Spark Streaming监控HDFS输入流

Spark Streaming实时监控HDFS文件系统写入的数据。

以下是相关代码和注解以及特殊方法的源代码注释：

object SourceHdfs {  /**    * Definition HDFS checkpoint directory address    */  val checkpointDirectory = "hdfs://master:9000/sparkStreaming/Checkpoint_Data"  def main(args: Array[String]): Unit = {    /**      * Either recreate a StreamingContext from checkpoint data or create a new StreamingContext.      */    val context = StreamingContext.getOrCreate(checkpointDirectory, createContext _)    /**      * Configure inputDStream source that HDFS address      * No Receiver, SparkStreaming application monitor batch by timer      */    val DStream = context.textFileStream("hdfs://master:9000/quality/clipper_erp/2017-07-11")    val wordCount = DStream.flatMap(_.split(" ")).map(word => (word, 1)).reduceByKey(_+_)    wordCount.print()    context.start()    context.awaitTermination()  }  /**    * Create spark streamingContext function for getOrCreate method    */  def createContext(): StreamingContext ={    val conf = new SparkConf()      .setAppName("HDFSInputData")      .setMaster("spark://master:7077")    val ssc = new StreamingContext(conf, Seconds(10))    ssc.checkpoint(checkpointDirectory)    ssc  }}

以下是完整代码的地址(https://github.com/DragonTong/Streaming/blob/master/src/main/scala/streaming/SourceHdfs.scala)

1. checkpoint 保持RDD状态和容错

  /** 1. Set the context to periodically checkpoint the DStream operations for driver 2. fault-tolerance. 3. @param directory HDFS-compatible directory where the checkpoint data will be reliably stored. 4.                  Note that this must be a fault-tolerant file system like HDFS.   */def checkpoint(directory: String) {    if (directory != null) {      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    }  }

2. getOrCreate 创建StreamingContext

 /**   * Either recreate a StreamingContext from checkpoint data or create a new StreamingContext.   * If checkpoint data exists in the provided `checkpointPath`, then StreamingContext will be   * recreated from the checkpoint data. If the data does not exist, then the StreamingContext   * will be created by called the provided `creatingFunc`.   *   * @param checkpointPath Checkpoint directory used in an earlier StreamingContext program   * @param creatingFunc   Function to create a new StreamingContext   * @param hadoopConf     Optional Hadoop configuration if necessary for reading from the   *                       file system   * @param createOnError  Optional, whether to create a new StreamingContext if there is an   *                       error in reading checkpoint data. By default, an exception will be   *                       thrown on error.   */  def getOrCreate(      checkpointPath: String,      creatingFunc: () => StreamingContext,      hadoopConf: Configuration = SparkHadoopUtil.get.conf,      createOnError: Boolean = false    ): StreamingContext = {    val checkpointOption = CheckpointReader.read(      checkpointPath, new SparkConf(), hadoopConf, createOnError)    checkpointOption.map(new StreamingContext(null, _, null)).getOrElse(creatingFunc())  }

编译程序，在集群环境下运行：
spark-submit –class streaming.SourceHdfs –master spark://master:7077 spark.streaming.pro-1.0-SNAPSHOT.jar

注：第二次运行时会报错需要把Checkpoint_Data文件夹里面的内容删除。深入理解Checkpoint后再解决这个BUG。

报错内容：

Exception in thread "main" org.apache.spark.SparkException: org.apache.spark.streaming.dstream.ShuffledDStream@321ca237 has not been initialized    at org.apache.spark.streaming.dstream.DStream.isTimeValid(DStream.scala:312)    at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:333)at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:333)    at scala.Option.orElse(Option.scala:289)    at org.apache.spark.streaming.dstream.DStream.getOrCompute(DStream.scala:330)    at org.apache.spark.streaming.dstream.ForEachDStream.generateJob(ForEachDStream.scala:48)    at org.apache.spark.streaming.DStreamGraph$$anonfun$1.apply(DStreamGraph.scala:117)at org.apache.spark.streaming.DStreamGraph$$anonfun$1.apply(DStreamGraph.scala:116)    at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)    at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)    at scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:241)    at scala.collection.AbstractTraversable.flatMap(Traversable.scala:104)    at org.apache.spark.streaming.DStreamGraph.generateJobs(DStreamGraph.scala:116)    at org.apache.spark.streaming.scheduler.JobGenerator$$anonfun$restart$4.apply(JobGenerator.scala:234)at org.apache.spark.streaming.scheduler.JobGenerator$$anonfun$restart$4.apply(JobGenerator.scala:229)    at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)    at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:186)    at org.apache.spark.streaming.scheduler.JobGenerator.restart(JobGenerator.scala:229)    at org.apache.spark.streaming.scheduler.JobGenerator.start(JobGenerator.scala:98)    at org.apache.spark.streaming.scheduler.JobScheduler.start(JobScheduler.scala:102)    at org.apache.spark.streaming.StreamingContext$$anonfun$liftedTree1$1$1.apply$mcV$sp(StreamingContext.scala:583)at org.apache.spark.streaming.StreamingContext$$anonfun$liftedTree1$1$1.apply(StreamingContext.scala:578)    at org.apache.spark.streaming.StreamingContext$$anonfun$liftedTree1$1$1.apply(StreamingContext.scala:578)at ... run in separate thread using org.apache.spark.util.ThreadUtils ... ()at org.apache.spark.streaming.StreamingContext.liftedTree1$1(StreamingContext.scala:578)at org.apache.spark.streaming.StreamingContext.start(StreamingContext.scala:572)at streaming.SourceHdfs$.main(SourceHdfs.scala:21)at streaming.SourceHdfs.main(SourceHdfs.scala)at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)at java.lang.reflect.Method.invoke(Method.java:497)at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:738)    at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:187)    at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:212)    at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:126)    at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)17/07/11 19:19:27 INFO spark.SparkContext: Invoking stop() from shutdown hook