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From Jeff Eastman <jeast...@Narus.com>
Subject RE: Clustering : Number of Reducers
Date Tue, 20 Sep 2011 18:05:48 GMT
As all the Mahout clustering implementations keep their clusters in memory, I don't believe
any of them will handle that many clusters. I'm a bit skeptical; however, that 5 million clusters
over a billion, 300-d vectors will produce anything useful by way of analytics. You've got
the curse of dimensionality working against you and your vectors will be nearly equidistant
from each other. This means that very small (=noise) differences in distance will be driving
the clustering.


-----Original Message-----
From: Paritosh Ranjan [mailto:pranjan@xebia.com] 
Sent: Tuesday, September 20, 2011 10:41 AM
To: user@mahout.apache.org
Subject: Re: Clustering : Number of Reducers


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:pranjan@xebia.com]
> Sent: Tuesday, September 20, 2011 9:46 AM
> To: user@mahout.apache.org
> 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:pranjan@xebia.com]
>> Sent: Tuesday, September 20, 2011 12:31 AM
>> To: user@mahout.apache.org
>> 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:pranjan@xebia.com]
>>> Sent: Sunday, September 18, 2011 10:03 PM
>>> To: user@mahout.apache.org
>>> 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<pranjan@xebia.com>
    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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>
>
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