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rocksdb/table/table_reader_bench.cc

346 lines
13 KiB

// 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).
#ifndef GFLAGS
#include <cstdio>
int main() {
fprintf(stderr, "Please install gflags to run rocksdb tools\n");
return 1;
}
#else
#include "db/db_impl/db_impl.h"
#include "db/dbformat.h"
#include "monitoring/histogram.h"
#include "rocksdb/db.h"
#include "rocksdb/slice_transform.h"
#include "rocksdb/table.h"
#include "table/block_based/block_based_table_factory.h"
#include "table/get_context.h"
#include "table/internal_iterator.h"
#include "table/plain/plain_table_factory.h"
#include "table/table_builder.h"
#include "test_util/testharness.h"
#include "test_util/testutil.h"
#include "util/file_reader_writer.h"
#include "util/gflags_compat.h"
using GFLAGS_NAMESPACE::ParseCommandLineFlags;
using GFLAGS_NAMESPACE::SetUsageMessage;
namespace rocksdb {
namespace {
// Make a key that i determines the first 4 characters and j determines the
// last 4 characters.
static std::string MakeKey(int i, int j, bool through_db) {
char buf[100];
snprintf(buf, sizeof(buf), "%04d__key___%04d", i, j);
if (through_db) {
return std::string(buf);
}
// If we directly query table, which operates on internal keys
// instead of user keys, we need to add 8 bytes of internal
// information (row type etc) to user key to make an internal
// key.
InternalKey key(std::string(buf), 0, ValueType::kTypeValue);
return key.Encode().ToString();
}
Benchmark table reader wiht nanoseconds Summary: nanosecnods gave us better view of the performance, especially when some operations are fast so that micro seconds may only reveal less informative results. Test Plan: sample output: ./table_reader_bench --plain_table --time_unit=nanosecond ======================================================================================================= InMemoryTableSimpleBenchmark: PlainTable num_key1: 4096 num_key2: 512 non_empty ======================================================================================================= Histogram (unit: nanosecond): Count: 6291456 Average: 475.3867 StdDev: 556.05 Min: 135.0000 Median: 400.1817 Max: 33370.0000 Percentiles: P50: 400.18 P75: 530.02 P99: 887.73 P99.9: 8843.26 P99.99: 9941.21 ------------------------------------------------------ [ 120, 140 ) 2 0.000% 0.000% [ 140, 160 ) 452 0.007% 0.007% [ 160, 180 ) 13683 0.217% 0.225% [ 180, 200 ) 54353 0.864% 1.089% [ 200, 250 ) 101004 1.605% 2.694% [ 250, 300 ) 729791 11.600% 14.294% ## [ 300, 350 ) 616070 9.792% 24.086% ## [ 350, 400 ) 1628021 25.877% 49.963% ##### [ 400, 450 ) 647220 10.287% 60.250% ## [ 450, 500 ) 577206 9.174% 69.424% ## [ 500, 600 ) 1168585 18.574% 87.999% #### [ 600, 700 ) 506875 8.057% 96.055% ## [ 700, 800 ) 147878 2.350% 98.406% [ 800, 900 ) 42633 0.678% 99.083% [ 900, 1000 ) 16304 0.259% 99.342% [ 1000, 1200 ) 7811 0.124% 99.466% [ 1200, 1400 ) 1453 0.023% 99.490% [ 1400, 1600 ) 307 0.005% 99.494% [ 1600, 1800 ) 81 0.001% 99.496% [ 1800, 2000 ) 18 0.000% 99.496% [ 2000, 2500 ) 8 0.000% 99.496% [ 2500, 3000 ) 6 0.000% 99.496% [ 3500, 4000 ) 3 0.000% 99.496% [ 4000, 4500 ) 116 0.002% 99.498% [ 4500, 5000 ) 1144 0.018% 99.516% [ 5000, 6000 ) 1087 0.017% 99.534% [ 6000, 7000 ) 2403 0.038% 99.572% [ 7000, 8000 ) 9840 0.156% 99.728% [ 8000, 9000 ) 12820 0.204% 99.932% [ 9000, 10000 ) 3881 0.062% 99.994% [ 10000, 12000 ) 135 0.002% 99.996% [ 12000, 14000 ) 159 0.003% 99.998% [ 14000, 16000 ) 58 0.001% 99.999% [ 16000, 18000 ) 30 0.000% 100.000% [ 18000, 20000 ) 14 0.000% 100.000% [ 20000, 25000 ) 2 0.000% 100.000% [ 25000, 30000 ) 2 0.000% 100.000% [ 30000, 35000 ) 1 0.000% 100.000% Reviewers: haobo, dhruba, sdong CC: leveldb Differential Revision: https://reviews.facebook.net/D16113
11 years ago
uint64_t Now(Env* env, bool measured_by_nanosecond) {
return measured_by_nanosecond ? env->NowNanos() : env->NowMicros();
}
} // namespace
Benchmark table reader wiht nanoseconds Summary: nanosecnods gave us better view of the performance, especially when some operations are fast so that micro seconds may only reveal less informative results. Test Plan: sample output: ./table_reader_bench --plain_table --time_unit=nanosecond ======================================================================================================= InMemoryTableSimpleBenchmark: PlainTable num_key1: 4096 num_key2: 512 non_empty ======================================================================================================= Histogram (unit: nanosecond): Count: 6291456 Average: 475.3867 StdDev: 556.05 Min: 135.0000 Median: 400.1817 Max: 33370.0000 Percentiles: P50: 400.18 P75: 530.02 P99: 887.73 P99.9: 8843.26 P99.99: 9941.21 ------------------------------------------------------ [ 120, 140 ) 2 0.000% 0.000% [ 140, 160 ) 452 0.007% 0.007% [ 160, 180 ) 13683 0.217% 0.225% [ 180, 200 ) 54353 0.864% 1.089% [ 200, 250 ) 101004 1.605% 2.694% [ 250, 300 ) 729791 11.600% 14.294% ## [ 300, 350 ) 616070 9.792% 24.086% ## [ 350, 400 ) 1628021 25.877% 49.963% ##### [ 400, 450 ) 647220 10.287% 60.250% ## [ 450, 500 ) 577206 9.174% 69.424% ## [ 500, 600 ) 1168585 18.574% 87.999% #### [ 600, 700 ) 506875 8.057% 96.055% ## [ 700, 800 ) 147878 2.350% 98.406% [ 800, 900 ) 42633 0.678% 99.083% [ 900, 1000 ) 16304 0.259% 99.342% [ 1000, 1200 ) 7811 0.124% 99.466% [ 1200, 1400 ) 1453 0.023% 99.490% [ 1400, 1600 ) 307 0.005% 99.494% [ 1600, 1800 ) 81 0.001% 99.496% [ 1800, 2000 ) 18 0.000% 99.496% [ 2000, 2500 ) 8 0.000% 99.496% [ 2500, 3000 ) 6 0.000% 99.496% [ 3500, 4000 ) 3 0.000% 99.496% [ 4000, 4500 ) 116 0.002% 99.498% [ 4500, 5000 ) 1144 0.018% 99.516% [ 5000, 6000 ) 1087 0.017% 99.534% [ 6000, 7000 ) 2403 0.038% 99.572% [ 7000, 8000 ) 9840 0.156% 99.728% [ 8000, 9000 ) 12820 0.204% 99.932% [ 9000, 10000 ) 3881 0.062% 99.994% [ 10000, 12000 ) 135 0.002% 99.996% [ 12000, 14000 ) 159 0.003% 99.998% [ 14000, 16000 ) 58 0.001% 99.999% [ 16000, 18000 ) 30 0.000% 100.000% [ 18000, 20000 ) 14 0.000% 100.000% [ 20000, 25000 ) 2 0.000% 100.000% [ 25000, 30000 ) 2 0.000% 100.000% [ 30000, 35000 ) 1 0.000% 100.000% Reviewers: haobo, dhruba, sdong CC: leveldb Differential Revision: https://reviews.facebook.net/D16113
11 years ago
// A very simple benchmark that.
// Create a table with roughly numKey1 * numKey2 keys,
// where there are numKey1 prefixes of the key, each has numKey2 number of
// distinguished key, differing in the suffix part.
// If if_query_empty_keys = false, query the existing keys numKey1 * numKey2
// times randomly.
// If if_query_empty_keys = true, query numKey1 * numKey2 random empty keys.
// Print out the total time.
// If through_db=true, a full DB will be created and queries will be against
// it. Otherwise, operations will be directly through table level.
//
// If for_terator=true, instead of just query one key each time, it queries
// a range sharing the same prefix.
namespace {
void TableReaderBenchmark(Options& opts, EnvOptions& env_options,
ReadOptions& read_options, int num_keys1,
int num_keys2, int num_iter, int /*prefix_len*/,
bool if_query_empty_keys, bool for_iterator,
Benchmark table reader wiht nanoseconds Summary: nanosecnods gave us better view of the performance, especially when some operations are fast so that micro seconds may only reveal less informative results. Test Plan: sample output: ./table_reader_bench --plain_table --time_unit=nanosecond ======================================================================================================= InMemoryTableSimpleBenchmark: PlainTable num_key1: 4096 num_key2: 512 non_empty ======================================================================================================= Histogram (unit: nanosecond): Count: 6291456 Average: 475.3867 StdDev: 556.05 Min: 135.0000 Median: 400.1817 Max: 33370.0000 Percentiles: P50: 400.18 P75: 530.02 P99: 887.73 P99.9: 8843.26 P99.99: 9941.21 ------------------------------------------------------ [ 120, 140 ) 2 0.000% 0.000% [ 140, 160 ) 452 0.007% 0.007% [ 160, 180 ) 13683 0.217% 0.225% [ 180, 200 ) 54353 0.864% 1.089% [ 200, 250 ) 101004 1.605% 2.694% [ 250, 300 ) 729791 11.600% 14.294% ## [ 300, 350 ) 616070 9.792% 24.086% ## [ 350, 400 ) 1628021 25.877% 49.963% ##### [ 400, 450 ) 647220 10.287% 60.250% ## [ 450, 500 ) 577206 9.174% 69.424% ## [ 500, 600 ) 1168585 18.574% 87.999% #### [ 600, 700 ) 506875 8.057% 96.055% ## [ 700, 800 ) 147878 2.350% 98.406% [ 800, 900 ) 42633 0.678% 99.083% [ 900, 1000 ) 16304 0.259% 99.342% [ 1000, 1200 ) 7811 0.124% 99.466% [ 1200, 1400 ) 1453 0.023% 99.490% [ 1400, 1600 ) 307 0.005% 99.494% [ 1600, 1800 ) 81 0.001% 99.496% [ 1800, 2000 ) 18 0.000% 99.496% [ 2000, 2500 ) 8 0.000% 99.496% [ 2500, 3000 ) 6 0.000% 99.496% [ 3500, 4000 ) 3 0.000% 99.496% [ 4000, 4500 ) 116 0.002% 99.498% [ 4500, 5000 ) 1144 0.018% 99.516% [ 5000, 6000 ) 1087 0.017% 99.534% [ 6000, 7000 ) 2403 0.038% 99.572% [ 7000, 8000 ) 9840 0.156% 99.728% [ 8000, 9000 ) 12820 0.204% 99.932% [ 9000, 10000 ) 3881 0.062% 99.994% [ 10000, 12000 ) 135 0.002% 99.996% [ 12000, 14000 ) 159 0.003% 99.998% [ 14000, 16000 ) 58 0.001% 99.999% [ 16000, 18000 ) 30 0.000% 100.000% [ 18000, 20000 ) 14 0.000% 100.000% [ 20000, 25000 ) 2 0.000% 100.000% [ 25000, 30000 ) 2 0.000% 100.000% [ 30000, 35000 ) 1 0.000% 100.000% Reviewers: haobo, dhruba, sdong CC: leveldb Differential Revision: https://reviews.facebook.net/D16113
11 years ago
bool through_db, bool measured_by_nanosecond) {
rocksdb::InternalKeyComparator ikc(opts.comparator);
std::string file_name =
test::PerThreadDBPath("rocksdb_table_reader_benchmark");
std::string dbname = test::PerThreadDBPath("rocksdb_table_reader_bench_db");
WriteOptions wo;
Env* env = Env::Default();
TableBuilder* tb = nullptr;
DB* db = nullptr;
Status s;
const ImmutableCFOptions ioptions(opts);
const ColumnFamilyOptions cfo(opts);
const MutableCFOptions moptions(cfo);
std::unique_ptr<WritableFileWriter> file_writer;
if (!through_db) {
std::unique_ptr<WritableFile> file;
env->NewWritableFile(file_name, &file, env_options);
std::vector<std::unique_ptr<IntTblPropCollectorFactory> >
int_tbl_prop_collector_factories;
file_writer.reset(
new WritableFileWriter(std::move(file), file_name, env_options));
int unknown_level = -1;
tb = opts.table_factory->NewTableBuilder(
TableBuilderOptions(
ioptions, moptions, ikc, &int_tbl_prop_collector_factories,
CompressionType::kNoCompression, 0 /* sample_for_compression */,
CompressionOptions(), false /* skip_filters */,
kDefaultColumnFamilyName, unknown_level),
0 /* column_family_id */, file_writer.get());
} else {
s = DB::Open(opts, dbname, &db);
ASSERT_OK(s);
ASSERT_TRUE(db != nullptr);
}
// Populate slightly more than 1M keys
for (int i = 0; i < num_keys1; i++) {
for (int j = 0; j < num_keys2; j++) {
std::string key = MakeKey(i * 2, j, through_db);
if (!through_db) {
tb->Add(key, key);
} else {
db->Put(wo, key, key);
}
}
}
if (!through_db) {
tb->Finish();
file_writer->Close();
} else {
db->Flush(FlushOptions());
}
std::unique_ptr<TableReader> table_reader;
if (!through_db) {
std::unique_ptr<RandomAccessFile> raf;
s = env->NewRandomAccessFile(file_name, &raf, env_options);
if (!s.ok()) {
fprintf(stderr, "Create File Error: %s\n", s.ToString().c_str());
exit(1);
}
uint64_t file_size;
env->GetFileSize(file_name, &file_size);
std::unique_ptr<RandomAccessFileReader> file_reader(
new RandomAccessFileReader(std::move(raf), file_name));
s = opts.table_factory->NewTableReader(
TableReaderOptions(ioptions, moptions.prefix_extractor.get(),
env_options, ikc),
std::move(file_reader), file_size, &table_reader);
if (!s.ok()) {
fprintf(stderr, "Open Table Error: %s\n", s.ToString().c_str());
exit(1);
}
}
Random rnd(301);
std::string result;
HistogramImpl hist;
for (int it = 0; it < num_iter; it++) {
for (int i = 0; i < num_keys1; i++) {
for (int j = 0; j < num_keys2; j++) {
int r1 = rnd.Uniform(num_keys1) * 2;
int r2 = rnd.Uniform(num_keys2);
if (if_query_empty_keys) {
r1++;
r2 = num_keys2 * 2 - r2;
}
if (!for_iterator) {
// Query one existing key;
std::string key = MakeKey(r1, r2, through_db);
uint64_t start_time = Now(env, measured_by_nanosecond);
if (!through_db) {
PinnableSlice value;
MergeContext merge_context;
Use only "local" range tombstones during Get (#4449) Summary: Previously, range tombstones were accumulated from every level, which was necessary if a range tombstone in a higher level covered a key in a lower level. However, RangeDelAggregator::AddTombstones's complexity is based on the number of tombstones that are currently stored in it, which is wasteful in the Get case, where we only need to know the highest sequence number of range tombstones that cover the key from higher levels, and compute the highest covering sequence number at the current level. This change introduces this optimization, and removes the use of RangeDelAggregator from the Get path. In the benchmark results, the following command was used to initialize the database: ``` ./db_bench -db=/dev/shm/5k-rts -use_existing_db=false -benchmarks=filluniquerandom -write_buffer_size=1048576 -compression_type=lz4 -target_file_size_base=1048576 -max_bytes_for_level_base=4194304 -value_size=112 -key_size=16 -block_size=4096 -level_compaction_dynamic_level_bytes=true -num=5000000 -max_background_jobs=12 -benchmark_write_rate_limit=20971520 -range_tombstone_width=100 -writes_per_range_tombstone=100 -max_num_range_tombstones=50000 -bloom_bits=8 ``` ...and the following command was used to measure read throughput: ``` ./db_bench -db=/dev/shm/5k-rts/ -use_existing_db=true -benchmarks=readrandom -disable_auto_compactions=true -num=5000000 -reads=100000 -threads=32 ``` The filluniquerandom command was only run once, and the resulting database was used to measure read performance before and after the PR. Both binaries were compiled with `DEBUG_LEVEL=0`. Readrandom results before PR: ``` readrandom : 4.544 micros/op 220090 ops/sec; 16.9 MB/s (63103 of 100000 found) ``` Readrandom results after PR: ``` readrandom : 11.147 micros/op 89707 ops/sec; 6.9 MB/s (63103 of 100000 found) ``` So it's actually slower right now, but this PR paves the way for future optimizations (see #4493). ---- Pull Request resolved: https://github.com/facebook/rocksdb/pull/4449 Differential Revision: D10370575 Pulled By: abhimadan fbshipit-source-id: 9a2e152be1ef36969055c0e9eb4beb0d96c11f4d
6 years ago
SequenceNumber max_covering_tombstone_seq = 0;
GetContext get_context(ioptions.user_comparator,
ioptions.merge_operator, ioptions.info_log,
ioptions.statistics, GetContext::kNotFound,
Slice(key), &value, nullptr, &merge_context,
Use only "local" range tombstones during Get (#4449) Summary: Previously, range tombstones were accumulated from every level, which was necessary if a range tombstone in a higher level covered a key in a lower level. However, RangeDelAggregator::AddTombstones's complexity is based on the number of tombstones that are currently stored in it, which is wasteful in the Get case, where we only need to know the highest sequence number of range tombstones that cover the key from higher levels, and compute the highest covering sequence number at the current level. This change introduces this optimization, and removes the use of RangeDelAggregator from the Get path. In the benchmark results, the following command was used to initialize the database: ``` ./db_bench -db=/dev/shm/5k-rts -use_existing_db=false -benchmarks=filluniquerandom -write_buffer_size=1048576 -compression_type=lz4 -target_file_size_base=1048576 -max_bytes_for_level_base=4194304 -value_size=112 -key_size=16 -block_size=4096 -level_compaction_dynamic_level_bytes=true -num=5000000 -max_background_jobs=12 -benchmark_write_rate_limit=20971520 -range_tombstone_width=100 -writes_per_range_tombstone=100 -max_num_range_tombstones=50000 -bloom_bits=8 ``` ...and the following command was used to measure read throughput: ``` ./db_bench -db=/dev/shm/5k-rts/ -use_existing_db=true -benchmarks=readrandom -disable_auto_compactions=true -num=5000000 -reads=100000 -threads=32 ``` The filluniquerandom command was only run once, and the resulting database was used to measure read performance before and after the PR. Both binaries were compiled with `DEBUG_LEVEL=0`. Readrandom results before PR: ``` readrandom : 4.544 micros/op 220090 ops/sec; 16.9 MB/s (63103 of 100000 found) ``` Readrandom results after PR: ``` readrandom : 11.147 micros/op 89707 ops/sec; 6.9 MB/s (63103 of 100000 found) ``` So it's actually slower right now, but this PR paves the way for future optimizations (see #4493). ---- Pull Request resolved: https://github.com/facebook/rocksdb/pull/4449 Differential Revision: D10370575 Pulled By: abhimadan fbshipit-source-id: 9a2e152be1ef36969055c0e9eb4beb0d96c11f4d
6 years ago
&max_covering_tombstone_seq, env);
s = table_reader->Get(read_options, key, &get_context, nullptr);
} else {
s = db->Get(read_options, key, &result);
}
hist.Add(Now(env, measured_by_nanosecond) - start_time);
} else {
int r2_len;
if (if_query_empty_keys) {
r2_len = 0;
} else {
r2_len = rnd.Uniform(num_keys2) + 1;
if (r2_len + r2 > num_keys2) {
r2_len = num_keys2 - r2;
}
}
std::string start_key = MakeKey(r1, r2, through_db);
std::string end_key = MakeKey(r1, r2 + r2_len, through_db);
uint64_t total_time = 0;
uint64_t start_time = Now(env, measured_by_nanosecond);
Iterator* iter = nullptr;
InternalIterator* iiter = nullptr;
if (!through_db) {
iiter = table_reader->NewIterator(
read_options, /*prefix_extractor=*/nullptr, /*arena=*/nullptr,
/*skip_filters=*/false, TableReaderCaller::kUncategorized);
} else {
iter = db->NewIterator(read_options);
}
int count = 0;
for (through_db ? iter->Seek(start_key) : iiter->Seek(start_key);
through_db ? iter->Valid() : iiter->Valid();
through_db ? iter->Next() : iiter->Next()) {
if (if_query_empty_keys) {
break;
}
// verify key;
total_time += Now(env, measured_by_nanosecond) - start_time;
assert(Slice(MakeKey(r1, r2 + count, through_db)) ==
(through_db ? iter->key() : iiter->key()));
start_time = Now(env, measured_by_nanosecond);
if (++count >= r2_len) {
break;
}
}
if (count != r2_len) {
fprintf(
stderr, "Iterator cannot iterate expected number of entries. "
"Expected %d but got %d\n", r2_len, count);
assert(false);
}
delete iter;
total_time += Now(env, measured_by_nanosecond) - start_time;
hist.Add(total_time);
}
}
}
}
fprintf(
stderr,
"==================================================="
"====================================================\n"
"InMemoryTableSimpleBenchmark: %20s num_key1: %5d "
"num_key2: %5d %10s\n"
"==================================================="
"===================================================="
Benchmark table reader wiht nanoseconds Summary: nanosecnods gave us better view of the performance, especially when some operations are fast so that micro seconds may only reveal less informative results. Test Plan: sample output: ./table_reader_bench --plain_table --time_unit=nanosecond ======================================================================================================= InMemoryTableSimpleBenchmark: PlainTable num_key1: 4096 num_key2: 512 non_empty ======================================================================================================= Histogram (unit: nanosecond): Count: 6291456 Average: 475.3867 StdDev: 556.05 Min: 135.0000 Median: 400.1817 Max: 33370.0000 Percentiles: P50: 400.18 P75: 530.02 P99: 887.73 P99.9: 8843.26 P99.99: 9941.21 ------------------------------------------------------ [ 120, 140 ) 2 0.000% 0.000% [ 140, 160 ) 452 0.007% 0.007% [ 160, 180 ) 13683 0.217% 0.225% [ 180, 200 ) 54353 0.864% 1.089% [ 200, 250 ) 101004 1.605% 2.694% [ 250, 300 ) 729791 11.600% 14.294% ## [ 300, 350 ) 616070 9.792% 24.086% ## [ 350, 400 ) 1628021 25.877% 49.963% ##### [ 400, 450 ) 647220 10.287% 60.250% ## [ 450, 500 ) 577206 9.174% 69.424% ## [ 500, 600 ) 1168585 18.574% 87.999% #### [ 600, 700 ) 506875 8.057% 96.055% ## [ 700, 800 ) 147878 2.350% 98.406% [ 800, 900 ) 42633 0.678% 99.083% [ 900, 1000 ) 16304 0.259% 99.342% [ 1000, 1200 ) 7811 0.124% 99.466% [ 1200, 1400 ) 1453 0.023% 99.490% [ 1400, 1600 ) 307 0.005% 99.494% [ 1600, 1800 ) 81 0.001% 99.496% [ 1800, 2000 ) 18 0.000% 99.496% [ 2000, 2500 ) 8 0.000% 99.496% [ 2500, 3000 ) 6 0.000% 99.496% [ 3500, 4000 ) 3 0.000% 99.496% [ 4000, 4500 ) 116 0.002% 99.498% [ 4500, 5000 ) 1144 0.018% 99.516% [ 5000, 6000 ) 1087 0.017% 99.534% [ 6000, 7000 ) 2403 0.038% 99.572% [ 7000, 8000 ) 9840 0.156% 99.728% [ 8000, 9000 ) 12820 0.204% 99.932% [ 9000, 10000 ) 3881 0.062% 99.994% [ 10000, 12000 ) 135 0.002% 99.996% [ 12000, 14000 ) 159 0.003% 99.998% [ 14000, 16000 ) 58 0.001% 99.999% [ 16000, 18000 ) 30 0.000% 100.000% [ 18000, 20000 ) 14 0.000% 100.000% [ 20000, 25000 ) 2 0.000% 100.000% [ 25000, 30000 ) 2 0.000% 100.000% [ 30000, 35000 ) 1 0.000% 100.000% Reviewers: haobo, dhruba, sdong CC: leveldb Differential Revision: https://reviews.facebook.net/D16113
11 years ago
"\nHistogram (unit: %s): \n%s",
opts.table_factory->Name(), num_keys1, num_keys2,
Benchmark table reader wiht nanoseconds Summary: nanosecnods gave us better view of the performance, especially when some operations are fast so that micro seconds may only reveal less informative results. Test Plan: sample output: ./table_reader_bench --plain_table --time_unit=nanosecond ======================================================================================================= InMemoryTableSimpleBenchmark: PlainTable num_key1: 4096 num_key2: 512 non_empty ======================================================================================================= Histogram (unit: nanosecond): Count: 6291456 Average: 475.3867 StdDev: 556.05 Min: 135.0000 Median: 400.1817 Max: 33370.0000 Percentiles: P50: 400.18 P75: 530.02 P99: 887.73 P99.9: 8843.26 P99.99: 9941.21 ------------------------------------------------------ [ 120, 140 ) 2 0.000% 0.000% [ 140, 160 ) 452 0.007% 0.007% [ 160, 180 ) 13683 0.217% 0.225% [ 180, 200 ) 54353 0.864% 1.089% [ 200, 250 ) 101004 1.605% 2.694% [ 250, 300 ) 729791 11.600% 14.294% ## [ 300, 350 ) 616070 9.792% 24.086% ## [ 350, 400 ) 1628021 25.877% 49.963% ##### [ 400, 450 ) 647220 10.287% 60.250% ## [ 450, 500 ) 577206 9.174% 69.424% ## [ 500, 600 ) 1168585 18.574% 87.999% #### [ 600, 700 ) 506875 8.057% 96.055% ## [ 700, 800 ) 147878 2.350% 98.406% [ 800, 900 ) 42633 0.678% 99.083% [ 900, 1000 ) 16304 0.259% 99.342% [ 1000, 1200 ) 7811 0.124% 99.466% [ 1200, 1400 ) 1453 0.023% 99.490% [ 1400, 1600 ) 307 0.005% 99.494% [ 1600, 1800 ) 81 0.001% 99.496% [ 1800, 2000 ) 18 0.000% 99.496% [ 2000, 2500 ) 8 0.000% 99.496% [ 2500, 3000 ) 6 0.000% 99.496% [ 3500, 4000 ) 3 0.000% 99.496% [ 4000, 4500 ) 116 0.002% 99.498% [ 4500, 5000 ) 1144 0.018% 99.516% [ 5000, 6000 ) 1087 0.017% 99.534% [ 6000, 7000 ) 2403 0.038% 99.572% [ 7000, 8000 ) 9840 0.156% 99.728% [ 8000, 9000 ) 12820 0.204% 99.932% [ 9000, 10000 ) 3881 0.062% 99.994% [ 10000, 12000 ) 135 0.002% 99.996% [ 12000, 14000 ) 159 0.003% 99.998% [ 14000, 16000 ) 58 0.001% 99.999% [ 16000, 18000 ) 30 0.000% 100.000% [ 18000, 20000 ) 14 0.000% 100.000% [ 20000, 25000 ) 2 0.000% 100.000% [ 25000, 30000 ) 2 0.000% 100.000% [ 30000, 35000 ) 1 0.000% 100.000% Reviewers: haobo, dhruba, sdong CC: leveldb Differential Revision: https://reviews.facebook.net/D16113
11 years ago
for_iterator ? "iterator" : (if_query_empty_keys ? "empty" : "non_empty"),
measured_by_nanosecond ? "nanosecond" : "microsecond",
hist.ToString().c_str());
if (!through_db) {
env->DeleteFile(file_name);
} else {
delete db;
db = nullptr;
DestroyDB(dbname, opts);
}
}
} // namespace
Benchmark table reader wiht nanoseconds Summary: nanosecnods gave us better view of the performance, especially when some operations are fast so that micro seconds may only reveal less informative results. Test Plan: sample output: ./table_reader_bench --plain_table --time_unit=nanosecond ======================================================================================================= InMemoryTableSimpleBenchmark: PlainTable num_key1: 4096 num_key2: 512 non_empty ======================================================================================================= Histogram (unit: nanosecond): Count: 6291456 Average: 475.3867 StdDev: 556.05 Min: 135.0000 Median: 400.1817 Max: 33370.0000 Percentiles: P50: 400.18 P75: 530.02 P99: 887.73 P99.9: 8843.26 P99.99: 9941.21 ------------------------------------------------------ [ 120, 140 ) 2 0.000% 0.000% [ 140, 160 ) 452 0.007% 0.007% [ 160, 180 ) 13683 0.217% 0.225% [ 180, 200 ) 54353 0.864% 1.089% [ 200, 250 ) 101004 1.605% 2.694% [ 250, 300 ) 729791 11.600% 14.294% ## [ 300, 350 ) 616070 9.792% 24.086% ## [ 350, 400 ) 1628021 25.877% 49.963% ##### [ 400, 450 ) 647220 10.287% 60.250% ## [ 450, 500 ) 577206 9.174% 69.424% ## [ 500, 600 ) 1168585 18.574% 87.999% #### [ 600, 700 ) 506875 8.057% 96.055% ## [ 700, 800 ) 147878 2.350% 98.406% [ 800, 900 ) 42633 0.678% 99.083% [ 900, 1000 ) 16304 0.259% 99.342% [ 1000, 1200 ) 7811 0.124% 99.466% [ 1200, 1400 ) 1453 0.023% 99.490% [ 1400, 1600 ) 307 0.005% 99.494% [ 1600, 1800 ) 81 0.001% 99.496% [ 1800, 2000 ) 18 0.000% 99.496% [ 2000, 2500 ) 8 0.000% 99.496% [ 2500, 3000 ) 6 0.000% 99.496% [ 3500, 4000 ) 3 0.000% 99.496% [ 4000, 4500 ) 116 0.002% 99.498% [ 4500, 5000 ) 1144 0.018% 99.516% [ 5000, 6000 ) 1087 0.017% 99.534% [ 6000, 7000 ) 2403 0.038% 99.572% [ 7000, 8000 ) 9840 0.156% 99.728% [ 8000, 9000 ) 12820 0.204% 99.932% [ 9000, 10000 ) 3881 0.062% 99.994% [ 10000, 12000 ) 135 0.002% 99.996% [ 12000, 14000 ) 159 0.003% 99.998% [ 14000, 16000 ) 58 0.001% 99.999% [ 16000, 18000 ) 30 0.000% 100.000% [ 18000, 20000 ) 14 0.000% 100.000% [ 20000, 25000 ) 2 0.000% 100.000% [ 25000, 30000 ) 2 0.000% 100.000% [ 30000, 35000 ) 1 0.000% 100.000% Reviewers: haobo, dhruba, sdong CC: leveldb Differential Revision: https://reviews.facebook.net/D16113
11 years ago
} // namespace rocksdb
DEFINE_bool(query_empty, false, "query non-existing keys instead of existing "
"ones.");
DEFINE_int32(num_keys1, 4096, "number of distinguish prefix of keys");
DEFINE_int32(num_keys2, 512, "number of distinguish keys for each prefix");
DEFINE_int32(iter, 3, "query non-existing keys instead of existing ones");
DEFINE_int32(prefix_len, 16, "Prefix length used for iterators and indexes");
DEFINE_bool(iterator, false, "For test iterator");
DEFINE_bool(through_db, false, "If enable, a DB instance will be created and "
"the query will be against DB. Otherwise, will be directly against "
"a table reader.");
DEFINE_bool(mmap_read, true, "Whether use mmap read");
DEFINE_string(table_factory, "block_based",
"Table factory to use: `block_based` (default), `plain_table` or "
"`cuckoo_hash`.");
Benchmark table reader wiht nanoseconds Summary: nanosecnods gave us better view of the performance, especially when some operations are fast so that micro seconds may only reveal less informative results. Test Plan: sample output: ./table_reader_bench --plain_table --time_unit=nanosecond ======================================================================================================= InMemoryTableSimpleBenchmark: PlainTable num_key1: 4096 num_key2: 512 non_empty ======================================================================================================= Histogram (unit: nanosecond): Count: 6291456 Average: 475.3867 StdDev: 556.05 Min: 135.0000 Median: 400.1817 Max: 33370.0000 Percentiles: P50: 400.18 P75: 530.02 P99: 887.73 P99.9: 8843.26 P99.99: 9941.21 ------------------------------------------------------ [ 120, 140 ) 2 0.000% 0.000% [ 140, 160 ) 452 0.007% 0.007% [ 160, 180 ) 13683 0.217% 0.225% [ 180, 200 ) 54353 0.864% 1.089% [ 200, 250 ) 101004 1.605% 2.694% [ 250, 300 ) 729791 11.600% 14.294% ## [ 300, 350 ) 616070 9.792% 24.086% ## [ 350, 400 ) 1628021 25.877% 49.963% ##### [ 400, 450 ) 647220 10.287% 60.250% ## [ 450, 500 ) 577206 9.174% 69.424% ## [ 500, 600 ) 1168585 18.574% 87.999% #### [ 600, 700 ) 506875 8.057% 96.055% ## [ 700, 800 ) 147878 2.350% 98.406% [ 800, 900 ) 42633 0.678% 99.083% [ 900, 1000 ) 16304 0.259% 99.342% [ 1000, 1200 ) 7811 0.124% 99.466% [ 1200, 1400 ) 1453 0.023% 99.490% [ 1400, 1600 ) 307 0.005% 99.494% [ 1600, 1800 ) 81 0.001% 99.496% [ 1800, 2000 ) 18 0.000% 99.496% [ 2000, 2500 ) 8 0.000% 99.496% [ 2500, 3000 ) 6 0.000% 99.496% [ 3500, 4000 ) 3 0.000% 99.496% [ 4000, 4500 ) 116 0.002% 99.498% [ 4500, 5000 ) 1144 0.018% 99.516% [ 5000, 6000 ) 1087 0.017% 99.534% [ 6000, 7000 ) 2403 0.038% 99.572% [ 7000, 8000 ) 9840 0.156% 99.728% [ 8000, 9000 ) 12820 0.204% 99.932% [ 9000, 10000 ) 3881 0.062% 99.994% [ 10000, 12000 ) 135 0.002% 99.996% [ 12000, 14000 ) 159 0.003% 99.998% [ 14000, 16000 ) 58 0.001% 99.999% [ 16000, 18000 ) 30 0.000% 100.000% [ 18000, 20000 ) 14 0.000% 100.000% [ 20000, 25000 ) 2 0.000% 100.000% [ 25000, 30000 ) 2 0.000% 100.000% [ 30000, 35000 ) 1 0.000% 100.000% Reviewers: haobo, dhruba, sdong CC: leveldb Differential Revision: https://reviews.facebook.net/D16113
11 years ago
DEFINE_string(time_unit, "microsecond",
"The time unit used for measuring performance. User can specify "
"`microsecond` (default) or `nanosecond`");
int main(int argc, char** argv) {
SetUsageMessage(std::string("\nUSAGE:\n") + std::string(argv[0]) +
" [OPTIONS]...");
ParseCommandLineFlags(&argc, &argv, true);
std::shared_ptr<rocksdb::TableFactory> tf;
rocksdb::Options options;
if (FLAGS_prefix_len < 16) {
options.prefix_extractor.reset(rocksdb::NewFixedPrefixTransform(
FLAGS_prefix_len));
}
rocksdb::ReadOptions ro;
rocksdb::EnvOptions env_options;
options.create_if_missing = true;
options.compression = rocksdb::CompressionType::kNoCompression;
if (FLAGS_table_factory == "cuckoo_hash") {
#ifndef ROCKSDB_LITE
options.allow_mmap_reads = FLAGS_mmap_read;
env_options.use_mmap_reads = FLAGS_mmap_read;
CuckooTable: add one option to allow identity function for the first hash function Summary: MurmurHash becomes expensive when we do millions Get() a second in one thread. Add this option to allow the first hash function to use identity function as hash function. It results in QPS increase from 3.7M/s to ~4.3M/s. I did not observe improvement for end to end RocksDB performance. This may be caused by other bottlenecks that I will address in a separate diff. Test Plan: ``` [ljin@dev1964 rocksdb] ./cuckoo_table_reader_test --enable_perf --file_dir=/dev/shm --write --identity_as_first_hash=0 ==== Test CuckooReaderTest.WhenKeyExists ==== Test CuckooReaderTest.WhenKeyExistsWithUint64Comparator ==== Test CuckooReaderTest.CheckIterator ==== Test CuckooReaderTest.CheckIteratorUint64 ==== Test CuckooReaderTest.WhenKeyNotFound ==== Test CuckooReaderTest.TestReadPerformance With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.272us (3.7 Mqps) with batch size of 0, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.138us (7.2 Mqps) with batch size of 10, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.142us (7.1 Mqps) with batch size of 25, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.142us (7.0 Mqps) with batch size of 50, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.144us (6.9 Mqps) with batch size of 100, # of found keys 125829120 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.201us (5.0 Mqps) with batch size of 0, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.121us (8.3 Mqps) with batch size of 10, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.123us (8.1 Mqps) with batch size of 25, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.121us (8.3 Mqps) with batch size of 50, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.112us (8.9 Mqps) with batch size of 100, # of found keys 104857600 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.251us (4.0 Mqps) with batch size of 0, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.107us (9.4 Mqps) with batch size of 10, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.099us (10.1 Mqps) with batch size of 25, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.100us (10.0 Mqps) with batch size of 50, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.116us (8.6 Mqps) with batch size of 100, # of found keys 83886080 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.189us (5.3 Mqps) with batch size of 0, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.095us (10.5 Mqps) with batch size of 10, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.096us (10.4 Mqps) with batch size of 25, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.098us (10.2 Mqps) with batch size of 50, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.105us (9.5 Mqps) with batch size of 100, # of found keys 73400320 [ljin@dev1964 rocksdb] ./cuckoo_table_reader_test --enable_perf --file_dir=/dev/shm --write --identity_as_first_hash=1 ==== Test CuckooReaderTest.WhenKeyExists ==== Test CuckooReaderTest.WhenKeyExistsWithUint64Comparator ==== Test CuckooReaderTest.CheckIterator ==== Test CuckooReaderTest.CheckIteratorUint64 ==== Test CuckooReaderTest.WhenKeyNotFound ==== Test CuckooReaderTest.TestReadPerformance With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.230us (4.3 Mqps) with batch size of 0, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.086us (11.7 Mqps) with batch size of 10, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.088us (11.3 Mqps) with batch size of 25, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.083us (12.1 Mqps) with batch size of 50, # of found keys 125829120 With 125829120 items, utilization is 93.75%, number of hash functions: 2. Time taken per op is 0.083us (12.1 Mqps) with batch size of 100, # of found keys 125829120 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.159us (6.3 Mqps) with batch size of 0, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.078us (12.8 Mqps) with batch size of 10, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.080us (12.6 Mqps) with batch size of 25, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.080us (12.5 Mqps) with batch size of 50, # of found keys 104857600 With 104857600 items, utilization is 78.12%, number of hash functions: 2. Time taken per op is 0.082us (12.2 Mqps) with batch size of 100, # of found keys 104857600 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.154us (6.5 Mqps) with batch size of 0, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.077us (13.0 Mqps) with batch size of 10, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.077us (12.9 Mqps) with batch size of 25, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.078us (12.8 Mqps) with batch size of 50, # of found keys 83886080 With 83886080 items, utilization is 62.50%, number of hash functions: 2. Time taken per op is 0.079us (12.6 Mqps) with batch size of 100, # of found keys 83886080 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.218us (4.6 Mqps) with batch size of 0, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.083us (12.0 Mqps) with batch size of 10, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.085us (11.7 Mqps) with batch size of 25, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.086us (11.6 Mqps) with batch size of 50, # of found keys 73400320 With 73400320 items, utilization is 54.69%, number of hash functions: 2. Time taken per op is 0.078us (12.8 Mqps) with batch size of 100, # of found keys 73400320 ``` Reviewers: sdong, igor, yhchiang Reviewed By: igor Subscribers: leveldb Differential Revision: https://reviews.facebook.net/D23451
10 years ago
rocksdb::CuckooTableOptions table_options;
table_options.hash_table_ratio = 0.75;
tf.reset(rocksdb::NewCuckooTableFactory(table_options));
#else
fprintf(stderr, "Plain table is not supported in lite mode\n");
exit(1);
#endif // ROCKSDB_LITE
} else if (FLAGS_table_factory == "plain_table") {
#ifndef ROCKSDB_LITE
options.allow_mmap_reads = FLAGS_mmap_read;
env_options.use_mmap_reads = FLAGS_mmap_read;
rocksdb::PlainTableOptions plain_table_options;
plain_table_options.user_key_len = 16;
plain_table_options.bloom_bits_per_key = (FLAGS_prefix_len == 16) ? 0 : 8;
plain_table_options.hash_table_ratio = 0.75;
tf.reset(new rocksdb::PlainTableFactory(plain_table_options));
options.prefix_extractor.reset(rocksdb::NewFixedPrefixTransform(
FLAGS_prefix_len));
#else
fprintf(stderr, "Cuckoo table is not supported in lite mode\n");
exit(1);
#endif // ROCKSDB_LITE
} else if (FLAGS_table_factory == "block_based") {
tf.reset(new rocksdb::BlockBasedTableFactory());
} else {
fprintf(stderr, "Invalid table type %s\n", FLAGS_table_factory.c_str());
}
if (tf) {
// if user provides invalid options, just fall back to microsecond.
bool measured_by_nanosecond = FLAGS_time_unit == "nanosecond";
options.table_factory = tf;
rocksdb::TableReaderBenchmark(options, env_options, ro, FLAGS_num_keys1,
FLAGS_num_keys2, FLAGS_iter, FLAGS_prefix_len,
FLAGS_query_empty, FLAGS_iterator,
FLAGS_through_db, measured_by_nanosecond);
} else {
return 1;
}
Benchmark table reader wiht nanoseconds Summary: nanosecnods gave us better view of the performance, especially when some operations are fast so that micro seconds may only reveal less informative results. Test Plan: sample output: ./table_reader_bench --plain_table --time_unit=nanosecond ======================================================================================================= InMemoryTableSimpleBenchmark: PlainTable num_key1: 4096 num_key2: 512 non_empty ======================================================================================================= Histogram (unit: nanosecond): Count: 6291456 Average: 475.3867 StdDev: 556.05 Min: 135.0000 Median: 400.1817 Max: 33370.0000 Percentiles: P50: 400.18 P75: 530.02 P99: 887.73 P99.9: 8843.26 P99.99: 9941.21 ------------------------------------------------------ [ 120, 140 ) 2 0.000% 0.000% [ 140, 160 ) 452 0.007% 0.007% [ 160, 180 ) 13683 0.217% 0.225% [ 180, 200 ) 54353 0.864% 1.089% [ 200, 250 ) 101004 1.605% 2.694% [ 250, 300 ) 729791 11.600% 14.294% ## [ 300, 350 ) 616070 9.792% 24.086% ## [ 350, 400 ) 1628021 25.877% 49.963% ##### [ 400, 450 ) 647220 10.287% 60.250% ## [ 450, 500 ) 577206 9.174% 69.424% ## [ 500, 600 ) 1168585 18.574% 87.999% #### [ 600, 700 ) 506875 8.057% 96.055% ## [ 700, 800 ) 147878 2.350% 98.406% [ 800, 900 ) 42633 0.678% 99.083% [ 900, 1000 ) 16304 0.259% 99.342% [ 1000, 1200 ) 7811 0.124% 99.466% [ 1200, 1400 ) 1453 0.023% 99.490% [ 1400, 1600 ) 307 0.005% 99.494% [ 1600, 1800 ) 81 0.001% 99.496% [ 1800, 2000 ) 18 0.000% 99.496% [ 2000, 2500 ) 8 0.000% 99.496% [ 2500, 3000 ) 6 0.000% 99.496% [ 3500, 4000 ) 3 0.000% 99.496% [ 4000, 4500 ) 116 0.002% 99.498% [ 4500, 5000 ) 1144 0.018% 99.516% [ 5000, 6000 ) 1087 0.017% 99.534% [ 6000, 7000 ) 2403 0.038% 99.572% [ 7000, 8000 ) 9840 0.156% 99.728% [ 8000, 9000 ) 12820 0.204% 99.932% [ 9000, 10000 ) 3881 0.062% 99.994% [ 10000, 12000 ) 135 0.002% 99.996% [ 12000, 14000 ) 159 0.003% 99.998% [ 14000, 16000 ) 58 0.001% 99.999% [ 16000, 18000 ) 30 0.000% 100.000% [ 18000, 20000 ) 14 0.000% 100.000% [ 20000, 25000 ) 2 0.000% 100.000% [ 25000, 30000 ) 2 0.000% 100.000% [ 30000, 35000 ) 1 0.000% 100.000% Reviewers: haobo, dhruba, sdong CC: leveldb Differential Revision: https://reviews.facebook.net/D16113
11 years ago
return 0;
}
#endif // GFLAGS