rocksdb/util/dynamic_bloom_test.cc

325 lines
9.2 KiB
C++
Raw Normal View History

// Copyright (c) 2011-present, Facebook, Inc. All rights reserved.
// This source code is licensed under both the GPLv2 (found in the
// COPYING file in the root directory) and Apache 2.0 License
// (found in the LICENSE.Apache file in the root directory).
2014-05-09 17:34:18 +02:00
#ifndef GFLAGS
#include <cstdio>
int main() {
fprintf(stderr, "Please install gflags to run this test... Skipping...\n");
return 0;
2014-05-09 17:34:18 +02:00
}
#else
#include <algorithm>
support for concurrent adds to memtable Summary: This diff adds support for concurrent adds to the skiplist memtable implementations. Memory allocation is made thread-safe by the addition of a spinlock, with small per-core buffers to avoid contention. Concurrent memtable writes are made via an additional method and don't impose a performance overhead on the non-concurrent case, so parallelism can be selected on a per-batch basis. Write thread synchronization is an increasing bottleneck for higher levels of concurrency, so this diff adds --enable_write_thread_adaptive_yield (default off). This feature causes threads joining a write batch group to spin for a short time (default 100 usec) using sched_yield, rather than going to sleep on a mutex. If the timing of the yield calls indicates that another thread has actually run during the yield then spinning is avoided. This option improves performance for concurrent situations even without parallel adds, although it has the potential to increase CPU usage (and the heuristic adaptation is not yet mature). Parallel writes are not currently compatible with inplace updates, update callbacks, or delete filtering. Enable it with --allow_concurrent_memtable_write (and --enable_write_thread_adaptive_yield). Parallel memtable writes are performance neutral when there is no actual parallelism, and in my experiments (SSD server-class Linux and varying contention and key sizes for fillrandom) they are always a performance win when there is more than one thread. Statistics are updated earlier in the write path, dropping the number of DB mutex acquisitions from 2 to 1 for almost all cases. This diff was motivated and inspired by Yahoo's cLSM work. It is more conservative than cLSM: RocksDB's write batch group leader role is preserved (along with all of the existing flush and write throttling logic) and concurrent writers are blocked until all memtable insertions have completed and the sequence number has been advanced, to preserve linearizability. My test config is "db_bench -benchmarks=fillrandom -threads=$T -batch_size=1 -memtablerep=skip_list -value_size=100 --num=1000000/$T -level0_slowdown_writes_trigger=9999 -level0_stop_writes_trigger=9999 -disable_auto_compactions --max_write_buffer_number=8 -max_background_flushes=8 --disable_wal --write_buffer_size=160000000 --block_size=16384 --allow_concurrent_memtable_write" on a two-socket Xeon E5-2660 @ 2.2Ghz with lots of memory and an SSD hard drive. With 1 thread I get ~440Kops/sec. Peak performance for 1 socket (numactl -N1) is slightly more than 1Mops/sec, at 16 threads. Peak performance across both sockets happens at 30 threads, and is ~900Kops/sec, although with fewer threads there is less performance loss when the system has background work. Test Plan: 1. concurrent stress tests for InlineSkipList and DynamicBloom 2. make clean; make check 3. make clean; DISABLE_JEMALLOC=1 make valgrind_check; valgrind db_bench 4. make clean; COMPILE_WITH_TSAN=1 make all check; db_bench 5. make clean; COMPILE_WITH_ASAN=1 make all check; db_bench 6. make clean; OPT=-DROCKSDB_LITE make check 7. verify no perf regressions when disabled Reviewers: igor, sdong Reviewed By: sdong Subscribers: MarkCallaghan, IslamAbdelRahman, anthony, yhchiang, rven, sdong, guyg8, kradhakrishnan, dhruba Differential Revision: https://reviews.facebook.net/D50589
2015-08-15 01:59:07 +02:00
#include <atomic>
#include <cinttypes>
#include <functional>
support for concurrent adds to memtable Summary: This diff adds support for concurrent adds to the skiplist memtable implementations. Memory allocation is made thread-safe by the addition of a spinlock, with small per-core buffers to avoid contention. Concurrent memtable writes are made via an additional method and don't impose a performance overhead on the non-concurrent case, so parallelism can be selected on a per-batch basis. Write thread synchronization is an increasing bottleneck for higher levels of concurrency, so this diff adds --enable_write_thread_adaptive_yield (default off). This feature causes threads joining a write batch group to spin for a short time (default 100 usec) using sched_yield, rather than going to sleep on a mutex. If the timing of the yield calls indicates that another thread has actually run during the yield then spinning is avoided. This option improves performance for concurrent situations even without parallel adds, although it has the potential to increase CPU usage (and the heuristic adaptation is not yet mature). Parallel writes are not currently compatible with inplace updates, update callbacks, or delete filtering. Enable it with --allow_concurrent_memtable_write (and --enable_write_thread_adaptive_yield). Parallel memtable writes are performance neutral when there is no actual parallelism, and in my experiments (SSD server-class Linux and varying contention and key sizes for fillrandom) they are always a performance win when there is more than one thread. Statistics are updated earlier in the write path, dropping the number of DB mutex acquisitions from 2 to 1 for almost all cases. This diff was motivated and inspired by Yahoo's cLSM work. It is more conservative than cLSM: RocksDB's write batch group leader role is preserved (along with all of the existing flush and write throttling logic) and concurrent writers are blocked until all memtable insertions have completed and the sequence number has been advanced, to preserve linearizability. My test config is "db_bench -benchmarks=fillrandom -threads=$T -batch_size=1 -memtablerep=skip_list -value_size=100 --num=1000000/$T -level0_slowdown_writes_trigger=9999 -level0_stop_writes_trigger=9999 -disable_auto_compactions --max_write_buffer_number=8 -max_background_flushes=8 --disable_wal --write_buffer_size=160000000 --block_size=16384 --allow_concurrent_memtable_write" on a two-socket Xeon E5-2660 @ 2.2Ghz with lots of memory and an SSD hard drive. With 1 thread I get ~440Kops/sec. Peak performance for 1 socket (numactl -N1) is slightly more than 1Mops/sec, at 16 threads. Peak performance across both sockets happens at 30 threads, and is ~900Kops/sec, although with fewer threads there is less performance loss when the system has background work. Test Plan: 1. concurrent stress tests for InlineSkipList and DynamicBloom 2. make clean; make check 3. make clean; DISABLE_JEMALLOC=1 make valgrind_check; valgrind db_bench 4. make clean; COMPILE_WITH_TSAN=1 make all check; db_bench 5. make clean; COMPILE_WITH_ASAN=1 make all check; db_bench 6. make clean; OPT=-DROCKSDB_LITE make check 7. verify no perf regressions when disabled Reviewers: igor, sdong Reviewed By: sdong Subscribers: MarkCallaghan, IslamAbdelRahman, anthony, yhchiang, rven, sdong, guyg8, kradhakrishnan, dhruba Differential Revision: https://reviews.facebook.net/D50589
2015-08-15 01:59:07 +02:00
#include <memory>
#include <thread>
#include <vector>
#include "dynamic_bloom.h"
#include "memory/arena.h"
#include "port/port.h"
#include "rocksdb/system_clock.h"
#include "test_util/testharness.h"
#include "test_util/testutil.h"
#include "util/gflags_compat.h"
#include "util/stop_watch.h"
using GFLAGS_NAMESPACE::ParseCommandLineFlags;
2014-05-09 17:34:18 +02:00
DEFINE_int32(bits_per_key, 10, "");
DEFINE_int32(num_probes, 6, "");
DEFINE_bool(enable_perf, false, "");
namespace ROCKSDB_NAMESPACE {
struct KeyMaker {
uint64_t a;
uint64_t b;
// Sequential, within a hash function block
inline Slice Seq(uint64_t i) {
a = i;
return Slice(reinterpret_cast<char *>(&a), sizeof(a));
}
// Not quite sequential, varies across hash function blocks
inline Slice Nonseq(uint64_t i) {
a = i;
b = i * 123;
return Slice(reinterpret_cast<char *>(this), sizeof(*this));
}
inline Slice Key(uint64_t i, bool nonseq) {
return nonseq ? Nonseq(i) : Seq(i);
}
};
rocksdb: switch to gtest Summary: Our existing test notation is very similar to what is used in gtest. It makes it easy to adopt what is different. In this diff I modify existing [[ https://code.google.com/p/googletest/wiki/Primer#Test_Fixtures:_Using_the_Same_Data_Configuration_for_Multiple_Te | test fixture ]] classes to inherit from `testing::Test`. Also for unit tests that use fixture class, `TEST` is replaced with `TEST_F` as required in gtest. There are several custom `main` functions in our existing tests. To make this transition easier, I modify all `main` functions to fallow gtest notation. But eventually we can remove them and use implementation of `main` that gtest provides. ```lang=bash % cat ~/transform #!/bin/sh files=$(git ls-files '*test\.cc') for file in $files do if grep -q "rocksdb::test::RunAllTests()" $file then if grep -Eq '^class \w+Test {' $file then perl -pi -e 's/^(class \w+Test) {/${1}: public testing::Test {/g' $file perl -pi -e 's/^(TEST)/${1}_F/g' $file fi perl -pi -e 's/(int main.*\{)/${1}::testing::InitGoogleTest(&argc, argv);/g' $file perl -pi -e 's/rocksdb::test::RunAllTests/RUN_ALL_TESTS/g' $file fi done % sh ~/transform % make format ``` Second iteration of this diff contains only scripted changes. Third iteration contains manual changes to fix last errors and make it compilable. Test Plan: Build and notice no errors. ```lang=bash % USE_CLANG=1 make check -j55 ``` Tests are still testing. Reviewers: meyering, sdong, rven, igor Reviewed By: igor Subscribers: dhruba, leveldb Differential Revision: https://reviews.facebook.net/D35157
2015-03-17 22:08:00 +01:00
class DynamicBloomTest : public testing::Test {};
rocksdb: switch to gtest Summary: Our existing test notation is very similar to what is used in gtest. It makes it easy to adopt what is different. In this diff I modify existing [[ https://code.google.com/p/googletest/wiki/Primer#Test_Fixtures:_Using_the_Same_Data_Configuration_for_Multiple_Te | test fixture ]] classes to inherit from `testing::Test`. Also for unit tests that use fixture class, `TEST` is replaced with `TEST_F` as required in gtest. There are several custom `main` functions in our existing tests. To make this transition easier, I modify all `main` functions to fallow gtest notation. But eventually we can remove them and use implementation of `main` that gtest provides. ```lang=bash % cat ~/transform #!/bin/sh files=$(git ls-files '*test\.cc') for file in $files do if grep -q "rocksdb::test::RunAllTests()" $file then if grep -Eq '^class \w+Test {' $file then perl -pi -e 's/^(class \w+Test) {/${1}: public testing::Test {/g' $file perl -pi -e 's/^(TEST)/${1}_F/g' $file fi perl -pi -e 's/(int main.*\{)/${1}::testing::InitGoogleTest(&argc, argv);/g' $file perl -pi -e 's/rocksdb::test::RunAllTests/RUN_ALL_TESTS/g' $file fi done % sh ~/transform % make format ``` Second iteration of this diff contains only scripted changes. Third iteration contains manual changes to fix last errors and make it compilable. Test Plan: Build and notice no errors. ```lang=bash % USE_CLANG=1 make check -j55 ``` Tests are still testing. Reviewers: meyering, sdong, rven, igor Reviewed By: igor Subscribers: dhruba, leveldb Differential Revision: https://reviews.facebook.net/D35157
2015-03-17 22:08:00 +01:00
TEST_F(DynamicBloomTest, EmptyFilter) {
Arena arena;
Faster new DynamicBloom implementation (for memtable) (#5762) Summary: Since DynamicBloom is now only used in-memory, we're free to change it without schema compatibility issues. The new implementation is drawn from (with manifest permission) https://github.com/pdillinger/wormhashing/blob/303542a767437f56d8b66cea6ebecaac0e6a61e9/bloom_simulation_tests/foo.cc#L613 This has several speed advantages over the prior implementation: * Uses fastrange instead of % * Minimum logic to determine first (and all) probed memory addresses * (Major) Two probes per 64-bit memory fetch/write. * Very fast and effective (murmur-like) hash expansion/re-mixing. (At least on recent CPUs, integer multiplication is very cheap.) While a Bloom filter with 512-bit cache locality has about a 1.15x FP rate penalty (e.g. 0.84% to 0.97%), further restricting to two probes per 64 bits incurs an additional 1.12x FP rate penalty (e.g. 0.97% to 1.09%). Nevertheless, the unit tests show no "mediocre" FP rate samples, unlike the old implementation with more erratic FP rates. Especially for the memtable, we expect speed to outweigh somewhat higher FP rates. For example, a negative table query would have to be 1000x slower than a BF query to justify doubling BF query time to shave 10% off FP rate (working assumption around 1% FP rate). While that seems likely for SSTs, my data suggests a speed factor of roughly 50x for the memtable (vs. BF; ~1.5% lower write throughput when enabling memtable Bloom filter, after this change). Thus, it's probably not worth even 5% more time in the Bloom filter to shave off 1/10th of the Bloom FP rate, or 0.1% in absolute terms, and it's probably at least 20% slower to recoup that much FP rate from this new implementation. Because of this, we do not see a need for a 'locality' option that affects the MemTable Bloom filter and have decoupled the MemTable Bloom filter from Options::bloom_locality. Note that just 3% more memory to the Bloom filter (10.3 bits per key vs. just 10) is able to make up for the ~12% FP rate drop in the new implementation: [] # Nearly "ideal" FP-wise but reasonably fast cache-local implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out time: 3.29372 sampled_fp_rate: 0.00985956 ... [] # Close match to this new implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out 10000000 6 10.3 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.10072 sampled_fp_rate: 0.00985655 ... [] # Old locality=1 implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out time: 3.95472 sampled_fp_rate: 0.00988943 ... Also note the dramatic speed improvement vs. alternatives. -- Performance unit test: DynamicBloomTest.concurrent_with_perf is updated to report more precise timing data. (Measure running time of each thread, not just longest running thread, etc.) Results averaged over various sizes enabled with --enable_perf and 20 runs each; old dynamic bloom refers to locality=1, the faster of the old: old dynamic bloom, avg add latency = 65.6468 new dynamic bloom, avg add latency = 44.3809 old dynamic bloom, avg query latency = 50.6485 new dynamic bloom, avg query latency = 43.2186 old avg parallel add latency = 41.678 new avg parallel add latency = 24.5238 old avg parallel hit latency = 14.6322 new avg parallel hit latency = 12.3939 old avg parallel miss latency = 16.7289 new avg parallel miss latency = 12.2134 Tested on a dedicated 64-bit production machine at Facebook. Significant improvement all around. Despite now using std::atomic<uint64_t>, quick before-and-after test on a 32-bit machine (Intel Atom N270, released 2008) shows no regression in performance, in some cases modest improvement. -- Performance integration test (synthetic): with DEBUG_LEVEL=0, used TEST_TMPDIR=/dev/shm ./db_bench --benchmarks=fillrandom,readmissing,readrandom,stats --num=2000000 and optionally with -memtable_whole_key_filtering -memtable_bloom_size_ratio=0.01 300 runs each configuration. Write throughput change by enabling memtable bloom: Old locality=0: -3.06% Old locality=1: -2.37% New: -1.50% conclusion -> seems to substantially close the gap Readmissing throughput change by enabling memtable bloom: Old locality=0: +34.47% Old locality=1: +34.80% New: +33.25% conclusion -> maybe a small new penalty from FP rate Readrandom throughput change by enabling memtable bloom: Old locality=0: +31.54% Old locality=1: +31.13% New: +30.60% conclusion -> maybe also from FP rate (after memtable flush) -- Another conclusion we can draw from this new implementation is that the existing 32-bit hash function is not inherently crippling the Bloom filter speed or accuracy, below about 5 million keys. For speed, the implementation is essentially the same whether starting with 32-bits or 64-bits of hash; it just determines whether the first multiplication after fastrange is a pseudorandom expansion or needed re-mix. Note that this multiplication can occur while memory is fetching. For accuracy, in a standard configuration, you need about 5 million keys before you have about a 1.1x FP penalty due to using a 32-bit hash vs. 64-bit: [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.52069 sampled_fp_rate: 0.0118267 ... [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out time: 2.43871 sampled_fp_rate: 0.0109059 Pull Request resolved: https://github.com/facebook/rocksdb/pull/5762 Differential Revision: D17214194 Pulled By: pdillinger fbshipit-source-id: ad9da031772e985fd6b62a0e1db8e81892520595
2019-09-05 23:57:39 +02:00
DynamicBloom bloom1(&arena, 100, 2);
ASSERT_TRUE(!bloom1.MayContain("hello"));
ASSERT_TRUE(!bloom1.MayContain("world"));
Faster new DynamicBloom implementation (for memtable) (#5762) Summary: Since DynamicBloom is now only used in-memory, we're free to change it without schema compatibility issues. The new implementation is drawn from (with manifest permission) https://github.com/pdillinger/wormhashing/blob/303542a767437f56d8b66cea6ebecaac0e6a61e9/bloom_simulation_tests/foo.cc#L613 This has several speed advantages over the prior implementation: * Uses fastrange instead of % * Minimum logic to determine first (and all) probed memory addresses * (Major) Two probes per 64-bit memory fetch/write. * Very fast and effective (murmur-like) hash expansion/re-mixing. (At least on recent CPUs, integer multiplication is very cheap.) While a Bloom filter with 512-bit cache locality has about a 1.15x FP rate penalty (e.g. 0.84% to 0.97%), further restricting to two probes per 64 bits incurs an additional 1.12x FP rate penalty (e.g. 0.97% to 1.09%). Nevertheless, the unit tests show no "mediocre" FP rate samples, unlike the old implementation with more erratic FP rates. Especially for the memtable, we expect speed to outweigh somewhat higher FP rates. For example, a negative table query would have to be 1000x slower than a BF query to justify doubling BF query time to shave 10% off FP rate (working assumption around 1% FP rate). While that seems likely for SSTs, my data suggests a speed factor of roughly 50x for the memtable (vs. BF; ~1.5% lower write throughput when enabling memtable Bloom filter, after this change). Thus, it's probably not worth even 5% more time in the Bloom filter to shave off 1/10th of the Bloom FP rate, or 0.1% in absolute terms, and it's probably at least 20% slower to recoup that much FP rate from this new implementation. Because of this, we do not see a need for a 'locality' option that affects the MemTable Bloom filter and have decoupled the MemTable Bloom filter from Options::bloom_locality. Note that just 3% more memory to the Bloom filter (10.3 bits per key vs. just 10) is able to make up for the ~12% FP rate drop in the new implementation: [] # Nearly "ideal" FP-wise but reasonably fast cache-local implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out time: 3.29372 sampled_fp_rate: 0.00985956 ... [] # Close match to this new implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out 10000000 6 10.3 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.10072 sampled_fp_rate: 0.00985655 ... [] # Old locality=1 implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out time: 3.95472 sampled_fp_rate: 0.00988943 ... Also note the dramatic speed improvement vs. alternatives. -- Performance unit test: DynamicBloomTest.concurrent_with_perf is updated to report more precise timing data. (Measure running time of each thread, not just longest running thread, etc.) Results averaged over various sizes enabled with --enable_perf and 20 runs each; old dynamic bloom refers to locality=1, the faster of the old: old dynamic bloom, avg add latency = 65.6468 new dynamic bloom, avg add latency = 44.3809 old dynamic bloom, avg query latency = 50.6485 new dynamic bloom, avg query latency = 43.2186 old avg parallel add latency = 41.678 new avg parallel add latency = 24.5238 old avg parallel hit latency = 14.6322 new avg parallel hit latency = 12.3939 old avg parallel miss latency = 16.7289 new avg parallel miss latency = 12.2134 Tested on a dedicated 64-bit production machine at Facebook. Significant improvement all around. Despite now using std::atomic<uint64_t>, quick before-and-after test on a 32-bit machine (Intel Atom N270, released 2008) shows no regression in performance, in some cases modest improvement. -- Performance integration test (synthetic): with DEBUG_LEVEL=0, used TEST_TMPDIR=/dev/shm ./db_bench --benchmarks=fillrandom,readmissing,readrandom,stats --num=2000000 and optionally with -memtable_whole_key_filtering -memtable_bloom_size_ratio=0.01 300 runs each configuration. Write throughput change by enabling memtable bloom: Old locality=0: -3.06% Old locality=1: -2.37% New: -1.50% conclusion -> seems to substantially close the gap Readmissing throughput change by enabling memtable bloom: Old locality=0: +34.47% Old locality=1: +34.80% New: +33.25% conclusion -> maybe a small new penalty from FP rate Readrandom throughput change by enabling memtable bloom: Old locality=0: +31.54% Old locality=1: +31.13% New: +30.60% conclusion -> maybe also from FP rate (after memtable flush) -- Another conclusion we can draw from this new implementation is that the existing 32-bit hash function is not inherently crippling the Bloom filter speed or accuracy, below about 5 million keys. For speed, the implementation is essentially the same whether starting with 32-bits or 64-bits of hash; it just determines whether the first multiplication after fastrange is a pseudorandom expansion or needed re-mix. Note that this multiplication can occur while memory is fetching. For accuracy, in a standard configuration, you need about 5 million keys before you have about a 1.1x FP penalty due to using a 32-bit hash vs. 64-bit: [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.52069 sampled_fp_rate: 0.0118267 ... [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out time: 2.43871 sampled_fp_rate: 0.0109059 Pull Request resolved: https://github.com/facebook/rocksdb/pull/5762 Differential Revision: D17214194 Pulled By: pdillinger fbshipit-source-id: ad9da031772e985fd6b62a0e1db8e81892520595
2019-09-05 23:57:39 +02:00
DynamicBloom bloom2(&arena, CACHE_LINE_SIZE * 8 * 2 - 1, 2);
ASSERT_TRUE(!bloom2.MayContain("hello"));
ASSERT_TRUE(!bloom2.MayContain("world"));
}
rocksdb: switch to gtest Summary: Our existing test notation is very similar to what is used in gtest. It makes it easy to adopt what is different. In this diff I modify existing [[ https://code.google.com/p/googletest/wiki/Primer#Test_Fixtures:_Using_the_Same_Data_Configuration_for_Multiple_Te | test fixture ]] classes to inherit from `testing::Test`. Also for unit tests that use fixture class, `TEST` is replaced with `TEST_F` as required in gtest. There are several custom `main` functions in our existing tests. To make this transition easier, I modify all `main` functions to fallow gtest notation. But eventually we can remove them and use implementation of `main` that gtest provides. ```lang=bash % cat ~/transform #!/bin/sh files=$(git ls-files '*test\.cc') for file in $files do if grep -q "rocksdb::test::RunAllTests()" $file then if grep -Eq '^class \w+Test {' $file then perl -pi -e 's/^(class \w+Test) {/${1}: public testing::Test {/g' $file perl -pi -e 's/^(TEST)/${1}_F/g' $file fi perl -pi -e 's/(int main.*\{)/${1}::testing::InitGoogleTest(&argc, argv);/g' $file perl -pi -e 's/rocksdb::test::RunAllTests/RUN_ALL_TESTS/g' $file fi done % sh ~/transform % make format ``` Second iteration of this diff contains only scripted changes. Third iteration contains manual changes to fix last errors and make it compilable. Test Plan: Build and notice no errors. ```lang=bash % USE_CLANG=1 make check -j55 ``` Tests are still testing. Reviewers: meyering, sdong, rven, igor Reviewed By: igor Subscribers: dhruba, leveldb Differential Revision: https://reviews.facebook.net/D35157
2015-03-17 22:08:00 +01:00
TEST_F(DynamicBloomTest, Small) {
Arena arena;
Faster new DynamicBloom implementation (for memtable) (#5762) Summary: Since DynamicBloom is now only used in-memory, we're free to change it without schema compatibility issues. The new implementation is drawn from (with manifest permission) https://github.com/pdillinger/wormhashing/blob/303542a767437f56d8b66cea6ebecaac0e6a61e9/bloom_simulation_tests/foo.cc#L613 This has several speed advantages over the prior implementation: * Uses fastrange instead of % * Minimum logic to determine first (and all) probed memory addresses * (Major) Two probes per 64-bit memory fetch/write. * Very fast and effective (murmur-like) hash expansion/re-mixing. (At least on recent CPUs, integer multiplication is very cheap.) While a Bloom filter with 512-bit cache locality has about a 1.15x FP rate penalty (e.g. 0.84% to 0.97%), further restricting to two probes per 64 bits incurs an additional 1.12x FP rate penalty (e.g. 0.97% to 1.09%). Nevertheless, the unit tests show no "mediocre" FP rate samples, unlike the old implementation with more erratic FP rates. Especially for the memtable, we expect speed to outweigh somewhat higher FP rates. For example, a negative table query would have to be 1000x slower than a BF query to justify doubling BF query time to shave 10% off FP rate (working assumption around 1% FP rate). While that seems likely for SSTs, my data suggests a speed factor of roughly 50x for the memtable (vs. BF; ~1.5% lower write throughput when enabling memtable Bloom filter, after this change). Thus, it's probably not worth even 5% more time in the Bloom filter to shave off 1/10th of the Bloom FP rate, or 0.1% in absolute terms, and it's probably at least 20% slower to recoup that much FP rate from this new implementation. Because of this, we do not see a need for a 'locality' option that affects the MemTable Bloom filter and have decoupled the MemTable Bloom filter from Options::bloom_locality. Note that just 3% more memory to the Bloom filter (10.3 bits per key vs. just 10) is able to make up for the ~12% FP rate drop in the new implementation: [] # Nearly "ideal" FP-wise but reasonably fast cache-local implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out time: 3.29372 sampled_fp_rate: 0.00985956 ... [] # Close match to this new implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out 10000000 6 10.3 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.10072 sampled_fp_rate: 0.00985655 ... [] # Old locality=1 implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out time: 3.95472 sampled_fp_rate: 0.00988943 ... Also note the dramatic speed improvement vs. alternatives. -- Performance unit test: DynamicBloomTest.concurrent_with_perf is updated to report more precise timing data. (Measure running time of each thread, not just longest running thread, etc.) Results averaged over various sizes enabled with --enable_perf and 20 runs each; old dynamic bloom refers to locality=1, the faster of the old: old dynamic bloom, avg add latency = 65.6468 new dynamic bloom, avg add latency = 44.3809 old dynamic bloom, avg query latency = 50.6485 new dynamic bloom, avg query latency = 43.2186 old avg parallel add latency = 41.678 new avg parallel add latency = 24.5238 old avg parallel hit latency = 14.6322 new avg parallel hit latency = 12.3939 old avg parallel miss latency = 16.7289 new avg parallel miss latency = 12.2134 Tested on a dedicated 64-bit production machine at Facebook. Significant improvement all around. Despite now using std::atomic<uint64_t>, quick before-and-after test on a 32-bit machine (Intel Atom N270, released 2008) shows no regression in performance, in some cases modest improvement. -- Performance integration test (synthetic): with DEBUG_LEVEL=0, used TEST_TMPDIR=/dev/shm ./db_bench --benchmarks=fillrandom,readmissing,readrandom,stats --num=2000000 and optionally with -memtable_whole_key_filtering -memtable_bloom_size_ratio=0.01 300 runs each configuration. Write throughput change by enabling memtable bloom: Old locality=0: -3.06% Old locality=1: -2.37% New: -1.50% conclusion -> seems to substantially close the gap Readmissing throughput change by enabling memtable bloom: Old locality=0: +34.47% Old locality=1: +34.80% New: +33.25% conclusion -> maybe a small new penalty from FP rate Readrandom throughput change by enabling memtable bloom: Old locality=0: +31.54% Old locality=1: +31.13% New: +30.60% conclusion -> maybe also from FP rate (after memtable flush) -- Another conclusion we can draw from this new implementation is that the existing 32-bit hash function is not inherently crippling the Bloom filter speed or accuracy, below about 5 million keys. For speed, the implementation is essentially the same whether starting with 32-bits or 64-bits of hash; it just determines whether the first multiplication after fastrange is a pseudorandom expansion or needed re-mix. Note that this multiplication can occur while memory is fetching. For accuracy, in a standard configuration, you need about 5 million keys before you have about a 1.1x FP penalty due to using a 32-bit hash vs. 64-bit: [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.52069 sampled_fp_rate: 0.0118267 ... [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out time: 2.43871 sampled_fp_rate: 0.0109059 Pull Request resolved: https://github.com/facebook/rocksdb/pull/5762 Differential Revision: D17214194 Pulled By: pdillinger fbshipit-source-id: ad9da031772e985fd6b62a0e1db8e81892520595
2019-09-05 23:57:39 +02:00
DynamicBloom bloom1(&arena, 100, 2);
bloom1.Add("hello");
bloom1.Add("world");
ASSERT_TRUE(bloom1.MayContain("hello"));
ASSERT_TRUE(bloom1.MayContain("world"));
ASSERT_TRUE(!bloom1.MayContain("x"));
ASSERT_TRUE(!bloom1.MayContain("foo"));
Faster new DynamicBloom implementation (for memtable) (#5762) Summary: Since DynamicBloom is now only used in-memory, we're free to change it without schema compatibility issues. The new implementation is drawn from (with manifest permission) https://github.com/pdillinger/wormhashing/blob/303542a767437f56d8b66cea6ebecaac0e6a61e9/bloom_simulation_tests/foo.cc#L613 This has several speed advantages over the prior implementation: * Uses fastrange instead of % * Minimum logic to determine first (and all) probed memory addresses * (Major) Two probes per 64-bit memory fetch/write. * Very fast and effective (murmur-like) hash expansion/re-mixing. (At least on recent CPUs, integer multiplication is very cheap.) While a Bloom filter with 512-bit cache locality has about a 1.15x FP rate penalty (e.g. 0.84% to 0.97%), further restricting to two probes per 64 bits incurs an additional 1.12x FP rate penalty (e.g. 0.97% to 1.09%). Nevertheless, the unit tests show no "mediocre" FP rate samples, unlike the old implementation with more erratic FP rates. Especially for the memtable, we expect speed to outweigh somewhat higher FP rates. For example, a negative table query would have to be 1000x slower than a BF query to justify doubling BF query time to shave 10% off FP rate (working assumption around 1% FP rate). While that seems likely for SSTs, my data suggests a speed factor of roughly 50x for the memtable (vs. BF; ~1.5% lower write throughput when enabling memtable Bloom filter, after this change). Thus, it's probably not worth even 5% more time in the Bloom filter to shave off 1/10th of the Bloom FP rate, or 0.1% in absolute terms, and it's probably at least 20% slower to recoup that much FP rate from this new implementation. Because of this, we do not see a need for a 'locality' option that affects the MemTable Bloom filter and have decoupled the MemTable Bloom filter from Options::bloom_locality. Note that just 3% more memory to the Bloom filter (10.3 bits per key vs. just 10) is able to make up for the ~12% FP rate drop in the new implementation: [] # Nearly "ideal" FP-wise but reasonably fast cache-local implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out time: 3.29372 sampled_fp_rate: 0.00985956 ... [] # Close match to this new implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out 10000000 6 10.3 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.10072 sampled_fp_rate: 0.00985655 ... [] # Old locality=1 implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out time: 3.95472 sampled_fp_rate: 0.00988943 ... Also note the dramatic speed improvement vs. alternatives. -- Performance unit test: DynamicBloomTest.concurrent_with_perf is updated to report more precise timing data. (Measure running time of each thread, not just longest running thread, etc.) Results averaged over various sizes enabled with --enable_perf and 20 runs each; old dynamic bloom refers to locality=1, the faster of the old: old dynamic bloom, avg add latency = 65.6468 new dynamic bloom, avg add latency = 44.3809 old dynamic bloom, avg query latency = 50.6485 new dynamic bloom, avg query latency = 43.2186 old avg parallel add latency = 41.678 new avg parallel add latency = 24.5238 old avg parallel hit latency = 14.6322 new avg parallel hit latency = 12.3939 old avg parallel miss latency = 16.7289 new avg parallel miss latency = 12.2134 Tested on a dedicated 64-bit production machine at Facebook. Significant improvement all around. Despite now using std::atomic<uint64_t>, quick before-and-after test on a 32-bit machine (Intel Atom N270, released 2008) shows no regression in performance, in some cases modest improvement. -- Performance integration test (synthetic): with DEBUG_LEVEL=0, used TEST_TMPDIR=/dev/shm ./db_bench --benchmarks=fillrandom,readmissing,readrandom,stats --num=2000000 and optionally with -memtable_whole_key_filtering -memtable_bloom_size_ratio=0.01 300 runs each configuration. Write throughput change by enabling memtable bloom: Old locality=0: -3.06% Old locality=1: -2.37% New: -1.50% conclusion -> seems to substantially close the gap Readmissing throughput change by enabling memtable bloom: Old locality=0: +34.47% Old locality=1: +34.80% New: +33.25% conclusion -> maybe a small new penalty from FP rate Readrandom throughput change by enabling memtable bloom: Old locality=0: +31.54% Old locality=1: +31.13% New: +30.60% conclusion -> maybe also from FP rate (after memtable flush) -- Another conclusion we can draw from this new implementation is that the existing 32-bit hash function is not inherently crippling the Bloom filter speed or accuracy, below about 5 million keys. For speed, the implementation is essentially the same whether starting with 32-bits or 64-bits of hash; it just determines whether the first multiplication after fastrange is a pseudorandom expansion or needed re-mix. Note that this multiplication can occur while memory is fetching. For accuracy, in a standard configuration, you need about 5 million keys before you have about a 1.1x FP penalty due to using a 32-bit hash vs. 64-bit: [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.52069 sampled_fp_rate: 0.0118267 ... [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out time: 2.43871 sampled_fp_rate: 0.0109059 Pull Request resolved: https://github.com/facebook/rocksdb/pull/5762 Differential Revision: D17214194 Pulled By: pdillinger fbshipit-source-id: ad9da031772e985fd6b62a0e1db8e81892520595
2019-09-05 23:57:39 +02:00
DynamicBloom bloom2(&arena, CACHE_LINE_SIZE * 8 * 2 - 1, 2);
bloom2.Add("hello");
bloom2.Add("world");
ASSERT_TRUE(bloom2.MayContain("hello"));
ASSERT_TRUE(bloom2.MayContain("world"));
ASSERT_TRUE(!bloom2.MayContain("x"));
ASSERT_TRUE(!bloom2.MayContain("foo"));
}
support for concurrent adds to memtable Summary: This diff adds support for concurrent adds to the skiplist memtable implementations. Memory allocation is made thread-safe by the addition of a spinlock, with small per-core buffers to avoid contention. Concurrent memtable writes are made via an additional method and don't impose a performance overhead on the non-concurrent case, so parallelism can be selected on a per-batch basis. Write thread synchronization is an increasing bottleneck for higher levels of concurrency, so this diff adds --enable_write_thread_adaptive_yield (default off). This feature causes threads joining a write batch group to spin for a short time (default 100 usec) using sched_yield, rather than going to sleep on a mutex. If the timing of the yield calls indicates that another thread has actually run during the yield then spinning is avoided. This option improves performance for concurrent situations even without parallel adds, although it has the potential to increase CPU usage (and the heuristic adaptation is not yet mature). Parallel writes are not currently compatible with inplace updates, update callbacks, or delete filtering. Enable it with --allow_concurrent_memtable_write (and --enable_write_thread_adaptive_yield). Parallel memtable writes are performance neutral when there is no actual parallelism, and in my experiments (SSD server-class Linux and varying contention and key sizes for fillrandom) they are always a performance win when there is more than one thread. Statistics are updated earlier in the write path, dropping the number of DB mutex acquisitions from 2 to 1 for almost all cases. This diff was motivated and inspired by Yahoo's cLSM work. It is more conservative than cLSM: RocksDB's write batch group leader role is preserved (along with all of the existing flush and write throttling logic) and concurrent writers are blocked until all memtable insertions have completed and the sequence number has been advanced, to preserve linearizability. My test config is "db_bench -benchmarks=fillrandom -threads=$T -batch_size=1 -memtablerep=skip_list -value_size=100 --num=1000000/$T -level0_slowdown_writes_trigger=9999 -level0_stop_writes_trigger=9999 -disable_auto_compactions --max_write_buffer_number=8 -max_background_flushes=8 --disable_wal --write_buffer_size=160000000 --block_size=16384 --allow_concurrent_memtable_write" on a two-socket Xeon E5-2660 @ 2.2Ghz with lots of memory and an SSD hard drive. With 1 thread I get ~440Kops/sec. Peak performance for 1 socket (numactl -N1) is slightly more than 1Mops/sec, at 16 threads. Peak performance across both sockets happens at 30 threads, and is ~900Kops/sec, although with fewer threads there is less performance loss when the system has background work. Test Plan: 1. concurrent stress tests for InlineSkipList and DynamicBloom 2. make clean; make check 3. make clean; DISABLE_JEMALLOC=1 make valgrind_check; valgrind db_bench 4. make clean; COMPILE_WITH_TSAN=1 make all check; db_bench 5. make clean; COMPILE_WITH_ASAN=1 make all check; db_bench 6. make clean; OPT=-DROCKSDB_LITE make check 7. verify no perf regressions when disabled Reviewers: igor, sdong Reviewed By: sdong Subscribers: MarkCallaghan, IslamAbdelRahman, anthony, yhchiang, rven, sdong, guyg8, kradhakrishnan, dhruba Differential Revision: https://reviews.facebook.net/D50589
2015-08-15 01:59:07 +02:00
TEST_F(DynamicBloomTest, SmallConcurrentAdd) {
Arena arena;
Faster new DynamicBloom implementation (for memtable) (#5762) Summary: Since DynamicBloom is now only used in-memory, we're free to change it without schema compatibility issues. The new implementation is drawn from (with manifest permission) https://github.com/pdillinger/wormhashing/blob/303542a767437f56d8b66cea6ebecaac0e6a61e9/bloom_simulation_tests/foo.cc#L613 This has several speed advantages over the prior implementation: * Uses fastrange instead of % * Minimum logic to determine first (and all) probed memory addresses * (Major) Two probes per 64-bit memory fetch/write. * Very fast and effective (murmur-like) hash expansion/re-mixing. (At least on recent CPUs, integer multiplication is very cheap.) While a Bloom filter with 512-bit cache locality has about a 1.15x FP rate penalty (e.g. 0.84% to 0.97%), further restricting to two probes per 64 bits incurs an additional 1.12x FP rate penalty (e.g. 0.97% to 1.09%). Nevertheless, the unit tests show no "mediocre" FP rate samples, unlike the old implementation with more erratic FP rates. Especially for the memtable, we expect speed to outweigh somewhat higher FP rates. For example, a negative table query would have to be 1000x slower than a BF query to justify doubling BF query time to shave 10% off FP rate (working assumption around 1% FP rate). While that seems likely for SSTs, my data suggests a speed factor of roughly 50x for the memtable (vs. BF; ~1.5% lower write throughput when enabling memtable Bloom filter, after this change). Thus, it's probably not worth even 5% more time in the Bloom filter to shave off 1/10th of the Bloom FP rate, or 0.1% in absolute terms, and it's probably at least 20% slower to recoup that much FP rate from this new implementation. Because of this, we do not see a need for a 'locality' option that affects the MemTable Bloom filter and have decoupled the MemTable Bloom filter from Options::bloom_locality. Note that just 3% more memory to the Bloom filter (10.3 bits per key vs. just 10) is able to make up for the ~12% FP rate drop in the new implementation: [] # Nearly "ideal" FP-wise but reasonably fast cache-local implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out time: 3.29372 sampled_fp_rate: 0.00985956 ... [] # Close match to this new implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out 10000000 6 10.3 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.10072 sampled_fp_rate: 0.00985655 ... [] # Old locality=1 implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out time: 3.95472 sampled_fp_rate: 0.00988943 ... Also note the dramatic speed improvement vs. alternatives. -- Performance unit test: DynamicBloomTest.concurrent_with_perf is updated to report more precise timing data. (Measure running time of each thread, not just longest running thread, etc.) Results averaged over various sizes enabled with --enable_perf and 20 runs each; old dynamic bloom refers to locality=1, the faster of the old: old dynamic bloom, avg add latency = 65.6468 new dynamic bloom, avg add latency = 44.3809 old dynamic bloom, avg query latency = 50.6485 new dynamic bloom, avg query latency = 43.2186 old avg parallel add latency = 41.678 new avg parallel add latency = 24.5238 old avg parallel hit latency = 14.6322 new avg parallel hit latency = 12.3939 old avg parallel miss latency = 16.7289 new avg parallel miss latency = 12.2134 Tested on a dedicated 64-bit production machine at Facebook. Significant improvement all around. Despite now using std::atomic<uint64_t>, quick before-and-after test on a 32-bit machine (Intel Atom N270, released 2008) shows no regression in performance, in some cases modest improvement. -- Performance integration test (synthetic): with DEBUG_LEVEL=0, used TEST_TMPDIR=/dev/shm ./db_bench --benchmarks=fillrandom,readmissing,readrandom,stats --num=2000000 and optionally with -memtable_whole_key_filtering -memtable_bloom_size_ratio=0.01 300 runs each configuration. Write throughput change by enabling memtable bloom: Old locality=0: -3.06% Old locality=1: -2.37% New: -1.50% conclusion -> seems to substantially close the gap Readmissing throughput change by enabling memtable bloom: Old locality=0: +34.47% Old locality=1: +34.80% New: +33.25% conclusion -> maybe a small new penalty from FP rate Readrandom throughput change by enabling memtable bloom: Old locality=0: +31.54% Old locality=1: +31.13% New: +30.60% conclusion -> maybe also from FP rate (after memtable flush) -- Another conclusion we can draw from this new implementation is that the existing 32-bit hash function is not inherently crippling the Bloom filter speed or accuracy, below about 5 million keys. For speed, the implementation is essentially the same whether starting with 32-bits or 64-bits of hash; it just determines whether the first multiplication after fastrange is a pseudorandom expansion or needed re-mix. Note that this multiplication can occur while memory is fetching. For accuracy, in a standard configuration, you need about 5 million keys before you have about a 1.1x FP penalty due to using a 32-bit hash vs. 64-bit: [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.52069 sampled_fp_rate: 0.0118267 ... [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out time: 2.43871 sampled_fp_rate: 0.0109059 Pull Request resolved: https://github.com/facebook/rocksdb/pull/5762 Differential Revision: D17214194 Pulled By: pdillinger fbshipit-source-id: ad9da031772e985fd6b62a0e1db8e81892520595
2019-09-05 23:57:39 +02:00
DynamicBloom bloom1(&arena, 100, 2);
support for concurrent adds to memtable Summary: This diff adds support for concurrent adds to the skiplist memtable implementations. Memory allocation is made thread-safe by the addition of a spinlock, with small per-core buffers to avoid contention. Concurrent memtable writes are made via an additional method and don't impose a performance overhead on the non-concurrent case, so parallelism can be selected on a per-batch basis. Write thread synchronization is an increasing bottleneck for higher levels of concurrency, so this diff adds --enable_write_thread_adaptive_yield (default off). This feature causes threads joining a write batch group to spin for a short time (default 100 usec) using sched_yield, rather than going to sleep on a mutex. If the timing of the yield calls indicates that another thread has actually run during the yield then spinning is avoided. This option improves performance for concurrent situations even without parallel adds, although it has the potential to increase CPU usage (and the heuristic adaptation is not yet mature). Parallel writes are not currently compatible with inplace updates, update callbacks, or delete filtering. Enable it with --allow_concurrent_memtable_write (and --enable_write_thread_adaptive_yield). Parallel memtable writes are performance neutral when there is no actual parallelism, and in my experiments (SSD server-class Linux and varying contention and key sizes for fillrandom) they are always a performance win when there is more than one thread. Statistics are updated earlier in the write path, dropping the number of DB mutex acquisitions from 2 to 1 for almost all cases. This diff was motivated and inspired by Yahoo's cLSM work. It is more conservative than cLSM: RocksDB's write batch group leader role is preserved (along with all of the existing flush and write throttling logic) and concurrent writers are blocked until all memtable insertions have completed and the sequence number has been advanced, to preserve linearizability. My test config is "db_bench -benchmarks=fillrandom -threads=$T -batch_size=1 -memtablerep=skip_list -value_size=100 --num=1000000/$T -level0_slowdown_writes_trigger=9999 -level0_stop_writes_trigger=9999 -disable_auto_compactions --max_write_buffer_number=8 -max_background_flushes=8 --disable_wal --write_buffer_size=160000000 --block_size=16384 --allow_concurrent_memtable_write" on a two-socket Xeon E5-2660 @ 2.2Ghz with lots of memory and an SSD hard drive. With 1 thread I get ~440Kops/sec. Peak performance for 1 socket (numactl -N1) is slightly more than 1Mops/sec, at 16 threads. Peak performance across both sockets happens at 30 threads, and is ~900Kops/sec, although with fewer threads there is less performance loss when the system has background work. Test Plan: 1. concurrent stress tests for InlineSkipList and DynamicBloom 2. make clean; make check 3. make clean; DISABLE_JEMALLOC=1 make valgrind_check; valgrind db_bench 4. make clean; COMPILE_WITH_TSAN=1 make all check; db_bench 5. make clean; COMPILE_WITH_ASAN=1 make all check; db_bench 6. make clean; OPT=-DROCKSDB_LITE make check 7. verify no perf regressions when disabled Reviewers: igor, sdong Reviewed By: sdong Subscribers: MarkCallaghan, IslamAbdelRahman, anthony, yhchiang, rven, sdong, guyg8, kradhakrishnan, dhruba Differential Revision: https://reviews.facebook.net/D50589
2015-08-15 01:59:07 +02:00
bloom1.AddConcurrently("hello");
bloom1.AddConcurrently("world");
ASSERT_TRUE(bloom1.MayContain("hello"));
ASSERT_TRUE(bloom1.MayContain("world"));
ASSERT_TRUE(!bloom1.MayContain("x"));
ASSERT_TRUE(!bloom1.MayContain("foo"));
Faster new DynamicBloom implementation (for memtable) (#5762) Summary: Since DynamicBloom is now only used in-memory, we're free to change it without schema compatibility issues. The new implementation is drawn from (with manifest permission) https://github.com/pdillinger/wormhashing/blob/303542a767437f56d8b66cea6ebecaac0e6a61e9/bloom_simulation_tests/foo.cc#L613 This has several speed advantages over the prior implementation: * Uses fastrange instead of % * Minimum logic to determine first (and all) probed memory addresses * (Major) Two probes per 64-bit memory fetch/write. * Very fast and effective (murmur-like) hash expansion/re-mixing. (At least on recent CPUs, integer multiplication is very cheap.) While a Bloom filter with 512-bit cache locality has about a 1.15x FP rate penalty (e.g. 0.84% to 0.97%), further restricting to two probes per 64 bits incurs an additional 1.12x FP rate penalty (e.g. 0.97% to 1.09%). Nevertheless, the unit tests show no "mediocre" FP rate samples, unlike the old implementation with more erratic FP rates. Especially for the memtable, we expect speed to outweigh somewhat higher FP rates. For example, a negative table query would have to be 1000x slower than a BF query to justify doubling BF query time to shave 10% off FP rate (working assumption around 1% FP rate). While that seems likely for SSTs, my data suggests a speed factor of roughly 50x for the memtable (vs. BF; ~1.5% lower write throughput when enabling memtable Bloom filter, after this change). Thus, it's probably not worth even 5% more time in the Bloom filter to shave off 1/10th of the Bloom FP rate, or 0.1% in absolute terms, and it's probably at least 20% slower to recoup that much FP rate from this new implementation. Because of this, we do not see a need for a 'locality' option that affects the MemTable Bloom filter and have decoupled the MemTable Bloom filter from Options::bloom_locality. Note that just 3% more memory to the Bloom filter (10.3 bits per key vs. just 10) is able to make up for the ~12% FP rate drop in the new implementation: [] # Nearly "ideal" FP-wise but reasonably fast cache-local implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out time: 3.29372 sampled_fp_rate: 0.00985956 ... [] # Close match to this new implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out 10000000 6 10.3 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.10072 sampled_fp_rate: 0.00985655 ... [] # Old locality=1 implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out time: 3.95472 sampled_fp_rate: 0.00988943 ... Also note the dramatic speed improvement vs. alternatives. -- Performance unit test: DynamicBloomTest.concurrent_with_perf is updated to report more precise timing data. (Measure running time of each thread, not just longest running thread, etc.) Results averaged over various sizes enabled with --enable_perf and 20 runs each; old dynamic bloom refers to locality=1, the faster of the old: old dynamic bloom, avg add latency = 65.6468 new dynamic bloom, avg add latency = 44.3809 old dynamic bloom, avg query latency = 50.6485 new dynamic bloom, avg query latency = 43.2186 old avg parallel add latency = 41.678 new avg parallel add latency = 24.5238 old avg parallel hit latency = 14.6322 new avg parallel hit latency = 12.3939 old avg parallel miss latency = 16.7289 new avg parallel miss latency = 12.2134 Tested on a dedicated 64-bit production machine at Facebook. Significant improvement all around. Despite now using std::atomic<uint64_t>, quick before-and-after test on a 32-bit machine (Intel Atom N270, released 2008) shows no regression in performance, in some cases modest improvement. -- Performance integration test (synthetic): with DEBUG_LEVEL=0, used TEST_TMPDIR=/dev/shm ./db_bench --benchmarks=fillrandom,readmissing,readrandom,stats --num=2000000 and optionally with -memtable_whole_key_filtering -memtable_bloom_size_ratio=0.01 300 runs each configuration. Write throughput change by enabling memtable bloom: Old locality=0: -3.06% Old locality=1: -2.37% New: -1.50% conclusion -> seems to substantially close the gap Readmissing throughput change by enabling memtable bloom: Old locality=0: +34.47% Old locality=1: +34.80% New: +33.25% conclusion -> maybe a small new penalty from FP rate Readrandom throughput change by enabling memtable bloom: Old locality=0: +31.54% Old locality=1: +31.13% New: +30.60% conclusion -> maybe also from FP rate (after memtable flush) -- Another conclusion we can draw from this new implementation is that the existing 32-bit hash function is not inherently crippling the Bloom filter speed or accuracy, below about 5 million keys. For speed, the implementation is essentially the same whether starting with 32-bits or 64-bits of hash; it just determines whether the first multiplication after fastrange is a pseudorandom expansion or needed re-mix. Note that this multiplication can occur while memory is fetching. For accuracy, in a standard configuration, you need about 5 million keys before you have about a 1.1x FP penalty due to using a 32-bit hash vs. 64-bit: [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.52069 sampled_fp_rate: 0.0118267 ... [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out time: 2.43871 sampled_fp_rate: 0.0109059 Pull Request resolved: https://github.com/facebook/rocksdb/pull/5762 Differential Revision: D17214194 Pulled By: pdillinger fbshipit-source-id: ad9da031772e985fd6b62a0e1db8e81892520595
2019-09-05 23:57:39 +02:00
DynamicBloom bloom2(&arena, CACHE_LINE_SIZE * 8 * 2 - 1, 2);
support for concurrent adds to memtable Summary: This diff adds support for concurrent adds to the skiplist memtable implementations. Memory allocation is made thread-safe by the addition of a spinlock, with small per-core buffers to avoid contention. Concurrent memtable writes are made via an additional method and don't impose a performance overhead on the non-concurrent case, so parallelism can be selected on a per-batch basis. Write thread synchronization is an increasing bottleneck for higher levels of concurrency, so this diff adds --enable_write_thread_adaptive_yield (default off). This feature causes threads joining a write batch group to spin for a short time (default 100 usec) using sched_yield, rather than going to sleep on a mutex. If the timing of the yield calls indicates that another thread has actually run during the yield then spinning is avoided. This option improves performance for concurrent situations even without parallel adds, although it has the potential to increase CPU usage (and the heuristic adaptation is not yet mature). Parallel writes are not currently compatible with inplace updates, update callbacks, or delete filtering. Enable it with --allow_concurrent_memtable_write (and --enable_write_thread_adaptive_yield). Parallel memtable writes are performance neutral when there is no actual parallelism, and in my experiments (SSD server-class Linux and varying contention and key sizes for fillrandom) they are always a performance win when there is more than one thread. Statistics are updated earlier in the write path, dropping the number of DB mutex acquisitions from 2 to 1 for almost all cases. This diff was motivated and inspired by Yahoo's cLSM work. It is more conservative than cLSM: RocksDB's write batch group leader role is preserved (along with all of the existing flush and write throttling logic) and concurrent writers are blocked until all memtable insertions have completed and the sequence number has been advanced, to preserve linearizability. My test config is "db_bench -benchmarks=fillrandom -threads=$T -batch_size=1 -memtablerep=skip_list -value_size=100 --num=1000000/$T -level0_slowdown_writes_trigger=9999 -level0_stop_writes_trigger=9999 -disable_auto_compactions --max_write_buffer_number=8 -max_background_flushes=8 --disable_wal --write_buffer_size=160000000 --block_size=16384 --allow_concurrent_memtable_write" on a two-socket Xeon E5-2660 @ 2.2Ghz with lots of memory and an SSD hard drive. With 1 thread I get ~440Kops/sec. Peak performance for 1 socket (numactl -N1) is slightly more than 1Mops/sec, at 16 threads. Peak performance across both sockets happens at 30 threads, and is ~900Kops/sec, although with fewer threads there is less performance loss when the system has background work. Test Plan: 1. concurrent stress tests for InlineSkipList and DynamicBloom 2. make clean; make check 3. make clean; DISABLE_JEMALLOC=1 make valgrind_check; valgrind db_bench 4. make clean; COMPILE_WITH_TSAN=1 make all check; db_bench 5. make clean; COMPILE_WITH_ASAN=1 make all check; db_bench 6. make clean; OPT=-DROCKSDB_LITE make check 7. verify no perf regressions when disabled Reviewers: igor, sdong Reviewed By: sdong Subscribers: MarkCallaghan, IslamAbdelRahman, anthony, yhchiang, rven, sdong, guyg8, kradhakrishnan, dhruba Differential Revision: https://reviews.facebook.net/D50589
2015-08-15 01:59:07 +02:00
bloom2.AddConcurrently("hello");
bloom2.AddConcurrently("world");
ASSERT_TRUE(bloom2.MayContain("hello"));
ASSERT_TRUE(bloom2.MayContain("world"));
ASSERT_TRUE(!bloom2.MayContain("x"));
ASSERT_TRUE(!bloom2.MayContain("foo"));
}
static uint32_t NextNum(uint32_t num) {
if (num < 10) {
num += 1;
} else if (num < 100) {
num += 10;
} else if (num < 1000) {
num += 100;
} else {
num = num * 26 / 10;
}
return num;
}
rocksdb: switch to gtest Summary: Our existing test notation is very similar to what is used in gtest. It makes it easy to adopt what is different. In this diff I modify existing [[ https://code.google.com/p/googletest/wiki/Primer#Test_Fixtures:_Using_the_Same_Data_Configuration_for_Multiple_Te | test fixture ]] classes to inherit from `testing::Test`. Also for unit tests that use fixture class, `TEST` is replaced with `TEST_F` as required in gtest. There are several custom `main` functions in our existing tests. To make this transition easier, I modify all `main` functions to fallow gtest notation. But eventually we can remove them and use implementation of `main` that gtest provides. ```lang=bash % cat ~/transform #!/bin/sh files=$(git ls-files '*test\.cc') for file in $files do if grep -q "rocksdb::test::RunAllTests()" $file then if grep -Eq '^class \w+Test {' $file then perl -pi -e 's/^(class \w+Test) {/${1}: public testing::Test {/g' $file perl -pi -e 's/^(TEST)/${1}_F/g' $file fi perl -pi -e 's/(int main.*\{)/${1}::testing::InitGoogleTest(&argc, argv);/g' $file perl -pi -e 's/rocksdb::test::RunAllTests/RUN_ALL_TESTS/g' $file fi done % sh ~/transform % make format ``` Second iteration of this diff contains only scripted changes. Third iteration contains manual changes to fix last errors and make it compilable. Test Plan: Build and notice no errors. ```lang=bash % USE_CLANG=1 make check -j55 ``` Tests are still testing. Reviewers: meyering, sdong, rven, igor Reviewed By: igor Subscribers: dhruba, leveldb Differential Revision: https://reviews.facebook.net/D35157
2015-03-17 22:08:00 +01:00
TEST_F(DynamicBloomTest, VaryingLengths) {
KeyMaker km;
// Count number of filters that significantly exceed the false positive rate
int mediocre_filters = 0;
int good_filters = 0;
uint32_t num_probes = static_cast<uint32_t>(FLAGS_num_probes);
support for concurrent adds to memtable Summary: This diff adds support for concurrent adds to the skiplist memtable implementations. Memory allocation is made thread-safe by the addition of a spinlock, with small per-core buffers to avoid contention. Concurrent memtable writes are made via an additional method and don't impose a performance overhead on the non-concurrent case, so parallelism can be selected on a per-batch basis. Write thread synchronization is an increasing bottleneck for higher levels of concurrency, so this diff adds --enable_write_thread_adaptive_yield (default off). This feature causes threads joining a write batch group to spin for a short time (default 100 usec) using sched_yield, rather than going to sleep on a mutex. If the timing of the yield calls indicates that another thread has actually run during the yield then spinning is avoided. This option improves performance for concurrent situations even without parallel adds, although it has the potential to increase CPU usage (and the heuristic adaptation is not yet mature). Parallel writes are not currently compatible with inplace updates, update callbacks, or delete filtering. Enable it with --allow_concurrent_memtable_write (and --enable_write_thread_adaptive_yield). Parallel memtable writes are performance neutral when there is no actual parallelism, and in my experiments (SSD server-class Linux and varying contention and key sizes for fillrandom) they are always a performance win when there is more than one thread. Statistics are updated earlier in the write path, dropping the number of DB mutex acquisitions from 2 to 1 for almost all cases. This diff was motivated and inspired by Yahoo's cLSM work. It is more conservative than cLSM: RocksDB's write batch group leader role is preserved (along with all of the existing flush and write throttling logic) and concurrent writers are blocked until all memtable insertions have completed and the sequence number has been advanced, to preserve linearizability. My test config is "db_bench -benchmarks=fillrandom -threads=$T -batch_size=1 -memtablerep=skip_list -value_size=100 --num=1000000/$T -level0_slowdown_writes_trigger=9999 -level0_stop_writes_trigger=9999 -disable_auto_compactions --max_write_buffer_number=8 -max_background_flushes=8 --disable_wal --write_buffer_size=160000000 --block_size=16384 --allow_concurrent_memtable_write" on a two-socket Xeon E5-2660 @ 2.2Ghz with lots of memory and an SSD hard drive. With 1 thread I get ~440Kops/sec. Peak performance for 1 socket (numactl -N1) is slightly more than 1Mops/sec, at 16 threads. Peak performance across both sockets happens at 30 threads, and is ~900Kops/sec, although with fewer threads there is less performance loss when the system has background work. Test Plan: 1. concurrent stress tests for InlineSkipList and DynamicBloom 2. make clean; make check 3. make clean; DISABLE_JEMALLOC=1 make valgrind_check; valgrind db_bench 4. make clean; COMPILE_WITH_TSAN=1 make all check; db_bench 5. make clean; COMPILE_WITH_ASAN=1 make all check; db_bench 6. make clean; OPT=-DROCKSDB_LITE make check 7. verify no perf regressions when disabled Reviewers: igor, sdong Reviewed By: sdong Subscribers: MarkCallaghan, IslamAbdelRahman, anthony, yhchiang, rven, sdong, guyg8, kradhakrishnan, dhruba Differential Revision: https://reviews.facebook.net/D50589
2015-08-15 01:59:07 +02:00
fprintf(stderr, "bits_per_key: %d num_probes: %d\n", FLAGS_bits_per_key,
num_probes);
// NB: FP rate impact of 32-bit hash is noticeable starting around 10M keys.
// But that effect is hidden if using sequential keys (unique hashes).
for (bool nonseq : {false, true}) {
const uint32_t max_num = FLAGS_enable_perf ? 40000000 : 400000;
for (uint32_t num = 1; num <= max_num; num = NextNum(num)) {
uint32_t bloom_bits = 0;
Arena arena;
bloom_bits = num * FLAGS_bits_per_key;
DynamicBloom bloom(&arena, bloom_bits, num_probes);
for (uint64_t i = 0; i < num; i++) {
bloom.Add(km.Key(i, nonseq));
ASSERT_TRUE(bloom.MayContain(km.Key(i, nonseq)));
}
// All added keys must match
for (uint64_t i = 0; i < num; i++) {
ASSERT_TRUE(bloom.MayContain(km.Key(i, nonseq)));
}
// Check false positive rate
int result = 0;
for (uint64_t i = 0; i < 30000; i++) {
if (bloom.MayContain(km.Key(i + 1000000000, nonseq))) {
result++;
}
}
double rate = result / 30000.0;
fprintf(stderr,
"False positives (%s keys): "
"%5.2f%% @ num = %6u, bloom_bits = %6u\n",
nonseq ? "nonseq" : "seq", rate * 100.0, num, bloom_bits);
Faster new DynamicBloom implementation (for memtable) (#5762) Summary: Since DynamicBloom is now only used in-memory, we're free to change it without schema compatibility issues. The new implementation is drawn from (with manifest permission) https://github.com/pdillinger/wormhashing/blob/303542a767437f56d8b66cea6ebecaac0e6a61e9/bloom_simulation_tests/foo.cc#L613 This has several speed advantages over the prior implementation: * Uses fastrange instead of % * Minimum logic to determine first (and all) probed memory addresses * (Major) Two probes per 64-bit memory fetch/write. * Very fast and effective (murmur-like) hash expansion/re-mixing. (At least on recent CPUs, integer multiplication is very cheap.) While a Bloom filter with 512-bit cache locality has about a 1.15x FP rate penalty (e.g. 0.84% to 0.97%), further restricting to two probes per 64 bits incurs an additional 1.12x FP rate penalty (e.g. 0.97% to 1.09%). Nevertheless, the unit tests show no "mediocre" FP rate samples, unlike the old implementation with more erratic FP rates. Especially for the memtable, we expect speed to outweigh somewhat higher FP rates. For example, a negative table query would have to be 1000x slower than a BF query to justify doubling BF query time to shave 10% off FP rate (working assumption around 1% FP rate). While that seems likely for SSTs, my data suggests a speed factor of roughly 50x for the memtable (vs. BF; ~1.5% lower write throughput when enabling memtable Bloom filter, after this change). Thus, it's probably not worth even 5% more time in the Bloom filter to shave off 1/10th of the Bloom FP rate, or 0.1% in absolute terms, and it's probably at least 20% slower to recoup that much FP rate from this new implementation. Because of this, we do not see a need for a 'locality' option that affects the MemTable Bloom filter and have decoupled the MemTable Bloom filter from Options::bloom_locality. Note that just 3% more memory to the Bloom filter (10.3 bits per key vs. just 10) is able to make up for the ~12% FP rate drop in the new implementation: [] # Nearly "ideal" FP-wise but reasonably fast cache-local implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out time: 3.29372 sampled_fp_rate: 0.00985956 ... [] # Close match to this new implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out 10000000 6 10.3 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.10072 sampled_fp_rate: 0.00985655 ... [] # Old locality=1 implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out time: 3.95472 sampled_fp_rate: 0.00988943 ... Also note the dramatic speed improvement vs. alternatives. -- Performance unit test: DynamicBloomTest.concurrent_with_perf is updated to report more precise timing data. (Measure running time of each thread, not just longest running thread, etc.) Results averaged over various sizes enabled with --enable_perf and 20 runs each; old dynamic bloom refers to locality=1, the faster of the old: old dynamic bloom, avg add latency = 65.6468 new dynamic bloom, avg add latency = 44.3809 old dynamic bloom, avg query latency = 50.6485 new dynamic bloom, avg query latency = 43.2186 old avg parallel add latency = 41.678 new avg parallel add latency = 24.5238 old avg parallel hit latency = 14.6322 new avg parallel hit latency = 12.3939 old avg parallel miss latency = 16.7289 new avg parallel miss latency = 12.2134 Tested on a dedicated 64-bit production machine at Facebook. Significant improvement all around. Despite now using std::atomic<uint64_t>, quick before-and-after test on a 32-bit machine (Intel Atom N270, released 2008) shows no regression in performance, in some cases modest improvement. -- Performance integration test (synthetic): with DEBUG_LEVEL=0, used TEST_TMPDIR=/dev/shm ./db_bench --benchmarks=fillrandom,readmissing,readrandom,stats --num=2000000 and optionally with -memtable_whole_key_filtering -memtable_bloom_size_ratio=0.01 300 runs each configuration. Write throughput change by enabling memtable bloom: Old locality=0: -3.06% Old locality=1: -2.37% New: -1.50% conclusion -> seems to substantially close the gap Readmissing throughput change by enabling memtable bloom: Old locality=0: +34.47% Old locality=1: +34.80% New: +33.25% conclusion -> maybe a small new penalty from FP rate Readrandom throughput change by enabling memtable bloom: Old locality=0: +31.54% Old locality=1: +31.13% New: +30.60% conclusion -> maybe also from FP rate (after memtable flush) -- Another conclusion we can draw from this new implementation is that the existing 32-bit hash function is not inherently crippling the Bloom filter speed or accuracy, below about 5 million keys. For speed, the implementation is essentially the same whether starting with 32-bits or 64-bits of hash; it just determines whether the first multiplication after fastrange is a pseudorandom expansion or needed re-mix. Note that this multiplication can occur while memory is fetching. For accuracy, in a standard configuration, you need about 5 million keys before you have about a 1.1x FP penalty due to using a 32-bit hash vs. 64-bit: [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.52069 sampled_fp_rate: 0.0118267 ... [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out time: 2.43871 sampled_fp_rate: 0.0109059 Pull Request resolved: https://github.com/facebook/rocksdb/pull/5762 Differential Revision: D17214194 Pulled By: pdillinger fbshipit-source-id: ad9da031772e985fd6b62a0e1db8e81892520595
2019-09-05 23:57:39 +02:00
if (rate > 0.0125)
mediocre_filters++; // Allowed, but not too often
else
good_filters++;
}
}
Faster new DynamicBloom implementation (for memtable) (#5762) Summary: Since DynamicBloom is now only used in-memory, we're free to change it without schema compatibility issues. The new implementation is drawn from (with manifest permission) https://github.com/pdillinger/wormhashing/blob/303542a767437f56d8b66cea6ebecaac0e6a61e9/bloom_simulation_tests/foo.cc#L613 This has several speed advantages over the prior implementation: * Uses fastrange instead of % * Minimum logic to determine first (and all) probed memory addresses * (Major) Two probes per 64-bit memory fetch/write. * Very fast and effective (murmur-like) hash expansion/re-mixing. (At least on recent CPUs, integer multiplication is very cheap.) While a Bloom filter with 512-bit cache locality has about a 1.15x FP rate penalty (e.g. 0.84% to 0.97%), further restricting to two probes per 64 bits incurs an additional 1.12x FP rate penalty (e.g. 0.97% to 1.09%). Nevertheless, the unit tests show no "mediocre" FP rate samples, unlike the old implementation with more erratic FP rates. Especially for the memtable, we expect speed to outweigh somewhat higher FP rates. For example, a negative table query would have to be 1000x slower than a BF query to justify doubling BF query time to shave 10% off FP rate (working assumption around 1% FP rate). While that seems likely for SSTs, my data suggests a speed factor of roughly 50x for the memtable (vs. BF; ~1.5% lower write throughput when enabling memtable Bloom filter, after this change). Thus, it's probably not worth even 5% more time in the Bloom filter to shave off 1/10th of the Bloom FP rate, or 0.1% in absolute terms, and it's probably at least 20% slower to recoup that much FP rate from this new implementation. Because of this, we do not see a need for a 'locality' option that affects the MemTable Bloom filter and have decoupled the MemTable Bloom filter from Options::bloom_locality. Note that just 3% more memory to the Bloom filter (10.3 bits per key vs. just 10) is able to make up for the ~12% FP rate drop in the new implementation: [] # Nearly "ideal" FP-wise but reasonably fast cache-local implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out time: 3.29372 sampled_fp_rate: 0.00985956 ... [] # Close match to this new implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out 10000000 6 10.3 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.10072 sampled_fp_rate: 0.00985655 ... [] # Old locality=1 implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out time: 3.95472 sampled_fp_rate: 0.00988943 ... Also note the dramatic speed improvement vs. alternatives. -- Performance unit test: DynamicBloomTest.concurrent_with_perf is updated to report more precise timing data. (Measure running time of each thread, not just longest running thread, etc.) Results averaged over various sizes enabled with --enable_perf and 20 runs each; old dynamic bloom refers to locality=1, the faster of the old: old dynamic bloom, avg add latency = 65.6468 new dynamic bloom, avg add latency = 44.3809 old dynamic bloom, avg query latency = 50.6485 new dynamic bloom, avg query latency = 43.2186 old avg parallel add latency = 41.678 new avg parallel add latency = 24.5238 old avg parallel hit latency = 14.6322 new avg parallel hit latency = 12.3939 old avg parallel miss latency = 16.7289 new avg parallel miss latency = 12.2134 Tested on a dedicated 64-bit production machine at Facebook. Significant improvement all around. Despite now using std::atomic<uint64_t>, quick before-and-after test on a 32-bit machine (Intel Atom N270, released 2008) shows no regression in performance, in some cases modest improvement. -- Performance integration test (synthetic): with DEBUG_LEVEL=0, used TEST_TMPDIR=/dev/shm ./db_bench --benchmarks=fillrandom,readmissing,readrandom,stats --num=2000000 and optionally with -memtable_whole_key_filtering -memtable_bloom_size_ratio=0.01 300 runs each configuration. Write throughput change by enabling memtable bloom: Old locality=0: -3.06% Old locality=1: -2.37% New: -1.50% conclusion -> seems to substantially close the gap Readmissing throughput change by enabling memtable bloom: Old locality=0: +34.47% Old locality=1: +34.80% New: +33.25% conclusion -> maybe a small new penalty from FP rate Readrandom throughput change by enabling memtable bloom: Old locality=0: +31.54% Old locality=1: +31.13% New: +30.60% conclusion -> maybe also from FP rate (after memtable flush) -- Another conclusion we can draw from this new implementation is that the existing 32-bit hash function is not inherently crippling the Bloom filter speed or accuracy, below about 5 million keys. For speed, the implementation is essentially the same whether starting with 32-bits or 64-bits of hash; it just determines whether the first multiplication after fastrange is a pseudorandom expansion or needed re-mix. Note that this multiplication can occur while memory is fetching. For accuracy, in a standard configuration, you need about 5 million keys before you have about a 1.1x FP penalty due to using a 32-bit hash vs. 64-bit: [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.52069 sampled_fp_rate: 0.0118267 ... [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out time: 2.43871 sampled_fp_rate: 0.0109059 Pull Request resolved: https://github.com/facebook/rocksdb/pull/5762 Differential Revision: D17214194 Pulled By: pdillinger fbshipit-source-id: ad9da031772e985fd6b62a0e1db8e81892520595
2019-09-05 23:57:39 +02:00
fprintf(stderr, "Filters: %d good, %d mediocre\n", good_filters,
mediocre_filters);
ASSERT_LE(mediocre_filters, good_filters / 25);
}
rocksdb: switch to gtest Summary: Our existing test notation is very similar to what is used in gtest. It makes it easy to adopt what is different. In this diff I modify existing [[ https://code.google.com/p/googletest/wiki/Primer#Test_Fixtures:_Using_the_Same_Data_Configuration_for_Multiple_Te | test fixture ]] classes to inherit from `testing::Test`. Also for unit tests that use fixture class, `TEST` is replaced with `TEST_F` as required in gtest. There are several custom `main` functions in our existing tests. To make this transition easier, I modify all `main` functions to fallow gtest notation. But eventually we can remove them and use implementation of `main` that gtest provides. ```lang=bash % cat ~/transform #!/bin/sh files=$(git ls-files '*test\.cc') for file in $files do if grep -q "rocksdb::test::RunAllTests()" $file then if grep -Eq '^class \w+Test {' $file then perl -pi -e 's/^(class \w+Test) {/${1}: public testing::Test {/g' $file perl -pi -e 's/^(TEST)/${1}_F/g' $file fi perl -pi -e 's/(int main.*\{)/${1}::testing::InitGoogleTest(&argc, argv);/g' $file perl -pi -e 's/rocksdb::test::RunAllTests/RUN_ALL_TESTS/g' $file fi done % sh ~/transform % make format ``` Second iteration of this diff contains only scripted changes. Third iteration contains manual changes to fix last errors and make it compilable. Test Plan: Build and notice no errors. ```lang=bash % USE_CLANG=1 make check -j55 ``` Tests are still testing. Reviewers: meyering, sdong, rven, igor Reviewed By: igor Subscribers: dhruba, leveldb Differential Revision: https://reviews.facebook.net/D35157
2015-03-17 22:08:00 +01:00
TEST_F(DynamicBloomTest, perf) {
KeyMaker km;
StopWatchNano timer(SystemClock::Default().get());
uint32_t num_probes = static_cast<uint32_t>(FLAGS_num_probes);
if (!FLAGS_enable_perf) {
return;
}
for (uint32_t m = 1; m <= 8; ++m) {
Arena arena;
const uint32_t num_keys = m * 8 * 1024 * 1024;
fprintf(stderr, "testing %" PRIu32 "M keys\n", m * 8);
Faster new DynamicBloom implementation (for memtable) (#5762) Summary: Since DynamicBloom is now only used in-memory, we're free to change it without schema compatibility issues. The new implementation is drawn from (with manifest permission) https://github.com/pdillinger/wormhashing/blob/303542a767437f56d8b66cea6ebecaac0e6a61e9/bloom_simulation_tests/foo.cc#L613 This has several speed advantages over the prior implementation: * Uses fastrange instead of % * Minimum logic to determine first (and all) probed memory addresses * (Major) Two probes per 64-bit memory fetch/write. * Very fast and effective (murmur-like) hash expansion/re-mixing. (At least on recent CPUs, integer multiplication is very cheap.) While a Bloom filter with 512-bit cache locality has about a 1.15x FP rate penalty (e.g. 0.84% to 0.97%), further restricting to two probes per 64 bits incurs an additional 1.12x FP rate penalty (e.g. 0.97% to 1.09%). Nevertheless, the unit tests show no "mediocre" FP rate samples, unlike the old implementation with more erratic FP rates. Especially for the memtable, we expect speed to outweigh somewhat higher FP rates. For example, a negative table query would have to be 1000x slower than a BF query to justify doubling BF query time to shave 10% off FP rate (working assumption around 1% FP rate). While that seems likely for SSTs, my data suggests a speed factor of roughly 50x for the memtable (vs. BF; ~1.5% lower write throughput when enabling memtable Bloom filter, after this change). Thus, it's probably not worth even 5% more time in the Bloom filter to shave off 1/10th of the Bloom FP rate, or 0.1% in absolute terms, and it's probably at least 20% slower to recoup that much FP rate from this new implementation. Because of this, we do not see a need for a 'locality' option that affects the MemTable Bloom filter and have decoupled the MemTable Bloom filter from Options::bloom_locality. Note that just 3% more memory to the Bloom filter (10.3 bits per key vs. just 10) is able to make up for the ~12% FP rate drop in the new implementation: [] # Nearly "ideal" FP-wise but reasonably fast cache-local implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out time: 3.29372 sampled_fp_rate: 0.00985956 ... [] # Close match to this new implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out 10000000 6 10.3 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.10072 sampled_fp_rate: 0.00985655 ... [] # Old locality=1 implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out time: 3.95472 sampled_fp_rate: 0.00988943 ... Also note the dramatic speed improvement vs. alternatives. -- Performance unit test: DynamicBloomTest.concurrent_with_perf is updated to report more precise timing data. (Measure running time of each thread, not just longest running thread, etc.) Results averaged over various sizes enabled with --enable_perf and 20 runs each; old dynamic bloom refers to locality=1, the faster of the old: old dynamic bloom, avg add latency = 65.6468 new dynamic bloom, avg add latency = 44.3809 old dynamic bloom, avg query latency = 50.6485 new dynamic bloom, avg query latency = 43.2186 old avg parallel add latency = 41.678 new avg parallel add latency = 24.5238 old avg parallel hit latency = 14.6322 new avg parallel hit latency = 12.3939 old avg parallel miss latency = 16.7289 new avg parallel miss latency = 12.2134 Tested on a dedicated 64-bit production machine at Facebook. Significant improvement all around. Despite now using std::atomic<uint64_t>, quick before-and-after test on a 32-bit machine (Intel Atom N270, released 2008) shows no regression in performance, in some cases modest improvement. -- Performance integration test (synthetic): with DEBUG_LEVEL=0, used TEST_TMPDIR=/dev/shm ./db_bench --benchmarks=fillrandom,readmissing,readrandom,stats --num=2000000 and optionally with -memtable_whole_key_filtering -memtable_bloom_size_ratio=0.01 300 runs each configuration. Write throughput change by enabling memtable bloom: Old locality=0: -3.06% Old locality=1: -2.37% New: -1.50% conclusion -> seems to substantially close the gap Readmissing throughput change by enabling memtable bloom: Old locality=0: +34.47% Old locality=1: +34.80% New: +33.25% conclusion -> maybe a small new penalty from FP rate Readrandom throughput change by enabling memtable bloom: Old locality=0: +31.54% Old locality=1: +31.13% New: +30.60% conclusion -> maybe also from FP rate (after memtable flush) -- Another conclusion we can draw from this new implementation is that the existing 32-bit hash function is not inherently crippling the Bloom filter speed or accuracy, below about 5 million keys. For speed, the implementation is essentially the same whether starting with 32-bits or 64-bits of hash; it just determines whether the first multiplication after fastrange is a pseudorandom expansion or needed re-mix. Note that this multiplication can occur while memory is fetching. For accuracy, in a standard configuration, you need about 5 million keys before you have about a 1.1x FP penalty due to using a 32-bit hash vs. 64-bit: [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.52069 sampled_fp_rate: 0.0118267 ... [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out time: 2.43871 sampled_fp_rate: 0.0109059 Pull Request resolved: https://github.com/facebook/rocksdb/pull/5762 Differential Revision: D17214194 Pulled By: pdillinger fbshipit-source-id: ad9da031772e985fd6b62a0e1db8e81892520595
2019-09-05 23:57:39 +02:00
DynamicBloom std_bloom(&arena, num_keys * 10, num_probes);
timer.Start();
support for concurrent adds to memtable Summary: This diff adds support for concurrent adds to the skiplist memtable implementations. Memory allocation is made thread-safe by the addition of a spinlock, with small per-core buffers to avoid contention. Concurrent memtable writes are made via an additional method and don't impose a performance overhead on the non-concurrent case, so parallelism can be selected on a per-batch basis. Write thread synchronization is an increasing bottleneck for higher levels of concurrency, so this diff adds --enable_write_thread_adaptive_yield (default off). This feature causes threads joining a write batch group to spin for a short time (default 100 usec) using sched_yield, rather than going to sleep on a mutex. If the timing of the yield calls indicates that another thread has actually run during the yield then spinning is avoided. This option improves performance for concurrent situations even without parallel adds, although it has the potential to increase CPU usage (and the heuristic adaptation is not yet mature). Parallel writes are not currently compatible with inplace updates, update callbacks, or delete filtering. Enable it with --allow_concurrent_memtable_write (and --enable_write_thread_adaptive_yield). Parallel memtable writes are performance neutral when there is no actual parallelism, and in my experiments (SSD server-class Linux and varying contention and key sizes for fillrandom) they are always a performance win when there is more than one thread. Statistics are updated earlier in the write path, dropping the number of DB mutex acquisitions from 2 to 1 for almost all cases. This diff was motivated and inspired by Yahoo's cLSM work. It is more conservative than cLSM: RocksDB's write batch group leader role is preserved (along with all of the existing flush and write throttling logic) and concurrent writers are blocked until all memtable insertions have completed and the sequence number has been advanced, to preserve linearizability. My test config is "db_bench -benchmarks=fillrandom -threads=$T -batch_size=1 -memtablerep=skip_list -value_size=100 --num=1000000/$T -level0_slowdown_writes_trigger=9999 -level0_stop_writes_trigger=9999 -disable_auto_compactions --max_write_buffer_number=8 -max_background_flushes=8 --disable_wal --write_buffer_size=160000000 --block_size=16384 --allow_concurrent_memtable_write" on a two-socket Xeon E5-2660 @ 2.2Ghz with lots of memory and an SSD hard drive. With 1 thread I get ~440Kops/sec. Peak performance for 1 socket (numactl -N1) is slightly more than 1Mops/sec, at 16 threads. Peak performance across both sockets happens at 30 threads, and is ~900Kops/sec, although with fewer threads there is less performance loss when the system has background work. Test Plan: 1. concurrent stress tests for InlineSkipList and DynamicBloom 2. make clean; make check 3. make clean; DISABLE_JEMALLOC=1 make valgrind_check; valgrind db_bench 4. make clean; COMPILE_WITH_TSAN=1 make all check; db_bench 5. make clean; COMPILE_WITH_ASAN=1 make all check; db_bench 6. make clean; OPT=-DROCKSDB_LITE make check 7. verify no perf regressions when disabled Reviewers: igor, sdong Reviewed By: sdong Subscribers: MarkCallaghan, IslamAbdelRahman, anthony, yhchiang, rven, sdong, guyg8, kradhakrishnan, dhruba Differential Revision: https://reviews.facebook.net/D50589
2015-08-15 01:59:07 +02:00
for (uint64_t i = 1; i <= num_keys; ++i) {
std_bloom.Add(km.Seq(i));
}
uint64_t elapsed = timer.ElapsedNanos();
Faster new DynamicBloom implementation (for memtable) (#5762) Summary: Since DynamicBloom is now only used in-memory, we're free to change it without schema compatibility issues. The new implementation is drawn from (with manifest permission) https://github.com/pdillinger/wormhashing/blob/303542a767437f56d8b66cea6ebecaac0e6a61e9/bloom_simulation_tests/foo.cc#L613 This has several speed advantages over the prior implementation: * Uses fastrange instead of % * Minimum logic to determine first (and all) probed memory addresses * (Major) Two probes per 64-bit memory fetch/write. * Very fast and effective (murmur-like) hash expansion/re-mixing. (At least on recent CPUs, integer multiplication is very cheap.) While a Bloom filter with 512-bit cache locality has about a 1.15x FP rate penalty (e.g. 0.84% to 0.97%), further restricting to two probes per 64 bits incurs an additional 1.12x FP rate penalty (e.g. 0.97% to 1.09%). Nevertheless, the unit tests show no "mediocre" FP rate samples, unlike the old implementation with more erratic FP rates. Especially for the memtable, we expect speed to outweigh somewhat higher FP rates. For example, a negative table query would have to be 1000x slower than a BF query to justify doubling BF query time to shave 10% off FP rate (working assumption around 1% FP rate). While that seems likely for SSTs, my data suggests a speed factor of roughly 50x for the memtable (vs. BF; ~1.5% lower write throughput when enabling memtable Bloom filter, after this change). Thus, it's probably not worth even 5% more time in the Bloom filter to shave off 1/10th of the Bloom FP rate, or 0.1% in absolute terms, and it's probably at least 20% slower to recoup that much FP rate from this new implementation. Because of this, we do not see a need for a 'locality' option that affects the MemTable Bloom filter and have decoupled the MemTable Bloom filter from Options::bloom_locality. Note that just 3% more memory to the Bloom filter (10.3 bits per key vs. just 10) is able to make up for the ~12% FP rate drop in the new implementation: [] # Nearly "ideal" FP-wise but reasonably fast cache-local implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out time: 3.29372 sampled_fp_rate: 0.00985956 ... [] # Close match to this new implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out 10000000 6 10.3 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.10072 sampled_fp_rate: 0.00985655 ... [] # Old locality=1 implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out time: 3.95472 sampled_fp_rate: 0.00988943 ... Also note the dramatic speed improvement vs. alternatives. -- Performance unit test: DynamicBloomTest.concurrent_with_perf is updated to report more precise timing data. (Measure running time of each thread, not just longest running thread, etc.) Results averaged over various sizes enabled with --enable_perf and 20 runs each; old dynamic bloom refers to locality=1, the faster of the old: old dynamic bloom, avg add latency = 65.6468 new dynamic bloom, avg add latency = 44.3809 old dynamic bloom, avg query latency = 50.6485 new dynamic bloom, avg query latency = 43.2186 old avg parallel add latency = 41.678 new avg parallel add latency = 24.5238 old avg parallel hit latency = 14.6322 new avg parallel hit latency = 12.3939 old avg parallel miss latency = 16.7289 new avg parallel miss latency = 12.2134 Tested on a dedicated 64-bit production machine at Facebook. Significant improvement all around. Despite now using std::atomic<uint64_t>, quick before-and-after test on a 32-bit machine (Intel Atom N270, released 2008) shows no regression in performance, in some cases modest improvement. -- Performance integration test (synthetic): with DEBUG_LEVEL=0, used TEST_TMPDIR=/dev/shm ./db_bench --benchmarks=fillrandom,readmissing,readrandom,stats --num=2000000 and optionally with -memtable_whole_key_filtering -memtable_bloom_size_ratio=0.01 300 runs each configuration. Write throughput change by enabling memtable bloom: Old locality=0: -3.06% Old locality=1: -2.37% New: -1.50% conclusion -> seems to substantially close the gap Readmissing throughput change by enabling memtable bloom: Old locality=0: +34.47% Old locality=1: +34.80% New: +33.25% conclusion -> maybe a small new penalty from FP rate Readrandom throughput change by enabling memtable bloom: Old locality=0: +31.54% Old locality=1: +31.13% New: +30.60% conclusion -> maybe also from FP rate (after memtable flush) -- Another conclusion we can draw from this new implementation is that the existing 32-bit hash function is not inherently crippling the Bloom filter speed or accuracy, below about 5 million keys. For speed, the implementation is essentially the same whether starting with 32-bits or 64-bits of hash; it just determines whether the first multiplication after fastrange is a pseudorandom expansion or needed re-mix. Note that this multiplication can occur while memory is fetching. For accuracy, in a standard configuration, you need about 5 million keys before you have about a 1.1x FP penalty due to using a 32-bit hash vs. 64-bit: [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.52069 sampled_fp_rate: 0.0118267 ... [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out time: 2.43871 sampled_fp_rate: 0.0109059 Pull Request resolved: https://github.com/facebook/rocksdb/pull/5762 Differential Revision: D17214194 Pulled By: pdillinger fbshipit-source-id: ad9da031772e985fd6b62a0e1db8e81892520595
2019-09-05 23:57:39 +02:00
fprintf(stderr, "dynamic bloom, avg add latency %3g\n",
static_cast<double>(elapsed) / num_keys);
uint32_t count = 0;
timer.Start();
support for concurrent adds to memtable Summary: This diff adds support for concurrent adds to the skiplist memtable implementations. Memory allocation is made thread-safe by the addition of a spinlock, with small per-core buffers to avoid contention. Concurrent memtable writes are made via an additional method and don't impose a performance overhead on the non-concurrent case, so parallelism can be selected on a per-batch basis. Write thread synchronization is an increasing bottleneck for higher levels of concurrency, so this diff adds --enable_write_thread_adaptive_yield (default off). This feature causes threads joining a write batch group to spin for a short time (default 100 usec) using sched_yield, rather than going to sleep on a mutex. If the timing of the yield calls indicates that another thread has actually run during the yield then spinning is avoided. This option improves performance for concurrent situations even without parallel adds, although it has the potential to increase CPU usage (and the heuristic adaptation is not yet mature). Parallel writes are not currently compatible with inplace updates, update callbacks, or delete filtering. Enable it with --allow_concurrent_memtable_write (and --enable_write_thread_adaptive_yield). Parallel memtable writes are performance neutral when there is no actual parallelism, and in my experiments (SSD server-class Linux and varying contention and key sizes for fillrandom) they are always a performance win when there is more than one thread. Statistics are updated earlier in the write path, dropping the number of DB mutex acquisitions from 2 to 1 for almost all cases. This diff was motivated and inspired by Yahoo's cLSM work. It is more conservative than cLSM: RocksDB's write batch group leader role is preserved (along with all of the existing flush and write throttling logic) and concurrent writers are blocked until all memtable insertions have completed and the sequence number has been advanced, to preserve linearizability. My test config is "db_bench -benchmarks=fillrandom -threads=$T -batch_size=1 -memtablerep=skip_list -value_size=100 --num=1000000/$T -level0_slowdown_writes_trigger=9999 -level0_stop_writes_trigger=9999 -disable_auto_compactions --max_write_buffer_number=8 -max_background_flushes=8 --disable_wal --write_buffer_size=160000000 --block_size=16384 --allow_concurrent_memtable_write" on a two-socket Xeon E5-2660 @ 2.2Ghz with lots of memory and an SSD hard drive. With 1 thread I get ~440Kops/sec. Peak performance for 1 socket (numactl -N1) is slightly more than 1Mops/sec, at 16 threads. Peak performance across both sockets happens at 30 threads, and is ~900Kops/sec, although with fewer threads there is less performance loss when the system has background work. Test Plan: 1. concurrent stress tests for InlineSkipList and DynamicBloom 2. make clean; make check 3. make clean; DISABLE_JEMALLOC=1 make valgrind_check; valgrind db_bench 4. make clean; COMPILE_WITH_TSAN=1 make all check; db_bench 5. make clean; COMPILE_WITH_ASAN=1 make all check; db_bench 6. make clean; OPT=-DROCKSDB_LITE make check 7. verify no perf regressions when disabled Reviewers: igor, sdong Reviewed By: sdong Subscribers: MarkCallaghan, IslamAbdelRahman, anthony, yhchiang, rven, sdong, guyg8, kradhakrishnan, dhruba Differential Revision: https://reviews.facebook.net/D50589
2015-08-15 01:59:07 +02:00
for (uint64_t i = 1; i <= num_keys; ++i) {
if (std_bloom.MayContain(km.Seq(i))) {
++count;
}
}
ASSERT_EQ(count, num_keys);
elapsed = timer.ElapsedNanos();
assert(count > 0);
Faster new DynamicBloom implementation (for memtable) (#5762) Summary: Since DynamicBloom is now only used in-memory, we're free to change it without schema compatibility issues. The new implementation is drawn from (with manifest permission) https://github.com/pdillinger/wormhashing/blob/303542a767437f56d8b66cea6ebecaac0e6a61e9/bloom_simulation_tests/foo.cc#L613 This has several speed advantages over the prior implementation: * Uses fastrange instead of % * Minimum logic to determine first (and all) probed memory addresses * (Major) Two probes per 64-bit memory fetch/write. * Very fast and effective (murmur-like) hash expansion/re-mixing. (At least on recent CPUs, integer multiplication is very cheap.) While a Bloom filter with 512-bit cache locality has about a 1.15x FP rate penalty (e.g. 0.84% to 0.97%), further restricting to two probes per 64 bits incurs an additional 1.12x FP rate penalty (e.g. 0.97% to 1.09%). Nevertheless, the unit tests show no "mediocre" FP rate samples, unlike the old implementation with more erratic FP rates. Especially for the memtable, we expect speed to outweigh somewhat higher FP rates. For example, a negative table query would have to be 1000x slower than a BF query to justify doubling BF query time to shave 10% off FP rate (working assumption around 1% FP rate). While that seems likely for SSTs, my data suggests a speed factor of roughly 50x for the memtable (vs. BF; ~1.5% lower write throughput when enabling memtable Bloom filter, after this change). Thus, it's probably not worth even 5% more time in the Bloom filter to shave off 1/10th of the Bloom FP rate, or 0.1% in absolute terms, and it's probably at least 20% slower to recoup that much FP rate from this new implementation. Because of this, we do not see a need for a 'locality' option that affects the MemTable Bloom filter and have decoupled the MemTable Bloom filter from Options::bloom_locality. Note that just 3% more memory to the Bloom filter (10.3 bits per key vs. just 10) is able to make up for the ~12% FP rate drop in the new implementation: [] # Nearly "ideal" FP-wise but reasonably fast cache-local implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out time: 3.29372 sampled_fp_rate: 0.00985956 ... [] # Close match to this new implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out 10000000 6 10.3 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.10072 sampled_fp_rate: 0.00985655 ... [] # Old locality=1 implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out time: 3.95472 sampled_fp_rate: 0.00988943 ... Also note the dramatic speed improvement vs. alternatives. -- Performance unit test: DynamicBloomTest.concurrent_with_perf is updated to report more precise timing data. (Measure running time of each thread, not just longest running thread, etc.) Results averaged over various sizes enabled with --enable_perf and 20 runs each; old dynamic bloom refers to locality=1, the faster of the old: old dynamic bloom, avg add latency = 65.6468 new dynamic bloom, avg add latency = 44.3809 old dynamic bloom, avg query latency = 50.6485 new dynamic bloom, avg query latency = 43.2186 old avg parallel add latency = 41.678 new avg parallel add latency = 24.5238 old avg parallel hit latency = 14.6322 new avg parallel hit latency = 12.3939 old avg parallel miss latency = 16.7289 new avg parallel miss latency = 12.2134 Tested on a dedicated 64-bit production machine at Facebook. Significant improvement all around. Despite now using std::atomic<uint64_t>, quick before-and-after test on a 32-bit machine (Intel Atom N270, released 2008) shows no regression in performance, in some cases modest improvement. -- Performance integration test (synthetic): with DEBUG_LEVEL=0, used TEST_TMPDIR=/dev/shm ./db_bench --benchmarks=fillrandom,readmissing,readrandom,stats --num=2000000 and optionally with -memtable_whole_key_filtering -memtable_bloom_size_ratio=0.01 300 runs each configuration. Write throughput change by enabling memtable bloom: Old locality=0: -3.06% Old locality=1: -2.37% New: -1.50% conclusion -> seems to substantially close the gap Readmissing throughput change by enabling memtable bloom: Old locality=0: +34.47% Old locality=1: +34.80% New: +33.25% conclusion -> maybe a small new penalty from FP rate Readrandom throughput change by enabling memtable bloom: Old locality=0: +31.54% Old locality=1: +31.13% New: +30.60% conclusion -> maybe also from FP rate (after memtable flush) -- Another conclusion we can draw from this new implementation is that the existing 32-bit hash function is not inherently crippling the Bloom filter speed or accuracy, below about 5 million keys. For speed, the implementation is essentially the same whether starting with 32-bits or 64-bits of hash; it just determines whether the first multiplication after fastrange is a pseudorandom expansion or needed re-mix. Note that this multiplication can occur while memory is fetching. For accuracy, in a standard configuration, you need about 5 million keys before you have about a 1.1x FP penalty due to using a 32-bit hash vs. 64-bit: [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.52069 sampled_fp_rate: 0.0118267 ... [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out time: 2.43871 sampled_fp_rate: 0.0109059 Pull Request resolved: https://github.com/facebook/rocksdb/pull/5762 Differential Revision: D17214194 Pulled By: pdillinger fbshipit-source-id: ad9da031772e985fd6b62a0e1db8e81892520595
2019-09-05 23:57:39 +02:00
fprintf(stderr, "dynamic bloom, avg query latency %3g\n",
static_cast<double>(elapsed) / count);
support for concurrent adds to memtable Summary: This diff adds support for concurrent adds to the skiplist memtable implementations. Memory allocation is made thread-safe by the addition of a spinlock, with small per-core buffers to avoid contention. Concurrent memtable writes are made via an additional method and don't impose a performance overhead on the non-concurrent case, so parallelism can be selected on a per-batch basis. Write thread synchronization is an increasing bottleneck for higher levels of concurrency, so this diff adds --enable_write_thread_adaptive_yield (default off). This feature causes threads joining a write batch group to spin for a short time (default 100 usec) using sched_yield, rather than going to sleep on a mutex. If the timing of the yield calls indicates that another thread has actually run during the yield then spinning is avoided. This option improves performance for concurrent situations even without parallel adds, although it has the potential to increase CPU usage (and the heuristic adaptation is not yet mature). Parallel writes are not currently compatible with inplace updates, update callbacks, or delete filtering. Enable it with --allow_concurrent_memtable_write (and --enable_write_thread_adaptive_yield). Parallel memtable writes are performance neutral when there is no actual parallelism, and in my experiments (SSD server-class Linux and varying contention and key sizes for fillrandom) they are always a performance win when there is more than one thread. Statistics are updated earlier in the write path, dropping the number of DB mutex acquisitions from 2 to 1 for almost all cases. This diff was motivated and inspired by Yahoo's cLSM work. It is more conservative than cLSM: RocksDB's write batch group leader role is preserved (along with all of the existing flush and write throttling logic) and concurrent writers are blocked until all memtable insertions have completed and the sequence number has been advanced, to preserve linearizability. My test config is "db_bench -benchmarks=fillrandom -threads=$T -batch_size=1 -memtablerep=skip_list -value_size=100 --num=1000000/$T -level0_slowdown_writes_trigger=9999 -level0_stop_writes_trigger=9999 -disable_auto_compactions --max_write_buffer_number=8 -max_background_flushes=8 --disable_wal --write_buffer_size=160000000 --block_size=16384 --allow_concurrent_memtable_write" on a two-socket Xeon E5-2660 @ 2.2Ghz with lots of memory and an SSD hard drive. With 1 thread I get ~440Kops/sec. Peak performance for 1 socket (numactl -N1) is slightly more than 1Mops/sec, at 16 threads. Peak performance across both sockets happens at 30 threads, and is ~900Kops/sec, although with fewer threads there is less performance loss when the system has background work. Test Plan: 1. concurrent stress tests for InlineSkipList and DynamicBloom 2. make clean; make check 3. make clean; DISABLE_JEMALLOC=1 make valgrind_check; valgrind db_bench 4. make clean; COMPILE_WITH_TSAN=1 make all check; db_bench 5. make clean; COMPILE_WITH_ASAN=1 make all check; db_bench 6. make clean; OPT=-DROCKSDB_LITE make check 7. verify no perf regressions when disabled Reviewers: igor, sdong Reviewed By: sdong Subscribers: MarkCallaghan, IslamAbdelRahman, anthony, yhchiang, rven, sdong, guyg8, kradhakrishnan, dhruba Differential Revision: https://reviews.facebook.net/D50589
2015-08-15 01:59:07 +02:00
}
}
TEST_F(DynamicBloomTest, concurrent_with_perf) {
uint32_t num_probes = static_cast<uint32_t>(FLAGS_num_probes);
uint32_t m_limit = FLAGS_enable_perf ? 8 : 1;
uint32_t num_threads = 4;
std::vector<port::Thread> threads;
support for concurrent adds to memtable Summary: This diff adds support for concurrent adds to the skiplist memtable implementations. Memory allocation is made thread-safe by the addition of a spinlock, with small per-core buffers to avoid contention. Concurrent memtable writes are made via an additional method and don't impose a performance overhead on the non-concurrent case, so parallelism can be selected on a per-batch basis. Write thread synchronization is an increasing bottleneck for higher levels of concurrency, so this diff adds --enable_write_thread_adaptive_yield (default off). This feature causes threads joining a write batch group to spin for a short time (default 100 usec) using sched_yield, rather than going to sleep on a mutex. If the timing of the yield calls indicates that another thread has actually run during the yield then spinning is avoided. This option improves performance for concurrent situations even without parallel adds, although it has the potential to increase CPU usage (and the heuristic adaptation is not yet mature). Parallel writes are not currently compatible with inplace updates, update callbacks, or delete filtering. Enable it with --allow_concurrent_memtable_write (and --enable_write_thread_adaptive_yield). Parallel memtable writes are performance neutral when there is no actual parallelism, and in my experiments (SSD server-class Linux and varying contention and key sizes for fillrandom) they are always a performance win when there is more than one thread. Statistics are updated earlier in the write path, dropping the number of DB mutex acquisitions from 2 to 1 for almost all cases. This diff was motivated and inspired by Yahoo's cLSM work. It is more conservative than cLSM: RocksDB's write batch group leader role is preserved (along with all of the existing flush and write throttling logic) and concurrent writers are blocked until all memtable insertions have completed and the sequence number has been advanced, to preserve linearizability. My test config is "db_bench -benchmarks=fillrandom -threads=$T -batch_size=1 -memtablerep=skip_list -value_size=100 --num=1000000/$T -level0_slowdown_writes_trigger=9999 -level0_stop_writes_trigger=9999 -disable_auto_compactions --max_write_buffer_number=8 -max_background_flushes=8 --disable_wal --write_buffer_size=160000000 --block_size=16384 --allow_concurrent_memtable_write" on a two-socket Xeon E5-2660 @ 2.2Ghz with lots of memory and an SSD hard drive. With 1 thread I get ~440Kops/sec. Peak performance for 1 socket (numactl -N1) is slightly more than 1Mops/sec, at 16 threads. Peak performance across both sockets happens at 30 threads, and is ~900Kops/sec, although with fewer threads there is less performance loss when the system has background work. Test Plan: 1. concurrent stress tests for InlineSkipList and DynamicBloom 2. make clean; make check 3. make clean; DISABLE_JEMALLOC=1 make valgrind_check; valgrind db_bench 4. make clean; COMPILE_WITH_TSAN=1 make all check; db_bench 5. make clean; COMPILE_WITH_ASAN=1 make all check; db_bench 6. make clean; OPT=-DROCKSDB_LITE make check 7. verify no perf regressions when disabled Reviewers: igor, sdong Reviewed By: sdong Subscribers: MarkCallaghan, IslamAbdelRahman, anthony, yhchiang, rven, sdong, guyg8, kradhakrishnan, dhruba Differential Revision: https://reviews.facebook.net/D50589
2015-08-15 01:59:07 +02:00
// NB: Uses sequential keys for speed, but that hides the FP rate
// impact of 32-bit hash, which is noticeable starting around 10M keys
// when they vary across hashing blocks.
support for concurrent adds to memtable Summary: This diff adds support for concurrent adds to the skiplist memtable implementations. Memory allocation is made thread-safe by the addition of a spinlock, with small per-core buffers to avoid contention. Concurrent memtable writes are made via an additional method and don't impose a performance overhead on the non-concurrent case, so parallelism can be selected on a per-batch basis. Write thread synchronization is an increasing bottleneck for higher levels of concurrency, so this diff adds --enable_write_thread_adaptive_yield (default off). This feature causes threads joining a write batch group to spin for a short time (default 100 usec) using sched_yield, rather than going to sleep on a mutex. If the timing of the yield calls indicates that another thread has actually run during the yield then spinning is avoided. This option improves performance for concurrent situations even without parallel adds, although it has the potential to increase CPU usage (and the heuristic adaptation is not yet mature). Parallel writes are not currently compatible with inplace updates, update callbacks, or delete filtering. Enable it with --allow_concurrent_memtable_write (and --enable_write_thread_adaptive_yield). Parallel memtable writes are performance neutral when there is no actual parallelism, and in my experiments (SSD server-class Linux and varying contention and key sizes for fillrandom) they are always a performance win when there is more than one thread. Statistics are updated earlier in the write path, dropping the number of DB mutex acquisitions from 2 to 1 for almost all cases. This diff was motivated and inspired by Yahoo's cLSM work. It is more conservative than cLSM: RocksDB's write batch group leader role is preserved (along with all of the existing flush and write throttling logic) and concurrent writers are blocked until all memtable insertions have completed and the sequence number has been advanced, to preserve linearizability. My test config is "db_bench -benchmarks=fillrandom -threads=$T -batch_size=1 -memtablerep=skip_list -value_size=100 --num=1000000/$T -level0_slowdown_writes_trigger=9999 -level0_stop_writes_trigger=9999 -disable_auto_compactions --max_write_buffer_number=8 -max_background_flushes=8 --disable_wal --write_buffer_size=160000000 --block_size=16384 --allow_concurrent_memtable_write" on a two-socket Xeon E5-2660 @ 2.2Ghz with lots of memory and an SSD hard drive. With 1 thread I get ~440Kops/sec. Peak performance for 1 socket (numactl -N1) is slightly more than 1Mops/sec, at 16 threads. Peak performance across both sockets happens at 30 threads, and is ~900Kops/sec, although with fewer threads there is less performance loss when the system has background work. Test Plan: 1. concurrent stress tests for InlineSkipList and DynamicBloom 2. make clean; make check 3. make clean; DISABLE_JEMALLOC=1 make valgrind_check; valgrind db_bench 4. make clean; COMPILE_WITH_TSAN=1 make all check; db_bench 5. make clean; COMPILE_WITH_ASAN=1 make all check; db_bench 6. make clean; OPT=-DROCKSDB_LITE make check 7. verify no perf regressions when disabled Reviewers: igor, sdong Reviewed By: sdong Subscribers: MarkCallaghan, IslamAbdelRahman, anthony, yhchiang, rven, sdong, guyg8, kradhakrishnan, dhruba Differential Revision: https://reviews.facebook.net/D50589
2015-08-15 01:59:07 +02:00
for (uint32_t m = 1; m <= m_limit; ++m) {
Faster new DynamicBloom implementation (for memtable) (#5762) Summary: Since DynamicBloom is now only used in-memory, we're free to change it without schema compatibility issues. The new implementation is drawn from (with manifest permission) https://github.com/pdillinger/wormhashing/blob/303542a767437f56d8b66cea6ebecaac0e6a61e9/bloom_simulation_tests/foo.cc#L613 This has several speed advantages over the prior implementation: * Uses fastrange instead of % * Minimum logic to determine first (and all) probed memory addresses * (Major) Two probes per 64-bit memory fetch/write. * Very fast and effective (murmur-like) hash expansion/re-mixing. (At least on recent CPUs, integer multiplication is very cheap.) While a Bloom filter with 512-bit cache locality has about a 1.15x FP rate penalty (e.g. 0.84% to 0.97%), further restricting to two probes per 64 bits incurs an additional 1.12x FP rate penalty (e.g. 0.97% to 1.09%). Nevertheless, the unit tests show no "mediocre" FP rate samples, unlike the old implementation with more erratic FP rates. Especially for the memtable, we expect speed to outweigh somewhat higher FP rates. For example, a negative table query would have to be 1000x slower than a BF query to justify doubling BF query time to shave 10% off FP rate (working assumption around 1% FP rate). While that seems likely for SSTs, my data suggests a speed factor of roughly 50x for the memtable (vs. BF; ~1.5% lower write throughput when enabling memtable Bloom filter, after this change). Thus, it's probably not worth even 5% more time in the Bloom filter to shave off 1/10th of the Bloom FP rate, or 0.1% in absolute terms, and it's probably at least 20% slower to recoup that much FP rate from this new implementation. Because of this, we do not see a need for a 'locality' option that affects the MemTable Bloom filter and have decoupled the MemTable Bloom filter from Options::bloom_locality. Note that just 3% more memory to the Bloom filter (10.3 bits per key vs. just 10) is able to make up for the ~12% FP rate drop in the new implementation: [] # Nearly "ideal" FP-wise but reasonably fast cache-local implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out time: 3.29372 sampled_fp_rate: 0.00985956 ... [] # Close match to this new implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out 10000000 6 10.3 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.10072 sampled_fp_rate: 0.00985655 ... [] # Old locality=1 implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out time: 3.95472 sampled_fp_rate: 0.00988943 ... Also note the dramatic speed improvement vs. alternatives. -- Performance unit test: DynamicBloomTest.concurrent_with_perf is updated to report more precise timing data. (Measure running time of each thread, not just longest running thread, etc.) Results averaged over various sizes enabled with --enable_perf and 20 runs each; old dynamic bloom refers to locality=1, the faster of the old: old dynamic bloom, avg add latency = 65.6468 new dynamic bloom, avg add latency = 44.3809 old dynamic bloom, avg query latency = 50.6485 new dynamic bloom, avg query latency = 43.2186 old avg parallel add latency = 41.678 new avg parallel add latency = 24.5238 old avg parallel hit latency = 14.6322 new avg parallel hit latency = 12.3939 old avg parallel miss latency = 16.7289 new avg parallel miss latency = 12.2134 Tested on a dedicated 64-bit production machine at Facebook. Significant improvement all around. Despite now using std::atomic<uint64_t>, quick before-and-after test on a 32-bit machine (Intel Atom N270, released 2008) shows no regression in performance, in some cases modest improvement. -- Performance integration test (synthetic): with DEBUG_LEVEL=0, used TEST_TMPDIR=/dev/shm ./db_bench --benchmarks=fillrandom,readmissing,readrandom,stats --num=2000000 and optionally with -memtable_whole_key_filtering -memtable_bloom_size_ratio=0.01 300 runs each configuration. Write throughput change by enabling memtable bloom: Old locality=0: -3.06% Old locality=1: -2.37% New: -1.50% conclusion -> seems to substantially close the gap Readmissing throughput change by enabling memtable bloom: Old locality=0: +34.47% Old locality=1: +34.80% New: +33.25% conclusion -> maybe a small new penalty from FP rate Readrandom throughput change by enabling memtable bloom: Old locality=0: +31.54% Old locality=1: +31.13% New: +30.60% conclusion -> maybe also from FP rate (after memtable flush) -- Another conclusion we can draw from this new implementation is that the existing 32-bit hash function is not inherently crippling the Bloom filter speed or accuracy, below about 5 million keys. For speed, the implementation is essentially the same whether starting with 32-bits or 64-bits of hash; it just determines whether the first multiplication after fastrange is a pseudorandom expansion or needed re-mix. Note that this multiplication can occur while memory is fetching. For accuracy, in a standard configuration, you need about 5 million keys before you have about a 1.1x FP penalty due to using a 32-bit hash vs. 64-bit: [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.52069 sampled_fp_rate: 0.0118267 ... [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out time: 2.43871 sampled_fp_rate: 0.0109059 Pull Request resolved: https://github.com/facebook/rocksdb/pull/5762 Differential Revision: D17214194 Pulled By: pdillinger fbshipit-source-id: ad9da031772e985fd6b62a0e1db8e81892520595
2019-09-05 23:57:39 +02:00
Arena arena;
const uint32_t num_keys = m * 8 * 1024 * 1024;
fprintf(stderr, "testing %" PRIu32 "M keys\n", m * 8);
support for concurrent adds to memtable Summary: This diff adds support for concurrent adds to the skiplist memtable implementations. Memory allocation is made thread-safe by the addition of a spinlock, with small per-core buffers to avoid contention. Concurrent memtable writes are made via an additional method and don't impose a performance overhead on the non-concurrent case, so parallelism can be selected on a per-batch basis. Write thread synchronization is an increasing bottleneck for higher levels of concurrency, so this diff adds --enable_write_thread_adaptive_yield (default off). This feature causes threads joining a write batch group to spin for a short time (default 100 usec) using sched_yield, rather than going to sleep on a mutex. If the timing of the yield calls indicates that another thread has actually run during the yield then spinning is avoided. This option improves performance for concurrent situations even without parallel adds, although it has the potential to increase CPU usage (and the heuristic adaptation is not yet mature). Parallel writes are not currently compatible with inplace updates, update callbacks, or delete filtering. Enable it with --allow_concurrent_memtable_write (and --enable_write_thread_adaptive_yield). Parallel memtable writes are performance neutral when there is no actual parallelism, and in my experiments (SSD server-class Linux and varying contention and key sizes for fillrandom) they are always a performance win when there is more than one thread. Statistics are updated earlier in the write path, dropping the number of DB mutex acquisitions from 2 to 1 for almost all cases. This diff was motivated and inspired by Yahoo's cLSM work. It is more conservative than cLSM: RocksDB's write batch group leader role is preserved (along with all of the existing flush and write throttling logic) and concurrent writers are blocked until all memtable insertions have completed and the sequence number has been advanced, to preserve linearizability. My test config is "db_bench -benchmarks=fillrandom -threads=$T -batch_size=1 -memtablerep=skip_list -value_size=100 --num=1000000/$T -level0_slowdown_writes_trigger=9999 -level0_stop_writes_trigger=9999 -disable_auto_compactions --max_write_buffer_number=8 -max_background_flushes=8 --disable_wal --write_buffer_size=160000000 --block_size=16384 --allow_concurrent_memtable_write" on a two-socket Xeon E5-2660 @ 2.2Ghz with lots of memory and an SSD hard drive. With 1 thread I get ~440Kops/sec. Peak performance for 1 socket (numactl -N1) is slightly more than 1Mops/sec, at 16 threads. Peak performance across both sockets happens at 30 threads, and is ~900Kops/sec, although with fewer threads there is less performance loss when the system has background work. Test Plan: 1. concurrent stress tests for InlineSkipList and DynamicBloom 2. make clean; make check 3. make clean; DISABLE_JEMALLOC=1 make valgrind_check; valgrind db_bench 4. make clean; COMPILE_WITH_TSAN=1 make all check; db_bench 5. make clean; COMPILE_WITH_ASAN=1 make all check; db_bench 6. make clean; OPT=-DROCKSDB_LITE make check 7. verify no perf regressions when disabled Reviewers: igor, sdong Reviewed By: sdong Subscribers: MarkCallaghan, IslamAbdelRahman, anthony, yhchiang, rven, sdong, guyg8, kradhakrishnan, dhruba Differential Revision: https://reviews.facebook.net/D50589
2015-08-15 01:59:07 +02:00
Faster new DynamicBloom implementation (for memtable) (#5762) Summary: Since DynamicBloom is now only used in-memory, we're free to change it without schema compatibility issues. The new implementation is drawn from (with manifest permission) https://github.com/pdillinger/wormhashing/blob/303542a767437f56d8b66cea6ebecaac0e6a61e9/bloom_simulation_tests/foo.cc#L613 This has several speed advantages over the prior implementation: * Uses fastrange instead of % * Minimum logic to determine first (and all) probed memory addresses * (Major) Two probes per 64-bit memory fetch/write. * Very fast and effective (murmur-like) hash expansion/re-mixing. (At least on recent CPUs, integer multiplication is very cheap.) While a Bloom filter with 512-bit cache locality has about a 1.15x FP rate penalty (e.g. 0.84% to 0.97%), further restricting to two probes per 64 bits incurs an additional 1.12x FP rate penalty (e.g. 0.97% to 1.09%). Nevertheless, the unit tests show no "mediocre" FP rate samples, unlike the old implementation with more erratic FP rates. Especially for the memtable, we expect speed to outweigh somewhat higher FP rates. For example, a negative table query would have to be 1000x slower than a BF query to justify doubling BF query time to shave 10% off FP rate (working assumption around 1% FP rate). While that seems likely for SSTs, my data suggests a speed factor of roughly 50x for the memtable (vs. BF; ~1.5% lower write throughput when enabling memtable Bloom filter, after this change). Thus, it's probably not worth even 5% more time in the Bloom filter to shave off 1/10th of the Bloom FP rate, or 0.1% in absolute terms, and it's probably at least 20% slower to recoup that much FP rate from this new implementation. Because of this, we do not see a need for a 'locality' option that affects the MemTable Bloom filter and have decoupled the MemTable Bloom filter from Options::bloom_locality. Note that just 3% more memory to the Bloom filter (10.3 bits per key vs. just 10) is able to make up for the ~12% FP rate drop in the new implementation: [] # Nearly "ideal" FP-wise but reasonably fast cache-local implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out time: 3.29372 sampled_fp_rate: 0.00985956 ... [] # Close match to this new implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out 10000000 6 10.3 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.10072 sampled_fp_rate: 0.00985655 ... [] # Old locality=1 implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out time: 3.95472 sampled_fp_rate: 0.00988943 ... Also note the dramatic speed improvement vs. alternatives. -- Performance unit test: DynamicBloomTest.concurrent_with_perf is updated to report more precise timing data. (Measure running time of each thread, not just longest running thread, etc.) Results averaged over various sizes enabled with --enable_perf and 20 runs each; old dynamic bloom refers to locality=1, the faster of the old: old dynamic bloom, avg add latency = 65.6468 new dynamic bloom, avg add latency = 44.3809 old dynamic bloom, avg query latency = 50.6485 new dynamic bloom, avg query latency = 43.2186 old avg parallel add latency = 41.678 new avg parallel add latency = 24.5238 old avg parallel hit latency = 14.6322 new avg parallel hit latency = 12.3939 old avg parallel miss latency = 16.7289 new avg parallel miss latency = 12.2134 Tested on a dedicated 64-bit production machine at Facebook. Significant improvement all around. Despite now using std::atomic<uint64_t>, quick before-and-after test on a 32-bit machine (Intel Atom N270, released 2008) shows no regression in performance, in some cases modest improvement. -- Performance integration test (synthetic): with DEBUG_LEVEL=0, used TEST_TMPDIR=/dev/shm ./db_bench --benchmarks=fillrandom,readmissing,readrandom,stats --num=2000000 and optionally with -memtable_whole_key_filtering -memtable_bloom_size_ratio=0.01 300 runs each configuration. Write throughput change by enabling memtable bloom: Old locality=0: -3.06% Old locality=1: -2.37% New: -1.50% conclusion -> seems to substantially close the gap Readmissing throughput change by enabling memtable bloom: Old locality=0: +34.47% Old locality=1: +34.80% New: +33.25% conclusion -> maybe a small new penalty from FP rate Readrandom throughput change by enabling memtable bloom: Old locality=0: +31.54% Old locality=1: +31.13% New: +30.60% conclusion -> maybe also from FP rate (after memtable flush) -- Another conclusion we can draw from this new implementation is that the existing 32-bit hash function is not inherently crippling the Bloom filter speed or accuracy, below about 5 million keys. For speed, the implementation is essentially the same whether starting with 32-bits or 64-bits of hash; it just determines whether the first multiplication after fastrange is a pseudorandom expansion or needed re-mix. Note that this multiplication can occur while memory is fetching. For accuracy, in a standard configuration, you need about 5 million keys before you have about a 1.1x FP penalty due to using a 32-bit hash vs. 64-bit: [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.52069 sampled_fp_rate: 0.0118267 ... [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out time: 2.43871 sampled_fp_rate: 0.0109059 Pull Request resolved: https://github.com/facebook/rocksdb/pull/5762 Differential Revision: D17214194 Pulled By: pdillinger fbshipit-source-id: ad9da031772e985fd6b62a0e1db8e81892520595
2019-09-05 23:57:39 +02:00
DynamicBloom std_bloom(&arena, num_keys * 10, num_probes);
support for concurrent adds to memtable Summary: This diff adds support for concurrent adds to the skiplist memtable implementations. Memory allocation is made thread-safe by the addition of a spinlock, with small per-core buffers to avoid contention. Concurrent memtable writes are made via an additional method and don't impose a performance overhead on the non-concurrent case, so parallelism can be selected on a per-batch basis. Write thread synchronization is an increasing bottleneck for higher levels of concurrency, so this diff adds --enable_write_thread_adaptive_yield (default off). This feature causes threads joining a write batch group to spin for a short time (default 100 usec) using sched_yield, rather than going to sleep on a mutex. If the timing of the yield calls indicates that another thread has actually run during the yield then spinning is avoided. This option improves performance for concurrent situations even without parallel adds, although it has the potential to increase CPU usage (and the heuristic adaptation is not yet mature). Parallel writes are not currently compatible with inplace updates, update callbacks, or delete filtering. Enable it with --allow_concurrent_memtable_write (and --enable_write_thread_adaptive_yield). Parallel memtable writes are performance neutral when there is no actual parallelism, and in my experiments (SSD server-class Linux and varying contention and key sizes for fillrandom) they are always a performance win when there is more than one thread. Statistics are updated earlier in the write path, dropping the number of DB mutex acquisitions from 2 to 1 for almost all cases. This diff was motivated and inspired by Yahoo's cLSM work. It is more conservative than cLSM: RocksDB's write batch group leader role is preserved (along with all of the existing flush and write throttling logic) and concurrent writers are blocked until all memtable insertions have completed and the sequence number has been advanced, to preserve linearizability. My test config is "db_bench -benchmarks=fillrandom -threads=$T -batch_size=1 -memtablerep=skip_list -value_size=100 --num=1000000/$T -level0_slowdown_writes_trigger=9999 -level0_stop_writes_trigger=9999 -disable_auto_compactions --max_write_buffer_number=8 -max_background_flushes=8 --disable_wal --write_buffer_size=160000000 --block_size=16384 --allow_concurrent_memtable_write" on a two-socket Xeon E5-2660 @ 2.2Ghz with lots of memory and an SSD hard drive. With 1 thread I get ~440Kops/sec. Peak performance for 1 socket (numactl -N1) is slightly more than 1Mops/sec, at 16 threads. Peak performance across both sockets happens at 30 threads, and is ~900Kops/sec, although with fewer threads there is less performance loss when the system has background work. Test Plan: 1. concurrent stress tests for InlineSkipList and DynamicBloom 2. make clean; make check 3. make clean; DISABLE_JEMALLOC=1 make valgrind_check; valgrind db_bench 4. make clean; COMPILE_WITH_TSAN=1 make all check; db_bench 5. make clean; COMPILE_WITH_ASAN=1 make all check; db_bench 6. make clean; OPT=-DROCKSDB_LITE make check 7. verify no perf regressions when disabled Reviewers: igor, sdong Reviewed By: sdong Subscribers: MarkCallaghan, IslamAbdelRahman, anthony, yhchiang, rven, sdong, guyg8, kradhakrishnan, dhruba Differential Revision: https://reviews.facebook.net/D50589
2015-08-15 01:59:07 +02:00
Faster new DynamicBloom implementation (for memtable) (#5762) Summary: Since DynamicBloom is now only used in-memory, we're free to change it without schema compatibility issues. The new implementation is drawn from (with manifest permission) https://github.com/pdillinger/wormhashing/blob/303542a767437f56d8b66cea6ebecaac0e6a61e9/bloom_simulation_tests/foo.cc#L613 This has several speed advantages over the prior implementation: * Uses fastrange instead of % * Minimum logic to determine first (and all) probed memory addresses * (Major) Two probes per 64-bit memory fetch/write. * Very fast and effective (murmur-like) hash expansion/re-mixing. (At least on recent CPUs, integer multiplication is very cheap.) While a Bloom filter with 512-bit cache locality has about a 1.15x FP rate penalty (e.g. 0.84% to 0.97%), further restricting to two probes per 64 bits incurs an additional 1.12x FP rate penalty (e.g. 0.97% to 1.09%). Nevertheless, the unit tests show no "mediocre" FP rate samples, unlike the old implementation with more erratic FP rates. Especially for the memtable, we expect speed to outweigh somewhat higher FP rates. For example, a negative table query would have to be 1000x slower than a BF query to justify doubling BF query time to shave 10% off FP rate (working assumption around 1% FP rate). While that seems likely for SSTs, my data suggests a speed factor of roughly 50x for the memtable (vs. BF; ~1.5% lower write throughput when enabling memtable Bloom filter, after this change). Thus, it's probably not worth even 5% more time in the Bloom filter to shave off 1/10th of the Bloom FP rate, or 0.1% in absolute terms, and it's probably at least 20% slower to recoup that much FP rate from this new implementation. Because of this, we do not see a need for a 'locality' option that affects the MemTable Bloom filter and have decoupled the MemTable Bloom filter from Options::bloom_locality. Note that just 3% more memory to the Bloom filter (10.3 bits per key vs. just 10) is able to make up for the ~12% FP rate drop in the new implementation: [] # Nearly "ideal" FP-wise but reasonably fast cache-local implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out time: 3.29372 sampled_fp_rate: 0.00985956 ... [] # Close match to this new implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out 10000000 6 10.3 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.10072 sampled_fp_rate: 0.00985655 ... [] # Old locality=1 implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out time: 3.95472 sampled_fp_rate: 0.00988943 ... Also note the dramatic speed improvement vs. alternatives. -- Performance unit test: DynamicBloomTest.concurrent_with_perf is updated to report more precise timing data. (Measure running time of each thread, not just longest running thread, etc.) Results averaged over various sizes enabled with --enable_perf and 20 runs each; old dynamic bloom refers to locality=1, the faster of the old: old dynamic bloom, avg add latency = 65.6468 new dynamic bloom, avg add latency = 44.3809 old dynamic bloom, avg query latency = 50.6485 new dynamic bloom, avg query latency = 43.2186 old avg parallel add latency = 41.678 new avg parallel add latency = 24.5238 old avg parallel hit latency = 14.6322 new avg parallel hit latency = 12.3939 old avg parallel miss latency = 16.7289 new avg parallel miss latency = 12.2134 Tested on a dedicated 64-bit production machine at Facebook. Significant improvement all around. Despite now using std::atomic<uint64_t>, quick before-and-after test on a 32-bit machine (Intel Atom N270, released 2008) shows no regression in performance, in some cases modest improvement. -- Performance integration test (synthetic): with DEBUG_LEVEL=0, used TEST_TMPDIR=/dev/shm ./db_bench --benchmarks=fillrandom,readmissing,readrandom,stats --num=2000000 and optionally with -memtable_whole_key_filtering -memtable_bloom_size_ratio=0.01 300 runs each configuration. Write throughput change by enabling memtable bloom: Old locality=0: -3.06% Old locality=1: -2.37% New: -1.50% conclusion -> seems to substantially close the gap Readmissing throughput change by enabling memtable bloom: Old locality=0: +34.47% Old locality=1: +34.80% New: +33.25% conclusion -> maybe a small new penalty from FP rate Readrandom throughput change by enabling memtable bloom: Old locality=0: +31.54% Old locality=1: +31.13% New: +30.60% conclusion -> maybe also from FP rate (after memtable flush) -- Another conclusion we can draw from this new implementation is that the existing 32-bit hash function is not inherently crippling the Bloom filter speed or accuracy, below about 5 million keys. For speed, the implementation is essentially the same whether starting with 32-bits or 64-bits of hash; it just determines whether the first multiplication after fastrange is a pseudorandom expansion or needed re-mix. Note that this multiplication can occur while memory is fetching. For accuracy, in a standard configuration, you need about 5 million keys before you have about a 1.1x FP penalty due to using a 32-bit hash vs. 64-bit: [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.52069 sampled_fp_rate: 0.0118267 ... [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out time: 2.43871 sampled_fp_rate: 0.0109059 Pull Request resolved: https://github.com/facebook/rocksdb/pull/5762 Differential Revision: D17214194 Pulled By: pdillinger fbshipit-source-id: ad9da031772e985fd6b62a0e1db8e81892520595
2019-09-05 23:57:39 +02:00
std::atomic<uint64_t> elapsed(0);
support for concurrent adds to memtable Summary: This diff adds support for concurrent adds to the skiplist memtable implementations. Memory allocation is made thread-safe by the addition of a spinlock, with small per-core buffers to avoid contention. Concurrent memtable writes are made via an additional method and don't impose a performance overhead on the non-concurrent case, so parallelism can be selected on a per-batch basis. Write thread synchronization is an increasing bottleneck for higher levels of concurrency, so this diff adds --enable_write_thread_adaptive_yield (default off). This feature causes threads joining a write batch group to spin for a short time (default 100 usec) using sched_yield, rather than going to sleep on a mutex. If the timing of the yield calls indicates that another thread has actually run during the yield then spinning is avoided. This option improves performance for concurrent situations even without parallel adds, although it has the potential to increase CPU usage (and the heuristic adaptation is not yet mature). Parallel writes are not currently compatible with inplace updates, update callbacks, or delete filtering. Enable it with --allow_concurrent_memtable_write (and --enable_write_thread_adaptive_yield). Parallel memtable writes are performance neutral when there is no actual parallelism, and in my experiments (SSD server-class Linux and varying contention and key sizes for fillrandom) they are always a performance win when there is more than one thread. Statistics are updated earlier in the write path, dropping the number of DB mutex acquisitions from 2 to 1 for almost all cases. This diff was motivated and inspired by Yahoo's cLSM work. It is more conservative than cLSM: RocksDB's write batch group leader role is preserved (along with all of the existing flush and write throttling logic) and concurrent writers are blocked until all memtable insertions have completed and the sequence number has been advanced, to preserve linearizability. My test config is "db_bench -benchmarks=fillrandom -threads=$T -batch_size=1 -memtablerep=skip_list -value_size=100 --num=1000000/$T -level0_slowdown_writes_trigger=9999 -level0_stop_writes_trigger=9999 -disable_auto_compactions --max_write_buffer_number=8 -max_background_flushes=8 --disable_wal --write_buffer_size=160000000 --block_size=16384 --allow_concurrent_memtable_write" on a two-socket Xeon E5-2660 @ 2.2Ghz with lots of memory and an SSD hard drive. With 1 thread I get ~440Kops/sec. Peak performance for 1 socket (numactl -N1) is slightly more than 1Mops/sec, at 16 threads. Peak performance across both sockets happens at 30 threads, and is ~900Kops/sec, although with fewer threads there is less performance loss when the system has background work. Test Plan: 1. concurrent stress tests for InlineSkipList and DynamicBloom 2. make clean; make check 3. make clean; DISABLE_JEMALLOC=1 make valgrind_check; valgrind db_bench 4. make clean; COMPILE_WITH_TSAN=1 make all check; db_bench 5. make clean; COMPILE_WITH_ASAN=1 make all check; db_bench 6. make clean; OPT=-DROCKSDB_LITE make check 7. verify no perf regressions when disabled Reviewers: igor, sdong Reviewed By: sdong Subscribers: MarkCallaghan, IslamAbdelRahman, anthony, yhchiang, rven, sdong, guyg8, kradhakrishnan, dhruba Differential Revision: https://reviews.facebook.net/D50589
2015-08-15 01:59:07 +02:00
Faster new DynamicBloom implementation (for memtable) (#5762) Summary: Since DynamicBloom is now only used in-memory, we're free to change it without schema compatibility issues. The new implementation is drawn from (with manifest permission) https://github.com/pdillinger/wormhashing/blob/303542a767437f56d8b66cea6ebecaac0e6a61e9/bloom_simulation_tests/foo.cc#L613 This has several speed advantages over the prior implementation: * Uses fastrange instead of % * Minimum logic to determine first (and all) probed memory addresses * (Major) Two probes per 64-bit memory fetch/write. * Very fast and effective (murmur-like) hash expansion/re-mixing. (At least on recent CPUs, integer multiplication is very cheap.) While a Bloom filter with 512-bit cache locality has about a 1.15x FP rate penalty (e.g. 0.84% to 0.97%), further restricting to two probes per 64 bits incurs an additional 1.12x FP rate penalty (e.g. 0.97% to 1.09%). Nevertheless, the unit tests show no "mediocre" FP rate samples, unlike the old implementation with more erratic FP rates. Especially for the memtable, we expect speed to outweigh somewhat higher FP rates. For example, a negative table query would have to be 1000x slower than a BF query to justify doubling BF query time to shave 10% off FP rate (working assumption around 1% FP rate). While that seems likely for SSTs, my data suggests a speed factor of roughly 50x for the memtable (vs. BF; ~1.5% lower write throughput when enabling memtable Bloom filter, after this change). Thus, it's probably not worth even 5% more time in the Bloom filter to shave off 1/10th of the Bloom FP rate, or 0.1% in absolute terms, and it's probably at least 20% slower to recoup that much FP rate from this new implementation. Because of this, we do not see a need for a 'locality' option that affects the MemTable Bloom filter and have decoupled the MemTable Bloom filter from Options::bloom_locality. Note that just 3% more memory to the Bloom filter (10.3 bits per key vs. just 10) is able to make up for the ~12% FP rate drop in the new implementation: [] # Nearly "ideal" FP-wise but reasonably fast cache-local implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out time: 3.29372 sampled_fp_rate: 0.00985956 ... [] # Close match to this new implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out 10000000 6 10.3 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.10072 sampled_fp_rate: 0.00985655 ... [] # Old locality=1 implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out time: 3.95472 sampled_fp_rate: 0.00988943 ... Also note the dramatic speed improvement vs. alternatives. -- Performance unit test: DynamicBloomTest.concurrent_with_perf is updated to report more precise timing data. (Measure running time of each thread, not just longest running thread, etc.) Results averaged over various sizes enabled with --enable_perf and 20 runs each; old dynamic bloom refers to locality=1, the faster of the old: old dynamic bloom, avg add latency = 65.6468 new dynamic bloom, avg add latency = 44.3809 old dynamic bloom, avg query latency = 50.6485 new dynamic bloom, avg query latency = 43.2186 old avg parallel add latency = 41.678 new avg parallel add latency = 24.5238 old avg parallel hit latency = 14.6322 new avg parallel hit latency = 12.3939 old avg parallel miss latency = 16.7289 new avg parallel miss latency = 12.2134 Tested on a dedicated 64-bit production machine at Facebook. Significant improvement all around. Despite now using std::atomic<uint64_t>, quick before-and-after test on a 32-bit machine (Intel Atom N270, released 2008) shows no regression in performance, in some cases modest improvement. -- Performance integration test (synthetic): with DEBUG_LEVEL=0, used TEST_TMPDIR=/dev/shm ./db_bench --benchmarks=fillrandom,readmissing,readrandom,stats --num=2000000 and optionally with -memtable_whole_key_filtering -memtable_bloom_size_ratio=0.01 300 runs each configuration. Write throughput change by enabling memtable bloom: Old locality=0: -3.06% Old locality=1: -2.37% New: -1.50% conclusion -> seems to substantially close the gap Readmissing throughput change by enabling memtable bloom: Old locality=0: +34.47% Old locality=1: +34.80% New: +33.25% conclusion -> maybe a small new penalty from FP rate Readrandom throughput change by enabling memtable bloom: Old locality=0: +31.54% Old locality=1: +31.13% New: +30.60% conclusion -> maybe also from FP rate (after memtable flush) -- Another conclusion we can draw from this new implementation is that the existing 32-bit hash function is not inherently crippling the Bloom filter speed or accuracy, below about 5 million keys. For speed, the implementation is essentially the same whether starting with 32-bits or 64-bits of hash; it just determines whether the first multiplication after fastrange is a pseudorandom expansion or needed re-mix. Note that this multiplication can occur while memory is fetching. For accuracy, in a standard configuration, you need about 5 million keys before you have about a 1.1x FP penalty due to using a 32-bit hash vs. 64-bit: [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.52069 sampled_fp_rate: 0.0118267 ... [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out time: 2.43871 sampled_fp_rate: 0.0109059 Pull Request resolved: https://github.com/facebook/rocksdb/pull/5762 Differential Revision: D17214194 Pulled By: pdillinger fbshipit-source-id: ad9da031772e985fd6b62a0e1db8e81892520595
2019-09-05 23:57:39 +02:00
std::function<void(size_t)> adder([&](size_t t) {
KeyMaker km;
StopWatchNano timer(SystemClock::Default().get());
Faster new DynamicBloom implementation (for memtable) (#5762) Summary: Since DynamicBloom is now only used in-memory, we're free to change it without schema compatibility issues. The new implementation is drawn from (with manifest permission) https://github.com/pdillinger/wormhashing/blob/303542a767437f56d8b66cea6ebecaac0e6a61e9/bloom_simulation_tests/foo.cc#L613 This has several speed advantages over the prior implementation: * Uses fastrange instead of % * Minimum logic to determine first (and all) probed memory addresses * (Major) Two probes per 64-bit memory fetch/write. * Very fast and effective (murmur-like) hash expansion/re-mixing. (At least on recent CPUs, integer multiplication is very cheap.) While a Bloom filter with 512-bit cache locality has about a 1.15x FP rate penalty (e.g. 0.84% to 0.97%), further restricting to two probes per 64 bits incurs an additional 1.12x FP rate penalty (e.g. 0.97% to 1.09%). Nevertheless, the unit tests show no "mediocre" FP rate samples, unlike the old implementation with more erratic FP rates. Especially for the memtable, we expect speed to outweigh somewhat higher FP rates. For example, a negative table query would have to be 1000x slower than a BF query to justify doubling BF query time to shave 10% off FP rate (working assumption around 1% FP rate). While that seems likely for SSTs, my data suggests a speed factor of roughly 50x for the memtable (vs. BF; ~1.5% lower write throughput when enabling memtable Bloom filter, after this change). Thus, it's probably not worth even 5% more time in the Bloom filter to shave off 1/10th of the Bloom FP rate, or 0.1% in absolute terms, and it's probably at least 20% slower to recoup that much FP rate from this new implementation. Because of this, we do not see a need for a 'locality' option that affects the MemTable Bloom filter and have decoupled the MemTable Bloom filter from Options::bloom_locality. Note that just 3% more memory to the Bloom filter (10.3 bits per key vs. just 10) is able to make up for the ~12% FP rate drop in the new implementation: [] # Nearly "ideal" FP-wise but reasonably fast cache-local implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out time: 3.29372 sampled_fp_rate: 0.00985956 ... [] # Close match to this new implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out 10000000 6 10.3 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.10072 sampled_fp_rate: 0.00985655 ... [] # Old locality=1 implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out time: 3.95472 sampled_fp_rate: 0.00988943 ... Also note the dramatic speed improvement vs. alternatives. -- Performance unit test: DynamicBloomTest.concurrent_with_perf is updated to report more precise timing data. (Measure running time of each thread, not just longest running thread, etc.) Results averaged over various sizes enabled with --enable_perf and 20 runs each; old dynamic bloom refers to locality=1, the faster of the old: old dynamic bloom, avg add latency = 65.6468 new dynamic bloom, avg add latency = 44.3809 old dynamic bloom, avg query latency = 50.6485 new dynamic bloom, avg query latency = 43.2186 old avg parallel add latency = 41.678 new avg parallel add latency = 24.5238 old avg parallel hit latency = 14.6322 new avg parallel hit latency = 12.3939 old avg parallel miss latency = 16.7289 new avg parallel miss latency = 12.2134 Tested on a dedicated 64-bit production machine at Facebook. Significant improvement all around. Despite now using std::atomic<uint64_t>, quick before-and-after test on a 32-bit machine (Intel Atom N270, released 2008) shows no regression in performance, in some cases modest improvement. -- Performance integration test (synthetic): with DEBUG_LEVEL=0, used TEST_TMPDIR=/dev/shm ./db_bench --benchmarks=fillrandom,readmissing,readrandom,stats --num=2000000 and optionally with -memtable_whole_key_filtering -memtable_bloom_size_ratio=0.01 300 runs each configuration. Write throughput change by enabling memtable bloom: Old locality=0: -3.06% Old locality=1: -2.37% New: -1.50% conclusion -> seems to substantially close the gap Readmissing throughput change by enabling memtable bloom: Old locality=0: +34.47% Old locality=1: +34.80% New: +33.25% conclusion -> maybe a small new penalty from FP rate Readrandom throughput change by enabling memtable bloom: Old locality=0: +31.54% Old locality=1: +31.13% New: +30.60% conclusion -> maybe also from FP rate (after memtable flush) -- Another conclusion we can draw from this new implementation is that the existing 32-bit hash function is not inherently crippling the Bloom filter speed or accuracy, below about 5 million keys. For speed, the implementation is essentially the same whether starting with 32-bits or 64-bits of hash; it just determines whether the first multiplication after fastrange is a pseudorandom expansion or needed re-mix. Note that this multiplication can occur while memory is fetching. For accuracy, in a standard configuration, you need about 5 million keys before you have about a 1.1x FP penalty due to using a 32-bit hash vs. 64-bit: [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.52069 sampled_fp_rate: 0.0118267 ... [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out time: 2.43871 sampled_fp_rate: 0.0109059 Pull Request resolved: https://github.com/facebook/rocksdb/pull/5762 Differential Revision: D17214194 Pulled By: pdillinger fbshipit-source-id: ad9da031772e985fd6b62a0e1db8e81892520595
2019-09-05 23:57:39 +02:00
timer.Start();
for (uint64_t i = 1 + t; i <= num_keys; i += num_threads) {
std_bloom.AddConcurrently(km.Seq(i));
support for concurrent adds to memtable Summary: This diff adds support for concurrent adds to the skiplist memtable implementations. Memory allocation is made thread-safe by the addition of a spinlock, with small per-core buffers to avoid contention. Concurrent memtable writes are made via an additional method and don't impose a performance overhead on the non-concurrent case, so parallelism can be selected on a per-batch basis. Write thread synchronization is an increasing bottleneck for higher levels of concurrency, so this diff adds --enable_write_thread_adaptive_yield (default off). This feature causes threads joining a write batch group to spin for a short time (default 100 usec) using sched_yield, rather than going to sleep on a mutex. If the timing of the yield calls indicates that another thread has actually run during the yield then spinning is avoided. This option improves performance for concurrent situations even without parallel adds, although it has the potential to increase CPU usage (and the heuristic adaptation is not yet mature). Parallel writes are not currently compatible with inplace updates, update callbacks, or delete filtering. Enable it with --allow_concurrent_memtable_write (and --enable_write_thread_adaptive_yield). Parallel memtable writes are performance neutral when there is no actual parallelism, and in my experiments (SSD server-class Linux and varying contention and key sizes for fillrandom) they are always a performance win when there is more than one thread. Statistics are updated earlier in the write path, dropping the number of DB mutex acquisitions from 2 to 1 for almost all cases. This diff was motivated and inspired by Yahoo's cLSM work. It is more conservative than cLSM: RocksDB's write batch group leader role is preserved (along with all of the existing flush and write throttling logic) and concurrent writers are blocked until all memtable insertions have completed and the sequence number has been advanced, to preserve linearizability. My test config is "db_bench -benchmarks=fillrandom -threads=$T -batch_size=1 -memtablerep=skip_list -value_size=100 --num=1000000/$T -level0_slowdown_writes_trigger=9999 -level0_stop_writes_trigger=9999 -disable_auto_compactions --max_write_buffer_number=8 -max_background_flushes=8 --disable_wal --write_buffer_size=160000000 --block_size=16384 --allow_concurrent_memtable_write" on a two-socket Xeon E5-2660 @ 2.2Ghz with lots of memory and an SSD hard drive. With 1 thread I get ~440Kops/sec. Peak performance for 1 socket (numactl -N1) is slightly more than 1Mops/sec, at 16 threads. Peak performance across both sockets happens at 30 threads, and is ~900Kops/sec, although with fewer threads there is less performance loss when the system has background work. Test Plan: 1. concurrent stress tests for InlineSkipList and DynamicBloom 2. make clean; make check 3. make clean; DISABLE_JEMALLOC=1 make valgrind_check; valgrind db_bench 4. make clean; COMPILE_WITH_TSAN=1 make all check; db_bench 5. make clean; COMPILE_WITH_ASAN=1 make all check; db_bench 6. make clean; OPT=-DROCKSDB_LITE make check 7. verify no perf regressions when disabled Reviewers: igor, sdong Reviewed By: sdong Subscribers: MarkCallaghan, IslamAbdelRahman, anthony, yhchiang, rven, sdong, guyg8, kradhakrishnan, dhruba Differential Revision: https://reviews.facebook.net/D50589
2015-08-15 01:59:07 +02:00
}
Faster new DynamicBloom implementation (for memtable) (#5762) Summary: Since DynamicBloom is now only used in-memory, we're free to change it without schema compatibility issues. The new implementation is drawn from (with manifest permission) https://github.com/pdillinger/wormhashing/blob/303542a767437f56d8b66cea6ebecaac0e6a61e9/bloom_simulation_tests/foo.cc#L613 This has several speed advantages over the prior implementation: * Uses fastrange instead of % * Minimum logic to determine first (and all) probed memory addresses * (Major) Two probes per 64-bit memory fetch/write. * Very fast and effective (murmur-like) hash expansion/re-mixing. (At least on recent CPUs, integer multiplication is very cheap.) While a Bloom filter with 512-bit cache locality has about a 1.15x FP rate penalty (e.g. 0.84% to 0.97%), further restricting to two probes per 64 bits incurs an additional 1.12x FP rate penalty (e.g. 0.97% to 1.09%). Nevertheless, the unit tests show no "mediocre" FP rate samples, unlike the old implementation with more erratic FP rates. Especially for the memtable, we expect speed to outweigh somewhat higher FP rates. For example, a negative table query would have to be 1000x slower than a BF query to justify doubling BF query time to shave 10% off FP rate (working assumption around 1% FP rate). While that seems likely for SSTs, my data suggests a speed factor of roughly 50x for the memtable (vs. BF; ~1.5% lower write throughput when enabling memtable Bloom filter, after this change). Thus, it's probably not worth even 5% more time in the Bloom filter to shave off 1/10th of the Bloom FP rate, or 0.1% in absolute terms, and it's probably at least 20% slower to recoup that much FP rate from this new implementation. Because of this, we do not see a need for a 'locality' option that affects the MemTable Bloom filter and have decoupled the MemTable Bloom filter from Options::bloom_locality. Note that just 3% more memory to the Bloom filter (10.3 bits per key vs. just 10) is able to make up for the ~12% FP rate drop in the new implementation: [] # Nearly "ideal" FP-wise but reasonably fast cache-local implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out time: 3.29372 sampled_fp_rate: 0.00985956 ... [] # Close match to this new implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out 10000000 6 10.3 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.10072 sampled_fp_rate: 0.00985655 ... [] # Old locality=1 implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out time: 3.95472 sampled_fp_rate: 0.00988943 ... Also note the dramatic speed improvement vs. alternatives. -- Performance unit test: DynamicBloomTest.concurrent_with_perf is updated to report more precise timing data. (Measure running time of each thread, not just longest running thread, etc.) Results averaged over various sizes enabled with --enable_perf and 20 runs each; old dynamic bloom refers to locality=1, the faster of the old: old dynamic bloom, avg add latency = 65.6468 new dynamic bloom, avg add latency = 44.3809 old dynamic bloom, avg query latency = 50.6485 new dynamic bloom, avg query latency = 43.2186 old avg parallel add latency = 41.678 new avg parallel add latency = 24.5238 old avg parallel hit latency = 14.6322 new avg parallel hit latency = 12.3939 old avg parallel miss latency = 16.7289 new avg parallel miss latency = 12.2134 Tested on a dedicated 64-bit production machine at Facebook. Significant improvement all around. Despite now using std::atomic<uint64_t>, quick before-and-after test on a 32-bit machine (Intel Atom N270, released 2008) shows no regression in performance, in some cases modest improvement. -- Performance integration test (synthetic): with DEBUG_LEVEL=0, used TEST_TMPDIR=/dev/shm ./db_bench --benchmarks=fillrandom,readmissing,readrandom,stats --num=2000000 and optionally with -memtable_whole_key_filtering -memtable_bloom_size_ratio=0.01 300 runs each configuration. Write throughput change by enabling memtable bloom: Old locality=0: -3.06% Old locality=1: -2.37% New: -1.50% conclusion -> seems to substantially close the gap Readmissing throughput change by enabling memtable bloom: Old locality=0: +34.47% Old locality=1: +34.80% New: +33.25% conclusion -> maybe a small new penalty from FP rate Readrandom throughput change by enabling memtable bloom: Old locality=0: +31.54% Old locality=1: +31.13% New: +30.60% conclusion -> maybe also from FP rate (after memtable flush) -- Another conclusion we can draw from this new implementation is that the existing 32-bit hash function is not inherently crippling the Bloom filter speed or accuracy, below about 5 million keys. For speed, the implementation is essentially the same whether starting with 32-bits or 64-bits of hash; it just determines whether the first multiplication after fastrange is a pseudorandom expansion or needed re-mix. Note that this multiplication can occur while memory is fetching. For accuracy, in a standard configuration, you need about 5 million keys before you have about a 1.1x FP penalty due to using a 32-bit hash vs. 64-bit: [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.52069 sampled_fp_rate: 0.0118267 ... [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out time: 2.43871 sampled_fp_rate: 0.0109059 Pull Request resolved: https://github.com/facebook/rocksdb/pull/5762 Differential Revision: D17214194 Pulled By: pdillinger fbshipit-source-id: ad9da031772e985fd6b62a0e1db8e81892520595
2019-09-05 23:57:39 +02:00
elapsed += timer.ElapsedNanos();
});
for (size_t t = 0; t < num_threads; ++t) {
threads.emplace_back(adder, t);
}
while (threads.size() > 0) {
threads.back().join();
threads.pop_back();
}
support for concurrent adds to memtable Summary: This diff adds support for concurrent adds to the skiplist memtable implementations. Memory allocation is made thread-safe by the addition of a spinlock, with small per-core buffers to avoid contention. Concurrent memtable writes are made via an additional method and don't impose a performance overhead on the non-concurrent case, so parallelism can be selected on a per-batch basis. Write thread synchronization is an increasing bottleneck for higher levels of concurrency, so this diff adds --enable_write_thread_adaptive_yield (default off). This feature causes threads joining a write batch group to spin for a short time (default 100 usec) using sched_yield, rather than going to sleep on a mutex. If the timing of the yield calls indicates that another thread has actually run during the yield then spinning is avoided. This option improves performance for concurrent situations even without parallel adds, although it has the potential to increase CPU usage (and the heuristic adaptation is not yet mature). Parallel writes are not currently compatible with inplace updates, update callbacks, or delete filtering. Enable it with --allow_concurrent_memtable_write (and --enable_write_thread_adaptive_yield). Parallel memtable writes are performance neutral when there is no actual parallelism, and in my experiments (SSD server-class Linux and varying contention and key sizes for fillrandom) they are always a performance win when there is more than one thread. Statistics are updated earlier in the write path, dropping the number of DB mutex acquisitions from 2 to 1 for almost all cases. This diff was motivated and inspired by Yahoo's cLSM work. It is more conservative than cLSM: RocksDB's write batch group leader role is preserved (along with all of the existing flush and write throttling logic) and concurrent writers are blocked until all memtable insertions have completed and the sequence number has been advanced, to preserve linearizability. My test config is "db_bench -benchmarks=fillrandom -threads=$T -batch_size=1 -memtablerep=skip_list -value_size=100 --num=1000000/$T -level0_slowdown_writes_trigger=9999 -level0_stop_writes_trigger=9999 -disable_auto_compactions --max_write_buffer_number=8 -max_background_flushes=8 --disable_wal --write_buffer_size=160000000 --block_size=16384 --allow_concurrent_memtable_write" on a two-socket Xeon E5-2660 @ 2.2Ghz with lots of memory and an SSD hard drive. With 1 thread I get ~440Kops/sec. Peak performance for 1 socket (numactl -N1) is slightly more than 1Mops/sec, at 16 threads. Peak performance across both sockets happens at 30 threads, and is ~900Kops/sec, although with fewer threads there is less performance loss when the system has background work. Test Plan: 1. concurrent stress tests for InlineSkipList and DynamicBloom 2. make clean; make check 3. make clean; DISABLE_JEMALLOC=1 make valgrind_check; valgrind db_bench 4. make clean; COMPILE_WITH_TSAN=1 make all check; db_bench 5. make clean; COMPILE_WITH_ASAN=1 make all check; db_bench 6. make clean; OPT=-DROCKSDB_LITE make check 7. verify no perf regressions when disabled Reviewers: igor, sdong Reviewed By: sdong Subscribers: MarkCallaghan, IslamAbdelRahman, anthony, yhchiang, rven, sdong, guyg8, kradhakrishnan, dhruba Differential Revision: https://reviews.facebook.net/D50589
2015-08-15 01:59:07 +02:00
fprintf(stderr,
"dynamic bloom, avg parallel add latency %3g"
" nanos/key\n",
Faster new DynamicBloom implementation (for memtable) (#5762) Summary: Since DynamicBloom is now only used in-memory, we're free to change it without schema compatibility issues. The new implementation is drawn from (with manifest permission) https://github.com/pdillinger/wormhashing/blob/303542a767437f56d8b66cea6ebecaac0e6a61e9/bloom_simulation_tests/foo.cc#L613 This has several speed advantages over the prior implementation: * Uses fastrange instead of % * Minimum logic to determine first (and all) probed memory addresses * (Major) Two probes per 64-bit memory fetch/write. * Very fast and effective (murmur-like) hash expansion/re-mixing. (At least on recent CPUs, integer multiplication is very cheap.) While a Bloom filter with 512-bit cache locality has about a 1.15x FP rate penalty (e.g. 0.84% to 0.97%), further restricting to two probes per 64 bits incurs an additional 1.12x FP rate penalty (e.g. 0.97% to 1.09%). Nevertheless, the unit tests show no "mediocre" FP rate samples, unlike the old implementation with more erratic FP rates. Especially for the memtable, we expect speed to outweigh somewhat higher FP rates. For example, a negative table query would have to be 1000x slower than a BF query to justify doubling BF query time to shave 10% off FP rate (working assumption around 1% FP rate). While that seems likely for SSTs, my data suggests a speed factor of roughly 50x for the memtable (vs. BF; ~1.5% lower write throughput when enabling memtable Bloom filter, after this change). Thus, it's probably not worth even 5% more time in the Bloom filter to shave off 1/10th of the Bloom FP rate, or 0.1% in absolute terms, and it's probably at least 20% slower to recoup that much FP rate from this new implementation. Because of this, we do not see a need for a 'locality' option that affects the MemTable Bloom filter and have decoupled the MemTable Bloom filter from Options::bloom_locality. Note that just 3% more memory to the Bloom filter (10.3 bits per key vs. just 10) is able to make up for the ~12% FP rate drop in the new implementation: [] # Nearly "ideal" FP-wise but reasonably fast cache-local implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out time: 3.29372 sampled_fp_rate: 0.00985956 ... [] # Close match to this new implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out 10000000 6 10.3 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.10072 sampled_fp_rate: 0.00985655 ... [] # Old locality=1 implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out time: 3.95472 sampled_fp_rate: 0.00988943 ... Also note the dramatic speed improvement vs. alternatives. -- Performance unit test: DynamicBloomTest.concurrent_with_perf is updated to report more precise timing data. (Measure running time of each thread, not just longest running thread, etc.) Results averaged over various sizes enabled with --enable_perf and 20 runs each; old dynamic bloom refers to locality=1, the faster of the old: old dynamic bloom, avg add latency = 65.6468 new dynamic bloom, avg add latency = 44.3809 old dynamic bloom, avg query latency = 50.6485 new dynamic bloom, avg query latency = 43.2186 old avg parallel add latency = 41.678 new avg parallel add latency = 24.5238 old avg parallel hit latency = 14.6322 new avg parallel hit latency = 12.3939 old avg parallel miss latency = 16.7289 new avg parallel miss latency = 12.2134 Tested on a dedicated 64-bit production machine at Facebook. Significant improvement all around. Despite now using std::atomic<uint64_t>, quick before-and-after test on a 32-bit machine (Intel Atom N270, released 2008) shows no regression in performance, in some cases modest improvement. -- Performance integration test (synthetic): with DEBUG_LEVEL=0, used TEST_TMPDIR=/dev/shm ./db_bench --benchmarks=fillrandom,readmissing,readrandom,stats --num=2000000 and optionally with -memtable_whole_key_filtering -memtable_bloom_size_ratio=0.01 300 runs each configuration. Write throughput change by enabling memtable bloom: Old locality=0: -3.06% Old locality=1: -2.37% New: -1.50% conclusion -> seems to substantially close the gap Readmissing throughput change by enabling memtable bloom: Old locality=0: +34.47% Old locality=1: +34.80% New: +33.25% conclusion -> maybe a small new penalty from FP rate Readrandom throughput change by enabling memtable bloom: Old locality=0: +31.54% Old locality=1: +31.13% New: +30.60% conclusion -> maybe also from FP rate (after memtable flush) -- Another conclusion we can draw from this new implementation is that the existing 32-bit hash function is not inherently crippling the Bloom filter speed or accuracy, below about 5 million keys. For speed, the implementation is essentially the same whether starting with 32-bits or 64-bits of hash; it just determines whether the first multiplication after fastrange is a pseudorandom expansion or needed re-mix. Note that this multiplication can occur while memory is fetching. For accuracy, in a standard configuration, you need about 5 million keys before you have about a 1.1x FP penalty due to using a 32-bit hash vs. 64-bit: [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.52069 sampled_fp_rate: 0.0118267 ... [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out time: 2.43871 sampled_fp_rate: 0.0109059 Pull Request resolved: https://github.com/facebook/rocksdb/pull/5762 Differential Revision: D17214194 Pulled By: pdillinger fbshipit-source-id: ad9da031772e985fd6b62a0e1db8e81892520595
2019-09-05 23:57:39 +02:00
static_cast<double>(elapsed) / num_threads / num_keys);
support for concurrent adds to memtable Summary: This diff adds support for concurrent adds to the skiplist memtable implementations. Memory allocation is made thread-safe by the addition of a spinlock, with small per-core buffers to avoid contention. Concurrent memtable writes are made via an additional method and don't impose a performance overhead on the non-concurrent case, so parallelism can be selected on a per-batch basis. Write thread synchronization is an increasing bottleneck for higher levels of concurrency, so this diff adds --enable_write_thread_adaptive_yield (default off). This feature causes threads joining a write batch group to spin for a short time (default 100 usec) using sched_yield, rather than going to sleep on a mutex. If the timing of the yield calls indicates that another thread has actually run during the yield then spinning is avoided. This option improves performance for concurrent situations even without parallel adds, although it has the potential to increase CPU usage (and the heuristic adaptation is not yet mature). Parallel writes are not currently compatible with inplace updates, update callbacks, or delete filtering. Enable it with --allow_concurrent_memtable_write (and --enable_write_thread_adaptive_yield). Parallel memtable writes are performance neutral when there is no actual parallelism, and in my experiments (SSD server-class Linux and varying contention and key sizes for fillrandom) they are always a performance win when there is more than one thread. Statistics are updated earlier in the write path, dropping the number of DB mutex acquisitions from 2 to 1 for almost all cases. This diff was motivated and inspired by Yahoo's cLSM work. It is more conservative than cLSM: RocksDB's write batch group leader role is preserved (along with all of the existing flush and write throttling logic) and concurrent writers are blocked until all memtable insertions have completed and the sequence number has been advanced, to preserve linearizability. My test config is "db_bench -benchmarks=fillrandom -threads=$T -batch_size=1 -memtablerep=skip_list -value_size=100 --num=1000000/$T -level0_slowdown_writes_trigger=9999 -level0_stop_writes_trigger=9999 -disable_auto_compactions --max_write_buffer_number=8 -max_background_flushes=8 --disable_wal --write_buffer_size=160000000 --block_size=16384 --allow_concurrent_memtable_write" on a two-socket Xeon E5-2660 @ 2.2Ghz with lots of memory and an SSD hard drive. With 1 thread I get ~440Kops/sec. Peak performance for 1 socket (numactl -N1) is slightly more than 1Mops/sec, at 16 threads. Peak performance across both sockets happens at 30 threads, and is ~900Kops/sec, although with fewer threads there is less performance loss when the system has background work. Test Plan: 1. concurrent stress tests for InlineSkipList and DynamicBloom 2. make clean; make check 3. make clean; DISABLE_JEMALLOC=1 make valgrind_check; valgrind db_bench 4. make clean; COMPILE_WITH_TSAN=1 make all check; db_bench 5. make clean; COMPILE_WITH_ASAN=1 make all check; db_bench 6. make clean; OPT=-DROCKSDB_LITE make check 7. verify no perf regressions when disabled Reviewers: igor, sdong Reviewed By: sdong Subscribers: MarkCallaghan, IslamAbdelRahman, anthony, yhchiang, rven, sdong, guyg8, kradhakrishnan, dhruba Differential Revision: https://reviews.facebook.net/D50589
2015-08-15 01:59:07 +02:00
Faster new DynamicBloom implementation (for memtable) (#5762) Summary: Since DynamicBloom is now only used in-memory, we're free to change it without schema compatibility issues. The new implementation is drawn from (with manifest permission) https://github.com/pdillinger/wormhashing/blob/303542a767437f56d8b66cea6ebecaac0e6a61e9/bloom_simulation_tests/foo.cc#L613 This has several speed advantages over the prior implementation: * Uses fastrange instead of % * Minimum logic to determine first (and all) probed memory addresses * (Major) Two probes per 64-bit memory fetch/write. * Very fast and effective (murmur-like) hash expansion/re-mixing. (At least on recent CPUs, integer multiplication is very cheap.) While a Bloom filter with 512-bit cache locality has about a 1.15x FP rate penalty (e.g. 0.84% to 0.97%), further restricting to two probes per 64 bits incurs an additional 1.12x FP rate penalty (e.g. 0.97% to 1.09%). Nevertheless, the unit tests show no "mediocre" FP rate samples, unlike the old implementation with more erratic FP rates. Especially for the memtable, we expect speed to outweigh somewhat higher FP rates. For example, a negative table query would have to be 1000x slower than a BF query to justify doubling BF query time to shave 10% off FP rate (working assumption around 1% FP rate). While that seems likely for SSTs, my data suggests a speed factor of roughly 50x for the memtable (vs. BF; ~1.5% lower write throughput when enabling memtable Bloom filter, after this change). Thus, it's probably not worth even 5% more time in the Bloom filter to shave off 1/10th of the Bloom FP rate, or 0.1% in absolute terms, and it's probably at least 20% slower to recoup that much FP rate from this new implementation. Because of this, we do not see a need for a 'locality' option that affects the MemTable Bloom filter and have decoupled the MemTable Bloom filter from Options::bloom_locality. Note that just 3% more memory to the Bloom filter (10.3 bits per key vs. just 10) is able to make up for the ~12% FP rate drop in the new implementation: [] # Nearly "ideal" FP-wise but reasonably fast cache-local implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out time: 3.29372 sampled_fp_rate: 0.00985956 ... [] # Close match to this new implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out 10000000 6 10.3 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.10072 sampled_fp_rate: 0.00985655 ... [] # Old locality=1 implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out time: 3.95472 sampled_fp_rate: 0.00988943 ... Also note the dramatic speed improvement vs. alternatives. -- Performance unit test: DynamicBloomTest.concurrent_with_perf is updated to report more precise timing data. (Measure running time of each thread, not just longest running thread, etc.) Results averaged over various sizes enabled with --enable_perf and 20 runs each; old dynamic bloom refers to locality=1, the faster of the old: old dynamic bloom, avg add latency = 65.6468 new dynamic bloom, avg add latency = 44.3809 old dynamic bloom, avg query latency = 50.6485 new dynamic bloom, avg query latency = 43.2186 old avg parallel add latency = 41.678 new avg parallel add latency = 24.5238 old avg parallel hit latency = 14.6322 new avg parallel hit latency = 12.3939 old avg parallel miss latency = 16.7289 new avg parallel miss latency = 12.2134 Tested on a dedicated 64-bit production machine at Facebook. Significant improvement all around. Despite now using std::atomic<uint64_t>, quick before-and-after test on a 32-bit machine (Intel Atom N270, released 2008) shows no regression in performance, in some cases modest improvement. -- Performance integration test (synthetic): with DEBUG_LEVEL=0, used TEST_TMPDIR=/dev/shm ./db_bench --benchmarks=fillrandom,readmissing,readrandom,stats --num=2000000 and optionally with -memtable_whole_key_filtering -memtable_bloom_size_ratio=0.01 300 runs each configuration. Write throughput change by enabling memtable bloom: Old locality=0: -3.06% Old locality=1: -2.37% New: -1.50% conclusion -> seems to substantially close the gap Readmissing throughput change by enabling memtable bloom: Old locality=0: +34.47% Old locality=1: +34.80% New: +33.25% conclusion -> maybe a small new penalty from FP rate Readrandom throughput change by enabling memtable bloom: Old locality=0: +31.54% Old locality=1: +31.13% New: +30.60% conclusion -> maybe also from FP rate (after memtable flush) -- Another conclusion we can draw from this new implementation is that the existing 32-bit hash function is not inherently crippling the Bloom filter speed or accuracy, below about 5 million keys. For speed, the implementation is essentially the same whether starting with 32-bits or 64-bits of hash; it just determines whether the first multiplication after fastrange is a pseudorandom expansion or needed re-mix. Note that this multiplication can occur while memory is fetching. For accuracy, in a standard configuration, you need about 5 million keys before you have about a 1.1x FP penalty due to using a 32-bit hash vs. 64-bit: [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.52069 sampled_fp_rate: 0.0118267 ... [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out time: 2.43871 sampled_fp_rate: 0.0109059 Pull Request resolved: https://github.com/facebook/rocksdb/pull/5762 Differential Revision: D17214194 Pulled By: pdillinger fbshipit-source-id: ad9da031772e985fd6b62a0e1db8e81892520595
2019-09-05 23:57:39 +02:00
elapsed = 0;
std::function<void(size_t)> hitter([&](size_t t) {
KeyMaker km;
StopWatchNano timer(SystemClock::Default().get());
support for concurrent adds to memtable Summary: This diff adds support for concurrent adds to the skiplist memtable implementations. Memory allocation is made thread-safe by the addition of a spinlock, with small per-core buffers to avoid contention. Concurrent memtable writes are made via an additional method and don't impose a performance overhead on the non-concurrent case, so parallelism can be selected on a per-batch basis. Write thread synchronization is an increasing bottleneck for higher levels of concurrency, so this diff adds --enable_write_thread_adaptive_yield (default off). This feature causes threads joining a write batch group to spin for a short time (default 100 usec) using sched_yield, rather than going to sleep on a mutex. If the timing of the yield calls indicates that another thread has actually run during the yield then spinning is avoided. This option improves performance for concurrent situations even without parallel adds, although it has the potential to increase CPU usage (and the heuristic adaptation is not yet mature). Parallel writes are not currently compatible with inplace updates, update callbacks, or delete filtering. Enable it with --allow_concurrent_memtable_write (and --enable_write_thread_adaptive_yield). Parallel memtable writes are performance neutral when there is no actual parallelism, and in my experiments (SSD server-class Linux and varying contention and key sizes for fillrandom) they are always a performance win when there is more than one thread. Statistics are updated earlier in the write path, dropping the number of DB mutex acquisitions from 2 to 1 for almost all cases. This diff was motivated and inspired by Yahoo's cLSM work. It is more conservative than cLSM: RocksDB's write batch group leader role is preserved (along with all of the existing flush and write throttling logic) and concurrent writers are blocked until all memtable insertions have completed and the sequence number has been advanced, to preserve linearizability. My test config is "db_bench -benchmarks=fillrandom -threads=$T -batch_size=1 -memtablerep=skip_list -value_size=100 --num=1000000/$T -level0_slowdown_writes_trigger=9999 -level0_stop_writes_trigger=9999 -disable_auto_compactions --max_write_buffer_number=8 -max_background_flushes=8 --disable_wal --write_buffer_size=160000000 --block_size=16384 --allow_concurrent_memtable_write" on a two-socket Xeon E5-2660 @ 2.2Ghz with lots of memory and an SSD hard drive. With 1 thread I get ~440Kops/sec. Peak performance for 1 socket (numactl -N1) is slightly more than 1Mops/sec, at 16 threads. Peak performance across both sockets happens at 30 threads, and is ~900Kops/sec, although with fewer threads there is less performance loss when the system has background work. Test Plan: 1. concurrent stress tests for InlineSkipList and DynamicBloom 2. make clean; make check 3. make clean; DISABLE_JEMALLOC=1 make valgrind_check; valgrind db_bench 4. make clean; COMPILE_WITH_TSAN=1 make all check; db_bench 5. make clean; COMPILE_WITH_ASAN=1 make all check; db_bench 6. make clean; OPT=-DROCKSDB_LITE make check 7. verify no perf regressions when disabled Reviewers: igor, sdong Reviewed By: sdong Subscribers: MarkCallaghan, IslamAbdelRahman, anthony, yhchiang, rven, sdong, guyg8, kradhakrishnan, dhruba Differential Revision: https://reviews.facebook.net/D50589
2015-08-15 01:59:07 +02:00
timer.Start();
Faster new DynamicBloom implementation (for memtable) (#5762) Summary: Since DynamicBloom is now only used in-memory, we're free to change it without schema compatibility issues. The new implementation is drawn from (with manifest permission) https://github.com/pdillinger/wormhashing/blob/303542a767437f56d8b66cea6ebecaac0e6a61e9/bloom_simulation_tests/foo.cc#L613 This has several speed advantages over the prior implementation: * Uses fastrange instead of % * Minimum logic to determine first (and all) probed memory addresses * (Major) Two probes per 64-bit memory fetch/write. * Very fast and effective (murmur-like) hash expansion/re-mixing. (At least on recent CPUs, integer multiplication is very cheap.) While a Bloom filter with 512-bit cache locality has about a 1.15x FP rate penalty (e.g. 0.84% to 0.97%), further restricting to two probes per 64 bits incurs an additional 1.12x FP rate penalty (e.g. 0.97% to 1.09%). Nevertheless, the unit tests show no "mediocre" FP rate samples, unlike the old implementation with more erratic FP rates. Especially for the memtable, we expect speed to outweigh somewhat higher FP rates. For example, a negative table query would have to be 1000x slower than a BF query to justify doubling BF query time to shave 10% off FP rate (working assumption around 1% FP rate). While that seems likely for SSTs, my data suggests a speed factor of roughly 50x for the memtable (vs. BF; ~1.5% lower write throughput when enabling memtable Bloom filter, after this change). Thus, it's probably not worth even 5% more time in the Bloom filter to shave off 1/10th of the Bloom FP rate, or 0.1% in absolute terms, and it's probably at least 20% slower to recoup that much FP rate from this new implementation. Because of this, we do not see a need for a 'locality' option that affects the MemTable Bloom filter and have decoupled the MemTable Bloom filter from Options::bloom_locality. Note that just 3% more memory to the Bloom filter (10.3 bits per key vs. just 10) is able to make up for the ~12% FP rate drop in the new implementation: [] # Nearly "ideal" FP-wise but reasonably fast cache-local implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out time: 3.29372 sampled_fp_rate: 0.00985956 ... [] # Close match to this new implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out 10000000 6 10.3 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.10072 sampled_fp_rate: 0.00985655 ... [] # Old locality=1 implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out time: 3.95472 sampled_fp_rate: 0.00988943 ... Also note the dramatic speed improvement vs. alternatives. -- Performance unit test: DynamicBloomTest.concurrent_with_perf is updated to report more precise timing data. (Measure running time of each thread, not just longest running thread, etc.) Results averaged over various sizes enabled with --enable_perf and 20 runs each; old dynamic bloom refers to locality=1, the faster of the old: old dynamic bloom, avg add latency = 65.6468 new dynamic bloom, avg add latency = 44.3809 old dynamic bloom, avg query latency = 50.6485 new dynamic bloom, avg query latency = 43.2186 old avg parallel add latency = 41.678 new avg parallel add latency = 24.5238 old avg parallel hit latency = 14.6322 new avg parallel hit latency = 12.3939 old avg parallel miss latency = 16.7289 new avg parallel miss latency = 12.2134 Tested on a dedicated 64-bit production machine at Facebook. Significant improvement all around. Despite now using std::atomic<uint64_t>, quick before-and-after test on a 32-bit machine (Intel Atom N270, released 2008) shows no regression in performance, in some cases modest improvement. -- Performance integration test (synthetic): with DEBUG_LEVEL=0, used TEST_TMPDIR=/dev/shm ./db_bench --benchmarks=fillrandom,readmissing,readrandom,stats --num=2000000 and optionally with -memtable_whole_key_filtering -memtable_bloom_size_ratio=0.01 300 runs each configuration. Write throughput change by enabling memtable bloom: Old locality=0: -3.06% Old locality=1: -2.37% New: -1.50% conclusion -> seems to substantially close the gap Readmissing throughput change by enabling memtable bloom: Old locality=0: +34.47% Old locality=1: +34.80% New: +33.25% conclusion -> maybe a small new penalty from FP rate Readrandom throughput change by enabling memtable bloom: Old locality=0: +31.54% Old locality=1: +31.13% New: +30.60% conclusion -> maybe also from FP rate (after memtable flush) -- Another conclusion we can draw from this new implementation is that the existing 32-bit hash function is not inherently crippling the Bloom filter speed or accuracy, below about 5 million keys. For speed, the implementation is essentially the same whether starting with 32-bits or 64-bits of hash; it just determines whether the first multiplication after fastrange is a pseudorandom expansion or needed re-mix. Note that this multiplication can occur while memory is fetching. For accuracy, in a standard configuration, you need about 5 million keys before you have about a 1.1x FP penalty due to using a 32-bit hash vs. 64-bit: [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.52069 sampled_fp_rate: 0.0118267 ... [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out time: 2.43871 sampled_fp_rate: 0.0109059 Pull Request resolved: https://github.com/facebook/rocksdb/pull/5762 Differential Revision: D17214194 Pulled By: pdillinger fbshipit-source-id: ad9da031772e985fd6b62a0e1db8e81892520595
2019-09-05 23:57:39 +02:00
for (uint64_t i = 1 + t; i <= num_keys; i += num_threads) {
bool f = std_bloom.MayContain(km.Seq(i));
Faster new DynamicBloom implementation (for memtable) (#5762) Summary: Since DynamicBloom is now only used in-memory, we're free to change it without schema compatibility issues. The new implementation is drawn from (with manifest permission) https://github.com/pdillinger/wormhashing/blob/303542a767437f56d8b66cea6ebecaac0e6a61e9/bloom_simulation_tests/foo.cc#L613 This has several speed advantages over the prior implementation: * Uses fastrange instead of % * Minimum logic to determine first (and all) probed memory addresses * (Major) Two probes per 64-bit memory fetch/write. * Very fast and effective (murmur-like) hash expansion/re-mixing. (At least on recent CPUs, integer multiplication is very cheap.) While a Bloom filter with 512-bit cache locality has about a 1.15x FP rate penalty (e.g. 0.84% to 0.97%), further restricting to two probes per 64 bits incurs an additional 1.12x FP rate penalty (e.g. 0.97% to 1.09%). Nevertheless, the unit tests show no "mediocre" FP rate samples, unlike the old implementation with more erratic FP rates. Especially for the memtable, we expect speed to outweigh somewhat higher FP rates. For example, a negative table query would have to be 1000x slower than a BF query to justify doubling BF query time to shave 10% off FP rate (working assumption around 1% FP rate). While that seems likely for SSTs, my data suggests a speed factor of roughly 50x for the memtable (vs. BF; ~1.5% lower write throughput when enabling memtable Bloom filter, after this change). Thus, it's probably not worth even 5% more time in the Bloom filter to shave off 1/10th of the Bloom FP rate, or 0.1% in absolute terms, and it's probably at least 20% slower to recoup that much FP rate from this new implementation. Because of this, we do not see a need for a 'locality' option that affects the MemTable Bloom filter and have decoupled the MemTable Bloom filter from Options::bloom_locality. Note that just 3% more memory to the Bloom filter (10.3 bits per key vs. just 10) is able to make up for the ~12% FP rate drop in the new implementation: [] # Nearly "ideal" FP-wise but reasonably fast cache-local implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out time: 3.29372 sampled_fp_rate: 0.00985956 ... [] # Close match to this new implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out 10000000 6 10.3 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.10072 sampled_fp_rate: 0.00985655 ... [] # Old locality=1 implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out time: 3.95472 sampled_fp_rate: 0.00988943 ... Also note the dramatic speed improvement vs. alternatives. -- Performance unit test: DynamicBloomTest.concurrent_with_perf is updated to report more precise timing data. (Measure running time of each thread, not just longest running thread, etc.) Results averaged over various sizes enabled with --enable_perf and 20 runs each; old dynamic bloom refers to locality=1, the faster of the old: old dynamic bloom, avg add latency = 65.6468 new dynamic bloom, avg add latency = 44.3809 old dynamic bloom, avg query latency = 50.6485 new dynamic bloom, avg query latency = 43.2186 old avg parallel add latency = 41.678 new avg parallel add latency = 24.5238 old avg parallel hit latency = 14.6322 new avg parallel hit latency = 12.3939 old avg parallel miss latency = 16.7289 new avg parallel miss latency = 12.2134 Tested on a dedicated 64-bit production machine at Facebook. Significant improvement all around. Despite now using std::atomic<uint64_t>, quick before-and-after test on a 32-bit machine (Intel Atom N270, released 2008) shows no regression in performance, in some cases modest improvement. -- Performance integration test (synthetic): with DEBUG_LEVEL=0, used TEST_TMPDIR=/dev/shm ./db_bench --benchmarks=fillrandom,readmissing,readrandom,stats --num=2000000 and optionally with -memtable_whole_key_filtering -memtable_bloom_size_ratio=0.01 300 runs each configuration. Write throughput change by enabling memtable bloom: Old locality=0: -3.06% Old locality=1: -2.37% New: -1.50% conclusion -> seems to substantially close the gap Readmissing throughput change by enabling memtable bloom: Old locality=0: +34.47% Old locality=1: +34.80% New: +33.25% conclusion -> maybe a small new penalty from FP rate Readrandom throughput change by enabling memtable bloom: Old locality=0: +31.54% Old locality=1: +31.13% New: +30.60% conclusion -> maybe also from FP rate (after memtable flush) -- Another conclusion we can draw from this new implementation is that the existing 32-bit hash function is not inherently crippling the Bloom filter speed or accuracy, below about 5 million keys. For speed, the implementation is essentially the same whether starting with 32-bits or 64-bits of hash; it just determines whether the first multiplication after fastrange is a pseudorandom expansion or needed re-mix. Note that this multiplication can occur while memory is fetching. For accuracy, in a standard configuration, you need about 5 million keys before you have about a 1.1x FP penalty due to using a 32-bit hash vs. 64-bit: [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.52069 sampled_fp_rate: 0.0118267 ... [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out time: 2.43871 sampled_fp_rate: 0.0109059 Pull Request resolved: https://github.com/facebook/rocksdb/pull/5762 Differential Revision: D17214194 Pulled By: pdillinger fbshipit-source-id: ad9da031772e985fd6b62a0e1db8e81892520595
2019-09-05 23:57:39 +02:00
ASSERT_TRUE(f);
}
Faster new DynamicBloom implementation (for memtable) (#5762) Summary: Since DynamicBloom is now only used in-memory, we're free to change it without schema compatibility issues. The new implementation is drawn from (with manifest permission) https://github.com/pdillinger/wormhashing/blob/303542a767437f56d8b66cea6ebecaac0e6a61e9/bloom_simulation_tests/foo.cc#L613 This has several speed advantages over the prior implementation: * Uses fastrange instead of % * Minimum logic to determine first (and all) probed memory addresses * (Major) Two probes per 64-bit memory fetch/write. * Very fast and effective (murmur-like) hash expansion/re-mixing. (At least on recent CPUs, integer multiplication is very cheap.) While a Bloom filter with 512-bit cache locality has about a 1.15x FP rate penalty (e.g. 0.84% to 0.97%), further restricting to two probes per 64 bits incurs an additional 1.12x FP rate penalty (e.g. 0.97% to 1.09%). Nevertheless, the unit tests show no "mediocre" FP rate samples, unlike the old implementation with more erratic FP rates. Especially for the memtable, we expect speed to outweigh somewhat higher FP rates. For example, a negative table query would have to be 1000x slower than a BF query to justify doubling BF query time to shave 10% off FP rate (working assumption around 1% FP rate). While that seems likely for SSTs, my data suggests a speed factor of roughly 50x for the memtable (vs. BF; ~1.5% lower write throughput when enabling memtable Bloom filter, after this change). Thus, it's probably not worth even 5% more time in the Bloom filter to shave off 1/10th of the Bloom FP rate, or 0.1% in absolute terms, and it's probably at least 20% slower to recoup that much FP rate from this new implementation. Because of this, we do not see a need for a 'locality' option that affects the MemTable Bloom filter and have decoupled the MemTable Bloom filter from Options::bloom_locality. Note that just 3% more memory to the Bloom filter (10.3 bits per key vs. just 10) is able to make up for the ~12% FP rate drop in the new implementation: [] # Nearly "ideal" FP-wise but reasonably fast cache-local implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out time: 3.29372 sampled_fp_rate: 0.00985956 ... [] # Close match to this new implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out 10000000 6 10.3 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.10072 sampled_fp_rate: 0.00985655 ... [] # Old locality=1 implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out time: 3.95472 sampled_fp_rate: 0.00988943 ... Also note the dramatic speed improvement vs. alternatives. -- Performance unit test: DynamicBloomTest.concurrent_with_perf is updated to report more precise timing data. (Measure running time of each thread, not just longest running thread, etc.) Results averaged over various sizes enabled with --enable_perf and 20 runs each; old dynamic bloom refers to locality=1, the faster of the old: old dynamic bloom, avg add latency = 65.6468 new dynamic bloom, avg add latency = 44.3809 old dynamic bloom, avg query latency = 50.6485 new dynamic bloom, avg query latency = 43.2186 old avg parallel add latency = 41.678 new avg parallel add latency = 24.5238 old avg parallel hit latency = 14.6322 new avg parallel hit latency = 12.3939 old avg parallel miss latency = 16.7289 new avg parallel miss latency = 12.2134 Tested on a dedicated 64-bit production machine at Facebook. Significant improvement all around. Despite now using std::atomic<uint64_t>, quick before-and-after test on a 32-bit machine (Intel Atom N270, released 2008) shows no regression in performance, in some cases modest improvement. -- Performance integration test (synthetic): with DEBUG_LEVEL=0, used TEST_TMPDIR=/dev/shm ./db_bench --benchmarks=fillrandom,readmissing,readrandom,stats --num=2000000 and optionally with -memtable_whole_key_filtering -memtable_bloom_size_ratio=0.01 300 runs each configuration. Write throughput change by enabling memtable bloom: Old locality=0: -3.06% Old locality=1: -2.37% New: -1.50% conclusion -> seems to substantially close the gap Readmissing throughput change by enabling memtable bloom: Old locality=0: +34.47% Old locality=1: +34.80% New: +33.25% conclusion -> maybe a small new penalty from FP rate Readrandom throughput change by enabling memtable bloom: Old locality=0: +31.54% Old locality=1: +31.13% New: +30.60% conclusion -> maybe also from FP rate (after memtable flush) -- Another conclusion we can draw from this new implementation is that the existing 32-bit hash function is not inherently crippling the Bloom filter speed or accuracy, below about 5 million keys. For speed, the implementation is essentially the same whether starting with 32-bits or 64-bits of hash; it just determines whether the first multiplication after fastrange is a pseudorandom expansion or needed re-mix. Note that this multiplication can occur while memory is fetching. For accuracy, in a standard configuration, you need about 5 million keys before you have about a 1.1x FP penalty due to using a 32-bit hash vs. 64-bit: [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.52069 sampled_fp_rate: 0.0118267 ... [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out time: 2.43871 sampled_fp_rate: 0.0109059 Pull Request resolved: https://github.com/facebook/rocksdb/pull/5762 Differential Revision: D17214194 Pulled By: pdillinger fbshipit-source-id: ad9da031772e985fd6b62a0e1db8e81892520595
2019-09-05 23:57:39 +02:00
elapsed += timer.ElapsedNanos();
});
for (size_t t = 0; t < num_threads; ++t) {
threads.emplace_back(hitter, t);
}
while (threads.size() > 0) {
threads.back().join();
threads.pop_back();
}
fprintf(stderr,
"dynamic bloom, avg parallel hit latency %3g"
" nanos/key\n",
Faster new DynamicBloom implementation (for memtable) (#5762) Summary: Since DynamicBloom is now only used in-memory, we're free to change it without schema compatibility issues. The new implementation is drawn from (with manifest permission) https://github.com/pdillinger/wormhashing/blob/303542a767437f56d8b66cea6ebecaac0e6a61e9/bloom_simulation_tests/foo.cc#L613 This has several speed advantages over the prior implementation: * Uses fastrange instead of % * Minimum logic to determine first (and all) probed memory addresses * (Major) Two probes per 64-bit memory fetch/write. * Very fast and effective (murmur-like) hash expansion/re-mixing. (At least on recent CPUs, integer multiplication is very cheap.) While a Bloom filter with 512-bit cache locality has about a 1.15x FP rate penalty (e.g. 0.84% to 0.97%), further restricting to two probes per 64 bits incurs an additional 1.12x FP rate penalty (e.g. 0.97% to 1.09%). Nevertheless, the unit tests show no "mediocre" FP rate samples, unlike the old implementation with more erratic FP rates. Especially for the memtable, we expect speed to outweigh somewhat higher FP rates. For example, a negative table query would have to be 1000x slower than a BF query to justify doubling BF query time to shave 10% off FP rate (working assumption around 1% FP rate). While that seems likely for SSTs, my data suggests a speed factor of roughly 50x for the memtable (vs. BF; ~1.5% lower write throughput when enabling memtable Bloom filter, after this change). Thus, it's probably not worth even 5% more time in the Bloom filter to shave off 1/10th of the Bloom FP rate, or 0.1% in absolute terms, and it's probably at least 20% slower to recoup that much FP rate from this new implementation. Because of this, we do not see a need for a 'locality' option that affects the MemTable Bloom filter and have decoupled the MemTable Bloom filter from Options::bloom_locality. Note that just 3% more memory to the Bloom filter (10.3 bits per key vs. just 10) is able to make up for the ~12% FP rate drop in the new implementation: [] # Nearly "ideal" FP-wise but reasonably fast cache-local implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out time: 3.29372 sampled_fp_rate: 0.00985956 ... [] # Close match to this new implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out 10000000 6 10.3 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.10072 sampled_fp_rate: 0.00985655 ... [] # Old locality=1 implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out time: 3.95472 sampled_fp_rate: 0.00988943 ... Also note the dramatic speed improvement vs. alternatives. -- Performance unit test: DynamicBloomTest.concurrent_with_perf is updated to report more precise timing data. (Measure running time of each thread, not just longest running thread, etc.) Results averaged over various sizes enabled with --enable_perf and 20 runs each; old dynamic bloom refers to locality=1, the faster of the old: old dynamic bloom, avg add latency = 65.6468 new dynamic bloom, avg add latency = 44.3809 old dynamic bloom, avg query latency = 50.6485 new dynamic bloom, avg query latency = 43.2186 old avg parallel add latency = 41.678 new avg parallel add latency = 24.5238 old avg parallel hit latency = 14.6322 new avg parallel hit latency = 12.3939 old avg parallel miss latency = 16.7289 new avg parallel miss latency = 12.2134 Tested on a dedicated 64-bit production machine at Facebook. Significant improvement all around. Despite now using std::atomic<uint64_t>, quick before-and-after test on a 32-bit machine (Intel Atom N270, released 2008) shows no regression in performance, in some cases modest improvement. -- Performance integration test (synthetic): with DEBUG_LEVEL=0, used TEST_TMPDIR=/dev/shm ./db_bench --benchmarks=fillrandom,readmissing,readrandom,stats --num=2000000 and optionally with -memtable_whole_key_filtering -memtable_bloom_size_ratio=0.01 300 runs each configuration. Write throughput change by enabling memtable bloom: Old locality=0: -3.06% Old locality=1: -2.37% New: -1.50% conclusion -> seems to substantially close the gap Readmissing throughput change by enabling memtable bloom: Old locality=0: +34.47% Old locality=1: +34.80% New: +33.25% conclusion -> maybe a small new penalty from FP rate Readrandom throughput change by enabling memtable bloom: Old locality=0: +31.54% Old locality=1: +31.13% New: +30.60% conclusion -> maybe also from FP rate (after memtable flush) -- Another conclusion we can draw from this new implementation is that the existing 32-bit hash function is not inherently crippling the Bloom filter speed or accuracy, below about 5 million keys. For speed, the implementation is essentially the same whether starting with 32-bits or 64-bits of hash; it just determines whether the first multiplication after fastrange is a pseudorandom expansion or needed re-mix. Note that this multiplication can occur while memory is fetching. For accuracy, in a standard configuration, you need about 5 million keys before you have about a 1.1x FP penalty due to using a 32-bit hash vs. 64-bit: [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.52069 sampled_fp_rate: 0.0118267 ... [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out time: 2.43871 sampled_fp_rate: 0.0109059 Pull Request resolved: https://github.com/facebook/rocksdb/pull/5762 Differential Revision: D17214194 Pulled By: pdillinger fbshipit-source-id: ad9da031772e985fd6b62a0e1db8e81892520595
2019-09-05 23:57:39 +02:00
static_cast<double>(elapsed) / num_threads / num_keys);
Faster new DynamicBloom implementation (for memtable) (#5762) Summary: Since DynamicBloom is now only used in-memory, we're free to change it without schema compatibility issues. The new implementation is drawn from (with manifest permission) https://github.com/pdillinger/wormhashing/blob/303542a767437f56d8b66cea6ebecaac0e6a61e9/bloom_simulation_tests/foo.cc#L613 This has several speed advantages over the prior implementation: * Uses fastrange instead of % * Minimum logic to determine first (and all) probed memory addresses * (Major) Two probes per 64-bit memory fetch/write. * Very fast and effective (murmur-like) hash expansion/re-mixing. (At least on recent CPUs, integer multiplication is very cheap.) While a Bloom filter with 512-bit cache locality has about a 1.15x FP rate penalty (e.g. 0.84% to 0.97%), further restricting to two probes per 64 bits incurs an additional 1.12x FP rate penalty (e.g. 0.97% to 1.09%). Nevertheless, the unit tests show no "mediocre" FP rate samples, unlike the old implementation with more erratic FP rates. Especially for the memtable, we expect speed to outweigh somewhat higher FP rates. For example, a negative table query would have to be 1000x slower than a BF query to justify doubling BF query time to shave 10% off FP rate (working assumption around 1% FP rate). While that seems likely for SSTs, my data suggests a speed factor of roughly 50x for the memtable (vs. BF; ~1.5% lower write throughput when enabling memtable Bloom filter, after this change). Thus, it's probably not worth even 5% more time in the Bloom filter to shave off 1/10th of the Bloom FP rate, or 0.1% in absolute terms, and it's probably at least 20% slower to recoup that much FP rate from this new implementation. Because of this, we do not see a need for a 'locality' option that affects the MemTable Bloom filter and have decoupled the MemTable Bloom filter from Options::bloom_locality. Note that just 3% more memory to the Bloom filter (10.3 bits per key vs. just 10) is able to make up for the ~12% FP rate drop in the new implementation: [] # Nearly "ideal" FP-wise but reasonably fast cache-local implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out time: 3.29372 sampled_fp_rate: 0.00985956 ... [] # Close match to this new implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out 10000000 6 10.3 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.10072 sampled_fp_rate: 0.00985655 ... [] # Old locality=1 implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out time: 3.95472 sampled_fp_rate: 0.00988943 ... Also note the dramatic speed improvement vs. alternatives. -- Performance unit test: DynamicBloomTest.concurrent_with_perf is updated to report more precise timing data. (Measure running time of each thread, not just longest running thread, etc.) Results averaged over various sizes enabled with --enable_perf and 20 runs each; old dynamic bloom refers to locality=1, the faster of the old: old dynamic bloom, avg add latency = 65.6468 new dynamic bloom, avg add latency = 44.3809 old dynamic bloom, avg query latency = 50.6485 new dynamic bloom, avg query latency = 43.2186 old avg parallel add latency = 41.678 new avg parallel add latency = 24.5238 old avg parallel hit latency = 14.6322 new avg parallel hit latency = 12.3939 old avg parallel miss latency = 16.7289 new avg parallel miss latency = 12.2134 Tested on a dedicated 64-bit production machine at Facebook. Significant improvement all around. Despite now using std::atomic<uint64_t>, quick before-and-after test on a 32-bit machine (Intel Atom N270, released 2008) shows no regression in performance, in some cases modest improvement. -- Performance integration test (synthetic): with DEBUG_LEVEL=0, used TEST_TMPDIR=/dev/shm ./db_bench --benchmarks=fillrandom,readmissing,readrandom,stats --num=2000000 and optionally with -memtable_whole_key_filtering -memtable_bloom_size_ratio=0.01 300 runs each configuration. Write throughput change by enabling memtable bloom: Old locality=0: -3.06% Old locality=1: -2.37% New: -1.50% conclusion -> seems to substantially close the gap Readmissing throughput change by enabling memtable bloom: Old locality=0: +34.47% Old locality=1: +34.80% New: +33.25% conclusion -> maybe a small new penalty from FP rate Readrandom throughput change by enabling memtable bloom: Old locality=0: +31.54% Old locality=1: +31.13% New: +30.60% conclusion -> maybe also from FP rate (after memtable flush) -- Another conclusion we can draw from this new implementation is that the existing 32-bit hash function is not inherently crippling the Bloom filter speed or accuracy, below about 5 million keys. For speed, the implementation is essentially the same whether starting with 32-bits or 64-bits of hash; it just determines whether the first multiplication after fastrange is a pseudorandom expansion or needed re-mix. Note that this multiplication can occur while memory is fetching. For accuracy, in a standard configuration, you need about 5 million keys before you have about a 1.1x FP penalty due to using a 32-bit hash vs. 64-bit: [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.52069 sampled_fp_rate: 0.0118267 ... [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out time: 2.43871 sampled_fp_rate: 0.0109059 Pull Request resolved: https://github.com/facebook/rocksdb/pull/5762 Differential Revision: D17214194 Pulled By: pdillinger fbshipit-source-id: ad9da031772e985fd6b62a0e1db8e81892520595
2019-09-05 23:57:39 +02:00
elapsed = 0;
std::atomic<uint32_t> false_positives(0);
std::function<void(size_t)> misser([&](size_t t) {
KeyMaker km;
StopWatchNano timer(SystemClock::Default().get());
timer.Start();
for (uint64_t i = num_keys + 1 + t; i <= 2 * num_keys; i += num_threads) {
bool f = std_bloom.MayContain(km.Seq(i));
Faster new DynamicBloom implementation (for memtable) (#5762) Summary: Since DynamicBloom is now only used in-memory, we're free to change it without schema compatibility issues. The new implementation is drawn from (with manifest permission) https://github.com/pdillinger/wormhashing/blob/303542a767437f56d8b66cea6ebecaac0e6a61e9/bloom_simulation_tests/foo.cc#L613 This has several speed advantages over the prior implementation: * Uses fastrange instead of % * Minimum logic to determine first (and all) probed memory addresses * (Major) Two probes per 64-bit memory fetch/write. * Very fast and effective (murmur-like) hash expansion/re-mixing. (At least on recent CPUs, integer multiplication is very cheap.) While a Bloom filter with 512-bit cache locality has about a 1.15x FP rate penalty (e.g. 0.84% to 0.97%), further restricting to two probes per 64 bits incurs an additional 1.12x FP rate penalty (e.g. 0.97% to 1.09%). Nevertheless, the unit tests show no "mediocre" FP rate samples, unlike the old implementation with more erratic FP rates. Especially for the memtable, we expect speed to outweigh somewhat higher FP rates. For example, a negative table query would have to be 1000x slower than a BF query to justify doubling BF query time to shave 10% off FP rate (working assumption around 1% FP rate). While that seems likely for SSTs, my data suggests a speed factor of roughly 50x for the memtable (vs. BF; ~1.5% lower write throughput when enabling memtable Bloom filter, after this change). Thus, it's probably not worth even 5% more time in the Bloom filter to shave off 1/10th of the Bloom FP rate, or 0.1% in absolute terms, and it's probably at least 20% slower to recoup that much FP rate from this new implementation. Because of this, we do not see a need for a 'locality' option that affects the MemTable Bloom filter and have decoupled the MemTable Bloom filter from Options::bloom_locality. Note that just 3% more memory to the Bloom filter (10.3 bits per key vs. just 10) is able to make up for the ~12% FP rate drop in the new implementation: [] # Nearly "ideal" FP-wise but reasonably fast cache-local implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out time: 3.29372 sampled_fp_rate: 0.00985956 ... [] # Close match to this new implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out 10000000 6 10.3 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.10072 sampled_fp_rate: 0.00985655 ... [] # Old locality=1 implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out time: 3.95472 sampled_fp_rate: 0.00988943 ... Also note the dramatic speed improvement vs. alternatives. -- Performance unit test: DynamicBloomTest.concurrent_with_perf is updated to report more precise timing data. (Measure running time of each thread, not just longest running thread, etc.) Results averaged over various sizes enabled with --enable_perf and 20 runs each; old dynamic bloom refers to locality=1, the faster of the old: old dynamic bloom, avg add latency = 65.6468 new dynamic bloom, avg add latency = 44.3809 old dynamic bloom, avg query latency = 50.6485 new dynamic bloom, avg query latency = 43.2186 old avg parallel add latency = 41.678 new avg parallel add latency = 24.5238 old avg parallel hit latency = 14.6322 new avg parallel hit latency = 12.3939 old avg parallel miss latency = 16.7289 new avg parallel miss latency = 12.2134 Tested on a dedicated 64-bit production machine at Facebook. Significant improvement all around. Despite now using std::atomic<uint64_t>, quick before-and-after test on a 32-bit machine (Intel Atom N270, released 2008) shows no regression in performance, in some cases modest improvement. -- Performance integration test (synthetic): with DEBUG_LEVEL=0, used TEST_TMPDIR=/dev/shm ./db_bench --benchmarks=fillrandom,readmissing,readrandom,stats --num=2000000 and optionally with -memtable_whole_key_filtering -memtable_bloom_size_ratio=0.01 300 runs each configuration. Write throughput change by enabling memtable bloom: Old locality=0: -3.06% Old locality=1: -2.37% New: -1.50% conclusion -> seems to substantially close the gap Readmissing throughput change by enabling memtable bloom: Old locality=0: +34.47% Old locality=1: +34.80% New: +33.25% conclusion -> maybe a small new penalty from FP rate Readrandom throughput change by enabling memtable bloom: Old locality=0: +31.54% Old locality=1: +31.13% New: +30.60% conclusion -> maybe also from FP rate (after memtable flush) -- Another conclusion we can draw from this new implementation is that the existing 32-bit hash function is not inherently crippling the Bloom filter speed or accuracy, below about 5 million keys. For speed, the implementation is essentially the same whether starting with 32-bits or 64-bits of hash; it just determines whether the first multiplication after fastrange is a pseudorandom expansion or needed re-mix. Note that this multiplication can occur while memory is fetching. For accuracy, in a standard configuration, you need about 5 million keys before you have about a 1.1x FP penalty due to using a 32-bit hash vs. 64-bit: [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.52069 sampled_fp_rate: 0.0118267 ... [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out time: 2.43871 sampled_fp_rate: 0.0109059 Pull Request resolved: https://github.com/facebook/rocksdb/pull/5762 Differential Revision: D17214194 Pulled By: pdillinger fbshipit-source-id: ad9da031772e985fd6b62a0e1db8e81892520595
2019-09-05 23:57:39 +02:00
if (f) {
++false_positives;
}
}
Faster new DynamicBloom implementation (for memtable) (#5762) Summary: Since DynamicBloom is now only used in-memory, we're free to change it without schema compatibility issues. The new implementation is drawn from (with manifest permission) https://github.com/pdillinger/wormhashing/blob/303542a767437f56d8b66cea6ebecaac0e6a61e9/bloom_simulation_tests/foo.cc#L613 This has several speed advantages over the prior implementation: * Uses fastrange instead of % * Minimum logic to determine first (and all) probed memory addresses * (Major) Two probes per 64-bit memory fetch/write. * Very fast and effective (murmur-like) hash expansion/re-mixing. (At least on recent CPUs, integer multiplication is very cheap.) While a Bloom filter with 512-bit cache locality has about a 1.15x FP rate penalty (e.g. 0.84% to 0.97%), further restricting to two probes per 64 bits incurs an additional 1.12x FP rate penalty (e.g. 0.97% to 1.09%). Nevertheless, the unit tests show no "mediocre" FP rate samples, unlike the old implementation with more erratic FP rates. Especially for the memtable, we expect speed to outweigh somewhat higher FP rates. For example, a negative table query would have to be 1000x slower than a BF query to justify doubling BF query time to shave 10% off FP rate (working assumption around 1% FP rate). While that seems likely for SSTs, my data suggests a speed factor of roughly 50x for the memtable (vs. BF; ~1.5% lower write throughput when enabling memtable Bloom filter, after this change). Thus, it's probably not worth even 5% more time in the Bloom filter to shave off 1/10th of the Bloom FP rate, or 0.1% in absolute terms, and it's probably at least 20% slower to recoup that much FP rate from this new implementation. Because of this, we do not see a need for a 'locality' option that affects the MemTable Bloom filter and have decoupled the MemTable Bloom filter from Options::bloom_locality. Note that just 3% more memory to the Bloom filter (10.3 bits per key vs. just 10) is able to make up for the ~12% FP rate drop in the new implementation: [] # Nearly "ideal" FP-wise but reasonably fast cache-local implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out time: 3.29372 sampled_fp_rate: 0.00985956 ... [] # Close match to this new implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out 10000000 6 10.3 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.10072 sampled_fp_rate: 0.00985655 ... [] # Old locality=1 implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out time: 3.95472 sampled_fp_rate: 0.00988943 ... Also note the dramatic speed improvement vs. alternatives. -- Performance unit test: DynamicBloomTest.concurrent_with_perf is updated to report more precise timing data. (Measure running time of each thread, not just longest running thread, etc.) Results averaged over various sizes enabled with --enable_perf and 20 runs each; old dynamic bloom refers to locality=1, the faster of the old: old dynamic bloom, avg add latency = 65.6468 new dynamic bloom, avg add latency = 44.3809 old dynamic bloom, avg query latency = 50.6485 new dynamic bloom, avg query latency = 43.2186 old avg parallel add latency = 41.678 new avg parallel add latency = 24.5238 old avg parallel hit latency = 14.6322 new avg parallel hit latency = 12.3939 old avg parallel miss latency = 16.7289 new avg parallel miss latency = 12.2134 Tested on a dedicated 64-bit production machine at Facebook. Significant improvement all around. Despite now using std::atomic<uint64_t>, quick before-and-after test on a 32-bit machine (Intel Atom N270, released 2008) shows no regression in performance, in some cases modest improvement. -- Performance integration test (synthetic): with DEBUG_LEVEL=0, used TEST_TMPDIR=/dev/shm ./db_bench --benchmarks=fillrandom,readmissing,readrandom,stats --num=2000000 and optionally with -memtable_whole_key_filtering -memtable_bloom_size_ratio=0.01 300 runs each configuration. Write throughput change by enabling memtable bloom: Old locality=0: -3.06% Old locality=1: -2.37% New: -1.50% conclusion -> seems to substantially close the gap Readmissing throughput change by enabling memtable bloom: Old locality=0: +34.47% Old locality=1: +34.80% New: +33.25% conclusion -> maybe a small new penalty from FP rate Readrandom throughput change by enabling memtable bloom: Old locality=0: +31.54% Old locality=1: +31.13% New: +30.60% conclusion -> maybe also from FP rate (after memtable flush) -- Another conclusion we can draw from this new implementation is that the existing 32-bit hash function is not inherently crippling the Bloom filter speed or accuracy, below about 5 million keys. For speed, the implementation is essentially the same whether starting with 32-bits or 64-bits of hash; it just determines whether the first multiplication after fastrange is a pseudorandom expansion or needed re-mix. Note that this multiplication can occur while memory is fetching. For accuracy, in a standard configuration, you need about 5 million keys before you have about a 1.1x FP penalty due to using a 32-bit hash vs. 64-bit: [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.52069 sampled_fp_rate: 0.0118267 ... [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out time: 2.43871 sampled_fp_rate: 0.0109059 Pull Request resolved: https://github.com/facebook/rocksdb/pull/5762 Differential Revision: D17214194 Pulled By: pdillinger fbshipit-source-id: ad9da031772e985fd6b62a0e1db8e81892520595
2019-09-05 23:57:39 +02:00
elapsed += timer.ElapsedNanos();
});
for (size_t t = 0; t < num_threads; ++t) {
threads.emplace_back(misser, t);
}
while (threads.size() > 0) {
threads.back().join();
threads.pop_back();
}
Faster new DynamicBloom implementation (for memtable) (#5762) Summary: Since DynamicBloom is now only used in-memory, we're free to change it without schema compatibility issues. The new implementation is drawn from (with manifest permission) https://github.com/pdillinger/wormhashing/blob/303542a767437f56d8b66cea6ebecaac0e6a61e9/bloom_simulation_tests/foo.cc#L613 This has several speed advantages over the prior implementation: * Uses fastrange instead of % * Minimum logic to determine first (and all) probed memory addresses * (Major) Two probes per 64-bit memory fetch/write. * Very fast and effective (murmur-like) hash expansion/re-mixing. (At least on recent CPUs, integer multiplication is very cheap.) While a Bloom filter with 512-bit cache locality has about a 1.15x FP rate penalty (e.g. 0.84% to 0.97%), further restricting to two probes per 64 bits incurs an additional 1.12x FP rate penalty (e.g. 0.97% to 1.09%). Nevertheless, the unit tests show no "mediocre" FP rate samples, unlike the old implementation with more erratic FP rates. Especially for the memtable, we expect speed to outweigh somewhat higher FP rates. For example, a negative table query would have to be 1000x slower than a BF query to justify doubling BF query time to shave 10% off FP rate (working assumption around 1% FP rate). While that seems likely for SSTs, my data suggests a speed factor of roughly 50x for the memtable (vs. BF; ~1.5% lower write throughput when enabling memtable Bloom filter, after this change). Thus, it's probably not worth even 5% more time in the Bloom filter to shave off 1/10th of the Bloom FP rate, or 0.1% in absolute terms, and it's probably at least 20% slower to recoup that much FP rate from this new implementation. Because of this, we do not see a need for a 'locality' option that affects the MemTable Bloom filter and have decoupled the MemTable Bloom filter from Options::bloom_locality. Note that just 3% more memory to the Bloom filter (10.3 bits per key vs. just 10) is able to make up for the ~12% FP rate drop in the new implementation: [] # Nearly "ideal" FP-wise but reasonably fast cache-local implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out time: 3.29372 sampled_fp_rate: 0.00985956 ... [] # Close match to this new implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out 10000000 6 10.3 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.10072 sampled_fp_rate: 0.00985655 ... [] # Old locality=1 implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out time: 3.95472 sampled_fp_rate: 0.00988943 ... Also note the dramatic speed improvement vs. alternatives. -- Performance unit test: DynamicBloomTest.concurrent_with_perf is updated to report more precise timing data. (Measure running time of each thread, not just longest running thread, etc.) Results averaged over various sizes enabled with --enable_perf and 20 runs each; old dynamic bloom refers to locality=1, the faster of the old: old dynamic bloom, avg add latency = 65.6468 new dynamic bloom, avg add latency = 44.3809 old dynamic bloom, avg query latency = 50.6485 new dynamic bloom, avg query latency = 43.2186 old avg parallel add latency = 41.678 new avg parallel add latency = 24.5238 old avg parallel hit latency = 14.6322 new avg parallel hit latency = 12.3939 old avg parallel miss latency = 16.7289 new avg parallel miss latency = 12.2134 Tested on a dedicated 64-bit production machine at Facebook. Significant improvement all around. Despite now using std::atomic<uint64_t>, quick before-and-after test on a 32-bit machine (Intel Atom N270, released 2008) shows no regression in performance, in some cases modest improvement. -- Performance integration test (synthetic): with DEBUG_LEVEL=0, used TEST_TMPDIR=/dev/shm ./db_bench --benchmarks=fillrandom,readmissing,readrandom,stats --num=2000000 and optionally with -memtable_whole_key_filtering -memtable_bloom_size_ratio=0.01 300 runs each configuration. Write throughput change by enabling memtable bloom: Old locality=0: -3.06% Old locality=1: -2.37% New: -1.50% conclusion -> seems to substantially close the gap Readmissing throughput change by enabling memtable bloom: Old locality=0: +34.47% Old locality=1: +34.80% New: +33.25% conclusion -> maybe a small new penalty from FP rate Readrandom throughput change by enabling memtable bloom: Old locality=0: +31.54% Old locality=1: +31.13% New: +30.60% conclusion -> maybe also from FP rate (after memtable flush) -- Another conclusion we can draw from this new implementation is that the existing 32-bit hash function is not inherently crippling the Bloom filter speed or accuracy, below about 5 million keys. For speed, the implementation is essentially the same whether starting with 32-bits or 64-bits of hash; it just determines whether the first multiplication after fastrange is a pseudorandom expansion or needed re-mix. Note that this multiplication can occur while memory is fetching. For accuracy, in a standard configuration, you need about 5 million keys before you have about a 1.1x FP penalty due to using a 32-bit hash vs. 64-bit: [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.52069 sampled_fp_rate: 0.0118267 ... [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out time: 2.43871 sampled_fp_rate: 0.0109059 Pull Request resolved: https://github.com/facebook/rocksdb/pull/5762 Differential Revision: D17214194 Pulled By: pdillinger fbshipit-source-id: ad9da031772e985fd6b62a0e1db8e81892520595
2019-09-05 23:57:39 +02:00
fprintf(stderr,
"dynamic bloom, avg parallel miss latency %3g"
" nanos/key, %f%% false positive rate\n",
Faster new DynamicBloom implementation (for memtable) (#5762) Summary: Since DynamicBloom is now only used in-memory, we're free to change it without schema compatibility issues. The new implementation is drawn from (with manifest permission) https://github.com/pdillinger/wormhashing/blob/303542a767437f56d8b66cea6ebecaac0e6a61e9/bloom_simulation_tests/foo.cc#L613 This has several speed advantages over the prior implementation: * Uses fastrange instead of % * Minimum logic to determine first (and all) probed memory addresses * (Major) Two probes per 64-bit memory fetch/write. * Very fast and effective (murmur-like) hash expansion/re-mixing. (At least on recent CPUs, integer multiplication is very cheap.) While a Bloom filter with 512-bit cache locality has about a 1.15x FP rate penalty (e.g. 0.84% to 0.97%), further restricting to two probes per 64 bits incurs an additional 1.12x FP rate penalty (e.g. 0.97% to 1.09%). Nevertheless, the unit tests show no "mediocre" FP rate samples, unlike the old implementation with more erratic FP rates. Especially for the memtable, we expect speed to outweigh somewhat higher FP rates. For example, a negative table query would have to be 1000x slower than a BF query to justify doubling BF query time to shave 10% off FP rate (working assumption around 1% FP rate). While that seems likely for SSTs, my data suggests a speed factor of roughly 50x for the memtable (vs. BF; ~1.5% lower write throughput when enabling memtable Bloom filter, after this change). Thus, it's probably not worth even 5% more time in the Bloom filter to shave off 1/10th of the Bloom FP rate, or 0.1% in absolute terms, and it's probably at least 20% slower to recoup that much FP rate from this new implementation. Because of this, we do not see a need for a 'locality' option that affects the MemTable Bloom filter and have decoupled the MemTable Bloom filter from Options::bloom_locality. Note that just 3% more memory to the Bloom filter (10.3 bits per key vs. just 10) is able to make up for the ~12% FP rate drop in the new implementation: [] # Nearly "ideal" FP-wise but reasonably fast cache-local implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_WORM64_FROM32_any.out time: 3.29372 sampled_fp_rate: 0.00985956 ... [] # Close match to this new implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out 10000000 6 10.3 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.10072 sampled_fp_rate: 0.00985655 ... [] # Old locality=1 implementation [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out 10000000 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_ROCKSDB_DYNAMIC_any.out time: 3.95472 sampled_fp_rate: 0.00988943 ... Also note the dramatic speed improvement vs. alternatives. -- Performance unit test: DynamicBloomTest.concurrent_with_perf is updated to report more precise timing data. (Measure running time of each thread, not just longest running thread, etc.) Results averaged over various sizes enabled with --enable_perf and 20 runs each; old dynamic bloom refers to locality=1, the faster of the old: old dynamic bloom, avg add latency = 65.6468 new dynamic bloom, avg add latency = 44.3809 old dynamic bloom, avg query latency = 50.6485 new dynamic bloom, avg query latency = 43.2186 old avg parallel add latency = 41.678 new avg parallel add latency = 24.5238 old avg parallel hit latency = 14.6322 new avg parallel hit latency = 12.3939 old avg parallel miss latency = 16.7289 new avg parallel miss latency = 12.2134 Tested on a dedicated 64-bit production machine at Facebook. Significant improvement all around. Despite now using std::atomic<uint64_t>, quick before-and-after test on a 32-bit machine (Intel Atom N270, released 2008) shows no regression in performance, in some cases modest improvement. -- Performance integration test (synthetic): with DEBUG_LEVEL=0, used TEST_TMPDIR=/dev/shm ./db_bench --benchmarks=fillrandom,readmissing,readrandom,stats --num=2000000 and optionally with -memtable_whole_key_filtering -memtable_bloom_size_ratio=0.01 300 runs each configuration. Write throughput change by enabling memtable bloom: Old locality=0: -3.06% Old locality=1: -2.37% New: -1.50% conclusion -> seems to substantially close the gap Readmissing throughput change by enabling memtable bloom: Old locality=0: +34.47% Old locality=1: +34.80% New: +33.25% conclusion -> maybe a small new penalty from FP rate Readrandom throughput change by enabling memtable bloom: Old locality=0: +31.54% Old locality=1: +31.13% New: +30.60% conclusion -> maybe also from FP rate (after memtable flush) -- Another conclusion we can draw from this new implementation is that the existing 32-bit hash function is not inherently crippling the Bloom filter speed or accuracy, below about 5 million keys. For speed, the implementation is essentially the same whether starting with 32-bits or 64-bits of hash; it just determines whether the first multiplication after fastrange is a pseudorandom expansion or needed re-mix. Note that this multiplication can occur while memory is fetching. For accuracy, in a standard configuration, you need about 5 million keys before you have about a 1.1x FP penalty due to using a 32-bit hash vs. 64-bit: [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_FROM32_any.out time: 2.52069 sampled_fp_rate: 0.0118267 ... [~/wormhashing/bloom_simulation_tests] ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out $((5 * 1000 * 1000 * 10)) 6 10 $RANDOM 100000000 ./foo_gcc_IMPL_CACHE_MUL64_BLOCK_any.out time: 2.43871 sampled_fp_rate: 0.0109059 Pull Request resolved: https://github.com/facebook/rocksdb/pull/5762 Differential Revision: D17214194 Pulled By: pdillinger fbshipit-source-id: ad9da031772e985fd6b62a0e1db8e81892520595
2019-09-05 23:57:39 +02:00
static_cast<double>(elapsed) / num_threads / num_keys,
false_positives.load() * 100.0 / num_keys);
support for concurrent adds to memtable Summary: This diff adds support for concurrent adds to the skiplist memtable implementations. Memory allocation is made thread-safe by the addition of a spinlock, with small per-core buffers to avoid contention. Concurrent memtable writes are made via an additional method and don't impose a performance overhead on the non-concurrent case, so parallelism can be selected on a per-batch basis. Write thread synchronization is an increasing bottleneck for higher levels of concurrency, so this diff adds --enable_write_thread_adaptive_yield (default off). This feature causes threads joining a write batch group to spin for a short time (default 100 usec) using sched_yield, rather than going to sleep on a mutex. If the timing of the yield calls indicates that another thread has actually run during the yield then spinning is avoided. This option improves performance for concurrent situations even without parallel adds, although it has the potential to increase CPU usage (and the heuristic adaptation is not yet mature). Parallel writes are not currently compatible with inplace updates, update callbacks, or delete filtering. Enable it with --allow_concurrent_memtable_write (and --enable_write_thread_adaptive_yield). Parallel memtable writes are performance neutral when there is no actual parallelism, and in my experiments (SSD server-class Linux and varying contention and key sizes for fillrandom) they are always a performance win when there is more than one thread. Statistics are updated earlier in the write path, dropping the number of DB mutex acquisitions from 2 to 1 for almost all cases. This diff was motivated and inspired by Yahoo's cLSM work. It is more conservative than cLSM: RocksDB's write batch group leader role is preserved (along with all of the existing flush and write throttling logic) and concurrent writers are blocked until all memtable insertions have completed and the sequence number has been advanced, to preserve linearizability. My test config is "db_bench -benchmarks=fillrandom -threads=$T -batch_size=1 -memtablerep=skip_list -value_size=100 --num=1000000/$T -level0_slowdown_writes_trigger=9999 -level0_stop_writes_trigger=9999 -disable_auto_compactions --max_write_buffer_number=8 -max_background_flushes=8 --disable_wal --write_buffer_size=160000000 --block_size=16384 --allow_concurrent_memtable_write" on a two-socket Xeon E5-2660 @ 2.2Ghz with lots of memory and an SSD hard drive. With 1 thread I get ~440Kops/sec. Peak performance for 1 socket (numactl -N1) is slightly more than 1Mops/sec, at 16 threads. Peak performance across both sockets happens at 30 threads, and is ~900Kops/sec, although with fewer threads there is less performance loss when the system has background work. Test Plan: 1. concurrent stress tests for InlineSkipList and DynamicBloom 2. make clean; make check 3. make clean; DISABLE_JEMALLOC=1 make valgrind_check; valgrind db_bench 4. make clean; COMPILE_WITH_TSAN=1 make all check; db_bench 5. make clean; COMPILE_WITH_ASAN=1 make all check; db_bench 6. make clean; OPT=-DROCKSDB_LITE make check 7. verify no perf regressions when disabled Reviewers: igor, sdong Reviewed By: sdong Subscribers: MarkCallaghan, IslamAbdelRahman, anthony, yhchiang, rven, sdong, guyg8, kradhakrishnan, dhruba Differential Revision: https://reviews.facebook.net/D50589
2015-08-15 01:59:07 +02:00
}
}
} // namespace ROCKSDB_NAMESPACE
int main(int argc, char** argv) {
rocksdb: switch to gtest Summary: Our existing test notation is very similar to what is used in gtest. It makes it easy to adopt what is different. In this diff I modify existing [[ https://code.google.com/p/googletest/wiki/Primer#Test_Fixtures:_Using_the_Same_Data_Configuration_for_Multiple_Te | test fixture ]] classes to inherit from `testing::Test`. Also for unit tests that use fixture class, `TEST` is replaced with `TEST_F` as required in gtest. There are several custom `main` functions in our existing tests. To make this transition easier, I modify all `main` functions to fallow gtest notation. But eventually we can remove them and use implementation of `main` that gtest provides. ```lang=bash % cat ~/transform #!/bin/sh files=$(git ls-files '*test\.cc') for file in $files do if grep -q "rocksdb::test::RunAllTests()" $file then if grep -Eq '^class \w+Test {' $file then perl -pi -e 's/^(class \w+Test) {/${1}: public testing::Test {/g' $file perl -pi -e 's/^(TEST)/${1}_F/g' $file fi perl -pi -e 's/(int main.*\{)/${1}::testing::InitGoogleTest(&argc, argv);/g' $file perl -pi -e 's/rocksdb::test::RunAllTests/RUN_ALL_TESTS/g' $file fi done % sh ~/transform % make format ``` Second iteration of this diff contains only scripted changes. Third iteration contains manual changes to fix last errors and make it compilable. Test Plan: Build and notice no errors. ```lang=bash % USE_CLANG=1 make check -j55 ``` Tests are still testing. Reviewers: meyering, sdong, rven, igor Reviewed By: igor Subscribers: dhruba, leveldb Differential Revision: https://reviews.facebook.net/D35157
2015-03-17 22:08:00 +01:00
::testing::InitGoogleTest(&argc, argv);
2014-05-09 17:34:18 +02:00
ParseCommandLineFlags(&argc, &argv, true);
rocksdb: switch to gtest Summary: Our existing test notation is very similar to what is used in gtest. It makes it easy to adopt what is different. In this diff I modify existing [[ https://code.google.com/p/googletest/wiki/Primer#Test_Fixtures:_Using_the_Same_Data_Configuration_for_Multiple_Te | test fixture ]] classes to inherit from `testing::Test`. Also for unit tests that use fixture class, `TEST` is replaced with `TEST_F` as required in gtest. There are several custom `main` functions in our existing tests. To make this transition easier, I modify all `main` functions to fallow gtest notation. But eventually we can remove them and use implementation of `main` that gtest provides. ```lang=bash % cat ~/transform #!/bin/sh files=$(git ls-files '*test\.cc') for file in $files do if grep -q "rocksdb::test::RunAllTests()" $file then if grep -Eq '^class \w+Test {' $file then perl -pi -e 's/^(class \w+Test) {/${1}: public testing::Test {/g' $file perl -pi -e 's/^(TEST)/${1}_F/g' $file fi perl -pi -e 's/(int main.*\{)/${1}::testing::InitGoogleTest(&argc, argv);/g' $file perl -pi -e 's/rocksdb::test::RunAllTests/RUN_ALL_TESTS/g' $file fi done % sh ~/transform % make format ``` Second iteration of this diff contains only scripted changes. Third iteration contains manual changes to fix last errors and make it compilable. Test Plan: Build and notice no errors. ```lang=bash % USE_CLANG=1 make check -j55 ``` Tests are still testing. Reviewers: meyering, sdong, rven, igor Reviewed By: igor Subscribers: dhruba, leveldb Differential Revision: https://reviews.facebook.net/D35157
2015-03-17 22:08:00 +01:00
return RUN_ALL_TESTS();
}
2014-05-09 17:34:18 +02:00
#endif // GFLAGS