claudevdm commented on code in PR #38015:
URL: https://github.com/apache/beam/pull/38015#discussion_r3018590463
##########
sdks/python/apache_beam/io/gcp/bigquery_change_history.py:
##########
@@ -1170,16 +1315,46 @@ def expand(self, pbegin: beam.pvalue.PBegin) ->
beam.PCollection:
row_filter=self._row_filter))
| 'CommitQueryResults' >> beam.Reshuffle())
+ emit_raw = self._decompress_shards is not None
+
+ read_sdf = beam.ParDo(
+ _ReadStorageStreamsSDF(
+ batch_arrow_read=self._batch_arrow_read,
+ change_timestamp_column=self._change_timestamp_column,
+ max_split_rounds=self._max_split_rounds,
+ emit_raw_batches=emit_raw))
+ if emit_raw:
+ read_sdf = read_sdf.with_output_types(Tuple[bytes, bytes])
+ else:
+ read_sdf = read_sdf.with_output_types(Dict[str, Any])
+
read_outputs = (
query_results
- | 'ReadStorageStreams' >> beam.ParDo(
- _ReadStorageStreamsSDF(
- batch_arrow_read=self._batch_arrow_read,
- change_timestamp_column=self._change_timestamp_column)).
- with_outputs(_CLEANUP_TAG, main='rows'))
+ | 'ReadStorageStreams' >> read_sdf.with_outputs(
+ _CLEANUP_TAG, main='rows'))
_ = (
read_outputs[_CLEANUP_TAG]
| 'CleanupTempTables' >> beam.ParDo(_CleanupTempTablesFn()))
- return read_outputs['rows']
+ if emit_raw:
+ # Fan out raw Arrow batches across decompress_shards workers
+ # via GBK, then decompress and convert to timestamped row dicts.
+ # Uses a discarding trigger so GBK fires per-element without
+ # waiting for the GlobalWindow to close.
+ num_shards = self._decompress_shards
+ rows = (
+ read_outputs['rows']
+ | 'ShardBatches' >>
+ beam.WithKeys(lambda _, n=num_shards: random.randint(0, n - 1))
+ | 'WindowForGBK' >> beam.WindowInto(
+ GlobalWindows(),
+ trigger=beam_trigger.Repeatedly(beam_trigger.AfterCount(1)),
+ accumulation_mode=(beam_trigger.AccumulationMode.DISCARDING))
+ | 'GroupByShardKey' >> beam.GroupByKey()
Review Comment:
Less shuffle if the the pipeline can handle the load without fanning out.
But probably better to just fanout since compressed data is very small shuffle
load
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