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From Mike Percy <mpe...@apache.org>
Subject Re: Analysis of Data
Date Fri, 08 Feb 2013 03:00:35 GMT
Hi Steven,
Thanks for chiming in! Please see my responses inline:

On Thu, Feb 7, 2013 at 3:04 PM, Steven Yates <syates@stevendyates.com>wrote:

> The only missing link within the Flume architecture I see in this
> conversation is the actual channel's and brokers themselves which
> orchestrate this lovely undertaking of data collection.

Can you define what you mean by channels and brokers in this context? Since
channel is a synonym for queueing event buffer in Flume parlance. Also, can
you elaborate more on what you mean by orchestration? I think I know where
you're going but I don't want to put words in your mouth.

One opportunity I do see (and I may be wrong) is for the data to offloaded
> into a system such as Apache Mahout  before being sent to the sink. Perhaps
> the concept of a ChannelAdapter of sorts? I.e Mahout Adapter ? Just
> thinking out loud and it may be well out of the question.

Why not a Mahout sink? Since Mahout often wants sequence files in a
particular format to begin its MapReduce processing (e.g. its k-Means
clustering implementation), Flume is already a good fit with its HDFS sink
and EventSerializers allowing for writing a plugin to format your data
however it needs to go in. In fact that works today if you have a batch
(even 5-minute batch) use case. With today's functionality, you could use
Oozie to coordinate kicking off the Mahout M/R job periodically, as new
data becomes available and the files are rolled.

Perhaps even more interestingly, I can see a use case where you might want
to use Mahout to do streaming / realtime updates driven by Flume in the
form of an interceptor or a Mahout sink. If online machine learning (e.g.
stochastic gradient descent or something else online) was what you were
thinking, I wonder if there are any folks on this list who might have an
interest in helping to work on putting such a thing together.

In any case, I'd like to hear more about specific use cases for streaming
analytics. :)


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