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https://issues.apache.org/jira/browse/HIVE-17270?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16119244#comment-16119244
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Rui Li commented on HIVE-17270:
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When automatically deciding numReducers, it should be no less than numCores. On
the other side, {{spark.executor.instances}} is numContainers spark will
request from YARN. How many containers can be really allocated is up to YARN.
So I can think of two possible reasons why we have 2 instead of 4 here.
# Only 1 container is allocated, in which case 2 is the right way to go.
# 2 containers are allocated but only 1 has started running when we get the
executor count. This is a common case in real cluster and can make our test
result unstable. We should find a way to fix it.
I guess we can monitor the log of the mini-yarn test to see how many cores we
really have during execution.
> Qtest results show wrong number of executors
> --------------------------------------------
>
> Key: HIVE-17270
> URL: https://issues.apache.org/jira/browse/HIVE-17270
> Project: Hive
> Issue Type: Bug
> Components: Spark
> Affects Versions: 3.0.0
> Reporter: Peter Vary
> Assignee: Peter Vary
>
> The hive-site.xml shows, that the TestMiniSparkOnYarnCliDriver uses 2 cores,
> and 2 executor instances to run the queries. See:
> https://github.com/apache/hive/blob/master/data/conf/spark/yarn-client/hive-site.xml#L233
> When reading the log files for the query tests, I see the following:
> {code}
> 2017-08-08T07:41:03,315 INFO [0381325d-2c8c-46fb-ab51-423defaddd84 main]
> session.SparkSession: Spark cluster current has executors: 1, total cores: 2,
> memory per executor: 512M, memoryFraction: 0.4
> {code}
> See:
> http://104.198.109.242/logs/PreCommit-HIVE-Build-6299/succeeded/171-TestMiniSparkOnYarnCliDriver-insert_overwrite_directory2.q-scriptfile1.q-vector_outer_join0.q-and-17-more/logs/hive.log
> When running the tests against a real cluster, I found that running an
> explain query for the first time I see 1 executor, but running it for the
> second time I see 2 executors.
> Also setting some spark configuration on the cluster resets this behavior.
> For the first time I will see 1 executor, and for the second time I will see
> 2 executors again.
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