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Testing structure: exponential growth options and whether they make sense

This document outlines how the test suite can be scaled beyond the current structure and whether that approach is appropriate for this project.

Current state (short)​

  • Task 1 (ETL): Many test files (unit, integration, Spark vs Pandas), shared conftest.py (Spark, S3/MinIO, metrics), fixtures/scenario_fixtures.py with a small set of ScenarioCsv definitions (A1, A2, B1, quarantine), and test_data_generator.py for synthetic data. Scenario tests are mostly one class per scenario with hardcoded CSV/content; no pytest.mark.parametrize in the project's tests.
  • Task 3 (SQL): Small focused tests (e.g. balance query).
  • Task 4 (DevOps): Workflow / Terraform / orchestration tests.
  • Reporting: scripts/test_report/ (aggregate, summary, assertion checks, quality gates).

Ways to grow the testing structure “exponentially”​

These levers multiply test coverage from a small amount of new test code or data.

1. Scenario × backend parametrization​

  • Idea: Register all scenarios in one place (e.g. list of ScenarioCsv or ids). One (or a few) test functions run the same assertions for each scenario; parametrize over (scenario_id, backend) with backend in ("pandas", "spark").
  • Growth: Add 1 scenario → N new test cases (N = number of backends). Add 1 backend → M new cases (M = number of scenarios). Scenarios × backends from a single test implementation.
  • Implementation: In scenario_fixtures.py, add e.g. ALL_SCENARIOS = [scenario_csv_a1(), scenario_csv_a2(), ...] and a scenario_id; in tests use @pytest.mark.parametrize("scenario,backend", [...]) and call run_ingest_pandas or run_ingest_spark based on backend.

2. Contract / adapter tests​

  • Idea: Define a single “contract” test suite (e.g. for the storage or ingest port): given these inputs, expect these outputs/side-effects. Run the same tests against Pandas adapter, Spark adapter, and (later) any new adapter.
  • Growth: One new adapter → full contract coverage with no new test logic. Coverage = contracts × adapters.

3. Property-based / fuzz testing​

  • Idea: Use Hypothesis (or similar) to generate many CSVs (valid/invalid, edge cases). One test asserts invariants: e.g. “silver row count + quarantine + condemned = input valid rows”, “no duplicate TransactionIDs in silver”, “partition keys match event_date”.
  • Growth: One test function can represent many generated cases; adding a new invariant adds one test that scales over the same generators.

4. More scenario dimensions​

  • Idea: Scenarios already vary by content; add dimensions: schema version, “with/without promotion”, “with/without loop-prevention”, quality-gate thresholds, or partition layout.
  • Growth: Scenarios × backends × schema_version × promotion_flag × … from the same parametrized test(s). Each new dimension multiplies.

5. Snapshot / golden-file testing​

  • Idea: For each scenario (and optionally backend), store expected outputs (e.g. Parquet row count per partition, or SQL result CSV). Test: run ETL (or SQL), compare to snapshot. New scenario = new snapshot file; test code stays “run and diff”.
  • Growth: Adding scenarios is adding data, not copy-pasting test methods. Can combine with (1) so one parametrized test + many snapshots = large effective coverage.

6. Layered “smoke” matrix​

  • Idea: Critical path (ingest → silver → promote → query) × (MinIO, optional real S3) × (Pandas, Spark) with a small fixed set of scenarios. Explicit N×M matrix; add environment or backend = add a dimension.
  • Growth: Clear, bounded growth adding environments or backends; suitable for CI smoke runs and evidence.

7. Shared test traits / one test, multiple runners​

  • Idea: Single “ETL scenario test” protocol: given CSV + expected counts/paths, run ETL, assert. Pandas and Spark tests differ only by which run_ingest_* is called (e.g. base class or composition). New behavior = one new scenario/test; new backend = one new runner.
  • Growth: Same as (1): scenarios × runners, with less duplication than today.

Does exponential growth make sense here?​

When it makes sense​

  • A single registry for scenarios (data and expected outcomes) with coverage for both Pandas and Spark (and future backends).
  • Regression safety as more scenarios or adapters are added without rewriting tests.
  • Clear scaling rules: new scenario is added to the registry; new backend is added to the parametrize list.
  • The project will evolve (more sources, backends, quality rules) and the test structure should support that.

Caveats​

  • CI time: More tests × more backends × integration (MinIO) can increase runtime significantly. Mitigations: markers (@pytest.mark.unit vs @pytest.mark.integration), parallel jobs, and running a reduced matrix on PRs (e.g. unit + one backend) and full matrix on main/nightly.
  • Debugging: Parametrized and generated tests require stable ids and reporting (JSON report and metrics are available); include scenario id and backend in test ids and logs.
  • Diminishing returns: After a point, many scenarios hit the same code paths. A small set of well-chosen scenarios plus property-based invariants often beats “maximum number of scenarios”.
  • Scope of the case: For a time-bounded case study, “exponential” might be overkill; structured and scalable (parametrized scenarios × backends, one contract suite) is often enough to show design thinking without maintaining a huge suite.

Recommendation​

  • Do:
    • Introduce scenario × backend parametrization (1) using a scenario registry in scenario_fixtures.py and one or a few parametrized integration tests. That gives exponential growth in the sense “add scenario or backend → more cases from same code.”
    • Optionally add contract tests (2) for storage/ingest so new adapters get full coverage from one suite.
    • Optionally add one or two property-based tests (3) for invariants (e.g. row conservation, no duplicate IDs in silver) to cover many generated inputs without writing many test methods.
  • Do not (for this project, unless requirements grow):
    • Push all dimensions (schema version × promotion × quality gates × …) into one giant matrix at once.
    • Aim for “maximum test count”; prefer clarity and maintainability and a small, representative scenario set that runs in CI within reasonable time.

So: yes, the structure can be designed to grow exponentially (scenarios × backends × optional dimensions). Whether it makes sense is “yes in a controlled way”: parametrization + scenario registry + optional property-based invariants, with CI and scope kept in check so the suite stays understandable and fast enough.

© 2026 Stephen Adei•CC BY 4.0