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Date: Mon, 26 Apr 2010 10:28:33 -0700
Subject: Re: Data partitioning for demux
From: Jerome Boulon <jboulon@netflix.com>
To: <chukwa-dev@hadoop.apache.org>
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Thread-Topic: Data partitioning for demux
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Hi,
The partitionning function should be driven by the user not decide at this
level.
The Mapper class, the reducer class and the partionner should all be driven
by configuration. 
There's no way for Demux to do the right thing based on static
configuration. Even for the same demux but different dataType you may want
to use a different partionning function so we need to have a
partionnerManager that will select the right partionner based on the
reduceType similar to what we are doing to select the right parser/reducer
class.

The reason I'm saying that is that in Hive world, nobody access the SeqFile
itself, just Hive engine is doing that and since there's no index it doesn't
make sense to spend time/cpu/memory to have a file that will be globally
sorted. So in that case, you want to have the same number of rows per
reducer (%reducerCount), your proposal will be better than the current
implementation but will not be good for anybody who does not need a file to
be globally sorted.

Could you open a Jira for this and I will add more comments on it?

/Jerome.

On 4/25/10 12:08 PM, "Eric Yang" <eyang@yahoo-inc.com> wrote:

> Hi all,
> 
> I am working on enhancing the reducer partitioning for demux.  It basically
> boils down to two main use cases.
> 
> Case #1, demux is responsible for crunching large volumes of the same data
> type (dozen of types).  It will probably make more sense to partition the
> reducer by time grouping + data type (extend TotalOrderPartitioner).  I.e. A
> user can have evenly distributed workload for each reducer base on time
> interval.  A distributed hash table like Hbase/voldermort could be the down
> stream system to store/cache the data for data serving.  This model is great
> for collecting fixed time interval logs like hadoop metrics, and ExecAdaptor
> which generates repetitive time series summary.
> 
> Case #2, demux is responsible for crunching hundred of different data type,
> but small volumn for each data type.  The current demux implementation is
> using this model, where a single data type is reduced by one reducer slot
> (ChukwaRecordPartitioner).  One draw back from this model,the data from each
> data type must have similar volume.  Otherwise, the largest data volume type
> becomes the long tail of the mapreduce job.  Materialized report is easy to
> generate by using this model because the single reducer per data type has
> view to all data of the given demux run.  This model works great for many
> different application and all logging through Chukwa Log4j appender.  I.e.
> web crawl, or log file indexing / viewing.
> 
> I am thinking to change the default Chukwa demux implementation to case #1,
> and restructure the current demux as Archive Organizer.  Any suggestion or
> objection?
> 
> Regards,
> Eric 
> 
>