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From Dominik Safaric <dominiksafa...@gmail.com>
Subject Re: Spark Streaming - java.lang.ClassNotFoundException Scala anonymous function
Date Wed, 01 Mar 2017 14:01:52 GMT
The jars I am submitting are the following:

bin/spark-submit --class topology.SimpleProcessingTopology --master spark://10.0.0.8:7077
--jars /tmp/spark_streaming-1.0-SNAPSHOT.jar /tmp//tmp/spark_streaming-1.0-SNAPSHOT.jar /tmp/streaming.properties

I’ve even tried using the spark.executor.extraClassPath option but unfortunately unsuccessfully.


What do you mean by conflicting copies of Spark classes? Could you elaborate it?

> On 1 Mar 2017, at 14:51, Sean Owen <sowen@cloudera.com> wrote:
> 
> What is the --jars you are submitting? You may have conflicting copies of Spark classes
that interfere.
> 
> 
> On Wed, Mar 1, 2017, 14:20 Dominik Safaric <dominiksafaric@gmail.com <mailto:dominiksafaric@gmail.com>>
wrote:
> I've been trying to submit a Spark Streaming application using spark-submit to a cluster
of mine consisting of a master and two worker nodes. The application has been written in Scala,
and build using Maven. Importantly, the Maven build is configured to produce a fat JAR containing
all dependencies. Furthermore, the JAR has been distributed to all of nodes. The streaming
job has been submitted using the following command:
> 
> bin/spark-submit --class topology.SimpleProcessingTopology --jars /tmp/spark_streaming-1.0-SNAPSHOT.jar
--master spark://10.0.0.8:7077 <http://10.0.0.8:7077/> --verbose /tmp/spark_streaming-1.0-SNAPSHOT.jar
/tmp/streaming-benchmark.properties 
> where 10.0.0.8 is the IP address of the master node within the VNET. 
> 
> However, I keep getting the following exception while starting the streaming application:
> 
> Driver stacktrace:
>     at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1435)
>     at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1423)
>     at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1422)
>     at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
>     at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
>     at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1422)
>     at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:802)
>     at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:802)
> 
> Caused by: java.lang.ClassNotFoundException: topology.SimpleProcessingTopology$$anonfun$main$1$$anonfun$apply$1
>     at java.net.URLClassLoader.findClass(URLClassLoader.java:381)
>     at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
>     at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
>     at java.lang.Class.forName0(Native Method)
>     at java.lang.Class.forName(Class.java:348)
>     at org.apache.spark.serializer.JavaDeserializationStream$$anon$1.resolveClass(JavaSerializer.scala:67)
> I've checked the content of the JAR using jar tvf and as you can see in the output below,
it does contain the class in question.
> 
> 1735 Wed Mar 01 12:29:20 UTC 2017 topology/SimpleProcessingTopology$$anonfun$main$1.class
>    702 Wed Mar 01 12:29:20 UTC 2017 topology/SimpleProcessingTopology.class
>   2415 Wed Mar 01 12:29:20 UTC 2017 topology/SimpleProcessingTopology$$anonfun$main$1$$anonfun$apply$1$$anonfun$apply$2.class
>   2500 Wed Mar 01 12:29:20 UTC 2017 topology/SimpleProcessingTopology$$anonfun$main$1$$anonfun$apply$1.class
>   7045 Wed Mar 01 12:29:20 UTC 2017 topology/SimpleProcessingTopology$.class
> This exception has been caused due to the anonymous function of the foreachPartition
call:
> 
> rdd.foreachPartition(partition => {
>       val outTopic = props.getString("application.simple.kafka.out.topic")
>       val producer = new KafkaProducer[Array[Byte],Array[Byte]](kafkaParams)
>       partition.foreach(record => {
>         val producerRecord = new ProducerRecord[Array[Byte], Array[Byte]](outTopic, record.key(),
record.value())
>         producer.send(producerRecord)
>       })
>       producer.close()
>     })
> Unfortunately, I am not able to find the root cause of this since so far. Hence, I would
appreciate if anyone could help me out fixing this issue.
> 


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