Benchmarks

Use this page to run existing benchmarks, qualify performance improvements, and write new benchmarks.

Micro benchmarks

When contributing performance improvements to very specific pieces of code, prefer using the existing micro-benches in benchmarks/benches.

If there’s no benchmark that exercises the code path you are aiming to improve, consider creating a separate PR adding the necessary benchmark first, following the same pattern as the existing ones.

The structure of new benchmarks should be unsurprising and consistent with existing ones.

Local Benchmarks

It’s recommended to run these benchmarks locally when contributing to ensure there are no performance regressions.

Generating Test Data

First, a TPCH dataset must be generated:

DATASET=$(cargo run -p datafusion-distributed-benchmarks --release -- prepare-tpch --scale-factor 10)

This might take a while. It prints the dataset’s path under testdata/, which records every generation parameter, and nothing else on stdout. See the benchmarks README for the available layout options.

Running Benchmarks

After generating the data, run the benchmarks with the dfbench run command. A good setup is to run 8 local workers. This provides a relatively accurate benchmarking environment for a distributed system locally.

cargo run -p datafusion-distributed-benchmarks --release -- run --workers 8 --dataset $DATASET

Subsequent runs will compare results against the previous one, so a useful trick to measure the impact of a PR is to first run the benchmarks on main, and then on the PR branch.

More information about these benchmarks can be found in the benchmarks README.

Remote Benchmarks

These benchmarks are hosted on https://github.com/gabotechs/datafusion-distributed-dev-tools, and they can be trigger on PRs by commenting benchmark run tpch/sf100 or any other relevant dataset (only whitelisted people can run those).

Qualifying performance improvements

A performance claim needs reproducible, representative evidence. A faster microbenchmark alone is not sufficient evidence for a cross-cutting production change.

Most benchmarks in this project are based on real data, and therefore, the variability of results is high. When reporting benchmarks results over TPC-H, TPC-DS or ClickBench data, keep into account that certain queries can easily show variabilities of +-50% just because of network noise when reading files from S3.

The single most important value is the total wall time of the full suite, not individual query results. For example a +100% improvement in one specific query is meaningless if the full test suite did just a +1% improvement.

Do not overindex in individual query results reported by a single benchmark run, when in doubt, re-run that specific query with a high number of iterations (e.g., -i 20), in main VS the new branch, and then evaluate if indeed there was an issue there.