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https://issues.apache.org/jira/browse/FLINK-25029?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17451022#comment-17451022
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刘方奇 commented on FLINK-25029:
-----------------------------
[~dmvk] [~arvid] , Hi, I just submitted my PR
(https://github.com/apache/flink/pull/17958), Could you help to review the code
and push the process go on.
> Hadoop Caller Context Setting In Flink
> --------------------------------------
>
> Key: FLINK-25029
> URL: https://issues.apache.org/jira/browse/FLINK-25029
> Project: Flink
> Issue Type: Improvement
> Components: FileSystems
> Reporter: 刘方奇
> Assignee: 刘方奇
> Priority: Major
> Labels: pull-request-available
>
> For a given HDFS operation (e.g. delete file), it's very helpful to track
> which upper level job issues it. The upper level callers may be specific
> Oozie tasks, MR jobs, and hive queries. One scenario is that the namenode
> (NN) is abused/spammed, the operator may want to know immediately which MR
> job should be blamed so that she can kill it. To this end, the caller context
> contains at least the application-dependent "tracking id".
> The above is the main effect of the Caller Context. HDFS Client set Caller
> Context, then name node get it in audit log to do some work.
> Now the Spark and hive have the Caller Context to meet the HDFS Job Audit
> requirement.
> In my company, flink jobs often cause some problems for HDFS, so we did it
> for preventing some cases.
> If the feature is general enough. Should we support it, then I can submit a
> PR for this.
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