Why does spark-shell --master yarn-client fail (yet pyspark --master yarn seems to work)?

hdfs apache-spark yarn
merours picture merours · Jul 7, 2014 · Viewed 14k times · Source

I'm trying to run the spark shell on my Hadoop cluster via Yarn. I use

  • Hadoop 2.4.1
  • Spark 1.0.0

My Hadoop cluster already works. In order to use Spark, I built Spark as described here : 

mvn -Pyarn -Phadoop-2.4 -Dhadoop.version=2.4.1 -DskipTests clean package

The compilation works fine, and I can run spark-shell without troubles. However, running it on yarn :

spark-shell --master yarn-client

gets me the following error :

14/07/07 11:30:32 INFO cluster.YarnClientSchedulerBackend: Application report from ASM:
         appMasterRpcPort: -1
         appStartTime: 1404725422955
         yarnAppState: ACCEPTED

14/07/07 11:30:33 INFO cluster.YarnClientSchedulerBackend: Application report from ASM:
         appMasterRpcPort: -1
         appStartTime: 1404725422955
         yarnAppState: FAILED

org.apache.spark.SparkException: Yarn application already ended,might be killed or not able to launch application master
.
        at org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.waitForApp(YarnClientSchedulerBackend.scala:105
)
        at org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:82)
        at org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:136)
        at org.apache.spark.SparkContext.<init>(SparkContext.scala:318)
        at org.apache.spark.repl.SparkILoop.createSparkContext(SparkILoop.scala:957)
        at $iwC$$iwC.<init>(<console>:8)
        at $iwC.<init>(<console>:14)
        at <init>(<console>:16)
        at .<init>(<console>:20)
        at .<clinit>(<console>)
        at .<init>(<console>:7)
        at .<clinit>(<console>)
        at $print(<console>)
        at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
        at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
        at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
        at java.lang.reflect.Method.invoke(Method.java:606)
        at org.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:788)
        at org.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1056)
        at org.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:614)
        at org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:645)
        at org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:609)
        at org.apache.spark.repl.SparkILoop.reallyInterpret$1(SparkILoop.scala:796)
        at org.apache.spark.repl.SparkILoop.interpretStartingWith(SparkILoop.scala:841)
        at org.apache.spark.repl.SparkILoop.command(SparkILoop.scala:753)
        at org.apache.spark.repl.SparkILoopInit$$anonfun$initializeSpark$1.apply(SparkILoopInit.scala:121)
        at org.apache.spark.repl.SparkILoopInit$$anonfun$initializeSpark$1.apply(SparkILoopInit.scala:120)
        at org.apache.spark.repl.SparkIMain.beQuietDuring(SparkIMain.scala:263)
        at org.apache.spark.repl.SparkILoopInit$class.initializeSpark(SparkILoopInit.scala:120)
        at org.apache.spark.repl.SparkILoop.initializeSpark(SparkILoop.scala:56)
        at org.apache.spark.repl.SparkILoop$$anonfun$process$1$$anonfun$apply$mcZ$sp$5.apply$mcV$sp(SparkILoop.scala:913)
        at org.apache.spark.repl.SparkILoopInit$class.runThunks(SparkILoopInit.scala:142)
        at org.apache.spark.repl.SparkILoop.runThunks(SparkILoop.scala:56)
        at org.apache.spark.repl.SparkILoopInit$class.postInitialization(SparkILoopInit.scala:104)
        at org.apache.spark.repl.SparkILoop.postInitialization(SparkILoop.scala:56)
        at org.apache.spark.repl.SparkILoop$$anonfun$process$1.apply$mcZ$sp(SparkILoop.scala:930)
        at org.apache.spark.repl.SparkILoop$$anonfun$process$1.apply(SparkILoop.scala:884)
        at org.apache.spark.repl.SparkILoop$$anonfun$process$1.apply(SparkILoop.scala:884)
        at scala.tools.nsc.util.ScalaClassLoader$.savingContextLoader(ScalaClassLoader.scala:135)
        at org.apache.spark.repl.SparkILoop.process(SparkILoop.scala:884)
        at org.apache.spark.repl.SparkILoop.process(SparkILoop.scala:982)
        at org.apache.spark.repl.Main$.main(Main.scala:31)
        at org.apache.spark.repl.Main.main(Main.scala)
        at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
        at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
        at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
        at java.lang.reflect.Method.invoke(Method.java:606)
        at org.apache.spark.deploy.SparkSubmit$.launch(SparkSubmit.scala:292)
        at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:55)
        at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)

Spark manages to communicate with my cluster, but it doesn't work out. Another interesting thing is that I can access my cluster using pyspark --master yarn. However, I get the following warning 

14/07/07 14:10:11 WARN cluster.YarnClientClusterScheduler: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient memory

and an infinite computation time when doing something as simple as 

sc.wholeTextFiles('hdfs://vm7x64.fr/').collect()

What may be causing this problem ?

Answer

Vit D picture Vit D · Jun 12, 2015

Please check does your Hadoop cluster is running correctly. On the master node next YARN process must be running:

$ jps
24970 ResourceManager

On slave nodes/executors:

$ jps
14389 NodeManager

Also make sure that you created a reference (or copied those files) to Hadoop configuration in Spark config directory :

$ ll /spark/conf/ | grep site
lrwxrwxrwx  1 hadoop hadoop   33 Jun  8 18:13 core-site.xml -> /hadoop/etc/hadoop/core-site.xml
lrwxrwxrwx  1 hadoop hadoop   33 Jun  8 18:13 hdfs-site.xml -> /hadoop/etc/hadoop/hdfs-site.xml

You also can check ResourceManager Web UI on port 8088 - http://master:8088/cluster/nodes. There must be a list of available nodes and resources.

You must take a look at your log files using next command (application ID you can find in Web UI):

$ yarn logs -applicationId <yourApplicationId>

Or you can look directly to entire log files on Master/ResourceManager host:

$ ll /hadoop/logs/ | grep resourcemanager
-rw-rw-r--  1 hadoop hadoop  368414 Jun 12 18:12 yarn-hadoop-resourcemanager-master.log
-rw-rw-r--  1 hadoop hadoop    2632 Jun 12 17:52 yarn-hadoop-resourcemanager-master.out

And on Slave/NodeManager hosts:

$ ll /hadoop/logs/ | grep nodemanager
-rw-rw-r--  1 hadoop hadoop  284134 Jun 12 18:12 yarn-hadoop-nodemanager-slave.log
-rw-rw-r--  1 hadoop hadoop     702 Jun  9 14:47 yarn-hadoop-nodemanager-slave.out

Also check if all environment variables are correct:

HADOOP_CONF_LIB_NATIVE_DIR=/hadoop/lib/native
HADOOP_MAPRED_HOME=/hadoop
HADOOP_COMMON_HOME=/hadoop
HADOOP_HDFS_HOME=/hadoop
YARN_HOME=/hadoop
HADOOP_INSTALL=/hadoop
HADOOP_CONF_DIR=/hadoop/etc/hadoop
YARN_CONF_DIR=/hadoop/etc/hadoop
SPARK_HOME=/spark

Related questions

issue Running Spark Job on Yarn Cluster

I want to run my spark Job in Hadoop YARN cluster mode, and I am using the following command: spark-submit --master yarn-cluster --driver-memory 1g --executor-memory 1g --executor-cores 1 --class com.dc.analysis.jobs.AggregationJob sparkanalitic.jar param1 param2 param3 I am …

Read More
Spark on yarn concept understanding

I am trying to understand how spark runs on YARN cluster/client. I have the following question in my mind. Is it necessary that spark is installed on all the nodes in yarn cluster? I think it should because worker …

Read More
Spark - load CSV file as DataFrame?

I would like to read a CSV in spark and convert it as DataFrame and store it in HDFS with df.registerTempTable("table_name") I have tried: scala> val df = sqlContext.load("hdfs:///csv/file/dir/file.csv") Error …

Read More