## Problem
`TYPE_CHECKING` is used inconsistently across Python tests.
## Summary of changes
- Update `ruff`: 0.7.0 -> 0.11.2
- Enable TC (flake8-type-checking):
https://docs.astral.sh/ruff/rules/#flake8-type-checking-tc
- (auto)fix all new issues
## Problem
The path to TPC-H queries was incorrectly changed in #9306.
This path is used for `test_tpch` parameterization, so all perf tests
started to fail:
```
==================================== ERRORS ====================================
__________ ERROR collecting test_runner/performance/test_perf_olap.py __________
test_runner/performance/test_perf_olap.py:205: in <module>
@pytest.mark.parametrize("query", tpch_queuies())
test_runner/performance/test_perf_olap.py:196: in tpch_queuies
assert queries_dir.exists(), f"TPC-H queries dir not found: {queries_dir}"
E AssertionError: TPC-H queries dir not found: /__w/neon/neon/test_runner/performance/performance/tpc-h/queries
E assert False
E + where False = <bound method Path.exists of PosixPath('/__w/neon/neon/test_runner/performance/performance/tpc-h/queries')>()
E + where <bound method Path.exists of PosixPath('/__w/neon/neon/test_runner/performance/performance/tpc-h/queries')> = PosixPath('/__w/neon/neon/test_runner/performance/performance/tpc-h/queries').exists
```
## Summary of changes
- Fix the path to tpc-h queries
It didn't serve much value, and was only used twice.
Path(__file__).parent is a pretty easy invocation to use.
Signed-off-by: Tristan Partin <tristan@neon.tech>
## Problem
halfvec data type was introduced in pgvector 0.7.0 and is popular
because
it allows smaller vectors, smaller indexes and potentially better
performance.
So far we have not tested halfvec in our periodic performance tests.
This PR adds halfvec indexing and halfvec queries to the test.
## Problem
We want to regularly verify the performance of pgvector HNSW parallel
index builds and parallel similarity search using HNSW indexes.
The first release that considerably improved the index-build parallelism
was pgvector 0.7.0 and we want to make sure that we do not regress by
our neon compute VM settings (swap, memory over commit, pg conf etc.)
## Summary of changes
Prepare a Neon project with 1 million openAI vector embeddings (vector
size 1536).
Run HNSW indexing operations in the regression test for the various
distance metrics.
Run similarity queries using pgbench with 100 concurrent clients.
I have also added the relevant metrics to the grafana dashboards pgbench
and olape
---------
Co-authored-by: Alexander Bayandin <alexander@neon.tech>
## Problem
`black` is slow sometimes, we can replace it with `ruff format` (a new
feature in 0.1.2 [0]), which produces pretty similar to black style [1].
On my local machine (MacBook M1 Pro 16GB):
```
# `black` on main
$ hyperfine "BLACK_CACHE_DIR=/dev/null poetry run black ."
Benchmark 1: BLACK_CACHE_DIR=/dev/null poetry run black .
Time (mean ± σ): 3.131 s ± 0.090 s [User: 5.194 s, System: 0.859 s]
Range (min … max): 3.047 s … 3.354 s 10 runs
```
```
# `ruff format` on the current PR
$ hyperfine "RUFF_NO_CACHE=true poetry run ruff format"
Benchmark 1: RUFF_NO_CACHE=true poetry run ruff format
Time (mean ± σ): 300.7 ms ± 50.2 ms [User: 259.5 ms, System: 76.1 ms]
Range (min … max): 267.5 ms … 420.2 ms 10 runs
```
## Summary of changes
- Replace `black` with `ruff format` everywhere
- [0] https://docs.astral.sh/ruff/formatter/
- [1] https://docs.astral.sh/ruff/formatter/#black-compatibility
## Problem
This is a comment only change.
To ensure that our benchmarking results are fair we need to have correct
stats in catalog. Otherwise optimizer chooses seq scan instead of index
only scan for some queries. Added comment to run vacuum after data prep.
## Problem
To understand differences in performance between neon, aurora and rds we
want to collect explain analyze plans and pg_stat_statements for
selected benchmarking runs
## Summary of changes
Add workflow input options to collect explain and pg_stat_statements for
benchmarking workflow
Co-authored-by: BodoBolero <bodobolero@gmail.com>
## Problem
It's hard to find out which DB size we use for OLAP benchmarks (TPC-H in
particular).
This PR adds handling of `TEST_OLAP_SCALE` env var, which is get added
to a test name as a parameter.
This is required for performing larger periodic benchmarks.
## Summary of changes
- Handle `TEST_OLAP_SCALE` in
`test_runner/performance/test_perf_olap.py`
- Set `TEST_OLAP_SCALE` in `.github/workflows/benchmarking.yml` to a
TPC-H scale
Add ClickBench benchmark, an OLAP-style benchmark, to Nightly
Benchmarks.
The full run of 43 queries on the original dataset takes more than 6h
(only 34 queries got processed on in 6h) on our default-sized compute.
Having this, currently, would mean having some really unstable tests
because of our regular deployment to staging/captest environment (see
https://github.com/neondatabase/cloud/issues/1872).
I've reduced the dataset size to the first 10^7 rows from the original
10^8 rows. Now it takes ~30-40 minutes to pass.
Ref https://github.com/ClickHouse/ClickBench/tree/main/aurora-postgresql
Ref https://benchmark.clickhouse.com/
