504fe4de80
Summary: This PR avoids allocations and copies for the result of `GetMergeOperands()` when the average operand size is at least 256 bytes and the total operands size is at least 32KB. The `GetMergeOperands()` already included `PinnableSlice` but was calling `PinSelf()` (i.e., allocating and copying) for each operand. When this optimization takes effect, we instead call `PinSlice()` to skip that allocation and copy. Resources are pinned in order for the `PinnableSlice` to point to valid memory even after `GetMergeOperands()` returns. The pinned resources include a referenced `SuperVersion`, a `MergingContext`, and a `PinnedIteratorsManager`. They are bundled into a `GetMergeOperandsState`. We use `SharedCleanablePtr` to share that bundle among all `PinnableSlice`s populated by `GetMergeOperands()`. That way, the last `PinnableSlice` to be `Reset()` will cleanup the bundle, including unreferencing the `SuperVersion`. Pull Request resolved: https://github.com/facebook/rocksdb/pull/10458 Test Plan: - new DB level test - measured benefit/regression in a number of memtable scenarios Setup command: ``` $ ./db_bench -benchmarks=mergerandom -merge_operator=StringAppendOperator -num=$num -writes=16384 -key_size=16 -value_size=$value_sz -compression_type=none -write_buffer_size=1048576000 ``` Benchmark command: ``` ./db_bench -threads=$threads -use_existing_db=true -avoid_flush_during_recovery=true -write_buffer_size=1048576000 -benchmarks=readrandomoperands -merge_operator=StringAppendOperator -num=$num -duration=10 ``` Worst regression is when a key has many tiny operands: - Parameters: num=1 (implying 16384 operands per key), value_sz=8, threads=1 - `GetMergeOperands()` latency increases 682 micros -> 800 micros (+17%) The regression disappears into the noise (<1% difference) if we remove the `Reset()` loop and the size counting loop. The former is arguably needed regardless of this PR as the convention in `Get()` and `MultiGet()` is to `Reset()` the input `PinnableSlice`s at the start. The latter could be optimized to count the size as we accumulate operands rather than after the fact. Best improvement is when a key has large operands and high concurrency: - Parameters: num=4 (implying 4096 operands per key), value_sz=2KB, threads=32 - `GetMergeOperands()` latency decreases 11492 micros -> 437 micros (-96%). Reviewed By: cbi42 Differential Revision: D38336578 Pulled By: ajkr fbshipit-source-id: 48146d127e04cb7f2d4d2939a2b9dff3aba18258 |
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| README.md | ||
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RocksDB Micro-Benchmark
Overview
RocksDB micro-benchmark is a set of tests for benchmarking a single component or simple DB operations. The test artificially generates input data and executes the same operation with it to collect and report performance metrics. As it's focusing on testing a single, well-defined operation, the result is more precise and reproducible, which also has its limitation of not representing a real production use case. The test author needs to carefully design the microbench to represent its true purpose.
The tests are based on Google Benchmark library, which provides a standard framework for writing benchmarks.
How to Run
Prerequisite
Install the Google Benchmark version 1.6.0 or above.
Note: Google Benchmark 1.6.x is incompatible with previous versions like 1.5.x, please make sure you're using the newer version.
Build and Run
With Makefile:
$ DEBUG_LEVEL=0 make run_microbench
Or with cmake:
$ mkdir build && cd build && cmake .. -DCMAKE_BUILD_TYPE=Release -DWITH_BENCHMARK
$ make run_microbench
Note: Please run the benchmark code in release build.
Run Single Test
Example:
$ make db_basic_bench
$ ./db_basic_bench --benchmark_filter=<TEST_NAME>
Best Practices
* Use the Same Test Directory Setting as Unittest
Most of the Micro-benchmark tests use the same test directory setup as unittest, so it could be overridden by:
$ TEST_TMPDIR=/mydata/tmp/ ./db_basic_bench --benchmark_filter=<TEST_NAME>
Please also follow that when designing new tests.
* Avoid Using Debug API
Even though micro-benchmark is a test, avoid using internal Debug API like TEST_WaitForRun() which is designed for unittest. As benchmark tests are designed for release build, don't use any of that.
* Pay Attention to Local Optimization
As a micro-benchmark is focusing on a single component or area, make sure it is a key part for impacting the overall application performance.
The compiler might be able to optimize the code that not the same way as the whole application, and if the test data input is simple and small, it may be able to all cached in CPU memory, which is leading to a wrong metric. Take these into consideration when designing the tests.
* Names of user-defined counters/metrics has to be [A-Za-z0-9_]
It's a restriction of the metrics collecting and reporting system RocksDB is using internally. It will also help integrate with more systems.
* Minimize the Metrics Variation
Try reducing the test result variation, one way to check that is running the test multiple times and check the CV (Coefficient of Variation) reported by gbenchmark.
$ ./db_basic_bench --benchmark_filter=<TEST_NAME> --benchmark_repetitions=10
...
<TEST_NAME>_cv 3.2%
RocksDB has background compaction jobs which may cause the test result to vary a lot. If the micro-benchmark is not purposely testing the operation while compaction is in progress, it should wait for the compaction to finish (db_impl->WaitForCompact()) or disable auto-compaction.