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https://issues.apache.org/jira/browse/FLINK-3477?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15301046#comment-15301046
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ASF GitHub Bot commented on FLINK-3477:
---------------------------------------
Github user fhueske commented on a diff in the pull request:
https://github.com/apache/flink/pull/1517#discussion_r64667048
--- Diff:
flink-java/src/main/java/org/apache/flink/api/java/operators/UnsortedGrouping.java
---
@@ -135,10 +136,37 @@ public UnsortedGrouping(DataSet<T> set, Keys<T> keys)
{
* @see DataSet
*/
public ReduceOperator<T> reduce(ReduceFunction<T> reducer) {
+ return reduce(Utils.getCallLocationName(), reducer,
CombineHint.OPTIMIZER_CHOOSES);
+ }
+
+ /**
+ * Applies a Reduce transformation on a grouped {@link DataSet}.<br>
+ * For each group, the transformation consecutively calls a {@link
org.apache.flink.api.common.functions.RichReduceFunction}
+ * until only a single element for each group remains.
+ * A ReduceFunction combines two elements into one new element of the
same type.
+ *
+ * @param reducer The ReduceFunction that is applied on each group of
the DataSet.
+ * @param strategy The strategy that should be used to execute the
combine phase of the reduce.
+ * If {@code null} is given, then the optimizer will
pick the strategy.
+ * @return A ReduceOperator that represents the reduced DataSet.
+ *
+ * @see org.apache.flink.api.common.functions.RichReduceFunction
+ * @see ReduceOperator
+ * @see DataSet
+ */
+ public ReduceOperator<T> reduce(ReduceFunction<T> reducer, CombineHint
strategy) {
--- End diff --
The `ReduceOperator.setCombineHint(CombineHint)` method should be annotated
with `@PublicEvolving`.
> Add hash-based combine strategy for ReduceFunction
> --------------------------------------------------
>
> Key: FLINK-3477
> URL: https://issues.apache.org/jira/browse/FLINK-3477
> Project: Flink
> Issue Type: Sub-task
> Components: Local Runtime
> Reporter: Fabian Hueske
> Assignee: Gabor Gevay
>
> This issue is about adding a hash-based combine strategy for ReduceFunctions.
> The interface of the {{reduce()}} method is as follows:
> {code}
> public T reduce(T v1, T v2)
> {code}
> Input type and output type are identical and the function returns only a
> single value. A Reduce function is incrementally applied to compute a final
> aggregated value. This allows to hold the preaggregated value in a hash-table
> and update it with each function call.
> The hash-based strategy requires special implementation of an in-memory hash
> table. The hash table should support in place updates of elements (if the
> updated value has the same size as the new value) but also appending updates
> with invalidation of the old value (if the binary length of the new value
> differs). The hash table needs to be able to evict and emit all elements if
> it runs out-of-memory.
> We should also add {{HASH}} and {{SORT}} compiler hints to
> {{DataSet.reduce()}} and {{Grouping.reduce()}} to allow users to pick the
> execution strategy.
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