Limit length of tag set by Spark datasource autologging #4809
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Signed-off-by: dbczumar <corey.zumar@databricks.com>
marijncv
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Sep 21, 2021
* Truncate dict Signed-off-by: dbczumar <corey.zumar@databricks.com> * Test case Signed-off-by: dbczumar <corey.zumar@databricks.com> * Docs Signed-off-by: dbczumar <corey.zumar@databricks.com> * Fix Signed-off-by: dbczumar <corey.zumar@databricks.com> * 150 Signed-off-by: dbczumar <corey.zumar@databricks.com> * +10 Signed-off-by: dbczumar <corey.zumar@databricks.com> * Fixes Signed-off-by: dbczumar <corey.zumar@databricks.com> * Fix Signed-off-by: dbczumar <corey.zumar@databricks.com> * Update __init__.py Signed-off-by: Marijn Valk <marijncv@hotmail.com>
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What changes are proposed in this pull request?
Limit length of tag set by Spark datasource autologging
How is this patch tested?
Unit test
Release Notes
Limit the length of the
sparkDatasourceInfotag set by Spark Datasource autologging to 5000 characters.Is this a user-facing change?
(Details in 1-2 sentences. You can just refer to another PR with a description if this PR is part of a larger change.)
What component(s), interfaces, languages, and integrations does this PR affect?
Components
area/artifacts: Artifact stores and artifact loggingarea/build: Build and test infrastructure for MLflowarea/docs: MLflow documentation pagesarea/examples: Example codearea/model-registry: Model Registry service, APIs, and the fluent client calls for Model Registryarea/models: MLmodel format, model serialization/deserialization, flavorsarea/projects: MLproject format, project running backendsarea/scoring: MLflow Model server, model deployment tools, Spark UDFsarea/server-infra: MLflow Tracking server backendarea/tracking: Tracking Service, tracking client APIs, autologgingInterface
area/uiux: Front-end, user experience, plotting, JavaScript, JavaScript dev serverarea/docker: Docker use across MLflow's components, such as MLflow Projects and MLflow Modelsarea/sqlalchemy: Use of SQLAlchemy in the Tracking Service or Model Registryarea/windows: Windows supportLanguage
language/r: R APIs and clientslanguage/java: Java APIs and clientslanguage/new: Proposals for new client languagesIntegrations
integrations/azure: Azure and Azure ML integrationsintegrations/sagemaker: SageMaker integrationsintegrations/databricks: Databricks integrationsHow should the PR be classified in the release notes? Choose one:
rn/breaking-change- The PR will be mentioned in the "Breaking Changes" sectionrn/none- No description will be included. The PR will be mentioned only by the PR number in the "Small Bugfixes and Documentation Updates" 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 notes