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https://issues.apache.org/jira/browse/FLINK-3477?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15368461#comment-15368461
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ASF GitHub Bot commented on FLINK-3477:
---------------------------------------
Github user greghogan commented on a diff in the pull request:
https://github.com/apache/flink/pull/1517#discussion_r70139954
--- Diff:
flink-scala/src/main/scala/org/apache/flink/api/scala/GroupedDataSet.scala ---
@@ -282,27 +283,57 @@ class GroupedDataSet[T: ClassTag](
}
/**
- * Creates a new [[DataSet]] by merging the elements of each group
(elements with the same key)
- * using an associative reduce function.
- */
+ * Creates a new [[DataSet]] by merging the elements of each group
(elements with the same key)
+ * using an associative reduce function.
+ */
def reduce(fun: (T, T) => T): DataSet[T] = {
+ reduce(getCallLocationName(), fun, CombineHint.OPTIMIZER_CHOOSES)
+ }
+
+ /**
+ * Special [[reduce]] operation for explicitly telling the system what
strategy to use for the
+ * combine phase.
+ * If null is given as the strategy, then the optimizer will pick the
strategy.
+ */
+ def reduce(fun: (T, T) => T, strategy: CombineHint): DataSet[T] = {
--- End diff --
@fhueske or @StephanEwen since we cannot break the scala DataSet API by
creating and returning a `ReduceOperator`, do you agree with Gábor's
recommendation to overload `DataSet.reduce` with `CombineHint`?
> 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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