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https://issues.apache.org/jira/browse/SPARK-22963?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Jose Torres updated SPARK-22963:
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Description:
Spark native task restarts don't work well for continuous processing. They will
process all data from the task's original start offset - even data which has
already been committed. This is not semantically incorrect under at least once
semantics, but it's awkward and bad.
Fortunately, they're also not necessary; the central coordinator can restart
every task from the checkpointed offsets without losing much. So we should make
that happen automatically on task failures instead.
was:
Spark native task restarts don't work well for continuous processing. They will
process all data from the task's original start offset - even data which has
already been committed. This is not semantically incorrect under at least once
semantics, but it's awkward and bad.
Fortunately, they're also not necessary; the central coordinator can restart
every task from the checkpointed offsets without losing much. So we should make
that happen on task failures instead.
> Make failure recovery global and automatic for continuous processing.
> ---------------------------------------------------------------------
>
> Key: SPARK-22963
> URL: https://issues.apache.org/jira/browse/SPARK-22963
> Project: Spark
> Issue Type: Improvement
> Components: Structured Streaming
> Affects Versions: 2.3.0
> Reporter: Jose Torres
>
> Spark native task restarts don't work well for continuous processing. They
> will process all data from the task's original start offset - even data which
> has already been committed. This is not semantically incorrect under at least
> once semantics, but it's awkward and bad.
> Fortunately, they're also not necessary; the central coordinator can restart
> every task from the checkpointed offsets without losing much. So we should
> make that happen automatically on task failures instead.
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