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https://issues.apache.org/jira/browse/FLINK-3477?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15351713#comment-15351713
]
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_r68646048
--- Diff:
flink-runtime/src/main/java/org/apache/flink/runtime/operators/hash/MutableHashTable.java
---
@@ -1480,28 +1480,17 @@ public static int getInitialTableSize(int
numBuffers, int bufferSize, int numPar
public static byte assignPartition(int bucket, byte numPartitions) {
return (byte) (bucket % numPartitions);
}
-
+
/**
- * This function hashes an integer value. It is adapted from Bob
Jenkins' website
- * <a
href="http://www.burtleburtle.net/bob/hash/integer.html";>http://www.burtleburtle.net/bob/hash/integer.html</a>.
- * The hash function has the <i>full avalanche</i> property, meaning
that every bit of the value to be hashed
- * affects every bit of the hash value.
- *
- * @param code The integer to be hashed.
- * @return The hash code for the integer.
- */
+ * The level parameter is needed so that we can have different hash
functions when we recursively apply
+ * the partitioning, so that the working set eventually fits into
memory.
+ */
public static int hash(int code, int level) {
final int rotation = level * 11;
code = (code << rotation) | (code >>> -rotation);
--- End diff --
Since this was included in the diff ... `Integer.rotateLeft`.
> 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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