Xuefu Zhang created HIVE-7493:
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Summary: 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
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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