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Posted to common-dev@hadoop.apache.org by "Runping Qi (JIRA)" <ji...@apache.org> on 2007/01/18 23:54:29 UTC
[jira] Updated: (HADOOP-908) Hadoop Abacus, a package for
performing simple counting/aggregation
[ https://issues.apache.org/jira/browse/HADOOP-908?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Runping Qi updated HADOOP-908:
------------------------------
Attachment: abacus.patch
> Hadoop Abacus, a package for performing simple counting/aggregation
> -------------------------------------------------------------------
>
> Key: HADOOP-908
> URL: https://issues.apache.org/jira/browse/HADOOP-908
> Project: Hadoop
> Issue Type: New Feature
> Components: contrib/streaming
> Reporter: Runping Qi
> Assigned To: Runping Qi
> Attachments: abacus.patch
>
>
> Hadoop Abacus package is a specialization of map/reduce framework,
> specilizing for performing various counting and aggregations.
> It offers similar functionalities to Google's SawZall.
> Generally speaking, in order to implement an application using Map/Reduce model,
> the developer needs to implement Map and Reduce functions (and possibly Combine function).
> However, for a lot of applications related to counting and statistics computing,
> these functions have very similar characteristics.
> Abacus abstracts out the general patterns and provides a package implementing those patterns.
> In particular, the package provides a generic mapper class, a reducer class and a combiner class,
> and a set of built-in value aggregators. It also provides a generic utility class, ValueAggregatorJob
> for creating Abacus jobs.
> To create an Abacus job, the user just needs to implement one plugin class that
> is responsible for specifying what aggregators to use and what values are for which aggregators.
> The mapper will call this class in the runtime to generate aggregation ids and values.
> The generic combiner and reducer will aggregate the values associated with the same
> aggregation ids accordingly. Thus, it is much easier to create and run an Abacus job than
> a normal map/reduce job. Since a built-in generic combiner is always used, the execution is very efficient.
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