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https://issues.apache.org/jira/browse/SPARK-8890?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14628620#comment-14628620
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Ilya Ganelin commented on SPARK-8890:
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[~yhuai] That makes sense, thank you. Wouldn't we still need to close/delete
output buffers for keys that have been completely written? Thus, would we, for
example, write all values associated with key=1, then close that output buffer,
write the next one etc.
Operational flow would become:
1) Attempt to create new outputWriter for each possible key
2) When maximum is exceeded, stop outputting rows.
3) Sort all remaining data by key (and persist this sorted set of
{code}InternalRow{code} objects in memory.
4) One key at a time, create an outputWriter and write all rows associated with
that key
5) Close outputWriter for that key and open a new outputWriter, continue from
step 4.
> Reduce memory consumption for dynamic partition insert
> ------------------------------------------------------
>
> Key: SPARK-8890
> URL: https://issues.apache.org/jira/browse/SPARK-8890
> Project: Spark
> Issue Type: Sub-task
> Components: SQL
> Reporter: Reynold Xin
> Priority: Critical
>
> Currently, InsertIntoHadoopFsRelation can run out of memory if the number of
> table partitions is large. The problem is that we open one output writer for
> each partition, and when data are randomized and when the number of
> partitions is large, we open a large number of output writers, leading to OOM.
> The solution here is to inject a sorting operation once the number of active
> partitions is beyond a certain point (e.g. 50?)
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