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https://issues.apache.org/jira/browse/HIVE-8262?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14153622#comment-14153622
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Hive QA commented on HIVE-8262:
-------------------------------
{color:red}Overall{color}: -1 at least one tests failed
Here are the results of testing the latest attachment:
https://issues.apache.org/jira/secure/attachment/12672078/HIVE-8262.1-spark.patch
{color:red}ERROR:{color} -1 due to 2 failed/errored test(s), 6509 tests executed
*Failed tests:*
{noformat}
org.apache.hadoop.hive.cli.TestCliDriver.testCliDriver_sample_islocalmode_hook
org.apache.hadoop.hive.cli.TestNegativeCliDriver.testNegativeCliDriver_fs_default_name2
{noformat}
Test results:
http://ec2-174-129-184-35.compute-1.amazonaws.com/jenkins/job/PreCommit-HIVE-SPARK-Build/183/testReport
Console output:
http://ec2-174-129-184-35.compute-1.amazonaws.com/jenkins/job/PreCommit-HIVE-SPARK-Build/183/console
Test logs:
http://ec2-174-129-184-35.compute-1.amazonaws.com/logs/PreCommit-HIVE-SPARK-Build-183/
Messages:
{noformat}
Executing org.apache.hive.ptest.execution.PrepPhase
Executing org.apache.hive.ptest.execution.ExecutionPhase
Executing org.apache.hive.ptest.execution.ReportingPhase
Tests exited with: TestsFailedException: 2 tests failed
{noformat}
This message is automatically generated.
ATTACHMENT ID: 12672078
> Create CacheTran that transforms the input RDD by caching it [Spark Branch]
> ---------------------------------------------------------------------------
>
> Key: HIVE-8262
> URL: https://issues.apache.org/jira/browse/HIVE-8262
> Project: Hive
> Issue Type: Sub-task
> Components: Spark
> Reporter: Xuefu Zhang
> Assignee: Chao
> Attachments: HIVE-8262.1-spark.patch
>
>
> In a few cases we need to cache a RDD to avoid recompute it for better
> performance. However, caching a map input RDD is different from caching a
> regular RDD due to SPARK-3693. The way to cache a Hadoop RDD, which is the
> input to MapWork, is to cache, the result RDD that is transformed from the
> original Hadoop RDD by applying a map function, in which <key, value> pairs
> are copied. To cache intermediate RDDs, such as that from a shuffle, is just
> calling .cache().
> This task is to create a CacheTran to capture this, which can be used to plug
> in Spark Plan when caching is desirable.
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