hadoop-common-user mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From "Joydeep Sen Sarma" <jssa...@facebook.com>
Subject RE: Appropriate use of Hadoop for non-map/reduce tasks?
Date Tue, 25 Dec 2007 10:38:10 GMT
in many cases - long running tasks are of low cpu util. i have trouble imagining how these
can mix well with cpu intensive short/batch tasks. afaik - hadoop's job scheduling is not
resource usage aware. long background tasks would consume per-machine task slots that would
block out other tasks from using available cpu bandwidth. 

-----Original Message-----
From: Chad Walters [mailto:chad@powerset.com]
Sent: Sat 12/22/2007 2:39 PM
To: hadoop-user@lucene.apache.org
Subject: Re: Appropriate use of Hadoop for non-map/reduce tasks?

I should further say that god functions only on a per machine basis. We have then built a
number of scripts that do auto-configuration of our various services, using configs pulled
from LDAP and code pulled from our package repo. We use this to configure our various server
processes and also to configure Hadoop clusters (HDFS and Map/Reduce). But god is a key part
of the system, since it helps us provide a uniform interface for starting and stopping all
our services.


On 12/22/07 1:30 PM, "Chad Walters" <chad@powerset.com> wrote:

I am not really sure that Hadoop is right for what Jeff is describing.

I think there may be two separate problems:

 1.  Batch tasks that may take a long time but are expected to have a finite termination
 2.  Long-lived server processes that have an indefinite lifetime

For #1, we pretty much use Hadoop, although we have built a fairly extensive framework inside
of these long map tasks to track progress and handle various failure conditions that can arise.
If people are really interested, I'll poke around and see if any of it is general enough to
warrant contributing back, but I think a lot of it is probably fairly specific to the kinds
of failure cases we expect from the components involved in the long map task.

For #2, we are using something called "god" (http://god.rubyforge.org/). One of our developers
ended up starting this project because he didn't like monit. We liked the way it was going
and now we now we use it throughout our datacenter to start, stop, and health check our server
processes. It supports both polling and event-driven actions and is pretty extensible. Check
it out to see if it might satisfy some of your needs.


On 12/22/07 11:40 AM, "Jeff Hammerbacher" <jeff.hammerbacher@gmail.com> wrote:

from my understanding, the map/reduce codebase grew out of the codebase for
"the borg", google's system for managing long-running processes.  we could
definitely use this sort of functionality, and the jobtracker/tasktracker
paradigm goes part of the way there.  sqs really helps when you want to run
a set of recurring, dependent processes (a problem our group definitely
needs to solve), but it doesn't really seem to address the issue of managing
those processes when they're long-lived.

for instance, when we deploy our search servers, we have a script that
basically says "daemonize this process on this many boxes, and if it enters
a condition that doesn't look healthy, take this action (like restart, or
rebuild the index, etc.)".  given how hard-coded the task-type is into
map/reduce (er, "map" and "reduce"), it's hard to specify new types of error
conditions and running conditions for your processes.  also, the jobtracker
doesn't have any high availability guarantees, so you could run into a
situation where your processes are fine but the jobtracker goes down.
 zookeeper could help here.  it'd be sweet if hadoop could handle this
long-lived process management scenario.

kirk, i'd be interested in hearing more about your processes and the
requirements you have of your process manager.  we're exploring other
solutions to this problem and i'd be happy to connect you with the folks
here who are thinking about the issue.


On Dec 21, 2007 12:42 PM, John Heidemann <johnh@isi.edu> wrote:

> On Fri, 21 Dec 2007 12:24:57 PST, John Heidemann wrote:
> >On Thu, 20 Dec 2007 18:46:58 PST, Kirk True wrote:
> >>Hi all,
> >>
> >>A lot of the ideas I have for incorporating Hadoop into internal
> projects revolves around distributing long-running tasks over multiple
> machines. I've been able to get a quick prototype up in Hadoop for one of
> those projects and it seems to work pretty well.
> >>...
> >He's not saying "is Hadoop optimal" for things that aren't really
> >map/reduce, but "is it reasonable" for those things?
> >(Kirk, is that right?)
> >...
> Sorry to double reply, but I left out my comment to (my view of) Kirk's
> question.
> In addition to what Ted said, I'm not sure how well Hadoop works with
> long-running jobs, particuarlly how well that interacts with its fault
> tolerance code.
> And more generally, if you're not doing map/reduce than you'd probably
> have to build your own fault tolerance methods.
>   -John Heidemann

  • Unnamed multipart/alternative (inline, None, 0 bytes)
View raw message