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From Chris Nauroth <cnaur...@hortonworks.com>
Subject Re: Distributed Cache For 100MB+ Data Structure
Date Thu, 11 Oct 2012 17:52:22 GMT
Hello Kyle,

Regarding the setup time of the radix tree, is it possible to precompute
the radix tree before job submission time, then create a serialized
representation (perhaps just Java object serialization), and send the
serialized form through distributed cache?  Then, each reducer would just
need to deserialize during setup() instead of recomputing the full radix
tree for every reducer task.  That might save time.

Regarding the memory consumption, when I've run into a situation like this,
I've generally solved it by caching the data in a separate process and
using some kind of IPC from the reducers to access it.  memcache is one
example, though that's probably not an ideal fit for this data structure.
 I'm aware of no equivalent solution directly in Hadoop and would be
curious to hear from others on the topic.


On Thu, Oct 11, 2012 at 10:12 AM, Kyle Moses <kmoses@cs.duke.edu> wrote:

> Problem Background:
> I have a Hadoop MapReduce program that uses a IPv6 radix tree to provide
> auxiliary input during the reduce phase of the second job in it's workflow,
> but doesn't need the data at any other point.
> It seems pretty straight forward to use the distributed cache to build
> this data structure inside each reducer in the setup() method.
> This solution is functional, but ends up using a large amount of memory if
> I have 3 or more reducers running on the same node and the setup time of
> the radix tree is non-trivial.
> Additionally, the IPv6 version of the structure is quite a bit larger in
> memory.
> Question:
> Is there a "good" way to share this data structure across all reducers on
> the same node within the Hadoop framework?
> Initial Thoughts:
> It seems like this might be possible by altering the Task JVM Reuse
> parameters, but from what I have read this would also affect map tasks and
> I'm concerned about drawbacks/side-effects.
> Thanks for your help!

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