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rocksdb/cache/clock_cache.cc

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// Copyright (c) 2011-present, Facebook, Inc. All rights reserved.
// This source code is licensed under both the GPLv2 (found in the
// COPYING file in the root directory) and Apache 2.0 License
// (found in the LICENSE.Apache file in the root directory).
//
// Copyright (c) 2011 The LevelDB Authors. All rights reserved.
// Use of this source code is governed by a BSD-style license that can be
// found in the LICENSE file. See the AUTHORS file for names of contributors.
#include "cache/clock_cache.h"
#include <cassert>
#include <cstdint>
#include <cstdio>
#include <functional>
#include "monitoring/perf_context_imp.h"
#include "monitoring/statistics.h"
#include "port/lang.h"
#include "util/distributed_mutex.h"
#include "util/hash.h"
#include "util/math.h"
#include "util/random.h"
namespace ROCKSDB_NAMESPACE {
namespace clock_cache {
ClockHandleTable::ClockHandleTable(int hash_bits)
: length_bits_(hash_bits),
length_bits_mask_((uint32_t{1} << length_bits_) - 1),
occupancy_(0),
occupancy_limit_(static_cast<uint32_t>((uint32_t{1} << length_bits_) *
kStrictLoadFactor)),
array_(new ClockHandle[size_t{1} << length_bits_]) {
assert(hash_bits <= 32);
}
ClockHandleTable::~ClockHandleTable() {
ApplyToEntriesRange([](ClockHandle* h) { h->FreeData(); }, 0, GetTableSize(),
true);
}
ClockHandle* ClockHandleTable::Lookup(const Slice& key, uint32_t hash) {
uint32_t probe = 0;
int slot = FindElement(key, hash, probe);
return (slot == -1) ? nullptr : &array_[slot];
}
ClockHandle* ClockHandleTable::Insert(ClockHandle* h, ClockHandle** old) {
uint32_t probe = 0;
int slot = FindElementOrAvailableSlot(h->key(), h->hash, probe);
*old = nullptr;
if (slot == -1) {
// The key is not already present, and there's no available slot to place
// the new copy.
return nullptr;
}
if (!array_[slot].IsElement()) {
// The slot is empty or is a tombstone.
ClockHandle* new_entry = &array_[slot];
new_entry->InternalToExclusiveRef();
Assign(new_entry, h);
if (new_entry->displacements == 0) {
// The slot was empty.
return new_entry;
}
// It used to be a tombstone, so there may already be a copy of the
// key in the table.
slot = FindElement(h->key(), h->hash, probe);
if (slot == -1) {
// Nope, no existing copy of the key.
return new_entry;
}
ClockHandle* old_entry = &array_[slot];
old_entry->ReleaseInternalRef();
*old = old_entry;
return new_entry;
} else {
// There is an existing copy of the key.
ClockHandle* old_entry = &array_[slot];
old_entry->ReleaseInternalRef();
*old = old_entry;
// Find an available slot for the new element.
old_entry->displacements++;
slot = FindAvailableSlot(h->key(), probe);
if (slot == -1) {
// No available slots.
return nullptr;
}
ClockHandle* new_entry = &array_[slot];
new_entry->InternalToExclusiveRef();
Assign(new_entry, h);
return new_entry;
}
}
void ClockHandleTable::Remove(ClockHandle* h) {
assert(!h->IsInClock()); // Already off clock.
uint32_t probe = 0;
FindSlot(
h->key(), [&](ClockHandle* e) { return e == h; },
[&](ClockHandle* /*e*/) { return false; },
[&](ClockHandle* e) { e->displacements--; }, probe);
h->SetWillBeDeleted(false);
h->SetIsElement(false);
occupancy_--;
}
void ClockHandleTable::Assign(ClockHandle* dst, ClockHandle* src) {
// DON'T touch displacements and refs.
dst->value = src->value;
dst->deleter = src->deleter;
dst->hash = src->hash;
dst->total_charge = src->total_charge;
dst->key_data = src->key_data;
dst->flags.store(0);
dst->SetIsElement(true);
dst->SetClockPriority(ClockHandle::ClockPriority::NONE);
dst->SetCachePriority(src->GetCachePriority());
occupancy_++;
}
int ClockHandleTable::FindElement(const Slice& key, uint32_t hash,
uint32_t& probe) {
return FindSlot(
key,
[&](ClockHandle* h) {
if (h->TryInternalRef()) {
if (h->Matches(key, hash)) {
return true;
}
h->ReleaseInternalRef();
}
return false;
},
[&](ClockHandle* h) { return h->displacements == 0; },
[&](ClockHandle* /*h*/) {}, probe);
}
int ClockHandleTable::FindAvailableSlot(const Slice& key, uint32_t& probe) {
int slot = FindSlot(
key,
[&](ClockHandle* h) {
if (h->TryInternalRef()) {
if (!h->IsElement()) {
return true;
}
h->ReleaseInternalRef();
}
return false;
},
[&](ClockHandle* /*h*/) { return false; },
[&](ClockHandle* h) { h->displacements++; }, probe);
if (slot == -1) {
Rollback(key, probe);
}
return slot;
}
int ClockHandleTable::FindElementOrAvailableSlot(const Slice& key,
uint32_t hash,
uint32_t& probe) {
int slot = FindSlot(
key,
[&](ClockHandle* h) {
if (h->TryInternalRef()) {
if (!h->IsElement() || h->Matches(key, hash)) {
return true;
}
h->ReleaseInternalRef();
}
return false;
},
[&](ClockHandle* /*h*/) { return false; },
[&](ClockHandle* h) { h->displacements++; }, probe);
if (slot == -1) {
Rollback(key, probe);
}
return slot;
}
int ClockHandleTable::FindSlot(const Slice& key,
std::function<bool(ClockHandle*)> match,
std::function<bool(ClockHandle*)> abort,
std::function<void(ClockHandle*)> update,
uint32_t& probe) {
// We use double-hashing probing. Every probe in the sequence is a
// pseudorandom integer, computed as a linear function of two random hashes,
// which we call base and increment. Specifically, the i-th probe is base + i
// * increment modulo the table size.
uint32_t base = ModTableSize(Hash(key.data(), key.size(), kProbingSeed1));
// We use an odd increment, which is relatively prime with the power-of-two
// table size. This implies that we cycle back to the first probe only
// after probing every slot exactly once.
uint32_t increment =
ModTableSize((Hash(key.data(), key.size(), kProbingSeed2) << 1) | 1);
uint32_t current = ModTableSize(base + probe * increment);
while (true) {
ClockHandle* h = &array_[current];
if (current == base && probe > 0) {
// We looped back.
return -1;
}
if (match(h)) {
probe++;
return current;
}
if (abort(h)) {
return -1;
}
probe++;
update(h);
current = ModTableSize(current + increment);
}
}
void ClockHandleTable::Rollback(const Slice& key, uint32_t probe) {
uint32_t current = ModTableSize(Hash(key.data(), key.size(), kProbingSeed1));
uint32_t increment =
ModTableSize((Hash(key.data(), key.size(), kProbingSeed2) << 1) | 1);
for (uint32_t i = 0; i < probe; i++) {
array_[current].displacements--;
current = ModTableSize(current + increment);
}
}
ClockCacheShard::ClockCacheShard(
size_t capacity, size_t estimated_value_size, bool strict_capacity_limit,
CacheMetadataChargePolicy metadata_charge_policy)
: capacity_(capacity),
strict_capacity_limit_(strict_capacity_limit),
clock_pointer_(0),
table_(
CalcHashBits(capacity, estimated_value_size, metadata_charge_policy)),
usage_(0) {
set_metadata_charge_policy(metadata_charge_policy);
}
void ClockCacheShard::EraseUnRefEntries() {
autovector<ClockHandle> last_reference_list;
{
DMutexLock l(mutex_);
table_.ApplyToEntriesRange(
[this, &last_reference_list](ClockHandle* h) {
// Externally unreferenced element.
last_reference_list.push_back(*h);
Evict(h);
},
0, table_.GetTableSize(), true);
}
// Free the entry outside of the mutex for performance reasons.
for (auto& h : last_reference_list) {
h.FreeData();
}
}
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
void ClockCacheShard::ApplyToSomeEntries(
const std::function<void(const Slice& key, void* value, size_t charge,
DeleterFn deleter)>& callback,
uint32_t average_entries_per_lock, uint32_t* state) {
// The state is essentially going to be the starting hash, which works
// nicely even if we resize between calls because we use upper-most
// hash bits for table indexes.
Use optimized folly DistributedMutex in LRUCache when available (#10179) Summary: folly DistributedMutex is faster than standard mutexes though imposes some static obligations on usage. See https://github.com/facebook/folly/blob/main/folly/synchronization/DistributedMutex.h for details. Here we use this alternative for our Cache implementations (especially LRUCache) for better locking performance, when RocksDB is compiled with folly. Also added information about which distributed mutex implementation is being used to cache_bench output and to DB LOG. Intended follow-up: * Use DMutex in more places, perhaps improving API to support non-scoped locking * Fix linking with fbcode compiler (needs ROCKSDB_NO_FBCODE=1 currently) Credit: Thanks Siying for reminding me about this line of work that was previously left unfinished. Pull Request resolved: https://github.com/facebook/rocksdb/pull/10179 Test Plan: for correctness, existing tests. CircleCI config updated. Also Meta-internal buck build updated. For performance, ran simultaneous before & after cache_bench. Out of three comparison runs, the middle improvement to ops/sec was +21%: Baseline: USE_CLANG=1 DEBUG_LEVEL=0 make -j24 cache_bench (fbcode compiler) ``` Complete in 20.201 s; Rough parallel ops/sec = 1584062 Thread ops/sec = 107176 Operation latency (ns): Count: 32000000 Average: 9257.9421 StdDev: 122412.04 Min: 134 Median: 3623.0493 Max: 56918500 Percentiles: P50: 3623.05 P75: 10288.02 P99: 30219.35 P99.9: 683522.04 P99.99: 7302791.63 ``` New: (add USE_FOLLY=1) ``` Complete in 16.674 s; Rough parallel ops/sec = 1919135 (+21%) Thread ops/sec = 135487 Operation latency (ns): Count: 32000000 Average: 7304.9294 StdDev: 108530.28 Min: 132 Median: 3777.6012 Max: 91030902 Percentiles: P50: 3777.60 P75: 10169.89 P99: 24504.51 P99.9: 59721.59 P99.99: 1861151.83 ``` Reviewed By: anand1976 Differential Revision: D37182983 Pulled By: pdillinger fbshipit-source-id: a17eb05f25b832b6a2c1356f5c657e831a5af8d1
3 years ago
DMutexLock l(mutex_);
uint32_t length_bits = table_.GetLengthBits();
uint32_t length = table_.GetTableSize();
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
assert(average_entries_per_lock > 0);
// Assuming we are called with same average_entries_per_lock repeatedly,
// this simplifies some logic (index_end will not overflow).
assert(average_entries_per_lock < length || *state == 0);
uint32_t index_begin = *state >> (32 - length_bits);
uint32_t index_end = index_begin + average_entries_per_lock;
if (index_end >= length) {
// Going to end
index_end = length;
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
*state = UINT32_MAX;
} else {
*state = index_end << (32 - length_bits);
}
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
table_.ApplyToEntriesRange(
[callback,
metadata_charge_policy = metadata_charge_policy_](ClockHandle* h) {
callback(h->key(), h->value, h->GetCharge(metadata_charge_policy),
h->deleter);
},
index_begin, index_end, false);
}
void ClockCacheShard::ClockOff(ClockHandle* h) {
h->SetClockPriority(ClockHandle::ClockPriority::NONE);
}
void ClockCacheShard::ClockOn(ClockHandle* h) {
assert(!h->IsInClock());
bool is_high_priority =
h->HasHit() || h->GetCachePriority() == Cache::Priority::HIGH;
h->SetClockPriority(static_cast<ClockHandle::ClockPriority>(
is_high_priority * ClockHandle::ClockPriority::HIGH +
(1 - is_high_priority) * ClockHandle::ClockPriority::MEDIUM));
}
void ClockCacheShard::Evict(ClockHandle* h) {
ClockOff(h);
table_.Remove(h);
assert(usage_ >= h->total_charge);
usage_ -= h->total_charge;
}
void ClockCacheShard::EvictFromClock(size_t charge,
autovector<ClockHandle>* deleted) {
// TODO(Guido) When an element is in the probe sequence of a
// hot element, it will be hard to get an exclusive ref.
// We may need a mechanism to avoid that an element sits forever
// in cache waiting to be evicted.
assert(charge <= capacity_);
uint32_t max_iterations = table_.GetTableSize();
while (usage_ + charge > capacity_ && max_iterations--) {
ClockHandle* h = &table_.array_[clock_pointer_];
clock_pointer_ = table_.ModTableSize(clock_pointer_ + 1);
if (h->TryExclusiveRef()) {
if (!h->IsInClock() && h->IsElement()) {
// We adjust the clock priority to make the element evictable again.
// Why? Elements that are not in clock are either currently
// externally referenced or used to be---because we are holding an
// exclusive ref, we know we are in the latter case. This can only
// happen when the last external reference to an element was released,
// and the element was not immediately removed.
ClockOn(h);
}
if (h->GetClockPriority() == ClockHandle::ClockPriority::LOW) {
deleted->push_back(*h);
Evict(h);
} else if (h->GetClockPriority() > ClockHandle::ClockPriority::LOW) {
h->DecreaseClockPriority();
}
h->ReleaseExclusiveRef();
}
}
}
size_t ClockCacheShard::CalcEstimatedHandleCharge(
size_t estimated_value_size,
CacheMetadataChargePolicy metadata_charge_policy) {
ClockHandle h;
h.CalcTotalCharge(estimated_value_size, metadata_charge_policy);
return h.total_charge;
}
int ClockCacheShard::CalcHashBits(
size_t capacity, size_t estimated_value_size,
CacheMetadataChargePolicy metadata_charge_policy) {
size_t handle_charge =
CalcEstimatedHandleCharge(estimated_value_size, metadata_charge_policy);
assert(handle_charge > 0);
uint32_t num_entries =
static_cast<uint32_t>(capacity / (kLoadFactor * handle_charge)) + 1;
assert(num_entries <= uint32_t{1} << 31);
return FloorLog2((num_entries << 1) - 1);
}
void ClockCacheShard::SetCapacity(size_t capacity) {
assert(false); // Not supported. TODO(Guido) Support it?
autovector<ClockHandle> last_reference_list;
{
Use optimized folly DistributedMutex in LRUCache when available (#10179) Summary: folly DistributedMutex is faster than standard mutexes though imposes some static obligations on usage. See https://github.com/facebook/folly/blob/main/folly/synchronization/DistributedMutex.h for details. Here we use this alternative for our Cache implementations (especially LRUCache) for better locking performance, when RocksDB is compiled with folly. Also added information about which distributed mutex implementation is being used to cache_bench output and to DB LOG. Intended follow-up: * Use DMutex in more places, perhaps improving API to support non-scoped locking * Fix linking with fbcode compiler (needs ROCKSDB_NO_FBCODE=1 currently) Credit: Thanks Siying for reminding me about this line of work that was previously left unfinished. Pull Request resolved: https://github.com/facebook/rocksdb/pull/10179 Test Plan: for correctness, existing tests. CircleCI config updated. Also Meta-internal buck build updated. For performance, ran simultaneous before & after cache_bench. Out of three comparison runs, the middle improvement to ops/sec was +21%: Baseline: USE_CLANG=1 DEBUG_LEVEL=0 make -j24 cache_bench (fbcode compiler) ``` Complete in 20.201 s; Rough parallel ops/sec = 1584062 Thread ops/sec = 107176 Operation latency (ns): Count: 32000000 Average: 9257.9421 StdDev: 122412.04 Min: 134 Median: 3623.0493 Max: 56918500 Percentiles: P50: 3623.05 P75: 10288.02 P99: 30219.35 P99.9: 683522.04 P99.99: 7302791.63 ``` New: (add USE_FOLLY=1) ``` Complete in 16.674 s; Rough parallel ops/sec = 1919135 (+21%) Thread ops/sec = 135487 Operation latency (ns): Count: 32000000 Average: 7304.9294 StdDev: 108530.28 Min: 132 Median: 3777.6012 Max: 91030902 Percentiles: P50: 3777.60 P75: 10169.89 P99: 24504.51 P99.9: 59721.59 P99.99: 1861151.83 ``` Reviewed By: anand1976 Differential Revision: D37182983 Pulled By: pdillinger fbshipit-source-id: a17eb05f25b832b6a2c1356f5c657e831a5af8d1
3 years ago
DMutexLock l(mutex_);
capacity_ = capacity;
EvictFromClock(0, &last_reference_list);
}
// Free the entry outside of the mutex for performance reasons.
for (auto& h : last_reference_list) {
h.FreeData();
}
}
void ClockCacheShard::SetStrictCapacityLimit(bool strict_capacity_limit) {
assert(false); // Not supported. TODO(Guido) Support it?
Use optimized folly DistributedMutex in LRUCache when available (#10179) Summary: folly DistributedMutex is faster than standard mutexes though imposes some static obligations on usage. See https://github.com/facebook/folly/blob/main/folly/synchronization/DistributedMutex.h for details. Here we use this alternative for our Cache implementations (especially LRUCache) for better locking performance, when RocksDB is compiled with folly. Also added information about which distributed mutex implementation is being used to cache_bench output and to DB LOG. Intended follow-up: * Use DMutex in more places, perhaps improving API to support non-scoped locking * Fix linking with fbcode compiler (needs ROCKSDB_NO_FBCODE=1 currently) Credit: Thanks Siying for reminding me about this line of work that was previously left unfinished. Pull Request resolved: https://github.com/facebook/rocksdb/pull/10179 Test Plan: for correctness, existing tests. CircleCI config updated. Also Meta-internal buck build updated. For performance, ran simultaneous before & after cache_bench. Out of three comparison runs, the middle improvement to ops/sec was +21%: Baseline: USE_CLANG=1 DEBUG_LEVEL=0 make -j24 cache_bench (fbcode compiler) ``` Complete in 20.201 s; Rough parallel ops/sec = 1584062 Thread ops/sec = 107176 Operation latency (ns): Count: 32000000 Average: 9257.9421 StdDev: 122412.04 Min: 134 Median: 3623.0493 Max: 56918500 Percentiles: P50: 3623.05 P75: 10288.02 P99: 30219.35 P99.9: 683522.04 P99.99: 7302791.63 ``` New: (add USE_FOLLY=1) ``` Complete in 16.674 s; Rough parallel ops/sec = 1919135 (+21%) Thread ops/sec = 135487 Operation latency (ns): Count: 32000000 Average: 7304.9294 StdDev: 108530.28 Min: 132 Median: 3777.6012 Max: 91030902 Percentiles: P50: 3777.60 P75: 10169.89 P99: 24504.51 P99.9: 59721.59 P99.99: 1861151.83 ``` Reviewed By: anand1976 Differential Revision: D37182983 Pulled By: pdillinger fbshipit-source-id: a17eb05f25b832b6a2c1356f5c657e831a5af8d1
3 years ago
DMutexLock l(mutex_);
strict_capacity_limit_ = strict_capacity_limit;
}
Status ClockCacheShard::Insert(const Slice& key, uint32_t hash, void* value,
size_t charge, Cache::DeleterFn deleter,
Cache::Handle** handle,
Cache::Priority priority) {
if (key.size() != kCacheKeySize) {
return Status::NotSupported("ClockCache only supports key size " +
std::to_string(kCacheKeySize) + "B");
}
ClockHandle tmp;
tmp.value = value;
tmp.deleter = deleter;
tmp.hash = hash;
tmp.CalcTotalCharge(charge, metadata_charge_policy_);
tmp.SetCachePriority(priority);
for (int i = 0; i < kCacheKeySize; i++) {
tmp.key_data[i] = key.data()[i];
}
Status s = Status::OK();
autovector<ClockHandle> last_reference_list;
{
DMutexLock l(mutex_);
assert(table_.GetOccupancy() <= table_.GetOccupancyLimit());
// Free the space following strict clock policy until enough space
// is freed or there are no evictable elements.
EvictFromClock(tmp.total_charge, &last_reference_list);
if ((usage_ + tmp.total_charge > capacity_ &&
(strict_capacity_limit_ || handle == nullptr)) ||
table_.GetOccupancy() == table_.GetOccupancyLimit()) {
if (handle == nullptr) {
// Don't insert the entry but still return ok, as if the entry inserted
// into cache and get evicted immediately.
last_reference_list.push_back(tmp);
} else {
if (table_.GetOccupancy() == table_.GetOccupancyLimit()) {
// TODO: Consider using a distinct status for this case, but usually
// it will be handled the same way as reaching charge capacity limit
s = Status::MemoryLimit(
"Insert failed because all slots in the hash table are full.");
} else {
s = Status::MemoryLimit(
"Insert failed because the total charge has exceeded the "
"capacity.");
}
}
} else {
// Insert into the cache. Note that the cache might get larger than its
// capacity if not enough space was freed up.
ClockHandle* old;
ClockHandle* h = table_.Insert(&tmp, &old);
assert(h != nullptr); // We're below occupancy, so this insertion should
// never fail.
usage_ += h->total_charge;
if (old != nullptr) {
s = Status::OkOverwritten();
assert(!old->WillBeDeleted());
old->SetWillBeDeleted(true);
// Try to evict the old copy of the element.
if (old->TryExclusiveRef()) {
last_reference_list.push_back(*old);
Evict(old);
old->ReleaseExclusiveRef();
}
}
if (handle == nullptr) {
// If the user didn't provide a handle, no reference is taken,
// so we make the element evictable.
ClockOn(h);
h->ReleaseExclusiveRef();
} else {
// The caller already holds a ref.
h->ExclusiveToExternalRef();
*handle = reinterpret_cast<Cache::Handle*>(h);
}
}
}
// Free the entry outside of the mutex for performance reasons.
for (auto& h : last_reference_list) {
h.FreeData();
Stats for redundant insertions into block cache (#6681) Summary: Since read threads do not coordinate on loading data into block cache, two threads between Lookup and Insert can end up loading and inserting the same data. This is particularly concerning with cache_index_and_filter_blocks since those are hot and more likely to be race targets if ejected from (or not pre-populated in) the cache. Particularly with moves toward disaggregated / network storage, the cost of redundant retrieval might be high, and we should at least have some hard statistics from which we can estimate impact. Example with full filter thrashing "cliff": $ ./db_bench --benchmarks=fillrandom --num=15000000 --cache_index_and_filter_blocks -bloom_bits=10 ... $ ./db_bench --db=/tmp/rocksdbtest-172704/dbbench --use_existing_db --benchmarks=readrandom,stats --num=200000 --cache_index_and_filter_blocks --cache_size=$((130 * 1024 * 1024)) --bloom_bits=10 --threads=16 -statistics 2>&1 | egrep '^rocksdb.block.cache.(.*add|.*redundant)' | grep -v compress | sort rocksdb.block.cache.add COUNT : 14181 rocksdb.block.cache.add.failures COUNT : 0 rocksdb.block.cache.add.redundant COUNT : 476 rocksdb.block.cache.data.add COUNT : 12749 rocksdb.block.cache.data.add.redundant COUNT : 18 rocksdb.block.cache.filter.add COUNT : 1003 rocksdb.block.cache.filter.add.redundant COUNT : 217 rocksdb.block.cache.index.add COUNT : 429 rocksdb.block.cache.index.add.redundant COUNT : 241 $ ./db_bench --db=/tmp/rocksdbtest-172704/dbbench --use_existing_db --benchmarks=readrandom,stats --num=200000 --cache_index_and_filter_blocks --cache_size=$((120 * 1024 * 1024)) --bloom_bits=10 --threads=16 -statistics 2>&1 | egrep '^rocksdb.block.cache.(.*add|.*redundant)' | grep -v compress | sort rocksdb.block.cache.add COUNT : 1182223 rocksdb.block.cache.add.failures COUNT : 0 rocksdb.block.cache.add.redundant COUNT : 302728 rocksdb.block.cache.data.add COUNT : 31425 rocksdb.block.cache.data.add.redundant COUNT : 12 rocksdb.block.cache.filter.add COUNT : 795455 rocksdb.block.cache.filter.add.redundant COUNT : 130238 rocksdb.block.cache.index.add COUNT : 355343 rocksdb.block.cache.index.add.redundant COUNT : 172478 Pull Request resolved: https://github.com/facebook/rocksdb/pull/6681 Test Plan: Some manual testing (above) and unit test covering key metrics is included Reviewed By: ltamasi Differential Revision: D21134113 Pulled By: pdillinger fbshipit-source-id: c11497b5f00f4ffdfe919823904e52d0a1a91d87
5 years ago
}
return s;
}
Cache::Handle* ClockCacheShard::Lookup(const Slice& key, uint32_t hash) {
ClockHandle* h = nullptr;
h = table_.Lookup(key, hash);
if (h != nullptr) {
// TODO(Guido) Comment from #10347: Here it looks like we have three atomic
// updates where it would be possible to combine into one CAS (more metadata
// under one atomic field) or maybe two atomic updates (one arithmetic, one
// bitwise). Something to think about optimizing.
h->InternalToExternalRef();
h->SetHit();
// The handle is now referenced, so we take it out of clock.
ClockOff(h);
}
return reinterpret_cast<Cache::Handle*>(h);
}
bool ClockCacheShard::Ref(Cache::Handle* h) {
ClockHandle* e = reinterpret_cast<ClockHandle*>(h);
assert(e->HasExternalRefs());
return e->TryExternalRef();
}
bool ClockCacheShard::Release(Cache::Handle* handle, bool erase_if_last_ref) {
// In contrast with LRUCache's Release, this function won't delete the handle
// when the reference is the last one and the cache is above capacity. Space
// is only freed up by EvictFromClock (called by Insert when space is needed)
// and Erase.
if (handle == nullptr) {
return false;
}
ClockHandle* h = reinterpret_cast<ClockHandle*>(handle);
uint32_t hash = h->hash;
uint32_t refs = h->ReleaseExternalRef();
bool last_reference = !(refs & ClockHandle::EXTERNAL_REFS);
bool will_be_deleted = refs & ClockHandle::WILL_BE_DELETED;
if (last_reference && (will_be_deleted || erase_if_last_ref)) {
// At this point we want to evict the element, so we need to take
// a lock and an exclusive reference. But there's a problem:
// as soon as we released the last reference, an Insert or Erase could've
// replaced this element, and by the time we take the lock and ref
// we could potentially be referencing a different element.
// Thus, before evicting the (potentially different) element, we need to
// re-check that it's unreferenced and marked as WILL_BE_DELETED, so the
// eviction is safe. Additionally, we check that the hash doesn't change,
// which will detect, most of the time, whether the element is a different
// one. The bottomline is that we only guarantee that the input handle will
// be deleted, and occasionally also another handle, but in any case all
// deleted handles are safe to delete.
// TODO(Guido) With lock-free inserts and deletes we may be able to
// "atomically" transition to an exclusive ref, without creating a deadlock.
ClockHandle copy;
{
DMutexLock l(mutex_);
if (h->TrySpinExclusiveRef()) {
will_be_deleted = h->refs & ClockHandle::WILL_BE_DELETED;
// Check that it's still safe to delete.
if (h->IsElement() && (will_be_deleted || erase_if_last_ref) &&
h->hash == hash) {
copy = *h;
Evict(h);
}
h->ReleaseExclusiveRef();
} else {
// An external ref was detected.
return false;
}
}
// Free the entry outside of the mutex for performance reasons.
copy.FreeData();
return true;
}
return false;
}
void ClockCacheShard::Erase(const Slice& key, uint32_t hash) {
ClockHandle copy;
bool last_reference = false;
{
Use optimized folly DistributedMutex in LRUCache when available (#10179) Summary: folly DistributedMutex is faster than standard mutexes though imposes some static obligations on usage. See https://github.com/facebook/folly/blob/main/folly/synchronization/DistributedMutex.h for details. Here we use this alternative for our Cache implementations (especially LRUCache) for better locking performance, when RocksDB is compiled with folly. Also added information about which distributed mutex implementation is being used to cache_bench output and to DB LOG. Intended follow-up: * Use DMutex in more places, perhaps improving API to support non-scoped locking * Fix linking with fbcode compiler (needs ROCKSDB_NO_FBCODE=1 currently) Credit: Thanks Siying for reminding me about this line of work that was previously left unfinished. Pull Request resolved: https://github.com/facebook/rocksdb/pull/10179 Test Plan: for correctness, existing tests. CircleCI config updated. Also Meta-internal buck build updated. For performance, ran simultaneous before & after cache_bench. Out of three comparison runs, the middle improvement to ops/sec was +21%: Baseline: USE_CLANG=1 DEBUG_LEVEL=0 make -j24 cache_bench (fbcode compiler) ``` Complete in 20.201 s; Rough parallel ops/sec = 1584062 Thread ops/sec = 107176 Operation latency (ns): Count: 32000000 Average: 9257.9421 StdDev: 122412.04 Min: 134 Median: 3623.0493 Max: 56918500 Percentiles: P50: 3623.05 P75: 10288.02 P99: 30219.35 P99.9: 683522.04 P99.99: 7302791.63 ``` New: (add USE_FOLLY=1) ``` Complete in 16.674 s; Rough parallel ops/sec = 1919135 (+21%) Thread ops/sec = 135487 Operation latency (ns): Count: 32000000 Average: 7304.9294 StdDev: 108530.28 Min: 132 Median: 3777.6012 Max: 91030902 Percentiles: P50: 3777.60 P75: 10169.89 P99: 24504.51 P99.9: 59721.59 P99.99: 1861151.83 ``` Reviewed By: anand1976 Differential Revision: D37182983 Pulled By: pdillinger fbshipit-source-id: a17eb05f25b832b6a2c1356f5c657e831a5af8d1
3 years ago
DMutexLock l(mutex_);
ClockHandle* h = table_.Lookup(key, hash);
if (h != nullptr) {
h->SetWillBeDeleted(true);
h->ReleaseInternalRef();
if (h->TryExclusiveRef()) {
copy = *h;
Evict(h);
last_reference = true;
h->ReleaseExclusiveRef();
}
}
}
// Free the entry outside of the mutex for performance reasons.
if (last_reference) {
copy.FreeData();
}
}
size_t ClockCacheShard::GetUsage() const {
DMutexLock l(mutex_);
return usage_;
}
size_t ClockCacheShard::GetPinnedUsage() const {
// Computes the pinned usage scanning the whole hash table. This
// is slow, but avoid keeping an exact counter on the clock usage,
// i.e., the number of not externally referenced elements.
// Why avoid this? Because Lookup removes elements from the clock
// list, so it would need to update the pinned usage every time,
// which creates additional synchronization costs.
DMutexLock l(mutex_);
size_t clock_usage = 0;
table_.ConstApplyToEntriesRange(
[&clock_usage](ClockHandle* h) {
if (h->HasExternalRefs()) {
clock_usage += h->total_charge;
}
},
0, table_.GetTableSize(), true);
return clock_usage;
}
std::string ClockCacheShard::GetPrintableOptions() const {
return std::string{};
}
ClockCache::ClockCache(size_t capacity, size_t estimated_value_size,
int num_shard_bits, bool strict_capacity_limit,
CacheMetadataChargePolicy metadata_charge_policy)
: ShardedCache(capacity, num_shard_bits, strict_capacity_limit) {
assert(estimated_value_size > 0 ||
metadata_charge_policy != kDontChargeCacheMetadata);
num_shards_ = 1 << num_shard_bits;
shards_ = reinterpret_cast<ClockCacheShard*>(
port::cacheline_aligned_alloc(sizeof(ClockCacheShard) * num_shards_));
size_t per_shard = (capacity + (num_shards_ - 1)) / num_shards_;
for (int i = 0; i < num_shards_; i++) {
new (&shards_[i])
ClockCacheShard(per_shard, estimated_value_size, strict_capacity_limit,
metadata_charge_policy);
}
}
ClockCache::~ClockCache() {
if (shards_ != nullptr) {
assert(num_shards_ > 0);
for (int i = 0; i < num_shards_; i++) {
shards_[i].~ClockCacheShard();
}
port::cacheline_aligned_free(shards_);
}
}
CacheShard* ClockCache::GetShard(uint32_t shard) {
return reinterpret_cast<CacheShard*>(&shards_[shard]);
}
const CacheShard* ClockCache::GetShard(uint32_t shard) const {
return reinterpret_cast<CacheShard*>(&shards_[shard]);
}
void* ClockCache::Value(Handle* handle) {
return reinterpret_cast<const ClockHandle*>(handle)->value;
}
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
size_t ClockCache::GetCharge(Handle* handle) const {
CacheMetadataChargePolicy metadata_charge_policy = kDontChargeCacheMetadata;
if (num_shards_ > 0) {
metadata_charge_policy = shards_[0].metadata_charge_policy_;
Fix use-after-free threading bug in ClockCache (#8261) Summary: In testing for https://github.com/facebook/rocksdb/issues/8225 I found cache_bench would crash with -use_clock_cache, as well as db_bench -use_clock_cache, but not single-threaded. Smaller cache size hits failure much faster. ASAN reported the failuer as calling malloc_usable_size on the `key` pointer of a ClockCache handle after it was reportedly freed. On detailed inspection I found this bad sequence of operations for a cache entry: state=InCache=1,refs=1 [thread 1] Start ClockCacheShard::Unref (from Release, no mutex) [thread 1] Decrement ref count state=InCache=1,refs=0 [thread 1] Suspend before CalcTotalCharge (no mutex) [thread 2] Start UnsetInCache (from Insert, mutex held) [thread 2] clear InCache bit state=InCache=0,refs=0 [thread 2] Calls RecycleHandle (based on pre-updated state) [thread 2] Returns to Insert which calls Cleanup which deletes `key` [thread 1] Resume ClockCacheShard::Unref [thread 1] Read `key` in CalcTotalCharge To fix this, I've added a field to the handle to store the metadata charge so that we can efficiently remember everything we need from the handle in Unref. We must not read from the handle again if we decrement the count to zero with InCache=1, which means we don't own the entry and someone else could eject/overwrite it immediately. Note before this change, on amd64 sizeof(Handle) == 56 even though there are only 48 bytes of data. Grouping together the uint32_t fields would cut it down to 48, but I've added another uint32_t, which takes it back up to 56. Not a big deal. Also fixed DisownData to cooperate with ASAN as in LRUCache. Pull Request resolved: https://github.com/facebook/rocksdb/pull/8261 Test Plan: Manual + adding use_clock_cache to db_crashtest.py Base performance ./cache_bench -use_clock_cache Complete in 17.060 s; QPS = 2458513 New performance ./cache_bench -use_clock_cache Complete in 17.052 s; QPS = 2459695 Any difference is easily buried in small noise. Crash test shows still more bug(s) in ClockCache, so I'm expecting to disable ClockCache from production code in a follow-up PR (if we can't find and fix the bug(s)) Reviewed By: mrambacher Differential Revision: D28207358 Pulled By: pdillinger fbshipit-source-id: aa7a9322afc6f18f30e462c75dbbe4a1206eb294
4 years ago
}
return reinterpret_cast<const ClockHandle*>(handle)->GetCharge(
metadata_charge_policy);
}
Cache::DeleterFn ClockCache::GetDeleter(Handle* handle) const {
auto h = reinterpret_cast<const ClockHandle*>(handle);
return h->deleter;
}
Initial support for secondary cache in LRUCache (#8271) Summary: Defined the abstract interface for a secondary cache in include/rocksdb/secondary_cache.h, and updated LRUCacheOptions to take a std::shared_ptr<SecondaryCache>. An item is initially inserted into the LRU (primary) cache. When it ages out and evicted from memory, its inserted into the secondary cache. On a LRU cache miss and successful lookup in the secondary cache, the item is promoted to the LRU cache. Only support synchronous lookup currently. The secondary cache would be used to implement a persistent (flash cache) or compressed cache. Tests: Results from cache_bench and db_bench don't show any regression due to these changes. cache_bench results before and after this change - Command ```./cache_bench -ops_per_thread=10000000 -threads=1``` Before ```Complete in 40.688 s; QPS = 245774``` ```Complete in 40.486 s; QPS = 246996``` ```Complete in 42.019 s; QPS = 237989``` After ```Complete in 40.672 s; QPS = 245869``` ```Complete in 44.622 s; QPS = 224107``` ```Complete in 42.445 s; QPS = 235599``` db_bench results before this change, and with this change + https://github.com/facebook/rocksdb/issues/8213 and https://github.com/facebook/rocksdb/issues/8191 - Commands ```./db_bench --benchmarks="fillseq,compact" -num=30000000 -key_size=32 -value_size=256 -use_direct_io_for_flush_and_compaction=true -db=/home/anand76/nvm_cache/db -partition_index_and_filters=true``` ```./db_bench -db=/home/anand76/nvm_cache/db -use_existing_db=true -benchmarks=readrandom -num=30000000 -key_size=32 -value_size=256 -use_direct_reads=true -cache_size=1073741824 -cache_numshardbits=6 -cache_index_and_filter_blocks=true -read_random_exp_range=17 -statistics -partition_index_and_filters=true -threads=16 -duration=300``` Before ``` DB path: [/home/anand76/nvm_cache/db] readrandom : 80.702 micros/op 198104 ops/sec; 54.4 MB/s (3708999 of 3708999 found) ``` ``` DB path: [/home/anand76/nvm_cache/db] readrandom : 87.124 micros/op 183625 ops/sec; 50.4 MB/s (3439999 of 3439999 found) ``` After ``` DB path: [/home/anand76/nvm_cache/db] readrandom : 77.653 micros/op 206025 ops/sec; 56.6 MB/s (3866999 of 3866999 found) ``` ``` DB path: [/home/anand76/nvm_cache/db] readrandom : 84.962 micros/op 188299 ops/sec; 51.7 MB/s (3535999 of 3535999 found) ``` Pull Request resolved: https://github.com/facebook/rocksdb/pull/8271 Reviewed By: zhichao-cao Differential Revision: D28357511 Pulled By: anand1976 fbshipit-source-id: d1cfa236f00e649a18c53328be10a8062a4b6da2
4 years ago
uint32_t ClockCache::GetHash(Handle* handle) const {
return reinterpret_cast<const ClockHandle*>(handle)->hash;
}
void ClockCache::DisownData() {
// Leak data only if that won't generate an ASAN/valgrind warning.
if (!kMustFreeHeapAllocations) {
shards_ = nullptr;
num_shards_ = 0;
}
}
} // namespace clock_cache
std::shared_ptr<Cache> NewClockCache(
size_t capacity, int num_shard_bits, bool strict_capacity_limit,
CacheMetadataChargePolicy metadata_charge_policy) {
return NewLRUCache(capacity, num_shard_bits, strict_capacity_limit, 0.5,
nullptr, kDefaultToAdaptiveMutex, metadata_charge_policy);
}
std::shared_ptr<Cache> ExperimentalNewClockCache(
size_t capacity, size_t estimated_value_size, int num_shard_bits,
bool strict_capacity_limit,
CacheMetadataChargePolicy metadata_charge_policy) {
if (num_shard_bits >= 20) {
return nullptr; // The cache cannot be sharded into too many fine pieces.
}
if (num_shard_bits < 0) {
num_shard_bits = GetDefaultCacheShardBits(capacity);
}
return std::make_shared<clock_cache::ClockCache>(
capacity, estimated_value_size, num_shard_bits, strict_capacity_limit,
metadata_charge_policy);
}
} // namespace ROCKSDB_NAMESPACE