HDFS is designed with Hadoop in mind, so there are certain advantages (e.g.
performance, reliability, and ease of use) to using HDFS for Hadoop.
However, it's not required. For example, when you run Hadoop in standalone
mode, it just uses the file system on your local machine. When you run it on
Amazon AWS, it can use S3 as a file system.


On Tue, Apr 28, 2009 at 6:15 AM, Adam Retter <adam.ret...@landmark.co.uk>wrote:

>
> > Each document processing is independent and can be processed
> > parallelly, so that part could be done in a map reduce job.
> > Now whether it suits this use case depends on rate at which new
> > URI's are discovered for processing and acceptable delay in processing
> > of a document. The way I see it you can batch the URI's
> > and input that to mapreduce job. Each mapper can work on sublist of
> URIs.
> > You can choose to make DB inserts from mapper itself. In that case
> > you can set no of reducers to 0. Otherwise if batching of the queries
> > is an option then you can consider making batch inserts in reducer. It
> > will help in reducing load on DB.
>
> So I don't have to use HDFS at all when using Hadoop?
>
>
>
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