Congrats!
On Mon, Sep 22, 2014 at 5:16 PM, P. Taylor Goetz ptgo...@gmail.com wrote:
I’m pleased to announce that Apache Storm has graduated to a Top-Level
Project (TLP), and I’d like to thank everyone in the Storm community for
your contributions and help in achieving this important
Awhile ago I had written a camel adapter for storm so that spout inputs
could come from camel. Not sure how useful it would be for you but its
located here:
https://github.com/calrissian/storm-recipes/blob/master/camel/src/main/java/org/calrissian/recipes/camel/spout/CamelConsumerSpout.java
Hi
Also, Trident is a DSL for rapidly producing useful analytics in Storm and
I've been working on a DSL that makes streams processing for complex event
processing possible.
That one is located here:
https://github.com/calrissian/flowmix
On Sep 16, 2014 4:29 AM, dominique.vill...@orange.com wrote:
Kafka is also distributed in nature, which is not something easily achieved
by queuing brokers like ActiveMQ or JMS (1.0) in general. Kafka allows data
to be partitioned across many machines which can grow as necessary as your
data grows.
On Thu, Aug 14, 2014 at 11:20 PM, Justin Workman
it handles IPv4/6.
Try adding the following JVM parameter when running your tests:
-Djava.net.preferIPv4Stack=true
-Taylor
On Aug 4, 2014, at 8:49 PM, Corey Nolet cjno...@gmail.com wrote:
I'm testing some sliding window algorithms with tuples emitted from a
mock spout based on a timer
Sorry- the ipv4 fix worked.
On Tue, Aug 5, 2014 at 9:13 PM, Corey Nolet cjno...@gmail.com wrote:
This did work. Thanks!
On Tue, Aug 5, 2014 at 2:23 PM, P. Taylor Goetz ptgo...@gmail.com wrote:
My guess is that the slowdown you are seeing is a result of the new
version of ZooKeeper
:
-Djava.net.preferIPv4Stack=true
-Taylor
On Aug 4, 2014, at 8:49 PM, Corey Nolet cjno...@gmail.com wrote:
I'm testing some sliding window algorithms with tuples emitted from a
mock spout based on a timer but the amount of time it takes the topology to
fully start up and activate seems to vary from
);
completeTopologyParam.setStormConf(daemonConf);
completeTopologyParam.setTopologyName(getTopologyName());
Map result = Testing.completeTopology(cluster,
topology, completeTopologyParam);
});
-Vincent
On Mon, Aug 4, 2014 at 8:49 PM, Corey Nolet cjno...@gmail.com wrote:
I'm testing some sliding window algorithms
Nevermind, I wrote that before looking. This has been around since 0.8.1.
Thanks again Vincent!
On Mon, Aug 4, 2014 at 11:01 PM, Corey Nolet cjno...@gmail.com wrote:
Oh Nice. Is this new in 0.9.*? I just updated so I haven't looked much
into what's changed yet, other than Netty.
On Mon
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On Fri, May 16, 2014 at 8:58 AM, eric perler ericper...@hotmail.com wrote:
unsubscribe
Interesting that the paper was written by IBM people defending an IBM
product. Not saying that it's biased or anything...
Nathan, I agree that the windowing is better served as a layer on top.
Personally, I appreciate that Storm deals with clustering, distributed
state, fault-tolerance, and
Raphael, in your case it sounds like a TickSpout could be useful where
you emit a tuple every n time slices and then sleep until needing to emit
another. I'm not sure how that'd work in a Trident aggregator, however.
I'm not sure if this is something Nathan or the community would approve of,
but
I'm trying to do some timed/count-based orchestrations of streams in
Storm/Trident. Some of my timing problems include timed or count-based
emissions of tuples from aggregators and tumbling windows (whereby I'm
batching up data and I emit every so often or I emit on every 500th tuple).
I am
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