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// Copyright (c) 2013, Facebook, Inc. All rights reserved.
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// This source code is licensed under the BSD-style license found in the
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// LICENSE file in the root directory of this source tree. An additional grant
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// of patent rights can be found in the PATENTS file in the same directory.
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#ifndef GFLAGS
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#include <cstdio>
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int main() {
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fprintf(stderr, "Please install gflags to run rocksdb tools\n");
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return 1;
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}
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#else
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#include <gflags/gflags.h>
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#include "rocksdb/db.h"
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#include "rocksdb/slice_transform.h"
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#include "rocksdb/table.h"
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#include "db/db_impl.h"
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#include "db/dbformat.h"
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#include "port/atomic_pointer.h"
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#include "table/block_based_table_factory.h"
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#include "table/plain_table_factory.h"
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#include "table/table_builder.h"
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#include "util/histogram.h"
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#include "util/testharness.h"
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#include "util/testutil.h"
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using GFLAGS::ParseCommandLineFlags;
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using GFLAGS::SetUsageMessage;
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namespace rocksdb {
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namespace {
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// Make a key that i determines the first 4 characters and j determines the
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// last 4 characters.
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static std::string MakeKey(int i, int j, bool through_db) {
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char buf[100];
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snprintf(buf, sizeof(buf), "%04d__key___%04d", i, j);
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if (through_db) {
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return std::string(buf);
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}
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// If we directly query table, which operates on internal keys
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// instead of user keys, we need to add 8 bytes of internal
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// information (row type etc) to user key to make an internal
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// key.
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InternalKey key(std::string(buf), 0, ValueType::kTypeValue);
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return key.Encode().ToString();
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}
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static bool DummySaveValue(void* arg, const ParsedInternalKey& ikey,
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const Slice& v) {
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return false;
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}
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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
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uint64_t Now(Env* env, bool measured_by_nanosecond) {
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return measured_by_nanosecond ? env->NowNanos() : env->NowMicros();
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}
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} // namespace
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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
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// A very simple benchmark that.
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// Create a table with roughly numKey1 * numKey2 keys,
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// where there are numKey1 prefixes of the key, each has numKey2 number of
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// distinguished key, differing in the suffix part.
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// If if_query_empty_keys = false, query the existing keys numKey1 * numKey2
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// times randomly.
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// If if_query_empty_keys = true, query numKey1 * numKey2 random empty keys.
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// Print out the total time.
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// If through_db=true, a full DB will be created and queries will be against
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// it. Otherwise, operations will be directly through table level.
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//
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// If for_terator=true, instead of just query one key each time, it queries
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// a range sharing the same prefix.
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namespace {
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void TableReaderBenchmark(Options& opts, EnvOptions& env_options,
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ReadOptions& read_options, int num_keys1,
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int num_keys2, int num_iter, int prefix_len,
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bool if_query_empty_keys, bool for_iterator,
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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
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bool through_db, bool measured_by_nanosecond) {
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rocksdb::InternalKeyComparator ikc(opts.comparator);
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std::string file_name = test::TmpDir()
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+ "/rocksdb_table_reader_benchmark";
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std::string dbname = test::TmpDir() + "/rocksdb_table_reader_bench_db";
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WriteOptions wo;
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unique_ptr<WritableFile> file;
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Env* env = Env::Default();
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TableBuilder* tb = nullptr;
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DB* db = nullptr;
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Status s;
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const ImmutableCFOptions ioptions(opts);
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if (!through_db) {
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env->NewWritableFile(file_name, &file, env_options);
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tb = opts.table_factory->NewTableBuilder(ioptions, ikc, file.get(),
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CompressionType::kNoCompression,
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CompressionOptions());
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} else {
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s = DB::Open(opts, dbname, &db);
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ASSERT_OK(s);
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ASSERT_TRUE(db != nullptr);
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}
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// Populate slightly more than 1M keys
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for (int i = 0; i < num_keys1; i++) {
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for (int j = 0; j < num_keys2; j++) {
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std::string key = MakeKey(i * 2, j, through_db);
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if (!through_db) {
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tb->Add(key, key);
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} else {
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db->Put(wo, key, key);
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}
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}
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}
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if (!through_db) {
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tb->Finish();
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file->Close();
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} else {
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db->Flush(FlushOptions());
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}
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unique_ptr<TableReader> table_reader;
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unique_ptr<RandomAccessFile> raf;
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if (!through_db) {
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Status s = env->NewRandomAccessFile(file_name, &raf, env_options);
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uint64_t file_size;
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env->GetFileSize(file_name, &file_size);
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s = opts.table_factory->NewTableReader(
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ioptions, env_options, ikc, std::move(raf), file_size, &table_reader);
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}
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Random rnd(301);
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std::string result;
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HistogramImpl hist;
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void* arg = nullptr;
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for (int it = 0; it < num_iter; it++) {
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for (int i = 0; i < num_keys1; i++) {
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for (int j = 0; j < num_keys2; j++) {
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int r1 = rnd.Uniform(num_keys1) * 2;
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int r2 = rnd.Uniform(num_keys2);
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if (if_query_empty_keys) {
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r1++;
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r2 = num_keys2 * 2 - r2;
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}
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if (!for_iterator) {
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// Query one existing key;
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std::string key = MakeKey(r1, r2, through_db);
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uint64_t start_time = Now(env, measured_by_nanosecond);
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if (!through_db) {
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s = table_reader->Get(read_options, key, arg, DummySaveValue,
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nullptr);
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} else {
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s = db->Get(read_options, key, &result);
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}
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hist.Add(Now(env, measured_by_nanosecond) - start_time);
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} else {
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int r2_len;
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if (if_query_empty_keys) {
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r2_len = 0;
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} else {
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r2_len = rnd.Uniform(num_keys2) + 1;
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if (r2_len + r2 > num_keys2) {
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r2_len = num_keys2 - r2;
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}
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}
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std::string start_key = MakeKey(r1, r2, through_db);
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std::string end_key = MakeKey(r1, r2 + r2_len, through_db);
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uint64_t total_time = 0;
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uint64_t start_time = Now(env, measured_by_nanosecond);
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Iterator* iter;
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if (!through_db) {
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iter = table_reader->NewIterator(read_options);
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} else {
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iter = db->NewIterator(read_options);
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}
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int count = 0;
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for(iter->Seek(start_key); iter->Valid(); iter->Next()) {
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if (if_query_empty_keys) {
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break;
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}
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// verify key;
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total_time += Now(env, measured_by_nanosecond) - start_time;
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assert(Slice(MakeKey(r1, r2 + count, through_db)) == iter->key());
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start_time = Now(env, measured_by_nanosecond);
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if (++count >= r2_len) {
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break;
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}
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}
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if (count != r2_len) {
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fprintf(
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stderr, "Iterator cannot iterate expected number of entries. "
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"Expected %d but got %d\n", r2_len, count);
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assert(false);
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}
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delete iter;
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total_time += Now(env, measured_by_nanosecond) - start_time;
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hist.Add(total_time);
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}
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}
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}
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}
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fprintf(
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stderr,
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"==================================================="
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"====================================================\n"
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"InMemoryTableSimpleBenchmark: %20s num_key1: %5d "
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"num_key2: %5d %10s\n"
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"==================================================="
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"===================================================="
|
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_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") {
|
|
|
|
options.allow_mmap_reads = true;
|
|
|
|
env_options.use_mmap_reads = true;
|
|
|
|
|
|
|
|
tf.reset(rocksdb::NewCuckooTableFactory(0.75));
|
|
|
|
} 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
11 years ago
|
|
|
|
|
|
|
return 0;
|
|
|
|
}
|
|
|
|
|
|
|
|
#endif // GFLAGS
|