65d690b21d
## Problem Currently, we only split tenants into 8 shards once, at the 64 GB split threshold. For very large tenants, we need to keep splitting to avoid huge shards. And we also want to eagerly split at a lower threshold to improve throughput during initial ingestion. See https://github.com/neondatabase/cloud/issues/22532#issuecomment-2706215907 for details. Touches https://github.com/neondatabase/cloud/issues/22532. Requires #11157. ## Summary of changes This adds parameters and logic to enable repeated splits when a tenant's largest timeline divided by shard count exceeds `split_threshold`, as well as eager initial splits at a lower threshold to speed up initial ingestion. The default parameters are all set such that they retain the current behavior in production (only split into 8 shards once, at 64 GB). * `split_threshold` now specifies a maximum shard size. When a shard exceeds it, all tenant shards are split by powers of 2 such that all tenant shards fall below `split_threshold`. Disabled by default, like today. * Add `max_split_shards` to specify a max shard count for autosplits. Defaults to 8 to retain current behavior. * Add `initial_split_threshold` and `initial_split_shards` to specify a threshold and target count for eager splits of unsharded tenants. Defaults to 64 GB and 8 shards to retain current production behavior. Because this PR sets `initial_split_threshold` to 64 GB by default, it has the effect of enabling autosplits by default. This was not the case previously, since `split_threshold` defaults to None, but it is already enabled across production and staging. This is temporary until we complete the production rollout. For more details, see code comments. This must wait until #11157 has been deployed to Pageservers. Once this has been deployed to production, we plan to change the parameters to: * `split-threshold`: 256 GB * `initial-split-threshold`: 16 GB * `initial-split-shards`: 4 * `max-split-shards`: 16 The final split points will thus be: * Start: 1 shard * 16 GB: 4 shards * 1 TB: 8 shards * 2 TB: 16 shards We will then change the default settings to be disabled by default. --------- Co-authored-by: John Spray <john@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.