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https://issues.apache.org/jira/browse/HIVE-8840?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14212783#comment-14212783
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Hive QA commented on HIVE-8840:
-------------------------------
{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/12681580/HIVE-8840.1-spark.patch
{color:red}ERROR:{color} -1 due to 4 failed/errored test(s), 7234 tests executed
*Failed tests:*
{noformat}
org.apache.hadoop.hive.cli.TestCliDriver.testCliDriver_sample_islocalmode_hook
org.apache.hadoop.hive.cli.TestMiniTezCliDriver.testCliDriver_optimize_nullscan
org.apache.hadoop.hive.cli.TestSparkCliDriver.testCliDriver_optimize_nullscan
org.apache.hadoop.hive.ql.exec.spark.TestHiveKVResultCache.testResultList
{noformat}
Test results:
http://ec2-174-129-184-35.compute-1.amazonaws.com/jenkins/job/PreCommit-HIVE-SPARK-Build/360/testReport
Console output:
http://ec2-174-129-184-35.compute-1.amazonaws.com/jenkins/job/PreCommit-HIVE-SPARK-Build/360/console
Test logs:
http://ec2-174-129-184-35.compute-1.amazonaws.com/logs/PreCommit-HIVE-SPARK-Build-360/
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: 4 tests failed
{noformat}
This message is automatically generated.
ATTACHMENT ID: 12681580 - PreCommit-HIVE-SPARK-Build
> Print prettier Spark work graph after HIVE-8793 [Spark Branch]
> --------------------------------------------------------------
>
> Key: HIVE-8840
> URL: https://issues.apache.org/jira/browse/HIVE-8840
> Project: Hive
> Issue Type: Sub-task
> Components: Spark
> Reporter: Xuefu Zhang
> Assignee: Jimmy Xiang
> Fix For: spark-branch
>
> Attachments: HIVE-8840.1-spark.patch, HIVE-8840.2-spark.patch
>
>
> Because of HIVE-8793, the work graph for Spark is possibly modified by
> SplitSparkWorkResolver. Original:
> {code}
> Spark
> Edges:
> Reducer 2 <- Map 1 (SORT, 1)
> Reducer 3 <- Reducer 2 (GROUP, 1)
> Reducer 4 <- Reducer 2 (GROUP, 1)
> {code}
> New graph
> {code}
> Spark
> Edges:
> Reducer 3 <- Reducer 5 (GROUP, 1)
> Reducer 4 <- Reducer 6 (GROUP, 1)
> Reducer 5 <- Map 1 (SORT, 1)
> Reducer 6 <- Map 1 (SORT, 1)
> {code}
> where Reducer2 was splitted into Reducer5 and Reducer6.
> Two types of ordering can be considered:
> 1. Topological order
> {code}
> Spark
> Edges:
> Reducer 5 <- Map 1 (SORT, 1)
> Reducer 6 <- Map 1 (SORT, 1)
> Reducer 3 <- Reducer 5 (GROUP, 1)
> Reducer 4 <- Reducer 6 (GROUP, 1)
> {code}
> 2. DFS
> {code}
> Spark
> Edges:
> Reducer 5 <- Map 1 (SORT, 1)
> Reducer 3 <- Reducer 5 (GROUP, 1)
> Reducer 6 <- Map 1 (SORT, 1)
> Reducer 4 <- Reducer 6 (GROUP, 1)
> {code}
> Both seems better, though topolical seems more suitable for a graph. Please
> feel free to create a patch on trunk if needed.
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