[ https://issues.apache.org/jira/browse/SPARK-18970?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Michael Armbrust updated SPARK-18970: ------------------------------------- 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: {code} 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$$anonfun$runJob$5.apply(SparkContext.scala:1897) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70) at org.apache.spark.scheduler.Task.run(Task.scala:85) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) at java.lang.Thread.run(Thread.java:745) {code} I believe 2 things should (or can) be fixed: 1. Application should fail in case of such an error. 2. Allow application to ignore such failure, since there is a chance that during next refresh the error will not resurface. (In my case I believe an error was cased by S3 cleaning the bucket exactly at the same moment when refresh was running) My code to create streaming processing looks as the following: {code} val cq = sqlContext.readStream .format("json") .schema(struct) .load(s"input") .writeStream .option("checkpointLocation", s"checkpoints") .foreach(new ForeachWriter[Row] {...}) .trigger(ProcessingTime("10 seconds")).start() cq.awaitTermination() {code} was: Spark streaming application uses S3 files as streaming sources. After running for several day processing stopped even though an application continued to run. Stack trace: java.io.FileNotFoundException: No such file or directory 's3n://XXXXXXXXXXXXXXXXX' at com.amazon.ws.emr.hadoop.fs.s3n.S3NativeFileSystem.getFileStatus(S3NativeFileSystem.java:818) at com.amazon.ws.emr.hadoop.fs.EmrFileSystem.getFileStatus(EmrFileSystem.java:511) at org.apache.spark.sql.execution.datasources.HadoopFsRelation$$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() > FileSource failure during file list 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, 2.0.2 > Reporter: Lev > Attachments: sparkerror.log > > > 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: > {code} > 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) > {code} > 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: > {code} > 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() > {code} -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org