrent_memtable_write" on a two-socket Xeon E5-2660 @ 2.2Ghz with lots of memory and an SSD hard drive. With 1 thread I get ~440Kops/sec. Peak performance for 1 socket (numactl -N1) is slightly more than 1Mops/sec, at 16 threads. Peak performance across both sockets happens at 30 threads, and is ~900Kops/sec, although with fewer threads there is less performance loss when the system has background work. Test Plan: 1. concurrent stress tests for InlineSkipList and DynamicBloom 2. make clean; make check 3. make clean; DISABLE_JEMALLOC=1 make valgrind_check; valgrind db_bench 4. make clean; COMPILE_WITH_TSAN=1 make all check; db_bench 5. make clean; COMPILE_WITH_ASAN=1 make all check; db_bench 6. make clean; OPT=-DROCKSDB_LITE make check 7. verify no perf regressions when disabled Reviewers: igor, sdong Reviewed By: sdong Subscribers: MarkCallaghan, IslamAbdelRahman, anthony, yhchiang, rven, sdong, guyg8, kradhakrishnan, dhruba Differential Revision: https://reviews.facebook.net/D50589
2015-08-15 01:59:07 +02:00
"Allow multi-writers to update mem tables in parallel.");
Memtable sampling for mempurge heuristic. (#8628) Summary: Changes the API of the MemPurge process: the `bool experimental_allow_mempurge` and `experimental_mempurge_policy` flags have been replaced by a `double experimental_mempurge_threshold` option. This change of API reflects another major change introduced in this PR: the MemPurgeDecider() function now works by sampling the memtables being flushed to estimate the overall amount of useful payload (payload minus the garbage), and then compare this useful payload estimate with the `double experimental_mempurge_threshold` value. Therefore, when the value of this flag is `0.0` (default value), mempurge is simply deactivated. On the other hand, a value of `DBL_MAX` would be equivalent to always going through a mempurge regardless of the garbage ratio estimate. At the moment, a `double experimental_mempurge_threshold` value else than 0.0 or `DBL_MAX` is opnly supported`with the `SkipList` memtable representation. Regarding the sampling, this PR includes the introduction of a `MemTable::UniqueRandomSample` function that collects (approximately) random entries from the memtable by using the new `SkipList::Iterator::RandomSeek()` under the hood, or by iterating through each memtable entry, depending on the target sample size and the total number of entries. The unit tests have been readapted to support this new API. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8628 Reviewed By: pdillinger Differential Revision: D30149315 Pulled By: bjlemaire fbshipit-source-id: 1feef5390c95db6f4480ab4434716533d3947f27
2021-08-11 03:07:48 +02:00
DEFINE_double(experimental_mempurge_threshold, 0.0,
"Maximum useful payload ratio estimate that triggers a mempurge "
"(memtable garbage collection).");
Add simple heuristics for experimental mempurge. (#8583) Summary: Add `experimental_mempurge_policy` option flag and introduce two new `MemPurge` (Memtable Garbage Collection) policies: 'ALWAYS' and 'ALTERNATE'. Default value: ALTERNATE. `ALWAYS`: every flush will first go through a `MemPurge` process. If the output is too big to fit into a single memtable, then the mempurge is aborted and a regular flush process carries on. `ALWAYS` is designed for user that need to reduce the number of L0 SST file created to a strict minimum, and can afford a small dent in performance (possibly hits to CPU usage, read efficiency, and maximum burst write throughput). `ALTERNATE`: a flush is transformed into a `MemPurge` except if one of the memtables being flushed is the product of a previous `MemPurge`. `ALTERNATE` is a good tradeoff between reduction in number of L0 SST files created and performance. `ALTERNATE` perform particularly well for completely random garbage ratios, or garbage ratios anywhere in (0%,50%], and even higher when there is a wild variability in garbage ratios. This PR also includes support for `experimental_mempurge_policy` in `db_bench`. Testing was done locally by replacing all the `MemPurge` policies of the unit tests with `ALTERNATE`, as well as local testing with `db_crashtest.py` `whitebox` and `blackbox`. Overall, if an `ALWAYS` mempurge policy passes the tests, there is no reasons why an `ALTERNATE` policy would fail, and therefore the mempurge policy was set to `ALWAYS` for all mempurge unit tests. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8583 Reviewed By: pdillinger Differential Revision: D29888050 Pulled By: bjlemaire fbshipit-source-id: e2cf26646d66679f6f5fb29842624615610759c1
2021-07-26 20:55:27 +02:00
DEFINE_bool(inplace_update_support,
ROCKSDB_NAMESPACE::Options().inplace_update_support,
"Support in-place memtable update for smaller or same-size values");
DEFINE_uint64(inplace_update_num_locks,
ROCKSDB_NAMESPACE::Options().inplace_update_num_locks,
"Number of RW locks to protect in-place memtable updates");
DEFINE_bool(enable_write_thread_adaptive_yield, true,
support for concurrent adds to memtable Summary: This diff adds support for concurrent adds to the skiplist memtable implementations. Memory allocation is made thread-safe by the addition of a spinlock, with small per-core buffers to avoid contention. Concurrent memtable writes are made via an additional method and don't impose a performance overhead on the non-concurrent case, so parallelism can be selected on a per-batch basis. Write thread synchronization is an increasing bottleneck for higher levels of concurrency, so this diff adds --enable_write_thread_adaptive_yield (default off). This feature causes threads joining a write batch group to spin for a short time (default 100 usec) using sched_yield, rather than going to sleep on a mutex. If the timing of the yield calls indicates that another thread has actually run during the yield then spinning is avoided. This option improves performance for concurrent situations even without parallel adds, although it has the potential to increase CPU usage (and the heuristic adaptation is not yet mature). Parallel writes are not currently compatible with inplace updates, update callbacks, or delete filtering. Enable it with --allow_concurrent_memtable_write (and --enable_write_thread_adaptive_yield). Parallel memtable writes are performance neutral when there is no actual parallelism, and in my experiments (SSD server-class Linux and varying contention and key sizes for fillrandom) they are always a performance win when there is more than one thread. Statistics are updated earlier in the write path, dropping the number of DB mutex acquisitions from 2 to 1 for almost all cases. This diff was motivated and inspired by Yahoo's cLSM work. It is more conservative than cLSM: RocksDB's write batch group leader role is preserved (along with all of the existing flush and write throttling logic) and concurrent writers are blocked until all memtable insertions have completed and the sequence number has been advanced, to preserve linearizability. My test config is "db_bench -benchmarks=fillrandom -threads=$T -batch_size=1 -memtablerep=skip_list -value_size=100 --num=1000000/$T -level0_slowdown_writes_trigger=9999 -level0_stop_writes_trigger=9999 -disable_auto_compactions --max_write_buffer_number=8 -max_background_flushes=8 --disable_wal --write_buffer_size=160000000 --block_size=16384 --allow_concurrent_memtable_write" on a two-socket Xeon E5-2660 @ 2.2Ghz with lots of memory and an SSD hard drive. With 1 thread I get ~440Kops/sec. Peak performance for 1 socket (numactl -N1) is slightly more than 1Mops/sec, at 16 threads. Peak performance across both sockets happens at 30 threads, and is ~900Kops/sec, although with fewer threads there is less performance loss when the system has background work. Test Plan: 1. concurrent stress tests for InlineSkipList and DynamicBloom 2. make clean; make check 3. make clean; DISABLE_JEMALLOC=1 make valgrind_check; valgrind db_bench 4. make clean; COMPILE_WITH_TSAN=1 make all check; db_bench 5. make clean; COMPILE_WITH_ASAN=1 make all check; db_bench 6. make clean; OPT=-DROCKSDB_LITE make check 7. verify no perf regressions when disabled Reviewers: igor, sdong Reviewed By: sdong Subscribers: MarkCallaghan, IslamAbdelRahman, anthony, yhchiang, rven, sdong, guyg8, kradhakrishnan, dhruba Differential Revision: https://reviews.facebook.net/D50589
2015-08-15 01:59:07 +02:00
"Use a yielding spin loop for brief writer thread waits.");
DEFINE_uint64(
write_thread_max_yield_usec, 100,
"Maximum microseconds for enable_write_thread_adaptive_yield operation.");
DEFINE_uint64(write_thread_slow_yield_usec, 3,
"The threshold at which a slow yield is considered a signal that "
"other processes or threads want the core.");
DEFINE_int32(rate_limit_delay_max_milliseconds, 1000,
"When hard_rate_limit is set then this is the max time a put will"
" be stalled.");
DEFINE_uint64(rate_limiter_bytes_per_sec, 0, "Set options.rate_limiter value.");
Simplify GenericRateLimiter algorithm (#8602) Summary: `GenericRateLimiter` slow path handles requests that cannot be satisfied immediately. Such requests enter a queue, and their thread stays in `Request()` until they are granted or the rate limiter is stopped. These threads are responsible for unblocking themselves. The work to do so is split into two main duties. (1) Waiting for the next refill time. (2) Refilling the bytes and granting requests. Prior to this PR, the slow path logic involved a leader election algorithm to pick one thread to perform (1) followed by (2). It elected the thread whose request was at the front of the highest priority non-empty queue since that request was most likely to be granted. This algorithm was efficient in terms of reducing intermediate wakeups, which is a thread waking up only to resume waiting after finding its request is not granted. However, the conceptual complexity of this algorithm was too high. It took me a long time to draw a timeline to understand how it works for just one edge case yet there were so many. This PR drops the leader election to reduce conceptual complexity. Now, the two duties can be performed by whichever thread acquires the lock first. The risk of this change is increasing the number of intermediate wakeups, however, we took steps to mitigate that. - `wait_until_refill_pending_` flag ensures only one thread performs (1). This\ prevents the thundering herd problem at the next refill time. The remaining\ threads wait on their condition variable with an unbounded duration -- thus we\ must remember to notify them to ensure forward progress. - (1) is typically done by a thread at the front of a queue. This is trivial\ when the queues are initially empty as the first choice that arrives must be\ the only entry in its queue. When queues are initially non-empty, we achieve\ this by having (2) notify a thread at the front of a queue (preferring higher\ priority) to perform the next duty. - We do not require any additional wakeup for (2). Typically it will just be\ done by the thread that finished (1). Combined, the second and third bullet points above suggest the refill/granting will typically be done by a request at the front of its queue. This is important because one wakeup is saved when a granted request happens to be in an already running thread. Note there are a few cases that still lead to intermediate wakeup, however. The first two are existing issues that also apply to the old algorithm, however, the third (including both subpoints) is new. - No request may be granted (only possible when rate limit dynamically\ decreases). - Requests from a different queue may be granted. - (2) may be run by a non-front request thread causing it to not be granted even\ if some requests in that same queue are granted. It can happen for a couple\ (unlikely) reasons. - A new request may sneak in and grab the lock at the refill time, before the\ thread finishing (1) can wake up and grab it. - A new request may sneak in and grab the lock and execute (1) before (2)'s\ chosen candidate can wake up and grab the lock. Then that non-front request\ thread performing (1) can carry over to perform (2). Pull Request resolved: https://github.com/facebook/rocksdb/pull/8602 Test Plan: - Use existing tests. The edge cases listed in the comment are all performance\ related; I could not really think of any related to correctness. The logic\ looks the same whether a thread wakes up/finishes its work early/on-time/late,\ or whether the thread is chosen vs. "steals" the work. - Verified write throughput and CPU overhead are basically the same with and\ without this change, even in a rate limiter heavy workload: Test command: ``` $ rm -rf /dev/shm/dbbench/ && TEST_TMPDIR=/dev/shm /usr/bin/time ./db_bench -benchmarks=fillrandom -num_multi_db=64 -num_low_pri_threads=64 -num_high_pri_threads=64 -write_buffer_size=262144 -target_file_size_base=262144 -max_bytes_for_level_base=1048576 -rate_limiter_bytes_per_sec=16777216 -key_size=24 -value_size=1000 -num=10000 -compression_type=none -rate_limiter_refill_period_us=1000 ``` Results before this PR: ``` fillrandom : 108.463 micros/op 9219 ops/sec; 9.0 MB/s 7.40user 8.84system 1:26.20elapsed 18%CPU (0avgtext+0avgdata 256140maxresident)k ``` Results after this PR: ``` fillrandom : 108.108 micros/op 9250 ops/sec; 9.0 MB/s 7.45user 8.23system 1:26.68elapsed 18%CPU (0avgtext+0avgdata 255688maxresident)k ``` Reviewed By: hx235 Differential Revision: D30048013 Pulled By: ajkr fbshipit-source-id: 6741bba9d9dfbccab359806d725105817fef818b
2021-08-10 01:46:14 +02:00
DEFINE_int64(rate_limiter_refill_period_us, 100 * 1000,
"Set refill period on "
"rate limiter.");
DEFINE_bool(rate_limiter_auto_tuned, false,
"Enable dynamic adjustment of rate limit according to demand for "
"background I/O");
DEFINE_bool(sine_write_rate, false,
"Use a sine wave write_rate_limit");
DEFINE_uint64(sine_write_rate_interval_milliseconds, 10000,
"Interval of which the sine wave write_rate_limit is recalculated");
DEFINE_double(sine_a, 1,
"A in f(x) = A sin(bx + c) + d");
DEFINE_double(sine_b, 1,
"B in f(x) = A sin(bx + c) + d");
DEFINE_double(sine_c, 0,
"C in f(x) = A sin(bx + c) + d");
DEFINE_double(sine_d, 1,
"D in f(x) = A sin(bx + c) + d");
DEFINE_bool(rate_limit_bg_reads, false,
"Use options.rate_limiter on compaction reads");
DEFINE_uint64(
benchmark_write_rate_limit, 0,
"If non-zero, db_bench will rate-limit the writes going into RocksDB. This "
"is the global rate in bytes/second.");
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 19:40:44 +01:00
// the parameters of mix_graph
Workload generator (Mixgraph) based on prefix hotness (#5953) Summary: In the previous PR https://github.com/facebook/rocksdb/issues/4788, user can use db_bench mix_graph option to generate the workload that is from the social graph. The key is generated based on the key access hotness. In this PR, user can further model the key-range hotness and fit those to two-term-exponential distribution. First, user cuts the whole key space into small key ranges (e.g., key-ranges are the same size and the key-range number is the number of SST files). Then, user calculates the average access count per key of each key-range as the key-range hotness. Next, user fits the key-range hotness to two-term-exponential distribution (f(x) = f(x) = a*exp(b*x) + c*exp(d*x)) and generate the value of a, b, c, and d. They are the parameters in db_bench: prefix_dist_a, prefix_dist_b, prefix_dist_c, and prefix_dist_d. Finally, user can run db_bench by specify the parameters. For example: `./db_bench --benchmarks="mixgraph" -use_direct_io_for_flush_and_compaction=true -use_direct_reads=true -cache_size=268435456 -key_dist_a=0.002312 -key_dist_b=0.3467 -keyrange_dist_a=14.18 -keyrange_dist_b=-2.917 -keyrange_dist_c=0.0164 -keyrange_dist_d=-0.08082 -keyrange_num=30 -value_k=0.2615 -value_sigma=25.45 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.85 -mix_put_ratio=0.14 -mix_seek_ratio=0.01 -sine_mix_rate_interval_milliseconds=5000 -sine_a=350 -sine_b=0.0105 -sine_d=50000 --perf_level=2 -reads=1000000 -num=5000000 -key_size=48` Pull Request resolved: https://github.com/facebook/rocksdb/pull/5953 Test Plan: run db_bench with different parameters and checked the results. Differential Revision: D18053527 Pulled By: zhichao-cao fbshipit-source-id: 171f8b3142bd76462f1967c58345ad7e4f84bab7
2019-11-06 21:50:33 +01:00
DEFINE_double(keyrange_dist_a, 0.0,
"The parameter 'a' of prefix average access distribution "
"f(x)=a*exp(b*x)+c*exp(d*x)");
DEFINE_double(keyrange_dist_b, 0.0,
"The parameter 'b' of prefix average access distribution "
"f(x)=a*exp(b*x)+c*exp(d*x)");
DEFINE_double(keyrange_dist_c, 0.0,
"The parameter 'c' of prefix average access distribution"
"f(x)=a*exp(b*x)+c*exp(d*x)");
DEFINE_double(keyrange_dist_d, 0.0,
"The parameter 'd' of prefix average access distribution"
"f(x)=a*exp(b*x)+c*exp(d*x)");
DEFINE_int64(keyrange_num, 1,
"The number of key ranges that are in the same prefix "
"group, each prefix range will have its key access "
Workload generator (Mixgraph) based on prefix hotness (#5953) Summary: In the previous PR https://github.com/facebook/rocksdb/issues/4788, user can use db_bench mix_graph option to generate the workload that is from the social graph. The key is generated based on the key access hotness. In this PR, user can further model the key-range hotness and fit those to two-term-exponential distribution. First, user cuts the whole key space into small key ranges (e.g., key-ranges are the same size and the key-range number is the number of SST files). Then, user calculates the average access count per key of each key-range as the key-range hotness. Next, user fits the key-range hotness to two-term-exponential distribution (f(x) = f(x) = a*exp(b*x) + c*exp(d*x)) and generate the value of a, b, c, and d. They are the parameters in db_bench: prefix_dist_a, prefix_dist_b, prefix_dist_c, and prefix_dist_d. Finally, user can run db_bench by specify the parameters. For example: `./db_bench --benchmarks="mixgraph" -use_direct_io_for_flush_and_compaction=true -use_direct_reads=true -cache_size=268435456 -key_dist_a=0.002312 -key_dist_b=0.3467 -keyrange_dist_a=14.18 -keyrange_dist_b=-2.917 -keyrange_dist_c=0.0164 -keyrange_dist_d=-0.08082 -keyrange_num=30 -value_k=0.2615 -value_sigma=25.45 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.85 -mix_put_ratio=0.14 -mix_seek_ratio=0.01 -sine_mix_rate_interval_milliseconds=5000 -sine_a=350 -sine_b=0.0105 -sine_d=50000 --perf_level=2 -reads=1000000 -num=5000000 -key_size=48` Pull Request resolved: https://github.com/facebook/rocksdb/pull/5953 Test Plan: run db_bench with different parameters and checked the results. Differential Revision: D18053527 Pulled By: zhichao-cao fbshipit-source-id: 171f8b3142bd76462f1967c58345ad7e4f84bab7
2019-11-06 21:50:33 +01:00
"distribution");
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 19:40:44 +01:00
DEFINE_double(key_dist_a, 0.0,
"The parameter 'a' of key access distribution model "
"f(x)=a*x^b");
DEFINE_double(key_dist_b, 0.0,
"The parameter 'b' of key access distribution model "
"f(x)=a*x^b");
DEFINE_double(value_theta, 0.0,
"The parameter 'theta' of Generized Pareto Distribution "
"f(x)=(1/sigma)*(1+k*(x-theta)/sigma)^-(1/k+1)");
DEFINE_double(value_k, 0.0,
"The parameter 'k' of Generized Pareto Distribution "
"f(x)=(1/sigma)*(1+k*(x-theta)/sigma)^-(1/k+1)");
DEFINE_double(value_sigma, 0.0,
"The parameter 'theta' of Generized Pareto Distribution "
"f(x)=(1/sigma)*(1+k*(x-theta)/sigma)^-(1/k+1)");
DEFINE_double(iter_theta, 0.0,
"The parameter 'theta' of Generized Pareto Distribution "
"f(x)=(1/sigma)*(1+k*(x-theta)/sigma)^-(1/k+1)");
DEFINE_double(iter_k, 0.0,
"The parameter 'k' of Generized Pareto Distribution "
"f(x)=(1/sigma)*(1+k*(x-theta)/sigma)^-(1/k+1)");
DEFINE_double(iter_sigma, 0.0,
"The parameter 'sigma' of Generized Pareto Distribution "
"f(x)=(1/sigma)*(1+k*(x-theta)/sigma)^-(1/k+1)");
DEFINE_double(mix_get_ratio, 1.0,
"The ratio of Get queries of mix_graph workload");
DEFINE_double(mix_put_ratio, 0.0,
"The ratio of Put queries of mix_graph workload");
DEFINE_double(mix_seek_ratio, 0.0,
"The ratio of Seek queries of mix_graph workload");
DEFINE_int64(mix_max_scan_len, 10000, "The max scan length of Iterator");
DEFINE_int64(mix_ave_kv_size, 512,
"The average key-value size of this workload");
DEFINE_int64(mix_max_value_size, 1024, "The max value size of this workload");
DEFINE_double(
sine_mix_rate_noise, 0.0,
"Add the noise ratio to the sine rate, it is between 0.0 and 1.0");
DEFINE_bool(sine_mix_rate, false,
"Enable the sine QPS control on the mix workload");
DEFINE_uint64(
sine_mix_rate_interval_milliseconds, 10000,
"Interval of which the sine wave read_rate_limit is recalculated");
DEFINE_int64(mix_accesses, -1,
"The total query accesses of mix_graph workload");
DEFINE_uint64(
benchmark_read_rate_limit, 0,
"If non-zero, db_bench will rate-limit the reads from RocksDB. This "
"is the global rate in ops/second.");
DEFINE_uint64(max_compaction_bytes,
ROCKSDB_NAMESPACE::Options().max_compaction_bytes,
"Max bytes allowed in one compaction");
#ifndef ROCKSDB_LITE
DEFINE_bool(readonly, false, "Run read only benchmarks.");
DEFINE_bool(print_malloc_stats, false,
"Print malloc stats to stdout after benchmarks finish.");
#endif // ROCKSDB_LITE
DEFINE_bool(disable_auto_compactions, false, "Do not auto trigger compactions");
DEFINE_uint64(wal_ttl_seconds, 0, "Set the TTL for the WAL Files in seconds.");
DEFINE_uint64(wal_size_limit_MB, 0, "Set the size limit for the WAL Files"
" in MB.");
DEFINE_uint64(max_total_wal_size, 0, "Set total max WAL size");
DEFINE_bool(mmap_read, ROCKSDB_NAMESPACE::Options().allow_mmap_reads,
"Allow reads to occur via mmap-ing files");
DEFINE_bool(mmap_write, ROCKSDB_NAMESPACE::Options().allow_mmap_writes,
"Allow writes to occur via mmap-ing files");
DEFINE_bool(use_direct_reads, ROCKSDB_NAMESPACE::Options().use_direct_reads,
"Use O_DIRECT for reading data");
DEFINE_bool(use_direct_io_for_flush_and_compaction,
ROCKSDB_NAMESPACE::Options().use_direct_io_for_flush_and_compaction,
"Use O_DIRECT for background flush and compaction writes");
DEFINE_bool(advise_random_on_open,
ROCKSDB_NAMESPACE::Options().advise_random_on_open,
"Advise random access on table file open");
DEFINE_string(compaction_fadvice, "NORMAL",
"Access pattern advice when a file is compacted");
static auto FLAGS_compaction_fadvice_e =
ROCKSDB_NAMESPACE::Options().access_hint_on_compaction_start;
DEFINE_bool(use_tailing_iterator, false,
"Use tailing iterator to access a series of keys instead of get");
DEFINE_bool(use_adaptive_mutex, ROCKSDB_NAMESPACE::Options().use_adaptive_mutex,
"Use adaptive mutex");
DEFINE_uint64(bytes_per_sync, ROCKSDB_NAMESPACE::Options().bytes_per_sync,
"Allows OS to incrementally sync SST files to disk while they are"
" being written, in the background. Issue one request for every"
" bytes_per_sync written. 0 turns it off.");
DEFINE_uint64(wal_bytes_per_sync,
ROCKSDB_NAMESPACE::Options().wal_bytes_per_sync,
"Allows OS to incrementally sync WAL files to disk while they are"
" being written, in the background. Issue one request for every"
" wal_bytes_per_sync written. 0 turns it off.");
Support for SingleDelete() Summary: This patch fixes #7460559. It introduces SingleDelete as a new database operation. This operation can be used to delete keys that were never overwritten (no put following another put of the same key). If an overwritten key is single deleted the behavior is undefined. Single deletion of a non-existent key has no effect but multiple consecutive single deletions are not allowed (see limitations). In contrast to the conventional Delete() operation, the deletion entry is removed along with the value when the two are lined up in a compaction. Note: The semantics are similar to @igor's prototype that allowed to have this behavior on the granularity of a column family ( https://reviews.facebook.net/D42093 ). This new patch, however, is more aggressive when it comes to removing tombstones: It removes the SingleDelete together with the value whenever there is no snapshot between them while the older patch only did this when the sequence number of the deletion was older than the earliest snapshot. Most of the complex additions are in the Compaction Iterator, all other changes should be relatively straightforward. The patch also includes basic support for single deletions in db_stress and db_bench. Limitations: - Not compatible with cuckoo hash tables - Single deletions cannot be used in combination with merges and normal deletions on the same key (other keys are not affected by this) - Consecutive single deletions are currently not allowed (and older version of this patch supported this so it could be resurrected if needed) Test Plan: make all check Reviewers: yhchiang, sdong, rven, anthony, yoshinorim, igor Reviewed By: igor Subscribers: maykov, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D43179
2015-09-17 20:42:56 +02:00
DEFINE_bool(use_single_deletes, true,
"Use single deletes (used in RandomReplaceKeys only).");
DEFINE_double(stddev, 2000.0,
"Standard deviation of normal distribution used for picking keys"
" (used in RandomReplaceKeys only).");
Write a benchmark to emulate time series data Summary: Add a benchmark to `db_bench`. In this benchmark, a write thread will populate time series data in the format of 'id | timestamp', and multiple read threads will randomly retrieve all data from one id at a time. Test Plan: Run the benchmark: `num=134217728;bpl=536870912;mb=67108864;overlap=10;mcz=2;del=300000000;levels=6;ctrig=4;delay=8;stop=12;wbn=3;mbc=20;wbs=134217728;dds=0;sync=0;t=32;vs=800;bs=4096;cs=17179869184;of=500000;wps=0;si=10000000; kir=100000; dir=/data/users/jhli/test/; ./db_bench --benchmarks=timeseries --disable_seek_compaction=1 --mmap_read=0 --statistics=1 --histogram=1 --num=$num --threads=$t --value_size=$vs --block_size=$bs --cache_size=$cs --bloom_bits=10 --cache_numshardbits=6 --open_files=$of --verify_checksum=1 --db=$dir --sync=$sync --disable_wal=0 --compression_type=none --stats_interval=$si --compression_ratio=1 --disable_data_sync=$dds --write_buffer_size=$wbs --target_file_size_base=$mb --max_write_buffer_number=$wbn --max_background_compactions=$mbc --level0_file_num_compaction_trigger=$ctrig --level0_slowdown_writes_trigger=$delay --level0_stop_writes_trigger=$stop --num_levels=$levels --delete_obsolete_files_period_micros=$del --min_level_to_compress=$mcz --max_grandparent_overlap_factor=$overlap --stats_per_interval=1 --max_bytes_for_level_base=$bpl --use_existing_db=0 --key_id_range=$kir` Reviewers: andrewkr, sdong Reviewed By: sdong Subscribers: lgalanis, andrewkr, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D60651
2016-08-01 21:28:51 +02:00
DEFINE_int32(key_id_range, 100000,
"Range of possible value of key id (used in TimeSeries only).");
DEFINE_string(expire_style, "none",
"Style to remove expired time entries. Can be one of the options "
"below: none (do not expired data), compaction_filter (use a "
"compaction filter to remove expired data), delete (seek IDs and "
"remove expired data) (used in TimeSeries only).");
DEFINE_uint64(
time_range, 100000,
"Range of timestamp that store in the database (used in TimeSeries"
" only).");
DEFINE_int32(num_deletion_threads, 1,
"Number of threads to do deletion (used in TimeSeries and delete "
"expire_style only).");
DEFINE_int32(max_successive_merges, 0, "Maximum number of successive merge"
" operations on a key in the memtable");
static bool ValidatePrefixSize(const char* flagname, int32_t value) {
if (value < 0 || value>=2000000000) {
fprintf(stderr, "Invalid value for --%s: %d. 0<= PrefixSize <=2000000000\n",
flagname, value);
return false;
}
return true;
}
DEFINE_int32(prefix_size, 0, "control the prefix size for HashSkipList and "
"plain table");
DEFINE_int64(keys_per_prefix, 0, "control average number of keys generated "
"per prefix, 0 means no special handling of the prefix, "
"i.e. use the prefix comes with the generated random number.");
DEFINE_bool(total_order_seek, false,
"Enable total order seek regardless of index format.");
DEFINE_bool(prefix_same_as_start, false,
"Enforce iterator to return keys with prefix same as seek key.");
DEFINE_bool(
seek_missing_prefix, false,
"Iterator seek to keys with non-exist prefixes. Require prefix_size > 8");
DEFINE_int32(memtable_insert_with_hint_prefix_size, 0,
"If non-zero, enable "
"memtable insert with hint with the given prefix size.");
DEFINE_bool(enable_io_prio, false, "Lower the background flush/compaction "
"threads' IO priority");
DEFINE_bool(enable_cpu_prio, false, "Lower the background flush/compaction "
"threads' CPU priority");
CuckooTable: add one option to allow identity function for the first hash function Summary: MurmurHash becomes expensive when we do millions Get() a second in one thread. Add this option to allow the first hash function to use identity function as hash function. It results in QPS increase from 3.7M/s to ~4.3M/s. I did not observe improvement for end to end RocksDB performance. This may be caused by other bottlenecks that I will address in a separate diff. Test Plan: ``` [ljin@dev1964 rocksdb] ./cuckoo_table_reader_test --enable_perf --file_dir=/dev/shm --write --identity_as_first_hash=0 ==== Test CuckooReaderTest.WhenKeyExists ==== Test CuckooReaderTest.WhenKeyExistsWithUint64Comparator ==== Test CuckooReaderTest.CheckIterator ==== Test CuckooReaderTest.CheckIteratorUint64 ==== Test CuckooReaderTest.WhenKeyNotFound ==== Test CuckooReaderTest.TestReadPerformance With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.272us (3.7 Mqps) with batch size of 0, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.138us (7.2 Mqps) with batch size of 10, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.142us (7.1 Mqps) with batch size of 25, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.142us (7.0 Mqps) with batch size of 50, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.144us (6.9 Mqps) with batch size of 100, # of found keys 125829120 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.201us (5.0 Mqps) with batch size of 0, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.121us (8.3 Mqps) with batch size of 10, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.123us (8.1 Mqps) with batch size of 25, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.121us (8.3 Mqps) with batch size of 50, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.112us (8.9 Mqps) with batch size of 100, # of found keys 104857600 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.251us (4.0 Mqps) with batch size of 0, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.107us (9.4 Mqps) with batch size of 10, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.099us (10.1 Mqps) with batch size of 25, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.100us (10.0 Mqps) with batch size of 50, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.116us (8.6 Mqps) with batch size of 100, # of found keys 83886080 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.189us (5.3 Mqps) with batch size of 0, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.095us (10.5 Mqps) with batch size of 10, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.096us (10.4 Mqps) with batch size of 25, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.098us (10.2 Mqps) with batch size of 50, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.105us (9.5 Mqps) with batch size of 100, # of found keys 73400320 [ljin@dev1964 rocksdb] ./cuckoo_table_reader_test --enable_perf --file_dir=/dev/shm --write --identity_as_first_hash=1 ==== Test CuckooReaderTest.WhenKeyExists ==== Test CuckooReaderTest.WhenKeyExistsWithUint64Comparator ==== Test CuckooReaderTest.CheckIterator ==== Test CuckooReaderTest.CheckIteratorUint64 ==== Test CuckooReaderTest.WhenKeyNotFound ==== Test CuckooReaderTest.TestReadPerformance With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.230us (4.3 Mqps) with batch size of 0, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.086us (11.7 Mqps) with batch size of 10, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.088us (11.3 Mqps) with batch size of 25, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.083us (12.1 Mqps) with batch size of 50, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.083us (12.1 Mqps) with batch size of 100, # of found keys 125829120 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.159us (6.3 Mqps) with batch size of 0, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.078us (12.8 Mqps) with batch size of 10, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.080us (12.6 Mqps) with batch size of 25, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.080us (12.5 Mqps) with batch size of 50, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.082us (12.2 Mqps) with batch size of 100, # of found keys 104857600 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.154us (6.5 Mqps) with batch size of 0, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.077us (13.0 Mqps) with batch size of 10, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.077us (12.9 Mqps) with batch size of 25, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.078us (12.8 Mqps) with batch size of 50, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.079us (12.6 Mqps) with batch size of 100, # of found keys 83886080 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.218us (4.6 Mqps) with batch size of 0, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.083us (12.0 Mqps) with batch size of 10, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.085us (11.7 Mqps) with batch size of 25, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.086us (11.6 Mqps) with batch size of 50, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.078us (12.8 Mqps) with batch size of 100, # of found keys 73400320 ``` Reviewers: sdong, igor, yhchiang Reviewed By: igor Subscribers: leveldb Differential Revision: https://reviews.facebook.net/D23451
2014-09-18 20:00:48 +02:00
DEFINE_bool(identity_as_first_hash, false, "the first hash function of cuckoo "
"table becomes an identity function. This is only valid when key "
"is 8 bytes");
DEFINE_bool(dump_malloc_stats, true, "Dump malloc stats in LOG ");
DEFINE_uint64(stats_dump_period_sec,
ROCKSDB_NAMESPACE::Options().stats_dump_period_sec,
"Gap between printing stats to log in seconds");
DEFINE_uint64(stats_persist_period_sec,
ROCKSDB_NAMESPACE::Options().stats_persist_period_sec,
"Gap between persisting stats in seconds");
DEFINE_bool(persist_stats_to_disk,
ROCKSDB_NAMESPACE::Options().persist_stats_to_disk,
"whether to persist stats to disk");
DEFINE_uint64(stats_history_buffer_size,
ROCKSDB_NAMESPACE::Options().stats_history_buffer_size,
"Max number of stats snapshots to keep in memory");
Introduce a new MultiGet batching implementation (#5011) Summary: This PR introduces a new MultiGet() API, with the underlying implementation grouping keys based on SST file and batching lookups in a file. The reason for the new API is twofold - the definition allows callers to allocate storage for status and values on stack instead of std::vector, as well as return values as PinnableSlices in order to avoid copying, and it keeps the original MultiGet() implementation intact while we experiment with batching. Batching is useful when there is some spatial locality to the keys being queries, as well as larger batch sizes. The main benefits are due to - 1. Fewer function calls, especially to BlockBasedTableReader::MultiGet() and FullFilterBlockReader::KeysMayMatch() 2. Bloom filter cachelines can be prefetched, hiding the cache miss latency The next step is to optimize the binary searches in the level_storage_info, index blocks and data blocks, since we could reduce the number of key comparisons if the keys are relatively close to each other. The batching optimizations also need to be extended to other formats, such as PlainTable and filter formats. This also needs to be added to db_stress. Benchmark results from db_bench for various batch size/locality of reference combinations are given below. Locality was simulated by offsetting the keys in a batch by a stride length. Each SST file is about 8.6MB uncompressed and key/value size is 16/100 uncompressed. To focus on the cpu benefit of batching, the runs were single threaded and bound to the same cpu to eliminate interference from other system events. The results show a 10-25% improvement in micros/op from smaller to larger batch sizes (4 - 32). Batch Sizes 1 | 2 | 4 | 8 | 16 | 32 Random pattern (Stride length 0) 4.158 | 4.109 | 4.026 | 4.05 | 4.1 | 4.074 - Get 4.438 | 4.302 | 4.165 | 4.122 | 4.096 | 4.075 - MultiGet (no batching) 4.461 | 4.256 | 4.277 | 4.11 | 4.182 | 4.14 - MultiGet (w/ batching) Good locality (Stride length 16) 4.048 | 3.659 | 3.248 | 2.99 | 2.84 | 2.753 4.429 | 3.728 | 3.406 | 3.053 | 2.911 | 2.781 4.452 | 3.45 | 2.833 | 2.451 | 2.233 | 2.135 Good locality (Stride length 256) 4.066 | 3.786 | 3.581 | 3.447 | 3.415 | 3.232 4.406 | 4.005 | 3.644 | 3.49 | 3.381 | 3.268 4.393 | 3.649 | 3.186 | 2.882 | 2.676 | 2.62 Medium locality (Stride length 4096) 4.012 | 3.922 | 3.768 | 3.61 | 3.582 | 3.555 4.364 | 4.057 | 3.791 | 3.65 | 3.57 | 3.465 4.479 | 3.758 | 3.316 | 3.077 | 2.959 | 2.891 dbbench command used (on a DB with 4 levels, 12 million keys)- TEST_TMPDIR=/dev/shm numactl -C 10 ./db_bench.tmp -use_existing_db=true -benchmarks="readseq,multireadrandom" -write_buffer_size=4194304 -target_file_size_base=4194304 -max_bytes_for_level_base=16777216 -num=12000000 -reads=12000000 -duration=90 -threads=1 -compression_type=none -cache_size=4194304000 -batch_size=32 -disable_auto_compactions=true -bloom_bits=10 -cache_index_and_filter_blocks=true -pin_l0_filter_and_index_blocks_in_cache=true -multiread_batched=true -multiread_stride=4 Pull Request resolved: https://github.com/facebook/rocksdb/pull/5011 Differential Revision: D14348703 Pulled By: anand1976 fbshipit-source-id: 774406dab3776d979c809522a67bedac6c17f84b
2019-04-11 23:24:09 +02:00
DEFINE_int64(multiread_stride, 0,
"Stride length for the keys in a MultiGet batch");
DEFINE_bool(multiread_batched, false, "Use the new MultiGet API");
DEFINE_string(memtablerep, "skip_list", "");
DEFINE_int64(hash_bucket_count, 1024 * 1024, "hash bucket count");
DEFINE_bool(use_plain_table, false, "if use plain table "
"instead of block-based table format");
DEFINE_bool(use_cuckoo_table, false, "if use cuckoo table format");
DEFINE_double(cuckoo_hash_ratio, 0.9, "Hash ratio for Cuckoo SST table.");
DEFINE_bool(use_hash_search, false, "if use kHashSearch "
"instead of kBinarySearch. "
"This is valid if only we use BlockTable");
Implement full filter for block based table. Summary: 1. Make filter_block.h a base class. Derive block_based_filter_block and full_filter_block. The previous one is the traditional filter block. The full_filter_block is newly added. It would generate a filter block that contain all the keys in SST file. 2. When querying a key, table would first check if full_filter is available. If not, it would go to the exact data block and check using block_based filter. 3. User could choose to use full_filter or tradional(block_based_filter). They would be stored in SST file with different meta index name. "filter.filter_policy" or "full_filter.filter_policy". Then, Table reader is able to know the fllter block type. 4. Some optimizations have been done for full_filter_block, thus it requires a different interface compared to the original one in filter_policy.h. 5. Actual implementation of filter bits coding/decoding is placed in util/bloom_impl.cc Benchmark: base commit 1d23b5c470844c1208301311f0889eca750431c0 Command: db_bench --db=/dev/shm/rocksdb --num_levels=6 --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --write_buffer_size=134217728 --max_write_buffer_number=2 --target_file_size_base=33554432 --max_bytes_for_level_base=1073741824 --verify_checksum=false --max_background_compactions=4 --use_plain_table=0 --memtablerep=prefix_hash --open_files=-1 --mmap_read=1 --mmap_write=0 --bloom_bits=10 --bloom_locality=1 --memtable_bloom_bits=500000 --compression_type=lz4 --num=393216000 --use_hash_search=1 --block_size=1024 --block_restart_interval=16 --use_existing_db=1 --threads=1 --benchmarks=readrandom —disable_auto_compactions=1 Read QPS increase for about 30% from 2230002 to 2991411. Test Plan: make all check valgrind db_test db_stress --use_block_based_filter = 0 ./auto_sanity_test.sh Reviewers: igor, yhchiang, ljin, sdong Reviewed By: sdong Subscribers: dhruba, leveldb Differential Revision: https://reviews.facebook.net/D20979
2014-09-08 19:37:05 +02:00
DEFINE_bool(use_block_based_filter, false, "if use kBlockBasedFilter "
"instead of kFullFilter for filter block. "
"This is valid if only we use BlockTable");
DEFINE_string(merge_operator, "", "The merge operator to use with the database."
"If a new merge operator is specified, be sure to use fresh"
" database The possible merge operators are defined in"
" utilities/merge_operators.h");
SkipListRep::LookaheadIterator Summary: This diff introduces the `lookahead` argument to `SkipListFactory()`. This is an optimization for the tailing use case which includes many seeks. E.g. consider the following operations on a skip list iterator: Seek(x), Next(), Next(), Seek(x+2), Next(), Seek(x+3), Next(), Next(), ... If `lookahead` is positive, `SkipListRep` will return an iterator which also keeps track of the previously visited node. Seek() then first does a linear search starting from that node (up to `lookahead` steps). As in the tailing example above, this may require fewer than ~log(n) comparisons as with regular skip list search. Test Plan: Added a new benchmark (`fillseekseq`) which simulates the usage pattern. It first writes N records (with consecutive keys), then measures how much time it takes to read them by calling `Seek()` and `Next()`. $ time ./db_bench -num 10000000 -benchmarks fillseekseq -prefix_size 1 \ -key_size 8 -write_buffer_size $[1024*1024*1024] -value_size 50 \ -seekseq_next 2 -skip_list_lookahead=0 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.389 micros/op 2569047 ops/sec; real 0m21.806s user 0m12.106s sys 0m9.672s $ time ./db_bench [...] -skip_list_lookahead=2 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.153 micros/op 6540684 ops/sec; real 0m19.469s user 0m10.192s sys 0m9.252s Reviewers: ljin, sdong, igor Reviewed By: igor Subscribers: dhruba, leveldb, march, lovro Differential Revision: https://reviews.facebook.net/D23997
2014-09-24 00:52:28 +02:00
DEFINE_int32(skip_list_lookahead, 0, "Used with skip_list memtablerep; try "
"linear search first for this many steps from the previous "
"position");
DEFINE_bool(report_file_operations, false, "if report number of file "
"operations");
Add -report_open_timing to db_bench (#8464) Summary: Hello and thanks for RocksDB, This PR adds support for ```-report_open_timing true``` to ```db_bench```. It can be useful when tuning RocksDB on filesystem/env with high latencies for file level operations (create/delete/rename...) seen during ```((Optimistic)Transaction)DB::Open```. Some examples: ``` > db_bench -benchmarks updaterandom -num 1 -db /dev/shm/db_bench > db_bench -benchmarks updaterandom -num 0 -db /dev/shm/db_bench -use_existing_db true -report_open_timing true -readonly true 2>&1 | grep OpenDb OpenDb: 3.90133 milliseconds > db_bench -benchmarks updaterandom -num 0 -db /dev/shm/db_bench -use_existing_db true -report_open_timing true -use_secondary_db true 2>&1 | grep OpenDb OpenDb: 3.33414 milliseconds > db_bench -benchmarks updaterandom -num 0 -db /dev/shm/db_bench -use_existing_db true -report_open_timing true 2>&1 | grep -A1 OpenDb OpenDb: 6.05423 milliseconds > db_bench -benchmarks updaterandom -num 1 > db_bench -benchmarks updaterandom -num 0 -use_existing_db true -report_open_timing true -readonly true 2>&1 | grep OpenDb OpenDb: 4.06859 milliseconds > db_bench -benchmarks updaterandom -num 0 -use_existing_db true -report_open_timing true -use_secondary_db true 2>&1 | grep OpenDb OpenDb: 2.85794 milliseconds > db_bench -benchmarks updaterandom -num 0 -use_existing_db true -report_open_timing true 2>&1 | grep OpenDb OpenDb: 6.46376 milliseconds > db_bench -benchmarks updaterandom -num 1 -db /clustered_fs/db_bench > db_bench -benchmarks updaterandom -num 0 -db /clustered_fs/db_bench -use_existing_db true -report_open_timing true -readonly true 2>&1 | grep OpenDb OpenDb: 3.79805 milliseconds > db_bench -benchmarks updaterandom -num 0 -db /clustered_fs/db_bench -use_existing_db true -report_open_timing true -use_secondary_db true 2>&1 | grep OpenDb OpenDb: 3.00174 milliseconds > db_bench -benchmarks updaterandom -num 0 -db /clustered_fs/db_bench -use_existing_db true -report_open_timing true 2>&1 | grep OpenDb OpenDb: 24.8732 milliseconds ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/8464 Reviewed By: hx235 Differential Revision: D29398096 Pulled By: zhichao-cao fbshipit-source-id: 8f05dc3284f084612a3f30234e39e1c37548f50c
2021-07-02 03:41:20 +02:00
DEFINE_bool(report_open_timing, false, "if report open timing");
DEFINE_int32(readahead_size, 0, "Iterator readahead size");
DEFINE_bool(read_with_latest_user_timestamp, true,
"If true, always use the current latest timestamp for read. If "
"false, choose a random timestamp from the past.");
#ifndef ROCKSDB_LITE
DEFINE_string(secondary_cache_uri, "",
"Full URI for creating a custom secondary cache object");
static class std::shared_ptr<ROCKSDB_NAMESPACE::SecondaryCache> secondary_cache;
#endif // ROCKSDB_LITE
static const bool FLAGS_soft_rate_limit_dummy __attribute__((__unused__)) =
RegisterFlagValidator(&FLAGS_soft_rate_limit, &ValidateRateLimit);
static const bool FLAGS_hard_rate_limit_dummy __attribute__((__unused__)) =
RegisterFlagValidator(&FLAGS_hard_rate_limit, &ValidateRateLimit);
static const bool FLAGS_prefix_size_dummy __attribute__((__unused__)) =
RegisterFlagValidator(&FLAGS_prefix_size, &ValidatePrefixSize);
static const bool FLAGS_key_size_dummy __attribute__((__unused__)) =
RegisterFlagValidator(&FLAGS_key_size, &ValidateKeySize);
static const bool FLAGS_cache_numshardbits_dummy __attribute__((__unused__)) =
RegisterFlagValidator(&FLAGS_cache_numshardbits,
&ValidateCacheNumshardbits);
static const bool FLAGS_readwritepercent_dummy __attribute__((__unused__)) =
RegisterFlagValidator(&FLAGS_readwritepercent, &ValidateInt32Percent);
DEFINE_int32(disable_seek_compaction, false,
"Not used, left here for backwards compatibility");
static const bool FLAGS_deletepercent_dummy __attribute__((__unused__)) =
RegisterFlagValidator(&FLAGS_deletepercent, &ValidateInt32Percent);
static const bool FLAGS_table_cache_numshardbits_dummy __attribute__((__unused__)) =
RegisterFlagValidator(&FLAGS_table_cache_numshardbits,
&ValidateTableCacheNumshardbits);
namespace ROCKSDB_NAMESPACE {
namespace {
static Status CreateMemTableRepFactory(
const ConfigOptions& config_options,
std::shared_ptr<MemTableRepFactory>* factory) {
Status s;
if (!strcasecmp(FLAGS_memtablerep.c_str(), SkipListFactory::kNickName())) {
factory->reset(new SkipListFactory(FLAGS_skip_list_lookahead));
#ifndef ROCKSDB_LITE
} else if (!strcasecmp(FLAGS_memtablerep.c_str(), "prefix_hash")) {
factory->reset(NewHashSkipListRepFactory(FLAGS_hash_bucket_count));
} else if (!strcasecmp(FLAGS_memtablerep.c_str(),
VectorRepFactory::kNickName())) {
factory->reset(new VectorRepFactory());
} else if (!strcasecmp(FLAGS_memtablerep.c_str(), "hash_linkedlist")) {
factory->reset(NewHashLinkListRepFactory(FLAGS_hash_bucket_count));
#endif // ROCKSDB_LITE
} else {
std::unique_ptr<MemTableRepFactory> unique;
s = MemTableRepFactory::CreateFromString(config_options, FLAGS_memtablerep,
&unique);
if (s.ok()) {
factory->reset(unique.release());
}
}
return s;
}
struct ReportFileOpCounters {
std::atomic<int> open_counter_;
Add more ops to: db_bench -report_file_operations (#8448) Summary: Hello and thanks for RocksDB, Here is a PR to add file deletes, renames and ```Flush()```, ```Sync()```, ```Fsync()``` and ```Close()``` to file ops report. The reason is to help tune RocksDB options when using an env/filesystem with high latencies for file level ("metadata") operations, typically seen during ```DB::Open``` (```db_bench -num 0``` also see https://github.com/facebook/rocksdb/pull/7203 where IOTracing does not trace ```DB::Open```). Before: ``` > db_bench -benchmarks updaterandom -num 0 -report_file_operations true ... Entries: 0 ... Num files opened: 12 Num Read(): 6 Num Append(): 8 Num bytes read: 6216 Num bytes written: 6289 ``` After: ``` > db_bench -benchmarks updaterandom -num 0 -report_file_operations true ... Entries: 0 ... Num files opened: 12 Num files deleted: 3 Num files renamed: 4 Num Flush(): 10 Num Sync(): 5 Num Fsync(): 1 Num Close(): 2 Num Read(): 6 Num Append(): 8 Num bytes read: 6216 Num bytes written: 6289 ``` Before: ``` > db_bench -benchmarks updaterandom -report_file_operations true ... Entries: 1000000 ... Num files opened: 18 Num Read(): 396339 Num Append(): 1000058 Num bytes read: 892030224 Num bytes written: 187569238 ``` After: ``` > db_bench -benchmarks updaterandom -report_file_operations true ... Entries: 1000000 ... Num files opened: 18 Num files deleted: 5 Num files renamed: 4 Num Flush(): 1000068 Num Sync(): 9 Num Fsync(): 1 Num Close(): 6 Num Read(): 396339 Num Append(): 1000058 Num bytes read: 892030224 Num bytes written: 187569238 ``` Another example showing how using ```DB::OpenForReadOnly``` reduces file operations compared to ```((Optimistic)Transaction)DB::Open```: ``` > db_bench -benchmarks updaterandom -num 1 > db_bench -benchmarks updaterandom -num 0 -use_existing_db true -readonly true -report_file_operations true ... Entries: 0 ... Num files opened: 8 Num files deleted: 0 Num files renamed: 0 Num Flush(): 0 Num Sync(): 0 Num Fsync(): 0 Num Close(): 0 Num Read(): 13 Num Append(): 0 Num bytes read: 374 Num bytes written: 0 ``` ``` > db_bench -benchmarks updaterandom -num 1 > db_bench -benchmarks updaterandom -num 0 -use_existing_db true -report_file_operations true ... Entries: 0 ... Num files opened: 14 Num files deleted: 3 Num files renamed: 4 Num Flush(): 14 Num Sync(): 5 Num Fsync(): 1 Num Close(): 3 Num Read(): 11 Num Append(): 10 Num bytes read: 7291 Num bytes written: 7357 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/8448 Reviewed By: anand1976 Differential Revision: D29333818 Pulled By: zhichao-cao fbshipit-source-id: a06a8c87f799806462319115195b3e94faf5f542
2021-06-24 20:56:03 +02:00
std::atomic<int> delete_counter_;
std::atomic<int> rename_counter_;
std::atomic<int> flush_counter_;
std::atomic<int> sync_counter_;
std::atomic<int> fsync_counter_;
std::atomic<int> close_counter_;
std::atomic<int> read_counter_;
std::atomic<int> append_counter_;
std::atomic<uint64_t> bytes_read_;
std::atomic<uint64_t> bytes_written_;
};
// A special Env to records and report file operations in db_bench
class ReportFileOpEnv : public EnvWrapper {
public:
explicit ReportFileOpEnv(Env* base) : EnvWrapper(base) { reset(); }
void reset() {
counters_.open_counter_ = 0;
Add more ops to: db_bench -report_file_operations (#8448) Summary: Hello and thanks for RocksDB, Here is a PR to add file deletes, renames and ```Flush()```, ```Sync()```, ```Fsync()``` and ```Close()``` to file ops report. The reason is to help tune RocksDB options when using an env/filesystem with high latencies for file level ("metadata") operations, typically seen during ```DB::Open``` (```db_bench -num 0``` also see https://github.com/facebook/rocksdb/pull/7203 where IOTracing does not trace ```DB::Open```). Before: ``` > db_bench -benchmarks updaterandom -num 0 -report_file_operations true ... Entries: 0 ... Num files opened: 12 Num Read(): 6 Num Append(): 8 Num bytes read: 6216 Num bytes written: 6289 ``` After: ``` > db_bench -benchmarks updaterandom -num 0 -report_file_operations true ... Entries: 0 ... Num files opened: 12 Num files deleted: 3 Num files renamed: 4 Num Flush(): 10 Num Sync(): 5 Num Fsync(): 1 Num Close(): 2 Num Read(): 6 Num Append(): 8 Num bytes read: 6216 Num bytes written: 6289 ``` Before: ``` > db_bench -benchmarks updaterandom -report_file_operations true ... Entries: 1000000 ... Num files opened: 18 Num Read(): 396339 Num Append(): 1000058 Num bytes read: 892030224 Num bytes written: 187569238 ``` After: ``` > db_bench -benchmarks updaterandom -report_file_operations true ... Entries: 1000000 ... Num files opened: 18 Num files deleted: 5 Num files renamed: 4 Num Flush(): 1000068 Num Sync(): 9 Num Fsync(): 1 Num Close(): 6 Num Read(): 396339 Num Append(): 1000058 Num bytes read: 892030224 Num bytes written: 187569238 ``` Another example showing how using ```DB::OpenForReadOnly``` reduces file operations compared to ```((Optimistic)Transaction)DB::Open```: ``` > db_bench -benchmarks updaterandom -num 1 > db_bench -benchmarks updaterandom -num 0 -use_existing_db true -readonly true -report_file_operations true ... Entries: 0 ... Num files opened: 8 Num files deleted: 0 Num files renamed: 0 Num Flush(): 0 Num Sync(): 0 Num Fsync(): 0 Num Close(): 0 Num Read(): 13 Num Append(): 0 Num bytes read: 374 Num bytes written: 0 ``` ``` > db_bench -benchmarks updaterandom -num 1 > db_bench -benchmarks updaterandom -num 0 -use_existing_db true -report_file_operations true ... Entries: 0 ... Num files opened: 14 Num files deleted: 3 Num files renamed: 4 Num Flush(): 14 Num Sync(): 5 Num Fsync(): 1 Num Close(): 3 Num Read(): 11 Num Append(): 10 Num bytes read: 7291 Num bytes written: 7357 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/8448 Reviewed By: anand1976 Differential Revision: D29333818 Pulled By: zhichao-cao fbshipit-source-id: a06a8c87f799806462319115195b3e94faf5f542
2021-06-24 20:56:03 +02:00
counters_.delete_counter_ = 0;
counters_.rename_counter_ = 0;
counters_.flush_counter_ = 0;
counters_.sync_counter_ = 0;
counters_.fsync_counter_ = 0;
counters_.close_counter_ = 0;
counters_.read_counter_ = 0;
counters_.append_counter_ = 0;
counters_.bytes_read_ = 0;
counters_.bytes_written_ = 0;
}
Status NewSequentialFile(const std::string& f,
std::unique_ptr<SequentialFile>* r,
const EnvOptions& soptions) override {
class CountingFile : public SequentialFile {
private:
std::unique_ptr<SequentialFile> target_;
ReportFileOpCounters* counters_;
public:
CountingFile(std::unique_ptr<SequentialFile>&& target,
ReportFileOpCounters* counters)
: target_(std::move(target)), counters_(counters) {}
Status Read(size_t n, Slice* result, char* scratch) override {
counters_->read_counter_.fetch_add(1, std::memory_order_relaxed);
Status rv = target_->Read(n, result, scratch);
counters_->bytes_read_.fetch_add(result->size(),
std::memory_order_relaxed);
return rv;
}
Status Skip(uint64_t n) override { return target_->Skip(n); }
};
Status s = target()->NewSequentialFile(f, r, soptions);
if (s.ok()) {
counters()->open_counter_.fetch_add(1, std::memory_order_relaxed);
r->reset(new CountingFile(std::move(*r), counters()));
}
return s;
}
Add more ops to: db_bench -report_file_operations (#8448) Summary: Hello and thanks for RocksDB, Here is a PR to add file deletes, renames and ```Flush()```, ```Sync()```, ```Fsync()``` and ```Close()``` to file ops report. The reason is to help tune RocksDB options when using an env/filesystem with high latencies for file level ("metadata") operations, typically seen during ```DB::Open``` (```db_bench -num 0``` also see https://github.com/facebook/rocksdb/pull/7203 where IOTracing does not trace ```DB::Open```). Before: ``` > db_bench -benchmarks updaterandom -num 0 -report_file_operations true ... Entries: 0 ... Num files opened: 12 Num Read(): 6 Num Append(): 8 Num bytes read: 6216 Num bytes written: 6289 ``` After: ``` > db_bench -benchmarks updaterandom -num 0 -report_file_operations true ... Entries: 0 ... Num files opened: 12 Num files deleted: 3 Num files renamed: 4 Num Flush(): 10 Num Sync(): 5 Num Fsync(): 1 Num Close(): 2 Num Read(): 6 Num Append(): 8 Num bytes read: 6216 Num bytes written: 6289 ``` Before: ``` > db_bench -benchmarks updaterandom -report_file_operations true ... Entries: 1000000 ... Num files opened: 18 Num Read(): 396339 Num Append(): 1000058 Num bytes read: 892030224 Num bytes written: 187569238 ``` After: ``` > db_bench -benchmarks updaterandom -report_file_operations true ... Entries: 1000000 ... Num files opened: 18 Num files deleted: 5 Num files renamed: 4 Num Flush(): 1000068 Num Sync(): 9 Num Fsync(): 1 Num Close(): 6 Num Read(): 396339 Num Append(): 1000058 Num bytes read: 892030224 Num bytes written: 187569238 ``` Another example showing how using ```DB::OpenForReadOnly``` reduces file operations compared to ```((Optimistic)Transaction)DB::Open```: ``` > db_bench -benchmarks updaterandom -num 1 > db_bench -benchmarks updaterandom -num 0 -use_existing_db true -readonly true -report_file_operations true ... Entries: 0 ... Num files opened: 8 Num files deleted: 0 Num files renamed: 0 Num Flush(): 0 Num Sync(): 0 Num Fsync(): 0 Num Close(): 0 Num Read(): 13 Num Append(): 0 Num bytes read: 374 Num bytes written: 0 ``` ``` > db_bench -benchmarks updaterandom -num 1 > db_bench -benchmarks updaterandom -num 0 -use_existing_db true -report_file_operations true ... Entries: 0 ... Num files opened: 14 Num files deleted: 3 Num files renamed: 4 Num Flush(): 14 Num Sync(): 5 Num Fsync(): 1 Num Close(): 3 Num Read(): 11 Num Append(): 10 Num bytes read: 7291 Num bytes written: 7357 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/8448 Reviewed By: anand1976 Differential Revision: D29333818 Pulled By: zhichao-cao fbshipit-source-id: a06a8c87f799806462319115195b3e94faf5f542
2021-06-24 20:56:03 +02:00
Status DeleteFile(const std::string& fname) override {
Status s = target()->DeleteFile(fname);
if (s.ok()) {
counters()->delete_counter_.fetch_add(1, std::memory_order_relaxed);
}
return s;
}
Status RenameFile(const std::string& s, const std::string& t) override {
Status st = target()->RenameFile(s, t);
if (st.ok()) {
counters()->rename_counter_.fetch_add(1, std::memory_order_relaxed);
}
return st;
}
Status NewRandomAccessFile(const std::string& f,
std::unique_ptr<RandomAccessFile>* r,
const EnvOptions& soptions) override {
class CountingFile : public RandomAccessFile {
private:
std::unique_ptr<RandomAccessFile> target_;
ReportFileOpCounters* counters_;
public:
CountingFile(std::unique_ptr<RandomAccessFile>&& target,
ReportFileOpCounters* counters)
: target_(std::move(target)), counters_(counters) {}
Status Read(uint64_t offset, size_t n, Slice* result,
char* scratch) const override {
counters_->read_counter_.fetch_add(1, std::memory_order_relaxed);
Status rv = target_->Read(offset, n, result, scratch);
counters_->bytes_read_.fetch_add(result->size(),
std::memory_order_relaxed);
return rv;
}
};
Status s = target()->NewRandomAccessFile(f, r, soptions);
if (s.ok()) {
counters()->open_counter_.fetch_add(1, std::memory_order_relaxed);
r->reset(new CountingFile(std::move(*r), counters()));
}
return s;
}
Status NewWritableFile(const std::string& f, std::unique_ptr<WritableFile>* r,
const EnvOptions& soptions) override {
class CountingFile : public WritableFile {
private:
std::unique_ptr<WritableFile> target_;
ReportFileOpCounters* counters_;
public:
CountingFile(std::unique_ptr<WritableFile>&& target,
ReportFileOpCounters* counters)
: target_(std::move(target)), counters_(counters) {}
Status Append(const Slice& data) override {
counters_->append_counter_.fetch_add(1, std::memory_order_relaxed);
Status rv = target_->Append(data);
counters_->bytes_written_.fetch_add(data.size(),
std::memory_order_relaxed);
return rv;
}
Status Append(
const Slice& data,
const DataVerificationInfo& /* verification_info */) override {
return Append(data);
}
Add more ops to: db_bench -report_file_operations (#8448) Summary: Hello and thanks for RocksDB, Here is a PR to add file deletes, renames and ```Flush()```, ```Sync()```, ```Fsync()``` and ```Close()``` to file ops report. The reason is to help tune RocksDB options when using an env/filesystem with high latencies for file level ("metadata") operations, typically seen during ```DB::Open``` (```db_bench -num 0``` also see https://github.com/facebook/rocksdb/pull/7203 where IOTracing does not trace ```DB::Open```). Before: ``` > db_bench -benchmarks updaterandom -num 0 -report_file_operations true ... Entries: 0 ... Num files opened: 12 Num Read(): 6 Num Append(): 8 Num bytes read: 6216 Num bytes written: 6289 ``` After: ``` > db_bench -benchmarks updaterandom -num 0 -report_file_operations true ... Entries: 0 ... Num files opened: 12 Num files deleted: 3 Num files renamed: 4 Num Flush(): 10 Num Sync(): 5 Num Fsync(): 1 Num Close(): 2 Num Read(): 6 Num Append(): 8 Num bytes read: 6216 Num bytes written: 6289 ``` Before: ``` > db_bench -benchmarks updaterandom -report_file_operations true ... Entries: 1000000 ... Num files opened: 18 Num Read(): 396339 Num Append(): 1000058 Num bytes read: 892030224 Num bytes written: 187569238 ``` After: ``` > db_bench -benchmarks updaterandom -report_file_operations true ... Entries: 1000000 ... Num files opened: 18 Num files deleted: 5 Num files renamed: 4 Num Flush(): 1000068 Num Sync(): 9 Num Fsync(): 1 Num Close(): 6 Num Read(): 396339 Num Append(): 1000058 Num bytes read: 892030224 Num bytes written: 187569238 ``` Another example showing how using ```DB::OpenForReadOnly``` reduces file operations compared to ```((Optimistic)Transaction)DB::Open```: ``` > db_bench -benchmarks updaterandom -num 1 > db_bench -benchmarks updaterandom -num 0 -use_existing_db true -readonly true -report_file_operations true ... Entries: 0 ... Num files opened: 8 Num files deleted: 0 Num files renamed: 0 Num Flush(): 0 Num Sync(): 0 Num Fsync(): 0 Num Close(): 0 Num Read(): 13 Num Append(): 0 Num bytes read: 374 Num bytes written: 0 ``` ``` > db_bench -benchmarks updaterandom -num 1 > db_bench -benchmarks updaterandom -num 0 -use_existing_db true -report_file_operations true ... Entries: 0 ... Num files opened: 14 Num files deleted: 3 Num files renamed: 4 Num Flush(): 14 Num Sync(): 5 Num Fsync(): 1 Num Close(): 3 Num Read(): 11 Num Append(): 10 Num bytes read: 7291 Num bytes written: 7357 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/8448 Reviewed By: anand1976 Differential Revision: D29333818 Pulled By: zhichao-cao fbshipit-source-id: a06a8c87f799806462319115195b3e94faf5f542
2021-06-24 20:56:03 +02:00
Status Truncate(uint64_t size) override {
return target_->Truncate(size);
}
Status Close() override {
Status s = target_->Close();
if (s.ok()) {
counters_->close_counter_.fetch_add(1, std::memory_order_relaxed);
}
return s;
}
Status Flush() override {
Status s = target_->Flush();
if (s.ok()) {
counters_->flush_counter_.fetch_add(1, std::memory_order_relaxed);
}
return s;
}
Status Sync() override {
Status s = target_->Sync();
if (s.ok()) {
counters_->sync_counter_.fetch_add(1, std::memory_order_relaxed);
}
return s;
}
Status Fsync() override {
Status s = target_->Fsync();
if (s.ok()) {
counters_->fsync_counter_.fetch_add(1, std::memory_order_relaxed);
}
return s;
}
};
Status s = target()->NewWritableFile(f, r, soptions);
if (s.ok()) {
counters()->open_counter_.fetch_add(1, std::memory_order_relaxed);
r->reset(new CountingFile(std::move(*r), counters()));
}
return s;
}
// getter
ReportFileOpCounters* counters() { return &counters_; }
private:
ReportFileOpCounters counters_;
};
} // namespace
enum DistributionType : unsigned char {
kFixed = 0,
kUniform,
kNormal
};
static enum DistributionType FLAGS_value_size_distribution_type_e = kFixed;
static enum DistributionType StringToDistributionType(const char* ctype) {
assert(ctype);
if (!strcasecmp(ctype, "fixed"))
return kFixed;
else if (!strcasecmp(ctype, "uniform"))
return kUniform;
else if (!strcasecmp(ctype, "normal"))
return kNormal;
fprintf(stdout, "Cannot parse distribution type '%s'\n", ctype);
return kFixed; // default value
}
class BaseDistribution {
public:
BaseDistribution(unsigned int _min, unsigned int _max)
: min_value_size_(_min), max_value_size_(_max) {}
virtual ~BaseDistribution() {}
unsigned int Generate() {
auto val = Get();
if (NeedTruncate()) {
val = std::max(min_value_size_, val);
val = std::min(max_value_size_, val);
}
return val;
}
private:
virtual unsigned int Get() = 0;
virtual bool NeedTruncate() {
return true;
}
unsigned int min_value_size_;
unsigned int max_value_size_;
};
class FixedDistribution : public BaseDistribution
{
public:
FixedDistribution(unsigned int size) :
BaseDistribution(size, size),
size_(size) {}
private:
virtual unsigned int Get() override {
return size_;
}
virtual bool NeedTruncate() override {
return false;
}
unsigned int size_;
};
class NormalDistribution
: public BaseDistribution, public std::normal_distribution<double> {
public:
NormalDistribution(unsigned int _min, unsigned int _max)
: BaseDistribution(_min, _max),
// 99.7% values within the range [min, max].
std::normal_distribution<double>(
(double)(_min + _max) / 2.0 /*mean*/,
(double)(_max - _min) / 6.0 /*stddev*/),
gen_(rd_()) {}
private:
virtual unsigned int Get() override {
return static_cast<unsigned int>((*this)(gen_));
}
std::random_device rd_;
std::mt19937 gen_;
};
class UniformDistribution
: public BaseDistribution,
public std::uniform_int_distribution<unsigned int> {
public:
UniformDistribution(unsigned int _min, unsigned int _max)
: BaseDistribution(_min, _max),
std::uniform_int_distribution<unsigned int>(_min, _max),
gen_(rd_()) {}
private:
virtual unsigned int Get() override {
return (*this)(gen_);
}
virtual bool NeedTruncate() override {
return false;
}
std::random_device rd_;
std::mt19937 gen_;
};
// Helper for quickly generating random data.
class RandomGenerator {
private:
std::string data_;
unsigned int pos_;
std::unique_ptr<BaseDistribution> dist_;
public:
RandomGenerator() {
auto max_value_size = FLAGS_value_size_max;
switch (FLAGS_value_size_distribution_type_e) {
case kUniform:
dist_.reset(new UniformDistribution(FLAGS_value_size_min,
FLAGS_value_size_max));
break;
case kNormal:
dist_.reset(new NormalDistribution(FLAGS_value_size_min,
FLAGS_value_size_max));
break;
case kFixed:
default:
dist_.reset(new FixedDistribution(value_size));
max_value_size = value_size;
}
// We use a limited amount of data over and over again and ensure
// that it is larger than the compression window (32KB), and also
// large enough to serve all typical value sizes we want to write.
Random rnd(301);
std::string piece;
while (data_.size() < (unsigned)std::max(1048576, max_value_size)) {
// Add a short fragment that is as compressible as specified
// by FLAGS_compression_ratio.
test::CompressibleString(&rnd, FLAGS_compression_ratio, 100, &piece);
data_.append(piece);
}
pos_ = 0;
}
Slice Generate(unsigned int len) {
assert(len <= data_.size());
if (pos_ + len > data_.size()) {
pos_ = 0;
}
pos_ += len;
return Slice(data_.data() + pos_ - len, len);
}
Slice Generate() {
auto len = dist_->Generate();
return Generate(len);
}
};
static void AppendWithSpace(std::string* str, Slice msg) {
if (msg.empty()) return;
if (!str->empty()) {
str->push_back(' ');
}
str->append(msg.data(), msg.size());
}
struct DBWithColumnFamilies {
std::vector<ColumnFamilyHandle*> cfh;
DB* db;
#ifndef ROCKSDB_LITE
OptimisticTransactionDB* opt_txn_db;
#endif // ROCKSDB_LITE
std::atomic<size_t> num_created; // Need to be updated after all the
// new entries in cfh are set.
size_t num_hot; // Number of column families to be queried at each moment.
// After each CreateNewCf(), another num_hot number of new
// Column families will be created and used to be queried.
port::Mutex create_cf_mutex; // Only one thread can execute CreateNewCf()
std::vector<int> cfh_idx_to_prob; // ith index holds probability of operating
// on cfh[i].
DBWithColumnFamilies()
: db(nullptr)
#ifndef ROCKSDB_LITE
, opt_txn_db(nullptr)
#endif // ROCKSDB_LITE
{
cfh.clear();
num_created = 0;
num_hot = 0;
}
DBWithColumnFamilies(const DBWithColumnFamilies& other)
: cfh(other.cfh),
db(other.db),
#ifndef ROCKSDB_LITE
opt_txn_db(other.opt_txn_db),
#endif // ROCKSDB_LITE
num_created(other.num_created.load()),
num_hot(other.num_hot),
cfh_idx_to_prob(other.cfh_idx_to_prob) {
}
void DeleteDBs() {
std::for_each(cfh.begin(), cfh.end(),
[](ColumnFamilyHandle* cfhi) { delete cfhi; });
cfh.clear();
#ifndef ROCKSDB_LITE
if (opt_txn_db) {
delete opt_txn_db;
opt_txn_db = nullptr;
} else {
delete db;
db = nullptr;
}
#else
delete db;
db = nullptr;
#endif // ROCKSDB_LITE
}
ColumnFamilyHandle* GetCfh(int64_t rand_num) {
assert(num_hot > 0);
size_t rand_offset = 0;
if (!cfh_idx_to_prob.empty()) {
assert(cfh_idx_to_prob.size() == num_hot);
int sum = 0;
while (sum + cfh_idx_to_prob[rand_offset] < rand_num % 100) {
sum += cfh_idx_to_prob[rand_offset];
++rand_offset;
}
assert(rand_offset < cfh_idx_to_prob.size());
} else {
rand_offset = rand_num % num_hot;
}
return cfh[num_created.load(std::memory_order_acquire) - num_hot +
rand_offset];
}
// stage: assume CF from 0 to stage * num_hot has be created. Need to create
// stage * num_hot + 1 to stage * (num_hot + 1).
void CreateNewCf(ColumnFamilyOptions options, int64_t stage) {
MutexLock l(&create_cf_mutex);
if ((stage + 1) * num_hot <= num_created) {
// Already created.
return;
}
auto new_num_created = num_created + num_hot;
assert(new_num_created <= cfh.size());
for (size_t i = num_created; i < new_num_created; i++) {
Status s =
db->CreateColumnFamily(options, ColumnFamilyName(i), &(cfh[i]));
if (!s.ok()) {
fprintf(stderr, "create column family error: %s\n",
s.ToString().c_str());
abort();
}
}
num_created.store(new_num_created, std::memory_order_release);
}
};
// a class that reports stats to CSV file
class ReporterAgent {
public:
ReporterAgent(Env* env, const std::string& fname,
uint64_t report_interval_secs)
: env_(env),
total_ops_done_(0),
last_report_(0),
report_interval_secs_(report_interval_secs),
stop_(false) {
auto s = env_->NewWritableFile(fname, &report_file_, EnvOptions());
if (s.ok()) {
s = report_file_->Append(Header() + "\n");
}
if (s.ok()) {
s = report_file_->Flush();
}
if (!s.ok()) {
fprintf(stderr, "Can't open %s: %s\n", fname.c_str(),
s.ToString().c_str());
abort();
}
reporting_thread_ = port::Thread([&]() { SleepAndReport(); });
}
~ReporterAgent() {
{
std::unique_lock<std::mutex> lk(mutex_);
stop_ = true;
stop_cv_.notify_all();
}
reporting_thread_.join();
}
// thread safe
void ReportFinishedOps(int64_t num_ops) {
total_ops_done_.fetch_add(num_ops);
}
private:
std::string Header() const { return "secs_elapsed,interval_qps"; }
void SleepAndReport() {
auto* clock = env_->GetSystemClock().get();
auto time_started = clock->NowMicros();
while (true) {
{
std::unique_lock<std::mutex> lk(mutex_);
if (stop_ ||
stop_cv_.wait_for(lk, std::chrono::seconds(report_interval_secs_),
[&]() { return stop_; })) {
// stopping
break;
}
// else -> timeout, which means time for a report!
}
auto total_ops_done_snapshot = total_ops_done_.load();
// round the seconds elapsed
auto secs_elapsed =
(clock->NowMicros() - time_started + kMicrosInSecond / 2) /
kMicrosInSecond;
std::string report = ToString(secs_elapsed) + "," +
ToString(total_ops_done_snapshot - last_report_) +
"\n";
auto s = report_file_->Append(report);
if (s.ok()) {
s = report_file_->Flush();
}
if (!s.ok()) {
fprintf(stderr,
"Can't write to report file (%s), stopping the reporting\n",
s.ToString().c_str());
break;
}
last_report_ = total_ops_done_snapshot;
}
}
Env* env_;
std::unique_ptr<WritableFile> report_file_;
std::atomic<int64_t> total_ops_done_;
int64_t last_report_;
const uint64_t report_interval_secs_;
ROCKSDB_NAMESPACE::port::Thread reporting_thread_;
std::mutex mutex_;
// will notify on stop
std::condition_variable stop_cv_;
bool stop_;
};
enum OperationType : unsigned char {
kRead = 0,
kWrite,
kDelete,
kSeek,
kMerge,
kUpdate,
kCompress,
kUncompress,
kCrc,
kHash,
kOthers
};
static std::unordered_map<OperationType, std::string, std::hash<unsigned char>>
OperationTypeString = {
{kRead, "read"},
{kWrite, "write"},
{kDelete, "delete"},
{kSeek, "seek"},
{kMerge, "merge"},
{kUpdate, "update"},
{kCompress, "compress"},
{kCompress, "uncompress"},
{kCrc, "crc"},
{kHash, "hash"},
{kOthers, "op"}
};
[db_bench] Support single benchmark arguments (Repeat for X times, Warm up for X times), Support CombinedStats (AVG / MEDIAN) Summary: This diff allow us to run a single benchmark X times and warm it up for Y times. and see the AVG & MEDIAN throughput of these X runs for example ``` $ ./db_bench --benchmarks="fillseq,readseq[X5-W2]" Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags RocksDB: version 4.12 Date: Wed Aug 24 10:45:26 2016 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Compression: Snappy Memtablerep: skip_list Perf Level: 1 WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags DB path: [/tmp/rocksdbtest-8616/dbbench] fillseq : 4.695 micros/op 212971 ops/sec; 23.6 MB/s DB path: [/tmp/rocksdbtest-8616/dbbench] Warming up benchmark by running 2 times readseq : 0.214 micros/op 4677005 ops/sec; 517.4 MB/s readseq : 0.212 micros/op 4706834 ops/sec; 520.7 MB/s Running benchmark for 5 times readseq : 0.218 micros/op 4588187 ops/sec; 507.6 MB/s readseq : 0.208 micros/op 4816538 ops/sec; 532.8 MB/s readseq : 0.213 micros/op 4685376 ops/sec; 518.3 MB/s readseq : 0.214 micros/op 4676787 ops/sec; 517.4 MB/s readseq : 0.217 micros/op 4618532 ops/sec; 510.9 MB/s readseq [AVG 5 runs] : 4677084 ops/sec; 517.4 MB/sec readseq [MEDIAN 5 runs] : 4676787 ops/sec; 517.4 MB/sec ``` Test Plan: run db_bench Reviewers: sdong, andrewkr, yhchiang Reviewed By: yhchiang Subscribers: andrewkr, dhruba Differential Revision: https://reviews.facebook.net/D62235
2016-08-25 21:57:35 +02:00
class CombinedStats;
class Stats {
private:
SystemClock* clock_;
int id_;
uint64_t start_ = 0;
uint64_t sine_interval_;
uint64_t finish_;
double seconds_;
uint64_t done_;
uint64_t last_report_done_;
uint64_t next_report_;
uint64_t bytes_;
uint64_t last_op_finish_;
uint64_t last_report_finish_;
std::unordered_map<OperationType, std::shared_ptr<HistogramImpl>,
std::hash<unsigned char>> hist_;
std::string message_;
bool exclude_from_merge_;
ReporterAgent* reporter_agent_; // does not own
[db_bench] Support single benchmark arguments (Repeat for X times, Warm up for X times), Support CombinedStats (AVG / MEDIAN) Summary: This diff allow us to run a single benchmark X times and warm it up for Y times. and see the AVG & MEDIAN throughput of these X runs for example ``` $ ./db_bench --benchmarks="fillseq,readseq[X5-W2]" Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags RocksDB: version 4.12 Date: Wed Aug 24 10:45:26 2016 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Compression: Snappy Memtablerep: skip_list Perf Level: 1 WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags DB path: [/tmp/rocksdbtest-8616/dbbench] fillseq : 4.695 micros/op 212971 ops/sec; 23.6 MB/s DB path: [/tmp/rocksdbtest-8616/dbbench] Warming up benchmark by running 2 times readseq : 0.214 micros/op 4677005 ops/sec; 517.4 MB/s readseq : 0.212 micros/op 4706834 ops/sec; 520.7 MB/s Running benchmark for 5 times readseq : 0.218 micros/op 4588187 ops/sec; 507.6 MB/s readseq : 0.208 micros/op 4816538 ops/sec; 532.8 MB/s readseq : 0.213 micros/op 4685376 ops/sec; 518.3 MB/s readseq : 0.214 micros/op 4676787 ops/sec; 517.4 MB/s readseq : 0.217 micros/op 4618532 ops/sec; 510.9 MB/s readseq [AVG 5 runs] : 4677084 ops/sec; 517.4 MB/sec readseq [MEDIAN 5 runs] : 4676787 ops/sec; 517.4 MB/sec ``` Test Plan: run db_bench Reviewers: sdong, andrewkr, yhchiang Reviewed By: yhchiang Subscribers: andrewkr, dhruba Differential Revision: https://reviews.facebook.net/D62235
2016-08-25 21:57:35 +02:00
friend class CombinedStats;
public:
Stats() : clock_(FLAGS_env->GetSystemClock().get()) { Start(-1); }
void SetReporterAgent(ReporterAgent* reporter_agent) {
reporter_agent_ = reporter_agent;
}
void Start(int id) {
id_ = id;
next_report_ = FLAGS_stats_interval ? FLAGS_stats_interval : 100;
last_op_finish_ = start_;
hist_.clear();
done_ = 0;
last_report_done_ = 0;
bytes_ = 0;
seconds_ = 0;
start_ = clock_->NowMicros();
sine_interval_ = clock_->NowMicros();
finish_ = start_;
last_report_finish_ = start_;
message_.clear();
// When set, stats from this thread won't be merged with others.
exclude_from_merge_ = false;
}
void Merge(const Stats& other) {
if (other.exclude_from_merge_)
return;
for (auto it = other.hist_.begin(); it != other.hist_.end(); ++it) {
auto this_it = hist_.find(it->first);
if (this_it != hist_.end()) {
this_it->second->Merge(*(other.hist_.at(it->first)));
} else {
hist_.insert({ it->first, it->second });
}
}
done_ += other.done_;
bytes_ += other.bytes_;
seconds_ += other.seconds_;
if (other.start_ < start_) start_ = other.start_;
if (other.finish_ > finish_) finish_ = other.finish_;
// Just keep the messages from one thread
if (message_.empty()) message_ = other.message_;
}
void Stop() {
finish_ = clock_->NowMicros();
seconds_ = (finish_ - start_) * 1e-6;
}
void AddMessage(Slice msg) {
AppendWithSpace(&message_, msg);
}
void SetId(int id) { id_ = id; }
void SetExcludeFromMerge() { exclude_from_merge_ = true; }
void PrintThreadStatus() {
std::vector<ThreadStatus> thread_list;
FLAGS_env->GetThreadList(&thread_list);
Allow GetThreadList() to report basic compaction operation properties. Summary: Now we're able to show more details about a compaction in GetThreadList() :) This patch allows GetThreadList() to report basic compaction operation properties. Basic compaction properties include: 1. job id 2. compaction input / output level 3. compaction property flags (is_manual, is_deletion, .. etc) 4. total input bytes 5. the number of bytes has been read currently. 6. the number of bytes has been written currently. Flush operation properties will be done in a seperate diff. Test Plan: /db_bench --threads=30 --num=1000000 --benchmarks=fillrandom --thread_status_per_interval=1 Sample output of tracking same job: ThreadID ThreadType cfName Operation ElapsedTime Stage State OperationProperties 140664171987072 Low Pri default Compaction 31.357 ms CompactionJob::FinishCompactionOutputFile BaseInputLevel 1 | BytesRead 2264663 | BytesWritten 1934241 | IsDeletion 0 | IsManual 0 | IsTrivialMove 0 | JobID 277 | OutputLevel 2 | TotalInputBytes 3964158 | ThreadID ThreadType cfName Operation ElapsedTime Stage State OperationProperties 140664171987072 Low Pri default Compaction 59.440 ms CompactionJob::FinishCompactionOutputFile BaseInputLevel 1 | BytesRead 2264663 | BytesWritten 1934241 | IsDeletion 0 | IsManual 0 | IsTrivialMove 0 | JobID 277 | OutputLevel 2 | TotalInputBytes 3964158 | ThreadID ThreadType cfName Operation ElapsedTime Stage State OperationProperties 140664171987072 Low Pri default Compaction 226.375 ms CompactionJob::Install BaseInputLevel 1 | BytesRead 3958013 | BytesWritten 3621940 | IsDeletion 0 | IsManual 0 | IsTrivialMove 0 | JobID 277 | OutputLevel 2 | TotalInputBytes 3964158 | Reviewers: sdong, rven, igor Reviewed By: igor Subscribers: dhruba, leveldb Differential Revision: https://reviews.facebook.net/D37653
2015-05-07 07:50:35 +02:00
fprintf(stderr, "\n%18s %10s %12s %20s %13s %45s %12s %s\n",
"ThreadID", "ThreadType", "cfName", "Operation",
Allow GetThreadList() to report basic compaction operation properties. Summary: Now we're able to show more details about a compaction in GetThreadList() :) This patch allows GetThreadList() to report basic compaction operation properties. Basic compaction properties include: 1. job id 2. compaction input / output level 3. compaction property flags (is_manual, is_deletion, .. etc) 4. total input bytes 5. the number of bytes has been read currently. 6. the number of bytes has been written currently. Flush operation properties will be done in a seperate diff. Test Plan: /db_bench --threads=30 --num=1000000 --benchmarks=fillrandom --thread_status_per_interval=1 Sample output of tracking same job: ThreadID ThreadType cfName Operation ElapsedTime Stage State OperationProperties 140664171987072 Low Pri default Compaction 31.357 ms CompactionJob::FinishCompactionOutputFile BaseInputLevel 1 | BytesRead 2264663 | BytesWritten 1934241 | IsDeletion 0 | IsManual 0 | IsTrivialMove 0 | JobID 277 | OutputLevel 2 | TotalInputBytes 3964158 | ThreadID ThreadType cfName Operation ElapsedTime Stage State OperationProperties 140664171987072 Low Pri default Compaction 59.440 ms CompactionJob::FinishCompactionOutputFile BaseInputLevel 1 | BytesRead 2264663 | BytesWritten 1934241 | IsDeletion 0 | IsManual 0 | IsTrivialMove 0 | JobID 277 | OutputLevel 2 | TotalInputBytes 3964158 | ThreadID ThreadType cfName Operation ElapsedTime Stage State OperationProperties 140664171987072 Low Pri default Compaction 226.375 ms CompactionJob::Install BaseInputLevel 1 | BytesRead 3958013 | BytesWritten 3621940 | IsDeletion 0 | IsManual 0 | IsTrivialMove 0 | JobID 277 | OutputLevel 2 | TotalInputBytes 3964158 | Reviewers: sdong, rven, igor Reviewed By: igor Subscribers: dhruba, leveldb Differential Revision: https://reviews.facebook.net/D37653
2015-05-07 07:50:35 +02:00
"ElapsedTime", "Stage", "State", "OperationProperties");
int64_t current_time = 0;
clock_->GetCurrentTime(&current_time).PermitUncheckedError();
for (auto ts : thread_list) {
Allow GetThreadList() to report basic compaction operation properties. Summary: Now we're able to show more details about a compaction in GetThreadList() :) This patch allows GetThreadList() to report basic compaction operation properties. Basic compaction properties include: 1. job id 2. compaction input / output level 3. compaction property flags (is_manual, is_deletion, .. etc) 4. total input bytes 5. the number of bytes has been read currently. 6. the number of bytes has been written currently. Flush operation properties will be done in a seperate diff. Test Plan: /db_bench --threads=30 --num=1000000 --benchmarks=fillrandom --thread_status_per_interval=1 Sample output of tracking same job: ThreadID ThreadType cfName Operation ElapsedTime Stage State OperationProperties 140664171987072 Low Pri default Compaction 31.357 ms CompactionJob::FinishCompactionOutputFile BaseInputLevel 1 | BytesRead 2264663 | BytesWritten 1934241 | IsDeletion 0 | IsManual 0 | IsTrivialMove 0 | JobID 277 | OutputLevel 2 | TotalInputBytes 3964158 | ThreadID ThreadType cfName Operation ElapsedTime Stage State OperationProperties 140664171987072 Low Pri default Compaction 59.440 ms CompactionJob::FinishCompactionOutputFile BaseInputLevel 1 | BytesRead 2264663 | BytesWritten 1934241 | IsDeletion 0 | IsManual 0 | IsTrivialMove 0 | JobID 277 | OutputLevel 2 | TotalInputBytes 3964158 | ThreadID ThreadType cfName Operation ElapsedTime Stage State OperationProperties 140664171987072 Low Pri default Compaction 226.375 ms CompactionJob::Install BaseInputLevel 1 | BytesRead 3958013 | BytesWritten 3621940 | IsDeletion 0 | IsManual 0 | IsTrivialMove 0 | JobID 277 | OutputLevel 2 | TotalInputBytes 3964158 | Reviewers: sdong, rven, igor Reviewed By: igor Subscribers: dhruba, leveldb Differential Revision: https://reviews.facebook.net/D37653
2015-05-07 07:50:35 +02:00
fprintf(stderr, "%18" PRIu64 " %10s %12s %20s %13s %45s %12s",
ts.thread_id,
ThreadStatus::GetThreadTypeName(ts.thread_type).c_str(),
ts.cf_name.c_str(),
ThreadStatus::GetOperationName(ts.operation_type).c_str(),
Report elapsed time in micros in ThreadStatus instead of start time. Summary: Report elapsed time of a thread operation in micros in ThreadStatus instead of start time of a thread operation in seconds since the Epoch, 1970-01-01 00:00:00 (UTC). Test Plan: ./db_bench --benchmarks=fillrandom --num=100000 --threads=40 \ --max_background_compactions=10 --max_background_flushes=3 \ --thread_status_per_interval=1000 --key_size=16 --value_size=1000 \ --num_column_families=10 Sample Output: ThreadID ThreadType cfName Operation ElapsedTime Stage State 140667724562496 High Pri column_family_name_000002 Flush 772.419 ms FlushJob::WriteLevel0Table 140667728756800 High Pri default Flush 617.845 ms FlushJob::WriteLevel0Table 140667732951104 High Pri column_family_name_000005 Flush 772.078 ms FlushJob::WriteLevel0Table 140667875557440 Low Pri column_family_name_000008 Compaction 1409.216 ms CompactionJob::Install 140667737145408 Low Pri 140667749728320 Low Pri 140667816837184 Low Pri column_family_name_000007 Compaction 1071.815 ms CompactionJob::ProcessKeyValueCompaction 140667787477056 Low Pri column_family_name_000009 Compaction 772.516 ms CompactionJob::ProcessKeyValueCompaction 140667741339712 Low Pri 140667758116928 Low Pri column_family_name_000004 Compaction 620.739 ms CompactionJob::ProcessKeyValueCompaction 140667753922624 Low Pri 140667842003008 Low Pri column_family_name_000006 Compaction 1260.079 ms CompactionJob::ProcessKeyValueCompaction 140667745534016 Low Pri Reviewers: sdong, igor, rven Subscribers: dhruba, leveldb Differential Revision: https://reviews.facebook.net/D35769
2015-03-24 00:35:04 +01:00
ThreadStatus::MicrosToString(ts.op_elapsed_micros).c_str(),
Allow GetThreadList() to report operation stage. Summary: Allow GetThreadList() to report operation stage. Test Plan: ./thread_list_test ./db_bench --benchmarks=fillrandom --num=100000 --threads=40 \ --max_background_compactions=10 --max_background_flushes=3 \ --thread_status_per_interval=1000 --key_size=16 --value_size=1000 \ --num_column_families=10 export ROCKSDB_TESTS=ThreadStatus ./db_test Sample output ThreadID ThreadType cfName Operation OP_StartTime ElapsedTime Stage State 140116265861184 Low Pri 140116270055488 Low Pri 140116274249792 High Pri column_family_name_000005 Flush 2015/03/10-14:58:11 0 us FlushJob::WriteLevel0Table 140116400078912 Low Pri column_family_name_000004 Compaction 2015/03/10-14:58:11 0 us CompactionJob::FinishCompactionOutputFile 140116358135872 Low Pri column_family_name_000006 Compaction 2015/03/10-14:58:10 1 us CompactionJob::FinishCompactionOutputFile 140116341358656 Low Pri 140116295221312 High Pri default Flush 2015/03/10-14:58:11 0 us FlushJob::WriteLevel0Table 140116324581440 Low Pri column_family_name_000009 Compaction 2015/03/10-14:58:11 0 us CompactionJob::ProcessKeyValueCompaction 140116278444096 Low Pri 140116299415616 Low Pri column_family_name_000008 Compaction 2015/03/10-14:58:11 0 us CompactionJob::FinishCompactionOutputFile 140116291027008 High Pri column_family_name_000001 Flush 2015/03/10-14:58:11 0 us FlushJob::WriteLevel0Table 140116286832704 Low Pri column_family_name_000002 Compaction 2015/03/10-14:58:11 0 us CompactionJob::FinishCompactionOutputFile 140116282638400 Low Pri Reviewers: rven, igor, sdong Reviewed By: sdong Subscribers: dhruba, leveldb Differential Revision: https://reviews.facebook.net/D34683
2015-03-13 18:45:40 +01:00
ThreadStatus::GetOperationStageName(ts.operation_stage).c_str(),
ThreadStatus::GetStateName(ts.state_type).c_str());
Allow GetThreadList() to report basic compaction operation properties. Summary: Now we're able to show more details about a compaction in GetThreadList() :) This patch allows GetThreadList() to report basic compaction operation properties. Basic compaction properties include: 1. job id 2. compaction input / output level 3. compaction property flags (is_manual, is_deletion, .. etc) 4. total input bytes 5. the number of bytes has been read currently. 6. the number of bytes has been written currently. Flush operation properties will be done in a seperate diff. Test Plan: /db_bench --threads=30 --num=1000000 --benchmarks=fillrandom --thread_status_per_interval=1 Sample output of tracking same job: ThreadID ThreadType cfName Operation ElapsedTime Stage State OperationProperties 140664171987072 Low Pri default Compaction 31.357 ms CompactionJob::FinishCompactionOutputFile BaseInputLevel 1 | BytesRead 2264663 | BytesWritten 1934241 | IsDeletion 0 | IsManual 0 | IsTrivialMove 0 | JobID 277 | OutputLevel 2 | TotalInputBytes 3964158 | ThreadID ThreadType cfName Operation ElapsedTime Stage State OperationProperties 140664171987072 Low Pri default Compaction 59.440 ms CompactionJob::FinishCompactionOutputFile BaseInputLevel 1 | BytesRead 2264663 | BytesWritten 1934241 | IsDeletion 0 | IsManual 0 | IsTrivialMove 0 | JobID 277 | OutputLevel 2 | TotalInputBytes 3964158 | ThreadID ThreadType cfName Operation ElapsedTime Stage State OperationProperties 140664171987072 Low Pri default Compaction 226.375 ms CompactionJob::Install BaseInputLevel 1 | BytesRead 3958013 | BytesWritten 3621940 | IsDeletion 0 | IsManual 0 | IsTrivialMove 0 | JobID 277 | OutputLevel 2 | TotalInputBytes 3964158 | Reviewers: sdong, rven, igor Reviewed By: igor Subscribers: dhruba, leveldb Differential Revision: https://reviews.facebook.net/D37653
2015-05-07 07:50:35 +02:00
auto op_properties = ThreadStatus::InterpretOperationProperties(
ts.operation_type, ts.op_properties);
for (const auto& op_prop : op_properties) {
fprintf(stderr, " %s %" PRIu64" |",
op_prop.first.c_str(), op_prop.second);
}
fprintf(stderr, "\n");
}
}
void ResetSineInterval() { sine_interval_ = clock_->NowMicros(); }
uint64_t GetSineInterval() {
return sine_interval_;
}
uint64_t GetStart() {
return start_;
}
void ResetLastOpTime() {
// Set to now to avoid latency from calls to SleepForMicroseconds
last_op_finish_ = clock_->NowMicros();
}
void FinishedOps(DBWithColumnFamilies* db_with_cfh, DB* db, int64_t num_ops,
enum OperationType op_type = kOthers) {
if (reporter_agent_) {
reporter_agent_->ReportFinishedOps(num_ops);
}
if (FLAGS_histogram) {
uint64_t now = clock_->NowMicros();
uint64_t micros = now - last_op_finish_;
if (hist_.find(op_type) == hist_.end())
{
auto hist_temp = std::make_shared<HistogramImpl>();
hist_.insert({op_type, std::move(hist_temp)});
}
hist_[op_type]->Add(micros);
if (micros > 20000 && !FLAGS_stats_interval) {
fprintf(stderr, "long op: %" PRIu64 " micros%30s\r", micros, "");
fflush(stderr);
}
last_op_finish_ = now;
}
done_ += num_ops;
if (done_ >= next_report_) {
if (!FLAGS_stats_interval) {
if (next_report_ < 1000) next_report_ += 100;
else if (next_report_ < 5000) next_report_ += 500;
else if (next_report_ < 10000) next_report_ += 1000;
else if (next_report_ < 50000) next_report_ += 5000;
else if (next_report_ < 100000) next_report_ += 10000;
else if (next_report_ < 500000) next_report_ += 50000;
else next_report_ += 100000;
fprintf(stderr, "... finished %" PRIu64 " ops%30s\r", done_, "");
} else {
uint64_t now = clock_->NowMicros();
int64_t usecs_since_last = now - last_report_finish_;
// Determine whether to print status where interval is either
// each N operations or each N seconds.
if (FLAGS_stats_interval_seconds &&
usecs_since_last < (FLAGS_stats_interval_seconds * 1000000)) {
// Don't check again for this many operations
next_report_ += FLAGS_stats_interval;
} else {
fprintf(stderr,
"%s ... thread %d: (%" PRIu64 ",%" PRIu64
") ops and "
"(%.1f,%.1f) ops/second in (%.6f,%.6f) seconds\n",
clock_->TimeToString(now / 1000000).c_str(), id_,
done_ - last_report_done_, done_,
(done_ - last_report_done_) / (usecs_since_last / 1000000.0),
done_ / ((now - start_) / 1000000.0),
(now - last_report_finish_) / 1000000.0,
(now - start_) / 1000000.0);
if (id_ == 0 && FLAGS_stats_per_interval) {
std::string stats;
if (db_with_cfh && db_with_cfh->num_created.load()) {
for (size_t i = 0; i < db_with_cfh->num_created.load(); ++i) {
if (db->GetProperty(db_with_cfh->cfh[i], "rocksdb.cfstats",
&stats))
fprintf(stderr, "%s\n", stats.c_str());
Add argument --show_table_properties to db_bench Summary: Add argument --show_table_properties to db_bench -show_table_properties (If true, then per-level table properties will be printed on every stats-interval when stats_interval is set and stats_per_interval is on.) type: bool default: false Test Plan: ./db_bench --show_table_properties=1 --stats_interval=100000 --stats_per_interval=1 ./db_bench --show_table_properties=1 --stats_interval=100000 --stats_per_interval=1 --num_column_families=2 Sample Output: Compaction Stats [column_family_name_000001] Level Files Size(MB) Score Read(GB) Rn(GB) Rnp1(GB) Write(GB) Wnew(GB) Moved(GB) W-Amp Rd(MB/s) Wr(MB/s) Comp(sec) Comp(cnt) Avg(sec) Stall(cnt) KeyIn KeyDrop --------------------------------------------------------------------------------------------------------------------------------------------------------------------- L0 3/0 5 0.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 86.3 0 17 0.021 0 0 0 L1 5/0 9 0.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 0 0.000 0 0 0 L2 9/0 16 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0 0 0.000 0 0 0 Sum 17/0 31 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 86.3 0 17 0.021 0 0 0 Int 0/0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 83.9 0 2 0.022 0 0 0 Flush(GB): cumulative 0.030, interval 0.004 Stalls(count): 0 level0_slowdown, 0 level0_numfiles, 0 memtable_compaction, 0 leveln_slowdown_soft, 0 leveln_slowdown_hard Level[0]: # data blocks=2571; # entries=84813; raw key size=2035512; raw average key size=24.000000; raw value size=8481300; raw average value size=100.000000; data block size=5690119; index block size=82415; filter block size=0; (estimated) table size=5772534; filter policy name=N/A; Level[1]: # data blocks=4285; # entries=141355; raw key size=3392520; raw average key size=24.000000; raw value size=14135500; raw average value size=100.000000; data block size=9487353; index block size=137377; filter block size=0; (estimated) table size=9624730; filter policy name=N/A; Level[2]: # data blocks=7713; # entries=254439; raw key size=6106536; raw average key size=24.000000; raw value size=25443900; raw average value size=100.000000; data block size=17077893; index block size=247269; filter block size=0; (estimated) table size=17325162; filter policy name=N/A; Level[3]: # data blocks=0; # entries=0; raw key size=0; raw average key size=0.000000; raw value size=0; raw average value size=0.000000; data block size=0; index block size=0; filter block size=0; (estimated) table size=0; filter policy name=N/A; Level[4]: # data blocks=0; # entries=0; raw key size=0; raw average key size=0.000000; raw value size=0; raw average value size=0.000000; data block size=0; index block size=0; filter block size=0; (estimated) table size=0; filter policy name=N/A; Level[5]: # data blocks=0; # entries=0; raw key size=0; raw average key size=0.000000; raw value size=0; raw average value size=0.000000; data block size=0; index block size=0; filter block size=0; (estimated) table size=0; filter policy name=N/A; Level[6]: # data blocks=0; # entries=0; raw key size=0; raw average key size=0.000000; raw value size=0; raw average value size=0.000000; data block size=0; index block size=0; filter block size=0; (estimated) table size=0; filter policy name=N/A; Reviewers: anthony, IslamAbdelRahman, MarkCallaghan, sdong, igor Reviewed By: igor Subscribers: dhruba, leveldb Differential Revision: https://reviews.facebook.net/D45651
2015-08-27 03:27:23 +02:00
if (FLAGS_show_table_properties) {
for (int level = 0; level < FLAGS_num_levels; ++level) {
if (db->GetProperty(
db_with_cfh->cfh[i],
"rocksdb.aggregated-table-properties-at-level" +
ToString(level),
&stats)) {
if (stats.find("# entries=0") == std::string::npos) {
fprintf(stderr, "Level[%d]: %s\n", level,
stats.c_str());
}
}
}
}
}
} else if (db) {
if (db->GetProperty("rocksdb.stats", &stats)) {
fprintf(stderr, "%s\n", stats.c_str());
}
if (FLAGS_show_table_properties) {
for (int level = 0; level < FLAGS_num_levels; ++level) {
if (db->GetProperty(
"rocksdb.aggregated-table-properties-at-level" +
ToString(level),
&stats)) {
if (stats.find("# entries=0") == std::string::npos) {
fprintf(stderr, "Level[%d]: %s\n", level, stats.c_str());
}
}
}
}
}
}
Improve statistics Summary: This adds more statistics to be reported by GetProperty("leveldb.stats"). The new stats include time spent waiting on stalls in MakeRoomForWrite. This also includes the total amplification rate where that is: (#bytes of sequential IO during compaction) / (#bytes from Put) This also includes a lot more data for the per-level compaction report. * Rn(MB) - MB read from level N during compaction between levels N and N+1 * Rnp1(MB) - MB read from level N+1 during compaction between levels N and N+1 * Wnew(MB) - new data written to the level during compaction * Amplify - ( Write(MB) + Rnp1(MB) ) / Rn(MB) * Rn - files read from level N during compaction between levels N and N+1 * Rnp1 - files read from level N+1 during compaction between levels N and N+1 * Wnp1 - files written to level N+1 during compaction between levels N and N+1 * NewW - new files written to level N+1 during compaction * Count - number of compactions done for this level This is the new output from DB::GetProperty("leveldb.stats"). The old output stopped at Write(MB) Compactions Level Files Size(MB) Time(sec) Read(MB) Write(MB) Rn(MB) Rnp1(MB) Wnew(MB) Amplify Read(MB/s) Write(MB/s) Rn Rnp1 Wnp1 NewW Count ------------------------------------------------------------------------------------------------------------------------------------- 0 3 6 33 0 576 0 0 576 -1.0 0.0 1.3 0 0 0 0 290 1 127 242 351 5316 5314 570 4747 567 17.0 12.1 12.1 287 2399 2685 286 32 2 161 328 54 822 824 326 496 328 4.0 1.9 1.9 160 251 411 160 161 Amplification: 22.3 rate, 0.56 GB in, 12.55 GB out Uptime(secs): 439.8 Stalls(secs): 206.938 level0_slowdown, 0.000 level0_numfiles, 24.129 memtable_compaction Task ID: # Blame Rev: Test Plan: run db_bench Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - (cherry picked from commit ecdeead38f86cc02e754d0032600742c4f02fec8) Reviewers: dhruba Differential Revision: https://reviews.facebook.net/D6153
2012-10-23 19:34:09 +02:00
next_report_ += FLAGS_stats_interval;
last_report_finish_ = now;
last_report_done_ = done_;
}
}
if (id_ == 0 && FLAGS_thread_status_per_interval) {
PrintThreadStatus();
}
fflush(stderr);
}
}
void AddBytes(int64_t n) {
bytes_ += n;
}
void Report(const Slice& name) {
// Pretend at least one op was done in case we are running a benchmark
// that does not call FinishedOps().
if (done_ < 1) done_ = 1;
std::string extra;
if (bytes_ > 0) {
// Rate is computed on actual elapsed time, not the sum of per-thread
// elapsed times.
double elapsed = (finish_ - start_) * 1e-6;
char rate[100];
snprintf(rate, sizeof(rate), "%6.1f MB/s",
(bytes_ / 1048576.0) / elapsed);
extra = rate;
}
AppendWithSpace(&extra, message_);
double elapsed = (finish_ - start_) * 1e-6;
double throughput = (double)done_/elapsed;
fprintf(stdout, "%-12s : %11.3f micros/op %ld ops/sec;%s%s\n",
name.ToString().c_str(),
seconds_ * 1e6 / done_,
(long)throughput,
(extra.empty() ? "" : " "),
extra.c_str());
if (FLAGS_histogram) {
for (auto it = hist_.begin(); it != hist_.end(); ++it) {
fprintf(stdout, "Microseconds per %s:\n%s\n",
OperationTypeString[it->first].c_str(),
it->second->ToString().c_str());
}
}
if (FLAGS_report_file_operations) {
ReportFileOpEnv* env = static_cast<ReportFileOpEnv*>(FLAGS_env);
ReportFileOpCounters* counters = env->counters();
fprintf(stdout, "Num files opened: %d\n",
counters->open_counter_.load(std::memory_order_relaxed));
Add more ops to: db_bench -report_file_operations (#8448) Summary: Hello and thanks for RocksDB, Here is a PR to add file deletes, renames and ```Flush()```, ```Sync()```, ```Fsync()``` and ```Close()``` to file ops report. The reason is to help tune RocksDB options when using an env/filesystem with high latencies for file level ("metadata") operations, typically seen during ```DB::Open``` (```db_bench -num 0``` also see https://github.com/facebook/rocksdb/pull/7203 where IOTracing does not trace ```DB::Open```). Before: ``` > db_bench -benchmarks updaterandom -num 0 -report_file_operations true ... Entries: 0 ... Num files opened: 12 Num Read(): 6 Num Append(): 8 Num bytes read: 6216 Num bytes written: 6289 ``` After: ``` > db_bench -benchmarks updaterandom -num 0 -report_file_operations true ... Entries: 0 ... Num files opened: 12 Num files deleted: 3 Num files renamed: 4 Num Flush(): 10 Num Sync(): 5 Num Fsync(): 1 Num Close(): 2 Num Read(): 6 Num Append(): 8 Num bytes read: 6216 Num bytes written: 6289 ``` Before: ``` > db_bench -benchmarks updaterandom -report_file_operations true ... Entries: 1000000 ... Num files opened: 18 Num Read(): 396339 Num Append(): 1000058 Num bytes read: 892030224 Num bytes written: 187569238 ``` After: ``` > db_bench -benchmarks updaterandom -report_file_operations true ... Entries: 1000000 ... Num files opened: 18 Num files deleted: 5 Num files renamed: 4 Num Flush(): 1000068 Num Sync(): 9 Num Fsync(): 1 Num Close(): 6 Num Read(): 396339 Num Append(): 1000058 Num bytes read: 892030224 Num bytes written: 187569238 ``` Another example showing how using ```DB::OpenForReadOnly``` reduces file operations compared to ```((Optimistic)Transaction)DB::Open```: ``` > db_bench -benchmarks updaterandom -num 1 > db_bench -benchmarks updaterandom -num 0 -use_existing_db true -readonly true -report_file_operations true ... Entries: 0 ... Num files opened: 8 Num files deleted: 0 Num files renamed: 0 Num Flush(): 0 Num Sync(): 0 Num Fsync(): 0 Num Close(): 0 Num Read(): 13 Num Append(): 0 Num bytes read: 374 Num bytes written: 0 ``` ``` > db_bench -benchmarks updaterandom -num 1 > db_bench -benchmarks updaterandom -num 0 -use_existing_db true -report_file_operations true ... Entries: 0 ... Num files opened: 14 Num files deleted: 3 Num files renamed: 4 Num Flush(): 14 Num Sync(): 5 Num Fsync(): 1 Num Close(): 3 Num Read(): 11 Num Append(): 10 Num bytes read: 7291 Num bytes written: 7357 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/8448 Reviewed By: anand1976 Differential Revision: D29333818 Pulled By: zhichao-cao fbshipit-source-id: a06a8c87f799806462319115195b3e94faf5f542
2021-06-24 20:56:03 +02:00
fprintf(stdout, "Num files deleted: %d\n",
counters->delete_counter_.load(std::memory_order_relaxed));
fprintf(stdout, "Num files renamed: %d\n",
counters->rename_counter_.load(std::memory_order_relaxed));
fprintf(stdout, "Num Flush(): %d\n",
counters->flush_counter_.load(std::memory_order_relaxed));
fprintf(stdout, "Num Sync(): %d\n",
counters->sync_counter_.load(std::memory_order_relaxed));
fprintf(stdout, "Num Fsync(): %d\n",
counters->fsync_counter_.load(std::memory_order_relaxed));
fprintf(stdout, "Num Close(): %d\n",
counters->close_counter_.load(std::memory_order_relaxed));
fprintf(stdout, "Num Read(): %d\n",
counters->read_counter_.load(std::memory_order_relaxed));
fprintf(stdout, "Num Append(): %d\n",
counters->append_counter_.load(std::memory_order_relaxed));
fprintf(stdout, "Num bytes read: %" PRIu64 "\n",
counters->bytes_read_.load(std::memory_order_relaxed));
fprintf(stdout, "Num bytes written: %" PRIu64 "\n",
counters->bytes_written_.load(std::memory_order_relaxed));
env->reset();
}
fflush(stdout);
}
};
[db_bench] Support single benchmark arguments (Repeat for X times, Warm up for X times), Support CombinedStats (AVG / MEDIAN) Summary: This diff allow us to run a single benchmark X times and warm it up for Y times. and see the AVG & MEDIAN throughput of these X runs for example ``` $ ./db_bench --benchmarks="fillseq,readseq[X5-W2]" Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags RocksDB: version 4.12 Date: Wed Aug 24 10:45:26 2016 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Compression: Snappy Memtablerep: skip_list Perf Level: 1 WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags DB path: [/tmp/rocksdbtest-8616/dbbench] fillseq : 4.695 micros/op 212971 ops/sec; 23.6 MB/s DB path: [/tmp/rocksdbtest-8616/dbbench] Warming up benchmark by running 2 times readseq : 0.214 micros/op 4677005 ops/sec; 517.4 MB/s readseq : 0.212 micros/op 4706834 ops/sec; 520.7 MB/s Running benchmark for 5 times readseq : 0.218 micros/op 4588187 ops/sec; 507.6 MB/s readseq : 0.208 micros/op 4816538 ops/sec; 532.8 MB/s readseq : 0.213 micros/op 4685376 ops/sec; 518.3 MB/s readseq : 0.214 micros/op 4676787 ops/sec; 517.4 MB/s readseq : 0.217 micros/op 4618532 ops/sec; 510.9 MB/s readseq [AVG 5 runs] : 4677084 ops/sec; 517.4 MB/sec readseq [MEDIAN 5 runs] : 4676787 ops/sec; 517.4 MB/sec ``` Test Plan: run db_bench Reviewers: sdong, andrewkr, yhchiang Reviewed By: yhchiang Subscribers: andrewkr, dhruba Differential Revision: https://reviews.facebook.net/D62235
2016-08-25 21:57:35 +02:00
class CombinedStats {
public:
void AddStats(const Stats& stat) {
uint64_t total_ops = stat.done_;
uint64_t total_bytes_ = stat.bytes_;
double elapsed;
if (total_ops < 1) {
total_ops = 1;
}
elapsed = (stat.finish_ - stat.start_) * 1e-6;
throughput_ops_.emplace_back(total_ops / elapsed);
if (total_bytes_ > 0) {
double mbs = (total_bytes_ / 1048576.0);
throughput_mbs_.emplace_back(mbs / elapsed);
}
}
void Report(const std::string& bench_name) {
const char* name = bench_name.c_str();
int num_runs = static_cast<int>(throughput_ops_.size());
if (throughput_mbs_.size() == throughput_ops_.size()) {
fprintf(stdout,
"%s [AVG %d runs] : %d ops/sec; %6.1f MB/sec\n"
"%s [MEDIAN %d runs] : %d ops/sec; %6.1f MB/sec\n",
name, num_runs, static_cast<int>(CalcAvg(throughput_ops_)),
CalcAvg(throughput_mbs_), name, num_runs,
static_cast<int>(CalcMedian(throughput_ops_)),
CalcMedian(throughput_mbs_));
} else {
fprintf(stdout,
"%s [AVG %d runs] : %d ops/sec\n"
"%s [MEDIAN %d runs] : %d ops/sec\n",
name, num_runs, static_cast<int>(CalcAvg(throughput_ops_)), name,
num_runs, static_cast<int>(CalcMedian(throughput_ops_)));
}
}
private:
double CalcAvg(std::vector<double> data) {
double avg = 0;
for (double x : data) {
avg += x;
}
avg = avg / data.size();
return avg;
}
double CalcMedian(std::vector<double> data) {
assert(data.size() > 0);
std::sort(data.begin(), data.end());
size_t mid = data.size() / 2;
if (data.size() % 2 == 1) {
// Odd number of entries
return data[mid];
} else {
// Even number of entries
return (data[mid] + data[mid - 1]) / 2;
}
}
std::vector<double> throughput_ops_;
std::vector<double> throughput_mbs_;
};
Write a benchmark to emulate time series data Summary: Add a benchmark to `db_bench`. In this benchmark, a write thread will populate time series data in the format of 'id | timestamp', and multiple read threads will randomly retrieve all data from one id at a time. Test Plan: Run the benchmark: `num=134217728;bpl=536870912;mb=67108864;overlap=10;mcz=2;del=300000000;levels=6;ctrig=4;delay=8;stop=12;wbn=3;mbc=20;wbs=134217728;dds=0;sync=0;t=32;vs=800;bs=4096;cs=17179869184;of=500000;wps=0;si=10000000; kir=100000; dir=/data/users/jhli/test/; ./db_bench --benchmarks=timeseries --disable_seek_compaction=1 --mmap_read=0 --statistics=1 --histogram=1 --num=$num --threads=$t --value_size=$vs --block_size=$bs --cache_size=$cs --bloom_bits=10 --cache_numshardbits=6 --open_files=$of --verify_checksum=1 --db=$dir --sync=$sync --disable_wal=0 --compression_type=none --stats_interval=$si --compression_ratio=1 --disable_data_sync=$dds --write_buffer_size=$wbs --target_file_size_base=$mb --max_write_buffer_number=$wbn --max_background_compactions=$mbc --level0_file_num_compaction_trigger=$ctrig --level0_slowdown_writes_trigger=$delay --level0_stop_writes_trigger=$stop --num_levels=$levels --delete_obsolete_files_period_micros=$del --min_level_to_compress=$mcz --max_grandparent_overlap_factor=$overlap --stats_per_interval=1 --max_bytes_for_level_base=$bpl --use_existing_db=0 --key_id_range=$kir` Reviewers: andrewkr, sdong Reviewed By: sdong Subscribers: lgalanis, andrewkr, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D60651
2016-08-01 21:28:51 +02:00
class TimestampEmulator {
private:
std::atomic<uint64_t> timestamp_;
public:
TimestampEmulator() : timestamp_(0) {}
uint64_t Get() const { return timestamp_.load(); }
void Inc() { timestamp_++; }
Slice Allocate(char* scratch) {
// TODO: support larger timestamp sizes
assert(FLAGS_user_timestamp_size == 8);
assert(scratch);
uint64_t ts = timestamp_.fetch_add(1);
EncodeFixed64(scratch, ts);
return Slice(scratch, FLAGS_user_timestamp_size);
}
Slice GetTimestampForRead(Random64& rand, char* scratch) {
assert(FLAGS_user_timestamp_size == 8);
assert(scratch);
if (FLAGS_read_with_latest_user_timestamp) {
return Allocate(scratch);
}
// Choose a random timestamp from the past.
uint64_t ts = rand.Next() % Get();
EncodeFixed64(scratch, ts);
return Slice(scratch, FLAGS_user_timestamp_size);
}
Write a benchmark to emulate time series data Summary: Add a benchmark to `db_bench`. In this benchmark, a write thread will populate time series data in the format of 'id | timestamp', and multiple read threads will randomly retrieve all data from one id at a time. Test Plan: Run the benchmark: `num=134217728;bpl=536870912;mb=67108864;overlap=10;mcz=2;del=300000000;levels=6;ctrig=4;delay=8;stop=12;wbn=3;mbc=20;wbs=134217728;dds=0;sync=0;t=32;vs=800;bs=4096;cs=17179869184;of=500000;wps=0;si=10000000; kir=100000; dir=/data/users/jhli/test/; ./db_bench --benchmarks=timeseries --disable_seek_compaction=1 --mmap_read=0 --statistics=1 --histogram=1 --num=$num --threads=$t --value_size=$vs --block_size=$bs --cache_size=$cs --bloom_bits=10 --cache_numshardbits=6 --open_files=$of --verify_checksum=1 --db=$dir --sync=$sync --disable_wal=0 --compression_type=none --stats_interval=$si --compression_ratio=1 --disable_data_sync=$dds --write_buffer_size=$wbs --target_file_size_base=$mb --max_write_buffer_number=$wbn --max_background_compactions=$mbc --level0_file_num_compaction_trigger=$ctrig --level0_slowdown_writes_trigger=$delay --level0_stop_writes_trigger=$stop --num_levels=$levels --delete_obsolete_files_period_micros=$del --min_level_to_compress=$mcz --max_grandparent_overlap_factor=$overlap --stats_per_interval=1 --max_bytes_for_level_base=$bpl --use_existing_db=0 --key_id_range=$kir` Reviewers: andrewkr, sdong Reviewed By: sdong Subscribers: lgalanis, andrewkr, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D60651
2016-08-01 21:28:51 +02:00
};
// State shared by all concurrent executions of the same benchmark.
struct SharedState {
port::Mutex mu;
port::CondVar cv;
int total;
int perf_level;
std::shared_ptr<RateLimiter> write_rate_limiter;
std::shared_ptr<RateLimiter> read_rate_limiter;
// Each thread goes through the following states:
// (1) initializing
// (2) waiting for others to be initialized
// (3) running
// (4) done
long num_initialized;
long num_done;
bool start;
SharedState() : cv(&mu), perf_level(FLAGS_perf_level) { }
};
// Per-thread state for concurrent executions of the same benchmark.
struct ThreadState {
int tid; // 0..n-1 when running in n threads
Pull from https://reviews.facebook.net/D10917 Summary: Pull Mark's patch and slightly revise it. I revised another place in db_impl.cc with similar new formula. Test Plan: make all check. Also run "time ./db_bench --num=2500000000 --numdistinct=2200000000". It has run for 20+ hours and hasn't finished. Looks good so far: Installed stack trace handler for SIGILL SIGSEGV SIGBUS SIGABRT LevelDB: version 2.0 Date: Tue Aug 20 23:11:55 2013 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 2500000000 RawSize: 276565.6 MB (estimated) FileSize: 157356.3 MB (estimated) Write rate limit: 0 Compression: snappy WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ DB path: [/tmp/leveldbtest-3088/dbbench] fillseq : 7202.000 micros/op 138 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] fillsync : 7148.000 micros/op 139 ops/sec; (2500000 ops) DB path: [/tmp/leveldbtest-3088/dbbench] fillrandom : 7105.000 micros/op 140 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] overwrite : 6930.000 micros/op 144 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] readrandom : 1.020 micros/op 980507 ops/sec; (0 of 2500000000 found) DB path: [/tmp/leveldbtest-3088/dbbench] readrandom : 1.021 micros/op 979620 ops/sec; (0 of 2500000000 found) DB path: [/tmp/leveldbtest-3088/dbbench] readseq : 113.000 micros/op 8849 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] readreverse : 102.000 micros/op 9803 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] Created bg thread 0x7f0ac17f7700 compact : 111701.000 micros/op 8 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] readrandom : 1.020 micros/op 980376 ops/sec; (0 of 2500000000 found) DB path: [/tmp/leveldbtest-3088/dbbench] readseq : 120.000 micros/op 8333 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] readreverse : 29.000 micros/op 34482 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] ... finished 618100000 ops Reviewers: MarkCallaghan, haobo, dhruba, chip Reviewed By: dhruba Differential Revision: https://reviews.facebook.net/D12441
2013-08-23 07:37:13 +02:00
Random64 rand; // Has different seeds for different threads
Stats stats;
SharedState* shared;
explicit ThreadState(int index)
: tid(index), rand((FLAGS_seed ? FLAGS_seed : 1000) + index) {}
};
class Duration {
public:
Duration(uint64_t max_seconds, int64_t max_ops, int64_t ops_per_stage = 0) {
max_seconds_ = max_seconds;
max_ops_= max_ops;
ops_per_stage_ = (ops_per_stage > 0) ? ops_per_stage : max_ops;
ops_ = 0;
start_at_ = FLAGS_env->NowMicros();
}
int64_t GetStage() { return std::min(ops_, max_ops_ - 1) / ops_per_stage_; }
bool Done(int64_t increment) {
if (increment <= 0) increment = 1; // avoid Done(0) and infinite loops
ops_ += increment;
if (max_seconds_) {
// Recheck every appx 1000 ops (exact iff increment is factor of 1000)
auto granularity = FLAGS_ops_between_duration_checks;
if ((ops_ / granularity) != ((ops_ - increment) / granularity)) {
uint64_t now = FLAGS_env->NowMicros();
return ((now - start_at_) / 1000000) >= max_seconds_;
} else {
return false;
}
} else {
return ops_ > max_ops_;
}
}
private:
uint64_t max_seconds_;
int64_t max_ops_;
int64_t ops_per_stage_;
int64_t ops_;
uint64_t start_at_;
};
class Benchmark {
private:
std::shared_ptr<Cache> cache_;
std::shared_ptr<Cache> compressed_cache_;
const SliceTransform* prefix_extractor_;
DBWithColumnFamilies db_;
std::vector<DBWithColumnFamilies> multi_dbs_;
int64_t num_;
int key_size_;
int user_timestamp_size_;
int prefix_size_;
int64_t keys_per_prefix_;
int64_t entries_per_batch_;
int64_t writes_before_delete_range_;
int64_t writes_per_range_tombstone_;
int64_t range_tombstone_width_;
int64_t max_num_range_tombstones_;
WriteOptions write_options_;
Options open_options_; // keep options around to properly destroy db later
#ifndef ROCKSDB_LITE
TraceOptions trace_options_;
TraceOptions block_cache_trace_options_;
#endif
int64_t reads_;
int64_t deletes_;
double read_random_exp_range_;
int64_t writes_;
int64_t readwrites_;
int64_t merge_keys_;
bool report_file_operations_;
bool use_blob_db_; // Stacked BlobDB
std::vector<std::string> keys_;
Auto recovery from out of space errors (#4164) Summary: This commit implements automatic recovery from a Status::NoSpace() error during background operations such as write callback, flush and compaction. The broad design is as follows - 1. Compaction errors are treated as soft errors and don't put the database in read-only mode. A compaction is delayed until enough free disk space is available to accomodate the compaction outputs, which is estimated based on the input size. This means that users can continue to write, and we rely on the WriteController to delay or stop writes if the compaction debt becomes too high due to persistent low disk space condition 2. Errors during write callback and flush are treated as hard errors, i.e the database is put in read-only mode and goes back to read-write only fater certain recovery actions are taken. 3. Both types of recovery rely on the SstFileManagerImpl to poll for sufficient disk space. We assume that there is a 1-1 mapping between an SFM and the underlying OS storage container. For cases where multiple DBs are hosted on a single storage container, the user is expected to allocate a single SFM instance and use the same one for all the DBs. If no SFM is specified by the user, DBImpl::Open() will allocate one, but this will be one per DB and each DB will recover independently. The recovery implemented by SFM is as follows - a) On the first occurance of an out of space error during compaction, subsequent compactions will be delayed until the disk free space check indicates enough available space. The required space is computed as the sum of input sizes. b) The free space check requirement will be removed once the amount of free space is greater than the size reserved by in progress compactions when the first error occured c) If the out of space error is a hard error, a background thread in SFM will poll for sufficient headroom before triggering the recovery of the database and putting it in write-only mode. The headroom is calculated as the sum of the write_buffer_size of all the DB instances associated with the SFM 4. EventListener callbacks will be called at the start and completion of automatic recovery. Users can disable the auto recov ery in the start callback, and later initiate it manually by calling DB::Resume() Todo: 1. More extensive testing 2. Add disk full condition to db_stress (follow-on PR) Pull Request resolved: https://github.com/facebook/rocksdb/pull/4164 Differential Revision: D9846378 Pulled By: anand1976 fbshipit-source-id: 80ea875dbd7f00205e19c82215ff6e37da10da4a
2018-09-15 22:36:19 +02:00
class ErrorHandlerListener : public EventListener {
public:
#ifndef ROCKSDB_LITE
Auto recovery from out of space errors (#4164) Summary: This commit implements automatic recovery from a Status::NoSpace() error during background operations such as write callback, flush and compaction. The broad design is as follows - 1. Compaction errors are treated as soft errors and don't put the database in read-only mode. A compaction is delayed until enough free disk space is available to accomodate the compaction outputs, which is estimated based on the input size. This means that users can continue to write, and we rely on the WriteController to delay or stop writes if the compaction debt becomes too high due to persistent low disk space condition 2. Errors during write callback and flush are treated as hard errors, i.e the database is put in read-only mode and goes back to read-write only fater certain recovery actions are taken. 3. Both types of recovery rely on the SstFileManagerImpl to poll for sufficient disk space. We assume that there is a 1-1 mapping between an SFM and the underlying OS storage container. For cases where multiple DBs are hosted on a single storage container, the user is expected to allocate a single SFM instance and use the same one for all the DBs. If no SFM is specified by the user, DBImpl::Open() will allocate one, but this will be one per DB and each DB will recover independently. The recovery implemented by SFM is as follows - a) On the first occurance of an out of space error during compaction, subsequent compactions will be delayed until the disk free space check indicates enough available space. The required space is computed as the sum of input sizes. b) The free space check requirement will be removed once the amount of free space is greater than the size reserved by in progress compactions when the first error occured c) If the out of space error is a hard error, a background thread in SFM will poll for sufficient headroom before triggering the recovery of the database and putting it in write-only mode. The headroom is calculated as the sum of the write_buffer_size of all the DB instances associated with the SFM 4. EventListener callbacks will be called at the start and completion of automatic recovery. Users can disable the auto recov ery in the start callback, and later initiate it manually by calling DB::Resume() Todo: 1. More extensive testing 2. Add disk full condition to db_stress (follow-on PR) Pull Request resolved: https://github.com/facebook/rocksdb/pull/4164 Differential Revision: D9846378 Pulled By: anand1976 fbshipit-source-id: 80ea875dbd7f00205e19c82215ff6e37da10da4a
2018-09-15 22:36:19 +02:00
ErrorHandlerListener()
: mutex_(),
cv_(&mutex_),
no_auto_recovery_(false),
recovery_complete_(false) {}
~ErrorHandlerListener() override {}
Auto recovery from out of space errors (#4164) Summary: This commit implements automatic recovery from a Status::NoSpace() error during background operations such as write callback, flush and compaction. The broad design is as follows - 1. Compaction errors are treated as soft errors and don't put the database in read-only mode. A compaction is delayed until enough free disk space is available to accomodate the compaction outputs, which is estimated based on the input size. This means that users can continue to write, and we rely on the WriteController to delay or stop writes if the compaction debt becomes too high due to persistent low disk space condition 2. Errors during write callback and flush are treated as hard errors, i.e the database is put in read-only mode and goes back to read-write only fater certain recovery actions are taken. 3. Both types of recovery rely on the SstFileManagerImpl to poll for sufficient disk space. We assume that there is a 1-1 mapping between an SFM and the underlying OS storage container. For cases where multiple DBs are hosted on a single storage container, the user is expected to allocate a single SFM instance and use the same one for all the DBs. If no SFM is specified by the user, DBImpl::Open() will allocate one, but this will be one per DB and each DB will recover independently. The recovery implemented by SFM is as follows - a) On the first occurance of an out of space error during compaction, subsequent compactions will be delayed until the disk free space check indicates enough available space. The required space is computed as the sum of input sizes. b) The free space check requirement will be removed once the amount of free space is greater than the size reserved by in progress compactions when the first error occured c) If the out of space error is a hard error, a background thread in SFM will poll for sufficient headroom before triggering the recovery of the database and putting it in write-only mode. The headroom is calculated as the sum of the write_buffer_size of all the DB instances associated with the SFM 4. EventListener callbacks will be called at the start and completion of automatic recovery. Users can disable the auto recov ery in the start callback, and later initiate it manually by calling DB::Resume() Todo: 1. More extensive testing 2. Add disk full condition to db_stress (follow-on PR) Pull Request resolved: https://github.com/facebook/rocksdb/pull/4164 Differential Revision: D9846378 Pulled By: anand1976 fbshipit-source-id: 80ea875dbd7f00205e19c82215ff6e37da10da4a
2018-09-15 22:36:19 +02:00
const char* Name() const override { return kClassName(); }
static const char* kClassName() { return "ErrorHandlerListener"; }
Auto recovery from out of space errors (#4164) Summary: This commit implements automatic recovery from a Status::NoSpace() error during background operations such as write callback, flush and compaction. The broad design is as follows - 1. Compaction errors are treated as soft errors and don't put the database in read-only mode. A compaction is delayed until enough free disk space is available to accomodate the compaction outputs, which is estimated based on the input size. This means that users can continue to write, and we rely on the WriteController to delay or stop writes if the compaction debt becomes too high due to persistent low disk space condition 2. Errors during write callback and flush are treated as hard errors, i.e the database is put in read-only mode and goes back to read-write only fater certain recovery actions are taken. 3. Both types of recovery rely on the SstFileManagerImpl to poll for sufficient disk space. We assume that there is a 1-1 mapping between an SFM and the underlying OS storage container. For cases where multiple DBs are hosted on a single storage container, the user is expected to allocate a single SFM instance and use the same one for all the DBs. If no SFM is specified by the user, DBImpl::Open() will allocate one, but this will be one per DB and each DB will recover independently. The recovery implemented by SFM is as follows - a) On the first occurance of an out of space error during compaction, subsequent compactions will be delayed until the disk free space check indicates enough available space. The required space is computed as the sum of input sizes. b) The free space check requirement will be removed once the amount of free space is greater than the size reserved by in progress compactions when the first error occured c) If the out of space error is a hard error, a background thread in SFM will poll for sufficient headroom before triggering the recovery of the database and putting it in write-only mode. The headroom is calculated as the sum of the write_buffer_size of all the DB instances associated with the SFM 4. EventListener callbacks will be called at the start and completion of automatic recovery. Users can disable the auto recov ery in the start callback, and later initiate it manually by calling DB::Resume() Todo: 1. More extensive testing 2. Add disk full condition to db_stress (follow-on PR) Pull Request resolved: https://github.com/facebook/rocksdb/pull/4164 Differential Revision: D9846378 Pulled By: anand1976 fbshipit-source-id: 80ea875dbd7f00205e19c82215ff6e37da10da4a
2018-09-15 22:36:19 +02:00
void OnErrorRecoveryBegin(BackgroundErrorReason /*reason*/,
Status /*bg_error*/,
bool* auto_recovery) override {
Auto recovery from out of space errors (#4164) Summary: This commit implements automatic recovery from a Status::NoSpace() error during background operations such as write callback, flush and compaction. The broad design is as follows - 1. Compaction errors are treated as soft errors and don't put the database in read-only mode. A compaction is delayed until enough free disk space is available to accomodate the compaction outputs, which is estimated based on the input size. This means that users can continue to write, and we rely on the WriteController to delay or stop writes if the compaction debt becomes too high due to persistent low disk space condition 2. Errors during write callback and flush are treated as hard errors, i.e the database is put in read-only mode and goes back to read-write only fater certain recovery actions are taken. 3. Both types of recovery rely on the SstFileManagerImpl to poll for sufficient disk space. We assume that there is a 1-1 mapping between an SFM and the underlying OS storage container. For cases where multiple DBs are hosted on a single storage container, the user is expected to allocate a single SFM instance and use the same one for all the DBs. If no SFM is specified by the user, DBImpl::Open() will allocate one, but this will be one per DB and each DB will recover independently. The recovery implemented by SFM is as follows - a) On the first occurance of an out of space error during compaction, subsequent compactions will be delayed until the disk free space check indicates enough available space. The required space is computed as the sum of input sizes. b) The free space check requirement will be removed once the amount of free space is greater than the size reserved by in progress compactions when the first error occured c) If the out of space error is a hard error, a background thread in SFM will poll for sufficient headroom before triggering the recovery of the database and putting it in write-only mode. The headroom is calculated as the sum of the write_buffer_size of all the DB instances associated with the SFM 4. EventListener callbacks will be called at the start and completion of automatic recovery. Users can disable the auto recov ery in the start callback, and later initiate it manually by calling DB::Resume() Todo: 1. More extensive testing 2. Add disk full condition to db_stress (follow-on PR) Pull Request resolved: https://github.com/facebook/rocksdb/pull/4164 Differential Revision: D9846378 Pulled By: anand1976 fbshipit-source-id: 80ea875dbd7f00205e19c82215ff6e37da10da4a
2018-09-15 22:36:19 +02:00
if (*auto_recovery && no_auto_recovery_) {
*auto_recovery = false;
}
}
void OnErrorRecoveryCompleted(Status /*old_bg_error*/) override {
Auto recovery from out of space errors (#4164) Summary: This commit implements automatic recovery from a Status::NoSpace() error during background operations such as write callback, flush and compaction. The broad design is as follows - 1. Compaction errors are treated as soft errors and don't put the database in read-only mode. A compaction is delayed until enough free disk space is available to accomodate the compaction outputs, which is estimated based on the input size. This means that users can continue to write, and we rely on the WriteController to delay or stop writes if the compaction debt becomes too high due to persistent low disk space condition 2. Errors during write callback and flush are treated as hard errors, i.e the database is put in read-only mode and goes back to read-write only fater certain recovery actions are taken. 3. Both types of recovery rely on the SstFileManagerImpl to poll for sufficient disk space. We assume that there is a 1-1 mapping between an SFM and the underlying OS storage container. For cases where multiple DBs are hosted on a single storage container, the user is expected to allocate a single SFM instance and use the same one for all the DBs. If no SFM is specified by the user, DBImpl::Open() will allocate one, but this will be one per DB and each DB will recover independently. The recovery implemented by SFM is as follows - a) On the first occurance of an out of space error during compaction, subsequent compactions will be delayed until the disk free space check indicates enough available space. The required space is computed as the sum of input sizes. b) The free space check requirement will be removed once the amount of free space is greater than the size reserved by in progress compactions when the first error occured c) If the out of space error is a hard error, a background thread in SFM will poll for sufficient headroom before triggering the recovery of the database and putting it in write-only mode. The headroom is calculated as the sum of the write_buffer_size of all the DB instances associated with the SFM 4. EventListener callbacks will be called at the start and completion of automatic recovery. Users can disable the auto recov ery in the start callback, and later initiate it manually by calling DB::Resume() Todo: 1. More extensive testing 2. Add disk full condition to db_stress (follow-on PR) Pull Request resolved: https://github.com/facebook/rocksdb/pull/4164 Differential Revision: D9846378 Pulled By: anand1976 fbshipit-source-id: 80ea875dbd7f00205e19c82215ff6e37da10da4a
2018-09-15 22:36:19 +02:00
InstrumentedMutexLock l(&mutex_);
recovery_complete_ = true;
cv_.SignalAll();
}
bool WaitForRecovery(uint64_t abs_time_us) {
Auto recovery from out of space errors (#4164) Summary: This commit implements automatic recovery from a Status::NoSpace() error during background operations such as write callback, flush and compaction. The broad design is as follows - 1. Compaction errors are treated as soft errors and don't put the database in read-only mode. A compaction is delayed until enough free disk space is available to accomodate the compaction outputs, which is estimated based on the input size. This means that users can continue to write, and we rely on the WriteController to delay or stop writes if the compaction debt becomes too high due to persistent low disk space condition 2. Errors during write callback and flush are treated as hard errors, i.e the database is put in read-only mode and goes back to read-write only fater certain recovery actions are taken. 3. Both types of recovery rely on the SstFileManagerImpl to poll for sufficient disk space. We assume that there is a 1-1 mapping between an SFM and the underlying OS storage container. For cases where multiple DBs are hosted on a single storage container, the user is expected to allocate a single SFM instance and use the same one for all the DBs. If no SFM is specified by the user, DBImpl::Open() will allocate one, but this will be one per DB and each DB will recover independently. The recovery implemented by SFM is as follows - a) On the first occurance of an out of space error during compaction, subsequent compactions will be delayed until the disk free space check indicates enough available space. The required space is computed as the sum of input sizes. b) The free space check requirement will be removed once the amount of free space is greater than the size reserved by in progress compactions when the first error occured c) If the out of space error is a hard error, a background thread in SFM will poll for sufficient headroom before triggering the recovery of the database and putting it in write-only mode. The headroom is calculated as the sum of the write_buffer_size of all the DB instances associated with the SFM 4. EventListener callbacks will be called at the start and completion of automatic recovery. Users can disable the auto recov ery in the start callback, and later initiate it manually by calling DB::Resume() Todo: 1. More extensive testing 2. Add disk full condition to db_stress (follow-on PR) Pull Request resolved: https://github.com/facebook/rocksdb/pull/4164 Differential Revision: D9846378 Pulled By: anand1976 fbshipit-source-id: 80ea875dbd7f00205e19c82215ff6e37da10da4a
2018-09-15 22:36:19 +02:00
InstrumentedMutexLock l(&mutex_);
if (!recovery_complete_) {
cv_.TimedWait(abs_time_us);
Auto recovery from out of space errors (#4164) Summary: This commit implements automatic recovery from a Status::NoSpace() error during background operations such as write callback, flush and compaction. The broad design is as follows - 1. Compaction errors are treated as soft errors and don't put the database in read-only mode. A compaction is delayed until enough free disk space is available to accomodate the compaction outputs, which is estimated based on the input size. This means that users can continue to write, and we rely on the WriteController to delay or stop writes if the compaction debt becomes too high due to persistent low disk space condition 2. Errors during write callback and flush are treated as hard errors, i.e the database is put in read-only mode and goes back to read-write only fater certain recovery actions are taken. 3. Both types of recovery rely on the SstFileManagerImpl to poll for sufficient disk space. We assume that there is a 1-1 mapping between an SFM and the underlying OS storage container. For cases where multiple DBs are hosted on a single storage container, the user is expected to allocate a single SFM instance and use the same one for all the DBs. If no SFM is specified by the user, DBImpl::Open() will allocate one, but this will be one per DB and each DB will recover independently. The recovery implemented by SFM is as follows - a) On the first occurance of an out of space error during compaction, subsequent compactions will be delayed until the disk free space check indicates enough available space. The required space is computed as the sum of input sizes. b) The free space check requirement will be removed once the amount of free space is greater than the size reserved by in progress compactions when the first error occured c) If the out of space error is a hard error, a background thread in SFM will poll for sufficient headroom before triggering the recovery of the database and putting it in write-only mode. The headroom is calculated as the sum of the write_buffer_size of all the DB instances associated with the SFM 4. EventListener callbacks will be called at the start and completion of automatic recovery. Users can disable the auto recov ery in the start callback, and later initiate it manually by calling DB::Resume() Todo: 1. More extensive testing 2. Add disk full condition to db_stress (follow-on PR) Pull Request resolved: https://github.com/facebook/rocksdb/pull/4164 Differential Revision: D9846378 Pulled By: anand1976 fbshipit-source-id: 80ea875dbd7f00205e19c82215ff6e37da10da4a
2018-09-15 22:36:19 +02:00
}
if (recovery_complete_) {
recovery_complete_ = false;
return true;
}
return false;
}
void EnableAutoRecovery(bool enable = true) { no_auto_recovery_ = !enable; }
private:
InstrumentedMutex mutex_;
InstrumentedCondVar cv_;
bool no_auto_recovery_;
bool recovery_complete_;
#else // ROCKSDB_LITE
bool WaitForRecovery(uint64_t /*abs_time_us*/) { return true; }
void EnableAutoRecovery(bool /*enable*/) {}
#endif // ROCKSDB_LITE
Auto recovery from out of space errors (#4164) Summary: This commit implements automatic recovery from a Status::NoSpace() error during background operations such as write callback, flush and compaction. The broad design is as follows - 1. Compaction errors are treated as soft errors and don't put the database in read-only mode. A compaction is delayed until enough free disk space is available to accomodate the compaction outputs, which is estimated based on the input size. This means that users can continue to write, and we rely on the WriteController to delay or stop writes if the compaction debt becomes too high due to persistent low disk space condition 2. Errors during write callback and flush are treated as hard errors, i.e the database is put in read-only mode and goes back to read-write only fater certain recovery actions are taken. 3. Both types of recovery rely on the SstFileManagerImpl to poll for sufficient disk space. We assume that there is a 1-1 mapping between an SFM and the underlying OS storage container. For cases where multiple DBs are hosted on a single storage container, the user is expected to allocate a single SFM instance and use the same one for all the DBs. If no SFM is specified by the user, DBImpl::Open() will allocate one, but this will be one per DB and each DB will recover independently. The recovery implemented by SFM is as follows - a) On the first occurance of an out of space error during compaction, subsequent compactions will be delayed until the disk free space check indicates enough available space. The required space is computed as the sum of input sizes. b) The free space check requirement will be removed once the amount of free space is greater than the size reserved by in progress compactions when the first error occured c) If the out of space error is a hard error, a background thread in SFM will poll for sufficient headroom before triggering the recovery of the database and putting it in write-only mode. The headroom is calculated as the sum of the write_buffer_size of all the DB instances associated with the SFM 4. EventListener callbacks will be called at the start and completion of automatic recovery. Users can disable the auto recov ery in the start callback, and later initiate it manually by calling DB::Resume() Todo: 1. More extensive testing 2. Add disk full condition to db_stress (follow-on PR) Pull Request resolved: https://github.com/facebook/rocksdb/pull/4164 Differential Revision: D9846378 Pulled By: anand1976 fbshipit-source-id: 80ea875dbd7f00205e19c82215ff6e37da10da4a
2018-09-15 22:36:19 +02:00
};
std::shared_ptr<ErrorHandlerListener> listener_;
std::unique_ptr<TimestampEmulator> mock_app_clock_;
bool SanityCheck() {
if (FLAGS_compression_ratio > 1) {
fprintf(stderr, "compression_ratio should be between 0 and 1\n");
return false;
}
return true;
}
inline bool CompressSlice(const CompressionInfo& compression_info,
const Slice& input, std::string* compressed) {
constexpr uint32_t compress_format_version = 2;
return CompressData(input, compression_info, compress_format_version,
compressed);
}
void PrintHeader(const Options& options) {
PrintEnvironment();
fprintf(stdout,
"Keys: %d bytes each (+ %d bytes user-defined timestamp)\n",
FLAGS_key_size, FLAGS_user_timestamp_size);
auto avg_value_size = FLAGS_value_size;
if (FLAGS_value_size_distribution_type_e == kFixed) {
fprintf(stdout, "Values: %d bytes each (%d bytes after compression)\n",
avg_value_size,
static_cast<int>(avg_value_size * FLAGS_compression_ratio + 0.5));
} else {
avg_value_size = (FLAGS_value_size_min + FLAGS_value_size_max) / 2;
fprintf(stdout, "Values: %d avg bytes each (%d bytes after compression)\n",
avg_value_size,
static_cast<int>(avg_value_size * FLAGS_compression_ratio + 0.5));
fprintf(stdout, "Values Distribution: %s (min: %d, max: %d)\n",
FLAGS_value_size_distribution_type.c_str(),
FLAGS_value_size_min, FLAGS_value_size_max);
}
fprintf(stdout, "Entries: %" PRIu64 "\n", num_);
fprintf(stdout, "Prefix: %d bytes\n", FLAGS_prefix_size);
fprintf(stdout, "Keys per prefix: %" PRIu64 "\n", keys_per_prefix_);
fprintf(stdout, "RawSize: %.1f MB (estimated)\n",
((static_cast<int64_t>(FLAGS_key_size + avg_value_size) * num_)
/ 1048576.0));
fprintf(stdout, "FileSize: %.1f MB (estimated)\n",
(((FLAGS_key_size + avg_value_size * FLAGS_compression_ratio)
* num_)
/ 1048576.0));
fprintf(stdout, "Write rate: %" PRIu64 " bytes/second\n",
FLAGS_benchmark_write_rate_limit);
fprintf(stdout, "Read rate: %" PRIu64 " ops/second\n",
FLAGS_benchmark_read_rate_limit);
Adding NUMA support to db_bench tests Summary: Changes: - Adding numa_aware flag to db_bench.cc - Using numa.h library to bind memory and cpu of threads to a fixed NUMA node Result: There seems to be no significant change in the micros/op time with numa_aware enabled. I also tried this with other implementations, including a combination of pthread_setaffinity_np, sched_setaffinity and set_mempolicy methods. It'd be great if someone could point out where I'm going wrong and if we can achieve a better micors/op. Test Plan: Ran db_bench tests using following command: ./db_bench --db=/mnt/tmp --num_levels=6 --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --block_size=4096 --cache_size=17179869184 --cache_numshardbits=6 --compression_type=none --compression_ratio=1 --min_level_to_compress=-1 --disable_seek_compaction=1 --hard_rate_limit=2 --write_buffer_size=134217728 --max_write_buffer_number=2 --level0_file_num_compaction_trigger=8 --target_file_size_base=134217728 --max_bytes_for_level_base=1073741824 --disable_wal=0 --wal_dir=/mnt/tmp --sync=0 --disable_data_sync=1 --verify_checksum=1 --delete_obsolete_files_period_micros=314572800 --max_grandparent_overlap_factor=10 --max_background_compactions=4 --max_background_flushes=0 --level0_slowdown_writes_trigger=16 --level0_stop_writes_trigger=24 --statistics=0 --stats_per_interval=0 --stats_interval=1048576 --histogram=0 --use_plain_table=1 --open_files=-1 --mmap_read=1 --mmap_write=0 --memtablerep=prefix_hash --bloom_bits=10 --bloom_locality=1 --perf_level=0 --duration=300 --benchmarks=readwhilewriting --use_existing_db=1 --num=157286400 --threads=24 --writes_per_second=10240 --numa_aware=[False/True] The tests were run in private devserver with 24 cores and the db was prepopulated using filluniquerandom test. The tests resulted in 0.145 us/op with numa_aware=False and 0.161 us/op with numa_aware=True. Reviewers: sdong, yhchiang, ljin, igor Reviewed By: ljin, igor Subscribers: igor, leveldb Differential Revision: https://reviews.facebook.net/D19353
2014-07-07 19:53:31 +02:00
if (FLAGS_enable_numa) {
fprintf(stderr, "Running in NUMA enabled mode.\n");
#ifndef NUMA
fprintf(stderr, "NUMA is not defined in the system.\n");
exit(1);
#else
if (numa_available() == -1) {
fprintf(stderr, "NUMA is not supported by the system.\n");
exit(1);
}
#endif
}
auto compression = CompressionTypeToString(FLAGS_compression_type_e);
fprintf(stdout, "Compression: %s\n", compression.c_str());
fprintf(stdout, "Compression sampling rate: %" PRId64 "\n",
FLAGS_sample_for_compression);
if (options.memtable_factory != nullptr) {
fprintf(stdout, "Memtablerep: %s\n",
options.memtable_factory->GetId().c_str());
}
fprintf(stdout, "Perf Level: %d\n", FLAGS_perf_level);
PrintWarnings(compression.c_str());
fprintf(stdout, "------------------------------------------------\n");
}
void PrintWarnings(const char* compression) {
#if defined(__GNUC__) && !defined(__OPTIMIZE__)
fprintf(stdout,
"WARNING: Optimization is disabled: benchmarks unnecessarily slow\n"
);
#endif
#ifndef NDEBUG
fprintf(stdout,
"WARNING: Assertions are enabled; benchmarks unnecessarily slow\n");
#endif
if (FLAGS_compression_type_e != ROCKSDB_NAMESPACE::kNoCompression) {
// The test string should not be too small.
const int len = FLAGS_block_size;
std::string input_str(len, 'y');
std::string compressed;
CompressionOptions opts;
CompressionContext context(FLAGS_compression_type_e);
CompressionInfo info(opts, context, CompressionDict::GetEmptyDict(),
FLAGS_compression_type_e,
FLAGS_sample_for_compression);
bool result = CompressSlice(info, Slice(input_str), &compressed);
if (!result) {
fprintf(stdout, "WARNING: %s compression is not enabled\n",
compression);
} else if (compressed.size() >= input_str.size()) {
fprintf(stdout, "WARNING: %s compression is not effective\n",
compression);
}
}
}
// Current the following isn't equivalent to OS_LINUX.
#if defined(__linux)
static Slice TrimSpace(Slice s) {
unsigned int start = 0;
while (start < s.size() && isspace(s[start])) {
start++;
}
unsigned int limit = static_cast<unsigned int>(s.size());
while (limit > start && isspace(s[limit-1])) {
limit--;
}
return Slice(s.data() + start, limit - start);
}
#endif
void PrintEnvironment() {
fprintf(stderr, "RocksDB: version %d.%d\n",
kMajorVersion, kMinorVersion);
#if defined(__linux) || defined(__APPLE__) || defined(__FreeBSD__)
time_t now = time(nullptr);
char buf[52];
// Lint complains about ctime() usage, so replace it with ctime_r(). The
// requirement is to provide a buffer which is at least 26 bytes.
fprintf(stderr, "Date: %s",
ctime_r(&now, buf)); // ctime_r() adds newline
#if defined(__linux)
FILE* cpuinfo = fopen("/proc/cpuinfo", "r");
if (cpuinfo != nullptr) {
char line[1000];
int num_cpus = 0;
std::string cpu_type;
std::string cache_size;
while (fgets(line, sizeof(line), cpuinfo) != nullptr) {
const char* sep = strchr(line, ':');
if (sep == nullptr) {
continue;
}
Slice key = TrimSpace(Slice(line, sep - 1 - line));
Slice val = TrimSpace(Slice(sep + 1));
if (key == "model name") {
++num_cpus;
cpu_type = val.ToString();
} else if (key == "cache size") {
cache_size = val.ToString();
}
}
fclose(cpuinfo);
fprintf(stderr, "CPU: %d * %s\n", num_cpus, cpu_type.c_str());
fprintf(stderr, "CPUCache: %s\n", cache_size.c_str());
}
#elif defined(__APPLE__)
struct host_basic_info h;
size_t hlen = HOST_BASIC_INFO_COUNT;
if (host_info(mach_host_self(), HOST_BASIC_INFO, (host_info_t)&h,
(uint32_t*)&hlen) == KERN_SUCCESS) {
std::string cpu_type;
std::string cache_size;
size_t hcache_size;
hlen = sizeof(hcache_size);
if (sysctlbyname("hw.cachelinesize", &hcache_size, &hlen, NULL, 0) == 0) {
cache_size = std::to_string(hcache_size);
}
switch (h.cpu_type) {
case CPU_TYPE_X86_64:
cpu_type = "x86_64";
break;
case CPU_TYPE_ARM64:
cpu_type = "arm64";
break;
default:
break;
}
fprintf(stderr, "CPU: %d * %s\n", h.max_cpus, cpu_type.c_str());
fprintf(stderr, "CPUCache: %s\n", cache_size.c_str());
}
#elif defined(__FreeBSD__)
int ncpus;
size_t len = sizeof(ncpus);
int mib[2] = {CTL_HW, HW_NCPU};
if (sysctl(mib, 2, &ncpus, &len, nullptr, 0) == 0) {
char cpu_type[16];
len = sizeof(cpu_type) - 1;
mib[1] = HW_MACHINE;
if (sysctl(mib, 2, cpu_type, &len, nullptr, 0) == 0) cpu_type[len] = 0;
fprintf(stderr, "CPU: %d * %s\n", ncpus, cpu_type);
// no programmatic way to get the cache line size except on PPC
}
#endif
#endif
}
Write a benchmark to emulate time series data Summary: Add a benchmark to `db_bench`. In this benchmark, a write thread will populate time series data in the format of 'id | timestamp', and multiple read threads will randomly retrieve all data from one id at a time. Test Plan: Run the benchmark: `num=134217728;bpl=536870912;mb=67108864;overlap=10;mcz=2;del=300000000;levels=6;ctrig=4;delay=8;stop=12;wbn=3;mbc=20;wbs=134217728;dds=0;sync=0;t=32;vs=800;bs=4096;cs=17179869184;of=500000;wps=0;si=10000000; kir=100000; dir=/data/users/jhli/test/; ./db_bench --benchmarks=timeseries --disable_seek_compaction=1 --mmap_read=0 --statistics=1 --histogram=1 --num=$num --threads=$t --value_size=$vs --block_size=$bs --cache_size=$cs --bloom_bits=10 --cache_numshardbits=6 --open_files=$of --verify_checksum=1 --db=$dir --sync=$sync --disable_wal=0 --compression_type=none --stats_interval=$si --compression_ratio=1 --disable_data_sync=$dds --write_buffer_size=$wbs --target_file_size_base=$mb --max_write_buffer_number=$wbn --max_background_compactions=$mbc --level0_file_num_compaction_trigger=$ctrig --level0_slowdown_writes_trigger=$delay --level0_stop_writes_trigger=$stop --num_levels=$levels --delete_obsolete_files_period_micros=$del --min_level_to_compress=$mcz --max_grandparent_overlap_factor=$overlap --stats_per_interval=1 --max_bytes_for_level_base=$bpl --use_existing_db=0 --key_id_range=$kir` Reviewers: andrewkr, sdong Reviewed By: sdong Subscribers: lgalanis, andrewkr, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D60651
2016-08-01 21:28:51 +02:00
static bool KeyExpired(const TimestampEmulator* timestamp_emulator,
const Slice& key) {
const char* pos = key.data();
pos += 8;
uint64_t timestamp = 0;
if (port::kLittleEndian) {
int bytes_to_fill = 8;
for (int i = 0; i < bytes_to_fill; ++i) {
timestamp |= (static_cast<uint64_t>(static_cast<unsigned char>(pos[i]))
<< ((bytes_to_fill - i - 1) << 3));
}
} else {
memcpy(&timestamp, pos, sizeof(timestamp));
}
return timestamp_emulator->Get() - timestamp > FLAGS_time_range;
}
class ExpiredTimeFilter : public CompactionFilter {
public:
explicit ExpiredTimeFilter(
const std::shared_ptr<TimestampEmulator>& timestamp_emulator)
: timestamp_emulator_(timestamp_emulator) {}
bool Filter(int /*level*/, const Slice& key,
const Slice& /*existing_value*/, std::string* /*new_value*/,
bool* /*value_changed*/) const override {
Write a benchmark to emulate time series data Summary: Add a benchmark to `db_bench`. In this benchmark, a write thread will populate time series data in the format of 'id | timestamp', and multiple read threads will randomly retrieve all data from one id at a time. Test Plan: Run the benchmark: `num=134217728;bpl=536870912;mb=67108864;overlap=10;mcz=2;del=300000000;levels=6;ctrig=4;delay=8;stop=12;wbn=3;mbc=20;wbs=134217728;dds=0;sync=0;t=32;vs=800;bs=4096;cs=17179869184;of=500000;wps=0;si=10000000; kir=100000; dir=/data/users/jhli/test/; ./db_bench --benchmarks=timeseries --disable_seek_compaction=1 --mmap_read=0 --statistics=1 --histogram=1 --num=$num --threads=$t --value_size=$vs --block_size=$bs --cache_size=$cs --bloom_bits=10 --cache_numshardbits=6 --open_files=$of --verify_checksum=1 --db=$dir --sync=$sync --disable_wal=0 --compression_type=none --stats_interval=$si --compression_ratio=1 --disable_data_sync=$dds --write_buffer_size=$wbs --target_file_size_base=$mb --max_write_buffer_number=$wbn --max_background_compactions=$mbc --level0_file_num_compaction_trigger=$ctrig --level0_slowdown_writes_trigger=$delay --level0_stop_writes_trigger=$stop --num_levels=$levels --delete_obsolete_files_period_micros=$del --min_level_to_compress=$mcz --max_grandparent_overlap_factor=$overlap --stats_per_interval=1 --max_bytes_for_level_base=$bpl --use_existing_db=0 --key_id_range=$kir` Reviewers: andrewkr, sdong Reviewed By: sdong Subscribers: lgalanis, andrewkr, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D60651
2016-08-01 21:28:51 +02:00
return KeyExpired(timestamp_emulator_.get(), key);
}
const char* Name() const override { return "ExpiredTimeFilter"; }
private:
std::shared_ptr<TimestampEmulator> timestamp_emulator_;
};
class KeepFilter : public CompactionFilter {
public:
bool Filter(int /*level*/, const Slice& /*key*/, const Slice& /*value*/,
std::string* /*new_value*/,
bool* /*value_changed*/) const override {
return false;
}
const char* Name() const override { return "KeepFilter"; }
};
std::shared_ptr<Cache> NewCache(int64_t capacity) {
if (capacity <= 0) {
return nullptr;
}
if (FLAGS_use_clock_cache) {
auto cache = NewClockCache(static_cast<size_t>(capacity),
FLAGS_cache_numshardbits);
if (!cache) {
fprintf(stderr, "Clock cache not supported.");
exit(1);
}
return cache;
} else {
LRUCacheOptions opts(
static_cast<size_t>(capacity), FLAGS_cache_numshardbits,
false /*strict_capacity_limit*/, FLAGS_cache_high_pri_pool_ratio,
Provide an allocator for new memory type to be used with RocksDB block cache (#6214) Summary: New memory technologies are being developed by various hardware vendors (Intel DCPMM is one such technology currently available). These new memory types require different libraries for allocation and management (such as PMDK and memkind). The high capacities available make it possible to provision large caches (up to several TBs in size), beyond what is achievable with DRAM. The new allocator provided in this PR uses the memkind library to allocate memory on different media. **Performance** We tested the new allocator using db_bench. - For each test, we vary the size of the block cache (relative to the size of the uncompressed data in the database). - The database is filled sequentially. Throughput is then measured with a readrandom benchmark. - We use a uniform distribution as a worst-case scenario. The plot shows throughput (ops/s) relative to a configuration with no block cache and default allocator. For all tests, p99 latency is below 500 us. ![image](https://user-images.githubusercontent.com/26400080/71108594-42479100-2178-11ea-8231-8a775bbc92db.png) **Changes** - Add MemkindKmemAllocator - Add --use_cache_memkind_kmem_allocator db_bench option (to create an LRU block cache with the new allocator) - Add detection of memkind library with KMEM DAX support - Add test for MemkindKmemAllocator **Minimum Requirements** - kernel 5.3.12 - ndctl v67 - https://github.com/pmem/ndctl - memkind v1.10.0 - https://github.com/memkind/memkind **Memory Configuration** The allocator uses the MEMKIND_DAX_KMEM memory kind. Follow the instructions on[ memkind’s GitHub page](https://github.com/memkind/memkind) to set up NVDIMM memory accordingly. Note on memory allocation with NVDIMM memory exposed as system memory. - The MemkindKmemAllocator will only allocate from NVDIMM memory (using memkind_malloc with MEMKIND_DAX_KMEM kind). - The default allocator is not restricted to RAM by default. Based on NUMA node latency, the kernel should allocate from local RAM preferentially, but it’s a kernel decision. numactl --preferred/--membind can be used to allocate preferentially/exclusively from the local RAM node. **Usage** When creating an LRU cache, pass a MemkindKmemAllocator object as argument. For example (replace capacity with the desired value in bytes): ``` #include "rocksdb/cache.h" #include "memory/memkind_kmem_allocator.h" NewLRUCache( capacity /*size_t*/, 6 /*cache_numshardbits*/, false /*strict_capacity_limit*/, false /*cache_high_pri_pool_ratio*/, std::make_shared<MemkindKmemAllocator>()); ``` Refer to [RocksDB’s block cache documentation](https://github.com/facebook/rocksdb/wiki/Block-Cache) to assign the LRU cache as block cache for a database. Pull Request resolved: https://github.com/facebook/rocksdb/pull/6214 Reviewed By: cheng-chang Differential Revision: D19292435 fbshipit-source-id: 7202f47b769e7722b539c86c2ffd669f64d7b4e1
2020-04-10 05:45:17 +02:00
#ifdef MEMKIND
FLAGS_use_cache_memkind_kmem_allocator
? std::make_shared<MemkindKmemAllocator>()
: nullptr
Provide an allocator for new memory type to be used with RocksDB block cache (#6214) Summary: New memory technologies are being developed by various hardware vendors (Intel DCPMM is one such technology currently available). These new memory types require different libraries for allocation and management (such as PMDK and memkind). The high capacities available make it possible to provision large caches (up to several TBs in size), beyond what is achievable with DRAM. The new allocator provided in this PR uses the memkind library to allocate memory on different media. **Performance** We tested the new allocator using db_bench. - For each test, we vary the size of the block cache (relative to the size of the uncompressed data in the database). - The database is filled sequentially. Throughput is then measured with a readrandom benchmark. - We use a uniform distribution as a worst-case scenario. The plot shows throughput (ops/s) relative to a configuration with no block cache and default allocator. For all tests, p99 latency is below 500 us. ![image](https://user-images.githubusercontent.com/26400080/71108594-42479100-2178-11ea-8231-8a775bbc92db.png) **Changes** - Add MemkindKmemAllocator - Add --use_cache_memkind_kmem_allocator db_bench option (to create an LRU block cache with the new allocator) - Add detection of memkind library with KMEM DAX support - Add test for MemkindKmemAllocator **Minimum Requirements** - kernel 5.3.12 - ndctl v67 - https://github.com/pmem/ndctl - memkind v1.10.0 - https://github.com/memkind/memkind **Memory Configuration** The allocator uses the MEMKIND_DAX_KMEM memory kind. Follow the instructions on[ memkind’s GitHub page](https://github.com/memkind/memkind) to set up NVDIMM memory accordingly. Note on memory allocation with NVDIMM memory exposed as system memory. - The MemkindKmemAllocator will only allocate from NVDIMM memory (using memkind_malloc with MEMKIND_DAX_KMEM kind). - The default allocator is not restricted to RAM by default. Based on NUMA node latency, the kernel should allocate from local RAM preferentially, but it’s a kernel decision. numactl --preferred/--membind can be used to allocate preferentially/exclusively from the local RAM node. **Usage** When creating an LRU cache, pass a MemkindKmemAllocator object as argument. For example (replace capacity with the desired value in bytes): ``` #include "rocksdb/cache.h" #include "memory/memkind_kmem_allocator.h" NewLRUCache( capacity /*size_t*/, 6 /*cache_numshardbits*/, false /*strict_capacity_limit*/, false /*cache_high_pri_pool_ratio*/, std::make_shared<MemkindKmemAllocator>()); ``` Refer to [RocksDB’s block cache documentation](https://github.com/facebook/rocksdb/wiki/Block-Cache) to assign the LRU cache as block cache for a database. Pull Request resolved: https://github.com/facebook/rocksdb/pull/6214 Reviewed By: cheng-chang Differential Revision: D19292435 fbshipit-source-id: 7202f47b769e7722b539c86c2ffd669f64d7b4e1
2020-04-10 05:45:17 +02:00
#else
nullptr
#endif
);
if (FLAGS_use_cache_memkind_kmem_allocator) {
#ifndef MEMKIND
Provide an allocator for new memory type to be used with RocksDB block cache (#6214) Summary: New memory technologies are being developed by various hardware vendors (Intel DCPMM is one such technology currently available). These new memory types require different libraries for allocation and management (such as PMDK and memkind). The high capacities available make it possible to provision large caches (up to several TBs in size), beyond what is achievable with DRAM. The new allocator provided in this PR uses the memkind library to allocate memory on different media. **Performance** We tested the new allocator using db_bench. - For each test, we vary the size of the block cache (relative to the size of the uncompressed data in the database). - The database is filled sequentially. Throughput is then measured with a readrandom benchmark. - We use a uniform distribution as a worst-case scenario. The plot shows throughput (ops/s) relative to a configuration with no block cache and default allocator. For all tests, p99 latency is below 500 us. ![image](https://user-images.githubusercontent.com/26400080/71108594-42479100-2178-11ea-8231-8a775bbc92db.png) **Changes** - Add MemkindKmemAllocator - Add --use_cache_memkind_kmem_allocator db_bench option (to create an LRU block cache with the new allocator) - Add detection of memkind library with KMEM DAX support - Add test for MemkindKmemAllocator **Minimum Requirements** - kernel 5.3.12 - ndctl v67 - https://github.com/pmem/ndctl - memkind v1.10.0 - https://github.com/memkind/memkind **Memory Configuration** The allocator uses the MEMKIND_DAX_KMEM memory kind. Follow the instructions on[ memkind’s GitHub page](https://github.com/memkind/memkind) to set up NVDIMM memory accordingly. Note on memory allocation with NVDIMM memory exposed as system memory. - The MemkindKmemAllocator will only allocate from NVDIMM memory (using memkind_malloc with MEMKIND_DAX_KMEM kind). - The default allocator is not restricted to RAM by default. Based on NUMA node latency, the kernel should allocate from local RAM preferentially, but it’s a kernel decision. numactl --preferred/--membind can be used to allocate preferentially/exclusively from the local RAM node. **Usage** When creating an LRU cache, pass a MemkindKmemAllocator object as argument. For example (replace capacity with the desired value in bytes): ``` #include "rocksdb/cache.h" #include "memory/memkind_kmem_allocator.h" NewLRUCache( capacity /*size_t*/, 6 /*cache_numshardbits*/, false /*strict_capacity_limit*/, false /*cache_high_pri_pool_ratio*/, std::make_shared<MemkindKmemAllocator>()); ``` Refer to [RocksDB’s block cache documentation](https://github.com/facebook/rocksdb/wiki/Block-Cache) to assign the LRU cache as block cache for a database. Pull Request resolved: https://github.com/facebook/rocksdb/pull/6214 Reviewed By: cheng-chang Differential Revision: D19292435 fbshipit-source-id: 7202f47b769e7722b539c86c2ffd669f64d7b4e1
2020-04-10 05:45:17 +02:00
fprintf(stderr, "Memkind library is not linked with the binary.");
exit(1);
#endif
}
#ifndef ROCKSDB_LITE
if (!FLAGS_secondary_cache_uri.empty()) {
Status s = SecondaryCache::CreateFromString(
ConfigOptions(), FLAGS_secondary_cache_uri, &secondary_cache);
if (secondary_cache == nullptr) {
fprintf(
stderr,
"No secondary cache registered matching string: %s status=%s\n",
FLAGS_secondary_cache_uri.c_str(), s.ToString().c_str());
exit(1);
}
opts.secondary_cache = secondary_cache;
}
#endif // ROCKSDB_LITE
return NewLRUCache(opts);
}
}
public:
Benchmark()
: cache_(NewCache(FLAGS_cache_size)),
compressed_cache_(NewCache(FLAGS_compressed_cache_size)),
prefix_extractor_(NewFixedPrefixTransform(FLAGS_prefix_size)),
num_(FLAGS_num),
key_size_(FLAGS_key_size),
user_timestamp_size_(FLAGS_user_timestamp_size),
prefix_size_(FLAGS_prefix_size),
keys_per_prefix_(FLAGS_keys_per_prefix),
entries_per_batch_(1),
reads_(FLAGS_reads < 0 ? FLAGS_num : FLAGS_reads),
read_random_exp_range_(0.0),
writes_(FLAGS_writes < 0 ? FLAGS_num : FLAGS_writes),
readwrites_(
(FLAGS_writes < 0 && FLAGS_reads < 0)
? FLAGS_num
: ((FLAGS_writes > FLAGS_reads) ? FLAGS_writes : FLAGS_reads)),
merge_keys_(FLAGS_merge_keys < 0 ? FLAGS_num : FLAGS_merge_keys),
report_file_operations_(FLAGS_report_file_operations),
#ifndef ROCKSDB_LITE
use_blob_db_(FLAGS_use_blob_db) // Stacked BlobDB
#else
use_blob_db_(false) // Stacked BlobDB
#endif // !ROCKSDB_LITE
{
add simulator Cache as class SimCache/SimLRUCache(with test) Summary: add class SimCache(base class with instrumentation api) and SimLRUCache(derived class with detailed implementation) which is used as an instrumented block cache that can predict hit rate for different cache size Test Plan: Add a test case in `db_block_cache_test.cc` called `SimCacheTest` to test basic logic of SimCache. Also add option `-simcache_size` in db_bench. if set with a value other than -1, then the benchmark will use this value as the size of the simulator cache and finally output the simulation result. ``` [gzh@dev9927.prn1 ~/local/rocksdb] ./db_bench -benchmarks "fillseq,readrandom" -cache_size 1000000 -simcache_size 1000000 RocksDB: version 4.8 Date: Tue May 17 16:56:16 2016 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Compression: Snappy Memtablerep: skip_list Perf Level: 0 WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ DB path: [/tmp/rocksdbtest-112628/dbbench] fillseq : 6.809 micros/op 146874 ops/sec; 16.2 MB/s DB path: [/tmp/rocksdbtest-112628/dbbench] readrandom : 6.343 micros/op 157665 ops/sec; 17.4 MB/s (1000000 of 1000000 found) SIMULATOR CACHE STATISTICS: SimCache LOOKUPs: 986559 SimCache HITs: 264760 SimCache HITRATE: 26.84% [gzh@dev9927.prn1 ~/local/rocksdb] ./db_bench -benchmarks "fillseq,readrandom" -cache_size 1000000 -simcache_size 10000000 RocksDB: version 4.8 Date: Tue May 17 16:57:10 2016 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Compression: Snappy Memtablerep: skip_list Perf Level: 0 WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ DB path: [/tmp/rocksdbtest-112628/dbbench] fillseq : 5.066 micros/op 197394 ops/sec; 21.8 MB/s DB path: [/tmp/rocksdbtest-112628/dbbench] readrandom : 6.457 micros/op 154870 ops/sec; 17.1 MB/s (1000000 of 1000000 found) SIMULATOR CACHE STATISTICS: SimCache LOOKUPs: 1059764 SimCache HITs: 374501 SimCache HITRATE: 35.34% [gzh@dev9927.prn1 ~/local/rocksdb] ./db_bench -benchmarks "fillseq,readrandom" -cache_size 1000000 -simcache_size 100000000 RocksDB: version 4.8 Date: Tue May 17 16:57:32 2016 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Compression: Snappy Memtablerep: skip_list Perf Level: 0 WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ DB path: [/tmp/rocksdbtest-112628/dbbench] fillseq : 5.632 micros/op 177572 ops/sec; 19.6 MB/s DB path: [/tmp/rocksdbtest-112628/dbbench] readrandom : 6.892 micros/op 145094 ops/sec; 16.1 MB/s (1000000 of 1000000 found) SIMULATOR CACHE STATISTICS: SimCache LOOKUPs: 1150767 SimCache HITs: 1034535 SimCache HITRATE: 89.90% ``` Reviewers: IslamAbdelRahman, andrewkr, sdong Reviewed By: sdong Subscribers: MarkCallaghan, andrewkr, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D57999
2016-05-24 08:35:23 +02:00
// use simcache instead of cache
if (FLAGS_simcache_size >= 0) {
if (FLAGS_cache_numshardbits >= 1) {
cache_ =
NewSimCache(cache_, FLAGS_simcache_size, FLAGS_cache_numshardbits);
} else {
cache_ = NewSimCache(cache_, FLAGS_simcache_size, 0);
}
}
if (report_file_operations_) {
if (!FLAGS_hdfs.empty()) {
fprintf(stderr,
"--hdfs and --report_file_operations cannot be enabled "
"at the same time");
exit(1);
}
FLAGS_env = new ReportFileOpEnv(FLAGS_env);
}
if (FLAGS_prefix_size > FLAGS_key_size) {
fprintf(stderr, "prefix size is larger than key size");
exit(1);
}
std::vector<std::string> files;
FLAGS_env->GetChildren(FLAGS_db, &files);
for (size_t i = 0; i < files.size(); i++) {
if (Slice(files[i]).starts_with("heap-")) {
FLAGS_env->DeleteFile(FLAGS_db + "/" + files[i]);
}
}
if (!FLAGS_use_existing_db) {
Options options;
options.env = FLAGS_env;
if (!FLAGS_wal_dir.empty()) {
options.wal_dir = FLAGS_wal_dir;
}
#ifndef ROCKSDB_LITE
if (use_blob_db_) {
// Stacked BlobDB
blob_db::DestroyBlobDB(FLAGS_db, options, blob_db::BlobDBOptions());
}
#endif // !ROCKSDB_LITE
DestroyDB(FLAGS_db, options);
if (!FLAGS_wal_dir.empty()) {
FLAGS_env->DeleteDir(FLAGS_wal_dir);
}
if (FLAGS_num_multi_db > 1) {
FLAGS_env->CreateDir(FLAGS_db);
if (!FLAGS_wal_dir.empty()) {
FLAGS_env->CreateDir(FLAGS_wal_dir);
}
}
}
Auto recovery from out of space errors (#4164) Summary: This commit implements automatic recovery from a Status::NoSpace() error during background operations such as write callback, flush and compaction. The broad design is as follows - 1. Compaction errors are treated as soft errors and don't put the database in read-only mode. A compaction is delayed until enough free disk space is available to accomodate the compaction outputs, which is estimated based on the input size. This means that users can continue to write, and we rely on the WriteController to delay or stop writes if the compaction debt becomes too high due to persistent low disk space condition 2. Errors during write callback and flush are treated as hard errors, i.e the database is put in read-only mode and goes back to read-write only fater certain recovery actions are taken. 3. Both types of recovery rely on the SstFileManagerImpl to poll for sufficient disk space. We assume that there is a 1-1 mapping between an SFM and the underlying OS storage container. For cases where multiple DBs are hosted on a single storage container, the user is expected to allocate a single SFM instance and use the same one for all the DBs. If no SFM is specified by the user, DBImpl::Open() will allocate one, but this will be one per DB and each DB will recover independently. The recovery implemented by SFM is as follows - a) On the first occurance of an out of space error during compaction, subsequent compactions will be delayed until the disk free space check indicates enough available space. The required space is computed as the sum of input sizes. b) The free space check requirement will be removed once the amount of free space is greater than the size reserved by in progress compactions when the first error occured c) If the out of space error is a hard error, a background thread in SFM will poll for sufficient headroom before triggering the recovery of the database and putting it in write-only mode. The headroom is calculated as the sum of the write_buffer_size of all the DB instances associated with the SFM 4. EventListener callbacks will be called at the start and completion of automatic recovery. Users can disable the auto recov ery in the start callback, and later initiate it manually by calling DB::Resume() Todo: 1. More extensive testing 2. Add disk full condition to db_stress (follow-on PR) Pull Request resolved: https://github.com/facebook/rocksdb/pull/4164 Differential Revision: D9846378 Pulled By: anand1976 fbshipit-source-id: 80ea875dbd7f00205e19c82215ff6e37da10da4a
2018-09-15 22:36:19 +02:00
listener_.reset(new ErrorHandlerListener());
if (user_timestamp_size_ > 0) {
mock_app_clock_.reset(new TimestampEmulator());
}
}
void DeleteDBs() {
db_.DeleteDBs();
for (const DBWithColumnFamilies& dbwcf : multi_dbs_) {
delete dbwcf.db;
}
}
~Benchmark() {
DeleteDBs();
delete prefix_extractor_;
2015-03-14 00:41:00 +01:00
if (cache_.get() != nullptr) {
Use deleters to label cache entries and collect stats (#8297) Summary: This change gathers and publishes statistics about the kinds of items in block cache. This is especially important for profiling relative usage of cache by index vs. filter vs. data blocks. It works by iterating over the cache during periodic stats dump (InternalStats, stats_dump_period_sec) or on demand when DB::Get(Map)Property(kBlockCacheEntryStats), except that for efficiency and sharing among column families, saved data from the last scan is used when the data is not considered too old. The new information can be seen in info LOG, for example: Block cache LRUCache@0x7fca62229330 capacity: 95.37 MB collections: 8 last_copies: 0 last_secs: 0.00178 secs_since: 0 Block cache entry stats(count,size,portion): DataBlock(7092,28.24 MB,29.6136%) FilterBlock(215,867.90 KB,0.888728%) FilterMetaBlock(2,5.31 KB,0.00544%) IndexBlock(217,180.11 KB,0.184432%) WriteBuffer(1,256.00 KB,0.262144%) Misc(1,0.00 KB,0%) And also through DB::GetProperty and GetMapProperty (here using ldb just for demonstration): $ ./ldb --db=/dev/shm/dbbench/ get_property rocksdb.block-cache-entry-stats rocksdb.block-cache-entry-stats.bytes.data-block: 0 rocksdb.block-cache-entry-stats.bytes.deprecated-filter-block: 0 rocksdb.block-cache-entry-stats.bytes.filter-block: 0 rocksdb.block-cache-entry-stats.bytes.filter-meta-block: 0 rocksdb.block-cache-entry-stats.bytes.index-block: 178992 rocksdb.block-cache-entry-stats.bytes.misc: 0 rocksdb.block-cache-entry-stats.bytes.other-block: 0 rocksdb.block-cache-entry-stats.bytes.write-buffer: 0 rocksdb.block-cache-entry-stats.capacity: 8388608 rocksdb.block-cache-entry-stats.count.data-block: 0 rocksdb.block-cache-entry-stats.count.deprecated-filter-block: 0 rocksdb.block-cache-entry-stats.count.filter-block: 0 rocksdb.block-cache-entry-stats.count.filter-meta-block: 0 rocksdb.block-cache-entry-stats.count.index-block: 215 rocksdb.block-cache-entry-stats.count.misc: 1 rocksdb.block-cache-entry-stats.count.other-block: 0 rocksdb.block-cache-entry-stats.count.write-buffer: 0 rocksdb.block-cache-entry-stats.id: LRUCache@0x7f3636661290 rocksdb.block-cache-entry-stats.percent.data-block: 0.000000 rocksdb.block-cache-entry-stats.percent.deprecated-filter-block: 0.000000 rocksdb.block-cache-entry-stats.percent.filter-block: 0.000000 rocksdb.block-cache-entry-stats.percent.filter-meta-block: 0.000000 rocksdb.block-cache-entry-stats.percent.index-block: 2.133751 rocksdb.block-cache-entry-stats.percent.misc: 0.000000 rocksdb.block-cache-entry-stats.percent.other-block: 0.000000 rocksdb.block-cache-entry-stats.percent.write-buffer: 0.000000 rocksdb.block-cache-entry-stats.secs_for_last_collection: 0.000052 rocksdb.block-cache-entry-stats.secs_since_last_collection: 0 Solution detail - We need some way to flag what kind of blocks each entry belongs to, preferably without changing the Cache API. One of the complications is that Cache is a general interface that could have other users that don't adhere to whichever convention we decide on for keys and values. Or we would pay for an extra field in the Handle that would only be used for this purpose. This change uses a back-door approach, the deleter, to indicate the "role" of a Cache entry (in addition to the value type, implicitly). This has the added benefit of ensuring proper code origin whenever we recognize a particular role for a cache entry; if the entry came from some other part of the code, it will use an unrecognized deleter, which we simply attribute to the "Misc" role. An internal API makes for simple instantiation and automatic registration of Cache deleters for a given value type and "role". Another internal API, CacheEntryStatsCollector, solves the problem of caching the results of a scan and sharing them, to ensure scans are neither excessive nor redundant so as not to harm Cache performance. Because code is added to BlocklikeTraits, it is pulled out of block_based_table_reader.cc into its own file. This is a reformulation of https://github.com/facebook/rocksdb/issues/8276, without the type checking option (could still be added), and with actual stat gathering. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8297 Test Plan: manual testing with db_bench, and a couple of basic unit tests Reviewed By: ltamasi Differential Revision: D28488721 Pulled By: pdillinger fbshipit-source-id: 472f524a9691b5afb107934be2d41d84f2b129fb
2021-05-20 01:45:51 +02:00
// Clear cache reference first
open_options_.write_buffer_manager.reset();
2015-03-14 00:41:00 +01:00
// this will leak, but we're shutting down so nobody cares
cache_->DisownData();
}
}
Slice AllocateKey(std::unique_ptr<const char[]>* key_guard) {
char* data = new char[key_size_];
const char* const_data = data;
key_guard->reset(const_data);
return Slice(key_guard->get(), key_size_);
}
// Generate key according to the given specification and random number.
// The resulting key will have the following format:
// - If keys_per_prefix_ is positive, extra trailing bytes are either cut
// off or padded with '0'.
// The prefix value is derived from key value.
// ----------------------------
// | prefix 00000 | key 00000 |
// ----------------------------
//
// - If keys_per_prefix_ is 0, the key is simply a binary representation of
// random number followed by trailing '0's
// ----------------------------
// | key 00000 |
// ----------------------------
void GenerateKeyFromInt(uint64_t v, int64_t num_keys, Slice* key) {
if (!keys_.empty()) {
assert(FLAGS_use_existing_keys);
assert(keys_.size() == static_cast<size_t>(num_keys));
assert(v < static_cast<uint64_t>(num_keys));
*key = keys_[v];
return;
}
char* start = const_cast<char*>(key->data());
char* pos = start;
if (keys_per_prefix_ > 0) {
int64_t num_prefix = num_keys / keys_per_prefix_;
int64_t prefix = v % num_prefix;
int bytes_to_fill = std::min(prefix_size_, 8);
if (port::kLittleEndian) {
for (int i = 0; i < bytes_to_fill; ++i) {
pos[i] = (prefix >> ((bytes_to_fill - i - 1) << 3)) & 0xFF;
}
} else {
memcpy(pos, static_cast<void*>(&prefix), bytes_to_fill);
}
if (prefix_size_ > 8) {
// fill the rest with 0s
memset(pos + 8, '0', prefix_size_ - 8);
}
pos += prefix_size_;
}
int bytes_to_fill = std::min(key_size_ - static_cast<int>(pos - start), 8);
if (port::kLittleEndian) {
for (int i = 0; i < bytes_to_fill; ++i) {
pos[i] = (v >> ((bytes_to_fill - i - 1) << 3)) & 0xFF;
}
} else {
memcpy(pos, static_cast<void*>(&v), bytes_to_fill);
}
pos += bytes_to_fill;
if (key_size_ > pos - start) {
memset(pos, '0', key_size_ - (pos - start));
}
}
void GenerateKeyFromIntForSeek(uint64_t v, int64_t num_keys, Slice* key) {
GenerateKeyFromInt(v, num_keys, key);
if (FLAGS_seek_missing_prefix) {
assert(prefix_size_ > 8);
char* key_ptr = const_cast<char*>(key->data());
// This rely on GenerateKeyFromInt filling paddings with '0's.
// Putting a '1' will create a non-existing prefix.
key_ptr[8] = '1';
}
}
std::string GetPathForMultiple(std::string base_name, size_t id) {
if (!base_name.empty()) {
#ifndef OS_WIN
if (base_name.back() != '/') {
base_name += '/';
}
#else
if (base_name.back() != '\\') {
base_name += '\\';
}
#endif
}
return base_name + ToString(id);
}
void VerifyDBFromDB(std::string& truth_db_name) {
DBWithColumnFamilies truth_db;
auto s = DB::OpenForReadOnly(open_options_, truth_db_name, &truth_db.db);
if (!s.ok()) {
fprintf(stderr, "open error: %s\n", s.ToString().c_str());
exit(1);
}
ReadOptions ro;
ro.total_order_seek = true;
std::unique_ptr<Iterator> truth_iter(truth_db.db->NewIterator(ro));
std::unique_ptr<Iterator> db_iter(db_.db->NewIterator(ro));
// Verify that all the key/values in truth_db are retrivable in db with
// ::Get
fprintf(stderr, "Verifying db >= truth_db with ::Get...\n");
for (truth_iter->SeekToFirst(); truth_iter->Valid(); truth_iter->Next()) {
std::string value;
s = db_.db->Get(ro, truth_iter->key(), &value);
assert(s.ok());
// TODO(myabandeh): provide debugging hints
assert(Slice(value) == truth_iter->value());
}
// Verify that the db iterator does not give any extra key/value
fprintf(stderr, "Verifying db == truth_db...\n");
for (db_iter->SeekToFirst(), truth_iter->SeekToFirst(); db_iter->Valid();
db_iter->Next(), truth_iter->Next()) {
assert(truth_iter->Valid());
assert(truth_iter->value() == db_iter->value());
}
// No more key should be left unchecked in truth_db
assert(!truth_iter->Valid());
fprintf(stderr, "...Verified\n");
}
void ErrorExit() {
DeleteDBs();
exit(1);
}
void Run() {
if (!SanityCheck()) {
ErrorExit();
}
Open(&open_options_);
PrintHeader(open_options_);
std::stringstream benchmark_stream(FLAGS_benchmarks);
std::string name;
Write a benchmark to emulate time series data Summary: Add a benchmark to `db_bench`. In this benchmark, a write thread will populate time series data in the format of 'id | timestamp', and multiple read threads will randomly retrieve all data from one id at a time. Test Plan: Run the benchmark: `num=134217728;bpl=536870912;mb=67108864;overlap=10;mcz=2;del=300000000;levels=6;ctrig=4;delay=8;stop=12;wbn=3;mbc=20;wbs=134217728;dds=0;sync=0;t=32;vs=800;bs=4096;cs=17179869184;of=500000;wps=0;si=10000000; kir=100000; dir=/data/users/jhli/test/; ./db_bench --benchmarks=timeseries --disable_seek_compaction=1 --mmap_read=0 --statistics=1 --histogram=1 --num=$num --threads=$t --value_size=$vs --block_size=$bs --cache_size=$cs --bloom_bits=10 --cache_numshardbits=6 --open_files=$of --verify_checksum=1 --db=$dir --sync=$sync --disable_wal=0 --compression_type=none --stats_interval=$si --compression_ratio=1 --disable_data_sync=$dds --write_buffer_size=$wbs --target_file_size_base=$mb --max_write_buffer_number=$wbn --max_background_compactions=$mbc --level0_file_num_compaction_trigger=$ctrig --level0_slowdown_writes_trigger=$delay --level0_stop_writes_trigger=$stop --num_levels=$levels --delete_obsolete_files_period_micros=$del --min_level_to_compress=$mcz --max_grandparent_overlap_factor=$overlap --stats_per_interval=1 --max_bytes_for_level_base=$bpl --use_existing_db=0 --key_id_range=$kir` Reviewers: andrewkr, sdong Reviewed By: sdong Subscribers: lgalanis, andrewkr, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D60651
2016-08-01 21:28:51 +02:00
std::unique_ptr<ExpiredTimeFilter> filter;
while (std::getline(benchmark_stream, name, ',')) {
// Sanitize parameters
num_ = FLAGS_num;
reads_ = (FLAGS_reads < 0 ? FLAGS_num : FLAGS_reads);
writes_ = (FLAGS_writes < 0 ? FLAGS_num : FLAGS_writes);
deletes_ = (FLAGS_deletes < 0 ? FLAGS_num : FLAGS_deletes);
value_size = FLAGS_value_size;
key_size_ = FLAGS_key_size;
entries_per_batch_ = FLAGS_batch_size;
writes_before_delete_range_ = FLAGS_writes_before_delete_range;
writes_per_range_tombstone_ = FLAGS_writes_per_range_tombstone;
range_tombstone_width_ = FLAGS_range_tombstone_width;
max_num_range_tombstones_ = FLAGS_max_num_range_tombstones;
write_options_ = WriteOptions();
read_random_exp_range_ = FLAGS_read_random_exp_range;
if (FLAGS_sync) {
write_options_.sync = true;
}
write_options_.disableWAL = FLAGS_disable_wal;
void (Benchmark::*method)(ThreadState*) = nullptr;
void (Benchmark::*post_process_method)() = nullptr;
bool fresh_db = false;
int num_threads = FLAGS_threads;
[db_bench] Support single benchmark arguments (Repeat for X times, Warm up for X times), Support CombinedStats (AVG / MEDIAN) Summary: This diff allow us to run a single benchmark X times and warm it up for Y times. and see the AVG & MEDIAN throughput of these X runs for example ``` $ ./db_bench --benchmarks="fillseq,readseq[X5-W2]" Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags RocksDB: version 4.12 Date: Wed Aug 24 10:45:26 2016 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Compression: Snappy Memtablerep: skip_list Perf Level: 1 WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags DB path: [/tmp/rocksdbtest-8616/dbbench] fillseq : 4.695 micros/op 212971 ops/sec; 23.6 MB/s DB path: [/tmp/rocksdbtest-8616/dbbench] Warming up benchmark by running 2 times readseq : 0.214 micros/op 4677005 ops/sec; 517.4 MB/s readseq : 0.212 micros/op 4706834 ops/sec; 520.7 MB/s Running benchmark for 5 times readseq : 0.218 micros/op 4588187 ops/sec; 507.6 MB/s readseq : 0.208 micros/op 4816538 ops/sec; 532.8 MB/s readseq : 0.213 micros/op 4685376 ops/sec; 518.3 MB/s readseq : 0.214 micros/op 4676787 ops/sec; 517.4 MB/s readseq : 0.217 micros/op 4618532 ops/sec; 510.9 MB/s readseq [AVG 5 runs] : 4677084 ops/sec; 517.4 MB/sec readseq [MEDIAN 5 runs] : 4676787 ops/sec; 517.4 MB/sec ``` Test Plan: run db_bench Reviewers: sdong, andrewkr, yhchiang Reviewed By: yhchiang Subscribers: andrewkr, dhruba Differential Revision: https://reviews.facebook.net/D62235
2016-08-25 21:57:35 +02:00
int num_repeat = 1;
int num_warmup = 0;
if (!name.empty() && *name.rbegin() == ']') {
auto it = name.find('[');
if (it == std::string::npos) {
fprintf(stderr, "unknown benchmark arguments '%s'\n", name.c_str());
ErrorExit();
[db_bench] Support single benchmark arguments (Repeat for X times, Warm up for X times), Support CombinedStats (AVG / MEDIAN) Summary: This diff allow us to run a single benchmark X times and warm it up for Y times. and see the AVG & MEDIAN throughput of these X runs for example ``` $ ./db_bench --benchmarks="fillseq,readseq[X5-W2]" Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags RocksDB: version 4.12 Date: Wed Aug 24 10:45:26 2016 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Compression: Snappy Memtablerep: skip_list Perf Level: 1 WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags DB path: [/tmp/rocksdbtest-8616/dbbench] fillseq : 4.695 micros/op 212971 ops/sec; 23.6 MB/s DB path: [/tmp/rocksdbtest-8616/dbbench] Warming up benchmark by running 2 times readseq : 0.214 micros/op 4677005 ops/sec; 517.4 MB/s readseq : 0.212 micros/op 4706834 ops/sec; 520.7 MB/s Running benchmark for 5 times readseq : 0.218 micros/op 4588187 ops/sec; 507.6 MB/s readseq : 0.208 micros/op 4816538 ops/sec; 532.8 MB/s readseq : 0.213 micros/op 4685376 ops/sec; 518.3 MB/s readseq : 0.214 micros/op 4676787 ops/sec; 517.4 MB/s readseq : 0.217 micros/op 4618532 ops/sec; 510.9 MB/s readseq [AVG 5 runs] : 4677084 ops/sec; 517.4 MB/sec readseq [MEDIAN 5 runs] : 4676787 ops/sec; 517.4 MB/sec ``` Test Plan: run db_bench Reviewers: sdong, andrewkr, yhchiang Reviewed By: yhchiang Subscribers: andrewkr, dhruba Differential Revision: https://reviews.facebook.net/D62235
2016-08-25 21:57:35 +02:00
}
std::string args = name.substr(it + 1);
args.resize(args.size() - 1);
name.resize(it);
std::string bench_arg;
std::stringstream args_stream(args);
while (std::getline(args_stream, bench_arg, '-')) {
if (bench_arg.empty()) {
continue;
}
if (bench_arg[0] == 'X') {
// Repeat the benchmark n times
std::string num_str = bench_arg.substr(1);
num_repeat = std::stoi(num_str);
} else if (bench_arg[0] == 'W') {
// Warm up the benchmark for n times
std::string num_str = bench_arg.substr(1);
num_warmup = std::stoi(num_str);
}
}
}
// Both fillseqdeterministic and filluniquerandomdeterministic
// fill the levels except the max level with UNIQUE_RANDOM
// and fill the max level with fillseq and filluniquerandom, respectively
if (name == "fillseqdeterministic" ||
name == "filluniquerandomdeterministic") {
if (!FLAGS_disable_auto_compactions) {
fprintf(stderr,
"Please disable_auto_compactions in FillDeterministic "
"benchmark\n");
ErrorExit();
}
if (num_threads > 1) {
fprintf(stderr,
"filldeterministic multithreaded not supported"
", use 1 thread\n");
num_threads = 1;
}
fresh_db = true;
if (name == "fillseqdeterministic") {
method = &Benchmark::WriteSeqDeterministic;
} else {
method = &Benchmark::WriteUniqueRandomDeterministic;
}
} else if (name == "fillseq") {
fresh_db = true;
method = &Benchmark::WriteSeq;
} else if (name == "fillbatch") {
fresh_db = true;
entries_per_batch_ = 1000;
method = &Benchmark::WriteSeq;
} else if (name == "fillrandom") {
fresh_db = true;
method = &Benchmark::WriteRandom;
Create fillanddeleteuniquerandom benchmark (db_bench), with new option flags. (#8593) Summary: Introduction of a new `fillanddeleteuniquerandom` benchmark (`db_bench`) with 5 new option flags to simulate a benchmark where the following sequence is repeated multiple times: "A set of keys S1 is inserted ('`disposable entries`'), then after some delay another set of keys S2 is inserted ('`persistent entries`') and the first set of keys S1 is deleted. S2 artificially represents the insertion of hypothetical results from some undefined computation done on the first set of keys S1. The next sequence can start as soon as the last disposable entry in the set S1 of this sequence is inserted, if the `delay` is non negligible." New flags: - `disposable_entries_delete_delay`: minimum delay in microseconds between insertion of the last `disposable` entry, and the start of the insertion of the first `persistent` entry. - `disposable_entries_batch_size`: number of `disposable` entries inserted at the beginning of each sequence. - `disposable_entries_value_size`: size of the random `value` string for the `disposable` entries. - `persistent_entries_batch_size`: number of `persistent` entries inserted at the end of each sequence, right before the deletion of the `disposable` entries starts. - `persistent_entries_value_size`: size of the random value string for the `persistent` entries. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8593 Reviewed By: pdillinger Differential Revision: D29974436 Pulled By: bjlemaire fbshipit-source-id: f578033e5b45e8268ba6fa6f38f4770c2e6e801d
2021-07-29 23:57:03 +02:00
} else if (name == "filluniquerandom" ||
name == "fillanddeleteuniquerandom") {
fresh_db = true;
if (num_threads > 1) {
fprintf(stderr,
Create fillanddeleteuniquerandom benchmark (db_bench), with new option flags. (#8593) Summary: Introduction of a new `fillanddeleteuniquerandom` benchmark (`db_bench`) with 5 new option flags to simulate a benchmark where the following sequence is repeated multiple times: "A set of keys S1 is inserted ('`disposable entries`'), then after some delay another set of keys S2 is inserted ('`persistent entries`') and the first set of keys S1 is deleted. S2 artificially represents the insertion of hypothetical results from some undefined computation done on the first set of keys S1. The next sequence can start as soon as the last disposable entry in the set S1 of this sequence is inserted, if the `delay` is non negligible." New flags: - `disposable_entries_delete_delay`: minimum delay in microseconds between insertion of the last `disposable` entry, and the start of the insertion of the first `persistent` entry. - `disposable_entries_batch_size`: number of `disposable` entries inserted at the beginning of each sequence. - `disposable_entries_value_size`: size of the random `value` string for the `disposable` entries. - `persistent_entries_batch_size`: number of `persistent` entries inserted at the end of each sequence, right before the deletion of the `disposable` entries starts. - `persistent_entries_value_size`: size of the random value string for the `persistent` entries. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8593 Reviewed By: pdillinger Differential Revision: D29974436 Pulled By: bjlemaire fbshipit-source-id: f578033e5b45e8268ba6fa6f38f4770c2e6e801d
2021-07-29 23:57:03 +02:00
"filluniquerandom and fillanddeleteuniquerandom "
"multithreaded not supported, use 1 thread");
num_threads = 1;
}
method = &Benchmark::WriteUniqueRandom;
} else if (name == "overwrite") {
method = &Benchmark::WriteRandom;
} else if (name == "fillsync") {
fresh_db = true;
num_ /= 1000;
write_options_.sync = true;
method = &Benchmark::WriteRandom;
} else if (name == "fill100K") {
fresh_db = true;
num_ /= 1000;
value_size = 100 * 1000;
method = &Benchmark::WriteRandom;
} else if (name == "readseq") {
method = &Benchmark::ReadSequential;
} else if (name == "readtorowcache") {
if (!FLAGS_use_existing_keys || !FLAGS_row_cache_size) {
fprintf(stderr,
"Please set use_existing_keys to true and specify a "
"row cache size in readtorowcache benchmark\n");
ErrorExit();
}
method = &Benchmark::ReadToRowCache;
} else if (name == "readtocache") {
method = &Benchmark::ReadSequential;
num_threads = 1;
reads_ = num_;
} else if (name == "readreverse") {
method = &Benchmark::ReadReverse;
} else if (name == "readrandom") {
Introduce a new MultiGet batching implementation (#5011) Summary: This PR introduces a new MultiGet() API, with the underlying implementation grouping keys based on SST file and batching lookups in a file. The reason for the new API is twofold - the definition allows callers to allocate storage for status and values on stack instead of std::vector, as well as return values as PinnableSlices in order to avoid copying, and it keeps the original MultiGet() implementation intact while we experiment with batching. Batching is useful when there is some spatial locality to the keys being queries, as well as larger batch sizes. The main benefits are due to - 1. Fewer function calls, especially to BlockBasedTableReader::MultiGet() and FullFilterBlockReader::KeysMayMatch() 2. Bloom filter cachelines can be prefetched, hiding the cache miss latency The next step is to optimize the binary searches in the level_storage_info, index blocks and data blocks, since we could reduce the number of key comparisons if the keys are relatively close to each other. The batching optimizations also need to be extended to other formats, such as PlainTable and filter formats. This also needs to be added to db_stress. Benchmark results from db_bench for various batch size/locality of reference combinations are given below. Locality was simulated by offsetting the keys in a batch by a stride length. Each SST file is about 8.6MB uncompressed and key/value size is 16/100 uncompressed. To focus on the cpu benefit of batching, the runs were single threaded and bound to the same cpu to eliminate interference from other system events. The results show a 10-25% improvement in micros/op from smaller to larger batch sizes (4 - 32). Batch Sizes 1 | 2 | 4 | 8 | 16 | 32 Random pattern (Stride length 0) 4.158 | 4.109 | 4.026 | 4.05 | 4.1 | 4.074 - Get 4.438 | 4.302 | 4.165 | 4.122 | 4.096 | 4.075 - MultiGet (no batching) 4.461 | 4.256 | 4.277 | 4.11 | 4.182 | 4.14 - MultiGet (w/ batching) Good locality (Stride length 16) 4.048 | 3.659 | 3.248 | 2.99 | 2.84 | 2.753 4.429 | 3.728 | 3.406 | 3.053 | 2.911 | 2.781 4.452 | 3.45 | 2.833 | 2.451 | 2.233 | 2.135 Good locality (Stride length 256) 4.066 | 3.786 | 3.581 | 3.447 | 3.415 | 3.232 4.406 | 4.005 | 3.644 | 3.49 | 3.381 | 3.268 4.393 | 3.649 | 3.186 | 2.882 | 2.676 | 2.62 Medium locality (Stride length 4096) 4.012 | 3.922 | 3.768 | 3.61 | 3.582 | 3.555 4.364 | 4.057 | 3.791 | 3.65 | 3.57 | 3.465 4.479 | 3.758 | 3.316 | 3.077 | 2.959 | 2.891 dbbench command used (on a DB with 4 levels, 12 million keys)- TEST_TMPDIR=/dev/shm numactl -C 10 ./db_bench.tmp -use_existing_db=true -benchmarks="readseq,multireadrandom" -write_buffer_size=4194304 -target_file_size_base=4194304 -max_bytes_for_level_base=16777216 -num=12000000 -reads=12000000 -duration=90 -threads=1 -compression_type=none -cache_size=4194304000 -batch_size=32 -disable_auto_compactions=true -bloom_bits=10 -cache_index_and_filter_blocks=true -pin_l0_filter_and_index_blocks_in_cache=true -multiread_batched=true -multiread_stride=4 Pull Request resolved: https://github.com/facebook/rocksdb/pull/5011 Differential Revision: D14348703 Pulled By: anand1976 fbshipit-source-id: 774406dab3776d979c809522a67bedac6c17f84b
2019-04-11 23:24:09 +02:00
if (FLAGS_multiread_stride) {
fprintf(stderr, "entries_per_batch = %" PRIi64 "\n",
entries_per_batch_);
}
method = &Benchmark::ReadRandom;
} else if (name == "readrandomfast") {
method = &Benchmark::ReadRandomFast;
} else if (name == "multireadrandom") {
fprintf(stderr, "entries_per_batch = %" PRIi64 "\n",
entries_per_batch_);
method = &Benchmark::MultiReadRandom;
For ApproximateSizes, pro-rate table metadata size over data blocks (#6784) Summary: The implementation of GetApproximateSizes was inconsistent in its treatment of the size of non-data blocks of SST files, sometimes including and sometimes now. This was at its worst with large portion of table file used by filters and querying a small range that crossed a table boundary: the size estimate would include large filter size. It's conceivable that someone might want only to know the size in terms of data blocks, but I believe that's unlikely enough to ignore for now. Similarly, there's no evidence the internal function AppoximateOffsetOf is used for anything other than a one-sided ApproximateSize, so I intend to refactor to remove redundancy in a follow-up commit. So to fix this, GetApproximateSizes (and implementation details ApproximateSize and ApproximateOffsetOf) now consistently include in their returned sizes a portion of table file metadata (incl filters and indexes) based on the size portion of the data blocks in range. In other words, if a key range covers data blocks that are X% by size of all the table's data blocks, returned approximate size is X% of the total file size. It would technically be more accurate to attribute metadata based on number of keys, but that's not computationally efficient with data available and rarely a meaningful difference. Also includes miscellaneous comment improvements / clarifications. Also included is a new approximatesizerandom benchmark for db_bench. No significant performance difference seen with this change, whether ~700 ops/sec with cache_index_and_filter_blocks and small cache or ~150k ops/sec without cache_index_and_filter_blocks. Pull Request resolved: https://github.com/facebook/rocksdb/pull/6784 Test Plan: Test added to DBTest.ApproximateSizesFilesWithErrorMargin. Old code running new test... [ RUN ] DBTest.ApproximateSizesFilesWithErrorMargin db/db_test.cc:1562: Failure Expected: (size) <= (11 * 100), actual: 9478 vs 1100 Other tests updated to reflect consistent accounting of metadata. Reviewed By: siying Differential Revision: D21334706 Pulled By: pdillinger fbshipit-source-id: 6f86870e45213334fedbe9c73b4ebb1d8d611185
2020-06-02 21:27:59 +02:00
} else if (name == "approximatesizerandom") {
fprintf(stderr, "entries_per_batch = %" PRIi64 "\n",
entries_per_batch_);
method = &Benchmark::ApproximateSizeRandom;
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 19:40:44 +01:00
} else if (name == "mixgraph") {
method = &Benchmark::MixGraph;
} else if (name == "readmissing") {
++key_size_;
method = &Benchmark::ReadRandom;
} else if (name == "newiterator") {
method = &Benchmark::IteratorCreation;
} else if (name == "newiteratorwhilewriting") {
num_threads++; // Add extra thread for writing
method = &Benchmark::IteratorCreationWhileWriting;
} else if (name == "seekrandom") {
method = &Benchmark::SeekRandom;
} else if (name == "seekrandomwhilewriting") {
num_threads++; // Add extra thread for writing
method = &Benchmark::SeekRandomWhileWriting;
} else if (name == "seekrandomwhilemerging") {
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
2015-03-30 20:28:25 +02:00
num_threads++; // Add extra thread for merging
method = &Benchmark::SeekRandomWhileMerging;
} else if (name == "readrandomsmall") {
reads_ /= 1000;
method = &Benchmark::ReadRandom;
} else if (name == "deleteseq") {
method = &Benchmark::DeleteSeq;
} else if (name == "deleterandom") {
method = &Benchmark::DeleteRandom;
} else if (name == "readwhilewriting") {
num_threads++; // Add extra thread for writing
method = &Benchmark::ReadWhileWriting;
} else if (name == "readwhilemerging") {
num_threads++; // Add extra thread for writing
method = &Benchmark::ReadWhileMerging;
} else if (name == "readwhilescanning") {
num_threads++; // Add extra thread for scaning
method = &Benchmark::ReadWhileScanning;
} else if (name == "readrandomwriterandom") {
method = &Benchmark::ReadRandomWriteRandom;
} else if (name == "readrandommergerandom") {
if (FLAGS_merge_operator.empty()) {
fprintf(stdout, "%-12s : skipped (--merge_operator is unknown)\n",
name.c_str());
ErrorExit();
}
method = &Benchmark::ReadRandomMergeRandom;
} else if (name == "updaterandom") {
method = &Benchmark::UpdateRandom;
} else if (name == "xorupdaterandom") {
method = &Benchmark::XORUpdateRandom;
} else if (name == "appendrandom") {
method = &Benchmark::AppendRandom;
} else if (name == "mergerandom") {
if (FLAGS_merge_operator.empty()) {
fprintf(stdout, "%-12s : skipped (--merge_operator is unknown)\n",
name.c_str());
exit(1);
}
method = &Benchmark::MergeRandom;
} else if (name == "randomwithverify") {
method = &Benchmark::RandomWithVerify;
} else if (name == "fillseekseq") {
SkipListRep::LookaheadIterator Summary: This diff introduces the `lookahead` argument to `SkipListFactory()`. This is an optimization for the tailing use case which includes many seeks. E.g. consider the following operations on a skip list iterator: Seek(x), Next(), Next(), Seek(x+2), Next(), Seek(x+3), Next(), Next(), ... If `lookahead` is positive, `SkipListRep` will return an iterator which also keeps track of the previously visited node. Seek() then first does a linear search starting from that node (up to `lookahead` steps). As in the tailing example above, this may require fewer than ~log(n) comparisons as with regular skip list search. Test Plan: Added a new benchmark (`fillseekseq`) which simulates the usage pattern. It first writes N records (with consecutive keys), then measures how much time it takes to read them by calling `Seek()` and `Next()`. $ time ./db_bench -num 10000000 -benchmarks fillseekseq -prefix_size 1 \ -key_size 8 -write_buffer_size $[1024*1024*1024] -value_size 50 \ -seekseq_next 2 -skip_list_lookahead=0 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.389 micros/op 2569047 ops/sec; real 0m21.806s user 0m12.106s sys 0m9.672s $ time ./db_bench [...] -skip_list_lookahead=2 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.153 micros/op 6540684 ops/sec; real 0m19.469s user 0m10.192s sys 0m9.252s Reviewers: ljin, sdong, igor Reviewed By: igor Subscribers: dhruba, leveldb, march, lovro Differential Revision: https://reviews.facebook.net/D23997
2014-09-24 00:52:28 +02:00
method = &Benchmark::WriteSeqSeekSeq;
} else if (name == "compact") {
method = &Benchmark::Compact;
} else if (name == "compactall") {
CompactAll();
#ifndef ROCKSDB_LITE
} else if (name == "compact0") {
CompactLevel(0);
} else if (name == "compact1") {
CompactLevel(1);
} else if (name == "waitforcompaction") {
WaitForCompaction();
#endif
} else if (name == "flush") {
Flush();
} else if (name == "crc32c") {
method = &Benchmark::Crc32c;
} else if (name == "xxhash") {
method = &Benchmark::xxHash;
} else if (name == "acquireload") {
method = &Benchmark::AcquireLoad;
} else if (name == "compress") {
2014-02-08 03:12:30 +01:00
method = &Benchmark::Compress;
} else if (name == "uncompress") {
2014-02-08 03:12:30 +01:00
method = &Benchmark::Uncompress;
#ifndef ROCKSDB_LITE
} else if (name == "randomtransaction") {
method = &Benchmark::RandomTransaction;
post_process_method = &Benchmark::RandomTransactionVerify;
#endif // ROCKSDB_LITE
Support for SingleDelete() Summary: This patch fixes #7460559. It introduces SingleDelete as a new database operation. This operation can be used to delete keys that were never overwritten (no put following another put of the same key). If an overwritten key is single deleted the behavior is undefined. Single deletion of a non-existent key has no effect but multiple consecutive single deletions are not allowed (see limitations). In contrast to the conventional Delete() operation, the deletion entry is removed along with the value when the two are lined up in a compaction. Note: The semantics are similar to @igor's prototype that allowed to have this behavior on the granularity of a column family ( https://reviews.facebook.net/D42093 ). This new patch, however, is more aggressive when it comes to removing tombstones: It removes the SingleDelete together with the value whenever there is no snapshot between them while the older patch only did this when the sequence number of the deletion was older than the earliest snapshot. Most of the complex additions are in the Compaction Iterator, all other changes should be relatively straightforward. The patch also includes basic support for single deletions in db_stress and db_bench. Limitations: - Not compatible with cuckoo hash tables - Single deletions cannot be used in combination with merges and normal deletions on the same key (other keys are not affected by this) - Consecutive single deletions are currently not allowed (and older version of this patch supported this so it could be resurrected if needed) Test Plan: make all check Reviewers: yhchiang, sdong, rven, anthony, yoshinorim, igor Reviewed By: igor Subscribers: maykov, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D43179
2015-09-17 20:42:56 +02:00
} else if (name == "randomreplacekeys") {
fresh_db = true;
method = &Benchmark::RandomReplaceKeys;
Write a benchmark to emulate time series data Summary: Add a benchmark to `db_bench`. In this benchmark, a write thread will populate time series data in the format of 'id | timestamp', and multiple read threads will randomly retrieve all data from one id at a time. Test Plan: Run the benchmark: `num=134217728;bpl=536870912;mb=67108864;overlap=10;mcz=2;del=300000000;levels=6;ctrig=4;delay=8;stop=12;wbn=3;mbc=20;wbs=134217728;dds=0;sync=0;t=32;vs=800;bs=4096;cs=17179869184;of=500000;wps=0;si=10000000; kir=100000; dir=/data/users/jhli/test/; ./db_bench --benchmarks=timeseries --disable_seek_compaction=1 --mmap_read=0 --statistics=1 --histogram=1 --num=$num --threads=$t --value_size=$vs --block_size=$bs --cache_size=$cs --bloom_bits=10 --cache_numshardbits=6 --open_files=$of --verify_checksum=1 --db=$dir --sync=$sync --disable_wal=0 --compression_type=none --stats_interval=$si --compression_ratio=1 --disable_data_sync=$dds --write_buffer_size=$wbs --target_file_size_base=$mb --max_write_buffer_number=$wbn --max_background_compactions=$mbc --level0_file_num_compaction_trigger=$ctrig --level0_slowdown_writes_trigger=$delay --level0_stop_writes_trigger=$stop --num_levels=$levels --delete_obsolete_files_period_micros=$del --min_level_to_compress=$mcz --max_grandparent_overlap_factor=$overlap --stats_per_interval=1 --max_bytes_for_level_base=$bpl --use_existing_db=0 --key_id_range=$kir` Reviewers: andrewkr, sdong Reviewed By: sdong Subscribers: lgalanis, andrewkr, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D60651
2016-08-01 21:28:51 +02:00
} else if (name == "timeseries") {
timestamp_emulator_.reset(new TimestampEmulator());
if (FLAGS_expire_style == "compaction_filter") {
filter.reset(new ExpiredTimeFilter(timestamp_emulator_));
fprintf(stdout, "Compaction filter is used to remove expired data");
open_options_.compaction_filter = filter.get();
}
fresh_db = true;
method = &Benchmark::TimeSeries;
} else if (name == "stats") {
PrintStats("rocksdb.stats");
} else if (name == "resetstats") {
ResetStats();
} else if (name == "verify") {
VerifyDBFromDB(FLAGS_truth_db);
} else if (name == "levelstats") {
PrintStats("rocksdb.levelstats");
} else if (name == "memstats") {
std::vector<std::string> keys{"rocksdb.num-immutable-mem-table",
"rocksdb.cur-size-active-mem-table",
"rocksdb.cur-size-all-mem-tables",
"rocksdb.size-all-mem-tables",
"rocksdb.num-entries-active-mem-table",
"rocksdb.num-entries-imm-mem-tables"};
PrintStats(keys);
} else if (name == "sstables") {
PrintStats("rocksdb.sstables");
} else if (name == "stats_history") {
PrintStatsHistory();
#ifndef ROCKSDB_LITE
} else if (name == "replay") {
if (num_threads > 1) {
fprintf(stderr, "Multi-threaded replay is not yet supported\n");
ErrorExit();
}
if (FLAGS_trace_file == "") {
fprintf(stderr, "Please set --trace_file to be replayed from\n");
ErrorExit();
}
method = &Benchmark::Replay;
#endif // ROCKSDB_LITE
New API to get all merge operands for a Key (#5604) Summary: This is a new API added to db.h to allow for fetching all merge operands associated with a Key. The main motivation for this API is to support use cases where doing a full online merge is not necessary as it is performance sensitive. Example use-cases: 1. Update subset of columns and read subset of columns - Imagine a SQL Table, a row is encoded as a K/V pair (as it is done in MyRocks). If there are many columns and users only updated one of them, we can use merge operator to reduce write amplification. While users only read one or two columns in the read query, this feature can avoid a full merging of the whole row, and save some CPU. 2. Updating very few attributes in a value which is a JSON-like document - Updating one attribute can be done efficiently using merge operator, while reading back one attribute can be done more efficiently if we don't need to do a full merge. ---------------------------------------------------------------------------------------------------- API : Status GetMergeOperands( const ReadOptions& options, ColumnFamilyHandle* column_family, const Slice& key, PinnableSlice* merge_operands, GetMergeOperandsOptions* get_merge_operands_options, int* number_of_operands) Example usage : int size = 100; int number_of_operands = 0; std::vector<PinnableSlice> values(size); GetMergeOperandsOptions merge_operands_info; db_->GetMergeOperands(ReadOptions(), db_->DefaultColumnFamily(), "k1", values.data(), merge_operands_info, &number_of_operands); Description : Returns all the merge operands corresponding to the key. If the number of merge operands in DB is greater than merge_operands_options.expected_max_number_of_operands no merge operands are returned and status is Incomplete. Merge operands returned are in the order of insertion. merge_operands-> Points to an array of at-least merge_operands_options.expected_max_number_of_operands and the caller is responsible for allocating it. If the status returned is Incomplete then number_of_operands will contain the total number of merge operands found in DB for key. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5604 Test Plan: Added unit test and perf test in db_bench that can be run using the command: ./db_bench -benchmarks=getmergeoperands --merge_operator=sortlist Differential Revision: D16657366 Pulled By: vjnadimpalli fbshipit-source-id: 0faadd752351745224ee12d4ae9ef3cb529951bf
2019-08-06 23:22:34 +02:00
} else if (name == "getmergeoperands") {
method = &Benchmark::GetMergeOperands;
} else if (!name.empty()) { // No error message for empty name
fprintf(stderr, "unknown benchmark '%s'\n", name.c_str());
ErrorExit();
}
if (fresh_db) {
if (FLAGS_use_existing_db) {
fprintf(stdout, "%-12s : skipped (--use_existing_db is true)\n",
name.c_str());
method = nullptr;
} else {
if (db_.db != nullptr) {
db_.DeleteDBs();
DestroyDB(FLAGS_db, open_options_);
}
Options options = open_options_;
for (size_t i = 0; i < multi_dbs_.size(); i++) {
delete multi_dbs_[i].db;
if (!open_options_.wal_dir.empty()) {
options.wal_dir = GetPathForMultiple(open_options_.wal_dir, i);
}
DestroyDB(GetPathForMultiple(FLAGS_db, i), options);
}
multi_dbs_.clear();
}
Open(&open_options_); // use open_options for the last accessed
}
if (method != nullptr) {
fprintf(stdout, "DB path: [%s]\n", FLAGS_db.c_str());
#ifndef ROCKSDB_LITE
// A trace_file option can be provided both for trace and replay
// operations. But db_bench does not support tracing and replaying at
// the same time, for now. So, start tracing only when it is not a
// replay.
if (FLAGS_trace_file != "" && name != "replay") {
std::unique_ptr<TraceWriter> trace_writer;
Status s = NewFileTraceWriter(FLAGS_env, EnvOptions(),
FLAGS_trace_file, &trace_writer);
if (!s.ok()) {
fprintf(stderr, "Encountered an error starting a trace, %s\n",
s.ToString().c_str());
ErrorExit();
}
s = db_.db->StartTrace(trace_options_, std::move(trace_writer));
if (!s.ok()) {
fprintf(stderr, "Encountered an error starting a trace, %s\n",
s.ToString().c_str());
ErrorExit();
}
fprintf(stdout, "Tracing the workload to: [%s]\n",
FLAGS_trace_file.c_str());
}
// Start block cache tracing.
if (!FLAGS_block_cache_trace_file.empty()) {
// Sanity checks.
if (FLAGS_block_cache_trace_sampling_frequency <= 0) {
fprintf(stderr,
"Block cache trace sampling frequency must be higher than "
"0.\n");
ErrorExit();
}
if (FLAGS_block_cache_trace_max_trace_file_size_in_bytes <= 0) {
fprintf(stderr,
"The maximum file size for block cache tracing must be "
"higher than 0.\n");
ErrorExit();
}
block_cache_trace_options_.max_trace_file_size =
FLAGS_block_cache_trace_max_trace_file_size_in_bytes;
block_cache_trace_options_.sampling_frequency =
FLAGS_block_cache_trace_sampling_frequency;
std::unique_ptr<TraceWriter> block_cache_trace_writer;
Status s = NewFileTraceWriter(FLAGS_env, EnvOptions(),
FLAGS_block_cache_trace_file,
&block_cache_trace_writer);
if (!s.ok()) {
fprintf(stderr,
"Encountered an error when creating trace writer, %s\n",
s.ToString().c_str());
ErrorExit();
}
s = db_.db->StartBlockCacheTrace(block_cache_trace_options_,
std::move(block_cache_trace_writer));
if (!s.ok()) {
fprintf(
stderr,
"Encountered an error when starting block cache tracing, %s\n",
s.ToString().c_str());
ErrorExit();
}
fprintf(stdout, "Tracing block cache accesses to: [%s]\n",
FLAGS_block_cache_trace_file.c_str());
}
#endif // ROCKSDB_LITE
[db_bench] Support single benchmark arguments (Repeat for X times, Warm up for X times), Support CombinedStats (AVG / MEDIAN) Summary: This diff allow us to run a single benchmark X times and warm it up for Y times. and see the AVG & MEDIAN throughput of these X runs for example ``` $ ./db_bench --benchmarks="fillseq,readseq[X5-W2]" Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags RocksDB: version 4.12 Date: Wed Aug 24 10:45:26 2016 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Compression: Snappy Memtablerep: skip_list Perf Level: 1 WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags DB path: [/tmp/rocksdbtest-8616/dbbench] fillseq : 4.695 micros/op 212971 ops/sec; 23.6 MB/s DB path: [/tmp/rocksdbtest-8616/dbbench] Warming up benchmark by running 2 times readseq : 0.214 micros/op 4677005 ops/sec; 517.4 MB/s readseq : 0.212 micros/op 4706834 ops/sec; 520.7 MB/s Running benchmark for 5 times readseq : 0.218 micros/op 4588187 ops/sec; 507.6 MB/s readseq : 0.208 micros/op 4816538 ops/sec; 532.8 MB/s readseq : 0.213 micros/op 4685376 ops/sec; 518.3 MB/s readseq : 0.214 micros/op 4676787 ops/sec; 517.4 MB/s readseq : 0.217 micros/op 4618532 ops/sec; 510.9 MB/s readseq [AVG 5 runs] : 4677084 ops/sec; 517.4 MB/sec readseq [MEDIAN 5 runs] : 4676787 ops/sec; 517.4 MB/sec ``` Test Plan: run db_bench Reviewers: sdong, andrewkr, yhchiang Reviewed By: yhchiang Subscribers: andrewkr, dhruba Differential Revision: https://reviews.facebook.net/D62235
2016-08-25 21:57:35 +02:00
if (num_warmup > 0) {
printf("Warming up benchmark by running %d times\n", num_warmup);
}
for (int i = 0; i < num_warmup; i++) {
RunBenchmark(num_threads, name, method);
}
if (num_repeat > 1) {
printf("Running benchmark for %d times\n", num_repeat);
}
CombinedStats combined_stats;
for (int i = 0; i < num_repeat; i++) {
Stats stats = RunBenchmark(num_threads, name, method);
combined_stats.AddStats(stats);
}
if (num_repeat > 1) {
combined_stats.Report(name);
}
}
if (post_process_method != nullptr) {
(this->*post_process_method)();
}
}
if (secondary_update_thread_) {
secondary_update_stopped_.store(1, std::memory_order_relaxed);
secondary_update_thread_->join();
secondary_update_thread_.reset();
}
#ifndef ROCKSDB_LITE
if (name != "replay" && FLAGS_trace_file != "") {
Status s = db_.db->EndTrace();
if (!s.ok()) {
fprintf(stderr, "Encountered an error ending the trace, %s\n",
s.ToString().c_str());
}
}
if (!FLAGS_block_cache_trace_file.empty()) {
Status s = db_.db->EndBlockCacheTrace();
if (!s.ok()) {
fprintf(stderr,
"Encountered an error ending the block cache tracing, %s\n",
s.ToString().c_str());
}
}
#endif // ROCKSDB_LITE
if (FLAGS_statistics) {
fprintf(stdout, "STATISTICS:\n%s\n", dbstats->ToString().c_str());
}
if (FLAGS_simcache_size >= 0) {
fprintf(
stdout, "SIMULATOR CACHE STATISTICS:\n%s\n",
static_cast_with_check<SimCache>(cache_.get())->ToString().c_str());
add simulator Cache as class SimCache/SimLRUCache(with test) Summary: add class SimCache(base class with instrumentation api) and SimLRUCache(derived class with detailed implementation) which is used as an instrumented block cache that can predict hit rate for different cache size Test Plan: Add a test case in `db_block_cache_test.cc` called `SimCacheTest` to test basic logic of SimCache. Also add option `-simcache_size` in db_bench. if set with a value other than -1, then the benchmark will use this value as the size of the simulator cache and finally output the simulation result. ``` [gzh@dev9927.prn1 ~/local/rocksdb] ./db_bench -benchmarks "fillseq,readrandom" -cache_size 1000000 -simcache_size 1000000 RocksDB: version 4.8 Date: Tue May 17 16:56:16 2016 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Compression: Snappy Memtablerep: skip_list Perf Level: 0 WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ DB path: [/tmp/rocksdbtest-112628/dbbench] fillseq : 6.809 micros/op 146874 ops/sec; 16.2 MB/s DB path: [/tmp/rocksdbtest-112628/dbbench] readrandom : 6.343 micros/op 157665 ops/sec; 17.4 MB/s (1000000 of 1000000 found) SIMULATOR CACHE STATISTICS: SimCache LOOKUPs: 986559 SimCache HITs: 264760 SimCache HITRATE: 26.84% [gzh@dev9927.prn1 ~/local/rocksdb] ./db_bench -benchmarks "fillseq,readrandom" -cache_size 1000000 -simcache_size 10000000 RocksDB: version 4.8 Date: Tue May 17 16:57:10 2016 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Compression: Snappy Memtablerep: skip_list Perf Level: 0 WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ DB path: [/tmp/rocksdbtest-112628/dbbench] fillseq : 5.066 micros/op 197394 ops/sec; 21.8 MB/s DB path: [/tmp/rocksdbtest-112628/dbbench] readrandom : 6.457 micros/op 154870 ops/sec; 17.1 MB/s (1000000 of 1000000 found) SIMULATOR CACHE STATISTICS: SimCache LOOKUPs: 1059764 SimCache HITs: 374501 SimCache HITRATE: 35.34% [gzh@dev9927.prn1 ~/local/rocksdb] ./db_bench -benchmarks "fillseq,readrandom" -cache_size 1000000 -simcache_size 100000000 RocksDB: version 4.8 Date: Tue May 17 16:57:32 2016 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Compression: Snappy Memtablerep: skip_list Perf Level: 0 WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ DB path: [/tmp/rocksdbtest-112628/dbbench] fillseq : 5.632 micros/op 177572 ops/sec; 19.6 MB/s DB path: [/tmp/rocksdbtest-112628/dbbench] readrandom : 6.892 micros/op 145094 ops/sec; 16.1 MB/s (1000000 of 1000000 found) SIMULATOR CACHE STATISTICS: SimCache LOOKUPs: 1150767 SimCache HITs: 1034535 SimCache HITRATE: 89.90% ``` Reviewers: IslamAbdelRahman, andrewkr, sdong Reviewed By: sdong Subscribers: MarkCallaghan, andrewkr, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D57999
2016-05-24 08:35:23 +02:00
}
#ifndef ROCKSDB_LITE
if (FLAGS_use_secondary_db) {
fprintf(stdout, "Secondary instance updated %" PRIu64 " times.\n",
secondary_db_updates_);
}
#endif // ROCKSDB_LITE
}
private:
Write a benchmark to emulate time series data Summary: Add a benchmark to `db_bench`. In this benchmark, a write thread will populate time series data in the format of 'id | timestamp', and multiple read threads will randomly retrieve all data from one id at a time. Test Plan: Run the benchmark: `num=134217728;bpl=536870912;mb=67108864;overlap=10;mcz=2;del=300000000;levels=6;ctrig=4;delay=8;stop=12;wbn=3;mbc=20;wbs=134217728;dds=0;sync=0;t=32;vs=800;bs=4096;cs=17179869184;of=500000;wps=0;si=10000000; kir=100000; dir=/data/users/jhli/test/; ./db_bench --benchmarks=timeseries --disable_seek_compaction=1 --mmap_read=0 --statistics=1 --histogram=1 --num=$num --threads=$t --value_size=$vs --block_size=$bs --cache_size=$cs --bloom_bits=10 --cache_numshardbits=6 --open_files=$of --verify_checksum=1 --db=$dir --sync=$sync --disable_wal=0 --compression_type=none --stats_interval=$si --compression_ratio=1 --disable_data_sync=$dds --write_buffer_size=$wbs --target_file_size_base=$mb --max_write_buffer_number=$wbn --max_background_compactions=$mbc --level0_file_num_compaction_trigger=$ctrig --level0_slowdown_writes_trigger=$delay --level0_stop_writes_trigger=$stop --num_levels=$levels --delete_obsolete_files_period_micros=$del --min_level_to_compress=$mcz --max_grandparent_overlap_factor=$overlap --stats_per_interval=1 --max_bytes_for_level_base=$bpl --use_existing_db=0 --key_id_range=$kir` Reviewers: andrewkr, sdong Reviewed By: sdong Subscribers: lgalanis, andrewkr, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D60651
2016-08-01 21:28:51 +02:00
std::shared_ptr<TimestampEmulator> timestamp_emulator_;
std::unique_ptr<port::Thread> secondary_update_thread_;
std::atomic<int> secondary_update_stopped_{0};
#ifndef ROCKSDB_LITE
uint64_t secondary_db_updates_ = 0;
#endif // ROCKSDB_LITE
struct ThreadArg {
Benchmark* bm;
SharedState* shared;
ThreadState* thread;
void (Benchmark::*method)(ThreadState*);
};
static void ThreadBody(void* v) {
ThreadArg* arg = reinterpret_cast<ThreadArg*>(v);
SharedState* shared = arg->shared;
ThreadState* thread = arg->thread;
{
MutexLock l(&shared->mu);
shared->num_initialized++;
if (shared->num_initialized >= shared->total) {
shared->cv.SignalAll();
}
while (!shared->start) {
shared->cv.Wait();
}
}
SetPerfLevel(static_cast<PerfLevel> (shared->perf_level));
perf_context.EnablePerLevelPerfContext();
thread->stats.Start(thread->tid);
(arg->bm->*(arg->method))(thread);
thread->stats.Stop();
{
MutexLock l(&shared->mu);
shared->num_done++;
if (shared->num_done >= shared->total) {
shared->cv.SignalAll();
}
}
}
[db_bench] Support single benchmark arguments (Repeat for X times, Warm up for X times), Support CombinedStats (AVG / MEDIAN) Summary: This diff allow us to run a single benchmark X times and warm it up for Y times. and see the AVG & MEDIAN throughput of these X runs for example ``` $ ./db_bench --benchmarks="fillseq,readseq[X5-W2]" Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags RocksDB: version 4.12 Date: Wed Aug 24 10:45:26 2016 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Compression: Snappy Memtablerep: skip_list Perf Level: 1 WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags DB path: [/tmp/rocksdbtest-8616/dbbench] fillseq : 4.695 micros/op 212971 ops/sec; 23.6 MB/s DB path: [/tmp/rocksdbtest-8616/dbbench] Warming up benchmark by running 2 times readseq : 0.214 micros/op 4677005 ops/sec; 517.4 MB/s readseq : 0.212 micros/op 4706834 ops/sec; 520.7 MB/s Running benchmark for 5 times readseq : 0.218 micros/op 4588187 ops/sec; 507.6 MB/s readseq : 0.208 micros/op 4816538 ops/sec; 532.8 MB/s readseq : 0.213 micros/op 4685376 ops/sec; 518.3 MB/s readseq : 0.214 micros/op 4676787 ops/sec; 517.4 MB/s readseq : 0.217 micros/op 4618532 ops/sec; 510.9 MB/s readseq [AVG 5 runs] : 4677084 ops/sec; 517.4 MB/sec readseq [MEDIAN 5 runs] : 4676787 ops/sec; 517.4 MB/sec ``` Test Plan: run db_bench Reviewers: sdong, andrewkr, yhchiang Reviewed By: yhchiang Subscribers: andrewkr, dhruba Differential Revision: https://reviews.facebook.net/D62235
2016-08-25 21:57:35 +02:00
Stats RunBenchmark(int n, Slice name,
void (Benchmark::*method)(ThreadState*)) {
SharedState shared;
shared.total = n;
shared.num_initialized = 0;
shared.num_done = 0;
shared.start = false;
if (FLAGS_benchmark_write_rate_limit > 0) {
shared.write_rate_limiter.reset(
NewGenericRateLimiter(FLAGS_benchmark_write_rate_limit));
}
if (FLAGS_benchmark_read_rate_limit > 0) {
shared.read_rate_limiter.reset(NewGenericRateLimiter(
FLAGS_benchmark_read_rate_limit, 100000 /* refill_period_us */,
10 /* fairness */, RateLimiter::Mode::kReadsOnly));
}
std::unique_ptr<ReporterAgent> reporter_agent;
if (FLAGS_report_interval_seconds > 0) {
reporter_agent.reset(new ReporterAgent(FLAGS_env, FLAGS_report_file,
FLAGS_report_interval_seconds));
}
ThreadArg* arg = new ThreadArg[n];
Adding NUMA support to db_bench tests Summary: Changes: - Adding numa_aware flag to db_bench.cc - Using numa.h library to bind memory and cpu of threads to a fixed NUMA node Result: There seems to be no significant change in the micros/op time with numa_aware enabled. I also tried this with other implementations, including a combination of pthread_setaffinity_np, sched_setaffinity and set_mempolicy methods. It'd be great if someone could point out where I'm going wrong and if we can achieve a better micors/op. Test Plan: Ran db_bench tests using following command: ./db_bench --db=/mnt/tmp --num_levels=6 --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --block_size=4096 --cache_size=17179869184 --cache_numshardbits=6 --compression_type=none --compression_ratio=1 --min_level_to_compress=-1 --disable_seek_compaction=1 --hard_rate_limit=2 --write_buffer_size=134217728 --max_write_buffer_number=2 --level0_file_num_compaction_trigger=8 --target_file_size_base=134217728 --max_bytes_for_level_base=1073741824 --disable_wal=0 --wal_dir=/mnt/tmp --sync=0 --disable_data_sync=1 --verify_checksum=1 --delete_obsolete_files_period_micros=314572800 --max_grandparent_overlap_factor=10 --max_background_compactions=4 --max_background_flushes=0 --level0_slowdown_writes_trigger=16 --level0_stop_writes_trigger=24 --statistics=0 --stats_per_interval=0 --stats_interval=1048576 --histogram=0 --use_plain_table=1 --open_files=-1 --mmap_read=1 --mmap_write=0 --memtablerep=prefix_hash --bloom_bits=10 --bloom_locality=1 --perf_level=0 --duration=300 --benchmarks=readwhilewriting --use_existing_db=1 --num=157286400 --threads=24 --writes_per_second=10240 --numa_aware=[False/True] The tests were run in private devserver with 24 cores and the db was prepopulated using filluniquerandom test. The tests resulted in 0.145 us/op with numa_aware=False and 0.161 us/op with numa_aware=True. Reviewers: sdong, yhchiang, ljin, igor Reviewed By: ljin, igor Subscribers: igor, leveldb Differential Revision: https://reviews.facebook.net/D19353
2014-07-07 19:53:31 +02:00
for (int i = 0; i < n; i++) {
Adding NUMA support to db_bench tests Summary: Changes: - Adding numa_aware flag to db_bench.cc - Using numa.h library to bind memory and cpu of threads to a fixed NUMA node Result: There seems to be no significant change in the micros/op time with numa_aware enabled. I also tried this with other implementations, including a combination of pthread_setaffinity_np, sched_setaffinity and set_mempolicy methods. It'd be great if someone could point out where I'm going wrong and if we can achieve a better micors/op. Test Plan: Ran db_bench tests using following command: ./db_bench --db=/mnt/tmp --num_levels=6 --key_size=20 --prefix_size=20 --keys_per_prefix=0 --value_size=100 --block_size=4096 --cache_size=17179869184 --cache_numshardbits=6 --compression_type=none --compression_ratio=1 --min_level_to_compress=-1 --disable_seek_compaction=1 --hard_rate_limit=2 --write_buffer_size=134217728 --max_write_buffer_number=2 --level0_file_num_compaction_trigger=8 --target_file_size_base=134217728 --max_bytes_for_level_base=1073741824 --disable_wal=0 --wal_dir=/mnt/tmp --sync=0 --disable_data_sync=1 --verify_checksum=1 --delete_obsolete_files_period_micros=314572800 --max_grandparent_overlap_factor=10 --max_background_compactions=4 --max_background_flushes=0 --level0_slowdown_writes_trigger=16 --level0_stop_writes_trigger=24 --statistics=0 --stats_per_interval=0 --stats_interval=1048576 --histogram=0 --use_plain_table=1 --open_files=-1 --mmap_read=1 --mmap_write=0 --memtablerep=prefix_hash --bloom_bits=10 --bloom_locality=1 --perf_level=0 --duration=300 --benchmarks=readwhilewriting --use_existing_db=1 --num=157286400 --threads=24 --writes_per_second=10240 --numa_aware=[False/True] The tests were run in private devserver with 24 cores and the db was prepopulated using filluniquerandom test. The tests resulted in 0.145 us/op with numa_aware=False and 0.161 us/op with numa_aware=True. Reviewers: sdong, yhchiang, ljin, igor Reviewed By: ljin, igor Subscribers: igor, leveldb Differential Revision: https://reviews.facebook.net/D19353
2014-07-07 19:53:31 +02:00
#ifdef NUMA
if (FLAGS_enable_numa) {
// Performs a local allocation of memory to threads in numa node.
int n_nodes = numa_num_task_nodes(); // Number of nodes in NUMA.
numa_exit_on_error = 1;
int numa_node = i % n_nodes;
bitmask* nodes = numa_allocate_nodemask();
numa_bitmask_clearall(nodes);
numa_bitmask_setbit(nodes, numa_node);
// numa_bind() call binds the process to the node and these
// properties are passed on to the thread that is created in
// StartThread method called later in the loop.
numa_bind(nodes);
numa_set_strict(1);
numa_free_nodemask(nodes);
}
#endif
arg[i].bm = this;
arg[i].method = method;
arg[i].shared = &shared;
arg[i].thread = new ThreadState(i);
arg[i].thread->stats.SetReporterAgent(reporter_agent.get());
arg[i].thread->shared = &shared;
FLAGS_env->StartThread(ThreadBody, &arg[i]);
}
shared.mu.Lock();
while (shared.num_initialized < n) {
shared.cv.Wait();
}
shared.start = true;
shared.cv.SignalAll();
while (shared.num_done < n) {
shared.cv.Wait();
}
shared.mu.Unlock();
// Stats for some threads can be excluded.
Stats merge_stats;
for (int i = 0; i < n; i++) {
merge_stats.Merge(arg[i].thread->stats);
}
merge_stats.Report(name);
for (int i = 0; i < n; i++) {
delete arg[i].thread;
}
delete[] arg;
[db_bench] Support single benchmark arguments (Repeat for X times, Warm up for X times), Support CombinedStats (AVG / MEDIAN) Summary: This diff allow us to run a single benchmark X times and warm it up for Y times. and see the AVG & MEDIAN throughput of these X runs for example ``` $ ./db_bench --benchmarks="fillseq,readseq[X5-W2]" Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags RocksDB: version 4.12 Date: Wed Aug 24 10:45:26 2016 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 1000000 Prefix: 0 bytes Keys per prefix: 0 RawSize: 110.6 MB (estimated) FileSize: 62.9 MB (estimated) Write rate: 0 bytes/second Compression: Snappy Memtablerep: skip_list Perf Level: 1 WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ Initializing RocksDB Options from the specified file Initializing RocksDB Options from command-line flags DB path: [/tmp/rocksdbtest-8616/dbbench] fillseq : 4.695 micros/op 212971 ops/sec; 23.6 MB/s DB path: [/tmp/rocksdbtest-8616/dbbench] Warming up benchmark by running 2 times readseq : 0.214 micros/op 4677005 ops/sec; 517.4 MB/s readseq : 0.212 micros/op 4706834 ops/sec; 520.7 MB/s Running benchmark for 5 times readseq : 0.218 micros/op 4588187 ops/sec; 507.6 MB/s readseq : 0.208 micros/op 4816538 ops/sec; 532.8 MB/s readseq : 0.213 micros/op 4685376 ops/sec; 518.3 MB/s readseq : 0.214 micros/op 4676787 ops/sec; 517.4 MB/s readseq : 0.217 micros/op 4618532 ops/sec; 510.9 MB/s readseq [AVG 5 runs] : 4677084 ops/sec; 517.4 MB/sec readseq [MEDIAN 5 runs] : 4676787 ops/sec; 517.4 MB/sec ``` Test Plan: run db_bench Reviewers: sdong, andrewkr, yhchiang Reviewed By: yhchiang Subscribers: andrewkr, dhruba Differential Revision: https://reviews.facebook.net/D62235
2016-08-25 21:57:35 +02:00
return merge_stats;
}
void Crc32c(ThreadState* thread) {
// Checksum about 500MB of data total
Port 3 way SSE4.2 crc32c implementation from Folly Summary: **# Summary** RocksDB uses SSE crc32 intrinsics to calculate the crc32 values but it does it in single way fashion (not pipelined on single CPU core). Intel's whitepaper () published an algorithm that uses 3-way pipelining for the crc32 intrinsics, then use pclmulqdq intrinsic to combine the values. Because pclmulqdq has overhead on its own, this algorithm will show perf gains on buffers larger than 216 bytes, which makes RocksDB a perfect user, since most of the buffers RocksDB call crc32c on is over 4KB. Initial db_bench show tremendous CPU gain. This change uses the 3-way SSE algorithm by default. The old SSE algorithm is now behind a compiler tag NO_THREEWAY_CRC32C. If user compiles the code with NO_THREEWAY_CRC32C=1 then the old SSE Crc32c algorithm would be used. If the server does not have SSE4.2 at the run time the slow way (Non SSE) will be used. **# Performance Test Results** We ran the FillRandom and ReadRandom benchmarks in db_bench. ReadRandom is the point of interest here since it calculates the CRC32 for the in-mem buffers. We did 3 runs for each algorithm. Before this change the CRC32 value computation takes about 11.5% of total CPU cost, and with the new 3-way algorithm it reduced to around 4.5%. The overall throughput also improved from 25.53MB/s to 27.63MB/s. 1) ReadRandom in db_bench overall metrics PER RUN Algorithm | run | micros/op | ops/sec |Throughput (MB/s) 3-way | 1 | 4.143 | 241387 | 26.7 3-way | 2 | 3.775 | 264872 | 29.3 3-way | 3 | 4.116 | 242929 | 26.9 FastCrc32c|1 | 4.037 | 247727 | 27.4 FastCrc32c|2 | 4.648 | 215166 | 23.8 FastCrc32c|3 | 4.352 | 229799 | 25.4 AVG Algorithm | Average of micros/op | Average of ops/sec | Average of Throughput (MB/s) 3-way | 4.01 | 249,729 | 27.63 FastCrc32c | 4.35 | 230,897 | 25.53 2) Crc32c computation CPU cost (inclusive samples percentage) PER RUN Implementation | run |  TotalSamples | Crc32c percentage 3-way   | 1    |  4,572,250,000 | 4.37% 3-way   | 2    |  3,779,250,000 | 4.62% 3-way   | 3    |  4,129,500,000 | 4.48% FastCrc32c     | 1    |  4,663,500,000 | 11.24% FastCrc32c     | 2    |  4,047,500,000 | 12.34% FastCrc32c     | 3    |  4,366,750,000 | 11.68% **# Test Plan** make -j64 corruption_test && ./corruption_test By default it uses 3-way SSE algorithm NO_THREEWAY_CRC32C=1 make -j64 corruption_test && ./corruption_test make clean && DEBUG_LEVEL=0 make -j64 db_bench make clean && DEBUG_LEVEL=0 NO_THREEWAY_CRC32C=1 make -j64 db_bench Closes https://github.com/facebook/rocksdb/pull/3173 Differential Revision: D6330882 Pulled By: yingsu00 fbshipit-source-id: 8ec3d89719533b63b536a736663ca6f0dd4482e9
2017-12-20 03:20:50 +01:00
const int size = FLAGS_block_size; // use --block_size option for db_bench
std::string labels = "(" + ToString(FLAGS_block_size) + " per op)";
const char* label = labels.c_str();
std::string data(size, 'x');
int64_t bytes = 0;
uint32_t crc = 0;
while (bytes < 500 * 1048576) {
crc = crc32c::Value(data.data(), size);
thread->stats.FinishedOps(nullptr, nullptr, 1, kCrc);
bytes += size;
}
// Print so result is not dead
fprintf(stderr, "... crc=0x%x\r", static_cast<unsigned int>(crc));
thread->stats.AddBytes(bytes);
thread->stats.AddMessage(label);
}
void xxHash(ThreadState* thread) {
// Checksum about 500MB of data total
const int size = 4096;
const char* label = "(4K per op)";
std::string data(size, 'x');
int64_t bytes = 0;
unsigned int xxh32 = 0;
while (bytes < 500 * 1048576) {
xxh32 = XXH32(data.data(), size, 0);
thread->stats.FinishedOps(nullptr, nullptr, 1, kHash);
bytes += size;
}
// Print so result is not dead
fprintf(stderr, "... xxh32=0x%x\r", static_cast<unsigned int>(xxh32));
thread->stats.AddBytes(bytes);
thread->stats.AddMessage(label);
}
void AcquireLoad(ThreadState* thread) {
int dummy;
2014-10-27 23:41:05 +01:00
std::atomic<void*> ap(&dummy);
int count = 0;
void *ptr = nullptr;
thread->stats.AddMessage("(each op is 1000 loads)");
while (count < 100000) {
for (int i = 0; i < 1000; i++) {
ptr = ap.load(std::memory_order_acquire);
}
count++;
thread->stats.FinishedOps(nullptr, nullptr, 1, kOthers);
}
Separeate main from bench functionality to allow cusomizations Summary: Isolate db_bench functionality from main so custom benchmark code can be written and managed Test Plan: Tested commands ./build_tools/regression_build_test.sh ./db_bench --db=/tmp/rocksdbtest-12321/dbbench --stats_interval_seconds=1 --num=1000 ./db_bench --db=/tmp/rocksdbtest-12321/dbbench --stats_interval_seconds=1 --num=1000 --reads=500 --writes=500 ./db_bench --db=/tmp/rocksdbtest-12321/dbbench --stats_interval_seconds=1 --num=1000 --merge_keys=100 --numdistinct=100 --num_column_families=3 --num_hot_column_families=1 ./db_bench --stats_interval_seconds=1 --num=1000 --bloom_locality=1 --seed=5 --threads=5 ./db_bench --duration=60 --value_size=50 --seek_nexts=10 --reverse_iterator=true --usee_uint64_comparator=true --batch-size=5 ./db_bench --duration=60 --value_size=50 --seek_nexts=10 --reverse_iterator=true --use_uint64_comparator=true --batch_size=5 ./db_bench --duration=60 --value_size=50 --seek_nexts=10 --reverse_iterator=true --usee_uint64_comparator=true --batch-size=5 Test Results - https://phabricator.fb.com/P56130387 Additional tests for: ./db_bench --duration=60 --value_size=50 --seek_nexts=10 --reverse_iterator=true --use_uint64_comparator=true --batch_size=5 --key_size=8 --merge_operator=put ./db_bench --stats_interval_seconds=1 --num=1000 --bloom_locality=1 --seed=5 --threads=5 --merge_operator=uint64add Results: https://phabricator.fb.com/P56130607 Reviewers: yhchiang, sdong Reviewed By: sdong Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D53991
2016-02-16 15:17:31 +01:00
if (ptr == nullptr) exit(1); // Disable unused variable warning.
}
2014-02-08 03:12:30 +01:00
void Compress(ThreadState *thread) {
RandomGenerator gen;
Slice input = gen.Generate(FLAGS_block_size);
int64_t bytes = 0;
int64_t produced = 0;
bool ok = true;
std::string compressed;
CompressionOptions opts;
CompressionContext context(FLAGS_compression_type_e);
CompressionInfo info(opts, context, CompressionDict::GetEmptyDict(),
FLAGS_compression_type_e,
FLAGS_sample_for_compression);
2014-02-08 03:12:30 +01:00
// Compress 1G
while (ok && bytes < int64_t(1) << 30) {
compressed.clear();
ok = CompressSlice(info, input, &compressed);
produced += compressed.size();
bytes += input.size();
thread->stats.FinishedOps(nullptr, nullptr, 1, kCompress);
}
if (!ok) {
2014-02-08 03:12:30 +01:00
thread->stats.AddMessage("(compression failure)");
} else {
char buf[340];
snprintf(buf, sizeof(buf), "(output: %.1f%%)",
(produced * 100.0) / bytes);
thread->stats.AddMessage(buf);
thread->stats.AddBytes(bytes);
}
}
2014-02-08 03:12:30 +01:00
void Uncompress(ThreadState *thread) {
RandomGenerator gen;
Slice input = gen.Generate(FLAGS_block_size);
std::string compressed;
2014-02-08 03:12:30 +01:00
CompressionContext compression_ctx(FLAGS_compression_type_e);
CompressionOptions compression_opts;
CompressionInfo compression_info(
compression_opts, compression_ctx, CompressionDict::GetEmptyDict(),
FLAGS_compression_type_e, FLAGS_sample_for_compression);
UncompressionContext uncompression_ctx(FLAGS_compression_type_e);
UncompressionInfo uncompression_info(uncompression_ctx,
UncompressionDict::GetEmptyDict(),
FLAGS_compression_type_e);
bool ok = CompressSlice(compression_info, input, &compressed);
int64_t bytes = 0;
size_t uncompressed_size = 0;
2014-02-08 03:12:30 +01:00
while (ok && bytes < 1024 * 1048576) {
constexpr uint32_t compress_format_version = 2;
CacheAllocationPtr uncompressed = UncompressData(
uncompression_info, compressed.data(), compressed.size(),
&uncompressed_size, compress_format_version);
ok = uncompressed.get() != nullptr;
bytes += input.size();
thread->stats.FinishedOps(nullptr, nullptr, 1, kUncompress);
}
if (!ok) {
2014-02-08 03:12:30 +01:00
thread->stats.AddMessage("(compression failure)");
} else {
thread->stats.AddBytes(bytes);
}
}
// Returns true if the options is initialized from the specified
// options file.
bool InitializeOptionsFromFile(Options* opts) {
#ifndef ROCKSDB_LITE
printf("Initializing RocksDB Options from the specified file\n");
DBOptions db_opts;
std::vector<ColumnFamilyDescriptor> cf_descs;
if (FLAGS_options_file != "") {
auto s = LoadOptionsFromFile(FLAGS_options_file, FLAGS_env, &db_opts,
&cf_descs);
db_opts.env = FLAGS_env;
if (s.ok()) {
*opts = Options(db_opts, cf_descs[0].options);
return true;
}
fprintf(stderr, "Unable to load options file %s --- %s\n",
FLAGS_options_file.c_str(), s.ToString().c_str());
exit(1);
}
#else
(void)opts;
#endif
return false;
}
void InitializeOptionsFromFlags(Options* opts) {
printf("Initializing RocksDB Options from command-line flags\n");
Options& options = *opts;
ConfigOptions config_options(options);
config_options.ignore_unsupported_options = false;
assert(db_.db == nullptr);
options.env = FLAGS_env;
options.max_open_files = FLAGS_open_files;
if (FLAGS_cost_write_buffer_to_cache || FLAGS_db_write_buffer_size != 0) {
options.write_buffer_manager.reset(
new WriteBufferManager(FLAGS_db_write_buffer_size, cache_));
}
options.arena_block_size = FLAGS_arena_block_size;
options.write_buffer_size = FLAGS_write_buffer_size;
options.max_write_buffer_number = FLAGS_max_write_buffer_number;
options.min_write_buffer_number_to_merge =
FLAGS_min_write_buffer_number_to_merge;
Support saving history in memtable_list Summary: For transactions, we are using the memtables to validate that there are no write conflicts. But after flushing, we don't have any memtables, and transactions could fail to commit. So we want to someone keep around some extra history to use for conflict checking. In addition, we want to provide a way to increase the size of this history if too many transactions fail to commit. After chatting with people, it seems like everyone prefers just using Memtables to store this history (instead of a separate history structure). It seems like the best place for this is abstracted inside the memtable_list. I decide to create a separate list in MemtableListVersion as using the same list complicated the flush/installalflushresults logic too much. This diff adds a new parameter to control how much memtable history to keep around after flushing. However, it sounds like people aren't too fond of adding new parameters. So I am making the default size of flushed+not-flushed memtables be set to max_write_buffers. This should not change the maximum amount of memory used, but make it more likely we're using closer the the limit. (We are now postponing deleting flushed memtables until the max_write_buffer limit is reached). So while we might use more memory on average, we are still obeying the limit set (and you could argue it's better to go ahead and use up memory now instead of waiting for a write stall to happen to test this limit). However, if people are opposed to this default behavior, we can easily set it to 0 and require this parameter be set in order to use transactions. Test Plan: Added a xfunc test to play around with setting different values of this parameter in all tests. Added testing in memtablelist_test and planning on adding more testing here. Reviewers: sdong, rven, igor Reviewed By: igor Subscribers: dhruba, leveldb Differential Revision: https://reviews.facebook.net/D37443
2015-05-29 01:34:24 +02:00
options.max_write_buffer_number_to_maintain =
FLAGS_max_write_buffer_number_to_maintain;
Refactor trimming logic for immutable memtables (#5022) Summary: MyRocks currently sets `max_write_buffer_number_to_maintain` in order to maintain enough history for transaction conflict checking. The effectiveness of this approach depends on the size of memtables. When memtables are small, it may not keep enough history; when memtables are large, this may consume too much memory. We are proposing a new way to configure memtable list history: by limiting the memory usage of immutable memtables. The new option is `max_write_buffer_size_to_maintain` and it will take precedence over the old `max_write_buffer_number_to_maintain` if they are both set to non-zero values. The new option accounts for the total memory usage of flushed immutable memtables and mutable memtable. When the total usage exceeds the limit, RocksDB may start dropping immutable memtables (which is also called trimming history), starting from the oldest one. The semantics of the old option actually works both as an upper bound and lower bound. History trimming will start if number of immutable memtables exceeds the limit, but it will never go below (limit-1) due to history trimming. In order the mimic the behavior with the new option, history trimming will stop if dropping the next immutable memtable causes the total memory usage go below the size limit. For example, assuming the size limit is set to 64MB, and there are 3 immutable memtables with sizes of 20, 30, 30. Although the total memory usage is 80MB > 64MB, dropping the oldest memtable will reduce the memory usage to 60MB < 64MB, so in this case no memtable will be dropped. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5022 Differential Revision: D14394062 Pulled By: miasantreble fbshipit-source-id: 60457a509c6af89d0993f988c9b5c2aa9e45f5c5
2019-08-23 22:54:09 +02:00
options.max_write_buffer_size_to_maintain =
FLAGS_max_write_buffer_size_to_maintain;
options.max_background_jobs = FLAGS_max_background_jobs;
options.max_background_compactions = FLAGS_max_background_compactions;
options.max_subcompactions = static_cast<uint32_t>(FLAGS_subcompactions);
options.max_background_flushes = FLAGS_max_background_flushes;
options.compaction_style = FLAGS_compaction_style_e;
options.compaction_pri = FLAGS_compaction_pri_e;
options.allow_mmap_reads = FLAGS_mmap_read;
options.allow_mmap_writes = FLAGS_mmap_write;
options.use_direct_reads = FLAGS_use_direct_reads;
options.use_direct_io_for_flush_and_compaction =
FLAGS_use_direct_io_for_flush_and_compaction;
#ifndef ROCKSDB_LITE
options.ttl = FLAGS_fifo_compaction_ttl;
options.compaction_options_fifo = CompactionOptionsFIFO(
FLAGS_fifo_compaction_max_table_files_size_mb * 1024 * 1024,
FLAGS_fifo_compaction_allow_compaction);
options.compaction_options_fifo.age_for_warm = FLAGS_fifo_age_for_warm;
#endif // ROCKSDB_LITE
if (FLAGS_prefix_size != 0) {
options.prefix_extractor.reset(
NewFixedPrefixTransform(FLAGS_prefix_size));
}
if (FLAGS_use_uint64_comparator) {
options.comparator = test::Uint64Comparator();
if (FLAGS_key_size != 8) {
fprintf(stderr, "Using Uint64 comparator but key size is not 8.\n");
exit(1);
}
}
if (FLAGS_use_stderr_info_logger) {
options.info_log.reset(new StderrLogger());
}
options.memtable_huge_page_size = FLAGS_memtable_use_huge_page ? 2048 : 0;
options.memtable_prefix_bloom_size_ratio = FLAGS_memtable_bloom_size_ratio;
options.memtable_whole_key_filtering = FLAGS_memtable_whole_key_filtering;
if (FLAGS_memtable_insert_with_hint_prefix_size > 0) {
options.memtable_insert_with_hint_prefix_extractor.reset(
NewCappedPrefixTransform(
FLAGS_memtable_insert_with_hint_prefix_size));
}
options.bloom_locality = FLAGS_bloom_locality;
options.max_file_opening_threads = FLAGS_file_opening_threads;
options.new_table_reader_for_compaction_inputs =
FLAGS_new_table_reader_for_compaction_inputs;
options.compaction_readahead_size = FLAGS_compaction_readahead_size;
options.log_readahead_size = FLAGS_log_readahead_size;
options.random_access_max_buffer_size = FLAGS_random_access_max_buffer_size;
options.writable_file_max_buffer_size = FLAGS_writable_file_max_buffer_size;
options.use_fsync = FLAGS_use_fsync;
options.num_levels = FLAGS_num_levels;
options.target_file_size_base = FLAGS_target_file_size_base;
options.target_file_size_multiplier = FLAGS_target_file_size_multiplier;
options.max_bytes_for_level_base = FLAGS_max_bytes_for_level_base;
options.level_compaction_dynamic_level_bytes =
FLAGS_level_compaction_dynamic_level_bytes;
options.max_bytes_for_level_multiplier =
FLAGS_max_bytes_for_level_multiplier;
Status s =
CreateMemTableRepFactory(config_options, &options.memtable_factory);
if (!s.ok()) {
fprintf(stderr, "Could not create memtable factory: %s\n",
s.ToString().c_str());
exit(1);
} else if ((FLAGS_prefix_size == 0) &&
(options.memtable_factory->IsInstanceOf("prefix_hash") ||
options.memtable_factory->IsInstanceOf("hash_linkedlist"))) {
fprintf(stderr, "prefix_size should be non-zero if PrefixHash or "
"HashLinkedList memtablerep is used\n");
exit(1);
}
if (FLAGS_use_plain_table) {
#ifndef ROCKSDB_LITE
if (!options.memtable_factory->IsInstanceOf("prefix_hash") &&
!options.memtable_factory->IsInstanceOf("hash_linkedlist")) {
fprintf(stderr, "Warning: plain table is used with %s\n",
options.memtable_factory->Name());
}
int bloom_bits_per_key = FLAGS_bloom_bits;
if (bloom_bits_per_key < 0) {
bloom_bits_per_key = PlainTableOptions().bloom_bits_per_key;
}
PlainTableOptions plain_table_options;
plain_table_options.user_key_len = FLAGS_key_size;
plain_table_options.bloom_bits_per_key = bloom_bits_per_key;
plain_table_options.hash_table_ratio = 0.75;
options.table_factory = std::shared_ptr<TableFactory>(
NewPlainTableFactory(plain_table_options));
#else
fprintf(stderr, "Plain table is not supported in lite mode\n");
exit(1);
#endif // ROCKSDB_LITE
} else if (FLAGS_use_cuckoo_table) {
#ifndef ROCKSDB_LITE
if (FLAGS_cuckoo_hash_ratio > 1 || FLAGS_cuckoo_hash_ratio < 0) {
fprintf(stderr, "Invalid cuckoo_hash_ratio\n");
exit(1);
}
if (!FLAGS_mmap_read) {
fprintf(stderr, "cuckoo table format requires mmap read to operate\n");
exit(1);
}
ROCKSDB_NAMESPACE::CuckooTableOptions table_options;
CuckooTable: add one option to allow identity function for the first hash function Summary: MurmurHash becomes expensive when we do millions Get() a second in one thread. Add this option to allow the first hash function to use identity function as hash function. It results in QPS increase from 3.7M/s to ~4.3M/s. I did not observe improvement for end to end RocksDB performance. This may be caused by other bottlenecks that I will address in a separate diff. Test Plan: ``` [ljin@dev1964 rocksdb] ./cuckoo_table_reader_test --enable_perf --file_dir=/dev/shm --write --identity_as_first_hash=0 ==== Test CuckooReaderTest.WhenKeyExists ==== Test CuckooReaderTest.WhenKeyExistsWithUint64Comparator ==== Test CuckooReaderTest.CheckIterator ==== Test CuckooReaderTest.CheckIteratorUint64 ==== Test CuckooReaderTest.WhenKeyNotFound ==== Test CuckooReaderTest.TestReadPerformance With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.272us (3.7 Mqps) with batch size of 0, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.138us (7.2 Mqps) with batch size of 10, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.142us (7.1 Mqps) with batch size of 25, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.142us (7.0 Mqps) with batch size of 50, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.144us (6.9 Mqps) with batch size of 100, # of found keys 125829120 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.201us (5.0 Mqps) with batch size of 0, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.121us (8.3 Mqps) with batch size of 10, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.123us (8.1 Mqps) with batch size of 25, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.121us (8.3 Mqps) with batch size of 50, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.112us (8.9 Mqps) with batch size of 100, # of found keys 104857600 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.251us (4.0 Mqps) with batch size of 0, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.107us (9.4 Mqps) with batch size of 10, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.099us (10.1 Mqps) with batch size of 25, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.100us (10.0 Mqps) with batch size of 50, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.116us (8.6 Mqps) with batch size of 100, # of found keys 83886080 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.189us (5.3 Mqps) with batch size of 0, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.095us (10.5 Mqps) with batch size of 10, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.096us (10.4 Mqps) with batch size of 25, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.098us (10.2 Mqps) with batch size of 50, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.105us (9.5 Mqps) with batch size of 100, # of found keys 73400320 [ljin@dev1964 rocksdb] ./cuckoo_table_reader_test --enable_perf --file_dir=/dev/shm --write --identity_as_first_hash=1 ==== Test CuckooReaderTest.WhenKeyExists ==== Test CuckooReaderTest.WhenKeyExistsWithUint64Comparator ==== Test CuckooReaderTest.CheckIterator ==== Test CuckooReaderTest.CheckIteratorUint64 ==== Test CuckooReaderTest.WhenKeyNotFound ==== Test CuckooReaderTest.TestReadPerformance With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.230us (4.3 Mqps) with batch size of 0, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.086us (11.7 Mqps) with batch size of 10, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.088us (11.3 Mqps) with batch size of 25, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.083us (12.1 Mqps) with batch size of 50, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.083us (12.1 Mqps) with batch size of 100, # of found keys 125829120 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.159us (6.3 Mqps) with batch size of 0, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.078us (12.8 Mqps) with batch size of 10, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.080us (12.6 Mqps) with batch size of 25, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.080us (12.5 Mqps) with batch size of 50, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.082us (12.2 Mqps) with batch size of 100, # of found keys 104857600 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.154us (6.5 Mqps) with batch size of 0, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.077us (13.0 Mqps) with batch size of 10, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.077us (12.9 Mqps) with batch size of 25, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.078us (12.8 Mqps) with batch size of 50, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.079us (12.6 Mqps) with batch size of 100, # of found keys 83886080 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.218us (4.6 Mqps) with batch size of 0, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.083us (12.0 Mqps) with batch size of 10, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.085us (11.7 Mqps) with batch size of 25, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.086us (11.6 Mqps) with batch size of 50, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.078us (12.8 Mqps) with batch size of 100, # of found keys 73400320 ``` Reviewers: sdong, igor, yhchiang Reviewed By: igor Subscribers: leveldb Differential Revision: https://reviews.facebook.net/D23451
2014-09-18 20:00:48 +02:00
table_options.hash_table_ratio = FLAGS_cuckoo_hash_ratio;
table_options.identity_as_first_hash = FLAGS_identity_as_first_hash;
options.table_factory = std::shared_ptr<TableFactory>(
CuckooTable: add one option to allow identity function for the first hash function Summary: MurmurHash becomes expensive when we do millions Get() a second in one thread. Add this option to allow the first hash function to use identity function as hash function. It results in QPS increase from 3.7M/s to ~4.3M/s. I did not observe improvement for end to end RocksDB performance. This may be caused by other bottlenecks that I will address in a separate diff. Test Plan: ``` [ljin@dev1964 rocksdb] ./cuckoo_table_reader_test --enable_perf --file_dir=/dev/shm --write --identity_as_first_hash=0 ==== Test CuckooReaderTest.WhenKeyExists ==== Test CuckooReaderTest.WhenKeyExistsWithUint64Comparator ==== Test CuckooReaderTest.CheckIterator ==== Test CuckooReaderTest.CheckIteratorUint64 ==== Test CuckooReaderTest.WhenKeyNotFound ==== Test CuckooReaderTest.TestReadPerformance With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.272us (3.7 Mqps) with batch size of 0, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.138us (7.2 Mqps) with batch size of 10, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.142us (7.1 Mqps) with batch size of 25, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.142us (7.0 Mqps) with batch size of 50, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.144us (6.9 Mqps) with batch size of 100, # of found keys 125829120 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.201us (5.0 Mqps) with batch size of 0, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.121us (8.3 Mqps) with batch size of 10, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.123us (8.1 Mqps) with batch size of 25, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.121us (8.3 Mqps) with batch size of 50, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.112us (8.9 Mqps) with batch size of 100, # of found keys 104857600 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.251us (4.0 Mqps) with batch size of 0, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.107us (9.4 Mqps) with batch size of 10, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.099us (10.1 Mqps) with batch size of 25, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.100us (10.0 Mqps) with batch size of 50, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.116us (8.6 Mqps) with batch size of 100, # of found keys 83886080 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.189us (5.3 Mqps) with batch size of 0, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.095us (10.5 Mqps) with batch size of 10, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.096us (10.4 Mqps) with batch size of 25, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.098us (10.2 Mqps) with batch size of 50, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.105us (9.5 Mqps) with batch size of 100, # of found keys 73400320 [ljin@dev1964 rocksdb] ./cuckoo_table_reader_test --enable_perf --file_dir=/dev/shm --write --identity_as_first_hash=1 ==== Test CuckooReaderTest.WhenKeyExists ==== Test CuckooReaderTest.WhenKeyExistsWithUint64Comparator ==== Test CuckooReaderTest.CheckIterator ==== Test CuckooReaderTest.CheckIteratorUint64 ==== Test CuckooReaderTest.WhenKeyNotFound ==== Test CuckooReaderTest.TestReadPerformance With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.230us (4.3 Mqps) with batch size of 0, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.086us (11.7 Mqps) with batch size of 10, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.088us (11.3 Mqps) with batch size of 25, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.083us (12.1 Mqps) with batch size of 50, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.083us (12.1 Mqps) with batch size of 100, # of found keys 125829120 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.159us (6.3 Mqps) with batch size of 0, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.078us (12.8 Mqps) with batch size of 10, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.080us (12.6 Mqps) with batch size of 25, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.080us (12.5 Mqps) with batch size of 50, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.082us (12.2 Mqps) with batch size of 100, # of found keys 104857600 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.154us (6.5 Mqps) with batch size of 0, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.077us (13.0 Mqps) with batch size of 10, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.077us (12.9 Mqps) with batch size of 25, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.078us (12.8 Mqps) with batch size of 50, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.079us (12.6 Mqps) with batch size of 100, # of found keys 83886080 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.218us (4.6 Mqps) with batch size of 0, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.083us (12.0 Mqps) with batch size of 10, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.085us (11.7 Mqps) with batch size of 25, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.086us (11.6 Mqps) with batch size of 50, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.078us (12.8 Mqps) with batch size of 100, # of found keys 73400320 ``` Reviewers: sdong, igor, yhchiang Reviewed By: igor Subscribers: leveldb Differential Revision: https://reviews.facebook.net/D23451
2014-09-18 20:00:48 +02:00
NewCuckooTableFactory(table_options));
#else
fprintf(stderr, "Cuckoo table is not supported in lite mode\n");
exit(1);
#endif // ROCKSDB_LITE
} else {
BlockBasedTableOptions block_based_options;
if (FLAGS_use_hash_search) {
if (FLAGS_prefix_size == 0) {
fprintf(stderr,
"prefix_size not assigned when enable use_hash_search \n");
exit(1);
}
block_based_options.index_type = BlockBasedTableOptions::kHashSearch;
} else {
block_based_options.index_type = BlockBasedTableOptions::kBinarySearch;
}
if (FLAGS_partition_index_and_filters || FLAGS_partition_index) {
if (FLAGS_index_with_first_key) {
fprintf(stderr,
"--index_with_first_key is not compatible with"
" partition index.");
}
if (FLAGS_use_hash_search) {
fprintf(stderr,
"use_hash_search is incompatible with "
"partition index and is ignored");
}
block_based_options.index_type =
BlockBasedTableOptions::kTwoLevelIndexSearch;
block_based_options.metadata_block_size = FLAGS_metadata_block_size;
if (FLAGS_partition_index_and_filters) {
block_based_options.partition_filters = true;
}
} else if (FLAGS_index_with_first_key) {
block_based_options.index_type =
BlockBasedTableOptions::kBinarySearchWithFirstKey;
}
BlockBasedTableOptions::IndexShorteningMode index_shortening =
block_based_options.index_shortening;
switch (FLAGS_index_shortening_mode) {
case 0:
index_shortening =
BlockBasedTableOptions::IndexShorteningMode::kNoShortening;
break;
case 1:
index_shortening =
BlockBasedTableOptions::IndexShorteningMode::kShortenSeparators;
break;
case 2:
index_shortening = BlockBasedTableOptions::IndexShorteningMode::
kShortenSeparatorsAndSuccessor;
break;
default:
fprintf(stderr, "Unknown key shortening mode\n");
}
Minimize memory internal fragmentation for Bloom filters (#6427) Summary: New experimental option BBTO::optimize_filters_for_memory builds filters that maximize their use of "usable size" from malloc_usable_size, which is also used to compute block cache charges. Rather than always "rounding up," we track state in the BloomFilterPolicy object to mix essentially "rounding down" and "rounding up" so that the average FP rate of all generated filters is the same as without the option. (YMMV as heavily accessed filters might be unluckily lower accuracy.) Thus, the option near-minimizes what the block cache considers as "memory used" for a given target Bloom filter false positive rate and Bloom filter implementation. There are no forward or backward compatibility issues with this change, though it only works on the format_version=5 Bloom filter. With Jemalloc, we see about 10% reduction in memory footprint (and block cache charge) for Bloom filters, but 1-2% increase in storage footprint, due to encoding efficiency losses (FP rate is non-linear with bits/key). Why not weighted random round up/down rather than state tracking? By only requiring malloc_usable_size, we don't actually know what the next larger and next smaller usable sizes for the allocator are. We pick a requested size, accept and use whatever usable size it has, and use the difference to inform our next choice. This allows us to narrow in on the right balance without tracking/predicting usable sizes. Why not weight history of generated filter false positive rates by number of keys? This could lead to excess skew in small filters after generating a large filter. Results from filter_bench with jemalloc (irrelevant details omitted): (normal keys/filter, but high variance) $ ./filter_bench -quick -impl=2 -average_keys_per_filter=30000 -vary_key_count_ratio=0.9 Build avg ns/key: 29.6278 Number of filters: 5516 Total size (MB): 200.046 Reported total allocated memory (MB): 220.597 Reported internal fragmentation: 10.2732% Bits/key stored: 10.0097 Average FP rate %: 0.965228 $ ./filter_bench -quick -impl=2 -average_keys_per_filter=30000 -vary_key_count_ratio=0.9 -optimize_filters_for_memory Build avg ns/key: 30.5104 Number of filters: 5464 Total size (MB): 200.015 Reported total allocated memory (MB): 200.322 Reported internal fragmentation: 0.153709% Bits/key stored: 10.1011 Average FP rate %: 0.966313 (very few keys / filter, optimization not as effective due to ~59 byte internal fragmentation in blocked Bloom filter representation) $ ./filter_bench -quick -impl=2 -average_keys_per_filter=1000 -vary_key_count_ratio=0.9 Build avg ns/key: 29.5649 Number of filters: 162950 Total size (MB): 200.001 Reported total allocated memory (MB): 224.624 Reported internal fragmentation: 12.3117% Bits/key stored: 10.2951 Average FP rate %: 0.821534 $ ./filter_bench -quick -impl=2 -average_keys_per_filter=1000 -vary_key_count_ratio=0.9 -optimize_filters_for_memory Build avg ns/key: 31.8057 Number of filters: 159849 Total size (MB): 200 Reported total allocated memory (MB): 208.846 Reported internal fragmentation: 4.42297% Bits/key stored: 10.4948 Average FP rate %: 0.811006 (high keys/filter) $ ./filter_bench -quick -impl=2 -average_keys_per_filter=1000000 -vary_key_count_ratio=0.9 Build avg ns/key: 29.7017 Number of filters: 164 Total size (MB): 200.352 Reported total allocated memory (MB): 221.5 Reported internal fragmentation: 10.5552% Bits/key stored: 10.0003 Average FP rate %: 0.969358 $ ./filter_bench -quick -impl=2 -average_keys_per_filter=1000000 -vary_key_count_ratio=0.9 -optimize_filters_for_memory Build avg ns/key: 30.7131 Number of filters: 160 Total size (MB): 200.928 Reported total allocated memory (MB): 200.938 Reported internal fragmentation: 0.00448054% Bits/key stored: 10.1852 Average FP rate %: 0.963387 And from db_bench (block cache) with jemalloc: $ ./db_bench -db=/dev/shm/dbbench.no_optimize -benchmarks=fillrandom -format_version=5 -value_size=90 -bloom_bits=10 -num=2000000 -threads=8 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=false $ ./db_bench -db=/dev/shm/dbbench -benchmarks=fillrandom -format_version=5 -value_size=90 -bloom_bits=10 -num=2000000 -threads=8 -optimize_filters_for_memory -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=false $ (for FILE in /dev/shm/dbbench.no_optimize/*.sst; do ./sst_dump --file=$FILE --show_properties | grep 'filter block' ; done) | awk '{ t += $4; } END { print t; }' 17063835 $ (for FILE in /dev/shm/dbbench/*.sst; do ./sst_dump --file=$FILE --show_properties | grep 'filter block' ; done) | awk '{ t += $4; } END { print t; }' 17430747 $ #^ 2.1% additional filter storage $ ./db_bench -db=/dev/shm/dbbench.no_optimize -use_existing_db -benchmarks=readrandom,stats -statistics -bloom_bits=10 -num=2000000 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=false -duration=10 -cache_index_and_filter_blocks -cache_size=1000000000 rocksdb.block.cache.index.add COUNT : 33 rocksdb.block.cache.index.bytes.insert COUNT : 8440400 rocksdb.block.cache.filter.add COUNT : 33 rocksdb.block.cache.filter.bytes.insert COUNT : 21087528 rocksdb.bloom.filter.useful COUNT : 4963889 rocksdb.bloom.filter.full.positive COUNT : 1214081 rocksdb.bloom.filter.full.true.positive COUNT : 1161999 $ #^ 1.04 % observed FP rate $ ./db_bench -db=/dev/shm/dbbench -use_existing_db -benchmarks=readrandom,stats -statistics -bloom_bits=10 -num=2000000 -compaction_style=2 -fifo_compaction_max_table_files_size_mb=10000 -fifo_compaction_allow_compaction=false -optimize_filters_for_memory -duration=10 -cache_index_and_filter_blocks -cache_size=1000000000 rocksdb.block.cache.index.add COUNT : 33 rocksdb.block.cache.index.bytes.insert COUNT : 8448592 rocksdb.block.cache.filter.add COUNT : 33 rocksdb.block.cache.filter.bytes.insert COUNT : 18220328 rocksdb.bloom.filter.useful COUNT : 5360933 rocksdb.bloom.filter.full.positive COUNT : 1321315 rocksdb.bloom.filter.full.true.positive COUNT : 1262999 $ #^ 1.08 % observed FP rate, 13.6% less memory usage for filters (Due to specific key density, this example tends to generate filters that are "worse than average" for internal fragmentation. "Better than average" cases can show little or no improvement.) Pull Request resolved: https://github.com/facebook/rocksdb/pull/6427 Test Plan: unit test added, 'make check' with gcc, clang and valgrind Reviewed By: siying Differential Revision: D22124374 Pulled By: pdillinger fbshipit-source-id: f3e3aa152f9043ddf4fae25799e76341d0d8714e
2020-06-22 22:30:57 +02:00
block_based_options.optimize_filters_for_memory =
FLAGS_optimize_filters_for_memory;
block_based_options.index_shortening = index_shortening;
if (cache_ == nullptr) {
block_based_options.no_block_cache = true;
}
block_based_options.cache_index_and_filter_blocks =
FLAGS_cache_index_and_filter_blocks;
block_based_options.pin_l0_filter_and_index_blocks_in_cache =
FLAGS_pin_l0_filter_and_index_blocks_in_cache;
block_based_options.pin_top_level_index_and_filter =
FLAGS_pin_top_level_index_and_filter;
if (FLAGS_cache_high_pri_pool_ratio > 1e-6) { // > 0.0 + eps
block_based_options.cache_index_and_filter_blocks_with_high_priority =
true;
}
block_based_options.block_cache = cache_;
block_based_options.block_cache_compressed = compressed_cache_;
block_based_options.block_size = FLAGS_block_size;
block_based_options.block_restart_interval = FLAGS_block_restart_interval;
block_based_options.index_block_restart_interval =
FLAGS_index_block_restart_interval;
block_based_options.format_version =
static_cast<uint32_t>(FLAGS_format_version);
block_based_options.read_amp_bytes_per_bit = FLAGS_read_amp_bytes_per_bit;
block_based_options.enable_index_compression =
FLAGS_enable_index_compression;
block_based_options.block_align = FLAGS_block_align;
BlockBasedTableOptions::PrepopulateBlockCache prepopulate_block_cache =
block_based_options.prepopulate_block_cache;
switch (FLAGS_prepopulate_block_cache) {
case 0:
prepopulate_block_cache =
BlockBasedTableOptions::PrepopulateBlockCache::kDisable;
break;
case 1:
prepopulate_block_cache =
BlockBasedTableOptions::PrepopulateBlockCache::kFlushOnly;
break;
default:
fprintf(stderr, "Unknown prepopulate block cache mode\n");
}
block_based_options.prepopulate_block_cache = prepopulate_block_cache;
if (FLAGS_use_data_block_hash_index) {
block_based_options.data_block_index_type =
ROCKSDB_NAMESPACE::BlockBasedTableOptions::kDataBlockBinaryAndHash;
} else {
block_based_options.data_block_index_type =
ROCKSDB_NAMESPACE::BlockBasedTableOptions::kDataBlockBinarySearch;
}
block_based_options.data_block_hash_table_util_ratio =
FLAGS_data_block_hash_table_util_ratio;
if (FLAGS_read_cache_path != "") {
#ifndef ROCKSDB_LITE
Status rc_status;
// Read cache need to be provided with a the Logger, we will put all
// reac cache logs in the read cache path in a file named rc_LOG
rc_status = FLAGS_env->CreateDirIfMissing(FLAGS_read_cache_path);
std::shared_ptr<Logger> read_cache_logger;
if (rc_status.ok()) {
rc_status = FLAGS_env->NewLogger(FLAGS_read_cache_path + "/rc_LOG",
&read_cache_logger);
}
if (rc_status.ok()) {
PersistentCacheConfig rc_cfg(FLAGS_env, FLAGS_read_cache_path,
FLAGS_read_cache_size,
read_cache_logger);
rc_cfg.enable_direct_reads = FLAGS_read_cache_direct_read;
rc_cfg.enable_direct_writes = FLAGS_read_cache_direct_write;
rc_cfg.writer_qdepth = 4;
rc_cfg.writer_dispatch_size = 4 * 1024;
auto pcache = std::make_shared<BlockCacheTier>(rc_cfg);
block_based_options.persistent_cache = pcache;
rc_status = pcache->Open();
}
if (!rc_status.ok()) {
fprintf(stderr, "Error initializing read cache, %s\n",
rc_status.ToString().c_str());
exit(1);
}
#else
fprintf(stderr, "Read cache is not supported in LITE\n");
exit(1);
#endif
}
options.table_factory.reset(
NewBlockBasedTableFactory(block_based_options));
}
if (FLAGS_max_bytes_for_level_multiplier_additional_v.size() > 0) {
if (FLAGS_max_bytes_for_level_multiplier_additional_v.size() !=
static_cast<unsigned int>(FLAGS_num_levels)) {
fprintf(stderr, "Insufficient number of fanouts specified %d\n",
static_cast<int>(
FLAGS_max_bytes_for_level_multiplier_additional_v.size()));
exit(1);
}
options.max_bytes_for_level_multiplier_additional =
FLAGS_max_bytes_for_level_multiplier_additional_v;
}
options.level0_stop_writes_trigger = FLAGS_level0_stop_writes_trigger;
Improve statistics Summary: This adds more statistics to be reported by GetProperty("leveldb.stats"). The new stats include time spent waiting on stalls in MakeRoomForWrite. This also includes the total amplification rate where that is: (#bytes of sequential IO during compaction) / (#bytes from Put) This also includes a lot more data for the per-level compaction report. * Rn(MB) - MB read from level N during compaction between levels N and N+1 * Rnp1(MB) - MB read from level N+1 during compaction between levels N and N+1 * Wnew(MB) - new data written to the level during compaction * Amplify - ( Write(MB) + Rnp1(MB) ) / Rn(MB) * Rn - files read from level N during compaction between levels N and N+1 * Rnp1 - files read from level N+1 during compaction between levels N and N+1 * Wnp1 - files written to level N+1 during compaction between levels N and N+1 * NewW - new files written to level N+1 during compaction * Count - number of compactions done for this level This is the new output from DB::GetProperty("leveldb.stats"). The old output stopped at Write(MB) Compactions Level Files Size(MB) Time(sec) Read(MB) Write(MB) Rn(MB) Rnp1(MB) Wnew(MB) Amplify Read(MB/s) Write(MB/s) Rn Rnp1 Wnp1 NewW Count ------------------------------------------------------------------------------------------------------------------------------------- 0 3 6 33 0 576 0 0 576 -1.0 0.0 1.3 0 0 0 0 290 1 127 242 351 5316 5314 570 4747 567 17.0 12.1 12.1 287 2399 2685 286 32 2 161 328 54 822 824 326 496 328 4.0 1.9 1.9 160 251 411 160 161 Amplification: 22.3 rate, 0.56 GB in, 12.55 GB out Uptime(secs): 439.8 Stalls(secs): 206.938 level0_slowdown, 0.000 level0_numfiles, 24.129 memtable_compaction Task ID: # Blame Rev: Test Plan: run db_bench Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - (cherry picked from commit ecdeead38f86cc02e754d0032600742c4f02fec8) Reviewers: dhruba Differential Revision: https://reviews.facebook.net/D6153
2012-10-23 19:34:09 +02:00
options.level0_file_num_compaction_trigger =
FLAGS_level0_file_num_compaction_trigger;
options.level0_slowdown_writes_trigger =
FLAGS_level0_slowdown_writes_trigger;
options.compression = FLAGS_compression_type_e;
if (FLAGS_simulate_hybrid_fs_file != "") {
options.bottommost_temperature = Temperature::kWarm;
}
options.sample_for_compression = FLAGS_sample_for_compression;
options.WAL_ttl_seconds = FLAGS_wal_ttl_seconds;
options.WAL_size_limit_MB = FLAGS_wal_size_limit_MB;
options.max_total_wal_size = FLAGS_max_total_wal_size;
Allow having different compression algorithms on different levels. Summary: The leveldb API is enhanced to support different compression algorithms at different levels. This adds the option min_level_to_compress to db_bench that specifies the minimum level for which compression should be done when compression is enabled. This can be used to disable compression for levels 0 and 1 which are likely to suffer from stalls because of the CPU load for memtable flushes and (L0,L1) compaction. Level 0 is special as it gets frequent memtable flushes. Level 1 is special as it frequently gets all:all file compactions between it and level 0. But all other levels could be the same. For any level N where N > 1, the rate of sequential IO for that level should be the same. The last level is the exception because it might not be full and because files from it are not read to compact with the next larger level. The same amount of time will be spent doing compaction at any level N excluding N=0, 1 or the last level. By this standard all of those levels should use the same compression. The difference is that the loss (using more disk space) from a faster compression algorithm is less significant for N=2 than for N=3. So we might be willing to trade disk space for faster write rates with no compression for L0 and L1, snappy for L2, zlib for L3. Using a faster compression algorithm for the mid levels also allows us to reclaim some cpu without trading off much loss in disk space overhead. Also note that little is to be gained by compressing levels 0 and 1. For a 4-level tree they account for 10% of the data. For a 5-level tree they account for 1% of the data. With compression enabled: * memtable flush rate is ~18MB/second * (L0,L1) compaction rate is ~30MB/second With compression enabled but min_level_to_compress=2 * memtable flush rate is ~320MB/second * (L0,L1) compaction rate is ~560MB/second This practicaly takes the same code from https://reviews.facebook.net/D6225 but makes the leveldb api more general purpose with a few additional lines of code. Test Plan: make check Differential Revision: https://reviews.facebook.net/D6261
2012-10-28 07:13:17 +01:00
if (FLAGS_min_level_to_compress >= 0) {
assert(FLAGS_min_level_to_compress <= FLAGS_num_levels);
options.compression_per_level.resize(FLAGS_num_levels);
for (int i = 0; i < FLAGS_min_level_to_compress; i++) {
Allow having different compression algorithms on different levels. Summary: The leveldb API is enhanced to support different compression algorithms at different levels. This adds the option min_level_to_compress to db_bench that specifies the minimum level for which compression should be done when compression is enabled. This can be used to disable compression for levels 0 and 1 which are likely to suffer from stalls because of the CPU load for memtable flushes and (L0,L1) compaction. Level 0 is special as it gets frequent memtable flushes. Level 1 is special as it frequently gets all:all file compactions between it and level 0. But all other levels could be the same. For any level N where N > 1, the rate of sequential IO for that level should be the same. The last level is the exception because it might not be full and because files from it are not read to compact with the next larger level. The same amount of time will be spent doing compaction at any level N excluding N=0, 1 or the last level. By this standard all of those levels should use the same compression. The difference is that the loss (using more disk space) from a faster compression algorithm is less significant for N=2 than for N=3. So we might be willing to trade disk space for faster write rates with no compression for L0 and L1, snappy for L2, zlib for L3. Using a faster compression algorithm for the mid levels also allows us to reclaim some cpu without trading off much loss in disk space overhead. Also note that little is to be gained by compressing levels 0 and 1. For a 4-level tree they account for 10% of the data. For a 5-level tree they account for 1% of the data. With compression enabled: * memtable flush rate is ~18MB/second * (L0,L1) compaction rate is ~30MB/second With compression enabled but min_level_to_compress=2 * memtable flush rate is ~320MB/second * (L0,L1) compaction rate is ~560MB/second This practicaly takes the same code from https://reviews.facebook.net/D6225 but makes the leveldb api more general purpose with a few additional lines of code. Test Plan: make check Differential Revision: https://reviews.facebook.net/D6261
2012-10-28 07:13:17 +01:00
options.compression_per_level[i] = kNoCompression;
}
for (int i = FLAGS_min_level_to_compress;
Allow having different compression algorithms on different levels. Summary: The leveldb API is enhanced to support different compression algorithms at different levels. This adds the option min_level_to_compress to db_bench that specifies the minimum level for which compression should be done when compression is enabled. This can be used to disable compression for levels 0 and 1 which are likely to suffer from stalls because of the CPU load for memtable flushes and (L0,L1) compaction. Level 0 is special as it gets frequent memtable flushes. Level 1 is special as it frequently gets all:all file compactions between it and level 0. But all other levels could be the same. For any level N where N > 1, the rate of sequential IO for that level should be the same. The last level is the exception because it might not be full and because files from it are not read to compact with the next larger level. The same amount of time will be spent doing compaction at any level N excluding N=0, 1 or the last level. By this standard all of those levels should use the same compression. The difference is that the loss (using more disk space) from a faster compression algorithm is less significant for N=2 than for N=3. So we might be willing to trade disk space for faster write rates with no compression for L0 and L1, snappy for L2, zlib for L3. Using a faster compression algorithm for the mid levels also allows us to reclaim some cpu without trading off much loss in disk space overhead. Also note that little is to be gained by compressing levels 0 and 1. For a 4-level tree they account for 10% of the data. For a 5-level tree they account for 1% of the data. With compression enabled: * memtable flush rate is ~18MB/second * (L0,L1) compaction rate is ~30MB/second With compression enabled but min_level_to_compress=2 * memtable flush rate is ~320MB/second * (L0,L1) compaction rate is ~560MB/second This practicaly takes the same code from https://reviews.facebook.net/D6225 but makes the leveldb api more general purpose with a few additional lines of code. Test Plan: make check Differential Revision: https://reviews.facebook.net/D6261
2012-10-28 07:13:17 +01:00
i < FLAGS_num_levels; i++) {
options.compression_per_level[i] = FLAGS_compression_type_e;
Allow having different compression algorithms on different levels. Summary: The leveldb API is enhanced to support different compression algorithms at different levels. This adds the option min_level_to_compress to db_bench that specifies the minimum level for which compression should be done when compression is enabled. This can be used to disable compression for levels 0 and 1 which are likely to suffer from stalls because of the CPU load for memtable flushes and (L0,L1) compaction. Level 0 is special as it gets frequent memtable flushes. Level 1 is special as it frequently gets all:all file compactions between it and level 0. But all other levels could be the same. For any level N where N > 1, the rate of sequential IO for that level should be the same. The last level is the exception because it might not be full and because files from it are not read to compact with the next larger level. The same amount of time will be spent doing compaction at any level N excluding N=0, 1 or the last level. By this standard all of those levels should use the same compression. The difference is that the loss (using more disk space) from a faster compression algorithm is less significant for N=2 than for N=3. So we might be willing to trade disk space for faster write rates with no compression for L0 and L1, snappy for L2, zlib for L3. Using a faster compression algorithm for the mid levels also allows us to reclaim some cpu without trading off much loss in disk space overhead. Also note that little is to be gained by compressing levels 0 and 1. For a 4-level tree they account for 10% of the data. For a 5-level tree they account for 1% of the data. With compression enabled: * memtable flush rate is ~18MB/second * (L0,L1) compaction rate is ~30MB/second With compression enabled but min_level_to_compress=2 * memtable flush rate is ~320MB/second * (L0,L1) compaction rate is ~560MB/second This practicaly takes the same code from https://reviews.facebook.net/D6225 but makes the leveldb api more general purpose with a few additional lines of code. Test Plan: make check Differential Revision: https://reviews.facebook.net/D6261
2012-10-28 07:13:17 +01:00
}
}
options.soft_rate_limit = FLAGS_soft_rate_limit;
options.hard_rate_limit = FLAGS_hard_rate_limit;
options.soft_pending_compaction_bytes_limit =
FLAGS_soft_pending_compaction_bytes_limit;
options.hard_pending_compaction_bytes_limit =
FLAGS_hard_pending_compaction_bytes_limit;
options.delayed_write_rate = FLAGS_delayed_write_rate;
support for concurrent adds to memtable Summary: This diff adds support for concurrent adds to the skiplist memtable implementations. Memory allocation is made thread-safe by the addition of a spinlock, with small per-core buffers to avoid contention. Concurrent memtable writes are made via an additional method and don't impose a performance overhead on the non-concurrent case, so parallelism can be selected on a per-batch basis. Write thread synchronization is an increasing bottleneck for higher levels of concurrency, so this diff adds --enable_write_thread_adaptive_yield (default off). This feature causes threads joining a write batch group to spin for a short time (default 100 usec) using sched_yield, rather than going to sleep on a mutex. If the timing of the yield calls indicates that another thread has actually run during the yield then spinning is avoided. This option improves performance for concurrent situations even without parallel adds, although it has the potential to increase CPU usage (and the heuristic adaptation is not yet mature). Parallel writes are not currently compatible with inplace updates, update callbacks, or delete filtering. Enable it with --allow_concurrent_memtable_write (and --enable_write_thread_adaptive_yield). Parallel memtable writes are performance neutral when there is no actual parallelism, and in my experiments (SSD server-class Linux and varying contention and key sizes for fillrandom) they are always a performance win when there is more than one thread. Statistics are updated earlier in the write path, dropping the number of DB mutex acquisitions from 2 to 1 for almost all cases. This diff was motivated and inspired by Yahoo's cLSM work. It is more conservative than cLSM: RocksDB's write batch group leader role is preserved (along with all of the existing flush and write throttling logic) and concurrent writers are blocked until all memtable insertions have completed and the sequence number has been advanced, to preserve linearizability. My test config is "db_bench -benchmarks=fillrandom -threads=$T -batch_size=1 -memtablerep=skip_list -value_size=100 --num=1000000/$T -level0_slowdown_writes_trigger=9999 -level0_stop_writes_trigger=9999 -disable_auto_compactions --max_write_buffer_number=8 -max_background_flushes=8 --disable_wal --write_buffer_size=160000000 --block_size=16384 --allow_concurrent_memtable_write" on a two-socket Xeon E5-2660 @ 2.2Ghz with lots of memory and an SSD hard drive. With 1 thread I get ~440Kops/sec. Peak performance for 1 socket (numactl -N1) is slightly more than 1Mops/sec, at 16 threads. Peak performance across both sockets happens at 30 threads, and is ~900Kops/sec, although with fewer threads there is less performance loss when the system has background work. Test Plan: 1. concurrent stress tests for InlineSkipList and DynamicBloom 2. make clean; make check 3. make clean; DISABLE_JEMALLOC=1 make valgrind_check; valgrind db_bench 4. make clean; COMPILE_WITH_TSAN=1 make all check; db_bench 5. make clean; COMPILE_WITH_ASAN=1 make all check; db_bench 6. make clean; OPT=-DROCKSDB_LITE make check 7. verify no perf regressions when disabled Reviewers: igor, sdong Reviewed By: sdong Subscribers: MarkCallaghan, IslamAbdelRahman, anthony, yhchiang, rven, sdong, guyg8, kradhakrishnan, dhruba Differential Revision: https://reviews.facebook.net/D50589
2015-08-15 01:59:07 +02:00
options.allow_concurrent_memtable_write =
FLAGS_allow_concurrent_memtable_write;
Memtable sampling for mempurge heuristic. (#8628) Summary: Changes the API of the MemPurge process: the `bool experimental_allow_mempurge` and `experimental_mempurge_policy` flags have been replaced by a `double experimental_mempurge_threshold` option. This change of API reflects another major change introduced in this PR: the MemPurgeDecider() function now works by sampling the memtables being flushed to estimate the overall amount of useful payload (payload minus the garbage), and then compare this useful payload estimate with the `double experimental_mempurge_threshold` value. Therefore, when the value of this flag is `0.0` (default value), mempurge is simply deactivated. On the other hand, a value of `DBL_MAX` would be equivalent to always going through a mempurge regardless of the garbage ratio estimate. At the moment, a `double experimental_mempurge_threshold` value else than 0.0 or `DBL_MAX` is opnly supported`with the `SkipList` memtable representation. Regarding the sampling, this PR includes the introduction of a `MemTable::UniqueRandomSample` function that collects (approximately) random entries from the memtable by using the new `SkipList::Iterator::RandomSeek()` under the hood, or by iterating through each memtable entry, depending on the target sample size and the total number of entries. The unit tests have been readapted to support this new API. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8628 Reviewed By: pdillinger Differential Revision: D30149315 Pulled By: bjlemaire fbshipit-source-id: 1feef5390c95db6f4480ab4434716533d3947f27
2021-08-11 03:07:48 +02:00
options.experimental_mempurge_threshold =
FLAGS_experimental_mempurge_threshold;
options.inplace_update_support = FLAGS_inplace_update_support;
options.inplace_update_num_locks = FLAGS_inplace_update_num_locks;
support for concurrent adds to memtable Summary: This diff adds support for concurrent adds to the skiplist memtable implementations. Memory allocation is made thread-safe by the addition of a spinlock, with small per-core buffers to avoid contention. Concurrent memtable writes are made via an additional method and don't impose a performance overhead on the non-concurrent case, so parallelism can be selected on a per-batch basis. Write thread synchronization is an increasing bottleneck for higher levels of concurrency, so this diff adds --enable_write_thread_adaptive_yield (default off). This feature causes threads joining a write batch group to spin for a short time (default 100 usec) using sched_yield, rather than going to sleep on a mutex. If the timing of the yield calls indicates that another thread has actually run during the yield then spinning is avoided. This option improves performance for concurrent situations even without parallel adds, although it has the potential to increase CPU usage (and the heuristic adaptation is not yet mature). Parallel writes are not currently compatible with inplace updates, update callbacks, or delete filtering. Enable it with --allow_concurrent_memtable_write (and --enable_write_thread_adaptive_yield). Parallel memtable writes are performance neutral when there is no actual parallelism, and in my experiments (SSD server-class Linux and varying contention and key sizes for fillrandom) they are always a performance win when there is more than one thread. Statistics are updated earlier in the write path, dropping the number of DB mutex acquisitions from 2 to 1 for almost all cases. This diff was motivated and inspired by Yahoo's cLSM work. It is more conservative than cLSM: RocksDB's write batch group leader role is preserved (along with all of the existing flush and write throttling logic) and concurrent writers are blocked until all memtable insertions have completed and the sequence number has been advanced, to preserve linearizability. My test config is "db_bench -benchmarks=fillrandom -threads=$T -batch_size=1 -memtablerep=skip_list -value_size=100 --num=1000000/$T -level0_slowdown_writes_trigger=9999 -level0_stop_writes_trigger=9999 -disable_auto_compactions --max_write_buffer_number=8 -max_background_flushes=8 --disable_wal --write_buffer_size=160000000 --block_size=16384 --allow_concurrent_memtable_write" on a two-socket Xeon E5-2660 @ 2.2Ghz with lots of memory and an SSD hard drive. With 1 thread I get ~440Kops/sec. Peak performance for 1 socket (numactl -N1) is slightly more than 1Mops/sec, at 16 threads. Peak performance across both sockets happens at 30 threads, and is ~900Kops/sec, although with fewer threads there is less performance loss when the system has background work. Test Plan: 1. concurrent stress tests for InlineSkipList and DynamicBloom 2. make clean; make check 3. make clean; DISABLE_JEMALLOC=1 make valgrind_check; valgrind db_bench 4. make clean; COMPILE_WITH_TSAN=1 make all check; db_bench 5. make clean; COMPILE_WITH_ASAN=1 make all check; db_bench 6. make clean; OPT=-DROCKSDB_LITE make check 7. verify no perf regressions when disabled Reviewers: igor, sdong Reviewed By: sdong Subscribers: MarkCallaghan, IslamAbdelRahman, anthony, yhchiang, rven, sdong, guyg8, kradhakrishnan, dhruba Differential Revision: https://reviews.facebook.net/D50589
2015-08-15 01:59:07 +02:00
options.enable_write_thread_adaptive_yield =
FLAGS_enable_write_thread_adaptive_yield;
options.enable_pipelined_write = FLAGS_enable_pipelined_write;
Unordered Writes (#5218) Summary: Performing unordered writes in rocksdb when unordered_write option is set to true. When enabled the writes to memtable are done without joining any write thread. This offers much higher write throughput since the upcoming writes would not have to wait for the slowest memtable write to finish. The tradeoff is that the writes visible to a snapshot might change over time. If the application cannot tolerate that, it should implement its own mechanisms to work around that. Using TransactionDB with WRITE_PREPARED write policy is one way to achieve that. Doing so increases the max throughput by 2.2x without however compromising the snapshot guarantees. The patch is prepared based on an original by siying Existing unit tests are extended to include unordered_write option. Benchmark Results: ``` TEST_TMPDIR=/dev/shm/ ./db_bench_unordered --benchmarks=fillrandom --threads=32 --num=10000000 -max_write_buffer_number=16 --max_background_jobs=64 --batch_size=8 --writes=3000000 -level0_file_num_compaction_trigger=99999 --level0_slowdown_writes_trigger=99999 --level0_stop_writes_trigger=99999 -enable_pipelined_write=false -disable_auto_compactions --unordered_write=1 ``` With WAL - Vanilla RocksDB: 78.6 MB/s - WRITER_PREPARED with unordered_write: 177.8 MB/s (2.2x) - unordered_write: 368.9 MB/s (4.7x with relaxed snapshot guarantees) Without WAL - Vanilla RocksDB: 111.3 MB/s - WRITER_PREPARED with unordered_write: 259.3 MB/s MB/s (2.3x) - unordered_write: 645.6 MB/s (5.8x with relaxed snapshot guarantees) - WRITER_PREPARED with unordered_write disable concurrency control: 185.3 MB/s MB/s (2.35x) Limitations: - The feature is not yet extended to `max_successive_merges` > 0. The feature is also incompatible with `enable_pipelined_write` = true as well as with `allow_concurrent_memtable_write` = false. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5218 Differential Revision: D15219029 Pulled By: maysamyabandeh fbshipit-source-id: 38f2abc4af8780148c6128acdba2b3227bc81759
2019-05-14 02:43:47 +02:00
options.unordered_write = FLAGS_unordered_write;
support for concurrent adds to memtable Summary: This diff adds support for concurrent adds to the skiplist memtable implementations. Memory allocation is made thread-safe by the addition of a spinlock, with small per-core buffers to avoid contention. Concurrent memtable writes are made via an additional method and don't impose a performance overhead on the non-concurrent case, so parallelism can be selected on a per-batch basis. Write thread synchronization is an increasing bottleneck for higher levels of concurrency, so this diff adds --enable_write_thread_adaptive_yield (default off). This feature causes threads joining a write batch group to spin for a short time (default 100 usec) using sched_yield, rather than going to sleep on a mutex. If the timing of the yield calls indicates that another thread has actually run during the yield then spinning is avoided. This option improves performance for concurrent situations even without parallel adds, although it has the potential to increase CPU usage (and the heuristic adaptation is not yet mature). Parallel writes are not currently compatible with inplace updates, update callbacks, or delete filtering. Enable it with --allow_concurrent_memtable_write (and --enable_write_thread_adaptive_yield). Parallel memtable writes are performance neutral when there is no actual parallelism, and in my experiments (SSD server-class Linux and varying contention and key sizes for fillrandom) they are always a performance win when there is more than one thread. Statistics are updated earlier in the write path, dropping the number of DB mutex acquisitions from 2 to 1 for almost all cases. This diff was motivated and inspired by Yahoo's cLSM work. It is more conservative than cLSM: RocksDB's write batch group leader role is preserved (along with all of the existing flush and write throttling logic) and concurrent writers are blocked until all memtable insertions have completed and the sequence number has been advanced, to preserve linearizability. My test config is "db_bench -benchmarks=fillrandom -threads=$T -batch_size=1 -memtablerep=skip_list -value_size=100 --num=1000000/$T -level0_slowdown_writes_trigger=9999 -level0_stop_writes_trigger=9999 -disable_auto_compactions --max_write_buffer_number=8 -max_background_flushes=8 --disable_wal --write_buffer_size=160000000 --block_size=16384 --allow_concurrent_memtable_write" on a two-socket Xeon E5-2660 @ 2.2Ghz with lots of memory and an SSD hard drive. With 1 thread I get ~440Kops/sec. Peak performance for 1 socket (numactl -N1) is slightly more than 1Mops/sec, at 16 threads. Peak performance across both sockets happens at 30 threads, and is ~900Kops/sec, although with fewer threads there is less performance loss when the system has background work. Test Plan: 1. concurrent stress tests for InlineSkipList and DynamicBloom 2. make clean; make check 3. make clean; DISABLE_JEMALLOC=1 make valgrind_check; valgrind db_bench 4. make clean; COMPILE_WITH_TSAN=1 make all check; db_bench 5. make clean; COMPILE_WITH_ASAN=1 make all check; db_bench 6. make clean; OPT=-DROCKSDB_LITE make check 7. verify no perf regressions when disabled Reviewers: igor, sdong Reviewed By: sdong Subscribers: MarkCallaghan, IslamAbdelRahman, anthony, yhchiang, rven, sdong, guyg8, kradhakrishnan, dhruba Differential Revision: https://reviews.facebook.net/D50589
2015-08-15 01:59:07 +02:00
options.write_thread_max_yield_usec = FLAGS_write_thread_max_yield_usec;
options.write_thread_slow_yield_usec = FLAGS_write_thread_slow_yield_usec;
options.rate_limit_delay_max_milliseconds =
FLAGS_rate_limit_delay_max_milliseconds;
options.table_cache_numshardbits = FLAGS_table_cache_numshardbits;
options.max_compaction_bytes = FLAGS_max_compaction_bytes;
options.disable_auto_compactions = FLAGS_disable_auto_compactions;
options.optimize_filters_for_hits = FLAGS_optimize_filters_for_hits;
options.periodic_compaction_seconds = FLAGS_periodic_compaction_seconds;
// fill storage options
options.advise_random_on_open = FLAGS_advise_random_on_open;
options.access_hint_on_compaction_start = FLAGS_compaction_fadvice_e;
options.use_adaptive_mutex = FLAGS_use_adaptive_mutex;
options.bytes_per_sync = FLAGS_bytes_per_sync;
options.wal_bytes_per_sync = FLAGS_wal_bytes_per_sync;
// merge operator options
if (!FLAGS_merge_operator.empty()) {
s = MergeOperator::CreateFromString(config_options, FLAGS_merge_operator,
&options.merge_operator);
if (!s.ok()) {
fprintf(stderr, "invalid merge operator[%s]: %s\n",
FLAGS_merge_operator.c_str(), s.ToString().c_str());
exit(1);
}
}
options.max_successive_merges = FLAGS_max_successive_merges;
options.report_bg_io_stats = FLAGS_report_bg_io_stats;
// set universal style compaction configurations, if applicable
if (FLAGS_universal_size_ratio != 0) {
options.compaction_options_universal.size_ratio =
FLAGS_universal_size_ratio;
}
if (FLAGS_universal_min_merge_width != 0) {
options.compaction_options_universal.min_merge_width =
FLAGS_universal_min_merge_width;
}
if (FLAGS_universal_max_merge_width != 0) {
options.compaction_options_universal.max_merge_width =
FLAGS_universal_max_merge_width;
}
if (FLAGS_universal_max_size_amplification_percent != 0) {
options.compaction_options_universal.max_size_amplification_percent =
FLAGS_universal_max_size_amplification_percent;
}
if (FLAGS_universal_compression_size_percent != -1) {
options.compaction_options_universal.compression_size_percent =
FLAGS_universal_compression_size_percent;
}
options.compaction_options_universal.allow_trivial_move =
FLAGS_universal_allow_trivial_move;
if (FLAGS_thread_status_per_interval > 0) {
options.enable_thread_tracking = true;
}
if (FLAGS_user_timestamp_size > 0) {
if (FLAGS_user_timestamp_size != 8) {
fprintf(stderr, "Only 64 bits timestamps are supported.\n");
exit(1);
}
options.comparator = ROCKSDB_NAMESPACE::test::ComparatorWithU64Ts();
}
// Integrated BlobDB
options.enable_blob_files = FLAGS_enable_blob_files;
options.min_blob_size = FLAGS_min_blob_size;
options.blob_file_size = FLAGS_blob_file_size;
options.blob_compression_type =
StringToCompressionType(FLAGS_blob_compression_type.c_str());
options.enable_blob_garbage_collection =
FLAGS_enable_blob_garbage_collection;
options.blob_garbage_collection_age_cutoff =
FLAGS_blob_garbage_collection_age_cutoff;
#ifndef ROCKSDB_LITE
if (FLAGS_readonly && FLAGS_transaction_db) {
fprintf(stderr, "Cannot use readonly flag with transaction_db\n");
exit(1);
}
if (FLAGS_use_secondary_db &&
(FLAGS_transaction_db || FLAGS_optimistic_transaction_db)) {
fprintf(stderr, "Cannot use use_secondary_db flag with transaction_db\n");
exit(1);
}
#endif // ROCKSDB_LITE
}
void InitializeOptionsGeneral(Options* opts) {
Options& options = *opts;
options.create_missing_column_families = FLAGS_num_column_families > 1;
options.statistics = dbstats;
options.wal_dir = FLAGS_wal_dir;
options.create_if_missing = !FLAGS_use_existing_db;
options.dump_malloc_stats = FLAGS_dump_malloc_stats;
options.stats_dump_period_sec =
static_cast<unsigned int>(FLAGS_stats_dump_period_sec);
options.stats_persist_period_sec =
static_cast<unsigned int>(FLAGS_stats_persist_period_sec);
options.persist_stats_to_disk = FLAGS_persist_stats_to_disk;
options.stats_history_buffer_size =
static_cast<size_t>(FLAGS_stats_history_buffer_size);
options.compression_opts.level = FLAGS_compression_level;
options.compression_opts.max_dict_bytes = FLAGS_compression_max_dict_bytes;
options.compression_opts.zstd_max_train_bytes =
FLAGS_compression_zstd_max_train_bytes;
options.compression_opts.parallel_threads =
FLAGS_compression_parallel_threads;
Limit buffering for collecting samples for compression dictionary (#7970) Summary: For dictionary compression, we need to collect some representative samples of the data to be compressed, which we use to either generate or train (when `CompressionOptions::zstd_max_train_bytes > 0`) a dictionary. Previously, the strategy was to buffer all the data blocks during flush, and up to the target file size during compaction. That strategy allowed us to randomly pick samples from as wide a range as possible that'd be guaranteed to land in a single output file. However, some users try to make huge files in memory-constrained environments, where this strategy can cause OOM. This PR introduces an option, `CompressionOptions::max_dict_buffer_bytes`, that limits how much data blocks are buffered before we switch to unbuffered mode (which means creating the per-SST dictionary, writing out the buffered data, and compressing/writing new blocks as soon as they are built). It is not strict as we currently buffer more than just data blocks -- also keys are buffered. But it does make a step towards giving users predictable memory usage. Related changes include: - Changed sampling for dictionary compression to select unique data blocks when there is limited availability of data blocks - Made use of `BlockBuilder::SwapAndReset()` to save an allocation+memcpy when buffering data blocks for building a dictionary - Changed `ParseBoolean()` to accept an input containing characters after the boolean. This is necessary since, with this PR, a value for `CompressionOptions::enabled` is no longer necessarily the final component in the `CompressionOptions` string. Pull Request resolved: https://github.com/facebook/rocksdb/pull/7970 Test Plan: - updated `CompressionOptions` unit tests to verify limit is respected (to the extent expected in the current implementation) in various scenarios of flush/compaction to bottommost/non-bottommost level - looked at jemalloc heap profiles right before and after switching to unbuffered mode during flush/compaction. Verified memory usage in buffering is proportional to the limit set. Reviewed By: pdillinger Differential Revision: D26467994 Pulled By: ajkr fbshipit-source-id: 3da4ef9fba59974e4ef40e40c01611002c861465
2021-02-19 23:06:59 +01:00
options.compression_opts.max_dict_buffer_bytes =
FLAGS_compression_max_dict_buffer_bytes;
// If this is a block based table, set some related options
auto table_options =
options.table_factory->GetOptions<BlockBasedTableOptions>();
if (table_options != nullptr) {
if (FLAGS_cache_size) {
table_options->block_cache = cache_;
}
if (FLAGS_bloom_bits < 0) {
table_options->filter_policy = BlockBasedTableOptions().filter_policy;
} else if (FLAGS_bloom_bits == 0) {
table_options->filter_policy.reset();
} else {
Support optimize_filters_for_memory for Ribbon filter (#7774) Summary: Primarily this change refactors the optimize_filters_for_memory code for Bloom filters, based on malloc_usable_size, to also work for Ribbon filters. This change also replaces the somewhat slow but general BuiltinFilterBitsBuilder::ApproximateNumEntries with implementation-specific versions for Ribbon (new) and Legacy Bloom (based on a recently deleted version). The reason is to emphasize speed in ApproximateNumEntries rather than 100% accuracy. Justification: ApproximateNumEntries (formerly CalculateNumEntry) is only used by RocksDB for range-partitioned filters, called each time we start to construct one. (In theory, it should be possible to reuse the estimate, but the abstractions provided by FilterPolicy don't really make that workable.) But this is only used as a heuristic estimate for hitting a desired partitioned filter size because of alignment to data blocks, which have various numbers of unique keys or prefixes. The two factors lead us to prioritize reasonable speed over 100% accuracy. optimize_filters_for_memory adds extra complication, because precisely calculating num_entries for some allowed number of bytes depends on state with optimize_filters_for_memory enabled. And the allocator-agnostic implementation of optimize_filters_for_memory, using malloc_usable_size, means we would have to actually allocate memory, many times, just to precisely determine how many entries (keys) could be added and stay below some size budget, for the current state. (In a draft, I got this working, and then realized the balance of speed vs. accuracy was all wrong.) So related to that, I have made CalculateSpace, an internal-only API only used for testing, non-authoritative also if optimize_filters_for_memory is enabled. This simplifies some code. Pull Request resolved: https://github.com/facebook/rocksdb/pull/7774 Test Plan: unit test updated, and for FilterSize test, range of tested values is greatly expanded (still super fast) Also tested `db_bench -benchmarks=fillrandom,stats -bloom_bits=10 -num=1000000 -partition_index_and_filters -format_version=5 [-optimize_filters_for_memory] [-use_ribbon_filter]` with temporary debug output of generated filter sizes. Bloom+optimize_filters_for_memory: 1 Filter size: 197 (224 in memory) 134 Filter size: 3525 (3584 in memory) 107 Filter size: 4037 (4096 in memory) Total on disk: 904,506 Total in memory: 918,752 Ribbon+optimize_filters_for_memory: 1 Filter size: 3061 (3072 in memory) 110 Filter size: 3573 (3584 in memory) 58 Filter size: 4085 (4096 in memory) Total on disk: 633,021 (-30.0%) Total in memory: 634,880 (-30.9%) Bloom (no offm): 1 Filter size: 261 (320 in memory) 1 Filter size: 3333 (3584 in memory) 240 Filter size: 3717 (4096 in memory) Total on disk: 895,674 (-1% on disk vs. +offm; known tolerable overhead of offm) Total in memory: 986,944 (+7.4% vs. +offm) Ribbon (no offm): 1 Filter size: 2949 (3072 in memory) 1 Filter size: 3381 (3584 in memory) 167 Filter size: 3701 (4096 in memory) Total on disk: 624,397 (-30.3% vs. Bloom) Total in memory: 690,688 (-30.0% vs. Bloom) Note that optimize_filters_for_memory is even more effective for Ribbon filter than for cache-local Bloom, because it can close the unused memory gap even tighter than Bloom filter, because of 16 byte increments for Ribbon vs. 64 byte increments for Bloom. Reviewed By: jay-zhuang Differential Revision: D25592970 Pulled By: pdillinger fbshipit-source-id: 606fdaa025bb790d7e9c21601e8ea86e10541912
2020-12-18 23:29:48 +01:00
table_options->filter_policy.reset(
FLAGS_use_ribbon_filter
? NewRibbonFilterPolicy(FLAGS_bloom_bits)
Support optimize_filters_for_memory for Ribbon filter (#7774) Summary: Primarily this change refactors the optimize_filters_for_memory code for Bloom filters, based on malloc_usable_size, to also work for Ribbon filters. This change also replaces the somewhat slow but general BuiltinFilterBitsBuilder::ApproximateNumEntries with implementation-specific versions for Ribbon (new) and Legacy Bloom (based on a recently deleted version). The reason is to emphasize speed in ApproximateNumEntries rather than 100% accuracy. Justification: ApproximateNumEntries (formerly CalculateNumEntry) is only used by RocksDB for range-partitioned filters, called each time we start to construct one. (In theory, it should be possible to reuse the estimate, but the abstractions provided by FilterPolicy don't really make that workable.) But this is only used as a heuristic estimate for hitting a desired partitioned filter size because of alignment to data blocks, which have various numbers of unique keys or prefixes. The two factors lead us to prioritize reasonable speed over 100% accuracy. optimize_filters_for_memory adds extra complication, because precisely calculating num_entries for some allowed number of bytes depends on state with optimize_filters_for_memory enabled. And the allocator-agnostic implementation of optimize_filters_for_memory, using malloc_usable_size, means we would have to actually allocate memory, many times, just to precisely determine how many entries (keys) could be added and stay below some size budget, for the current state. (In a draft, I got this working, and then realized the balance of speed vs. accuracy was all wrong.) So related to that, I have made CalculateSpace, an internal-only API only used for testing, non-authoritative also if optimize_filters_for_memory is enabled. This simplifies some code. Pull Request resolved: https://github.com/facebook/rocksdb/pull/7774 Test Plan: unit test updated, and for FilterSize test, range of tested values is greatly expanded (still super fast) Also tested `db_bench -benchmarks=fillrandom,stats -bloom_bits=10 -num=1000000 -partition_index_and_filters -format_version=5 [-optimize_filters_for_memory] [-use_ribbon_filter]` with temporary debug output of generated filter sizes. Bloom+optimize_filters_for_memory: 1 Filter size: 197 (224 in memory) 134 Filter size: 3525 (3584 in memory) 107 Filter size: 4037 (4096 in memory) Total on disk: 904,506 Total in memory: 918,752 Ribbon+optimize_filters_for_memory: 1 Filter size: 3061 (3072 in memory) 110 Filter size: 3573 (3584 in memory) 58 Filter size: 4085 (4096 in memory) Total on disk: 633,021 (-30.0%) Total in memory: 634,880 (-30.9%) Bloom (no offm): 1 Filter size: 261 (320 in memory) 1 Filter size: 3333 (3584 in memory) 240 Filter size: 3717 (4096 in memory) Total on disk: 895,674 (-1% on disk vs. +offm; known tolerable overhead of offm) Total in memory: 986,944 (+7.4% vs. +offm) Ribbon (no offm): 1 Filter size: 2949 (3072 in memory) 1 Filter size: 3381 (3584 in memory) 167 Filter size: 3701 (4096 in memory) Total on disk: 624,397 (-30.3% vs. Bloom) Total in memory: 690,688 (-30.0% vs. Bloom) Note that optimize_filters_for_memory is even more effective for Ribbon filter than for cache-local Bloom, because it can close the unused memory gap even tighter than Bloom filter, because of 16 byte increments for Ribbon vs. 64 byte increments for Bloom. Reviewed By: jay-zhuang Differential Revision: D25592970 Pulled By: pdillinger fbshipit-source-id: 606fdaa025bb790d7e9c21601e8ea86e10541912
2020-12-18 23:29:48 +01:00
: NewBloomFilterPolicy(FLAGS_bloom_bits,
FLAGS_use_block_based_filter));
}
}
if (FLAGS_row_cache_size) {
if (FLAGS_cache_numshardbits >= 1) {
options.row_cache =
NewLRUCache(FLAGS_row_cache_size, FLAGS_cache_numshardbits);
} else {
options.row_cache = NewLRUCache(FLAGS_row_cache_size);
}
}
if (FLAGS_enable_io_prio) {
FLAGS_env->LowerThreadPoolIOPriority(Env::LOW);
FLAGS_env->LowerThreadPoolIOPriority(Env::HIGH);
}
if (FLAGS_enable_cpu_prio) {
FLAGS_env->LowerThreadPoolCPUPriority(Env::LOW);
FLAGS_env->LowerThreadPoolCPUPriority(Env::HIGH);
}
options.env = FLAGS_env;
if (FLAGS_sine_write_rate) {
FLAGS_benchmark_write_rate_limit = static_cast<uint64_t>(SineRate(0));
}
if (FLAGS_rate_limiter_bytes_per_sec > 0) {
if (FLAGS_rate_limit_bg_reads &&
!FLAGS_new_table_reader_for_compaction_inputs) {
fprintf(stderr,
"rate limit compaction reads must have "
"new_table_reader_for_compaction_inputs set\n");
exit(1);
}
options.rate_limiter.reset(NewGenericRateLimiter(
Simplify GenericRateLimiter algorithm (#8602) Summary: `GenericRateLimiter` slow path handles requests that cannot be satisfied immediately. Such requests enter a queue, and their thread stays in `Request()` until they are granted or the rate limiter is stopped. These threads are responsible for unblocking themselves. The work to do so is split into two main duties. (1) Waiting for the next refill time. (2) Refilling the bytes and granting requests. Prior to this PR, the slow path logic involved a leader election algorithm to pick one thread to perform (1) followed by (2). It elected the thread whose request was at the front of the highest priority non-empty queue since that request was most likely to be granted. This algorithm was efficient in terms of reducing intermediate wakeups, which is a thread waking up only to resume waiting after finding its request is not granted. However, the conceptual complexity of this algorithm was too high. It took me a long time to draw a timeline to understand how it works for just one edge case yet there were so many. This PR drops the leader election to reduce conceptual complexity. Now, the two duties can be performed by whichever thread acquires the lock first. The risk of this change is increasing the number of intermediate wakeups, however, we took steps to mitigate that. - `wait_until_refill_pending_` flag ensures only one thread performs (1). This\ prevents the thundering herd problem at the next refill time. The remaining\ threads wait on their condition variable with an unbounded duration -- thus we\ must remember to notify them to ensure forward progress. - (1) is typically done by a thread at the front of a queue. This is trivial\ when the queues are initially empty as the first choice that arrives must be\ the only entry in its queue. When queues are initially non-empty, we achieve\ this by having (2) notify a thread at the front of a queue (preferring higher\ priority) to perform the next duty. - We do not require any additional wakeup for (2). Typically it will just be\ done by the thread that finished (1). Combined, the second and third bullet points above suggest the refill/granting will typically be done by a request at the front of its queue. This is important because one wakeup is saved when a granted request happens to be in an already running thread. Note there are a few cases that still lead to intermediate wakeup, however. The first two are existing issues that also apply to the old algorithm, however, the third (including both subpoints) is new. - No request may be granted (only possible when rate limit dynamically\ decreases). - Requests from a different queue may be granted. - (2) may be run by a non-front request thread causing it to not be granted even\ if some requests in that same queue are granted. It can happen for a couple\ (unlikely) reasons. - A new request may sneak in and grab the lock at the refill time, before the\ thread finishing (1) can wake up and grab it. - A new request may sneak in and grab the lock and execute (1) before (2)'s\ chosen candidate can wake up and grab the lock. Then that non-front request\ thread performing (1) can carry over to perform (2). Pull Request resolved: https://github.com/facebook/rocksdb/pull/8602 Test Plan: - Use existing tests. The edge cases listed in the comment are all performance\ related; I could not really think of any related to correctness. The logic\ looks the same whether a thread wakes up/finishes its work early/on-time/late,\ or whether the thread is chosen vs. "steals" the work. - Verified write throughput and CPU overhead are basically the same with and\ without this change, even in a rate limiter heavy workload: Test command: ``` $ rm -rf /dev/shm/dbbench/ && TEST_TMPDIR=/dev/shm /usr/bin/time ./db_bench -benchmarks=fillrandom -num_multi_db=64 -num_low_pri_threads=64 -num_high_pri_threads=64 -write_buffer_size=262144 -target_file_size_base=262144 -max_bytes_for_level_base=1048576 -rate_limiter_bytes_per_sec=16777216 -key_size=24 -value_size=1000 -num=10000 -compression_type=none -rate_limiter_refill_period_us=1000 ``` Results before this PR: ``` fillrandom : 108.463 micros/op 9219 ops/sec; 9.0 MB/s 7.40user 8.84system 1:26.20elapsed 18%CPU (0avgtext+0avgdata 256140maxresident)k ``` Results after this PR: ``` fillrandom : 108.108 micros/op 9250 ops/sec; 9.0 MB/s 7.45user 8.23system 1:26.68elapsed 18%CPU (0avgtext+0avgdata 255688maxresident)k ``` Reviewed By: hx235 Differential Revision: D30048013 Pulled By: ajkr fbshipit-source-id: 6741bba9d9dfbccab359806d725105817fef818b
2021-08-10 01:46:14 +02:00
FLAGS_rate_limiter_bytes_per_sec, FLAGS_rate_limiter_refill_period_us,
10 /* fairness */,
FLAGS_rate_limit_bg_reads ? RateLimiter::Mode::kReadsOnly
: RateLimiter::Mode::kWritesOnly,
FLAGS_rate_limiter_auto_tuned));
}
Auto recovery from out of space errors (#4164) Summary: This commit implements automatic recovery from a Status::NoSpace() error during background operations such as write callback, flush and compaction. The broad design is as follows - 1. Compaction errors are treated as soft errors and don't put the database in read-only mode. A compaction is delayed until enough free disk space is available to accomodate the compaction outputs, which is estimated based on the input size. This means that users can continue to write, and we rely on the WriteController to delay or stop writes if the compaction debt becomes too high due to persistent low disk space condition 2. Errors during write callback and flush are treated as hard errors, i.e the database is put in read-only mode and goes back to read-write only fater certain recovery actions are taken. 3. Both types of recovery rely on the SstFileManagerImpl to poll for sufficient disk space. We assume that there is a 1-1 mapping between an SFM and the underlying OS storage container. For cases where multiple DBs are hosted on a single storage container, the user is expected to allocate a single SFM instance and use the same one for all the DBs. If no SFM is specified by the user, DBImpl::Open() will allocate one, but this will be one per DB and each DB will recover independently. The recovery implemented by SFM is as follows - a) On the first occurance of an out of space error during compaction, subsequent compactions will be delayed until the disk free space check indicates enough available space. The required space is computed as the sum of input sizes. b) The free space check requirement will be removed once the amount of free space is greater than the size reserved by in progress compactions when the first error occured c) If the out of space error is a hard error, a background thread in SFM will poll for sufficient headroom before triggering the recovery of the database and putting it in write-only mode. The headroom is calculated as the sum of the write_buffer_size of all the DB instances associated with the SFM 4. EventListener callbacks will be called at the start and completion of automatic recovery. Users can disable the auto recov ery in the start callback, and later initiate it manually by calling DB::Resume() Todo: 1. More extensive testing 2. Add disk full condition to db_stress (follow-on PR) Pull Request resolved: https://github.com/facebook/rocksdb/pull/4164 Differential Revision: D9846378 Pulled By: anand1976 fbshipit-source-id: 80ea875dbd7f00205e19c82215ff6e37da10da4a
2018-09-15 22:36:19 +02:00
options.listeners.emplace_back(listener_);
if (FLAGS_num_multi_db <= 1) {
OpenDb(options, FLAGS_db, &db_);
} else {
multi_dbs_.clear();
multi_dbs_.resize(FLAGS_num_multi_db);
auto wal_dir = options.wal_dir;
2014-04-29 21:33:57 +02:00
for (int i = 0; i < FLAGS_num_multi_db; i++) {
if (!wal_dir.empty()) {
options.wal_dir = GetPathForMultiple(wal_dir, i);
}
OpenDb(options, GetPathForMultiple(FLAGS_db, i), &multi_dbs_[i]);
}
options.wal_dir = wal_dir;
}
// KeepFilter is a noop filter, this can be used to test compaction filter
if (FLAGS_use_keep_filter) {
options.compaction_filter = new KeepFilter();
fprintf(stdout, "A noop compaction filter is used\n");
}
if (FLAGS_use_existing_keys) {
// Only work on single database
assert(db_.db != nullptr);
ReadOptions read_opts;
read_opts.total_order_seek = true;
Iterator* iter = db_.db->NewIterator(read_opts);
for (iter->SeekToFirst(); iter->Valid(); iter->Next()) {
keys_.emplace_back(iter->key().ToString());
}
delete iter;
FLAGS_num = keys_.size();
}
}
void Open(Options* opts) {
if (!InitializeOptionsFromFile(opts)) {
InitializeOptionsFromFlags(opts);
}
InitializeOptionsGeneral(opts);
}
void OpenDb(Options options, const std::string& db_name,
DBWithColumnFamilies* db) {
Add -report_open_timing to db_bench (#8464) Summary: Hello and thanks for RocksDB, This PR adds support for ```-report_open_timing true``` to ```db_bench```. It can be useful when tuning RocksDB on filesystem/env with high latencies for file level operations (create/delete/rename...) seen during ```((Optimistic)Transaction)DB::Open```. Some examples: ``` > db_bench -benchmarks updaterandom -num 1 -db /dev/shm/db_bench > db_bench -benchmarks updaterandom -num 0 -db /dev/shm/db_bench -use_existing_db true -report_open_timing true -readonly true 2>&1 | grep OpenDb OpenDb: 3.90133 milliseconds > db_bench -benchmarks updaterandom -num 0 -db /dev/shm/db_bench -use_existing_db true -report_open_timing true -use_secondary_db true 2>&1 | grep OpenDb OpenDb: 3.33414 milliseconds > db_bench -benchmarks updaterandom -num 0 -db /dev/shm/db_bench -use_existing_db true -report_open_timing true 2>&1 | grep -A1 OpenDb OpenDb: 6.05423 milliseconds > db_bench -benchmarks updaterandom -num 1 > db_bench -benchmarks updaterandom -num 0 -use_existing_db true -report_open_timing true -readonly true 2>&1 | grep OpenDb OpenDb: 4.06859 milliseconds > db_bench -benchmarks updaterandom -num 0 -use_existing_db true -report_open_timing true -use_secondary_db true 2>&1 | grep OpenDb OpenDb: 2.85794 milliseconds > db_bench -benchmarks updaterandom -num 0 -use_existing_db true -report_open_timing true 2>&1 | grep OpenDb OpenDb: 6.46376 milliseconds > db_bench -benchmarks updaterandom -num 1 -db /clustered_fs/db_bench > db_bench -benchmarks updaterandom -num 0 -db /clustered_fs/db_bench -use_existing_db true -report_open_timing true -readonly true 2>&1 | grep OpenDb OpenDb: 3.79805 milliseconds > db_bench -benchmarks updaterandom -num 0 -db /clustered_fs/db_bench -use_existing_db true -report_open_timing true -use_secondary_db true 2>&1 | grep OpenDb OpenDb: 3.00174 milliseconds > db_bench -benchmarks updaterandom -num 0 -db /clustered_fs/db_bench -use_existing_db true -report_open_timing true 2>&1 | grep OpenDb OpenDb: 24.8732 milliseconds ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/8464 Reviewed By: hx235 Differential Revision: D29398096 Pulled By: zhichao-cao fbshipit-source-id: 8f05dc3284f084612a3f30234e39e1c37548f50c
2021-07-02 03:41:20 +02:00
uint64_t open_start = FLAGS_report_open_timing ? FLAGS_env->NowNanos() : 0;
Status s;
// Open with column families if necessary.
if (FLAGS_num_column_families > 1) {
size_t num_hot = FLAGS_num_column_families;
if (FLAGS_num_hot_column_families > 0 &&
FLAGS_num_hot_column_families < FLAGS_num_column_families) {
num_hot = FLAGS_num_hot_column_families;
} else {
FLAGS_num_hot_column_families = FLAGS_num_column_families;
}
std::vector<ColumnFamilyDescriptor> column_families;
for (size_t i = 0; i < num_hot; i++) {
column_families.push_back(ColumnFamilyDescriptor(
ColumnFamilyName(i), ColumnFamilyOptions(options)));
}
std::vector<int> cfh_idx_to_prob;
if (!FLAGS_column_family_distribution.empty()) {
std::stringstream cf_prob_stream(FLAGS_column_family_distribution);
std::string cf_prob;
int sum = 0;
while (std::getline(cf_prob_stream, cf_prob, ',')) {
cfh_idx_to_prob.push_back(std::stoi(cf_prob));
sum += cfh_idx_to_prob.back();
}
if (sum != 100) {
fprintf(stderr, "column_family_distribution items must sum to 100\n");
exit(1);
}
if (cfh_idx_to_prob.size() != num_hot) {
fprintf(stderr,
"got %" ROCKSDB_PRIszt
" column_family_distribution items; expected "
"%" ROCKSDB_PRIszt "\n",
cfh_idx_to_prob.size(), num_hot);
exit(1);
}
}
#ifndef ROCKSDB_LITE
if (FLAGS_readonly) {
s = DB::OpenForReadOnly(options, db_name, column_families,
&db->cfh, &db->db);
} else if (FLAGS_optimistic_transaction_db) {
s = OptimisticTransactionDB::Open(options, db_name, column_families,
&db->cfh, &db->opt_txn_db);
if (s.ok()) {
db->db = db->opt_txn_db->GetBaseDB();
}
} else if (FLAGS_transaction_db) {
TransactionDB* ptr;
TransactionDBOptions txn_db_options;
if (options.unordered_write) {
options.two_write_queues = true;
txn_db_options.skip_concurrency_control = true;
txn_db_options.write_policy = WRITE_PREPARED;
}
s = TransactionDB::Open(options, txn_db_options, db_name,
column_families, &db->cfh, &ptr);
if (s.ok()) {
db->db = ptr;
}
} else {
s = DB::Open(options, db_name, column_families, &db->cfh, &db->db);
}
#else
s = DB::Open(options, db_name, column_families, &db->cfh, &db->db);
#endif // ROCKSDB_LITE
db->cfh.resize(FLAGS_num_column_families);
db->num_created = num_hot;
db->num_hot = num_hot;
db->cfh_idx_to_prob = std::move(cfh_idx_to_prob);
#ifndef ROCKSDB_LITE
} else if (FLAGS_readonly) {
s = DB::OpenForReadOnly(options, db_name, &db->db);
} else if (FLAGS_optimistic_transaction_db) {
s = OptimisticTransactionDB::Open(options, db_name, &db->opt_txn_db);
if (s.ok()) {
db->db = db->opt_txn_db->GetBaseDB();
}
} else if (FLAGS_transaction_db) {
TransactionDB* ptr = nullptr;
TransactionDBOptions txn_db_options;
if (options.unordered_write) {
options.two_write_queues = true;
txn_db_options.skip_concurrency_control = true;
txn_db_options.write_policy = WRITE_PREPARED;
}
s = CreateLoggerFromOptions(db_name, options, &options.info_log);
if (s.ok()) {
s = TransactionDB::Open(options, txn_db_options, db_name, &ptr);
}
if (s.ok()) {
db->db = ptr;
}
} else if (FLAGS_use_blob_db) {
// Stacked BlobDB
blob_db::BlobDBOptions blob_db_options;
blob_db_options.enable_garbage_collection = FLAGS_blob_db_enable_gc;
blob_db_options.garbage_collection_cutoff = FLAGS_blob_db_gc_cutoff;
blob_db_options.is_fifo = FLAGS_blob_db_is_fifo;
blob_db_options.max_db_size = FLAGS_blob_db_max_db_size;
blob_db_options.ttl_range_secs = FLAGS_blob_db_ttl_range_secs;
blob_db_options.min_blob_size = FLAGS_blob_db_min_blob_size;
blob_db_options.bytes_per_sync = FLAGS_blob_db_bytes_per_sync;
blob_db_options.blob_file_size = FLAGS_blob_db_file_size;
blob_db_options.compression = FLAGS_blob_db_compression_type_e;
blob_db::BlobDB* ptr = nullptr;
s = blob_db::BlobDB::Open(options, blob_db_options, db_name, &ptr);
if (s.ok()) {
db->db = ptr;
}
} else if (FLAGS_use_secondary_db) {
if (FLAGS_secondary_path.empty()) {
std::string default_secondary_path;
FLAGS_env->GetTestDirectory(&default_secondary_path);
default_secondary_path += "/dbbench_secondary";
FLAGS_secondary_path = default_secondary_path;
}
s = DB::OpenAsSecondary(options, db_name, FLAGS_secondary_path, &db->db);
if (s.ok() && FLAGS_secondary_update_interval > 0) {
secondary_update_thread_.reset(new port::Thread(
[this](int interval, DBWithColumnFamilies* _db) {
while (0 == secondary_update_stopped_.load(
std::memory_order_relaxed)) {
Status secondary_update_status =
_db->db->TryCatchUpWithPrimary();
if (!secondary_update_status.ok()) {
fprintf(stderr, "Failed to catch up with primary: %s\n",
secondary_update_status.ToString().c_str());
break;
}
++secondary_db_updates_;
FLAGS_env->SleepForMicroseconds(interval * 1000000);
}
},
FLAGS_secondary_update_interval, db));
}
#endif // ROCKSDB_LITE
} else {
s = DB::Open(options, db_name, &db->db);
}
Add -report_open_timing to db_bench (#8464) Summary: Hello and thanks for RocksDB, This PR adds support for ```-report_open_timing true``` to ```db_bench```. It can be useful when tuning RocksDB on filesystem/env with high latencies for file level operations (create/delete/rename...) seen during ```((Optimistic)Transaction)DB::Open```. Some examples: ``` > db_bench -benchmarks updaterandom -num 1 -db /dev/shm/db_bench > db_bench -benchmarks updaterandom -num 0 -db /dev/shm/db_bench -use_existing_db true -report_open_timing true -readonly true 2>&1 | grep OpenDb OpenDb: 3.90133 milliseconds > db_bench -benchmarks updaterandom -num 0 -db /dev/shm/db_bench -use_existing_db true -report_open_timing true -use_secondary_db true 2>&1 | grep OpenDb OpenDb: 3.33414 milliseconds > db_bench -benchmarks updaterandom -num 0 -db /dev/shm/db_bench -use_existing_db true -report_open_timing true 2>&1 | grep -A1 OpenDb OpenDb: 6.05423 milliseconds > db_bench -benchmarks updaterandom -num 1 > db_bench -benchmarks updaterandom -num 0 -use_existing_db true -report_open_timing true -readonly true 2>&1 | grep OpenDb OpenDb: 4.06859 milliseconds > db_bench -benchmarks updaterandom -num 0 -use_existing_db true -report_open_timing true -use_secondary_db true 2>&1 | grep OpenDb OpenDb: 2.85794 milliseconds > db_bench -benchmarks updaterandom -num 0 -use_existing_db true -report_open_timing true 2>&1 | grep OpenDb OpenDb: 6.46376 milliseconds > db_bench -benchmarks updaterandom -num 1 -db /clustered_fs/db_bench > db_bench -benchmarks updaterandom -num 0 -db /clustered_fs/db_bench -use_existing_db true -report_open_timing true -readonly true 2>&1 | grep OpenDb OpenDb: 3.79805 milliseconds > db_bench -benchmarks updaterandom -num 0 -db /clustered_fs/db_bench -use_existing_db true -report_open_timing true -use_secondary_db true 2>&1 | grep OpenDb OpenDb: 3.00174 milliseconds > db_bench -benchmarks updaterandom -num 0 -db /clustered_fs/db_bench -use_existing_db true -report_open_timing true 2>&1 | grep OpenDb OpenDb: 24.8732 milliseconds ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/8464 Reviewed By: hx235 Differential Revision: D29398096 Pulled By: zhichao-cao fbshipit-source-id: 8f05dc3284f084612a3f30234e39e1c37548f50c
2021-07-02 03:41:20 +02:00
if (FLAGS_report_open_timing) {
std::cout << "OpenDb: "
<< (FLAGS_env->NowNanos() - open_start) / 1000000.0
<< " milliseconds\n";
}
if (!s.ok()) {
fprintf(stderr, "open error: %s\n", s.ToString().c_str());
exit(1);
}
}
enum WriteMode {
RANDOM, SEQUENTIAL, UNIQUE_RANDOM
};
void WriteSeqDeterministic(ThreadState* thread) {
DoDeterministicCompact(thread, open_options_.compaction_style, SEQUENTIAL);
}
void WriteUniqueRandomDeterministic(ThreadState* thread) {
DoDeterministicCompact(thread, open_options_.compaction_style,
UNIQUE_RANDOM);
}
void WriteSeq(ThreadState* thread) {
DoWrite(thread, SEQUENTIAL);
}
void WriteRandom(ThreadState* thread) {
DoWrite(thread, RANDOM);
}
void WriteUniqueRandom(ThreadState* thread) {
DoWrite(thread, UNIQUE_RANDOM);
}
class KeyGenerator {
public:
KeyGenerator(Random64* rand, WriteMode mode, uint64_t num,
uint64_t /*num_per_set*/ = 64 * 1024)
: rand_(rand), mode_(mode), num_(num), next_(0) {
if (mode_ == UNIQUE_RANDOM) {
// NOTE: if memory consumption of this approach becomes a concern,
// we can either break it into pieces and only random shuffle a section
// each time. Alternatively, use a bit map implementation
// (https://reviews.facebook.net/differential/diff/54627/)
values_.resize(num_);
for (uint64_t i = 0; i < num_; ++i) {
values_[i] = i;
}
RandomShuffle(values_.begin(), values_.end(),
static_cast<uint32_t>(FLAGS_seed));
}
}
uint64_t Next() {
switch (mode_) {
case SEQUENTIAL:
return next_++;
case RANDOM:
return rand_->Next() % num_;
case UNIQUE_RANDOM:
assert(next_ < num_);
return values_[next_++];
}
assert(false);
return std::numeric_limits<uint64_t>::max();
}
Create fillanddeleteuniquerandom benchmark (db_bench), with new option flags. (#8593) Summary: Introduction of a new `fillanddeleteuniquerandom` benchmark (`db_bench`) with 5 new option flags to simulate a benchmark where the following sequence is repeated multiple times: "A set of keys S1 is inserted ('`disposable entries`'), then after some delay another set of keys S2 is inserted ('`persistent entries`') and the first set of keys S1 is deleted. S2 artificially represents the insertion of hypothetical results from some undefined computation done on the first set of keys S1. The next sequence can start as soon as the last disposable entry in the set S1 of this sequence is inserted, if the `delay` is non negligible." New flags: - `disposable_entries_delete_delay`: minimum delay in microseconds between insertion of the last `disposable` entry, and the start of the insertion of the first `persistent` entry. - `disposable_entries_batch_size`: number of `disposable` entries inserted at the beginning of each sequence. - `disposable_entries_value_size`: size of the random `value` string for the `disposable` entries. - `persistent_entries_batch_size`: number of `persistent` entries inserted at the end of each sequence, right before the deletion of the `disposable` entries starts. - `persistent_entries_value_size`: size of the random value string for the `persistent` entries. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8593 Reviewed By: pdillinger Differential Revision: D29974436 Pulled By: bjlemaire fbshipit-source-id: f578033e5b45e8268ba6fa6f38f4770c2e6e801d
2021-07-29 23:57:03 +02:00
// Only available for UNIQUE_RANDOM mode.
uint64_t Fetch(uint64_t index) {
assert(mode_ == UNIQUE_RANDOM);
assert(index < values_.size());
return values_[index];
}
private:
Random64* rand_;
WriteMode mode_;
const uint64_t num_;
uint64_t next_;
std::vector<uint64_t> values_;
};
DB* SelectDB(ThreadState* thread) {
return SelectDBWithCfh(thread)->db;
}
DBWithColumnFamilies* SelectDBWithCfh(ThreadState* thread) {
return SelectDBWithCfh(thread->rand.Next());
}
DBWithColumnFamilies* SelectDBWithCfh(uint64_t rand_int) {
if (db_.db != nullptr) {
return &db_;
} else {
return &multi_dbs_[rand_int % multi_dbs_.size()];
}
}
double SineRate(double x) {
return FLAGS_sine_a*sin((FLAGS_sine_b*x) + FLAGS_sine_c) + FLAGS_sine_d;
}
void DoWrite(ThreadState* thread, WriteMode write_mode) {
const int test_duration = write_mode == RANDOM ? FLAGS_duration : 0;
const int64_t num_ops = writes_ == 0 ? num_ : writes_;
size_t num_key_gens = 1;
if (db_.db == nullptr) {
num_key_gens = multi_dbs_.size();
}
std::vector<std::unique_ptr<KeyGenerator>> key_gens(num_key_gens);
int64_t max_ops = num_ops * num_key_gens;
int64_t ops_per_stage = max_ops;
if (FLAGS_num_column_families > 1 && FLAGS_num_hot_column_families > 0) {
ops_per_stage = (max_ops - 1) / (FLAGS_num_column_families /
FLAGS_num_hot_column_families) +
1;
}
Duration duration(test_duration, max_ops, ops_per_stage);
for (size_t i = 0; i < num_key_gens; i++) {
key_gens[i].reset(new KeyGenerator(&(thread->rand), write_mode,
num_ + max_num_range_tombstones_,
ops_per_stage));
}
if (num_ != FLAGS_num) {
char msg[100];
snprintf(msg, sizeof(msg), "(%" PRIu64 " ops)", num_);
thread->stats.AddMessage(msg);
}
RandomGenerator gen;
WriteBatch batch(/*reserved_bytes=*/0, /*max_bytes=*/0,
user_timestamp_size_);
Status s;
int64_t bytes = 0;
std::unique_ptr<const char[]> key_guard;
Slice key = AllocateKey(&key_guard);
std::unique_ptr<const char[]> begin_key_guard;
Slice begin_key = AllocateKey(&begin_key_guard);
std::unique_ptr<const char[]> end_key_guard;
Slice end_key = AllocateKey(&end_key_guard);
Add overwrite_probability for filluniquerandom benchmark in db_bench (#8569) Summary: Add flags `overwrite_probability` and `overwrite_window_size` flag to `db_bench`. Add the possibility of performing a `filluniquerandom` benchmark with an overwrite probability. For each write operation, there is a probability _p_ that the write is an overwrite (_p_=`overwrite_probability`). When an overwrite is decided, the key is randomly chosen from the last _N_ keys previously inserted into the DB (with _N_=`overwrite_window_size`). When a pure write is decided, the key inserted into the DB is unique and therefore will not be an overwrite. The `overwrite_window_size` is used so that the user can decide if the overwrite are mostly targeting recently inserted keys (when `overwrite_window_size` is small compared to the total number of writes), or can also target keys inserted "a long time ago" (when `overwrite_window_size` is comparable to total number of writes). Note that total number of writes = # of unique insertions + # of overwrites. No unit test specifically added. Local testing show the following **throughputs** for `filluniquerandom` with 1M total writes: - bypass the code inserts (no `overwrite_probability` flag specified): ~14.0MB/s - `overwrite_probability=0.99`, `overwrite_window_size=10`: ~17.0MB/s - `overwrite_probability=0.10`, `overwrite_window_size=10`: ~14.0MB/s - `overwrite_probability=0.99`, `overwrite_window_size=1M`: ~14.5MB/s - `overwrite_probability=0.10`, `overwrite_window_size=1M`: ~14.0MB/s Pull Request resolved: https://github.com/facebook/rocksdb/pull/8569 Reviewed By: pdillinger Differential Revision: D29818631 Pulled By: bjlemaire fbshipit-source-id: d472b4ea4e457a4da7c4ee4f14b40cccd6a4587a
2021-07-21 20:32:36 +02:00
double p = 0.0;
Create fillanddeleteuniquerandom benchmark (db_bench), with new option flags. (#8593) Summary: Introduction of a new `fillanddeleteuniquerandom` benchmark (`db_bench`) with 5 new option flags to simulate a benchmark where the following sequence is repeated multiple times: "A set of keys S1 is inserted ('`disposable entries`'), then after some delay another set of keys S2 is inserted ('`persistent entries`') and the first set of keys S1 is deleted. S2 artificially represents the insertion of hypothetical results from some undefined computation done on the first set of keys S1. The next sequence can start as soon as the last disposable entry in the set S1 of this sequence is inserted, if the `delay` is non negligible." New flags: - `disposable_entries_delete_delay`: minimum delay in microseconds between insertion of the last `disposable` entry, and the start of the insertion of the first `persistent` entry. - `disposable_entries_batch_size`: number of `disposable` entries inserted at the beginning of each sequence. - `disposable_entries_value_size`: size of the random `value` string for the `disposable` entries. - `persistent_entries_batch_size`: number of `persistent` entries inserted at the end of each sequence, right before the deletion of the `disposable` entries starts. - `persistent_entries_value_size`: size of the random value string for the `persistent` entries. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8593 Reviewed By: pdillinger Differential Revision: D29974436 Pulled By: bjlemaire fbshipit-source-id: f578033e5b45e8268ba6fa6f38f4770c2e6e801d
2021-07-29 23:57:03 +02:00
uint64_t num_overwrites = 0, num_unique_keys = 0, num_selective_deletes = 0;
Add overwrite_probability for filluniquerandom benchmark in db_bench (#8569) Summary: Add flags `overwrite_probability` and `overwrite_window_size` flag to `db_bench`. Add the possibility of performing a `filluniquerandom` benchmark with an overwrite probability. For each write operation, there is a probability _p_ that the write is an overwrite (_p_=`overwrite_probability`). When an overwrite is decided, the key is randomly chosen from the last _N_ keys previously inserted into the DB (with _N_=`overwrite_window_size`). When a pure write is decided, the key inserted into the DB is unique and therefore will not be an overwrite. The `overwrite_window_size` is used so that the user can decide if the overwrite are mostly targeting recently inserted keys (when `overwrite_window_size` is small compared to the total number of writes), or can also target keys inserted "a long time ago" (when `overwrite_window_size` is comparable to total number of writes). Note that total number of writes = # of unique insertions + # of overwrites. No unit test specifically added. Local testing show the following **throughputs** for `filluniquerandom` with 1M total writes: - bypass the code inserts (no `overwrite_probability` flag specified): ~14.0MB/s - `overwrite_probability=0.99`, `overwrite_window_size=10`: ~17.0MB/s - `overwrite_probability=0.10`, `overwrite_window_size=10`: ~14.0MB/s - `overwrite_probability=0.99`, `overwrite_window_size=1M`: ~14.5MB/s - `overwrite_probability=0.10`, `overwrite_window_size=1M`: ~14.0MB/s Pull Request resolved: https://github.com/facebook/rocksdb/pull/8569 Reviewed By: pdillinger Differential Revision: D29818631 Pulled By: bjlemaire fbshipit-source-id: d472b4ea4e457a4da7c4ee4f14b40cccd6a4587a
2021-07-21 20:32:36 +02:00
// If user set overwrite_probability flag,
// check if value is in [0.0,1.0].
if (FLAGS_overwrite_probability > 0.0) {
p = FLAGS_overwrite_probability > 1.0 ? 1.0 : FLAGS_overwrite_probability;
// If overwrite set by user, and UNIQUE_RANDOM mode on,
// the overwrite_window_size must be > 0.
if (write_mode == UNIQUE_RANDOM && FLAGS_overwrite_window_size == 0) {
fprintf(stderr,
"Overwrite_window_size must be strictly greater than 0.\n");
ErrorExit();
}
}
// Default_random_engine provides slightly
// improved throughput over mt19937.
std::default_random_engine overwrite_gen{
static_cast<unsigned int>(FLAGS_seed)};
std::bernoulli_distribution overwrite_decider(p);
// Inserted key window is filled with the last N
// keys previously inserted into the DB (with
// N=FLAGS_overwrite_window_size).
// We use a deque struct because:
// - random access is O(1)
// - insertion/removal at beginning/end is also O(1).
std::deque<int64_t> inserted_key_window;
Random64 reservoir_id_gen(FLAGS_seed);
Create fillanddeleteuniquerandom benchmark (db_bench), with new option flags. (#8593) Summary: Introduction of a new `fillanddeleteuniquerandom` benchmark (`db_bench`) with 5 new option flags to simulate a benchmark where the following sequence is repeated multiple times: "A set of keys S1 is inserted ('`disposable entries`'), then after some delay another set of keys S2 is inserted ('`persistent entries`') and the first set of keys S1 is deleted. S2 artificially represents the insertion of hypothetical results from some undefined computation done on the first set of keys S1. The next sequence can start as soon as the last disposable entry in the set S1 of this sequence is inserted, if the `delay` is non negligible." New flags: - `disposable_entries_delete_delay`: minimum delay in microseconds between insertion of the last `disposable` entry, and the start of the insertion of the first `persistent` entry. - `disposable_entries_batch_size`: number of `disposable` entries inserted at the beginning of each sequence. - `disposable_entries_value_size`: size of the random `value` string for the `disposable` entries. - `persistent_entries_batch_size`: number of `persistent` entries inserted at the end of each sequence, right before the deletion of the `disposable` entries starts. - `persistent_entries_value_size`: size of the random value string for the `persistent` entries. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8593 Reviewed By: pdillinger Differential Revision: D29974436 Pulled By: bjlemaire fbshipit-source-id: f578033e5b45e8268ba6fa6f38f4770c2e6e801d
2021-07-29 23:57:03 +02:00
// --- Variables used in disposable/persistent keys simulation:
// The following variables are used when
// disposable_entries_batch_size is >0. We simualte a workload
// where the following sequence is repeated multiple times:
// "A set of keys S1 is inserted ('disposable entries'), then after
// some delay another set of keys S2 is inserted ('persistent entries')
// and the first set of keys S1 is deleted. S2 artificially represents
// the insertion of hypothetical results from some undefined computation
// done on the first set of keys S1. The next sequence can start as soon
// as the last disposable entry in the set S1 of this sequence is
// inserted, if the delay is non negligible"
bool skip_for_loop = false, is_disposable_entry = true;
std::vector<uint64_t> disposable_entries_index(num_key_gens, 0);
std::vector<uint64_t> persistent_ent_and_del_index(num_key_gens, 0);
const uint64_t kNumDispAndPersEntries =
FLAGS_disposable_entries_batch_size +
FLAGS_persistent_entries_batch_size;
if (kNumDispAndPersEntries > 0) {
if ((write_mode != UNIQUE_RANDOM) || (writes_per_range_tombstone_ > 0) ||
(p > 0.0)) {
fprintf(
stderr,
"Disposable/persistent deletes are not compatible with overwrites "
"and DeleteRanges; and are only supported in filluniquerandom.\n");
ErrorExit();
}
if (FLAGS_disposable_entries_value_size < 0 ||
FLAGS_persistent_entries_value_size < 0) {
fprintf(
stderr,
"disposable_entries_value_size and persistent_entries_value_size"
"have to be positive.\n");
ErrorExit();
}
}
Random rnd_disposable_entry(static_cast<uint32_t>(FLAGS_seed));
std::string random_value;
// Queue that stores scheduled timestamp of disposable entries deletes,
// along with starting index of disposable entry keys to delete.
std::vector<std::queue<std::pair<uint64_t, uint64_t>>> disposable_entries_q(
num_key_gens);
// --- End of variables used in disposable/persistent keys simulation.
std::vector<std::unique_ptr<const char[]>> expanded_key_guards;
std::vector<Slice> expanded_keys;
if (FLAGS_expand_range_tombstones) {
expanded_key_guards.resize(range_tombstone_width_);
for (auto& expanded_key_guard : expanded_key_guards) {
expanded_keys.emplace_back(AllocateKey(&expanded_key_guard));
}
}
std::unique_ptr<char[]> ts_guard;
if (user_timestamp_size_ > 0) {
ts_guard.reset(new char[user_timestamp_size_]);
}
int64_t stage = 0;
int64_t num_written = 0;
while (!duration.Done(entries_per_batch_)) {
if (duration.GetStage() != stage) {
stage = duration.GetStage();
if (db_.db != nullptr) {
db_.CreateNewCf(open_options_, stage);
} else {
for (auto& db : multi_dbs_) {
db.CreateNewCf(open_options_, stage);
}
}
}
size_t id = thread->rand.Next() % num_key_gens;
DBWithColumnFamilies* db_with_cfh = SelectDBWithCfh(id);
batch.Clear();
int64_t batch_bytes = 0;
for (int64_t j = 0; j < entries_per_batch_; j++) {
Add overwrite_probability for filluniquerandom benchmark in db_bench (#8569) Summary: Add flags `overwrite_probability` and `overwrite_window_size` flag to `db_bench`. Add the possibility of performing a `filluniquerandom` benchmark with an overwrite probability. For each write operation, there is a probability _p_ that the write is an overwrite (_p_=`overwrite_probability`). When an overwrite is decided, the key is randomly chosen from the last _N_ keys previously inserted into the DB (with _N_=`overwrite_window_size`). When a pure write is decided, the key inserted into the DB is unique and therefore will not be an overwrite. The `overwrite_window_size` is used so that the user can decide if the overwrite are mostly targeting recently inserted keys (when `overwrite_window_size` is small compared to the total number of writes), or can also target keys inserted "a long time ago" (when `overwrite_window_size` is comparable to total number of writes). Note that total number of writes = # of unique insertions + # of overwrites. No unit test specifically added. Local testing show the following **throughputs** for `filluniquerandom` with 1M total writes: - bypass the code inserts (no `overwrite_probability` flag specified): ~14.0MB/s - `overwrite_probability=0.99`, `overwrite_window_size=10`: ~17.0MB/s - `overwrite_probability=0.10`, `overwrite_window_size=10`: ~14.0MB/s - `overwrite_probability=0.99`, `overwrite_window_size=1M`: ~14.5MB/s - `overwrite_probability=0.10`, `overwrite_window_size=1M`: ~14.0MB/s Pull Request resolved: https://github.com/facebook/rocksdb/pull/8569 Reviewed By: pdillinger Differential Revision: D29818631 Pulled By: bjlemaire fbshipit-source-id: d472b4ea4e457a4da7c4ee4f14b40cccd6a4587a
2021-07-21 20:32:36 +02:00
int64_t rand_num = 0;
if ((write_mode == UNIQUE_RANDOM) && (p > 0.0)) {
if ((inserted_key_window.size() > 0) &&
overwrite_decider(overwrite_gen)) {
num_overwrites++;
rand_num = inserted_key_window[reservoir_id_gen.Next() %
inserted_key_window.size()];
} else {
num_unique_keys++;
rand_num = key_gens[id]->Next();
if (inserted_key_window.size() < FLAGS_overwrite_window_size) {
inserted_key_window.push_back(rand_num);
} else {
inserted_key_window.pop_front();
inserted_key_window.push_back(rand_num);
}
}
Create fillanddeleteuniquerandom benchmark (db_bench), with new option flags. (#8593) Summary: Introduction of a new `fillanddeleteuniquerandom` benchmark (`db_bench`) with 5 new option flags to simulate a benchmark where the following sequence is repeated multiple times: "A set of keys S1 is inserted ('`disposable entries`'), then after some delay another set of keys S2 is inserted ('`persistent entries`') and the first set of keys S1 is deleted. S2 artificially represents the insertion of hypothetical results from some undefined computation done on the first set of keys S1. The next sequence can start as soon as the last disposable entry in the set S1 of this sequence is inserted, if the `delay` is non negligible." New flags: - `disposable_entries_delete_delay`: minimum delay in microseconds between insertion of the last `disposable` entry, and the start of the insertion of the first `persistent` entry. - `disposable_entries_batch_size`: number of `disposable` entries inserted at the beginning of each sequence. - `disposable_entries_value_size`: size of the random `value` string for the `disposable` entries. - `persistent_entries_batch_size`: number of `persistent` entries inserted at the end of each sequence, right before the deletion of the `disposable` entries starts. - `persistent_entries_value_size`: size of the random value string for the `persistent` entries. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8593 Reviewed By: pdillinger Differential Revision: D29974436 Pulled By: bjlemaire fbshipit-source-id: f578033e5b45e8268ba6fa6f38f4770c2e6e801d
2021-07-29 23:57:03 +02:00
} else if (kNumDispAndPersEntries > 0) {
// Check if queue is non-empty and if we need to insert
// 'persistent' KV entries (KV entries that are never deleted)
// and delete disposable entries previously inserted.
if (!disposable_entries_q[id].empty() &&
(disposable_entries_q[id].front().first <
FLAGS_env->NowMicros())) {
// If we need to perform a "merge op" pattern,
// we first write all the persistent KV entries not targeted
// by deletes, and then we write the disposable entries deletes.
if (persistent_ent_and_del_index[id] <
FLAGS_persistent_entries_batch_size) {
// Generate key to insert.
rand_num =
key_gens[id]->Fetch(disposable_entries_q[id].front().second +
FLAGS_disposable_entries_batch_size +
persistent_ent_and_del_index[id]);
persistent_ent_and_del_index[id]++;
is_disposable_entry = false;
skip_for_loop = false;
} else if (persistent_ent_and_del_index[id] <
kNumDispAndPersEntries) {
// Find key of the entry to delete.
rand_num =
key_gens[id]->Fetch(disposable_entries_q[id].front().second +
(persistent_ent_and_del_index[id] -
FLAGS_persistent_entries_batch_size));
persistent_ent_and_del_index[id]++;
GenerateKeyFromInt(rand_num, FLAGS_num, &key);
// For the delete operation, everything happens here and we
// skip the rest of the for-loop, which is designed for
// inserts.
if (FLAGS_num_column_families <= 1) {
batch.Delete(key);
} else {
// We use same rand_num as seed for key and column family so
// that we can deterministically find the cfh corresponding to a
// particular key while reading the key.
batch.Delete(db_with_cfh->GetCfh(rand_num), key);
}
// A delete only includes Key+Timestamp (no value).
batch_bytes += key_size_ + user_timestamp_size_;
bytes += key_size_ + user_timestamp_size_;
num_selective_deletes++;
// Skip rest of the for-loop (j=0, j<entries_per_batch_,j++).
skip_for_loop = true;
} else {
assert(false); // should never reach this point.
}
// If disposable_entries_q needs to be updated (ie: when a selective
// insert+delete was successfully completed, pop the job out of the
// queue).
if (!disposable_entries_q[id].empty() &&
(disposable_entries_q[id].front().first <
FLAGS_env->NowMicros()) &&
persistent_ent_and_del_index[id] == kNumDispAndPersEntries) {
disposable_entries_q[id].pop();
persistent_ent_and_del_index[id] = 0;
}
// If we are deleting disposable entries, skip the rest of the
// for-loop since there is no key-value inserts at this moment in
// time.
if (skip_for_loop) {
continue;
}
}
// If no job is in the queue, then we keep inserting disposable KV
// entries that will be deleted later by a series of deletes.
else {
rand_num = key_gens[id]->Fetch(disposable_entries_index[id]);
disposable_entries_index[id]++;
is_disposable_entry = true;
if ((disposable_entries_index[id] %
FLAGS_disposable_entries_batch_size) == 0) {
// Skip the persistent KV entries inserts for now
disposable_entries_index[id] +=
FLAGS_persistent_entries_batch_size;
}
}
Add overwrite_probability for filluniquerandom benchmark in db_bench (#8569) Summary: Add flags `overwrite_probability` and `overwrite_window_size` flag to `db_bench`. Add the possibility of performing a `filluniquerandom` benchmark with an overwrite probability. For each write operation, there is a probability _p_ that the write is an overwrite (_p_=`overwrite_probability`). When an overwrite is decided, the key is randomly chosen from the last _N_ keys previously inserted into the DB (with _N_=`overwrite_window_size`). When a pure write is decided, the key inserted into the DB is unique and therefore will not be an overwrite. The `overwrite_window_size` is used so that the user can decide if the overwrite are mostly targeting recently inserted keys (when `overwrite_window_size` is small compared to the total number of writes), or can also target keys inserted "a long time ago" (when `overwrite_window_size` is comparable to total number of writes). Note that total number of writes = # of unique insertions + # of overwrites. No unit test specifically added. Local testing show the following **throughputs** for `filluniquerandom` with 1M total writes: - bypass the code inserts (no `overwrite_probability` flag specified): ~14.0MB/s - `overwrite_probability=0.99`, `overwrite_window_size=10`: ~17.0MB/s - `overwrite_probability=0.10`, `overwrite_window_size=10`: ~14.0MB/s - `overwrite_probability=0.99`, `overwrite_window_size=1M`: ~14.5MB/s - `overwrite_probability=0.10`, `overwrite_window_size=1M`: ~14.0MB/s Pull Request resolved: https://github.com/facebook/rocksdb/pull/8569 Reviewed By: pdillinger Differential Revision: D29818631 Pulled By: bjlemaire fbshipit-source-id: d472b4ea4e457a4da7c4ee4f14b40cccd6a4587a
2021-07-21 20:32:36 +02:00
} else {
rand_num = key_gens[id]->Next();
}
GenerateKeyFromInt(rand_num, FLAGS_num, &key);
Create fillanddeleteuniquerandom benchmark (db_bench), with new option flags. (#8593) Summary: Introduction of a new `fillanddeleteuniquerandom` benchmark (`db_bench`) with 5 new option flags to simulate a benchmark where the following sequence is repeated multiple times: "A set of keys S1 is inserted ('`disposable entries`'), then after some delay another set of keys S2 is inserted ('`persistent entries`') and the first set of keys S1 is deleted. S2 artificially represents the insertion of hypothetical results from some undefined computation done on the first set of keys S1. The next sequence can start as soon as the last disposable entry in the set S1 of this sequence is inserted, if the `delay` is non negligible." New flags: - `disposable_entries_delete_delay`: minimum delay in microseconds between insertion of the last `disposable` entry, and the start of the insertion of the first `persistent` entry. - `disposable_entries_batch_size`: number of `disposable` entries inserted at the beginning of each sequence. - `disposable_entries_value_size`: size of the random `value` string for the `disposable` entries. - `persistent_entries_batch_size`: number of `persistent` entries inserted at the end of each sequence, right before the deletion of the `disposable` entries starts. - `persistent_entries_value_size`: size of the random value string for the `persistent` entries. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8593 Reviewed By: pdillinger Differential Revision: D29974436 Pulled By: bjlemaire fbshipit-source-id: f578033e5b45e8268ba6fa6f38f4770c2e6e801d
2021-07-29 23:57:03 +02:00
Slice val;
if (kNumDispAndPersEntries > 0) {
random_value = rnd_disposable_entry.RandomString(
is_disposable_entry ? FLAGS_disposable_entries_value_size
: FLAGS_persistent_entries_value_size);
val = Slice(random_value);
num_unique_keys++;
} else {
val = gen.Generate();
}
if (use_blob_db_) {
#ifndef ROCKSDB_LITE
// Stacked BlobDB
blob_db::BlobDB* blobdb =
static_cast<blob_db::BlobDB*>(db_with_cfh->db);
if (FLAGS_blob_db_max_ttl_range > 0) {
int ttl = rand() % FLAGS_blob_db_max_ttl_range;
s = blobdb->PutWithTTL(write_options_, key, val, ttl);
} else {
s = blobdb->Put(write_options_, key, val);
}
#endif // ROCKSDB_LITE
} else if (FLAGS_num_column_families <= 1) {
batch.Put(key, val);
} else {
// We use same rand_num as seed for key and column family so that we
// can deterministically find the cfh corresponding to a particular
// key while reading the key.
batch.Put(db_with_cfh->GetCfh(rand_num), key,
val);
}
batch_bytes += val.size() + key_size_ + user_timestamp_size_;
bytes += val.size() + key_size_ + user_timestamp_size_;
++num_written;
Create fillanddeleteuniquerandom benchmark (db_bench), with new option flags. (#8593) Summary: Introduction of a new `fillanddeleteuniquerandom` benchmark (`db_bench`) with 5 new option flags to simulate a benchmark where the following sequence is repeated multiple times: "A set of keys S1 is inserted ('`disposable entries`'), then after some delay another set of keys S2 is inserted ('`persistent entries`') and the first set of keys S1 is deleted. S2 artificially represents the insertion of hypothetical results from some undefined computation done on the first set of keys S1. The next sequence can start as soon as the last disposable entry in the set S1 of this sequence is inserted, if the `delay` is non negligible." New flags: - `disposable_entries_delete_delay`: minimum delay in microseconds between insertion of the last `disposable` entry, and the start of the insertion of the first `persistent` entry. - `disposable_entries_batch_size`: number of `disposable` entries inserted at the beginning of each sequence. - `disposable_entries_value_size`: size of the random `value` string for the `disposable` entries. - `persistent_entries_batch_size`: number of `persistent` entries inserted at the end of each sequence, right before the deletion of the `disposable` entries starts. - `persistent_entries_value_size`: size of the random value string for the `persistent` entries. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8593 Reviewed By: pdillinger Differential Revision: D29974436 Pulled By: bjlemaire fbshipit-source-id: f578033e5b45e8268ba6fa6f38f4770c2e6e801d
2021-07-29 23:57:03 +02:00
// If all disposable entries have been inserted, then we need to
// add in the job queue a call for 'persistent entry insertions +
// disposable entry deletions'.
if (kNumDispAndPersEntries > 0 && is_disposable_entry &&
((disposable_entries_index[id] % kNumDispAndPersEntries) == 0)) {
// Queue contains [timestamp, starting_idx],
// timestamp = current_time + delay (minimum aboslute time when to
// start inserting the selective deletes) starting_idx = index in the
// keygen of the rand_num to generate the key of the first KV entry to
// delete (= key of the first selective delete).
disposable_entries_q[id].push(std::make_pair(
FLAGS_env->NowMicros() +
FLAGS_disposable_entries_delete_delay /* timestamp */,
disposable_entries_index[id] - kNumDispAndPersEntries
/*starting idx*/));
}
if (writes_per_range_tombstone_ > 0 &&
num_written > writes_before_delete_range_ &&
(num_written - writes_before_delete_range_) /
writes_per_range_tombstone_ <=
max_num_range_tombstones_ &&
(num_written - writes_before_delete_range_) %
writes_per_range_tombstone_ ==
0) {
int64_t begin_num = key_gens[id]->Next();
if (FLAGS_expand_range_tombstones) {
for (int64_t offset = 0; offset < range_tombstone_width_;
++offset) {
GenerateKeyFromInt(begin_num + offset, FLAGS_num,
&expanded_keys[offset]);
if (use_blob_db_) {
#ifndef ROCKSDB_LITE
// Stacked BlobDB
s = db_with_cfh->db->Delete(write_options_,
expanded_keys[offset]);
#endif // ROCKSDB_LITE
} else if (FLAGS_num_column_families <= 1) {
batch.Delete(expanded_keys[offset]);
} else {
batch.Delete(db_with_cfh->GetCfh(rand_num),
expanded_keys[offset]);
}
}
} else {
GenerateKeyFromInt(begin_num, FLAGS_num, &begin_key);
GenerateKeyFromInt(begin_num + range_tombstone_width_, FLAGS_num,
&end_key);
if (use_blob_db_) {
#ifndef ROCKSDB_LITE
// Stacked BlobDB
s = db_with_cfh->db->DeleteRange(
write_options_, db_with_cfh->db->DefaultColumnFamily(),
begin_key, end_key);
#endif // ROCKSDB_LITE
} else if (FLAGS_num_column_families <= 1) {
batch.DeleteRange(begin_key, end_key);
} else {
batch.DeleteRange(db_with_cfh->GetCfh(rand_num), begin_key,
end_key);
}
}
}
}
if (thread->shared->write_rate_limiter.get() != nullptr) {
thread->shared->write_rate_limiter->Request(
batch_bytes, Env::IO_HIGH,
nullptr /* stats */, RateLimiter::OpType::kWrite);
// Set time at which last op finished to Now() to hide latency and
// sleep from rate limiter. Also, do the check once per batch, not
// once per write.
thread->stats.ResetLastOpTime();
}
if (user_timestamp_size_ > 0) {
Slice user_ts = mock_app_clock_->Allocate(ts_guard.get());
s = batch.AssignTimestamp(user_ts);
if (!s.ok()) {
fprintf(stderr, "assign timestamp to write batch: %s\n",
s.ToString().c_str());
ErrorExit();
}
}
if (!use_blob_db_) {
// Not stacked BlobDB
s = db_with_cfh->db->Write(write_options_, &batch);
}
thread->stats.FinishedOps(db_with_cfh, db_with_cfh->db,
entries_per_batch_, kWrite);
if (FLAGS_sine_write_rate) {
uint64_t now = FLAGS_env->NowMicros();
uint64_t usecs_since_last;
if (now > thread->stats.GetSineInterval()) {
usecs_since_last = now - thread->stats.GetSineInterval();
} else {
usecs_since_last = 0;
}
if (usecs_since_last >
(FLAGS_sine_write_rate_interval_milliseconds * uint64_t{1000})) {
double usecs_since_start =
static_cast<double>(now - thread->stats.GetStart());
thread->stats.ResetSineInterval();
uint64_t write_rate =
static_cast<uint64_t>(SineRate(usecs_since_start / 1000000.0));
thread->shared->write_rate_limiter.reset(
NewGenericRateLimiter(write_rate));
}
}
Auto recovery from out of space errors (#4164) Summary: This commit implements automatic recovery from a Status::NoSpace() error during background operations such as write callback, flush and compaction. The broad design is as follows - 1. Compaction errors are treated as soft errors and don't put the database in read-only mode. A compaction is delayed until enough free disk space is available to accomodate the compaction outputs, which is estimated based on the input size. This means that users can continue to write, and we rely on the WriteController to delay or stop writes if the compaction debt becomes too high due to persistent low disk space condition 2. Errors during write callback and flush are treated as hard errors, i.e the database is put in read-only mode and goes back to read-write only fater certain recovery actions are taken. 3. Both types of recovery rely on the SstFileManagerImpl to poll for sufficient disk space. We assume that there is a 1-1 mapping between an SFM and the underlying OS storage container. For cases where multiple DBs are hosted on a single storage container, the user is expected to allocate a single SFM instance and use the same one for all the DBs. If no SFM is specified by the user, DBImpl::Open() will allocate one, but this will be one per DB and each DB will recover independently. The recovery implemented by SFM is as follows - a) On the first occurance of an out of space error during compaction, subsequent compactions will be delayed until the disk free space check indicates enough available space. The required space is computed as the sum of input sizes. b) The free space check requirement will be removed once the amount of free space is greater than the size reserved by in progress compactions when the first error occured c) If the out of space error is a hard error, a background thread in SFM will poll for sufficient headroom before triggering the recovery of the database and putting it in write-only mode. The headroom is calculated as the sum of the write_buffer_size of all the DB instances associated with the SFM 4. EventListener callbacks will be called at the start and completion of automatic recovery. Users can disable the auto recov ery in the start callback, and later initiate it manually by calling DB::Resume() Todo: 1. More extensive testing 2. Add disk full condition to db_stress (follow-on PR) Pull Request resolved: https://github.com/facebook/rocksdb/pull/4164 Differential Revision: D9846378 Pulled By: anand1976 fbshipit-source-id: 80ea875dbd7f00205e19c82215ff6e37da10da4a
2018-09-15 22:36:19 +02:00
if (!s.ok()) {
s = listener_->WaitForRecovery(600000000) ? Status::OK() : s;
}
if (!s.ok()) {
fprintf(stderr, "put error: %s\n", s.ToString().c_str());
ErrorExit();
}
}
Add overwrite_probability for filluniquerandom benchmark in db_bench (#8569) Summary: Add flags `overwrite_probability` and `overwrite_window_size` flag to `db_bench`. Add the possibility of performing a `filluniquerandom` benchmark with an overwrite probability. For each write operation, there is a probability _p_ that the write is an overwrite (_p_=`overwrite_probability`). When an overwrite is decided, the key is randomly chosen from the last _N_ keys previously inserted into the DB (with _N_=`overwrite_window_size`). When a pure write is decided, the key inserted into the DB is unique and therefore will not be an overwrite. The `overwrite_window_size` is used so that the user can decide if the overwrite are mostly targeting recently inserted keys (when `overwrite_window_size` is small compared to the total number of writes), or can also target keys inserted "a long time ago" (when `overwrite_window_size` is comparable to total number of writes). Note that total number of writes = # of unique insertions + # of overwrites. No unit test specifically added. Local testing show the following **throughputs** for `filluniquerandom` with 1M total writes: - bypass the code inserts (no `overwrite_probability` flag specified): ~14.0MB/s - `overwrite_probability=0.99`, `overwrite_window_size=10`: ~17.0MB/s - `overwrite_probability=0.10`, `overwrite_window_size=10`: ~14.0MB/s - `overwrite_probability=0.99`, `overwrite_window_size=1M`: ~14.5MB/s - `overwrite_probability=0.10`, `overwrite_window_size=1M`: ~14.0MB/s Pull Request resolved: https://github.com/facebook/rocksdb/pull/8569 Reviewed By: pdillinger Differential Revision: D29818631 Pulled By: bjlemaire fbshipit-source-id: d472b4ea4e457a4da7c4ee4f14b40cccd6a4587a
2021-07-21 20:32:36 +02:00
if ((write_mode == UNIQUE_RANDOM) && (p > 0.0)) {
fprintf(stdout,
Create fillanddeleteuniquerandom benchmark (db_bench), with new option flags. (#8593) Summary: Introduction of a new `fillanddeleteuniquerandom` benchmark (`db_bench`) with 5 new option flags to simulate a benchmark where the following sequence is repeated multiple times: "A set of keys S1 is inserted ('`disposable entries`'), then after some delay another set of keys S2 is inserted ('`persistent entries`') and the first set of keys S1 is deleted. S2 artificially represents the insertion of hypothetical results from some undefined computation done on the first set of keys S1. The next sequence can start as soon as the last disposable entry in the set S1 of this sequence is inserted, if the `delay` is non negligible." New flags: - `disposable_entries_delete_delay`: minimum delay in microseconds between insertion of the last `disposable` entry, and the start of the insertion of the first `persistent` entry. - `disposable_entries_batch_size`: number of `disposable` entries inserted at the beginning of each sequence. - `disposable_entries_value_size`: size of the random `value` string for the `disposable` entries. - `persistent_entries_batch_size`: number of `persistent` entries inserted at the end of each sequence, right before the deletion of the `disposable` entries starts. - `persistent_entries_value_size`: size of the random value string for the `persistent` entries. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8593 Reviewed By: pdillinger Differential Revision: D29974436 Pulled By: bjlemaire fbshipit-source-id: f578033e5b45e8268ba6fa6f38f4770c2e6e801d
2021-07-29 23:57:03 +02:00
"Number of unique keys inserted: %" PRIu64
Add overwrite_probability for filluniquerandom benchmark in db_bench (#8569) Summary: Add flags `overwrite_probability` and `overwrite_window_size` flag to `db_bench`. Add the possibility of performing a `filluniquerandom` benchmark with an overwrite probability. For each write operation, there is a probability _p_ that the write is an overwrite (_p_=`overwrite_probability`). When an overwrite is decided, the key is randomly chosen from the last _N_ keys previously inserted into the DB (with _N_=`overwrite_window_size`). When a pure write is decided, the key inserted into the DB is unique and therefore will not be an overwrite. The `overwrite_window_size` is used so that the user can decide if the overwrite are mostly targeting recently inserted keys (when `overwrite_window_size` is small compared to the total number of writes), or can also target keys inserted "a long time ago" (when `overwrite_window_size` is comparable to total number of writes). Note that total number of writes = # of unique insertions + # of overwrites. No unit test specifically added. Local testing show the following **throughputs** for `filluniquerandom` with 1M total writes: - bypass the code inserts (no `overwrite_probability` flag specified): ~14.0MB/s - `overwrite_probability=0.99`, `overwrite_window_size=10`: ~17.0MB/s - `overwrite_probability=0.10`, `overwrite_window_size=10`: ~14.0MB/s - `overwrite_probability=0.99`, `overwrite_window_size=1M`: ~14.5MB/s - `overwrite_probability=0.10`, `overwrite_window_size=1M`: ~14.0MB/s Pull Request resolved: https://github.com/facebook/rocksdb/pull/8569 Reviewed By: pdillinger Differential Revision: D29818631 Pulled By: bjlemaire fbshipit-source-id: d472b4ea4e457a4da7c4ee4f14b40cccd6a4587a
2021-07-21 20:32:36 +02:00
".\nNumber of overwrites: %" PRIu64 "\n",
num_unique_keys, num_overwrites);
Create fillanddeleteuniquerandom benchmark (db_bench), with new option flags. (#8593) Summary: Introduction of a new `fillanddeleteuniquerandom` benchmark (`db_bench`) with 5 new option flags to simulate a benchmark where the following sequence is repeated multiple times: "A set of keys S1 is inserted ('`disposable entries`'), then after some delay another set of keys S2 is inserted ('`persistent entries`') and the first set of keys S1 is deleted. S2 artificially represents the insertion of hypothetical results from some undefined computation done on the first set of keys S1. The next sequence can start as soon as the last disposable entry in the set S1 of this sequence is inserted, if the `delay` is non negligible." New flags: - `disposable_entries_delete_delay`: minimum delay in microseconds between insertion of the last `disposable` entry, and the start of the insertion of the first `persistent` entry. - `disposable_entries_batch_size`: number of `disposable` entries inserted at the beginning of each sequence. - `disposable_entries_value_size`: size of the random `value` string for the `disposable` entries. - `persistent_entries_batch_size`: number of `persistent` entries inserted at the end of each sequence, right before the deletion of the `disposable` entries starts. - `persistent_entries_value_size`: size of the random value string for the `persistent` entries. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8593 Reviewed By: pdillinger Differential Revision: D29974436 Pulled By: bjlemaire fbshipit-source-id: f578033e5b45e8268ba6fa6f38f4770c2e6e801d
2021-07-29 23:57:03 +02:00
} else if (kNumDispAndPersEntries > 0) {
fprintf(stdout,
"Number of unique keys inserted (disposable+persistent): %" PRIu64
".\nNumber of 'disposable entry delete': %" PRIu64 "\n",
num_written, num_selective_deletes);
Add overwrite_probability for filluniquerandom benchmark in db_bench (#8569) Summary: Add flags `overwrite_probability` and `overwrite_window_size` flag to `db_bench`. Add the possibility of performing a `filluniquerandom` benchmark with an overwrite probability. For each write operation, there is a probability _p_ that the write is an overwrite (_p_=`overwrite_probability`). When an overwrite is decided, the key is randomly chosen from the last _N_ keys previously inserted into the DB (with _N_=`overwrite_window_size`). When a pure write is decided, the key inserted into the DB is unique and therefore will not be an overwrite. The `overwrite_window_size` is used so that the user can decide if the overwrite are mostly targeting recently inserted keys (when `overwrite_window_size` is small compared to the total number of writes), or can also target keys inserted "a long time ago" (when `overwrite_window_size` is comparable to total number of writes). Note that total number of writes = # of unique insertions + # of overwrites. No unit test specifically added. Local testing show the following **throughputs** for `filluniquerandom` with 1M total writes: - bypass the code inserts (no `overwrite_probability` flag specified): ~14.0MB/s - `overwrite_probability=0.99`, `overwrite_window_size=10`: ~17.0MB/s - `overwrite_probability=0.10`, `overwrite_window_size=10`: ~14.0MB/s - `overwrite_probability=0.99`, `overwrite_window_size=1M`: ~14.5MB/s - `overwrite_probability=0.10`, `overwrite_window_size=1M`: ~14.0MB/s Pull Request resolved: https://github.com/facebook/rocksdb/pull/8569 Reviewed By: pdillinger Differential Revision: D29818631 Pulled By: bjlemaire fbshipit-source-id: d472b4ea4e457a4da7c4ee4f14b40cccd6a4587a
2021-07-21 20:32:36 +02:00
}
thread->stats.AddBytes(bytes);
}
Status DoDeterministicCompact(ThreadState* thread,
CompactionStyle compaction_style,
WriteMode write_mode) {
#ifndef ROCKSDB_LITE
ColumnFamilyMetaData meta;
std::vector<DB*> db_list;
if (db_.db != nullptr) {
db_list.push_back(db_.db);
} else {
for (auto& db : multi_dbs_) {
db_list.push_back(db.db);
}
}
std::vector<Options> options_list;
for (auto db : db_list) {
options_list.push_back(db->GetOptions());
if (compaction_style != kCompactionStyleFIFO) {
db->SetOptions({{"disable_auto_compactions", "1"},
{"level0_slowdown_writes_trigger", "400000000"},
{"level0_stop_writes_trigger", "400000000"}});
} else {
db->SetOptions({{"disable_auto_compactions", "1"}});
}
}
assert(!db_list.empty());
auto num_db = db_list.size();
size_t num_levels = static_cast<size_t>(open_options_.num_levels);
size_t output_level = open_options_.num_levels - 1;
std::vector<std::vector<std::vector<SstFileMetaData>>> sorted_runs(num_db);
std::vector<size_t> num_files_at_level0(num_db, 0);
if (compaction_style == kCompactionStyleLevel) {
if (num_levels == 0) {
return Status::InvalidArgument("num_levels should be larger than 1");
}
bool should_stop = false;
while (!should_stop) {
if (sorted_runs[0].empty()) {
DoWrite(thread, write_mode);
} else {
DoWrite(thread, UNIQUE_RANDOM);
}
for (size_t i = 0; i < num_db; i++) {
auto db = db_list[i];
db->Flush(FlushOptions());
db->GetColumnFamilyMetaData(&meta);
if (num_files_at_level0[i] == meta.levels[0].files.size() ||
writes_ == 0) {
should_stop = true;
continue;
}
sorted_runs[i].emplace_back(
meta.levels[0].files.begin(),
meta.levels[0].files.end() - num_files_at_level0[i]);
num_files_at_level0[i] = meta.levels[0].files.size();
if (sorted_runs[i].back().size() == 1) {
should_stop = true;
continue;
}
if (sorted_runs[i].size() == output_level) {
auto& L1 = sorted_runs[i].back();
L1.erase(L1.begin(), L1.begin() + L1.size() / 3);
should_stop = true;
continue;
}
}
writes_ /= static_cast<int64_t>(open_options_.max_bytes_for_level_multiplier);
}
for (size_t i = 0; i < num_db; i++) {
if (sorted_runs[i].size() < num_levels - 1) {
fprintf(stderr, "n is too small to fill %" ROCKSDB_PRIszt " levels\n", num_levels);
exit(1);
}
}
for (size_t i = 0; i < num_db; i++) {
auto db = db_list[i];
auto compactionOptions = CompactionOptions();
compactionOptions.compression = FLAGS_compression_type_e;
auto options = db->GetOptions();
MutableCFOptions mutable_cf_options(options);
for (size_t j = 0; j < sorted_runs[i].size(); j++) {
compactionOptions.output_file_size_limit =
MaxFileSizeForLevel(mutable_cf_options,
static_cast<int>(output_level), compaction_style);
std::cout << sorted_runs[i][j].size() << std::endl;
db->CompactFiles(compactionOptions, {sorted_runs[i][j].back().name,
sorted_runs[i][j].front().name},
static_cast<int>(output_level - j) /*level*/);
}
}
} else if (compaction_style == kCompactionStyleUniversal) {
auto ratio = open_options_.compaction_options_universal.size_ratio;
bool should_stop = false;
while (!should_stop) {
if (sorted_runs[0].empty()) {
DoWrite(thread, write_mode);
} else {
DoWrite(thread, UNIQUE_RANDOM);
}
for (size_t i = 0; i < num_db; i++) {
auto db = db_list[i];
db->Flush(FlushOptions());
db->GetColumnFamilyMetaData(&meta);
if (num_files_at_level0[i] == meta.levels[0].files.size() ||
writes_ == 0) {
should_stop = true;
continue;
}
sorted_runs[i].emplace_back(
meta.levels[0].files.begin(),
meta.levels[0].files.end() - num_files_at_level0[i]);
num_files_at_level0[i] = meta.levels[0].files.size();
if (sorted_runs[i].back().size() == 1) {
should_stop = true;
continue;
}
num_files_at_level0[i] = meta.levels[0].files.size();
}
writes_ = static_cast<int64_t>(writes_* static_cast<double>(100) / (ratio + 200));
}
for (size_t i = 0; i < num_db; i++) {
if (sorted_runs[i].size() < num_levels) {
fprintf(stderr, "n is too small to fill %" ROCKSDB_PRIszt " levels\n", num_levels);
exit(1);
}
}
for (size_t i = 0; i < num_db; i++) {
auto db = db_list[i];
auto compactionOptions = CompactionOptions();
compactionOptions.compression = FLAGS_compression_type_e;
auto options = db->GetOptions();
MutableCFOptions mutable_cf_options(options);
for (size_t j = 0; j < sorted_runs[i].size(); j++) {
compactionOptions.output_file_size_limit =
MaxFileSizeForLevel(mutable_cf_options,
static_cast<int>(output_level), compaction_style);
db->CompactFiles(
compactionOptions,
{sorted_runs[i][j].back().name, sorted_runs[i][j].front().name},
(output_level > j ? static_cast<int>(output_level - j)
: 0) /*level*/);
}
}
} else if (compaction_style == kCompactionStyleFIFO) {
if (num_levels != 1) {
return Status::InvalidArgument(
"num_levels should be 1 for FIFO compaction");
}
if (FLAGS_num_multi_db != 0) {
return Status::InvalidArgument("Doesn't support multiDB");
}
auto db = db_list[0];
std::vector<std::string> file_names;
while (true) {
if (sorted_runs[0].empty()) {
DoWrite(thread, write_mode);
} else {
DoWrite(thread, UNIQUE_RANDOM);
}
db->Flush(FlushOptions());
db->GetColumnFamilyMetaData(&meta);
auto total_size = meta.levels[0].size;
if (total_size >=
db->GetOptions().compaction_options_fifo.max_table_files_size) {
for (auto file_meta : meta.levels[0].files) {
file_names.emplace_back(file_meta.name);
}
break;
}
}
// TODO(shuzhang1989): Investigate why CompactFiles not working
// auto compactionOptions = CompactionOptions();
// db->CompactFiles(compactionOptions, file_names, 0);
auto compactionOptions = CompactRangeOptions();
db->CompactRange(compactionOptions, nullptr, nullptr);
} else {
fprintf(stdout,
"%-12s : skipped (-compaction_stype=kCompactionStyleNone)\n",
"filldeterministic");
return Status::InvalidArgument("None compaction is not supported");
}
// Verify seqno and key range
// Note: the seqno get changed at the max level by implementation
// optimization, so skip the check of the max level.
#ifndef NDEBUG
for (size_t k = 0; k < num_db; k++) {
auto db = db_list[k];
db->GetColumnFamilyMetaData(&meta);
// verify the number of sorted runs
if (compaction_style == kCompactionStyleLevel) {
assert(num_levels - 1 == sorted_runs[k].size());
} else if (compaction_style == kCompactionStyleUniversal) {
assert(meta.levels[0].files.size() + num_levels - 1 ==
sorted_runs[k].size());
} else if (compaction_style == kCompactionStyleFIFO) {
// TODO(gzh): FIFO compaction
db->GetColumnFamilyMetaData(&meta);
auto total_size = meta.levels[0].size;
assert(total_size <=
db->GetOptions().compaction_options_fifo.max_table_files_size);
break;
}
// verify smallest/largest seqno and key range of each sorted run
auto max_level = num_levels - 1;
int level;
for (size_t i = 0; i < sorted_runs[k].size(); i++) {
level = static_cast<int>(max_level - i);
SequenceNumber sorted_run_smallest_seqno = kMaxSequenceNumber;
SequenceNumber sorted_run_largest_seqno = 0;
std::string sorted_run_smallest_key, sorted_run_largest_key;
bool first_key = true;
for (auto fileMeta : sorted_runs[k][i]) {
sorted_run_smallest_seqno =
std::min(sorted_run_smallest_seqno, fileMeta.smallest_seqno);
sorted_run_largest_seqno =
std::max(sorted_run_largest_seqno, fileMeta.largest_seqno);
if (first_key ||
db->DefaultColumnFamily()->GetComparator()->Compare(
fileMeta.smallestkey, sorted_run_smallest_key) < 0) {
sorted_run_smallest_key = fileMeta.smallestkey;
}
if (first_key ||
db->DefaultColumnFamily()->GetComparator()->Compare(
fileMeta.largestkey, sorted_run_largest_key) > 0) {
sorted_run_largest_key = fileMeta.largestkey;
}
first_key = false;
}
if (compaction_style == kCompactionStyleLevel ||
(compaction_style == kCompactionStyleUniversal && level > 0)) {
SequenceNumber level_smallest_seqno = kMaxSequenceNumber;
SequenceNumber level_largest_seqno = 0;
for (auto fileMeta : meta.levels[level].files) {
level_smallest_seqno =
std::min(level_smallest_seqno, fileMeta.smallest_seqno);
level_largest_seqno =
std::max(level_largest_seqno, fileMeta.largest_seqno);
}
assert(sorted_run_smallest_key ==
meta.levels[level].files.front().smallestkey);
assert(sorted_run_largest_key ==
meta.levels[level].files.back().largestkey);
if (level != static_cast<int>(max_level)) {
// compaction at max_level would change sequence number
assert(sorted_run_smallest_seqno == level_smallest_seqno);
assert(sorted_run_largest_seqno == level_largest_seqno);
}
} else if (compaction_style == kCompactionStyleUniversal) {
// level <= 0 means sorted runs on level 0
auto level0_file =
meta.levels[0].files[sorted_runs[k].size() - 1 - i];
assert(sorted_run_smallest_key == level0_file.smallestkey);
assert(sorted_run_largest_key == level0_file.largestkey);
if (level != static_cast<int>(max_level)) {
assert(sorted_run_smallest_seqno == level0_file.smallest_seqno);
assert(sorted_run_largest_seqno == level0_file.largest_seqno);
}
}
}
}
#endif
// print the size of each sorted_run
for (size_t k = 0; k < num_db; k++) {
auto db = db_list[k];
fprintf(stdout,
"---------------------- DB %" ROCKSDB_PRIszt " LSM ---------------------\n", k);
db->GetColumnFamilyMetaData(&meta);
for (auto& levelMeta : meta.levels) {
if (levelMeta.files.empty()) {
continue;
}
if (levelMeta.level == 0) {
for (auto& fileMeta : levelMeta.files) {
fprintf(stdout, "Level[%d]: %s(size: %" PRIi64 " bytes)\n",
levelMeta.level, fileMeta.name.c_str(), fileMeta.size);
}
} else {
fprintf(stdout, "Level[%d]: %s - %s(total size: %" PRIi64 " bytes)\n",
levelMeta.level, levelMeta.files.front().name.c_str(),
levelMeta.files.back().name.c_str(), levelMeta.size);
}
}
}
for (size_t i = 0; i < num_db; i++) {
db_list[i]->SetOptions(
{{"disable_auto_compactions",
std::to_string(options_list[i].disable_auto_compactions)},
{"level0_slowdown_writes_trigger",
std::to_string(options_list[i].level0_slowdown_writes_trigger)},
{"level0_stop_writes_trigger",
std::to_string(options_list[i].level0_stop_writes_trigger)}});
}
return Status::OK();
#else
(void)thread;
(void)compaction_style;
(void)write_mode;
fprintf(stderr, "Rocksdb Lite doesn't support filldeterministic\n");
return Status::NotSupported(
"Rocksdb Lite doesn't support filldeterministic");
#endif // ROCKSDB_LITE
}
void ReadSequential(ThreadState* thread) {
if (db_.db != nullptr) {
ReadSequential(thread, db_.db);
} else {
for (const auto& db_with_cfh : multi_dbs_) {
ReadSequential(thread, db_with_cfh.db);
}
}
}
void ReadSequential(ThreadState* thread, DB* db) {
ReadOptions options(FLAGS_verify_checksum, true);
options.tailing = FLAGS_use_tailing_iterator;
std::unique_ptr<char[]> ts_guard;
Slice ts;
if (user_timestamp_size_ > 0) {
ts_guard.reset(new char[user_timestamp_size_]);
ts = mock_app_clock_->GetTimestampForRead(thread->rand, ts_guard.get());
options.timestamp = &ts;
}
Iterator* iter = db->NewIterator(options);
int64_t i = 0;
int64_t bytes = 0;
for (iter->SeekToFirst(); i < reads_ && iter->Valid(); iter->Next()) {
bytes += iter->key().size() + iter->value().size();
thread->stats.FinishedOps(nullptr, db, 1, kRead);
++i;
if (thread->shared->read_rate_limiter.get() != nullptr &&
i % 1024 == 1023) {
thread->shared->read_rate_limiter->Request(1024, Env::IO_HIGH,
nullptr /* stats */,
RateLimiter::OpType::kRead);
}
}
delete iter;
thread->stats.AddBytes(bytes);
if (FLAGS_perf_level > ROCKSDB_NAMESPACE::PerfLevel::kDisable) {
thread->stats.AddMessage(std::string("PERF_CONTEXT:\n") +
get_perf_context()->ToString());
}
}
void ReadToRowCache(ThreadState* thread) {
int64_t read = 0;
int64_t found = 0;
int64_t bytes = 0;
int64_t key_rand = 0;
ReadOptions options(FLAGS_verify_checksum, true);
std::unique_ptr<const char[]> key_guard;
Slice key = AllocateKey(&key_guard);
PinnableSlice pinnable_val;
while (key_rand < FLAGS_num) {
DBWithColumnFamilies* db_with_cfh = SelectDBWithCfh(thread);
// We use same key_rand as seed for key and column family so that we can
// deterministically find the cfh corresponding to a particular key, as it
// is done in DoWrite method.
GenerateKeyFromInt(key_rand, FLAGS_num, &key);
key_rand++;
read++;
Status s;
if (FLAGS_num_column_families > 1) {
s = db_with_cfh->db->Get(options, db_with_cfh->GetCfh(key_rand), key,
&pinnable_val);
} else {
pinnable_val.Reset();
s = db_with_cfh->db->Get(options,
db_with_cfh->db->DefaultColumnFamily(), key,
&pinnable_val);
}
if (s.ok()) {
found++;
bytes += key.size() + pinnable_val.size();
} else if (!s.IsNotFound()) {
fprintf(stderr, "Get returned an error: %s\n", s.ToString().c_str());
abort();
}
if (thread->shared->read_rate_limiter.get() != nullptr &&
read % 256 == 255) {
thread->shared->read_rate_limiter->Request(
256, Env::IO_HIGH, nullptr /* stats */, RateLimiter::OpType::kRead);
}
thread->stats.FinishedOps(db_with_cfh, db_with_cfh->db, 1, kRead);
}
char msg[100];
snprintf(msg, sizeof(msg), "(%" PRIu64 " of %" PRIu64 " found)\n", found,
read);
thread->stats.AddBytes(bytes);
thread->stats.AddMessage(msg);
if (FLAGS_perf_level > ROCKSDB_NAMESPACE::PerfLevel::kDisable) {
thread->stats.AddMessage(std::string("PERF_CONTEXT:\n") +
get_perf_context()->ToString());
}
}
void ReadReverse(ThreadState* thread) {
if (db_.db != nullptr) {
ReadReverse(thread, db_.db);
} else {
for (const auto& db_with_cfh : multi_dbs_) {
ReadReverse(thread, db_with_cfh.db);
}
}
}
void ReadReverse(ThreadState* thread, DB* db) {
Iterator* iter = db->NewIterator(ReadOptions(FLAGS_verify_checksum, true));
int64_t i = 0;
int64_t bytes = 0;
for (iter->SeekToLast(); i < reads_ && iter->Valid(); iter->Prev()) {
bytes += iter->key().size() + iter->value().size();
thread->stats.FinishedOps(nullptr, db, 1, kRead);
++i;
if (thread->shared->read_rate_limiter.get() != nullptr &&
i % 1024 == 1023) {
thread->shared->read_rate_limiter->Request(1024, Env::IO_HIGH,
nullptr /* stats */,
RateLimiter::OpType::kRead);
}
}
delete iter;
thread->stats.AddBytes(bytes);
}
void ReadRandomFast(ThreadState* thread) {
int64_t read = 0;
int64_t found = 0;
int64_t nonexist = 0;
ReadOptions options(FLAGS_verify_checksum, true);
std::unique_ptr<const char[]> key_guard;
Slice key = AllocateKey(&key_guard);
std::string value;
Slice ts;
std::unique_ptr<char[]> ts_guard;
if (user_timestamp_size_ > 0) {
ts_guard.reset(new char[user_timestamp_size_]);
}
DB* db = SelectDBWithCfh(thread)->db;
int64_t pot = 1;
while (pot < FLAGS_num) {
pot <<= 1;
}
Duration duration(FLAGS_duration, reads_);
do {
for (int i = 0; i < 100; ++i) {
int64_t key_rand = thread->rand.Next() & (pot - 1);
GenerateKeyFromInt(key_rand, FLAGS_num, &key);
++read;
std::string ts_ret;
std::string* ts_ptr = nullptr;
if (user_timestamp_size_ > 0) {
ts = mock_app_clock_->GetTimestampForRead(thread->rand,
ts_guard.get());
options.timestamp = &ts;
ts_ptr = &ts_ret;
}
auto status = db->Get(options, key, &value, ts_ptr);
if (status.ok()) {
++found;
} else if (!status.IsNotFound()) {
2015-01-22 03:23:12 +01:00
fprintf(stderr, "Get returned an error: %s\n",
status.ToString().c_str());
abort();
}
if (key_rand >= FLAGS_num) {
++nonexist;
}
}
if (thread->shared->read_rate_limiter.get() != nullptr) {
thread->shared->read_rate_limiter->Request(
100, Env::IO_HIGH, nullptr /* stats */, RateLimiter::OpType::kRead);
}
thread->stats.FinishedOps(nullptr, db, 100, kRead);
} while (!duration.Done(100));
char msg[100];
snprintf(msg, sizeof(msg), "(%" PRIu64 " of %" PRIu64 " found, "
"issued %" PRIu64 " non-exist keys)\n",
found, read, nonexist);
thread->stats.AddMessage(msg);
if (FLAGS_perf_level > ROCKSDB_NAMESPACE::PerfLevel::kDisable) {
thread->stats.AddMessage(std::string("PERF_CONTEXT:\n") +
get_perf_context()->ToString());
}
}
int64_t GetRandomKey(Random64* rand) {
uint64_t rand_int = rand->Next();
int64_t key_rand;
if (read_random_exp_range_ == 0) {
key_rand = rand_int % FLAGS_num;
} else {
const uint64_t kBigInt = static_cast<uint64_t>(1U) << 62;
long double order = -static_cast<long double>(rand_int % kBigInt) /
static_cast<long double>(kBigInt) *
read_random_exp_range_;
long double exp_ran = std::exp(order);
uint64_t rand_num =
static_cast<int64_t>(exp_ran * static_cast<long double>(FLAGS_num));
// Map to a different number to avoid locality.
const uint64_t kBigPrime = 0x5bd1e995;
// Overflow is like %(2^64). Will have little impact of results.
key_rand = static_cast<int64_t>((rand_num * kBigPrime) % FLAGS_num);
}
return key_rand;
}
void ReadRandom(ThreadState* thread) {
int64_t read = 0;
int64_t found = 0;
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
2015-03-30 20:28:25 +02:00
int64_t bytes = 0;
Introduce a new MultiGet batching implementation (#5011) Summary: This PR introduces a new MultiGet() API, with the underlying implementation grouping keys based on SST file and batching lookups in a file. The reason for the new API is twofold - the definition allows callers to allocate storage for status and values on stack instead of std::vector, as well as return values as PinnableSlices in order to avoid copying, and it keeps the original MultiGet() implementation intact while we experiment with batching. Batching is useful when there is some spatial locality to the keys being queries, as well as larger batch sizes. The main benefits are due to - 1. Fewer function calls, especially to BlockBasedTableReader::MultiGet() and FullFilterBlockReader::KeysMayMatch() 2. Bloom filter cachelines can be prefetched, hiding the cache miss latency The next step is to optimize the binary searches in the level_storage_info, index blocks and data blocks, since we could reduce the number of key comparisons if the keys are relatively close to each other. The batching optimizations also need to be extended to other formats, such as PlainTable and filter formats. This also needs to be added to db_stress. Benchmark results from db_bench for various batch size/locality of reference combinations are given below. Locality was simulated by offsetting the keys in a batch by a stride length. Each SST file is about 8.6MB uncompressed and key/value size is 16/100 uncompressed. To focus on the cpu benefit of batching, the runs were single threaded and bound to the same cpu to eliminate interference from other system events. The results show a 10-25% improvement in micros/op from smaller to larger batch sizes (4 - 32). Batch Sizes 1 | 2 | 4 | 8 | 16 | 32 Random pattern (Stride length 0) 4.158 | 4.109 | 4.026 | 4.05 | 4.1 | 4.074 - Get 4.438 | 4.302 | 4.165 | 4.122 | 4.096 | 4.075 - MultiGet (no batching) 4.461 | 4.256 | 4.277 | 4.11 | 4.182 | 4.14 - MultiGet (w/ batching) Good locality (Stride length 16) 4.048 | 3.659 | 3.248 | 2.99 | 2.84 | 2.753 4.429 | 3.728 | 3.406 | 3.053 | 2.911 | 2.781 4.452 | 3.45 | 2.833 | 2.451 | 2.233 | 2.135 Good locality (Stride length 256) 4.066 | 3.786 | 3.581 | 3.447 | 3.415 | 3.232 4.406 | 4.005 | 3.644 | 3.49 | 3.381 | 3.268 4.393 | 3.649 | 3.186 | 2.882 | 2.676 | 2.62 Medium locality (Stride length 4096) 4.012 | 3.922 | 3.768 | 3.61 | 3.582 | 3.555 4.364 | 4.057 | 3.791 | 3.65 | 3.57 | 3.465 4.479 | 3.758 | 3.316 | 3.077 | 2.959 | 2.891 dbbench command used (on a DB with 4 levels, 12 million keys)- TEST_TMPDIR=/dev/shm numactl -C 10 ./db_bench.tmp -use_existing_db=true -benchmarks="readseq,multireadrandom" -write_buffer_size=4194304 -target_file_size_base=4194304 -max_bytes_for_level_base=16777216 -num=12000000 -reads=12000000 -duration=90 -threads=1 -compression_type=none -cache_size=4194304000 -batch_size=32 -disable_auto_compactions=true -bloom_bits=10 -cache_index_and_filter_blocks=true -pin_l0_filter_and_index_blocks_in_cache=true -multiread_batched=true -multiread_stride=4 Pull Request resolved: https://github.com/facebook/rocksdb/pull/5011 Differential Revision: D14348703 Pulled By: anand1976 fbshipit-source-id: 774406dab3776d979c809522a67bedac6c17f84b
2019-04-11 23:24:09 +02:00
int num_keys = 0;
int64_t key_rand = 0;
ReadOptions options(FLAGS_verify_checksum, true);
std::unique_ptr<const char[]> key_guard;
Slice key = AllocateKey(&key_guard);
PinnableSlice pinnable_val;
std::unique_ptr<char[]> ts_guard;
Slice ts;
if (user_timestamp_size_ > 0) {
ts_guard.reset(new char[user_timestamp_size_]);
}
Duration duration(FLAGS_duration, reads_);
while (!duration.Done(1)) {
DBWithColumnFamilies* db_with_cfh = SelectDBWithCfh(thread);
// We use same key_rand as seed for key and column family so that we can
// deterministically find the cfh corresponding to a particular key, as it
// is done in DoWrite method.
Introduce a new MultiGet batching implementation (#5011) Summary: This PR introduces a new MultiGet() API, with the underlying implementation grouping keys based on SST file and batching lookups in a file. The reason for the new API is twofold - the definition allows callers to allocate storage for status and values on stack instead of std::vector, as well as return values as PinnableSlices in order to avoid copying, and it keeps the original MultiGet() implementation intact while we experiment with batching. Batching is useful when there is some spatial locality to the keys being queries, as well as larger batch sizes. The main benefits are due to - 1. Fewer function calls, especially to BlockBasedTableReader::MultiGet() and FullFilterBlockReader::KeysMayMatch() 2. Bloom filter cachelines can be prefetched, hiding the cache miss latency The next step is to optimize the binary searches in the level_storage_info, index blocks and data blocks, since we could reduce the number of key comparisons if the keys are relatively close to each other. The batching optimizations also need to be extended to other formats, such as PlainTable and filter formats. This also needs to be added to db_stress. Benchmark results from db_bench for various batch size/locality of reference combinations are given below. Locality was simulated by offsetting the keys in a batch by a stride length. Each SST file is about 8.6MB uncompressed and key/value size is 16/100 uncompressed. To focus on the cpu benefit of batching, the runs were single threaded and bound to the same cpu to eliminate interference from other system events. The results show a 10-25% improvement in micros/op from smaller to larger batch sizes (4 - 32). Batch Sizes 1 | 2 | 4 | 8 | 16 | 32 Random pattern (Stride length 0) 4.158 | 4.109 | 4.026 | 4.05 | 4.1 | 4.074 - Get 4.438 | 4.302 | 4.165 | 4.122 | 4.096 | 4.075 - MultiGet (no batching) 4.461 | 4.256 | 4.277 | 4.11 | 4.182 | 4.14 - MultiGet (w/ batching) Good locality (Stride length 16) 4.048 | 3.659 | 3.248 | 2.99 | 2.84 | 2.753 4.429 | 3.728 | 3.406 | 3.053 | 2.911 | 2.781 4.452 | 3.45 | 2.833 | 2.451 | 2.233 | 2.135 Good locality (Stride length 256) 4.066 | 3.786 | 3.581 | 3.447 | 3.415 | 3.232 4.406 | 4.005 | 3.644 | 3.49 | 3.381 | 3.268 4.393 | 3.649 | 3.186 | 2.882 | 2.676 | 2.62 Medium locality (Stride length 4096) 4.012 | 3.922 | 3.768 | 3.61 | 3.582 | 3.555 4.364 | 4.057 | 3.791 | 3.65 | 3.57 | 3.465 4.479 | 3.758 | 3.316 | 3.077 | 2.959 | 2.891 dbbench command used (on a DB with 4 levels, 12 million keys)- TEST_TMPDIR=/dev/shm numactl -C 10 ./db_bench.tmp -use_existing_db=true -benchmarks="readseq,multireadrandom" -write_buffer_size=4194304 -target_file_size_base=4194304 -max_bytes_for_level_base=16777216 -num=12000000 -reads=12000000 -duration=90 -threads=1 -compression_type=none -cache_size=4194304000 -batch_size=32 -disable_auto_compactions=true -bloom_bits=10 -cache_index_and_filter_blocks=true -pin_l0_filter_and_index_blocks_in_cache=true -multiread_batched=true -multiread_stride=4 Pull Request resolved: https://github.com/facebook/rocksdb/pull/5011 Differential Revision: D14348703 Pulled By: anand1976 fbshipit-source-id: 774406dab3776d979c809522a67bedac6c17f84b
2019-04-11 23:24:09 +02:00
if (entries_per_batch_ > 1 && FLAGS_multiread_stride) {
if (++num_keys == entries_per_batch_) {
num_keys = 0;
key_rand = GetRandomKey(&thread->rand);
if ((key_rand + (entries_per_batch_ - 1) * FLAGS_multiread_stride) >=
FLAGS_num) {
key_rand = FLAGS_num - entries_per_batch_ * FLAGS_multiread_stride;
}
} else {
key_rand += FLAGS_multiread_stride;
}
} else {
key_rand = GetRandomKey(&thread->rand);
}
GenerateKeyFromInt(key_rand, FLAGS_num, &key);
read++;
std::string ts_ret;
std::string* ts_ptr = nullptr;
if (user_timestamp_size_ > 0) {
ts = mock_app_clock_->GetTimestampForRead(thread->rand, ts_guard.get());
options.timestamp = &ts;
ts_ptr = &ts_ret;
}
Status s;
pinnable_val.Reset();
if (FLAGS_num_column_families > 1) {
s = db_with_cfh->db->Get(options, db_with_cfh->GetCfh(key_rand), key,
&pinnable_val, ts_ptr);
} else {
s = db_with_cfh->db->Get(options,
db_with_cfh->db->DefaultColumnFamily(), key,
&pinnable_val, ts_ptr);
}
if (s.ok()) {
found++;
bytes += key.size() + pinnable_val.size() + user_timestamp_size_;
} else if (!s.IsNotFound()) {
2015-01-22 03:23:12 +01:00
fprintf(stderr, "Get returned an error: %s\n", s.ToString().c_str());
abort();
}
if (thread->shared->read_rate_limiter.get() != nullptr &&
read % 256 == 255) {
thread->shared->read_rate_limiter->Request(
256, Env::IO_HIGH, nullptr /* stats */, RateLimiter::OpType::kRead);
}
thread->stats.FinishedOps(db_with_cfh, db_with_cfh->db, 1, kRead);
}
char msg[100];
snprintf(msg, sizeof(msg), "(%" PRIu64 " of %" PRIu64 " found)\n",
found, read);
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
2015-03-30 20:28:25 +02:00
thread->stats.AddBytes(bytes);
thread->stats.AddMessage(msg);
if (FLAGS_perf_level > ROCKSDB_NAMESPACE::PerfLevel::kDisable) {
thread->stats.AddMessage(std::string("PERF_CONTEXT:\n") +
get_perf_context()->ToString());
}
}
// Calls MultiGet over a list of keys from a random distribution.
// Returns the total number of keys found.
void MultiReadRandom(ThreadState* thread) {
int64_t read = 0;
int64_t bytes = 0;
int64_t num_multireads = 0;
int64_t found = 0;
ReadOptions options(FLAGS_verify_checksum, true);
std::vector<Slice> keys;
std::vector<std::unique_ptr<const char[]> > key_guards;
std::vector<std::string> values(entries_per_batch_);
Introduce a new MultiGet batching implementation (#5011) Summary: This PR introduces a new MultiGet() API, with the underlying implementation grouping keys based on SST file and batching lookups in a file. The reason for the new API is twofold - the definition allows callers to allocate storage for status and values on stack instead of std::vector, as well as return values as PinnableSlices in order to avoid copying, and it keeps the original MultiGet() implementation intact while we experiment with batching. Batching is useful when there is some spatial locality to the keys being queries, as well as larger batch sizes. The main benefits are due to - 1. Fewer function calls, especially to BlockBasedTableReader::MultiGet() and FullFilterBlockReader::KeysMayMatch() 2. Bloom filter cachelines can be prefetched, hiding the cache miss latency The next step is to optimize the binary searches in the level_storage_info, index blocks and data blocks, since we could reduce the number of key comparisons if the keys are relatively close to each other. The batching optimizations also need to be extended to other formats, such as PlainTable and filter formats. This also needs to be added to db_stress. Benchmark results from db_bench for various batch size/locality of reference combinations are given below. Locality was simulated by offsetting the keys in a batch by a stride length. Each SST file is about 8.6MB uncompressed and key/value size is 16/100 uncompressed. To focus on the cpu benefit of batching, the runs were single threaded and bound to the same cpu to eliminate interference from other system events. The results show a 10-25% improvement in micros/op from smaller to larger batch sizes (4 - 32). Batch Sizes 1 | 2 | 4 | 8 | 16 | 32 Random pattern (Stride length 0) 4.158 | 4.109 | 4.026 | 4.05 | 4.1 | 4.074 - Get 4.438 | 4.302 | 4.165 | 4.122 | 4.096 | 4.075 - MultiGet (no batching) 4.461 | 4.256 | 4.277 | 4.11 | 4.182 | 4.14 - MultiGet (w/ batching) Good locality (Stride length 16) 4.048 | 3.659 | 3.248 | 2.99 | 2.84 | 2.753 4.429 | 3.728 | 3.406 | 3.053 | 2.911 | 2.781 4.452 | 3.45 | 2.833 | 2.451 | 2.233 | 2.135 Good locality (Stride length 256) 4.066 | 3.786 | 3.581 | 3.447 | 3.415 | 3.232 4.406 | 4.005 | 3.644 | 3.49 | 3.381 | 3.268 4.393 | 3.649 | 3.186 | 2.882 | 2.676 | 2.62 Medium locality (Stride length 4096) 4.012 | 3.922 | 3.768 | 3.61 | 3.582 | 3.555 4.364 | 4.057 | 3.791 | 3.65 | 3.57 | 3.465 4.479 | 3.758 | 3.316 | 3.077 | 2.959 | 2.891 dbbench command used (on a DB with 4 levels, 12 million keys)- TEST_TMPDIR=/dev/shm numactl -C 10 ./db_bench.tmp -use_existing_db=true -benchmarks="readseq,multireadrandom" -write_buffer_size=4194304 -target_file_size_base=4194304 -max_bytes_for_level_base=16777216 -num=12000000 -reads=12000000 -duration=90 -threads=1 -compression_type=none -cache_size=4194304000 -batch_size=32 -disable_auto_compactions=true -bloom_bits=10 -cache_index_and_filter_blocks=true -pin_l0_filter_and_index_blocks_in_cache=true -multiread_batched=true -multiread_stride=4 Pull Request resolved: https://github.com/facebook/rocksdb/pull/5011 Differential Revision: D14348703 Pulled By: anand1976 fbshipit-source-id: 774406dab3776d979c809522a67bedac6c17f84b
2019-04-11 23:24:09 +02:00
PinnableSlice* pin_values = new PinnableSlice[entries_per_batch_];
std::unique_ptr<PinnableSlice[]> pin_values_guard(pin_values);
Introduce a new MultiGet batching implementation (#5011) Summary: This PR introduces a new MultiGet() API, with the underlying implementation grouping keys based on SST file and batching lookups in a file. The reason for the new API is twofold - the definition allows callers to allocate storage for status and values on stack instead of std::vector, as well as return values as PinnableSlices in order to avoid copying, and it keeps the original MultiGet() implementation intact while we experiment with batching. Batching is useful when there is some spatial locality to the keys being queries, as well as larger batch sizes. The main benefits are due to - 1. Fewer function calls, especially to BlockBasedTableReader::MultiGet() and FullFilterBlockReader::KeysMayMatch() 2. Bloom filter cachelines can be prefetched, hiding the cache miss latency The next step is to optimize the binary searches in the level_storage_info, index blocks and data blocks, since we could reduce the number of key comparisons if the keys are relatively close to each other. The batching optimizations also need to be extended to other formats, such as PlainTable and filter formats. This also needs to be added to db_stress. Benchmark results from db_bench for various batch size/locality of reference combinations are given below. Locality was simulated by offsetting the keys in a batch by a stride length. Each SST file is about 8.6MB uncompressed and key/value size is 16/100 uncompressed. To focus on the cpu benefit of batching, the runs were single threaded and bound to the same cpu to eliminate interference from other system events. The results show a 10-25% improvement in micros/op from smaller to larger batch sizes (4 - 32). Batch Sizes 1 | 2 | 4 | 8 | 16 | 32 Random pattern (Stride length 0) 4.158 | 4.109 | 4.026 | 4.05 | 4.1 | 4.074 - Get 4.438 | 4.302 | 4.165 | 4.122 | 4.096 | 4.075 - MultiGet (no batching) 4.461 | 4.256 | 4.277 | 4.11 | 4.182 | 4.14 - MultiGet (w/ batching) Good locality (Stride length 16) 4.048 | 3.659 | 3.248 | 2.99 | 2.84 | 2.753 4.429 | 3.728 | 3.406 | 3.053 | 2.911 | 2.781 4.452 | 3.45 | 2.833 | 2.451 | 2.233 | 2.135 Good locality (Stride length 256) 4.066 | 3.786 | 3.581 | 3.447 | 3.415 | 3.232 4.406 | 4.005 | 3.644 | 3.49 | 3.381 | 3.268 4.393 | 3.649 | 3.186 | 2.882 | 2.676 | 2.62 Medium locality (Stride length 4096) 4.012 | 3.922 | 3.768 | 3.61 | 3.582 | 3.555 4.364 | 4.057 | 3.791 | 3.65 | 3.57 | 3.465 4.479 | 3.758 | 3.316 | 3.077 | 2.959 | 2.891 dbbench command used (on a DB with 4 levels, 12 million keys)- TEST_TMPDIR=/dev/shm numactl -C 10 ./db_bench.tmp -use_existing_db=true -benchmarks="readseq,multireadrandom" -write_buffer_size=4194304 -target_file_size_base=4194304 -max_bytes_for_level_base=16777216 -num=12000000 -reads=12000000 -duration=90 -threads=1 -compression_type=none -cache_size=4194304000 -batch_size=32 -disable_auto_compactions=true -bloom_bits=10 -cache_index_and_filter_blocks=true -pin_l0_filter_and_index_blocks_in_cache=true -multiread_batched=true -multiread_stride=4 Pull Request resolved: https://github.com/facebook/rocksdb/pull/5011 Differential Revision: D14348703 Pulled By: anand1976 fbshipit-source-id: 774406dab3776d979c809522a67bedac6c17f84b
2019-04-11 23:24:09 +02:00
std::vector<Status> stat_list(entries_per_batch_);
2014-04-29 21:33:57 +02:00
while (static_cast<int64_t>(keys.size()) < entries_per_batch_) {
key_guards.push_back(std::unique_ptr<const char[]>());
keys.push_back(AllocateKey(&key_guards.back()));
}
std::unique_ptr<char[]> ts_guard;
if (user_timestamp_size_ > 0) {
ts_guard.reset(new char[user_timestamp_size_]);
}
Duration duration(FLAGS_duration, reads_);
while (!duration.Done(entries_per_batch_)) {
DB* db = SelectDB(thread);
Introduce a new MultiGet batching implementation (#5011) Summary: This PR introduces a new MultiGet() API, with the underlying implementation grouping keys based on SST file and batching lookups in a file. The reason for the new API is twofold - the definition allows callers to allocate storage for status and values on stack instead of std::vector, as well as return values as PinnableSlices in order to avoid copying, and it keeps the original MultiGet() implementation intact while we experiment with batching. Batching is useful when there is some spatial locality to the keys being queries, as well as larger batch sizes. The main benefits are due to - 1. Fewer function calls, especially to BlockBasedTableReader::MultiGet() and FullFilterBlockReader::KeysMayMatch() 2. Bloom filter cachelines can be prefetched, hiding the cache miss latency The next step is to optimize the binary searches in the level_storage_info, index blocks and data blocks, since we could reduce the number of key comparisons if the keys are relatively close to each other. The batching optimizations also need to be extended to other formats, such as PlainTable and filter formats. This also needs to be added to db_stress. Benchmark results from db_bench for various batch size/locality of reference combinations are given below. Locality was simulated by offsetting the keys in a batch by a stride length. Each SST file is about 8.6MB uncompressed and key/value size is 16/100 uncompressed. To focus on the cpu benefit of batching, the runs were single threaded and bound to the same cpu to eliminate interference from other system events. The results show a 10-25% improvement in micros/op from smaller to larger batch sizes (4 - 32). Batch Sizes 1 | 2 | 4 | 8 | 16 | 32 Random pattern (Stride length 0) 4.158 | 4.109 | 4.026 | 4.05 | 4.1 | 4.074 - Get 4.438 | 4.302 | 4.165 | 4.122 | 4.096 | 4.075 - MultiGet (no batching) 4.461 | 4.256 | 4.277 | 4.11 | 4.182 | 4.14 - MultiGet (w/ batching) Good locality (Stride length 16) 4.048 | 3.659 | 3.248 | 2.99 | 2.84 | 2.753 4.429 | 3.728 | 3.406 | 3.053 | 2.911 | 2.781 4.452 | 3.45 | 2.833 | 2.451 | 2.233 | 2.135 Good locality (Stride length 256) 4.066 | 3.786 | 3.581 | 3.447 | 3.415 | 3.232 4.406 | 4.005 | 3.644 | 3.49 | 3.381 | 3.268 4.393 | 3.649 | 3.186 | 2.882 | 2.676 | 2.62 Medium locality (Stride length 4096) 4.012 | 3.922 | 3.768 | 3.61 | 3.582 | 3.555 4.364 | 4.057 | 3.791 | 3.65 | 3.57 | 3.465 4.479 | 3.758 | 3.316 | 3.077 | 2.959 | 2.891 dbbench command used (on a DB with 4 levels, 12 million keys)- TEST_TMPDIR=/dev/shm numactl -C 10 ./db_bench.tmp -use_existing_db=true -benchmarks="readseq,multireadrandom" -write_buffer_size=4194304 -target_file_size_base=4194304 -max_bytes_for_level_base=16777216 -num=12000000 -reads=12000000 -duration=90 -threads=1 -compression_type=none -cache_size=4194304000 -batch_size=32 -disable_auto_compactions=true -bloom_bits=10 -cache_index_and_filter_blocks=true -pin_l0_filter_and_index_blocks_in_cache=true -multiread_batched=true -multiread_stride=4 Pull Request resolved: https://github.com/facebook/rocksdb/pull/5011 Differential Revision: D14348703 Pulled By: anand1976 fbshipit-source-id: 774406dab3776d979c809522a67bedac6c17f84b
2019-04-11 23:24:09 +02:00
if (FLAGS_multiread_stride) {
int64_t key = GetRandomKey(&thread->rand);
if ((key + (entries_per_batch_ - 1) * FLAGS_multiread_stride) >=
static_cast<int64_t>(FLAGS_num)) {
Introduce a new MultiGet batching implementation (#5011) Summary: This PR introduces a new MultiGet() API, with the underlying implementation grouping keys based on SST file and batching lookups in a file. The reason for the new API is twofold - the definition allows callers to allocate storage for status and values on stack instead of std::vector, as well as return values as PinnableSlices in order to avoid copying, and it keeps the original MultiGet() implementation intact while we experiment with batching. Batching is useful when there is some spatial locality to the keys being queries, as well as larger batch sizes. The main benefits are due to - 1. Fewer function calls, especially to BlockBasedTableReader::MultiGet() and FullFilterBlockReader::KeysMayMatch() 2. Bloom filter cachelines can be prefetched, hiding the cache miss latency The next step is to optimize the binary searches in the level_storage_info, index blocks and data blocks, since we could reduce the number of key comparisons if the keys are relatively close to each other. The batching optimizations also need to be extended to other formats, such as PlainTable and filter formats. This also needs to be added to db_stress. Benchmark results from db_bench for various batch size/locality of reference combinations are given below. Locality was simulated by offsetting the keys in a batch by a stride length. Each SST file is about 8.6MB uncompressed and key/value size is 16/100 uncompressed. To focus on the cpu benefit of batching, the runs were single threaded and bound to the same cpu to eliminate interference from other system events. The results show a 10-25% improvement in micros/op from smaller to larger batch sizes (4 - 32). Batch Sizes 1 | 2 | 4 | 8 | 16 | 32 Random pattern (Stride length 0) 4.158 | 4.109 | 4.026 | 4.05 | 4.1 | 4.074 - Get 4.438 | 4.302 | 4.165 | 4.122 | 4.096 | 4.075 - MultiGet (no batching) 4.461 | 4.256 | 4.277 | 4.11 | 4.182 | 4.14 - MultiGet (w/ batching) Good locality (Stride length 16) 4.048 | 3.659 | 3.248 | 2.99 | 2.84 | 2.753 4.429 | 3.728 | 3.406 | 3.053 | 2.911 | 2.781 4.452 | 3.45 | 2.833 | 2.451 | 2.233 | 2.135 Good locality (Stride length 256) 4.066 | 3.786 | 3.581 | 3.447 | 3.415 | 3.232 4.406 | 4.005 | 3.644 | 3.49 | 3.381 | 3.268 4.393 | 3.649 | 3.186 | 2.882 | 2.676 | 2.62 Medium locality (Stride length 4096) 4.012 | 3.922 | 3.768 | 3.61 | 3.582 | 3.555 4.364 | 4.057 | 3.791 | 3.65 | 3.57 | 3.465 4.479 | 3.758 | 3.316 | 3.077 | 2.959 | 2.891 dbbench command used (on a DB with 4 levels, 12 million keys)- TEST_TMPDIR=/dev/shm numactl -C 10 ./db_bench.tmp -use_existing_db=true -benchmarks="readseq,multireadrandom" -write_buffer_size=4194304 -target_file_size_base=4194304 -max_bytes_for_level_base=16777216 -num=12000000 -reads=12000000 -duration=90 -threads=1 -compression_type=none -cache_size=4194304000 -batch_size=32 -disable_auto_compactions=true -bloom_bits=10 -cache_index_and_filter_blocks=true -pin_l0_filter_and_index_blocks_in_cache=true -multiread_batched=true -multiread_stride=4 Pull Request resolved: https://github.com/facebook/rocksdb/pull/5011 Differential Revision: D14348703 Pulled By: anand1976 fbshipit-source-id: 774406dab3776d979c809522a67bedac6c17f84b
2019-04-11 23:24:09 +02:00
key = FLAGS_num - entries_per_batch_ * FLAGS_multiread_stride;
}
for (int64_t i = 0; i < entries_per_batch_; ++i) {
GenerateKeyFromInt(key, FLAGS_num, &keys[i]);
key += FLAGS_multiread_stride;
}
} else {
for (int64_t i = 0; i < entries_per_batch_; ++i) {
GenerateKeyFromInt(GetRandomKey(&thread->rand), FLAGS_num, &keys[i]);
}
}
Slice ts;
if (user_timestamp_size_ > 0) {
ts = mock_app_clock_->GetTimestampForRead(thread->rand, ts_guard.get());
options.timestamp = &ts;
}
Introduce a new MultiGet batching implementation (#5011) Summary: This PR introduces a new MultiGet() API, with the underlying implementation grouping keys based on SST file and batching lookups in a file. The reason for the new API is twofold - the definition allows callers to allocate storage for status and values on stack instead of std::vector, as well as return values as PinnableSlices in order to avoid copying, and it keeps the original MultiGet() implementation intact while we experiment with batching. Batching is useful when there is some spatial locality to the keys being queries, as well as larger batch sizes. The main benefits are due to - 1. Fewer function calls, especially to BlockBasedTableReader::MultiGet() and FullFilterBlockReader::KeysMayMatch() 2. Bloom filter cachelines can be prefetched, hiding the cache miss latency The next step is to optimize the binary searches in the level_storage_info, index blocks and data blocks, since we could reduce the number of key comparisons if the keys are relatively close to each other. The batching optimizations also need to be extended to other formats, such as PlainTable and filter formats. This also needs to be added to db_stress. Benchmark results from db_bench for various batch size/locality of reference combinations are given below. Locality was simulated by offsetting the keys in a batch by a stride length. Each SST file is about 8.6MB uncompressed and key/value size is 16/100 uncompressed. To focus on the cpu benefit of batching, the runs were single threaded and bound to the same cpu to eliminate interference from other system events. The results show a 10-25% improvement in micros/op from smaller to larger batch sizes (4 - 32). Batch Sizes 1 | 2 | 4 | 8 | 16 | 32 Random pattern (Stride length 0) 4.158 | 4.109 | 4.026 | 4.05 | 4.1 | 4.074 - Get 4.438 | 4.302 | 4.165 | 4.122 | 4.096 | 4.075 - MultiGet (no batching) 4.461 | 4.256 | 4.277 | 4.11 | 4.182 | 4.14 - MultiGet (w/ batching) Good locality (Stride length 16) 4.048 | 3.659 | 3.248 | 2.99 | 2.84 | 2.753 4.429 | 3.728 | 3.406 | 3.053 | 2.911 | 2.781 4.452 | 3.45 | 2.833 | 2.451 | 2.233 | 2.135 Good locality (Stride length 256) 4.066 | 3.786 | 3.581 | 3.447 | 3.415 | 3.232 4.406 | 4.005 | 3.644 | 3.49 | 3.381 | 3.268 4.393 | 3.649 | 3.186 | 2.882 | 2.676 | 2.62 Medium locality (Stride length 4096) 4.012 | 3.922 | 3.768 | 3.61 | 3.582 | 3.555 4.364 | 4.057 | 3.791 | 3.65 | 3.57 | 3.465 4.479 | 3.758 | 3.316 | 3.077 | 2.959 | 2.891 dbbench command used (on a DB with 4 levels, 12 million keys)- TEST_TMPDIR=/dev/shm numactl -C 10 ./db_bench.tmp -use_existing_db=true -benchmarks="readseq,multireadrandom" -write_buffer_size=4194304 -target_file_size_base=4194304 -max_bytes_for_level_base=16777216 -num=12000000 -reads=12000000 -duration=90 -threads=1 -compression_type=none -cache_size=4194304000 -batch_size=32 -disable_auto_compactions=true -bloom_bits=10 -cache_index_and_filter_blocks=true -pin_l0_filter_and_index_blocks_in_cache=true -multiread_batched=true -multiread_stride=4 Pull Request resolved: https://github.com/facebook/rocksdb/pull/5011 Differential Revision: D14348703 Pulled By: anand1976 fbshipit-source-id: 774406dab3776d979c809522a67bedac6c17f84b
2019-04-11 23:24:09 +02:00
if (!FLAGS_multiread_batched) {
std::vector<Status> statuses = db->MultiGet(options, keys, &values);
assert(static_cast<int64_t>(statuses.size()) == entries_per_batch_);
read += entries_per_batch_;
num_multireads++;
for (int64_t i = 0; i < entries_per_batch_; ++i) {
if (statuses[i].ok()) {
bytes += keys[i].size() + values[i].size() + user_timestamp_size_;
Introduce a new MultiGet batching implementation (#5011) Summary: This PR introduces a new MultiGet() API, with the underlying implementation grouping keys based on SST file and batching lookups in a file. The reason for the new API is twofold - the definition allows callers to allocate storage for status and values on stack instead of std::vector, as well as return values as PinnableSlices in order to avoid copying, and it keeps the original MultiGet() implementation intact while we experiment with batching. Batching is useful when there is some spatial locality to the keys being queries, as well as larger batch sizes. The main benefits are due to - 1. Fewer function calls, especially to BlockBasedTableReader::MultiGet() and FullFilterBlockReader::KeysMayMatch() 2. Bloom filter cachelines can be prefetched, hiding the cache miss latency The next step is to optimize the binary searches in the level_storage_info, index blocks and data blocks, since we could reduce the number of key comparisons if the keys are relatively close to each other. The batching optimizations also need to be extended to other formats, such as PlainTable and filter formats. This also needs to be added to db_stress. Benchmark results from db_bench for various batch size/locality of reference combinations are given below. Locality was simulated by offsetting the keys in a batch by a stride length. Each SST file is about 8.6MB uncompressed and key/value size is 16/100 uncompressed. To focus on the cpu benefit of batching, the runs were single threaded and bound to the same cpu to eliminate interference from other system events. The results show a 10-25% improvement in micros/op from smaller to larger batch sizes (4 - 32). Batch Sizes 1 | 2 | 4 | 8 | 16 | 32 Random pattern (Stride length 0) 4.158 | 4.109 | 4.026 | 4.05 | 4.1 | 4.074 - Get 4.438 | 4.302 | 4.165 | 4.122 | 4.096 | 4.075 - MultiGet (no batching) 4.461 | 4.256 | 4.277 | 4.11 | 4.182 | 4.14 - MultiGet (w/ batching) Good locality (Stride length 16) 4.048 | 3.659 | 3.248 | 2.99 | 2.84 | 2.753 4.429 | 3.728 | 3.406 | 3.053 | 2.911 | 2.781 4.452 | 3.45 | 2.833 | 2.451 | 2.233 | 2.135 Good locality (Stride length 256) 4.066 | 3.786 | 3.581 | 3.447 | 3.415 | 3.232 4.406 | 4.005 | 3.644 | 3.49 | 3.381 | 3.268 4.393 | 3.649 | 3.186 | 2.882 | 2.676 | 2.62 Medium locality (Stride length 4096) 4.012 | 3.922 | 3.768 | 3.61 | 3.582 | 3.555 4.364 | 4.057 | 3.791 | 3.65 | 3.57 | 3.465 4.479 | 3.758 | 3.316 | 3.077 | 2.959 | 2.891 dbbench command used (on a DB with 4 levels, 12 million keys)- TEST_TMPDIR=/dev/shm numactl -C 10 ./db_bench.tmp -use_existing_db=true -benchmarks="readseq,multireadrandom" -write_buffer_size=4194304 -target_file_size_base=4194304 -max_bytes_for_level_base=16777216 -num=12000000 -reads=12000000 -duration=90 -threads=1 -compression_type=none -cache_size=4194304000 -batch_size=32 -disable_auto_compactions=true -bloom_bits=10 -cache_index_and_filter_blocks=true -pin_l0_filter_and_index_blocks_in_cache=true -multiread_batched=true -multiread_stride=4 Pull Request resolved: https://github.com/facebook/rocksdb/pull/5011 Differential Revision: D14348703 Pulled By: anand1976 fbshipit-source-id: 774406dab3776d979c809522a67bedac6c17f84b
2019-04-11 23:24:09 +02:00
++found;
} else if (!statuses[i].IsNotFound()) {
fprintf(stderr, "MultiGet returned an error: %s\n",
statuses[i].ToString().c_str());
abort();
}
}
} else {
db->MultiGet(options, db->DefaultColumnFamily(), keys.size(),
keys.data(), pin_values, stat_list.data());
read += entries_per_batch_;
num_multireads++;
for (int64_t i = 0; i < entries_per_batch_; ++i) {
if (stat_list[i].ok()) {
bytes +=
keys[i].size() + pin_values[i].size() + user_timestamp_size_;
Introduce a new MultiGet batching implementation (#5011) Summary: This PR introduces a new MultiGet() API, with the underlying implementation grouping keys based on SST file and batching lookups in a file. The reason for the new API is twofold - the definition allows callers to allocate storage for status and values on stack instead of std::vector, as well as return values as PinnableSlices in order to avoid copying, and it keeps the original MultiGet() implementation intact while we experiment with batching. Batching is useful when there is some spatial locality to the keys being queries, as well as larger batch sizes. The main benefits are due to - 1. Fewer function calls, especially to BlockBasedTableReader::MultiGet() and FullFilterBlockReader::KeysMayMatch() 2. Bloom filter cachelines can be prefetched, hiding the cache miss latency The next step is to optimize the binary searches in the level_storage_info, index blocks and data blocks, since we could reduce the number of key comparisons if the keys are relatively close to each other. The batching optimizations also need to be extended to other formats, such as PlainTable and filter formats. This also needs to be added to db_stress. Benchmark results from db_bench for various batch size/locality of reference combinations are given below. Locality was simulated by offsetting the keys in a batch by a stride length. Each SST file is about 8.6MB uncompressed and key/value size is 16/100 uncompressed. To focus on the cpu benefit of batching, the runs were single threaded and bound to the same cpu to eliminate interference from other system events. The results show a 10-25% improvement in micros/op from smaller to larger batch sizes (4 - 32). Batch Sizes 1 | 2 | 4 | 8 | 16 | 32 Random pattern (Stride length 0) 4.158 | 4.109 | 4.026 | 4.05 | 4.1 | 4.074 - Get 4.438 | 4.302 | 4.165 | 4.122 | 4.096 | 4.075 - MultiGet (no batching) 4.461 | 4.256 | 4.277 | 4.11 | 4.182 | 4.14 - MultiGet (w/ batching) Good locality (Stride length 16) 4.048 | 3.659 | 3.248 | 2.99 | 2.84 | 2.753 4.429 | 3.728 | 3.406 | 3.053 | 2.911 | 2.781 4.452 | 3.45 | 2.833 | 2.451 | 2.233 | 2.135 Good locality (Stride length 256) 4.066 | 3.786 | 3.581 | 3.447 | 3.415 | 3.232 4.406 | 4.005 | 3.644 | 3.49 | 3.381 | 3.268 4.393 | 3.649 | 3.186 | 2.882 | 2.676 | 2.62 Medium locality (Stride length 4096) 4.012 | 3.922 | 3.768 | 3.61 | 3.582 | 3.555 4.364 | 4.057 | 3.791 | 3.65 | 3.57 | 3.465 4.479 | 3.758 | 3.316 | 3.077 | 2.959 | 2.891 dbbench command used (on a DB with 4 levels, 12 million keys)- TEST_TMPDIR=/dev/shm numactl -C 10 ./db_bench.tmp -use_existing_db=true -benchmarks="readseq,multireadrandom" -write_buffer_size=4194304 -target_file_size_base=4194304 -max_bytes_for_level_base=16777216 -num=12000000 -reads=12000000 -duration=90 -threads=1 -compression_type=none -cache_size=4194304000 -batch_size=32 -disable_auto_compactions=true -bloom_bits=10 -cache_index_and_filter_blocks=true -pin_l0_filter_and_index_blocks_in_cache=true -multiread_batched=true -multiread_stride=4 Pull Request resolved: https://github.com/facebook/rocksdb/pull/5011 Differential Revision: D14348703 Pulled By: anand1976 fbshipit-source-id: 774406dab3776d979c809522a67bedac6c17f84b
2019-04-11 23:24:09 +02:00
++found;
} else if (!stat_list[i].IsNotFound()) {
fprintf(stderr, "MultiGet returned an error: %s\n",
stat_list[i].ToString().c_str());
abort();
}
stat_list[i] = Status::OK();
pin_values[i].Reset();
}
}
if (thread->shared->read_rate_limiter.get() != nullptr &&
num_multireads % 256 == 255) {
thread->shared->read_rate_limiter->Request(
256 * entries_per_batch_, Env::IO_HIGH, nullptr /* stats */,
RateLimiter::OpType::kRead);
}
thread->stats.FinishedOps(nullptr, db, entries_per_batch_, kRead);
}
char msg[100];
snprintf(msg, sizeof(msg), "(%" PRIu64 " of %" PRIu64 " found)",
found, read);
thread->stats.AddBytes(bytes);
thread->stats.AddMessage(msg);
}
For ApproximateSizes, pro-rate table metadata size over data blocks (#6784) Summary: The implementation of GetApproximateSizes was inconsistent in its treatment of the size of non-data blocks of SST files, sometimes including and sometimes now. This was at its worst with large portion of table file used by filters and querying a small range that crossed a table boundary: the size estimate would include large filter size. It's conceivable that someone might want only to know the size in terms of data blocks, but I believe that's unlikely enough to ignore for now. Similarly, there's no evidence the internal function AppoximateOffsetOf is used for anything other than a one-sided ApproximateSize, so I intend to refactor to remove redundancy in a follow-up commit. So to fix this, GetApproximateSizes (and implementation details ApproximateSize and ApproximateOffsetOf) now consistently include in their returned sizes a portion of table file metadata (incl filters and indexes) based on the size portion of the data blocks in range. In other words, if a key range covers data blocks that are X% by size of all the table's data blocks, returned approximate size is X% of the total file size. It would technically be more accurate to attribute metadata based on number of keys, but that's not computationally efficient with data available and rarely a meaningful difference. Also includes miscellaneous comment improvements / clarifications. Also included is a new approximatesizerandom benchmark for db_bench. No significant performance difference seen with this change, whether ~700 ops/sec with cache_index_and_filter_blocks and small cache or ~150k ops/sec without cache_index_and_filter_blocks. Pull Request resolved: https://github.com/facebook/rocksdb/pull/6784 Test Plan: Test added to DBTest.ApproximateSizesFilesWithErrorMargin. Old code running new test... [ RUN ] DBTest.ApproximateSizesFilesWithErrorMargin db/db_test.cc:1562: Failure Expected: (size) <= (11 * 100), actual: 9478 vs 1100 Other tests updated to reflect consistent accounting of metadata. Reviewed By: siying Differential Revision: D21334706 Pulled By: pdillinger fbshipit-source-id: 6f86870e45213334fedbe9c73b4ebb1d8d611185
2020-06-02 21:27:59 +02:00
// Calls ApproximateSize over random key ranges.
void ApproximateSizeRandom(ThreadState* thread) {
int64_t size_sum = 0;
int64_t num_sizes = 0;
const size_t batch_size = entries_per_batch_;
std::vector<Range> ranges;
std::vector<Slice> lkeys;
std::vector<std::unique_ptr<const char[]>> lkey_guards;
std::vector<Slice> rkeys;
std::vector<std::unique_ptr<const char[]>> rkey_guards;
std::vector<uint64_t> sizes;
while (ranges.size() < batch_size) {
// Ugly without C++17 return from emplace_back
lkey_guards.emplace_back();
rkey_guards.emplace_back();
lkeys.emplace_back(AllocateKey(&lkey_guards.back()));
rkeys.emplace_back(AllocateKey(&rkey_guards.back()));
ranges.emplace_back(lkeys.back(), rkeys.back());
sizes.push_back(0);
}
Duration duration(FLAGS_duration, reads_);
while (!duration.Done(1)) {
DB* db = SelectDB(thread);
for (size_t i = 0; i < batch_size; ++i) {
int64_t lkey = GetRandomKey(&thread->rand);
int64_t rkey = GetRandomKey(&thread->rand);
if (lkey > rkey) {
std::swap(lkey, rkey);
}
GenerateKeyFromInt(lkey, FLAGS_num, &lkeys[i]);
GenerateKeyFromInt(rkey, FLAGS_num, &rkeys[i]);
}
db->GetApproximateSizes(&ranges[0], static_cast<int>(entries_per_batch_),
&sizes[0]);
num_sizes += entries_per_batch_;
for (int64_t size : sizes) {
size_sum += size;
}
thread->stats.FinishedOps(nullptr, db, entries_per_batch_, kOthers);
}
char msg[100];
snprintf(msg, sizeof(msg), "(Avg approx size=%g)",
static_cast<double>(size_sum) / static_cast<double>(num_sizes));
thread->stats.AddMessage(msg);
}
Workload generator (Mixgraph) based on prefix hotness (#5953) Summary: In the previous PR https://github.com/facebook/rocksdb/issues/4788, user can use db_bench mix_graph option to generate the workload that is from the social graph. The key is generated based on the key access hotness. In this PR, user can further model the key-range hotness and fit those to two-term-exponential distribution. First, user cuts the whole key space into small key ranges (e.g., key-ranges are the same size and the key-range number is the number of SST files). Then, user calculates the average access count per key of each key-range as the key-range hotness. Next, user fits the key-range hotness to two-term-exponential distribution (f(x) = f(x) = a*exp(b*x) + c*exp(d*x)) and generate the value of a, b, c, and d. They are the parameters in db_bench: prefix_dist_a, prefix_dist_b, prefix_dist_c, and prefix_dist_d. Finally, user can run db_bench by specify the parameters. For example: `./db_bench --benchmarks="mixgraph" -use_direct_io_for_flush_and_compaction=true -use_direct_reads=true -cache_size=268435456 -key_dist_a=0.002312 -key_dist_b=0.3467 -keyrange_dist_a=14.18 -keyrange_dist_b=-2.917 -keyrange_dist_c=0.0164 -keyrange_dist_d=-0.08082 -keyrange_num=30 -value_k=0.2615 -value_sigma=25.45 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.85 -mix_put_ratio=0.14 -mix_seek_ratio=0.01 -sine_mix_rate_interval_milliseconds=5000 -sine_a=350 -sine_b=0.0105 -sine_d=50000 --perf_level=2 -reads=1000000 -num=5000000 -key_size=48` Pull Request resolved: https://github.com/facebook/rocksdb/pull/5953 Test Plan: run db_bench with different parameters and checked the results. Differential Revision: D18053527 Pulled By: zhichao-cao fbshipit-source-id: 171f8b3142bd76462f1967c58345ad7e4f84bab7
2019-11-06 21:50:33 +01:00
// The inverse function of Pareto distribution
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 19:40:44 +01:00
int64_t ParetoCdfInversion(double u, double theta, double k, double sigma) {
double ret;
if (k == 0.0) {
ret = theta - sigma * std::log(u);
} else {
ret = theta + sigma * (std::pow(u, -1 * k) - 1) / k;
}
return static_cast<int64_t>(ceil(ret));
}
Workload generator (Mixgraph) based on prefix hotness (#5953) Summary: In the previous PR https://github.com/facebook/rocksdb/issues/4788, user can use db_bench mix_graph option to generate the workload that is from the social graph. The key is generated based on the key access hotness. In this PR, user can further model the key-range hotness and fit those to two-term-exponential distribution. First, user cuts the whole key space into small key ranges (e.g., key-ranges are the same size and the key-range number is the number of SST files). Then, user calculates the average access count per key of each key-range as the key-range hotness. Next, user fits the key-range hotness to two-term-exponential distribution (f(x) = f(x) = a*exp(b*x) + c*exp(d*x)) and generate the value of a, b, c, and d. They are the parameters in db_bench: prefix_dist_a, prefix_dist_b, prefix_dist_c, and prefix_dist_d. Finally, user can run db_bench by specify the parameters. For example: `./db_bench --benchmarks="mixgraph" -use_direct_io_for_flush_and_compaction=true -use_direct_reads=true -cache_size=268435456 -key_dist_a=0.002312 -key_dist_b=0.3467 -keyrange_dist_a=14.18 -keyrange_dist_b=-2.917 -keyrange_dist_c=0.0164 -keyrange_dist_d=-0.08082 -keyrange_num=30 -value_k=0.2615 -value_sigma=25.45 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.85 -mix_put_ratio=0.14 -mix_seek_ratio=0.01 -sine_mix_rate_interval_milliseconds=5000 -sine_a=350 -sine_b=0.0105 -sine_d=50000 --perf_level=2 -reads=1000000 -num=5000000 -key_size=48` Pull Request resolved: https://github.com/facebook/rocksdb/pull/5953 Test Plan: run db_bench with different parameters and checked the results. Differential Revision: D18053527 Pulled By: zhichao-cao fbshipit-source-id: 171f8b3142bd76462f1967c58345ad7e4f84bab7
2019-11-06 21:50:33 +01:00
// The inverse function of power distribution (y=ax^b)
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 19:40:44 +01:00
int64_t PowerCdfInversion(double u, double a, double b) {
double ret;
ret = std::pow((u / a), (1 / b));
return static_cast<int64_t>(ceil(ret));
}
// Add the noice to the QPS
double AddNoise(double origin, double noise_ratio) {
if (noise_ratio < 0.0 || noise_ratio > 1.0) {
return origin;
}
int band_int = static_cast<int>(FLAGS_sine_a);
double delta = (rand() % band_int - band_int / 2) * noise_ratio;
if (origin + delta < 0) {
return origin;
} else {
return (origin + delta);
}
}
Workload generator (Mixgraph) based on prefix hotness (#5953) Summary: In the previous PR https://github.com/facebook/rocksdb/issues/4788, user can use db_bench mix_graph option to generate the workload that is from the social graph. The key is generated based on the key access hotness. In this PR, user can further model the key-range hotness and fit those to two-term-exponential distribution. First, user cuts the whole key space into small key ranges (e.g., key-ranges are the same size and the key-range number is the number of SST files). Then, user calculates the average access count per key of each key-range as the key-range hotness. Next, user fits the key-range hotness to two-term-exponential distribution (f(x) = f(x) = a*exp(b*x) + c*exp(d*x)) and generate the value of a, b, c, and d. They are the parameters in db_bench: prefix_dist_a, prefix_dist_b, prefix_dist_c, and prefix_dist_d. Finally, user can run db_bench by specify the parameters. For example: `./db_bench --benchmarks="mixgraph" -use_direct_io_for_flush_and_compaction=true -use_direct_reads=true -cache_size=268435456 -key_dist_a=0.002312 -key_dist_b=0.3467 -keyrange_dist_a=14.18 -keyrange_dist_b=-2.917 -keyrange_dist_c=0.0164 -keyrange_dist_d=-0.08082 -keyrange_num=30 -value_k=0.2615 -value_sigma=25.45 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.85 -mix_put_ratio=0.14 -mix_seek_ratio=0.01 -sine_mix_rate_interval_milliseconds=5000 -sine_a=350 -sine_b=0.0105 -sine_d=50000 --perf_level=2 -reads=1000000 -num=5000000 -key_size=48` Pull Request resolved: https://github.com/facebook/rocksdb/pull/5953 Test Plan: run db_bench with different parameters and checked the results. Differential Revision: D18053527 Pulled By: zhichao-cao fbshipit-source-id: 171f8b3142bd76462f1967c58345ad7e4f84bab7
2019-11-06 21:50:33 +01:00
// Decide the ratio of different query types
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 19:40:44 +01:00
// 0 Get, 1 Put, 2 Seek, 3 SeekForPrev, 4 Delete, 5 SingleDelete, 6 merge
class QueryDecider {
public:
std::vector<int> type_;
std::vector<double> ratio_;
int range_;
QueryDecider() {}
~QueryDecider() {}
Status Initiate(std::vector<double> ratio_input) {
int range_max = 1000;
double sum = 0.0;
for (auto& ratio : ratio_input) {
sum += ratio;
}
range_ = 0;
for (auto& ratio : ratio_input) {
range_ += static_cast<int>(ceil(range_max * (ratio / sum)));
type_.push_back(range_);
ratio_.push_back(ratio / sum);
}
return Status::OK();
}
int GetType(int64_t rand_num) {
if (rand_num < 0) {
rand_num = rand_num * (-1);
}
assert(range_ != 0);
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 19:40:44 +01:00
int pos = static_cast<int>(rand_num % range_);
for (int i = 0; i < static_cast<int>(type_.size()); i++) {
if (pos < type_[i]) {
return i;
}
}
return 0;
}
};
Workload generator (Mixgraph) based on prefix hotness (#5953) Summary: In the previous PR https://github.com/facebook/rocksdb/issues/4788, user can use db_bench mix_graph option to generate the workload that is from the social graph. The key is generated based on the key access hotness. In this PR, user can further model the key-range hotness and fit those to two-term-exponential distribution. First, user cuts the whole key space into small key ranges (e.g., key-ranges are the same size and the key-range number is the number of SST files). Then, user calculates the average access count per key of each key-range as the key-range hotness. Next, user fits the key-range hotness to two-term-exponential distribution (f(x) = f(x) = a*exp(b*x) + c*exp(d*x)) and generate the value of a, b, c, and d. They are the parameters in db_bench: prefix_dist_a, prefix_dist_b, prefix_dist_c, and prefix_dist_d. Finally, user can run db_bench by specify the parameters. For example: `./db_bench --benchmarks="mixgraph" -use_direct_io_for_flush_and_compaction=true -use_direct_reads=true -cache_size=268435456 -key_dist_a=0.002312 -key_dist_b=0.3467 -keyrange_dist_a=14.18 -keyrange_dist_b=-2.917 -keyrange_dist_c=0.0164 -keyrange_dist_d=-0.08082 -keyrange_num=30 -value_k=0.2615 -value_sigma=25.45 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.85 -mix_put_ratio=0.14 -mix_seek_ratio=0.01 -sine_mix_rate_interval_milliseconds=5000 -sine_a=350 -sine_b=0.0105 -sine_d=50000 --perf_level=2 -reads=1000000 -num=5000000 -key_size=48` Pull Request resolved: https://github.com/facebook/rocksdb/pull/5953 Test Plan: run db_bench with different parameters and checked the results. Differential Revision: D18053527 Pulled By: zhichao-cao fbshipit-source-id: 171f8b3142bd76462f1967c58345ad7e4f84bab7
2019-11-06 21:50:33 +01:00
// KeyrangeUnit is the struct of a keyrange. It is used in a keyrange vector
// to transfer a random value to one keyrange based on the hotness.
struct KeyrangeUnit {
int64_t keyrange_start;
int64_t keyrange_access;
int64_t keyrange_keys;
};
// From our observations, the prefix hotness (key-range hotness) follows
// the two-term-exponential distribution: f(x) = a*exp(b*x) + c*exp(d*x).
// However, we cannot directly use the inverse function to decide a
// key-range from a random distribution. To achieve it, we create a list of
// KeyrangeUnit, each KeyrangeUnit occupies a range of integers whose size is
// decided based on the hotness of the key-range. When a random value is
// generated based on uniform distribution, we map it to the KeyrangeUnit Vec
// and one KeyrangeUnit is selected. The probability of a KeyrangeUnit being
// selected is the same as the hotness of this KeyrangeUnit. After that, the
// key can be randomly allocated to the key-range of this KeyrangeUnit, or we
// can based on the power distribution (y=ax^b) to generate the offset of
// the key in the selected key-range. In this way, we generate the keyID
// based on the hotness of the prefix and also the key hotness distribution.
class GenerateTwoTermExpKeys {
public:
// Avoid uninitialized warning-as-error in some compilers
int64_t keyrange_rand_max_ = 0;
int64_t keyrange_size_ = 0;
int64_t keyrange_num_ = 0;
Workload generator (Mixgraph) based on prefix hotness (#5953) Summary: In the previous PR https://github.com/facebook/rocksdb/issues/4788, user can use db_bench mix_graph option to generate the workload that is from the social graph. The key is generated based on the key access hotness. In this PR, user can further model the key-range hotness and fit those to two-term-exponential distribution. First, user cuts the whole key space into small key ranges (e.g., key-ranges are the same size and the key-range number is the number of SST files). Then, user calculates the average access count per key of each key-range as the key-range hotness. Next, user fits the key-range hotness to two-term-exponential distribution (f(x) = f(x) = a*exp(b*x) + c*exp(d*x)) and generate the value of a, b, c, and d. They are the parameters in db_bench: prefix_dist_a, prefix_dist_b, prefix_dist_c, and prefix_dist_d. Finally, user can run db_bench by specify the parameters. For example: `./db_bench --benchmarks="mixgraph" -use_direct_io_for_flush_and_compaction=true -use_direct_reads=true -cache_size=268435456 -key_dist_a=0.002312 -key_dist_b=0.3467 -keyrange_dist_a=14.18 -keyrange_dist_b=-2.917 -keyrange_dist_c=0.0164 -keyrange_dist_d=-0.08082 -keyrange_num=30 -value_k=0.2615 -value_sigma=25.45 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.85 -mix_put_ratio=0.14 -mix_seek_ratio=0.01 -sine_mix_rate_interval_milliseconds=5000 -sine_a=350 -sine_b=0.0105 -sine_d=50000 --perf_level=2 -reads=1000000 -num=5000000 -key_size=48` Pull Request resolved: https://github.com/facebook/rocksdb/pull/5953 Test Plan: run db_bench with different parameters and checked the results. Differential Revision: D18053527 Pulled By: zhichao-cao fbshipit-source-id: 171f8b3142bd76462f1967c58345ad7e4f84bab7
2019-11-06 21:50:33 +01:00
std::vector<KeyrangeUnit> keyrange_set_;
// Initiate the KeyrangeUnit vector and calculate the size of each
// KeyrangeUnit.
Status InitiateExpDistribution(int64_t total_keys, double prefix_a,
double prefix_b, double prefix_c,
double prefix_d) {
int64_t amplify = 0;
int64_t keyrange_start = 0;
if (FLAGS_keyrange_num <= 0) {
keyrange_num_ = 1;
} else {
keyrange_num_ = FLAGS_keyrange_num;
}
keyrange_size_ = total_keys / keyrange_num_;
// Calculate the key-range shares size based on the input parameters
for (int64_t pfx = keyrange_num_; pfx >= 1; pfx--) {
// Step 1. Calculate the probability that this key range will be
// accessed in a query. It is based on the two-term expoential
// distribution
double keyrange_p = prefix_a * std::exp(prefix_b * pfx) +
prefix_c * std::exp(prefix_d * pfx);
if (keyrange_p < std::pow(10.0, -16.0)) {
keyrange_p = 0.0;
}
// Step 2. Calculate the amplify
// In order to allocate a query to a key-range based on the random
// number generated for this query, we need to extend the probability
// of each key range from [0,1] to [0, amplify]. Amplify is calculated
// by 1/(smallest key-range probability). In this way, we ensure that
// all key-ranges are assigned with an Integer that >=0
if (amplify == 0 && keyrange_p > 0) {
amplify = static_cast<int64_t>(std::floor(1 / keyrange_p)) + 1;
}
// Step 3. For each key-range, we calculate its position in the
// [0, amplify] range, including the start, the size (keyrange_access)
KeyrangeUnit p_unit;
p_unit.keyrange_start = keyrange_start;
if (0.0 >= keyrange_p) {
p_unit.keyrange_access = 0;
} else {
p_unit.keyrange_access =
static_cast<int64_t>(std::floor(amplify * keyrange_p));
}
p_unit.keyrange_keys = keyrange_size_;
keyrange_set_.push_back(p_unit);
keyrange_start += p_unit.keyrange_access;
}
keyrange_rand_max_ = keyrange_start;
// Step 4. Shuffle the key-ranges randomly
// Since the access probability is calculated from small to large,
// If we do not re-allocate them, hot key-ranges are always at the end
// and cold key-ranges are at the begin of the key space. Therefore, the
// key-ranges are shuffled and the rand seed is only decide by the
// key-range hotness distribution. With the same distribution parameters
// the shuffle results are the same.
Random64 rand_loca(keyrange_rand_max_);
for (int64_t i = 0; i < FLAGS_keyrange_num; i++) {
int64_t pos = rand_loca.Next() % FLAGS_keyrange_num;
assert(i >= 0 && i < static_cast<int64_t>(keyrange_set_.size()) &&
pos >= 0 && pos < static_cast<int64_t>(keyrange_set_.size()));
std::swap(keyrange_set_[i], keyrange_set_[pos]);
}
// Step 5. Recalculate the prefix start postion after shuffling
int64_t offset = 0;
for (auto& p_unit : keyrange_set_) {
p_unit.keyrange_start = offset;
offset += p_unit.keyrange_access;
}
return Status::OK();
}
// Generate the Key ID according to the input ini_rand and key distribution
int64_t DistGetKeyID(int64_t ini_rand, double key_dist_a,
double key_dist_b) {
int64_t keyrange_rand = ini_rand % keyrange_rand_max_;
// Calculate and select one key-range that contains the new key
int64_t start = 0, end = static_cast<int64_t>(keyrange_set_.size());
while (start + 1 < end) {
int64_t mid = start + (end - start) / 2;
assert(mid >= 0 && mid < static_cast<int64_t>(keyrange_set_.size()));
if (keyrange_rand < keyrange_set_[mid].keyrange_start) {
end = mid;
} else {
start = mid;
}
}
int64_t keyrange_id = start;
// Select one key in the key-range and compose the keyID
int64_t key_offset = 0, key_seed;
if (key_dist_a == 0.0 || key_dist_b == 0.0) {
Workload generator (Mixgraph) based on prefix hotness (#5953) Summary: In the previous PR https://github.com/facebook/rocksdb/issues/4788, user can use db_bench mix_graph option to generate the workload that is from the social graph. The key is generated based on the key access hotness. In this PR, user can further model the key-range hotness and fit those to two-term-exponential distribution. First, user cuts the whole key space into small key ranges (e.g., key-ranges are the same size and the key-range number is the number of SST files). Then, user calculates the average access count per key of each key-range as the key-range hotness. Next, user fits the key-range hotness to two-term-exponential distribution (f(x) = f(x) = a*exp(b*x) + c*exp(d*x)) and generate the value of a, b, c, and d. They are the parameters in db_bench: prefix_dist_a, prefix_dist_b, prefix_dist_c, and prefix_dist_d. Finally, user can run db_bench by specify the parameters. For example: `./db_bench --benchmarks="mixgraph" -use_direct_io_for_flush_and_compaction=true -use_direct_reads=true -cache_size=268435456 -key_dist_a=0.002312 -key_dist_b=0.3467 -keyrange_dist_a=14.18 -keyrange_dist_b=-2.917 -keyrange_dist_c=0.0164 -keyrange_dist_d=-0.08082 -keyrange_num=30 -value_k=0.2615 -value_sigma=25.45 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.85 -mix_put_ratio=0.14 -mix_seek_ratio=0.01 -sine_mix_rate_interval_milliseconds=5000 -sine_a=350 -sine_b=0.0105 -sine_d=50000 --perf_level=2 -reads=1000000 -num=5000000 -key_size=48` Pull Request resolved: https://github.com/facebook/rocksdb/pull/5953 Test Plan: run db_bench with different parameters and checked the results. Differential Revision: D18053527 Pulled By: zhichao-cao fbshipit-source-id: 171f8b3142bd76462f1967c58345ad7e4f84bab7
2019-11-06 21:50:33 +01:00
key_offset = ini_rand % keyrange_size_;
} else {
double u =
static_cast<double>(ini_rand % keyrange_size_) / keyrange_size_;
Workload generator (Mixgraph) based on prefix hotness (#5953) Summary: In the previous PR https://github.com/facebook/rocksdb/issues/4788, user can use db_bench mix_graph option to generate the workload that is from the social graph. The key is generated based on the key access hotness. In this PR, user can further model the key-range hotness and fit those to two-term-exponential distribution. First, user cuts the whole key space into small key ranges (e.g., key-ranges are the same size and the key-range number is the number of SST files). Then, user calculates the average access count per key of each key-range as the key-range hotness. Next, user fits the key-range hotness to two-term-exponential distribution (f(x) = f(x) = a*exp(b*x) + c*exp(d*x)) and generate the value of a, b, c, and d. They are the parameters in db_bench: prefix_dist_a, prefix_dist_b, prefix_dist_c, and prefix_dist_d. Finally, user can run db_bench by specify the parameters. For example: `./db_bench --benchmarks="mixgraph" -use_direct_io_for_flush_and_compaction=true -use_direct_reads=true -cache_size=268435456 -key_dist_a=0.002312 -key_dist_b=0.3467 -keyrange_dist_a=14.18 -keyrange_dist_b=-2.917 -keyrange_dist_c=0.0164 -keyrange_dist_d=-0.08082 -keyrange_num=30 -value_k=0.2615 -value_sigma=25.45 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.85 -mix_put_ratio=0.14 -mix_seek_ratio=0.01 -sine_mix_rate_interval_milliseconds=5000 -sine_a=350 -sine_b=0.0105 -sine_d=50000 --perf_level=2 -reads=1000000 -num=5000000 -key_size=48` Pull Request resolved: https://github.com/facebook/rocksdb/pull/5953 Test Plan: run db_bench with different parameters and checked the results. Differential Revision: D18053527 Pulled By: zhichao-cao fbshipit-source-id: 171f8b3142bd76462f1967c58345ad7e4f84bab7
2019-11-06 21:50:33 +01:00
key_seed = static_cast<int64_t>(
ceil(std::pow((u / key_dist_a), (1 / key_dist_b))));
Workload generator (Mixgraph) based on prefix hotness (#5953) Summary: In the previous PR https://github.com/facebook/rocksdb/issues/4788, user can use db_bench mix_graph option to generate the workload that is from the social graph. The key is generated based on the key access hotness. In this PR, user can further model the key-range hotness and fit those to two-term-exponential distribution. First, user cuts the whole key space into small key ranges (e.g., key-ranges are the same size and the key-range number is the number of SST files). Then, user calculates the average access count per key of each key-range as the key-range hotness. Next, user fits the key-range hotness to two-term-exponential distribution (f(x) = f(x) = a*exp(b*x) + c*exp(d*x)) and generate the value of a, b, c, and d. They are the parameters in db_bench: prefix_dist_a, prefix_dist_b, prefix_dist_c, and prefix_dist_d. Finally, user can run db_bench by specify the parameters. For example: `./db_bench --benchmarks="mixgraph" -use_direct_io_for_flush_and_compaction=true -use_direct_reads=true -cache_size=268435456 -key_dist_a=0.002312 -key_dist_b=0.3467 -keyrange_dist_a=14.18 -keyrange_dist_b=-2.917 -keyrange_dist_c=0.0164 -keyrange_dist_d=-0.08082 -keyrange_num=30 -value_k=0.2615 -value_sigma=25.45 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.85 -mix_put_ratio=0.14 -mix_seek_ratio=0.01 -sine_mix_rate_interval_milliseconds=5000 -sine_a=350 -sine_b=0.0105 -sine_d=50000 --perf_level=2 -reads=1000000 -num=5000000 -key_size=48` Pull Request resolved: https://github.com/facebook/rocksdb/pull/5953 Test Plan: run db_bench with different parameters and checked the results. Differential Revision: D18053527 Pulled By: zhichao-cao fbshipit-source-id: 171f8b3142bd76462f1967c58345ad7e4f84bab7
2019-11-06 21:50:33 +01:00
Random64 rand_key(key_seed);
key_offset = rand_key.Next() % keyrange_size_;
Workload generator (Mixgraph) based on prefix hotness (#5953) Summary: In the previous PR https://github.com/facebook/rocksdb/issues/4788, user can use db_bench mix_graph option to generate the workload that is from the social graph. The key is generated based on the key access hotness. In this PR, user can further model the key-range hotness and fit those to two-term-exponential distribution. First, user cuts the whole key space into small key ranges (e.g., key-ranges are the same size and the key-range number is the number of SST files). Then, user calculates the average access count per key of each key-range as the key-range hotness. Next, user fits the key-range hotness to two-term-exponential distribution (f(x) = f(x) = a*exp(b*x) + c*exp(d*x)) and generate the value of a, b, c, and d. They are the parameters in db_bench: prefix_dist_a, prefix_dist_b, prefix_dist_c, and prefix_dist_d. Finally, user can run db_bench by specify the parameters. For example: `./db_bench --benchmarks="mixgraph" -use_direct_io_for_flush_and_compaction=true -use_direct_reads=true -cache_size=268435456 -key_dist_a=0.002312 -key_dist_b=0.3467 -keyrange_dist_a=14.18 -keyrange_dist_b=-2.917 -keyrange_dist_c=0.0164 -keyrange_dist_d=-0.08082 -keyrange_num=30 -value_k=0.2615 -value_sigma=25.45 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.85 -mix_put_ratio=0.14 -mix_seek_ratio=0.01 -sine_mix_rate_interval_milliseconds=5000 -sine_a=350 -sine_b=0.0105 -sine_d=50000 --perf_level=2 -reads=1000000 -num=5000000 -key_size=48` Pull Request resolved: https://github.com/facebook/rocksdb/pull/5953 Test Plan: run db_bench with different parameters and checked the results. Differential Revision: D18053527 Pulled By: zhichao-cao fbshipit-source-id: 171f8b3142bd76462f1967c58345ad7e4f84bab7
2019-11-06 21:50:33 +01:00
}
return keyrange_size_ * keyrange_id + key_offset;
}
};
// The social graph workload mixed with Get, Put, Iterator queries.
Workload generator (Mixgraph) based on prefix hotness (#5953) Summary: In the previous PR https://github.com/facebook/rocksdb/issues/4788, user can use db_bench mix_graph option to generate the workload that is from the social graph. The key is generated based on the key access hotness. In this PR, user can further model the key-range hotness and fit those to two-term-exponential distribution. First, user cuts the whole key space into small key ranges (e.g., key-ranges are the same size and the key-range number is the number of SST files). Then, user calculates the average access count per key of each key-range as the key-range hotness. Next, user fits the key-range hotness to two-term-exponential distribution (f(x) = f(x) = a*exp(b*x) + c*exp(d*x)) and generate the value of a, b, c, and d. They are the parameters in db_bench: prefix_dist_a, prefix_dist_b, prefix_dist_c, and prefix_dist_d. Finally, user can run db_bench by specify the parameters. For example: `./db_bench --benchmarks="mixgraph" -use_direct_io_for_flush_and_compaction=true -use_direct_reads=true -cache_size=268435456 -key_dist_a=0.002312 -key_dist_b=0.3467 -keyrange_dist_a=14.18 -keyrange_dist_b=-2.917 -keyrange_dist_c=0.0164 -keyrange_dist_d=-0.08082 -keyrange_num=30 -value_k=0.2615 -value_sigma=25.45 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.85 -mix_put_ratio=0.14 -mix_seek_ratio=0.01 -sine_mix_rate_interval_milliseconds=5000 -sine_a=350 -sine_b=0.0105 -sine_d=50000 --perf_level=2 -reads=1000000 -num=5000000 -key_size=48` Pull Request resolved: https://github.com/facebook/rocksdb/pull/5953 Test Plan: run db_bench with different parameters and checked the results. Differential Revision: D18053527 Pulled By: zhichao-cao fbshipit-source-id: 171f8b3142bd76462f1967c58345ad7e4f84bab7
2019-11-06 21:50:33 +01:00
// The value size and iterator length follow Pareto distribution.
// The overall key access follow power distribution. If user models the
// workload based on different key-ranges (or different prefixes), user
// can use two-term-exponential distribution to fit the workload. User
// needs to decide the ratio between Get, Put, Iterator queries before
Workload generator (Mixgraph) based on prefix hotness (#5953) Summary: In the previous PR https://github.com/facebook/rocksdb/issues/4788, user can use db_bench mix_graph option to generate the workload that is from the social graph. The key is generated based on the key access hotness. In this PR, user can further model the key-range hotness and fit those to two-term-exponential distribution. First, user cuts the whole key space into small key ranges (e.g., key-ranges are the same size and the key-range number is the number of SST files). Then, user calculates the average access count per key of each key-range as the key-range hotness. Next, user fits the key-range hotness to two-term-exponential distribution (f(x) = f(x) = a*exp(b*x) + c*exp(d*x)) and generate the value of a, b, c, and d. They are the parameters in db_bench: prefix_dist_a, prefix_dist_b, prefix_dist_c, and prefix_dist_d. Finally, user can run db_bench by specify the parameters. For example: `./db_bench --benchmarks="mixgraph" -use_direct_io_for_flush_and_compaction=true -use_direct_reads=true -cache_size=268435456 -key_dist_a=0.002312 -key_dist_b=0.3467 -keyrange_dist_a=14.18 -keyrange_dist_b=-2.917 -keyrange_dist_c=0.0164 -keyrange_dist_d=-0.08082 -keyrange_num=30 -value_k=0.2615 -value_sigma=25.45 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.85 -mix_put_ratio=0.14 -mix_seek_ratio=0.01 -sine_mix_rate_interval_milliseconds=5000 -sine_a=350 -sine_b=0.0105 -sine_d=50000 --perf_level=2 -reads=1000000 -num=5000000 -key_size=48` Pull Request resolved: https://github.com/facebook/rocksdb/pull/5953 Test Plan: run db_bench with different parameters and checked the results. Differential Revision: D18053527 Pulled By: zhichao-cao fbshipit-source-id: 171f8b3142bd76462f1967c58345ad7e4f84bab7
2019-11-06 21:50:33 +01:00
// starting the benchmark.
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 19:40:44 +01:00
void MixGraph(ThreadState* thread) {
int64_t read = 0; // including single gets and Next of iterators
int64_t gets = 0;
int64_t puts = 0;
int64_t found = 0;
int64_t seek = 0;
int64_t seek_found = 0;
int64_t bytes = 0;
const int64_t default_value_max = 1 * 1024 * 1024;
int64_t value_max = default_value_max;
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 19:40:44 +01:00
int64_t scan_len_max = FLAGS_mix_max_scan_len;
double write_rate = 1000000.0;
double read_rate = 1000000.0;
Workload generator (Mixgraph) based on prefix hotness (#5953) Summary: In the previous PR https://github.com/facebook/rocksdb/issues/4788, user can use db_bench mix_graph option to generate the workload that is from the social graph. The key is generated based on the key access hotness. In this PR, user can further model the key-range hotness and fit those to two-term-exponential distribution. First, user cuts the whole key space into small key ranges (e.g., key-ranges are the same size and the key-range number is the number of SST files). Then, user calculates the average access count per key of each key-range as the key-range hotness. Next, user fits the key-range hotness to two-term-exponential distribution (f(x) = f(x) = a*exp(b*x) + c*exp(d*x)) and generate the value of a, b, c, and d. They are the parameters in db_bench: prefix_dist_a, prefix_dist_b, prefix_dist_c, and prefix_dist_d. Finally, user can run db_bench by specify the parameters. For example: `./db_bench --benchmarks="mixgraph" -use_direct_io_for_flush_and_compaction=true -use_direct_reads=true -cache_size=268435456 -key_dist_a=0.002312 -key_dist_b=0.3467 -keyrange_dist_a=14.18 -keyrange_dist_b=-2.917 -keyrange_dist_c=0.0164 -keyrange_dist_d=-0.08082 -keyrange_num=30 -value_k=0.2615 -value_sigma=25.45 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.85 -mix_put_ratio=0.14 -mix_seek_ratio=0.01 -sine_mix_rate_interval_milliseconds=5000 -sine_a=350 -sine_b=0.0105 -sine_d=50000 --perf_level=2 -reads=1000000 -num=5000000 -key_size=48` Pull Request resolved: https://github.com/facebook/rocksdb/pull/5953 Test Plan: run db_bench with different parameters and checked the results. Differential Revision: D18053527 Pulled By: zhichao-cao fbshipit-source-id: 171f8b3142bd76462f1967c58345ad7e4f84bab7
2019-11-06 21:50:33 +01:00
bool use_prefix_modeling = false;
bool use_random_modeling = false;
Workload generator (Mixgraph) based on prefix hotness (#5953) Summary: In the previous PR https://github.com/facebook/rocksdb/issues/4788, user can use db_bench mix_graph option to generate the workload that is from the social graph. The key is generated based on the key access hotness. In this PR, user can further model the key-range hotness and fit those to two-term-exponential distribution. First, user cuts the whole key space into small key ranges (e.g., key-ranges are the same size and the key-range number is the number of SST files). Then, user calculates the average access count per key of each key-range as the key-range hotness. Next, user fits the key-range hotness to two-term-exponential distribution (f(x) = f(x) = a*exp(b*x) + c*exp(d*x)) and generate the value of a, b, c, and d. They are the parameters in db_bench: prefix_dist_a, prefix_dist_b, prefix_dist_c, and prefix_dist_d. Finally, user can run db_bench by specify the parameters. For example: `./db_bench --benchmarks="mixgraph" -use_direct_io_for_flush_and_compaction=true -use_direct_reads=true -cache_size=268435456 -key_dist_a=0.002312 -key_dist_b=0.3467 -keyrange_dist_a=14.18 -keyrange_dist_b=-2.917 -keyrange_dist_c=0.0164 -keyrange_dist_d=-0.08082 -keyrange_num=30 -value_k=0.2615 -value_sigma=25.45 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.85 -mix_put_ratio=0.14 -mix_seek_ratio=0.01 -sine_mix_rate_interval_milliseconds=5000 -sine_a=350 -sine_b=0.0105 -sine_d=50000 --perf_level=2 -reads=1000000 -num=5000000 -key_size=48` Pull Request resolved: https://github.com/facebook/rocksdb/pull/5953 Test Plan: run db_bench with different parameters and checked the results. Differential Revision: D18053527 Pulled By: zhichao-cao fbshipit-source-id: 171f8b3142bd76462f1967c58345ad7e4f84bab7
2019-11-06 21:50:33 +01:00
GenerateTwoTermExpKeys gen_exp;
std::vector<double> ratio{FLAGS_mix_get_ratio, FLAGS_mix_put_ratio,
FLAGS_mix_seek_ratio};
char value_buffer[default_value_max];
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 19:40:44 +01:00
QueryDecider query;
RandomGenerator gen;
Status s;
if (value_max > FLAGS_mix_max_value_size) {
value_max = FLAGS_mix_max_value_size;
}
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 19:40:44 +01:00
ReadOptions options(FLAGS_verify_checksum, true);
std::unique_ptr<const char[]> key_guard;
Slice key = AllocateKey(&key_guard);
PinnableSlice pinnable_val;
query.Initiate(ratio);
// the limit of qps initiation
if (FLAGS_sine_a != 0 || FLAGS_sine_d != 0) {
thread->shared->read_rate_limiter.reset(NewGenericRateLimiter(
static_cast<int64_t>(read_rate), 100000 /* refill_period_us */, 10 /* fairness */,
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 19:40:44 +01:00
RateLimiter::Mode::kReadsOnly));
thread->shared->write_rate_limiter.reset(
NewGenericRateLimiter(static_cast<int64_t>(write_rate)));
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 19:40:44 +01:00
}
Workload generator (Mixgraph) based on prefix hotness (#5953) Summary: In the previous PR https://github.com/facebook/rocksdb/issues/4788, user can use db_bench mix_graph option to generate the workload that is from the social graph. The key is generated based on the key access hotness. In this PR, user can further model the key-range hotness and fit those to two-term-exponential distribution. First, user cuts the whole key space into small key ranges (e.g., key-ranges are the same size and the key-range number is the number of SST files). Then, user calculates the average access count per key of each key-range as the key-range hotness. Next, user fits the key-range hotness to two-term-exponential distribution (f(x) = f(x) = a*exp(b*x) + c*exp(d*x)) and generate the value of a, b, c, and d. They are the parameters in db_bench: prefix_dist_a, prefix_dist_b, prefix_dist_c, and prefix_dist_d. Finally, user can run db_bench by specify the parameters. For example: `./db_bench --benchmarks="mixgraph" -use_direct_io_for_flush_and_compaction=true -use_direct_reads=true -cache_size=268435456 -key_dist_a=0.002312 -key_dist_b=0.3467 -keyrange_dist_a=14.18 -keyrange_dist_b=-2.917 -keyrange_dist_c=0.0164 -keyrange_dist_d=-0.08082 -keyrange_num=30 -value_k=0.2615 -value_sigma=25.45 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.85 -mix_put_ratio=0.14 -mix_seek_ratio=0.01 -sine_mix_rate_interval_milliseconds=5000 -sine_a=350 -sine_b=0.0105 -sine_d=50000 --perf_level=2 -reads=1000000 -num=5000000 -key_size=48` Pull Request resolved: https://github.com/facebook/rocksdb/pull/5953 Test Plan: run db_bench with different parameters and checked the results. Differential Revision: D18053527 Pulled By: zhichao-cao fbshipit-source-id: 171f8b3142bd76462f1967c58345ad7e4f84bab7
2019-11-06 21:50:33 +01:00
// Decide if user wants to use prefix based key generation
if (FLAGS_keyrange_dist_a != 0.0 || FLAGS_keyrange_dist_b != 0.0 ||
FLAGS_keyrange_dist_c != 0.0 || FLAGS_keyrange_dist_d != 0.0) {
use_prefix_modeling = true;
gen_exp.InitiateExpDistribution(
FLAGS_num, FLAGS_keyrange_dist_a, FLAGS_keyrange_dist_b,
FLAGS_keyrange_dist_c, FLAGS_keyrange_dist_d);
}
if (FLAGS_key_dist_a == 0 || FLAGS_key_dist_b == 0) {
use_random_modeling = true;
}
Workload generator (Mixgraph) based on prefix hotness (#5953) Summary: In the previous PR https://github.com/facebook/rocksdb/issues/4788, user can use db_bench mix_graph option to generate the workload that is from the social graph. The key is generated based on the key access hotness. In this PR, user can further model the key-range hotness and fit those to two-term-exponential distribution. First, user cuts the whole key space into small key ranges (e.g., key-ranges are the same size and the key-range number is the number of SST files). Then, user calculates the average access count per key of each key-range as the key-range hotness. Next, user fits the key-range hotness to two-term-exponential distribution (f(x) = f(x) = a*exp(b*x) + c*exp(d*x)) and generate the value of a, b, c, and d. They are the parameters in db_bench: prefix_dist_a, prefix_dist_b, prefix_dist_c, and prefix_dist_d. Finally, user can run db_bench by specify the parameters. For example: `./db_bench --benchmarks="mixgraph" -use_direct_io_for_flush_and_compaction=true -use_direct_reads=true -cache_size=268435456 -key_dist_a=0.002312 -key_dist_b=0.3467 -keyrange_dist_a=14.18 -keyrange_dist_b=-2.917 -keyrange_dist_c=0.0164 -keyrange_dist_d=-0.08082 -keyrange_num=30 -value_k=0.2615 -value_sigma=25.45 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.85 -mix_put_ratio=0.14 -mix_seek_ratio=0.01 -sine_mix_rate_interval_milliseconds=5000 -sine_a=350 -sine_b=0.0105 -sine_d=50000 --perf_level=2 -reads=1000000 -num=5000000 -key_size=48` Pull Request resolved: https://github.com/facebook/rocksdb/pull/5953 Test Plan: run db_bench with different parameters and checked the results. Differential Revision: D18053527 Pulled By: zhichao-cao fbshipit-source-id: 171f8b3142bd76462f1967c58345ad7e4f84bab7
2019-11-06 21:50:33 +01:00
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 19:40:44 +01:00
Duration duration(FLAGS_duration, reads_);
while (!duration.Done(1)) {
DBWithColumnFamilies* db_with_cfh = SelectDBWithCfh(thread);
Workload generator (Mixgraph) based on prefix hotness (#5953) Summary: In the previous PR https://github.com/facebook/rocksdb/issues/4788, user can use db_bench mix_graph option to generate the workload that is from the social graph. The key is generated based on the key access hotness. In this PR, user can further model the key-range hotness and fit those to two-term-exponential distribution. First, user cuts the whole key space into small key ranges (e.g., key-ranges are the same size and the key-range number is the number of SST files). Then, user calculates the average access count per key of each key-range as the key-range hotness. Next, user fits the key-range hotness to two-term-exponential distribution (f(x) = f(x) = a*exp(b*x) + c*exp(d*x)) and generate the value of a, b, c, and d. They are the parameters in db_bench: prefix_dist_a, prefix_dist_b, prefix_dist_c, and prefix_dist_d. Finally, user can run db_bench by specify the parameters. For example: `./db_bench --benchmarks="mixgraph" -use_direct_io_for_flush_and_compaction=true -use_direct_reads=true -cache_size=268435456 -key_dist_a=0.002312 -key_dist_b=0.3467 -keyrange_dist_a=14.18 -keyrange_dist_b=-2.917 -keyrange_dist_c=0.0164 -keyrange_dist_d=-0.08082 -keyrange_num=30 -value_k=0.2615 -value_sigma=25.45 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.85 -mix_put_ratio=0.14 -mix_seek_ratio=0.01 -sine_mix_rate_interval_milliseconds=5000 -sine_a=350 -sine_b=0.0105 -sine_d=50000 --perf_level=2 -reads=1000000 -num=5000000 -key_size=48` Pull Request resolved: https://github.com/facebook/rocksdb/pull/5953 Test Plan: run db_bench with different parameters and checked the results. Differential Revision: D18053527 Pulled By: zhichao-cao fbshipit-source-id: 171f8b3142bd76462f1967c58345ad7e4f84bab7
2019-11-06 21:50:33 +01:00
int64_t ini_rand, rand_v, key_rand, key_seed;
ini_rand = GetRandomKey(&thread->rand);
rand_v = ini_rand % FLAGS_num;
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 19:40:44 +01:00
double u = static_cast<double>(rand_v) / FLAGS_num;
Workload generator (Mixgraph) based on prefix hotness (#5953) Summary: In the previous PR https://github.com/facebook/rocksdb/issues/4788, user can use db_bench mix_graph option to generate the workload that is from the social graph. The key is generated based on the key access hotness. In this PR, user can further model the key-range hotness and fit those to two-term-exponential distribution. First, user cuts the whole key space into small key ranges (e.g., key-ranges are the same size and the key-range number is the number of SST files). Then, user calculates the average access count per key of each key-range as the key-range hotness. Next, user fits the key-range hotness to two-term-exponential distribution (f(x) = f(x) = a*exp(b*x) + c*exp(d*x)) and generate the value of a, b, c, and d. They are the parameters in db_bench: prefix_dist_a, prefix_dist_b, prefix_dist_c, and prefix_dist_d. Finally, user can run db_bench by specify the parameters. For example: `./db_bench --benchmarks="mixgraph" -use_direct_io_for_flush_and_compaction=true -use_direct_reads=true -cache_size=268435456 -key_dist_a=0.002312 -key_dist_b=0.3467 -keyrange_dist_a=14.18 -keyrange_dist_b=-2.917 -keyrange_dist_c=0.0164 -keyrange_dist_d=-0.08082 -keyrange_num=30 -value_k=0.2615 -value_sigma=25.45 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.85 -mix_put_ratio=0.14 -mix_seek_ratio=0.01 -sine_mix_rate_interval_milliseconds=5000 -sine_a=350 -sine_b=0.0105 -sine_d=50000 --perf_level=2 -reads=1000000 -num=5000000 -key_size=48` Pull Request resolved: https://github.com/facebook/rocksdb/pull/5953 Test Plan: run db_bench with different parameters and checked the results. Differential Revision: D18053527 Pulled By: zhichao-cao fbshipit-source-id: 171f8b3142bd76462f1967c58345ad7e4f84bab7
2019-11-06 21:50:33 +01:00
// Generate the keyID based on the key hotness and prefix hotness
if (use_random_modeling) {
key_rand = ini_rand;
} else if (use_prefix_modeling) {
Workload generator (Mixgraph) based on prefix hotness (#5953) Summary: In the previous PR https://github.com/facebook/rocksdb/issues/4788, user can use db_bench mix_graph option to generate the workload that is from the social graph. The key is generated based on the key access hotness. In this PR, user can further model the key-range hotness and fit those to two-term-exponential distribution. First, user cuts the whole key space into small key ranges (e.g., key-ranges are the same size and the key-range number is the number of SST files). Then, user calculates the average access count per key of each key-range as the key-range hotness. Next, user fits the key-range hotness to two-term-exponential distribution (f(x) = f(x) = a*exp(b*x) + c*exp(d*x)) and generate the value of a, b, c, and d. They are the parameters in db_bench: prefix_dist_a, prefix_dist_b, prefix_dist_c, and prefix_dist_d. Finally, user can run db_bench by specify the parameters. For example: `./db_bench --benchmarks="mixgraph" -use_direct_io_for_flush_and_compaction=true -use_direct_reads=true -cache_size=268435456 -key_dist_a=0.002312 -key_dist_b=0.3467 -keyrange_dist_a=14.18 -keyrange_dist_b=-2.917 -keyrange_dist_c=0.0164 -keyrange_dist_d=-0.08082 -keyrange_num=30 -value_k=0.2615 -value_sigma=25.45 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.85 -mix_put_ratio=0.14 -mix_seek_ratio=0.01 -sine_mix_rate_interval_milliseconds=5000 -sine_a=350 -sine_b=0.0105 -sine_d=50000 --perf_level=2 -reads=1000000 -num=5000000 -key_size=48` Pull Request resolved: https://github.com/facebook/rocksdb/pull/5953 Test Plan: run db_bench with different parameters and checked the results. Differential Revision: D18053527 Pulled By: zhichao-cao fbshipit-source-id: 171f8b3142bd76462f1967c58345ad7e4f84bab7
2019-11-06 21:50:33 +01:00
key_rand =
gen_exp.DistGetKeyID(ini_rand, FLAGS_key_dist_a, FLAGS_key_dist_b);
} else {
key_seed = PowerCdfInversion(u, FLAGS_key_dist_a, FLAGS_key_dist_b);
Random64 rand(key_seed);
key_rand = static_cast<int64_t>(rand.Next()) % FLAGS_num;
}
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 19:40:44 +01:00
GenerateKeyFromInt(key_rand, FLAGS_num, &key);
int query_type = query.GetType(rand_v);
// change the qps
uint64_t now = FLAGS_env->NowMicros();
uint64_t usecs_since_last;
if (now > thread->stats.GetSineInterval()) {
usecs_since_last = now - thread->stats.GetSineInterval();
} else {
usecs_since_last = 0;
}
if (usecs_since_last >
(FLAGS_sine_mix_rate_interval_milliseconds * uint64_t{1000})) {
double usecs_since_start =
static_cast<double>(now - thread->stats.GetStart());
thread->stats.ResetSineInterval();
double mix_rate_with_noise = AddNoise(
SineRate(usecs_since_start / 1000000.0), FLAGS_sine_mix_rate_noise);
read_rate = mix_rate_with_noise * (query.ratio_[0] + query.ratio_[2]);
write_rate =
mix_rate_with_noise * query.ratio_[1] * FLAGS_mix_ave_kv_size;
thread->shared->write_rate_limiter.reset(
NewGenericRateLimiter(static_cast<int64_t>(write_rate)));
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 19:40:44 +01:00
thread->shared->read_rate_limiter.reset(NewGenericRateLimiter(
static_cast<int64_t>(read_rate),
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 19:40:44 +01:00
FLAGS_sine_mix_rate_interval_milliseconds * uint64_t{1000}, 10,
RateLimiter::Mode::kReadsOnly));
}
// Start the query
if (query_type == 0) {
// the Get query
gets++;
read++;
if (FLAGS_num_column_families > 1) {
s = db_with_cfh->db->Get(options, db_with_cfh->GetCfh(key_rand), key,
&pinnable_val);
} else {
pinnable_val.Reset();
s = db_with_cfh->db->Get(options,
db_with_cfh->db->DefaultColumnFamily(), key,
&pinnable_val);
}
if (s.ok()) {
found++;
bytes += key.size() + pinnable_val.size();
} else if (!s.IsNotFound()) {
fprintf(stderr, "Get returned an error: %s\n", s.ToString().c_str());
abort();
}
if (thread->shared->read_rate_limiter.get() != nullptr &&
read % 256 == 255) {
thread->shared->read_rate_limiter->Request(
256, Env::IO_HIGH, nullptr /* stats */,
RateLimiter::OpType::kRead);
}
thread->stats.FinishedOps(db_with_cfh, db_with_cfh->db, 1, kRead);
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 19:40:44 +01:00
} else if (query_type == 1) {
// the Put query
puts++;
int64_t val_size = ParetoCdfInversion(
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 19:40:44 +01:00
u, FLAGS_value_theta, FLAGS_value_k, FLAGS_value_sigma);
if (val_size < 0) {
val_size = 10;
} else if (val_size > value_max) {
val_size = val_size % value_max;
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 19:40:44 +01:00
}
s = db_with_cfh->db->Put(
write_options_, key,
gen.Generate(static_cast<unsigned int>(val_size)));
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 19:40:44 +01:00
if (!s.ok()) {
fprintf(stderr, "put error: %s\n", s.ToString().c_str());
ErrorExit();
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 19:40:44 +01:00
}
if (thread->shared->write_rate_limiter) {
thread->shared->write_rate_limiter->Request(
key.size() + val_size, Env::IO_HIGH, nullptr /*stats*/,
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 19:40:44 +01:00
RateLimiter::OpType::kWrite);
}
thread->stats.FinishedOps(db_with_cfh, db_with_cfh->db, 1, kWrite);
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 19:40:44 +01:00
} else if (query_type == 2) {
// Seek query
if (db_with_cfh->db != nullptr) {
Iterator* single_iter = nullptr;
single_iter = db_with_cfh->db->NewIterator(options);
if (single_iter != nullptr) {
single_iter->Seek(key);
seek++;
read++;
if (single_iter->Valid() && single_iter->key().compare(key) == 0) {
seek_found++;
}
int64_t scan_length =
ParetoCdfInversion(u, FLAGS_iter_theta, FLAGS_iter_k,
FLAGS_iter_sigma) %
scan_len_max;
for (int64_t j = 0; j < scan_length && single_iter->Valid(); j++) {
Slice value = single_iter->value();
memcpy(value_buffer, value.data(),
std::min(value.size(), sizeof(value_buffer)));
bytes += single_iter->key().size() + single_iter->value().size();
single_iter->Next();
assert(single_iter->status().ok());
}
}
delete single_iter;
}
thread->stats.FinishedOps(db_with_cfh, db_with_cfh->db, 1, kSeek);
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 19:40:44 +01:00
}
}
char msg[256];
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 19:40:44 +01:00
snprintf(msg, sizeof(msg),
"( Gets:%" PRIu64 " Puts:%" PRIu64 " Seek:%" PRIu64 " of %" PRIu64
" in %" PRIu64 " found)\n",
gets, puts, seek, found, read);
thread->stats.AddBytes(bytes);
thread->stats.AddMessage(msg);
if (FLAGS_perf_level > ROCKSDB_NAMESPACE::PerfLevel::kDisable) {
Generate mixed workload with Get, Put, Seek in db_bench (#4788) Summary: Based on the specific workload models (key access distribution, value size distribution, and iterator scan length distribution, the QPS variation), the MixGraph benchmark generate the synthetic workload according to these distributions which can reflect the real-world workload characteristics. After user enable the tracing function, they will get the trace file. By analyzing the trace file with the trace_analyzer tool, user can generate a set of statistic data files including. The *_accessed_key_stats.txt, *-accessed_value_size_distribution.txt, *-iterator_length_distribution.txt, and *-qps_stats.txt are mainly used to fit the Matlab model fitting. After that, user can get the parameters of the workload distributions (the modeling details are described: [here](https://github.com/facebook/rocksdb/wiki/RocksDB-Trace%2C-Replay%2C-and-Analyzer)) The key access distribution follows the The two-term power model. The probability density function is: `f(x) = ax^{b}+c`. The corresponding parameters are key_dist_a, key_dist_b, and key_dist_c in db_bench For the value size distribution and iterator scan length distribution, they both follow the Generalized Pareto Distribution. The probability density function is `f(x) = (1/sigma)(1+k*(x-theta)/sigma))^{-1-1/k)`. The parameters are: value_k, value_theta, value_sigma and iter_k, iter_theta, iter_sigma. For more information about the Generalized Pareto Distribution, users can find the [wiki](https://en.wikipedia.org/wiki/Generalized_Pareto_distribution) and [Matalb page](https://www.mathworks.com/help/stats/generalized-pareto-distribution.html) As for the QPS, it follows the diurnal pattern. So Sine is a good model to fit it. `F(x) = sine_a*sin(sine_b*x + sine_c) + sine_d`. The trace_will tell you the average QPS in the print out resutls, which is sine_d. After user fit the "*-qps_stats.txt" to the Matlab model, user can get the sine_a, sine_b, and sine_c. By using the 4 parameters, user can control the QPS variation including the period, average, changes. To use the bench mark, user can indicate the following parameters as examples: ``` -benchmarks="mixgraph" -key_dist_a=0.002312 -key_dist_b=0.3467 -value_k=0.9233 -value_sigma=226.4092 -iter_k=2.517 -iter_sigma=14.236 -mix_get_ratio=0.7 -mix_put_ratio=0.25 -mix_seek_ratio=0.05 -sine_mix_rate_interval_milliseconds=500 -sine_a=15000 -sine_b=1 -sine_d=20000 ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/4788 Differential Revision: D13573940 Pulled By: sagar0 fbshipit-source-id: e184c27e07b4f1bc0b436c2be36c5090c1fb0222
2019-01-22 19:40:44 +01:00
thread->stats.AddMessage(std::string("PERF_CONTEXT:\n") +
get_perf_context()->ToString());
}
}
void IteratorCreation(ThreadState* thread) {
Duration duration(FLAGS_duration, reads_);
ReadOptions options(FLAGS_verify_checksum, true);
std::unique_ptr<char[]> ts_guard;
if (user_timestamp_size_ > 0) {
ts_guard.reset(new char[user_timestamp_size_]);
}
while (!duration.Done(1)) {
DB* db = SelectDB(thread);
Slice ts;
if (user_timestamp_size_ > 0) {
ts = mock_app_clock_->GetTimestampForRead(thread->rand, ts_guard.get());
options.timestamp = &ts;
}
Iterator* iter = db->NewIterator(options);
delete iter;
thread->stats.FinishedOps(nullptr, db, 1, kOthers);
}
}
void IteratorCreationWhileWriting(ThreadState* thread) {
if (thread->tid > 0) {
IteratorCreation(thread);
} else {
BGWriter(thread, kWrite);
}
}
void SeekRandom(ThreadState* thread) {
int64_t read = 0;
int64_t found = 0;
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
2015-03-30 20:28:25 +02:00
int64_t bytes = 0;
ReadOptions options(FLAGS_verify_checksum, true);
options.total_order_seek = FLAGS_total_order_seek;
options.prefix_same_as_start = FLAGS_prefix_same_as_start;
options.tailing = FLAGS_use_tailing_iterator;
options.readahead_size = FLAGS_readahead_size;
std::unique_ptr<char[]> ts_guard;
Slice ts;
if (user_timestamp_size_ > 0) {
ts_guard.reset(new char[user_timestamp_size_]);
ts = mock_app_clock_->GetTimestampForRead(thread->rand, ts_guard.get());
options.timestamp = &ts;
}
std::vector<Iterator*> tailing_iters;
if (FLAGS_use_tailing_iterator) {
if (db_.db != nullptr) {
tailing_iters.push_back(db_.db->NewIterator(options));
} else {
for (const auto& db_with_cfh : multi_dbs_) {
tailing_iters.push_back(db_with_cfh.db->NewIterator(options));
}
}
}
std::unique_ptr<const char[]> key_guard;
Slice key = AllocateKey(&key_guard);
std::unique_ptr<const char[]> upper_bound_key_guard;
Slice upper_bound = AllocateKey(&upper_bound_key_guard);
std::unique_ptr<const char[]> lower_bound_key_guard;
Slice lower_bound = AllocateKey(&lower_bound_key_guard);
Duration duration(FLAGS_duration, reads_);
char value_buffer[256];
while (!duration.Done(1)) {
int64_t seek_pos = thread->rand.Next() % FLAGS_num;
GenerateKeyFromIntForSeek(static_cast<uint64_t>(seek_pos), FLAGS_num,
&key);
if (FLAGS_max_scan_distance != 0) {
if (FLAGS_reverse_iterator) {
GenerateKeyFromInt(
static_cast<uint64_t>(std::max(
static_cast<int64_t>(0), seek_pos - FLAGS_max_scan_distance)),
FLAGS_num, &lower_bound);
options.iterate_lower_bound = &lower_bound;
} else {
auto min_num =
std::min(FLAGS_num, seek_pos + FLAGS_max_scan_distance);
GenerateKeyFromInt(static_cast<uint64_t>(min_num), FLAGS_num,
&upper_bound);
options.iterate_upper_bound = &upper_bound;
}
}
// Pick a Iterator to use
size_t db_idx_to_use =
(db_.db == nullptr)
? (size_t{thread->rand.Next()} % multi_dbs_.size())
: 0;
std::unique_ptr<Iterator> single_iter;
Iterator* iter_to_use;
if (FLAGS_use_tailing_iterator) {
iter_to_use = tailing_iters[db_idx_to_use];
} else {
if (db_.db != nullptr) {
single_iter.reset(db_.db->NewIterator(options));
} else {
single_iter.reset(multi_dbs_[db_idx_to_use].db->NewIterator(options));
}
iter_to_use = single_iter.get();
}
iter_to_use->Seek(key);
read++;
if (iter_to_use->Valid() && iter_to_use->key().compare(key) == 0) {
found++;
}
for (int j = 0; j < FLAGS_seek_nexts && iter_to_use->Valid(); ++j) {
// Copy out iterator's value to make sure we read them.
Slice value = iter_to_use->value();
memcpy(value_buffer, value.data(),
std::min(value.size(), sizeof(value_buffer)));
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
2015-03-30 20:28:25 +02:00
bytes += iter_to_use->key().size() + iter_to_use->value().size();
if (!FLAGS_reverse_iterator) {
iter_to_use->Next();
} else {
iter_to_use->Prev();
}
assert(iter_to_use->status().ok());
}
if (thread->shared->read_rate_limiter.get() != nullptr &&
read % 256 == 255) {
thread->shared->read_rate_limiter->Request(
256, Env::IO_HIGH, nullptr /* stats */, RateLimiter::OpType::kRead);
}
thread->stats.FinishedOps(&db_, db_.db, 1, kSeek);
}
for (auto iter : tailing_iters) {
delete iter;
}
char msg[100];
snprintf(msg, sizeof(msg), "(%" PRIu64 " of %" PRIu64 " found)\n",
found, read);
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
2015-03-30 20:28:25 +02:00
thread->stats.AddBytes(bytes);
thread->stats.AddMessage(msg);
if (FLAGS_perf_level > ROCKSDB_NAMESPACE::PerfLevel::kDisable) {
thread->stats.AddMessage(std::string("PERF_CONTEXT:\n") +
get_perf_context()->ToString());
}
}
void SeekRandomWhileWriting(ThreadState* thread) {
if (thread->tid > 0) {
SeekRandom(thread);
} else {
BGWriter(thread, kWrite);
}
}
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
2015-03-30 20:28:25 +02:00
void SeekRandomWhileMerging(ThreadState* thread) {
if (thread->tid > 0) {
SeekRandom(thread);
} else {
BGWriter(thread, kMerge);
}
}
void DoDelete(ThreadState* thread, bool seq) {
WriteBatch batch(/*reserved_bytes=*/0, /*max_bytes=*/0,
user_timestamp_size_);
Duration duration(seq ? 0 : FLAGS_duration, deletes_);
int64_t i = 0;
std::unique_ptr<const char[]> key_guard;
Slice key = AllocateKey(&key_guard);
std::unique_ptr<char[]> ts_guard;
Slice ts;
if (user_timestamp_size_ > 0) {
ts_guard.reset(new char[user_timestamp_size_]);
}
while (!duration.Done(entries_per_batch_)) {
DB* db = SelectDB(thread);
batch.Clear();
for (int64_t j = 0; j < entries_per_batch_; ++j) {
const int64_t k = seq ? i + j : (thread->rand.Next() % FLAGS_num);
GenerateKeyFromInt(k, FLAGS_num, &key);
batch.Delete(key);
}
Status s;
if (user_timestamp_size_ > 0) {
ts = mock_app_clock_->Allocate(ts_guard.get());
s = batch.AssignTimestamp(ts);
if (!s.ok()) {
fprintf(stderr, "assign timestamp: %s\n", s.ToString().c_str());
ErrorExit();
}
}
s = db->Write(write_options_, &batch);
thread->stats.FinishedOps(nullptr, db, entries_per_batch_, kDelete);
if (!s.ok()) {
fprintf(stderr, "del error: %s\n", s.ToString().c_str());
exit(1);
}
i += entries_per_batch_;
}
}
void DeleteSeq(ThreadState* thread) {
DoDelete(thread, true);
}
void DeleteRandom(ThreadState* thread) {
DoDelete(thread, false);
}
void ReadWhileWriting(ThreadState* thread) {
if (thread->tid > 0) {
ReadRandom(thread);
} else {
BGWriter(thread, kWrite);
}
}
void ReadWhileMerging(ThreadState* thread) {
if (thread->tid > 0) {
ReadRandom(thread);
} else {
BGWriter(thread, kMerge);
}
}
void BGWriter(ThreadState* thread, enum OperationType write_merge) {
// Special thread that keeps writing until other threads are done.
RandomGenerator gen;
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
2015-03-30 20:28:25 +02:00
int64_t bytes = 0;
std::unique_ptr<RateLimiter> write_rate_limiter;
if (FLAGS_benchmark_write_rate_limit > 0) {
write_rate_limiter.reset(
NewGenericRateLimiter(FLAGS_benchmark_write_rate_limit));
}
// Don't merge stats from this thread with the readers.
thread->stats.SetExcludeFromMerge();
std::unique_ptr<const char[]> key_guard;
Slice key = AllocateKey(&key_guard);
std::unique_ptr<char[]> ts_guard;
if (user_timestamp_size_ > 0) {
ts_guard.reset(new char[user_timestamp_size_]);
}
uint32_t written = 0;
bool hint_printed = false;
while (true) {
DB* db = SelectDB(thread);
{
MutexLock l(&thread->shared->mu);
if (FLAGS_finish_after_writes && written == writes_) {
fprintf(stderr, "Exiting the writer after %u writes...\n", written);
break;
}
if (thread->shared->num_done + 1 >= thread->shared->num_initialized) {
// Other threads have finished
if (FLAGS_finish_after_writes) {
// Wait for the writes to be finished
if (!hint_printed) {
fprintf(stderr, "Reads are finished. Have %d more writes to do\n",
static_cast<int>(writes_) - written);
hint_printed = true;
}
} else {
// Finish the write immediately
break;
}
}
}
GenerateKeyFromInt(thread->rand.Next() % FLAGS_num, FLAGS_num, &key);
Status s;
Slice val = gen.Generate();
Slice ts;
if (user_timestamp_size_ > 0) {
ts = mock_app_clock_->Allocate(ts_guard.get());
write_options_.timestamp = &ts;
}
if (write_merge == kWrite) {
s = db->Put(write_options_, key, val);
} else {
s = db->Merge(write_options_, key, val);
}
// Restore write_options_
if (user_timestamp_size_ > 0) {
write_options_.timestamp = nullptr;
}
written++;
if (!s.ok()) {
fprintf(stderr, "put or merge error: %s\n", s.ToString().c_str());
exit(1);
}
bytes += key.size() + val.size() + user_timestamp_size_;
thread->stats.FinishedOps(&db_, db_.db, 1, kWrite);
if (FLAGS_benchmark_write_rate_limit > 0) {
write_rate_limiter->Request(
key.size() + val.size(), Env::IO_HIGH,
nullptr /* stats */, RateLimiter::OpType::kWrite);
}
}
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
2015-03-30 20:28:25 +02:00
thread->stats.AddBytes(bytes);
}
void ReadWhileScanning(ThreadState* thread) {
if (thread->tid > 0) {
ReadRandom(thread);
} else {
BGScan(thread);
}
}
void BGScan(ThreadState* thread) {
if (FLAGS_num_multi_db > 0) {
fprintf(stderr, "Not supporting multiple DBs.\n");
abort();
}
assert(db_.db != nullptr);
ReadOptions read_options;
std::unique_ptr<char[]> ts_guard;
Slice ts;
if (user_timestamp_size_ > 0) {
ts_guard.reset(new char[user_timestamp_size_]);
ts = mock_app_clock_->GetTimestampForRead(thread->rand, ts_guard.get());
read_options.timestamp = &ts;
}
Iterator* iter = db_.db->NewIterator(read_options);
fprintf(stderr, "num reads to do %" PRIu64 "\n", reads_);
Duration duration(FLAGS_duration, reads_);
uint64_t num_seek_to_first = 0;
uint64_t num_next = 0;
while (!duration.Done(1)) {
if (!iter->Valid()) {
iter->SeekToFirst();
num_seek_to_first++;
} else if (!iter->status().ok()) {
fprintf(stderr, "Iterator error: %s\n",
iter->status().ToString().c_str());
abort();
} else {
iter->Next();
num_next++;
}
thread->stats.FinishedOps(&db_, db_.db, 1, kSeek);
}
delete iter;
}
// Given a key K and value V, this puts (K+"0", V), (K+"1", V), (K+"2", V)
// in DB atomically i.e in a single batch. Also refer GetMany.
Status PutMany(DB* db, const WriteOptions& writeoptions, const Slice& key,
const Slice& value) {
std::string suffixes[3] = {"2", "1", "0"};
std::string keys[3];
WriteBatch batch(/*reserved_bytes=*/0, /*max_bytes=*/0,
user_timestamp_size_);
Status s;
for (int i = 0; i < 3; i++) {
keys[i] = key.ToString() + suffixes[i];
batch.Put(keys[i], value);
}
std::unique_ptr<char[]> ts_guard;
if (user_timestamp_size_ > 0) {
ts_guard.reset(new char[user_timestamp_size_]);
Slice ts = mock_app_clock_->Allocate(ts_guard.get());
s = batch.AssignTimestamp(ts);
if (!s.ok()) {
fprintf(stderr, "assign timestamp to batch: %s\n",
s.ToString().c_str());
ErrorExit();
}
}
s = db->Write(writeoptions, &batch);
return s;
}
// Given a key K, this deletes (K+"0", V), (K+"1", V), (K+"2", V)
// in DB atomically i.e in a single batch. Also refer GetMany.
Status DeleteMany(DB* db, const WriteOptions& writeoptions,
const Slice& key) {
std::string suffixes[3] = {"1", "2", "0"};
std::string keys[3];
WriteBatch batch(0, 0, user_timestamp_size_);
Status s;
for (int i = 0; i < 3; i++) {
keys[i] = key.ToString() + suffixes[i];
batch.Delete(keys[i]);
}
std::unique_ptr<char[]> ts_guard;
if (user_timestamp_size_ > 0) {
ts_guard.reset(new char[user_timestamp_size_]);
Slice ts = mock_app_clock_->Allocate(ts_guard.get());
s = batch.AssignTimestamp(ts);
if (!s.ok()) {
fprintf(stderr, "assign timestamp to batch: %s\n",
s.ToString().c_str());
ErrorExit();
}
}
s = db->Write(writeoptions, &batch);
return s;
}
// Given a key K and value V, this gets values for K+"0", K+"1" and K+"2"
// in the same snapshot, and verifies that all the values are identical.
// ASSUMES that PutMany was used to put (K, V) into the DB.
Status GetMany(DB* db, const ReadOptions& readoptions, const Slice& key,
std::string* value) {
std::string suffixes[3] = {"0", "1", "2"};
std::string keys[3];
Slice key_slices[3];
std::string values[3];
ReadOptions readoptionscopy = readoptions;
std::unique_ptr<char[]> ts_guard;
Slice ts;
if (user_timestamp_size_ > 0) {
ts_guard.reset(new char[user_timestamp_size_]);
ts = mock_app_clock_->Allocate(ts_guard.get());
readoptionscopy.timestamp = &ts;
}
readoptionscopy.snapshot = db->GetSnapshot();
Status s;
for (int i = 0; i < 3; i++) {
keys[i] = key.ToString() + suffixes[i];
key_slices[i] = keys[i];
s = db->Get(readoptionscopy, key_slices[i], value);
if (!s.ok() && !s.IsNotFound()) {
fprintf(stderr, "get error: %s\n", s.ToString().c_str());
values[i] = "";
// we continue after error rather than exiting so that we can
// find more errors if any
} else if (s.IsNotFound()) {
values[i] = "";
} else {
values[i] = *value;
}
}
db->ReleaseSnapshot(readoptionscopy.snapshot);
if ((values[0] != values[1]) || (values[1] != values[2])) {
fprintf(stderr, "inconsistent values for key %s: %s, %s, %s\n",
key.ToString().c_str(), values[0].c_str(), values[1].c_str(),
values[2].c_str());
// we continue after error rather than exiting so that we can
// find more errors if any
}
return s;
}
// Differs from readrandomwriterandom in the following ways:
// (a) Uses GetMany/PutMany to read/write key values. Refer to those funcs.
// (b) Does deletes as well (per FLAGS_deletepercent)
// (c) In order to achieve high % of 'found' during lookups, and to do
// multiple writes (including puts and deletes) it uses upto
// FLAGS_numdistinct distinct keys instead of FLAGS_num distinct keys.
// (d) Does not have a MultiGet option.
void RandomWithVerify(ThreadState* thread) {
ReadOptions options(FLAGS_verify_checksum, true);
RandomGenerator gen;
std::string value;
int64_t found = 0;
int get_weight = 0;
int put_weight = 0;
int delete_weight = 0;
int64_t gets_done = 0;
int64_t puts_done = 0;
int64_t deletes_done = 0;
std::unique_ptr<const char[]> key_guard;
Slice key = AllocateKey(&key_guard);
// the number of iterations is the larger of read_ or write_
for (int64_t i = 0; i < readwrites_; i++) {
DB* db = SelectDB(thread);
if (get_weight == 0 && put_weight == 0 && delete_weight == 0) {
// one batch completed, reinitialize for next batch
get_weight = FLAGS_readwritepercent;
delete_weight = FLAGS_deletepercent;
put_weight = 100 - get_weight - delete_weight;
}
GenerateKeyFromInt(thread->rand.Next() % FLAGS_numdistinct,
FLAGS_numdistinct, &key);
if (get_weight > 0) {
// do all the gets first
Status s = GetMany(db, options, key, &value);
if (!s.ok() && !s.IsNotFound()) {
fprintf(stderr, "getmany error: %s\n", s.ToString().c_str());
// we continue after error rather than exiting so that we can
// find more errors if any
} else if (!s.IsNotFound()) {
found++;
}
get_weight--;
gets_done++;
thread->stats.FinishedOps(&db_, db_.db, 1, kRead);
} else if (put_weight > 0) {
// then do all the corresponding number of puts
// for all the gets we have done earlier
Status s = PutMany(db, write_options_, key, gen.Generate());
if (!s.ok()) {
fprintf(stderr, "putmany error: %s\n", s.ToString().c_str());
exit(1);
}
put_weight--;
puts_done++;
thread->stats.FinishedOps(&db_, db_.db, 1, kWrite);
} else if (delete_weight > 0) {
Status s = DeleteMany(db, write_options_, key);
if (!s.ok()) {
fprintf(stderr, "deletemany error: %s\n", s.ToString().c_str());
exit(1);
}
delete_weight--;
deletes_done++;
thread->stats.FinishedOps(&db_, db_.db, 1, kDelete);
}
}
char msg[128];
Pull from https://reviews.facebook.net/D10917 Summary: Pull Mark's patch and slightly revise it. I revised another place in db_impl.cc with similar new formula. Test Plan: make all check. Also run "time ./db_bench --num=2500000000 --numdistinct=2200000000". It has run for 20+ hours and hasn't finished. Looks good so far: Installed stack trace handler for SIGILL SIGSEGV SIGBUS SIGABRT LevelDB: version 2.0 Date: Tue Aug 20 23:11:55 2013 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 2500000000 RawSize: 276565.6 MB (estimated) FileSize: 157356.3 MB (estimated) Write rate limit: 0 Compression: snappy WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ DB path: [/tmp/leveldbtest-3088/dbbench] fillseq : 7202.000 micros/op 138 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] fillsync : 7148.000 micros/op 139 ops/sec; (2500000 ops) DB path: [/tmp/leveldbtest-3088/dbbench] fillrandom : 7105.000 micros/op 140 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] overwrite : 6930.000 micros/op 144 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] readrandom : 1.020 micros/op 980507 ops/sec; (0 of 2500000000 found) DB path: [/tmp/leveldbtest-3088/dbbench] readrandom : 1.021 micros/op 979620 ops/sec; (0 of 2500000000 found) DB path: [/tmp/leveldbtest-3088/dbbench] readseq : 113.000 micros/op 8849 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] readreverse : 102.000 micros/op 9803 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] Created bg thread 0x7f0ac17f7700 compact : 111701.000 micros/op 8 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] readrandom : 1.020 micros/op 980376 ops/sec; (0 of 2500000000 found) DB path: [/tmp/leveldbtest-3088/dbbench] readseq : 120.000 micros/op 8333 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] readreverse : 29.000 micros/op 34482 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] ... finished 618100000 ops Reviewers: MarkCallaghan, haobo, dhruba, chip Reviewed By: dhruba Differential Revision: https://reviews.facebook.net/D12441
2013-08-23 07:37:13 +02:00
snprintf(msg, sizeof(msg),
"( get:%" PRIu64 " put:%" PRIu64 " del:%" PRIu64 " total:%" \
PRIu64 " found:%" PRIu64 ")",
gets_done, puts_done, deletes_done, readwrites_, found);
thread->stats.AddMessage(msg);
}
// This is different from ReadWhileWriting because it does not use
// an extra thread.
void ReadRandomWriteRandom(ThreadState* thread) {
ReadOptions options(FLAGS_verify_checksum, true);
RandomGenerator gen;
std::string value;
int64_t found = 0;
int get_weight = 0;
int put_weight = 0;
int64_t reads_done = 0;
int64_t writes_done = 0;
Duration duration(FLAGS_duration, readwrites_);
std::unique_ptr<const char[]> key_guard;
Slice key = AllocateKey(&key_guard);
std::unique_ptr<char[]> ts_guard;
if (user_timestamp_size_ > 0) {
ts_guard.reset(new char[user_timestamp_size_]);
}
// the number of iterations is the larger of read_ or write_
while (!duration.Done(1)) {
DB* db = SelectDB(thread);
GenerateKeyFromInt(thread->rand.Next() % FLAGS_num, FLAGS_num, &key);
if (get_weight == 0 && put_weight == 0) {
// one batch completed, reinitialize for next batch
get_weight = FLAGS_readwritepercent;
put_weight = 100 - get_weight;
}
if (get_weight > 0) {
// do all the gets first
Slice ts;
if (user_timestamp_size_ > 0) {
ts = mock_app_clock_->GetTimestampForRead(thread->rand,
ts_guard.get());
options.timestamp = &ts;
}
Status s = db->Get(options, key, &value);
if (!s.ok() && !s.IsNotFound()) {
fprintf(stderr, "get error: %s\n", s.ToString().c_str());
// we continue after error rather than exiting so that we can
// find more errors if any
} else if (!s.IsNotFound()) {
found++;
}
get_weight--;
reads_done++;
thread->stats.FinishedOps(nullptr, db, 1, kRead);
} else if (put_weight > 0) {
// then do all the corresponding number of puts
// for all the gets we have done earlier
Slice ts;
if (user_timestamp_size_ > 0) {
ts = mock_app_clock_->Allocate(ts_guard.get());
write_options_.timestamp = &ts;
}
Status s = db->Put(write_options_, key, gen.Generate());
if (!s.ok()) {
fprintf(stderr, "put error: %s\n", s.ToString().c_str());
ErrorExit();
}
put_weight--;
writes_done++;
thread->stats.FinishedOps(nullptr, db, 1, kWrite);
}
}
char msg[100];
snprintf(msg, sizeof(msg), "( reads:%" PRIu64 " writes:%" PRIu64 \
" total:%" PRIu64 " found:%" PRIu64 ")",
reads_done, writes_done, readwrites_, found);
thread->stats.AddMessage(msg);
}
//
// Read-modify-write for random keys
void UpdateRandom(ThreadState* thread) {
ReadOptions options(FLAGS_verify_checksum, true);
RandomGenerator gen;
std::string value;
int64_t found = 0;
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
2015-03-30 20:28:25 +02:00
int64_t bytes = 0;
Duration duration(FLAGS_duration, readwrites_);
std::unique_ptr<const char[]> key_guard;
Slice key = AllocateKey(&key_guard);
std::unique_ptr<char[]> ts_guard;
if (user_timestamp_size_ > 0) {
ts_guard.reset(new char[user_timestamp_size_]);
}
// the number of iterations is the larger of read_ or write_
while (!duration.Done(1)) {
DB* db = SelectDB(thread);
GenerateKeyFromInt(thread->rand.Next() % FLAGS_num, FLAGS_num, &key);
Slice ts;
if (user_timestamp_size_ > 0) {
// Read with newest timestamp because we are doing rmw.
ts = mock_app_clock_->Allocate(ts_guard.get());
options.timestamp = &ts;
}
auto status = db->Get(options, key, &value);
if (status.ok()) {
++found;
bytes += key.size() + value.size() + user_timestamp_size_;
} else if (!status.IsNotFound()) {
2015-01-22 03:23:12 +01:00
fprintf(stderr, "Get returned an error: %s\n",
status.ToString().c_str());
abort();
}
if (thread->shared->write_rate_limiter) {
thread->shared->write_rate_limiter->Request(
key.size() + value.size(), Env::IO_HIGH, nullptr /*stats*/,
RateLimiter::OpType::kWrite);
}
Slice val = gen.Generate();
if (user_timestamp_size_ > 0) {
ts = mock_app_clock_->Allocate(ts_guard.get());
write_options_.timestamp = &ts;
}
Status s = db->Put(write_options_, key, val);
if (!s.ok()) {
fprintf(stderr, "put error: %s\n", s.ToString().c_str());
exit(1);
}
bytes += key.size() + val.size() + user_timestamp_size_;
thread->stats.FinishedOps(nullptr, db, 1, kUpdate);
}
char msg[100];
Pull from https://reviews.facebook.net/D10917 Summary: Pull Mark's patch and slightly revise it. I revised another place in db_impl.cc with similar new formula. Test Plan: make all check. Also run "time ./db_bench --num=2500000000 --numdistinct=2200000000". It has run for 20+ hours and hasn't finished. Looks good so far: Installed stack trace handler for SIGILL SIGSEGV SIGBUS SIGABRT LevelDB: version 2.0 Date: Tue Aug 20 23:11:55 2013 CPU: 32 * Intel(R) Xeon(R) CPU E5-2660 0 @ 2.20GHz CPUCache: 20480 KB Keys: 16 bytes each Values: 100 bytes each (50 bytes after compression) Entries: 2500000000 RawSize: 276565.6 MB (estimated) FileSize: 157356.3 MB (estimated) Write rate limit: 0 Compression: snappy WARNING: Assertions are enabled; benchmarks unnecessarily slow ------------------------------------------------ DB path: [/tmp/leveldbtest-3088/dbbench] fillseq : 7202.000 micros/op 138 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] fillsync : 7148.000 micros/op 139 ops/sec; (2500000 ops) DB path: [/tmp/leveldbtest-3088/dbbench] fillrandom : 7105.000 micros/op 140 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] overwrite : 6930.000 micros/op 144 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] readrandom : 1.020 micros/op 980507 ops/sec; (0 of 2500000000 found) DB path: [/tmp/leveldbtest-3088/dbbench] readrandom : 1.021 micros/op 979620 ops/sec; (0 of 2500000000 found) DB path: [/tmp/leveldbtest-3088/dbbench] readseq : 113.000 micros/op 8849 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] readreverse : 102.000 micros/op 9803 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] Created bg thread 0x7f0ac17f7700 compact : 111701.000 micros/op 8 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] readrandom : 1.020 micros/op 980376 ops/sec; (0 of 2500000000 found) DB path: [/tmp/leveldbtest-3088/dbbench] readseq : 120.000 micros/op 8333 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] readreverse : 29.000 micros/op 34482 ops/sec; DB path: [/tmp/leveldbtest-3088/dbbench] ... finished 618100000 ops Reviewers: MarkCallaghan, haobo, dhruba, chip Reviewed By: dhruba Differential Revision: https://reviews.facebook.net/D12441
2013-08-23 07:37:13 +02:00
snprintf(msg, sizeof(msg),
"( updates:%" PRIu64 " found:%" PRIu64 ")", readwrites_, found);
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
2015-03-30 20:28:25 +02:00
thread->stats.AddBytes(bytes);
thread->stats.AddMessage(msg);
}
// Read-XOR-write for random keys. Xors the existing value with a randomly
// generated value, and stores the result. Assuming A in the array of bytes
// representing the existing value, we generate an array B of the same size,
// then compute C = A^B as C[i]=A[i]^B[i], and store C
void XORUpdateRandom(ThreadState* thread) {
ReadOptions options(FLAGS_verify_checksum, true);
RandomGenerator gen;
std::string existing_value;
int64_t found = 0;
Duration duration(FLAGS_duration, readwrites_);
BytesXOROperator xor_operator;
std::unique_ptr<const char[]> key_guard;
Slice key = AllocateKey(&key_guard);
std::unique_ptr<char[]> ts_guard;
if (user_timestamp_size_ > 0) {
ts_guard.reset(new char[user_timestamp_size_]);
}
// the number of iterations is the larger of read_ or write_
while (!duration.Done(1)) {
DB* db = SelectDB(thread);
GenerateKeyFromInt(thread->rand.Next() % FLAGS_num, FLAGS_num, &key);
Slice ts;
if (user_timestamp_size_ > 0) {
ts = mock_app_clock_->Allocate(ts_guard.get());
options.timestamp = &ts;
}
auto status = db->Get(options, key, &existing_value);
if (status.ok()) {
++found;
} else if (!status.IsNotFound()) {
fprintf(stderr, "Get returned an error: %s\n",
status.ToString().c_str());
exit(1);
}
Slice value = gen.Generate(static_cast<unsigned int>(existing_value.size()));
std::string new_value;
if (status.ok()) {
Slice existing_value_slice = Slice(existing_value);
xor_operator.XOR(&existing_value_slice, value, &new_value);
} else {
xor_operator.XOR(nullptr, value, &new_value);
}
if (user_timestamp_size_ > 0) {
ts = mock_app_clock_->Allocate(ts_guard.get());
write_options_.timestamp = &ts;
}
Status s = db->Put(write_options_, key, Slice(new_value));
if (!s.ok()) {
fprintf(stderr, "put error: %s\n", s.ToString().c_str());
ErrorExit();
}
thread->stats.FinishedOps(nullptr, db, 1);
}
char msg[100];
snprintf(msg, sizeof(msg),
"( updates:%" PRIu64 " found:%" PRIu64 ")", readwrites_, found);
thread->stats.AddMessage(msg);
}
// Read-modify-write for random keys.
// Each operation causes the key grow by value_size (simulating an append).
// Generally used for benchmarking against merges of similar type
void AppendRandom(ThreadState* thread) {
ReadOptions options(FLAGS_verify_checksum, true);
RandomGenerator gen;
std::string value;
int64_t found = 0;
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
2015-03-30 20:28:25 +02:00
int64_t bytes = 0;
std::unique_ptr<const char[]> key_guard;
Slice key = AllocateKey(&key_guard);
std::unique_ptr<char[]> ts_guard;
if (user_timestamp_size_ > 0) {
ts_guard.reset(new char[user_timestamp_size_]);
}
// The number of iterations is the larger of read_ or write_
Duration duration(FLAGS_duration, readwrites_);
while (!duration.Done(1)) {
DB* db = SelectDB(thread);
GenerateKeyFromInt(thread->rand.Next() % FLAGS_num, FLAGS_num, &key);
Slice ts;
if (user_timestamp_size_ > 0) {
ts = mock_app_clock_->Allocate(ts_guard.get());
options.timestamp = &ts;
}
auto status = db->Get(options, key, &value);
if (status.ok()) {
++found;
bytes += key.size() + value.size() + user_timestamp_size_;
} else if (!status.IsNotFound()) {
2015-01-22 03:23:12 +01:00
fprintf(stderr, "Get returned an error: %s\n",
status.ToString().c_str());
abort();
} else {
// If not existing, then just assume an empty string of data
value.clear();
}
// Update the value (by appending data)
Slice operand = gen.Generate();
if (value.size() > 0) {
// Use a delimiter to match the semantics for StringAppendOperator
value.append(1,',');
}
value.append(operand.data(), operand.size());
if (user_timestamp_size_ > 0) {
ts = mock_app_clock_->Allocate(ts_guard.get());
write_options_.timestamp = &ts;
}
// Write back to the database
Status s = db->Put(write_options_, key, value);
if (!s.ok()) {
fprintf(stderr, "put error: %s\n", s.ToString().c_str());
ErrorExit();
}
bytes += key.size() + value.size() + user_timestamp_size_;
thread->stats.FinishedOps(nullptr, db, 1, kUpdate);
}
char msg[100];
snprintf(msg, sizeof(msg), "( updates:%" PRIu64 " found:%" PRIu64 ")",
readwrites_, found);
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
2015-03-30 20:28:25 +02:00
thread->stats.AddBytes(bytes);
thread->stats.AddMessage(msg);
}
// Read-modify-write for random keys (using MergeOperator)
// The merge operator to use should be defined by FLAGS_merge_operator
// Adjust FLAGS_value_size so that the keys are reasonable for this operator
// Assumes that the merge operator is non-null (i.e.: is well-defined)
//
// For example, use FLAGS_merge_operator="uint64add" and FLAGS_value_size=8
// to simulate random additions over 64-bit integers using merge.
//
// The number of merges on the same key can be controlled by adjusting
// FLAGS_merge_keys.
void MergeRandom(ThreadState* thread) {
RandomGenerator gen;
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
2015-03-30 20:28:25 +02:00
int64_t bytes = 0;
std::unique_ptr<const char[]> key_guard;
Slice key = AllocateKey(&key_guard);
// The number of iterations is the larger of read_ or write_
Duration duration(FLAGS_duration, readwrites_);
while (!duration.Done(1)) {
DBWithColumnFamilies* db_with_cfh = SelectDBWithCfh(thread);
int64_t key_rand = thread->rand.Next() % merge_keys_;
GenerateKeyFromInt(key_rand, merge_keys_, &key);
Status s;
Slice val = gen.Generate();
if (FLAGS_num_column_families > 1) {
s = db_with_cfh->db->Merge(write_options_,
db_with_cfh->GetCfh(key_rand), key,
val);
} else {
s = db_with_cfh->db->Merge(write_options_,
db_with_cfh->db->DefaultColumnFamily(), key,
val);
}
if (!s.ok()) {
fprintf(stderr, "merge error: %s\n", s.ToString().c_str());
exit(1);
}
bytes += key.size() + val.size();
thread->stats.FinishedOps(nullptr, db_with_cfh->db, 1, kMerge);
}
// Print some statistics
char msg[100];
snprintf(msg, sizeof(msg), "( updates:%" PRIu64 ")", readwrites_);
Make the benchmark scripts configurable and add tests Summary: This makes run_flash_bench.sh configurable. Previously it was hardwired for 1B keys and tests ran for 12 hours each. That kept me from using it. This makes it configuable, adds more tests, makes the duration per-test configurable and refactors the test scripts. Adds the seekrandomwhilemerging test to db_bench which is the same as seekrandomwhilewriting except the writer thread does Merge rather than Put. Forces the stall-time column in compaction IO stats to use a fixed format (H:M:S) which makes it easier to scrape and parse. Also adds an option to AppendHumanMicros to force a fixed format. Sometimes automation and humans want different format. Calls thread->stats.AddBytes(bytes); in db_bench for more tests to get the MB/sec summary stats in the output at test end. Adds the average ingest rate to compaction IO stats. Output now looks like: https://gist.github.com/mdcallag/2bd64d18be1b93adc494 More information on the benchmark output is at https://gist.github.com/mdcallag/db43a58bd5ac624f01e1 For benchmark.sh changes default RocksDB configuration to reduce stalls: * min_level_to_compress from 2 to 3 * hard_rate_limit from 2 to 3 * max_grandparent_overlap_factor and max_bytes_for_level_multiplier from 10 to 8 * L0 file count triggers from 4,8,12 to 4,12,20 for (start,stall,stop) Task ID: #6596829 Blame Rev: Test Plan: run tools/run_flash_bench.sh Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: igor Reviewed By: igor Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D36075
2015-03-30 20:28:25 +02:00
thread->stats.AddBytes(bytes);
thread->stats.AddMessage(msg);
}
// Read and merge random keys. The amount of reads and merges are controlled
// by adjusting FLAGS_num and FLAGS_mergereadpercent. The number of distinct
// keys (and thus also the number of reads and merges on the same key) can be
// adjusted with FLAGS_merge_keys.
//
// As with MergeRandom, the merge operator to use should be defined by
// FLAGS_merge_operator.
void ReadRandomMergeRandom(ThreadState* thread) {
ReadOptions options(FLAGS_verify_checksum, true);
RandomGenerator gen;
std::string value;
int64_t num_hits = 0;
int64_t num_gets = 0;
int64_t num_merges = 0;
size_t max_length = 0;
std::unique_ptr<const char[]> key_guard;
Slice key = AllocateKey(&key_guard);
// the number of iterations is the larger of read_ or write_
Duration duration(FLAGS_duration, readwrites_);
while (!duration.Done(1)) {
DB* db = SelectDB(thread);
GenerateKeyFromInt(thread->rand.Next() % merge_keys_, merge_keys_, &key);
bool do_merge = int(thread->rand.Next() % 100) < FLAGS_mergereadpercent;
if (do_merge) {
Status s = db->Merge(write_options_, key, gen.Generate());
if (!s.ok()) {
fprintf(stderr, "merge error: %s\n", s.ToString().c_str());
exit(1);
}
num_merges++;
thread->stats.FinishedOps(nullptr, db, 1, kMerge);
} else {
Status s = db->Get(options, key, &value);
if (value.length() > max_length)
max_length = value.length();
if (!s.ok() && !s.IsNotFound()) {
fprintf(stderr, "get error: %s\n", s.ToString().c_str());
// we continue after error rather than exiting so that we can
// find more errors if any
} else if (!s.IsNotFound()) {
num_hits++;
}
num_gets++;
thread->stats.FinishedOps(nullptr, db, 1, kRead);
}
}
char msg[100];
snprintf(msg, sizeof(msg),
"(reads:%" PRIu64 " merges:%" PRIu64 " total:%" PRIu64
" hits:%" PRIu64 " maxlength:%" ROCKSDB_PRIszt ")",
num_gets, num_merges, readwrites_, num_hits, max_length);
thread->stats.AddMessage(msg);
}
SkipListRep::LookaheadIterator Summary: This diff introduces the `lookahead` argument to `SkipListFactory()`. This is an optimization for the tailing use case which includes many seeks. E.g. consider the following operations on a skip list iterator: Seek(x), Next(), Next(), Seek(x+2), Next(), Seek(x+3), Next(), Next(), ... If `lookahead` is positive, `SkipListRep` will return an iterator which also keeps track of the previously visited node. Seek() then first does a linear search starting from that node (up to `lookahead` steps). As in the tailing example above, this may require fewer than ~log(n) comparisons as with regular skip list search. Test Plan: Added a new benchmark (`fillseekseq`) which simulates the usage pattern. It first writes N records (with consecutive keys), then measures how much time it takes to read them by calling `Seek()` and `Next()`. $ time ./db_bench -num 10000000 -benchmarks fillseekseq -prefix_size 1 \ -key_size 8 -write_buffer_size $[1024*1024*1024] -value_size 50 \ -seekseq_next 2 -skip_list_lookahead=0 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.389 micros/op 2569047 ops/sec; real 0m21.806s user 0m12.106s sys 0m9.672s $ time ./db_bench [...] -skip_list_lookahead=2 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.153 micros/op 6540684 ops/sec; real 0m19.469s user 0m10.192s sys 0m9.252s Reviewers: ljin, sdong, igor Reviewed By: igor Subscribers: dhruba, leveldb, march, lovro Differential Revision: https://reviews.facebook.net/D23997
2014-09-24 00:52:28 +02:00
void WriteSeqSeekSeq(ThreadState* thread) {
writes_ = FLAGS_num;
DoWrite(thread, SEQUENTIAL);
// exclude writes from the ops/sec calculation
thread->stats.Start(thread->tid);
DB* db = SelectDB(thread);
ReadOptions read_opts(FLAGS_verify_checksum, true);
std::unique_ptr<char[]> ts_guard;
Slice ts;
if (user_timestamp_size_ > 0) {
ts_guard.reset(new char[user_timestamp_size_]);
ts = mock_app_clock_->GetTimestampForRead(thread->rand, ts_guard.get());
read_opts.timestamp = &ts;
}
std::unique_ptr<Iterator> iter(db->NewIterator(read_opts));
SkipListRep::LookaheadIterator Summary: This diff introduces the `lookahead` argument to `SkipListFactory()`. This is an optimization for the tailing use case which includes many seeks. E.g. consider the following operations on a skip list iterator: Seek(x), Next(), Next(), Seek(x+2), Next(), Seek(x+3), Next(), Next(), ... If `lookahead` is positive, `SkipListRep` will return an iterator which also keeps track of the previously visited node. Seek() then first does a linear search starting from that node (up to `lookahead` steps). As in the tailing example above, this may require fewer than ~log(n) comparisons as with regular skip list search. Test Plan: Added a new benchmark (`fillseekseq`) which simulates the usage pattern. It first writes N records (with consecutive keys), then measures how much time it takes to read them by calling `Seek()` and `Next()`. $ time ./db_bench -num 10000000 -benchmarks fillseekseq -prefix_size 1 \ -key_size 8 -write_buffer_size $[1024*1024*1024] -value_size 50 \ -seekseq_next 2 -skip_list_lookahead=0 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.389 micros/op 2569047 ops/sec; real 0m21.806s user 0m12.106s sys 0m9.672s $ time ./db_bench [...] -skip_list_lookahead=2 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.153 micros/op 6540684 ops/sec; real 0m19.469s user 0m10.192s sys 0m9.252s Reviewers: ljin, sdong, igor Reviewed By: igor Subscribers: dhruba, leveldb, march, lovro Differential Revision: https://reviews.facebook.net/D23997
2014-09-24 00:52:28 +02:00
std::unique_ptr<const char[]> key_guard;
Slice key = AllocateKey(&key_guard);
SkipListRep::LookaheadIterator Summary: This diff introduces the `lookahead` argument to `SkipListFactory()`. This is an optimization for the tailing use case which includes many seeks. E.g. consider the following operations on a skip list iterator: Seek(x), Next(), Next(), Seek(x+2), Next(), Seek(x+3), Next(), Next(), ... If `lookahead` is positive, `SkipListRep` will return an iterator which also keeps track of the previously visited node. Seek() then first does a linear search starting from that node (up to `lookahead` steps). As in the tailing example above, this may require fewer than ~log(n) comparisons as with regular skip list search. Test Plan: Added a new benchmark (`fillseekseq`) which simulates the usage pattern. It first writes N records (with consecutive keys), then measures how much time it takes to read them by calling `Seek()` and `Next()`. $ time ./db_bench -num 10000000 -benchmarks fillseekseq -prefix_size 1 \ -key_size 8 -write_buffer_size $[1024*1024*1024] -value_size 50 \ -seekseq_next 2 -skip_list_lookahead=0 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.389 micros/op 2569047 ops/sec; real 0m21.806s user 0m12.106s sys 0m9.672s $ time ./db_bench [...] -skip_list_lookahead=2 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.153 micros/op 6540684 ops/sec; real 0m19.469s user 0m10.192s sys 0m9.252s Reviewers: ljin, sdong, igor Reviewed By: igor Subscribers: dhruba, leveldb, march, lovro Differential Revision: https://reviews.facebook.net/D23997
2014-09-24 00:52:28 +02:00
for (int64_t i = 0; i < FLAGS_num; ++i) {
GenerateKeyFromInt(i, FLAGS_num, &key);
iter->Seek(key);
assert(iter->Valid() && iter->key() == key);
thread->stats.FinishedOps(nullptr, db, 1, kSeek);
SkipListRep::LookaheadIterator Summary: This diff introduces the `lookahead` argument to `SkipListFactory()`. This is an optimization for the tailing use case which includes many seeks. E.g. consider the following operations on a skip list iterator: Seek(x), Next(), Next(), Seek(x+2), Next(), Seek(x+3), Next(), Next(), ... If `lookahead` is positive, `SkipListRep` will return an iterator which also keeps track of the previously visited node. Seek() then first does a linear search starting from that node (up to `lookahead` steps). As in the tailing example above, this may require fewer than ~log(n) comparisons as with regular skip list search. Test Plan: Added a new benchmark (`fillseekseq`) which simulates the usage pattern. It first writes N records (with consecutive keys), then measures how much time it takes to read them by calling `Seek()` and `Next()`. $ time ./db_bench -num 10000000 -benchmarks fillseekseq -prefix_size 1 \ -key_size 8 -write_buffer_size $[1024*1024*1024] -value_size 50 \ -seekseq_next 2 -skip_list_lookahead=0 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.389 micros/op 2569047 ops/sec; real 0m21.806s user 0m12.106s sys 0m9.672s $ time ./db_bench [...] -skip_list_lookahead=2 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.153 micros/op 6540684 ops/sec; real 0m19.469s user 0m10.192s sys 0m9.252s Reviewers: ljin, sdong, igor Reviewed By: igor Subscribers: dhruba, leveldb, march, lovro Differential Revision: https://reviews.facebook.net/D23997
2014-09-24 00:52:28 +02:00
for (int j = 0; j < FLAGS_seek_nexts && i + 1 < FLAGS_num; ++j) {
if (!FLAGS_reverse_iterator) {
iter->Next();
} else {
iter->Prev();
}
SkipListRep::LookaheadIterator Summary: This diff introduces the `lookahead` argument to `SkipListFactory()`. This is an optimization for the tailing use case which includes many seeks. E.g. consider the following operations on a skip list iterator: Seek(x), Next(), Next(), Seek(x+2), Next(), Seek(x+3), Next(), Next(), ... If `lookahead` is positive, `SkipListRep` will return an iterator which also keeps track of the previously visited node. Seek() then first does a linear search starting from that node (up to `lookahead` steps). As in the tailing example above, this may require fewer than ~log(n) comparisons as with regular skip list search. Test Plan: Added a new benchmark (`fillseekseq`) which simulates the usage pattern. It first writes N records (with consecutive keys), then measures how much time it takes to read them by calling `Seek()` and `Next()`. $ time ./db_bench -num 10000000 -benchmarks fillseekseq -prefix_size 1 \ -key_size 8 -write_buffer_size $[1024*1024*1024] -value_size 50 \ -seekseq_next 2 -skip_list_lookahead=0 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.389 micros/op 2569047 ops/sec; real 0m21.806s user 0m12.106s sys 0m9.672s $ time ./db_bench [...] -skip_list_lookahead=2 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.153 micros/op 6540684 ops/sec; real 0m19.469s user 0m10.192s sys 0m9.252s Reviewers: ljin, sdong, igor Reviewed By: igor Subscribers: dhruba, leveldb, march, lovro Differential Revision: https://reviews.facebook.net/D23997
2014-09-24 00:52:28 +02:00
GenerateKeyFromInt(++i, FLAGS_num, &key);
assert(iter->Valid() && iter->key() == key);
thread->stats.FinishedOps(nullptr, db, 1, kSeek);
SkipListRep::LookaheadIterator Summary: This diff introduces the `lookahead` argument to `SkipListFactory()`. This is an optimization for the tailing use case which includes many seeks. E.g. consider the following operations on a skip list iterator: Seek(x), Next(), Next(), Seek(x+2), Next(), Seek(x+3), Next(), Next(), ... If `lookahead` is positive, `SkipListRep` will return an iterator which also keeps track of the previously visited node. Seek() then first does a linear search starting from that node (up to `lookahead` steps). As in the tailing example above, this may require fewer than ~log(n) comparisons as with regular skip list search. Test Plan: Added a new benchmark (`fillseekseq`) which simulates the usage pattern. It first writes N records (with consecutive keys), then measures how much time it takes to read them by calling `Seek()` and `Next()`. $ time ./db_bench -num 10000000 -benchmarks fillseekseq -prefix_size 1 \ -key_size 8 -write_buffer_size $[1024*1024*1024] -value_size 50 \ -seekseq_next 2 -skip_list_lookahead=0 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.389 micros/op 2569047 ops/sec; real 0m21.806s user 0m12.106s sys 0m9.672s $ time ./db_bench [...] -skip_list_lookahead=2 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.153 micros/op 6540684 ops/sec; real 0m19.469s user 0m10.192s sys 0m9.252s Reviewers: ljin, sdong, igor Reviewed By: igor Subscribers: dhruba, leveldb, march, lovro Differential Revision: https://reviews.facebook.net/D23997
2014-09-24 00:52:28 +02:00
}
iter->Seek(key);
assert(iter->Valid() && iter->key() == key);
thread->stats.FinishedOps(nullptr, db, 1, kSeek);
SkipListRep::LookaheadIterator Summary: This diff introduces the `lookahead` argument to `SkipListFactory()`. This is an optimization for the tailing use case which includes many seeks. E.g. consider the following operations on a skip list iterator: Seek(x), Next(), Next(), Seek(x+2), Next(), Seek(x+3), Next(), Next(), ... If `lookahead` is positive, `SkipListRep` will return an iterator which also keeps track of the previously visited node. Seek() then first does a linear search starting from that node (up to `lookahead` steps). As in the tailing example above, this may require fewer than ~log(n) comparisons as with regular skip list search. Test Plan: Added a new benchmark (`fillseekseq`) which simulates the usage pattern. It first writes N records (with consecutive keys), then measures how much time it takes to read them by calling `Seek()` and `Next()`. $ time ./db_bench -num 10000000 -benchmarks fillseekseq -prefix_size 1 \ -key_size 8 -write_buffer_size $[1024*1024*1024] -value_size 50 \ -seekseq_next 2 -skip_list_lookahead=0 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.389 micros/op 2569047 ops/sec; real 0m21.806s user 0m12.106s sys 0m9.672s $ time ./db_bench [...] -skip_list_lookahead=2 [...] DB path: [/dev/shm/rocksdbtest/dbbench] fillseekseq : 0.153 micros/op 6540684 ops/sec; real 0m19.469s user 0m10.192s sys 0m9.252s Reviewers: ljin, sdong, igor Reviewed By: igor Subscribers: dhruba, leveldb, march, lovro Differential Revision: https://reviews.facebook.net/D23997
2014-09-24 00:52:28 +02:00
}
}
New API to get all merge operands for a Key (#5604) Summary: This is a new API added to db.h to allow for fetching all merge operands associated with a Key. The main motivation for this API is to support use cases where doing a full online merge is not necessary as it is performance sensitive. Example use-cases: 1. Update subset of columns and read subset of columns - Imagine a SQL Table, a row is encoded as a K/V pair (as it is done in MyRocks). If there are many columns and users only updated one of them, we can use merge operator to reduce write amplification. While users only read one or two columns in the read query, this feature can avoid a full merging of the whole row, and save some CPU. 2. Updating very few attributes in a value which is a JSON-like document - Updating one attribute can be done efficiently using merge operator, while reading back one attribute can be done more efficiently if we don't need to do a full merge. ---------------------------------------------------------------------------------------------------- API : Status GetMergeOperands( const ReadOptions& options, ColumnFamilyHandle* column_family, const Slice& key, PinnableSlice* merge_operands, GetMergeOperandsOptions* get_merge_operands_options, int* number_of_operands) Example usage : int size = 100; int number_of_operands = 0; std::vector<PinnableSlice> values(size); GetMergeOperandsOptions merge_operands_info; db_->GetMergeOperands(ReadOptions(), db_->DefaultColumnFamily(), "k1", values.data(), merge_operands_info, &number_of_operands); Description : Returns all the merge operands corresponding to the key. If the number of merge operands in DB is greater than merge_operands_options.expected_max_number_of_operands no merge operands are returned and status is Incomplete. Merge operands returned are in the order of insertion. merge_operands-> Points to an array of at-least merge_operands_options.expected_max_number_of_operands and the caller is responsible for allocating it. If the status returned is Incomplete then number_of_operands will contain the total number of merge operands found in DB for key. Pull Request resolved: https://github.com/facebook/rocksdb/pull/5604 Test Plan: Added unit test and perf test in db_bench that can be run using the command: ./db_bench -benchmarks=getmergeoperands --merge_operator=sortlist Differential Revision: D16657366 Pulled By: vjnadimpalli fbshipit-source-id: 0faadd752351745224ee12d4ae9ef3cb529951bf
2019-08-06 23:22:34 +02:00
bool binary_search(std::vector<int>& data, int start, int end, int key) {
if (data.empty()) return false;
if (start > end) return false;
int mid = start + (end - start) / 2;
if (mid > static_cast<int>(data.size()) - 1) return false;
if (data[mid] == key) {
return true;
} else if (data[mid] > key) {
return binary_search(data, start, mid - 1, key);
} else {
return binary_search(data, mid + 1, end, key);
}
}
// Does a bunch of merge operations for a key(key1) where the merge operand
// is a sorted list. Next performance comparison is done between doing a Get
// for key1 followed by searching for another key(key2) in the large sorted
// list vs calling GetMergeOperands for key1 and then searching for the key2
// in all the sorted sub-lists. Later case is expected to be a lot faster.
void GetMergeOperands(ThreadState* thread) {
DB* db = SelectDB(thread);
const int kTotalValues = 100000;
const int kListSize = 100;
std::string key = "my_key";
std::string value;
for (int i = 1; i < kTotalValues; i++) {
if (i % kListSize == 0) {
// Remove trailing ','
value.pop_back();
db->Merge(WriteOptions(), key, value);
value.clear();
} else {
value.append(std::to_string(i)).append(",");
}
}
SortList s;
std::vector<int> data;
// This value can be experimented with and it will demonstrate the
// perf difference between doing a Get and searching for lookup_key in the
// resultant large sorted list vs doing GetMergeOperands and searching
// for lookup_key within this resultant sorted sub-lists.
int lookup_key = 1;
// Get API call
std::cout << "--- Get API call --- \n";
PinnableSlice p_slice;
uint64_t st = FLAGS_env->NowNanos();
db->Get(ReadOptions(), db->DefaultColumnFamily(), key, &p_slice);
s.MakeVector(data, p_slice);
bool found =
binary_search(data, 0, static_cast<int>(data.size() - 1), lookup_key);
std::cout << "Found key? " << std::to_string(found) << "\n";
uint64_t sp = FLAGS_env->NowNanos();
std::cout << "Get: " << (sp - st) / 1000000000.0 << " seconds\n";
std::string* dat_ = p_slice.GetSelf();
std::cout << "Sample data from Get API call: " << dat_->substr(0, 10)
<< "\n";
data.clear();
// GetMergeOperands API call
std::cout << "--- GetMergeOperands API --- \n";
std::vector<PinnableSlice> a_slice((kTotalValues / kListSize) + 1);
st = FLAGS_env->NowNanos();
int number_of_operands = 0;
GetMergeOperandsOptions get_merge_operands_options;
get_merge_operands_options.expected_max_number_of_operands =
(kTotalValues / 100) + 1;
db->GetMergeOperands(ReadOptions(), db->DefaultColumnFamily(), key,
a_slice.data(), &get_merge_operands_options,
&number_of_operands);
for (PinnableSlice& psl : a_slice) {
s.MakeVector(data, psl);
found =
binary_search(data, 0, static_cast<int>(data.size() - 1), lookup_key);
data.clear();
if (found) break;
}
std::cout << "Found key? " << std::to_string(found) << "\n";
sp = FLAGS_env->NowNanos();
std::cout << "Get Merge operands: " << (sp - st) / 1000000000.0
<< " seconds \n";
int to_print = 0;
std::cout << "Sample data from GetMergeOperands API call: ";
for (PinnableSlice& psl : a_slice) {
std::cout << "List: " << to_print << " : " << *psl.GetSelf() << "\n";
if (to_print++ > 2) break;
}
}
#ifndef ROCKSDB_LITE
// This benchmark stress tests Transactions. For a given --duration (or
// total number of --writes, a Transaction will perform a read-modify-write
// to increment the value of a key in each of N(--transaction-sets) sets of
// keys (where each set has --num keys). If --threads is set, this will be
// done in parallel.
//
// To test transactions, use --transaction_db=true. Not setting this
// parameter
// will run the same benchmark without transactions.
//
// RandomTransactionVerify() will then validate the correctness of the results
// by checking if the sum of all keys in each set is the same.
void RandomTransaction(ThreadState* thread) {
ReadOptions options(FLAGS_verify_checksum, true);
Duration duration(FLAGS_duration, readwrites_);
ReadOptions read_options(FLAGS_verify_checksum, true);
2016-03-15 18:57:33 +01:00
uint16_t num_prefix_ranges = static_cast<uint16_t>(FLAGS_transaction_sets);
uint64_t transactions_done = 0;
if (num_prefix_ranges == 0 || num_prefix_ranges > 9999) {
fprintf(stderr, "invalid value for transaction_sets\n");
abort();
}
TransactionOptions txn_options;
txn_options.lock_timeout = FLAGS_transaction_lock_timeout;
txn_options.set_snapshot = FLAGS_transaction_set_snapshot;
RandomTransactionInserter inserter(&thread->rand, write_options_,
read_options, FLAGS_num,
num_prefix_ranges);
if (FLAGS_num_multi_db > 1) {
fprintf(stderr,
"Cannot run RandomTransaction benchmark with "
"FLAGS_multi_db > 1.");
abort();
}
while (!duration.Done(1)) {
bool success;
// RandomTransactionInserter will attempt to insert a key for each
// # of FLAGS_transaction_sets
if (FLAGS_optimistic_transaction_db) {
success = inserter.OptimisticTransactionDBInsert(db_.opt_txn_db);
} else if (FLAGS_transaction_db) {
TransactionDB* txn_db = reinterpret_cast<TransactionDB*>(db_.db);
success = inserter.TransactionDBInsert(txn_db, txn_options);
} else {
success = inserter.DBInsert(db_.db);
}
if (!success) {
fprintf(stderr, "Unexpected error: %s\n",
inserter.GetLastStatus().ToString().c_str());
abort();
}
thread->stats.FinishedOps(nullptr, db_.db, 1, kOthers);
transactions_done++;
}
char msg[100];
if (FLAGS_optimistic_transaction_db || FLAGS_transaction_db) {
snprintf(msg, sizeof(msg),
"( transactions:%" PRIu64 " aborts:%" PRIu64 ")",
transactions_done, inserter.GetFailureCount());
} else {
snprintf(msg, sizeof(msg), "( batches:%" PRIu64 " )", transactions_done);
}
thread->stats.AddMessage(msg);
if (FLAGS_perf_level > ROCKSDB_NAMESPACE::PerfLevel::kDisable) {
thread->stats.AddMessage(std::string("PERF_CONTEXT:\n") +
get_perf_context()->ToString());
}
thread->stats.AddBytes(static_cast<int64_t>(inserter.GetBytesInserted()));
}
// Verifies consistency of data after RandomTransaction() has been run.
// Since each iteration of RandomTransaction() incremented a key in each set
// by the same value, the sum of the keys in each set should be the same.
void RandomTransactionVerify() {
if (!FLAGS_transaction_db && !FLAGS_optimistic_transaction_db) {
// transactions not used, nothing to verify.
return;
}
Status s =
2016-03-15 18:57:33 +01:00
RandomTransactionInserter::Verify(db_.db,
static_cast<uint16_t>(FLAGS_transaction_sets));
if (s.ok()) {
fprintf(stdout, "RandomTransactionVerify Success.\n");
} else {
fprintf(stdout, "RandomTransactionVerify FAILED!!\n");
}
}
#endif // ROCKSDB_LITE
Support for SingleDelete() Summary: This patch fixes #7460559. It introduces SingleDelete as a new database operation. This operation can be used to delete keys that were never overwritten (no put following another put of the same key). If an overwritten key is single deleted the behavior is undefined. Single deletion of a non-existent key has no effect but multiple consecutive single deletions are not allowed (see limitations). In contrast to the conventional Delete() operation, the deletion entry is removed along with the value when the two are lined up in a compaction. Note: The semantics are similar to @igor's prototype that allowed to have this behavior on the granularity of a column family ( https://reviews.facebook.net/D42093 ). This new patch, however, is more aggressive when it comes to removing tombstones: It removes the SingleDelete together with the value whenever there is no snapshot between them while the older patch only did this when the sequence number of the deletion was older than the earliest snapshot. Most of the complex additions are in the Compaction Iterator, all other changes should be relatively straightforward. The patch also includes basic support for single deletions in db_stress and db_bench. Limitations: - Not compatible with cuckoo hash tables - Single deletions cannot be used in combination with merges and normal deletions on the same key (other keys are not affected by this) - Consecutive single deletions are currently not allowed (and older version of this patch supported this so it could be resurrected if needed) Test Plan: make all check Reviewers: yhchiang, sdong, rven, anthony, yoshinorim, igor Reviewed By: igor Subscribers: maykov, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D43179
2015-09-17 20:42:56 +02:00
// Writes and deletes random keys without overwriting keys.
//
// This benchmark is intended to partially replicate the behavior of MyRocks
// secondary indices: All data is stored in keys and updates happen by
// deleting the old version of the key and inserting the new version.
void RandomReplaceKeys(ThreadState* thread) {
std::unique_ptr<const char[]> key_guard;
Slice key = AllocateKey(&key_guard);
std::unique_ptr<char[]> ts_guard;
if (user_timestamp_size_ > 0) {
ts_guard.reset(new char[user_timestamp_size_]);
}
Support for SingleDelete() Summary: This patch fixes #7460559. It introduces SingleDelete as a new database operation. This operation can be used to delete keys that were never overwritten (no put following another put of the same key). If an overwritten key is single deleted the behavior is undefined. Single deletion of a non-existent key has no effect but multiple consecutive single deletions are not allowed (see limitations). In contrast to the conventional Delete() operation, the deletion entry is removed along with the value when the two are lined up in a compaction. Note: The semantics are similar to @igor's prototype that allowed to have this behavior on the granularity of a column family ( https://reviews.facebook.net/D42093 ). This new patch, however, is more aggressive when it comes to removing tombstones: It removes the SingleDelete together with the value whenever there is no snapshot between them while the older patch only did this when the sequence number of the deletion was older than the earliest snapshot. Most of the complex additions are in the Compaction Iterator, all other changes should be relatively straightforward. The patch also includes basic support for single deletions in db_stress and db_bench. Limitations: - Not compatible with cuckoo hash tables - Single deletions cannot be used in combination with merges and normal deletions on the same key (other keys are not affected by this) - Consecutive single deletions are currently not allowed (and older version of this patch supported this so it could be resurrected if needed) Test Plan: make all check Reviewers: yhchiang, sdong, rven, anthony, yoshinorim, igor Reviewed By: igor Subscribers: maykov, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D43179
2015-09-17 20:42:56 +02:00
std::vector<uint32_t> counters(FLAGS_numdistinct, 0);
size_t max_counter = 50;
RandomGenerator gen;
Status s;
DB* db = SelectDB(thread);
for (int64_t i = 0; i < FLAGS_numdistinct; i++) {
GenerateKeyFromInt(i * max_counter, FLAGS_num, &key);
Slice ts;
if (user_timestamp_size_ > 0) {
ts = mock_app_clock_->Allocate(ts_guard.get());
write_options_.timestamp = &ts;
}
s = db->Put(write_options_, key, gen.Generate());
Support for SingleDelete() Summary: This patch fixes #7460559. It introduces SingleDelete as a new database operation. This operation can be used to delete keys that were never overwritten (no put following another put of the same key). If an overwritten key is single deleted the behavior is undefined. Single deletion of a non-existent key has no effect but multiple consecutive single deletions are not allowed (see limitations). In contrast to the conventional Delete() operation, the deletion entry is removed along with the value when the two are lined up in a compaction. Note: The semantics are similar to @igor's prototype that allowed to have this behavior on the granularity of a column family ( https://reviews.facebook.net/D42093 ). This new patch, however, is more aggressive when it comes to removing tombstones: It removes the SingleDelete together with the value whenever there is no snapshot between them while the older patch only did this when the sequence number of the deletion was older than the earliest snapshot. Most of the complex additions are in the Compaction Iterator, all other changes should be relatively straightforward. The patch also includes basic support for single deletions in db_stress and db_bench. Limitations: - Not compatible with cuckoo hash tables - Single deletions cannot be used in combination with merges and normal deletions on the same key (other keys are not affected by this) - Consecutive single deletions are currently not allowed (and older version of this patch supported this so it could be resurrected if needed) Test Plan: make all check Reviewers: yhchiang, sdong, rven, anthony, yoshinorim, igor Reviewed By: igor Subscribers: maykov, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D43179
2015-09-17 20:42:56 +02:00
if (!s.ok()) {
fprintf(stderr, "Operation failed: %s\n", s.ToString().c_str());
exit(1);
}
}
db->GetSnapshot();
std::default_random_engine generator;
std::normal_distribution<double> distribution(FLAGS_numdistinct / 2.0,
FLAGS_stddev);
Duration duration(FLAGS_duration, FLAGS_num);
while (!duration.Done(1)) {
int64_t rnd_id = static_cast<int64_t>(distribution(generator));
int64_t key_id = std::max(std::min(FLAGS_numdistinct - 1, rnd_id),
static_cast<int64_t>(0));
GenerateKeyFromInt(key_id * max_counter + counters[key_id], FLAGS_num,
&key);
Slice ts;
if (user_timestamp_size_ > 0) {
ts = mock_app_clock_->Allocate(ts_guard.get());
write_options_.timestamp = &ts;
}
Support for SingleDelete() Summary: This patch fixes #7460559. It introduces SingleDelete as a new database operation. This operation can be used to delete keys that were never overwritten (no put following another put of the same key). If an overwritten key is single deleted the behavior is undefined. Single deletion of a non-existent key has no effect but multiple consecutive single deletions are not allowed (see limitations). In contrast to the conventional Delete() operation, the deletion entry is removed along with the value when the two are lined up in a compaction. Note: The semantics are similar to @igor's prototype that allowed to have this behavior on the granularity of a column family ( https://reviews.facebook.net/D42093 ). This new patch, however, is more aggressive when it comes to removing tombstones: It removes the SingleDelete together with the value whenever there is no snapshot between them while the older patch only did this when the sequence number of the deletion was older than the earliest snapshot. Most of the complex additions are in the Compaction Iterator, all other changes should be relatively straightforward. The patch also includes basic support for single deletions in db_stress and db_bench. Limitations: - Not compatible with cuckoo hash tables - Single deletions cannot be used in combination with merges and normal deletions on the same key (other keys are not affected by this) - Consecutive single deletions are currently not allowed (and older version of this patch supported this so it could be resurrected if needed) Test Plan: make all check Reviewers: yhchiang, sdong, rven, anthony, yoshinorim, igor Reviewed By: igor Subscribers: maykov, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D43179
2015-09-17 20:42:56 +02:00
s = FLAGS_use_single_deletes ? db->SingleDelete(write_options_, key)
: db->Delete(write_options_, key);
if (s.ok()) {
counters[key_id] = (counters[key_id] + 1) % max_counter;
GenerateKeyFromInt(key_id * max_counter + counters[key_id], FLAGS_num,
&key);
if (user_timestamp_size_ > 0) {
ts = mock_app_clock_->Allocate(ts_guard.get());
write_options_.timestamp = &ts;
}
Support for SingleDelete() Summary: This patch fixes #7460559. It introduces SingleDelete as a new database operation. This operation can be used to delete keys that were never overwritten (no put following another put of the same key). If an overwritten key is single deleted the behavior is undefined. Single deletion of a non-existent key has no effect but multiple consecutive single deletions are not allowed (see limitations). In contrast to the conventional Delete() operation, the deletion entry is removed along with the value when the two are lined up in a compaction. Note: The semantics are similar to @igor's prototype that allowed to have this behavior on the granularity of a column family ( https://reviews.facebook.net/D42093 ). This new patch, however, is more aggressive when it comes to removing tombstones: It removes the SingleDelete together with the value whenever there is no snapshot between them while the older patch only did this when the sequence number of the deletion was older than the earliest snapshot. Most of the complex additions are in the Compaction Iterator, all other changes should be relatively straightforward. The patch also includes basic support for single deletions in db_stress and db_bench. Limitations: - Not compatible with cuckoo hash tables - Single deletions cannot be used in combination with merges and normal deletions on the same key (other keys are not affected by this) - Consecutive single deletions are currently not allowed (and older version of this patch supported this so it could be resurrected if needed) Test Plan: make all check Reviewers: yhchiang, sdong, rven, anthony, yoshinorim, igor Reviewed By: igor Subscribers: maykov, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D43179
2015-09-17 20:42:56 +02:00
s = db->Put(write_options_, key, Slice());
}
if (!s.ok()) {
fprintf(stderr, "Operation failed: %s\n", s.ToString().c_str());
exit(1);
}
thread->stats.FinishedOps(nullptr, db, 1, kOthers);
Support for SingleDelete() Summary: This patch fixes #7460559. It introduces SingleDelete as a new database operation. This operation can be used to delete keys that were never overwritten (no put following another put of the same key). If an overwritten key is single deleted the behavior is undefined. Single deletion of a non-existent key has no effect but multiple consecutive single deletions are not allowed (see limitations). In contrast to the conventional Delete() operation, the deletion entry is removed along with the value when the two are lined up in a compaction. Note: The semantics are similar to @igor's prototype that allowed to have this behavior on the granularity of a column family ( https://reviews.facebook.net/D42093 ). This new patch, however, is more aggressive when it comes to removing tombstones: It removes the SingleDelete together with the value whenever there is no snapshot between them while the older patch only did this when the sequence number of the deletion was older than the earliest snapshot. Most of the complex additions are in the Compaction Iterator, all other changes should be relatively straightforward. The patch also includes basic support for single deletions in db_stress and db_bench. Limitations: - Not compatible with cuckoo hash tables - Single deletions cannot be used in combination with merges and normal deletions on the same key (other keys are not affected by this) - Consecutive single deletions are currently not allowed (and older version of this patch supported this so it could be resurrected if needed) Test Plan: make all check Reviewers: yhchiang, sdong, rven, anthony, yoshinorim, igor Reviewed By: igor Subscribers: maykov, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D43179
2015-09-17 20:42:56 +02:00
}
char msg[200];
snprintf(msg, sizeof(msg),
"use single deletes: %d, "
"standard deviation: %lf\n",
FLAGS_use_single_deletes, FLAGS_stddev);
thread->stats.AddMessage(msg);
}
Write a benchmark to emulate time series data Summary: Add a benchmark to `db_bench`. In this benchmark, a write thread will populate time series data in the format of 'id | timestamp', and multiple read threads will randomly retrieve all data from one id at a time. Test Plan: Run the benchmark: `num=134217728;bpl=536870912;mb=67108864;overlap=10;mcz=2;del=300000000;levels=6;ctrig=4;delay=8;stop=12;wbn=3;mbc=20;wbs=134217728;dds=0;sync=0;t=32;vs=800;bs=4096;cs=17179869184;of=500000;wps=0;si=10000000; kir=100000; dir=/data/users/jhli/test/; ./db_bench --benchmarks=timeseries --disable_seek_compaction=1 --mmap_read=0 --statistics=1 --histogram=1 --num=$num --threads=$t --value_size=$vs --block_size=$bs --cache_size=$cs --bloom_bits=10 --cache_numshardbits=6 --open_files=$of --verify_checksum=1 --db=$dir --sync=$sync --disable_wal=0 --compression_type=none --stats_interval=$si --compression_ratio=1 --disable_data_sync=$dds --write_buffer_size=$wbs --target_file_size_base=$mb --max_write_buffer_number=$wbn --max_background_compactions=$mbc --level0_file_num_compaction_trigger=$ctrig --level0_slowdown_writes_trigger=$delay --level0_stop_writes_trigger=$stop --num_levels=$levels --delete_obsolete_files_period_micros=$del --min_level_to_compress=$mcz --max_grandparent_overlap_factor=$overlap --stats_per_interval=1 --max_bytes_for_level_base=$bpl --use_existing_db=0 --key_id_range=$kir` Reviewers: andrewkr, sdong Reviewed By: sdong Subscribers: lgalanis, andrewkr, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D60651
2016-08-01 21:28:51 +02:00
void TimeSeriesReadOrDelete(ThreadState* thread, bool do_deletion) {
ReadOptions options(FLAGS_verify_checksum, true);
int64_t read = 0;
int64_t found = 0;
int64_t bytes = 0;
Iterator* iter = nullptr;
// Only work on single database
assert(db_.db != nullptr);
iter = db_.db->NewIterator(options);
std::unique_ptr<const char[]> key_guard;
Slice key = AllocateKey(&key_guard);
char value_buffer[256];
while (true) {
{
MutexLock l(&thread->shared->mu);
if (thread->shared->num_done >= 1) {
// Write thread have finished
break;
}
}
if (!FLAGS_use_tailing_iterator) {
delete iter;
iter = db_.db->NewIterator(options);
}
// Pick a Iterator to use
int64_t key_id = thread->rand.Next() % FLAGS_key_id_range;
GenerateKeyFromInt(key_id, FLAGS_num, &key);
// Reset last 8 bytes to 0
char* start = const_cast<char*>(key.data());
start += key.size() - 8;
memset(start, 0, 8);
++read;
bool key_found = false;
// Seek the prefix
for (iter->Seek(key); iter->Valid() && iter->key().starts_with(key);
iter->Next()) {
key_found = true;
// Copy out iterator's value to make sure we read them.
if (do_deletion) {
bytes += iter->key().size();
if (KeyExpired(timestamp_emulator_.get(), iter->key())) {
thread->stats.FinishedOps(&db_, db_.db, 1, kDelete);
db_.db->Delete(write_options_, iter->key());
} else {
break;
}
} else {
bytes += iter->key().size() + iter->value().size();
thread->stats.FinishedOps(&db_, db_.db, 1, kRead);
Slice value = iter->value();
memcpy(value_buffer, value.data(),
std::min(value.size(), sizeof(value_buffer)));
assert(iter->status().ok());
}
}
found += key_found;
if (thread->shared->read_rate_limiter.get() != nullptr) {
thread->shared->read_rate_limiter->Request(
1, Env::IO_HIGH, nullptr /* stats */, RateLimiter::OpType::kRead);
}
Write a benchmark to emulate time series data Summary: Add a benchmark to `db_bench`. In this benchmark, a write thread will populate time series data in the format of 'id | timestamp', and multiple read threads will randomly retrieve all data from one id at a time. Test Plan: Run the benchmark: `num=134217728;bpl=536870912;mb=67108864;overlap=10;mcz=2;del=300000000;levels=6;ctrig=4;delay=8;stop=12;wbn=3;mbc=20;wbs=134217728;dds=0;sync=0;t=32;vs=800;bs=4096;cs=17179869184;of=500000;wps=0;si=10000000; kir=100000; dir=/data/users/jhli/test/; ./db_bench --benchmarks=timeseries --disable_seek_compaction=1 --mmap_read=0 --statistics=1 --histogram=1 --num=$num --threads=$t --value_size=$vs --block_size=$bs --cache_size=$cs --bloom_bits=10 --cache_numshardbits=6 --open_files=$of --verify_checksum=1 --db=$dir --sync=$sync --disable_wal=0 --compression_type=none --stats_interval=$si --compression_ratio=1 --disable_data_sync=$dds --write_buffer_size=$wbs --target_file_size_base=$mb --max_write_buffer_number=$wbn --max_background_compactions=$mbc --level0_file_num_compaction_trigger=$ctrig --level0_slowdown_writes_trigger=$delay --level0_stop_writes_trigger=$stop --num_levels=$levels --delete_obsolete_files_period_micros=$del --min_level_to_compress=$mcz --max_grandparent_overlap_factor=$overlap --stats_per_interval=1 --max_bytes_for_level_base=$bpl --use_existing_db=0 --key_id_range=$kir` Reviewers: andrewkr, sdong Reviewed By: sdong Subscribers: lgalanis, andrewkr, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D60651
2016-08-01 21:28:51 +02:00
}
delete iter;
char msg[100];
snprintf(msg, sizeof(msg), "(%" PRIu64 " of %" PRIu64 " found)", found,
read);
thread->stats.AddBytes(bytes);
thread->stats.AddMessage(msg);
if (FLAGS_perf_level > ROCKSDB_NAMESPACE::PerfLevel::kDisable) {
thread->stats.AddMessage(std::string("PERF_CONTEXT:\n") +
get_perf_context()->ToString());
Write a benchmark to emulate time series data Summary: Add a benchmark to `db_bench`. In this benchmark, a write thread will populate time series data in the format of 'id | timestamp', and multiple read threads will randomly retrieve all data from one id at a time. Test Plan: Run the benchmark: `num=134217728;bpl=536870912;mb=67108864;overlap=10;mcz=2;del=300000000;levels=6;ctrig=4;delay=8;stop=12;wbn=3;mbc=20;wbs=134217728;dds=0;sync=0;t=32;vs=800;bs=4096;cs=17179869184;of=500000;wps=0;si=10000000; kir=100000; dir=/data/users/jhli/test/; ./db_bench --benchmarks=timeseries --disable_seek_compaction=1 --mmap_read=0 --statistics=1 --histogram=1 --num=$num --threads=$t --value_size=$vs --block_size=$bs --cache_size=$cs --bloom_bits=10 --cache_numshardbits=6 --open_files=$of --verify_checksum=1 --db=$dir --sync=$sync --disable_wal=0 --compression_type=none --stats_interval=$si --compression_ratio=1 --disable_data_sync=$dds --write_buffer_size=$wbs --target_file_size_base=$mb --max_write_buffer_number=$wbn --max_background_compactions=$mbc --level0_file_num_compaction_trigger=$ctrig --level0_slowdown_writes_trigger=$delay --level0_stop_writes_trigger=$stop --num_levels=$levels --delete_obsolete_files_period_micros=$del --min_level_to_compress=$mcz --max_grandparent_overlap_factor=$overlap --stats_per_interval=1 --max_bytes_for_level_base=$bpl --use_existing_db=0 --key_id_range=$kir` Reviewers: andrewkr, sdong Reviewed By: sdong Subscribers: lgalanis, andrewkr, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D60651
2016-08-01 21:28:51 +02:00
}
}
void TimeSeriesWrite(ThreadState* thread) {
// Special thread that keeps writing until other threads are done.
RandomGenerator gen;
int64_t bytes = 0;
// Don't merge stats from this thread with the readers.
thread->stats.SetExcludeFromMerge();
std::unique_ptr<RateLimiter> write_rate_limiter;
if (FLAGS_benchmark_write_rate_limit > 0) {
write_rate_limiter.reset(
NewGenericRateLimiter(FLAGS_benchmark_write_rate_limit));
}
std::unique_ptr<const char[]> key_guard;
Slice key = AllocateKey(&key_guard);
Duration duration(FLAGS_duration, writes_);
while (!duration.Done(1)) {
DB* db = SelectDB(thread);
uint64_t key_id = thread->rand.Next() % FLAGS_key_id_range;
// Write key id
GenerateKeyFromInt(key_id, FLAGS_num, &key);
// Write timestamp
char* start = const_cast<char*>(key.data());
char* pos = start + 8;
int bytes_to_fill =
std::min(key_size_ - static_cast<int>(pos - start), 8);
uint64_t timestamp_value = timestamp_emulator_->Get();
if (port::kLittleEndian) {
for (int i = 0; i < bytes_to_fill; ++i) {
pos[i] = (timestamp_value >> ((bytes_to_fill - i - 1) << 3)) & 0xFF;
}
} else {
memcpy(pos, static_cast<void*>(&timestamp_value), bytes_to_fill);
}
timestamp_emulator_->Inc();
Status s;
Slice val = gen.Generate();
s = db->Put(write_options_, key, val);
Write a benchmark to emulate time series data Summary: Add a benchmark to `db_bench`. In this benchmark, a write thread will populate time series data in the format of 'id | timestamp', and multiple read threads will randomly retrieve all data from one id at a time. Test Plan: Run the benchmark: `num=134217728;bpl=536870912;mb=67108864;overlap=10;mcz=2;del=300000000;levels=6;ctrig=4;delay=8;stop=12;wbn=3;mbc=20;wbs=134217728;dds=0;sync=0;t=32;vs=800;bs=4096;cs=17179869184;of=500000;wps=0;si=10000000; kir=100000; dir=/data/users/jhli/test/; ./db_bench --benchmarks=timeseries --disable_seek_compaction=1 --mmap_read=0 --statistics=1 --histogram=1 --num=$num --threads=$t --value_size=$vs --block_size=$bs --cache_size=$cs --bloom_bits=10 --cache_numshardbits=6 --open_files=$of --verify_checksum=1 --db=$dir --sync=$sync --disable_wal=0 --compression_type=none --stats_interval=$si --compression_ratio=1 --disable_data_sync=$dds --write_buffer_size=$wbs --target_file_size_base=$mb --max_write_buffer_number=$wbn --max_background_compactions=$mbc --level0_file_num_compaction_trigger=$ctrig --level0_slowdown_writes_trigger=$delay --level0_stop_writes_trigger=$stop --num_levels=$levels --delete_obsolete_files_period_micros=$del --min_level_to_compress=$mcz --max_grandparent_overlap_factor=$overlap --stats_per_interval=1 --max_bytes_for_level_base=$bpl --use_existing_db=0 --key_id_range=$kir` Reviewers: andrewkr, sdong Reviewed By: sdong Subscribers: lgalanis, andrewkr, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D60651
2016-08-01 21:28:51 +02:00
if (!s.ok()) {
fprintf(stderr, "put error: %s\n", s.ToString().c_str());
ErrorExit();
Write a benchmark to emulate time series data Summary: Add a benchmark to `db_bench`. In this benchmark, a write thread will populate time series data in the format of 'id | timestamp', and multiple read threads will randomly retrieve all data from one id at a time. Test Plan: Run the benchmark: `num=134217728;bpl=536870912;mb=67108864;overlap=10;mcz=2;del=300000000;levels=6;ctrig=4;delay=8;stop=12;wbn=3;mbc=20;wbs=134217728;dds=0;sync=0;t=32;vs=800;bs=4096;cs=17179869184;of=500000;wps=0;si=10000000; kir=100000; dir=/data/users/jhli/test/; ./db_bench --benchmarks=timeseries --disable_seek_compaction=1 --mmap_read=0 --statistics=1 --histogram=1 --num=$num --threads=$t --value_size=$vs --block_size=$bs --cache_size=$cs --bloom_bits=10 --cache_numshardbits=6 --open_files=$of --verify_checksum=1 --db=$dir --sync=$sync --disable_wal=0 --compression_type=none --stats_interval=$si --compression_ratio=1 --disable_data_sync=$dds --write_buffer_size=$wbs --target_file_size_base=$mb --max_write_buffer_number=$wbn --max_background_compactions=$mbc --level0_file_num_compaction_trigger=$ctrig --level0_slowdown_writes_trigger=$delay --level0_stop_writes_trigger=$stop --num_levels=$levels --delete_obsolete_files_period_micros=$del --min_level_to_compress=$mcz --max_grandparent_overlap_factor=$overlap --stats_per_interval=1 --max_bytes_for_level_base=$bpl --use_existing_db=0 --key_id_range=$kir` Reviewers: andrewkr, sdong Reviewed By: sdong Subscribers: lgalanis, andrewkr, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D60651
2016-08-01 21:28:51 +02:00
}
bytes = key.size() + val.size();
Write a benchmark to emulate time series data Summary: Add a benchmark to `db_bench`. In this benchmark, a write thread will populate time series data in the format of 'id | timestamp', and multiple read threads will randomly retrieve all data from one id at a time. Test Plan: Run the benchmark: `num=134217728;bpl=536870912;mb=67108864;overlap=10;mcz=2;del=300000000;levels=6;ctrig=4;delay=8;stop=12;wbn=3;mbc=20;wbs=134217728;dds=0;sync=0;t=32;vs=800;bs=4096;cs=17179869184;of=500000;wps=0;si=10000000; kir=100000; dir=/data/users/jhli/test/; ./db_bench --benchmarks=timeseries --disable_seek_compaction=1 --mmap_read=0 --statistics=1 --histogram=1 --num=$num --threads=$t --value_size=$vs --block_size=$bs --cache_size=$cs --bloom_bits=10 --cache_numshardbits=6 --open_files=$of --verify_checksum=1 --db=$dir --sync=$sync --disable_wal=0 --compression_type=none --stats_interval=$si --compression_ratio=1 --disable_data_sync=$dds --write_buffer_size=$wbs --target_file_size_base=$mb --max_write_buffer_number=$wbn --max_background_compactions=$mbc --level0_file_num_compaction_trigger=$ctrig --level0_slowdown_writes_trigger=$delay --level0_stop_writes_trigger=$stop --num_levels=$levels --delete_obsolete_files_period_micros=$del --min_level_to_compress=$mcz --max_grandparent_overlap_factor=$overlap --stats_per_interval=1 --max_bytes_for_level_base=$bpl --use_existing_db=0 --key_id_range=$kir` Reviewers: andrewkr, sdong Reviewed By: sdong Subscribers: lgalanis, andrewkr, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D60651
2016-08-01 21:28:51 +02:00
thread->stats.FinishedOps(&db_, db_.db, 1, kWrite);
thread->stats.AddBytes(bytes);
if (FLAGS_benchmark_write_rate_limit > 0) {
write_rate_limiter->Request(
key.size() + val.size(), Env::IO_HIGH,
nullptr /* stats */, RateLimiter::OpType::kWrite);
Write a benchmark to emulate time series data Summary: Add a benchmark to `db_bench`. In this benchmark, a write thread will populate time series data in the format of 'id | timestamp', and multiple read threads will randomly retrieve all data from one id at a time. Test Plan: Run the benchmark: `num=134217728;bpl=536870912;mb=67108864;overlap=10;mcz=2;del=300000000;levels=6;ctrig=4;delay=8;stop=12;wbn=3;mbc=20;wbs=134217728;dds=0;sync=0;t=32;vs=800;bs=4096;cs=17179869184;of=500000;wps=0;si=10000000; kir=100000; dir=/data/users/jhli/test/; ./db_bench --benchmarks=timeseries --disable_seek_compaction=1 --mmap_read=0 --statistics=1 --histogram=1 --num=$num --threads=$t --value_size=$vs --block_size=$bs --cache_size=$cs --bloom_bits=10 --cache_numshardbits=6 --open_files=$of --verify_checksum=1 --db=$dir --sync=$sync --disable_wal=0 --compression_type=none --stats_interval=$si --compression_ratio=1 --disable_data_sync=$dds --write_buffer_size=$wbs --target_file_size_base=$mb --max_write_buffer_number=$wbn --max_background_compactions=$mbc --level0_file_num_compaction_trigger=$ctrig --level0_slowdown_writes_trigger=$delay --level0_stop_writes_trigger=$stop --num_levels=$levels --delete_obsolete_files_period_micros=$del --min_level_to_compress=$mcz --max_grandparent_overlap_factor=$overlap --stats_per_interval=1 --max_bytes_for_level_base=$bpl --use_existing_db=0 --key_id_range=$kir` Reviewers: andrewkr, sdong Reviewed By: sdong Subscribers: lgalanis, andrewkr, dhruba, leveldb Differential Revision: https://reviews.facebook.net/D60651
2016-08-01 21:28:51 +02:00
}
}
}
void TimeSeries(ThreadState* thread) {
if (thread->tid > 0) {
bool do_deletion = FLAGS_expire_style == "delete" &&
thread->tid <= FLAGS_num_deletion_threads;
TimeSeriesReadOrDelete(thread, do_deletion);
} else {
TimeSeriesWrite(thread);
thread->stats.Stop();
thread->stats.Report("timeseries write");
}
}
void Compact(ThreadState* thread) {
DB* db = SelectDB(thread);
CompactRangeOptions cro;
cro.bottommost_level_compaction =
BottommostLevelCompaction::kForceOptimized;
db->CompactRange(cro, nullptr, nullptr);
}
void CompactAll() {
if (db_.db != nullptr) {
db_.db->CompactRange(CompactRangeOptions(), nullptr, nullptr);
}
for (const auto& db_with_cfh : multi_dbs_) {
db_with_cfh.db->CompactRange(CompactRangeOptions(), nullptr, nullptr);
}
}
#ifndef ROCKSDB_LITE
void WaitForCompactionHelper(DBWithColumnFamilies& db) {
// This is an imperfect way of waiting for compaction. The loop and sleep
// is done because a thread that finishes a compaction job should get a
// chance to pickup a new compaction job.
std::vector<std::string> keys = {DB::Properties::kMemTableFlushPending,
DB::Properties::kNumRunningFlushes,
DB::Properties::kCompactionPending,
DB::Properties::kNumRunningCompactions};
fprintf(stdout, "waitforcompaction(%s): started\n",
db.db->GetName().c_str());
while (true) {
bool retry = false;
for (const auto& k : keys) {
uint64_t v;
if (!db.db->GetIntProperty(k, &v)) {
fprintf(stderr, "waitforcompaction(%s): GetIntProperty(%s) failed\n",
db.db->GetName().c_str(), k.c_str());
exit(1);
} else if (v > 0) {
fprintf(stdout,
"waitforcompaction(%s): active(%s). Sleep 10 seconds\n",
db.db->GetName().c_str(), k.c_str());
FLAGS_env->SleepForMicroseconds(10 * 1000000);
retry = true;
break;
}
}
if (!retry) {
fprintf(stdout, "waitforcompaction(%s): finished\n",
db.db->GetName().c_str());
return;
}
}
}
void WaitForCompaction() {
// Give background threads a chance to wake
FLAGS_env->SleepForMicroseconds(5 * 1000000);
// I am skeptical that this check race free. I hope that checking twice
// reduces the chance.
if (db_.db != nullptr) {
WaitForCompactionHelper(db_);
WaitForCompactionHelper(db_);
} else {
for (auto& db_with_cfh : multi_dbs_) {
WaitForCompactionHelper(db_with_cfh);
WaitForCompactionHelper(db_with_cfh);
}
}
}
bool CompactLevelHelper(DBWithColumnFamilies& db_with_cfh, int from_level) {
std::vector<LiveFileMetaData> files;
db_with_cfh.db->GetLiveFilesMetaData(&files);
assert(from_level == 0 || from_level == 1);
int real_from_level = from_level;
if (real_from_level > 0) {
// With dynamic leveled compaction the first level with data beyond L0
// might not be L1.
real_from_level = std::numeric_limits<int>::max();
for (auto& f : files) {
if (f.level > 0 && f.level < real_from_level) real_from_level = f.level;
}
if (real_from_level == std::numeric_limits<int>::max()) {
fprintf(stdout, "compact%d found 0 files to compact\n", from_level);
return true;
}
}
// The goal is to compact from from_level to the level that follows it,
// and with dynamic leveled compaction the next level might not be
// real_from_level+1
int next_level = std::numeric_limits<int>::max();
std::vector<std::string> files_to_compact;
for (auto& f : files) {
if (f.level == real_from_level)
files_to_compact.push_back(f.name);
else if (f.level > real_from_level && f.level < next_level)
next_level = f.level;
}
if (files_to_compact.empty()) {
fprintf(stdout, "compact%d found 0 files to compact\n", from_level);
return true;
} else if (next_level == std::numeric_limits<int>::max()) {
// There is no data beyond real_from_level. So we are done.
fprintf(stdout, "compact%d found no data beyond L%d\n", from_level,
real_from_level);
return true;
}
fprintf(stdout, "compact%d found %d files to compact from L%d to L%d\n",
from_level, static_cast<int>(files_to_compact.size()),
real_from_level, next_level);
ROCKSDB_NAMESPACE::CompactionOptions options;
// Lets RocksDB use the configured compression for this level
options.compression = ROCKSDB_NAMESPACE::kDisableCompressionOption;
ROCKSDB_NAMESPACE::ColumnFamilyDescriptor cfDesc;
db_with_cfh.db->DefaultColumnFamily()->GetDescriptor(&cfDesc);
options.output_file_size_limit = cfDesc.options.target_file_size_base;
Status status =
db_with_cfh.db->CompactFiles(options, files_to_compact, next_level);
if (!status.ok()) {
// This can fail for valid reasons including the operation was aborted
// or a filename is invalid because background compaction removed it.
// Having read the current cases for which an error is raised I prefer
// not to figure out whether an exception should be thrown here.
fprintf(stderr, "compact%d CompactFiles failed: %s\n", from_level,
status.ToString().c_str());
return false;
}
return true;
}
void CompactLevel(int from_level) {
if (db_.db != nullptr) {
while (!CompactLevelHelper(db_, from_level)) WaitForCompaction();
}
for (auto& db_with_cfh : multi_dbs_) {
while (!CompactLevelHelper(db_with_cfh, from_level)) WaitForCompaction();
}
}
#endif
void Flush() {
FlushOptions flush_opt;
flush_opt.wait = true;
if (db_.db != nullptr) {
Status s = db_.db->Flush(flush_opt, db_.cfh);
if (!s.ok()) {
fprintf(stderr, "Flush failed: %s\n", s.ToString().c_str());
exit(1);
}
} else {
for (const auto& db_with_cfh : multi_dbs_) {
Status s = db_with_cfh.db->Flush(flush_opt, db_with_cfh.cfh);
if (!s.ok()) {
fprintf(stderr, "Flush failed: %s\n", s.ToString().c_str());
exit(1);
}
}
}
fprintf(stdout, "flush memtable\n");
}
void ResetStats() {
if (db_.db != nullptr) {
db_.db->ResetStats();
}
for (const auto& db_with_cfh : multi_dbs_) {
db_with_cfh.db->ResetStats();
}
}
void PrintStatsHistory() {
if (db_.db != nullptr) {
PrintStatsHistoryImpl(db_.db, false);
}
for (const auto& db_with_cfh : multi_dbs_) {
PrintStatsHistoryImpl(db_with_cfh.db, true);
}
}
void PrintStatsHistoryImpl(DB* db, bool print_header) {
if (print_header) {
fprintf(stdout, "\n==== DB: %s ===\n", db->GetName().c_str());
}
std::unique_ptr<StatsHistoryIterator> shi;
Status s = db->GetStatsHistory(0, port::kMaxUint64, &shi);
if (!s.ok()) {
fprintf(stdout, "%s\n", s.ToString().c_str());
return;
}
assert(shi);
while (shi->Valid()) {
uint64_t stats_time = shi->GetStatsTime();
fprintf(stdout, "------ %s ------\n",
TimeToHumanString(static_cast<int>(stats_time)).c_str());
for (auto& entry : shi->GetStatsMap()) {
fprintf(stdout, " %" PRIu64 " %s %" PRIu64 "\n", stats_time,
entry.first.c_str(), entry.second);
}
shi->Next();
}
}
void PrintStats(const char* key) {
if (db_.db != nullptr) {
PrintStats(db_.db, key, false);
}
for (const auto& db_with_cfh : multi_dbs_) {
PrintStats(db_with_cfh.db, key, true);
}
}
void PrintStats(DB* db, const char* key, bool print_header = false) {
if (print_header) {
fprintf(stdout, "\n==== DB: %s ===\n", db->GetName().c_str());
}
std::string stats;
if (!db->GetProperty(key, &stats)) {
stats = "(failed)";
}
fprintf(stdout, "\n%s\n", stats.c_str());
}
void PrintStats(const std::vector<std::string>& keys) {
if (db_.db != nullptr) {
PrintStats(db_.db, keys);
}
for (const auto& db_with_cfh : multi_dbs_) {
PrintStats(db_with_cfh.db, keys, true);
}
}
void PrintStats(DB* db, const std::vector<std::string>& keys,
bool print_header = false) {
if (print_header) {
fprintf(stdout, "\n==== DB: %s ===\n", db->GetName().c_str());
}
for (const auto& key : keys) {
std::string stats;
if (!db->GetProperty(key, &stats)) {
stats = "(failed)";
}
fprintf(stdout, "%s: %s\n", key.c_str(), stats.c_str());
}
}
#ifndef ROCKSDB_LITE
void Replay(ThreadState* thread) {
if (db_.db != nullptr) {
Replay(thread, &db_);
}
}
void Replay(ThreadState* /*thread*/, DBWithColumnFamilies* db_with_cfh) {
Status s;
std::unique_ptr<TraceReader> trace_reader;
s = NewFileTraceReader(FLAGS_env, EnvOptions(), FLAGS_trace_file,
&trace_reader);
if (!s.ok()) {
fprintf(
stderr,
"Encountered an error creating a TraceReader from the trace file. "
"Error: %s\n",
s.ToString().c_str());
exit(1);
}
std::unique_ptr<Replayer> replayer;
s = db_with_cfh->db->NewDefaultReplayer(db_with_cfh->cfh,
std::move(trace_reader), &replayer);
if (!s.ok()) {
fprintf(stderr,
"Encountered an error creating a default Replayer. "
"Error: %s\n",
s.ToString().c_str());
exit(1);
}
s = replayer->Prepare();
if (!s.ok()) {
fprintf(stderr, "Prepare for replay failed. Error: %s\n",
s.ToString().c_str());
}
s = replayer->Replay(
ReplayOptions(static_cast<uint32_t>(FLAGS_trace_replay_threads),
FLAGS_trace_replay_fast_forward),
nullptr);
replayer.reset();
if (s.ok()) {
fprintf(stdout, "Replay completed from trace_file: %s\n",
FLAGS_trace_file.c_str());
} else {
fprintf(stderr, "Replay failed. Error: %s\n", s.ToString().c_str());
}
}
#endif // ROCKSDB_LITE
};
Separeate main from bench functionality to allow cusomizations Summary: Isolate db_bench functionality from main so custom benchmark code can be written and managed Test Plan: Tested commands ./build_tools/regression_build_test.sh ./db_bench --db=/tmp/rocksdbtest-12321/dbbench --stats_interval_seconds=1 --num=1000 ./db_bench --db=/tmp/rocksdbtest-12321/dbbench --stats_interval_seconds=1 --num=1000 --reads=500 --writes=500 ./db_bench --db=/tmp/rocksdbtest-12321/dbbench --stats_interval_seconds=1 --num=1000 --merge_keys=100 --numdistinct=100 --num_column_families=3 --num_hot_column_families=1 ./db_bench --stats_interval_seconds=1 --num=1000 --bloom_locality=1 --seed=5 --threads=5 ./db_bench --duration=60 --value_size=50 --seek_nexts=10 --reverse_iterator=true --usee_uint64_comparator=true --batch-size=5 ./db_bench --duration=60 --value_size=50 --seek_nexts=10 --reverse_iterator=true --use_uint64_comparator=true --batch_size=5 ./db_bench --duration=60 --value_size=50 --seek_nexts=10 --reverse_iterator=true --usee_uint64_comparator=true --batch-size=5 Test Results - https://phabricator.fb.com/P56130387 Additional tests for: ./db_bench --duration=60 --value_size=50 --seek_nexts=10 --reverse_iterator=true --use_uint64_comparator=true --batch_size=5 --key_size=8 --merge_operator=put ./db_bench --stats_interval_seconds=1 --num=1000 --bloom_locality=1 --seed=5 --threads=5 --merge_operator=uint64add Results: https://phabricator.fb.com/P56130607 Reviewers: yhchiang, sdong Reviewed By: sdong Subscribers: dhruba Differential Revision: https://reviews.facebook.net/D53991
2016-02-16 15:17:31 +01:00
int db_bench_tool(int argc, char** argv) {
ROCKSDB_NAMESPACE::port::InstallStackTraceHandler();
ConfigOptions config_options;
static bool initialized = false;
if (!initialized) {
SetUsageMessage(std::string("\nUSAGE:\n") + std::string(argv[0]) +
" [OPTIONS]...");
initialized = true;
}
ParseCommandLineFlags(&argc, &argv, true);
FLAGS_compaction_style_e =
(ROCKSDB_NAMESPACE::CompactionStyle)FLAGS_compaction_style;
#ifndef ROCKSDB_LITE
if (FLAGS_statistics && !FLAGS_statistics_string.empty()) {
fprintf(stderr,
"Cannot provide both --statistics and --statistics_string.\n");
exit(1);
}
if (!FLAGS_statistics_string.empty()) {
Status s = Statistics::CreateFromString(config_options,
FLAGS_statistics_string, &dbstats);
if (dbstats == nullptr) {
fprintf(stderr,
"No Statistics registered matching string: %s status=%s\n",
FLAGS_statistics_string.c_str(), s.ToString().c_str());
exit(1);
}
}
#endif // ROCKSDB_LITE
if (FLAGS_statistics) {
dbstats = ROCKSDB_NAMESPACE::CreateDBStatistics();
}
if (dbstats) {
dbstats->set_stats_level(static_cast<StatsLevel>(FLAGS_stats_level));
}
FLAGS_compaction_pri_e =
(ROCKSDB_NAMESPACE::CompactionPri)FLAGS_compaction_pri;
std::vector<std::string> fanout = ROCKSDB_NAMESPACE::StringSplit(
FLAGS_max_bytes_for_level_multiplier_additional, ',');
for (size_t j = 0; j < fanout.size(); j++) {
FLAGS_max_bytes_for_level_multiplier_additional_v.push_back(
#ifndef CYGWIN
std::stoi(fanout[j]));
#else
stoi(fanout[j]));
#endif
}
FLAGS_compression_type_e =
StringToCompressionType(FLAGS_compression_type.c_str());
#ifndef ROCKSDB_LITE
// Stacked BlobDB
FLAGS_blob_db_compression_type_e =
StringToCompressionType(FLAGS_blob_db_compression_type.c_str());
int env_opts =
!FLAGS_hdfs.empty() + !FLAGS_env_uri.empty() + !FLAGS_fs_uri.empty();
if (env_opts > 1) {
fprintf(stderr,
"Error: --hdfs, --env_uri and --fs_uri are mutually exclusive\n");
exit(1);
}
if (env_opts == 1) {
Status s = Env::CreateFromUri(config_options, FLAGS_env_uri, FLAGS_fs_uri,
&FLAGS_env, &env_guard);
if (!s.ok()) {
fprintf(stderr, "Failed creating env: %s\n", s.ToString().c_str());
exit(1);
}
} else if (FLAGS_simulate_hybrid_fs_file != "") {
//**TODO: Make the simulate fs something that can be loaded
// from the ObjectRegistry...
static std::shared_ptr<ROCKSDB_NAMESPACE::Env> composite_env =
NewCompositeEnv(std::make_shared<SimulatedHybridFileSystem>(
FileSystem::Default(), FLAGS_simulate_hybrid_fs_file));
FLAGS_env = composite_env.get();
}
#endif // ROCKSDB_LITE
if (FLAGS_use_existing_keys && !FLAGS_use_existing_db) {
fprintf(stderr,
"`-use_existing_db` must be true for `-use_existing_keys` to be "
"settable\n");
exit(1);
}
if (!FLAGS_hdfs.empty()) {
FLAGS_env = new ROCKSDB_NAMESPACE::HdfsEnv(FLAGS_hdfs);
}
if (!strcasecmp(FLAGS_compaction_fadvice.c_str(), "NONE"))
FLAGS_compaction_fadvice_e = ROCKSDB_NAMESPACE::Options::NONE;
else if (!strcasecmp(FLAGS_compaction_fadvice.c_str(), "NORMAL"))
FLAGS_compaction_fadvice_e = ROCKSDB_NAMESPACE::Options::NORMAL;
else if (!strcasecmp(FLAGS_compaction_fadvice.c_str(), "SEQUENTIAL"))
FLAGS_compaction_fadvice_e = ROCKSDB_NAMESPACE::Options::SEQUENTIAL;
else if (!strcasecmp(FLAGS_compaction_fadvice.c_str(), "WILLNEED"))
FLAGS_compaction_fadvice_e = ROCKSDB_NAMESPACE::Options::WILLNEED;
else {
fprintf(stdout, "Unknown compaction fadvice:%s\n",
FLAGS_compaction_fadvice.c_str());
}
FLAGS_value_size_distribution_type_e =
StringToDistributionType(FLAGS_value_size_distribution_type.c_str());
// Note options sanitization may increase thread pool sizes according to
// max_background_flushes/max_background_compactions/max_background_jobs
FLAGS_env->SetBackgroundThreads(FLAGS_num_high_pri_threads,
ROCKSDB_NAMESPACE::Env::Priority::HIGH);
Introduce bottom-pri thread pool for large universal compactions Summary: When we had a single thread pool for compactions, a thread could be busy for a long time (minutes) executing a compaction involving the bottom level. In multi-instance setups, the entire thread pool could be consumed by such bottom-level compactions. Then, top-level compactions (e.g., a few L0 files) would be blocked for a long time ("head-of-line blocking"). Such top-level compactions are critical to prevent compaction stalls as they can quickly reduce number of L0 files / sorted runs. This diff introduces a bottom-priority queue for universal compactions including the bottom level. This alleviates the head-of-line blocking situation for fast, top-level compactions. - Added `Env::Priority::BOTTOM` thread pool. This feature is only enabled if user explicitly configures it to have a positive number of threads. - Changed `ThreadPoolImpl`'s default thread limit from one to zero. This change is invisible to users as we call `IncBackgroundThreadsIfNeeded` on the low-pri/high-pri pools during `DB::Open` with values of at least one. It is necessary, though, for bottom-pri to start with zero threads so the feature is disabled by default. - Separated `ManualCompaction` into two parts in `PrepickedCompaction`. `PrepickedCompaction` is used for any compaction that's picked outside of its execution thread, either manual or automatic. - Forward universal compactions involving last level to the bottom pool (worker thread's entry point is `BGWorkBottomCompaction`). - Track `bg_bottom_compaction_scheduled_` so we can wait for bottom-level compactions to finish. We don't count them against the background jobs limits. So users of this feature will get an extra compaction for free. Closes https://github.com/facebook/rocksdb/pull/2580 Differential Revision: D5422916 Pulled By: ajkr fbshipit-source-id: a74bd11f1ea4933df3739b16808bb21fcd512333
2017-08-04 00:36:28 +02:00
FLAGS_env->SetBackgroundThreads(FLAGS_num_bottom_pri_threads,
ROCKSDB_NAMESPACE::Env::Priority::BOTTOM);
FLAGS_env->SetBackgroundThreads(FLAGS_num_low_pri_threads,
ROCKSDB_NAMESPACE::Env::Priority::LOW);
// Choose a location for the test database if none given with --db=<path>
if (FLAGS_db.empty()) {
std::string default_db_path;
FLAGS_env->GetTestDirectory(&default_db_path);
default_db_path += "/dbbench";
FLAGS_db = default_db_path;
}
if (FLAGS_stats_interval_seconds > 0) {
// When both are set then FLAGS_stats_interval determines the frequency
// at which the timer is checked for FLAGS_stats_interval_seconds
FLAGS_stats_interval = 1000;
}
if (FLAGS_seek_missing_prefix && FLAGS_prefix_size <= 8) {
fprintf(stderr, "prefix_size > 8 required by --seek_missing_prefix\n");
exit(1);
}
ROCKSDB_NAMESPACE::Benchmark benchmark;
benchmark.Run();
#ifndef ROCKSDB_LITE
if (FLAGS_print_malloc_stats) {
std::string stats_string;
ROCKSDB_NAMESPACE::DumpMallocStats(&stats_string);
fprintf(stdout, "Malloc stats:\n%s\n", stats_string.c_str());
}
#endif // ROCKSDB_LITE
return 0;
}
} // namespace ROCKSDB_NAMESPACE
#endif