fd1368d31e
## Problem We want to do a more robust job of scheduling tenants into their home AZ: https://github.com/neondatabase/neon/issues/8264. Closes: https://github.com/neondatabase/neon/issues/8969 ## Summary of changes ### Scope This PR combines prioritizing AZ with a larger rework of how we do optimisation. The rationale is that just bumping AZ in the order of Score attributes is a very tiny change: the interesting part is lining up all the optimisation logic to respect this properly, which means rewriting it to use the same scores as the scheduler, rather than the fragile hand-crafted logic that we had before. Separating these changes out is possible, but would involve doing two rounds of test updates instead of one. ### Scheduling optimisation `TenantShard`'s `optimize_attachment` and `optimize_secondary` methods now both use the scheduler to pick a new "favourite" location. Then there is some refined logic for whether + how to migrate to it: - To decide if a new location is sufficiently "better", we generate scores using some projected ScheduleContexts that exclude the shard under consideration, so that we avoid migrating from a node with AffinityScore(2) to a node with AffinityScore(1), only to migrate back later. - Score types get a `for_optimization` method so that when we compare scores, we will only do an optimisation if the scores differ by their highest-ranking attributes, not just because one pageserver is lower in utilization. Eventually we _will_ want a mode that does this, but doing it here would make scheduling logic unstable and harder to test, and to do this correctly one needs to know the size of the tenant that one is migrating. - When we find a new attached location that we would like to move to, we will create a new secondary location there, even if we already had one on some other node. This handles the case where we have a home AZ A, and want to migrate the attachment between pageservers in that AZ while retaining a secondary location in some other AZ as well. - A unit test is added for https://github.com/neondatabase/neon/issues/8969, which is implicitly fixed by reworking optimisation to use the same scheduling scores as scheduling.
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.