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Introduction to RFX for Backend Developer

Introduction to RFX for Backend Developer
Reactive Function (X) - the Open Source Framework for solving Fast Data Problem

Introduction to RFX for Backend Developer

  1. 1. Introduction to RFX for Backend Developer Reactive Function (X) the Open Source Framework for solving Fast Data Problem and reacting to the World with Deep Learning By Triều Nguyễn, the creator of RFX http://mc2ads.com (Reactive Big Data Lab) λ(x)
  2. 2. 2008: Java Developer, develop Social Trading Network for a startup (Yopco) 2011: joined FPT Online, software engineer, worked in banbe. net social network and VnExpress Mobile Restful API 2012: backend engineer at Greengar Studios 12/2012 to now - back to FPT Online, lead engineer, developed new version Data Analytics Platform (ad-network eclick.vn and VnExpress News) Introduction about myself
  3. 3. 1. What is Rfx ? 2. Inception and Ideas 3. How Rfx was born 4. Why is Rfx ? a. from big picture view (business) b. from business view c. from specific problems 5. Concepts and architecture: The BIG picture 6. Coding and tutorials from practical problems 7. Resources for self-studying Contents of this talk
  4. 4. ● A framework for reactive real-time big fast data ● A collection of Open Source Tools ● The mission of RFX → “BUILD digital data-driven brain for every company in the World” What is RFX or Reactive Function X ?
  5. 5. INCEPTION and Ideas Ideas when I was student, internship at DRD, non-profit Organization More info at http://activefunctor.blogspot.com
  6. 6. Challenges for today computing Ask bigger questions
  7. 7. http://singularityhub.com/2014/04/20/new-imaging-method-shows-young-neurons-making-connections-exchanging-information/
  8. 8. Complex brain network topology
  9. 9. Digital Brain Topology
  10. 10. Think about this module
  11. 11. Why Rfx ? ● Ideas since 2007 (from Haskell and Actor model theory) ● R&D and Deployed in Production since 2013 ● Open Source: Apache License, Version 2.0 ● Full Stack: Front-end and Back-end ● Apply Agile for Analytics and Data Science ● Apply Reactive Lambda Architecture ● Really fast and near-real-time processing ● Tested with 1.000.000 logs / second (1 million in 1 second) ● Simple development model for big data developer
  12. 12. What problems can be solved with Rfx
  13. 13. Domain (in business) where Rfx can be used ● real-time data analytics for digital marketing, advertising ● hospital systems ● personal banking system ● financial institution to detect frauds ● manufacturing plant ● airline systems ● online trading system ● emergency control system ● manufacturing plant management system ● road tolling system detects ● social networking site
  14. 14. Paper: http://vialab.science.uoit.ca/textvis2011/papers/textvis%202011-rohrdantz.pdf
  15. 15. Problem: How to monitor Mobile Web Performance and react to slow response time http://sixrevisions.com/mobile/pay-attention-to-mobile-web-performance
  16. 16. Luggage management system, events are produced by the check-in process and by the various radio-frequency identification (RFID) readers, which emit events about the movement of the luggage in the system. The events generated by the event processing system are consumed by the luggage control system itself, by airport staff, or even by the passengers themselves. Problem: Monitoring sensor data and real-time security checking
  17. 17. Actor User, Mobile, Browser, ... Reactive Lambda Architecture System Rfx- Topology data + context + metadata useful (data + relationship) Database NoSQL 1. Actor → System 2. System → Database 3. Database → System 4. System → Actor
  18. 18. Concepts ● Each user, who uses the services and creates data, is the actor in system ● Actor is the source of all events (aka: logs), (click, reading news, sending message to friends, playing games, ...) ● Functor (aka: neuron) is a computing object, used for storing, processing data and emitting results to subscribed functors ● Topology is the directed graph, define how functors that are connected with stream data and process data
  19. 19. Philosophy and the Architecture
  20. 20. There are 3 demos, from simple to advanced user story User story 1: Counting Real-time URL Pageview User story 2: Monitoring Social Media Statistics User story 3: Social Ranking for Recommendation Engine
  21. 21. User Story Domain problem: Reactive Real-time Marketing User story’s details: 1. User does read news from a website → tracking user activities (pageview, time on site) 2. User does login with Facebook Account 3. User clicks on like Facebook button → tracking what user liked, commented 4. The marketer/data analyst should see the trending most read article in real-time ● → Personalized articles for the reader ● → Native advertising in real-time
  22. 22. Demo user story 1: Counting Real-time URL Pageview Input: 1. The pageview logs from HTTP Output: 1. The total number of page-view 2. The total number of page-view per hour 3. The total number of page-view per minute 4. The total number of page-view per second 5. The total number of page-view for URL
  23. 23. Demo user story 2: Monitoring Social Media Statistics Input: 1. The pageview logs from HTTP Output: The social media statistics from: 1. Facebook: Like, Share, Comment 2. Twitter: Tweet Count 3. LinkedIn: Share Count 4. Geolocation heat-map report
  24. 24. Demo user story 3: Social Ranking for Recommendation Engine Input: ● Data: the URL of article ● Context: where (User's Location), when (time visit), from where (referer url) ● Metadata: keywords, category of article Output: Real-time Statistics about pageview, social media statistics (Share, Like, Comment), recommended articles The list of articles are ranked by: ● most liked and same category ● most viewed and same category ● most liked, same category and near user's location
  25. 25. Reference Resources Main website for Rfx: http://www.mc2ads.com Ideas: ● http://journal.frontiersin.org/Journal/10.3389/fninf.2010.00112/full ● http://singularityhub.com/2014/04/20/new-imaging-method-shows-young-neurons-making- connections-exchanging-information ● http://en.wikipedia.org/wiki/Actor_model_theory ● http://java.dzone.com/articles/introduction-event-processing ● http://www.technologyreview.com/featuredstory/526501/brain-mapping ● http://www.technologyreview.com/featuredstory/513696/deep-learning Apache Storm: http://storm.incubator.apache.org Apache Kafka: http://kafka.apache.org In-memory NoSQL: http://redis.io Deep Learning for Java: http://deeplearning4j.org Distributed processing with Actor Model: http://akka.io Papers: ● Real-Time Visualization of Streaming Text Data ● Hypernetworks for the Science of. Complex Systems Main Blogs: http://www.mc2ads.org
  26. 26. The end and thank you https://github.com/mc2ads/rfx http://www.mc2ads.org λ(x)

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