bq. have tried these settings with the hbase protocol jar, to no avail In that case, HBaseZeroCopyByteString is contained in hbase-protocol.jar. In HBaseZeroCopyByteString , you can see:
package com.google.protobuf; // This is a lie. If protobuf jar is loaded ahead of hbase-protocol.jar, things start to get interesting ... On Tue, Sep 29, 2015 at 6:12 PM, Dmitry Goldenberg <dgoldenberg...@gmail.com > wrote: > 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) >>>>> >>>> >>>> >>> >> >
