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https://issues.apache.org/jira/browse/SPARK-45138?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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ASF GitHub Bot updated SPARK-45138:
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Labels: pull-request-available (was: )
> Define a new error class and apply for the case where checkpointing state to
> DFS fails
> --------------------------------------------------------------------------------------
>
> Key: SPARK-45138
> URL: https://issues.apache.org/jira/browse/SPARK-45138
> Project: Spark
> Issue Type: Task
> Components: Structured Streaming
> Affects Versions: 4.0.0
> Reporter: Raghu Angadi
> Priority: Major
> Labels: pull-request-available
>
> [From Neil Ramswamy]:
> When there is an exception during storing state from DFS (Hadoop, object
> store, etc), we just propagate the exception without the context. There is a
> context if customers look into stack trace, but that is definitely not
> something we want customers to look into by themselves.
> For example, let’s say file system related exception happened during
> checkpointing state. Since we just let the exception be bubbled up to the
> very top without adding any context, it is quite confusing for customers to
> quickly indicate whether there is an issue with source/sink (if source/sink
> data source is based on file), or offset/commit log, or state
> load/checkpoint. Too many operations can throw the same exception.
> The ticket aims to wrap the exception during the commit of the state, to
> assign error class properly. With assigning error class, we can classify the
> errors which help us to determine what errors customers are struggling much.
> StateStore.commit() is the entry point. Each state store provider has its own
> implementation, but use the same error class across implementations, as we
> want to categorize it as the same. Even better if we can put it to the
> higher-level of caller, but would be OK to handle it in built-in impls.
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