[jira] [Commented] (SPARK-15343) NoClassDefFoundError when initializing Spark with YARN
[ https://issues.apache.org/jira/browse/SPARK-15343?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16545225#comment-16545225 ] Antony commented on SPARK-15343: {{--conf spark.hadoop.yarn.timeline-service.enabled=false is work for me}} > NoClassDefFoundError when initializing Spark with YARN > -- > > Key: SPARK-15343 > URL: https://issues.apache.org/jira/browse/SPARK-15343 > Project: Spark > Issue Type: Bug > Components: YARN >Affects Versions: 2.0.0 >Reporter: Maciej Bryński >Priority: Critical > > I'm trying to connect Spark 2.0 (compiled from branch-2.0) with Hadoop. > Spark compiled with: > {code} > ./dev/make-distribution.sh -Pyarn -Phadoop-2.6 -Phive -Phive-thriftserver > -Dhadoop.version=2.6.0 -DskipTests > {code} > I'm getting following error > {code} > mbrynski@jupyter:~/spark$ bin/pyspark > Python 3.4.0 (default, Apr 11 2014, 13:05:11) > [GCC 4.8.2] on linux > Type "help", "copyright", "credits" or "license" for more information. > Warning: Master yarn-client is deprecated since 2.0. Please use master "yarn" > with specified deploy mode instead. > Setting default log level to "WARN". > To adjust logging level use sc.setLogLevel(newLevel). > 16/05/16 11:54:41 WARN SparkConf: The configuration key 'spark.yarn.jar' has > been deprecated as of Spark 2.0 and may be removed in the future. Please use > the new key 'spark.yarn.jars' instead. > 16/05/16 11:54:41 WARN NativeCodeLoader: Unable to load native-hadoop library > for your platform... using builtin-java classes where applicable > 16/05/16 11:54:42 WARN AbstractHandler: No Server set for > org.spark_project.jetty.server.handler.ErrorHandler@f7989f6 > 16/05/16 11:54:43 WARN DomainSocketFactory: The short-circuit local reads > feature cannot be used because libhadoop cannot be loaded. > Traceback (most recent call last): > File "/home/mbrynski/spark/python/pyspark/shell.py", line 38, in > sc = SparkContext() > File "/home/mbrynski/spark/python/pyspark/context.py", line 115, in __init__ > conf, jsc, profiler_cls) > File "/home/mbrynski/spark/python/pyspark/context.py", line 172, in _do_init > self._jsc = jsc or self._initialize_context(self._conf._jconf) > File "/home/mbrynski/spark/python/pyspark/context.py", line 235, in > _initialize_context > return self._jvm.JavaSparkContext(jconf) > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/java_gateway.py", > line 1183, in __call__ > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/protocol.py", line > 312, in get_return_value > py4j.protocol.Py4JJavaError: An error occurred while calling > None.org.apache.spark.api.java.JavaSparkContext. > : java.lang.NoClassDefFoundError: > com/sun/jersey/api/client/config/ClientConfig > at > org.apache.hadoop.yarn.client.api.TimelineClient.createTimelineClient(TimelineClient.java:45) > at > org.apache.hadoop.yarn.client.api.impl.YarnClientImpl.serviceInit(YarnClientImpl.java:163) > at > org.apache.hadoop.service.AbstractService.init(AbstractService.java:163) > at > org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:150) > at > org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:56) > at > org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:148) > at org.apache.spark.SparkContext.(SparkContext.scala:502) > at > org.apache.spark.api.java.JavaSparkContext.(JavaSparkContext.scala:58) > at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native > Method) > at > sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62) > at > sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45) > at java.lang.reflect.Constructor.newInstance(Constructor.java:422) > at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:240) > at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) > at py4j.Gateway.invoke(Gateway.java:236) > at > py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:80) > at > py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:69) > at py4j.GatewayConnection.run(GatewayConnection.java:211) > at java.lang.Thread.run(Thread.java:745) > Caused by: java.lang.ClassNotFoundException: > com.sun.jersey.api.client.config.ClientConfig > at java.net.URLClassLoader.findClass(URLClassLoader.java:381) > at java.lang.ClassLoader.loadClass(ClassLoader.java:424) > at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:331) > at java.lang.ClassLoader.loadClass(ClassLoader.java:357) >
[jira] [Commented] (SPARK-15343) NoClassDefFoundError when initializing Spark with YARN
[ https://issues.apache.org/jira/browse/SPARK-15343?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16386554#comment-16386554 ] ASF GitHub Bot commented on SPARK-15343: Github user kr-arjun commented on the issue: https://github.com/apache/drill/pull/1011 @paul-rogers I was able to resolve this issue by workaround of setting 'yarn.timeline-service.enabled' to false ( Copied yarn-site.xml with this property set to $DRILL_SITE directory). This issue is specific to environment where Timeline server is enabled. Initially , it failed with 'java.lang.NoClassDefFoundError: com/sun/jersey/api/client/config/ClientConfig'. On copying required jars to Drill classpath , it failed with exception I have shared in the previous attachment. The same issue is reported in Spark as well (https://issues.apache.org/jira/browse/SPARK-15343). To find the error stack trace, I had to modify the DrillOnYarn.java to print StackTrace. Thought it would be useful if stack trace can be logged for troubleshooting purpose. > NoClassDefFoundError when initializing Spark with YARN > -- > > Key: SPARK-15343 > URL: https://issues.apache.org/jira/browse/SPARK-15343 > Project: Spark > Issue Type: Bug > Components: YARN >Affects Versions: 2.0.0 >Reporter: Maciej Bryński >Priority: Critical > > I'm trying to connect Spark 2.0 (compiled from branch-2.0) with Hadoop. > Spark compiled with: > {code} > ./dev/make-distribution.sh -Pyarn -Phadoop-2.6 -Phive -Phive-thriftserver > -Dhadoop.version=2.6.0 -DskipTests > {code} > I'm getting following error > {code} > mbrynski@jupyter:~/spark$ bin/pyspark > Python 3.4.0 (default, Apr 11 2014, 13:05:11) > [GCC 4.8.2] on linux > Type "help", "copyright", "credits" or "license" for more information. > Warning: Master yarn-client is deprecated since 2.0. Please use master "yarn" > with specified deploy mode instead. > Setting default log level to "WARN". > To adjust logging level use sc.setLogLevel(newLevel). > 16/05/16 11:54:41 WARN SparkConf: The configuration key 'spark.yarn.jar' has > been deprecated as of Spark 2.0 and may be removed in the future. Please use > the new key 'spark.yarn.jars' instead. > 16/05/16 11:54:41 WARN NativeCodeLoader: Unable to load native-hadoop library > for your platform... using builtin-java classes where applicable > 16/05/16 11:54:42 WARN AbstractHandler: No Server set for > org.spark_project.jetty.server.handler.ErrorHandler@f7989f6 > 16/05/16 11:54:43 WARN DomainSocketFactory: The short-circuit local reads > feature cannot be used because libhadoop cannot be loaded. > Traceback (most recent call last): > File "/home/mbrynski/spark/python/pyspark/shell.py", line 38, in > sc = SparkContext() > File "/home/mbrynski/spark/python/pyspark/context.py", line 115, in __init__ > conf, jsc, profiler_cls) > File "/home/mbrynski/spark/python/pyspark/context.py", line 172, in _do_init > self._jsc = jsc or self._initialize_context(self._conf._jconf) > File "/home/mbrynski/spark/python/pyspark/context.py", line 235, in > _initialize_context > return self._jvm.JavaSparkContext(jconf) > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/java_gateway.py", > line 1183, in __call__ > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/protocol.py", line > 312, in get_return_value > py4j.protocol.Py4JJavaError: An error occurred while calling > None.org.apache.spark.api.java.JavaSparkContext. > : java.lang.NoClassDefFoundError: > com/sun/jersey/api/client/config/ClientConfig > at > org.apache.hadoop.yarn.client.api.TimelineClient.createTimelineClient(TimelineClient.java:45) > at > org.apache.hadoop.yarn.client.api.impl.YarnClientImpl.serviceInit(YarnClientImpl.java:163) > at > org.apache.hadoop.service.AbstractService.init(AbstractService.java:163) > at > org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:150) > at > org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:56) > at > org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:148) > at org.apache.spark.SparkContext.(SparkContext.scala:502) > at > org.apache.spark.api.java.JavaSparkContext.(JavaSparkContext.scala:58) > at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native > Method) > at > sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62) > at > sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45) > at java.lang.reflect.Constructor.newInstance(Constructor.java:422) > at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:240) >
[jira] [Commented] (SPARK-15343) NoClassDefFoundError when initializing Spark with YARN
[ https://issues.apache.org/jira/browse/SPARK-15343?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16021310#comment-16021310 ] Bing Li commented on SPARK-15343: - The property in Yarn should be yarn.timeline-service.enabled=false, instead of hadoop.yarn.timeline-service.enabled. > NoClassDefFoundError when initializing Spark with YARN > -- > > Key: SPARK-15343 > URL: https://issues.apache.org/jira/browse/SPARK-15343 > Project: Spark > Issue Type: Bug > Components: YARN >Affects Versions: 2.0.0 >Reporter: Maciej Bryński >Priority: Critical > > I'm trying to connect Spark 2.0 (compiled from branch-2.0) with Hadoop. > Spark compiled with: > {code} > ./dev/make-distribution.sh -Pyarn -Phadoop-2.6 -Phive -Phive-thriftserver > -Dhadoop.version=2.6.0 -DskipTests > {code} > I'm getting following error > {code} > mbrynski@jupyter:~/spark$ bin/pyspark > Python 3.4.0 (default, Apr 11 2014, 13:05:11) > [GCC 4.8.2] on linux > Type "help", "copyright", "credits" or "license" for more information. > Warning: Master yarn-client is deprecated since 2.0. Please use master "yarn" > with specified deploy mode instead. > Setting default log level to "WARN". > To adjust logging level use sc.setLogLevel(newLevel). > 16/05/16 11:54:41 WARN SparkConf: The configuration key 'spark.yarn.jar' has > been deprecated as of Spark 2.0 and may be removed in the future. Please use > the new key 'spark.yarn.jars' instead. > 16/05/16 11:54:41 WARN NativeCodeLoader: Unable to load native-hadoop library > for your platform... using builtin-java classes where applicable > 16/05/16 11:54:42 WARN AbstractHandler: No Server set for > org.spark_project.jetty.server.handler.ErrorHandler@f7989f6 > 16/05/16 11:54:43 WARN DomainSocketFactory: The short-circuit local reads > feature cannot be used because libhadoop cannot be loaded. > Traceback (most recent call last): > File "/home/mbrynski/spark/python/pyspark/shell.py", line 38, in > sc = SparkContext() > File "/home/mbrynski/spark/python/pyspark/context.py", line 115, in __init__ > conf, jsc, profiler_cls) > File "/home/mbrynski/spark/python/pyspark/context.py", line 172, in _do_init > self._jsc = jsc or self._initialize_context(self._conf._jconf) > File "/home/mbrynski/spark/python/pyspark/context.py", line 235, in > _initialize_context > return self._jvm.JavaSparkContext(jconf) > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/java_gateway.py", > line 1183, in __call__ > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/protocol.py", line > 312, in get_return_value > py4j.protocol.Py4JJavaError: An error occurred while calling > None.org.apache.spark.api.java.JavaSparkContext. > : java.lang.NoClassDefFoundError: > com/sun/jersey/api/client/config/ClientConfig > at > org.apache.hadoop.yarn.client.api.TimelineClient.createTimelineClient(TimelineClient.java:45) > at > org.apache.hadoop.yarn.client.api.impl.YarnClientImpl.serviceInit(YarnClientImpl.java:163) > at > org.apache.hadoop.service.AbstractService.init(AbstractService.java:163) > at > org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:150) > at > org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:56) > at > org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:148) > at org.apache.spark.SparkContext.(SparkContext.scala:502) > at > org.apache.spark.api.java.JavaSparkContext.(JavaSparkContext.scala:58) > at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native > Method) > at > sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62) > at > sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45) > at java.lang.reflect.Constructor.newInstance(Constructor.java:422) > at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:240) > at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) > at py4j.Gateway.invoke(Gateway.java:236) > at > py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:80) > at > py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:69) > at py4j.GatewayConnection.run(GatewayConnection.java:211) > at java.lang.Thread.run(Thread.java:745) > Caused by: java.lang.ClassNotFoundException: > com.sun.jersey.api.client.config.ClientConfig > at java.net.URLClassLoader.findClass(URLClassLoader.java:381) > at java.lang.ClassLoader.loadClass(ClassLoader.java:424) > at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:331) > at
[jira] [Commented] (SPARK-15343) NoClassDefFoundError when initializing Spark with YARN
[ https://issues.apache.org/jira/browse/SPARK-15343?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15991945#comment-15991945 ] Jo Desmet commented on SPARK-15343: --- So the issue is fixed by moving to at least Hadoop Yarn Version 2.8.0 as per [YARN-5271 |https://issues.apache.org/jira/browse/YARN-5271]. > NoClassDefFoundError when initializing Spark with YARN > -- > > Key: SPARK-15343 > URL: https://issues.apache.org/jira/browse/SPARK-15343 > Project: Spark > Issue Type: Bug > Components: YARN >Affects Versions: 2.0.0 >Reporter: Maciej Bryński >Priority: Critical > > I'm trying to connect Spark 2.0 (compiled from branch-2.0) with Hadoop. > Spark compiled with: > {code} > ./dev/make-distribution.sh -Pyarn -Phadoop-2.6 -Phive -Phive-thriftserver > -Dhadoop.version=2.6.0 -DskipTests > {code} > I'm getting following error > {code} > mbrynski@jupyter:~/spark$ bin/pyspark > Python 3.4.0 (default, Apr 11 2014, 13:05:11) > [GCC 4.8.2] on linux > Type "help", "copyright", "credits" or "license" for more information. > Warning: Master yarn-client is deprecated since 2.0. Please use master "yarn" > with specified deploy mode instead. > Setting default log level to "WARN". > To adjust logging level use sc.setLogLevel(newLevel). > 16/05/16 11:54:41 WARN SparkConf: The configuration key 'spark.yarn.jar' has > been deprecated as of Spark 2.0 and may be removed in the future. Please use > the new key 'spark.yarn.jars' instead. > 16/05/16 11:54:41 WARN NativeCodeLoader: Unable to load native-hadoop library > for your platform... using builtin-java classes where applicable > 16/05/16 11:54:42 WARN AbstractHandler: No Server set for > org.spark_project.jetty.server.handler.ErrorHandler@f7989f6 > 16/05/16 11:54:43 WARN DomainSocketFactory: The short-circuit local reads > feature cannot be used because libhadoop cannot be loaded. > Traceback (most recent call last): > File "/home/mbrynski/spark/python/pyspark/shell.py", line 38, in > sc = SparkContext() > File "/home/mbrynski/spark/python/pyspark/context.py", line 115, in __init__ > conf, jsc, profiler_cls) > File "/home/mbrynski/spark/python/pyspark/context.py", line 172, in _do_init > self._jsc = jsc or self._initialize_context(self._conf._jconf) > File "/home/mbrynski/spark/python/pyspark/context.py", line 235, in > _initialize_context > return self._jvm.JavaSparkContext(jconf) > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/java_gateway.py", > line 1183, in __call__ > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/protocol.py", line > 312, in get_return_value > py4j.protocol.Py4JJavaError: An error occurred while calling > None.org.apache.spark.api.java.JavaSparkContext. > : java.lang.NoClassDefFoundError: > com/sun/jersey/api/client/config/ClientConfig > at > org.apache.hadoop.yarn.client.api.TimelineClient.createTimelineClient(TimelineClient.java:45) > at > org.apache.hadoop.yarn.client.api.impl.YarnClientImpl.serviceInit(YarnClientImpl.java:163) > at > org.apache.hadoop.service.AbstractService.init(AbstractService.java:163) > at > org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:150) > at > org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:56) > at > org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:148) > at org.apache.spark.SparkContext.(SparkContext.scala:502) > at > org.apache.spark.api.java.JavaSparkContext.(JavaSparkContext.scala:58) > at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native > Method) > at > sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62) > at > sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45) > at java.lang.reflect.Constructor.newInstance(Constructor.java:422) > at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:240) > at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) > at py4j.Gateway.invoke(Gateway.java:236) > at > py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:80) > at > py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:69) > at py4j.GatewayConnection.run(GatewayConnection.java:211) > at java.lang.Thread.run(Thread.java:745) > Caused by: java.lang.ClassNotFoundException: > com.sun.jersey.api.client.config.ClientConfig > at java.net.URLClassLoader.findClass(URLClassLoader.java:381) > at java.lang.ClassLoader.loadClass(ClassLoader.java:424) > at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:331) >
[jira] [Commented] (SPARK-15343) NoClassDefFoundError when initializing Spark with YARN
[ https://issues.apache.org/jira/browse/SPARK-15343?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15568187#comment-15568187 ] Steve Loughran commented on SPARK-15343: There is a very quick fix here, to stop the problem surfacing. When the Spark AM come up, it patches its Hadoop config to set {{hadoop.yarn.timeline-service.enabled=false}} before calling {{yarnClient.initialize()}}. The YarnClient won't do anything with the ATS Do people want a PR here? > NoClassDefFoundError when initializing Spark with YARN > -- > > Key: SPARK-15343 > URL: https://issues.apache.org/jira/browse/SPARK-15343 > Project: Spark > Issue Type: Bug > Components: YARN >Affects Versions: 2.0.0 >Reporter: Maciej Bryński >Priority: Critical > > I'm trying to connect Spark 2.0 (compiled from branch-2.0) with Hadoop. > Spark compiled with: > {code} > ./dev/make-distribution.sh -Pyarn -Phadoop-2.6 -Phive -Phive-thriftserver > -Dhadoop.version=2.6.0 -DskipTests > {code} > I'm getting following error > {code} > mbrynski@jupyter:~/spark$ bin/pyspark > Python 3.4.0 (default, Apr 11 2014, 13:05:11) > [GCC 4.8.2] on linux > Type "help", "copyright", "credits" or "license" for more information. > Warning: Master yarn-client is deprecated since 2.0. Please use master "yarn" > with specified deploy mode instead. > Setting default log level to "WARN". > To adjust logging level use sc.setLogLevel(newLevel). > 16/05/16 11:54:41 WARN SparkConf: The configuration key 'spark.yarn.jar' has > been deprecated as of Spark 2.0 and may be removed in the future. Please use > the new key 'spark.yarn.jars' instead. > 16/05/16 11:54:41 WARN NativeCodeLoader: Unable to load native-hadoop library > for your platform... using builtin-java classes where applicable > 16/05/16 11:54:42 WARN AbstractHandler: No Server set for > org.spark_project.jetty.server.handler.ErrorHandler@f7989f6 > 16/05/16 11:54:43 WARN DomainSocketFactory: The short-circuit local reads > feature cannot be used because libhadoop cannot be loaded. > Traceback (most recent call last): > File "/home/mbrynski/spark/python/pyspark/shell.py", line 38, in > sc = SparkContext() > File "/home/mbrynski/spark/python/pyspark/context.py", line 115, in __init__ > conf, jsc, profiler_cls) > File "/home/mbrynski/spark/python/pyspark/context.py", line 172, in _do_init > self._jsc = jsc or self._initialize_context(self._conf._jconf) > File "/home/mbrynski/spark/python/pyspark/context.py", line 235, in > _initialize_context > return self._jvm.JavaSparkContext(jconf) > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/java_gateway.py", > line 1183, in __call__ > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/protocol.py", line > 312, in get_return_value > py4j.protocol.Py4JJavaError: An error occurred while calling > None.org.apache.spark.api.java.JavaSparkContext. > : java.lang.NoClassDefFoundError: > com/sun/jersey/api/client/config/ClientConfig > at > org.apache.hadoop.yarn.client.api.TimelineClient.createTimelineClient(TimelineClient.java:45) > at > org.apache.hadoop.yarn.client.api.impl.YarnClientImpl.serviceInit(YarnClientImpl.java:163) > at > org.apache.hadoop.service.AbstractService.init(AbstractService.java:163) > at > org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:150) > at > org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:56) > at > org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:148) > at org.apache.spark.SparkContext.(SparkContext.scala:502) > at > org.apache.spark.api.java.JavaSparkContext.(JavaSparkContext.scala:58) > at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native > Method) > at > sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62) > at > sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45) > at java.lang.reflect.Constructor.newInstance(Constructor.java:422) > at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:240) > at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) > at py4j.Gateway.invoke(Gateway.java:236) > at > py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:80) > at > py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:69) > at py4j.GatewayConnection.run(GatewayConnection.java:211) > at java.lang.Thread.run(Thread.java:745) > Caused by: java.lang.ClassNotFoundException: > com.sun.jersey.api.client.config.ClientConfig > at
[jira] [Commented] (SPARK-15343) NoClassDefFoundError when initializing Spark with YARN
[ https://issues.apache.org/jira/browse/SPARK-15343?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15568180#comment-15568180 ] Steve Loughran commented on SPARK-15343: this is a tough problem with Hadoop core, as if it moves foward too fast things downstream break; after I did a slew of HADOOP-9991 updates I managed to get the HBase team unhappy on a jackson update. Jersey is an example: it has been bumped up in HADOOP-9613; but that is going to break things that wanted Jersey 1.9, so postponed until Hadoop 3. Now, returning to the specifics of Yarn ATS integration. It seems to be that the YARN client code could be tweaked so that if all publishing is via HDFS (as is now recommended for scalability & availability), there's no reason to load jersey at all...its how the code has been written that the path is coded in. It should be possible to alter the YARN Code so that jersey libs are only need of the combination of (timeline enabled, timeline 1.0 REST client); the combination of (enabled, 1.5 API) would work. As usual, we're going to need someone to sit down and do that... > NoClassDefFoundError when initializing Spark with YARN > -- > > Key: SPARK-15343 > URL: https://issues.apache.org/jira/browse/SPARK-15343 > Project: Spark > Issue Type: Bug > Components: YARN >Affects Versions: 2.0.0 >Reporter: Maciej Bryński >Priority: Critical > > I'm trying to connect Spark 2.0 (compiled from branch-2.0) with Hadoop. > Spark compiled with: > {code} > ./dev/make-distribution.sh -Pyarn -Phadoop-2.6 -Phive -Phive-thriftserver > -Dhadoop.version=2.6.0 -DskipTests > {code} > I'm getting following error > {code} > mbrynski@jupyter:~/spark$ bin/pyspark > Python 3.4.0 (default, Apr 11 2014, 13:05:11) > [GCC 4.8.2] on linux > Type "help", "copyright", "credits" or "license" for more information. > Warning: Master yarn-client is deprecated since 2.0. Please use master "yarn" > with specified deploy mode instead. > Setting default log level to "WARN". > To adjust logging level use sc.setLogLevel(newLevel). > 16/05/16 11:54:41 WARN SparkConf: The configuration key 'spark.yarn.jar' has > been deprecated as of Spark 2.0 and may be removed in the future. Please use > the new key 'spark.yarn.jars' instead. > 16/05/16 11:54:41 WARN NativeCodeLoader: Unable to load native-hadoop library > for your platform... using builtin-java classes where applicable > 16/05/16 11:54:42 WARN AbstractHandler: No Server set for > org.spark_project.jetty.server.handler.ErrorHandler@f7989f6 > 16/05/16 11:54:43 WARN DomainSocketFactory: The short-circuit local reads > feature cannot be used because libhadoop cannot be loaded. > Traceback (most recent call last): > File "/home/mbrynski/spark/python/pyspark/shell.py", line 38, in > sc = SparkContext() > File "/home/mbrynski/spark/python/pyspark/context.py", line 115, in __init__ > conf, jsc, profiler_cls) > File "/home/mbrynski/spark/python/pyspark/context.py", line 172, in _do_init > self._jsc = jsc or self._initialize_context(self._conf._jconf) > File "/home/mbrynski/spark/python/pyspark/context.py", line 235, in > _initialize_context > return self._jvm.JavaSparkContext(jconf) > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/java_gateway.py", > line 1183, in __call__ > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/protocol.py", line > 312, in get_return_value > py4j.protocol.Py4JJavaError: An error occurred while calling > None.org.apache.spark.api.java.JavaSparkContext. > : java.lang.NoClassDefFoundError: > com/sun/jersey/api/client/config/ClientConfig > at > org.apache.hadoop.yarn.client.api.TimelineClient.createTimelineClient(TimelineClient.java:45) > at > org.apache.hadoop.yarn.client.api.impl.YarnClientImpl.serviceInit(YarnClientImpl.java:163) > at > org.apache.hadoop.service.AbstractService.init(AbstractService.java:163) > at > org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:150) > at > org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:56) > at > org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:148) > at org.apache.spark.SparkContext.(SparkContext.scala:502) > at > org.apache.spark.api.java.JavaSparkContext.(JavaSparkContext.scala:58) > at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native > Method) > at > sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62) > at > sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45) > at java.lang.reflect.Constructor.newInstance(Constructor.java:422) > at
[jira] [Commented] (SPARK-15343) NoClassDefFoundError when initializing Spark with YARN
[ https://issues.apache.org/jira/browse/SPARK-15343?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15566019#comment-15566019 ] Jo Desmet commented on SPARK-15343: --- By design we apparently have a very tight coupling of scheduling and execution environment(?). Unless this gets better decoupling with a remote-call API, it is as much YARN's as it is SPARK's classpath. Isn't that what Jersey is about? What I advocate is a more subdued approach where we keep supporting new versions of Spark (2.0 and beyond) that remain compatible with mainstream used versions of Hadoop Yarn. We should do so until we have more mainstream adoption of a YARN environment with the more recent libraries, or until other fixes or features are implemented on either Spark or Yarn side. The desire for a newer Jersey library just does not seem that much worth to me compared to this. I am no Yarn fan, but it just feels like we are breaking the bond with Yarn just because we feel it is not going fast enough on that side. > NoClassDefFoundError when initializing Spark with YARN > -- > > Key: SPARK-15343 > URL: https://issues.apache.org/jira/browse/SPARK-15343 > Project: Spark > Issue Type: Bug > Components: YARN >Affects Versions: 2.0.0 >Reporter: Maciej Bryński >Priority: Critical > > I'm trying to connect Spark 2.0 (compiled from branch-2.0) with Hadoop. > Spark compiled with: > {code} > ./dev/make-distribution.sh -Pyarn -Phadoop-2.6 -Phive -Phive-thriftserver > -Dhadoop.version=2.6.0 -DskipTests > {code} > I'm getting following error > {code} > mbrynski@jupyter:~/spark$ bin/pyspark > Python 3.4.0 (default, Apr 11 2014, 13:05:11) > [GCC 4.8.2] on linux > Type "help", "copyright", "credits" or "license" for more information. > Warning: Master yarn-client is deprecated since 2.0. Please use master "yarn" > with specified deploy mode instead. > Setting default log level to "WARN". > To adjust logging level use sc.setLogLevel(newLevel). > 16/05/16 11:54:41 WARN SparkConf: The configuration key 'spark.yarn.jar' has > been deprecated as of Spark 2.0 and may be removed in the future. Please use > the new key 'spark.yarn.jars' instead. > 16/05/16 11:54:41 WARN NativeCodeLoader: Unable to load native-hadoop library > for your platform... using builtin-java classes where applicable > 16/05/16 11:54:42 WARN AbstractHandler: No Server set for > org.spark_project.jetty.server.handler.ErrorHandler@f7989f6 > 16/05/16 11:54:43 WARN DomainSocketFactory: The short-circuit local reads > feature cannot be used because libhadoop cannot be loaded. > Traceback (most recent call last): > File "/home/mbrynski/spark/python/pyspark/shell.py", line 38, in > sc = SparkContext() > File "/home/mbrynski/spark/python/pyspark/context.py", line 115, in __init__ > conf, jsc, profiler_cls) > File "/home/mbrynski/spark/python/pyspark/context.py", line 172, in _do_init > self._jsc = jsc or self._initialize_context(self._conf._jconf) > File "/home/mbrynski/spark/python/pyspark/context.py", line 235, in > _initialize_context > return self._jvm.JavaSparkContext(jconf) > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/java_gateway.py", > line 1183, in __call__ > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/protocol.py", line > 312, in get_return_value > py4j.protocol.Py4JJavaError: An error occurred while calling > None.org.apache.spark.api.java.JavaSparkContext. > : java.lang.NoClassDefFoundError: > com/sun/jersey/api/client/config/ClientConfig > at > org.apache.hadoop.yarn.client.api.TimelineClient.createTimelineClient(TimelineClient.java:45) > at > org.apache.hadoop.yarn.client.api.impl.YarnClientImpl.serviceInit(YarnClientImpl.java:163) > at > org.apache.hadoop.service.AbstractService.init(AbstractService.java:163) > at > org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:150) > at > org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:56) > at > org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:148) > at org.apache.spark.SparkContext.(SparkContext.scala:502) > at > org.apache.spark.api.java.JavaSparkContext.(JavaSparkContext.scala:58) > at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native > Method) > at > sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62) > at > sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45) > at java.lang.reflect.Constructor.newInstance(Constructor.java:422) > at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:240) > at
[jira] [Commented] (SPARK-15343) NoClassDefFoundError when initializing Spark with YARN
[ https://issues.apache.org/jira/browse/SPARK-15343?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15564578#comment-15564578 ] Sean Owen commented on SPARK-15343: --- [~jdesmet] I'm not clear what you're advocating _in Spark_. See the discussion above. You're running into a problem with the YARN classpath. > NoClassDefFoundError when initializing Spark with YARN > -- > > Key: SPARK-15343 > URL: https://issues.apache.org/jira/browse/SPARK-15343 > Project: Spark > Issue Type: Bug > Components: YARN >Affects Versions: 2.0.0 >Reporter: Maciej Bryński >Priority: Critical > > I'm trying to connect Spark 2.0 (compiled from branch-2.0) with Hadoop. > Spark compiled with: > {code} > ./dev/make-distribution.sh -Pyarn -Phadoop-2.6 -Phive -Phive-thriftserver > -Dhadoop.version=2.6.0 -DskipTests > {code} > I'm getting following error > {code} > mbrynski@jupyter:~/spark$ bin/pyspark > Python 3.4.0 (default, Apr 11 2014, 13:05:11) > [GCC 4.8.2] on linux > Type "help", "copyright", "credits" or "license" for more information. > Warning: Master yarn-client is deprecated since 2.0. Please use master "yarn" > with specified deploy mode instead. > Setting default log level to "WARN". > To adjust logging level use sc.setLogLevel(newLevel). > 16/05/16 11:54:41 WARN SparkConf: The configuration key 'spark.yarn.jar' has > been deprecated as of Spark 2.0 and may be removed in the future. Please use > the new key 'spark.yarn.jars' instead. > 16/05/16 11:54:41 WARN NativeCodeLoader: Unable to load native-hadoop library > for your platform... using builtin-java classes where applicable > 16/05/16 11:54:42 WARN AbstractHandler: No Server set for > org.spark_project.jetty.server.handler.ErrorHandler@f7989f6 > 16/05/16 11:54:43 WARN DomainSocketFactory: The short-circuit local reads > feature cannot be used because libhadoop cannot be loaded. > Traceback (most recent call last): > File "/home/mbrynski/spark/python/pyspark/shell.py", line 38, in > sc = SparkContext() > File "/home/mbrynski/spark/python/pyspark/context.py", line 115, in __init__ > conf, jsc, profiler_cls) > File "/home/mbrynski/spark/python/pyspark/context.py", line 172, in _do_init > self._jsc = jsc or self._initialize_context(self._conf._jconf) > File "/home/mbrynski/spark/python/pyspark/context.py", line 235, in > _initialize_context > return self._jvm.JavaSparkContext(jconf) > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/java_gateway.py", > line 1183, in __call__ > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/protocol.py", line > 312, in get_return_value > py4j.protocol.Py4JJavaError: An error occurred while calling > None.org.apache.spark.api.java.JavaSparkContext. > : java.lang.NoClassDefFoundError: > com/sun/jersey/api/client/config/ClientConfig > at > org.apache.hadoop.yarn.client.api.TimelineClient.createTimelineClient(TimelineClient.java:45) > at > org.apache.hadoop.yarn.client.api.impl.YarnClientImpl.serviceInit(YarnClientImpl.java:163) > at > org.apache.hadoop.service.AbstractService.init(AbstractService.java:163) > at > org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:150) > at > org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:56) > at > org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:148) > at org.apache.spark.SparkContext.(SparkContext.scala:502) > at > org.apache.spark.api.java.JavaSparkContext.(JavaSparkContext.scala:58) > at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native > Method) > at > sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62) > at > sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45) > at java.lang.reflect.Constructor.newInstance(Constructor.java:422) > at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:240) > at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) > at py4j.Gateway.invoke(Gateway.java:236) > at > py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:80) > at > py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:69) > at py4j.GatewayConnection.run(GatewayConnection.java:211) > at java.lang.Thread.run(Thread.java:745) > Caused by: java.lang.ClassNotFoundException: > com.sun.jersey.api.client.config.ClientConfig > at java.net.URLClassLoader.findClass(URLClassLoader.java:381) > at java.lang.ClassLoader.loadClass(ClassLoader.java:424) > at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:331) >
[jira] [Commented] (SPARK-15343) NoClassDefFoundError when initializing Spark with YARN
[ https://issues.apache.org/jira/browse/SPARK-15343?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15563894#comment-15563894 ] Jo Desmet commented on SPARK-15343: --- Still not acceptable, I mean how can we. This tool has been presented as a tool with near-perfect Hadoop integration, which we are about to drop. It absolutely does not make sense to drop this as 'not-a-problem': it is a clearly described regression for a mainstream deployment. I am all for galvanizing the Hadoop community in upping their libraries, and Spark is a good motivation, but this is all too harsh. There is absolutely no way back once we go that direction. It might mean loosing and alienating our existing user-base. For example, big corporations are using vetted Hadoop Stacks. I do work for one such bigger corporation, and I know that that kind of thing means a lot to them. Before we know we will end up with a complex maze of what version of hadoop works with what version of Mesos, Hadoop, etc. If Java 9 provides the solution, or providing shaded libraries, then we should wait till that is in place before moving forward. > NoClassDefFoundError when initializing Spark with YARN > -- > > Key: SPARK-15343 > URL: https://issues.apache.org/jira/browse/SPARK-15343 > Project: Spark > Issue Type: Bug > Components: YARN >Affects Versions: 2.0.0 >Reporter: Maciej Bryński >Priority: Critical > > I'm trying to connect Spark 2.0 (compiled from branch-2.0) with Hadoop. > Spark compiled with: > {code} > ./dev/make-distribution.sh -Pyarn -Phadoop-2.6 -Phive -Phive-thriftserver > -Dhadoop.version=2.6.0 -DskipTests > {code} > I'm getting following error > {code} > mbrynski@jupyter:~/spark$ bin/pyspark > Python 3.4.0 (default, Apr 11 2014, 13:05:11) > [GCC 4.8.2] on linux > Type "help", "copyright", "credits" or "license" for more information. > Warning: Master yarn-client is deprecated since 2.0. Please use master "yarn" > with specified deploy mode instead. > Setting default log level to "WARN". > To adjust logging level use sc.setLogLevel(newLevel). > 16/05/16 11:54:41 WARN SparkConf: The configuration key 'spark.yarn.jar' has > been deprecated as of Spark 2.0 and may be removed in the future. Please use > the new key 'spark.yarn.jars' instead. > 16/05/16 11:54:41 WARN NativeCodeLoader: Unable to load native-hadoop library > for your platform... using builtin-java classes where applicable > 16/05/16 11:54:42 WARN AbstractHandler: No Server set for > org.spark_project.jetty.server.handler.ErrorHandler@f7989f6 > 16/05/16 11:54:43 WARN DomainSocketFactory: The short-circuit local reads > feature cannot be used because libhadoop cannot be loaded. > Traceback (most recent call last): > File "/home/mbrynski/spark/python/pyspark/shell.py", line 38, in > sc = SparkContext() > File "/home/mbrynski/spark/python/pyspark/context.py", line 115, in __init__ > conf, jsc, profiler_cls) > File "/home/mbrynski/spark/python/pyspark/context.py", line 172, in _do_init > self._jsc = jsc or self._initialize_context(self._conf._jconf) > File "/home/mbrynski/spark/python/pyspark/context.py", line 235, in > _initialize_context > return self._jvm.JavaSparkContext(jconf) > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/java_gateway.py", > line 1183, in __call__ > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/protocol.py", line > 312, in get_return_value > py4j.protocol.Py4JJavaError: An error occurred while calling > None.org.apache.spark.api.java.JavaSparkContext. > : java.lang.NoClassDefFoundError: > com/sun/jersey/api/client/config/ClientConfig > at > org.apache.hadoop.yarn.client.api.TimelineClient.createTimelineClient(TimelineClient.java:45) > at > org.apache.hadoop.yarn.client.api.impl.YarnClientImpl.serviceInit(YarnClientImpl.java:163) > at > org.apache.hadoop.service.AbstractService.init(AbstractService.java:163) > at > org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:150) > at > org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:56) > at > org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:148) > at org.apache.spark.SparkContext.(SparkContext.scala:502) > at > org.apache.spark.api.java.JavaSparkContext.(JavaSparkContext.scala:58) > at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native > Method) > at > sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62) > at > sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45) > at java.lang.reflect.Constructor.newInstance(Constructor.java:422) >
[jira] [Commented] (SPARK-15343) NoClassDefFoundError when initializing Spark with YARN
[ https://issues.apache.org/jira/browse/SPARK-15343?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15535594#comment-15535594 ] Steve Loughran commented on SPARK-15343: Jo, this is an eternal problem with the Hadoop stack: transitive dependency pain. We're euqally trapped at the low level (HADOOP-9991) with things like Jackson; if you look at something minor like the Jersey upgrade of HADOOP-10075, the discussion comes down to "what is going to break", rather than whether. I really hope this can be fixed in Java 9, otherwise theres a permanent problem: whoever upgrades a library breaks something else, hold back and you stop people upgrading. There is some PoC of a fully shaded Hadoop client going on, but that's initially focused on filesystem interaction (driver: HBase); if someone has the time maybe a yarn equivalent could be kicked off. For now, you are going to have to turn off ATS integration in your spark defaults. Sorry. > NoClassDefFoundError when initializing Spark with YARN > -- > > Key: SPARK-15343 > URL: https://issues.apache.org/jira/browse/SPARK-15343 > Project: Spark > Issue Type: Bug > Components: YARN >Affects Versions: 2.0.0 >Reporter: Maciej Bryński >Priority: Critical > > I'm trying to connect Spark 2.0 (compiled from branch-2.0) with Hadoop. > Spark compiled with: > {code} > ./dev/make-distribution.sh -Pyarn -Phadoop-2.6 -Phive -Phive-thriftserver > -Dhadoop.version=2.6.0 -DskipTests > {code} > I'm getting following error > {code} > mbrynski@jupyter:~/spark$ bin/pyspark > Python 3.4.0 (default, Apr 11 2014, 13:05:11) > [GCC 4.8.2] on linux > Type "help", "copyright", "credits" or "license" for more information. > Warning: Master yarn-client is deprecated since 2.0. Please use master "yarn" > with specified deploy mode instead. > Setting default log level to "WARN". > To adjust logging level use sc.setLogLevel(newLevel). > 16/05/16 11:54:41 WARN SparkConf: The configuration key 'spark.yarn.jar' has > been deprecated as of Spark 2.0 and may be removed in the future. Please use > the new key 'spark.yarn.jars' instead. > 16/05/16 11:54:41 WARN NativeCodeLoader: Unable to load native-hadoop library > for your platform... using builtin-java classes where applicable > 16/05/16 11:54:42 WARN AbstractHandler: No Server set for > org.spark_project.jetty.server.handler.ErrorHandler@f7989f6 > 16/05/16 11:54:43 WARN DomainSocketFactory: The short-circuit local reads > feature cannot be used because libhadoop cannot be loaded. > Traceback (most recent call last): > File "/home/mbrynski/spark/python/pyspark/shell.py", line 38, in > sc = SparkContext() > File "/home/mbrynski/spark/python/pyspark/context.py", line 115, in __init__ > conf, jsc, profiler_cls) > File "/home/mbrynski/spark/python/pyspark/context.py", line 172, in _do_init > self._jsc = jsc or self._initialize_context(self._conf._jconf) > File "/home/mbrynski/spark/python/pyspark/context.py", line 235, in > _initialize_context > return self._jvm.JavaSparkContext(jconf) > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/java_gateway.py", > line 1183, in __call__ > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/protocol.py", line > 312, in get_return_value > py4j.protocol.Py4JJavaError: An error occurred while calling > None.org.apache.spark.api.java.JavaSparkContext. > : java.lang.NoClassDefFoundError: > com/sun/jersey/api/client/config/ClientConfig > at > org.apache.hadoop.yarn.client.api.TimelineClient.createTimelineClient(TimelineClient.java:45) > at > org.apache.hadoop.yarn.client.api.impl.YarnClientImpl.serviceInit(YarnClientImpl.java:163) > at > org.apache.hadoop.service.AbstractService.init(AbstractService.java:163) > at > org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:150) > at > org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:56) > at > org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:148) > at org.apache.spark.SparkContext.(SparkContext.scala:502) > at > org.apache.spark.api.java.JavaSparkContext.(JavaSparkContext.scala:58) > at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native > Method) > at > sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62) > at > sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45) > at java.lang.reflect.Constructor.newInstance(Constructor.java:422) > at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:240) > at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) >
[jira] [Commented] (SPARK-15343) NoClassDefFoundError when initializing Spark with YARN
[ https://issues.apache.org/jira/browse/SPARK-15343?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15534607#comment-15534607 ] Jo Desmet commented on SPARK-15343: --- Hadoop Yarn is not 'just' 3rd party. It is an important framework to run Spark on. Compatibility is paramount. > NoClassDefFoundError when initializing Spark with YARN > -- > > Key: SPARK-15343 > URL: https://issues.apache.org/jira/browse/SPARK-15343 > Project: Spark > Issue Type: Bug > Components: YARN >Affects Versions: 2.0.0 >Reporter: Maciej Bryński >Priority: Critical > > I'm trying to connect Spark 2.0 (compiled from branch-2.0) with Hadoop. > Spark compiled with: > {code} > ./dev/make-distribution.sh -Pyarn -Phadoop-2.6 -Phive -Phive-thriftserver > -Dhadoop.version=2.6.0 -DskipTests > {code} > I'm getting following error > {code} > mbrynski@jupyter:~/spark$ bin/pyspark > Python 3.4.0 (default, Apr 11 2014, 13:05:11) > [GCC 4.8.2] on linux > Type "help", "copyright", "credits" or "license" for more information. > Warning: Master yarn-client is deprecated since 2.0. Please use master "yarn" > with specified deploy mode instead. > Setting default log level to "WARN". > To adjust logging level use sc.setLogLevel(newLevel). > 16/05/16 11:54:41 WARN SparkConf: The configuration key 'spark.yarn.jar' has > been deprecated as of Spark 2.0 and may be removed in the future. Please use > the new key 'spark.yarn.jars' instead. > 16/05/16 11:54:41 WARN NativeCodeLoader: Unable to load native-hadoop library > for your platform... using builtin-java classes where applicable > 16/05/16 11:54:42 WARN AbstractHandler: No Server set for > org.spark_project.jetty.server.handler.ErrorHandler@f7989f6 > 16/05/16 11:54:43 WARN DomainSocketFactory: The short-circuit local reads > feature cannot be used because libhadoop cannot be loaded. > Traceback (most recent call last): > File "/home/mbrynski/spark/python/pyspark/shell.py", line 38, in > sc = SparkContext() > File "/home/mbrynski/spark/python/pyspark/context.py", line 115, in __init__ > conf, jsc, profiler_cls) > File "/home/mbrynski/spark/python/pyspark/context.py", line 172, in _do_init > self._jsc = jsc or self._initialize_context(self._conf._jconf) > File "/home/mbrynski/spark/python/pyspark/context.py", line 235, in > _initialize_context > return self._jvm.JavaSparkContext(jconf) > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/java_gateway.py", > line 1183, in __call__ > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/protocol.py", line > 312, in get_return_value > py4j.protocol.Py4JJavaError: An error occurred while calling > None.org.apache.spark.api.java.JavaSparkContext. > : java.lang.NoClassDefFoundError: > com/sun/jersey/api/client/config/ClientConfig > at > org.apache.hadoop.yarn.client.api.TimelineClient.createTimelineClient(TimelineClient.java:45) > at > org.apache.hadoop.yarn.client.api.impl.YarnClientImpl.serviceInit(YarnClientImpl.java:163) > at > org.apache.hadoop.service.AbstractService.init(AbstractService.java:163) > at > org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:150) > at > org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:56) > at > org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:148) > at org.apache.spark.SparkContext.(SparkContext.scala:502) > at > org.apache.spark.api.java.JavaSparkContext.(JavaSparkContext.scala:58) > at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native > Method) > at > sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62) > at > sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45) > at java.lang.reflect.Constructor.newInstance(Constructor.java:422) > at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:240) > at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) > at py4j.Gateway.invoke(Gateway.java:236) > at > py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:80) > at > py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:69) > at py4j.GatewayConnection.run(GatewayConnection.java:211) > at java.lang.Thread.run(Thread.java:745) > Caused by: java.lang.ClassNotFoundException: > com.sun.jersey.api.client.config.ClientConfig > at java.net.URLClassLoader.findClass(URLClassLoader.java:381) > at java.lang.ClassLoader.loadClass(ClassLoader.java:424) > at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:331) > at
[jira] [Commented] (SPARK-15343) NoClassDefFoundError when initializing Spark with YARN
[ https://issues.apache.org/jira/browse/SPARK-15343?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15534603#comment-15534603 ] Jo Desmet commented on SPARK-15343: --- I think this issue has not been properly addressed, and should be reopened. We are bound by current versions of Hadoop, and can just not simply ignore users running on top of a Yarn framework. Compatibility with Yarn should take precedence over unleashing new features. Coordination and compatibility with Yarn/Hadoop is paramount. What possibly could happen is pushing the hadoop libraries for jersey in a different namespace - a custom repackaging of the library. But I guess once you start that, you can as well up the Jersey version. We are already building against specific versions of Hadoop, this should be the case here too, and once the opportunity arises, we can start supporting Jersey 2, but Now is not the time. > NoClassDefFoundError when initializing Spark with YARN > -- > > Key: SPARK-15343 > URL: https://issues.apache.org/jira/browse/SPARK-15343 > Project: Spark > Issue Type: Bug > Components: YARN >Affects Versions: 2.0.0 >Reporter: Maciej Bryński >Priority: Critical > > I'm trying to connect Spark 2.0 (compiled from branch-2.0) with Hadoop. > Spark compiled with: > {code} > ./dev/make-distribution.sh -Pyarn -Phadoop-2.6 -Phive -Phive-thriftserver > -Dhadoop.version=2.6.0 -DskipTests > {code} > I'm getting following error > {code} > mbrynski@jupyter:~/spark$ bin/pyspark > Python 3.4.0 (default, Apr 11 2014, 13:05:11) > [GCC 4.8.2] on linux > Type "help", "copyright", "credits" or "license" for more information. > Warning: Master yarn-client is deprecated since 2.0. Please use master "yarn" > with specified deploy mode instead. > Setting default log level to "WARN". > To adjust logging level use sc.setLogLevel(newLevel). > 16/05/16 11:54:41 WARN SparkConf: The configuration key 'spark.yarn.jar' has > been deprecated as of Spark 2.0 and may be removed in the future. Please use > the new key 'spark.yarn.jars' instead. > 16/05/16 11:54:41 WARN NativeCodeLoader: Unable to load native-hadoop library > for your platform... using builtin-java classes where applicable > 16/05/16 11:54:42 WARN AbstractHandler: No Server set for > org.spark_project.jetty.server.handler.ErrorHandler@f7989f6 > 16/05/16 11:54:43 WARN DomainSocketFactory: The short-circuit local reads > feature cannot be used because libhadoop cannot be loaded. > Traceback (most recent call last): > File "/home/mbrynski/spark/python/pyspark/shell.py", line 38, in > sc = SparkContext() > File "/home/mbrynski/spark/python/pyspark/context.py", line 115, in __init__ > conf, jsc, profiler_cls) > File "/home/mbrynski/spark/python/pyspark/context.py", line 172, in _do_init > self._jsc = jsc or self._initialize_context(self._conf._jconf) > File "/home/mbrynski/spark/python/pyspark/context.py", line 235, in > _initialize_context > return self._jvm.JavaSparkContext(jconf) > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/java_gateway.py", > line 1183, in __call__ > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/protocol.py", line > 312, in get_return_value > py4j.protocol.Py4JJavaError: An error occurred while calling > None.org.apache.spark.api.java.JavaSparkContext. > : java.lang.NoClassDefFoundError: > com/sun/jersey/api/client/config/ClientConfig > at > org.apache.hadoop.yarn.client.api.TimelineClient.createTimelineClient(TimelineClient.java:45) > at > org.apache.hadoop.yarn.client.api.impl.YarnClientImpl.serviceInit(YarnClientImpl.java:163) > at > org.apache.hadoop.service.AbstractService.init(AbstractService.java:163) > at > org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:150) > at > org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:56) > at > org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:148) > at org.apache.spark.SparkContext.(SparkContext.scala:502) > at > org.apache.spark.api.java.JavaSparkContext.(JavaSparkContext.scala:58) > at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native > Method) > at > sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62) > at > sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45) > at java.lang.reflect.Constructor.newInstance(Constructor.java:422) > at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:240) > at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) > at py4j.Gateway.invoke(Gateway.java:236) > at >
[jira] [Commented] (SPARK-15343) NoClassDefFoundError when initializing Spark with YARN
[ https://issues.apache.org/jira/browse/SPARK-15343?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15335878#comment-15335878 ] Steve Loughran commented on SPARK-15343: ooh, this is ia pain. FWIW, my current stance on upgrading bits of Hadoop http://steveloughran.blogspot.co.uk/2016/05/fear-of-dependencies.html Should Hadoop make the leap to Jersey 2? I think for trunk/Hadoop 3, yes. For Hadoop 2? we'd be hated by too many people downstream. What Hadoop Timeline client could do is be reworked so that it doesn't try to instantiate the jersey client if you are using the filesystem timeline writer (ATS 1.5+). It's not needed there, even though the current class hierarchy does store it in the base class. The {{TimelineClient}} class creates that {{com.sun.jersey.api.client.Client}} instance to pass in...it's where the config is used (and it's the *only* place in the hadoop codebase which uses it). Created YARN-5271 : essentially the client code could be reworked to create the Jersey client lower down, even without upgrading Hadoop Jersey. You'd still see a stack trace trying to talk to an ATS1.0 server, but for a 1.5 endpoint, all would be well. Too bad Restlet's LGPL license stops ASF code using it: it's a better API. Though it probably doesn't handle Kerberos anyway. > NoClassDefFoundError when initializing Spark with YARN > -- > > Key: SPARK-15343 > URL: https://issues.apache.org/jira/browse/SPARK-15343 > Project: Spark > Issue Type: Bug > Components: YARN >Affects Versions: 2.0.0 >Reporter: Maciej Bryński >Priority: Critical > > I'm trying to connect Spark 2.0 (compiled from branch-2.0) with Hadoop. > Spark compiled with: > {code} > ./dev/make-distribution.sh -Pyarn -Phadoop-2.6 -Phive -Phive-thriftserver > -Dhadoop.version=2.6.0 -DskipTests > {code} > I'm getting following error > {code} > mbrynski@jupyter:~/spark$ bin/pyspark > Python 3.4.0 (default, Apr 11 2014, 13:05:11) > [GCC 4.8.2] on linux > Type "help", "copyright", "credits" or "license" for more information. > Warning: Master yarn-client is deprecated since 2.0. Please use master "yarn" > with specified deploy mode instead. > Setting default log level to "WARN". > To adjust logging level use sc.setLogLevel(newLevel). > 16/05/16 11:54:41 WARN SparkConf: The configuration key 'spark.yarn.jar' has > been deprecated as of Spark 2.0 and may be removed in the future. Please use > the new key 'spark.yarn.jars' instead. > 16/05/16 11:54:41 WARN NativeCodeLoader: Unable to load native-hadoop library > for your platform... using builtin-java classes where applicable > 16/05/16 11:54:42 WARN AbstractHandler: No Server set for > org.spark_project.jetty.server.handler.ErrorHandler@f7989f6 > 16/05/16 11:54:43 WARN DomainSocketFactory: The short-circuit local reads > feature cannot be used because libhadoop cannot be loaded. > Traceback (most recent call last): > File "/home/mbrynski/spark/python/pyspark/shell.py", line 38, in > sc = SparkContext() > File "/home/mbrynski/spark/python/pyspark/context.py", line 115, in __init__ > conf, jsc, profiler_cls) > File "/home/mbrynski/spark/python/pyspark/context.py", line 172, in _do_init > self._jsc = jsc or self._initialize_context(self._conf._jconf) > File "/home/mbrynski/spark/python/pyspark/context.py", line 235, in > _initialize_context > return self._jvm.JavaSparkContext(jconf) > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/java_gateway.py", > line 1183, in __call__ > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/protocol.py", line > 312, in get_return_value > py4j.protocol.Py4JJavaError: An error occurred while calling > None.org.apache.spark.api.java.JavaSparkContext. > : java.lang.NoClassDefFoundError: > com/sun/jersey/api/client/config/ClientConfig > at > org.apache.hadoop.yarn.client.api.TimelineClient.createTimelineClient(TimelineClient.java:45) > at > org.apache.hadoop.yarn.client.api.impl.YarnClientImpl.serviceInit(YarnClientImpl.java:163) > at > org.apache.hadoop.service.AbstractService.init(AbstractService.java:163) > at > org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:150) > at > org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:56) > at > org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:148) > at org.apache.spark.SparkContext.(SparkContext.scala:502) > at > org.apache.spark.api.java.JavaSparkContext.(JavaSparkContext.scala:58) > at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native > Method) > at > sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62) >
[jira] [Commented] (SPARK-15343) NoClassDefFoundError when initializing Spark with YARN
[ https://issues.apache.org/jira/browse/SPARK-15343?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15335055#comment-15335055 ] Marcelo Vanzin commented on SPARK-15343: Yes, Spark 2.0 updated the version of Jersey, because many people want to use Jersey and it's pretty much impossible to use a new version if the old version YARN uses is in the classpath. See SPARK-12154 and SPARK-11081. If there's a way to include the *client* jars from the old jersey without breaking the server side used by Spark, then we could do that. But if any of those jars has entries in {{META-INF/services/}}, then they probably will cause problems. > NoClassDefFoundError when initializing Spark with YARN > -- > > Key: SPARK-15343 > URL: https://issues.apache.org/jira/browse/SPARK-15343 > Project: Spark > Issue Type: Bug > Components: YARN >Affects Versions: 2.0.0 >Reporter: Maciej Bryński >Priority: Critical > > I'm trying to connect Spark 2.0 (compiled from branch-2.0) with Hadoop. > Spark compiled with: > {code} > ./dev/make-distribution.sh -Pyarn -Phadoop-2.6 -Phive -Phive-thriftserver > -Dhadoop.version=2.6.0 -DskipTests > {code} > I'm getting following error > {code} > mbrynski@jupyter:~/spark$ bin/pyspark > Python 3.4.0 (default, Apr 11 2014, 13:05:11) > [GCC 4.8.2] on linux > Type "help", "copyright", "credits" or "license" for more information. > Warning: Master yarn-client is deprecated since 2.0. Please use master "yarn" > with specified deploy mode instead. > Setting default log level to "WARN". > To adjust logging level use sc.setLogLevel(newLevel). > 16/05/16 11:54:41 WARN SparkConf: The configuration key 'spark.yarn.jar' has > been deprecated as of Spark 2.0 and may be removed in the future. Please use > the new key 'spark.yarn.jars' instead. > 16/05/16 11:54:41 WARN NativeCodeLoader: Unable to load native-hadoop library > for your platform... using builtin-java classes where applicable > 16/05/16 11:54:42 WARN AbstractHandler: No Server set for > org.spark_project.jetty.server.handler.ErrorHandler@f7989f6 > 16/05/16 11:54:43 WARN DomainSocketFactory: The short-circuit local reads > feature cannot be used because libhadoop cannot be loaded. > Traceback (most recent call last): > File "/home/mbrynski/spark/python/pyspark/shell.py", line 38, in > sc = SparkContext() > File "/home/mbrynski/spark/python/pyspark/context.py", line 115, in __init__ > conf, jsc, profiler_cls) > File "/home/mbrynski/spark/python/pyspark/context.py", line 172, in _do_init > self._jsc = jsc or self._initialize_context(self._conf._jconf) > File "/home/mbrynski/spark/python/pyspark/context.py", line 235, in > _initialize_context > return self._jvm.JavaSparkContext(jconf) > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/java_gateway.py", > line 1183, in __call__ > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/protocol.py", line > 312, in get_return_value > py4j.protocol.Py4JJavaError: An error occurred while calling > None.org.apache.spark.api.java.JavaSparkContext. > : java.lang.NoClassDefFoundError: > com/sun/jersey/api/client/config/ClientConfig > at > org.apache.hadoop.yarn.client.api.TimelineClient.createTimelineClient(TimelineClient.java:45) > at > org.apache.hadoop.yarn.client.api.impl.YarnClientImpl.serviceInit(YarnClientImpl.java:163) > at > org.apache.hadoop.service.AbstractService.init(AbstractService.java:163) > at > org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:150) > at > org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:56) > at > org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:148) > at org.apache.spark.SparkContext.(SparkContext.scala:502) > at > org.apache.spark.api.java.JavaSparkContext.(JavaSparkContext.scala:58) > at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native > Method) > at > sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62) > at > sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45) > at java.lang.reflect.Constructor.newInstance(Constructor.java:422) > at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:240) > at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) > at py4j.Gateway.invoke(Gateway.java:236) > at > py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:80) > at > py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:69) > at py4j.GatewayConnection.run(GatewayConnection.java:211) > at
[jira] [Commented] (SPARK-15343) NoClassDefFoundError when initializing Spark with YARN
[ https://issues.apache.org/jira/browse/SPARK-15343?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15335039#comment-15335039 ] Saisai Shao commented on SPARK-15343: - If timeline is enabled, YarnClient will also post some events to ATS server, this is a general part no matter Spark or other yarn applications. > NoClassDefFoundError when initializing Spark with YARN > -- > > Key: SPARK-15343 > URL: https://issues.apache.org/jira/browse/SPARK-15343 > Project: Spark > Issue Type: Bug > Components: YARN >Affects Versions: 2.0.0 >Reporter: Maciej Bryński >Priority: Critical > > I'm trying to connect Spark 2.0 (compiled from branch-2.0) with Hadoop. > Spark compiled with: > {code} > ./dev/make-distribution.sh -Pyarn -Phadoop-2.6 -Phive -Phive-thriftserver > -Dhadoop.version=2.6.0 -DskipTests > {code} > I'm getting following error > {code} > mbrynski@jupyter:~/spark$ bin/pyspark > Python 3.4.0 (default, Apr 11 2014, 13:05:11) > [GCC 4.8.2] on linux > Type "help", "copyright", "credits" or "license" for more information. > Warning: Master yarn-client is deprecated since 2.0. Please use master "yarn" > with specified deploy mode instead. > Setting default log level to "WARN". > To adjust logging level use sc.setLogLevel(newLevel). > 16/05/16 11:54:41 WARN SparkConf: The configuration key 'spark.yarn.jar' has > been deprecated as of Spark 2.0 and may be removed in the future. Please use > the new key 'spark.yarn.jars' instead. > 16/05/16 11:54:41 WARN NativeCodeLoader: Unable to load native-hadoop library > for your platform... using builtin-java classes where applicable > 16/05/16 11:54:42 WARN AbstractHandler: No Server set for > org.spark_project.jetty.server.handler.ErrorHandler@f7989f6 > 16/05/16 11:54:43 WARN DomainSocketFactory: The short-circuit local reads > feature cannot be used because libhadoop cannot be loaded. > Traceback (most recent call last): > File "/home/mbrynski/spark/python/pyspark/shell.py", line 38, in > sc = SparkContext() > File "/home/mbrynski/spark/python/pyspark/context.py", line 115, in __init__ > conf, jsc, profiler_cls) > File "/home/mbrynski/spark/python/pyspark/context.py", line 172, in _do_init > self._jsc = jsc or self._initialize_context(self._conf._jconf) > File "/home/mbrynski/spark/python/pyspark/context.py", line 235, in > _initialize_context > return self._jvm.JavaSparkContext(jconf) > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/java_gateway.py", > line 1183, in __call__ > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/protocol.py", line > 312, in get_return_value > py4j.protocol.Py4JJavaError: An error occurred while calling > None.org.apache.spark.api.java.JavaSparkContext. > : java.lang.NoClassDefFoundError: > com/sun/jersey/api/client/config/ClientConfig > at > org.apache.hadoop.yarn.client.api.TimelineClient.createTimelineClient(TimelineClient.java:45) > at > org.apache.hadoop.yarn.client.api.impl.YarnClientImpl.serviceInit(YarnClientImpl.java:163) > at > org.apache.hadoop.service.AbstractService.init(AbstractService.java:163) > at > org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:150) > at > org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:56) > at > org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:148) > at org.apache.spark.SparkContext.(SparkContext.scala:502) > at > org.apache.spark.api.java.JavaSparkContext.(JavaSparkContext.scala:58) > at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native > Method) > at > sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62) > at > sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45) > at java.lang.reflect.Constructor.newInstance(Constructor.java:422) > at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:240) > at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) > at py4j.Gateway.invoke(Gateway.java:236) > at > py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:80) > at > py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:69) > at py4j.GatewayConnection.run(GatewayConnection.java:211) > at java.lang.Thread.run(Thread.java:745) > Caused by: java.lang.ClassNotFoundException: > com.sun.jersey.api.client.config.ClientConfig > at java.net.URLClassLoader.findClass(URLClassLoader.java:381) > at java.lang.ClassLoader.loadClass(ClassLoader.java:424) > at
[jira] [Commented] (SPARK-15343) NoClassDefFoundError when initializing Spark with YARN
[ https://issues.apache.org/jira/browse/SPARK-15343?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15334719#comment-15334719 ] Saisai Shao commented on SPARK-15343: - The class ClientConfig is still existed but the package name is change to org.glassfish.xx. > NoClassDefFoundError when initializing Spark with YARN > -- > > Key: SPARK-15343 > URL: https://issues.apache.org/jira/browse/SPARK-15343 > Project: Spark > Issue Type: Bug > Components: YARN >Affects Versions: 2.0.0 >Reporter: Maciej Bryński >Priority: Critical > Attachments: jersey-client-2.22.2.jar > > > I'm trying to connect Spark 2.0 (compiled from branch-2.0) with Hadoop. > Spark compiled with: > {code} > ./dev/make-distribution.sh -Pyarn -Phadoop-2.6 -Phive -Phive-thriftserver > -Dhadoop.version=2.6.0 -DskipTests > {code} > I'm getting following error > {code} > mbrynski@jupyter:~/spark$ bin/pyspark > Python 3.4.0 (default, Apr 11 2014, 13:05:11) > [GCC 4.8.2] on linux > Type "help", "copyright", "credits" or "license" for more information. > Warning: Master yarn-client is deprecated since 2.0. Please use master "yarn" > with specified deploy mode instead. > Setting default log level to "WARN". > To adjust logging level use sc.setLogLevel(newLevel). > 16/05/16 11:54:41 WARN SparkConf: The configuration key 'spark.yarn.jar' has > been deprecated as of Spark 2.0 and may be removed in the future. Please use > the new key 'spark.yarn.jars' instead. > 16/05/16 11:54:41 WARN NativeCodeLoader: Unable to load native-hadoop library > for your platform... using builtin-java classes where applicable > 16/05/16 11:54:42 WARN AbstractHandler: No Server set for > org.spark_project.jetty.server.handler.ErrorHandler@f7989f6 > 16/05/16 11:54:43 WARN DomainSocketFactory: The short-circuit local reads > feature cannot be used because libhadoop cannot be loaded. > Traceback (most recent call last): > File "/home/mbrynski/spark/python/pyspark/shell.py", line 38, in > sc = SparkContext() > File "/home/mbrynski/spark/python/pyspark/context.py", line 115, in __init__ > conf, jsc, profiler_cls) > File "/home/mbrynski/spark/python/pyspark/context.py", line 172, in _do_init > self._jsc = jsc or self._initialize_context(self._conf._jconf) > File "/home/mbrynski/spark/python/pyspark/context.py", line 235, in > _initialize_context > return self._jvm.JavaSparkContext(jconf) > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/java_gateway.py", > line 1183, in __call__ > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/protocol.py", line > 312, in get_return_value > py4j.protocol.Py4JJavaError: An error occurred while calling > None.org.apache.spark.api.java.JavaSparkContext. > : java.lang.NoClassDefFoundError: > com/sun/jersey/api/client/config/ClientConfig > at > org.apache.hadoop.yarn.client.api.TimelineClient.createTimelineClient(TimelineClient.java:45) > at > org.apache.hadoop.yarn.client.api.impl.YarnClientImpl.serviceInit(YarnClientImpl.java:163) > at > org.apache.hadoop.service.AbstractService.init(AbstractService.java:163) > at > org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:150) > at > org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:56) > at > org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:148) > at org.apache.spark.SparkContext.(SparkContext.scala:502) > at > org.apache.spark.api.java.JavaSparkContext.(JavaSparkContext.scala:58) > at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native > Method) > at > sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62) > at > sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45) > at java.lang.reflect.Constructor.newInstance(Constructor.java:422) > at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:240) > at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) > at py4j.Gateway.invoke(Gateway.java:236) > at > py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:80) > at > py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:69) > at py4j.GatewayConnection.run(GatewayConnection.java:211) > at java.lang.Thread.run(Thread.java:745) > Caused by: java.lang.ClassNotFoundException: > com.sun.jersey.api.client.config.ClientConfig > at java.net.URLClassLoader.findClass(URLClassLoader.java:381) > at java.lang.ClassLoader.loadClass(ClassLoader.java:424) > at
[jira] [Commented] (SPARK-15343) NoClassDefFoundError when initializing Spark with YARN
[ https://issues.apache.org/jira/browse/SPARK-15343?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15334712#comment-15334712 ] Marcelo Vanzin commented on SPARK-15343: Fair point. But I think the right thing then is to just not enable that setting. We can't just stick to really old libraries that cause other problems just because YARN has decided not to move on. Jersey 1.9 causes too many problems when it's in the classpath, making it really hard for people to use newer versions when they need to. Since vanilla Spark has no ATS support, disabling that setting should be ok. Also, it's kinda weird that YARN is even instantiating that client automatically when Spark has no need for it, but I assume there's a good reason for that. > NoClassDefFoundError when initializing Spark with YARN > -- > > Key: SPARK-15343 > URL: https://issues.apache.org/jira/browse/SPARK-15343 > Project: Spark > Issue Type: Bug > Components: YARN >Affects Versions: 2.0.0 >Reporter: Maciej Bryński >Priority: Critical > > I'm trying to connect Spark 2.0 (compiled from branch-2.0) with Hadoop. > Spark compiled with: > {code} > ./dev/make-distribution.sh -Pyarn -Phadoop-2.6 -Phive -Phive-thriftserver > -Dhadoop.version=2.6.0 -DskipTests > {code} > I'm getting following error > {code} > mbrynski@jupyter:~/spark$ bin/pyspark > Python 3.4.0 (default, Apr 11 2014, 13:05:11) > [GCC 4.8.2] on linux > Type "help", "copyright", "credits" or "license" for more information. > Warning: Master yarn-client is deprecated since 2.0. Please use master "yarn" > with specified deploy mode instead. > Setting default log level to "WARN". > To adjust logging level use sc.setLogLevel(newLevel). > 16/05/16 11:54:41 WARN SparkConf: The configuration key 'spark.yarn.jar' has > been deprecated as of Spark 2.0 and may be removed in the future. Please use > the new key 'spark.yarn.jars' instead. > 16/05/16 11:54:41 WARN NativeCodeLoader: Unable to load native-hadoop library > for your platform... using builtin-java classes where applicable > 16/05/16 11:54:42 WARN AbstractHandler: No Server set for > org.spark_project.jetty.server.handler.ErrorHandler@f7989f6 > 16/05/16 11:54:43 WARN DomainSocketFactory: The short-circuit local reads > feature cannot be used because libhadoop cannot be loaded. > Traceback (most recent call last): > File "/home/mbrynski/spark/python/pyspark/shell.py", line 38, in > sc = SparkContext() > File "/home/mbrynski/spark/python/pyspark/context.py", line 115, in __init__ > conf, jsc, profiler_cls) > File "/home/mbrynski/spark/python/pyspark/context.py", line 172, in _do_init > self._jsc = jsc or self._initialize_context(self._conf._jconf) > File "/home/mbrynski/spark/python/pyspark/context.py", line 235, in > _initialize_context > return self._jvm.JavaSparkContext(jconf) > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/java_gateway.py", > line 1183, in __call__ > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/protocol.py", line > 312, in get_return_value > py4j.protocol.Py4JJavaError: An error occurred while calling > None.org.apache.spark.api.java.JavaSparkContext. > : java.lang.NoClassDefFoundError: > com/sun/jersey/api/client/config/ClientConfig > at > org.apache.hadoop.yarn.client.api.TimelineClient.createTimelineClient(TimelineClient.java:45) > at > org.apache.hadoop.yarn.client.api.impl.YarnClientImpl.serviceInit(YarnClientImpl.java:163) > at > org.apache.hadoop.service.AbstractService.init(AbstractService.java:163) > at > org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:150) > at > org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:56) > at > org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:148) > at org.apache.spark.SparkContext.(SparkContext.scala:502) > at > org.apache.spark.api.java.JavaSparkContext.(JavaSparkContext.scala:58) > at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native > Method) > at > sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62) > at > sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45) > at java.lang.reflect.Constructor.newInstance(Constructor.java:422) > at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:240) > at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) > at py4j.Gateway.invoke(Gateway.java:236) > at > py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:80) > at >
[jira] [Commented] (SPARK-15343) NoClassDefFoundError when initializing Spark with YARN
[ https://issues.apache.org/jira/browse/SPARK-15343?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15334683#comment-15334683 ] Saisai Shao commented on SPARK-15343: - [~vanzin] [~srowen], I don't think it is a vendor specific code, look at the stack trace, it is thrown from {{YarnClientImpl}}, if we enable {{hadoop.yarn.timeline-service.enabled}} we will always meet this problem, no matter in Hadoop 2.6, 2.7. > NoClassDefFoundError when initializing Spark with YARN > -- > > Key: SPARK-15343 > URL: https://issues.apache.org/jira/browse/SPARK-15343 > Project: Spark > Issue Type: Bug > Components: YARN >Affects Versions: 2.0.0 >Reporter: Maciej Bryński >Priority: Critical > > I'm trying to connect Spark 2.0 (compiled from branch-2.0) with Hadoop. > Spark compiled with: > {code} > ./dev/make-distribution.sh -Pyarn -Phadoop-2.6 -Phive -Phive-thriftserver > -Dhadoop.version=2.6.0 -DskipTests > {code} > I'm getting following error > {code} > mbrynski@jupyter:~/spark$ bin/pyspark > Python 3.4.0 (default, Apr 11 2014, 13:05:11) > [GCC 4.8.2] on linux > Type "help", "copyright", "credits" or "license" for more information. > Warning: Master yarn-client is deprecated since 2.0. Please use master "yarn" > with specified deploy mode instead. > Setting default log level to "WARN". > To adjust logging level use sc.setLogLevel(newLevel). > 16/05/16 11:54:41 WARN SparkConf: The configuration key 'spark.yarn.jar' has > been deprecated as of Spark 2.0 and may be removed in the future. Please use > the new key 'spark.yarn.jars' instead. > 16/05/16 11:54:41 WARN NativeCodeLoader: Unable to load native-hadoop library > for your platform... using builtin-java classes where applicable > 16/05/16 11:54:42 WARN AbstractHandler: No Server set for > org.spark_project.jetty.server.handler.ErrorHandler@f7989f6 > 16/05/16 11:54:43 WARN DomainSocketFactory: The short-circuit local reads > feature cannot be used because libhadoop cannot be loaded. > Traceback (most recent call last): > File "/home/mbrynski/spark/python/pyspark/shell.py", line 38, in > sc = SparkContext() > File "/home/mbrynski/spark/python/pyspark/context.py", line 115, in __init__ > conf, jsc, profiler_cls) > File "/home/mbrynski/spark/python/pyspark/context.py", line 172, in _do_init > self._jsc = jsc or self._initialize_context(self._conf._jconf) > File "/home/mbrynski/spark/python/pyspark/context.py", line 235, in > _initialize_context > return self._jvm.JavaSparkContext(jconf) > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/java_gateway.py", > line 1183, in __call__ > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/protocol.py", line > 312, in get_return_value > py4j.protocol.Py4JJavaError: An error occurred while calling > None.org.apache.spark.api.java.JavaSparkContext. > : java.lang.NoClassDefFoundError: > com/sun/jersey/api/client/config/ClientConfig > at > org.apache.hadoop.yarn.client.api.TimelineClient.createTimelineClient(TimelineClient.java:45) > at > org.apache.hadoop.yarn.client.api.impl.YarnClientImpl.serviceInit(YarnClientImpl.java:163) > at > org.apache.hadoop.service.AbstractService.init(AbstractService.java:163) > at > org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:150) > at > org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:56) > at > org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:148) > at org.apache.spark.SparkContext.(SparkContext.scala:502) > at > org.apache.spark.api.java.JavaSparkContext.(JavaSparkContext.scala:58) > at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native > Method) > at > sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62) > at > sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45) > at java.lang.reflect.Constructor.newInstance(Constructor.java:422) > at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:240) > at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) > at py4j.Gateway.invoke(Gateway.java:236) > at > py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:80) > at > py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:69) > at py4j.GatewayConnection.run(GatewayConnection.java:211) > at java.lang.Thread.run(Thread.java:745) > Caused by: java.lang.ClassNotFoundException: > com.sun.jersey.api.client.config.ClientConfig > at java.net.URLClassLoader.findClass(URLClassLoader.java:381) > at
[jira] [Commented] (SPARK-15343) NoClassDefFoundError when initializing Spark with YARN
[ https://issues.apache.org/jira/browse/SPARK-15343?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15286954#comment-15286954 ] Steve Loughran commented on SPARK-15343: thanks for this, I'll look at it. FWIW it should work with branch-2, but will inevitably need a rebuild > NoClassDefFoundError when initializing Spark with YARN > -- > > Key: SPARK-15343 > URL: https://issues.apache.org/jira/browse/SPARK-15343 > Project: Spark > Issue Type: Bug > Components: YARN >Affects Versions: 2.0.0 >Reporter: Maciej Bryński >Priority: Critical > > I'm trying to connect Spark 2.0 (compiled from branch-2.0) with Hadoop. > Spark compiled with: > {code} > ./dev/make-distribution.sh -Pyarn -Phadoop-2.6 -Phive -Phive-thriftserver > -Dhadoop.version=2.6.0 -DskipTests > {code} > I'm getting following error > {code} > mbrynski@jupyter:~/spark$ bin/pyspark > Python 3.4.0 (default, Apr 11 2014, 13:05:11) > [GCC 4.8.2] on linux > Type "help", "copyright", "credits" or "license" for more information. > Warning: Master yarn-client is deprecated since 2.0. Please use master "yarn" > with specified deploy mode instead. > Setting default log level to "WARN". > To adjust logging level use sc.setLogLevel(newLevel). > 16/05/16 11:54:41 WARN SparkConf: The configuration key 'spark.yarn.jar' has > been deprecated as of Spark 2.0 and may be removed in the future. Please use > the new key 'spark.yarn.jars' instead. > 16/05/16 11:54:41 WARN NativeCodeLoader: Unable to load native-hadoop library > for your platform... using builtin-java classes where applicable > 16/05/16 11:54:42 WARN AbstractHandler: No Server set for > org.spark_project.jetty.server.handler.ErrorHandler@f7989f6 > 16/05/16 11:54:43 WARN DomainSocketFactory: The short-circuit local reads > feature cannot be used because libhadoop cannot be loaded. > Traceback (most recent call last): > File "/home/mbrynski/spark/python/pyspark/shell.py", line 38, in > sc = SparkContext() > File "/home/mbrynski/spark/python/pyspark/context.py", line 115, in __init__ > conf, jsc, profiler_cls) > File "/home/mbrynski/spark/python/pyspark/context.py", line 172, in _do_init > self._jsc = jsc or self._initialize_context(self._conf._jconf) > File "/home/mbrynski/spark/python/pyspark/context.py", line 235, in > _initialize_context > return self._jvm.JavaSparkContext(jconf) > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/java_gateway.py", > line 1183, in __call__ > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/protocol.py", line > 312, in get_return_value > py4j.protocol.Py4JJavaError: An error occurred while calling > None.org.apache.spark.api.java.JavaSparkContext. > : java.lang.NoClassDefFoundError: > com/sun/jersey/api/client/config/ClientConfig > at > org.apache.hadoop.yarn.client.api.TimelineClient.createTimelineClient(TimelineClient.java:45) > at > org.apache.hadoop.yarn.client.api.impl.YarnClientImpl.serviceInit(YarnClientImpl.java:163) > at > org.apache.hadoop.service.AbstractService.init(AbstractService.java:163) > at > org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:150) > at > org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:56) > at > org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:148) > at org.apache.spark.SparkContext.(SparkContext.scala:502) > at > org.apache.spark.api.java.JavaSparkContext.(JavaSparkContext.scala:58) > at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native > Method) > at > sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62) > at > sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45) > at java.lang.reflect.Constructor.newInstance(Constructor.java:422) > at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:240) > at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) > at py4j.Gateway.invoke(Gateway.java:236) > at > py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:80) > at > py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:69) > at py4j.GatewayConnection.run(GatewayConnection.java:211) > at java.lang.Thread.run(Thread.java:745) > Caused by: java.lang.ClassNotFoundException: > com.sun.jersey.api.client.config.ClientConfig > at java.net.URLClassLoader.findClass(URLClassLoader.java:381) > at java.lang.ClassLoader.loadClass(ClassLoader.java:424) > at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:331) > at
[jira] [Commented] (SPARK-15343) NoClassDefFoundError when initializing Spark with YARN
[ https://issues.apache.org/jira/browse/SPARK-15343?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15286421#comment-15286421 ] Maciej Bryński commented on SPARK-15343: CC: [~ste...@apache.org] > NoClassDefFoundError when initializing Spark with YARN > -- > > Key: SPARK-15343 > URL: https://issues.apache.org/jira/browse/SPARK-15343 > Project: Spark > Issue Type: Bug > Components: YARN >Affects Versions: 2.0.0 >Reporter: Maciej Bryński >Priority: Critical > > I'm trying to connect Spark 2.0 (compiled from branch-2.0) with Hadoop. > Spark compiled with: > {code} > ./dev/make-distribution.sh -Pyarn -Phadoop-2.6 -Phive -Phive-thriftserver > -Dhadoop.version=2.6.0 -DskipTests > {code} > I'm getting following error > {code} > mbrynski@jupyter:~/spark$ bin/pyspark > Python 3.4.0 (default, Apr 11 2014, 13:05:11) > [GCC 4.8.2] on linux > Type "help", "copyright", "credits" or "license" for more information. > Warning: Master yarn-client is deprecated since 2.0. Please use master "yarn" > with specified deploy mode instead. > Setting default log level to "WARN". > To adjust logging level use sc.setLogLevel(newLevel). > 16/05/16 11:54:41 WARN SparkConf: The configuration key 'spark.yarn.jar' has > been deprecated as of Spark 2.0 and may be removed in the future. Please use > the new key 'spark.yarn.jars' instead. > 16/05/16 11:54:41 WARN NativeCodeLoader: Unable to load native-hadoop library > for your platform... using builtin-java classes where applicable > 16/05/16 11:54:42 WARN AbstractHandler: No Server set for > org.spark_project.jetty.server.handler.ErrorHandler@f7989f6 > 16/05/16 11:54:43 WARN DomainSocketFactory: The short-circuit local reads > feature cannot be used because libhadoop cannot be loaded. > Traceback (most recent call last): > File "/home/mbrynski/spark/python/pyspark/shell.py", line 38, in > sc = SparkContext() > File "/home/mbrynski/spark/python/pyspark/context.py", line 115, in __init__ > conf, jsc, profiler_cls) > File "/home/mbrynski/spark/python/pyspark/context.py", line 172, in _do_init > self._jsc = jsc or self._initialize_context(self._conf._jconf) > File "/home/mbrynski/spark/python/pyspark/context.py", line 235, in > _initialize_context > return self._jvm.JavaSparkContext(jconf) > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/java_gateway.py", > line 1183, in __call__ > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/protocol.py", line > 312, in get_return_value > py4j.protocol.Py4JJavaError: An error occurred while calling > None.org.apache.spark.api.java.JavaSparkContext. > : java.lang.NoClassDefFoundError: > com/sun/jersey/api/client/config/ClientConfig > at > org.apache.hadoop.yarn.client.api.TimelineClient.createTimelineClient(TimelineClient.java:45) > at > org.apache.hadoop.yarn.client.api.impl.YarnClientImpl.serviceInit(YarnClientImpl.java:163) > at > org.apache.hadoop.service.AbstractService.init(AbstractService.java:163) > at > org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:150) > at > org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:56) > at > org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:148) > at org.apache.spark.SparkContext.(SparkContext.scala:502) > at > org.apache.spark.api.java.JavaSparkContext.(JavaSparkContext.scala:58) > at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native > Method) > at > sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62) > at > sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45) > at java.lang.reflect.Constructor.newInstance(Constructor.java:422) > at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:240) > at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) > at py4j.Gateway.invoke(Gateway.java:236) > at > py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:80) > at > py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:69) > at py4j.GatewayConnection.run(GatewayConnection.java:211) > at java.lang.Thread.run(Thread.java:745) > Caused by: java.lang.ClassNotFoundException: > com.sun.jersey.api.client.config.ClientConfig > at java.net.URLClassLoader.findClass(URLClassLoader.java:381) > at java.lang.ClassLoader.loadClass(ClassLoader.java:424) > at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:331) > at java.lang.ClassLoader.loadClass(ClassLoader.java:357) > ... 19 more > {code} > On 1.6
[jira] [Commented] (SPARK-15343) NoClassDefFoundError when initializing Spark with YARN
[ https://issues.apache.org/jira/browse/SPARK-15343?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15284981#comment-15284981 ] Marcelo Vanzin commented on SPARK-15343: bq. at org.apache.hadoop.yarn.client.api.TimelineClient.createTimelineClient(TimelineClient.java:45) You're using a 3rd-party module developed by Hortonworks to talk to the YARN ATS; they include it as part of their distribution, but I believe it's not yet compatible with Spark 2.0. So you need to follow up with them, since this is not an issue with Spark, or disable that feature. > NoClassDefFoundError when initializing Spark with YARN > -- > > Key: SPARK-15343 > URL: https://issues.apache.org/jira/browse/SPARK-15343 > Project: Spark > Issue Type: Bug > Components: YARN >Affects Versions: 2.0.0 >Reporter: Maciej Bryński >Priority: Critical > > I'm trying to connect Spark 2.0 (compiled from branch-2.0) with Hadoop. > Spark compiled with: > {code} > ./dev/make-distribution.sh -Pyarn -Phadoop-2.6 -Phive -Phive-thriftserver > -Dhadoop.version=2.6.0 -DskipTests > {code} > I'm getting following error > {code} > mbrynski@jupyter:~/spark$ bin/pyspark > Python 3.4.0 (default, Apr 11 2014, 13:05:11) > [GCC 4.8.2] on linux > Type "help", "copyright", "credits" or "license" for more information. > Warning: Master yarn-client is deprecated since 2.0. Please use master "yarn" > with specified deploy mode instead. > Setting default log level to "WARN". > To adjust logging level use sc.setLogLevel(newLevel). > 16/05/16 11:54:41 WARN SparkConf: The configuration key 'spark.yarn.jar' has > been deprecated as of Spark 2.0 and may be removed in the future. Please use > the new key 'spark.yarn.jars' instead. > 16/05/16 11:54:41 WARN NativeCodeLoader: Unable to load native-hadoop library > for your platform... using builtin-java classes where applicable > 16/05/16 11:54:42 WARN AbstractHandler: No Server set for > org.spark_project.jetty.server.handler.ErrorHandler@f7989f6 > 16/05/16 11:54:43 WARN DomainSocketFactory: The short-circuit local reads > feature cannot be used because libhadoop cannot be loaded. > Traceback (most recent call last): > File "/home/mbrynski/spark/python/pyspark/shell.py", line 38, in > sc = SparkContext() > File "/home/mbrynski/spark/python/pyspark/context.py", line 115, in __init__ > conf, jsc, profiler_cls) > File "/home/mbrynski/spark/python/pyspark/context.py", line 172, in _do_init > self._jsc = jsc or self._initialize_context(self._conf._jconf) > File "/home/mbrynski/spark/python/pyspark/context.py", line 235, in > _initialize_context > return self._jvm.JavaSparkContext(jconf) > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/java_gateway.py", > line 1183, in __call__ > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/protocol.py", line > 312, in get_return_value > py4j.protocol.Py4JJavaError: An error occurred while calling > None.org.apache.spark.api.java.JavaSparkContext. > : java.lang.NoClassDefFoundError: > com/sun/jersey/api/client/config/ClientConfig > at > org.apache.hadoop.yarn.client.api.TimelineClient.createTimelineClient(TimelineClient.java:45) > at > org.apache.hadoop.yarn.client.api.impl.YarnClientImpl.serviceInit(YarnClientImpl.java:163) > at > org.apache.hadoop.service.AbstractService.init(AbstractService.java:163) > at > org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:150) > at > org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:56) > at > org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:148) > at org.apache.spark.SparkContext.(SparkContext.scala:502) > at > org.apache.spark.api.java.JavaSparkContext.(JavaSparkContext.scala:58) > at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native > Method) > at > sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62) > at > sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45) > at java.lang.reflect.Constructor.newInstance(Constructor.java:422) > at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:240) > at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) > at py4j.Gateway.invoke(Gateway.java:236) > at > py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:80) > at > py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:69) > at py4j.GatewayConnection.run(GatewayConnection.java:211) > at java.lang.Thread.run(Thread.java:745) > Caused by: java.lang.ClassNotFoundException:
[jira] [Commented] (SPARK-15343) NoClassDefFoundError when initializing Spark with YARN
[ https://issues.apache.org/jira/browse/SPARK-15343?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15284623#comment-15284623 ] Sean Owen commented on SPARK-15343: --- SInce you're executing in a cluster, I think perhaps a better and more canonical solution is to build with "-Phadoop-provided" and get the Hadoop dependencies from the cluster? then you're inheriting the version that's consistent with the cluster config. > NoClassDefFoundError when initializing Spark with YARN > -- > > Key: SPARK-15343 > URL: https://issues.apache.org/jira/browse/SPARK-15343 > Project: Spark > Issue Type: Bug > Components: YARN >Affects Versions: 2.0.0 >Reporter: Maciej Bryński >Priority: Critical > > I'm trying to connect Spark 2.0 (compiled from branch-2.0) with Hadoop. > Spark compiled with: > {code} > ./dev/make-distribution.sh -Pyarn -Phadoop-2.6 -Phive -Phive-thriftserver > -Dhadoop.version=2.6.0 -DskipTests > {code} > I'm getting following error > {code} > mbrynski@jupyter:~/spark$ bin/pyspark > Python 3.4.0 (default, Apr 11 2014, 13:05:11) > [GCC 4.8.2] on linux > Type "help", "copyright", "credits" or "license" for more information. > Warning: Master yarn-client is deprecated since 2.0. Please use master "yarn" > with specified deploy mode instead. > Setting default log level to "WARN". > To adjust logging level use sc.setLogLevel(newLevel). > 16/05/16 11:54:41 WARN SparkConf: The configuration key 'spark.yarn.jar' has > been deprecated as of Spark 2.0 and may be removed in the future. Please use > the new key 'spark.yarn.jars' instead. > 16/05/16 11:54:41 WARN NativeCodeLoader: Unable to load native-hadoop library > for your platform... using builtin-java classes where applicable > 16/05/16 11:54:42 WARN AbstractHandler: No Server set for > org.spark_project.jetty.server.handler.ErrorHandler@f7989f6 > 16/05/16 11:54:43 WARN DomainSocketFactory: The short-circuit local reads > feature cannot be used because libhadoop cannot be loaded. > Traceback (most recent call last): > File "/home/mbrynski/spark/python/pyspark/shell.py", line 38, in > sc = SparkContext() > File "/home/mbrynski/spark/python/pyspark/context.py", line 115, in __init__ > conf, jsc, profiler_cls) > File "/home/mbrynski/spark/python/pyspark/context.py", line 172, in _do_init > self._jsc = jsc or self._initialize_context(self._conf._jconf) > File "/home/mbrynski/spark/python/pyspark/context.py", line 235, in > _initialize_context > return self._jvm.JavaSparkContext(jconf) > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/java_gateway.py", > line 1183, in __call__ > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/protocol.py", line > 312, in get_return_value > py4j.protocol.Py4JJavaError: An error occurred while calling > None.org.apache.spark.api.java.JavaSparkContext. > : java.lang.NoClassDefFoundError: > com/sun/jersey/api/client/config/ClientConfig > at > org.apache.hadoop.yarn.client.api.TimelineClient.createTimelineClient(TimelineClient.java:45) > at > org.apache.hadoop.yarn.client.api.impl.YarnClientImpl.serviceInit(YarnClientImpl.java:163) > at > org.apache.hadoop.service.AbstractService.init(AbstractService.java:163) > at > org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:150) > at > org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:56) > at > org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:148) > at org.apache.spark.SparkContext.(SparkContext.scala:502) > at > org.apache.spark.api.java.JavaSparkContext.(JavaSparkContext.scala:58) > at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native > Method) > at > sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62) > at > sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45) > at java.lang.reflect.Constructor.newInstance(Constructor.java:422) > at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:240) > at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) > at py4j.Gateway.invoke(Gateway.java:236) > at > py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:80) > at > py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:69) > at py4j.GatewayConnection.run(GatewayConnection.java:211) > at java.lang.Thread.run(Thread.java:745) > Caused by: java.lang.ClassNotFoundException: > com.sun.jersey.api.client.config.ClientConfig > at java.net.URLClassLoader.findClass(URLClassLoader.java:381) > at
[jira] [Commented] (SPARK-15343) NoClassDefFoundError when initializing Spark with YARN
[ https://issues.apache.org/jira/browse/SPARK-15343?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15284619#comment-15284619 ] Maciej Bryński commented on SPARK-15343: I set spark.hadoop.yarn.timeline-service.enabled to false. It's nasty workaround but it works. > NoClassDefFoundError when initializing Spark with YARN > -- > > Key: SPARK-15343 > URL: https://issues.apache.org/jira/browse/SPARK-15343 > Project: Spark > Issue Type: Bug > Components: YARN >Affects Versions: 2.0.0 >Reporter: Maciej Bryński >Priority: Critical > > I'm trying to connect Spark 2.0 (compiled from branch-2.0) with Hadoop. > Spark compiled with: > {code} > ./dev/make-distribution.sh -Pyarn -Phadoop-2.6 -Phive -Phive-thriftserver > -Dhadoop.version=2.6.0 -DskipTests > {code} > I'm getting following error > {code} > mbrynski@jupyter:~/spark$ bin/pyspark > Python 3.4.0 (default, Apr 11 2014, 13:05:11) > [GCC 4.8.2] on linux > Type "help", "copyright", "credits" or "license" for more information. > Warning: Master yarn-client is deprecated since 2.0. Please use master "yarn" > with specified deploy mode instead. > Setting default log level to "WARN". > To adjust logging level use sc.setLogLevel(newLevel). > 16/05/16 11:54:41 WARN SparkConf: The configuration key 'spark.yarn.jar' has > been deprecated as of Spark 2.0 and may be removed in the future. Please use > the new key 'spark.yarn.jars' instead. > 16/05/16 11:54:41 WARN NativeCodeLoader: Unable to load native-hadoop library > for your platform... using builtin-java classes where applicable > 16/05/16 11:54:42 WARN AbstractHandler: No Server set for > org.spark_project.jetty.server.handler.ErrorHandler@f7989f6 > 16/05/16 11:54:43 WARN DomainSocketFactory: The short-circuit local reads > feature cannot be used because libhadoop cannot be loaded. > Traceback (most recent call last): > File "/home/mbrynski/spark/python/pyspark/shell.py", line 38, in > sc = SparkContext() > File "/home/mbrynski/spark/python/pyspark/context.py", line 115, in __init__ > conf, jsc, profiler_cls) > File "/home/mbrynski/spark/python/pyspark/context.py", line 172, in _do_init > self._jsc = jsc or self._initialize_context(self._conf._jconf) > File "/home/mbrynski/spark/python/pyspark/context.py", line 235, in > _initialize_context > return self._jvm.JavaSparkContext(jconf) > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/java_gateway.py", > line 1183, in __call__ > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/protocol.py", line > 312, in get_return_value > py4j.protocol.Py4JJavaError: An error occurred while calling > None.org.apache.spark.api.java.JavaSparkContext. > : java.lang.NoClassDefFoundError: > com/sun/jersey/api/client/config/ClientConfig > at > org.apache.hadoop.yarn.client.api.TimelineClient.createTimelineClient(TimelineClient.java:45) > at > org.apache.hadoop.yarn.client.api.impl.YarnClientImpl.serviceInit(YarnClientImpl.java:163) > at > org.apache.hadoop.service.AbstractService.init(AbstractService.java:163) > at > org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:150) > at > org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:56) > at > org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:148) > at org.apache.spark.SparkContext.(SparkContext.scala:502) > at > org.apache.spark.api.java.JavaSparkContext.(JavaSparkContext.scala:58) > at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native > Method) > at > sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62) > at > sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45) > at java.lang.reflect.Constructor.newInstance(Constructor.java:422) > at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:240) > at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) > at py4j.Gateway.invoke(Gateway.java:236) > at > py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:80) > at > py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:69) > at py4j.GatewayConnection.run(GatewayConnection.java:211) > at java.lang.Thread.run(Thread.java:745) > Caused by: java.lang.ClassNotFoundException: > com.sun.jersey.api.client.config.ClientConfig > at java.net.URLClassLoader.findClass(URLClassLoader.java:381) > at java.lang.ClassLoader.loadClass(ClassLoader.java:424) > at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:331) > at
[jira] [Commented] (SPARK-15343) NoClassDefFoundError when initializing Spark with YARN
[ https://issues.apache.org/jira/browse/SPARK-15343?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15284612#comment-15284612 ] Sean Owen commented on SPARK-15343: --- Yes, of course that's the change that caused the behavior you're seeing, but it should be OK for all of Spark's usages. At least, that was the conclusion before, and all of the Spark tests work. > NoClassDefFoundError when initializing Spark with YARN > -- > > Key: SPARK-15343 > URL: https://issues.apache.org/jira/browse/SPARK-15343 > Project: Spark > Issue Type: Bug > Components: YARN >Affects Versions: 2.0.0 >Reporter: Maciej Bryński >Priority: Critical > > I'm trying to connect Spark 2.0 (compiled from branch-2.0) with Hadoop. > Spark compiled with: > {code} > ./dev/make-distribution.sh -Pyarn -Phadoop-2.6 -Phive -Phive-thriftserver > -Dhadoop.version=2.6.0 -DskipTests > {code} > I'm getting following error > {code} > mbrynski@jupyter:~/spark$ bin/pyspark > Python 3.4.0 (default, Apr 11 2014, 13:05:11) > [GCC 4.8.2] on linux > Type "help", "copyright", "credits" or "license" for more information. > Warning: Master yarn-client is deprecated since 2.0. Please use master "yarn" > with specified deploy mode instead. > Setting default log level to "WARN". > To adjust logging level use sc.setLogLevel(newLevel). > 16/05/16 11:54:41 WARN SparkConf: The configuration key 'spark.yarn.jar' has > been deprecated as of Spark 2.0 and may be removed in the future. Please use > the new key 'spark.yarn.jars' instead. > 16/05/16 11:54:41 WARN NativeCodeLoader: Unable to load native-hadoop library > for your platform... using builtin-java classes where applicable > 16/05/16 11:54:42 WARN AbstractHandler: No Server set for > org.spark_project.jetty.server.handler.ErrorHandler@f7989f6 > 16/05/16 11:54:43 WARN DomainSocketFactory: The short-circuit local reads > feature cannot be used because libhadoop cannot be loaded. > Traceback (most recent call last): > File "/home/mbrynski/spark/python/pyspark/shell.py", line 38, in > sc = SparkContext() > File "/home/mbrynski/spark/python/pyspark/context.py", line 115, in __init__ > conf, jsc, profiler_cls) > File "/home/mbrynski/spark/python/pyspark/context.py", line 172, in _do_init > self._jsc = jsc or self._initialize_context(self._conf._jconf) > File "/home/mbrynski/spark/python/pyspark/context.py", line 235, in > _initialize_context > return self._jvm.JavaSparkContext(jconf) > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/java_gateway.py", > line 1183, in __call__ > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/protocol.py", line > 312, in get_return_value > py4j.protocol.Py4JJavaError: An error occurred while calling > None.org.apache.spark.api.java.JavaSparkContext. > : java.lang.NoClassDefFoundError: > com/sun/jersey/api/client/config/ClientConfig > at > org.apache.hadoop.yarn.client.api.TimelineClient.createTimelineClient(TimelineClient.java:45) > at > org.apache.hadoop.yarn.client.api.impl.YarnClientImpl.serviceInit(YarnClientImpl.java:163) > at > org.apache.hadoop.service.AbstractService.init(AbstractService.java:163) > at > org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:150) > at > org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:56) > at > org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:148) > at org.apache.spark.SparkContext.(SparkContext.scala:502) > at > org.apache.spark.api.java.JavaSparkContext.(JavaSparkContext.scala:58) > at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native > Method) > at > sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62) > at > sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45) > at java.lang.reflect.Constructor.newInstance(Constructor.java:422) > at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:240) > at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) > at py4j.Gateway.invoke(Gateway.java:236) > at > py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:80) > at > py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:69) > at py4j.GatewayConnection.run(GatewayConnection.java:211) > at java.lang.Thread.run(Thread.java:745) > Caused by: java.lang.ClassNotFoundException: > com.sun.jersey.api.client.config.ClientConfig > at java.net.URLClassLoader.findClass(URLClassLoader.java:381) > at java.lang.ClassLoader.loadClass(ClassLoader.java:424) > at
[jira] [Commented] (SPARK-15343) NoClassDefFoundError when initializing Spark with YARN
[ https://issues.apache.org/jira/browse/SPARK-15343?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15284538#comment-15284538 ] Sean Owen commented on SPARK-15343: --- No, it's clearly a class needed by YARN and that's where it fails -- have a look at the stack. Yes, YARN certainly is the one using Jersey 1.x and it is in a different namespace. When this came up before I was wondering if we needed to adjust exclusions to allow both into the assembly, but have a look at this: http://apache-spark-developers-list.1001551.n3.nabble.com/spark-2-0-issue-with-yarn-td17440.html#a17448 I think the conclusion was that the thing that needs Jersey isn't a part of Spark? > NoClassDefFoundError when initializing Spark with YARN > -- > > Key: SPARK-15343 > URL: https://issues.apache.org/jira/browse/SPARK-15343 > Project: Spark > Issue Type: Bug > Components: YARN >Affects Versions: 2.0.0 >Reporter: Maciej Bryński >Priority: Critical > > I'm trying to connect Spark 2.0 (compiled from branch-2.0) with Hadoop. > Spark compiled with: > {code} > ./dev/make-distribution.sh -Pyarn -Phadoop-2.6 -Phive -Phive-thriftserver > -Dhadoop.version=2.6.0 -DskipTests > {code} > I'm getting following error > {code} > mbrynski@jupyter:~/spark$ bin/pyspark > Python 3.4.0 (default, Apr 11 2014, 13:05:11) > [GCC 4.8.2] on linux > Type "help", "copyright", "credits" or "license" for more information. > Warning: Master yarn-client is deprecated since 2.0. Please use master "yarn" > with specified deploy mode instead. > Setting default log level to "WARN". > To adjust logging level use sc.setLogLevel(newLevel). > 16/05/16 11:54:41 WARN SparkConf: The configuration key 'spark.yarn.jar' has > been deprecated as of Spark 2.0 and may be removed in the future. Please use > the new key 'spark.yarn.jars' instead. > 16/05/16 11:54:41 WARN NativeCodeLoader: Unable to load native-hadoop library > for your platform... using builtin-java classes where applicable > 16/05/16 11:54:42 WARN AbstractHandler: No Server set for > org.spark_project.jetty.server.handler.ErrorHandler@f7989f6 > 16/05/16 11:54:43 WARN DomainSocketFactory: The short-circuit local reads > feature cannot be used because libhadoop cannot be loaded. > Traceback (most recent call last): > File "/home/mbrynski/spark/python/pyspark/shell.py", line 38, in > sc = SparkContext() > File "/home/mbrynski/spark/python/pyspark/context.py", line 115, in __init__ > conf, jsc, profiler_cls) > File "/home/mbrynski/spark/python/pyspark/context.py", line 172, in _do_init > self._jsc = jsc or self._initialize_context(self._conf._jconf) > File "/home/mbrynski/spark/python/pyspark/context.py", line 235, in > _initialize_context > return self._jvm.JavaSparkContext(jconf) > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/java_gateway.py", > line 1183, in __call__ > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/protocol.py", line > 312, in get_return_value > py4j.protocol.Py4JJavaError: An error occurred while calling > None.org.apache.spark.api.java.JavaSparkContext. > : java.lang.NoClassDefFoundError: > com/sun/jersey/api/client/config/ClientConfig > at > org.apache.hadoop.yarn.client.api.TimelineClient.createTimelineClient(TimelineClient.java:45) > at > org.apache.hadoop.yarn.client.api.impl.YarnClientImpl.serviceInit(YarnClientImpl.java:163) > at > org.apache.hadoop.service.AbstractService.init(AbstractService.java:163) > at > org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:150) > at > org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:56) > at > org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:148) > at org.apache.spark.SparkContext.(SparkContext.scala:502) > at > org.apache.spark.api.java.JavaSparkContext.(JavaSparkContext.scala:58) > at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native > Method) > at > sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62) > at > sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45) > at java.lang.reflect.Constructor.newInstance(Constructor.java:422) > at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:240) > at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) > at py4j.Gateway.invoke(Gateway.java:236) > at > py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:80) > at > py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:69) > at
[jira] [Commented] (SPARK-15343) NoClassDefFoundError when initializing Spark with YARN
[ https://issues.apache.org/jira/browse/SPARK-15343?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15284507#comment-15284507 ] Maciej Bryński commented on SPARK-15343: And the likely reason of problem. https://issues.apache.org/jira/browse/SPARK-12154 > NoClassDefFoundError when initializing Spark with YARN > -- > > Key: SPARK-15343 > URL: https://issues.apache.org/jira/browse/SPARK-15343 > Project: Spark > Issue Type: Bug > Components: YARN >Affects Versions: 2.0.0 >Reporter: Maciej Bryński >Priority: Critical > > I'm trying to connect Spark 2.0 (compiled from branch-2.0) with Hadoop. > Spark compiled with: > {code} > ./dev/make-distribution.sh -Pyarn -Phadoop-2.6 -Phive -Phive-thriftserver > -Dhadoop.version=2.6.0 -DskipTests > {code} > I'm getting following error > {code} > mbrynski@jupyter:~/spark$ bin/pyspark > Python 3.4.0 (default, Apr 11 2014, 13:05:11) > [GCC 4.8.2] on linux > Type "help", "copyright", "credits" or "license" for more information. > Warning: Master yarn-client is deprecated since 2.0. Please use master "yarn" > with specified deploy mode instead. > Setting default log level to "WARN". > To adjust logging level use sc.setLogLevel(newLevel). > 16/05/16 11:54:41 WARN SparkConf: The configuration key 'spark.yarn.jar' has > been deprecated as of Spark 2.0 and may be removed in the future. Please use > the new key 'spark.yarn.jars' instead. > 16/05/16 11:54:41 WARN NativeCodeLoader: Unable to load native-hadoop library > for your platform... using builtin-java classes where applicable > 16/05/16 11:54:42 WARN AbstractHandler: No Server set for > org.spark_project.jetty.server.handler.ErrorHandler@f7989f6 > 16/05/16 11:54:43 WARN DomainSocketFactory: The short-circuit local reads > feature cannot be used because libhadoop cannot be loaded. > Traceback (most recent call last): > File "/home/mbrynski/spark/python/pyspark/shell.py", line 38, in > sc = SparkContext() > File "/home/mbrynski/spark/python/pyspark/context.py", line 115, in __init__ > conf, jsc, profiler_cls) > File "/home/mbrynski/spark/python/pyspark/context.py", line 172, in _do_init > self._jsc = jsc or self._initialize_context(self._conf._jconf) > File "/home/mbrynski/spark/python/pyspark/context.py", line 235, in > _initialize_context > return self._jvm.JavaSparkContext(jconf) > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/java_gateway.py", > line 1183, in __call__ > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/protocol.py", line > 312, in get_return_value > py4j.protocol.Py4JJavaError: An error occurred while calling > None.org.apache.spark.api.java.JavaSparkContext. > : java.lang.NoClassDefFoundError: > com/sun/jersey/api/client/config/ClientConfig > at > org.apache.hadoop.yarn.client.api.TimelineClient.createTimelineClient(TimelineClient.java:45) > at > org.apache.hadoop.yarn.client.api.impl.YarnClientImpl.serviceInit(YarnClientImpl.java:163) > at > org.apache.hadoop.service.AbstractService.init(AbstractService.java:163) > at > org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:150) > at > org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:56) > at > org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:148) > at org.apache.spark.SparkContext.(SparkContext.scala:502) > at > org.apache.spark.api.java.JavaSparkContext.(JavaSparkContext.scala:58) > at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native > Method) > at > sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62) > at > sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45) > at java.lang.reflect.Constructor.newInstance(Constructor.java:422) > at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:240) > at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) > at py4j.Gateway.invoke(Gateway.java:236) > at > py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:80) > at > py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:69) > at py4j.GatewayConnection.run(GatewayConnection.java:211) > at java.lang.Thread.run(Thread.java:745) > Caused by: java.lang.ClassNotFoundException: > com.sun.jersey.api.client.config.ClientConfig > at java.net.URLClassLoader.findClass(URLClassLoader.java:381) > at java.lang.ClassLoader.loadClass(ClassLoader.java:424) > at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:331) > at
[jira] [Commented] (SPARK-15343) NoClassDefFoundError when initializing Spark with YARN
[ https://issues.apache.org/jira/browse/SPARK-15343?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15284506#comment-15284506 ] Maciej Bryński commented on SPARK-15343: I think it's too early for that. Exception is thrown on JavaSparkContext initialization. So before connection to YARN. I checked jersey-client-1.19.1.jar and com/sun/jersey/api/client/config/ClientConfig is inside. Maybe we should include both versions ? > NoClassDefFoundError when initializing Spark with YARN > -- > > Key: SPARK-15343 > URL: https://issues.apache.org/jira/browse/SPARK-15343 > Project: Spark > Issue Type: Bug > Components: YARN >Affects Versions: 2.0.0 >Reporter: Maciej Bryński >Priority: Critical > > I'm trying to connect Spark 2.0 (compiled from branch-2.0) with Hadoop. > Spark compiled with: > {code} > ./dev/make-distribution.sh -Pyarn -Phadoop-2.6 -Phive -Phive-thriftserver > -Dhadoop.version=2.6.0 -DskipTests > {code} > I'm getting following error > {code} > mbrynski@jupyter:~/spark$ bin/pyspark > Python 3.4.0 (default, Apr 11 2014, 13:05:11) > [GCC 4.8.2] on linux > Type "help", "copyright", "credits" or "license" for more information. > Warning: Master yarn-client is deprecated since 2.0. Please use master "yarn" > with specified deploy mode instead. > Setting default log level to "WARN". > To adjust logging level use sc.setLogLevel(newLevel). > 16/05/16 11:54:41 WARN SparkConf: The configuration key 'spark.yarn.jar' has > been deprecated as of Spark 2.0 and may be removed in the future. Please use > the new key 'spark.yarn.jars' instead. > 16/05/16 11:54:41 WARN NativeCodeLoader: Unable to load native-hadoop library > for your platform... using builtin-java classes where applicable > 16/05/16 11:54:42 WARN AbstractHandler: No Server set for > org.spark_project.jetty.server.handler.ErrorHandler@f7989f6 > 16/05/16 11:54:43 WARN DomainSocketFactory: The short-circuit local reads > feature cannot be used because libhadoop cannot be loaded. > Traceback (most recent call last): > File "/home/mbrynski/spark/python/pyspark/shell.py", line 38, in > sc = SparkContext() > File "/home/mbrynski/spark/python/pyspark/context.py", line 115, in __init__ > conf, jsc, profiler_cls) > File "/home/mbrynski/spark/python/pyspark/context.py", line 172, in _do_init > self._jsc = jsc or self._initialize_context(self._conf._jconf) > File "/home/mbrynski/spark/python/pyspark/context.py", line 235, in > _initialize_context > return self._jvm.JavaSparkContext(jconf) > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/java_gateway.py", > line 1183, in __call__ > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/protocol.py", line > 312, in get_return_value > py4j.protocol.Py4JJavaError: An error occurred while calling > None.org.apache.spark.api.java.JavaSparkContext. > : java.lang.NoClassDefFoundError: > com/sun/jersey/api/client/config/ClientConfig > at > org.apache.hadoop.yarn.client.api.TimelineClient.createTimelineClient(TimelineClient.java:45) > at > org.apache.hadoop.yarn.client.api.impl.YarnClientImpl.serviceInit(YarnClientImpl.java:163) > at > org.apache.hadoop.service.AbstractService.init(AbstractService.java:163) > at > org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:150) > at > org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:56) > at > org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:148) > at org.apache.spark.SparkContext.(SparkContext.scala:502) > at > org.apache.spark.api.java.JavaSparkContext.(JavaSparkContext.scala:58) > at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native > Method) > at > sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62) > at > sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45) > at java.lang.reflect.Constructor.newInstance(Constructor.java:422) > at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:240) > at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) > at py4j.Gateway.invoke(Gateway.java:236) > at > py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:80) > at > py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:69) > at py4j.GatewayConnection.run(GatewayConnection.java:211) > at java.lang.Thread.run(Thread.java:745) > Caused by: java.lang.ClassNotFoundException: > com.sun.jersey.api.client.config.ClientConfig > at java.net.URLClassLoader.findClass(URLClassLoader.java:381) > at
[jira] [Commented] (SPARK-15343) NoClassDefFoundError when initializing Spark with YARN
[ https://issues.apache.org/jira/browse/SPARK-15343?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15284480#comment-15284480 ] Sean Owen commented on SPARK-15343: --- Yeah, though in theory that doesn't prevent it from being pulled in by YARN from its own copy. You should have YARN being 'provided' at runtime by the cluster -- not bundled in your app though right? > NoClassDefFoundError when initializing Spark with YARN > -- > > Key: SPARK-15343 > URL: https://issues.apache.org/jira/browse/SPARK-15343 > Project: Spark > Issue Type: Bug > Components: YARN >Affects Versions: 2.0.0 >Reporter: Maciej Bryński >Priority: Critical > > I'm trying to connect Spark 2.0 (compiled from branch-2.0) with Hadoop. > Spark compiled with: > {code} > ./dev/make-distribution.sh -Pyarn -Phadoop-2.6 -Phive -Phive-thriftserver > -Dhadoop.version=2.6.0 -DskipTests > {code} > I'm getting following error > {code} > mbrynski@jupyter:~/spark$ bin/pyspark > Python 3.4.0 (default, Apr 11 2014, 13:05:11) > [GCC 4.8.2] on linux > Type "help", "copyright", "credits" or "license" for more information. > Warning: Master yarn-client is deprecated since 2.0. Please use master "yarn" > with specified deploy mode instead. > Setting default log level to "WARN". > To adjust logging level use sc.setLogLevel(newLevel). > 16/05/16 11:54:41 WARN SparkConf: The configuration key 'spark.yarn.jar' has > been deprecated as of Spark 2.0 and may be removed in the future. Please use > the new key 'spark.yarn.jars' instead. > 16/05/16 11:54:41 WARN NativeCodeLoader: Unable to load native-hadoop library > for your platform... using builtin-java classes where applicable > 16/05/16 11:54:42 WARN AbstractHandler: No Server set for > org.spark_project.jetty.server.handler.ErrorHandler@f7989f6 > 16/05/16 11:54:43 WARN DomainSocketFactory: The short-circuit local reads > feature cannot be used because libhadoop cannot be loaded. > Traceback (most recent call last): > File "/home/mbrynski/spark/python/pyspark/shell.py", line 38, in > sc = SparkContext() > File "/home/mbrynski/spark/python/pyspark/context.py", line 115, in __init__ > conf, jsc, profiler_cls) > File "/home/mbrynski/spark/python/pyspark/context.py", line 172, in _do_init > self._jsc = jsc or self._initialize_context(self._conf._jconf) > File "/home/mbrynski/spark/python/pyspark/context.py", line 235, in > _initialize_context > return self._jvm.JavaSparkContext(jconf) > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/java_gateway.py", > line 1183, in __call__ > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/protocol.py", line > 312, in get_return_value > py4j.protocol.Py4JJavaError: An error occurred while calling > None.org.apache.spark.api.java.JavaSparkContext. > : java.lang.NoClassDefFoundError: > com/sun/jersey/api/client/config/ClientConfig > at > org.apache.hadoop.yarn.client.api.TimelineClient.createTimelineClient(TimelineClient.java:45) > at > org.apache.hadoop.yarn.client.api.impl.YarnClientImpl.serviceInit(YarnClientImpl.java:163) > at > org.apache.hadoop.service.AbstractService.init(AbstractService.java:163) > at > org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:150) > at > org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:56) > at > org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:148) > at org.apache.spark.SparkContext.(SparkContext.scala:502) > at > org.apache.spark.api.java.JavaSparkContext.(JavaSparkContext.scala:58) > at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native > Method) > at > sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62) > at > sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45) > at java.lang.reflect.Constructor.newInstance(Constructor.java:422) > at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:240) > at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) > at py4j.Gateway.invoke(Gateway.java:236) > at > py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:80) > at > py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:69) > at py4j.GatewayConnection.run(GatewayConnection.java:211) > at java.lang.Thread.run(Thread.java:745) > Caused by: java.lang.ClassNotFoundException: > com.sun.jersey.api.client.config.ClientConfig > at java.net.URLClassLoader.findClass(URLClassLoader.java:381) > at java.lang.ClassLoader.loadClass(ClassLoader.java:424) > at
[jira] [Commented] (SPARK-15343) NoClassDefFoundError when initializing Spark with YARN
[ https://issues.apache.org/jira/browse/SPARK-15343?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15284465#comment-15284465 ] Maciej Bryński commented on SPARK-15343: [~srowen] I found that we change version of jersey library from 1.9 (https://github.com/apache/spark/blob/branch-1.6/pom.xml#L182) to 2.22.2 (https://github.com/apache/spark/blob/master/pom.xml#L175). Maybe that's the reason. > NoClassDefFoundError when initializing Spark with YARN > -- > > Key: SPARK-15343 > URL: https://issues.apache.org/jira/browse/SPARK-15343 > Project: Spark > Issue Type: Bug > Components: YARN >Affects Versions: 2.0.0 >Reporter: Maciej Bryński >Priority: Critical > > I'm trying to connect Spark 2.0 (compiled from branch-2.0) with Hadoop. > Spark compiled with: > {code} > ./dev/make-distribution.sh -Pyarn -Phadoop-2.6 -Phive -Phive-thriftserver > -Dhadoop.version=2.6.0 -DskipTests > {code} > I'm getting following error > {code} > mbrynski@jupyter:~/spark$ bin/pyspark > Python 3.4.0 (default, Apr 11 2014, 13:05:11) > [GCC 4.8.2] on linux > Type "help", "copyright", "credits" or "license" for more information. > Warning: Master yarn-client is deprecated since 2.0. Please use master "yarn" > with specified deploy mode instead. > Setting default log level to "WARN". > To adjust logging level use sc.setLogLevel(newLevel). > 16/05/16 11:54:41 WARN SparkConf: The configuration key 'spark.yarn.jar' has > been deprecated as of Spark 2.0 and may be removed in the future. Please use > the new key 'spark.yarn.jars' instead. > 16/05/16 11:54:41 WARN NativeCodeLoader: Unable to load native-hadoop library > for your platform... using builtin-java classes where applicable > 16/05/16 11:54:42 WARN AbstractHandler: No Server set for > org.spark_project.jetty.server.handler.ErrorHandler@f7989f6 > 16/05/16 11:54:43 WARN DomainSocketFactory: The short-circuit local reads > feature cannot be used because libhadoop cannot be loaded. > Traceback (most recent call last): > File "/home/mbrynski/spark/python/pyspark/shell.py", line 38, in > sc = SparkContext() > File "/home/mbrynski/spark/python/pyspark/context.py", line 115, in __init__ > conf, jsc, profiler_cls) > File "/home/mbrynski/spark/python/pyspark/context.py", line 172, in _do_init > self._jsc = jsc or self._initialize_context(self._conf._jconf) > File "/home/mbrynski/spark/python/pyspark/context.py", line 235, in > _initialize_context > return self._jvm.JavaSparkContext(jconf) > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/java_gateway.py", > line 1183, in __call__ > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/protocol.py", line > 312, in get_return_value > py4j.protocol.Py4JJavaError: An error occurred while calling > None.org.apache.spark.api.java.JavaSparkContext. > : java.lang.NoClassDefFoundError: > com/sun/jersey/api/client/config/ClientConfig > at > org.apache.hadoop.yarn.client.api.TimelineClient.createTimelineClient(TimelineClient.java:45) > at > org.apache.hadoop.yarn.client.api.impl.YarnClientImpl.serviceInit(YarnClientImpl.java:163) > at > org.apache.hadoop.service.AbstractService.init(AbstractService.java:163) > at > org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:150) > at > org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:56) > at > org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:148) > at org.apache.spark.SparkContext.(SparkContext.scala:502) > at > org.apache.spark.api.java.JavaSparkContext.(JavaSparkContext.scala:58) > at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native > Method) > at > sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62) > at > sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45) > at java.lang.reflect.Constructor.newInstance(Constructor.java:422) > at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:240) > at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) > at py4j.Gateway.invoke(Gateway.java:236) > at > py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:80) > at > py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:69) > at py4j.GatewayConnection.run(GatewayConnection.java:211) > at java.lang.Thread.run(Thread.java:745) > Caused by: java.lang.ClassNotFoundException: > com.sun.jersey.api.client.config.ClientConfig > at java.net.URLClassLoader.findClass(URLClassLoader.java:381) > at
[jira] [Commented] (SPARK-15343) NoClassDefFoundError when initializing Spark with YARN
[ https://issues.apache.org/jira/browse/SPARK-15343?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15284433#comment-15284433 ] Maciej Bryński commented on SPARK-15343: CC: [~vanzin] > NoClassDefFoundError when initializing Spark with YARN > -- > > Key: SPARK-15343 > URL: https://issues.apache.org/jira/browse/SPARK-15343 > Project: Spark > Issue Type: Bug > Components: YARN >Affects Versions: 2.0.0 >Reporter: Maciej Bryński >Priority: Critical > > I'm trying to connect Spark 2.0 (compiled from branch-2.0) with Hadoop. > Spark compiled with: > {code} > ./dev/make-distribution.sh -Pyarn -Phadoop-2.6 -Phive -Phive-thriftserver > -Dhadoop.version=2.6.0 -DskipTests > {code} > I'm getting following error > {code} > mbrynski@jupyter:~/spark$ bin/pyspark > Python 3.4.0 (default, Apr 11 2014, 13:05:11) > [GCC 4.8.2] on linux > Type "help", "copyright", "credits" or "license" for more information. > Warning: Master yarn-client is deprecated since 2.0. Please use master "yarn" > with specified deploy mode instead. > Setting default log level to "WARN". > To adjust logging level use sc.setLogLevel(newLevel). > 16/05/16 11:54:41 WARN SparkConf: The configuration key 'spark.yarn.jar' has > been deprecated as of Spark 2.0 and may be removed in the future. Please use > the new key 'spark.yarn.jars' instead. > 16/05/16 11:54:41 WARN NativeCodeLoader: Unable to load native-hadoop library > for your platform... using builtin-java classes where applicable > 16/05/16 11:54:42 WARN AbstractHandler: No Server set for > org.spark_project.jetty.server.handler.ErrorHandler@f7989f6 > 16/05/16 11:54:43 WARN DomainSocketFactory: The short-circuit local reads > feature cannot be used because libhadoop cannot be loaded. > Traceback (most recent call last): > File "/home/mbrynski/spark/python/pyspark/shell.py", line 38, in > sc = SparkContext() > File "/home/mbrynski/spark/python/pyspark/context.py", line 115, in __init__ > conf, jsc, profiler_cls) > File "/home/mbrynski/spark/python/pyspark/context.py", line 172, in _do_init > self._jsc = jsc or self._initialize_context(self._conf._jconf) > File "/home/mbrynski/spark/python/pyspark/context.py", line 235, in > _initialize_context > return self._jvm.JavaSparkContext(jconf) > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/java_gateway.py", > line 1183, in __call__ > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/protocol.py", line > 312, in get_return_value > py4j.protocol.Py4JJavaError: An error occurred while calling > None.org.apache.spark.api.java.JavaSparkContext. > : java.lang.NoClassDefFoundError: > com/sun/jersey/api/client/config/ClientConfig > at > org.apache.hadoop.yarn.client.api.TimelineClient.createTimelineClient(TimelineClient.java:45) > at > org.apache.hadoop.yarn.client.api.impl.YarnClientImpl.serviceInit(YarnClientImpl.java:163) > at > org.apache.hadoop.service.AbstractService.init(AbstractService.java:163) > at > org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:150) > at > org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:56) > at > org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:148) > at org.apache.spark.SparkContext.(SparkContext.scala:502) > at > org.apache.spark.api.java.JavaSparkContext.(JavaSparkContext.scala:58) > at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native > Method) > at > sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62) > at > sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45) > at java.lang.reflect.Constructor.newInstance(Constructor.java:422) > at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:240) > at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) > at py4j.Gateway.invoke(Gateway.java:236) > at > py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:80) > at > py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:69) > at py4j.GatewayConnection.run(GatewayConnection.java:211) > at java.lang.Thread.run(Thread.java:745) > Caused by: java.lang.ClassNotFoundException: > com.sun.jersey.api.client.config.ClientConfig > at java.net.URLClassLoader.findClass(URLClassLoader.java:381) > at java.lang.ClassLoader.loadClass(ClassLoader.java:424) > at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:331) > at java.lang.ClassLoader.loadClass(ClassLoader.java:357) > ... 19 more > {code} > On 1.6 everything
[jira] [Commented] (SPARK-15343) NoClassDefFoundError when initializing Spark with YARN
[ https://issues.apache.org/jira/browse/SPARK-15343?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15284428#comment-15284428 ] Sean Owen commented on SPARK-15343: --- @vanzin did you think this was a HDP-specific thing -- if so why? is it something in the YARN traceback? I forget. > NoClassDefFoundError when initializing Spark with YARN > -- > > Key: SPARK-15343 > URL: https://issues.apache.org/jira/browse/SPARK-15343 > Project: Spark > Issue Type: Bug > Components: YARN >Affects Versions: 2.0.0 >Reporter: Maciej Bryński >Priority: Critical > > I'm trying to connect Spark 2.0 (compiled from branch-2.0) with Hadoop. > Spark compiled with: > {code} > ./dev/make-distribution.sh -Pyarn -Phadoop-2.6 -Phive -Phive-thriftserver > -Dhadoop.version=2.6.0 -DskipTests > {code} > I'm getting following error > {code} > mbrynski@jupyter:~/spark$ bin/pyspark > Python 3.4.0 (default, Apr 11 2014, 13:05:11) > [GCC 4.8.2] on linux > Type "help", "copyright", "credits" or "license" for more information. > Warning: Master yarn-client is deprecated since 2.0. Please use master "yarn" > with specified deploy mode instead. > Setting default log level to "WARN". > To adjust logging level use sc.setLogLevel(newLevel). > 16/05/16 11:54:41 WARN SparkConf: The configuration key 'spark.yarn.jar' has > been deprecated as of Spark 2.0 and may be removed in the future. Please use > the new key 'spark.yarn.jars' instead. > 16/05/16 11:54:41 WARN NativeCodeLoader: Unable to load native-hadoop library > for your platform... using builtin-java classes where applicable > 16/05/16 11:54:42 WARN AbstractHandler: No Server set for > org.spark_project.jetty.server.handler.ErrorHandler@f7989f6 > 16/05/16 11:54:43 WARN DomainSocketFactory: The short-circuit local reads > feature cannot be used because libhadoop cannot be loaded. > Traceback (most recent call last): > File "/home/mbrynski/spark/python/pyspark/shell.py", line 38, in > sc = SparkContext() > File "/home/mbrynski/spark/python/pyspark/context.py", line 115, in __init__ > conf, jsc, profiler_cls) > File "/home/mbrynski/spark/python/pyspark/context.py", line 172, in _do_init > self._jsc = jsc or self._initialize_context(self._conf._jconf) > File "/home/mbrynski/spark/python/pyspark/context.py", line 235, in > _initialize_context > return self._jvm.JavaSparkContext(jconf) > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/java_gateway.py", > line 1183, in __call__ > File > "/home/mbrynski/spark/python/lib/py4j-0.10.1-src.zip/py4j/protocol.py", line > 312, in get_return_value > py4j.protocol.Py4JJavaError: An error occurred while calling > None.org.apache.spark.api.java.JavaSparkContext. > : java.lang.NoClassDefFoundError: > com/sun/jersey/api/client/config/ClientConfig > at > org.apache.hadoop.yarn.client.api.TimelineClient.createTimelineClient(TimelineClient.java:45) > at > org.apache.hadoop.yarn.client.api.impl.YarnClientImpl.serviceInit(YarnClientImpl.java:163) > at > org.apache.hadoop.service.AbstractService.init(AbstractService.java:163) > at > org.apache.spark.deploy.yarn.Client.submitApplication(Client.scala:150) > at > org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend.start(YarnClientSchedulerBackend.scala:56) > at > org.apache.spark.scheduler.TaskSchedulerImpl.start(TaskSchedulerImpl.scala:148) > at org.apache.spark.SparkContext.(SparkContext.scala:502) > at > org.apache.spark.api.java.JavaSparkContext.(JavaSparkContext.scala:58) > at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native > Method) > at > sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62) > at > sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45) > at java.lang.reflect.Constructor.newInstance(Constructor.java:422) > at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:240) > at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) > at py4j.Gateway.invoke(Gateway.java:236) > at > py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:80) > at > py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:69) > at py4j.GatewayConnection.run(GatewayConnection.java:211) > at java.lang.Thread.run(Thread.java:745) > Caused by: java.lang.ClassNotFoundException: > com.sun.jersey.api.client.config.ClientConfig > at java.net.URLClassLoader.findClass(URLClassLoader.java:381) > at java.lang.ClassLoader.loadClass(ClassLoader.java:424) > at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:331) > at