hbase-dev mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From "stack (JIRA)" <j...@apache.org>
Subject [jira] Commented: (HBASE-745) scaling of one regionserver, improving memory and cpu usage
Date Tue, 22 Jul 2008 06:08:31 GMT

    [ https://issues.apache.org/jira/browse/HBASE-745?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12615532#action_12615532
] 

stack commented on HBASE-745:
-----------------------------

Hmm.  Took another look.  Comparison is a little more complicated than I above suppose.  
I did a recheck of the number of data files post completion of the without patch run, about
ten minutes after it ended; about the same amount of time that had elapsed when I went to
check the withpatch test.  The number of data files is rising as is the aggregate of all time
spent compacting.  Would seem then that the patch cuts time spent compacting by some 10-20%
or so in the test I just ran.

> scaling of one regionserver, improving memory and cpu usage
> -----------------------------------------------------------
>
>                 Key: HBASE-745
>                 URL: https://issues.apache.org/jira/browse/HBASE-745
>             Project: Hadoop HBase
>          Issue Type: Improvement
>          Components: regionserver
>    Affects Versions: 0.1.3, 0.2.0
>         Environment: hadoop 0.17.1
>            Reporter: LN
>            Priority: Minor
>         Attachments: hbase-745-for-0.2.patch, HBASE-745.compact.patch
>
>
> after weeks testing hbase 0.1.3 and hadoop(0.16.4, 0.17.1), i found there are many works
to do,  before a particular regionserver can handle data about 100G, or even more. i'd share
my opions here with stack, and other developers.
> first, the easiest way improving scalability of regionserver is upgrading hardware, use
64bit os and 8G memory for the regionserver process, and speed up disk io. 
> besides hardware, following are software bottlenecks i found in regionserver:
> 1. as data increasing, compaction was eating cpu(with io) times, the total compaction
time is basicly linear relative to whole data size, even worse, sometimes square relavtive
to that size.
> 2. memory usage are depends on opened mapfiles
> 3. network connection are depends on opened mapfiles, see HADOOP-2341 and HBASE-24. 

-- 
This message is automatically generated by JIRA.
-
You can reply to this email to add a comment to the issue online.


Mime
View raw message