Spark Streaming into HBase with filtering logic - scala

Question

I have been trying to understand how spark streaming and hbase connect, but have not been successful. What I am trying to do is given a spark stream, process that stream and store the results in an hbase table. So far this is what I have:
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()
I am currently running the above code in spark-shell. I am not sure what I am doing wrong.
I get the following error in the shell:
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)
I also double checked the hbase table, just in case, and nothing new is written in there.
I am running nc -lk 9999 on another terminal to feed in data into the spark-shell for testing.

Answer 1

With help from users on the spark user group, I was able to figure out how to get this to work. It looks like I needed to wrap my streaming, mapping and foreach call around a serializable object:
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

Seems to be a typical antipattern.
See "Design Patterns for using foreachRDD" chapter at http://spark.apache.org/docs/latest/streaming-programming-guide.html for correct pattern.