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https://issues.apache.org/jira/browse/FLINK-3477?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15308205#comment-15308205
]
ASF GitHub Bot commented on FLINK-3477:
---------------------------------------
Github user ggevay commented on a diff in the pull request:
https://github.com/apache/flink/pull/1517#discussion_r65229499
--- Diff:
flink-runtime/src/main/java/org/apache/flink/runtime/operators/ReduceCombineDriver.java
---
@@ -42,34 +44,38 @@
* Combine operator for Reduce functions, standalone (not chained).
* Sorts and groups and reduces data, but never spills the sort. May
produce multiple
* partially aggregated groups.
- *
+ *
* @param <T> The data type consumed and produced by the combiner.
*/
public class ReduceCombineDriver<T> implements Driver<ReduceFunction<T>,
T> {
-
+
private static final Logger LOG =
LoggerFactory.getLogger(ReduceCombineDriver.class);
/** Fix length records with a length below this threshold will be
in-place sorted, if possible. */
private static final int THRESHOLD_FOR_IN_PLACE_SORTING = 32;
-
-
+
+
private TaskContext<ReduceFunction<T>, T> taskContext;
private TypeSerializer<T> serializer;
private TypeComparator<T> comparator;
-
+
private ReduceFunction<T> reducer;
-
+
private Collector<T> output;
-
+
+ private DriverStrategy strategy;
+
private InMemorySorter<T> sorter;
-
+
private QuickSort sortAlgo = new QuickSort();
+ private ReduceHashTable<T> table;
+
private List<MemorySegment> memory;
- private boolean running;
+ private volatile boolean canceled;
--- End diff --
Oops, it's `volatile` in the other drivers as well, sorry! So then it can
be just volatile here as well, right?
> 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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