a338984dc7
## Problem Get page batching stops when we encounter requests at different LSNs. We are leaving batching factor on the table. ## Summary of changes The goal is to support keys with different LSNs in a single batch and still serve them with a single vectored get. Important restriction: the same key at different LSNs is not supported in one batch. Returning different key versions is a much more intrusive change. Firstly, the read path is changed to support "scattered" queries. This is a conceptually simple step from https://github.com/neondatabase/neon/pull/11463. Instead of initializing the fringe for one keyspace, we do it for multiple at different LSNs and let the logic already present into the fringe handle selection. Secondly, page service code is updated to support batching at different LSNs. Eeach request parsed from the wire determines its effective request LSN and keeps it in mem for the batcher toinspect. The batcher allows keys at different LSNs in one batch as long one key is not requested at different LSNs. I'd suggest doing the first pass commit by commit to get a feel for the changes. ## Results I used the batching test from [Christian's PR](https://github.com/neondatabase/neon/pull/11391) which increases the change of batch breaks. Looking at the logs I think the new code is at the max batching factor for the workload (we only break batches due to them being oversized or because the executor is idle). ``` Main: Reasons for stopping batching: {'LSN changed': 22843, 'of batch size': 33417} test_throughput[release-pg16-50-pipelining_config0-30-100-128-batchable {'max_batch_size': 32, 'execution': 'concurrent-futures', 'mode': 'pipelined'}].perfmetric.batching_factor: 14.6662 My branch: Reasons for stopping batching: {'of batch size': 37024} test_throughput[release-pg16-50-pipelining_config0-30-100-128-batchable {'max_batch_size': 32, 'execution': 'concurrent-futures', 'mode': 'pipelined'}].perfmetric.batching_factor: 19.8333 ``` Related: https://github.com/neondatabase/neon/issues/10765
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.