docs(samples): Update BigQuery Storage Arrow samples batching logic

[AlexZMLyu]

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> 🦕

[gemini-code-assist]

Summary of Changes

Hello @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 AppendRowsRequest payloads from exceeding BigQuery's internal 10MB size limit by implementing a size-aware batching strategy. This change improves the robustness and reliability of data ingestion when using PyArrow with BigQuery Storage.

Highlights

• Batching Logic Update: The generate_write_requests function in the BigQuery Storage Arrow sample has been updated to base batching on the serialized size of PyArrow record batches rather than row count. This ensures that individual AppendRowsRequest payloads do not exceed the 10MB internal limit, using a conservative 7MB soft limit.
• Improved Error Handling: A new check has been added to raise a ValueError if a single PyArrow batch containing just one row is larger than the maximum allowed request size (7MB), preventing potential issues with oversized individual records.

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[gemini-code-assist]

Code Review

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.