http://www.mail-archive.com/[email protected]/msg02394.html

"
Teruhiko Kurosaka wrote:

   Can I use MapReduce to run Nutch on a multi CPU system?
Yes.


   I want to run the index job on two (or four) CPUs
   on a single system.  I'm not trying to distribute the job
   over multiple systems.

   If the MapReduce is the way to go,
   do I just specify config parameters like these:
   mapred.tasktracker.tasks.maxiumum=2
   mapred.job.tracker=localhost:9001
   mapred.reduce.tasks=2 (or 1?)

   and
   bin/start-all.sh

   ?
That should work. You'd probably want to set the default number of map tasks to be a multiple of the number of CPUs, and the number of reduce tasks to be exactly the number of cpus.

Don't use start-all.sh, but rather just:

bin/nutch-daemon.sh start tasktracker
bin/nutch-daemon.sh start jobtracker


   Must I use NDFS for MapReduce?
No.

Doug

"





Doug Cook wrote:
Hi,

I've recently switched to 0.8 from 0.7, and after some initial fits and
starts, I'm past the "get it working at all" stage to the "get reasonable
performance" stage.

I've got a single machine with 4 CPUs and a lot of memory. URL fetching
works great because it's (mostly) multithreaded. But as soon as I hit the
reduce phase of fetch, it's dog slow. I'm down to running on one CPU, and
the phase can take days, leaving me vulnerable to losing everything should a
process fail.

Wait! you say. That's just what Hadoop is for! I'm all ears. I'd love some
help getting my configuration right. I've seen examples/tutorials of
configurations for multiple machines; am I just "faking" multiple machines
on my single node (will that work?) or is there a cleaner, simpler approach?

Alternatively, I was all excited to get an easy improvement with
-numFetchers, and run 4 fetchers simultaneously to use all my CPUs, but it
looks like -numFetchers has gone away, and though there was an 0.8 version
patch, at a quick glance this didn't seem to have made it into the mainline
source, and I don't see the value of trying to merge this in if there's a
cleaner Hadoop-based approach.

Many thanks for any help.

Doug

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