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https://issues.apache.org/jira/browse/HIVE-9455?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Chao resolved HIVE-9455.
------------------------
    Resolution: Duplicate

Looks like this is a duplicate of HIVE-9428..

> MapJoin task shouldn't start if HashTableSink task failed [Spark Branch] 
> -------------------------------------------------------------------------
>
>                 Key: HIVE-9455
>                 URL: https://issues.apache.org/jira/browse/HIVE-9455
>             Project: Hive
>          Issue Type: Sub-task
>          Components: Spark
>    Affects Versions: spark-branch
>            Reporter: Chao
>
> While playing with {{auto_join25.q}}, I noticed that even though the task for 
> hash table sink failed, HOS will still continue launch the task for map join. 
> This is not the desired result. Instead, like MR, we should abandon the 
> second task.
> Console output:
> {code}
> Total jobs = 2
> Launching Job 1 out of 2
> In order to change the average load for a reducer (in bytes):
>   set hive.exec.reducers.bytes.per.reducer=<number>
> In order to limit the maximum number of reducers:
>   set hive.exec.reducers.max=<number>
> In order to set a constant number of reducers:
>   set mapreduce.job.reduces=<number>
> Query Hive on Spark job[0] stages:
> 0
> Status: Running (Hive on Spark job[0])
> Job Progress Format
> CurrentTime StageId_StageAttemptId: 
> SucceededTasksCount(+RunningTasksCount-FailedTasksCount)/TotalTasksCount 
> [StageCost]
> 2015-01-23 16:18:14,604       Stage-0_0: 0/1
> 2015-01-23 04:18:14   Processing rows:        4       Hashtable size: 3       
> Memory usage:   119199408       percentage:     0.25
> 2015-01-23 16:18:15,611       Stage-0_0: 0(+0,-1)/1
> Status: Finished successfully in 1.07 seconds
> Launching Job 2 out of 2
> In order to change the average load for a reducer (in bytes):
>   set hive.exec.reducers.bytes.per.reducer=<number>
> In order to limit the maximum number of reducers:
>   set hive.exec.reducers.max=<number>
> In order to set a constant number of reducers:
>   set mapreduce.job.reduces=<number>
> 2015-01-23 16:22:27,854       Stage-1_0: 0(+0,-1)/1
> Status: Finished successfully in 1.01 seconds
> Loading data to table default.dest1
> Table default.dest1 stats: [numFiles=0, numRows=0, totalSize=0, rawDataSize=0]
> OK
> Time taken: 311.979 seconds
> {code}



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