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Using Spark, Kafka, Cassandra and Akka on Mesos for Real-Time Personalization


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The gambling industry has arguably been one of the most comprehensively affected by the internet revolution, and if an organization such as William Hill hadn't adapted successfully it would have disappeared. We call this, “Going Reactive.”

The company's latest innovations are very cutting edge platforms for personalization, recommendation, and big data, which are based on Akka, Scala, Play Framework, Kafka, Cassandra, Spark, and Mesos.

Published in: Software

Using Spark, Kafka, Cassandra and Akka on Mesos for Real-Time Personalization

  1. 1. Presented by Patrick Di Loreto R&D Engineering Lead 14th June 2015 Site: BLOG: Twitter: Using Spark, Kafka, Cassandra and Akka on Mesos for Real-Time Personalization
  2. 2. •  WH Labs •  Omnia – Data Management Platform –  Omnia Chronos – A distributed Integration Middleware with Akka and Kafka –  Omnia Fates – The long term memory with Apache Cassandra –  Omnia NeoCortex – Real time and Machine Learning using Apache Spark –  Omnia Hermes – Serving layer with Akka CQRS –  Omnia Infrastructure - Mesos, Marathon and Docker Introduction
  3. 3. We're  Hiring   h+ps://   WH  Apple  Watch  App   Interac:ve  Scoreboard   Virtual  Reality  Horse  Race   Oculus  RiD  
  4. 4. Omnia Platform Reactive Distributed Data Platform
  5. 5. Based on a Lambda Architecture Respecting Reactive Principles •  Chronos – Data Source •  Fates – Batch Layer •  NeoCortex – Speed Layer •  Hermes – Serving Layer Omnia – Data Management Platform Omnia   Chronos   Fates   Hermes   NeoCortex  
  6. 6. Omnia & Lambda Architecture Chronos   (Data  Source)   NeoCortex   (Speed  Layer)   Fates   (Batch  Layer)   Hermes   (Serving  Layer)                      
  7. 7. Omnia Principles h+p://   •  Scalable •  Fault Tolerant •  Highly Available
  8. 8. Omnia Chronos – Data Source
  9. 9. Omnia Chronos Is in charge to collect the data from different sources and organise them into a stream of observable events. Observable [ ] • Social  media   • Facebook   • Twi+er   • Affiliates   • Page  viewing   • Ar:cles  read,   following  and   followers,  bets  etc…   • Sports  related   • Tweets   • News   • Gaming   • Web  Analy:cs   • Ac:vi:es  with  in   our  applica:ons   Internal   Product   Centric   External   Customer   Centric   {      “type”  :  “bet”,      “version”  :  “1.0”      “Ame”  :  “2015-­‐06-­‐03   08:00:31”,      “acquisiAonTime:  “  .  .  .”,      “source”  :  “WHBetSystem”      “payload”  :  {  …  any  valid  json  }   }  
  10. 10. Omnia Chronos In Chronos you define streams that collect data and convert/ persist into a stream of Observable[Incident]. Chronos   Stream   3   Stream   2   Stream   1   Stream  
  11. 11. Omnia Chronos - Clustering Chronos  1   Chronos  2   Chronos  3   Twi+er    
  12. 12. Omnia Chronos •  Each stream is an actor which supervises its children: –  Adapter Actor –  Converter Actor –  Persistence Manager Actor •  Streams Actor are referential transparent with the usage of Akka Cluster: We have extended Akka Cluster to migrate the Stream Actors based on resource KPIs •  Data are persisted in Kafka for durability •  Chronos is built on top of Akka, ScalaRx and Play framework: planning migration to Akka Streaming
  13. 13. Omnia Fates
  14. 14. Fates represents the long term memory of Omnia. Is in charge to organise all the incidents recorded by Chronos into timelines and create new information as views by using machine learning, logical reasoning and time series analysis. •  A timeline represents the history, the sequence of incidents performed by a specific entity over the time. Timelines are organised per categories. An example of timeline can be the customer timeline, which might contain all the bets placed, deposit and withdraw activities, tweets etc... performed by the specific customer. A timeline category is not limited just to customers, it can be anything, for example: Sport Event: football match, competition •  Views are the result of job task that elaborates data from: –  Timelines –  Other Views Omnia Fates
  15. 15. Timelines are created from timeline streams, each timeline stream read data from a Chronos stream and fed the right timeline. Omnia FatesChronos               Fates                  
  16. 16. •  Fates persist timelines of incidents. •  Column Family Name: <TimelineCategory>_tl •  Key Definition: ( (entityId, date), timestamp ) •  The partition key is a strong hash key : well balanced Cassandra Cluster •  Composite key: incidents are ordered by timestamp under a specific entity within a day (date = yyyy-MM-dd ) Omnia Fates - Cassandra
  17. 17. Omnia Fates •  We build views with job able to do: Jobs are performed on top of NeoCortex Logical   Reasoning   • Deduc:on   • Induc:on   • Abduc:on   Time  line  analysis   • Trends   • Cycles   • Seasonality   Other  ML   • Classifica:on   • Clustering   • Predic:ons  
  18. 18. Omnia Neo Cortex
  19. 19. Omnia Neo Cortex •  Neo Cortex is a library developed on top of Apache Spark in order to provide to the developers an easy way to write micro services on top of Omnia. •  In NeoCortex we use the distribute nature of Spark to perform fast, real time data processing and we hide to the developer the problematic relative to the connection to the source system (Chronos) and the publishing layer •  Typeclass definition for: Timeline, View, ChronosStream etc… •  Typeclass definition for Algebrical structures: –  Monoids, Rings, Groups, providing advanced functions for: moving averages, ARX, ARMA etc
  20. 20. Omnia Neo Cortex
  21. 21. Omnia Neo Cortex - Parallelism chronos   stream   Driver   Executor  1   Executor  2   Executor  3   Executor  4   Executor  3   Executor  4   Hermes   (Serving  Layer)             Stage  1   (map)   Stage  2   (reduceByKey)   Fates   :melines   views  
  22. 22. Omnia Hermes
  23. 23. Hermes Is the layer on which data get represented for consumption: B2B and B2C. At its foundation micro-services, notifications and data as API are key aspects of the design Scalable and simple full duplex communication for the web Express the correlation between the entities of the model Inspired by Falcor (Netflix) and GraphQL (Facebook)
  24. 24. Hermes Hermes   Distributed  Cache   Hermes  Node                    Local  Cache   Subscrip:on  Manager   Client  Manager   Authen:ca:on  Handler   Dispatcher   HTTP   WS   TCP   Browser   Hermes  JS   WH  Apps  
  25. 25. Omnia Infrastructure – Mesos/Marathon/Docker
  26. 26. Omnia Infrastructure Omnia   Docker   Marathon     Mesos   Node   Node   Node   Node   Node  
  27. 27. Use Omnia on Omnia Mesos   Marathon   Docker   (Applica:on  Repository)                                 Docker   Omnia   App           Docker   Omnia   App           Docker   Omnia   App   Chronos     NeoCortex   (Speed  Layer)   Fates   (Batch  Layer)             JMX   JMX   JMX   Health  Stream  
  28. 28. Thank you Q&A