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Based on https://github.com/neondatabase/neon/pull/11139 ## Problem We want to export performance traces from the pageserver in OTEL format. End goal is to see them in Grafana. ## Summary of changes https://github.com/neondatabase/neon/pull/11139 introduces the infrastructure required to run the otel collector alongside the pageserver. ### Design Requirements: 1. We'd like to avoid implementing our own performance tracing stack if possible and use the `tracing` crate if possible. 2. Ideally, we'd like zero overhead of a sampling rate of zero and be a be able to change the tracing config for a tenant on the fly. 3. We should leave the current span hierarchy intact. This includes adding perf traces without modifying existing tracing. To satisfy (3) (and (2) in part) a separate span hierarchy is used. `RequestContext` gains an optional `perf_span` member that's only set when the request was chosen by sampling. All perf span related methods added to `RequestContext` are no-ops for requests that are not sampled. This on its own is not enough for (3), so performance spans use a separate tracing subscriber. The `tracing` crate doesn't have great support for this, so there's a fair amount of boilerplate to override the subscriber at all points of the perf span lifecycle. ### Perf Impact [Periodic pagebench](https://neonprod.grafana.net/d/ddqtbfykfqfi8d/e904990?orgId=1&from=2025-02-08T14:15:59.362Z&to=2025-03-10T14:15:59.362Z&timezone=utc) shows no statistically significant regression with a sample ratio of 0. There's an annotation on the dashboard on 2025-03-06. ### Overview of changes: 1. Clean up the `RequestContext` API a bit. Namely, get rid of the `RequestContext::extend` API and use the builder instead. 2. Add pageserver level configs for tracing: sampling ratio, otel endpoint, etc. 3. Introduce some perf span tracking utilities and expose them via `RequestContext`. We add a `tracing::Span` wrapper to be used for perf spans and a `tracing::Instrumented` equivalent for it. See doc comments for reason. 4. Set up OTEL tracing infra according to configuration. A separate runtime is used for the collector. 5. Add perf traces to the read path. ## Refs - epic https://github.com/neondatabase/neon/issues/9873 --------- Co-authored-by: Christian Schwarz <christian@neon.tech>
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=16 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
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.