You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "Steve Loughran (JIRA)" <ji...@apache.org> on 2017/03/23 13:51:41 UTC

[jira] [Commented] (SPARK-19013) java.util.ConcurrentModificationException when using s3 path as checkpointLocation

    [ https://issues.apache.org/jira/browse/SPARK-19013?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15938321#comment-15938321 ] 

Steve Loughran commented on SPARK-19013:
----------------------------------------

One thing that code be done here would be to worry about checkpointing to object stores differently; do a put rather than a create + rename. The best way to do that would rather than try and be clever about filesystem types (the way hive are doing), is probably just to provide a plugin point for the checkpoint commit; the normal one would be rename, with external ones offering the ability to commit differently, using whatever per-store mechanisms they have available (s3: multipart put, azure, path leases, etc). If you were to start something on that I'd help out

> java.util.ConcurrentModificationException when using s3 path as checkpointLocation 
> -----------------------------------------------------------------------------------
>
>                 Key: SPARK-19013
>                 URL: https://issues.apache.org/jira/browse/SPARK-19013
>             Project: Spark
>          Issue Type: Bug
>          Components: Structured Streaming
>    Affects Versions: 2.0.2
>            Reporter: Tim Chan
>
> I have a structured stream job running on EMR. The job will fail due to this
> {code}
> Multiple HDFSMetadataLog are using s3://mybucket/myapp org.apache.spark.sql.execution.streaming.HDFSMetadataLog.org$apache$spark$sql$execution$streaming$HDFSMetadataLog$$writeBatch(HDFSMetadataLog.scala:162)
> {code}
> There is only one instance of this stream job running.



--
This message was sent by Atlassian JIRA
(v6.3.15#6346)

---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org