@Harsh: Thanks for the reply. Would the patch work in Hadoop 1.0.4 release?
From: Harsh J [mailto:[EMAIL PROTECTED]]
Sent: Monday, May 13, 2013 1:03 PM
To: <[EMAIL PROTECTED]>
Subject: Re: How to combine input files for a MapReduce job
For "control number of mappers" question: You can use http://hadoop.apache.org/docs/current/api/org/apache/hadoop/mapred/lib/CombineFileInputFormat.html
which is designed to solve similar cases. However, you cannot beat the speed you get out of a single large file (or a few large files), as you'll still have file open/close overheads which will bog you down.
For "which file is being submitted to which" question: Having
https://issues.apache.org/jira/browse/MAPREDUCE-3678 in the version/distribution of Apache Hadoop you use would help.
On Mon, May 13, 2013 at 12:50 PM, Agarwal, Nikhil <[EMAIL PROTECTED]> wrote:
> I have a 3-node cluster, with JobTracker running on one machine and
> TaskTrackers on other two. Instead of using HDFS, I have written my
> own FileSystem implementation. As an experiment, I kept 1000 text
> files (all of same size) on both the slave nodes and ran a simple
> Wordcount MR job. It took around 50 mins to complete the task.
> Afterwards, I concatenated all the
> 1000 files into a single file and then ran a Wordcount MR job, it took
> 35 secs. From the JobTracker UI I could make out that the problem is
> because of the number of mappers that JobTracker is creating. For 1000
> files it creates
> 1000 maps and for 1 file it creates 1 map (irrespective of file size).
> Thus, is there a way to reduce the number of mappers i.e. can I
> control the number of mappers through some configuration parameter so
> that Hadoop would club all the files until it reaches some specified
> size (say, 64 MB) and then make 1 map per 64 MB block?
> Also, I wanted to know how to see which file is being submitted to
> which TaskTracker or if that is not possible then how do I check if
> some data transfer is happening in between my slave nodes during a MR job?
> Sorry for so many questions and Thank you for your time.