rocksdb/db/db_bench.cc

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// Copyright (c) 2013, Facebook, Inc. All rights reserved.
// This source code is licensed under the BSD-style license found in the
// LICENSE file in the root directory of this source tree. An additional grant
// of patent rights can be found in the PATENTS file in the same 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.
#ifndef __STDC_FORMAT_MACROS
#define __STDC_FORMAT_MACROS
#endif
#ifndef GFLAGS
#include <cstdio>
int main() {
fprintf(stderr, "Please install gflags to run rocksdb tools\n");
return 1;
}
#else
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>
#include <numaif.h>
#endif
#include <inttypes.h>
#include <cstddef>
#include <sys/types.h>
#include <stdio.h>
#include <stdlib.h>
#include <gflags/gflags.h>
#include "db/db_impl.h"
#include "db/version_set.h"
#include "rocksdb/options.h"
#include "rocksdb/cache.h"
#include "rocksdb/db.h"
#include "rocksdb/env.h"
#include "rocksdb/memtablerep.h"
#include "rocksdb/write_batch.h"
#include "rocksdb/slice.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/slice_transform.h"
#include "rocksdb/perf_context.h"
#include "port/port.h"
#include "port/stack_trace.h"
#include "util/crc32c.h"
#include "util/histogram.h"
#include "util/mutexlock.h"
#include "util/random.h"
#include "util/string_util.h"
#include "util/statistics.h"
#include "util/testutil.h"
#include "util/xxhash.h"
#include "hdfs/env_hdfs.h"
#include "utilities/merge_operators.h"
using GFLAGS::ParseCommandLineFlags;
using GFLAGS::RegisterFlagValidator;
using GFLAGS::SetUsageMessage;
DEFINE_string(benchmarks,
"fillseq,"
"fillsync,"
"fillrandom,"
"overwrite,"
"readrandom,"
"newiterator,"
"newiteratorwhilewriting,"
"seekrandom,"
"seekrandomwhilewriting,"
"readseq,"
"readreverse,"
"compact,"
"readrandom,"
"multireadrandom,"
"readseq,"
"readtocache,"
"readreverse,"
"readwhilewriting,"
"readrandomwriterandom,"
"updaterandom,"
"randomwithverify,"
"fill100K,"
"crc32c,"
"xxhash,"
2014-02-08 03:12:30 +01:00
"compress,"
"uncompress,"
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
"acquireload,"
"fillseekseq,",
"Comma-separated list of operations to run in the specified order"
"Actual benchmarks:\n"
"\tfillseq -- write N values in sequential key"
" order in async mode\n"
"\tfillrandom -- write N values in random key order in async"
" mode\n"
"\toverwrite -- overwrite N values in random key order in"
" async mode\n"
"\tfillsync -- write N/100 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"
"\treadhot -- read N times in random order from 1% section "
"of DB\n"
"\treadwhilewriting -- 1 writer, N threads doing random "
"reads\n"
"\treadrandomwriterandom -- N threads doing random-read, "
"random-write\n"
"\tprefixscanrandom -- prefix scan N times in random order\n"
"\tupdaterandom -- N threads doing read-modify-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\n"
"\tseekrandom -- 1 writer, N threads doing random seeks\n"
"\tcrc32c -- repeated crc32c of 4K of data\n"
"\txxhash -- repeated xxHash of 4K of data\n"
"\tacquireload -- load N*1000 times\n"
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
"\tfillseekseq -- write N values in sequential key, then read "
"them by seeking to each key\n"
"Meta operations:\n"
"\tcompact -- Compact the entire DB\n"
"\tstats -- Print DB stats\n"
"\tlevelstats -- Print the number of files and bytes per level\n"
"\tsstables -- Print sstable info\n"
"\theapprofile -- Dump a heap profile (if supported by this"
" port)\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_int64(reads, -1, "Number of read operations to do. "
"If negative, do FLAGS_num reads.");
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_int32(value_size, 100, "Size of each value");
DEFINE_int32(seek_nexts, 0,
"How many times to call Next() after Seek() in "
"fillseekseq and seekrandom");
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(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;
}
DEFINE_int32(key_size, 16, "size of each key");
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");
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.");
Add monitoring for universal compaction and add counters for compaction IO Summary: Adds these counters { WAL_FILE_SYNCED, "rocksdb.wal.synced" } number of writes that request a WAL sync { WAL_FILE_BYTES, "rocksdb.wal.bytes" }, number of bytes written to the WAL { WRITE_DONE_BY_SELF, "rocksdb.write.self" }, number of writes processed by the calling thread { WRITE_DONE_BY_OTHER, "rocksdb.write.other" }, number of writes not processed by the calling thread. Instead these were processed by the current holder of the write lock { WRITE_WITH_WAL, "rocksdb.write.wal" }, number of writes that request WAL logging { COMPACT_READ_BYTES, "rocksdb.compact.read.bytes" }, number of bytes read during compaction { COMPACT_WRITE_BYTES, "rocksdb.compact.write.bytes" }, number of bytes written during compaction Per-interval stats output was updated with WAL stats and correct stats for universal compaction including a correct value for write-amplification. It now looks like: Compactions Level Files Size(MB) Score Time(sec) Read(MB) Write(MB) Rn(MB) Rnp1(MB) Wnew(MB) RW-Amplify Read(MB/s) Write(MB/s) Rn Rnp1 Wnp1 NewW Count Ln-stall Stall-cnt -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 0 7 464 46.4 281 3411 3875 3411 0 3875 2.1 12.1 13.8 621 0 240 240 628 0.0 0 Uptime(secs): 310.8 total, 2.0 interval Writes cumulative: 9999999 total, 9999999 batches, 1.0 per batch, 1.22 ingest GB WAL cumulative: 9999999 WAL writes, 9999999 WAL syncs, 1.00 writes per sync, 1.22 GB written Compaction IO cumulative (GB): 1.22 new, 3.33 read, 3.78 write, 7.12 read+write Compaction IO cumulative (MB/sec): 4.0 new, 11.0 read, 12.5 write, 23.4 read+write Amplification cumulative: 4.1 write, 6.8 compaction Writes interval: 100000 total, 100000 batches, 1.0 per batch, 12.5 ingest MB WAL interval: 100000 WAL writes, 100000 WAL syncs, 1.00 writes per sync, 0.01 MB written Compaction IO interval (MB): 12.49 new, 14.98 read, 21.50 write, 36.48 read+write Compaction IO interval (MB/sec): 6.4 new, 7.6 read, 11.0 write, 18.6 read+write Amplification interval: 101.7 write, 102.9 compaction Stalls(secs): 142.924 level0_slowdown, 0.000 level0_numfiles, 0.805 memtable_compaction, 0.000 leveln_slowdown Stalls(count): 132461 level0_slowdown, 0 level0_numfiles, 3 memtable_compaction, 0 leveln_slowdown Task ID: #3329644, #3301695 Blame Rev: Test Plan: Revert Plan: Database Impact: Memcache Impact: Other Notes: EImportant: - begin *PUBLIC* platform impact section - Bugzilla: # - end platform impact - Reviewers: dhruba CC: leveldb Differential Revision: https://reviews.facebook.net/D14583
2013-12-09 22:43:34 +01:00
DEFINE_int64(write_buffer_size, rocksdb::Options().write_buffer_size,
"Number of bytes to buffer in memtable before compacting");
DEFINE_int32(max_write_buffer_number,
rocksdb::Options().max_write_buffer_number,
"The number of in-memory memtables. Each memtable is of size"
"write_buffer_size.");
DEFINE_int32(min_write_buffer_number_to_merge,
rocksdb::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.");
DEFINE_int32(max_background_compactions,
rocksdb::Options().max_background_compactions,
"The maximum number of concurrent background compactions"
" that can occur in parallel.");
DEFINE_int32(max_background_flushes,
rocksdb::Options().max_background_flushes,
"The maximum number of concurrent background flushes"
" that can occur in parallel.");
static rocksdb::CompactionStyle FLAGS_compaction_style_e;
DEFINE_int32(compaction_style, (int32_t) rocksdb::Options().compaction_style,
"style of compaction: level-based vs universal");
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_int64(cache_size, -1, "Number of bytes to use as a cache of uncompressed"
"data. Negative means use default settings.");
DEFINE_int32(block_size, rocksdb::BlockBasedTableOptions().block_size,
"Number of bytes in a block.");
DEFINE_int32(block_restart_interval,
rocksdb::BlockBasedTableOptions().block_restart_interval,
"Number of keys between restart points "
"for delta encoding of keys.");
DEFINE_int64(compressed_cache_size, -1,
"Number of bytes to use as a cache of compressed data.");
DEFINE_int32(open_files, rocksdb::Options().max_open_files,
"Maximum number of files to keep open at the same time"
" (use default if == 0)");
DEFINE_int32(bloom_bits, -1, "Bloom filter bits per key. Negative means"
" use default settings.");
DEFINE_int32(memtable_bloom_bits, 0, "Bloom filter bits per key for memtable. "
"Negative means no bloom filter.");
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_string(db, "", "Use the db with the following name.");
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_int32(cache_numshardbits, -1, "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_int32(cache_remove_scan_count_limit, 32, "");
DEFINE_bool(verify_checksum, false, "Verify checksum for every block read"
" from storage");
DEFINE_bool(statistics, false, "Database statistics");
static class std::shared_ptr<rocksdb::Statistics> dbstats;
DEFINE_int64(writes, -1, "Number of write operations to do. If negative, do"
" --num reads.");
DEFINE_int32(writes_per_second, 0, "Per-thread rate limit on writes per second."
" No limit when <= 0. Only for the readwhilewriting test.");
DEFINE_bool(sync, false, "Sync all writes to disk");
DEFINE_bool(disable_data_sync, false, "If true, do not wait until data is"
" synced 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_int32(num_levels, 7, "The total number of levels");
DEFINE_int64(target_file_size_base, 2 * 1048576, "Target file size at level-1");
DEFINE_int32(target_file_size_multiplier, 1,
"A multiplier to compute target level-N file size (N >= 2)");
DEFINE_uint64(max_bytes_for_level_base, 10 * 1048576, "Max bytes for level-1");
DEFINE_int32(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, 12, "Number of files in level-0"
" that will trigger put stop.");
DEFINE_int32(level0_slowdown_writes_trigger, 8, "Number of files in level-0"
" that will slow down writes.");
DEFINE_int32(level0_file_num_compaction_trigger, 4, "Number of files in level-0"
" when compactions start");
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_uint64(delete_obsolete_files_period_micros, 0, "Option to delete "
"obsolete files periodically. 0 means that obsolete files are"
" deleted after every compaction run.");
namespace {
enum rocksdb::CompressionType StringToCompressionType(const char* ctype) {
assert(ctype);
if (!strcasecmp(ctype, "none"))
return rocksdb::kNoCompression;
else if (!strcasecmp(ctype, "snappy"))
return rocksdb::kSnappyCompression;
else if (!strcasecmp(ctype, "zlib"))
return rocksdb::kZlibCompression;
else if (!strcasecmp(ctype, "bzip2"))
return rocksdb::kBZip2Compression;
2014-02-08 03:12:30 +01:00
else if (!strcasecmp(ctype, "lz4"))
return rocksdb::kLZ4Compression;
else if (!strcasecmp(ctype, "lz4hc"))
return rocksdb::kLZ4HCCompression;
fprintf(stdout, "Cannot parse compression type '%s'\n", ctype);
return rocksdb::kSnappyCompression; //default value
}
} // namespace
DEFINE_string(compression_type, "snappy",
"Algorithm to use to compress the database");
static enum rocksdb::CompressionType FLAGS_compression_type_e =
rocksdb::kSnappyCompression;
DEFINE_int32(compression_level, -1,
"Compression level. For zlib this should be -1 for the "
"default level, or between 0 and 9.");
static bool ValidateCompressionLevel(const char* flagname, int32_t value) {
if (value < -1 || value > 9) {
fprintf(stderr, "Invalid value for --%s: %d, must be between -1 and 9\n",
flagname, value);
return false;
}
return true;
}
static const bool FLAGS_compression_level_dummy __attribute__((unused)) =
RegisterFlagValidator(&FLAGS_compression_level, &ValidateCompressionLevel);
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.");
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, "");
DEFINE_string(hdfs, "", "Name of hdfs environment");
// posix or hdfs environment
static rocksdb::Env* FLAGS_env = rocksdb::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_int32(stats_per_interval, 0, "Reports additional stats per interval when"
" this is greater than 0.");
DEFINE_int32(perf_level, 0, "Level of perf collection");
static bool ValidateRateLimit(const char* flagname, double value) {
static constexpr 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, "");
DEFINE_double(hard_rate_limit, 0.0, "When not equal to 0 this make threads "
"sleep at each stats reporting interval until the compaction"
" score for all levels is less than or equal to this value.");
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_int32(max_grandparent_overlap_factor, 10, "Control maximum bytes of "
"overlaps in grandparent (i.e., level+2) before we stop building a"
" single file in a level->level+1 compaction.");
DEFINE_bool(readonly, false, "Run read only benchmarks.");
DEFINE_bool(disable_auto_compactions, false, "Do not auto trigger compactions");
DEFINE_int32(source_compaction_factor, 1, "Cap the size of data in level-K for"
" a compaction run that compacts Level-K with Level-(K+1) (for"
" K >= 1)");
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_bool(bufferedio, rocksdb::EnvOptions().use_os_buffer,
"Allow buffered io using OS buffers");
DEFINE_bool(mmap_read, rocksdb::EnvOptions().use_mmap_reads,
"Allow reads to occur via mmap-ing files");
DEFINE_bool(mmap_write, rocksdb::EnvOptions().use_mmap_writes,
"Allow writes to occur via mmap-ing files");
DEFINE_bool(advise_random_on_open, rocksdb::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::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_int64(iter_refresh_interval_us, -1,
"How often to refresh iterators. Disable refresh when -1");
DEFINE_bool(use_adaptive_mutex, rocksdb::Options().use_adaptive_mutex,
"Use adaptive mutex");
DEFINE_uint64(bytes_per_sync, rocksdb::Options().bytes_per_sync,
"Allows OS to incrementally sync 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_bool(filter_deletes, false, " On true, deletes use bloom-filter and drop"
" the delete if key not present");
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(enable_io_prio, false, "Lower the background flush/compaction "
"threads' IO 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");
enum RepFactory {
kSkipList,
kPrefixHash,
kVectorRep,
Add a new mem-table representation based on cuckoo hash. Summary: = Major Changes = * Add a new mem-table representation, HashCuckooRep, which is based cuckoo hash. Cuckoo hash uses multiple hash functions. This allows each key to have multiple possible locations in the mem-table. - Put: When insert a key, it will try to find whether one of its possible locations is vacant and store the key. If none of its possible locations are available, then it will kick out a victim key and store at that location. The kicked-out victim key will then be stored at a vacant space of its possible locations or kick-out another victim. In this diff, the kick-out path (known as cuckoo-path) is found using BFS, which guarantees to be the shortest. - Get: Simply tries all possible locations of a key --- this guarantees worst-case constant time complexity. - Time complexity: O(1) for Get, and average O(1) for Put if the fullness of the mem-table is below 80%. - Default using two hash functions, the number of hash functions used by the cuckoo-hash may dynamically increase if it fails to find a short-enough kick-out path. - Currently, HashCuckooRep does not support iteration and snapshots, as our current main purpose of this is to optimize point access. = Minor Changes = * Add IsSnapshotSupported() to DB to indicate whether the current DB supports snapshots. If it returns false, then DB::GetSnapshot() will always return nullptr. Test Plan: Run existing tests. Will develop a test specifically for cuckoo hash in the next diff. Reviewers: sdong, haobo Reviewed By: sdong CC: leveldb, dhruba, igor Differential Revision: https://reviews.facebook.net/D16155
2014-04-30 02:13:46 +02:00
kHashLinkedList,
kCuckoo
};
namespace {
enum RepFactory StringToRepFactory(const char* ctype) {
assert(ctype);
if (!strcasecmp(ctype, "skip_list"))
return kSkipList;
else if (!strcasecmp(ctype, "prefix_hash"))
return kPrefixHash;
else if (!strcasecmp(ctype, "vector"))
return kVectorRep;
else if (!strcasecmp(ctype, "hash_linkedlist"))
return kHashLinkedList;
Add a new mem-table representation based on cuckoo hash. Summary: = Major Changes = * Add a new mem-table representation, HashCuckooRep, which is based cuckoo hash. Cuckoo hash uses multiple hash functions. This allows each key to have multiple possible locations in the mem-table. - Put: When insert a key, it will try to find whether one of its possible locations is vacant and store the key. If none of its possible locations are available, then it will kick out a victim key and store at that location. The kicked-out victim key will then be stored at a vacant space of its possible locations or kick-out another victim. In this diff, the kick-out path (known as cuckoo-path) is found using BFS, which guarantees to be the shortest. - Get: Simply tries all possible locations of a key --- this guarantees worst-case constant time complexity. - Time complexity: O(1) for Get, and average O(1) for Put if the fullness of the mem-table is below 80%. - Default using two hash functions, the number of hash functions used by the cuckoo-hash may dynamically increase if it fails to find a short-enough kick-out path. - Currently, HashCuckooRep does not support iteration and snapshots, as our current main purpose of this is to optimize point access. = Minor Changes = * Add IsSnapshotSupported() to DB to indicate whether the current DB supports snapshots. If it returns false, then DB::GetSnapshot() will always return nullptr. Test Plan: Run existing tests. Will develop a test specifically for cuckoo hash in the next diff. Reviewers: sdong, haobo Reviewed By: sdong CC: leveldb, dhruba, igor Differential Revision: https://reviews.facebook.net/D16155
2014-04-30 02:13:46 +02:00
else if (!strcasecmp(ctype, "cuckoo"))
return kCuckoo;
fprintf(stdout, "Cannot parse memreptable %s\n", ctype);
return kSkipList;
}
} // namespace
static enum RepFactory FLAGS_rep_factory;
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");
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 {
// Helper for quickly generating random data.
class RandomGenerator {
private:
std::string data_;
unsigned int pos_;
public:
RandomGenerator() {
// 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, FLAGS_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);
}
};
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;
DBWithColumnFamilies() : db(nullptr) {
cfh.clear();
}
};
class Stats {
private:
int id_;
double start_;
double finish_;
double seconds_;
int64_t done_;
int64_t last_report_done_;
int64_t next_report_;
int64_t bytes_;
double last_op_finish_;
double last_report_finish_;
HistogramImpl hist_;
std::string message_;
bool exclude_from_merge_;
public:
Stats() { Start(-1); }
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_ = FLAGS_env->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;
hist_.Merge(other.hist_);
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_ = FLAGS_env->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 FinishedOps(DBWithColumnFamilies* db_with_cfh, DB* db, int64_t num_ops) {
if (FLAGS_histogram) {
double now = FLAGS_env->NowMicros();
double micros = now - last_op_finish_;
hist_.Add(micros);
if (micros > 20000 && !FLAGS_stats_interval) {
fprintf(stderr, "long op: %.1f 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_, "");
fflush(stderr);
} else {
double now = FLAGS_env->NowMicros();
fprintf(stderr,
"%s ... thread %d: (%" PRIu64 ",%" PRIu64 ") ops and "
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
"(%.1f,%.1f) ops/second in (%.6f,%.6f) seconds\n",
FLAGS_env->TimeToString((uint64_t) now/1000000).c_str(),
id_,
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
done_ - last_report_done_, done_,
(done_ - last_report_done_) /
((now - last_report_finish_) / 1000000.0),
done_ / ((now - start_) / 1000000.0),
(now - last_report_finish_) / 1000000.0,
(now - start_) / 1000000.0);
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
if (FLAGS_stats_per_interval) {
std::string stats;
if (db_with_cfh && db_with_cfh->cfh.size()) {
for (size_t i = 0; i < db_with_cfh->cfh.size(); ++i) {
if (db->GetProperty(db_with_cfh->cfh[i], "rocksdb.cfstats",
&stats))
fprintf(stderr, "%s\n", stats.c_str());
}
} else if (db && db->GetProperty("rocksdb.stats", &stats)) {
fprintf(stderr, "%s\n", 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
fflush(stderr);
next_report_ += FLAGS_stats_interval;
last_report_finish_ = now;
last_report_done_ = done_;
}
}
}
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(),
elapsed * 1e6 / done_,
(long)throughput,
(extra.empty() ? "" : " "),
extra.c_str());
if (FLAGS_histogram) {
fprintf(stdout, "Microseconds per op:\n%s\n", hist_.ToString().c_str());
}
fflush(stdout);
}
};
// State shared by all concurrent executions of the same benchmark.
struct SharedState {
port::Mutex mu;
port::CondVar cv;
int total;
int perf_level;
// 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;
/* implicit */ ThreadState(int index)
: tid(index),
rand((FLAGS_seed ? FLAGS_seed : 1000) + index) {
}
};
class Duration {
public:
Duration(int max_seconds, int64_t max_ops) {
max_seconds_ = max_seconds;
max_ops_= max_ops;
ops_ = 0;
start_at_ = FLAGS_env->NowMicros();
}
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)
if ((ops_/1000) != ((ops_-increment)/1000)) {
double now = FLAGS_env->NowMicros();
return ((now - start_at_) / 1000000.0) >= max_seconds_;
} else {
return false;
}
} else {
return ops_ > max_ops_;
}
}
private:
int max_seconds_;
int64_t max_ops_;
int64_t ops_;
double start_at_;
};
class Benchmark {
private:
std::shared_ptr<Cache> cache_;
std::shared_ptr<Cache> compressed_cache_;
std::shared_ptr<const FilterPolicy> filter_policy_;
const SliceTransform* prefix_extractor_;
DBWithColumnFamilies db_;
std::vector<DBWithColumnFamilies> multi_dbs_;
int64_t num_;
int value_size_;
int key_size_;
int prefix_size_;
int64_t keys_per_prefix_;
int64_t entries_per_batch_;
WriteOptions write_options_;
int64_t reads_;
int64_t writes_;
int64_t readwrites_;
int64_t merge_keys_;
bool SanityCheck() {
if (FLAGS_compression_ratio > 1) {
fprintf(stderr, "compression_ratio should be between 0 and 1\n");
return false;
}
return true;
}
void PrintHeader() {
PrintEnvironment();
fprintf(stdout, "Keys: %d bytes each\n", FLAGS_key_size);
fprintf(stdout, "Values: %d bytes each (%d bytes after compression)\n",
FLAGS_value_size,
static_cast<int>(FLAGS_value_size * FLAGS_compression_ratio + 0.5));
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 + FLAGS_value_size) * num_)
/ 1048576.0));
fprintf(stdout, "FileSize: %.1f MB (estimated)\n",
(((FLAGS_key_size + FLAGS_value_size * FLAGS_compression_ratio)
* num_)
/ 1048576.0));
fprintf(stdout, "Write rate limit: %d\n", FLAGS_writes_per_second);
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
}
switch (FLAGS_compression_type_e) {
case rocksdb::kNoCompression:
fprintf(stdout, "Compression: none\n");
break;
case rocksdb::kSnappyCompression:
fprintf(stdout, "Compression: snappy\n");
break;
case rocksdb::kZlibCompression:
fprintf(stdout, "Compression: zlib\n");
break;
case rocksdb::kBZip2Compression:
fprintf(stdout, "Compression: bzip2\n");
break;
2014-02-08 03:12:30 +01:00
case rocksdb::kLZ4Compression:
fprintf(stdout, "Compression: lz4\n");
break;
case rocksdb::kLZ4HCCompression:
fprintf(stdout, "Compression: lz4hc\n");
break;
}
switch (FLAGS_rep_factory) {
case kPrefixHash:
fprintf(stdout, "Memtablerep: prefix_hash\n");
break;
case kSkipList:
fprintf(stdout, "Memtablerep: skip_list\n");
break;
case kVectorRep:
fprintf(stdout, "Memtablerep: vector\n");
break;
case kHashLinkedList:
fprintf(stdout, "Memtablerep: hash_linkedlist\n");
break;
Add a new mem-table representation based on cuckoo hash. Summary: = Major Changes = * Add a new mem-table representation, HashCuckooRep, which is based cuckoo hash. Cuckoo hash uses multiple hash functions. This allows each key to have multiple possible locations in the mem-table. - Put: When insert a key, it will try to find whether one of its possible locations is vacant and store the key. If none of its possible locations are available, then it will kick out a victim key and store at that location. The kicked-out victim key will then be stored at a vacant space of its possible locations or kick-out another victim. In this diff, the kick-out path (known as cuckoo-path) is found using BFS, which guarantees to be the shortest. - Get: Simply tries all possible locations of a key --- this guarantees worst-case constant time complexity. - Time complexity: O(1) for Get, and average O(1) for Put if the fullness of the mem-table is below 80%. - Default using two hash functions, the number of hash functions used by the cuckoo-hash may dynamically increase if it fails to find a short-enough kick-out path. - Currently, HashCuckooRep does not support iteration and snapshots, as our current main purpose of this is to optimize point access. = Minor Changes = * Add IsSnapshotSupported() to DB to indicate whether the current DB supports snapshots. If it returns false, then DB::GetSnapshot() will always return nullptr. Test Plan: Run existing tests. Will develop a test specifically for cuckoo hash in the next diff. Reviewers: sdong, haobo Reviewed By: sdong CC: leveldb, dhruba, igor Differential Revision: https://reviews.facebook.net/D16155
2014-04-30 02:13:46 +02:00
case kCuckoo:
fprintf(stdout, "Memtablerep: cuckoo\n");
break;
}
fprintf(stdout, "Perf Level: %d\n", FLAGS_perf_level);
PrintWarnings();
fprintf(stdout, "------------------------------------------------\n");
}
void PrintWarnings() {
#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::kNoCompression) {
// The test string should not be too small.
const int len = FLAGS_block_size;
char* text = (char*) malloc(len+1);
bool result = true;
const char* name = nullptr;
std::string compressed;
memset(text, (int) 'y', len);
text[len] = '\0';
switch (FLAGS_compression_type_e) {
case kSnappyCompression:
result = port::Snappy_Compress(Options().compression_opts, text,
strlen(text), &compressed);
name = "Snappy";
break;
case kZlibCompression:
result = port::Zlib_Compress(Options().compression_opts, text,
strlen(text), &compressed);
name = "Zlib";
break;
case kBZip2Compression:
result = port::BZip2_Compress(Options().compression_opts, text,
strlen(text), &compressed);
name = "BZip2";
break;
2014-02-08 03:12:30 +01:00
case kLZ4Compression:
result = port::LZ4_Compress(Options().compression_opts, text,
strlen(text), &compressed);
name = "LZ4";
break;
case kLZ4HCCompression:
result = port::LZ4HC_Compress(Options().compression_opts, text,
strlen(text), &compressed);
name = "LZ4HC";
break;
case kNoCompression:
assert(false); // cannot happen
break;
}
if (!result) {
fprintf(stdout, "WARNING: %s compression is not enabled\n", name);
} else if (name && compressed.size() >= strlen(text)) {
fprintf(stdout, "WARNING: %s compression is not effective\n", name);
}
free(text);
}
}
// 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 = s.size();
while (limit > start && isspace(s[limit-1])) {
limit--;
}
return Slice(s.data() + start, limit - start);
}
#endif
void PrintEnvironment() {
fprintf(stderr, "LevelDB: version %d.%d\n",
kMajorVersion, kMinorVersion);
#if defined(__linux)
time_t now = time(nullptr);
fprintf(stderr, "Date: %s", ctime(&now)); // ctime() adds newline
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());
}
#endif
}
public:
Benchmark()
: cache_(FLAGS_cache_size >= 0 ?
(FLAGS_cache_numshardbits >= 1 ?
NewLRUCache(FLAGS_cache_size, FLAGS_cache_numshardbits,
FLAGS_cache_remove_scan_count_limit) :
NewLRUCache(FLAGS_cache_size)) : nullptr),
compressed_cache_(FLAGS_compressed_cache_size >= 0 ?
(FLAGS_cache_numshardbits >= 1 ?
NewLRUCache(FLAGS_compressed_cache_size, FLAGS_cache_numshardbits) :
NewLRUCache(FLAGS_compressed_cache_size)) : nullptr),
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
filter_policy_(FLAGS_bloom_bits >= 0 ?
NewBloomFilterPolicy(FLAGS_bloom_bits, FLAGS_use_block_based_filter)
: nullptr),
prefix_extractor_(NewFixedPrefixTransform(FLAGS_prefix_size)),
num_(FLAGS_num),
value_size_(FLAGS_value_size),
key_size_(FLAGS_key_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),
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)
),
2014-04-08 23:01:09 +02:00
merge_keys_(FLAGS_merge_keys < 0 ? FLAGS_num : FLAGS_merge_keys) {
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 (unsigned int 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) {
DestroyDB(FLAGS_db, Options());
}
}
~Benchmark() {
std::for_each(db_.cfh.begin(), db_.cfh.end(),
[](ColumnFamilyHandle* cfh) { delete cfh; });
delete db_.db;
delete prefix_extractor_;
}
Slice AllocateKey() {
return Slice(new char[key_size_], 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 paddd 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) {
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));
}
}
std::string GetDbNameForMultiple(std::string base_name, size_t id) {
return base_name + std::to_string(id);
}
std::string ColumnFamilyName(int i) {
if (i == 0) {
return kDefaultColumnFamilyName;
} else {
char name[100];
snprintf(name, sizeof(name), "column_family_name_%06d", i);
return std::string(name);
}
}
void Run() {
if (!SanityCheck()) {
exit(1);
}
PrintHeader();
Open();
const char* benchmarks = FLAGS_benchmarks.c_str();
while (benchmarks != nullptr) {
const char* sep = strchr(benchmarks, ',');
Slice name;
if (sep == nullptr) {
name = benchmarks;
benchmarks = nullptr;
} else {
name = Slice(benchmarks, sep - benchmarks);
benchmarks = sep + 1;
}
// Sanitize parameters
num_ = FLAGS_num;
reads_ = (FLAGS_reads < 0 ? FLAGS_num : FLAGS_reads);
writes_ = (FLAGS_writes < 0 ? FLAGS_num : FLAGS_writes);
value_size_ = FLAGS_value_size;
key_size_ = FLAGS_key_size;
entries_per_batch_ = FLAGS_batch_size;
write_options_ = WriteOptions();
if (FLAGS_sync) {
write_options_.sync = true;
}
write_options_.disableWAL = FLAGS_disable_wal;
void (Benchmark::*method)(ThreadState*) = nullptr;
bool fresh_db = false;
int num_threads = FLAGS_threads;
if (name == Slice("fillseq")) {
fresh_db = true;
method = &Benchmark::WriteSeq;
} else if (name == Slice("fillbatch")) {
fresh_db = true;
entries_per_batch_ = 1000;
method = &Benchmark::WriteSeq;
} else if (name == Slice("fillrandom")) {
fresh_db = true;
method = &Benchmark::WriteRandom;
} else if (name == Slice("filluniquerandom")) {
fresh_db = true;
if (num_threads > 1) {
fprintf(stderr, "filluniquerandom multithreaded not supported"
", use 1 thread");
num_threads = 1;
}
method = &Benchmark::WriteUniqueRandom;
} else if (name == Slice("overwrite")) {
fresh_db = false;
method = &Benchmark::WriteRandom;
} else if (name == Slice("fillsync")) {
fresh_db = true;
num_ /= 1000;
write_options_.sync = true;
method = &Benchmark::WriteRandom;
} else if (name == Slice("fill100K")) {
fresh_db = true;
num_ /= 1000;
value_size_ = 100 * 1000;
method = &Benchmark::WriteRandom;
} else if (name == Slice("readseq")) {
method = &Benchmark::ReadSequential;
} else if (name == Slice("readtocache")) {
method = &Benchmark::ReadSequential;
num_threads = 1;
reads_ = num_;
} else if (name == Slice("readreverse")) {
method = &Benchmark::ReadReverse;
} else if (name == Slice("readrandom")) {
method = &Benchmark::ReadRandom;
} else if (name == Slice("readrandomfast")) {
method = &Benchmark::ReadRandomFast;
} else if (name == Slice("multireadrandom")) {
fprintf(stderr, "entries_per_batch = %" PRIi64 "\n",
entries_per_batch_);
method = &Benchmark::MultiReadRandom;
} else if (name == Slice("readmissing")) {
++key_size_;
method = &Benchmark::ReadRandom;
} else if (name == Slice("newiterator")) {
method = &Benchmark::IteratorCreation;
} else if (name == Slice("newiteratorwhilewriting")) {
num_threads++; // Add extra thread for writing
method = &Benchmark::IteratorCreationWhileWriting;
} else if (name == Slice("seekrandom")) {
method = &Benchmark::SeekRandom;
} else if (name == Slice("seekrandomwhilewriting")) {
num_threads++; // Add extra thread for writing
method = &Benchmark::SeekRandomWhileWriting;
} else if (name == Slice("readrandomsmall")) {
reads_ /= 1000;
method = &Benchmark::ReadRandom;
} else if (name == Slice("deleteseq")) {
method = &Benchmark::DeleteSeq;
} else if (name == Slice("deleterandom")) {
method = &Benchmark::DeleteRandom;
} else if (name == Slice("readwhilewriting")) {
num_threads++; // Add extra thread for writing
method = &Benchmark::ReadWhileWriting;
} else if (name == Slice("readrandomwriterandom")) {
method = &Benchmark::ReadRandomWriteRandom;
} else if (name == Slice("readrandommergerandom")) {
if (FLAGS_merge_operator.empty()) {
fprintf(stdout, "%-12s : skipped (--merge_operator is unknown)\n",
name.ToString().c_str());
exit(1);
}
method = &Benchmark::ReadRandomMergeRandom;
} else if (name == Slice("updaterandom")) {
method = &Benchmark::UpdateRandom;
} else if (name == Slice("appendrandom")) {
method = &Benchmark::AppendRandom;
} else if (name == Slice("mergerandom")) {
if (FLAGS_merge_operator.empty()) {
fprintf(stdout, "%-12s : skipped (--merge_operator is unknown)\n",
name.ToString().c_str());
exit(1);
}
method = &Benchmark::MergeRandom;
} else if (name == Slice("randomwithverify")) {
method = &Benchmark::RandomWithVerify;
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
} else if (name == Slice("fillseekseq")) {
method = &Benchmark::WriteSeqSeekSeq;
} else if (name == Slice("compact")) {
method = &Benchmark::Compact;
} else if (name == Slice("crc32c")) {
method = &Benchmark::Crc32c;
} else if (name == Slice("xxhash")) {
method = &Benchmark::xxHash;
} else if (name == Slice("acquireload")) {
method = &Benchmark::AcquireLoad;
2014-02-08 03:12:30 +01:00
} else if (name == Slice("compress")) {
method = &Benchmark::Compress;
} else if (name == Slice("uncompress")) {
method = &Benchmark::Uncompress;
} else if (name == Slice("stats")) {
PrintStats("rocksdb.stats");
} else if (name == Slice("levelstats")) {
PrintStats("rocksdb.levelstats");
} else if (name == Slice("sstables")) {
PrintStats("rocksdb.sstables");
} else {
if (name != Slice()) { // No error message for empty name
fprintf(stderr, "unknown benchmark '%s'\n", name.ToString().c_str());
exit(1);
}
}
if (fresh_db) {
if (FLAGS_use_existing_db) {
fprintf(stdout, "%-12s : skipped (--use_existing_db is true)\n",
name.ToString().c_str());
method = nullptr;
} else {
if (db_.db != nullptr) {
std::for_each(db_.cfh.begin(), db_.cfh.end(),
[](ColumnFamilyHandle* cfh) { delete cfh; });
delete db_.db;
db_.db = nullptr;
db_.cfh.clear();
DestroyDB(FLAGS_db, Options());
}
for (size_t i = 0; i < multi_dbs_.size(); i++) {
delete multi_dbs_[i].db;
DestroyDB(GetDbNameForMultiple(FLAGS_db, i), Options());
}
multi_dbs_.clear();
}
Open();
}
if (method != nullptr) {
fprintf(stdout, "DB path: [%s]\n", FLAGS_db.c_str());
RunBenchmark(num_threads, name, method);
}
}
if (FLAGS_statistics) {
fprintf(stdout, "STATISTICS:\n%s\n", dbstats->ToString().c_str());
}
}
private:
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));
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();
}
}
}
void 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;
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->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;
}
void Crc32c(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;
uint32_t crc = 0;
while (bytes < 500 * 1048576) {
crc = crc32c::Value(data.data(), size);
thread->stats.FinishedOps(nullptr, nullptr, 1);
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);
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);
}
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;
2014-02-08 03:12:30 +01:00
// Compress 1G
while (ok && bytes < int64_t(1) << 30) {
switch (FLAGS_compression_type_e) {
case rocksdb::kSnappyCompression:
ok = port::Snappy_Compress(Options().compression_opts, input.data(),
input.size(), &compressed);
break;
case rocksdb::kZlibCompression:
ok = port::Zlib_Compress(Options().compression_opts, input.data(),
input.size(), &compressed);
2014-02-08 03:12:30 +01:00
break;
case rocksdb::kBZip2Compression:
ok = port::BZip2_Compress(Options().compression_opts, input.data(),
input.size(), &compressed);
break;
case rocksdb::kLZ4Compression:
ok = port::LZ4_Compress(Options().compression_opts, input.data(),
input.size(), &compressed);
break;
case rocksdb::kLZ4HCCompression:
ok = port::LZ4HC_Compress(Options().compression_opts, input.data(),
input.size(), &compressed);
break;
default:
ok = false;
}
produced += compressed.size();
bytes += input.size();
thread->stats.FinishedOps(nullptr, nullptr, 1);
}
if (!ok) {
2014-02-08 03:12:30 +01:00
thread->stats.AddMessage("(compression failure)");
} else {
char buf[100];
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
bool ok;
switch (FLAGS_compression_type_e) {
case rocksdb::kSnappyCompression:
ok = port::Snappy_Compress(Options().compression_opts, input.data(),
input.size(), &compressed);
break;
case rocksdb::kZlibCompression:
ok = port::Zlib_Compress(Options().compression_opts, input.data(),
input.size(), &compressed);
break;
case rocksdb::kBZip2Compression:
ok = port::BZip2_Compress(Options().compression_opts, input.data(),
input.size(), &compressed);
break;
case rocksdb::kLZ4Compression:
ok = port::LZ4_Compress(Options().compression_opts, input.data(),
input.size(), &compressed);
break;
case rocksdb::kLZ4HCCompression:
ok = port::LZ4HC_Compress(Options().compression_opts, input.data(),
input.size(), &compressed);
break;
default:
ok = false;
}
int64_t bytes = 0;
2014-02-08 03:12:30 +01:00
int decompress_size;
while (ok && bytes < 1024 * 1048576) {
char *uncompressed = nullptr;
switch (FLAGS_compression_type_e) {
case rocksdb::kSnappyCompression:
// allocate here to make comparison fair
uncompressed = new char[input.size()];
ok = port::Snappy_Uncompress(compressed.data(), compressed.size(),
uncompressed);
break;
case rocksdb::kZlibCompression:
uncompressed = port::Zlib_Uncompress(
compressed.data(), compressed.size(), &decompress_size);
ok = uncompressed != nullptr;
break;
case rocksdb::kBZip2Compression:
uncompressed = port::BZip2_Uncompress(
compressed.data(), compressed.size(), &decompress_size);
ok = uncompressed != nullptr;
break;
case rocksdb::kLZ4Compression:
uncompressed = port::LZ4_Uncompress(
compressed.data(), compressed.size(), &decompress_size);
ok = uncompressed != nullptr;
break;
case rocksdb::kLZ4HCCompression:
uncompressed = port::LZ4_Uncompress(
compressed.data(), compressed.size(), &decompress_size);
ok = uncompressed != nullptr;
break;
default:
ok = false;
}
delete[] uncompressed;
bytes += input.size();
thread->stats.FinishedOps(nullptr, nullptr, 1);
}
if (!ok) {
2014-02-08 03:12:30 +01:00
thread->stats.AddMessage("(compression failure)");
} else {
thread->stats.AddBytes(bytes);
}
}
void Open() {
assert(db_.db == nullptr);
Options options;
options.create_if_missing = !FLAGS_use_existing_db;
options.create_missing_column_families = FLAGS_num_column_families > 1;
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;
options.max_background_compactions = FLAGS_max_background_compactions;
options.max_background_flushes = FLAGS_max_background_flushes;
options.compaction_style = FLAGS_compaction_style_e;
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);
}
}
options.memtable_prefix_bloom_bits = FLAGS_memtable_bloom_bits;
options.bloom_locality = FLAGS_bloom_locality;
options.max_open_files = FLAGS_open_files;
options.statistics = dbstats;
if (FLAGS_enable_io_prio) {
FLAGS_env->LowerThreadPoolIOPriority(Env::LOW);
FLAGS_env->LowerThreadPoolIOPriority(Env::HIGH);
}
options.env = FLAGS_env;
options.disableDataSync = FLAGS_disable_data_sync;
options.use_fsync = FLAGS_use_fsync;
options.wal_dir = FLAGS_wal_dir;
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.max_bytes_for_level_multiplier =
FLAGS_max_bytes_for_level_multiplier;
options.filter_deletes = FLAGS_filter_deletes;
if ((FLAGS_prefix_size == 0) && (FLAGS_rep_factory == kPrefixHash ||
FLAGS_rep_factory == kHashLinkedList)) {
fprintf(stderr, "prefix_size should be non-zero if PrefixHash or "
"HashLinkedList memtablerep is used\n");
exit(1);
}
switch (FLAGS_rep_factory) {
case kPrefixHash:
options.memtable_factory.reset(NewHashSkipListRepFactory(
FLAGS_hash_bucket_count));
break;
case kSkipList:
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
options.memtable_factory.reset(new SkipListFactory(
FLAGS_skip_list_lookahead));
break;
case kHashLinkedList:
options.memtable_factory.reset(NewHashLinkListRepFactory(
FLAGS_hash_bucket_count));
break;
case kVectorRep:
options.memtable_factory.reset(
new VectorRepFactory
);
break;
Add a new mem-table representation based on cuckoo hash. Summary: = Major Changes = * Add a new mem-table representation, HashCuckooRep, which is based cuckoo hash. Cuckoo hash uses multiple hash functions. This allows each key to have multiple possible locations in the mem-table. - Put: When insert a key, it will try to find whether one of its possible locations is vacant and store the key. If none of its possible locations are available, then it will kick out a victim key and store at that location. The kicked-out victim key will then be stored at a vacant space of its possible locations or kick-out another victim. In this diff, the kick-out path (known as cuckoo-path) is found using BFS, which guarantees to be the shortest. - Get: Simply tries all possible locations of a key --- this guarantees worst-case constant time complexity. - Time complexity: O(1) for Get, and average O(1) for Put if the fullness of the mem-table is below 80%. - Default using two hash functions, the number of hash functions used by the cuckoo-hash may dynamically increase if it fails to find a short-enough kick-out path. - Currently, HashCuckooRep does not support iteration and snapshots, as our current main purpose of this is to optimize point access. = Minor Changes = * Add IsSnapshotSupported() to DB to indicate whether the current DB supports snapshots. If it returns false, then DB::GetSnapshot() will always return nullptr. Test Plan: Run existing tests. Will develop a test specifically for cuckoo hash in the next diff. Reviewers: sdong, haobo Reviewed By: sdong CC: leveldb, dhruba, igor Differential Revision: https://reviews.facebook.net/D16155
2014-04-30 02:13:46 +02:00
case kCuckoo:
options.memtable_factory.reset(NewHashCuckooRepFactory(
options.write_buffer_size, FLAGS_key_size + FLAGS_value_size));
break;
}
if (FLAGS_use_plain_table) {
if (FLAGS_rep_factory != kPrefixHash &&
FLAGS_rep_factory != kHashLinkedList) {
fprintf(stderr, "Waring: plain table is used with skipList\n");
}
if (!FLAGS_mmap_read && !FLAGS_mmap_write) {
fprintf(stderr, "plain table format requires mmap to operate\n");
exit(1);
}
int bloom_bits_per_key = FLAGS_bloom_bits;
if (bloom_bits_per_key < 0) {
bloom_bits_per_key = 0;
}
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 if (FLAGS_use_cuckoo_table) {
if (FLAGS_cuckoo_hash_ratio > 1 || FLAGS_cuckoo_hash_ratio < 0) {
fprintf(stderr, "Invalid cuckoo_hash_ratio\n");
exit(1);
}
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
rocksdb::CuckooTableOptions table_options;
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 {
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 (cache_ == nullptr) {
block_based_options.no_block_cache = 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.filter_policy = filter_policy_;
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() !=
(unsigned int)FLAGS_num_levels) {
fprintf(stderr, "Insufficient number of fanouts specified %d\n",
(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;
options.compression_opts.level = FLAGS_compression_level;
options.WAL_ttl_seconds = FLAGS_wal_ttl_seconds;
options.WAL_size_limit_MB = FLAGS_wal_size_limit_MB;
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.delete_obsolete_files_period_micros =
FLAGS_delete_obsolete_files_period_micros;
options.soft_rate_limit = FLAGS_soft_rate_limit;
options.hard_rate_limit = FLAGS_hard_rate_limit;
options.rate_limit_delay_max_milliseconds =
FLAGS_rate_limit_delay_max_milliseconds;
options.table_cache_numshardbits = FLAGS_table_cache_numshardbits;
options.max_grandparent_overlap_factor =
FLAGS_max_grandparent_overlap_factor;
options.disable_auto_compactions = FLAGS_disable_auto_compactions;
options.source_compaction_factor = FLAGS_source_compaction_factor;
// fill storage options
options.allow_os_buffer = FLAGS_bufferedio;
options.allow_mmap_reads = FLAGS_mmap_read;
options.allow_mmap_writes = FLAGS_mmap_write;
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;
// merge operator options
options.merge_operator = MergeOperators::CreateFromStringId(
FLAGS_merge_operator);
if (options.merge_operator == nullptr && !FLAGS_merge_operator.empty()) {
fprintf(stderr, "invalid merge operator: %s\n",
FLAGS_merge_operator.c_str());
exit(1);
}
options.max_successive_merges = FLAGS_max_successive_merges;
// 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;
}
if (FLAGS_num_multi_db <= 1) {
OpenDb(options, FLAGS_db, &db_);
} else {
multi_dbs_.clear();
multi_dbs_.resize(FLAGS_num_multi_db);
2014-04-29 21:33:57 +02:00
for (int i = 0; i < FLAGS_num_multi_db; i++) {
OpenDb(options, GetDbNameForMultiple(FLAGS_db, i), &multi_dbs_[i]);
}
}
if (FLAGS_min_level_to_compress >= 0) {
options.compression_per_level.clear();
}
}
void OpenDb(const Options& options, const std::string& db_name,
DBWithColumnFamilies* db) {
Status s;
// Open with column families if necessary.
if (FLAGS_num_column_families > 1) {
db->cfh.resize(FLAGS_num_column_families);
std::vector<ColumnFamilyDescriptor> column_families;
for (int i = 0; i < FLAGS_num_column_families; i++) {
column_families.push_back(ColumnFamilyDescriptor(
ColumnFamilyName(i), ColumnFamilyOptions(options)));
}
if (FLAGS_readonly) {
s = DB::OpenForReadOnly(options, db_name, column_families,
&db->cfh, &db->db);
} else {
s = DB::Open(options, db_name, column_families, &db->cfh, &db->db);
}
} else if (FLAGS_readonly) {
s = DB::OpenForReadOnly(options, db_name, &db->db);
} else {
s = DB::Open(options, db_name, &db->db);
}
if (!s.ok()) {
fprintf(stderr, "open error: %s\n", s.ToString().c_str());
exit(1);
}
}
enum WriteMode {
RANDOM, SEQUENTIAL, 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;
}
std::shuffle(values_.begin(), values_.end(),
std::default_random_engine(FLAGS_seed));
}
}
uint64_t Next() {
switch (mode_) {
case SEQUENTIAL:
return next_++;
case RANDOM:
return rand_->Next() % num_;
case UNIQUE_RANDOM:
return values_[next_++];
}
assert(false);
return std::numeric_limits<uint64_t>::max();
}
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()];
}
}
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);
Duration duration(test_duration, num_ops * num_key_gens);
for (size_t i = 0; i < num_key_gens; i++) {
key_gens[i].reset(new KeyGenerator(&(thread->rand), write_mode, num_ops));
}
if (num_ != FLAGS_num) {
char msg[100];
snprintf(msg, sizeof(msg), "(%" PRIu64 " ops)", num_);
thread->stats.AddMessage(msg);
}
RandomGenerator gen;
WriteBatch batch;
Status s;
int64_t bytes = 0;
Slice key = AllocateKey();
std::unique_ptr<const char[]> key_guard(key.data());
while (!duration.Done(entries_per_batch_)) {
size_t id = thread->rand.Next() % num_key_gens;
DBWithColumnFamilies* db_with_cfh = SelectDBWithCfh(id);
batch.Clear();
for (int64_t j = 0; j < entries_per_batch_; j++) {
int64_t rand_num = key_gens[id]->Next();
GenerateKeyFromInt(rand_num, FLAGS_num, &key);
if (FLAGS_num_column_families <= 1) {
batch.Put(key, gen.Generate(value_size_));
} 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->cfh[rand_num % db_with_cfh->cfh.size()],
key, gen.Generate(value_size_));
}
bytes += value_size_ + key_size_;
}
s = db_with_cfh->db->Write(write_options_, &batch);
thread->stats.FinishedOps(db_with_cfh, db_with_cfh->db,
entries_per_batch_);
if (!s.ok()) {
fprintf(stderr, "put error: %s\n", s.ToString().c_str());
exit(1);
}
}
thread->stats.AddBytes(bytes);
}
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) {
Iterator* iter = db->NewIterator(ReadOptions(FLAGS_verify_checksum, true));
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);
++i;
}
delete iter;
thread->stats.AddBytes(bytes);
}
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);
++i;
}
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);
Slice key = AllocateKey();
std::unique_ptr<const char[]> key_guard(key.data());
std::string value;
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;
if (db->Get(options, key, &value).ok()) {
++found;
}
if (key_rand >= FLAGS_num) {
++nonexist;
}
}
thread->stats.FinishedOps(nullptr, db, 100);
} 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 > 0) {
thread->stats.AddMessage(perf_context.ToString());
}
}
void ReadRandom(ThreadState* thread) {
int64_t read = 0;
int64_t found = 0;
ReadOptions options(FLAGS_verify_checksum, true);
Slice key = AllocateKey();
std::unique_ptr<const char[]> key_guard(key.data());
std::string value;
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.
int64_t key_rand = thread->rand.Next() % FLAGS_num;
GenerateKeyFromInt(key_rand, FLAGS_num, &key);
read++;
Status s;
if (FLAGS_num_column_families > 1) {
s = db_with_cfh->db->Get(options,
db_with_cfh->cfh[key_rand % db_with_cfh->cfh.size()], key, &value);
} else {
s = db_with_cfh->db->Get(options, key, &value);
}
if (s.ok()) {
found++;
}
thread->stats.FinishedOps(db_with_cfh, db_with_cfh->db, 1);
}
char msg[100];
snprintf(msg, sizeof(msg), "(%" PRIu64 " of %" PRIu64 " found)\n",
found, read);
thread->stats.AddMessage(msg);
if (FLAGS_perf_level > 0) {
thread->stats.AddMessage(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 found = 0;
ReadOptions options(FLAGS_verify_checksum, true);
std::vector<Slice> keys;
std::vector<std::string> values(entries_per_batch_);
2014-04-29 21:33:57 +02:00
while (static_cast<int64_t>(keys.size()) < entries_per_batch_) {
keys.push_back(AllocateKey());
}
Duration duration(FLAGS_duration, reads_);
while (!duration.Done(1)) {
DB* db = SelectDB(thread);
for (int64_t i = 0; i < entries_per_batch_; ++i) {
GenerateKeyFromInt(thread->rand.Next() % FLAGS_num,
FLAGS_num, &keys[i]);
}
std::vector<Status> statuses = db->MultiGet(options, keys, &values);
2014-04-29 21:33:57 +02:00
assert(static_cast<int64_t>(statuses.size()) == entries_per_batch_);
read += entries_per_batch_;
for (int64_t i = 0; i < entries_per_batch_; ++i) {
if (statuses[i].ok()) {
++found;
}
}
thread->stats.FinishedOps(nullptr, db, entries_per_batch_);
}
for (auto& k : keys) {
delete k.data();
}
char msg[100];
snprintf(msg, sizeof(msg), "(%" PRIu64 " of %" PRIu64 " found)",
found, read);
thread->stats.AddMessage(msg);
}
void IteratorCreation(ThreadState* thread) {
Duration duration(FLAGS_duration, reads_);
ReadOptions options(FLAGS_verify_checksum, true);
while (!duration.Done(1)) {
DB* db = SelectDB(thread);
Iterator* iter = db->NewIterator(options);
delete iter;
thread->stats.FinishedOps(nullptr, db, 1);
}
}
void IteratorCreationWhileWriting(ThreadState* thread) {
if (thread->tid > 0) {
IteratorCreation(thread);
} else {
BGWriter(thread);
}
}
void SeekRandom(ThreadState* thread) {
int64_t read = 0;
int64_t found = 0;
ReadOptions options(FLAGS_verify_checksum, true);
options.tailing = FLAGS_use_tailing_iterator;
Iterator* single_iter = nullptr;
std::vector<Iterator*> multi_iters;
if (db_.db != nullptr) {
single_iter = db_.db->NewIterator(options);
} else {
for (const auto& db_with_cfh : multi_dbs_) {
multi_iters.push_back(db_with_cfh.db->NewIterator(options));
}
}
uint64_t last_refresh = FLAGS_env->NowMicros();
Slice key = AllocateKey();
std::unique_ptr<const char[]> key_guard(key.data());
Duration duration(FLAGS_duration, reads_);
char value_buffer[256];
while (!duration.Done(1)) {
if (!FLAGS_use_tailing_iterator && FLAGS_iter_refresh_interval_us >= 0) {
uint64_t now = FLAGS_env->NowMicros();
if (now - last_refresh > (uint64_t)FLAGS_iter_refresh_interval_us) {
if (db_.db != nullptr) {
delete single_iter;
single_iter = db_.db->NewIterator(options);
} else {
for (auto iter : multi_iters) {
delete iter;
}
multi_iters.clear();
for (const auto& db_with_cfh : multi_dbs_) {
multi_iters.push_back(db_with_cfh.db->NewIterator(options));
}
}
}
last_refresh = now;
}
// Pick a Iterator to use
Iterator* iter_to_use = single_iter;
if (single_iter == nullptr) {
iter_to_use = multi_iters[thread->rand.Next() % multi_iters.size()];
}
GenerateKeyFromInt(thread->rand.Next() % FLAGS_num, FLAGS_num, &key);
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)));
iter_to_use->Next();
assert(iter_to_use->status().ok());
}
thread->stats.FinishedOps(&db_, db_.db, 1);
}
delete single_iter;
for (auto iter : multi_iters) {
delete iter;
}
char msg[100];
snprintf(msg, sizeof(msg), "(%" PRIu64 " of %" PRIu64 " found)\n",
found, read);
thread->stats.AddMessage(msg);
if (FLAGS_perf_level > 0) {
thread->stats.AddMessage(perf_context.ToString());
}
}
void SeekRandomWhileWriting(ThreadState* thread) {
if (thread->tid > 0) {
SeekRandom(thread);
} else {
BGWriter(thread);
}
}
void DoDelete(ThreadState* thread, bool seq) {
WriteBatch batch;
Duration duration(seq ? 0 : FLAGS_duration, num_);
int64_t i = 0;
Slice key = AllocateKey();
std::unique_ptr<const char[]> key_guard(key.data());
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);
}
auto s = db->Write(write_options_, &batch);
thread->stats.FinishedOps(nullptr, db, entries_per_batch_);
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);
}
}
void BGWriter(ThreadState* thread) {
// Special thread that keeps writing until other threads are done.
RandomGenerator gen;
double last = FLAGS_env->NowMicros();
int writes_per_second_by_10 = 0;
int num_writes = 0;
// --writes_per_second rate limit is enforced per 100 milliseconds
// intervals to avoid a burst of writes at the start of each second.
if (FLAGS_writes_per_second > 0)
writes_per_second_by_10 = FLAGS_writes_per_second / 10;
// Don't merge stats from this thread with the readers.
thread->stats.SetExcludeFromMerge();
Slice key = AllocateKey();
std::unique_ptr<const char[]> key_guard(key.data());
while (true) {
DB* db = SelectDB(thread);
{
MutexLock l(&thread->shared->mu);
if (thread->shared->num_done + 1 >= thread->shared->num_initialized) {
// Other threads have finished
break;
}
}
GenerateKeyFromInt(thread->rand.Next() % FLAGS_num, FLAGS_num, &key);
Status s = db->Put(write_options_, key, gen.Generate(value_size_));
if (!s.ok()) {
fprintf(stderr, "put error: %s\n", s.ToString().c_str());
exit(1);
}
thread->stats.FinishedOps(&db_, db_.db, 1);
++num_writes;
if (writes_per_second_by_10 && num_writes >= writes_per_second_by_10) {
double now = FLAGS_env->NowMicros();
double usecs_since_last = now - last;
num_writes = 0;
last = now;
if (usecs_since_last < 100000.0) {
FLAGS_env->SleepForMicroseconds(100000.0 - usecs_since_last);
last = FLAGS_env->NowMicros();
}
}
}
}
// 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;
Status s;
for (int i = 0; i < 3; i++) {
keys[i] = key.ToString() + suffixes[i];
batch.Put(keys[i], value);
}
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;
Status s;
for (int i = 0; i < 3; i++) {
keys[i] = key.ToString() + suffixes[i];
batch.Delete(keys[i]);
}
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;
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;
Slice key = AllocateKey();
std::unique_ptr<const char[]> key_guard(key.data());
// 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++;
} 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(value_size_));
if (!s.ok()) {
fprintf(stderr, "putmany error: %s\n", s.ToString().c_str());
exit(1);
}
put_weight--;
puts_done++;
} 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);
}
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),
"( 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_);
Slice key = AllocateKey();
std::unique_ptr<const char[]> key_guard(key.data());
// 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
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++;
} else if (put_weight > 0) {
// then do all the corresponding number of puts
// for all the gets we have done earlier
Status s = db->Put(write_options_, key, gen.Generate(value_size_));
if (!s.ok()) {
fprintf(stderr, "put error: %s\n", s.ToString().c_str());
exit(1);
}
put_weight--;
writes_done++;
}
thread->stats.FinishedOps(nullptr, db, 1);
}
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;
Duration duration(FLAGS_duration, readwrites_);
Slice key = AllocateKey();
std::unique_ptr<const char[]> key_guard(key.data());
// 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 (db->Get(options, key, &value).ok()) {
found++;
}
Status s = db->Put(write_options_, key, gen.Generate(value_size_));
if (!s.ok()) {
fprintf(stderr, "put error: %s\n", s.ToString().c_str());
exit(1);
}
thread->stats.FinishedOps(nullptr, db, 1);
}
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);
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;
Slice key = AllocateKey();
std::unique_ptr<const char[]> key_guard(key.data());
// 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);
// Get the existing value
if (db->Get(options, key, &value).ok()) {
found++;
} else {
// If not existing, then just assume an empty string of data
value.clear();
}
// Update the value (by appending data)
Slice operand = gen.Generate(value_size_);
if (value.size() > 0) {
// Use a delimeter to match the semantics for StringAppendOperator
value.append(1,',');
}
value.append(operand.data(), operand.size());
// 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());
exit(1);
}
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 (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;
Slice key = AllocateKey();
std::unique_ptr<const char[]> key_guard(key.data());
// 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);
Status s = db->Merge(write_options_, key, gen.Generate(value_size_));
if (!s.ok()) {
fprintf(stderr, "merge error: %s\n", s.ToString().c_str());
exit(1);
}
thread->stats.FinishedOps(nullptr, db, 1);
}
// Print some statistics
char msg[100];
snprintf(msg, sizeof(msg), "( updates:%" PRIu64 ")", readwrites_);
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;
Slice key = AllocateKey();
std::unique_ptr<const char[]> key_guard(key.data());
// 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(value_size_));
if (!s.ok()) {
fprintf(stderr, "merge error: %s\n", s.ToString().c_str());
exit(1);
}
num_merges++;
} 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);
}
char msg[100];
snprintf(msg, sizeof(msg),
"(reads:%" PRIu64 " merges:%" PRIu64 " total:%" PRIu64 " hits:%" \
PRIu64 " maxlength:%zu)",
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);
std::unique_ptr<Iterator> iter(
db->NewIterator(ReadOptions(FLAGS_verify_checksum, true)));
Slice key = AllocateKey();
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);
for (int j = 0; j < FLAGS_seek_nexts && i + 1 < FLAGS_num; ++j) {
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->Next();
GenerateKeyFromInt(++i, FLAGS_num, &key);
assert(iter->Valid() && iter->key() == key);
thread->stats.FinishedOps(nullptr, db, 1);
}
iter->Seek(key);
assert(iter->Valid() && iter->key() == key);
thread->stats.FinishedOps(nullptr, db, 1);
}
}
void Compact(ThreadState* thread) {
DB* db = SelectDB(thread);
db->CompactRange(nullptr, nullptr);
}
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());
}
};
} // namespace rocksdb
int main(int argc, char** argv) {
rocksdb::port::InstallStackTraceHandler();
SetUsageMessage(std::string("\nUSAGE:\n") + std::string(argv[0]) +
" [OPTIONS]...");
ParseCommandLineFlags(&argc, &argv, true);
[RocksDB] Add stacktrace signal handler Summary: This diff provides the ability to print out a stacktrace when the process receives certain signals. Currently, we enable this for the following signals (program error related): SIGILL SIGSEGV SIGBUS SIGABRT Application simply #include "util/stack_trace.h" and call leveldb::InstallStackTraceHandler() during initialization, if signal handler is needed. It's not done automatically when openning db, because it's the application(process)'s responsibility to install signal handler and some applications might already have their own (like fbcode). Sample output: Received signal 11 (Segmentation fault) #0 0x408ff0 ./signal_test() [0x408ff0] /home/haobo/rocksdb/util/signal_test.cc:4 #1 0x40827d ./signal_test() [0x40827d] /home/haobo/rocksdb/util/signal_test.cc:24 #2 0x7f8bb183172e /usr/local/fbcode/gcc-4.7.1-glibc-2.14.1/lib/libc.so.6(__libc_start_main+0x10e) [0x7f8bb183172e] ??:0 #3 0x408ebc ./signal_test() [0x408ebc] /home/engshare/third-party/src/glibc/glibc-2.14.1/glibc-2.14.1/csu/../sysdeps/x86_64/elf/start.S:113 Segmentation fault (core dumped) For each frame, we print the raw pointer, the symbol provided by backtrace_symbols (still not good enough), and the source file/line. Note that address translation is done by directly shell out to addr2line. ??:0 means addr2line fails to do the translation. Hacky, but I think it's good for now. Test Plan: signal_test.cc Reviewers: dhruba, MarkCallaghan Reviewed By: dhruba CC: leveldb Differential Revision: https://reviews.facebook.net/D10173
2013-04-11 19:54:35 +02:00
FLAGS_compaction_style_e = (rocksdb::CompactionStyle) FLAGS_compaction_style;
if (FLAGS_statistics) {
dbstats = rocksdb::CreateDBStatistics();
}
std::vector<std::string> fanout =
rocksdb::stringSplit(FLAGS_max_bytes_for_level_multiplier_additional, ',');
for (unsigned int j= 0; j < fanout.size(); j++) {
FLAGS_max_bytes_for_level_multiplier_additional_v.push_back(
std::stoi(fanout[j]));
}
FLAGS_compression_type_e =
StringToCompressionType(FLAGS_compression_type.c_str());
if (!FLAGS_hdfs.empty()) {
FLAGS_env = new rocksdb::HdfsEnv(FLAGS_hdfs);
}
if (!strcasecmp(FLAGS_compaction_fadvice.c_str(), "NONE"))
FLAGS_compaction_fadvice_e = rocksdb::Options::NONE;
else if (!strcasecmp(FLAGS_compaction_fadvice.c_str(), "NORMAL"))
FLAGS_compaction_fadvice_e = rocksdb::Options::NORMAL;
else if (!strcasecmp(FLAGS_compaction_fadvice.c_str(), "SEQUENTIAL"))
FLAGS_compaction_fadvice_e = rocksdb::Options::SEQUENTIAL;
else if (!strcasecmp(FLAGS_compaction_fadvice.c_str(), "WILLNEED"))
FLAGS_compaction_fadvice_e = rocksdb::Options::WILLNEED;
else {
fprintf(stdout, "Unknown compaction fadvice:%s\n",
FLAGS_compaction_fadvice.c_str());
}
FLAGS_rep_factory = StringToRepFactory(FLAGS_memtablerep.c_str());
// The number of background threads should be at least as much the
// max number of concurrent compactions.
FLAGS_env->SetBackgroundThreads(FLAGS_max_background_compactions);
FLAGS_env->SetBackgroundThreads(FLAGS_max_background_flushes,
rocksdb::Env::Priority::HIGH);
// Choose a location for the test database if none given with --db=<path>
if (FLAGS_db.empty()) {
std::string default_db_path;
rocksdb::Env::Default()->GetTestDirectory(&default_db_path);
default_db_path += "/dbbench";
FLAGS_db = default_db_path;
}
rocksdb::Benchmark benchmark;
benchmark.Run();
return 0;
}
#endif // GFLAGS