[
https://issues.apache.org/jira/browse/KYLIN-2764?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16178081#comment-16178081
]
kangkaisen commented on KYLIN-2764:
-----------------------------------
If the UHC columns have Hundreds of billions of rows and we use one reducer to
handle it , the {{FactDistinctColumnsReducer}} will be very very slow. In
other words，if we could use multiple reducers to build one global dict, we will
needn't add a new UHCDictionaryJob, but it is very hard build global dict with
multi reducers.
> Build the dict for UHC column with MR
> -------------------------------------
>
> Key: KYLIN-2764
> URL: https://issues.apache.org/jira/browse/KYLIN-2764
> Project: Kylin
> Issue Type: Improvement
> Components: Job Engine
> Affects Versions: v2.0.0
> Reporter: kangkaisen
> Assignee: kangkaisen
> Attachments: job-memory-after.png, job-memory-before.png
>
>
> KYLIN-2217 has built dict for normal column with MR, but the UHC column
> still build dict in JobServer. Like KYLIN-2217, we also could use MR build
> dict for UHC column. which could thoroughly release the memory pressure and
> improve job concurrent for JobServer as well as speed up multi UHC columns
> procedure.
> The MR input is the output of "Extract Fact Table Distinct Columns", the MR
> output is the UHC column dict. Because it is very hard build global dict with
> multi reducers, I use one reducer handle one UHC column and allocate enough
> memory to the reducer. According to my test, 8G memory is enough.
--
This message was sent by Atlassian JIRA
(v6.4.14#64029)
