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From Brian O'Neill <b...@alumni.brown.edu>
Subject Re: High-level architecture
Date Fri, 01 Feb 2013 18:19:30 GMT

Excellent points Ted. (again)
You've got me thinking.

I haven't delved into the algorithm enough to understand how widely the
communication patterns vary.  I'm sure your right, but it'd be really cool
if we could find a static topology construct that can accommodate the
different communication patterns.
(even if we have to sacrifice locality for now)

If I get some time, I may have a look.

thanks,
-brian


---
Brian O'Neill
Lead Architect, Software Development
Health Market Science
The Science of Better Results
2700 Horizon Drive € King of Prussia, PA € 19406
M: 215.588.6024 € @boneill42 <http://www.twitter.com/boneill42>  €
healthmarketscience.com

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On 1/31/13 6:33 PM, "Ted Dunning" <ted.dunning@gmail.com> wrote:

>I hear you.  Deployment complexity is an evil thing.
>
>And your comment about being willing to trade some performance for
>flexibility is also interesting.
>
>A big mismatch here, however, is that every query is going to cause
>different desired communication patterns.  One way to handle that is to
>build a new topology for every query.  That isn't going to fly due to long
>topology deployment times.  Essentially Nimbus becomes the out of band
>communication mechanism.
>
>The other option would be to use Storm to move query components around.
> The communication patterns are much simpler in this case, but bolts
>suddenly need the ability to communicate to arbitrary other bolts to
>implement the data flow.  This makes Storm handle the out-of-band
>communication and leaves us with implementation of the data transform
>outside of Storm.  Since the out-of-band comms are tiny, this is perverse
>and doesn't use Storm for what it should be doing.
>
>So I really think that the takeaway here is that we need to be able to pop
>up workers very quickly and easily.  That is the lesson learned from Storm
>here and it really needs to happen.  This also impacts features like
>elasticity (where Drill might soak up excess capability in a cluster, but
>not hurt batch performance).
>
>
>On Thu, Jan 31, 2013 at 12:43 PM, Brian O'Neill
><bone@alumni.brown.edu>wrote:
>
>> Great points. Thanks Ted.
>>
>> I'm not sure if it is possible, but if there were a Storm topology
>> deployment option, I think there might be appetite for that since it
>>would
>> reduce the operations/admin complexity significantly for consumers that
>> already have Storm deployed.  (IMHO) I would be willing to sacrifice
>>some
>> performance to maintain only one set of distributed processing
>> infrastructure.
>>
>> With respect to locality information, I think Storm will eventually need
>> to add out-of-band information to optimize the tuple routing.  We
>>developed
>> the storm-cassandra bolt, and I'm eager to get to the point where we can
>> supply ring/token information to Storm so it can route the tuples to the
>> nodes that contain the data.
>>
>> (Maybe it gets carried around in the tuple and leveraged by the
>>underlying
>> infrastructure -- much like Nathan did with transaction id for Trident?)
>>
>> But I fully appreciate your points. (especially regarding
>>java-centricity,
>> serialization, kryo, etc.)
>>
>> -brian
>>
>> --
>> Brian O'Neill
>> Lead Architect, Software Development
>> Health Market Science
>> The Science of Better Results
>> 2700 Horizon Drive € King of Prussia, PA € 19406
>> M: 215.588.6024 € @boneill42  € healthmarketscience.com
>>
>> On Jan 30, 2013, at 3:16 PM, Ted Dunning wrote:
>>
>> > On Wed, Jan 30, 2013 at 11:53 AM, Brian O'Neill <bone@alumni.brown.edu
>> >wrote:
>> >
>> >> ...
>> >> How do we intend to distribute the execution engine across a set of
>> >> machines?
>> >>
>> >
>> > There are a variety of thoughts.  These include:
>> >
>> > - custom built execution controller similar to Storm's Nimbus
>> >
>> > - use Storm's Nimbus
>> >
>> > - use the custom built controller via Yarn.  Or Mesos.  Or the MapR
>> warden
>> >
>> > - start them by hand.
>> >
>> > Obviously the last option will be the one that is used in initial
>> testing.
>> >
>> > Any thought to deploying the engine as a Storm topology?
>> >>
>> >
>> > Using Storm probably limits the performance that we can get.  Storm's
>> > performance is creditable but not super awesomely impressive.
>> >
>> > Some of the performance issues with Storm include:
>> >
>> > - limited to Java.  This may or may not make a difference in the end
>>in
>> > terms of performance, but we definitely want flexibility here.  Java
>>can
>> be
>> > awesomely fast (see LMAX and Disruptor), but C++ may be more
>>predictable.
>> > We definitely *don't* want to decide for all time right now which
>>option
>> > we take and we definitely *do* want to have the C++ option in our
>> > hip-pocket later regardless of how we build execution engines now.
>>Part
>> of
>> > Storm's limitations here have to do with the use of Kryo instead of a
>> > portable serializer like protobufs.
>> >
>> > - the designs I have seen or heard batting around tend to deal with
>> batches
>> > of records represented in an ephemeral column oriented design.  It
>>will
>> > also be important for records to be kept in unmaterialized, virtual
>>form
>> to
>> > minimize copying, especially when flattening arrays and such.  Storm
>> allows
>> > tuples to be kept in memory when bolts are on the same machine, but
>> insists
>> > on serializing and deserializing them at the frontier.  To control
>>this,
>> we
>> > would have to nest serializations which seems a bit like incipient
>> insanity.
>> >
>> > Other issues include:
>> >
>> > - Drill execution engines will need access to a considerable amount of
>> > out-of-band information such as schemas and statistics.  How do we
>>manage
>> > that in a restrictive paradigm like Storm
>> >
>> > - Storm hides location from Bolts.  Drill needs to make decisions
>>based
>> on
>> > location of execution engines and data.
>>
>>



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