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From "Haohui Mai (JIRA)" <j...@apache.org>
Subject [jira] [Assigned] (HDFS-5389) A Namenode that keeps only a part of the namespace in memory
Date Wed, 18 Jun 2014 22:22:28 GMT

     [ https://issues.apache.org/jira/browse/HDFS-5389?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel

Haohui Mai reassigned HDFS-5389:

    Assignee: Haohui Mai

> A Namenode that keeps only a part of the namespace in memory
> ------------------------------------------------------------
>                 Key: HDFS-5389
>                 URL: https://issues.apache.org/jira/browse/HDFS-5389
>             Project: Hadoop HDFS
>          Issue Type: Sub-task
>          Components: namenode
>    Affects Versions: 0.23.1
>            Reporter: Lin Xiao
>            Assignee: Haohui Mai
>            Priority: Minor
> *Background:*
> Currently, the NN Keeps all its namespace in memory. This has had the benefit that the
NN code is very simple and, more importantly, helps the NN scale to over 4.5K machines with
60K  to 100K concurrently tasks.  HDFS namespace can be scaled currently using more Ram on
the NN and/or using Federation which scales both namespace and performance. The current federation
implementation does not allow renames across volumes without data copying but there are proposals
to remove that limitation.
> *Motivation:*
>  Hadoop lets customers store huge amounts of data at very economical prices and hence
allows customers to store their data for several years. While most customers perform analytics
on recent  data (last hour, day, week, months, quarter, year), the ability to have five year
old data online for analytics is very attractive for many businesses. Although one can use
larger RAM in a NN and/or use Federation, it not really necessary to store the entire namespace
in memory since only the recent data is typically heavily accessed. 
> *Proposed Solution:*
> Store a portion of the NN's namespace in memory- the "working set" of the applications
that are currently operating. LSM data structures are quite appropriate for maintaining the
full namespace in memory. One choice is Google's LevelDB open-source implementation.
> *Benefits:*
>  *  Store larger namespaces without resorting to Federated namespace volumes.
>  * Complementary to NN Federated namespace volumes,  indeed will allow a single NN to
easily store multiple larger volumes.
>  *  Faster cold startup - the NN does not have read its full namespace before responding
to clients.

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