All of production is using it now as of
https://github.com/neondatabase/aws/pull/1121
The change in `flaky_tests.py` resets the flakiness detection logic.
The alternative would have been to repeat the choice of io engine in
each test name, which would junk up the various test reports too much.
---------
Co-authored-by: Alexander Bayandin <alexander@neon.tech>
## Problem
We started to store test results in a new format in
https://github.com/neondatabase/neon/pull/4549.
This PR switches scripts to query this db.
(we can completely remove old DB/ingestions scripts in a couple of
weeks after the PR merged)
## Summary of changes
- `scripts/benchmark_durations.py` query new database
- `scripts/flaky_tests.py` query new database
## Problem
We still need to rerun some builds manually because flaky tests weren't
detected automatically.
I found two reasons for it:
- If a test is flaky on a particular build type, on a particular
Postgres version, there's a high chance that this test is flaky on all
configurations, but we don't automatically detect such cases.
- We detect flaky tests only on the main branch, which requires manual
retrigger runs for freshly made flaky tests.
Both of them are fixed in the PR.
## Summary of changes
- Spread flakiness of a single test to all configurations
- Detect flaky tests in all branches (not only in the main)
- Look back only at 7 days of test history (instead of 10)
- Increase `connect_timeout` to 30s, which should be enough for
most of the cases
- If the script cannot connect to the DB (or any other
`psycopg2.OperationalError` occur) — do not fail the script, log
the error and proceed. Problems with fetching flaky tests shouldn't
block the PR
This PR adds a plugin that automatically reruns (up to 3 times) flaky
tests. Internally, it uses data from `TEST_RESULT_CONNSTR` database and
`pytest-rerunfailures` plugin.
As the first approximation we consider the test flaky if it has failed on
the main branch in the last 10 days.
Flaky tests are fetched by `scripts/flaky_tests.py` script (it's
possible to use it in a standalone mode to learn which tests are flaky),
stored to a JSON file, and then the file is passed to the pytest plugin.
