docs(samples): Update BigQuery Storage Arrow samples batching logic #14961
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Bases batching on size rather than row count to avoid exceeding an internal 10MB limit. Also removes an obsolete assertion in the test.
Summary of ChangesHello @AlexZMLyu, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed! This pull request refactors the data batching mechanism within the BigQuery Storage Arrow sample. The primary goal is to prevent Highlights
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This pull request updates the batching logic in the BigQuery Storage Arrow sample to be based on byte size instead of row count. This is a great improvement as it prevents requests from exceeding the 10MB size limit. The new implementation is more robust. I've suggested a small refactoring to reduce code duplication, which will improve maintainability.
packages/google-cloud-bigquery-storage/samples/pyarrow/append_rows_with_arrow.py
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packages/google-cloud-bigquery-storage/samples/pyarrow/append_rows_with_arrow.py
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- Changes query to SELECT DISTINCT int64_col to count unique rows. - Asserts the count is exactly TABLE_LENGTH, removing the allowance for extra rows from potential retries.
parthea
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…ample - Updates batching logic to use serialized size to avoid exceeding API limits. - Ensures all rows in the PyArrow table are serialized for the request. - Includes enhancements for measuring serialized row sizes.
- Changed `generate_write_requests` to be a generator, yielding requests instead of returning a list. - Made `stream.send()` calls blocking by calling `future.result()` immediately, ensuring requests are sent sequentially.
packages/google-cloud-bigquery-storage/samples/pyarrow/append_rows_with_arrow.py
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Bases batching on size rather than row count to avoid exceeding an internal 10MB limit. Also removes an obsolete assertion in the test.
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Fixes #<issue_number_goes_here> 🦕