You could create a lazily initialized singleton factory and connection
pool. Whenever an executor starts running the firt task that needs to
push out data, it will create the connection pool as a singleton. And
subsequent tasks running on the executor is going to use the
connection pool. You will also have to intelligently shutdown the
connections because there is not a obvious way to shut them down. You
could have a usage timeout - shutdown connection after not being used
for 10 x batch interval.
TD
On Thu, Dec 11, 2014 at 4:28 AM, Ashic Mahtab <as...@live.com> wrote:
> Hi,
> I was wondering if there's any way of having long running session type
> behaviour in spark. For example, let's say we're using Spark Streaming to
> listen to a stream of events. Upon receiving an event, we process it, and if
> certain conditions are met, we wish to send a message to rabbitmq. Now,
> rabbit clients have the concept of a connection factory, from which you
> create a connection, from which you create a channel. You use the channel to
> get a queue, and finally the queue is what you publish messages on.
>
> Currently, what I'm doing can be summarised as :
>
> dstream.foreachRDD(x => x.forEachPartition(y => {
> val factory = ..
> val connection = ...
> val channel = ...
> val queue = channel.declareQueue(...);
>
> y.foreach(z => Processor.Process(z, queue));
>
> cleanup the queue stuff.
> }));
>
> I'm doing the same thing for using Cassandra, etc. Now in these cases, the
> session initiation is expensive, so foing it per message is not a good idea.
> However, I can't find a way to say "hey...do this per worker once and only
> once".
>
> Is there a better pattern to do this?
>
> Regards,
> Ashic.
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