how to monitor multi directories in spark streaming task - apache-spark

Question

I want to use use fileStream in spark streaming to monitor multi hdfs directories, such as:
val list_join_action_stream = ssc.fileStream[LongWritable, Text, TextInputFormat]("/user/root/*/*", check_valid_file(_), false).map(_._2.toString).print
Buy the way, i could not under the meaning of the three class : LongWritable, Text, TextInputFormat
but it doesn't work...
java.io.FileNotFoundException: File /user/root/*/*
at org.apache.hadoop.hdfs.DistributedFileSystem.listStatusInternal(DistributedFileSystem.java:697)
at org.apache.hadoop.hdfs.DistributedFileSystem.access$600(DistributedFileSystem.java:105)
at org.apache.hadoop.hdfs.DistributedFileSystem$15.doCall(DistributedFileSystem.java:755)
at org.apache.hadoop.hdfs.DistributedFileSystem$15.doCall(DistributedFileSystem.java:751)
at org.apache.hadoop.fs.FileSystemLinkResolver.resolve(FileSystemLinkResolver.java:81)
at org.apache.hadoop.hdfs.DistributedFileSystem.listStatus(DistributedFileSystem.java:751)
at org.apache.hadoop.fs.FileSystem.listStatus(FileSystem.java:1485)
at org.apache.hadoop.fs.FileSystem.listStatus(FileSystem.java:1525)
at org.apache.spark.streaming.dstream.FileInputDStream.findNewFiles(FileInputDStream.scala:176)
at org.apache.spark.streaming.dstream.FileInputDStream.compute(FileInputDStream.scala:134)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1.apply(DStream.scala:300)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1.apply(DStream.scala:300)
at scala.util.DynamicVariable.withValue(DynamicVariable.scala:57)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:299)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:287)
at scala.Option.orElse(Option.scala:257)
at org.apache.spark.streaming.dstream.DStream.getOrCompute(DStream.scala:284)
at org.apache.spark.streaming.dstream.MappedDStream.compute(MappedDStream.scala:35)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1.apply(DStream.scala:300)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1.apply(DStream.scala:300)
at scala.util.DynamicVariable.withValue(DynamicVariable.scala:57)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:299)
at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:287)
at scala.Option.orElse(Option.scala:257)
at org.apache.spark.streaming.dstream.DStream.getOrCompute(DStream.scala:284)
at org.apache.spark.streaming.dstream.ForEachDStream.generateJob(ForEachDStream.scala:38)
at org.apache.spark.streaming.DStreamGraph$$anonfun$1.apply(DStreamGraph.scala:116)
at org.apache.spark.streaming.DStreamGraph$$anonfun$1.apply(DStreamGraph.scala:116)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:251)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:251)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
at scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:251)
at scala.collection.AbstractTraversable.flatMap(Traversable.scala:105)
at org.apache.spark.streaming.DStreamGraph.generateJobs(DStreamGraph.scala:116)
at org.apache.spark.streaming.scheduler.JobGenerator$$anonfun$2.apply(JobGenerator.scala:243)
at org.apache.spark.streaming.scheduler.JobGenerator$$anonfun$2.apply(JobGenerator.scala:241)
at scala.util.Try$.apply(Try.scala:161)
at org.apache.spark.streaming.scheduler.JobGenerator.generateJobs(JobGenerator.scala:241)
at org.apache.spark.streaming.scheduler.JobGenerator.org$apache$spark$streaming$scheduler$JobGenerator$$processEvent(JobGenerator.scala:177)
at org.apache.spark.streaming.scheduler.JobGenerator$$anonfun$start$1$$anon$1$$anonfun$receive$1.applyOrElse(JobGenerator.scala:86)
at akka.actor.Actor$class.aroundReceive(Actor.scala:465)
at org.apache.spark.streaming.scheduler.JobGenerator$$anonfun$start$1$$anon$1.aroundReceive(JobGenerator.scala:84)
at akka.actor.ActorCell.receiveMessage(ActorCell.scala:516)
at akka.actor.ActorCell.invoke(ActorCell.scala:487)
at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:238)
at akka.dispatch.Mailbox.run(Mailbox.scala:220)
at akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:393)
at scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260)
at scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339)
at scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979)
at scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107)

Answer 1

From the Spark documentation:
def fileStream[K, V, F <: InputFormat[K, V]](directory: String, filter: (Path) ⇒ Boolean, newFilesOnly: Boolean, conf: Configuration)(implicit arg0: ClassTag[K], arg1: ClassTag[V], arg2: ClassTag[F]): InputDStream[(K, V)]
Create a input stream that monitors a Hadoop-compatible filesystem for new files and reads them using the given key-value types and input format. Files must be written to the monitored directory by "moving" them from another location within the same file system. File names starting with . are ignored.
K Key type for reading HDFS file
V Value type for reading HDFS file
F Input format for reading HDFS file
directory HDFS directory to monitor for new file
filter Function to filter paths to process
newFilesOnly Should process only new files and ignore existing files in the directory
conf Hadoop configuration
filter parameter takes a method which identifies the input files for processing.
new Function<Path, Boolean>() {
#Override
public Boolean call(Path v1) throws Exception {
return Boolean.TRUE;
}
}
TextInputFormat is the default input format and used for plain or compressed text files.
Files are broken into lines.
Keys are the position (offset) in the file, and values are the line of text.
ssc.fileStream[LongWritable, Text, TextInputFormat] will work exactly as ssc.textFileStream(directory)
If you want to customize your file reading process then you need to define a custom input format and specify what are the key-value pair returned from
To implement custom input format you can refer defining Hadoop mapreduce custom input format.
Spark API Reference
Source code