pabloem commented on a change in pull request #13170:
URL: https://github.com/apache/beam/pull/13170#discussion_r528029880
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File path: sdks/python/apache_beam/io/gcp/bigquery.py
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@@ -79,6 +79,41 @@
`ReadFromBigQuery`, you can use the flag `use_json_exports` to export
data as JSON, and receive base64-encoded bytes.
+ReadAllFromBigQuery
+-------------------
+Beam 2.27.0 introduces a new transform called `ReadAllFromBigQuery` which
+allows you to define table and query reads from BigQuery at pipeline
+runtime.:::
+
+ read_requests = p | beam.Create([
+ ReadFromBigQueryRequest(query='SELECT * FROM mydataset.mytable'),
+ ReadFromBigQueryRequest(table='myproject.mydataset.mytable')])
+ results = read_requests | ReadAllFromBigQuery()
+
+A good application for this transform is in streaming pipelines to
+refresh a side input coming from BigQuery. This would work like so:::
+
+ side_input = (
+ p
+ | 'PeriodicImpulse' >> PeriodicImpulse(
+ first_timestamp, last_timestamp, interval, True)
+ | 'MapToReadRequest' >> beam.Map(
+ lambda x: ReadFromBigQueryRequest(table='dataset.table'))
+ | beam.io.ReadAllFromBigQuery())
+ main_input = (
+ p
+ | 'MpImpulse' >> beam.Create(sample_main_input_elements)
+ |
+ 'MapMpToTimestamped' >> beam.Map(lambda src: TimestampedValue(src, src))
+ | 'WindowMpInto' >> beam.WindowInto(
+ window.FixedWindows(main_input_windowing_interval)))
+ result = (
+ main_input
+ | 'ApplyCrossJoin' >> beam.FlatMap(
+ cross_join, rights=beam.pvalue.AsIter(side_input)))
+
+**Note**: This transform is supported on Portable runners only.
Review comment:
yes, runner v2 supports this as well
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