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https://issues.apache.org/jira/browse/MAHOUT-923?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13167406#comment-13167406
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jirapos...@reviews.apache.org commented on MAHOUT-923:
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bq. On 2011-12-12 02:10:01, Dmitriy Lyubimov wrote:
bq. > Hm. I hope i did not read the code or miss something.
bq. >
bq. > 1 -- i am not sure this will actually work as intended unless # of
reducers is corced to 1, of which i see no mention in the code.
bq. > 2 -- mappers do nothing, passing on all the row pressure to sort which
is absolutely not necessary. Even if you use combiners. This is going to be
especially the case if you coerce 1 reducer an no combiners. IMO mean
computation should be pushed up to mappers to avoid sort pressures of map
reduce. Then reduction becomes largely symbolical(but you do need pass on the #
of rows mapper has seen, to the reducer, in order for that operation to apply
correctly).
bq. > 3 -- i am not sure -- is NullWritable as a key legit? In my experience
sequence file reader cannot instantiate it because NullWritable is a singleton
and its creation is prohibited by making constructor private.
bq.
bq. Raphael Cendrillon wrote:
bq. Thanks Dmitry.
bq.
bq. Regarding 1, if I understand correctly the number of reducers depends
on the number of unique keys. Since all keys are set to the same value (null),
then all of the mapper outputs should arrive at the same reducer. This seems to
work in the unit test, but I may be missing something?
bq.
bq. Regarding 2, that makes alot of sense. I'm wondering how many rows
should be processed per mapper? I guess there is a trade-off between
scalability (processing more rows within a single map job means that each row
must have less columns) and speed? Is there someplace in the SSVD code where
the matrix is split into slices of rows that I could use as a reference?
bq.
bq. Regarding 3, I believe NullWritable is OK. It's used pretty
extensively in TimesSquaredJob in DistributedRowMatrx. However if you feel
there is some disadvantage to this I could replace "NullWritable.get()" with
"new IntWritable(1)" (that is, set all of the keys to 1). Would that be more
suitable?
bq.
bq.
NullWritable objection is withdrawn. Apparently i haven't looked into hadoop
for too long, amazingly it seems to work now.
1 -- I don't think your statement about # of reduce tasks is true.
The job (or, rather, user) sets the number of reduce tasks via config propery.
All users will follow hadoop recommendation to set that to 95% of capacity they
want to take. (usually the whole cluster). So in production environment you are
virtually _guaranteed_ to have number of reducers of something like 75 on a
40-noder and consequently 75 output files (unless users really want to read the
details of your job and figure you meant it to be just 1).
Now, it is true that only one file will actually end up having something and
the rest of task slots will just be occupied doing nothing .
So there are two problems with that scheme: a) is that job that allocates so
many task slots that do nothing is not a good citizen, since in real production
cluster is always shared with multiple jobs. b) your code assumes result will
end up in partition 0, whereas contractually it may end up in any of 75 files.
(in reality with default hash partitioner for key 1 it will wind up in partion
0001 unless there's one reducer as i guess in your test was).
2-- it is simple. when you send n rows to reducers, they are shuffled - and -
sorted. Sending massive sets to reducers has 2 effects: first, even if they all
group under the same key, they are still sorted with ~ n log (n/p) where p is
number of partitions assuming uniform distribution (which it is not because you
are sending everything to the same place). Just because we can run distributed
sort, doesn't mean we should. Secondly, all these rows are physically moved to
reduce tasks, which is still ~n rows. Finally what has made your case
especially problematic is that you are sending everything to the same reducer,
i.e. you are not actually doing sort in distributed way but rather simple
single threaded sort at the reducer that happens to get all the input.
So that would allocate a lot of tasks slots that are not used; but do a sort
that is not needed; and do it in a single reducer thread for the entire input
which is not parallel at all.
Instead, consider this: map has a state consisting of (sum(X), k). it keeps
updating it sum+=x, k++ for every new x. At the end of the cycle (in cleanup)
it writes only 1 tuple (sum(x), k) as output. so we just reduced complexity of
the sort and io from millions of elements to just # of maps (which is perhaps
just handful and in reality rarely overshoots 500 mappers). That is, about at
least 4 orders of magnitude.
Now, we send that handful tuples to single reducer and just do combining
(sum(X)+= sum_i(X); n+= n_i) where i is the tuple in reducer. And because it is
only a handful, reducer also runs very quickly, so the fact that we coerced it
to be 1, is pretty benign. That volume of anywhere between 1 to 500 vectors it
sums up doesn't warrant distributed computation.
But, you have to make sure there's only 1 reducer no matter what user put into
the config, and you have to make sure you do all heavy lifting in the mappers.
Finally, you don't even to coerce to 1 reducer. You still can have several (but
uniformly distributed) and do final combine in front end of the method.
However, given small size and triviality of the reduction processing, it is
probably not warranted. Coercing to 1 reducer is ok in this case IMO.
3 i guess any writable is ok but NullWritable. Maybe something has changed. i
remember falling into that pitfall several generations of hadoop ago. You can
verify by staging a simple experiment of writing a sequence file with
nullwritable as either key or value and try to read it back. in my test long
ago it would write ok but not read back. I beleive similar approach is used
with keys in shuffle and sort. There is a reflection writable factory inside
which is trying to use default constructor of the class to bring it up which
is(was) not available for NullWritable.
- Dmitriy
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On 2011-12-12 00:30:24, Raphael Cendrillon wrote:
bq.
bq. -----------------------------------------------------------
bq. This is an automatically generated e-mail. To reply, visit:
bq. https://reviews.apache.org/r/3147/
bq. -----------------------------------------------------------
bq.
bq. (Updated 2011-12-12 00:30:24)
bq.
bq.
bq. Review request for mahout.
bq.
bq.
bq. Summary
bq. -------
bq.
bq. Here's a patch with a simple job to calculate the row mean (column-wise
mean). One outstanding issue is the combiner, this requires a wrtiable class
IntVectorTupleWritable, where the Int stores the number of rows, and the Vector
stores the column-wise sum.
bq.
bq.
bq. This addresses bug MAHOUT-923.
bq. https://issues.apache.org/jira/browse/MAHOUT-923
bq.
bq.
bq. Diffs
bq. -----
bq.
bq.
/trunk/core/src/main/java/org/apache/mahout/math/hadoop/DistributedRowMatrix.java
1213095
bq.
/trunk/core/src/main/java/org/apache/mahout/math/hadoop/MatrixRowMeanJob.java
PRE-CREATION
bq.
/trunk/core/src/test/java/org/apache/mahout/math/hadoop/TestDistributedRowMatrix.java
1213095
bq.
bq. Diff: https://reviews.apache.org/r/3147/diff
bq.
bq.
bq. Testing
bq. -------
bq.
bq. Junit test
bq.
bq.
bq. Thanks,
bq.
bq. Raphael
bq.
bq.
> Row mean job for PCA
> --------------------
>
> Key: MAHOUT-923
> URL: https://issues.apache.org/jira/browse/MAHOUT-923
> Project: Mahout
> Issue Type: Improvement
> Components: Math
> Affects Versions: 0.6
> Reporter: Raphael Cendrillon
> Assignee: Raphael Cendrillon
> Fix For: Backlog
>
> Attachments: MAHOUT-923.patch
>
>
> Add map reduce job for calculating mean row (column-wise mean) of a
> Distributed Row Matrix for use in PCA.
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