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https://issues.apache.org/jira/browse/HDFS-15079?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17461176#comment-17461176
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Hadoop QA commented on HDFS-15079:
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| (x) *{color:red}-1 overall{color}* |
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|| Vote || Subsystem || Runtime || Logfile || Comment ||
| {color:blue}0{color} | {color:blue} reexec {color} | {color:blue} 0m
0s{color} | {color:blue}{color} | {color:blue} Docker mode activated. {color} |
| {color:red}-1{color} | {color:red} patch {color} | {color:red} 0m 10s{color}
| {color:red}{color} | {color:red} HDFS-15079 does not apply to trunk. Rebase
required? Wrong Branch? See
https://cwiki.apache.org/confluence/display/HADOOP/How+To+Contribute for help.
{color} |
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|| Subsystem || Report/Notes ||
| JIRA Issue | HDFS-15079 |
| JIRA Patch URL |
https://issues.apache.org/jira/secure/attachment/12999335/HDFS-15079.002.patch |
| Console output |
https://ci-hadoop.apache.org/job/PreCommit-HDFS-Build/751/console |
| versions | git=2.17.1 |
| Powered by | Apache Yetus 0.13.0-SNAPSHOT https://yetus.apache.org |
This message was automatically generated.
> RBF: Client maybe get an unexpected result with network anomaly
> ----------------------------------------------------------------
>
> Key: HDFS-15079
> URL: https://issues.apache.org/jira/browse/HDFS-15079
> Project: Hadoop HDFS
> Issue Type: Sub-task
> Components: rbf
> Affects Versions: 3.3.0
> Reporter: Hui Fei
> Priority: Critical
> Attachments: HDFS-15079.001.patch, HDFS-15079.002.patch,
> UnexpectedOverWriteUT.patch
>
>
> I find there is a critical problem on RBF, HDFS-15078 can resolve it on some
> Scenarios, but i have no idea about the overall resolution.
> The problem is that
> Client with RBF(r0, r1) create a file HDFS file via r0, it gets Exception and
> failovers to r1
> r0 has been send create rpc to namenode(1st create)
> Client create a HDFS file via r1(2nd create)
> Client writes the HDFS file and close it finally(3rd close)
> Maybe namenode receiving the rpc in order as follow
> 2nd create
> 3rd close
> 1st create
> And overwrite is true by default, this would make the file had been written
> an empty file. This is an critical problem
> We had encountered this problem. There are many hive and spark jobs running
> on our cluster, sometimes it occurs
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