Merge pull request #289 from tdas/filestream-fix

Bug fixes for file input stream and checkpointing

- Fixed bugs in the file input stream that led the stream to fail due to transient HDFS errors (listing files when a background thread it deleting fails caused errors, etc.)
- Updated Spark's CheckpointRDD and Streaming's CheckpointWriter to use SparkContext.hadoopConfiguration, to allow checkpoints to be written to any HDFS compatible store requiring special configuration.
- Changed the API of SparkContext.setCheckpointDir() - eliminated the unnecessary 'useExisting' parameter. Now SparkContext will always create a unique subdirectory within the user specified checkpoint directory. This is to ensure that previous checkpoint files are not accidentally overwritten.
- Fixed bug where setting checkpoint directory as a relative local path caused the checkpointing to fail.

14 files changed, 269 insertions, 196 deletions

diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index ad3337d94c..7514ce58fb 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -19,7 +19,7 @@ package org.apache.spark
 
 import java.io._
 import java.net.URI
-import java.util.Properties
+import java.util.{UUID, Properties}
 import java.util.concurrent.atomic.AtomicInteger
 
 import scala.collection.Map
@@ -855,22 +855,15 @@ class SparkContext(
 
   /**
    * Set the directory under which RDDs are going to be checkpointed. The directory must
-   * be a HDFS path if running on a cluster. If the directory does not exist, it will
-   * be created. If the directory exists and useExisting is set to true, then the
-   * exisiting directory will be used. Otherwise an exception will be thrown to
-   * prevent accidental overriding of checkpoint files in the existing directory.
+   * be a HDFS path if running on a cluster.
    */
-  def setCheckpointDir(dir: String, useExisting: Boolean = false) {
-    val path = new Path(dir)
-    val fs = path.getFileSystem(SparkHadoopUtil.get.newConfiguration())
-    if (!useExisting) {
-      if (fs.exists(path)) {
-        throw new Exception("Checkpoint directory '" + path + "' already exists.")
-      } else {
-        fs.mkdirs(path)
-      }
+  def setCheckpointDir(directory: String) {
+    checkpointDir = Option(directory).map { dir =>
+      val path = new Path(dir, UUID.randomUUID().toString)
+      val fs = path.getFileSystem(hadoopConfiguration)
+      fs.mkdirs(path)
+      fs.getFileStatus(path).getPath().toString
     }
-    checkpointDir = Some(dir)
   }
 
   /** Default level of parallelism to use when not given by user (e.g. parallelize and makeRDD). */
diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala b/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala
index acf328aa6a..50f2021d01 100644
--- a/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala
@@ -381,20 +381,7 @@ class JavaSparkContext(val sc: SparkContext) extends JavaSparkContextVarargsWork
 
   /**
    * Set the directory under which RDDs are going to be checkpointed. The directory must
-   * be a HDFS path if running on a cluster. If the directory does not exist, it will
-   * be created. If the directory exists and useExisting is set to true, then the
-   * exisiting directory will be used. Otherwise an exception will be thrown to
-   * prevent accidental overriding of checkpoint files in the existing directory.
-   */
-  def setCheckpointDir(dir: String, useExisting: Boolean) {
-    sc.setCheckpointDir(dir, useExisting)
-  }
-
-  /**
-   * Set the directory under which RDDs are going to be checkpointed. The directory must
-   * be a HDFS path if running on a cluster. If the directory does not exist, it will
-   * be created. If the directory exists, an exception will be thrown to prevent accidental
-   * overriding of checkpoint files.
+   * be a HDFS path if running on a cluster.
    */
   def setCheckpointDir(dir: String) {
     sc.setCheckpointDir(dir)
diff --git a/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala b/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala
index a712ef1c27..293a7d1f68 100644
--- a/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala
@@ -18,12 +18,12 @@
 package org.apache.spark.rdd
 
 import java.io.IOException
-
 import scala.reflect.ClassTag
-
-import org.apache.hadoop.fs.Path
 import org.apache.spark._
+import org.apache.spark.broadcast.Broadcast
 import org.apache.spark.deploy.SparkHadoopUtil
+import org.apache.hadoop.conf.Configuration
+import org.apache.hadoop.fs.Path
 
 private[spark] class CheckpointRDDPartition(val index: Int) extends Partition {}
 
@@ -34,6 +34,8 @@ private[spark]
 class CheckpointRDD[T: ClassTag](sc: SparkContext, val checkpointPath: String)
   extends RDD[T](sc, Nil) {
 
+  val broadcastedConf = sc.broadcast(new SerializableWritable(sc.hadoopConfiguration))
+
   @transient val fs = new Path(checkpointPath).getFileSystem(sc.hadoopConfiguration)
 
   override def getPartitions: Array[Partition] = {
@@ -65,7 +67,7 @@ class CheckpointRDD[T: ClassTag](sc: SparkContext, val checkpointPath: String)
 
   override def compute(split: Partition, context: TaskContext): Iterator[T] = {
     val file = new Path(checkpointPath, CheckpointRDD.splitIdToFile(split.index))
-    CheckpointRDD.readFromFile(file, context)
+    CheckpointRDD.readFromFile(file, broadcastedConf, context)
   }
 
   override def checkpoint() {
@@ -79,10 +81,14 @@ private[spark] object CheckpointRDD extends Logging {
     "part-%05d".format(splitId)
   }
 
-  def writeToFile[T](path: String, blockSize: Int = -1)(ctx: TaskContext, iterator: Iterator[T]) {
+  def writeToFile[T](
+      path: String,
+      broadcastedConf: Broadcast[SerializableWritable[Configuration]],
+      blockSize: Int = -1
+    )(ctx: TaskContext, iterator: Iterator[T]) {
     val env = SparkEnv.get
     val outputDir = new Path(path)
-    val fs = outputDir.getFileSystem(SparkHadoopUtil.get.newConfiguration())
+    val fs = outputDir.getFileSystem(broadcastedConf.value.value)
 
     val finalOutputName = splitIdToFile(ctx.partitionId)
     val finalOutputPath = new Path(outputDir, finalOutputName)
@@ -119,9 +125,13 @@ private[spark] object CheckpointRDD extends Logging {
     }
   }
 
-  def readFromFile[T](path: Path, context: TaskContext): Iterator[T] = {
+  def readFromFile[T](
+      path: Path,
+      broadcastedConf: Broadcast[SerializableWritable[Configuration]],
+      context: TaskContext
+    ): Iterator[T] = {
     val env = SparkEnv.get
-    val fs = path.getFileSystem(SparkHadoopUtil.get.newConfiguration())
+    val fs = path.getFileSystem(broadcastedConf.value.value)
     val bufferSize = System.getProperty("spark.buffer.size", "65536").toInt
     val fileInputStream = fs.open(path, bufferSize)
     val serializer = env.serializer.newInstance()
@@ -144,8 +154,10 @@ private[spark] object CheckpointRDD extends Logging {
     val sc = new SparkContext(cluster, "CheckpointRDD Test")
     val rdd = sc.makeRDD(1 to 10, 10).flatMap(x => 1 to 10000)
     val path = new Path(hdfsPath, "temp")
-    val fs = path.getFileSystem(SparkHadoopUtil.get.newConfiguration())
-    sc.runJob(rdd, CheckpointRDD.writeToFile(path.toString, 1024) _)
+    val conf = SparkHadoopUtil.get.newConfiguration()
+    val fs = path.getFileSystem(conf)
+    val broadcastedConf = sc.broadcast(new SerializableWritable(conf))
+    sc.runJob(rdd, CheckpointRDD.writeToFile(path.toString, broadcastedConf, 1024) _)
     val cpRDD = new CheckpointRDD[Int](sc, path.toString)
     assert(cpRDD.partitions.length == rdd.partitions.length, "Number of partitions is not the same")
     assert(cpRDD.collect.toList == rdd.collect.toList, "Data of partitions not the same")
diff --git a/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala b/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala
index 3b56e45aa9..642dabaad5 100644
--- a/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala
@@ -22,7 +22,7 @@ import scala.reflect.ClassTag
 import org.apache.hadoop.fs.Path
 import org.apache.hadoop.conf.Configuration
 
-import org.apache.spark.{Partition, SparkException, Logging}
+import org.apache.spark.{SerializableWritable, Partition, SparkException, Logging}
 import org.apache.spark.scheduler.{ResultTask, ShuffleMapTask}
 
 /**
@@ -85,14 +85,21 @@ private[spark] class RDDCheckpointData[T: ClassTag](rdd: RDD[T])
 
     // Create the output path for the checkpoint
     val path = new Path(rdd.context.checkpointDir.get, "rdd-" + rdd.id)
-    val fs = path.getFileSystem(new Configuration())
+    val fs = path.getFileSystem(rdd.context.hadoopConfiguration)
     if (!fs.mkdirs(path)) {
       throw new SparkException("Failed to create checkpoint path " + path)
     }
 
     // Save to file, and reload it as an RDD
-    rdd.context.runJob(rdd, CheckpointRDD.writeToFile(path.toString) _)
+    val broadcastedConf = rdd.context.broadcast(
+      new SerializableWritable(rdd.context.hadoopConfiguration))
+    rdd.context.runJob(rdd, CheckpointRDD.writeToFile(path.toString, broadcastedConf) _)
     val newRDD = new CheckpointRDD[T](rdd.context, path.toString)
+    if (newRDD.partitions.size != rdd.partitions.size) {
+      throw new SparkException(
+        "Checkpoint RDD " + newRDD + "("+ newRDD.partitions.size + ") has different " +
+          "number of partitions than original RDD " + rdd + "(" + rdd.partitions.size + ")")
+    }
 
     // Change the dependencies and partitions of the RDD
     RDDCheckpointData.synchronized {
@@ -101,8 +108,8 @@ private[spark] class RDDCheckpointData[T: ClassTag](rdd: RDD[T])
       rdd.markCheckpointed(newRDD)   // Update the RDD's dependencies and partitions
       cpState = Checkpointed
       RDDCheckpointData.clearTaskCaches()
-      logInfo("Done checkpointing RDD " + rdd.id + ", new parent is RDD " + newRDD.id)
     }
+    logInfo("Done checkpointing RDD " + rdd.id + " to " + path + ", new parent is RDD " + newRDD.id)
   }
 
   // Get preferred location of a split after checkpointing
diff --git a/core/src/test/scala/org/apache/spark/JavaAPISuite.java b/core/src/test/scala/org/apache/spark/JavaAPISuite.java
index 79913dc718..5e2899c97b 100644
--- a/core/src/test/scala/org/apache/spark/JavaAPISuite.java
+++ b/core/src/test/scala/org/apache/spark/JavaAPISuite.java
@@ -851,7 +851,7 @@ public class JavaAPISuite implements Serializable {
   public void checkpointAndComputation() {
     File tempDir = Files.createTempDir();
     JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 2, 3, 4, 5));
-    sc.setCheckpointDir(tempDir.getAbsolutePath(), true);
+    sc.setCheckpointDir(tempDir.getAbsolutePath());
     Assert.assertEquals(false, rdd.isCheckpointed());
     rdd.checkpoint();
     rdd.count(); // Forces the DAG to cause a checkpoint
@@ -863,7 +863,7 @@ public class JavaAPISuite implements Serializable {
   public void checkpointAndRestore() {
     File tempDir = Files.createTempDir();
     JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 2, 3, 4, 5));
-    sc.setCheckpointDir(tempDir.getAbsolutePath(), true);
+    sc.setCheckpointDir(tempDir.getAbsolutePath());
     Assert.assertEquals(false, rdd.isCheckpointed());
     rdd.checkpoint();
     rdd.count(); // Forces the DAG to cause a checkpoint
diff --git a/python/pyspark/context.py b/python/pyspark/context.py
index 0604f6836c..108f36576a 100644
--- a/python/pyspark/context.py
+++ b/python/pyspark/context.py
@@ -320,17 +320,12 @@ class SparkContext(object):
             self._python_includes.append(filename)
             sys.path.append(os.path.join(SparkFiles.getRootDirectory(), filename)) # for tests in local mode
 
-    def setCheckpointDir(self, dirName, useExisting=False):
+    def setCheckpointDir(self, dirName):
         """
         Set the directory under which RDDs are going to be checkpointed. The
         directory must be a HDFS path if running on a cluster.
-
-        If the directory does not exist, it will be created. If the directory
-        exists and C{useExisting} is set to true, then the exisiting directory
-        will be used.  Otherwise an exception will be thrown to prevent
-        accidental overriding of checkpoint files in the existing directory.
         """
-        self._jsc.sc().setCheckpointDir(dirName, useExisting)
+        self._jsc.sc().setCheckpointDir(dirName)
 
     def _getJavaStorageLevel(self, storageLevel):
         """
diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py
index 3987642bf4..7acb6eaf10 100644
--- a/python/pyspark/tests.py
+++ b/python/pyspark/tests.py
@@ -73,8 +73,8 @@ class TestCheckpoint(PySparkTestCase):
         time.sleep(1)  # 1 second
         self.assertTrue(flatMappedRDD.isCheckpointed())
         self.assertEqual(flatMappedRDD.collect(), result)
-        self.assertEqual(self.checkpointDir.name,
-                         os.path.dirname(flatMappedRDD.getCheckpointFile()))
+        self.assertEqual("file:" + self.checkpointDir.name,
+                         os.path.dirname(os.path.dirname(flatMappedRDD.getCheckpointFile())))
 
     def test_checkpoint_and_restore(self):
         parCollection = self.sc.parallelize([1, 2, 3, 4])
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala b/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala
index 7b343d2376..4960a85b97 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala
@@ -21,12 +21,13 @@ import java.io._
 import java.util.concurrent.Executors
 import java.util.concurrent.RejectedExecutionException
 
-import org.apache.hadoop.fs.Path
+import org.apache.hadoop.fs.{FileSystem, Path}
 import org.apache.hadoop.conf.Configuration
 
 import org.apache.spark.Logging
 import org.apache.spark.io.CompressionCodec
 import org.apache.spark.util.MetadataCleaner
+import org.apache.spark.deploy.SparkHadoopUtil
 
 
 private[streaming]
@@ -54,36 +55,34 @@ class Checkpoint(@transient ssc: StreamingContext, val checkpointTime: Time)
 
 
 /**
- * Convenience class to speed up the writing of graph checkpoint to file
+ * Convenience class to handle the writing of graph checkpoint to file
  */
 private[streaming]
-class CheckpointWriter(checkpointDir: String) extends Logging {
+class CheckpointWriter(checkpointDir: String, hadoopConf: Configuration) extends Logging {
   val file = new Path(checkpointDir, "graph")
-  // The file to which we actually write - and then "move" to file.
-  private val writeFile = new Path(file.getParent, file.getName + ".next")
-  private val bakFile = new Path(file.getParent, file.getName + ".bk")
-
-  private var stopped = false
-
-  val conf = new Configuration()
-  var fs = file.getFileSystem(conf)
-  val maxAttempts = 3
+  val MAX_ATTEMPTS = 3
   val executor = Executors.newFixedThreadPool(1)
+  val compressionCodec = CompressionCodec.createCodec()
+  // The file to which we actually write - and then "move" to file
+  val writeFile = new Path(file.getParent, file.getName + ".next")
+  // The file to which existing checkpoint is backed up (i.e. "moved")
+  val bakFile = new Path(file.getParent, file.getName + ".bk")
 
-  private val compressionCodec = CompressionCodec.createCodec()
+  private var stopped = false
+  private var fs_ : FileSystem = _
 
-  // Removed code which validates whether there is only one CheckpointWriter per path 'file' since 
+  // Removed code which validates whether there is only one CheckpointWriter per path 'file' since
   // I did not notice any errors - reintroduce it ?
-
   class CheckpointWriteHandler(checkpointTime: Time, bytes: Array[Byte]) extends Runnable {
     def run() {
       var attempts = 0
       val startTime = System.currentTimeMillis()
-      while (attempts < maxAttempts) {
+      while (attempts < MAX_ATTEMPTS && !stopped) {
         attempts += 1
         try {
           logDebug("Saving checkpoint for time " + checkpointTime + " to file '" + file + "'")
-          // This is inherently thread unsafe .. so alleviating it by writing to '.new' and then doing moves : which should be pretty fast.
+          // This is inherently thread unsafe, so alleviating it by writing to '.new' and
+          // then moving it to the final file
           val fos = fs.create(writeFile)
           fos.write(bytes)
           fos.close()
@@ -101,6 +100,7 @@ class CheckpointWriter(checkpointDir: String) extends Logging {
         } catch {
           case ioe: IOException =>
             logWarning("Error writing checkpoint to file in " + attempts + " attempts", ioe)
+            reset()
         }
       }
       logError("Could not write checkpoint for time " + checkpointTime + " to file '" + file + "'")
@@ -133,7 +133,17 @@ class CheckpointWriter(checkpointDir: String) extends Logging {
     val startTime = System.currentTimeMillis()
     val terminated = executor.awaitTermination(10, java.util.concurrent.TimeUnit.SECONDS)
     val endTime = System.currentTimeMillis()
-    logInfo("CheckpointWriter executor terminated ? " + terminated + ", waited for " + (endTime - startTime) + " ms.")
+    logInfo("CheckpointWriter executor terminated ? " + terminated +
+      ", waited for " + (endTime - startTime) + " ms.")
+  }
+
+  private def fs = synchronized {
+    if (fs_ == null) fs_ = file.getFileSystem(hadoopConf)
+    fs_
+  }
+
+  private def reset() = synchronized {
+    fs_ = null
   }
 }
 
@@ -143,7 +153,8 @@ object CheckpointReader extends Logging {
 
   def read(path: String): Checkpoint = {
     val fs = new Path(path).getFileSystem(new Configuration())
-    val attempts = Seq(new Path(path, "graph"), new Path(path, "graph.bk"), new Path(path), new Path(path + ".bk"))
+    val attempts = Seq(new Path(path, "graph"), new Path(path, "graph.bk"),
+      new Path(path), new Path(path + ".bk"))
 
     val compressionCodec = CompressionCodec.createCodec()
 
@@ -158,7 +169,8 @@ object CheckpointReader extends Logging {
           // loader to find and load classes. This is a well know Java issue and has popped up
           // in other places (e.g., http://jira.codehaus.org/browse/GROOVY-1627)
           val zis = compressionCodec.compressedInputStream(fis)
-          val ois = new ObjectInputStreamWithLoader(zis, Thread.currentThread().getContextClassLoader)
+          val ois = new ObjectInputStreamWithLoader(zis,
+            Thread.currentThread().getContextClassLoader)
           val cp = ois.readObject.asInstanceOf[Checkpoint]
           ois.close()
           fs.close()
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala b/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala
index 41da028a3c..8898fdcb7f 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala
@@ -20,17 +20,19 @@ package org.apache.spark.streaming
 import akka.actor.Props
 import akka.actor.SupervisorStrategy
 import akka.zeromq.Subscribe
+import akka.util.ByteString
 
 import org.apache.spark.streaming.dstream._
+import org.apache.spark.streaming.scheduler.StreamingListener
 
 import org.apache.spark._
 import org.apache.spark.rdd.RDD
-import org.apache.spark.streaming.receivers.ActorReceiver
 import org.apache.spark.streaming.receivers.ReceiverSupervisorStrategy
 import org.apache.spark.streaming.receivers.ZeroMQReceiver
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.util.MetadataCleaner
 import org.apache.spark.streaming.receivers.ActorReceiver
+import org.apache.spark.streaming.scheduler.{JobScheduler, NetworkInputTracker}
 
 import scala.collection.mutable.Queue
 import scala.collection.Map
@@ -38,17 +40,15 @@ import scala.reflect.ClassTag
 
 import java.io.InputStream
 import java.util.concurrent.atomic.AtomicInteger
-import java.util.UUID
 
 import org.apache.hadoop.io.LongWritable
 import org.apache.hadoop.io.Text
 import org.apache.hadoop.mapreduce.{InputFormat => NewInputFormat}
 import org.apache.hadoop.mapreduce.lib.input.TextInputFormat
 import org.apache.hadoop.fs.Path
+
 import twitter4j.Status
 import twitter4j.auth.Authorization
-import org.apache.spark.streaming.scheduler._
-import akka.util.ByteString
 
 /**
  * A StreamingContext is the main entry point for Spark Streaming functionality. Besides the basic
@@ -87,7 +87,6 @@ class StreamingContext private (
          null, batchDuration)
   }
 
-
   /**
    * Re-create a StreamingContext from a checkpoint file.
    * @param path Path either to the directory that was specified as the checkpoint directory, or
@@ -141,7 +140,7 @@ class StreamingContext private (
 
   protected[streaming] var checkpointDir: String = {
     if (isCheckpointPresent) {
-      sc.setCheckpointDir(StreamingContext.getSparkCheckpointDir(cp_.checkpointDir), true)
+      sc.setCheckpointDir(cp_.checkpointDir)
       cp_.checkpointDir
     } else {
       null
@@ -176,8 +175,12 @@ class StreamingContext private (
    */
   def checkpoint(directory: String) {
     if (directory != null) {
-      sc.setCheckpointDir(StreamingContext.getSparkCheckpointDir(directory))
-      checkpointDir = directory
+      val path = new Path(directory)
+      val fs = path.getFileSystem(sparkContext.hadoopConfiguration)
+      fs.mkdirs(path)
+      val fullPath = fs.getFileStatus(path).getPath().toString
+      sc.setCheckpointDir(fullPath)
+      checkpointDir = fullPath
     } else {
       checkpointDir = null
     }
@@ -368,7 +371,8 @@ class StreamingContext private (
   /**
    * 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.
-   * File names starting with . are ignored.
+   * 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.
    * @param directory HDFS directory to monitor for new file
    * @tparam K Key type for reading HDFS file
    * @tparam V Value type for reading HDFS file
@@ -387,6 +391,8 @@ class StreamingContext private (
   /**
    * 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.
    * @param directory HDFS directory to monitor for new file
    * @param filter Function to filter paths to process
    * @param newFilesOnly Should process only new files and ignore existing files in the directory
@@ -407,7 +413,9 @@ class StreamingContext private (
   /**
    * Create a input stream that monitors a Hadoop-compatible filesystem
    * for new files and reads them as text files (using key as LongWritable, value
-   * as Text and input format as TextInputFormat). File names starting with . are ignored.
+   * as Text and input format as TextInputFormat). 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.
    * @param directory HDFS directory to monitor for new file
    */
   def textFileStream(directory: String): DStream[String] = {
@@ -599,8 +607,4 @@ object StreamingContext {
       prefix + "-" + time.milliseconds + "." + suffix
     }
   }
-
-  protected[streaming] def getSparkCheckpointDir(sscCheckpointDir: String): String = {
-    new Path(sscCheckpointDir, UUID.randomUUID.toString).toString
-  }
 }
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala
index 78d318cf27..aad0d931e7 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala
@@ -256,9 +256,11 @@ class JavaStreamingContext(val ssc: StreamingContext) {
   }
 
   /**
-   * Creates a input stream that monitors a Hadoop-compatible filesystem
+   * Create a input stream that monitors a Hadoop-compatible filesystem
    * for new files and reads them as text files (using key as LongWritable, value
-   * as Text and input format as TextInputFormat). File names starting with . are ignored.
+   * as Text and input format as TextInputFormat). 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.
    * @param directory HDFS directory to monitor for new file
    */
   def textFileStream(directory: String): JavaDStream[String] = {
@@ -300,9 +302,10 @@ class JavaStreamingContext(val ssc: StreamingContext) {
   }
 
   /**
-   * Creates a input stream that monitors a Hadoop-compatible filesystem
+   * 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.
-   * File names starting with . are ignored.
+   * 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.
    * @param directory HDFS directory to monitor for new file
    * @tparam K Key type for reading HDFS file
    * @tparam V Value type for reading HDFS file
@@ -331,7 +334,7 @@ class JavaStreamingContext(val ssc: StreamingContext) {
 
 
   /**
-   * Creates a input stream from a Flume source.
+   * Create a input stream from a Flume source.
    * @param hostname Hostname of the slave machine to which the flume data will be sent
    * @param port     Port of the slave machine to which the flume data will be sent
    */
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/FileInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/FileInputDStream.scala
index 39e25239bf..fb9eda8996 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/FileInputDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/FileInputDStream.scala
@@ -17,18 +17,17 @@
 
 package org.apache.spark.streaming.dstream
 
-import org.apache.spark.rdd.RDD
-import org.apache.spark.rdd.UnionRDD
-import org.apache.spark.streaming.{DStreamCheckpointData, StreamingContext, Time}
-
+import java.io.{ObjectInputStream, IOException}
+import scala.collection.mutable.{HashSet, HashMap}
+import scala.reflect.ClassTag
 import org.apache.hadoop.fs.{FileSystem, Path, PathFilter}
 import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.mapreduce.{InputFormat => NewInputFormat}
+import org.apache.spark.SparkException
+import org.apache.spark.rdd.RDD
+import org.apache.spark.rdd.UnionRDD
+import org.apache.spark.streaming.{DStreamCheckpointData, StreamingContext, Time}
 
-import scala.collection.mutable.{HashSet, HashMap}
-import scala.reflect.ClassTag
-
-import java.io.{ObjectInputStream, IOException}
 
 private[streaming]
 class FileInputDStream[K: ClassTag, V: ClassTag, F <: NewInputFormat[K,V] : ClassTag](
@@ -41,8 +40,8 @@ class FileInputDStream[K: ClassTag, V: ClassTag, F <: NewInputFormat[K,V] : Clas
   protected[streaming] override val checkpointData = new FileInputDStreamCheckpointData
 
   // Latest file mod time seen till any point of time
-  private val lastModTimeFiles = new HashSet[String]()
-  private var lastModTime = 0L
+  private val prevModTimeFiles = new HashSet[String]()
+  private var prevModTime = 0L
 
   @transient private var path_ : Path = null
   @transient private var fs_ : FileSystem = null
@@ -50,11 +49,11 @@ class FileInputDStream[K: ClassTag, V: ClassTag, F <: NewInputFormat[K,V] : Clas
 
   override def start() {
     if (newFilesOnly) {
-      lastModTime = graph.zeroTime.milliseconds
+      prevModTime = graph.zeroTime.milliseconds
     } else {
-      lastModTime = 0
+      prevModTime = 0
     }
-    logDebug("LastModTime initialized to " + lastModTime + ", new files only = " + newFilesOnly)
+    logDebug("LastModTime initialized to " + prevModTime + ", new files only = " + newFilesOnly)
   }
 
   override def stop() { }
@@ -69,55 +68,22 @@ class FileInputDStream[K: ClassTag, V: ClassTag, F <: NewInputFormat[K,V] : Clas
    * the previous call.
    */
   override def compute(validTime: Time): Option[RDD[(K, V)]] = {
-    assert(validTime.milliseconds >= lastModTime, "Trying to get new files for really old time [" + validTime + " < " + lastModTime)
+    assert(validTime.milliseconds >= prevModTime,
+      "Trying to get new files for really old time [" + validTime + " < " + prevModTime + "]")
 
-    // Create the filter for selecting new files
-    val newFilter = new PathFilter() {
-      // Latest file mod time seen in this round of fetching files and its corresponding files
-      var latestModTime = 0L
-      val latestModTimeFiles = new HashSet[String]()
-
-      def accept(path: Path): Boolean = {
-        if (!filter(path)) {  // Reject file if it does not satisfy filter
-          logDebug("Rejected by filter " + path)
-          return false
-        } else {              // Accept file only if
-          val modTime = fs.getFileStatus(path).getModificationTime()
-          logDebug("Mod time for " + path + " is " + modTime)
-          if (modTime < lastModTime) {
-            logDebug("Mod time less than last mod time")
-            return false  // If the file was created before the last time it was called
-          } else if (modTime == lastModTime && lastModTimeFiles.contains(path.toString)) {
-            logDebug("Mod time equal to last mod time, but file considered already")
-            return false  // If the file was created exactly as lastModTime but not reported yet
-          } else if (modTime > validTime.milliseconds) {
-            logDebug("Mod time more than valid time")
-            return false  // If the file was created after the time this function call requires
-          }
-          if (modTime > latestModTime) {
-            latestModTime = modTime
-            latestModTimeFiles.clear()
-            logDebug("Latest mod time updated to " + latestModTime)
-          }
-          latestModTimeFiles += path.toString
-          logDebug("Accepted " + path)
-          return true
-        }
-      }
-    }
-    logDebug("Finding new files at time " + validTime + " for last mod time = " + lastModTime)
-    val newFiles = fs.listStatus(path, newFilter).map(_.getPath.toString)
+    // Find new files
+    val (newFiles, latestModTime, latestModTimeFiles) = findNewFiles(validTime.milliseconds)
     logInfo("New files at time " + validTime + ":\n" + newFiles.mkString("\n"))
     if (newFiles.length > 0) {
       // Update the modification time and the files processed for that modification time
-      if (lastModTime != newFilter.latestModTime) {
-        lastModTime = newFilter.latestModTime
-        lastModTimeFiles.clear()
+      if (prevModTime < latestModTime) {
+        prevModTime = latestModTime
+        prevModTimeFiles.clear()
       }
-      lastModTimeFiles ++= newFilter.latestModTimeFiles
-      logDebug("Last mod time updated to " + lastModTime)
+      prevModTimeFiles ++= latestModTimeFiles
+      logDebug("Last mod time updated to " + prevModTime)
     }
-    files += ((validTime, newFiles))
+    files += ((validTime, newFiles.toArray))
     Some(filesToRDD(newFiles))
   }
 
@@ -132,12 +98,28 @@ class FileInputDStream[K: ClassTag, V: ClassTag, F <: NewInputFormat[K,V] : Clas
       oldFiles.map(p => (p._1, p._2.mkString(", "))).mkString("\n"))
   }
 
+  /**
+   * Find files which have modification timestamp <= current time and return a 3-tuple of
+   * (new files found, latest modification time among them, files with latest modification time)
+   */
+  private def findNewFiles(currentTime: Long): (Seq[String], Long, Seq[String]) = {
+    logDebug("Trying to get new files for time " + currentTime)
+    val filter = new CustomPathFilter(currentTime)
+    val newFiles = fs.listStatus(path, filter).map(_.getPath.toString)
+    (newFiles, filter.latestModTime, filter.latestModTimeFiles.toSeq)
+  }
+
   /** Generate one RDD from an array of files */
-  protected[streaming] def filesToRDD(files: Seq[String]): RDD[(K, V)] = {
-    new UnionRDD(
-      context.sparkContext,
-      files.map(file => context.sparkContext.newAPIHadoopFile[K, V, F](file))
-    )
+  private def filesToRDD(files: Seq[String]): RDD[(K, V)] = {
+    val fileRDDs = files.map(file => context.sparkContext.newAPIHadoopFile[K, V, F](file))
+    files.zip(fileRDDs).foreach { case (file, rdd) => {
+      if (rdd.partitions.size == 0) {
+        logError("File " + file + " has no data in it. Spark Streaming can only ingest " +
+          "files that have been \"moved\" to the directory assigned to the file stream. " +
+          "Refer to the streaming programming guide for more details.")
+      }
+    }}
+    new UnionRDD(context.sparkContext, fileRDDs)
   }
 
   private def path: Path = {
@@ -150,6 +132,10 @@ class FileInputDStream[K: ClassTag, V: ClassTag, F <: NewInputFormat[K,V] : Clas
     fs_
   }
 
+  private def reset()  {
+    fs_ = null
+  }
+
   @throws(classOf[IOException])
   private def readObject(ois: ObjectInputStream) {
     logDebug(this.getClass().getSimpleName + ".readObject used")
@@ -191,6 +177,51 @@ class FileInputDStream[K: ClassTag, V: ClassTag, F <: NewInputFormat[K,V] : Clas
         hadoopFiles.map(p => (p._1, p._2.mkString(", "))).mkString("\n") + "\n]"
     }
   }
+
+  /**
+   * Custom PathFilter class to find new files that have modification timestamps <= current time,
+   * but have not been seen before (i.e. the file should not be in lastModTimeFiles)
+   */
+  private[streaming]
+  class CustomPathFilter(maxModTime: Long) extends PathFilter {
+    // Latest file mod time seen in this round of fetching files and its corresponding files
+    var latestModTime = 0L
+    val latestModTimeFiles = new HashSet[String]()
+
+    def accept(path: Path): Boolean = {
+      try {
+        if (!filter(path)) {  // Reject file if it does not satisfy filter
+          logDebug("Rejected by filter " + path)
+          return false
+        }
+        val modTime = fs.getFileStatus(path).getModificationTime()
+        logDebug("Mod time for " + path + " is " + modTime)
+        if (modTime < prevModTime) {
+          logDebug("Mod time less than last mod time")
+          return false  // If the file was created before the last time it was called
+        } else if (modTime == prevModTime && prevModTimeFiles.contains(path.toString)) {
+          logDebug("Mod time equal to last mod time, but file considered already")
+          return false  // If the file was created exactly as lastModTime but not reported yet
+        } else if (modTime > maxModTime) {
+          logDebug("Mod time more than ")
+          return false  // If the file is too new that considering it may give errors
+        }
+        if (modTime > latestModTime) {
+          latestModTime = modTime
+          latestModTimeFiles.clear()
+          logDebug("Latest mod time updated to " + latestModTime)
+        }
+        latestModTimeFiles += path.toString
+        logDebug("Accepted " + path)
+      } catch {
+        case fnfe: java.io.FileNotFoundException => 
+          logWarning("Error finding new files", fnfe)
+          reset()
+          return false
+      }
+      return true
+    }
+  }
 }
 
 private[streaming]
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala
index 1cd0b9b0a4..921a33a4cb 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala
@@ -17,11 +17,18 @@
 
 package org.apache.spark.streaming.scheduler
 
+import akka.actor.{Props, Actor}
 import org.apache.spark.SparkEnv
 import org.apache.spark.Logging
 import org.apache.spark.streaming.{Checkpoint, Time, CheckpointWriter}
 import org.apache.spark.streaming.util.{ManualClock, RecurringTimer, Clock}
 
+/** Event classes for JobGenerator */
+private[scheduler] sealed trait JobGeneratorEvent
+private[scheduler] case class GenerateJobs(time: Time) extends JobGeneratorEvent
+private[scheduler] case class ClearOldMetadata(time: Time) extends JobGeneratorEvent
+private[scheduler] case class DoCheckpoint(time: Time) extends JobGeneratorEvent
+
 /**
  * This class generates jobs from DStreams as well as drives checkpointing and cleaning
  * up DStream metadata.
@@ -30,44 +37,69 @@ private[streaming]
 class JobGenerator(jobScheduler: JobScheduler) extends Logging {
 
   initLogging()
+
   val ssc = jobScheduler.ssc
-  val clockClass = System.getProperty(
-    "spark.streaming.clock", "org.apache.spark.streaming.util.SystemClock")
-  val clock = Class.forName(clockClass).newInstance().asInstanceOf[Clock]
-  val timer = new RecurringTimer(clock, ssc.graph.batchDuration.milliseconds,
-    longTime => generateJobs(new Time(longTime)))
   val graph = ssc.graph
+  val eventProcessorActor = ssc.env.actorSystem.actorOf(Props(new Actor {
+    def receive = {
+      case event: JobGeneratorEvent =>
+        logDebug("Got event of type " + event.getClass.getName)
+        processEvent(event)
+    }
+  }))
+  val clock = {
+    val clockClass = System.getProperty(
+      "spark.streaming.clock", "org.apache.spark.streaming.util.SystemClock")
+    Class.forName(clockClass).newInstance().asInstanceOf[Clock]
+  }
+  val timer = new RecurringTimer(clock, ssc.graph.batchDuration.milliseconds,
+    longTime => eventProcessorActor ! GenerateJobs(new Time(longTime)))
   lazy val checkpointWriter = if (ssc.checkpointDuration != null && ssc.checkpointDir != null) {
-    new CheckpointWriter(ssc.checkpointDir)
+    new CheckpointWriter(ssc.checkpointDir, ssc.sparkContext.hadoopConfiguration)
   } else {
     null
   }
 
-  var latestTime: Time = null
-
   def start() = synchronized {
     if (ssc.isCheckpointPresent) {
       restart()
     } else {
       startFirstTime()
     }
-    logInfo("JobGenerator started")
   }
   
-  def stop() = synchronized {
+  def stop() {
     timer.stop()
     if (checkpointWriter != null) checkpointWriter.stop()
     ssc.graph.stop()
     logInfo("JobGenerator stopped")
   }
 
+  /**
+   * On batch completion, clear old metadata and checkpoint computation.
+   */
+  private[scheduler] def onBatchCompletion(time: Time) {
+    eventProcessorActor ! ClearOldMetadata(time)
+  }
+
+  /** Processes all events */
+  private def processEvent(event: JobGeneratorEvent) {
+    event match {
+      case GenerateJobs(time) => generateJobs(time)
+      case ClearOldMetadata(time) => clearOldMetadata(time)
+      case DoCheckpoint(time) => doCheckpoint(time)
+    }
+  }
+
+  /** Starts the generator for the first time */
   private def startFirstTime() {
     val startTime = new Time(timer.getStartTime())
     graph.start(startTime - graph.batchDuration)
     timer.start(startTime.milliseconds)
-    logInfo("JobGenerator's timer started at " + startTime)
+    logInfo("JobGenerator started at " + startTime)
   }
 
+  /** Restarts the generator based on the information in checkpoint */
   private def restart() {
     // If manual clock is being used for testing, then
     // either set the manual clock to the last checkpointed time,
@@ -99,7 +131,7 @@ class JobGenerator(jobScheduler: JobScheduler) extends Logging {
 
     // Restart the timer
     timer.start(restartTime.milliseconds)
-    logInfo("JobGenerator's timer restarted at " + restartTime)
+    logInfo("JobGenerator restarted at " + restartTime)
   }
 
   /** Generate jobs and perform checkpoint for the given `time`.  */
@@ -107,16 +139,13 @@ class JobGenerator(jobScheduler: JobScheduler) extends Logging {
     SparkEnv.set(ssc.env)
     logInfo("\n-----------------------------------------------------\n")
     jobScheduler.runJobs(time, graph.generateJobs(time))
-    latestTime = time
-    doCheckpoint(time)
+    eventProcessorActor ! DoCheckpoint(time)
   }
 
-  /**
-   * On batch completion, clear old metadata and checkpoint computation.
-   */
-  private[streaming] def onBatchCompletion(time: Time) {
+  /** Clear DStream metadata for the given `time`. */
+  private def clearOldMetadata(time: Time) {
     ssc.graph.clearOldMetadata(time)
-    doCheckpoint(time)
+    eventProcessorActor ! DoCheckpoint(time)
   }
 
   /** Perform checkpoint for the give `time`. */
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala
index 67a0841535..4e25c9566c 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala
@@ -17,24 +17,18 @@
 
 package org.apache.spark.streaming
 
-import dstream.FileInputDStream
-import org.apache.spark.streaming.StreamingContext._
 import java.io.File
 
 import scala.collection.mutable.ArrayBuffer
 import scala.reflect.ClassTag
-
 import org.apache.commons.io.FileUtils
-import org.scalatest.BeforeAndAfter
-
 import com.google.common.io.Files
-
-import org.apache.spark.streaming.StreamingContext.toPairDStreamFunctions
+import org.apache.hadoop.fs.{Path, FileSystem}
+import org.apache.hadoop.conf.Configuration
+import org.apache.spark.streaming.StreamingContext._
 import org.apache.spark.streaming.dstream.FileInputDStream
 import org.apache.spark.streaming.util.ManualClock
 
-
-
 /**
  * This test suites tests the checkpointing functionality of DStreams -
  * the checkpointing of a DStream's RDDs as well as the checkpointing of
@@ -66,7 +60,7 @@ class CheckpointSuite extends TestSuiteBase {
     System.setProperty("spark.streaming.clock", "org.apache.spark.streaming.util.ManualClock")
 
     val stateStreamCheckpointInterval = Seconds(1)
-
+    val fs = FileSystem.getLocal(new Configuration())
     // this ensure checkpointing occurs at least once
     val firstNumBatches = (stateStreamCheckpointInterval / batchDuration).toLong * 2
     val secondNumBatches = firstNumBatches
@@ -90,11 +84,12 @@ class CheckpointSuite extends TestSuiteBase {
     ssc.start()
     advanceTimeWithRealDelay(ssc, firstNumBatches)
     logInfo("Checkpoint data of state stream = \n" + stateStream.checkpointData)
-    assert(!stateStream.checkpointData.checkpointFiles.isEmpty, "No checkpointed RDDs in state stream before first failure")
+    assert(!stateStream.checkpointData.checkpointFiles.isEmpty,
+      "No checkpointed RDDs in state stream before first failure")
     stateStream.checkpointData.checkpointFiles.foreach {
-      case (time, data) => {
-        val file = new File(data.toString)
-        assert(file.exists(), "Checkpoint file '" + file +"' for time " + time + " for state stream before first failure does not exist")
+      case (time, file) => {
+        assert(fs.exists(new Path(file)), "Checkpoint file '" + file +"' for time " + time +
+            " for state stream before first failure does not exist")
       }
     }
 
@@ -102,7 +97,8 @@ class CheckpointSuite extends TestSuiteBase {
     // and check whether the earlier checkpoint files are deleted
     val checkpointFiles = stateStream.checkpointData.checkpointFiles.map(x => new File(x._2))
     advanceTimeWithRealDelay(ssc, secondNumBatches)
-    checkpointFiles.foreach(file => assert(!file.exists, "Checkpoint file '" + file + "' was not deleted"))
+    checkpointFiles.foreach(file =>
+      assert(!file.exists, "Checkpoint file '" + file + "' was not deleted"))
     ssc.stop()
 
     // Restart stream computation using the checkpoint file and check whether
@@ -110,19 +106,20 @@ class CheckpointSuite extends TestSuiteBase {
     ssc = new StreamingContext(checkpointDir)
     stateStream = ssc.graph.getOutputStreams().head.dependencies.head.dependencies.head
     logInfo("Restored data of state stream = \n[" + stateStream.generatedRDDs.mkString("\n") + "]")
-    assert(!stateStream.generatedRDDs.isEmpty, "No restored RDDs in state stream after recovery from first failure")
+    assert(!stateStream.generatedRDDs.isEmpty,
+      "No restored RDDs in state stream after recovery from first failure")
 
 
     // Run one batch to generate a new checkpoint file and check whether some RDD
     // is present in the checkpoint data or not
     ssc.start()
     advanceTimeWithRealDelay(ssc, 1)
-    assert(!stateStream.checkpointData.checkpointFiles.isEmpty, "No checkpointed RDDs in state stream before second failure")
+    assert(!stateStream.checkpointData.checkpointFiles.isEmpty,
+      "No checkpointed RDDs in state stream before second failure")
     stateStream.checkpointData.checkpointFiles.foreach {
-      case (time, data) => {
-        val file = new File(data.toString)
-        assert(file.exists(),
-          "Checkpoint file '" + file +"' for time " + time + " for state stream before seconds failure does not exist")
+      case (time, file) => {
+        assert(fs.exists(new Path(file)), "Checkpoint file '" + file +"' for time " + time +
+          " for state stream before seconds failure does not exist")
       }
     }
     ssc.stop()
@@ -132,7 +129,8 @@ class CheckpointSuite extends TestSuiteBase {
     ssc = new StreamingContext(checkpointDir)
     stateStream = ssc.graph.getOutputStreams().head.dependencies.head.dependencies.head
     logInfo("Restored data of state stream = \n[" + stateStream.generatedRDDs.mkString("\n") + "]")
-    assert(!stateStream.generatedRDDs.isEmpty, "No restored RDDs in state stream after recovery from second failure")
+    assert(!stateStream.generatedRDDs.isEmpty,
+      "No restored RDDs in state stream after recovery from second failure")
 
     // Adjust manual clock time as if it is being restarted after a delay
     System.setProperty("spark.streaming.manualClock.jump", (batchDuration.milliseconds * 7).toString)
@@ -143,6 +141,7 @@ class CheckpointSuite extends TestSuiteBase {
     ssc = null
   }
 
+
   // This tests whether the systm can recover from a master failure with simple
   // non-stateful operations. This assumes as reliable, replayable input
   // source - TestInputDStream.
@@ -191,6 +190,7 @@ class CheckpointSuite extends TestSuiteBase {
     testCheckpointedOperation(input, operation, output, 7)
   }
 
+
   // This tests whether file input stream remembers what files were seen before
   // the master failure and uses them again to process a large window operation.
   // It also tests whether batches, whose processing was incomplete due to the
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala
index 62a9f120b4..5fa14ad7c4 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala
@@ -23,7 +23,7 @@ import akka.actor.IOManager
 import akka.actor.Props
 import akka.util.ByteString
 
-import org.apache.spark.streaming.dstream.{NetworkReceiver, SparkFlumeEvent}
+import org.apache.spark.streaming.dstream.{FileInputDStream, NetworkReceiver, SparkFlumeEvent}
 import java.net.{InetSocketAddress, SocketException, Socket, ServerSocket}
 import java.io.{File, BufferedWriter, OutputStreamWriter}
 import java.util.concurrent.{Executors, TimeUnit, ArrayBlockingQueue}