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From Arun Ahuja <aahuj...@gmail.com>
Subject Re: Yarn configuration file doesn't work when run with yarn-client mode
Date Tue, 20 May 2014 19:45:29 GMT
Yes, we are on Spark 0.9.0 so that explains the first piece, thanks!

Also, yes, I meant SPARK_WORKER_MEMORY.  Thanks for the hierarchy.
Similarly is there some best practice on setting SPARK_WORKER_INSTANCES and
spark.default.parallelism?

Thanks,
Arun


On Tue, May 20, 2014 at 3:04 PM, Andrew Or <andrew@databricks.com> wrote:

> 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 <aahuja11@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 <andrew@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.dg19@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@&lt;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#noconfigfor
>>>> 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.
>>>>
>>>
>>>
>>
>

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