Alonso, The CDH VM uses YARN and the default deploy mode is client. I’ve been able to use the CDH VM for many learning scenarios.
http://www.cloudera.com/documentation/enterprise/latest.html http://www.cloudera.com/documentation/enterprise/latest/topics/spark.html David Newberger From: Alonso [mailto:alons...@gmail.com] Sent: Friday, June 3, 2016 5:39 AM To: user@spark.apache.org Subject: About a problem running a spark job in a cdh-5.7.0 vmware image. Hi, i am developing a project that needs to use kafka, spark-streaming and spark-mllib, this is the github project<https://github.com/alonsoir/awesome-recommendation-engine/tree/develop>. I am using a vmware cdh-5.7-0 image, with 4 cores and 8 GB of ram, the file that i want to use is only 16 MB, if i finding problems related with resources because the process outputs this message: .set("spark.driver.allowMultipleContexts", "true") <https://about.me/alonso.isidoro.roman?promo=email_sig&utm_source=email_sig&utm_medium=email_sig&utm_campaign=external_links> 16/06/03 11:58:09 WARN TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources when i go to spark-master page, i can see this: Spark Master at spark://192.168.30.137:7077 URL: spark://192.168.30.137:7077 REST URL: spark://192.168.30.137:6066 (cluster mode) Alive Workers: 0 Cores in use: 0 Total, 0 Used Memory in use: 0.0 B Total, 0.0 B Used Applications: 2 Running, 0 Completed Drivers: 0 Running, 0 Completed Status: ALIVE Workers Worker Id Address State Cores Memory Running Applications Application ID Name Cores Memory per Node Submitted Time User State Duration app-20160603115752-0001 (kill) AmazonKafkaConnector 0 1024.0 MB 2016/06/03 11:57:52 cloudera WAITING 2.0 min app-20160603115751-0000 (kill) AmazonKafkaConnector 0 1024.0 MB 2016/06/03 11:57:51 cloudera WAITING 2.0 min And this is the spark-worker output: Spark Worker at 192.168.30.137:7078 ID: worker-20160603115937-192.168.30.137-7078 Master URL: Cores: 4 (0 Used) Memory: 6.7 GB (0.0 B Used) Back to Master Running Executors (0) ExecutorID Cores State Memory Job Details Logs It is weird isn't ? master url is not set up and there is not any ExecutorID, Cores, so on so forth... If i do a ps xa | grep spark, this is the output: [cloudera@quickstart bin]$ ps xa | grep spark 6330 ? Sl 0:11 /usr/java/jdk1.7.0_67-cloudera/bin/java -cp /usr/lib/spark/conf/:/usr/lib/spark/lib/spark-assembly-1.6.0-cdh5.7.0-hadoop2.6.0-cdh5.7.0.jar:/etc/hadoop/conf/:/usr/lib/spark/lib/spark-assembly.jar:/usr/lib/hadoop/lib/*:/usr/lib/hadoop/*:/usr/lib/hadoop-hdfs/lib/*:/usr/lib/hadoop-hdfs/*:/usr/lib/hadoop-mapreduce/lib/*:/usr/lib/hadoop-mapreduce/*:/usr/lib/hadoop-yarn/lib/*:/usr/lib/hadoop-yarn/*:/usr/lib/hive/lib/*:/usr/lib/flume-ng/lib/*:/usr/lib/paquet/lib/*:/usr/lib/avro/lib/* -Dspark.deploy.defaultCores=4 -Xms1g -Xmx1g -XX:MaxPermSize=256m org.apache.spark.deploy.master.Master 6674 ? Sl 0:12 /usr/java/jdk1.7.0_67-cloudera/bin/java -cp /etc/spark/conf/:/usr/lib/spark/lib/spark-assembly-1.6.0-cdh5.7.0-hadoop2.6.0-cdh5.7.0.jar:/etc/hadoop/conf/:/usr/lib/spark/lib/spark-assembly.jar:/usr/lib/hadoop/lib/*:/usr/lib/hadoop/*:/usr/lib/hadoop-hdfs/lib/*:/usr/lib/hadoop-hdfs/*:/usr/lib/hadoop-mapreduce/lib/*:/usr/lib/hadoop-mapreduce/*:/usr/lib/hadoop-yarn/lib/*:/usr/lib/hadoop-yarn/*:/usr/lib/hive/lib/*:/usr/lib/flume-ng/lib/*:/usr/lib/paquet/lib/*:/usr/lib/avro/lib/* -Dspark.history.fs.logDirectory=hdfs:///user/spark/applicationHistory -Dspark.history.ui.port=18088 -Xms1g -Xmx1g -XX:MaxPermSize=256m org.apache.spark.deploy.history.HistoryServer 8153 pts/1 Sl+ 0:14 /usr/java/jdk1.7.0_67-cloudera/bin/java -cp /home/cloudera/awesome-recommendation-engine/target/pack/lib/* -Dprog.home=/home/cloudera/awesome-recommendation-engine/target/pack -Dprog.version=1.0-SNAPSHOT example.spark.AmazonKafkaConnector 192.168.1.35:9092 amazonRatingsTopic 8413 ? Sl 0:04 /usr/java/jdk1.7.0_67-cloudera/bin/java -cp /usr/lib/spark/conf/:/usr/lib/spark/lib/spark-assembly-1.6.0-cdh5.7.0-hadoop2.6.0-cdh5.7.0.jar:/etc/hadoop/conf/:/usr/lib/spark/lib/spark-assembly.jar:/usr/lib/hadoop/lib/*:/usr/lib/hadoop/*:/usr/lib/hadoop-hdfs/lib/*:/usr/lib/hadoop-hdfs/*:/usr/lib/hadoop-mapreduce/lib/*:/usr/lib/hadoop-mapreduce/*:/usr/lib/hadoop-yarn/lib/*:/usr/lib/hadoop-yarn/*:/usr/lib/hive/lib/*:/usr/lib/flume-ng/lib/*:/usr/lib/paquet/lib/*:/usr/lib/avro/lib/* -Xms1g -Xmx1g -XX:MaxPermSize=256m org.apache.spark.deploy.worker.Worker spark://quickstart.cloudera:7077 8619 pts/3 S+ 0:00 grep spark master is set up with four cores and 1 GB and worker has not any dedicated core and it is using 1GB, that is weird isn't ? I have configured the vmware image with 4 cores (from eight) and 8 GB (from 16). This is how it looks my build.sbt: libraryDependencies ++= Seq( "org.apache.kafka" % "kafka_2.10" % "0.8.1" exclude("javax.jms", "jms") exclude("com.sun.jdmk", "jmxtools") exclude("com.sun.jmx", "jmxri"), //not working play module!! check //jdbc, //anorm, //cache, // HTTP client "net.databinder.dispatch" %% "dispatch-core" % "0.11.1", // HTML parser "org.jodd" % "jodd-lagarto" % "3.5.2", "com.typesafe" % "config" % "1.2.1", "com.typesafe.play" % "play-json_2.10" % "2.4.0-M2", "org.scalatest" % "scalatest_2.10" % "2.2.1" % "test", "org.twitter4j" % "twitter4j-core" % "4.0.2", "org.twitter4j" % "twitter4j-stream" % "4.0.2", "org.codehaus.jackson" % "jackson-core-asl" % "1.6.1", "org.scala-tools.testing" % "specs_2.8.0" % "1.6.5" % "test", "org.apache.spark" % "spark-streaming-kafka_2.10" % "1.6.0-cdh5.7.0", "org.apache.spark" % "spark-core_2.10" % "1.6.0-cdh5.7.0", "org.apache.spark" % "spark-streaming_2.10" % "1.6.0-cdh5.7.0", "org.apache.spark" % "spark-sql_2.10" % "1.6.0-cdh5.7.0", "org.apache.spark" % "spark-mllib_2.10" % "1.6.0-cdh5.7.0", "com.google.code.gson" % "gson" % "2.6.2", "commons-cli" % "commons-cli" % "1.3.1", "com.stratio.datasource" % "spark-mongodb_2.10" % "0.11.1", // Akka "com.typesafe.akka" %% "akka-actor" % akkaVersion, "com.typesafe.akka" %% "akka-slf4j" % akkaVersion, // MongoDB "org.reactivemongo" %% "reactivemongo" % "0.10.0" ) packAutoSettings As you can see, i am using the exact version of spark modules for the pseudo cluster and i want to use sbt-pack in order to create an unix command, this is how i am declaring programmatically the spark context : val sparkConf = new SparkConf().setAppName("AmazonKafkaConnector") //.setMaster("local[4]") .setMaster("spark://192.168.30.137:7077") .set("spark.cores.max", "2") ... val ratingFile= "hdfs://192.168.30.137:8020/user/cloudera/ratings.csv" println("Using this ratingFile: " + ratingFile) // first create an RDD out of the rating file val rawTrainingRatings = sc.textFile(ratingFile).map { line => val Array(userId, productId, scoreStr) = line.split(",") AmazonRating(userId, productId, scoreStr.toDouble) } // only keep users that have rated between MinRecommendationsPerUser and MaxRecommendationsPerUser products //THIS IS THE LINE THAT PROVOKES the WARN TaskSchedulerImp ! val trainingRatings = rawTrainingRatings.groupBy(_.userId) .filter(r => MinRecommendationsPerUser <= r._2.size && r._2.size < MaxRecommendationsPerUser) .flatMap(_._2) .repartition(NumPartitions) .cache() println(s"Parsed $ratingFile. Kept ${trainingRatings.count()} ratings out of ${rawTrainingRatings.count()}") My question is, do you see anything wrong with the code? is there anything terrible wrong that i have to change? and, what can i do to have this up and running with my resources? What most annoys me is that the above code works perfectly in the console spark of the virtual image but when I try to make it run programmatically creating the unix with SBT-pack command does not work. If the dedicated resources are too few to develop this project, what else can i do? i mean, do i need to hire a tiny cluster with AWS or any another provider? if that is a correct answer, which are yours recommendation? Thank you very much for reading until here. Regards, Alonso ________________________________ View this message in context: About a problem running a spark job in a cdh-5.7.0 vmware image.<http://apache-spark-user-list.1001560.n3.nabble.com/About-a-problem-running-a-spark-job-in-a-cdh-5-7-0-vmware-image-tp27082.html> Sent from the Apache Spark User List mailing list archive<http://apache-spark-user-list.1001560.n3.nabble.com/> at Nabble.com.
