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From "Greg Hogan (JIRA)" <j...@apache.org>
Subject [jira] [Closed] (FLINK-3477) Add hash-based combine strategy for ReduceFunction
Date Thu, 14 Jul 2016 01:16:20 GMT

     [ https://issues.apache.org/jira/browse/FLINK-3477?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel

Greg Hogan closed FLINK-3477.
    Resolution: Implemented

Implemented in 52e191a5067322e82192314c16e70ae9e937ae2c

> 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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