2013-10-16 21:59:46 +00:00
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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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//
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2013-09-10 16:35:19 +00:00
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#include <algorithm>
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2013-08-13 06:59:04 +00:00
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#include <iostream>
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[RocksDB] Added nano second stopwatch and new perf counters to track block read cost
Summary: The pupose of this diff is to expose per user-call level precise timing of block read, so that we can answer questions like: a Get() costs me 100ms, is that somehow related to loading blocks from file system, or sth else? We will answer that with EXACTLY how many blocks have been read, how much time was spent on transfering the bytes from os, how much time was spent on checksum verification and how much time was spent on block decompression, just for that one Get. A nano second stopwatch was introduced to track time with higher precision. The cost/precision of the stopwatch is also measured in unit-test. On my dev box, retrieving one time instance costs about 30ns, on average. The deviation of timing results is good enough to track 100ns-1us level events. And the overhead could be safely ignored for 100us level events (10000 instances/s), for example, a viewstate thrift call.
Test Plan: perf_context_test, also testing with viewstate shadow traffic.
Reviewers: dhruba
Reviewed By: dhruba
CC: leveldb, xjin
Differential Revision: https://reviews.facebook.net/D12351
2013-06-04 06:09:15 +00:00
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#include <vector>
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2013-10-02 17:28:25 +00:00
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#include "/usr/include/valgrind/callgrind.h"
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2013-08-13 06:59:04 +00:00
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2013-08-23 15:38:13 +00:00
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#include "rocksdb/db.h"
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#include "rocksdb/perf_context.h"
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[RocksDB] Added nano second stopwatch and new perf counters to track block read cost
Summary: The pupose of this diff is to expose per user-call level precise timing of block read, so that we can answer questions like: a Get() costs me 100ms, is that somehow related to loading blocks from file system, or sth else? We will answer that with EXACTLY how many blocks have been read, how much time was spent on transfering the bytes from os, how much time was spent on checksum verification and how much time was spent on block decompression, just for that one Get. A nano second stopwatch was introduced to track time with higher precision. The cost/precision of the stopwatch is also measured in unit-test. On my dev box, retrieving one time instance costs about 30ns, on average. The deviation of timing results is good enough to track 100ns-1us level events. And the overhead could be safely ignored for 100us level events (10000 instances/s), for example, a viewstate thrift call.
Test Plan: perf_context_test, also testing with viewstate shadow traffic.
Reviewers: dhruba
Reviewed By: dhruba
CC: leveldb, xjin
Differential Revision: https://reviews.facebook.net/D12351
2013-06-04 06:09:15 +00:00
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#include "util/histogram.h"
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#include "util/stop_watch.h"
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2013-08-13 06:59:04 +00:00
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#include "util/testharness.h"
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2013-10-02 17:28:25 +00:00
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2013-09-10 16:35:19 +00:00
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bool FLAGS_random_key = false;
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2013-09-25 21:35:01 +00:00
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bool FLAGS_use_set_based_memetable = false;
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2013-09-10 16:35:19 +00:00
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int FLAGS_total_keys = 100;
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int FLAGS_write_buffer_size = 1000000000;
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int FLAGS_max_write_buffer_number = 8;
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int FLAGS_min_write_buffer_number_to_merge = 7;
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2013-08-13 06:59:04 +00:00
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// Path to the database on file system
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2013-10-04 04:49:15 +00:00
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const std::string kDbName = rocksdb::test::TmpDir() + "/perf_context_test";
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2013-08-13 06:59:04 +00:00
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2013-10-04 04:49:15 +00:00
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namespace rocksdb {
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2013-09-10 16:35:19 +00:00
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std::shared_ptr<DB> OpenDb() {
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2013-08-13 06:59:04 +00:00
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DB* db;
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Options options;
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options.create_if_missing = true;
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2013-09-10 16:35:19 +00:00
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options.write_buffer_size = FLAGS_write_buffer_size;
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options.max_write_buffer_number = FLAGS_max_write_buffer_number;
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options.min_write_buffer_number_to_merge =
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FLAGS_min_write_buffer_number_to_merge;
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2013-09-25 21:35:01 +00:00
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if (FLAGS_use_set_based_memetable) {
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2013-10-04 04:49:15 +00:00
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auto prefix_extractor = rocksdb::NewFixedPrefixTransform(0);
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2013-09-25 21:35:01 +00:00
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options.memtable_factory =
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2013-10-04 04:49:15 +00:00
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std::make_shared<rocksdb::PrefixHashRepFactory>(prefix_extractor);
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2013-09-25 21:35:01 +00:00
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}
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2013-08-13 06:59:04 +00:00
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Status s = DB::Open(options, kDbName, &db);
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ASSERT_OK(s);
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return std::shared_ptr<DB>(db);
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}
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class PerfContextTest { };
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2013-10-02 17:28:25 +00:00
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TEST(PerfContextTest, SeekIntoDeletion) {
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DestroyDB(kDbName, Options());
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auto db = OpenDb();
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WriteOptions write_options;
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ReadOptions read_options;
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for (int i = 0; i < FLAGS_total_keys; ++i) {
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std::string key = "k" + std::to_string(i);
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std::string value = "v" + std::to_string(i);
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db->Put(write_options, key, value);
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}
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for (int i = 0; i < FLAGS_total_keys -1 ; ++i) {
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std::string key = "k" + std::to_string(i);
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db->Delete(write_options, key);
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}
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HistogramImpl hist_get;
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HistogramImpl hist_get_time;
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for (int i = 0; i < FLAGS_total_keys - 1; ++i) {
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std::string key = "k" + std::to_string(i);
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std::string value;
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perf_context.Reset();
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StopWatchNano timer(Env::Default(), true);
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auto status = db->Get(read_options, key, &value);
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auto elapsed_nanos = timer.ElapsedNanos();
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ASSERT_TRUE(status.IsNotFound());
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hist_get.Add(perf_context.user_key_comparison_count);
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hist_get_time.Add(elapsed_nanos);
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}
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std::cout << "Get uesr key comparison: \n" << hist_get.ToString()
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<< "Get time: \n" << hist_get_time.ToString();
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HistogramImpl hist_seek_to_first;
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std::unique_ptr<Iterator> iter(db->NewIterator(read_options));
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perf_context.Reset();
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StopWatchNano timer(Env::Default(), true);
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2013-10-23 06:26:51 +00:00
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iter->SeekToFirst();
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2013-10-02 17:28:25 +00:00
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hist_seek_to_first.Add(perf_context.user_key_comparison_count);
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auto elapsed_nanos = timer.ElapsedNanos();
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std::cout << "SeekToFirst uesr key comparison: \n" << hist_seek_to_first.ToString()
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<< "ikey skipped: " << perf_context.internal_key_skipped_count << "\n"
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<< "idelete skipped: " << perf_context.internal_delete_skipped_count << "\n"
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<< "elapsed: " << elapsed_nanos << "\n";
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HistogramImpl hist_seek;
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for (int i = 0; i < FLAGS_total_keys; ++i) {
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std::unique_ptr<Iterator> iter(db->NewIterator(read_options));
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std::string key = "k" + std::to_string(i);
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perf_context.Reset();
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StopWatchNano timer(Env::Default(), true);
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iter->Seek(key);
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auto elapsed_nanos = timer.ElapsedNanos();
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hist_seek.Add(perf_context.user_key_comparison_count);
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std::cout << "seek cmp: " << perf_context.user_key_comparison_count
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<< " ikey skipped " << perf_context.internal_key_skipped_count
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<< " idelete skipped " << perf_context.internal_delete_skipped_count
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<< " elapsed: " << elapsed_nanos << "ns\n";
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perf_context.Reset();
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ASSERT_TRUE(iter->Valid());
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StopWatchNano timer2(Env::Default(), true);
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iter->Next();
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auto elapsed_nanos2 = timer2.ElapsedNanos();
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std::cout << "next cmp: " << perf_context.user_key_comparison_count
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<< "elapsed: " << elapsed_nanos2 << "ns\n";
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}
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std::cout << "Seek uesr key comparison: \n" << hist_seek.ToString();
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}
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[RocksDB] Added nano second stopwatch and new perf counters to track block read cost
Summary: The pupose of this diff is to expose per user-call level precise timing of block read, so that we can answer questions like: a Get() costs me 100ms, is that somehow related to loading blocks from file system, or sth else? We will answer that with EXACTLY how many blocks have been read, how much time was spent on transfering the bytes from os, how much time was spent on checksum verification and how much time was spent on block decompression, just for that one Get. A nano second stopwatch was introduced to track time with higher precision. The cost/precision of the stopwatch is also measured in unit-test. On my dev box, retrieving one time instance costs about 30ns, on average. The deviation of timing results is good enough to track 100ns-1us level events. And the overhead could be safely ignored for 100us level events (10000 instances/s), for example, a viewstate thrift call.
Test Plan: perf_context_test, also testing with viewstate shadow traffic.
Reviewers: dhruba
Reviewed By: dhruba
CC: leveldb, xjin
Differential Revision: https://reviews.facebook.net/D12351
2013-06-04 06:09:15 +00:00
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TEST(PerfContextTest, StopWatchNanoOverhead) {
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// profile the timer cost by itself!
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const int kTotalIterations = 1000000;
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std::vector<uint64_t> timings(kTotalIterations);
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StopWatchNano timer(Env::Default(), true);
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for (auto& timing : timings) {
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timing = timer.ElapsedNanos(true /* reset */);
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}
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HistogramImpl histogram;
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for (const auto timing : timings) {
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histogram.Add(timing);
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}
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std::cout << histogram.ToString();
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}
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TEST(PerfContextTest, StopWatchOverhead) {
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// profile the timer cost by itself!
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const int kTotalIterations = 1000000;
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std::vector<uint64_t> timings(kTotalIterations);
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StopWatch timer(Env::Default());
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for (auto& timing : timings) {
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timing = timer.ElapsedMicros();
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}
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HistogramImpl histogram;
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uint64_t prev_timing = 0;
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for (const auto timing : timings) {
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histogram.Add(timing - prev_timing);
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prev_timing = timing;
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}
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std::cout << histogram.ToString();
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}
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2013-08-13 06:59:04 +00:00
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[RocksDB] Added nano second stopwatch and new perf counters to track block read cost
Summary: The pupose of this diff is to expose per user-call level precise timing of block read, so that we can answer questions like: a Get() costs me 100ms, is that somehow related to loading blocks from file system, or sth else? We will answer that with EXACTLY how many blocks have been read, how much time was spent on transfering the bytes from os, how much time was spent on checksum verification and how much time was spent on block decompression, just for that one Get. A nano second stopwatch was introduced to track time with higher precision. The cost/precision of the stopwatch is also measured in unit-test. On my dev box, retrieving one time instance costs about 30ns, on average. The deviation of timing results is good enough to track 100ns-1us level events. And the overhead could be safely ignored for 100us level events (10000 instances/s), for example, a viewstate thrift call.
Test Plan: perf_context_test, also testing with viewstate shadow traffic.
Reviewers: dhruba
Reviewed By: dhruba
CC: leveldb, xjin
Differential Revision: https://reviews.facebook.net/D12351
2013-06-04 06:09:15 +00:00
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void ProfileKeyComparison() {
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2013-08-13 06:59:04 +00:00
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DestroyDB(kDbName, Options()); // Start this test with a fresh DB
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2013-09-10 16:35:19 +00:00
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auto db = OpenDb();
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2013-08-13 06:59:04 +00:00
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WriteOptions write_options;
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ReadOptions read_options;
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2013-09-25 21:35:01 +00:00
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HistogramImpl hist_put;
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HistogramImpl hist_get;
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2013-08-13 06:59:04 +00:00
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2013-09-10 16:35:19 +00:00
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std::cout << "Inserting " << FLAGS_total_keys << " key/value pairs\n...\n";
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2013-08-13 06:59:04 +00:00
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2013-09-25 21:35:01 +00:00
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std::vector<int> keys;
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2013-09-10 16:35:19 +00:00
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for (int i = 0; i < FLAGS_total_keys; ++i) {
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2013-09-25 21:35:01 +00:00
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keys.push_back(i);
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}
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if (FLAGS_random_key) {
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std::random_shuffle(keys.begin(), keys.end());
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}
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for (const int i : keys) {
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2013-08-13 06:59:04 +00:00
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std::string key = "k" + std::to_string(i);
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std::string value = "v" + std::to_string(i);
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perf_context.Reset();
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db->Put(write_options, key, value);
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2013-09-25 21:35:01 +00:00
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hist_put.Add(perf_context.user_key_comparison_count);
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2013-08-13 06:59:04 +00:00
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perf_context.Reset();
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db->Get(read_options, key, &value);
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2013-09-25 21:35:01 +00:00
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hist_get.Add(perf_context.user_key_comparison_count);
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2013-08-13 06:59:04 +00:00
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}
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2013-09-25 21:35:01 +00:00
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std::cout << "Put uesr key comparison: \n" << hist_put.ToString()
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<< "Get uesr key comparison: \n" << hist_get.ToString();
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2013-09-10 16:35:19 +00:00
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2013-08-13 06:59:04 +00:00
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}
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[RocksDB] Added nano second stopwatch and new perf counters to track block read cost
Summary: The pupose of this diff is to expose per user-call level precise timing of block read, so that we can answer questions like: a Get() costs me 100ms, is that somehow related to loading blocks from file system, or sth else? We will answer that with EXACTLY how many blocks have been read, how much time was spent on transfering the bytes from os, how much time was spent on checksum verification and how much time was spent on block decompression, just for that one Get. A nano second stopwatch was introduced to track time with higher precision. The cost/precision of the stopwatch is also measured in unit-test. On my dev box, retrieving one time instance costs about 30ns, on average. The deviation of timing results is good enough to track 100ns-1us level events. And the overhead could be safely ignored for 100us level events (10000 instances/s), for example, a viewstate thrift call.
Test Plan: perf_context_test, also testing with viewstate shadow traffic.
Reviewers: dhruba
Reviewed By: dhruba
CC: leveldb, xjin
Differential Revision: https://reviews.facebook.net/D12351
2013-06-04 06:09:15 +00:00
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TEST(PerfContextTest, KeyComparisonCount) {
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2013-09-10 16:35:19 +00:00
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SetPerfLevel(kEnableCount);
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[RocksDB] Added nano second stopwatch and new perf counters to track block read cost
Summary: The pupose of this diff is to expose per user-call level precise timing of block read, so that we can answer questions like: a Get() costs me 100ms, is that somehow related to loading blocks from file system, or sth else? We will answer that with EXACTLY how many blocks have been read, how much time was spent on transfering the bytes from os, how much time was spent on checksum verification and how much time was spent on block decompression, just for that one Get. A nano second stopwatch was introduced to track time with higher precision. The cost/precision of the stopwatch is also measured in unit-test. On my dev box, retrieving one time instance costs about 30ns, on average. The deviation of timing results is good enough to track 100ns-1us level events. And the overhead could be safely ignored for 100us level events (10000 instances/s), for example, a viewstate thrift call.
Test Plan: perf_context_test, also testing with viewstate shadow traffic.
Reviewers: dhruba
Reviewed By: dhruba
CC: leveldb, xjin
Differential Revision: https://reviews.facebook.net/D12351
2013-06-04 06:09:15 +00:00
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ProfileKeyComparison();
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2013-09-10 16:35:19 +00:00
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SetPerfLevel(kDisable);
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[RocksDB] Added nano second stopwatch and new perf counters to track block read cost
Summary: The pupose of this diff is to expose per user-call level precise timing of block read, so that we can answer questions like: a Get() costs me 100ms, is that somehow related to loading blocks from file system, or sth else? We will answer that with EXACTLY how many blocks have been read, how much time was spent on transfering the bytes from os, how much time was spent on checksum verification and how much time was spent on block decompression, just for that one Get. A nano second stopwatch was introduced to track time with higher precision. The cost/precision of the stopwatch is also measured in unit-test. On my dev box, retrieving one time instance costs about 30ns, on average. The deviation of timing results is good enough to track 100ns-1us level events. And the overhead could be safely ignored for 100us level events (10000 instances/s), for example, a viewstate thrift call.
Test Plan: perf_context_test, also testing with viewstate shadow traffic.
Reviewers: dhruba
Reviewed By: dhruba
CC: leveldb, xjin
Differential Revision: https://reviews.facebook.net/D12351
2013-06-04 06:09:15 +00:00
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ProfileKeyComparison();
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2013-08-13 06:59:04 +00:00
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[RocksDB] Added nano second stopwatch and new perf counters to track block read cost
Summary: The pupose of this diff is to expose per user-call level precise timing of block read, so that we can answer questions like: a Get() costs me 100ms, is that somehow related to loading blocks from file system, or sth else? We will answer that with EXACTLY how many blocks have been read, how much time was spent on transfering the bytes from os, how much time was spent on checksum verification and how much time was spent on block decompression, just for that one Get. A nano second stopwatch was introduced to track time with higher precision. The cost/precision of the stopwatch is also measured in unit-test. On my dev box, retrieving one time instance costs about 30ns, on average. The deviation of timing results is good enough to track 100ns-1us level events. And the overhead could be safely ignored for 100us level events (10000 instances/s), for example, a viewstate thrift call.
Test Plan: perf_context_test, also testing with viewstate shadow traffic.
Reviewers: dhruba
Reviewed By: dhruba
CC: leveldb, xjin
Differential Revision: https://reviews.facebook.net/D12351
2013-06-04 06:09:15 +00:00
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SetPerfLevel(kEnableTime);
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ProfileKeyComparison();
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2013-08-13 06:59:04 +00:00
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}
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2013-09-10 16:35:19 +00:00
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// make perf_context_test
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2013-10-05 05:32:05 +00:00
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// export ROCKSDB_TESTS=PerfContextTest.SeekKeyComparison
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2013-09-10 16:35:19 +00:00
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// For one memtable:
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// ./perf_context_test --write_buffer_size=500000 --total_keys=10000
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// For two memtables:
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// ./perf_context_test --write_buffer_size=250000 --total_keys=10000
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// Specify --random_key=1 to shuffle the key before insertion
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// Results show that, for sequential insertion, worst-case Seek Key comparison
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// is close to the total number of keys (linear), when there is only one
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// memtable. When there are two memtables, even the avg Seek Key comparison
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// starts to become linear to the input size.
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TEST(PerfContextTest, SeekKeyComparison) {
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DestroyDB(kDbName, Options());
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auto db = OpenDb();
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WriteOptions write_options;
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ReadOptions read_options;
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std::cout << "Inserting " << FLAGS_total_keys << " key/value pairs\n...\n";
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std::vector<int> keys;
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for (int i = 0; i < FLAGS_total_keys; ++i) {
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keys.push_back(i);
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}
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if (FLAGS_random_key) {
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std::random_shuffle(keys.begin(), keys.end());
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}
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2013-10-23 06:26:51 +00:00
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HistogramImpl hist_put_time;
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HistogramImpl hist_wal_time;
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HistogramImpl hist_time_diff;
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SetPerfLevel(kEnableTime);
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StopWatchNano timer(Env::Default());
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2013-09-10 16:35:19 +00:00
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for (const int i : keys) {
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std::string key = "k" + std::to_string(i);
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std::string value = "v" + std::to_string(i);
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2013-10-23 06:26:51 +00:00
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perf_context.Reset();
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timer.Start();
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2013-09-10 16:35:19 +00:00
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|
db->Put(write_options, key, value);
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2013-10-23 06:26:51 +00:00
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|
|
auto put_time = timer.ElapsedNanos();
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|
hist_put_time.Add(put_time);
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hist_wal_time.Add(perf_context.wal_write_time);
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hist_time_diff.Add(put_time - perf_context.wal_write_time);
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2013-09-10 16:35:19 +00:00
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|
|
}
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|
2013-10-23 06:26:51 +00:00
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|
|
std::cout << "Put time:\n" << hist_put_time.ToString()
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|
|
<< "WAL time:\n" << hist_wal_time.ToString()
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|
|
<< "time diff:\n" << hist_time_diff.ToString();
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|
2013-09-25 21:35:01 +00:00
|
|
|
HistogramImpl hist_seek;
|
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|
|
HistogramImpl hist_next;
|
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|
|
2013-09-10 16:35:19 +00:00
|
|
|
for (int i = 0; i < FLAGS_total_keys; ++i) {
|
|
|
|
std::string key = "k" + std::to_string(i);
|
|
|
|
std::string value = "v" + std::to_string(i);
|
|
|
|
|
|
|
|
std::unique_ptr<Iterator> iter(db->NewIterator(read_options));
|
|
|
|
perf_context.Reset();
|
|
|
|
iter->Seek(key);
|
|
|
|
ASSERT_TRUE(iter->Valid());
|
|
|
|
ASSERT_EQ(iter->value().ToString(), value);
|
2013-09-25 21:35:01 +00:00
|
|
|
hist_seek.Add(perf_context.user_key_comparison_count);
|
2013-09-10 16:35:19 +00:00
|
|
|
}
|
2013-09-25 21:35:01 +00:00
|
|
|
|
|
|
|
std::unique_ptr<Iterator> iter(db->NewIterator(read_options));
|
|
|
|
for (iter->SeekToFirst(); iter->Valid();) {
|
|
|
|
perf_context.Reset();
|
|
|
|
iter->Next();
|
|
|
|
hist_next.Add(perf_context.user_key_comparison_count);
|
|
|
|
}
|
|
|
|
|
|
|
|
std::cout << "Seek:\n" << hist_seek.ToString()
|
|
|
|
<< "Next:\n" << hist_next.ToString();
|
2013-09-10 16:35:19 +00:00
|
|
|
}
|
|
|
|
|
[RocksDB] Added nano second stopwatch and new perf counters to track block read cost
Summary: The pupose of this diff is to expose per user-call level precise timing of block read, so that we can answer questions like: a Get() costs me 100ms, is that somehow related to loading blocks from file system, or sth else? We will answer that with EXACTLY how many blocks have been read, how much time was spent on transfering the bytes from os, how much time was spent on checksum verification and how much time was spent on block decompression, just for that one Get. A nano second stopwatch was introduced to track time with higher precision. The cost/precision of the stopwatch is also measured in unit-test. On my dev box, retrieving one time instance costs about 30ns, on average. The deviation of timing results is good enough to track 100ns-1us level events. And the overhead could be safely ignored for 100us level events (10000 instances/s), for example, a viewstate thrift call.
Test Plan: perf_context_test, also testing with viewstate shadow traffic.
Reviewers: dhruba
Reviewed By: dhruba
CC: leveldb, xjin
Differential Revision: https://reviews.facebook.net/D12351
2013-06-04 06:09:15 +00:00
|
|
|
}
|
2013-08-13 06:59:04 +00:00
|
|
|
|
|
|
|
int main(int argc, char** argv) {
|
|
|
|
|
2013-09-10 16:35:19 +00:00
|
|
|
for (int i = 1; i < argc; i++) {
|
|
|
|
int n;
|
|
|
|
char junk;
|
|
|
|
|
|
|
|
if (sscanf(argv[i], "--write_buffer_size=%d%c", &n, &junk) == 1) {
|
|
|
|
FLAGS_write_buffer_size = n;
|
|
|
|
}
|
|
|
|
|
|
|
|
if (sscanf(argv[i], "--total_keys=%d%c", &n, &junk) == 1) {
|
|
|
|
FLAGS_total_keys = n;
|
|
|
|
}
|
|
|
|
|
|
|
|
if (sscanf(argv[i], "--random_key=%d%c", &n, &junk) == 1 &&
|
|
|
|
(n == 0 || n == 1)) {
|
|
|
|
FLAGS_random_key = n;
|
|
|
|
}
|
|
|
|
|
2013-09-25 21:35:01 +00:00
|
|
|
if (sscanf(argv[i], "--use_set_based_memetable=%d%c", &n, &junk) == 1 &&
|
|
|
|
(n == 0 || n == 1)) {
|
|
|
|
FLAGS_use_set_based_memetable = n;
|
|
|
|
}
|
|
|
|
|
2013-08-13 06:59:04 +00:00
|
|
|
}
|
|
|
|
|
2013-09-10 16:35:19 +00:00
|
|
|
std::cout << kDbName << "\n";
|
|
|
|
|
2013-10-04 04:49:15 +00:00
|
|
|
rocksdb::test::RunAllTests();
|
2013-08-13 06:59:04 +00:00
|
|
|
return 0;
|
|
|
|
}
|