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https://issues.apache.org/jira/browse/SPARK-10339?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Apache Spark reassigned SPARK-10339:
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Assignee: Apache Spark (was: Yin Huai)
> When scanning a partitioned table having thousands of partitions, Driver has
> a very high memory pressure because of SQL metrics
> -------------------------------------------------------------------------------------------------------------------------------
>
> Key: SPARK-10339
> URL: https://issues.apache.org/jira/browse/SPARK-10339
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 1.5.0
> Reporter: Yin Huai
> Assignee: Apache Spark
> Priority: Blocker
>
> I have a local dataset having 5000 partitions stored in {{/tmp/partitioned}}.
> When I run the following code, the free memory space in driver's old gen
> gradually decreases and eventually there is pretty much no free space in
> driver's old gen. Finally, all kinds of timeouts happen and the cluster is
> died.
> {code}
> val df = sqlContext.read.format("parquet").load("/tmp/partitioned")
> df.filter("a > -100").selectExpr("hash(a, b)").queryExecution.toRdd.foreach(_
> => Unit)
> {code}
> I did a quick test by deleting SQL metrics from project and filter operator,
> my job works fine.
> The reason is that for a partitioned table, when we scan it, the actual plan
> is like
> {code}
> other operators
> |
> |
> /--|------\
> / | \
> / | \
> / | \
> project project ... project
> | | |
> filter filter ... filter
> | | |
> part1 part2 ... part n
> {code}
> We create SQL metrics for every filter and project, which causing the
> extremely high memory pressure to the driver.
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