only expand update graph if needed#38332
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Summary of ChangesHello, 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 introduces a performance optimization by conditionally expanding the update graph in the BigQuery Storage API conversion logic. By checking for the presence of schema update options before building the graph, the pipeline avoids unnecessary overhead when these features are not in use. Highlights
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FYI @ahmedabu98 |
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This pull request introduces a conditional check in StorageApiConvertMessages.java to skip the schema update graph expansion when hasSchemaUpdateOptions is false, optimizing performance when updates are unnecessary. The review feedback suggests using a guard clause to return early, which would simplify the code structure by reducing unnecessary nesting in the method implementation.
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| } else { | ||
| final int numShards = | ||
| input | ||
| .getPipeline() | ||
| .getOptions() | ||
| .as(BigQueryOptions.class) | ||
| .getSchemaUpgradeBufferingShards(); | ||
|
|
||
| final int numShards = | ||
| input | ||
| .getPipeline() | ||
| .getOptions() | ||
| .as(BigQueryOptions.class) | ||
| .getSchemaUpgradeBufferingShards(); | ||
| // Throttle the stream to the patch-table function so that only a single update per table per | ||
| // two seconds gets processed (to match quotas). The combiner merges incremental schemas, so | ||
| // we | ||
| // won't miss any updates. | ||
| PCollection<KV<ShardedKey<DestinationT>, ElementT>> tablesPatched = | ||
| result | ||
| .get(patchTableSchemaTag) | ||
| .apply( | ||
| "rewindow", | ||
| Window.<KV<DestinationT, TableSchema>>configure() | ||
| .triggering( | ||
| Repeatedly.forever( | ||
| AfterProcessingTime.pastFirstElementInPane() | ||
| .plusDelayOf(Duration.standardSeconds(2)))) | ||
| .discardingFiredPanes()) | ||
| .apply("merge schemas", Combine.fewKeys(new MergeSchemaCombineFn())) | ||
| .setCoder(KvCoder.of(destinationCoder, ProtoCoder.of(TableSchema.class))) | ||
| .apply( | ||
| "Patch table schema", | ||
| ParDo.of( | ||
| new PatchTableSchemaDoFn<>(operationName, bqServices, dynamicDestinations))) | ||
| .setCoder(KvCoder.of(destinationCoder, NullableCoder.of(elementCoder))) | ||
| // We need to make sure that all shards of the buffering transform are notified. | ||
| .apply( | ||
| "fanout to all shards", | ||
| FlatMapElements.via( | ||
| new SimpleFunction< | ||
| KV<DestinationT, ElementT>, | ||
| Iterable<KV<ShardedKey<DestinationT>, ElementT>>>() { | ||
| @Override | ||
| public Iterable<KV<ShardedKey<DestinationT>, ElementT>> apply( | ||
| KV<DestinationT, ElementT> elem) { | ||
| return IntStream.range(0, numShards) | ||
| .mapToObj( | ||
| i -> | ||
| KV.of( | ||
| StorageApiConvertMessages.AssignShardFn.getShardedKey( | ||
| elem.getKey(), i, numShards), | ||
| elem.getValue())) | ||
| .collect(Collectors.toList()); | ||
| } | ||
| })) | ||
| .setCoder( | ||
| KvCoder.of(ShardedKey.Coder.of(destinationCoder), NullableCoder.of(elementCoder))) | ||
| .apply( | ||
| Window.<KV<ShardedKey<DestinationT>, ElementT>>configure() | ||
| .triggering(DefaultTrigger.of())); | ||
|
|
||
| // Throttle the stream to the patch-table function so that only a single update per table per | ||
| // two seconds gets processed (to match quotas). The combiner merges incremental schemas, so we | ||
| // won't miss any updates. | ||
| PCollection<KV<ShardedKey<DestinationT>, ElementT>> tablesPatched = | ||
| result | ||
| .get(patchTableSchemaTag) | ||
| .apply( | ||
| "rewindow", | ||
| Window.<KV<DestinationT, TableSchema>>configure() | ||
| .triggering( | ||
| Repeatedly.forever( | ||
| AfterProcessingTime.pastFirstElementInPane() | ||
| .plusDelayOf(Duration.standardSeconds(2)))) | ||
| .discardingFiredPanes()) | ||
| .apply("merge schemas", Combine.fewKeys(new MergeSchemaCombineFn())) | ||
| .setCoder(KvCoder.of(destinationCoder, ProtoCoder.of(TableSchema.class))) | ||
| .apply( | ||
| "Patch table schema", | ||
| ParDo.of( | ||
| new PatchTableSchemaDoFn<>(operationName, bqServices, dynamicDestinations))) | ||
| .setCoder(KvCoder.of(destinationCoder, NullableCoder.of(elementCoder))) | ||
| // We need to make sure that all shards of the buffering transform are notified. | ||
| .apply( | ||
| "fanout to all shards", | ||
| FlatMapElements.via( | ||
| new SimpleFunction< | ||
| KV<DestinationT, ElementT>, | ||
| Iterable<KV<ShardedKey<DestinationT>, ElementT>>>() { | ||
| @Override | ||
| public Iterable<KV<ShardedKey<DestinationT>, ElementT>> apply( | ||
| KV<DestinationT, ElementT> elem) { | ||
| return IntStream.range(0, numShards) | ||
| .mapToObj( | ||
| i -> | ||
| KV.of( | ||
| StorageApiConvertMessages.AssignShardFn.getShardedKey( | ||
| elem.getKey(), i, numShards), | ||
| elem.getValue())) | ||
| .collect(Collectors.toList()); | ||
| } | ||
| })) | ||
| .setCoder( | ||
| KvCoder.of(ShardedKey.Coder.of(destinationCoder), NullableCoder.of(elementCoder))) | ||
| .apply( | ||
| Window.<KV<ShardedKey<DestinationT>, ElementT>>configure() | ||
| .triggering(DefaultTrigger.of())); | ||
| // Any elements that are waiting for a schema update are sent to this stateful DoFn to be | ||
| // buffered. | ||
| // Note: we currently do not provide the DynamicDestinations object access to the side input | ||
| // in | ||
| // this path. | ||
| // This is because side inputs are not currently available from timer callbacks. Since side | ||
| // inputs are generally | ||
| // used for getSchema and in this case we read the schema from the table, this is unlikely to | ||
| // be | ||
| // a problem. | ||
| PCollection<KV<ShardedKey<DestinationT>, ElementT>> shardedWaitingElements = | ||
| result | ||
| .get(elementsWaitingForSchemaTag) | ||
| // TODO: Consider using GroupIntoBatchs.withShardingKey to get auto sharding here | ||
| // instead of fixed sharding. | ||
| .apply("assignShard", ParDo.of(new AssignShardFn<>(numShards))) | ||
| .setCoder( | ||
| KvCoder.of( | ||
| ShardedKey.Coder.of(destinationCoder), NullableCoder.of(elementCoder))); | ||
|
|
||
| // Any elements that are waiting for a schema update are sent to this stateful DoFn to be | ||
| // buffered. | ||
| // Note: we currently do not provide the DynamicDestinations object access to the side input in | ||
| // this path. | ||
| // This is because side inputs are not currently available from timer callbacks. Since side | ||
| // inputs are generally | ||
| // used for getSchema and in this case we read the schema from the table, this is unlikely to be | ||
| // a problem. | ||
| PCollection<KV<ShardedKey<DestinationT>, ElementT>> shardedWaitingElements = | ||
| result | ||
| .get(elementsWaitingForSchemaTag) | ||
| // TODO: Consider using GroupIntoBatchs.withShardingKey to get auto sharding here | ||
| // instead of fixed sharding. | ||
| .apply("assignShard", ParDo.of(new AssignShardFn<>(numShards))) | ||
| .setCoder( | ||
| KvCoder.of(ShardedKey.Coder.of(destinationCoder), NullableCoder.of(elementCoder))); | ||
| PCollectionList<KV<ShardedKey<DestinationT>, ElementT>> waitingElementsList = | ||
| PCollectionList.of(shardedWaitingElements).and(tablesPatched); | ||
| PCollectionTuple retryResult = | ||
| waitingElementsList | ||
| .apply("Buffered flatten", Flatten.pCollections()) | ||
| .apply( | ||
| "bufferElements", | ||
| ParDo.of(new SchemaUpdateHoldingFn<>(elementCoder, convertMessagesDoFn)) | ||
| .withOutputTags( | ||
| successfulWritesTag, | ||
| TupleTagList.of(ImmutableList.of(failedWritesTag, BAD_RECORD_TAG)))); | ||
| retryResult.get(successfulWritesTag).setCoder(successCoder); | ||
| retryResult.get(failedWritesTag).setCoder(errorCoder); | ||
| retryResult.get(BAD_RECORD_TAG).setCoder(BadRecord.getCoder(input.getPipeline())); | ||
|
|
||
| PCollectionList<KV<ShardedKey<DestinationT>, ElementT>> waitingElementsList = | ||
| PCollectionList.of(shardedWaitingElements).and(tablesPatched); | ||
| PCollectionTuple retryResult = | ||
| waitingElementsList | ||
| .apply("Buffered flatten", Flatten.pCollections()) | ||
| .apply( | ||
| "bufferElements", | ||
| ParDo.of(new SchemaUpdateHoldingFn<>(elementCoder, convertMessagesDoFn)) | ||
| .withOutputTags( | ||
| successfulWritesTag, | ||
| TupleTagList.of(ImmutableList.of(failedWritesTag, BAD_RECORD_TAG)))); | ||
| retryResult.get(successfulWritesTag).setCoder(successCoder); | ||
| retryResult.get(failedWritesTag).setCoder(errorCoder); | ||
| retryResult.get(BAD_RECORD_TAG).setCoder(BadRecord.getCoder(input.getPipeline())); | ||
|
|
||
| // Flatten successes and failures from both the regular transform and the retry transform. | ||
| PCollection<KV<DestinationT, StorageApiWritePayload>> allSuccesses = | ||
| PCollectionList.of(result.get(successfulWritesTag)) | ||
| .and(retryResult.get(successfulWritesTag)) | ||
| .apply("flattenSuccesses", Flatten.pCollections()); | ||
| PCollection<BigQueryStorageApiInsertError> allFailures = | ||
| PCollectionList.of(result.get(failedWritesTag)) | ||
| .and(retryResult.get(failedWritesTag)) | ||
| .apply("flattenFailures", Flatten.pCollections()); | ||
| PCollection<BadRecord> allBadRecords = | ||
| PCollectionList.of(result.get(BAD_RECORD_TAG)) | ||
| .and(retryResult.get(BAD_RECORD_TAG)) | ||
| .apply("flattenBadRecords", Flatten.pCollections()); | ||
| return PCollectionTuple.of(successfulWritesTag, allSuccesses) | ||
| .and(failedWritesTag, allFailures) | ||
| .and(BAD_RECORD_TAG, allBadRecords); | ||
| // Flatten successes and failures from both the regular transform and the retry transform. | ||
| PCollection<KV<DestinationT, StorageApiWritePayload>> allSuccesses = | ||
| PCollectionList.of(result.get(successfulWritesTag)) | ||
| .and(retryResult.get(successfulWritesTag)) | ||
| .apply("flattenSuccesses", Flatten.pCollections()); | ||
| PCollection<BigQueryStorageApiInsertError> allFailures = | ||
| PCollectionList.of(result.get(failedWritesTag)) | ||
| .and(retryResult.get(failedWritesTag)) | ||
| .apply("flattenFailures", Flatten.pCollections()); | ||
| PCollection<BadRecord> allBadRecords = | ||
| PCollectionList.of(result.get(BAD_RECORD_TAG)) | ||
| .and(retryResult.get(BAD_RECORD_TAG)) | ||
| .apply("flattenBadRecords", Flatten.pCollections()); | ||
| return PCollectionTuple.of(successfulWritesTag, allSuccesses) | ||
| .and(failedWritesTag, allFailures) | ||
| .and(BAD_RECORD_TAG, allBadRecords); | ||
| } |
Contributor
There was a problem hiding this comment.
To improve readability and reduce nesting, you can use a guard clause. Instead of wrapping the rest of the method in an else block, you can return early and then have the main logic at the top level of the method.
final int numShards =
input
.getPipeline()
.getOptions()
.as(BigQueryOptions.class)
.getSchemaUpgradeBufferingShards();
// Throttle the stream to the patch-table function so that only a single update per table per
// two seconds gets processed (to match quotas). The combiner merges incremental schemas, so
// we
// won't miss any updates.
PCollection<KV<ShardedKey<DestinationT>, ElementT>> tablesPatched =
result
.get(patchTableSchemaTag)
.apply(
"rewindow",
Window.<KV<DestinationT, TableSchema>>configure()
.triggering(
Repeatedly.forever(
AfterProcessingTime.pastFirstElementInPane()
.plusDelayOf(Duration.standardSeconds(2))))
.discardingFiredPanes())
.apply("merge schemas", Combine.fewKeys(new MergeSchemaCombineFn()))
.setCoder(KvCoder.of(destinationCoder, ProtoCoder.of(TableSchema.class)))
.apply(
"Patch table schema",
ParDo.of(
new PatchTableSchemaDoFn<>(operationName, bqServices, dynamicDestinations)))
.setCoder(KvCoder.of(destinationCoder, NullableCoder.of(elementCoder)))
// We need to make sure that all shards of the buffering transform are notified.
.apply(
"fanout to all shards",
FlatMapElements.via(
new SimpleFunction<
KV<DestinationT, ElementT>,
Iterable<KV<ShardedKey<DestinationT>, ElementT>>>() {
@Override
public Iterable<KV<ShardedKey<DestinationT>, ElementT>> apply(
KV<DestinationT, ElementT> elem) {
return IntStream.range(0, numShards)
.mapToObj(
i ->
KV.of(
StorageApiConvertMessages.AssignShardFn.getShardedKey(
elem.getKey(), i, numShards),
elem.getValue()))
.collect(Collectors.toList());
}
}))
.setCoder(
KvCoder.of(ShardedKey.Coder.of(destinationCoder), NullableCoder.of(elementCoder)))
.apply(
Window.<KV<ShardedKey<DestinationT>, ElementT>>configure()
.triggering(DefaultTrigger.of()));
// Any elements that are waiting for a schema update are sent to this stateful DoFn to be
// buffered.
// Note: we currently do not provide the DynamicDestinations object access to the side input
// in
// this path.
// This is because side inputs are not currently available from timer callbacks. Since side
// inputs are generally
// used for getSchema and in this case we read the schema from the table, this is unlikely to
// be
// a problem.
PCollection<KV<ShardedKey<DestinationT>, ElementT>> shardedWaitingElements =
result
.get(elementsWaitingForSchemaTag)
// TODO: Consider using GroupIntoBatchs.withShardingKey to get auto sharding here
// instead of fixed sharding.
.apply("assignShard", ParDo.of(new AssignShardFn<>(numShards)))
.setCoder(
KvCoder.of(
ShardedKey.Coder.of(destinationCoder), NullableCoder.of(elementCoder)));
PCollectionList<KV<ShardedKey<DestinationT>, ElementT>> waitingElementsList =
PCollectionList.of(shardedWaitingElements).and(tablesPatched);
PCollectionTuple retryResult =
waitingElementsList
.apply("Buffered flatten", Flatten.pCollections())
.apply(
"bufferElements",
ParDo.of(new SchemaUpdateHoldingFn<>(elementCoder, convertMessagesDoFn))
.withOutputTags(
successfulWritesTag,
TupleTagList.of(ImmutableList.of(failedWritesTag, BAD_RECORD_TAG))));
retryResult.get(successfulWritesTag).setCoder(successCoder);
retryResult.get(failedWritesTag).setCoder(errorCoder);
retryResult.get(BAD_RECORD_TAG).setCoder(BadRecord.getCoder(input.getPipeline()));
// Flatten successes and failures from both the regular transform and the retry transform.
PCollection<KV<DestinationT, StorageApiWritePayload>> allSuccesses =
PCollectionList.of(result.get(successfulWritesTag))
.and(retryResult.get(successfulWritesTag))
.apply("flattenSuccesses", Flatten.pCollections());
PCollection<BigQueryStorageApiInsertError> allFailures =
PCollectionList.of(result.get(failedWritesTag))
.and(retryResult.get(failedWritesTag))
.apply("flattenFailures", Flatten.pCollections());
PCollection<BadRecord> allBadRecords =
PCollectionList.of(result.get(BAD_RECORD_TAG))
.and(retryResult.get(BAD_RECORD_TAG))
.apply("flattenBadRecords", Flatten.pCollections());
return PCollectionTuple.of(successfulWritesTag, allSuccesses)
.and(failedWritesTag, allFailures)
.and(BAD_RECORD_TAG, allBadRecords);
3 tasks
Contributor
Author
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Adding @ahmedabu98 as a reviewer, since @Abacn will be out of office. |
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