rocksdb/table/table_reader_bench.cc

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// Copyright (c) 2013, Facebook, Inc. All rights reserved.
// This source code is licensed under the BSD-style license found in the
// LICENSE file in the root directory of this source tree. An additional grant
// of patent rights can be found in the PATENTS file in the same directory.
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#ifndef GFLAGS
#include <cstdio>
int main() {
fprintf(stderr, "Please install gflags to run rocksdb tools\n");
return 1;
}
#else
#include <gflags/gflags.h>
#include "rocksdb/db.h"
#include "rocksdb/slice_transform.h"
#include "rocksdb/table.h"
#include "db/db_impl.h"
#include "db/dbformat.h"
#include "table/block_based_table_factory.h"
#include "table/plain_table_factory.h"
#include "table/table_builder.h"
#include "table/get_context.h"
#include "util/histogram.h"
#include "util/testharness.h"
#include "util/testutil.h"
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using GFLAGS::ParseCommandLineFlags;
using GFLAGS::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
2014-02-13 22:55:04 +01:00
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
2014-02-13 22:55:04 +01:00
// 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
2014-02-13 22:55:04 +01:00
bool through_db, bool measured_by_nanosecond) {
rocksdb::InternalKeyComparator ikc(opts.comparator);
std::string file_name = test::TmpDir()
+ "/rocksdb_table_reader_benchmark";
std::string dbname = test::TmpDir() + "/rocksdb_table_reader_bench_db";
WriteOptions wo;
unique_ptr<WritableFile> file;
Env* env = Env::Default();
TableBuilder* tb = nullptr;
DB* db = nullptr;
Status s;
const ImmutableCFOptions ioptions(opts);
if (!through_db) {
env->NewWritableFile(file_name, &file, env_options);
tb = opts.table_factory->NewTableBuilder(ioptions, ikc, file.get(),
CompressionType::kNoCompression,
CompressionOptions());
} 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->Close();
} else {
db->Flush(FlushOptions());
}
unique_ptr<TableReader> table_reader;
unique_ptr<RandomAccessFile> raf;
if (!through_db) {
Status s = env->NewRandomAccessFile(file_name, &raf, env_options);
uint64_t file_size;
env->GetFileSize(file_name, &file_size);
s = opts.table_factory->NewTableReader(
ioptions, env_options, ikc, std::move(raf), file_size, &table_reader);
}
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) {
std::string value;
MergeContext merge_context;
GetContext get_context(ioptions.comparator, ioptions.merge_operator,
ioptions.info_log, ioptions.statistics,
GetContext::kNotFound, Slice(key), &value,
nullptr, &merge_context);
s = table_reader->Get(read_options, key, &get_context);
} 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;
if (!through_db) {
iter = table_reader->NewIterator(read_options);
} else {
iter = db->NewIterator(read_options);
}
int count = 0;
for(iter->Seek(start_key); iter->Valid(); iter->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)) == iter->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
2014-02-13 22:55:04 +01:00
"\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
2014-02-13 22:55:04 +01:00
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
2014-02-13 22:55:04 +01:00
} // 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_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
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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) {
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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") {
options.allow_mmap_reads = true;
env_options.use_mmap_reads = true;
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
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rocksdb::CuckooTableOptions table_options;
table_options.hash_table_ratio = 0.75;
tf.reset(rocksdb::NewCuckooTableFactory(table_options));
} else if (FLAGS_table_factory == "plain_table") {
options.allow_mmap_reads = true;
env_options.use_mmap_reads = true;
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 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
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return 0;
}
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#endif // GFLAGS