Streamcase

Streamcase is an early-development pytest-oriented toolkit for deterministic Apache Spark Structured Streaming tests.

Alpha status: Streamcase is available on PyPI, but its API remains experimental and may change in later alphas. The features in a source checkout can be newer than a published package. Check the release notes for the exact capabilities and tested combinations of the version you install.

The 0.2.0a1 release notes describe its expanded alpha scope and limitations. The verified prerelease is available on PyPI.

Problem

Ordinary DataFrame assertions do not exercise streaming semantics such as micro-batch boundaries, late data, watermarks, checkpoint restarts, or state growth. Streamcase will make those behaviors explicit and reproducible in Python tests while executing against Spark's public Structured Streaming interfaces.

Installation

Install the lightweight backend-independent package from PyPI. The --pre option allows alpha releases; pin a version when reproducibility matters:

python -m pip install --pre streamcase

For the Spark runner, install the optional spark extra and provide a compatible Java JDK separately:

python -m pip install --pre "streamcase[spark]"

The 0.2.0a1 release adds a separate spark35 extra for PySpark 3.5.9 or later 3.5 patches. Install it with:

python -m pip install "streamcase[spark35]==0.2.0a1"

For the opt-in pytest fixture, combine its pytest extra with exactly one Spark extra. For the Spark 4.2 line of 0.2.0a1:

python -m pip install "streamcase[pytest,spark]==0.2.0a1"

See the pytest integration guide for explicit activation, a runnable example, and session ownership. The older 0.1.0a2 distribution does not include the pytest or spark35 extras.

These extras keep PySpark out of the core dependency set. See the Spark compatibility policy for the supported Python, Java, and Spark combination. Contributors working from a checkout can use the editable-install instructions.

Capabilities in 0.2.0a1

Streamcase can describe immutable logical input batches and checkpoint-preserving restart boundaries, represent captured output, and assert row equality, batch counts, unique keys, and captured progress metrics without importing PySpark. The public Spark runner executes batches and restarts against a caller-owned session. It also offers an opt-in pytest runner fixture. See the scenario model guide and results and assertions guide, plus the progress and state assertions guide, two-batch Spark quick start and runner contract and checkpoint restart example, and pytest integration guide.

Repository status

This bootstrap establishes:

• Python packaging with a src layout;
• Apache License 2.0 and project governance;
• issue and pull-request conventions;
• formatting, linting, type checking, tests, and package validation in CI;
• security and release policies.

See ROADMAP.md for the planned delivery sequence and docs/architecture.md for the proposed technical shape.

Local checks

python -m venv .venv
python -m pip install -e ".[dev]"
ruff format --check .
ruff check .
mypy
pytest -m "not spark"
python -m build
python -m twine check dist/*

Spark contributors can run the isolated integration suite with the setup in the contribution guide.

Contributing

All material work starts with a GitHub issue and reaches main only through a reviewed pull request. Read CONTRIBUTING.md before making a change.

Trademark notice

Apache Spark, Spark, Apache Flink, and Flink are trademarks of the Apache Software Foundation. Streamcase is independent software and is not endorsed by the Apache Software Foundation.

License

Licensed under the Apache License 2.0.