Optimize test performance by reusing cached database in tracking_uri_mock#19258
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Optimize test performance by reusing cached database in tracking_uri_mock#19258
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- Changed tracking_uri_mock to use db_uri fixture instead of tmp_path - Added proper type hints to tracking_uri_mock fixture - Removed unused path_to_local_sqlite_uri import - This improves test execution speed by reusing cached database Co-authored-by: harupy <17039389+harupy@users.noreply.github.com>
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[WIP] Update tracking_uri_mock to use db_uri fixture
Optimize test performance by reusing cached database in tracking_uri_mock
Dec 7, 2025
Signed-off-by: harupy <17039389+harupy@users.noreply.github.com>
Signed-off-by: harupy <17039389+harupy@users.noreply.github.com>
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Signed-off-by: harupy <17039389+harupy@users.noreply.github.com>
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After mlflow#19258, tracking_uri_mock in tests/conftest.py now uses db_uri and is autouse by default. This makes per-file tracking_uri fixtures redundant. Signed-off-by: harupy <17039389+harupy@users.noreply.github.com>
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Related Issues/PRs
#xxxWhat changes are proposed in this pull request?
The
tracking_uri_mockfixture created a fresh SQLite database for each test, running migrations every time. This PR updates it to use the existingdb_urifixture, which copies a session-scoped pre-migrated database instead.Changes:
tracking_uri_mockto depend ondb_uriinstead oftmp_pathdb_uri: str,request: pytest.FixtureRequest,-> Iterator[str | None]path_to_local_sqlite_uriimportPerformance impact:
cached_dbruns migrations once per test sessiondb_uricopies the cached database per testHow is this PR tested?
Verified with
tests/test_version.py,tests/tracking/test_rest_tracking.py, andtests/tracking/_model_registry/test_utils.py.Does this PR require documentation update?
Release Notes
Is this a user-facing change?
What component(s), interfaces, languages, and integrations does this PR affect?
Components
area/tracking: Tracking Service, tracking client APIs, autologgingarea/models: MLmodel format, model serialization/deserialization, flavorsarea/model-registry: Model Registry service, APIs, and the fluent client calls for Model Registryarea/scoring: MLflow Model server, model deployment tools, Spark UDFsarea/evaluation: MLflow model evaluation features, evaluation metrics, and evaluation workflowsarea/gateway: MLflow AI Gateway client APIs, server, and third-party integrationsarea/prompts: MLflow prompt engineering features, prompt templates, and prompt managementarea/tracing: MLflow Tracing features, tracing APIs, and LLM tracing functionalityarea/projects: MLproject format, project running backendsarea/uiux: Front-end, user experience, plotting, JavaScript, JavaScript dev serverarea/build: Build and test infrastructure for MLflowarea/docs: MLflow documentation pagesHow should the PR be classified in the release notes? Choose one:
rn/none- No description will be included. The PR will be mentioned only by the PR number in the "Small Bugfixes and Documentation Updates" sectionrn/breaking-change- The PR will be mentioned in the "Breaking Changes" sectionrn/feature- A new user-facing feature worth mentioning in the release notesrn/bug-fix- A user-facing bug fix worth mentioning in the release notesrn/documentation- A user-facing documentation change worth mentioning in the release notesShould this PR be included in the next patch release?
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