通过过滤逻辑将Spark流式传输到HBase

Question

我一直在尝试了解火花流和hbase的连接方式，但是没有成功。 我正在尝试做的是提供一个火花流，对该流进行处理并将结果存储在hbase表中。 到目前为止，这就是我所拥有的：

import org.apache.spark.SparkConf
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.streaming.StreamingContext._
import org.apache.spark.storage.StorageLevel
import org.apache.hadoop.hbase.HBaseConfiguration
import org.apache.hadoop.hbase.client.{HBaseAdmin,HTable,Put,Get}
import org.apache.hadoop.hbase.util.Bytes

def blah(row: Array[String]) {
  val hConf = new HBaseConfiguration()
  val hTable = new HTable(hConf, "table")
  val thePut = new Put(Bytes.toBytes(row(0)))
  thePut.add(Bytes.toBytes("cf"), Bytes.toBytes(row(0)), Bytes.toBytes(row(0)))
  hTable.put(thePut)
}

val ssc = new StreamingContext(sc, Seconds(1))
val lines = ssc.socketTextStream("localhost", 9999, StorageLevel.MEMORY_AND_DISK_SER)
val words = lines.map(_.split(","))
val store = words.foreachRDD(rdd => rdd.foreach(blah))
ssc.start()

我目前正在spark-shell中运行以上代码。 我不确定自己在做什么错。
我在shell中收到以下错误：

14/09/03 16:21:03 ERROR scheduler.JobScheduler: Error running job streaming job 1409786463000 ms.0

org.apache.spark.SparkException: Job aborted due to stage failure: Task not serializable: java.io.NotSerializableException: org.apache.spark.streaming.StreamingContext

at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1033)

at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1017)

at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1015)

at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)

at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)

at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1015)

at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$submitMissingTasks(DAGScheduler.scala:770)

at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$submitStage(DAGScheduler.scala:713)

at org.apache.spark.scheduler.DAGScheduler.handleJobSubmitted(DAGScheduler.scala:697)

at org.apache.spark.scheduler.DAGSchedulerEventProcessActor$$anonfun$receive$2.applyOrElse(DAGScheduler.scala:1176)

at akka.actor.ActorCell.receiveMessage(ActorCell.scala:498)

at akka.actor.ActorCell.invoke(ActorCell.scala:456)

at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:237)

at akka.dispatch.Mailbox.run(Mailbox.scala:219)

at akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:386)

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)

我还仔细检查了hbase表，以防万一，没有任何新内容写入其中。

我在另一个终端上运行nc -lk 9999，将数据输入到spark-shell中进行测试。

Answer 1

在spark用户组的用户的帮助下，我能够弄清楚如何使其工作。 看来我需要将流，映射和foreach调用包装在可序列化的对象周围：

import org.apache.spark.SparkConf 
import org.apache.spark.streaming.{Seconds, StreamingContext} 
import org.apache.spark.streaming.StreamingContext._ 
import org.apache.spark.storage.StorageLevel 
import org.apache.hadoop.hbase.HBaseConfiguration 
import org.apache.hadoop.hbase.client.{HBaseAdmin,HTable,Put,Get} 
import org.apache.hadoop.hbase.util.Bytes 

object Blaher {
  def blah(row: Array[String]) { 
    val hConf = new HBaseConfiguration() 
    val hTable = new HTable(hConf, "table") 
    val thePut = new Put(Bytes.toBytes(row(0))) 
    thePut.add(Bytes.toBytes("cf"), Bytes.toBytes(row(0)), Bytes.toBytes(row(0))) 
    hTable.put(thePut) 
  } 
}

object TheMain extends Serializable{
  def run() {
    val ssc = new StreamingContext(sc, Seconds(1)) 
    val lines = ssc.socketTextStream("localhost", 9999, StorageLevel.MEMORY_AND_DISK_SER) 
    val words = lines.map(_.split(",")) 
    val store = words.foreachRDD(rdd => rdd.foreach(Blaher.blah)) 
    ssc.start()
  } 
}

TheMain.run()

Answer 2

似乎是典型的反模式。 有关正确的模式，请参阅http://spark.apache.org/docs/latest/streaming-programming-guide.html上的 “使用foreachRDD的设计模式”一章。