DDevelopment & Design

TouK contest – some important rules

By entering TouK contest at 33rd Degree Conference you are accepting these simple conditions:

  1. The contest is open to all 33rd Degree Conference participians, except TouK people and their family members, of course (sorry, guys).
  2. To take part, you must collect two TouK feathers (in two different colours) and hack an application. The apllication is avaliable at http://reg.touk.pl. Then you must write down the solution (with your name and surname) on a sheet of paper received at our stand. and leave it to us to enter a prize draw. You must have TouK feathers in both colors with you.
  3. Only one entry is allowed per person.
  4. The prizes are 6 Raspberry Pi computers (2 per each conference day).
  5. All entries (except the winners’) will be rolled over into subsequent draws.
  6. All prize draws take place at our stand in following dates: ·Day 1, 13 of March – 16:30 ·Day 2, 14 of March – 15:50 ·Day 3, 15 of March – 12:50
  7. Only correct and complete solutions will be awarded.
  8. The winner have to be present during the prize draw. If not, we reserve the right to award the prize to an alternative winner, drawn in accordance with these terms and conditions.
  9. If you have any questions about how to enter or in connection with the contest, please ask at TouK’s stand.

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Thanks for visiting TouK stand at the 33rd Degree Conference

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DDevelopment & Design

Recently at storm-users

I've been reading through storm-users Google Group recently. This resolution was heavily inspired by Adam Kawa's post "Football zero, Apache Pig hero". Since I've encountered a lot of insightful and very interesting information I've decided to describe some of those in this post.

  • nimbus will work in HA mode - There's a pull request open for it already... but some recent work (distributing topology files via Bittorrent) will greatly simplify the implementation. Once the Bittorrent work is done we'll look at reworking the HA pull request. (storm’s pull request)

  • pig on storm - Pig on Trident would be a cool and welcome project. Join and groupBy have very clear semantics there, as those concepts exist directly in Trident. The extensions needed to Pig are the concept of incremental, persistent state across batches (mirroring those concepts in Trident). You can read a complete proposal.

  • implementing topologies in pure python with petrel looks like this:

class Bolt(storm.BasicBolt):
    def initialize(self, conf, context):
       ''' This method executed only once '''
        storm.log('initializing bolt')

    def process(self, tup):
       ''' This method executed every time a new tuple arrived '''       
       msg = tup.values[0]
       storm.log('Got tuple %s' %msg)

if __name__ == "__main__":
    Bolt().run()

  • Fliptop is happy with storm - see their presentation here

  • topology metrics in 0.9.0: The new metrics feature allows you to collect arbitrarily custom metrics over fixed windows. Those metrics are exported to a metrics stream that you can consume by implementing IMetricsConsumer and configure with Config.java#L473. Use TopologyContext#registerMetric to register new metrics.

  • storm vs flume - some users' point of view: I use Storm and Flume and find that they are better at different things - it really depends on your use case as to which one is better suited. First and foremost, they were originally designed to do different things: Flume is a reliable service for collecting, aggregating, and moving large amounts of data from source to destination (e.g. log data from many web servers to HDFS). Storm is more for real-time computation (e.g. streaming analytics) where you analyse data in flight and don't necessarily land it anywhere. Having said that, Storm is also fault-tolerant and can write to external data stores (e.g. HBase) and you can do real-time computation in Flume (using interceptors)

That's all for this day - however, I'll keep on reading through storm-users, so watch this space for more info on storm development.

I've been reading through storm-users Google Group recently. This resolution was heavily inspired by Adam Kawa's post "Football zero, Apache Pig hero". Since I've encountered a lot of insightful and very interesting information I've decided to describe some of those in this post.

  • nimbus will work in HA mode - There's a pull request open for it already... but some recent work (distributing topology files via Bittorrent) will greatly simplify the implementation. Once the Bittorrent work is done we'll look at reworking the HA pull request. (storm’s pull request)

  • pig on storm - Pig on Trident would be a cool and welcome project. Join and groupBy have very clear semantics there, as those concepts exist directly in Trident. The extensions needed to Pig are the concept of incremental, persistent state across batches (mirroring those concepts in Trident). You can read a complete proposal.

  • implementing topologies in pure python with petrel looks like this:

class Bolt(storm.BasicBolt):
    def initialize(self, conf, context):
       ''' This method executed only once '''
        storm.log('initializing bolt')

    def process(self, tup):
       ''' This method executed every time a new tuple arrived '''       
       msg = tup.values[0]
       storm.log('Got tuple %s' %msg)

if __name__ == "__main__":
    Bolt().run()

  • Fliptop is happy with storm - see their presentation here

  • topology metrics in 0.9.0: The new metrics feature allows you to collect arbitrarily custom metrics over fixed windows. Those metrics are exported to a metrics stream that you can consume by implementing IMetricsConsumer and configure with Config.java#L473. Use TopologyContext#registerMetric to register new metrics.

  • storm vs flume - some users' point of view: I use Storm and Flume and find that they are better at different things - it really depends on your use case as to which one is better suited. First and foremost, they were originally designed to do different things: Flume is a reliable service for collecting, aggregating, and moving large amounts of data from source to destination (e.g. log data from many web servers to HDFS). Storm is more for real-time computation (e.g. streaming analytics) where you analyse data in flight and don't necessarily land it anywhere. Having said that, Storm is also fault-tolerant and can write to external data stores (e.g. HBase) and you can do real-time computation in Flume (using interceptors)

That's all for this day - however, I'll keep on reading through storm-users, so watch this space for more info on storm development.

DDevelopment & Design

Open IMS Core Mr interface

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TouK na targach pracyTouK at the job fair

Zapraszamy na XI Targi Pracy i Praktyk dla Elektroników i Informatyków. Odwiedź nasze stoisko w dniach 4-5 marca w godz. 9:30-15:30. Politechnika Warszawska Pierwsze piętro budynku Wydziału Elektroniki i Techniki Informacyjnych Nowowiejska 15/19We invite you to 11th Job and Internship Fair for Electronic Engineers and IT Specialists. Come and visit our stand between 4-5 March 9:30 am and 15 :30 pm Warsaw University of Technology the first floor of the Electronics faculty building Nowowiejska 15/19