[
https://issues.apache.org/jira/browse/SPARK-14408?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15227110#comment-15227110
]
Sean Owen commented on SPARK-14408:
-----------------------------------
Yeah it's right-er at least. There's a test for foldByKey with a mutable type:
"foldByKey with mutable result type", though maybe it could be tweaked to use
multiple partitions explicitly. I suppose you could assert this explicitly in
the docs since apparently it's tested for.
> Is RDD.treeAggregate implemented correctly?
> -------------------------------------------
>
> Key: SPARK-14408
> URL: https://issues.apache.org/jira/browse/SPARK-14408
> Project: Spark
> Issue Type: Question
> Components: Spark Core
> Reporter: Joseph K. Bradley
>
> **Issue**
> In MLlib, we have assumed that {{RDD.treeAggregate}} allows the {{seqOp}} and
> {{combOp}} functions to modify and return their first argument, just like
> {{RDD.aggregate}}. However, it is not documented that way.
> I started to add docs to this effect, but then noticed that {{treeAggregate}}
> uses {{reduceByKey}} and {{reduce}} in its implementation, neither of which
> technically allows the seq/combOps to modify and return their first arguments.
> **Question**: Is the implementation safe, or does it need to be updated?
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
