spark-cassandra connector in local gives Spark cluster looks down - apache-spark

Question

I am very new to spark and cassandra. I am trying a simple java progam where I am trying to add new rows to cassandra table using spark-cassandra-connector provided by datastax.
I am running dse on my laptop . Using java, I am trying to save the data to cassandra DB thru Spark . Following is the code :
Map<String, String> extra = new HashMap<String, String>();
extra.put("city", "bangalore");
extra.put("dept", "software");
List<User> products = Arrays.asList(new User(1, "vamsi", extra));
JavaRDD<User> productsRDD = sc.parallelize(products);
javaFunctions(productsRDD, User.class).saveToCassandra("test", "users");
When i execute this code I am getting following error
16/03/26 20:57:31 INFO client.AppClient$ClientActor: Connecting to master spark://127.0.0.1:7077...
16/03/26 20:57:44 WARN scheduler.TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient memory
16/03/26 20:57:51 INFO client.AppClient$ClientActor: Connecting to master spark://127.0.0.1:7077...
16/03/26 20:57:59 WARN scheduler.TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient memory
16/03/26 20:58:11 ERROR client.AppClient$ClientActor: All masters are unresponsive! Giving up.
16/03/26 20:58:11 ERROR cluster.SparkDeploySchedulerBackend: Spark cluster looks dead, giving up.
16/03/26 20:58:11 INFO scheduler.TaskSchedulerImpl: Removed TaskSet 0.0, whose tasks have all completed, from pool
16/03/26 20:58:11 INFO scheduler.DAGScheduler: Failed to run runJob at RDDFunctions.scala:48
Exception in thread "main" org.apache.spark.SparkException: Job aborted: Spark cluster looks down
at org.apache.spark.scheduler.DAGScheduler$$anonfun$org$apache$spark$scheduler$DAGScheduler$$abortStage$1.apply(DAGScheduler.scala:1020)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$org$apache$spark$scheduler$DAGScheduler$$abortStage$1.apply(DAGScheduler.scala:1018)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$abortStage(DAGScheduler.scala:1018)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$processEvent$10.apply(DAGScheduler.scala:604)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$processEvent$10.apply(DAGScheduler.scala:604)
at scala.Option.foreach(Option.scala:236)
at org.apache.spark.scheduler.DAGScheduler.processEvent(DAGScheduler.scala:604)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$start$1$$anon$2$$anonfun$receive$1.applyOrElse(DAGScheduler.scala:190)
at akka.actor.ActorCell.receiveMessage(ActorCell.scala:498)
at akka.actor.ActorCell.invoke(ActorCell.scala:456)
at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:237)
at akka.dispatch.Mailbox.run(Mailbox.scala:219)
at akka.dispatch.ForkJoinExecutorConfigurator$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)

Answer 1

Looks like you need to fix your Spark configuration...see this:
http://www.datastax.com/dev/blog/common-spark-troubleshooting