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https://issues.apache.org/jira/browse/PIG-4903?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15311028#comment-15311028
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Rohini Palaniswamy commented on PIG-4903:
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Please refer to my comment in
https://issues.apache.org/jira/browse/PIG-4893?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15311013#comment-15311013
You should avoid shipping spark jars altogether similar to mapreduce and tez.
Please take hdfs location of spark jar(s) as input from the user. This will be
an extra setup step that user will have to do. But that should be fine as we
already have same thing to be done for Tez and Mapreduce . For mapreduce, tar
balls approach is required to have smooth rolling upgrades. Older method of
using mapreduce installation of node manager also works and most still do that.
But recommended approach with yarn is to use hdfs tarballs for application
master dependencies.
> Avoid add all spark dependency jars to SPARK_YARN_DIST_FILES and
> SPARK_DIST_CLASSPATH
> --------------------------------------------------------------------------------------
>
> Key: PIG-4903
> URL: https://issues.apache.org/jira/browse/PIG-4903
> Project: Pig
> Issue Type: Sub-task
> Components: spark
> Reporter: liyunzhang_intel
> Attachments: PIG-4903.patch
>
>
> There are some comments about bin/pig on
> https://reviews.apache.org/r/45667/#comment198955.
> {code}
> ################# ADDING SPARK DEPENDENCIES ##################
> # Spark typically works with a single assembly file. However this
> # assembly isn't available as a artifact to pull in via ivy.
> # To work around this short coming, we add all the jars barring
> # spark-yarn to DIST through dist-files and then add them to classpath
> # of the executors through an independent env variable. The reason
> # for excluding spark-yarn is because spark-yarn is already being added
> # by the spark-yarn-client via jarOf(Client.Class)
> for f in $PIG_HOME/lib/*.jar; do
> if [[ $f == $PIG_HOME/lib/spark-assembly* ]]; then
> # Exclude spark-assembly.jar from shipped jars, but retain in
> classpath
> SPARK_JARS=${SPARK_JARS}:$f;
> else
> SPARK_JARS=${SPARK_JARS}:$f;
> SPARK_YARN_DIST_FILES=${SPARK_YARN_DIST_FILES},file://$f;
> SPARK_DIST_CLASSPATH=${SPARK_DIST_CLASSPATH}:\${PWD}/`basename $f`
> fi
> done
> CLASSPATH=${CLASSPATH}:${SPARK_JARS}
> export SPARK_YARN_DIST_FILES=`echo ${SPARK_YARN_DIST_FILES} | sed 's/^,//g'`
> export SPARK_JARS=${SPARK_YARN_DIST_FILES}
> export SPARK_DIST_CLASSPATH
> {code}
> Here we first copy all spark dependency jar like
> spark-network-shuffle_2.10-1.6.1 jar to distcache(SPARK_YARN_DIST_FILES) then
> add them to the classpath of executor(SPARK_DIST_CLASSPATH). Actually we need
> not copy all these depency jar to SPARK_DIST_CLASSPATH because all these
> dependency jars are included in spark-assembly.jar and spark-assembly.jar is
> uploaded with the spark job.
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