You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "Shixiong Zhu (JIRA)" <ji...@apache.org> on 2018/01/12 00:57:00 UTC
[jira] [Commented] (SPARK-23050) Structured Streaming with S3 file
source duplicates data because of eventual consistency.
[ https://issues.apache.org/jira/browse/SPARK-23050?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16323320#comment-16323320 ]
Shixiong Zhu commented on SPARK-23050:
--------------------------------------
How do you read the output? If you use Spark to read the output, it will only read the successful files which are stored in the query metadata.
> 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
>
> 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}
--
This message was sent by Atlassian JIRA
(v6.4.14#64029)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org