Ted, I think I have tried these settings with the hbase protocol jar, to no avail.
I'm going to see if I can try and use these with this SolrException issue though it now may be harder to reproduce it. Thanks for the suggestion. On Tue, Sep 29, 2015 at 8:03 PM, Ted Yu <yuzhih...@gmail.com> wrote: > Have you tried the following ? > --conf spark.driver.userClassPathFirst=true --conf spark.executor. > userClassPathFirst=true > > On Tue, Sep 29, 2015 at 4:38 PM, Dmitry Goldenberg < > dgoldenberg...@gmail.com> wrote: > >> Release of Spark: 1.5.0. >> >> Command line invokation: >> >> ACME_INGEST_HOME=/mnt/acme/acme-ingest >> ACME_INGEST_VERSION=0.0.1-SNAPSHOT >> ACME_BATCH_DURATION_MILLIS=5000 >> SPARK_MASTER_URL=spark://data1:7077 >> JAVA_OPTIONS="-Dspark.streaming.kafka.maxRatePerPartition=1000" >> JAVA_OPTIONS="$JAVA_OPTIONS -Dspark.executor.memory=2g" >> >> $SPARK_HOME/bin/spark-submit \ >> --driver-class-path $ACME_INGEST_HOME \ >> --driver-java-options "$JAVA_OPTIONS" \ >> --class "com.acme.consumer.kafka.spark.KafkaSparkStreamingDriver" >> \ >> --master $SPARK_MASTER_URL \ >> --conf >> "spark.executor.extraClassPath=$ACME_INGEST_HOME/conf:$ACME_INGEST_HOME/lib/hbase-protocol-0.98.9-hadoop2.jar" >> \ >> >> $ACME_INGEST_HOME/lib/acme-ingest-kafka-spark-$ACME_INGEST_VERSION.jar \ >> -brokerlist $METADATA_BROKER_LIST \ >> -topic acme.topic1 \ >> -autooffsetreset largest \ >> -batchdurationmillis $ACME_BATCH_DURATION_MILLIS \ >> -appname Acme.App1 \ >> -checkpointdir file://$SPARK_HOME/acme/checkpoint-acme-app1 >> Note that SolrException is definitely in our consumer jar >> acme-ingest-kafka-spark-$ACME_INGEST_VERSION.jar which gets deployed to >> $ACME_INGEST_HOME. >> >> For the extraClassPath on the executors, we've got additionally >> hbase-protocol-0.98.9-hadoop2.jar: we're using Apache Phoenix from the >> Spark jobs to communicate with HBase. The only way to force Phoenix to >> successfully communicate with HBase was to have that JAR explicitly added >> to the executor classpath regardless of the fact that the contents of the >> hbase-protocol hadoop jar get rolled up into the consumer jar at build time. >> >> I'm starting to wonder whether there's some class loading pattern here >> where some classes may not get loaded out of the consumer jar and therefore >> have to have their respective jars added to the executor extraClassPath? >> >> Or is this a serialization problem for SolrException as Divya >> Ravichandran suggested? >> >> >> >> >> On Tue, Sep 29, 2015 at 6:16 PM, Ted Yu <yuzhih...@gmail.com> wrote: >> >>> Mind providing a bit more information: >>> >>> release of Spark >>> command line for running Spark job >>> >>> Cheers >>> >>> On Tue, Sep 29, 2015 at 1:37 PM, Dmitry Goldenberg < >>> dgoldenberg...@gmail.com> wrote: >>> >>>> We're seeing this occasionally. Granted, this was caused by a wrinkle >>>> in the Solr schema but this bubbled up all the way in Spark and caused job >>>> failures. >>>> >>>> I just checked and SolrException class is actually in the consumer job >>>> jar we use. Is there any reason why Spark cannot find the SolrException >>>> class? >>>> >>>> 15/09/29 15:41:58 WARN ThrowableSerializationWrapper: Task exception >>>> could not be deserialized >>>> java.lang.ClassNotFoundException: org.apache.solr.common.SolrException >>>> at java.net.URLClassLoader.findClass(URLClassLoader.java:381) >>>> at java.lang.ClassLoader.loadClass(ClassLoader.java:424) >>>> at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:331) >>>> at java.lang.ClassLoader.loadClass(ClassLoader.java:357) >>>> 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) >>>> at >>>> java.io.ObjectInputStream.readNonProxyDesc(ObjectInputStream.java:1613) >>>> at java.io.ObjectInputStream.readClassDesc(ObjectInputStream.java:1518) >>>> at >>>> java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1774) >>>> at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1351) >>>> at java.io.ObjectInputStream.readObject(ObjectInputStream.java:371) >>>> at >>>> org.apache.spark.ThrowableSerializationWrapper.readObject(TaskEndReason.scala:163) >>>> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) >>>> at >>>> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) >>>> at >>>> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) >>>> at java.lang.reflect.Method.invoke(Method.java:497) >>>> at >>>> java.io.ObjectStreamClass.invokeReadObject(ObjectStreamClass.java:1017) >>>> at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1900) >>>> at >>>> java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1801) >>>> at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1351) >>>> at >>>> java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:2000) >>>> at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1924) >>>> at >>>> java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1801) >>>> at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1351) >>>> at >>>> java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:2000) >>>> at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1924) >>>> at >>>> java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1801) >>>> at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1351) >>>> at java.io.ObjectInputStream.readObject(ObjectInputStream.java:371) >>>> at >>>> org.apache.spark.serializer.JavaDeserializationStream.readObject(JavaSerializer.scala:72) >>>> at >>>> org.apache.spark.serializer.JavaSerializerInstance.deserialize(JavaSerializer.scala:98) >>>> at >>>> org.apache.spark.scheduler.TaskResultGetter$$anon$3$$anonfun$run$2.apply$mcV$sp(TaskResultGetter.scala:108) >>>> at >>>> org.apache.spark.scheduler.TaskResultGetter$$anon$3$$anonfun$run$2.apply(TaskResultGetter.scala:105) >>>> at >>>> org.apache.spark.scheduler.TaskResultGetter$$anon$3$$anonfun$run$2.apply(TaskResultGetter.scala:105) >>>> at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1699) >>>> at >>>> org.apache.spark.scheduler.TaskResultGetter$$anon$3.run(TaskResultGetter.scala:105) >>>> at >>>> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) >>>> at >>>> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) >>>> at java.lang.Thread.run(Thread.java:745) >>>> >>> >>> >> >
