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From "Hive QA (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (HIVE-6455) Scalable dynamic partitioning and bucketing optimization
Date Thu, 13 Mar 2014 20:25:42 GMT

    [ https://issues.apache.org/jira/browse/HIVE-6455?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13933995#comment-13933995
] 

Hive QA commented on HIVE-6455:
-------------------------------



{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/12634395/HIVE-6455.16.patch

{color:red}ERROR:{color} -1 due to 14 failed/errored test(s), 5388 tests executed
*Failed tests:*
{noformat}
org.apache.hadoop.hive.cli.TestMinimrCliDriver.testCliDriver_auto_sortmerge_join_16
org.apache.hadoop.hive.ql.parse.TestParse.testParse_input1
org.apache.hadoop.hive.ql.parse.TestParse.testParse_input2
org.apache.hadoop.hive.ql.parse.TestParse.testParse_input3
org.apache.hadoop.hive.ql.parse.TestParse.testParse_input6
org.apache.hadoop.hive.ql.parse.TestParse.testParse_input7
org.apache.hadoop.hive.ql.parse.TestParse.testParse_input9
org.apache.hadoop.hive.ql.parse.TestParse.testParse_sample2
org.apache.hadoop.hive.ql.parse.TestParse.testParse_sample3
org.apache.hadoop.hive.ql.parse.TestParse.testParse_sample4
org.apache.hadoop.hive.ql.parse.TestParse.testParse_sample5
org.apache.hadoop.hive.ql.parse.TestParse.testParse_sample6
org.apache.hadoop.hive.ql.parse.TestParse.testParse_sample7
org.apache.hadoop.hive.ql.parse.TestParse.testParse_union
{noformat}

Test results: http://bigtop01.cloudera.org:8080/job/PreCommit-HIVE-Build/1765/testReport
Console output: http://bigtop01.cloudera.org:8080/job/PreCommit-HIVE-Build/1765/console

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: 14 tests failed
{noformat}

This message is automatically generated.

ATTACHMENT ID: 12634395

> Scalable dynamic partitioning and bucketing optimization
> --------------------------------------------------------
>
>                 Key: HIVE-6455
>                 URL: https://issues.apache.org/jira/browse/HIVE-6455
>             Project: Hive
>          Issue Type: New Feature
>          Components: Query Processor
>    Affects Versions: 0.13.0
>            Reporter: Prasanth J
>            Assignee: Prasanth J
>              Labels: optimization
>         Attachments: HIVE-6455.1.patch, HIVE-6455.1.patch, HIVE-6455.10.patch, HIVE-6455.10.patch,
HIVE-6455.11.patch, HIVE-6455.12.patch, HIVE-6455.13.patch, HIVE-6455.13.patch, HIVE-6455.14.patch,
HIVE-6455.15.patch, HIVE-6455.16.patch, HIVE-6455.2.patch, HIVE-6455.3.patch, HIVE-6455.4.patch,
HIVE-6455.4.patch, HIVE-6455.5.patch, HIVE-6455.6.patch, HIVE-6455.7.patch, HIVE-6455.8.patch,
HIVE-6455.9.patch, HIVE-6455.9.patch
>
>
> The current implementation of dynamic partition works by keeping at least one record
writer open per dynamic partition directory. In case of bucketing there can be multispray
file writers which further adds up to the number of open record writers. The record writers
of column oriented file format (like ORC, RCFile etc.) keeps some sort of in-memory buffers
(value buffer or compression buffers) open all the time to buffer up the rows and compress
them before flushing it to disk. Since these buffers are maintained per column basis the amount
of constant memory that will required at runtime increases as the number of partitions and
number of columns per partition increases. This often leads to OutOfMemory (OOM) exception
in mappers or reducers depending on the number of open record writers. Users often tune the
JVM heapsize (runtime memory) to get over such OOM issues. 
> With this optimization, the dynamic partition columns and bucketing columns (in case
of bucketed tables) are sorted before being fed to the reducers. Since the partitioning and
bucketing columns are sorted, each reducers can keep only one record writer open at any time
thereby reducing the memory pressure on the reducers. This optimization is highly scalable
as the number of partition and number of columns per partition increases at the cost of sorting
the columns.



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