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From "Robert Joseph Evans (Commented) (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (HADOOP-7714) Add support in native libs for OS buffer cache management
Date Mon, 03 Oct 2011 18:09:34 GMT

    [ https://issues.apache.org/jira/browse/HADOOP-7714?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13119448#comment-13119448

Robert Joseph Evans commented on HADOOP-7714:

Why is the change across the entire datanode?  And why are we trying to guess the usage pattern
of the data based off of a hard coded read size of 256k?

It seems to me that there are other HDFS use cases that are not random reads of < 256K,
like the distributed cache or who knows what in the future with MRV2 where we want to keep
the data cached in memory on the data node if possible.

I would much rather see an optional parameter to the open API for flags like O_DIRECT.  Then
when HDFS talks to the data node it can also pass that information off to it when initiating
a block read.  That way the application can indicate how it expects to use the data.  MAPREDCUE
can say this is a streaming read where we will probably never reread the data again and it
can also say this is reading a file from the distributed cache and 900 others are going to
read this same file so keep it in memory if possible.
> Add support in native libs for OS buffer cache management
> ---------------------------------------------------------
>                 Key: HADOOP-7714
>                 URL: https://issues.apache.org/jira/browse/HADOOP-7714
>             Project: Hadoop Common
>          Issue Type: Bug
>          Components: native
>    Affects Versions: 0.24.0
>            Reporter: Todd Lipcon
>            Assignee: Todd Lipcon
>         Attachments: hadoop-7714-20s-prelim.txt
> Especially in shared HBase/MR situations, management of the OS buffer cache is important.
Currently, running a big MR job will evict all of HBase's hot data from cache, causing HBase
performance to really suffer. However, caching of the MR input/output is rarely useful, since
the datasets tend to be larger than cache and not re-read often enough that the cache is used.
Having access to the native calls {{posix_fadvise}} and {{sync_data_range}} on platforms where
they are supported would allow us to do a better job of managing this cache.

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