feat(bigquery): Support queryResultsFormat and compressionCodec in query_and_wait()

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Assessment

Difficulty
4/5
Estimated time
3-5 days
Newbie friendliness
52/100
Issue type
Feature
Clarity
Mostly clear
Activity status
Quiet
Tech stack
python
Domain
api, data

Research direction

Start at Client.query_and_wait(), then trace _job_helpers.py for the BigQuery request payload and table.py for RowIterator and Arrow parsing. Compare the related PR 18027 and verify that both options are accepted, sent to the API, and that compressed Arrow results support the stated to_arrow() and to_dataframe() use cases.

Written by the indexing model from the issue text.

Description

api: bigquery priority: p3 type: feature request
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Summary of the feature request
Feature Description

Add support for query_results_format (e.g., "ARROW") and compression_codec (e.g., "LZ4_FRAME") in Client.query_and_wait() and related job helper functions in google-cloud-bigquery.

Use Case & Problem Statement

Currently, Client.query_and_wait() executes a query and fetches results, but does not allow callers to request the underlying query results format as Apache Arrow streams or specify compression options directly during the fetch process.

For high-performance data processing pipelines using Apache Arrow or PyArrow, requesting query results in Arrow format with compression significantly reduces network payload size, deserialization overhead, and total query result fetch latency.

Proposed Solution
  1. Update google.cloud.bigquery.client.Client.query_and_wait() to accept optional parameters:
    • query_results_format: Optional string specifying result serialization format (e.g. "ARROW").
    • compression_codec: Optional string specifying compression algorithm
  2. Update underlying helper functions in _job_helpers.py to populate these options in the request payload sent to the BigQuery API.
  3. Update RowIterator and Arrow parsing logic in table.py to seamlessly handle Arrow record batch streams when query_results_format="ARROW" is specified.
Alternative Approaches Considered
  • Manually constructing custom QueryJob instances and extracting Arrow record batches post-execution, which requires boilerplate code and loses the convenience of query_and_wait().
Additional Context

Related PR: https://github.com/googleapis/google-cloud-python/pull/18027

Desired code experience

from google.cloud import bigquery

client = bigquery.Client()

query = "SELECT * FROM bigquery-public-data.usa_names.usa_1910_2013 LIMIT 10000"

Request query results directly in Apache Arrow format with LZ4_FRAME compression

results = client.query_and_wait(
query,
query_results_format="ARROW",
compression_codec="LZ4_FRAME",
)

Convert directly to PyArrow Table or Pandas DataFrame

arrow_table = results.to_arrow()
df = results.to_dataframe()

Expected results
  1. Client.query_and_wait() accepts query_results_format and compression_codec parameters.
  2. The BigQuery API receives these parameters in the job execution request.
  3. Query results are returned and parsed efficiently via compressed Apache Arrow streams into the resulting RowIterator / Arrow table without client errors.
API client name and version

No response

Use case

e.g. This feature would be useful in a scenario where I need to process large amounts of data efficiently.

Additional context

https://github.com/googleapis/google-cloud-python/pull/18027

Dominant language
Python
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