Would it make sense to write a map job that takes an unsplittable XML file (which defines all of the files I need to download); that one map job then kicks off the downloads in multiple threads. This way I can easily manage the most efficient download pattern within the map job, and my output is emitted as key,values straight to the reducer step?
From: [email protected] [mailto:[email protected]] On Behalf Of Seetharam Venkatesh Sent: Tuesday, October 23, 2012 7:28 AM To: [email protected] Subject: Re: Large input files via HTTP One possible way is to first create a list of files with tuples<host:port, filePath>. Then use a map-only job to pull each file using NLineInputFormat. Another way is to write a HttpInputFormat and HttpRecordReader and stream the data in a map-only job. On Mon, Oct 22, 2012 at 1:54 AM, David Parks <[email protected]> wrote: I want to create a MapReduce job which reads many multi-gigabyte input files from various HTTP sources & processes them nightly. Is there a reasonably flexible way to acquire the files in the Hadoop job its self? I expect the initial downloads to take many hours and I'd hope I can optimize the # of connections (example: I'm limited to 5 connections to one host, whereas another host has a 3-connection limit, so maximize as much as possible). Also the set of files to download will change a little over time so the input list should be easily configurable (in a config file or equivalent). - Is it normal to perform batch downloads like this *before* running the mapreduce job? - Or is it ok to include such steps in with the job? - It seems handy to keep the whole process as one neat package in Hadoop if possible. - What class should I implement if I wanted to manage this myself? Would I just extend TextInputFormat for example, and do the HTTP processing there? Or am I created a FileSystem? Thanks, David -- Regards, Venkatesh Perfection (in design) is achieved not when there is nothing more to add, but rather when there is nothing more to take away. - Antoine de Saint-Exupéry
