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From goutham patnaik <goutham.patn...@gmail.com>
Subject Re: Introducing Cloud MapReduce
Date Thu, 03 Dec 2009 19:28:42 GMT
see inline

On Thu, Dec 3, 2009 at 9:59 AM, <huan.liu@accenture.com> wrote:

> Owen,
>
> Great questions. You are hitting some key points, answers are inline.
>
> > > 1) It is faster than other implementations (e.g., 60 times faster
> > than
> > > Hadoop in one case. Speedup depends on the application and data.).
> >
> > Based on your report, it looks like your comparison was done using
> > 1.2GB of data in 92,000 files. If you took the default configuration,
> > you'll end up with 92,000 maps, each of them processing a very small
> > input. You either need to use MultiFileInputFormat or use Hadoop
> > Archives to bundle your tiny files into reasonable size. For input
> > data that small you should not have more than 8 maps unless each map
> > is doing a lot of CPU work.
>
> We used default because we were not sure how to optimize it. We can
> certainly rerun the test with your suggestion. The point is not about the
> 60x speedup, it is about a potential bottleneck in the master/slave
> architecture. When you scale up the number of slaves nodes and the number of
> tasks, you will run into the same problem even if you use larger chunk size.
> Due to the lack of access to a large cluster, we are not able to run an
> experiment to show that the master node will choke at some point. This is
> essentially a scaled-down version of the same large-scale test.
>
> > In your reduce push iterator model, you give up a lot of determinism
> > in your application. Many applications that run in Hadoop depend on
> > the reduces getting keys in sorted order. Getting keys in a random
> > order won't work.
>
> Sorting at the end after the values have been reduced is much cheaper than
> sorting the whole set of intermediate key-value pairs. We will add a
> function in our implementation to sort at the end, for those applications
> requiring ordered output.
>

Owen is right though - there are some applications that require the keys to
be sorted when they reach a reducer - secondary sorting is one such example

>
> > Furthermore, I don't see any way that you can scale
> > up your approach. You effectively need to hold open all of the reduce
> > states for the keys you've seen and can't close them until it is done.
> > Again, that will quickly exhaust memory. So the push model will work
> > for something like word count, but fail on large problems.
>
> Our strategy is to have a large number of partitions to limit the number of
> keys in each partition. Having a large number of partitions would not
> overload our system, so it is easy for us. As I expressed in a related
> thread answering Todd's question, I believe the state each key holds is
> small for most applications. Loved to be proved wrong, so that we have real
> motivations to refine the architecture to handle that case.
>
> > When you add types in, you should add them in as:
> >
> > K1, V1 -> map -> K2,V2 -> reduce -> K3,V3
> >
> > with the combiner taking:
> >
> > K2,V2 -> combiner -> K2,V2
> >
> > Of course the types are often not primitives and using something like
> > Avro is a good thing.
>
> Thanks for clarifying. Adding types is in the roadmap now.
>
> Regards.
>
> -Huan
>
>
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