Lambda architecture for real time big data

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Lambda Architecture in Real-time Big Data Project
Concepts & Techniques “Thinking with Lambda”
Case study in some real projects
Why lambda architecture is correct solution for big data?

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Lambda architecture for real time big data

  1. 1. Lambda Architecture in Real-time Big Data ● Concepts & Techniques “Thinking with Lambda” ● Case studies in Practice Trieu Nguyen - http://nguyentantrieu.info/blog or @tantrieuf31 Lead Engineer at eClick Data Analytics team at FPT Online All contents and thoughts in this slide are my subjective ideas and compiled from Open Source Communities
  2. 2. Just a little introduction ● 2008 Java Developer, developed Social Trading Network for a small startup (Yopco) ● 2011 worked at FPT Online, software engineer in Banbe Project, Restful API for VnExpress Mobile App ● 2012 joined Greengar Studio in 6 months, scaling backend API mobile games (iOS, Android) ● 2013 back to FPT Online, R&D about Big Data & Analytics, developing the new core Analytics Platform (on JVM Platform)
  3. 3. Stupid questions ● Big Data means big logs storage ? ● I just installed Hadoop, and it works! Do we really get a big data solution ? ● We have lots data, so let’s play with cool big data technologies x,y, z! Do we get profits from that ? ● We can hire or outsource a professional team to build big data solution, but do they answer what problem we get ?
  4. 4. Contents for this talk ● A little introduction about Lambda in history ● Trends of Now and the Future ● Why lambda architecture is correct solution for big data? ● Lambda in Practice, case studies from Greengar Studios and eClick ● Lessons ● Questions & Answers
  5. 5. History The best way to predict the future is looking at the past and now ?
  6. 6. Lambda is the symbol to denote: ● Half-life game ? ● Anonymous function, aka: Closure ? ● functional computation/programming? ● scalable system ?
  7. 7. http://en.wikipedia.org/wiki/Lambda
  8. 8. When I study “lambda” ? I studied Haskell in 2007 with Dr.Peter Gammie http://peteg.org/ when internship at DRD (a non-profit organization). ● Imperative programs will always be vulnerable to data races because they contain mutable variables. ● There are no data races in purely functional languages because they don't have mutable variables.
  9. 9. http://stackoverflow.com/questions/6087834/how- scalable-is-mapreduce-in-the-original-functional- languages
  10. 10. How did Google scale their search engine ? How does Hadoop really work ?
  11. 11. The Closure in JavaScript, running by billion websites !
  12. 12. (Lambda) is everywhere !
  13. 13. Trends of Now and the Future ● Big Data ● Data Analytics ● Reactive Programming ● Functional Programming ● Streaming Computation => All just the special cases of Lambda
  14. 14. Question: Is mobile app generating more data than traditional web ?
  15. 15. Question: Is the Open Source Big Data Solution like Hadoop, that makes big data more popular to enterprises and startups ? 2009, a big-data startup, Cloudera was founded !
  16. 16. What is the λ (Lambda) Architecture ?
  17. 17. the Lambda Architecture: ● apply the (λ) Lambda philosophy in designing big data system ● equation “query = function(all data)” which is the basis of all data systems ● proposed by Nathan Marz (http://nathanmarz.com/), a software engineer from Twitter in his “Big Data” book. ● is based on three main design principles: ○ human fault-tolerance – the system is unsusceptible to data loss or data corruption because at scale it could be irreparable. (BUGS ?) ○ data immutability – store data in it’s rawest form immutable and for perpetuity. (INSERT/ SELECT/DELETE but no UPDATE !) ○ recomputation – with the two principles above it is always possible to (re)-compute results by running a function on the raw data.
  18. 18. “lambda architecture” proposed by @nathanmarz We, at FPT Online, have applied the lambda architecture since April, 2013
  19. 19. Lambda In Practice 2 case studies from my experiences
  20. 20. Case Study 1: Greengar Studios API Backend Monitor + Statistics http://www.greengar.com/
  21. 21. Backend System at Greengar Studio I applied “Lambda” here
  22. 22. The data and the size, not too big for a small startup! Where is the lambda ? I used Groovy + GPars (Groovy Parallel Systems) + MongoDB for fast parallel computation (actor model) on statistical data http://gpars.codehaus.org/ The GPars framework offers Java developers intuitive and safe ways to handle Java or Groovy tasks concurrently. Support: ● Dataflow concurrency ● Actor programming model ● CSP ● Agent - an thread-safe reference to mutable state ● Concurrent collection processing ● Composable asynchronous functions ● Fork/Join ● STM (Software Transactional Memory)
  23. 23. Mobile Apps => Backend APIs => Statistics => Find the Trends & Insights?
  24. 24. Case Study 2: eClick Ad-Network ● Real-time Data Analytics ● Monitoring Stream Data (Reactive) http://eclick.vn
  25. 25. at eClick we have 30~40 GB Logs in Stream 10~20 GB Bandwidth just for tracking user actions (click, impression,...) in ONE day ! at eClick we must check campaigns in near-real-time (seconds) ! at eClick we have many types of log (video, web, mobile, system logs, ad-campaign, articles, … )
  26. 26. Our big-data system Leverage Open Source Projects ● Netty (http://netty.io/) a framework using reactive programming pattern for scaling HTTP system easier ● Kafka (http://kafka.apache.org/) a publish-subscribe messaging rethought as a distributed commit log. ● Storm (http://storm-project.net/) a framework for distributed realtime computation system. ● Redis (http://redis.io/) a advanced key-value in-memory NoSQL database, all fast statistical computations in here. ● Groovy for scripting layer, dynamic query on Redis + RDBMSs ● Hadoop ecosystem: HDFS, Hive, HBase for batch processing ● RxJava https://github.com/Netflix/RxJava a library for composing asynchronous and event-based programs
  27. 27. Some new ideas for the future: Connecting the active functor pattern + reactive programming + stream computation + in-memory computing to make: ● real-time data analytics easier ● better recommendation system ● build more profitable big data solutions More Information: ● http://activefunctor.blogspot.com/ (a special case of Lambda that actively search best connections to form optimal topology) - from ideas when internship at DRD with my advisor. ● Can a function be persistent (stored as data), distributed in a cluster (cloud), reactive to right data (best value in network)?
  28. 28. We can't solve problems by using the same kind of thinking we used when we created them. Albert Einstein Think more Lambda and Reactive
  29. 29. How could we see "user interest graph" in our user's database ?
  30. 30. ● Social Graph => Keep the connection ● Interest Graph => Make new connection => recommendation platform Source: http://en.wikipedia.org/wiki/Interest_graph
  31. 31. Lessons What I have learned from Lambda and Big Data World
  32. 32. What I have learned ● Keep it as simple as possible, but no simpler ! ● Ask right questions=> deep analytics=>Profit ● Reactive and Lambda for your data products ● Implement it! Just right tools for right jobs. ● Turn your data into the things everyone can "look & feel"
  33. 33. How to build profitable big data solutions? => read these Behavioral Economics Books http://www.goodreads.com/shelf/show/behavioral-economics
  34. 34. Stay focused, keep innovating Big Data is not profitable if you do not know what you want and ask right questions
  35. 35. “Logic will get you from A to Z; imagination will get you everywhere.” - Albert Einstein Use your imaginationwith data analytics, not just logic

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