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From "Viktor Rosenfeld (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (FLINK-1269) Easy way to "group count" dataset
Date Sat, 22 Nov 2014 19:11:12 GMT

    [ https://issues.apache.org/jira/browse/FLINK-1269?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14222117#comment-14222117
] 

Viktor Rosenfeld commented on FLINK-1269:
-----------------------------------------

Then all what's needed is a convenience function that checks that input is a Tuple2 and then
wraps:

{noformat}
input.groupBy(0).aggregate(count());
{noformat}

> Easy way to "group count" dataset
> ---------------------------------
>
>                 Key: FLINK-1269
>                 URL: https://issues.apache.org/jira/browse/FLINK-1269
>             Project: Flink
>          Issue Type: New Feature
>          Components: Java API, Scala API
>    Affects Versions: 0.7.0-incubating
>            Reporter: Sebastian Schelter
>            Assignee: Suneel Marthi
>
> Flink should offer an easy way to group datasets and compute the sizes of the resulting
groups. This is one of the most essential operations in distributed processing, yet it is
very hard to implement in Flink.
> I assume it could be a show-stopper for people trying Flink, because at the moment, users
have to perform the grouping and then write a groupReduce that counts the tuples in the group
and extracts the group key at the same time.
> Here is what I would semantically expect to happen:
> {noformat}
> def groupCount[T, K](data: DataSet[T], extractKey: (T) => K): DataSet[(K, Long)] =
{
>     data.groupBy { extractKey }
>         .reduceGroup { group => countBy(extractKey, group) }
>   }
>   private[this] def countBy[T, K](extractKey: T => K,
>                                   group: Iterator[T]): (K, Long) = {
>     val key = extractKey(group.next())
>     var count = 1L
>     while (group.hasNext) {
>       group.next()
>       count += 1
>     }
>     key -> count
>   }
> {noformat}
>   



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