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https://issues.apache.org/jira/browse/SPARK-21082?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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DjvuLee updated SPARK-21082:
----------------------------
Description:
Spark Scheduler do not consider the memory usage during dispatch tasks, this
can lead to Executor OOM if the RDD is cached sometimes, because Spark can not
estimate the memory usage enough well(especially when the RDD type is not
flatten), scheduler may dispatch so many tasks on one Executor.
We can offer a configuration for user to decide whether scheduler will consider
the memory usage.
was:
Spark Scheduler do not consider the memory usage during dispatch tasks, this
can lead to Executor OOM if the RDD is cached sometimes, because Spark can not
estimate the memory usage enough well(especially when the RDD type is not
flatten), scheduler may dispatch so many task on one Executor.
We can offer a configuration for user to decide whether scheduler will consider
the memory usage.
> Consider Executor's memory usage when scheduling task
> ------------------------------------------------------
>
> Key: SPARK-21082
> URL: https://issues.apache.org/jira/browse/SPARK-21082
> Project: Spark
> Issue Type: Improvement
> Components: Scheduler, Spark Core
> Affects Versions: 2.2.1
> Reporter: DjvuLee
>
> Spark Scheduler do not consider the memory usage during dispatch tasks, this
> can lead to Executor OOM if the RDD is cached sometimes, because Spark can
> not estimate the memory usage enough well(especially when the RDD type is not
> flatten), scheduler may dispatch so many tasks on one Executor.
> We can offer a configuration for user to decide whether scheduler will
> consider the memory usage.
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