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https://issues.apache.org/jira/browse/SPARK-14408?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15226944#comment-15226944
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Sean Owen commented on SPARK-14408:
-----------------------------------
Is it right-er to use the fold methods? I had thought this was one of the key
differences, yes, that the fold methods were definitely allowed to modify their
first args. In practice, it might happen to be fine for reduce too.
> 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?
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