[jira] [Assigned] (HIVE-7493) Enhance HiveReduceFunction's row clustering

["Xuefu Zhang (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/HIVE-7493?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Xuefu Zhang reassigned HIVE-7493:
---------------------------------

    Assignee: Chao

> Enhance HiveReduceFunction's row clustering
> -------------------------------------------
>
>                 Key: HIVE-7493
>                 URL: https://issues.apache.org/jira/browse/HIVE-7493
>             Project: Hive
>          Issue Type: Sub-task
>          Components: Spark
>            Reporter: Xuefu Zhang
>            Assignee: Chao
>
> HiveReduceFunction is backed by Hive's ExecReducer, whose reduce function takes an input in the form of <key, value list>. However, HiveReduceFunction's input is an iterator over a set of <key, value> pairs. To reuse Hive's ExecReducer, we need to "stage and cluster" the input rows by key, and then feed the <key, value list> to ExecMapper's reduce method. There are several problems with the current approach:
> 1. unbounded memory usage.
> 2. memory inefficient: input has be cached until all input is consumed.
> 3. this functionality seems generic enough to have it in Spark itself.
> Thus, we'd like to check:
> 1. Whether Spark can provide a different version of PairFlatMapFunction, where the input to the call method is an iterator over tuples of <key, iterator<value>>. Something like this:
> {code}
>   public Iterable<Tuple2<BytesWritable, BytesWritable>> call(Iterator<Tuple2<BytesWritable, Iterator<BytesWritable>>> it);
> {code}
> 2. If above effort fails, we need to enhance our row clustering mechanism so that it has bounded memory usage and is able to spill if needed.



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