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From "Giaccone, Tony" <anthony.giacc...@nytimes.com>
Subject Re: Partitioning a query result..
Date Fri, 16 Dec 2016 19:54:17 GMT
So the essential bit of this that's not perhaps been exposed is that we're
publishing this data to google's pubsub.  The intent is to generate the
events as a result of actions taken in the main application and store the
event data in a new event database  Then a periodic job, reads the data
from the database/table and pushes the data into the pubsub system.  One of
the other interesting aspects is that we are using avro to serialize the
event into a blob of json. The tables then store the event audit data (who,
where, when)  and the blob. The publisher pulls the blob and a has that
identifies the schema of the json mesg and publishes it to pubsub updating
the event to indicate it's been published.

Ideally we would just publish the events to pubsub as they happen from the
main app. It's not clear to me why we're using the DB as a temporary
storage location, but that's the deal for now.


   - In my POC, I pushed 10,000 events into the event queues using cayenne
   in about 13 seconds (a completely unoptimized local postgress db, event
   data generated randomly, app and db both running on my laptop).
   - If I avoid the pub sub step, I can then process those events, read
   them from the db, update the is_published field and write them back out in
   17 seconds.
   - If I include the pubsub step. then that 17 seconds expands to 10
   minutes.

So you see it's the single dispatcher -> message queue part that I want to
parallelize because that's where I think I could get the biggest speed up
now.  If pushing to pubsub is slow, and the results are guaranteed to be
retrieved out of order, it doesn't matter in what order I push them to
pubsub.


Tony

On Fri, Dec 16, 2016 at 1:35 PM, Andrus Adamchik <andrus@objectstyle.org>
wrote:

> Actually this is an interesting architectural discussion. Speaking for
> myself, I certainly like having it here.
>
> The 2 main approaches have already been mentioned:
>
> 1. Single dispatcher -> message queue -> multiple workers.
> 2. Multiple workers that somehow guess their part of the workload.
>
> Both can be made to work. Generally I like #1 as it is not brittle at all.
> This is how work is parallelized on the cloud essentially. Dispatcher
> instance would poll the DB and post a stream of IDs to the queue. Workers
> would grab the ids from the queue and do their processing. Worker instances
> can come and go. A good choice for the message queue is Apache Kafka, that
> supports automatically spreading messages to multiple consumers (and yes
> there's Bootique support for it). If your can make your dispatcher fast
> (and I don't see why you can't... fetching 10K IDs can be done in
> milliseconds with proper DB indexes), you can keep adding as many workers
> as needed.
>
> So, to confirm your scenario:
>
> * On each job run do you need to reprocess previously seen records, or do
> you only care about new records since the last run?
> * On a single instance, do you have an idea of "the main query time" vs
> "processing time + any extra queries and commits"?
>
> Andrus
>
>
> > On Dec 16, 2016, at 8:50 PM, Giaccone, Tony <
> anthony.giaccone@nytimes.com> wrote:
> >
> > Right so I agree with the partitioning of the database, that's a thing
> that
> > can be done.
> >
> > Andrus, I'm a bit less confident in the proposal you're suggesting. I
> want
> > to be able to spin up new instances potentially in new containers and run
> > them in different environments. If we're moving to a cloud based
> > infrastructure, then paralyzing in a single app doesn't match up with
> that
> > kind of deployment. I recognize there are limits on my solution as well.
> > You have to deal with how you split up the rows into partitions.
> >
> > The problem generally stated is. If I have 10,000 records and I want to
> > distribute them across N number of workers. How do I do that?  How can I
> > partition the result set at run time, into an arbitrary number of
> workers?
> >
> > I also realize this is quickly expanding out side the scope of the
> cayenne
> > users mailing list.
> >
> > On Thu, Dec 15, 2016 at 3:18 AM, Andrus Adamchik <andrus@objectstyle.org
> >
> > wrote:
> >
> >> Here is another idea:
> >>
> >> * read all data in one thread using iterated query and DataRows
> >> * append received rows to an in-memory queue (individually or in small
> >> batches)
> >> * run a thread pool of processors that read from the queue and do the
> work.
> >>
> >> As with all things performance, this needs to be measured and compared
> >> with a single-threaded base line. This will not help with IO bottleneck,
> >> but the processing part will happen in parallel. If you see any Cayenne
> >> bottlenecks during the last step, you can start multiple ServerRuntimes
> -
> >> one per thread.
> >>
> >> Andrus
> >>
> >>> On Dec 15, 2016, at 3:06 AM, John Huss <johnthuss@gmail.com> wrote:
> >>>
> >>> Unless your DB disk is stripped into at least four parts this won't be
> >>> faster.
> >>> On Wed, Dec 14, 2016 at 5:46 PM Tony Giaccone <tgiaccone@gmail.com>
> >> wrote:
> >>>
> >>>> I want to speed thing up, by running multiple instances of a job that
> >>>> fetches data from a table.  So that for example if I need to process
> >> 10,000
> >>>> rows
> >>>> the query runs on each instance and returns 4 sets of 2500 rows one
> for
> >>>> each instance with no duplication.
> >>>>
> >>>> My first thought in SQL was to add something like this to the where
> >>>> clause..
> >>>>
> >>>> and MOD(ID, INSTANCE_COUNT) == INSTANCE_ID;
> >>>>
> >>>> so that if the instance count was 4 then the instance IDs would run
> >>>> 0,1,2,3.
> >>>>
> >>>> I'm not quite sure how you would structure that using the queryAPI.
> Any
> >>>> suggestions about that?
> >>>>
> >>>> And there are some problems with this idea, as you have to be certain
> >> your
> >>>> IDs increase in a manner that aligns with your math so that the
> >>>> partitioning is equal in size.
> >>>> For example if your sequence increments by 20, then you would have to
> >> futz
> >>>> around with your math to get the right partitioning and that is the
> >> problem
> >>>> with this technique.
> >>>> It's brittle it depends on getting a bunch of things in  "sync".
> >>>>
> >>>> Does anyone have another idea of how to segment out rows that would
> >> yield a
> >>>> solution that's not quite so brittle?
> >>>>
> >>>>
> >>>>
> >>>> Tony Giaccone
> >>>>
> >>
> >>
>
>

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