Looks like just worker and master processes are running: [hivedata@hivecluster2 ~]$ jps
10425 Jps [hivedata@hivecluster2 ~]$ ps aux|grep spark hivedata 10424 0.0 0.0 103248 820 pts/3 S+ 10:05 0:00 grep spark root 10918 0.5 1.4 4752880 230512 ? Sl May27 41:43 java -cp :/opt/cloudera/parcels/SPARK-0.9.0-1.cdh4.6.0.p0.98/lib/spark/conf:/opt/cloudera/parcels/SPARK-0.9.0-1.cdh4.6.0.p0.98/lib/spark/core/lib/*:/opt/cloudera/parcels/SPARK-0.9.0-1.cdh4.6.0.p0.98/lib/spark/repl/lib/*:/opt/cloudera/parcels/SPARK-0.9.0-1.cdh4.6.0.p0.98/lib/spark/examples/lib/*:/opt/cloudera/parcels/SPARK-0.9.0-1.cdh4.6.0.p0.98/lib/spark/bagel/lib/*:/opt/cloudera/parcels/SPARK-0.9.0-1.cdh4.6.0.p0.98/lib/spark/mllib/lib/*:/opt/cloudera/parcels/SPARK-0.9.0-1.cdh4.6.0.p0.98/lib/spark/streaming/lib/*:/opt/cloudera/parcels/SPARK-0.9.0-1.cdh4.6.0.p0.98/lib/spark/lib/*:/etc/hadoop/conf:/opt/cloudera/parcels/CDH/lib/hadoop/*:/opt/cloudera/parcels/CDH/lib/hadoop/../hadoop-hdfs/*:/opt/cloudera/parcels/CDH/lib/hadoop/../hadoop-yarn/*:/opt/cloudera/parcels/CDH/lib/hadoop/../hadoop-mapreduce/*:/opt/cloudera/parcels/SPARK-0.9.0-1.cdh4.6.0.p0.98/lib/spark/lib/scala-library.jar:/opt/cloudera/parcels/SPARK-0.9.0-1.cdh4.6.0.p0.98/lib/spark/lib/scala-compiler.jar:/opt/cloudera/parcels/SPARK-0.9.0-1.cdh4.6.0.p0.98/lib/spark/lib/jline.jar -Dspark.akka.logLifecycleEvents=true -Djava.library.path=/opt/cloudera/parcels/SPARK-0.9.0-1.cdh4.6.0.p0.98/lib/spark/lib:/opt/cloudera/parcels/CDH/lib/hadoop/lib/native -Xms512m -Xmx512m org.apache.spark.deploy.master.Master --ip hivecluster2 --port 7077 --webui-port 18080 root 12715 0.0 0.0 148028 656 ? S May27 0:00 sudo /opt/cloudera/parcels/SPARK/lib/spark/bin/spark-class org.apache.spark.deploy.worker.Worker spark://hivecluster2:7077 root 12716 0.3 1.1 4155884 191340 ? Sl May27 30:21 java -cp :/opt/cloudera/parcels/SPARK/lib/spark/conf:/opt/cloudera/parcels/SPARK/lib/spark/core/lib/*:/opt/cloudera/parcels/SPARK/lib/spark/repl/lib/*:/opt/cloudera/parcels/SPARK/lib/spark/examples/lib/*:/opt/cloudera/parcels/SPARK/lib/spark/bagel/lib/*:/opt/cloudera/parcels/SPARK/lib/spark/mllib/lib/*:/opt/cloudera/parcels/SPARK/lib/spark/streaming/lib/*:/opt/cloudera/parcels/SPARK/lib/spark/lib/*:/etc/hadoop/conf:/opt/cloudera/parcels/CDH/lib/hadoop/*:/opt/cloudera/parcels/CDH/lib/hadoop/../hadoop-hdfs/*:/opt/cloudera/parcels/CDH/lib/hadoop/../hadoop-yarn/*:/opt/cloudera/parcels/CDH/lib/hadoop/../hadoop-mapreduce/*:/opt/cloudera/parcels/SPARK/lib/spark/lib/scala-library.jar:/opt/cloudera/parcels/SPARK/lib/spark/lib/scala-compiler.jar:/opt/cloudera/parcels/SPARK/lib/spark/lib/jline.jar -Dspark.akka.logLifecycleEvents=true -Djava.library.path=/opt/cloudera/parcels/SPARK/lib/spark/lib:/opt/cloudera/parcels/CDH/lib/hadoop/lib/native -Xms512m -Xmx512m org.apache.spark.deploy.worker.Worker spark://hivecluster2:7077 On Sun, Jun 1, 2014 at 7:41 PM, Aaron Davidson <ilike...@gmail.com> wrote: > Sounds like you have two shells running, and the first one is talking all > your resources. Do a "jps" and kill the other guy, then try again. > > By the way, you can look at http://localhost:8080 (replace localhost with > the server your Spark Master is running on) to see what applications are > currently started, and what resource allocations they have. > > > On Sun, Jun 1, 2014 at 6:47 PM, Russell Jurney <russell.jur...@gmail.com> > wrote: > >> Thanks again. Run results here: >> https://gist.github.com/rjurney/dc0efae486ba7d55b7d5 >> >> This time I get a port already in use exception on 4040, but it isn't >> fatal. Then when I run rdd.first, I get this over and over: >> >> 14/06/01 18:35:40 WARN scheduler.TaskSchedulerImpl: Initial job has not >> accepted any resources; check your cluster UI to ensure that workers are >> registered and have sufficient memory >> >> >> >> >> >> >> On Sun, Jun 1, 2014 at 3:09 PM, Aaron Davidson <ilike...@gmail.com> >> wrote: >> >>> You can avoid that by using the constructor that takes a SparkConf, a la >>> >>> val conf = new SparkConf() >>> conf.setJars("avro.jar", ...) >>> val sc = new SparkContext(conf) >>> >>> >>> On Sun, Jun 1, 2014 at 2:32 PM, Russell Jurney <russell.jur...@gmail.com >>> > wrote: >>> >>>> Followup question: the docs to make a new SparkContext require that I >>>> know where $SPARK_HOME is. However, I have no idea. Any idea where that >>>> might be? >>>> >>>> >>>> On Sun, Jun 1, 2014 at 10:28 AM, Aaron Davidson <ilike...@gmail.com> >>>> wrote: >>>> >>>>> Gotcha. The easiest way to get your dependencies to your Executors >>>>> would probably be to construct your SparkContext with all necessary jars >>>>> passed in (as the "jars" parameter), or inside a SparkConf with setJars(). >>>>> Avro is a "necessary jar", but it's possible your application also needs >>>>> to >>>>> distribute other ones to the cluster. >>>>> >>>>> An easy way to make sure all your dependencies get shipped to the >>>>> cluster is to create an assembly jar of your application, and then you >>>>> just >>>>> need to tell Spark about that jar, which includes all your application's >>>>> transitive dependencies. Maven and sbt both have pretty straightforward >>>>> ways of producing assembly jars. >>>>> >>>>> >>>>> On Sat, May 31, 2014 at 11:23 PM, Russell Jurney < >>>>> russell.jur...@gmail.com> wrote: >>>>> >>>>>> Thanks for the fast reply. >>>>>> >>>>>> I am running CDH 4.4 with the Cloudera Parcel of Spark 0.9.0, in >>>>>> standalone mode. >>>>>> >>>>>> >>>>>> On Saturday, May 31, 2014, Aaron Davidson <ilike...@gmail.com> wrote: >>>>>> >>>>>>> First issue was because your cluster was configured incorrectly. You >>>>>>> could probably read 1 file because that was done on the driver node, but >>>>>>> when it tried to run a job on the cluster, it failed. >>>>>>> >>>>>>> Second issue, it seems that the jar containing avro is not getting >>>>>>> propagated to the Executors. What version of Spark are you running on? >>>>>>> What >>>>>>> deployment mode (YARN, standalone, Mesos)? >>>>>>> >>>>>>> >>>>>>> On Sat, May 31, 2014 at 9:37 PM, Russell Jurney < >>>>>>> russell.jur...@gmail.com> wrote: >>>>>>> >>>>>>> Now I get this: >>>>>>> >>>>>>> scala> rdd.first >>>>>>> >>>>>>> 14/05/31 21:36:28 INFO spark.SparkContext: Starting job: first at >>>>>>> <console>:41 >>>>>>> >>>>>>> 14/05/31 21:36:28 INFO scheduler.DAGScheduler: Got job 4 (first at >>>>>>> <console>:41) with 1 output partitions (allowLocal=true) >>>>>>> >>>>>>> 14/05/31 21:36:28 INFO scheduler.DAGScheduler: Final stage: Stage 4 >>>>>>> (first at <console>:41) >>>>>>> >>>>>>> 14/05/31 21:36:28 INFO scheduler.DAGScheduler: Parents of final >>>>>>> stage: List() >>>>>>> >>>>>>> 14/05/31 21:36:28 INFO scheduler.DAGScheduler: Missing parents: >>>>>>> List() >>>>>>> >>>>>>> 14/05/31 21:36:28 INFO scheduler.DAGScheduler: Computing the >>>>>>> requested partition locally >>>>>>> >>>>>>> 14/05/31 21:36:28 INFO rdd.HadoopRDD: Input split: >>>>>>> hdfs://hivecluster2/securityx/web_proxy_mef/2014/05/29/22/part-m-00000.avro:0+3864 >>>>>>> >>>>>>> 14/05/31 21:36:28 INFO spark.SparkContext: Job finished: first at >>>>>>> <console>:41, took 0.037371256 s >>>>>>> >>>>>>> 14/05/31 21:36:28 INFO spark.SparkContext: Starting job: first at >>>>>>> <console>:41 >>>>>>> >>>>>>> 14/05/31 21:36:28 INFO scheduler.DAGScheduler: Got job 5 (first at >>>>>>> <console>:41) with 16 output partitions (allowLocal=true) >>>>>>> >>>>>>> 14/05/31 21:36:28 INFO scheduler.DAGScheduler: Final stage: Stage 5 >>>>>>> (first at <console>:41) >>>>>>> >>>>>>> 14/05/31 21:36:28 INFO scheduler.DAGScheduler: Parents of final >>>>>>> stage: List() >>>>>>> >>>>>>> 14/05/31 21:36:28 INFO scheduler.DAGScheduler: Missing parents: >>>>>>> List() >>>>>>> >>>>>>> 14/05/31 21:36:28 INFO scheduler.DAGScheduler: Submitting Stage 5 >>>>>>> (HadoopRDD[0] at hadoopRDD at <console>:37), which has no missing >>>>>>> parents >>>>>>> >>>>>>> 14/05/31 21:36:28 INFO scheduler.DAGScheduler: Submitting 16 missing >>>>>>> tasks from Stage 5 (HadoopRDD[0] at hadoopRDD at <console>:37) >>>>>>> >>>>>>> 14/05/31 21:36:28 INFO scheduler.TaskSchedulerImpl: Adding task set >>>>>>> 5.0 with 16 tasks >>>>>>> >>>>>>> 14/05/31 21:36:28 INFO scheduler.TaskSetManager: Starting task 5.0:0 >>>>>>> as TID 92 on executor 2: hivecluster3 (NODE_LOCAL) >>>>>>> >>>>>>> 14/05/31 21:36:28 INFO scheduler.TaskSetManager: Serialized task >>>>>>> 5.0:0 as 1294 bytes in 1 ms >>>>>>> >>>>>>> 14/05/31 21:36:28 INFO scheduler.TaskSetManager: Starting task 5.0:3 >>>>>>> as TID 93 on executor 1: hivecluster5.labs.lan (NODE_LOCAL) >>>>>>> >>>>>>> 14/05/31 21:36:28 INFO scheduler.TaskSetManager: Serialized task >>>>>>> 5.0:3 as 1294 bytes in 0 ms >>>>>>> >>>>>>> 14/05/31 21:36:28 INFO scheduler.TaskSetManager: Starting task 5.0:1 >>>>>>> as TID 94 on executor 4: hivecluster4 (NODE_LOCAL) >>>>>>> >>>>>>> 14/05/31 21:36:28 INFO scheduler.TaskSetManager: Serialized task >>>>>>> 5.0:1 as 1294 bytes in 1 ms >>>>>>> >>>>>>> 14/05/31 21:36:28 INFO scheduler.TaskSetManager: Starting task 5.0:2 >>>>>>> as TID 95 on executor 0: hivecluster6.labs.lan (NODE_LOCAL) >>>>>>> >>>>>>> 14/05/31 21:36:28 INFO scheduler.TaskSetManager: Serialized task >>>>>>> 5.0:2 as 1294 bytes in 0 ms >>>>>>> >>>>>>> 14/05/31 21:36:28 INFO scheduler.TaskSetManager: Starting task 5.0:4 >>>>>>> as TID 96 on executor 3: hivecluster1.labs.lan (NODE_LOCAL) >>>>>>> >>>>>>> 14/05/31 21:36:28 INFO scheduler.TaskSetManager: Serialized task >>>>>>> 5.0:4 as 1294 bytes in 0 ms >>>>>>> >>>>>>> 14/05/31 21:36:28 INFO scheduler.TaskSetManager: Starting task 5.0:6 >>>>>>> as TID 97 on executor 2: hivecluster3 (NODE_LOCAL) >>>>>>> >>>>>>> 14/05/31 21:36:28 INFO scheduler.TaskSetManager: Serialized task >>>>>>> 5.0:6 as 1294 bytes in 0 ms >>>>>>> >>>>>>> 14/05/31 21:36:28 INFO scheduler.TaskSetManager: Starting task 5.0:5 >>>>>>> as TID 98 on executor 1: hivecluster5.labs.lan (NODE_LOCAL) >>>>>>> >>>>>>> 14/05/31 21:36:28 INFO scheduler.TaskSetManager: Serialized task >>>>>>> 5.0:5 as 1294 bytes in 0 ms >>>>>>> >>>>>>> 14/05/31 21:36:28 INFO scheduler.TaskSetManager: Starting task 5.0:8 >>>>>>> as TID 99 on executor 4: hivecluster4 (NODE_LOCAL) >>>>>>> >>>>>>> 14/05/31 21:36:28 INFO scheduler.TaskSetManager: Serialized task >>>>>>> 5.0:8 as 1294 bytes in 0 ms >>>>>>> >>>>>>> 14/05/31 21:36:28 INFO scheduler.TaskSetManager: Starting task 5.0:7 >>>>>>> as TID 100 on executor 0: hivecluster6.labs.lan (NODE_LOCAL) >>>>>>> >>>>>>> 14/05/31 21:36:28 INFO scheduler.TaskSetManager: Serialized task >>>>>>> 5.0:7 as 1294 bytes in 0 ms >>>>>>> >>>>>>> 14/05/31 21:36:28 INFO scheduler.TaskSetManager: Starting task >>>>>>> 5.0:10 as TID 101 on executor 3: hivecluster1.labs.lan (NODE_LOCAL) >>>>>>> >>>>>>> 14/05/31 21:36:28 INFO scheduler.TaskSetManager: Serialized task >>>>>>> 5.0:10 as 1294 bytes in 0 ms >>>>>>> >>>>>>> 14/05/31 21:36:28 INFO scheduler.TaskSetManager: Starting task >>>>>>> 5.0:14 as TID 102 on executor 2: hivecluster3 (NODE_LOCAL) >>>>>>> >>>>>>> 14/05/31 21:36:28 INFO scheduler.TaskSetManager: Serialized task >>>>>>> 5.0:14 as 1294 bytes in 0 ms >>>>>>> >>>>>>> 14/05/31 21:36:28 INFO scheduler.TaskSetManager: Starting task 5.0:9 >>>>>>> as TID 103 on executor 1: hivecluster5.labs.lan (NODE_LOCAL) >>>>>>> >>>>>>> 14/05/31 21:36:28 INFO scheduler.TaskSetManager: Serialized task >>>>>>> 5.0:9 as 1294 bytes in 0 ms >>>>>>> >>>>>>> 14/05/31 21:36:28 INFO scheduler.TaskSetManager: Starting task >>>>>>> 5.0:11 as TID 104 on executor 4: hivecluster4 (N >>>>>>> >>>>>>> >>>>>> >>>>>> -- >>>>>> Russell Jurney twitter.com/rjurney russell.jur...@gmail.com >>>>>> datasyndrome.com >>>>>> >>>>> >>>>> >>>> >>>> >>>> -- >>>> Russell Jurney twitter.com/rjurney russell.jur...@gmail.com >>>> datasyndrome.com >>>> >>> >>> >> >> >> -- >> Russell Jurney twitter.com/rjurney russell.jur...@gmail.com datasyndrome. >> com >> > > -- Russell Jurney twitter.com/rjurney russell.jur...@gmail.com datasyndrome.com
