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// Copyright (c) 2011-present, Facebook, Inc. All rights reserved.
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// This source code is licensed under both the GPLv2 (found in the
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// COPYING file in the root directory) and Apache 2.0 License
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// (found in the LICENSE.Apache file in the root directory).
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#include "rocksdb/utilities/sim_cache.h"
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#include <atomic>
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#include <iomanip>
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#include "file/writable_file_writer.h"
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#include "monitoring/statistics.h"
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#include "port/port.h"
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#include "rocksdb/env.h"
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#include "rocksdb/file_system.h"
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#include "util/mutexlock.h"
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namespace ROCKSDB_NAMESPACE {
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namespace {
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class CacheActivityLogger {
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public:
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CacheActivityLogger()
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: activity_logging_enabled_(false), max_logging_size_(0) {}
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~CacheActivityLogger() {
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MutexLock l(&mutex_);
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StopLoggingInternal();
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bg_status_.PermitUncheckedError();
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}
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Status StartLogging(const std::string& activity_log_file, Env* env,
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uint64_t max_logging_size = 0) {
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assert(activity_log_file != "");
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assert(env != nullptr);
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Status status;
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FileOptions file_opts;
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MutexLock l(&mutex_);
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// Stop existing logging if any
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StopLoggingInternal();
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// Open log file
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status = WritableFileWriter::Create(env->GetFileSystem(), activity_log_file,
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file_opts, &file_writer_, nullptr);
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if (!status.ok()) {
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return status;
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}
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max_logging_size_ = max_logging_size;
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activity_logging_enabled_.store(true);
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return status;
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}
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void StopLogging() {
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MutexLock l(&mutex_);
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StopLoggingInternal();
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}
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void ReportLookup(const Slice& key) {
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if (activity_logging_enabled_.load() == false) {
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return;
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}
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std::ostringstream oss;
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// line format: "LOOKUP - <KEY>"
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oss << "LOOKUP - " << key.ToString(true) << std::endl;
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MutexLock l(&mutex_);
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Status s = file_writer_->Append(oss.str());
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if (!s.ok() && bg_status_.ok()) {
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bg_status_ = s;
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}
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if (MaxLoggingSizeReached() || !bg_status_.ok()) {
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// Stop logging if we have reached the max file size or
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// encountered an error
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StopLoggingInternal();
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}
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}
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void ReportAdd(const Slice& key, size_t size) {
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if (activity_logging_enabled_.load() == false) {
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return;
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}
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std::ostringstream oss;
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// line format: "ADD - <KEY> - <KEY-SIZE>"
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oss << "ADD - " << key.ToString(true) << " - " << size << std::endl;
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MutexLock l(&mutex_);
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Status s = file_writer_->Append(oss.str());
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if (!s.ok() && bg_status_.ok()) {
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bg_status_ = s;
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}
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if (MaxLoggingSizeReached() || !bg_status_.ok()) {
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// Stop logging if we have reached the max file size or
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// encountered an error
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StopLoggingInternal();
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}
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}
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Status& bg_status() {
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MutexLock l(&mutex_);
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return bg_status_;
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}
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private:
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bool MaxLoggingSizeReached() {
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mutex_.AssertHeld();
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return (max_logging_size_ > 0 &&
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file_writer_->GetFileSize() >= max_logging_size_);
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}
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void StopLoggingInternal() {
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mutex_.AssertHeld();
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if (!activity_logging_enabled_) {
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return;
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}
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activity_logging_enabled_.store(false);
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Status s = file_writer_->Close();
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if (!s.ok() && bg_status_.ok()) {
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bg_status_ = s;
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}
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}
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// Mutex to sync writes to file_writer, and all following
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// class data members
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port::Mutex mutex_;
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// Indicates if logging is currently enabled
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// atomic to allow reads without mutex
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std::atomic<bool> activity_logging_enabled_;
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// When reached, we will stop logging and close the file
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// Value of 0 means unlimited
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uint64_t max_logging_size_;
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std::unique_ptr<WritableFileWriter> file_writer_;
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Status bg_status_;
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};
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// SimCacheImpl definition
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class SimCacheImpl : public SimCache {
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public:
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// capacity for real cache (ShardedLRUCache)
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// test_capacity for key only cache
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Support computing miss ratio curves using sim_cache. (#5449)
Summary:
This PR adds a BlockCacheTraceSimulator that reports the miss ratios given different cache configurations. A cache configuration contains "cache_name,num_shard_bits,cache_capacities". For example, "lru, 1, 1K, 2K, 4M, 4G".
When we replay the trace, we also perform lookups and inserts on the simulated caches.
In the end, it reports the miss ratio for each tuple <cache_name, num_shard_bits, cache_capacity> in a output file.
This PR also adds a main source block_cache_trace_analyzer so that we can run the analyzer in command line.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/5449
Test Plan:
Added tests for block_cache_trace_analyzer.
COMPILE_WITH_ASAN=1 make check -j32.
Differential Revision: D15797073
Pulled By: HaoyuHuang
fbshipit-source-id: aef0c5c2e7938f3e8b6a10d4a6a50e6928ecf408
5 years ago
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SimCacheImpl(std::shared_ptr<Cache> sim_cache, std::shared_ptr<Cache> cache)
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: cache_(cache),
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Support computing miss ratio curves using sim_cache. (#5449)
Summary:
This PR adds a BlockCacheTraceSimulator that reports the miss ratios given different cache configurations. A cache configuration contains "cache_name,num_shard_bits,cache_capacities". For example, "lru, 1, 1K, 2K, 4M, 4G".
When we replay the trace, we also perform lookups and inserts on the simulated caches.
In the end, it reports the miss ratio for each tuple <cache_name, num_shard_bits, cache_capacity> in a output file.
This PR also adds a main source block_cache_trace_analyzer so that we can run the analyzer in command line.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/5449
Test Plan:
Added tests for block_cache_trace_analyzer.
COMPILE_WITH_ASAN=1 make check -j32.
Differential Revision: D15797073
Pulled By: HaoyuHuang
fbshipit-source-id: aef0c5c2e7938f3e8b6a10d4a6a50e6928ecf408
5 years ago
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key_only_cache_(sim_cache),
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miss_times_(0),
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hit_times_(0),
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stats_(nullptr) {}
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~SimCacheImpl() override {}
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void SetCapacity(size_t capacity) override { cache_->SetCapacity(capacity); }
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void SetStrictCapacityLimit(bool strict_capacity_limit) override {
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cache_->SetStrictCapacityLimit(strict_capacity_limit);
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}
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using Cache::Insert;
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Status Insert(const Slice& key, void* value, size_t charge,
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void (*deleter)(const Slice& key, void* value), Handle** handle,
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Priority priority) override {
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// The handle and value passed in are for real cache, so we pass nullptr
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// to key_only_cache_ for both instead. Also, the deleter function pointer
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// will be called by user to perform some external operation which should
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// be applied only once. Thus key_only_cache accepts an empty function.
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// *Lambda function without capture can be assgined to a function pointer
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Handle* h = key_only_cache_->Lookup(key);
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if (h == nullptr) {
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// TODO: Check for error here?
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auto s = key_only_cache_->Insert(
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key, nullptr, charge, [](const Slice& /*k*/, void* /*v*/) {}, nullptr,
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priority);
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s.PermitUncheckedError();
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} else {
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key_only_cache_->Release(h);
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}
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cache_activity_logger_.ReportAdd(key, charge);
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Support computing miss ratio curves using sim_cache. (#5449)
Summary:
This PR adds a BlockCacheTraceSimulator that reports the miss ratios given different cache configurations. A cache configuration contains "cache_name,num_shard_bits,cache_capacities". For example, "lru, 1, 1K, 2K, 4M, 4G".
When we replay the trace, we also perform lookups and inserts on the simulated caches.
In the end, it reports the miss ratio for each tuple <cache_name, num_shard_bits, cache_capacity> in a output file.
This PR also adds a main source block_cache_trace_analyzer so that we can run the analyzer in command line.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/5449
Test Plan:
Added tests for block_cache_trace_analyzer.
COMPILE_WITH_ASAN=1 make check -j32.
Differential Revision: D15797073
Pulled By: HaoyuHuang
fbshipit-source-id: aef0c5c2e7938f3e8b6a10d4a6a50e6928ecf408
5 years ago
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if (!cache_) {
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return Status::OK();
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}
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return cache_->Insert(key, value, charge, deleter, handle, priority);
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}
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using Cache::Lookup;
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Handle* Lookup(const Slice& key, Statistics* stats) override {
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Handle* h = key_only_cache_->Lookup(key);
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if (h != nullptr) {
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key_only_cache_->Release(h);
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inc_hit_counter();
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RecordTick(stats, SIM_BLOCK_CACHE_HIT);
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} else {
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inc_miss_counter();
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RecordTick(stats, SIM_BLOCK_CACHE_MISS);
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}
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cache_activity_logger_.ReportLookup(key);
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Support computing miss ratio curves using sim_cache. (#5449)
Summary:
This PR adds a BlockCacheTraceSimulator that reports the miss ratios given different cache configurations. A cache configuration contains "cache_name,num_shard_bits,cache_capacities". For example, "lru, 1, 1K, 2K, 4M, 4G".
When we replay the trace, we also perform lookups and inserts on the simulated caches.
In the end, it reports the miss ratio for each tuple <cache_name, num_shard_bits, cache_capacity> in a output file.
This PR also adds a main source block_cache_trace_analyzer so that we can run the analyzer in command line.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/5449
Test Plan:
Added tests for block_cache_trace_analyzer.
COMPILE_WITH_ASAN=1 make check -j32.
Differential Revision: D15797073
Pulled By: HaoyuHuang
fbshipit-source-id: aef0c5c2e7938f3e8b6a10d4a6a50e6928ecf408
5 years ago
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if (!cache_) {
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return nullptr;
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}
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return cache_->Lookup(key, stats);
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}
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bool Ref(Handle* handle) override { return cache_->Ref(handle); }
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using Cache::Release;
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bool Release(Handle* handle, bool erase_if_last_ref = false) override {
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return cache_->Release(handle, erase_if_last_ref);
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}
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void Erase(const Slice& key) override {
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cache_->Erase(key);
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key_only_cache_->Erase(key);
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}
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void* Value(Handle* handle) override { return cache_->Value(handle); }
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uint64_t NewId() override { return cache_->NewId(); }
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size_t GetCapacity() const override { return cache_->GetCapacity(); }
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bool HasStrictCapacityLimit() const override {
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return cache_->HasStrictCapacityLimit();
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}
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size_t GetUsage() const override { return cache_->GetUsage(); }
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size_t GetUsage(Handle* handle) const override {
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return cache_->GetUsage(handle);
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}
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size_t GetCharge(Handle* handle) const override {
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return cache_->GetCharge(handle);
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}
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Use deleters to label cache entries and collect stats (#8297)
Summary:
This change gathers and publishes statistics about the
kinds of items in block cache. This is especially important for
profiling relative usage of cache by index vs. filter vs. data blocks.
It works by iterating over the cache during periodic stats dump
(InternalStats, stats_dump_period_sec) or on demand when
DB::Get(Map)Property(kBlockCacheEntryStats), except that for
efficiency and sharing among column families, saved data from
the last scan is used when the data is not considered too old.
The new information can be seen in info LOG, for example:
Block cache LRUCache@0x7fca62229330 capacity: 95.37 MB collections: 8 last_copies: 0 last_secs: 0.00178 secs_since: 0
Block cache entry stats(count,size,portion): DataBlock(7092,28.24 MB,29.6136%) FilterBlock(215,867.90 KB,0.888728%) FilterMetaBlock(2,5.31 KB,0.00544%) IndexBlock(217,180.11 KB,0.184432%) WriteBuffer(1,256.00 KB,0.262144%) Misc(1,0.00 KB,0%)
And also through DB::GetProperty and GetMapProperty (here using
ldb just for demonstration):
$ ./ldb --db=/dev/shm/dbbench/ get_property rocksdb.block-cache-entry-stats
rocksdb.block-cache-entry-stats.bytes.data-block: 0
rocksdb.block-cache-entry-stats.bytes.deprecated-filter-block: 0
rocksdb.block-cache-entry-stats.bytes.filter-block: 0
rocksdb.block-cache-entry-stats.bytes.filter-meta-block: 0
rocksdb.block-cache-entry-stats.bytes.index-block: 178992
rocksdb.block-cache-entry-stats.bytes.misc: 0
rocksdb.block-cache-entry-stats.bytes.other-block: 0
rocksdb.block-cache-entry-stats.bytes.write-buffer: 0
rocksdb.block-cache-entry-stats.capacity: 8388608
rocksdb.block-cache-entry-stats.count.data-block: 0
rocksdb.block-cache-entry-stats.count.deprecated-filter-block: 0
rocksdb.block-cache-entry-stats.count.filter-block: 0
rocksdb.block-cache-entry-stats.count.filter-meta-block: 0
rocksdb.block-cache-entry-stats.count.index-block: 215
rocksdb.block-cache-entry-stats.count.misc: 1
rocksdb.block-cache-entry-stats.count.other-block: 0
rocksdb.block-cache-entry-stats.count.write-buffer: 0
rocksdb.block-cache-entry-stats.id: LRUCache@0x7f3636661290
rocksdb.block-cache-entry-stats.percent.data-block: 0.000000
rocksdb.block-cache-entry-stats.percent.deprecated-filter-block: 0.000000
rocksdb.block-cache-entry-stats.percent.filter-block: 0.000000
rocksdb.block-cache-entry-stats.percent.filter-meta-block: 0.000000
rocksdb.block-cache-entry-stats.percent.index-block: 2.133751
rocksdb.block-cache-entry-stats.percent.misc: 0.000000
rocksdb.block-cache-entry-stats.percent.other-block: 0.000000
rocksdb.block-cache-entry-stats.percent.write-buffer: 0.000000
rocksdb.block-cache-entry-stats.secs_for_last_collection: 0.000052
rocksdb.block-cache-entry-stats.secs_since_last_collection: 0
Solution detail - We need some way to flag what kind of blocks each
entry belongs to, preferably without changing the Cache API.
One of the complications is that Cache is a general interface that could
have other users that don't adhere to whichever convention we decide
on for keys and values. Or we would pay for an extra field in the Handle
that would only be used for this purpose.
This change uses a back-door approach, the deleter, to indicate the
"role" of a Cache entry (in addition to the value type, implicitly).
This has the added benefit of ensuring proper code origin whenever we
recognize a particular role for a cache entry; if the entry came from
some other part of the code, it will use an unrecognized deleter, which
we simply attribute to the "Misc" role.
An internal API makes for simple instantiation and automatic
registration of Cache deleters for a given value type and "role".
Another internal API, CacheEntryStatsCollector, solves the problem of
caching the results of a scan and sharing them, to ensure scans are
neither excessive nor redundant so as not to harm Cache performance.
Because code is added to BlocklikeTraits, it is pulled out of
block_based_table_reader.cc into its own file.
This is a reformulation of https://github.com/facebook/rocksdb/issues/8276, without the type checking option
(could still be added), and with actual stat gathering.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/8297
Test Plan: manual testing with db_bench, and a couple of basic unit tests
Reviewed By: ltamasi
Differential Revision: D28488721
Pulled By: pdillinger
fbshipit-source-id: 472f524a9691b5afb107934be2d41d84f2b129fb
4 years ago
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DeleterFn GetDeleter(Handle* handle) const override {
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return cache_->GetDeleter(handle);
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}
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size_t GetPinnedUsage() const override { return cache_->GetPinnedUsage(); }
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void DisownData() override {
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cache_->DisownData();
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key_only_cache_->DisownData();
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}
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void ApplyToAllCacheEntries(void (*callback)(void*, size_t),
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bool thread_safe) override {
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// only apply to _cache since key_only_cache doesn't hold value
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cache_->ApplyToAllCacheEntries(callback, thread_safe);
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}
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New Cache API for gathering statistics (#8225)
Summary:
Adds a new Cache::ApplyToAllEntries API that we expect to use
(in follow-up PRs) for efficiently gathering block cache statistics.
Notable features vs. old ApplyToAllCacheEntries:
* Includes key and deleter (in addition to value and charge). We could
have passed in a Handle but then more virtual function calls would be
needed to get the "fields" of each entry. We expect to use the 'deleter'
to identify the origin of entries, perhaps even more.
* Heavily tuned to minimize latency impact on operating cache. It
does this by iterating over small sections of each cache shard while
cycling through the shards.
* Supports tuning roughly how many entries to operate on for each
lock acquire and release, to control the impact on the latency of other
operations without excessive lock acquire & release. The right balance
can depend on the cost of the callback. Good default seems to be
around 256.
* There should be no need to disable thread safety. (I would expect
uncontended locks to be sufficiently fast.)
I have enhanced cache_bench to validate this approach:
* Reports a histogram of ns per operation, so we can look at the
ditribution of times, not just throughput (average).
* Can add a thread for simulated "gather stats" which calls
ApplyToAllEntries at a specified interval. We also generate a histogram
of time to run ApplyToAllEntries.
To make the iteration over some entries of each shard work as cleanly as
possible, even with resize between next set of entries, I have
re-arranged which hash bits are used for sharding and which for indexing
within a shard.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/8225
Test Plan:
A couple of unit tests are added, but primary validation is manual, as
the primary risk is to performance.
The primary validation is using cache_bench to ensure that neither
the minor hashing changes nor the simulated stats gathering
significantly impact QPS or latency distribution. Note that adding op
latency histogram seriously impacts the benchmark QPS, so for a
fair baseline, we need the cache_bench changes (except remove simulated
stat gathering to make it compile). In short, we don't see any
reproducible difference in ops/sec or op latency unless we are gathering
stats nearly continuously. Test uses 10GB block cache with
8KB values to be somewhat realistic in the number of items to iterate
over.
Baseline typical output:
```
Complete in 92.017 s; Rough parallel ops/sec = 869401
Thread ops/sec = 54662
Operation latency (ns):
Count: 80000000 Average: 11223.9494 StdDev: 29.61
Min: 0 Median: 7759.3973 Max: 9620500
Percentiles: P50: 7759.40 P75: 14190.73 P99: 46922.75 P99.9: 77509.84 P99.99: 217030.58
------------------------------------------------------
[ 0, 1 ] 68 0.000% 0.000%
( 2900, 4400 ] 89 0.000% 0.000%
( 4400, 6600 ] 33630240 42.038% 42.038% ########
( 6600, 9900 ] 18129842 22.662% 64.700% #####
( 9900, 14000 ] 7877533 9.847% 74.547% ##
( 14000, 22000 ] 15193238 18.992% 93.539% ####
( 22000, 33000 ] 3037061 3.796% 97.335% #
( 33000, 50000 ] 1626316 2.033% 99.368%
( 50000, 75000 ] 421532 0.527% 99.895%
( 75000, 110000 ] 56910 0.071% 99.966%
( 110000, 170000 ] 16134 0.020% 99.986%
( 170000, 250000 ] 5166 0.006% 99.993%
( 250000, 380000 ] 3017 0.004% 99.996%
( 380000, 570000 ] 1337 0.002% 99.998%
( 570000, 860000 ] 805 0.001% 99.999%
( 860000, 1200000 ] 319 0.000% 100.000%
( 1200000, 1900000 ] 231 0.000% 100.000%
( 1900000, 2900000 ] 100 0.000% 100.000%
( 2900000, 4300000 ] 39 0.000% 100.000%
( 4300000, 6500000 ] 16 0.000% 100.000%
( 6500000, 9800000 ] 7 0.000% 100.000%
```
New, gather_stats=false. Median thread ops/sec of 5 runs:
```
Complete in 92.030 s; Rough parallel ops/sec = 869285
Thread ops/sec = 54458
Operation latency (ns):
Count: 80000000 Average: 11298.1027 StdDev: 42.18
Min: 0 Median: 7722.0822 Max: 6398720
Percentiles: P50: 7722.08 P75: 14294.68 P99: 47522.95 P99.9: 85292.16 P99.99: 228077.78
------------------------------------------------------
[ 0, 1 ] 109 0.000% 0.000%
( 2900, 4400 ] 793 0.001% 0.001%
( 4400, 6600 ] 34054563 42.568% 42.569% #########
( 6600, 9900 ] 17482646 21.853% 64.423% ####
( 9900, 14000 ] 7908180 9.885% 74.308% ##
( 14000, 22000 ] 15032072 18.790% 93.098% ####
( 22000, 33000 ] 3237834 4.047% 97.145% #
( 33000, 50000 ] 1736882 2.171% 99.316%
( 50000, 75000 ] 446851 0.559% 99.875%
( 75000, 110000 ] 68251 0.085% 99.960%
( 110000, 170000 ] 18592 0.023% 99.983%
( 170000, 250000 ] 7200 0.009% 99.992%
( 250000, 380000 ] 3334 0.004% 99.997%
( 380000, 570000 ] 1393 0.002% 99.998%
( 570000, 860000 ] 700 0.001% 99.999%
( 860000, 1200000 ] 293 0.000% 100.000%
( 1200000, 1900000 ] 196 0.000% 100.000%
( 1900000, 2900000 ] 69 0.000% 100.000%
( 2900000, 4300000 ] 32 0.000% 100.000%
( 4300000, 6500000 ] 10 0.000% 100.000%
```
New, gather_stats=true, 1 second delay between scans. Scans take about
1 second here so it's spending about 50% time scanning. Still the effect on
ops/sec and latency seems to be in the noise. Median thread ops/sec of 5 runs:
```
Complete in 91.890 s; Rough parallel ops/sec = 870608
Thread ops/sec = 54551
Operation latency (ns):
Count: 80000000 Average: 11311.2629 StdDev: 45.28
Min: 0 Median: 7686.5458 Max: 10018340
Percentiles: P50: 7686.55 P75: 14481.95 P99: 47232.60 P99.9: 79230.18 P99.99: 232998.86
------------------------------------------------------
[ 0, 1 ] 71 0.000% 0.000%
( 2900, 4400 ] 291 0.000% 0.000%
( 4400, 6600 ] 34492060 43.115% 43.116% #########
( 6600, 9900 ] 16727328 20.909% 64.025% ####
( 9900, 14000 ] 7845828 9.807% 73.832% ##
( 14000, 22000 ] 15510654 19.388% 93.220% ####
( 22000, 33000 ] 3216533 4.021% 97.241% #
( 33000, 50000 ] 1680859 2.101% 99.342%
( 50000, 75000 ] 439059 0.549% 99.891%
( 75000, 110000 ] 60540 0.076% 99.967%
( 110000, 170000 ] 14649 0.018% 99.985%
( 170000, 250000 ] 5242 0.007% 99.991%
( 250000, 380000 ] 3260 0.004% 99.995%
( 380000, 570000 ] 1599 0.002% 99.997%
( 570000, 860000 ] 1043 0.001% 99.999%
( 860000, 1200000 ] 471 0.001% 99.999%
( 1200000, 1900000 ] 275 0.000% 100.000%
( 1900000, 2900000 ] 143 0.000% 100.000%
( 2900000, 4300000 ] 60 0.000% 100.000%
( 4300000, 6500000 ] 27 0.000% 100.000%
( 6500000, 9800000 ] 7 0.000% 100.000%
( 9800000, 14000000 ] 1 0.000% 100.000%
Gather stats latency (us):
Count: 46 Average: 980387.5870 StdDev: 60911.18
Min: 879155 Median: 1033777.7778 Max: 1261431
Percentiles: P50: 1033777.78 P75: 1120666.67 P99: 1261431.00 P99.9: 1261431.00 P99.99: 1261431.00
------------------------------------------------------
( 860000, 1200000 ] 45 97.826% 97.826% ####################
( 1200000, 1900000 ] 1 2.174% 100.000%
Most recent cache entry stats:
Number of entries: 1295133
Total charge: 9.88 GB
Average key size: 23.4982
Average charge: 8.00 KB
Unique deleters: 3
```
Reviewed By: mrambacher
Differential Revision: D28295742
Pulled By: pdillinger
fbshipit-source-id: bbc4a552f91ba0fe10e5cc025c42cef5a81f2b95
4 years ago
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void ApplyToAllEntries(
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const std::function<void(const Slice& key, void* value, size_t charge,
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DeleterFn deleter)>& callback,
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const ApplyToAllEntriesOptions& opts) override {
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cache_->ApplyToAllEntries(callback, opts);
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}
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void EraseUnRefEntries() override {
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cache_->EraseUnRefEntries();
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key_only_cache_->EraseUnRefEntries();
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}
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size_t GetSimCapacity() const override {
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return key_only_cache_->GetCapacity();
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}
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size_t GetSimUsage() const override { return key_only_cache_->GetUsage(); }
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void SetSimCapacity(size_t capacity) override {
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key_only_cache_->SetCapacity(capacity);
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}
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uint64_t get_miss_counter() const override {
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return miss_times_.load(std::memory_order_relaxed);
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}
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uint64_t get_hit_counter() const override {
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return hit_times_.load(std::memory_order_relaxed);
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}
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void reset_counter() override {
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miss_times_.store(0, std::memory_order_relaxed);
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hit_times_.store(0, std::memory_order_relaxed);
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SetTickerCount(stats_, SIM_BLOCK_CACHE_HIT, 0);
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SetTickerCount(stats_, SIM_BLOCK_CACHE_MISS, 0);
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}
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std::string ToString() const override {
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std::ostringstream oss;
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oss << "SimCache MISSes: " << get_miss_counter() << std::endl;
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oss << "SimCache HITs: " << get_hit_counter() << std::endl;
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auto lookups = get_miss_counter() + get_hit_counter();
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oss << "SimCache HITRATE: " << std::fixed << std::setprecision(2)
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<< (lookups == 0 ? 0 : get_hit_counter() * 100.0f / lookups)
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<< std::endl;
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return oss.str();
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}
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std::string GetPrintableOptions() const override {
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std::ostringstream oss;
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oss << " cache_options:" << std::endl;
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oss << cache_->GetPrintableOptions();
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oss << " sim_cache_options:" << std::endl;
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oss << key_only_cache_->GetPrintableOptions();
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return oss.str();
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}
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Status StartActivityLogging(const std::string& activity_log_file, Env* env,
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uint64_t max_logging_size = 0) override {
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return cache_activity_logger_.StartLogging(activity_log_file, env,
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max_logging_size);
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}
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void StopActivityLogging() override { cache_activity_logger_.StopLogging(); }
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Status GetActivityLoggingStatus() override {
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return cache_activity_logger_.bg_status();
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}
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private:
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std::shared_ptr<Cache> cache_;
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std::shared_ptr<Cache> key_only_cache_;
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std::atomic<uint64_t> miss_times_;
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std::atomic<uint64_t> hit_times_;
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Statistics* stats_;
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CacheActivityLogger cache_activity_logger_;
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void inc_miss_counter() {
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miss_times_.fetch_add(1, std::memory_order_relaxed);
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}
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void inc_hit_counter() { hit_times_.fetch_add(1, std::memory_order_relaxed); }
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};
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} // end anonymous namespace
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// For instrumentation purpose, use NewSimCache instead
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std::shared_ptr<SimCache> NewSimCache(std::shared_ptr<Cache> cache,
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size_t sim_capacity, int num_shard_bits) {
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LRUCacheOptions co;
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co.capacity = sim_capacity;
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co.num_shard_bits = num_shard_bits;
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co.metadata_charge_policy = kDontChargeCacheMetadata;
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return NewSimCache(NewLRUCache(co), cache, num_shard_bits);
|
Support computing miss ratio curves using sim_cache. (#5449)
Summary:
This PR adds a BlockCacheTraceSimulator that reports the miss ratios given different cache configurations. A cache configuration contains "cache_name,num_shard_bits,cache_capacities". For example, "lru, 1, 1K, 2K, 4M, 4G".
When we replay the trace, we also perform lookups and inserts on the simulated caches.
In the end, it reports the miss ratio for each tuple <cache_name, num_shard_bits, cache_capacity> in a output file.
This PR also adds a main source block_cache_trace_analyzer so that we can run the analyzer in command line.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/5449
Test Plan:
Added tests for block_cache_trace_analyzer.
COMPILE_WITH_ASAN=1 make check -j32.
Differential Revision: D15797073
Pulled By: HaoyuHuang
fbshipit-source-id: aef0c5c2e7938f3e8b6a10d4a6a50e6928ecf408
5 years ago
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|
}
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std::shared_ptr<SimCache> NewSimCache(std::shared_ptr<Cache> sim_cache,
|
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std::shared_ptr<Cache> cache,
|
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|
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int num_shard_bits) {
|
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|
|
if (num_shard_bits >= 20) {
|
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|
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return nullptr; // the cache cannot be sharded into too many fine pieces
|
|
|
|
}
|
Support computing miss ratio curves using sim_cache. (#5449)
Summary:
This PR adds a BlockCacheTraceSimulator that reports the miss ratios given different cache configurations. A cache configuration contains "cache_name,num_shard_bits,cache_capacities". For example, "lru, 1, 1K, 2K, 4M, 4G".
When we replay the trace, we also perform lookups and inserts on the simulated caches.
In the end, it reports the miss ratio for each tuple <cache_name, num_shard_bits, cache_capacity> in a output file.
This PR also adds a main source block_cache_trace_analyzer so that we can run the analyzer in command line.
Pull Request resolved: https://github.com/facebook/rocksdb/pull/5449
Test Plan:
Added tests for block_cache_trace_analyzer.
COMPILE_WITH_ASAN=1 make check -j32.
Differential Revision: D15797073
Pulled By: HaoyuHuang
fbshipit-source-id: aef0c5c2e7938f3e8b6a10d4a6a50e6928ecf408
5 years ago
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return std::make_shared<SimCacheImpl>(sim_cache, cache);
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}
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} // namespace ROCKSDB_NAMESPACE
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