[SPARK-18970] FileSource failure during file list refresh doesn't cause an application to fail, but stops further processing

• Type: Bug
• Status: Resolved
• Priority: Major
• Resolution: Fixed
• Affects Version/s: 2.0.0, 2.0.2
• Fix Version/s: 2.1.0
• Component/s: SQL, Structured Streaming
• Assignee: Unassigned
• Reporter: Lev
• Created: 22/Dec/16 02:01
• Updated: 23/Mar/17 00:28
• Resolved: 23/Mar/17 00:28

Description

Spark streaming application uses S3 files as streaming sources. After running for several day processing stopped even though an application continued to run.
Stack trace:

java.io.FileNotFoundException: No such file or directory 's3n://XXXXXXXXXXXXXXXXX'
	at com.amazon.ws.emr.hadoop.fs.s3n.S3NativeFileSystem.getFileStatus(S3NativeFileSystem.java:818)
	at com.amazon.ws.emr.hadoop.fs.EmrFileSystem.getFileStatus(EmrFileSystem.java:511)
	at org.apache.spark.sql.execution.datasources.HadoopFsRelation$$anonfun$7$$anonfun$apply$3.apply(fileSourceInterfaces.scala:465)
	at org.apache.spark.sql.execution.datasources.HadoopFsRelation$$anonfun$7$$anonfun$apply$3.apply(fileSourceInterfaces.scala:462)
	at scala.collection.Iterator$$anon$12.nextCur(Iterator.scala:434)
	at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440)
	at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
	at scala.collection.Iterator$class.foreach(Iterator.scala:893)
	at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
	at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:59)
	at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:104)
	at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:48)
	at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:310)
	at scala.collection.AbstractIterator.to(Iterator.scala:1336)
	at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:302)
	at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1336)
	at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:289)
	at scala.collection.AbstractIterator.toArray(Iterator.scala:1336)
	at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$13.apply(RDD.scala:893)
	at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$13.apply(RDD.scala:893)
	at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1897)
	at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1897)
	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70)
	at org.apache.spark.scheduler.Task.run(Task.scala:85)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
	at java.lang.Thread.run(Thread.java:745)

I believe 2 things should (or can) be fixed:
1. Application should fail in case of such an error.
2. Allow application to ignore such failure, since there is a chance that during next refresh the error will not resurface. (In my case I believe an error was cased by S3 cleaning the bucket exactly at the same moment when refresh was running)

My code to create streaming processing looks as the following:

      val cq = sqlContext.readStream
        .format("json")
        .schema(struct)
        .load(s"input")
        .writeStream
        .option("checkpointLocation", s"checkpoints")
        .foreach(new ForeachWriter[Row] {...})
        .trigger(ProcessingTime("10 seconds")).start()
		
	  cq.awaitTermination()