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https://issues.apache.org/jira/browse/BEAM-9748?focusedWorklogId=435753&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-435753
]
ASF GitHub Bot logged work on BEAM-9748:
----------------------------------------
Author: ASF GitHub Bot
Created on: 20/May/20 23:05
Start Date: 20/May/20 23:05
Worklog Time Spent: 10m
Work Description: jkff commented on a change in pull request #11406:
URL: https://github.com/apache/beam/pull/11406#discussion_r428356257
##########
File path:
sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/Reshuffle.java
##########
@@ -107,10 +108,57 @@ public void processElement(
/** Implementation of {@link #viaRandomKey()}. */
public static class ViaRandomKey<T> extends PTransform<PCollection<T>,
PCollection<T>> {
+ private boolean isHighFanoutAndLimitedInputParallelism;
+
private ViaRandomKey() {}
+ /**
+ * Use a different strategy that materializes the input and prepares it to
be consumed in a
+ * highly parallel fashion.
+ *
+ * <p>It is tailored to the case when input was produced in an extremely
sequential way -
+ * typically by a ParDo that emits millions of outputs _per input
element_, e.g., executing a
+ * large database query or a large simulation and emitting all of their
results.
+ *
+ * <p>Internally, it materializes the input at a moderate cost before
reshuffling it, making the
+ * reshuffling itself significantly cheaper in these extreme cases on some
runners. Use this
+ * only if your benchmarks show an improvement.
+ */
+ public ViaRandomKey<T> withHintHighFanoutAndLimitedInputParallelism() {
+ this.isHighFanoutAndLimitedInputParallelism = true;
+ return this;
+ }
+
@Override
public PCollection<T> expand(PCollection<T> input) {
+ if (isHighFanoutAndLimitedInputParallelism) {
+ // See https://issues.apache.org/jira/browse/BEAM-2803
+ // We use a combined approach to "break fusion" here:
+ // (see
https://cloud.google.com/dataflow/service/dataflow-service-desc#preventing-fusion)
+ // 1) force the data to be materialized by passing it as a side input
to an identity fn,
+ // then 2) reshuffle it with a random key. Initial materialization
provides some parallelism
+ // and ensures that data to be shuffled can be generated in parallel,
while reshuffling
+ // provides perfect parallelism.
+ // In most cases where a "fusion break" is needed, a simple reshuffle
would be sufficient.
+ // The current approach is necessary only to support the particular
case of JdbcIO where
+ // a single query may produce many gigabytes of query results.
+ PCollectionView<Iterable<T>> empty =
+ input
+ .apply("Consume",
Filter.by(SerializableFunctions.constant(false)))
+ .apply(View.asIterable());
+ PCollection<T> materialized =
+ input.apply(
+ "Identity",
+ ParDo.of(
Review comment:
nit: here you could use a `MapElements.via(Contextful.of(t -> t,
requiresSideInputs(empty)))`
##########
File path:
sdks/java/core/src/main/java/org/apache/beam/sdk/transforms/Reshuffle.java
##########
@@ -107,10 +108,57 @@ public void processElement(
/** Implementation of {@link #viaRandomKey()}. */
public static class ViaRandomKey<T> extends PTransform<PCollection<T>,
PCollection<T>> {
+ private boolean isHighFanoutAndLimitedInputParallelism;
Review comment:
Please use an immutable AutoValue with a builder here. PTransforms can't
have mutable member variables.
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Issue Time Tracking
-------------------
Worklog Id: (was: 435753)
Time Spent: 4h 40m (was: 4.5h)
> Refactor Reparallelize as an alternative Reshuffle implementation
> -----------------------------------------------------------------
>
> Key: BEAM-9748
> URL: https://issues.apache.org/jira/browse/BEAM-9748
> Project: Beam
> Issue Type: Improvement
> Components: sdk-java-core
> Reporter: Ismaël Mejía
> Assignee: Ismaël Mejía
> Priority: P3
> Time Spent: 4h 40m
> Remaining Estimate: 0h
>
> Some DoFn based IOs like JdbcIO and RedisIO rely on a different approach to
> Reparallelize outputs using a combination of a an empty PCollectionView to
> force materialization and Reshuffle.viaRandomkey to reparallelize a
> PCollection. This issue extracts this transform and expose it as part of the
> Reshuffle to avoid repeating the code for transforms (notably IOs) that
> produce lots of sequentially generated data where and benefit of this
> alternative approach to perform better reparallelization of its output.
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