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https://issues.apache.org/jira/browse/HIVE-14919?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15923492#comment-15923492
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liyunzhang_intel commented on HIVE-14919:
-----------------------------------------
[~lirui]: {quote}
One thing I noted is the Xms flag was removed from the executor's options via
SPARK-12384. We may want to set it the same as Xmx to achieve better
performance.
{quote}
not very understand this point, because now spark does not allow to use Xmx to
specify max heap memory settings and only use
${{spark.executor.memory}}
org.apache.spark.SparkConf#validateSettings
{code}
// Validate spark.executor.extraJavaOptions
getOption(executorOptsKey).foreach { javaOpts =>
if (javaOpts.contains("-Dspark")) {
val msg = s"$executorOptsKey is not allowed to set Spark options (was
'$javaOpts'). " +
"Set them directly on a SparkConf or in a properties file when using
./bin/spark-submit."
throw new Exception(msg)
}
if (javaOpts.contains("-Xmx")) {
val msg = s"$executorOptsKey is not allowed to specify max heap memory
settings " +
s"(was '$javaOpts'). Use spark.executor.memory instead."
throw new Exception(msg)
}
}
{code}
> Improve the performance of Hive on Spark 2.0.0
> ----------------------------------------------
>
> Key: HIVE-14919
> URL: https://issues.apache.org/jira/browse/HIVE-14919
> Project: Hive
> Issue Type: Improvement
> Reporter: Ferdinand Xu
> Assignee: Ferdinand Xu
>
> In HIVE-14029, we have updated Spark dependency to 2.0.0. We use Intel
> BigBench[1] to run benchmark with Spark 2.0 over 1 TB data set comparing with
> Spark 1.6. We can see performance improvments about 5.4% in general and 45%
> for the best case. However, some queries doesn't have significant performance
> improvements. This JIRA is the umbrella ticket addressing those performance
> issues.
> [1] https://github.com/intel-hadoop/Big-Data-Benchmark-for-Big-Bench
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