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Posted to dev@pig.apache.org by "Prasanth J (JIRA)" <ji...@apache.org> on 2012/10/09 00:26:03 UTC
[jira] [Commented] (PIG-2831) MR-Cube implementation (Distributed
cubing for holistic measures)
[ https://issues.apache.org/jira/browse/PIG-2831?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13471919#comment-13471919 ]
Prasanth J commented on PIG-2831:
---------------------------------
Updated the patch with following changes
1) Partition factor algorithm is tweaked to better distributed the reducer workload.
2) Partition factor in PartitionLargeGroups UDF is initialized to 0 (earlier it was 1), which generates many smaller bags (depends on cardinality of algebraic attribute). Earlier method initialized to 1 which generated few large bags.
The above changes also reduced the amount of records/bags spilled during full cube materialization job. In a test experiment, with 3M tuples and rollup on 3 dimensions following improvements were observed with the above changes
PROACTIVE_SPILL_COUNT_RECS improved by ~34% (from 5206793 to 3440694)
PROACTIVE_SPILL_COUNT_BAGS improved by ~54% (from 22 to 10)
> MR-Cube implementation (Distributed cubing for holistic measures)
> -----------------------------------------------------------------
>
> Key: PIG-2831
> URL: https://issues.apache.org/jira/browse/PIG-2831
> Project: Pig
> Issue Type: Sub-task
> Reporter: Prasanth J
> Assignee: Prasanth J
> Attachments: PIG-2831.1.git.patch, PIG-2831.2.git.patch, PIG-2831.3.git.patch, PIG-2831.4.git.patch, PIG-2831.5.git.patch, PIG-2831.6.git.patch, PIG-2831.7.git.patch, PIG-2831.8.git.patch, PIG-2831.9.git.patch
>
>
> Implementing distributed cube materialization on holistic measure based on MR-Cube approach as described in http://arnab.org/files/mrcube.pdf.
> Primary steps involved:
> 1) Identify if the measure is holistic or not
> 2) Determine algebraic attribute (can be detected automatically for few cases, if automatic detection fails user should hint the algebraic attribute)
> 3) Modify MRPlan to insert a sampling job which executes naive cube algorithm and generates annotated cube lattice (contains large group partitioning information)
> 4) Modify plan to distribute annotated cube lattice to all mappers using distributed cache
> 5) Execute actual cube materialization on full dataset
> 6) Modify MRPlan to insert a post process job for combining the results of actual cube materialization job
> 7) OOM exception handling
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