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ASF GitHub Bot commented on FLINK-3477: --------------------------------------- Github user aalexandrov commented on the pull request: https://github.com/apache/flink/pull/1517#issuecomment-208083609 We've summarized the use-case around the hash aggregation experiments in [a blog post on the Peel webpage](http://peel-framework.org/2016/04/07/hash-aggregations-in-flink.html). If you follow the instructions from the **Repeatability** section you should be able to reproduce the results on other environments without too much hastle. I hope that this will be the first of many Flink-related public Peel bundles. > 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. -- This message was sent by Atlassian JIRA (v6.3.4#6332)