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From "Raghotham Murthy (JIRA)" <j...@apache.org>
Subject [jira] Commented: (HIVE-467) Scratch data location should be on different filesystems for different types of intermediate data
Date Fri, 22 May 2009 01:27:45 GMT

    [ https://issues.apache.org/jira/browse/HIVE-467?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12711914#action_12711914
] 

Raghotham Murthy commented on HIVE-467:
---------------------------------------

The diff looks good. However, there are several space issues - there should be a space after
'if' and 'for'. once you fix those, i can commit after running tests.

> Scratch data location should be on different filesystems for different types of intermediate
data
> -------------------------------------------------------------------------------------------------
>
>                 Key: HIVE-467
>                 URL: https://issues.apache.org/jira/browse/HIVE-467
>             Project: Hadoop Hive
>          Issue Type: Bug
>          Components: Query Processor
>         Environment: S3/EC2
>            Reporter: Joydeep Sen Sarma
>            Assignee: Joydeep Sen Sarma
>         Attachments: hive-467.3.patch, hive-467.4.patch, hive-467.patch.1, hive-467.patch.2
>
>
> Currently Hive uses the same scratch directory/path for all sorts of temporary and intermediate
data. This is problematic:
> 1. Temporary location for writing out DDL output should just be temp file on local file
system. This divorces the dependence of metadata and browsing operations on a functioning
hadoop cluster.
> 2. Temporary location of intermediate map-reduce data should be the default file system
(which is typically the hdfs instance on the compute cluster)
> 3. Temporary location for data that needs to be 'moved' into tables should be on the
same file system as the table's location (table's location may not be same as hdfs instance
of processing cluster).
> ie. - local storage, map-reduce intermediate storage and table storage should be distinguished.
Without this distinction - using hive on environments like S3/EC2 causes problems. In such
an environment - i would like to be able to:
> - do metadata operations without a provisioned hadoop cluster (using data stored in S3
and metastore on local disk)
> - attach to a provisioned hadoop cluster and run queries
> - store data back in tables that are created over s3 file system

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