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From "Hive QA (JIRA)" <>
Subject [jira] [Commented] (HIVE-16318) LLAP cache: address some issues in 2.2/2.3
Date Tue, 28 Mar 2017 22:13:41 GMT


Hive QA commented on HIVE-16318:

Here are the results of testing the latest attachment:

{color:red}ERROR:{color} -1 due to no test(s) being added or modified.

{color:red}ERROR:{color} -1 due to 6 failed/errored test(s), 10518 tests executed
*Failed tests:*
org.apache.hadoop.hive.cli.TestBeeLineDriver.testCliDriver[escape_comments] (batchId=231)
org.apache.hadoop.hive.cli.TestCliDriver.testCliDriver[auto_sortmerge_join_2] (batchId=45)
org.apache.hadoop.hive.cli.TestCliDriver.testCliDriver[comments] (batchId=35)
org.apache.hadoop.hive.cli.TestMiniLlapLocalCliDriver.testCliDriver[vector_if_expr] (batchId=141)
org.apache.hadoop.hive.cli.TestSparkCliDriver.testCliDriver[load_dyn_part5] (batchId=115)
org.apache.hive.hcatalog.api.TestHCatClient.testTransportFailure (batchId=172)

Test results:
Console output:
Test logs:

Executing org.apache.hive.ptest.execution.TestCheckPhase
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: 6 tests failed

This message is automatically generated.

ATTACHMENT ID: 12860926 - PreCommit-HIVE-Build

> LLAP cache: address some issues in 2.2/2.3
> ------------------------------------------
>                 Key: HIVE-16318
>                 URL:
>             Project: Hive
>          Issue Type: Bug
>            Reporter: Sergey Shelukhin
>            Assignee: Sergey Shelukhin
>         Attachments: HIVE-16318.01.patch, HIVE-16318.patch
> We've run into HIVE-16233 and HIVE-15665 and given that 2.2 and 2.3 releases are approaching
we are going to add workarounds for them, and then commit the above patches and revert the
workarounds as soon as we can.
> Unfortunately this will result in cache wasting some memory on some datasets, but the
alternatives, when they are encountered (usually only on large datasets), are worse.

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