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Elasticsearch Microbenchmark Suite

This directory contains the microbenchmark suite of Elasticsearch. It relies on JMH.

Purpose

We do not want to microbenchmark everything but the kitchen sink and should typically rely on our macrobenchmarks with Rally. Microbenchmarks are intended to spot performance regressions in performance-critical components. The microbenchmark suite is also handy for ad-hoc microbenchmarks but please remove them again before merging your PR.

Getting Started

Just run gradlew -p benchmarks run from the project root directory. It will build all microbenchmarks, execute them and print the result.

Running Microbenchmarks

Running via an IDE is not supported as the results are meaningless because we have no control over the JVM running the benchmarks.

If you want to run a specific benchmark class like, say, MemoryStatsBenchmark, you can use --args:

gradlew -p benchmarks run --args ' AllocationBenchmark'

Everything in the ' gets sent on the command line to JMH. The leading inside the 's is important. Without it parameters are sometimes sent to gradle.

Adding Microbenchmarks

Before adding a new microbenchmark, make yourself familiar with the JMH API. You can check our existing microbenchmarks and also the JMH samples.

In contrast to tests, the actual name of the benchmark class is not relevant to JMH. However, stick to the naming convention and end the class name of a benchmark with Benchmark. To have JMH execute a benchmark, annotate the respective methods with @Benchmark.

Tips and Best Practices

To get realistic results, you should exercise care when running benchmarks. Here are a few tips:

Do

Don't