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Posted to github@beam.apache.org by "atognolas (via GitHub)" <gi...@apache.org> on 2026/06/29 17:30:20 UTC
[PR] [IcebergIO] Use GroupByKey for bounded inputs to avoid OOM [beam]
atognolas opened a new pull request, #39156:
URL: https://github.com/apache/beam/pull/39156
## Summary
- Replace `GroupIntoBatches` with `GroupByKey` for bounded (batch) inputs in `WriteToPartitions`
- Keep `GroupIntoBatches` only for unbounded (streaming) inputs where triggering semantics and optional `autoSharding` (via `withShardedKey`) are needed
- `GroupIntoBatches.ofByteSize()` materializes all values for a key into a Java `List` in memory — `RowCoder` reports wire size (~500 bytes/row) but actual heap is ~10× higher for nested schemas
## Motivation
Observed OOM on production pipelines with nested schemas (99 columns, 3 protobuf structs):
- `n4-standard-16` (64GB): GC thrashing at 99.79%, SDK harness killed
- `n4-highmem-48` (384GB): 277GB/377GB heap used for a single batch
`GroupByKey` returns a lazy `Iterable` backed by the shuffle service, consuming constant ~100MB memory regardless of partition size.
## Impact
- **Batch**: lazy iteration, O(100MB) memory regardless of shard size
- **Streaming**: unchanged — `GroupIntoBatches` with triggering + optional `autoSharding`
- `autoSharding` only meaningful for streaming (Dataflow dynamic key splitting)
## Test plan
- [ ] Existing `WriteToPartitionsTest` passes
- [ ] Run IcebergIO integration tests for both batch and streaming
- [ ] Verify no OOM on large-partition batch pipelines
🤖 Generated with [Claude Code](https://claude.com/claude-code)
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Re: [PR] [IcebergIO] Use GroupByKey for bounded inputs to avoid OOM [beam]
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Re: [PR] [IcebergIO] Use GroupByKey for bounded inputs to avoid OOM [beam]
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Re: [PR] [IcebergIO] Use GroupByKey for bounded inputs to avoid OOM [beam]
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Re: [PR] [IcebergIO] Use GroupByKey for bounded inputs to avoid OOM [beam]
Posted by "atognolas (via GitHub)" <gi...@apache.org>.
atognolas commented on PR #39156:
URL: https://github.com/apache/beam/pull/39156#issuecomment-4835312947
@ahmedabu98 — Would you mind reviewing this? These are a set of IcebergIO fixes validated on production-scale benchmarks (39M users, 400 partitions, 99-column schema). The four PRs are independent and can be reviewed/merged in any order.
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Re: [PR] [IcebergIO] Use GroupByKey for bounded inputs to avoid OOM [beam]
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Re: [PR] [IcebergIO] Use GroupByKey for bounded inputs to avoid OOM [beam]
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Re: [PR] [IcebergIO] Use GroupByKey for bounded inputs to avoid OOM [beam]
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Re: [PR] [IcebergIO] Use GroupByKey for bounded inputs to avoid OOM [beam]
Posted by "github-actions[bot] (via GitHub)" <gi...@apache.org>.
github-actions[bot] commented on PR #39156:
URL: https://github.com/apache/beam/pull/39156#issuecomment-5104337732
Reminder, please take a look at this pr: @ahmedabu98
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Re: [PR] [IcebergIO] Use GroupByKey for bounded inputs to avoid OOM [beam]
Posted by "ahmedabu98 (via GitHub)" <gi...@apache.org>.
ahmedabu98 commented on PR #39156:
URL: https://github.com/apache/beam/pull/39156#issuecomment-6353847615
Hey @atognolas, sorry for the slow turnaround on this.
I think we still need to address the batch-with-autosharding case (see [comment above](https://github.com/apache/beam/pull/39156#discussion_r3494205093)).
If a user has a batch pipeline and sets `autosharding=true`, we should honor that and use `GroupIntoBatches.withShardedKey()`. Your change overrides to use GroupByKey in all cases.
Regarding @bvolpato's [comment](https://github.com/apache/beam/pull/39156#pullrequestreview-5003252448) above about letting the writer roll over to new files when it hits a byte size limit, I opened a PR some time ago to do exactly this but it's gone stale: https://github.com/apache/beam/pull/38930
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Re: [PR] [IcebergIO] Use GroupByKey for bounded inputs to avoid OOM [beam]
Posted by "gemini-code-assist[bot] (via GitHub)" <gi...@apache.org>.
gemini-code-assist[bot] commented on PR #39156:
URL: https://github.com/apache/beam/pull/39156#issuecomment-4835285782
## Summary of Changes
Hello, I'm Gemini Code Assist[^1]! 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 optimizes memory usage in the IcebergIO connector by switching from GroupIntoBatches to GroupByKey for batch processing. By leveraging the shuffle service's lazy iteration, the connector now maintains a constant memory footprint regardless of partition size, effectively resolving OOM issues observed in production pipelines with complex nested schemas.
### Highlights
* **Performance Optimization**: Replaced GroupIntoBatches with GroupByKey for bounded (batch) inputs to prevent OutOfMemory (OOM) errors caused by materializing large batches in memory.
* **Conditional Logic**: Maintained GroupIntoBatches specifically for unbounded (streaming) inputs to preserve necessary triggering semantics and auto-sharding capabilities.
<details>
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Re: [PR] [IcebergIO] Use GroupByKey for bounded inputs to avoid OOM [beam]
Posted by "ahmedabu98 (via GitHub)" <gi...@apache.org>.
ahmedabu98 commented on code in PR #39156:
URL: https://github.com/apache/beam/pull/39156#discussion_r3494205093
##########
sdks/java/io/iceberg/src/main/java/org/apache/beam/sdk/io/iceberg/WriteToPartitions.java:
##########
@@ -64,27 +65,32 @@ private PCollection<KV<Row, Iterable<Row>>> groupByPartition(PCollection<KV<Row,
RowCoder destinationCoder = RowCoder.of(AssignDestinationsAndPartitions.OUTPUT_SCHEMA);
RowCoder dataCoder = RowCoder.of(dynamicDestinations.getDataSchema());
- GroupIntoBatches<Row, Row> groupIntoPartitions =
- GroupIntoBatches.ofByteSize(DEFAULT_BYTES_PER_FILE);
- if (IcebergUtils.isUnbounded(input) && triggeringFrequency != null) {
- groupIntoPartitions = groupIntoPartitions.withMaxBufferingDuration(triggeringFrequency);
- }
+ if (IcebergUtils.isUnbounded(input)) {
+ GroupIntoBatches<Row, Row> groupIntoPartitions =
+ GroupIntoBatches.ofByteSize(DEFAULT_BYTES_PER_FILE);
+ if (triggeringFrequency != null) {
+ groupIntoPartitions = groupIntoPartitions.withMaxBufferingDuration(triggeringFrequency);
+ }
- if (autoSharding) {
+ if (autoSharding) {
+ return input
+ .apply(groupIntoPartitions.withShardedKey())
+ .setCoder(
+ KvCoder.of(
+ org.apache.beam.sdk.util.ShardedKey.Coder.of(destinationCoder),
+ IterableCoder.of(dataCoder)))
+ .apply(
+ "DropShardId",
+ MapElements.into(kvs(rows(), iterables(rows())))
+ .via(kv -> KV.of(kv.getKey().getKey(), kv.getValue())))
+ .setCoder(KvCoder.of(destinationCoder, IterableCoder.of(dataCoder)));
+ }
return input
- .apply(groupIntoPartitions.withShardedKey())
- .setCoder(
- KvCoder.of(
- org.apache.beam.sdk.util.ShardedKey.Coder.of(destinationCoder),
- IterableCoder.of(dataCoder)))
- .apply(
- "DropShardId",
- MapElements.into(kvs(rows(), iterables(rows())))
- .via(kv -> KV.of(kv.getKey().getKey(), kv.getValue())))
+ .apply(groupIntoPartitions)
.setCoder(KvCoder.of(destinationCoder, IterableCoder.of(dataCoder)));
} else {
return input
- .apply(groupIntoPartitions)
+ .apply(GroupByKey.create())
.setCoder(KvCoder.of(destinationCoder, IterableCoder.of(dataCoder)));
}
Review Comment:
@atognolas I think this is a valid point.
Were you seeing OOMs even with autosharding enabled? AFAIK autosharding should distribute to different keys, so the state for any one key shouldn't get too large.
If it makes sense, we can keep the GroupIntoBatches.withShardedKey() path for autosharding batch writes.
Otherwise maybe we can add a `num_shards` config to do fixed sharding per partition, and stick with GBK
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Re: [PR] [IcebergIO] Use GroupByKey for bounded inputs to avoid OOM [beam]
Posted by "github-actions[bot] (via GitHub)" <gi...@apache.org>.
github-actions[bot] commented on PR #39156:
URL: https://github.com/apache/beam/pull/39156#issuecomment-4925479297
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Re: [PR] [IcebergIO] Use GroupByKey for bounded inputs to avoid OOM [beam]
Posted by "github-actions[bot] (via GitHub)" <gi...@apache.org>.
github-actions[bot] commented on PR #39156:
URL: https://github.com/apache/beam/pull/39156#issuecomment-5226062640
Reminder, please take a look at this pr: @chamikaramj
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Re: [PR] [IcebergIO] Use GroupByKey for bounded inputs to avoid OOM [beam]
Posted by "gemini-code-assist[bot] (via GitHub)" <gi...@apache.org>.
gemini-code-assist[bot] commented on code in PR #39156:
URL: https://github.com/apache/beam/pull/39156#discussion_r3493578375
##########
sdks/java/io/iceberg/src/main/java/org/apache/beam/sdk/io/iceberg/WriteToPartitions.java:
##########
@@ -64,27 +65,32 @@ private PCollection<KV<Row, Iterable<Row>>> groupByPartition(PCollection<KV<Row,
RowCoder destinationCoder = RowCoder.of(AssignDestinationsAndPartitions.OUTPUT_SCHEMA);
RowCoder dataCoder = RowCoder.of(dynamicDestinations.getDataSchema());
- GroupIntoBatches<Row, Row> groupIntoPartitions =
- GroupIntoBatches.ofByteSize(DEFAULT_BYTES_PER_FILE);
- if (IcebergUtils.isUnbounded(input) && triggeringFrequency != null) {
- groupIntoPartitions = groupIntoPartitions.withMaxBufferingDuration(triggeringFrequency);
- }
+ if (IcebergUtils.isUnbounded(input)) {
+ GroupIntoBatches<Row, Row> groupIntoPartitions =
+ GroupIntoBatches.ofByteSize(DEFAULT_BYTES_PER_FILE);
+ if (triggeringFrequency != null) {
+ groupIntoPartitions = groupIntoPartitions.withMaxBufferingDuration(triggeringFrequency);
+ }
- if (autoSharding) {
+ if (autoSharding) {
+ return input
+ .apply(groupIntoPartitions.withShardedKey())
+ .setCoder(
+ KvCoder.of(
+ org.apache.beam.sdk.util.ShardedKey.Coder.of(destinationCoder),
+ IterableCoder.of(dataCoder)))
+ .apply(
+ "DropShardId",
+ MapElements.into(kvs(rows(), iterables(rows())))
+ .via(kv -> KV.of(kv.getKey().getKey(), kv.getValue())))
+ .setCoder(KvCoder.of(destinationCoder, IterableCoder.of(dataCoder)));
+ }
return input
- .apply(groupIntoPartitions.withShardedKey())
- .setCoder(
- KvCoder.of(
- org.apache.beam.sdk.util.ShardedKey.Coder.of(destinationCoder),
- IterableCoder.of(dataCoder)))
- .apply(
- "DropShardId",
- MapElements.into(kvs(rows(), iterables(rows())))
- .via(kv -> KV.of(kv.getKey().getKey(), kv.getValue())))
+ .apply(groupIntoPartitions)
.setCoder(KvCoder.of(destinationCoder, IterableCoder.of(dataCoder)));
} else {
return input
- .apply(groupIntoPartitions)
+ .apply(GroupByKey.create())
.setCoder(KvCoder.of(destinationCoder, IterableCoder.of(dataCoder)));
}
Review Comment:
Silently ignoring `autoSharding` for bounded inputs can lead to severe performance regressions (such as hot key bottlenecks) for batch pipelines that explicitly enabled this option to parallelize writes to large partitions.
We should preserve the `GroupIntoBatches` path with sharding when `autoSharding` is explicitly enabled, even for bounded inputs, while defaulting to the memory-efficient `GroupByKey` when `autoSharding` is disabled.
```java
if (IcebergUtils.isUnbounded(input) || autoSharding) {
GroupIntoBatches<Row, Row> groupIntoPartitions =
GroupIntoBatches.ofByteSize(DEFAULT_BYTES_PER_FILE);
if (IcebergUtils.isUnbounded(input) && triggeringFrequency != null) {
groupIntoPartitions = groupIntoPartitions.withMaxBufferingDuration(triggeringFrequency);
}
if (autoSharding) {
return input
.apply(groupIntoPartitions.withShardedKey())
.setCoder(
KvCoder.of(
org.apache.beam.sdk.util.ShardedKey.Coder.of(destinationCoder),
IterableCoder.of(dataCoder)))
.apply(
"DropShardId",
MapElements.into(kvs(rows(), iterables(rows())))
.via(kv -> KV.of(kv.getKey().getKey(), kv.getValue())))
.setCoder(KvCoder.of(destinationCoder, IterableCoder.of(dataCoder)));
}
return input
.apply(groupIntoPartitions)
.setCoder(KvCoder.of(destinationCoder, IterableCoder.of(dataCoder)));
} else {
return input
.apply(GroupByKey.create())
.setCoder(KvCoder.of(destinationCoder, IterableCoder.of(dataCoder)));
}
```
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Re: [PR] [IcebergIO] Use GroupByKey for bounded inputs to avoid OOM [beam]
Posted by "github-actions[bot] (via GitHub)" <gi...@apache.org>.
github-actions[bot] commented on PR #39156:
URL: https://github.com/apache/beam/pull/39156#issuecomment-5003341470
Reminder, please take a look at this pr: @chamikaramj
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Re: [PR] [IcebergIO] Use GroupByKey for bounded inputs to avoid OOM [beam]
Posted by "github-actions[bot] (via GitHub)" <gi...@apache.org>.
github-actions[bot] commented on PR #39156:
URL: https://github.com/apache/beam/pull/39156#issuecomment-5034124309
Assigning new set of reviewers because Pr has gone too long without review. If you would like to opt out of this review, comment `assign to next reviewer`:
R: @ahmedabu98 for label java.
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Re: [PR] [IcebergIO] Use GroupByKey for bounded inputs to avoid OOM [beam]
Posted by "kennknowles (via GitHub)" <gi...@apache.org>.
kennknowles commented on PR #39156:
URL: https://github.com/apache/beam/pull/39156#issuecomment-5590947766
R: @ahmedabu98
The best reviewer for Iceberg stuff.
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Re: [PR] [IcebergIO] Use GroupByKey for bounded inputs to avoid OOM [beam]
Posted by "github-actions[bot] (via GitHub)" <gi...@apache.org>.
github-actions[bot] commented on PR #39156:
URL: https://github.com/apache/beam/pull/39156#issuecomment-4904072486
Reminder, please take a look at this pr: @ahmedabu98
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Re: [PR] [IcebergIO] Use GroupByKey for bounded inputs to avoid OOM [beam]
Posted by "github-actions[bot] (via GitHub)" <gi...@apache.org>.
github-actions[bot] commented on PR #39156:
URL: https://github.com/apache/beam/pull/39156#issuecomment-5509595347
Reminder, please take a look at this pr: @ahmedabu98
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Re: [PR] [IcebergIO] Use GroupByKey for bounded inputs to avoid OOM [beam]
Posted by "github-actions[bot] (via GitHub)" <gi...@apache.org>.
github-actions[bot] commented on PR #39156:
URL: https://github.com/apache/beam/pull/39156#issuecomment-5355759228
Reminder, please take a look at this pr: @Abacn
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Re: [PR] [IcebergIO] Use GroupByKey for bounded inputs to avoid OOM [beam]
Posted by "ahmedabu98 (via GitHub)" <gi...@apache.org>.
ahmedabu98 commented on PR #39156:
URL: https://github.com/apache/beam/pull/39156#issuecomment-6353851585
waiting on author
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