I'm assuming you're running Spark 0.9.x, because in the latest version of Spark you shouldn't have to add the HADOOP_CONF_DIR to the java class path manually. I tested this out on my own YARN cluster and was able to confirm that.
In Spark 1.0, SPARK_MEM is deprecated and should not be used. Instead, you should set the per-executor memory through spark.executor.memory, which has the same effect but takes higher priority. By YARN_WORKER_MEM, do you mean SPARK_EXECUTOR_MEMORY? It also does the same thing. In Spark 1.0, the priority hierarchy is as follows: spark.executor.memory (set through spark-defaults.conf) > SPARK_EXECUTOR_MEMORY > SPARK_MEM (deprecated) In Spark 0.9, the hierarchy very similar: spark.executor.memory (set through SPARK_JAVA_OPTS in spark-env) > SPARK_MEM For more information: http://people.apache.org/~pwendell/spark-1.0.0-rc7-docs/configuration.html http://spark.apache.org/docs/0.9.1/configuration.html 2014-05-20 11:30 GMT-07:00 Arun Ahuja <aahuj...@gmail.com>: > I was actually able to get this to work. I was NOT setting the classpath > properly originally. > > Simply running > java -cp /etc/hadoop/conf/:<yarn, hadoop jars> com.domain.JobClass > > and setting yarn-client as the spark master worked for me. Originally I > had not put the configuration on the classpath. Also, I used > $SPARK_HOME/bin/compute_classpath.sh now now to get all of the relevant > jars. The job properly connects to the am at the correct port. > > Is there any intuition on how spark executor map to yarn workers or how > the different memory settings interplay, SPARK_MEM vs YARN_WORKER_MEM? > > Thanks, > Arun > > > On Tue, May 20, 2014 at 2:25 PM, Andrew Or <and...@databricks.com> wrote: > >> Hi Gaurav and Arun, >> >> Your settings seem reasonable; as long as YARN_CONF_DIR or >> HADOOP_CONF_DIR is properly set, the application should be able to find the >> correct RM port. Have you tried running the examples in yarn-client mode, >> and your custom application in yarn-standalone (now yarn-cluster) mode? >> >> >> >> 2014-05-20 5:17 GMT-07:00 gaurav.dasgupta <gaurav.d...@gmail.com>: >> >> Few more details I would like to provide (Sorry as I should have provided >>> with the previous post): >>> >>> *- Spark Version = 0.9.1 (using pre-built spark-0.9.1-bin-hadoop2) >>> - Hadoop Version = 2.4.0 (Hortonworks) >>> - I am trying to execute a Spark Streaming program* >>> >>> Because I am using Hortornworks Hadoop (HDP), YARN is configured with >>> different port numbers than the default Apache's default configurations. >>> For >>> example, *resourcemanager.address* is <IP>:8050 in HDP whereas it >>> defaults >>> to <IP>:8032. >>> >>> When I run the Spark examples using bin/run-example, I can see in the >>> console logs, that it is connecting to the right port configured by HDP, >>> i.e., 8050. Please refer the below console log: >>> >>> */[root@host spark-0.9.1-bin-hadoop2]# SPARK_YARN_MODE=true >>> >>> SPARK_JAR=assembly/target/scala-2.10/spark-assembly_2.10-0.9.1-hadoop2.2.0.jar >>> >>> SPARK_YARN_APP_JAR=examples/target/scala-2.10/spark-examples_2.10-assembly-0.9.1.jar >>> bin/run-example org.apache.spark.examples.HdfsTest yarn-client >>> /user/root/test >>> SLF4J: Class path contains multiple SLF4J bindings. >>> SLF4J: Found binding in >>> >>> [jar:file:/usr/local/spark-0.9.1-bin-hadoop2/examples/target/scala-2.10/spark-examples_2.10-assembly-0.9.1.jar!/org/slf4j/impl/StaticLoggerBinder.class] >>> SLF4J: Found binding in >>> >>> [jar:file:/usr/local/spark-0.9.1-bin-hadoop2/assembly/target/scala-2.10/spark-assembly_2.10-0.9.1-hadoop2.2.0.jar!/org/slf4j/impl/StaticLoggerBinder.class] >>> SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an >>> explanation. >>> SLF4J: Actual binding is of type [org.slf4j.impl.Log4jLoggerFactory] >>> 14/05/20 06:55:29 INFO slf4j.Slf4jLogger: Slf4jLogger started >>> 14/05/20 06:55:29 INFO Remoting: Starting remoting >>> 14/05/20 06:55:29 INFO Remoting: Remoting started; listening on addresses >>> :[akka.tcp://spark@<IP:60988] >>> 14/05/20 06:55:29 INFO Remoting: Remoting now listens on addresses: >>> [akka.tcp://spark@<IP>:60988] >>> 14/05/20 06:55:29 INFO spark.SparkEnv: Registering BlockManagerMaster >>> 14/05/20 06:55:29 INFO storage.DiskBlockManager: Created local directory >>> at >>> /tmp/spark-local-20140520065529-924f >>> 14/05/20 06:55:29 INFO storage.MemoryStore: MemoryStore started with >>> capacity 4.2 GB. >>> 14/05/20 06:55:29 INFO network.ConnectionManager: Bound socket to port >>> 35359 >>> with id = ConnectionManagerId(<IP>,35359) >>> 14/05/20 06:55:29 INFO storage.BlockManagerMaster: Trying to register >>> BlockManager >>> 14/05/20 06:55:29 INFO storage.BlockManagerMasterActor$BlockManagerInfo: >>> Registering block manager <IP>:35359 with 4.2 GB RAM >>> 14/05/20 06:55:29 INFO storage.BlockManagerMaster: Registered >>> BlockManager >>> 14/05/20 06:55:29 INFO spark.HttpServer: Starting HTTP Server >>> 14/05/20 06:55:29 INFO server.Server: jetty-7.x.y-SNAPSHOT >>> 14/05/20 06:55:29 INFO server.AbstractConnector: Started >>> SocketConnector@0.0.0.0:59418 >>> 14/05/20 06:55:29 INFO broadcast.HttpBroadcast: Broadcast server started >>> at >>> http://<IP>:59418 >>> 14/05/20 06:55:29 INFO spark.SparkEnv: Registering MapOutputTracker >>> 14/05/20 06:55:29 INFO spark.HttpFileServer: HTTP File server directory >>> is >>> /tmp/spark-fc34fdc8-d940-420b-b184-fc7a8a65501a >>> 14/05/20 06:55:29 INFO spark.HttpServer: Starting HTTP Server >>> 14/05/20 06:55:29 INFO server.Server: jetty-7.x.y-SNAPSHOT >>> 14/05/20 06:55:29 INFO server.AbstractConnector: Started >>> SocketConnector@0.0.0.0:53425 >>> 14/05/20 06:55:29 INFO server.Server: jetty-7.x.y-SNAPSHOT >>> 14/05/20 06:55:29 INFO handler.ContextHandler: started >>> o.e.j.s.h.ContextHandler{/storage/rdd,null} >>> 14/05/20 06:55:29 INFO handler.ContextHandler: started >>> o.e.j.s.h.ContextHandler{/storage,null} >>> 14/05/20 06:55:29 INFO handler.ContextHandler: started >>> o.e.j.s.h.ContextHandler{/stages/stage,null} >>> 14/05/20 06:55:29 INFO handler.ContextHandler: started >>> o.e.j.s.h.ContextHandler{/stages/pool,null} >>> 14/05/20 06:55:29 INFO handler.ContextHandler: started >>> o.e.j.s.h.ContextHandler{/stages,null} >>> 14/05/20 06:55:29 INFO handler.ContextHandler: started >>> o.e.j.s.h.ContextHandler{/environment,null} >>> 14/05/20 06:55:29 INFO handler.ContextHandler: started >>> o.e.j.s.h.ContextHandler{/executors,null} >>> 14/05/20 06:55:29 INFO handler.ContextHandler: started >>> o.e.j.s.h.ContextHandler{/metrics/json,null} >>> 14/05/20 06:55:29 INFO handler.ContextHandler: started >>> o.e.j.s.h.ContextHandler{/static,null} >>> 14/05/20 06:55:29 INFO handler.ContextHandler: started >>> o.e.j.s.h.ContextHandler{/,null} >>> 14/05/20 06:55:29 INFO server.AbstractConnector: Started >>> SelectChannelConnector@0.0.0.0:4040 >>> 14/05/20 06:55:29 INFO ui.SparkUI: Started Spark Web UI at http:// >>> <IP>:4040 >>> 14/05/20 06:55:29 WARN util.NativeCodeLoader: Unable to load >>> native-hadoop >>> library for your platform... using builtin-java classes where applicable >>> 14/05/20 06:55:29 INFO spark.SparkContext: Added JAR >>> >>> /usr/local/spark-0.9.1-bin-hadoop2/examples/target/scala-2.10/spark-examples_2.10-assembly-0.9.1.jar >>> at http://<IP>:53425/jars/spark-examples_2.10-assembly-0.9.1.jar with >>> timestamp 1400586929921 >>> 14/05/20 06:55:30 INFO client.RMProxy: Connecting to ResourceManager at >>> <IP>:8050 >>> 14/05/20 06:55:30 INFO yarn.Client: Got Cluster metric info from >>> ApplicationsManager (ASM), number of NodeManagers: 9 >>> 14/05/20 06:55:30 INFO yarn.Client: Queue info ... queueName: default, >>> queueCurrentCapacity: 0.0, queueMaxCapacity: 1.0,/* >>> >>> But, when I running my own custom spark streaming code, it is trying to >>> connect to port number 8032 instead and hence unable to connect. Refer >>> the >>> below log: >>> >>> */[root@host spark-0.9.1-bin-hadoop2]# SPARK_YARN_MODE=true >>> >>> SPARK_JAR=assembly/target/scala-2.10/spark-assembly_2.10-0.9.1-hadoop2.2.0.jar >>> SPARK_YARN_APP_JAR=/home/gaurav/SparkStreamExample.jar java -cp >>> >>> /home/gaurav/SparkStreamExample.jar:assembly/target/scala-2.10/spark-assembly_2.10-0.9.1-hadoop2.2.0.jar >>> SparkStreamExample yarn-client <IP> 9999 >>> log4j:WARN No appenders could be found for logger >>> (akka.event.slf4j.Slf4jLogger). >>> log4j:WARN Please initialize the log4j system properly. >>> log4j:WARN See http://logging.apache.org/log4j/1.2/faq.html#noconfig for >>> more info. >>> 14/05/20 07:04:38 INFO SparkEnv: Using Spark's default log4j profile: >>> org/apache/spark/log4j-defaults.properties >>> 14/05/20 07:04:38 INFO SparkEnv: Registering BlockManagerMaster >>> 14/05/20 07:04:38 INFO DiskBlockManager: Created local directory at >>> /tmp/spark-local-20140520070438-5eae >>> 14/05/20 07:04:38 INFO MemoryStore: MemoryStore started with capacity 4.2 >>> GB. >>> 14/05/20 07:04:38 INFO ConnectionManager: Bound socket to port 49869 >>> with id >>> = ConnectionManagerId(<IP>,49869) >>> 14/05/20 07:04:38 INFO BlockManagerMaster: Trying to register >>> BlockManager >>> 14/05/20 07:04:38 INFO BlockManagerMasterActor$BlockManagerInfo: >>> Registering >>> block manager <IP>:49869 with 4.2 GB RAM >>> 14/05/20 07:04:38 INFO BlockManagerMaster: Registered BlockManager >>> 14/05/20 07:04:38 INFO HttpServer: Starting HTTP Server >>> 14/05/20 07:04:38 INFO HttpBroadcast: Broadcast server started at >>> http://<IP>:36946 >>> 14/05/20 07:04:38 INFO SparkEnv: Registering MapOutputTracker >>> 14/05/20 07:04:38 INFO HttpFileServer: HTTP File server directory is >>> /tmp/spark-414ba274-adc0-4a0e-b1a4-9c1f048cbf37 >>> 14/05/20 07:04:38 INFO HttpServer: Starting HTTP Server >>> 14/05/20 07:04:38 INFO SparkUI: Started Spark Web UI at http://<IP>:4040 >>> 14/05/20 07:04:38 WARN NativeCodeLoader: Unable to load native-hadoop >>> library for your platform... using builtin-java classes where applicable >>> 14/05/20 07:04:38 INFO SparkContext: Added JAR >>> /home/gaurav/SparkStreamExample.jar at >>> http://<IP>:40053/jars/SparkStreamExample.jar with timestamp >>> 1400587478500 >>> 14/05/20 07:04:38 INFO RMProxy: Connecting to ResourceManager at >>> /0.0.0.0:8032 >>> 14/05/20 07:04:39 INFO Client: Retrying connect to server: >>> 0.0.0.0/0.0.0.0:8032. Already tried 0 time(s); retry policy is >>> RetryUpToMaximumCountWithFixedSleep(maxRetries=10, sleepTime=1 SECONDS) >>> 14/05/20 07:04:40 INFO Client: Retrying connect to server: >>> 0.0.0.0/0.0.0.0:8032. Already tried 1 time(s); retry policy is >>> RetryUpToMaximumCountWithFixedSleep(maxRetries=10, sleepTime=1 SECONDS) >>> 14/05/20 07:04:41 INFO Client: Retrying connect to server: >>> 0.0.0.0/0.0.0.0:8032. Already tried 2 time(s); retry policy is >>> RetryUpToMaximumCountWithFixedSleep(maxRetries=10, sleepTime=1 SECONDS) >>> 14/05/20 07:04:42 INFO Client: Retrying connect to server: >>> 0.0.0.0/0.0.0.0:8032. Already tried 3 time(s); retry policy is >>> RetryUpToMaximumCountWithFixedSleep(maxRetries=10, sleepTime=1 SECONDS)/* >>> >>> Do I need to specify the YARN ports configured by HDP to Spark somehow? >>> How >>> the example jobs can detect the correct YARN ports? >>> >>> Thanks in advance. >>> >>> -- Gaurav >>> >>> >>> >>> -- >>> View this message in context: >>> http://apache-spark-user-list.1001560.n3.nabble.com/Yarn-configuration-file-doesn-t-work-when-run-with-yarn-client-mode-tp1418p6097.html >>> Sent from the Apache Spark User List mailing list archive at Nabble.com. >>> >> >> >
