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From Ted Dunning <tdunn...@maprtech.com>
Subject Re: breadth-first search
Date Tue, 21 Dec 2010 15:28:52 GMT
Ahh... I see what you mean.

This algorithm can be implemented with all of the iterations for all points
proceeding in parallel.  You should only need 4 map-reduce steps, not 400.
 This will still take several minutes on Hadoop, but as your problem
increases in size, this overhead becomes less important.

2010/12/21 Peng, Wei <Wei.Peng@xerox.com>

> The graph that my BFS algorithm is running on only needs 4 levels to reach
> all nodes. The reason I say "many iterations" is that there are 100 sources
> nodes, so totally 400 iterations. The algorithm should be right, and I
> cannot think of anything to reduce the number of iterations.
>
> Ted, I will check out the links that you sent to me.
> I really appreciate your help.
>
> Wei
> -----Original Message-----
> From: Edward J. Yoon [mailto:edward@udanax.org]
> Sent: Tuesday, December 21, 2010 1:27 AM
> To: common-user@hadoop.apache.org
> Subject: Re: breadth-first search
>
> There's no release yet.
>
> But, I had tested the BFS using hama and, hbase.
>
> Sent from my iPhone
>
> On 2010. 12. 21., at 오전 11:30, "Peng, Wei" <Wei.Peng@xerox.com> wrote:
>
> > Yoon,
> >
> > Can I use HAMA now, or it is still in development?
> >
> > Thanks
> >
> > Wei
> >
> > -----Original Message-----
> > From: Edward J. Yoon [mailto:edwardyoon@apache.org]
> > Sent: Monday, December 20, 2010 6:23 PM
> > To: common-user@hadoop.apache.org
> > Subject: Re: breadth-first search
> >
> > Check this slide out -
> > http://people.apache.org/~edwardyoon/papers/Apache_HAMA_BSP.pdf
> >
> > On Tue, Dec 21, 2010 at 10:49 AM, Peng, Wei <Wei.Peng@xerox.com> wrote:
> >>
> >>  I implemented an algorithm to run hadoop on a 25GB graph data to
> >> calculate its average separation length.
> >> The input format is V1(tab)V2 (where V2 is the friend of V1).
> >> My purpose is to first randomly select some seed nodes, and then for
> >> each node, calculate the shortest paths from this node to all other
> >> nodes on the graph.
> >>
> >> To do this, I first run a simple python code in a single machine to get
> >> some random seed nodes.
> >> Then I run a hadoop job to generate adjacent list for each node as the
> >> input for the second job.
> >>
> >> The second job takes the adjacent list input and output the first level
> >> breadth-first search result. The nodes which are the friends of the seed
> >> node have distance 1. Then this output is the input for the next hadoop
> >> job so on so forth, until all the nodes are reached.
> >>
> >> I generated a simulated graph for testing. This data has only 100 nodes.
> >> Normal python code can find the separation length within 1 second (100
> >> seed nodes). However, the hadoop took almost 3 hours to do that
> >> (pseudo-distributed mode on one machine)!!
> >>
> >> I wonder if there is a more efficient way to do breadth-first search in
> >> hadoop? It is very inefficient to output so many intermediate results.
> >> Totally there would be seedNodeNumber*levelNumber+1 jobs,
> >> seedNodeNumber*levelNumber intermediate files. Why is hadoop so slow?
> >>
> >> Please help.  Thanks!
> >>
> >> Wei
> >>
> >
> >
> >
> > --
> > Best Regards, Edward J. Yoon
> > edwardyoon@apache.org
> > http://blog.udanax.org
>

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