If it matters, I have servers running at http://hivecluster2:4040/stages/ and http://hivecluster2:4041/stages/
When I run rdd.first, I see an item at http://hivecluster2:4041/stages/ but no tasks are running. Stage ID 1, first at <console>:46, Tasks: Succeeded/Total 0/16. On Mon, Jun 2, 2014 at 10:09 AM, Russell Jurney <russell.jur...@gmail.com> wrote: > 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 -- Russell Jurney twitter.com/rjurney russell.jur...@gmail.com datasyndrome.com
