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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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//
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// Copyright (c) 2011 The LevelDB Authors. All rights reserved.
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// Use of this source code is governed by a BSD-style license that can be
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// found in the LICENSE file. See the AUTHORS file for names of contributors.
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#include "cache/lru_cache.h"
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#include <cassert>
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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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#include <cstdint>
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#include <cstdio>
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#include "util/mutexlock.h"
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namespace ROCKSDB_NAMESPACE {
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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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LRUHandleTable::LRUHandleTable(int max_upper_hash_bits)
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: length_bits_(/* historical starting size*/ 4),
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list_(new LRUHandle* [size_t{1} << length_bits_] {}),
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elems_(0),
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max_length_bits_(max_upper_hash_bits) {}
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LRUHandleTable::~LRUHandleTable() {
|
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
|
|
|
ApplyToEntriesRange(
|
|
|
|
[](LRUHandle* h) {
|
|
|
|
if (!h->HasRefs()) {
|
|
|
|
h->Free();
|
|
|
|
}
|
|
|
|
},
|
|
|
|
0, uint32_t{1} << length_bits_);
|
|
|
|
}
|
|
|
|
|
|
|
|
LRUHandle* LRUHandleTable::Lookup(const Slice& key, uint32_t hash) {
|
|
|
|
return *FindPointer(key, hash);
|
|
|
|
}
|
|
|
|
|
|
|
|
LRUHandle* LRUHandleTable::Insert(LRUHandle* h) {
|
|
|
|
LRUHandle** ptr = FindPointer(h->key(), h->hash);
|
|
|
|
LRUHandle* old = *ptr;
|
|
|
|
h->next_hash = (old == nullptr ? nullptr : old->next_hash);
|
|
|
|
*ptr = h;
|
|
|
|
if (old == nullptr) {
|
|
|
|
++elems_;
|
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
|
|
|
if ((elems_ >> length_bits_) > 0) { // elems_ >= length
|
|
|
|
// Since each cache entry is fairly large, we aim for a small
|
|
|
|
// average linked list length (<= 1).
|
|
|
|
Resize();
|
|
|
|
}
|
|
|
|
}
|
|
|
|
return old;
|
|
|
|
}
|
|
|
|
|
|
|
|
LRUHandle* LRUHandleTable::Remove(const Slice& key, uint32_t hash) {
|
|
|
|
LRUHandle** ptr = FindPointer(key, hash);
|
|
|
|
LRUHandle* result = *ptr;
|
|
|
|
if (result != nullptr) {
|
|
|
|
*ptr = result->next_hash;
|
|
|
|
--elems_;
|
|
|
|
}
|
|
|
|
return result;
|
|
|
|
}
|
|
|
|
|
|
|
|
LRUHandle** LRUHandleTable::FindPointer(const Slice& key, uint32_t hash) {
|
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
|
|
|
LRUHandle** ptr = &list_[hash >> (32 - length_bits_)];
|
|
|
|
while (*ptr != nullptr && ((*ptr)->hash != hash || key != (*ptr)->key())) {
|
|
|
|
ptr = &(*ptr)->next_hash;
|
|
|
|
}
|
|
|
|
return ptr;
|
|
|
|
}
|
|
|
|
|
|
|
|
void LRUHandleTable::Resize() {
|
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
|
|
|
if (length_bits_ >= max_length_bits_) {
|
|
|
|
// Due to reaching limit of hash information, if we made the table
|
|
|
|
// bigger, we would allocate more addresses but only the same
|
|
|
|
// number would be used.
|
|
|
|
return;
|
|
|
|
}
|
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
|
|
|
if (length_bits_ >= 31) {
|
|
|
|
// Avoid undefined behavior shifting uint32_t by 32
|
|
|
|
return;
|
|
|
|
}
|
|
|
|
|
|
|
|
uint32_t old_length = uint32_t{1} << length_bits_;
|
|
|
|
int new_length_bits = length_bits_ + 1;
|
|
|
|
std::unique_ptr<LRUHandle* []> new_list {
|
|
|
|
new LRUHandle* [size_t{1} << new_length_bits] {}
|
|
|
|
};
|
|
|
|
uint32_t count = 0;
|
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
|
|
|
for (uint32_t i = 0; i < old_length; i++) {
|
|
|
|
LRUHandle* h = list_[i];
|
|
|
|
while (h != nullptr) {
|
|
|
|
LRUHandle* next = h->next_hash;
|
|
|
|
uint32_t hash = h->hash;
|
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
|
|
|
LRUHandle** ptr = &new_list[hash >> (32 - new_length_bits)];
|
|
|
|
h->next_hash = *ptr;
|
|
|
|
*ptr = h;
|
|
|
|
h = next;
|
|
|
|
count++;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
assert(elems_ == count);
|
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
|
|
|
list_ = std::move(new_list);
|
|
|
|
length_bits_ = new_length_bits;
|
|
|
|
}
|
|
|
|
|
|
|
|
LRUCacheShard::LRUCacheShard(size_t capacity, bool strict_capacity_limit,
|
|
|
|
double high_pri_pool_ratio,
|
|
|
|
bool use_adaptive_mutex,
|
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
|
|
|
CacheMetadataChargePolicy metadata_charge_policy,
|
|
|
|
int max_upper_hash_bits)
|
|
|
|
: capacity_(0),
|
|
|
|
high_pri_pool_usage_(0),
|
|
|
|
strict_capacity_limit_(strict_capacity_limit),
|
|
|
|
high_pri_pool_ratio_(high_pri_pool_ratio),
|
|
|
|
high_pri_pool_capacity_(0),
|
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_(max_upper_hash_bits),
|
|
|
|
usage_(0),
|
|
|
|
lru_usage_(0),
|
|
|
|
mutex_(use_adaptive_mutex) {
|
|
|
|
set_metadata_charge_policy(metadata_charge_policy);
|
|
|
|
// Make empty circular linked list
|
|
|
|
lru_.next = &lru_;
|
|
|
|
lru_.prev = &lru_;
|
|
|
|
lru_low_pri_ = &lru_;
|
|
|
|
SetCapacity(capacity);
|
|
|
|
}
|
|
|
|
|
|
|
|
void LRUCacheShard::EraseUnRefEntries() {
|
Adding pin_l0_filter_and_index_blocks_in_cache feature and related fixes.
Summary:
When a block based table file is opened, if prefetch_index_and_filter is true, it will prefetch the index and filter blocks, putting them into the block cache.
What this feature adds: when a L0 block based table file is opened, if pin_l0_filter_and_index_blocks_in_cache is true in the options (and prefetch_index_and_filter is true), then the filter and index blocks aren't released back to the block cache at the end of BlockBasedTableReader::Open(). Instead the table reader takes ownership of them, hence pinning them, ie. the LRU cache will never push them out. Meanwhile in the table reader, further accesses will not hit the block cache, thus avoiding lock contention.
Test Plan:
'export TEST_TMPDIR=/dev/shm/ && DISABLE_JEMALLOC=1 OPT=-g make all valgrind_check -j32' is OK.
I didn't run the Java tests, I don't have Java set up on my devserver.
Reviewers: sdong
Reviewed By: sdong
Subscribers: andrewkr, dhruba
Differential Revision: https://reviews.facebook.net/D56133
9 years ago
|
|
|
autovector<LRUHandle*> last_reference_list;
|
|
|
|
{
|
|
|
|
MutexLock l(&mutex_);
|
|
|
|
while (lru_.next != &lru_) {
|
|
|
|
LRUHandle* old = lru_.next;
|
|
|
|
// LRU list contains only elements which can be evicted
|
|
|
|
assert(old->InCache() && !old->HasRefs());
|
Adding pin_l0_filter_and_index_blocks_in_cache feature and related fixes.
Summary:
When a block based table file is opened, if prefetch_index_and_filter is true, it will prefetch the index and filter blocks, putting them into the block cache.
What this feature adds: when a L0 block based table file is opened, if pin_l0_filter_and_index_blocks_in_cache is true in the options (and prefetch_index_and_filter is true), then the filter and index blocks aren't released back to the block cache at the end of BlockBasedTableReader::Open(). Instead the table reader takes ownership of them, hence pinning them, ie. the LRU cache will never push them out. Meanwhile in the table reader, further accesses will not hit the block cache, thus avoiding lock contention.
Test Plan:
'export TEST_TMPDIR=/dev/shm/ && DISABLE_JEMALLOC=1 OPT=-g make all valgrind_check -j32' is OK.
I didn't run the Java tests, I don't have Java set up on my devserver.
Reviewers: sdong
Reviewed By: sdong
Subscribers: andrewkr, dhruba
Differential Revision: https://reviews.facebook.net/D56133
9 years ago
|
|
|
LRU_Remove(old);
|
|
|
|
table_.Remove(old->key(), old->hash);
|
|
|
|
old->SetInCache(false);
|
|
|
|
size_t total_charge = old->CalcTotalCharge(metadata_charge_policy_);
|
|
|
|
assert(usage_ >= total_charge);
|
|
|
|
usage_ -= total_charge;
|
Adding pin_l0_filter_and_index_blocks_in_cache feature and related fixes.
Summary:
When a block based table file is opened, if prefetch_index_and_filter is true, it will prefetch the index and filter blocks, putting them into the block cache.
What this feature adds: when a L0 block based table file is opened, if pin_l0_filter_and_index_blocks_in_cache is true in the options (and prefetch_index_and_filter is true), then the filter and index blocks aren't released back to the block cache at the end of BlockBasedTableReader::Open(). Instead the table reader takes ownership of them, hence pinning them, ie. the LRU cache will never push them out. Meanwhile in the table reader, further accesses will not hit the block cache, thus avoiding lock contention.
Test Plan:
'export TEST_TMPDIR=/dev/shm/ && DISABLE_JEMALLOC=1 OPT=-g make all valgrind_check -j32' is OK.
I didn't run the Java tests, I don't have Java set up on my devserver.
Reviewers: sdong
Reviewed By: sdong
Subscribers: andrewkr, dhruba
Differential Revision: https://reviews.facebook.net/D56133
9 years ago
|
|
|
last_reference_list.push_back(old);
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
for (auto entry : last_reference_list) {
|
|
|
|
entry->Free();
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
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 LRUCacheShard::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.
|
|
|
|
MutexLock l(&mutex_);
|
|
|
|
uint32_t length_bits = table_.GetLengthBits();
|
|
|
|
uint32_t length = uint32_t{1} << length_bits;
|
|
|
|
|
|
|
|
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;
|
|
|
|
*state = UINT32_MAX;
|
|
|
|
} else {
|
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 = 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](LRUHandle* h) {
|
|
|
|
callback(h->key(), h->value, h->charge, h->deleter);
|
|
|
|
},
|
|
|
|
index_begin, index_end);
|
|
|
|
}
|
|
|
|
|
|
|
|
void LRUCacheShard::TEST_GetLRUList(LRUHandle** lru, LRUHandle** lru_low_pri) {
|
|
|
|
MutexLock l(&mutex_);
|
|
|
|
*lru = &lru_;
|
|
|
|
*lru_low_pri = lru_low_pri_;
|
|
|
|
}
|
|
|
|
|
|
|
|
size_t LRUCacheShard::TEST_GetLRUSize() {
|
|
|
|
MutexLock l(&mutex_);
|
|
|
|
LRUHandle* lru_handle = lru_.next;
|
|
|
|
size_t lru_size = 0;
|
|
|
|
while (lru_handle != &lru_) {
|
|
|
|
lru_size++;
|
|
|
|
lru_handle = lru_handle->next;
|
|
|
|
}
|
|
|
|
return lru_size;
|
|
|
|
}
|
|
|
|
|
|
|
|
double LRUCacheShard::GetHighPriPoolRatio() {
|
|
|
|
MutexLock l(&mutex_);
|
|
|
|
return high_pri_pool_ratio_;
|
|
|
|
}
|
|
|
|
|
|
|
|
void LRUCacheShard::LRU_Remove(LRUHandle* e) {
|
Modifed the LRU cache eviction code so that it doesn't evict blocks which have exteranl references
Summary:
Currently, blocks which have more than one reference (ie referenced by something other than cache itself) are evicted from cache. This doesn't make much sense:
- blocks are still in RAM, so the RAM usage reported by the cache is incorrect
- if the same block is needed by another iterator, it will be loaded and decompressed again
This diff changes the reference counting scheme a bit. Previously, if the cache contained the block, this was accounted for in its refcount. After this change, the refcount is only used to track external references. There is a boolean flag which indicates whether or not the block is contained in the cache.
This diff also changes how LRU list is used. Previously, both hashtable and the LRU list contained all blocks. After this change, the LRU list contains blocks with the refcount==0, ie those which can be evicted from the cache.
Note that this change still allows for cache to grow beyond its capacity. This happens when all blocks are pinned (ie refcount>0). This is consistent with the current behavior. The cache's insert function never fails. I spent lots of time trying to make table_reader and other places work with the insert which might failed. It turned out to be pretty hard. It might really destabilize some customers, so finally, I decided against doing this.
table_cache_remove_scan_count_limit option will be unneeded after this change, but I will remove it in the following diff, if this one gets approved
Test Plan: Ran tests, made sure they pass
Reviewers: sdong, ljin
Differential Revision: https://reviews.facebook.net/D25503
10 years ago
|
|
|
assert(e->next != nullptr);
|
|
|
|
assert(e->prev != nullptr);
|
|
|
|
if (lru_low_pri_ == e) {
|
|
|
|
lru_low_pri_ = e->prev;
|
|
|
|
}
|
|
|
|
e->next->prev = e->prev;
|
|
|
|
e->prev->next = e->next;
|
Modifed the LRU cache eviction code so that it doesn't evict blocks which have exteranl references
Summary:
Currently, blocks which have more than one reference (ie referenced by something other than cache itself) are evicted from cache. This doesn't make much sense:
- blocks are still in RAM, so the RAM usage reported by the cache is incorrect
- if the same block is needed by another iterator, it will be loaded and decompressed again
This diff changes the reference counting scheme a bit. Previously, if the cache contained the block, this was accounted for in its refcount. After this change, the refcount is only used to track external references. There is a boolean flag which indicates whether or not the block is contained in the cache.
This diff also changes how LRU list is used. Previously, both hashtable and the LRU list contained all blocks. After this change, the LRU list contains blocks with the refcount==0, ie those which can be evicted from the cache.
Note that this change still allows for cache to grow beyond its capacity. This happens when all blocks are pinned (ie refcount>0). This is consistent with the current behavior. The cache's insert function never fails. I spent lots of time trying to make table_reader and other places work with the insert which might failed. It turned out to be pretty hard. It might really destabilize some customers, so finally, I decided against doing this.
table_cache_remove_scan_count_limit option will be unneeded after this change, but I will remove it in the following diff, if this one gets approved
Test Plan: Ran tests, made sure they pass
Reviewers: sdong, ljin
Differential Revision: https://reviews.facebook.net/D25503
10 years ago
|
|
|
e->prev = e->next = nullptr;
|
|
|
|
size_t total_charge = e->CalcTotalCharge(metadata_charge_policy_);
|
|
|
|
assert(lru_usage_ >= total_charge);
|
|
|
|
lru_usage_ -= total_charge;
|
|
|
|
if (e->InHighPriPool()) {
|
|
|
|
assert(high_pri_pool_usage_ >= total_charge);
|
|
|
|
high_pri_pool_usage_ -= total_charge;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
void LRUCacheShard::LRU_Insert(LRUHandle* e) {
|
Modifed the LRU cache eviction code so that it doesn't evict blocks which have exteranl references
Summary:
Currently, blocks which have more than one reference (ie referenced by something other than cache itself) are evicted from cache. This doesn't make much sense:
- blocks are still in RAM, so the RAM usage reported by the cache is incorrect
- if the same block is needed by another iterator, it will be loaded and decompressed again
This diff changes the reference counting scheme a bit. Previously, if the cache contained the block, this was accounted for in its refcount. After this change, the refcount is only used to track external references. There is a boolean flag which indicates whether or not the block is contained in the cache.
This diff also changes how LRU list is used. Previously, both hashtable and the LRU list contained all blocks. After this change, the LRU list contains blocks with the refcount==0, ie those which can be evicted from the cache.
Note that this change still allows for cache to grow beyond its capacity. This happens when all blocks are pinned (ie refcount>0). This is consistent with the current behavior. The cache's insert function never fails. I spent lots of time trying to make table_reader and other places work with the insert which might failed. It turned out to be pretty hard. It might really destabilize some customers, so finally, I decided against doing this.
table_cache_remove_scan_count_limit option will be unneeded after this change, but I will remove it in the following diff, if this one gets approved
Test Plan: Ran tests, made sure they pass
Reviewers: sdong, ljin
Differential Revision: https://reviews.facebook.net/D25503
10 years ago
|
|
|
assert(e->next == nullptr);
|
|
|
|
assert(e->prev == nullptr);
|
|
|
|
size_t total_charge = e->CalcTotalCharge(metadata_charge_policy_);
|
|
|
|
if (high_pri_pool_ratio_ > 0 && (e->IsHighPri() || e->HasHit())) {
|
|
|
|
// Inset "e" to head of LRU list.
|
|
|
|
e->next = &lru_;
|
|
|
|
e->prev = lru_.prev;
|
|
|
|
e->prev->next = e;
|
|
|
|
e->next->prev = e;
|
|
|
|
e->SetInHighPriPool(true);
|
|
|
|
high_pri_pool_usage_ += total_charge;
|
|
|
|
MaintainPoolSize();
|
|
|
|
} else {
|
|
|
|
// Insert "e" to the head of low-pri pool. Note that when
|
|
|
|
// high_pri_pool_ratio is 0, head of low-pri pool is also head of LRU list.
|
|
|
|
e->next = lru_low_pri_->next;
|
|
|
|
e->prev = lru_low_pri_;
|
|
|
|
e->prev->next = e;
|
|
|
|
e->next->prev = e;
|
|
|
|
e->SetInHighPriPool(false);
|
|
|
|
lru_low_pri_ = e;
|
|
|
|
}
|
|
|
|
lru_usage_ += total_charge;
|
|
|
|
}
|
|
|
|
|
|
|
|
void LRUCacheShard::MaintainPoolSize() {
|
|
|
|
while (high_pri_pool_usage_ > high_pri_pool_capacity_) {
|
|
|
|
// Overflow last entry in high-pri pool to low-pri pool.
|
|
|
|
lru_low_pri_ = lru_low_pri_->next;
|
|
|
|
assert(lru_low_pri_ != &lru_);
|
|
|
|
lru_low_pri_->SetInHighPriPool(false);
|
|
|
|
size_t total_charge =
|
|
|
|
lru_low_pri_->CalcTotalCharge(metadata_charge_policy_);
|
|
|
|
assert(high_pri_pool_usage_ >= total_charge);
|
|
|
|
high_pri_pool_usage_ -= total_charge;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
void LRUCacheShard::EvictFromLRU(size_t charge,
|
|
|
|
autovector<LRUHandle*>* deleted) {
|
|
|
|
while ((usage_ + charge) > capacity_ && lru_.next != &lru_) {
|
|
|
|
LRUHandle* old = lru_.next;
|
|
|
|
// LRU list contains only elements which can be evicted
|
|
|
|
assert(old->InCache() && !old->HasRefs());
|
|
|
|
LRU_Remove(old);
|
|
|
|
table_.Remove(old->key(), old->hash);
|
|
|
|
old->SetInCache(false);
|
|
|
|
size_t old_total_charge = old->CalcTotalCharge(metadata_charge_policy_);
|
|
|
|
assert(usage_ >= old_total_charge);
|
|
|
|
usage_ -= old_total_charge;
|
|
|
|
deleted->push_back(old);
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
void LRUCacheShard::SetCapacity(size_t capacity) {
|
|
|
|
autovector<LRUHandle*> last_reference_list;
|
|
|
|
{
|
|
|
|
MutexLock l(&mutex_);
|
|
|
|
capacity_ = capacity;
|
|
|
|
high_pri_pool_capacity_ = capacity_ * high_pri_pool_ratio_;
|
|
|
|
EvictFromLRU(0, &last_reference_list);
|
|
|
|
}
|
|
|
|
|
|
|
|
// Free the entries outside of mutex for performance reasons
|
|
|
|
for (auto entry : last_reference_list) {
|
|
|
|
entry->Free();
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
void LRUCacheShard::SetStrictCapacityLimit(bool strict_capacity_limit) {
|
|
|
|
MutexLock l(&mutex_);
|
|
|
|
strict_capacity_limit_ = strict_capacity_limit;
|
|
|
|
}
|
|
|
|
|
|
|
|
Cache::Handle* LRUCacheShard::Lookup(const Slice& key, uint32_t hash) {
|
|
|
|
MutexLock l(&mutex_);
|
|
|
|
LRUHandle* e = table_.Lookup(key, hash);
|
|
|
|
if (e != nullptr) {
|
|
|
|
assert(e->InCache());
|
|
|
|
if (!e->HasRefs()) {
|
|
|
|
// The entry is in LRU since it's in hash and has no external references
|
Modifed the LRU cache eviction code so that it doesn't evict blocks which have exteranl references
Summary:
Currently, blocks which have more than one reference (ie referenced by something other than cache itself) are evicted from cache. This doesn't make much sense:
- blocks are still in RAM, so the RAM usage reported by the cache is incorrect
- if the same block is needed by another iterator, it will be loaded and decompressed again
This diff changes the reference counting scheme a bit. Previously, if the cache contained the block, this was accounted for in its refcount. After this change, the refcount is only used to track external references. There is a boolean flag which indicates whether or not the block is contained in the cache.
This diff also changes how LRU list is used. Previously, both hashtable and the LRU list contained all blocks. After this change, the LRU list contains blocks with the refcount==0, ie those which can be evicted from the cache.
Note that this change still allows for cache to grow beyond its capacity. This happens when all blocks are pinned (ie refcount>0). This is consistent with the current behavior. The cache's insert function never fails. I spent lots of time trying to make table_reader and other places work with the insert which might failed. It turned out to be pretty hard. It might really destabilize some customers, so finally, I decided against doing this.
table_cache_remove_scan_count_limit option will be unneeded after this change, but I will remove it in the following diff, if this one gets approved
Test Plan: Ran tests, made sure they pass
Reviewers: sdong, ljin
Differential Revision: https://reviews.facebook.net/D25503
10 years ago
|
|
|
LRU_Remove(e);
|
|
|
|
}
|
|
|
|
e->Ref();
|
|
|
|
e->SetHit();
|
|
|
|
}
|
|
|
|
return reinterpret_cast<Cache::Handle*>(e);
|
|
|
|
}
|
|
|
|
|
|
|
|
bool LRUCacheShard::Ref(Cache::Handle* h) {
|
|
|
|
LRUHandle* e = reinterpret_cast<LRUHandle*>(h);
|
|
|
|
MutexLock l(&mutex_);
|
|
|
|
// To create another reference - entry must be already externally referenced
|
|
|
|
assert(e->HasRefs());
|
|
|
|
e->Ref();
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
|
|
|
|
void LRUCacheShard::SetHighPriorityPoolRatio(double high_pri_pool_ratio) {
|
|
|
|
MutexLock l(&mutex_);
|
|
|
|
high_pri_pool_ratio_ = high_pri_pool_ratio;
|
|
|
|
high_pri_pool_capacity_ = capacity_ * high_pri_pool_ratio_;
|
|
|
|
MaintainPoolSize();
|
|
|
|
}
|
|
|
|
|
|
|
|
bool LRUCacheShard::Release(Cache::Handle* handle, bool force_erase) {
|
|
|
|
if (handle == nullptr) {
|
|
|
|
return false;
|
|
|
|
}
|
|
|
|
LRUHandle* e = reinterpret_cast<LRUHandle*>(handle);
|
|
|
|
bool last_reference = false;
|
|
|
|
{
|
|
|
|
MutexLock l(&mutex_);
|
|
|
|
last_reference = e->Unref();
|
|
|
|
if (last_reference && e->InCache()) {
|
Modifed the LRU cache eviction code so that it doesn't evict blocks which have exteranl references
Summary:
Currently, blocks which have more than one reference (ie referenced by something other than cache itself) are evicted from cache. This doesn't make much sense:
- blocks are still in RAM, so the RAM usage reported by the cache is incorrect
- if the same block is needed by another iterator, it will be loaded and decompressed again
This diff changes the reference counting scheme a bit. Previously, if the cache contained the block, this was accounted for in its refcount. After this change, the refcount is only used to track external references. There is a boolean flag which indicates whether or not the block is contained in the cache.
This diff also changes how LRU list is used. Previously, both hashtable and the LRU list contained all blocks. After this change, the LRU list contains blocks with the refcount==0, ie those which can be evicted from the cache.
Note that this change still allows for cache to grow beyond its capacity. This happens when all blocks are pinned (ie refcount>0). This is consistent with the current behavior. The cache's insert function never fails. I spent lots of time trying to make table_reader and other places work with the insert which might failed. It turned out to be pretty hard. It might really destabilize some customers, so finally, I decided against doing this.
table_cache_remove_scan_count_limit option will be unneeded after this change, but I will remove it in the following diff, if this one gets approved
Test Plan: Ran tests, made sure they pass
Reviewers: sdong, ljin
Differential Revision: https://reviews.facebook.net/D25503
10 years ago
|
|
|
// The item is still in cache, and nobody else holds a reference to it
|
|
|
|
if (usage_ > capacity_ || force_erase) {
|
Modifed the LRU cache eviction code so that it doesn't evict blocks which have exteranl references
Summary:
Currently, blocks which have more than one reference (ie referenced by something other than cache itself) are evicted from cache. This doesn't make much sense:
- blocks are still in RAM, so the RAM usage reported by the cache is incorrect
- if the same block is needed by another iterator, it will be loaded and decompressed again
This diff changes the reference counting scheme a bit. Previously, if the cache contained the block, this was accounted for in its refcount. After this change, the refcount is only used to track external references. There is a boolean flag which indicates whether or not the block is contained in the cache.
This diff also changes how LRU list is used. Previously, both hashtable and the LRU list contained all blocks. After this change, the LRU list contains blocks with the refcount==0, ie those which can be evicted from the cache.
Note that this change still allows for cache to grow beyond its capacity. This happens when all blocks are pinned (ie refcount>0). This is consistent with the current behavior. The cache's insert function never fails. I spent lots of time trying to make table_reader and other places work with the insert which might failed. It turned out to be pretty hard. It might really destabilize some customers, so finally, I decided against doing this.
table_cache_remove_scan_count_limit option will be unneeded after this change, but I will remove it in the following diff, if this one gets approved
Test Plan: Ran tests, made sure they pass
Reviewers: sdong, ljin
Differential Revision: https://reviews.facebook.net/D25503
10 years ago
|
|
|
// The LRU list must be empty since the cache is full
|
|
|
|
assert(lru_.next == &lru_ || force_erase);
|
|
|
|
// Take this opportunity and remove the item
|
Modifed the LRU cache eviction code so that it doesn't evict blocks which have exteranl references
Summary:
Currently, blocks which have more than one reference (ie referenced by something other than cache itself) are evicted from cache. This doesn't make much sense:
- blocks are still in RAM, so the RAM usage reported by the cache is incorrect
- if the same block is needed by another iterator, it will be loaded and decompressed again
This diff changes the reference counting scheme a bit. Previously, if the cache contained the block, this was accounted for in its refcount. After this change, the refcount is only used to track external references. There is a boolean flag which indicates whether or not the block is contained in the cache.
This diff also changes how LRU list is used. Previously, both hashtable and the LRU list contained all blocks. After this change, the LRU list contains blocks with the refcount==0, ie those which can be evicted from the cache.
Note that this change still allows for cache to grow beyond its capacity. This happens when all blocks are pinned (ie refcount>0). This is consistent with the current behavior. The cache's insert function never fails. I spent lots of time trying to make table_reader and other places work with the insert which might failed. It turned out to be pretty hard. It might really destabilize some customers, so finally, I decided against doing this.
table_cache_remove_scan_count_limit option will be unneeded after this change, but I will remove it in the following diff, if this one gets approved
Test Plan: Ran tests, made sure they pass
Reviewers: sdong, ljin
Differential Revision: https://reviews.facebook.net/D25503
10 years ago
|
|
|
table_.Remove(e->key(), e->hash);
|
|
|
|
e->SetInCache(false);
|
Modifed the LRU cache eviction code so that it doesn't evict blocks which have exteranl references
Summary:
Currently, blocks which have more than one reference (ie referenced by something other than cache itself) are evicted from cache. This doesn't make much sense:
- blocks are still in RAM, so the RAM usage reported by the cache is incorrect
- if the same block is needed by another iterator, it will be loaded and decompressed again
This diff changes the reference counting scheme a bit. Previously, if the cache contained the block, this was accounted for in its refcount. After this change, the refcount is only used to track external references. There is a boolean flag which indicates whether or not the block is contained in the cache.
This diff also changes how LRU list is used. Previously, both hashtable and the LRU list contained all blocks. After this change, the LRU list contains blocks with the refcount==0, ie those which can be evicted from the cache.
Note that this change still allows for cache to grow beyond its capacity. This happens when all blocks are pinned (ie refcount>0). This is consistent with the current behavior. The cache's insert function never fails. I spent lots of time trying to make table_reader and other places work with the insert which might failed. It turned out to be pretty hard. It might really destabilize some customers, so finally, I decided against doing this.
table_cache_remove_scan_count_limit option will be unneeded after this change, but I will remove it in the following diff, if this one gets approved
Test Plan: Ran tests, made sure they pass
Reviewers: sdong, ljin
Differential Revision: https://reviews.facebook.net/D25503
10 years ago
|
|
|
} else {
|
|
|
|
// Put the item back on the LRU list, and don't free it
|
|
|
|
LRU_Insert(e);
|
|
|
|
last_reference = false;
|
Modifed the LRU cache eviction code so that it doesn't evict blocks which have exteranl references
Summary:
Currently, blocks which have more than one reference (ie referenced by something other than cache itself) are evicted from cache. This doesn't make much sense:
- blocks are still in RAM, so the RAM usage reported by the cache is incorrect
- if the same block is needed by another iterator, it will be loaded and decompressed again
This diff changes the reference counting scheme a bit. Previously, if the cache contained the block, this was accounted for in its refcount. After this change, the refcount is only used to track external references. There is a boolean flag which indicates whether or not the block is contained in the cache.
This diff also changes how LRU list is used. Previously, both hashtable and the LRU list contained all blocks. After this change, the LRU list contains blocks with the refcount==0, ie those which can be evicted from the cache.
Note that this change still allows for cache to grow beyond its capacity. This happens when all blocks are pinned (ie refcount>0). This is consistent with the current behavior. The cache's insert function never fails. I spent lots of time trying to make table_reader and other places work with the insert which might failed. It turned out to be pretty hard. It might really destabilize some customers, so finally, I decided against doing this.
table_cache_remove_scan_count_limit option will be unneeded after this change, but I will remove it in the following diff, if this one gets approved
Test Plan: Ran tests, made sure they pass
Reviewers: sdong, ljin
Differential Revision: https://reviews.facebook.net/D25503
10 years ago
|
|
|
}
|
|
|
|
}
|
|
|
|
if (last_reference) {
|
|
|
|
size_t total_charge = e->CalcTotalCharge(metadata_charge_policy_);
|
|
|
|
assert(usage_ >= total_charge);
|
|
|
|
usage_ -= total_charge;
|
|
|
|
}
|
|
|
|
}
|
Modifed the LRU cache eviction code so that it doesn't evict blocks which have exteranl references
Summary:
Currently, blocks which have more than one reference (ie referenced by something other than cache itself) are evicted from cache. This doesn't make much sense:
- blocks are still in RAM, so the RAM usage reported by the cache is incorrect
- if the same block is needed by another iterator, it will be loaded and decompressed again
This diff changes the reference counting scheme a bit. Previously, if the cache contained the block, this was accounted for in its refcount. After this change, the refcount is only used to track external references. There is a boolean flag which indicates whether or not the block is contained in the cache.
This diff also changes how LRU list is used. Previously, both hashtable and the LRU list contained all blocks. After this change, the LRU list contains blocks with the refcount==0, ie those which can be evicted from the cache.
Note that this change still allows for cache to grow beyond its capacity. This happens when all blocks are pinned (ie refcount>0). This is consistent with the current behavior. The cache's insert function never fails. I spent lots of time trying to make table_reader and other places work with the insert which might failed. It turned out to be pretty hard. It might really destabilize some customers, so finally, I decided against doing this.
table_cache_remove_scan_count_limit option will be unneeded after this change, but I will remove it in the following diff, if this one gets approved
Test Plan: Ran tests, made sure they pass
Reviewers: sdong, ljin
Differential Revision: https://reviews.facebook.net/D25503
10 years ago
|
|
|
|
|
|
|
// Free the entry here outside of mutex for performance reasons
|
|
|
|
if (last_reference) {
|
Modifed the LRU cache eviction code so that it doesn't evict blocks which have exteranl references
Summary:
Currently, blocks which have more than one reference (ie referenced by something other than cache itself) are evicted from cache. This doesn't make much sense:
- blocks are still in RAM, so the RAM usage reported by the cache is incorrect
- if the same block is needed by another iterator, it will be loaded and decompressed again
This diff changes the reference counting scheme a bit. Previously, if the cache contained the block, this was accounted for in its refcount. After this change, the refcount is only used to track external references. There is a boolean flag which indicates whether or not the block is contained in the cache.
This diff also changes how LRU list is used. Previously, both hashtable and the LRU list contained all blocks. After this change, the LRU list contains blocks with the refcount==0, ie those which can be evicted from the cache.
Note that this change still allows for cache to grow beyond its capacity. This happens when all blocks are pinned (ie refcount>0). This is consistent with the current behavior. The cache's insert function never fails. I spent lots of time trying to make table_reader and other places work with the insert which might failed. It turned out to be pretty hard. It might really destabilize some customers, so finally, I decided against doing this.
table_cache_remove_scan_count_limit option will be unneeded after this change, but I will remove it in the following diff, if this one gets approved
Test Plan: Ran tests, made sure they pass
Reviewers: sdong, ljin
Differential Revision: https://reviews.facebook.net/D25503
10 years ago
|
|
|
e->Free();
|
|
|
|
}
|
|
|
|
return last_reference;
|
|
|
|
}
|
|
|
|
|
|
|
|
Status LRUCacheShard::Insert(const Slice& key, uint32_t hash, void* value,
|
|
|
|
size_t charge,
|
|
|
|
void (*deleter)(const Slice& key, void* value),
|
|
|
|
Cache::Handle** handle, Cache::Priority priority) {
|
Modifed the LRU cache eviction code so that it doesn't evict blocks which have exteranl references
Summary:
Currently, blocks which have more than one reference (ie referenced by something other than cache itself) are evicted from cache. This doesn't make much sense:
- blocks are still in RAM, so the RAM usage reported by the cache is incorrect
- if the same block is needed by another iterator, it will be loaded and decompressed again
This diff changes the reference counting scheme a bit. Previously, if the cache contained the block, this was accounted for in its refcount. After this change, the refcount is only used to track external references. There is a boolean flag which indicates whether or not the block is contained in the cache.
This diff also changes how LRU list is used. Previously, both hashtable and the LRU list contained all blocks. After this change, the LRU list contains blocks with the refcount==0, ie those which can be evicted from the cache.
Note that this change still allows for cache to grow beyond its capacity. This happens when all blocks are pinned (ie refcount>0). This is consistent with the current behavior. The cache's insert function never fails. I spent lots of time trying to make table_reader and other places work with the insert which might failed. It turned out to be pretty hard. It might really destabilize some customers, so finally, I decided against doing this.
table_cache_remove_scan_count_limit option will be unneeded after this change, but I will remove it in the following diff, if this one gets approved
Test Plan: Ran tests, made sure they pass
Reviewers: sdong, ljin
Differential Revision: https://reviews.facebook.net/D25503
10 years ago
|
|
|
// Allocate the memory here outside of the mutex
|
|
|
|
// If the cache is full, we'll have to release it
|
|
|
|
// It shouldn't happen very often though.
|
|
|
|
LRUHandle* e = reinterpret_cast<LRUHandle*>(
|
|
|
|
new char[sizeof(LRUHandle) - 1 + key.size()]);
|
|
|
|
Status s = Status::OK();
|
|
|
|
autovector<LRUHandle*> last_reference_list;
|
|
|
|
|
|
|
|
e->value = value;
|
|
|
|
e->deleter = deleter;
|
|
|
|
e->charge = charge;
|
|
|
|
e->key_length = key.size();
|
|
|
|
e->flags = 0;
|
|
|
|
e->hash = hash;
|
|
|
|
e->refs = 0;
|
Modifed the LRU cache eviction code so that it doesn't evict blocks which have exteranl references
Summary:
Currently, blocks which have more than one reference (ie referenced by something other than cache itself) are evicted from cache. This doesn't make much sense:
- blocks are still in RAM, so the RAM usage reported by the cache is incorrect
- if the same block is needed by another iterator, it will be loaded and decompressed again
This diff changes the reference counting scheme a bit. Previously, if the cache contained the block, this was accounted for in its refcount. After this change, the refcount is only used to track external references. There is a boolean flag which indicates whether or not the block is contained in the cache.
This diff also changes how LRU list is used. Previously, both hashtable and the LRU list contained all blocks. After this change, the LRU list contains blocks with the refcount==0, ie those which can be evicted from the cache.
Note that this change still allows for cache to grow beyond its capacity. This happens when all blocks are pinned (ie refcount>0). This is consistent with the current behavior. The cache's insert function never fails. I spent lots of time trying to make table_reader and other places work with the insert which might failed. It turned out to be pretty hard. It might really destabilize some customers, so finally, I decided against doing this.
table_cache_remove_scan_count_limit option will be unneeded after this change, but I will remove it in the following diff, if this one gets approved
Test Plan: Ran tests, made sure they pass
Reviewers: sdong, ljin
Differential Revision: https://reviews.facebook.net/D25503
10 years ago
|
|
|
e->next = e->prev = nullptr;
|
|
|
|
e->SetInCache(true);
|
|
|
|
e->SetPriority(priority);
|
|
|
|
memcpy(e->key_data, key.data(), key.size());
|
|
|
|
size_t total_charge = e->CalcTotalCharge(metadata_charge_policy_);
|
|
|
|
|
|
|
|
{
|
|
|
|
MutexLock l(&mutex_);
|
|
|
|
|
|
|
|
// Free the space following strict LRU policy until enough space
|
Modifed the LRU cache eviction code so that it doesn't evict blocks which have exteranl references
Summary:
Currently, blocks which have more than one reference (ie referenced by something other than cache itself) are evicted from cache. This doesn't make much sense:
- blocks are still in RAM, so the RAM usage reported by the cache is incorrect
- if the same block is needed by another iterator, it will be loaded and decompressed again
This diff changes the reference counting scheme a bit. Previously, if the cache contained the block, this was accounted for in its refcount. After this change, the refcount is only used to track external references. There is a boolean flag which indicates whether or not the block is contained in the cache.
This diff also changes how LRU list is used. Previously, both hashtable and the LRU list contained all blocks. After this change, the LRU list contains blocks with the refcount==0, ie those which can be evicted from the cache.
Note that this change still allows for cache to grow beyond its capacity. This happens when all blocks are pinned (ie refcount>0). This is consistent with the current behavior. The cache's insert function never fails. I spent lots of time trying to make table_reader and other places work with the insert which might failed. It turned out to be pretty hard. It might really destabilize some customers, so finally, I decided against doing this.
table_cache_remove_scan_count_limit option will be unneeded after this change, but I will remove it in the following diff, if this one gets approved
Test Plan: Ran tests, made sure they pass
Reviewers: sdong, ljin
Differential Revision: https://reviews.facebook.net/D25503
10 years ago
|
|
|
// is freed or the lru list is empty
|
|
|
|
EvictFromLRU(total_charge, &last_reference_list);
|
Modifed the LRU cache eviction code so that it doesn't evict blocks which have exteranl references
Summary:
Currently, blocks which have more than one reference (ie referenced by something other than cache itself) are evicted from cache. This doesn't make much sense:
- blocks are still in RAM, so the RAM usage reported by the cache is incorrect
- if the same block is needed by another iterator, it will be loaded and decompressed again
This diff changes the reference counting scheme a bit. Previously, if the cache contained the block, this was accounted for in its refcount. After this change, the refcount is only used to track external references. There is a boolean flag which indicates whether or not the block is contained in the cache.
This diff also changes how LRU list is used. Previously, both hashtable and the LRU list contained all blocks. After this change, the LRU list contains blocks with the refcount==0, ie those which can be evicted from the cache.
Note that this change still allows for cache to grow beyond its capacity. This happens when all blocks are pinned (ie refcount>0). This is consistent with the current behavior. The cache's insert function never fails. I spent lots of time trying to make table_reader and other places work with the insert which might failed. It turned out to be pretty hard. It might really destabilize some customers, so finally, I decided against doing this.
table_cache_remove_scan_count_limit option will be unneeded after this change, but I will remove it in the following diff, if this one gets approved
Test Plan: Ran tests, made sure they pass
Reviewers: sdong, ljin
Differential Revision: https://reviews.facebook.net/D25503
10 years ago
|
|
|
|
|
|
|
if ((usage_ + total_charge) > capacity_ &&
|
|
|
|
(strict_capacity_limit_ || handle == nullptr)) {
|
|
|
|
if (handle == nullptr) {
|
|
|
|
// Don't insert the entry but still return ok, as if the entry inserted
|
|
|
|
// into cache and get evicted immediately.
|
|
|
|
e->SetInCache(false);
|
|
|
|
last_reference_list.push_back(e);
|
|
|
|
} else {
|
|
|
|
delete[] reinterpret_cast<char*>(e);
|
|
|
|
*handle = nullptr;
|
|
|
|
s = Status::Incomplete("Insert failed due to LRU cache being full.");
|
|
|
|
}
|
|
|
|
} else {
|
|
|
|
// Insert into the cache. Note that the cache might get larger than its
|
|
|
|
// capacity if not enough space was freed up.
|
|
|
|
LRUHandle* old = table_.Insert(e);
|
|
|
|
usage_ += total_charge;
|
|
|
|
if (old != nullptr) {
|
|
|
|
s = Status::OkOverwritten();
|
|
|
|
assert(old->InCache());
|
|
|
|
old->SetInCache(false);
|
|
|
|
if (!old->HasRefs()) {
|
|
|
|
// old is on LRU because it's in cache and its reference count is 0
|
|
|
|
LRU_Remove(old);
|
|
|
|
size_t old_total_charge =
|
|
|
|
old->CalcTotalCharge(metadata_charge_policy_);
|
|
|
|
assert(usage_ >= old_total_charge);
|
|
|
|
usage_ -= old_total_charge;
|
|
|
|
last_reference_list.push_back(old);
|
|
|
|
}
|
|
|
|
}
|
|
|
|
if (handle == nullptr) {
|
|
|
|
LRU_Insert(e);
|
|
|
|
} else {
|
|
|
|
e->Ref();
|
|
|
|
*handle = reinterpret_cast<Cache::Handle*>(e);
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
// Free the entries here outside of mutex for performance reasons
|
|
|
|
for (auto entry : last_reference_list) {
|
Modifed the LRU cache eviction code so that it doesn't evict blocks which have exteranl references
Summary:
Currently, blocks which have more than one reference (ie referenced by something other than cache itself) are evicted from cache. This doesn't make much sense:
- blocks are still in RAM, so the RAM usage reported by the cache is incorrect
- if the same block is needed by another iterator, it will be loaded and decompressed again
This diff changes the reference counting scheme a bit. Previously, if the cache contained the block, this was accounted for in its refcount. After this change, the refcount is only used to track external references. There is a boolean flag which indicates whether or not the block is contained in the cache.
This diff also changes how LRU list is used. Previously, both hashtable and the LRU list contained all blocks. After this change, the LRU list contains blocks with the refcount==0, ie those which can be evicted from the cache.
Note that this change still allows for cache to grow beyond its capacity. This happens when all blocks are pinned (ie refcount>0). This is consistent with the current behavior. The cache's insert function never fails. I spent lots of time trying to make table_reader and other places work with the insert which might failed. It turned out to be pretty hard. It might really destabilize some customers, so finally, I decided against doing this.
table_cache_remove_scan_count_limit option will be unneeded after this change, but I will remove it in the following diff, if this one gets approved
Test Plan: Ran tests, made sure they pass
Reviewers: sdong, ljin
Differential Revision: https://reviews.facebook.net/D25503
10 years ago
|
|
|
entry->Free();
|
|
|
|
}
|
|
|
|
|
|
|
|
return s;
|
|
|
|
}
|
|
|
|
|
|
|
|
void LRUCacheShard::Erase(const Slice& key, uint32_t hash) {
|
|
|
|
LRUHandle* e;
|
|
|
|
bool last_reference = false;
|
|
|
|
{
|
|
|
|
MutexLock l(&mutex_);
|
|
|
|
e = table_.Remove(key, hash);
|
|
|
|
if (e != nullptr) {
|
|
|
|
assert(e->InCache());
|
|
|
|
e->SetInCache(false);
|
|
|
|
if (!e->HasRefs()) {
|
|
|
|
// The entry is in LRU since it's in hash and has no external references
|
Modifed the LRU cache eviction code so that it doesn't evict blocks which have exteranl references
Summary:
Currently, blocks which have more than one reference (ie referenced by something other than cache itself) are evicted from cache. This doesn't make much sense:
- blocks are still in RAM, so the RAM usage reported by the cache is incorrect
- if the same block is needed by another iterator, it will be loaded and decompressed again
This diff changes the reference counting scheme a bit. Previously, if the cache contained the block, this was accounted for in its refcount. After this change, the refcount is only used to track external references. There is a boolean flag which indicates whether or not the block is contained in the cache.
This diff also changes how LRU list is used. Previously, both hashtable and the LRU list contained all blocks. After this change, the LRU list contains blocks with the refcount==0, ie those which can be evicted from the cache.
Note that this change still allows for cache to grow beyond its capacity. This happens when all blocks are pinned (ie refcount>0). This is consistent with the current behavior. The cache's insert function never fails. I spent lots of time trying to make table_reader and other places work with the insert which might failed. It turned out to be pretty hard. It might really destabilize some customers, so finally, I decided against doing this.
table_cache_remove_scan_count_limit option will be unneeded after this change, but I will remove it in the following diff, if this one gets approved
Test Plan: Ran tests, made sure they pass
Reviewers: sdong, ljin
Differential Revision: https://reviews.facebook.net/D25503
10 years ago
|
|
|
LRU_Remove(e);
|
|
|
|
size_t total_charge = e->CalcTotalCharge(metadata_charge_policy_);
|
|
|
|
assert(usage_ >= total_charge);
|
|
|
|
usage_ -= total_charge;
|
|
|
|
last_reference = true;
|
Modifed the LRU cache eviction code so that it doesn't evict blocks which have exteranl references
Summary:
Currently, blocks which have more than one reference (ie referenced by something other than cache itself) are evicted from cache. This doesn't make much sense:
- blocks are still in RAM, so the RAM usage reported by the cache is incorrect
- if the same block is needed by another iterator, it will be loaded and decompressed again
This diff changes the reference counting scheme a bit. Previously, if the cache contained the block, this was accounted for in its refcount. After this change, the refcount is only used to track external references. There is a boolean flag which indicates whether or not the block is contained in the cache.
This diff also changes how LRU list is used. Previously, both hashtable and the LRU list contained all blocks. After this change, the LRU list contains blocks with the refcount==0, ie those which can be evicted from the cache.
Note that this change still allows for cache to grow beyond its capacity. This happens when all blocks are pinned (ie refcount>0). This is consistent with the current behavior. The cache's insert function never fails. I spent lots of time trying to make table_reader and other places work with the insert which might failed. It turned out to be pretty hard. It might really destabilize some customers, so finally, I decided against doing this.
table_cache_remove_scan_count_limit option will be unneeded after this change, but I will remove it in the following diff, if this one gets approved
Test Plan: Ran tests, made sure they pass
Reviewers: sdong, ljin
Differential Revision: https://reviews.facebook.net/D25503
10 years ago
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
Modifed the LRU cache eviction code so that it doesn't evict blocks which have exteranl references
Summary:
Currently, blocks which have more than one reference (ie referenced by something other than cache itself) are evicted from cache. This doesn't make much sense:
- blocks are still in RAM, so the RAM usage reported by the cache is incorrect
- if the same block is needed by another iterator, it will be loaded and decompressed again
This diff changes the reference counting scheme a bit. Previously, if the cache contained the block, this was accounted for in its refcount. After this change, the refcount is only used to track external references. There is a boolean flag which indicates whether or not the block is contained in the cache.
This diff also changes how LRU list is used. Previously, both hashtable and the LRU list contained all blocks. After this change, the LRU list contains blocks with the refcount==0, ie those which can be evicted from the cache.
Note that this change still allows for cache to grow beyond its capacity. This happens when all blocks are pinned (ie refcount>0). This is consistent with the current behavior. The cache's insert function never fails. I spent lots of time trying to make table_reader and other places work with the insert which might failed. It turned out to be pretty hard. It might really destabilize some customers, so finally, I decided against doing this.
table_cache_remove_scan_count_limit option will be unneeded after this change, but I will remove it in the following diff, if this one gets approved
Test Plan: Ran tests, made sure they pass
Reviewers: sdong, ljin
Differential Revision: https://reviews.facebook.net/D25503
10 years ago
|
|
|
|
|
|
|
// Free the entry here outside of mutex for performance reasons
|
|
|
|
// last_reference will only be true if e != nullptr
|
|
|
|
if (last_reference) {
|
Modifed the LRU cache eviction code so that it doesn't evict blocks which have exteranl references
Summary:
Currently, blocks which have more than one reference (ie referenced by something other than cache itself) are evicted from cache. This doesn't make much sense:
- blocks are still in RAM, so the RAM usage reported by the cache is incorrect
- if the same block is needed by another iterator, it will be loaded and decompressed again
This diff changes the reference counting scheme a bit. Previously, if the cache contained the block, this was accounted for in its refcount. After this change, the refcount is only used to track external references. There is a boolean flag which indicates whether or not the block is contained in the cache.
This diff also changes how LRU list is used. Previously, both hashtable and the LRU list contained all blocks. After this change, the LRU list contains blocks with the refcount==0, ie those which can be evicted from the cache.
Note that this change still allows for cache to grow beyond its capacity. This happens when all blocks are pinned (ie refcount>0). This is consistent with the current behavior. The cache's insert function never fails. I spent lots of time trying to make table_reader and other places work with the insert which might failed. It turned out to be pretty hard. It might really destabilize some customers, so finally, I decided against doing this.
table_cache_remove_scan_count_limit option will be unneeded after this change, but I will remove it in the following diff, if this one gets approved
Test Plan: Ran tests, made sure they pass
Reviewers: sdong, ljin
Differential Revision: https://reviews.facebook.net/D25503
10 years ago
|
|
|
e->Free();
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
size_t LRUCacheShard::GetUsage() const {
|
|
|
|
MutexLock l(&mutex_);
|
|
|
|
return usage_;
|
|
|
|
}
|
|
|
|
|
|
|
|
size_t LRUCacheShard::GetPinnedUsage() const {
|
|
|
|
MutexLock l(&mutex_);
|
|
|
|
assert(usage_ >= lru_usage_);
|
|
|
|
return usage_ - lru_usage_;
|
|
|
|
}
|
|
|
|
|
|
|
|
std::string LRUCacheShard::GetPrintableOptions() const {
|
|
|
|
const int kBufferSize = 200;
|
|
|
|
char buffer[kBufferSize];
|
|
|
|
{
|
|
|
|
MutexLock l(&mutex_);
|
|
|
|
snprintf(buffer, kBufferSize, " high_pri_pool_ratio: %.3lf\n",
|
|
|
|
high_pri_pool_ratio_);
|
|
|
|
}
|
|
|
|
return std::string(buffer);
|
|
|
|
}
|
|
|
|
|
|
|
|
LRUCache::LRUCache(size_t capacity, int num_shard_bits,
|
|
|
|
bool strict_capacity_limit, double high_pri_pool_ratio,
|
|
|
|
std::shared_ptr<MemoryAllocator> allocator,
|
|
|
|
bool use_adaptive_mutex,
|
|
|
|
CacheMetadataChargePolicy metadata_charge_policy)
|
|
|
|
: ShardedCache(capacity, num_shard_bits, strict_capacity_limit,
|
|
|
|
std::move(allocator)) {
|
|
|
|
num_shards_ = 1 << num_shard_bits;
|
|
|
|
shards_ = reinterpret_cast<LRUCacheShard*>(
|
|
|
|
port::cacheline_aligned_alloc(sizeof(LRUCacheShard) * num_shards_));
|
|
|
|
size_t per_shard = (capacity + (num_shards_ - 1)) / num_shards_;
|
|
|
|
for (int i = 0; i < num_shards_; i++) {
|
|
|
|
new (&shards_[i])
|
|
|
|
LRUCacheShard(per_shard, strict_capacity_limit, high_pri_pool_ratio,
|
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
|
|
|
use_adaptive_mutex, metadata_charge_policy,
|
|
|
|
/* max_upper_hash_bits */ 32 - num_shard_bits);
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
LRUCache::~LRUCache() {
|
|
|
|
if (shards_ != nullptr) {
|
|
|
|
assert(num_shards_ > 0);
|
|
|
|
for (int i = 0; i < num_shards_; i++) {
|
|
|
|
shards_[i].~LRUCacheShard();
|
|
|
|
}
|
|
|
|
port::cacheline_aligned_free(shards_);
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
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
|
|
|
CacheShard* LRUCache::GetShard(uint32_t shard) {
|
|
|
|
return reinterpret_cast<CacheShard*>(&shards_[shard]);
|
|
|
|
}
|
|
|
|
|
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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const CacheShard* LRUCache::GetShard(uint32_t shard) const {
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return reinterpret_cast<CacheShard*>(&shards_[shard]);
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}
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void* LRUCache::Value(Handle* handle) {
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return reinterpret_cast<const LRUHandle*>(handle)->value;
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}
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size_t LRUCache::GetCharge(Handle* handle) const {
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return reinterpret_cast<const LRUHandle*>(handle)->charge;
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}
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uint32_t LRUCache::GetHash(Handle* handle) const {
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return reinterpret_cast<const LRUHandle*>(handle)->hash;
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}
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void LRUCache::DisownData() {
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// Do not drop data if compile with ASAN to suppress leak warning.
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#if defined(__clang__)
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#if !defined(__has_feature) || !__has_feature(address_sanitizer)
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shards_ = nullptr;
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num_shards_ = 0;
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#endif
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#else // __clang__
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#ifndef __SANITIZE_ADDRESS__
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shards_ = nullptr;
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num_shards_ = 0;
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#endif // !__SANITIZE_ADDRESS__
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#endif // __clang__
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}
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size_t LRUCache::TEST_GetLRUSize() {
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size_t lru_size_of_all_shards = 0;
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for (int i = 0; i < num_shards_; i++) {
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lru_size_of_all_shards += shards_[i].TEST_GetLRUSize();
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}
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return lru_size_of_all_shards;
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}
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double LRUCache::GetHighPriPoolRatio() {
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double result = 0.0;
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if (num_shards_ > 0) {
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result = shards_[0].GetHighPriPoolRatio();
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}
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return result;
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}
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std::shared_ptr<Cache> NewLRUCache(const LRUCacheOptions& cache_opts) {
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return NewLRUCache(cache_opts.capacity, cache_opts.num_shard_bits,
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cache_opts.strict_capacity_limit,
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cache_opts.high_pri_pool_ratio,
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cache_opts.memory_allocator, cache_opts.use_adaptive_mutex,
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cache_opts.metadata_charge_policy);
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}
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std::shared_ptr<Cache> NewLRUCache(
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size_t capacity, int num_shard_bits, bool strict_capacity_limit,
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double high_pri_pool_ratio,
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std::shared_ptr<MemoryAllocator> memory_allocator, bool use_adaptive_mutex,
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CacheMetadataChargePolicy metadata_charge_policy) {
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if (num_shard_bits >= 20) {
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return nullptr; // the cache cannot be sharded into too many fine pieces
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}
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if (high_pri_pool_ratio < 0.0 || high_pri_pool_ratio > 1.0) {
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// invalid high_pri_pool_ratio
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return nullptr;
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}
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if (num_shard_bits < 0) {
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num_shard_bits = GetDefaultCacheShardBits(capacity);
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}
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return std::make_shared<LRUCache>(
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capacity, num_shard_bits, strict_capacity_limit, high_pri_pool_ratio,
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std::move(memory_allocator), use_adaptive_mutex, metadata_charge_policy);
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}
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} // namespace ROCKSDB_NAMESPACE
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