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https://issues.apache.org/jira/browse/SPARK-36810?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17866776#comment-17866776
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Nikita Blagodarnyi edited comment on SPARK-36810 at 7/17/24 2:23 PM:
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It can be fixed with submit configuration
{code:java}
spark.hadoop.dfs.client.failover.observer.auto-msync-period.cluster=0
spark.hadoop.dfs.client.failover.observer.auto-msync-period.[your_nameservice]=0{code}
was (Author: JIRAUSER305315):
It can be fixed with submit configuration
{{}}
{code:java}
spark.hadoop.dfs.client.failover.observer.auto-msync-period.cluster=0
spark.hadoop.dfs.client.failover.observer.auto-msync-period.[your_nameservice]=0{code}
{{}}
{{}}
> Handle HDFS read inconsistencies on Spark when observer Namenode is used
> ------------------------------------------------------------------------
>
> Key: SPARK-36810
> URL: https://issues.apache.org/jira/browse/SPARK-36810
> Project: Spark
> Issue Type: Bug
> Components: Spark Core, SQL
> Affects Versions: 3.2.0
> Reporter: Venkata krishnan Sowrirajan
> Priority: Major
>
> In short, with HDFS HA and with the use of [Observer
> Namenode|https://hadoop.apache.org/docs/stable/hadoop-project-dist/hadoop-hdfs/ObserverNameNode.html]
> the read-after-write consistency is only available when both the write and
> the read happens from the same client.
> But if the write happens on executor and the read happens on the driver, then
> the reads would be inconsistent causing application failure issues. This can
> be fixed by calling `FileSystem.msync` before making any read calls where the
> client thinks the write could have possibly happened elsewhere.
> This issue is discussed in greater detail in this
> [discussion|https://mail-archives.apache.org/mod_mbox/spark-dev/202108.mbox/browser]
>
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