feat: support automatic per-cell execution history filtering and isolated callbacks#17144
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feat: support automatic per-cell execution history filtering and isolated callbacks#17144shuoweil wants to merge 21 commits into
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This pull request introduces a callback parameter to the _read_gbq_colab function, allowing users to receive query execution events. The changes include updates to the public API, the session implementation, and the addition of a unit test to verify the callback functionality. A review comment suggests refactoring the _read_gbq_colab method to eliminate code duplication in the query execution logic by using a local helper function.
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This change introduces scoped query tracking and event callback management for BigQuery DataFrames within interactive notebook environments (Jupyter/Colab).
Key Changes
Jupyter Cell Scoping: Resolves and carries the active IPython cell execution count (cell_execution_count) through TableWidget, ExecutionSpec, query executors, and final JobMetadata.
Execution History Filtering: Adds events, job_ids, and all_cells parameters to session.execution_history(). When all_cells=False, it filters query logs down to the current active notebook cell.
Scoped Callback Support: Adds a callback parameter to _read_gbq_colab that automatically subscribes to the query progress publisher during execution and automatically unsubscribes upon completion.
Robustness Fixes:
Verified at:
https://screencast.googleplex.com/cast/NjQzOTAzMTUwMzA2MDk5MnwzZWQ2MTMzYS0xYg
Colab notebook test: screen/7d6Yt3C28BUAKEH
Fixes #<513337964> 🦕