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https://issues.apache.org/jira/browse/SPARK-23050?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16323883#comment-16323883
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Michael Armbrust commented on SPARK-23050:
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[~zsxwing] is correct. While it is possible for files to get written out more
than once, the whole point of the log is to make sure that readers will only
see exactly one copy. It is possible we will come up with a new streaming
commit protocol as part of the migration to the V2 data source, but for now I
would call this working as intended.
> 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}
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