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Testing strategy

DataCoolie uses plain pytest with pytest-xdist. The default repo behavior is defined in pyproject.toml, not in a separate CLI wrapper.

Default test run

# From datacoolie/
python -m pytest

This picks up the current default pytest options from pyproject.toml:

  • -n auto
  • --dist loadgroup
  • -m "not spark"
  • --strict-markers
  • --tb=short
  • -q
  • --import-mode=importlib

So the default run is a parallel non-spark test run. It does not execute the Spark-marked tests unless you override the marker selection explicitly. GitHub Actions runs this non-Spark suite as a required job after synchronizing the locked polars, deltalake, and polars-hash extras. A restored virtual environment cache never replaces poetry sync.

Markers

Marker Description
default selection pytest runs with -m "not spark", so all non-spark tests are included by default.
slow Defined marker. Still included by default unless you exclude it yourself.
integration Defined marker. Still included by default unless you exclude it yourself.
spark Spark-specific tests. Excluded by default by the repo pytest config. When run directly, the Spark module also uses pytest.importorskip(...) for pyspark and delta-spark. 
python -m pytest -m "not slow and not integration and not spark"

Coverage

The current pyproject.toml does not configure pytest-cov, branch coverage, omissions, or a repository-wide failure threshold. Run coverage explicitly when needed, and do not treat a normal python -m pytest result as a coverage gate.

Parallel execution contract

pytest-xdist distributes by test group (--dist loadgroup). Tests that share fixtures use the @pytest.mark.xdist_group(...) marker to pin into the same worker. The current Spark engine module is grouped this way so one JVM is reused safely.

Spark remains a local-only release gate because JVM and Delta startup are too expensive for the hosted CI job:

poetry sync --with dev -E spark -E delta-spark
poetry run pytest tests/unit/engines/test_spark_engine.py -m spark -n auto --dist loadgroup

Scope

The main automated test surface is the pytest suite under tests/. Separately, usecase-sim/ provides coarse-grained execution scenarios and runner scripts for end-to-end validation outside the core pytest unit suite.