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https://issues.apache.org/jira/browse/SPARK-42694?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17755329#comment-17755329
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Shuaipeng Lee commented on SPARK-42694:
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Are there any exeptions when the data-loss occur?
> Data duplication and loss occur after executing 'insert overwrite...' in
> Spark 3.1.1
> ------------------------------------------------------------------------------------
>
> Key: SPARK-42694
> URL: https://issues.apache.org/jira/browse/SPARK-42694
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Affects Versions: 3.1.1
> Environment: Spark 3.1.1
> Hadoop 3.2.1
> Hive 3.1.2
> Reporter: FengZhou
> Priority: Critical
> Labels: shuffle, spark
> Attachments: image-2023-03-07-15-59-08-818.png,
> image-2023-03-07-15-59-27-665.png
>
>
> We are currently using Spark version 3.1.1 in our production environment. We
> have noticed that occasionally, after executing 'insert overwrite ...
> select', the resulting data is inconsistent, with some data being duplicated
> or lost. This issue does not occur all the time and seems to be more
> prevalent on large tables with tens of millions of records.
> We compared the execution plans for two runs of the same SQL and found that
> they were identical. In the case where the SQL was executed successfully, the
> amount of data being written and read during the shuffle stage was the same.
> However, in the case where the problem occurred, the amount of data being
> written and read during the shuffle stage was different. Please see the
> attached screenshots for the write/read data during shuffle stage.
>
> Normal SQL:
> !image-2023-03-07-15-59-08-818.png!
> SQL with issues:
> !image-2023-03-07-15-59-27-665.png!
>
> Is this problem caused by a bug in version 3.1.1, specifically (SPARK-34534):
> 'New protocol FetchShuffleBlocks in OneForOneBlockFetcher lead to data loss
> or correctness'? Or is it caused by something else? What could be the root
> cause of this problem?
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