The max load I expect is 1 billion vectors. Around 300 dimensions per
vector. The number of clusters with more than one vector inside it can
be around 5 million, with an average of 10-20 vector per cluster.
But, When most of the vectors are really far away in the worst case
(apart from the similar ones, which will be inside the canopy) , most of
the canopies might contain only one vector. So, the number of canopies
will be really high ( As lots of canopies will result into clusters
having single vector ).
On 20-09-2011 22:56, Jeff Eastman wrote:
I guess it depends upon what you expect from your HUGE data set: How many
clusters do you believe it contains? A hundred? A thousand? A million? A
billion? With the right T-values I believe Canopy can handle the first three
but not the last. It will also depend upon the size of your vectors. This is
because, as canopy centroids are calculated, the centroid vectors become more
dense and these take up more space in memory. So a million, really wide
clusters might have trouble fitting into a 4GB reducer memory. But what are you
really going to do with a million clusters? This number seems vastly larger
than one might find useful in summarizing a data set. I would think a couple
hundred clusters would be the limit of human-understandable clustering. Canopy
can do that with no problem.
MeanShiftCanopy, as its name implies, is really just an iterative canopy
implementation. It allows the specification of an arbitrary number of initial
reducers, but it counts them down to 1 in each iteration in order to properly
process all the input. It is an agglomerative clustering algorithm, and the
clusters it builds contain the indices of each of the input points that have
been agglomerated. This makes the mean shift canopy larger in memory than
vanilla canopies since the list of points is maintained too. It is possible to
avoid the points accumulation and it won't happen unless the -cl option is
provided. In this case the memory consumption will be about the same as vanilla
canopy.
Bottom line: How many clusters do you expect to find?
-----Original Message-----
From: Paritosh Ranjan [mailto:[email protected]]
Sent: Tuesday, September 20, 2011 9:46 AM
To: [email protected]
Subject: Re: Clustering : Number of Reducers
"but all the canopies gotta fit in memory."
If this is true, then CanopyDriver would not be able to cluster HUGE
data ( as the memory might blow up ).
I am using MeanShiftCanopyDriver of 0.6-snapshot which can use any
number of reducers. Will it also need all the canopies in memory?
Or, which Clustering technique would you suggest to cluster really big
data ( considering performance and big size as parameters )?
Thanks and Regards,
Paritosh Ranjan
On 20-09-2011 21:35, Jeff Eastman wrote:
Well, while it is true that the CanopyDriver writes all its canopies to the
file system, they are written at the end of the reduce method. The mappers all
output the same key, so the one reducer gets all the mapper pairs and these
must fit into memory before they can be output. With T1/T2 values that are too
small given the data, there will be a very large number of clusters output by
each mapper and a corresponding deluge of clusters at the reducer. T3/T4 may be
used to supply different thresholds in the reduce step, but all the canopies
gotta fit in memory.
-----Original Message-----
From: Paritosh Ranjan [mailto:[email protected]]
Sent: Tuesday, September 20, 2011 12:31 AM
To: [email protected]
Subject: Re: Clustering : Number of Reducers
"The limit is that all the canopies need to fit into memory."
I don't think so. I think you can use CanopyDriver to write canopies in
a filesystem. This is done as a mapreduce job. Then the KMeansDriver
needs these canopy points as input to run KMeans.
On 20-09-2011 01:39, Jeff Eastman wrote:
Actually, most of the clustering jobs (including DirichletDriver) accept the
-Dmapred.reduce.tasks=n argument as noted below. Canopy is the only job which
forces n=1 and this is so the reducer will see all of the mapper outputs.
Generally, by adjusting T2& T1 to suitably-large values you can get canopy
to handle pretty large datasets. The limit is that all the canopies need to fit
into memory.
-----Original Message-----
From: Paritosh Ranjan [mailto:[email protected]]
Sent: Sunday, September 18, 2011 10:03 PM
To: [email protected]
Subject: Re: Clustering : Number of Reducers
So, does this mean that Mahout can not support clustering for large data?
Even in DirichletDriver the number of reducers is hardcoded to 1. And we
need canopies to run KMeansDriver.
Paritosh
On 19-09-2011 01:47, Konstantin Shmakov wrote:
For most of the tasks one can force the number of reducers with
mapred.reduce.tasks=<N>
where<N> the desired number of reducers.
It will not necessary increase the performance though - with kmeans and
fuzzykmeans combiners do reducers job and increasing the number of reducers
won't usually affect performance.
With the canopy the distributed
algorithm<http://svn.apache.org/viewvc/mahout/trunk/core/src/main/java/org/apache/mahout/clustering/canopy/CanopyDriver.java?revision=1134456&view=markup>has
no combiners and has 1 reducer hardcoded
- trying to increase #reducers won't have any effect as the algorithm
doesn't work with>1 reducer. My experience that the canopy won't scale to
large data and need improvement.
-- Konstantin
On Sun, Sep 18, 2011 at 10:50 AM, Paritosh Ranjan<[email protected]> wrote:
Hi,
I have been trying to cluster some hundreds of millions of records using
Mahout Clustering techniques.
The number of reducers is always one which I am not able to change. This is
effecting the performance. I am using Mahout 0.5
In 0.6-SNAPSHOT, I see that the MeanShiftCanopyDriver has been changed to
use any number of reducers. Will other ClusterDrivers also get changed to
use any number of reducers in 0.6?
Thanks and Regards,
Paritosh Ranjan
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