echauchot commented on a change in pull request #11055: [BEAM-9436] Improve GBK
in spark structured streaming runner
URL: https://github.com/apache/beam/pull/11055#discussion_r397230829
##########
File path:
runners/spark/src/main/java/org/apache/beam/runners/spark/structuredstreaming/translation/batch/GroupByKeyTranslatorBatch.java
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@@ -53,50 +49,23 @@ public void translateTransform(
@SuppressWarnings("unchecked")
final PCollection<KV<K, V>> inputPCollection = (PCollection<KV<K, V>>)
context.getInput();
-
Dataset<WindowedValue<KV<K, V>>> input =
context.getDataset(inputPCollection);
-
WindowingStrategy<?, ?> windowingStrategy =
inputPCollection.getWindowingStrategy();
KvCoder<K, V> kvCoder = (KvCoder<K, V>) inputPCollection.getCoder();
+ Coder<V> valueCoder = kvCoder.getValueCoder();
// group by key only
Coder<K> keyCoder = kvCoder.getKeyCoder();
KeyValueGroupedDataset<K, WindowedValue<KV<K, V>>> groupByKeyOnly =
input.groupByKey(KVHelpers.extractKey(),
EncoderHelpers.fromBeamCoder(keyCoder));
- // Materialize groupByKeyOnly values, potential OOM because of creation of
new iterable
- Coder<V> valueCoder = kvCoder.getValueCoder();
- WindowedValue.WindowedValueCoder<V> wvCoder =
- WindowedValue.FullWindowedValueCoder.of(
- valueCoder,
inputPCollection.getWindowingStrategy().getWindowFn().windowCoder());
- IterableCoder<WindowedValue<V>> iterableCoder = IterableCoder.of(wvCoder);
- Dataset<KV<K, Iterable<WindowedValue<V>>>> materialized =
- groupByKeyOnly.mapGroups(
- (MapGroupsFunction<K, WindowedValue<KV<K, V>>, KV<K,
Iterable<WindowedValue<V>>>>)
- (key, iterator) -> {
- List<WindowedValue<V>> values = new ArrayList<>();
- while (iterator.hasNext()) {
- WindowedValue<KV<K, V>> next = iterator.next();
- values.add(
- WindowedValue.of(
- next.getValue().getValue(),
- next.getTimestamp(),
- next.getWindows(),
- next.getPane()));
- }
- KV<K, Iterable<WindowedValue<V>>> kv =
- KV.of(key, Iterables.unmodifiableIterable(values));
- return kv;
- },
- EncoderHelpers.fromBeamCoder(KvCoder.of(keyCoder, iterableCoder)));
-
// group also by windows
WindowedValue.FullWindowedValueCoder<KV<K, Iterable<V>>> outputCoder =
WindowedValue.FullWindowedValueCoder.of(
KvCoder.of(keyCoder, IterableCoder.of(valueCoder)),
windowingStrategy.getWindowFn().windowCoder());
Dataset<WindowedValue<KV<K, Iterable<V>>>> output =
- materialized.flatMap(
+ groupByKeyOnly.flatMapGroups(
Review comment:
GBK will always trigger a shuffle. dataset.gbk is a logical operation (see
catalyst). One needs to take at the physical plans. In the previous version the
physical exec plan showed that the shuffle occurred before the mapgroups. Now
it occurres before the flatmapGroups. The 30% gain (see numbers) of this change
resides in the fact that we previously had a mapgroups + flatmap and now we
have only one flatmapgroups + we no more have to instanciate KV pairs. Take a
look at the phusical plans if you need to be convinced
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