Databricks SDK for Python v0.143.0 Adds ML Feature Fields, Fixes Logging
The Databricks SDK for Python v0.143.0 has been released, introducing enhancements for Machine Learning features and addressing a critical logging configuration bug. This update adds `job_id` and `pipeline_id` fields to `MaterializedFeature`, providing more detailed metadata for ML operations. It also resolves an issue where importing `databricks.sdk.runtime` or `WorkspaceClient`/`dbutils` would silently override `logging.basicConfig()`, ensuring consistent logging behavior for developers. These changes primarily affect engineers working with Databricks ML features or those with custom logging setups in their Python applications.
- →Add job_id and pipeline_id fields for databricks.sdk.service.ml.MaterializedFeature
Enhancements (1) ›
- Add job_id and pipeline_id fields for databricks.sdk.service.ml.MaterializedFeature
Fixes (1) ›
Don't configure the root logger when importing databricks.sdk.runtime. Its import-time notebook-globals initialization logged through root-level logging helpers, which install a handler on the root logger when it has none. This also happened transitively through WorkspaceClient and dbutils, and made a later logging.basicConfig() a silent no-op. These messages now go through the SDK's databricks.sdk logger
https://github.com/databricks/databricks-sdk-py/releases/tag/v0.143.0
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