Spark Streaming之一：StreamingContext解析

1.0 前言

目前Spark Streaming编程指南地址：

http://spark./docs/latest/streaming-programming-guide.html

1.1 创建StreamingContext对象

1.1.1通过SparkContext创建

源码如下：

def this(sparkContext:SparkContext, batchDuration: Duration) = {

   this(sparkContext,null,batchDuration)

  }

第一参数为sparkContext对象，第二个参数为批次时间；

创建实例：

val ssc = new StreamingContext(sc, Seconds(5))

 

1.1.2通过SparkConf创建

源码如下：

 defthis(conf:SparkConf, batchDuration: Duration) = {

   this(StreamingContext.createNewSparkContext(conf),null,batchDuration)

  }

第一参数为SparkConf对象，第二个参数为批次时间；

创建实例：

   valconf =newSparkConf().setAppName("StreamTest")

val ssc = newStreamingContext(conf,Seconds(5))

 

1.1.3通过SparkConf参数创建

源码如下：

 defthis(

     master: String,

     appName: String,

     batchDuration: Duration,

     sparkHome: String = null,

     jars: Seq[String] = Nil,

     environment: Map[String, String] = Map()) = {

   this(StreamingContext.createNewSparkContext(master,appName, sparkHome, jars, environment),

         null, batchDuration)

  }

第一参数为需要创建SparkConf对象的详细参数，master-spark地址，appName-对象名称，sparkHome- sparkHome环境变量，jars, environment，第二个参数为批次时间；

创建实例：

val ssc = newStreamingContext(“ spark://host:port”, "StreamTest", Seconds(5),      System.getenv("SPARK_HOME"),StreamingContext.jarOfClass(this.getClass))

 

1.1.4通过checkpointfile参数创建

源码如下：

 defthis(path:String, hadoopConf: Configuration) =

    this(null, CheckpointReader.read(path,new SparkConf(), hadoopConf).get,null)

第一参数为checkpoint file的路径，第二个参数为haoop的配置

源码如下：

def this(path:String) = this(path,newConfiguration)

第一参数为checkpoint file的路径

 

1.2创建Dstream监听对象

1.2.1 fileStream

源码如下：

/**

  * Create a input stream that monitors a Hadoop-compatible filesystem

  * for new files and reads them using the given key-value types and inputformat.

  * Files must be written to the monitored directory by "moving"them from another

  * location within the same file system. File names starting with . areignored.

  * @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

  * @tparam F Input format for reading HDFS file

  */

 deffileStream[

   K: ClassTag,

   V: ClassTag,

   F <: NewInputFormat[K, V]: ClassTag

 ] (directory: String): InputDStream[(K, V)] = {

   newFileInputDStream[K, V, F](this, directory)

 }

 

 /**

  * Create a input stream that monitors a Hadoop-compatible filesystem

  * for new files and reads them using the given key-value types and inputformat.

  * 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 ignoreexisting files in the directory

  * @tparam K Key type for reading HDFS file

  * @tparam V Value type for reading HDFS file

  * @tparam F Input format for reading HDFS file

  */

 deffileStream[

   K: ClassTag,

   V: ClassTag,

   F <: NewInputFormat[K, V]: ClassTag

 ] (directory: String, filter: Path => Boolean, newFilesOnly:Boolean): InputDStream[(K, V)] = {

   newFileInputDStream[K, V, F](this, directory, filter, newFilesOnly)

 }

参数：K-读入HDFS的Key的类型，V-读入HDFS的Value的类型，F-读入HDFS     的类型；directory-监听HDFS的路径，filter-对监听HDFS的文件进行过滤的函数，newFilesOnly-是否只监听新增文件；

fileStream可以通过设置filter函数，对监听目录下的文件进行过滤，只对满足条件的文件进行监听和处理；

默认过滤方法：

 defdefaultFilter(path: Path): Boolean = !path.getName().startsWith(".")

该方法是过滤以隐藏文件。

fileStream可以通过设置newFilesOnly为TRUE或者FALES，是否处理监听目录下已存在的文件，默认是不处理已存在文件，只处理新增加文件，如果设置为FALES，可以处理前一个窗口时间内的老文件。

源码如下：

 privatevalinitialModTimeIgnoreThreshold =if (newFilesOnly) System.currentTimeMillis()else0L

 

val modTimeIgnoreThreshold = math.max(

        initialModTimeIgnoreThreshold,   // initialthreshold based on newFilesOnly setting

        currentTime -durationToRemember.milliseconds  // trailing end of the remember window

      )

modTimeIgnoreThreshold是时间窗口过滤条件，通过newFilesOnly值来取的是当前时间或者前一个窗口时间。

创建实例：

// 创建新过滤函数

   defmyFilter(path:Path): Boolean = path.getName().contains("data")

// 创建fileStream

val data1 = ssc.fileStream[LongWritable,Text, TextInputFormat](Spath1, pa => myFilter(pa),false).map(_._2.toString)

 

1.2.2 textFileStream

源码如下：

/**

  * 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). Files must be written tothe

  * monitored directory by "moving" them from another locationwithin the same

  * file system. File names starting with . are ignored.

  * @param directory HDFS directory to monitor for new file

  */

 deftextFileStream(directory: String): DStream[String] = {

   fileStream[LongWritable, Text,TextInputFormat](directory).map(_._2.toString)

 }

参数：directory监听的目录；

其实textFileStream是fileStream的一个实例。

创建实例：

   valStreamFile1=ssc.textFileStream(Spath1)

 

1.2.3 socketTextStream

源码如下：

 /**

  * Create a input stream from TCP source hostname:port. Data is receivedusing

  * a TCP socket and the receive bytes is interpreted as UTF8 encoded `\n`delimited

  * lines.

  * @param hostname     Hostname to connect to for receiving data

  * @param port          Portto connect to for receiving data

  * @param storageLevel Storage level to use for storing the received objects

  *                      (default:StorageLevel.MEMORY_AND_DISK_SER_2)

  */

 defsocketTextStream(

     hostname: String,

     port: Int,

     storageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK_SER_2

   ): ReceiverInputDStream[String] = {

   socketStream[String](hostname, port, SocketReceiver.bytesToLines,storageLevel)

  }

参数：hostname是主机IP，port是端口号，storageLevel数据的存储级别，默认2份MEMORY_AND_DISK；

创建实例：

val lines = ssc.socketTextStream(serverIP, serverPort);

 

1.2.4 rawSocketStream

源码如下：

 /**

  * Create a input stream from network source hostname:port, where data isreceived

  * as serialized blocks (serialized using the Spark's serializer) thatcan be directly

  * pushed into the block manager without deserializing them. This is themost efficient

  * way to receive data.

  * @param hostname     Hostname to connect to for receiving data

  * @param port          Portto connect to for receiving data

  * @param storageLevel Storage level to use for storing the received objects

  *                      (default:StorageLevel.MEMORY_AND_DISK_SER_2)

  * @tparam T            Typeof the objects in the received blocks

  */

 defrawSocketStream[T: ClassTag](

     hostname: String,

     port: Int,

     storageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK_SER_2

   ): ReceiverInputDStream[T] = {

   newRawInputDStream[T](this, hostname, port, storageLevel)

  }

rawSocketStream类似于socketTextStream；参照socketTextStream。

 

1.2.5 networkStream

源码如下：

 /**

  * Create an input stream with any arbitrary user implemented receiver.

  * Find more details at:http://spark./docs/latest/streaming-custom-receivers.html

  * @param receiver Custom implementation of Receiver

  */

 @deprecated("Use receiverStream","1.0.0")

 defnetworkStream[T: ClassTag](

   receiver: Receiver[T]): ReceiverInputDStream[T] = {

   receiverStream(receiver)

  }

创建实例：

参照：http://spark./docs/latest/streaming-custom-receivers.html

 

1.2.6 receiverStream

源码如下：

 /**

  * Create an input stream with any arbitrary user implemented receiver.

  * Find more details at:http://spark./docs/latest/streaming-custom-receivers.html

  * @param receiver Custom implementation of Receiver

  */

 defreceiverStream[T: ClassTag](

   receiver: Receiver[T]): ReceiverInputDStream[T] = {

   newPluggableInputDStream[T](this, receiver)

  }

创建实例：

    val StreamFile1 = ssc.receiverStream (newCustomReceiver(host, port))

参照：http://spark./docs/latest/streaming-custom-receivers.html

 

1.2.7 actorStream

源码如下：

 /**

  * Create an input stream with any arbitrary user implemented actorreceiver.

  * Find more details at:http://spark./docs/latest/streaming-custom-receivers.html

  * @param props Props object defining creation of the actor

  * @param name Name of the actor

  * @param storageLevel RDD storage level (default:StorageLevel.MEMORY_AND_DISK_SER_2)

  *

  * @note An important point to note:

  *       Since Actor may exist outsidethe spark framework, It is thus user's responsibility

  *       to ensure the type safety,i.e parametrized type of data received and actorStream

  *       should be same.

  */

 defactorStream[T: ClassTag](

     props: Props,

     name: String,

     storageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK_SER_2,

     supervisorStrategy: SupervisorStrategy =ActorSupervisorStrategy.defaultStrategy

   ): ReceiverInputDStream[T] = {

   receiverStream(new ActorReceiver[T](props, name, storageLevel, supervisorStrategy))

  }

创建实例：

val StreamFile1 = ssc.actorStream[String](Props(newCustomActor()),"CustomReceiver")

参照：http://spark./docs/latest/streaming-custom-receivers.html

 

1.2.8 queueStream

源码如下：

/**

  * Create an input stream from a queue of RDDs. In each batch,

  * it will process either one or all of the RDDs returned by the queue.

  * @param queue      Queueof RDDs

  * @param oneAtATime Whether only one RDD should be consumed fromthe queue in every interval

  * @tparam T         Type ofobjects in the RDD

  */

 defqueueStream[T: ClassTag](

     queue: Queue[RDD[T]],

     oneAtATime: Boolean = true

   ): InputDStream[T] = {

   queueStream(queue, oneAtATime, sc.makeRDD(Seq[T](), 1))

 }

 

 /**

  * Create an input stream from a queue of RDDs. In each batch,

  * it will process either one or all of the RDDs returned by the queue.

  * @param queue      Queueof RDDs

  * @param oneAtATime Whether only one RDD should be consumed fromthe queue in every interval

  * @param defaultRDD Default RDD is returned by the DStream whenthe queue is empty.

  *                   Set as null ifno RDD should be returned when empty

  * @tparam T         Type ofobjects in the RDD

  */

 defqueueStream[T: ClassTag](

     queue: Queue[RDD[T]],

     oneAtATime: Boolean,

     defaultRDD: RDD[T]

   ): InputDStream[T] = {

   newQueueInputDStream(this, queue, oneAtATime, defaultRDD)

 }

 

1.2.9 union DStream

源码如下：

 /**

  * Create a unified DStream from multiple DStreams of the same type andsame slide duration.

  */

 defunion[T: ClassTag](streams: Seq[DStream[T]]): DStream[T] = {

   newUnionDStream[T](streams.toArray)

 }

对同一类型的DStream进行合并，生成一个新的DStream，其中要求DStream的数据格式一致，批次时间间隔一致。

 

1.2.10 transform DStream

源码如下：

 /**

  * Create a new DStream in which each RDD is generated by applying afunction on RDDs of

  * the DStreams.

  */

 deftransform[T: ClassTag](

     dstreams: Seq[DStream[_]],

     transformFunc: (Seq[RDD[_]], Time) => RDD[T]

   ): DStream[T] = {

   newTransformedDStream[T](dstreams, sparkContext.clean(transformFunc))

  }

对Dstream进行transform操作生成一个新的Dstream。

 

1.3 Checkpointing

状态的操作是基于多个批次的数据的。它包括基于window的操作和updateStateByKey。因为状态的操作要依赖于上一个批次的数据，所以它要根据时间，不断累积元数据。为了清空数据，它支持周期性的检查点，通过把中间结果保存到hdfs上。因为检查操作会导致保存到hdfs上的开销，所以设置这个时间间隔，要很慎重。对于小批次的数据，比如一秒的，检查操作会大大降低吞吐量。但是检查的间隔太长，会导致任务变大。通常来说，5-10秒的检查间隔时间是比较合适的。

实例：

   ssc.checkpoint("hdfs://192.168.1.100:9000/check")

   valStreamFile1=ssc.textFileStream(Spath1)

StreamFile1.checkpoint(Seconds(30))

 

转载请注明出处：

http://blog.csdn.NET/sunbow0/article/details/42966467