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## Problem We don't have a well-documented, periodic benchmark for TPC-C like OLTP workload. ## Summary of changes # Benchbase TPC-C-like Performance Results Runs TPC-C-like benchmarks on Neon databases using [Benchbase](https://github.com/cmu-db/benchbase). Docker images are built [here](https://github.com/neondatabase-labs/benchbase-docker-images) We run the benchmarks at different scale factors aligned with different compute sizes we offer to customers. For each scale factor, we determine a max rate (see Throughput in warmup phase) and then run the benchmark at a target rate of approx. 70 % of the max rate. We use different warehouse sizes which determine the working set size - it is optimized for LFC size of the respected pricing tier. Usually we should get LFC hit rates above 70 % for this setup and quite good, consistent (non-flaky) latencies. ## Expected performance as of first testing this | Tier | CU | Warehouses | Terminals | Max TPS | LFC size | Working set size | LFC hit rate | Median latency | p95 latency | |------------|------------|---------------|-----------|---------|----------|------------------|--------------|----------------|-------------| | free | 0.25-2 | 50 - 5 GB | 150 | 800 | 5 GB | 6.3 GB | 95 % | 170 ms | 600 ms | | serverless | 2-8 | 500 - 50 GB | 230 | 2000 | 26 GB | ?? GB | 91 % | 50 ms | 200 ms | | business | 2-16 | 1000 - 100 GB | 330 | 2900 | 51 GB | 50 GB | 72 % | 40 ms | 180 ms | Each run - first loads the database (not shown in the dashboard). - Then we run a warmup phase for 20 minutes to warm up the database and the LFC at unlimited target rate (max rate) (highest throughput but flaky latencies). The warmup phase can be used to determine the max rate and adjust it in the github workflow in case Neon is faster in the future. - Then we run the benchmark at a target rate of approx. 70 % of the max rate for 1 hour (expecting consistent latencies and throughput). ## Important notes on implementation: - we want to eventually publish the process how to reproduce these benchmarks - thus we want to reduce all dependencies necessary to run the benchmark, the only thing needed are - docker - the docker images referenced above for benchbase - python >= 3.9 to run some config generation steps and create diagrams - to reduce dependencies we deliberatly do NOT use some of our python fixture test infrastructure to make the dependency chain really small - so pls don't add a review comment "should reuse fixture xy" - we also upload all generator python scripts, generated bash shell scripts and configs as well as raw results to S3 bucket that we later want to publish once this benchmark is reviewed and approved.
Running locally
First make a release build. The -s flag silences a lot of output, and makes it
easier to see if you have compile errors without scrolling up.
BUILD_TYPE=release CARGO_BUILD_FLAGS="--features=testing" make -s -j8
You may also need to run ./scripts/pysync.
Then run the tests
DEFAULT_PG_VERSION=17 NEON_BIN=./target/release poetry run pytest test_runner/performance
Some handy pytest flags for local development:
-xtells pytest to stop on first error-sshows test output-kselects a test to run--timeout=0disables our default timeout of 300s (seesetup.cfg)--preserve-database-filesto skip cleanup--out-dirto produce a JSON with the recorded test metrics. There is a post-processing tool attest_runner/performance/out_dir_to_csv.py.
What performance tests do we have and how we run them
Performance tests are built using the same infrastructure as our usual python integration tests. There are some extra fixtures that help to collect performance metrics, and to run tests against both vanilla PostgreSQL and Neon for comparison.
Tests that are run against local installation
Most of the performance tests run against a local installation. This is not very representative of a production environment. Firstly, Postgres, safekeeper(s) and the pageserver have to share CPU and I/O resources, which can add noise to the results. Secondly, network overhead is eliminated.
In the CI, the performance tests are run in the same environment as the other integration tests. We don't have control over the host that the CI runs on, so the environment may vary widely from one run to another, which makes the results across different runs noisy to compare.
Remote tests
There are a few tests that marked with pytest.mark.remote_cluster. These tests do not set up a local environment, and instead require a libpq connection string to connect to. So they can be run on any Postgres compatible database. Currently, the CI runs these tests on our staging and captest environments daily. Those are not an isolated environments, so there can be noise in the results due to activity of other clusters.
Noise
All tests run only once. Usually to obtain more consistent performance numbers, a test should be repeated multiple times and the results be aggregated, for example by taking min, max, avg, or median.
Results collection
Local test results for main branch, and results of daily performance tests, are stored in a neon project deployed in production environment. There is a Grafana dashboard that visualizes the results. Here is the dashboard. The main problem with it is the unavailability to point at particular commit, though the data for that is available in the database. Needs some tweaking from someone who knows Grafana tricks.
There is also an inconsistency in test naming. Test name should be the same across platforms, and results can be differentiated by the platform field. But currently, platform is sometimes included in test name because of the way how parametrization works in pytest. I.e. there is a platform switch in the dashboard with neon-local-ci and neon-staging variants. I.e. some tests under neon-local-ci value for a platform switch are displayed as Test test_runner/performance/test_bulk_insert.py::test_bulk_insert[vanilla] and Test test_runner/performance/test_bulk_insert.py::test_bulk_insert[neon] which is highly confusing.