[jira] [Comment Edited] (SPARK-10063) Remove DirectParquetOutputCommitter

["Matt Martin (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-10063?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15377301#comment-15377301 ] 

Matt Martin edited comment on SPARK-10063 at 7/14/16 5:16 PM:
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For what it's worth, I gather that the Netflix folks have their own S3-specific solution (based purely on the couple minutes they spend talking about in this Hadoop Summit talk: https://youtu.be/85sew9OFaYc?t=8m39s).


was (Author: matt.martin):
For what it's worth, I gather that the Netflix folks have their own S3-specific solution (based purely on the couple minutes they spend talking about in this Hadoop summit talk: https://youtu.be/85sew9OFaYc?t=8m39s).

> Remove DirectParquetOutputCommitter
> -----------------------------------
>
>                 Key: SPARK-10063
>                 URL: https://issues.apache.org/jira/browse/SPARK-10063
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>            Reporter: Yin Huai
>            Assignee: Reynold Xin
>            Priority: Critical
>             Fix For: 2.0.0
>
>
> When we use DirectParquetOutputCommitter on S3 and speculation is enabled, there is a chance that we can loss data. 
> Here is the code to reproduce the problem.
> {code}
> import org.apache.spark.sql.functions._
> val failSpeculativeTask = sqlContext.udf.register("failSpeculativeTask", (i: Int, partitionId: Int, attemptNumber: Int) => {
>   if (partitionId == 0 && i == 5) {
>     if (attemptNumber > 0) {
>       Thread.sleep(15000)
>       throw new Exception("new exception")
>     } else {
>       Thread.sleep(10000)
>     }
>   }
>   
>   i
> })
> val df = sc.parallelize((1 to 100), 20).mapPartitions { iter =>
>   val context = org.apache.spark.TaskContext.get()
>   val partitionId = context.partitionId
>   val attemptNumber = context.attemptNumber
>   iter.map(i => (i, partitionId, attemptNumber))
> }.toDF("i", "partitionId", "attemptNumber")
> df
>   .select(failSpeculativeTask($"i", $"partitionId", $"attemptNumber").as("i"), $"partitionId", $"attemptNumber")
>   .write.mode("overwrite").format("parquet").save("/home/yin/outputCommitter")
> sqlContext.read.load("/home/yin/outputCommitter").count
> // The result is 99 and 5 is missing from the output.
> {code}
> What happened is that the original task finishes first and uploads its output file to S3, then the speculative task somehow fails. Because we have to call output stream's close method, which uploads data to S3, we actually uploads the partial result generated by the failed speculative task to S3 and this file overwrites the correct file generated by the original task.



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