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From "Steve Loughran (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-23050) Structured Streaming with S3 file source duplicates data because of eventual consistency.
Date Thu, 09 Aug 2018 04:33:00 GMT

    [ https://issues.apache.org/jira/browse/SPARK-23050?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16574276#comment-16574276
] 

Steve Loughran commented on SPARK-23050:
----------------------------------------

bq. Is there any way we can avoid happening this?

With Amazon EMR, pay the premium for EMR [Consistent View|https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-plan-consistent-view.html].


For ASF Hadoop, plus CDH and HDP, [S3Guard|https://hadoop.apache.org/docs/r3.1.0/hadoop-aws/tools/hadoop-aws/s3guard.html]

Now, what's really needed is a checkpoint mechanism for Spark streaming which works with the
S3 consistency (create consistency iff you don't do a HEAD/GET first); if you do want to get
involved in this, then what I've promised in HADOOP-15460 might help, as it promises faster
writes without any inconsistency-inducing HEAD call first. Being open source projects, this
is somewhere where you have an opportunity to get involved

> Structured Streaming with S3 file source duplicates data because of eventual consistency.
> -----------------------------------------------------------------------------------------
>
>                 Key: SPARK-23050
>                 URL: https://issues.apache.org/jira/browse/SPARK-23050
>             Project: Spark
>          Issue Type: Bug
>          Components: Structured Streaming
>    Affects Versions: 2.2.0
>            Reporter: Yash Sharma
>            Priority: Major
>
> Spark Structured streaming with S3 file source duplicates data because of eventual consistency.
> Re producing the scenario -
> - Structured streaming reading from S3 source. Writing back to S3.
> - Spark tries to commitTask on completion of a task, by verifying if all the files have
been written to Filesystem. {{ManifestFileCommitProtocol.commitTask}}.
> - [Eventual consistency issue] Spark finds that the file is not present and fails the
task. {{org.apache.spark.SparkException: Task failed while writing rows. No such file or directory
's3://path/data/part-00256-65ae782d-e32e-48fb-8652-e1d0defc370b-c000.snappy.parquet'}}
> - By this time S3 eventually gets the file.
> - Spark reruns the task and completes the task, but gets a new file name this time. {{ManifestFileCommitProtocol.newTaskTempFile.
part-00256-b62fa7a4-b7e0-43d6-8c38-9705076a7ee1-c000.snappy.parquet.}}
> - Data duplicates in results and the same data is processed twice and written to S3.
> - There is no data duplication if spark is able to list presence of all committed files
and all tasks succeed.
> Code:
> {code}
> query = selected_df.writeStream \
>     .format("parquet") \
>     .option("compression", "snappy") \
>     .option("path", "s3://path/data/") \
>     .option("checkpointLocation", "s3://path/checkpoint/") \
>     .start()
> {code}
> Same sized duplicate S3 Files:
> {code}
> $ aws s3 ls s3://path/data/ | grep part-00256
> 2018-01-11 03:37:00      17070 part-00256-65ae782d-e32e-48fb-8652-e1d0defc370b-c000.snappy.parquet
> 2018-01-11 03:37:10      17070 part-00256-b62fa7a4-b7e0-43d6-8c38-9705076a7ee1-c000.snappy.parquet
> {code}
> Exception on S3 listing and task failure:
> {code}
> [Stage 5:========================>                            (277 + 100) / 597]18/01/11
03:36:59 WARN TaskSetManager: Lost task 256.0 in stage 5.0 (TID  org.apache.spark.SparkException:
Task failed while writing rows
>  	at org.apache.spark.sql.execution.datasources.FileFormatWriter$.org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask(FileFormatWriter.scala:272)
>  	at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1$$anonfun$apply$mcV$sp$1.apply(FileFormatWriter.scala:191)
>  	at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1$$anonfun$apply$mcV$sp$1.apply(FileFormatWriter.scala:190)
>  	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
>  	at org.apache.spark.scheduler.Task.run(Task.scala:108)
>  	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335)
>  	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
>  	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
>  	at java.lang.Thread.run(Thread.java:748)
>  Caused by: java.io.FileNotFoundException: No such file or directory 's3://path/data/part-00256-65ae782d-e32e-48fb-8652-e1d0defc370b-c000.snappy.parquet'
>  	at com.amazon.ws.emr.hadoop.fs.s3n.S3NativeFileSystem.getFileStatus(S3NativeFileSystem.java:816)
>  	at com.amazon.ws.emr.hadoop.fs.EmrFileSystem.getFileStatus(EmrFileSystem.java:509)
>  	at org.apache.spark.sql.execution.streaming.ManifestFileCommitProtocol$$anonfun$4.apply(ManifestFileCommitProtocol.scala:109)
>  	at org.apache.spark.sql.execution.streaming.ManifestFileCommitProtocol$$anonfun$4.apply(ManifestFileCommitProtocol.scala:109)
>  	at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
>  	at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
>  	at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
>  	at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
>  	at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
>  	at scala.collection.AbstractTraversable.map(Traversable.scala:104)
>  	at org.apache.spark.sql.execution.streaming.ManifestFileCommitProtocol.commitTask(ManifestFileCommitProtocol.scala:109)
>  	at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask$3.apply(FileFormatWriter.scala:260)
>  	at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask$3.apply(FileFormatWriter.scala:256)
>  	at org.apache.spark.util.Utils$.tryWithSafeFinallyAndFailureCallbacks(Utils.scala:1375)
>  	at org.apache.spark.sql.execution.datasources.FileFormatWriter$.org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask(FileFormatWriter.scala:261)
>  	... 8 more
> {code}



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