Lambda Architecture and open source technology stack for real time big data
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Lambda Architecture and open source technology stack for real time big data

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Concepts & Techniques “Thinking with Lambda”

Concepts & Techniques “Thinking with Lambda”
Case studies in Practice using Lambda architecture

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Lambda Architecture and open source technology stack for real time big data Lambda Architecture and open source technology stack for real time big data Presentation Transcript

  • Lambda Architecture and Open Source Tools for Real-time Big Data ● Concepts & Techniques “Thinking with Lambda” ● Case studies in Practice Trieu Nguyen - http://nguyentantrieu.info or @tantrieuf31 Principal Engineer at eClick Data Analytics team, FPT Online All contents and thoughts in this slide are my subjective ideas and compiled from Communities
  • 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 Studios 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)
  • Contents for this talk ● ● ● ● ● ● ● ● The lessons from history Problems In Practice What is the Lambda Architecture? Why lambda architecture for real-time big data ? Open Source Technology Stack Lambda in Practice (Mobile Data and Web Data) Lessons I have learned Questions & Answers
  • History ? The best way to predict the future is looking at the past and now ?
  • Big data is a buzzword for old problems
  • Explaining Big Data http://www.youtube.com/watch?v=7D1CQ_LOizA
  • Learning ?
  • Working ?
  • Big Data + Old History http://www.youtube.com/watch?v=tp4y-_VoXdA
  • This is Big DATA This is most valuable things!
  • We can't solve problems by using the same kind of thinking we used when we created them. Albert Einstein Think more with Lambda and Reactive
  • Where Big Data can be used
  • BBC Horizon 2013 The Age of Big Data http://www.youtube.com/watch?v=RE0ITQ7XQjM
  • Google’s mission is to organize the world’s information and make it universally accessible and useful.
  • Organize the world’s information?
  • How did Google scale their search engine ? How does Hadoop really work ?
  • http://stackoverflow.com/questions/6087834/howscalable-is-mapreduce-in-the-original-functionallanguages
  • Trends of Now and the Future MapReduce Programming Reactive Programming Functional Programming Streaming Computation => All just the special cases of Lambda ● ● ● ●
  • So what is the λ (Lambda) Architecture ?
  • 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.
  • Lambda In Practice 2 case studies from my experiences
  • Case Study 1: Mobile Data Monitor API Backend + System KPI
  • Problem: Inside “mobile data”, What's the most valuable piece of information
  • I applied “Lambda” here Backend System for mobile app
  • Web vs Mobile App Web Visitors Visits Pageviews Events Mobile App Users Sessions Events
  • Metrics: Cause and Effect ● ● ● ● ● ● ● Screen Size => App Design, UI/UX, Usability App version => Deployment, Marketing Connectivity => Code, User Experience Location => Marketing, User Behaviour OS => Marketing, Cost, Development Memory => User Experience Feature Session => How to engage app users
  • 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)
  • Mobile Apps => Backend APIs => Statistics => Find the Trends & Insights?
  • Reactive Data Analytics for Mobile Apps It means real-time recommendation by: ➔ context (location, time) ➔ user profile (preferences, level, ...)
  • Big Data on Small Devices: Data Science goes Mobile http://strataconf.com/strata2013/public/schedule/detail/27605
  • Case Study 2: Web Data ● Real-time Data Analytics ● Monitoring Stream Data (Reactive) http://eclick.vn
  • at eClick we must check campaigns in near-real-time (seconds) ! 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 have many types of log (video, web, mobile, system logs, ad-campaign, articles, … )
  • “lambda architecture” proposed by @nathanmarz
  • Internet Netty Http Server TCP Connection Kafka Akka Workers Hadoop Tools Storm Redis Redis KPI Report the open-source lambda architecture at eClick
  • The big-data technology stack ● Netty (http://netty.io/) a framework using reactive programming pattern for scaling HTTP system easier, by JBoss http://www.jboss.org ● Kafka (http://kafka.apache.org/) a publish-subscribe messaging rethought as a distributed commit log, open sourced by Linkedin ● Storm (http://storm-project.net/) the framework for distributed realtime computation system, by Twitter ● Redis (http://redis.io/) a advanced key-value in-memory NoSQL database, all fast statistical computations in here. ● Groovy for scripting layer on JVM, ad-hoc query on Redis ● Hadoop ecosystem: HDFS, Hive, HBase for batch processing ● RxJava https://github.com/Netflix/RxJava a library for composing asynchronous and event-based programs ● Hystrix https://github.com/Netflix/Hystrix : for Latency and Fault Tolerance for Distributed Systems
  • My 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 in big data 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) ? ● http://www.reactivemanifesto.org/ (reactive pattern)
  • Lessons What I have learned from Lambda and Big Data World
  • What I have learned ● ● ● ● ● Study about lambda and read some books Ask questions=> analytics=> Profit & Value Collect any data you can, learn inside ! Implement it! Just right tools for right jobs. Turn your data into the things everyone can "look & feel"
  • read papers
  • Study the “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.
  • Reading some books
  • Improve your business knowledge ! => read the Behavioral Economics Books http://www.goodreads.com/shelf/show/behavioral-economics
  • Collect the data ?
  • Use your imagination is more than just knowledge you have
  • Think more about Butterfly Effect!
  • Z; om A to fr l get you you il “Logic w n will get in ginatio - Albert Einste ima .” ywhere ever Use you r with da imagination ta just log analytics, not ic Learn Data Visualization
  • Questions & Answers The link of this slide is here: ● http://nguyentantrieu.info/blog/lambda-architecture-andopen-source-tools-for-real-time-big-data/ More useful resources: ● http://nguyentantrieu.info/blog ● http://www.mc2ads.com