Lev created SPARK-18970:
---------------------------
Summary: FileSource filure during refresh doesn't cause an
application to fail, but stops further processing
Key: SPARK-18970
URL: https://issues.apache.org/jira/browse/SPARK-18970
Project: Spark
Issue Type: Bug
Components: SQL, Structured Streaming
Affects Versions: 2.0.0
Reporter: Lev
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()
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