use behind the scenes when we tweet? https://dev.twitter.com/rest/reference/post/statuses/update Your first stop as a tweet: Twitter Front End (TFE) A fancy reverse proxy for HTTP traffic built on the JVM Handles authentication, rate limits and more! Powered by the open source project Netty: http://netty.io tweet fanout write search batch fin
Used heavily at Twitter Healthy adopter community: http://netty.io/wiki/adopters.html ! Cloudhopper sends billions of SMS messages per month using Netty https://github.com/twitter/cloudhopper-smpp ! We contributed SPDY support to Netty: http://netty.io/news/2012/02/04/3-3-1-spdy.html *https://blog.twitter.com/2013/netty-4-at-twitter-reduced-gc-overhead
use behind the scenes when we tweet? Twitter backend architecture is *service-oriented (on the JVM) Core services are built on top of Finagle (using an API framework) Finagle is written in Scala and built on top of Netty https://github.com/twitter/finagle tweet fanout write search batch *http://www.slideshare.net/InfoQ/decomposing-twitter-adventures-in-serviceoriented-architecture fin
Java) Less typing is less reading; brevity enhances clarity Two open source Scala/Finagle guides from Twitter: https://twitter.github.io/effectivescala/ https://twitter.github.io/scala_school/ ! Finagle is our fault tolerant protocol- agnostic RCP framework built on Netty Emphasizes services modularity via async futures Handles failover semantics, metrics, logging etc… *https://blog.twitter.com/2014/netty-at-twitter-with-finagle
service! val timelineSvc = Thrift.newIface[TimelineService](...)! val tweetSvc = Thrift.newIface[TweetService](...)! val authSvc = Thrift.newIface[AuthService](...)! ! // #2 Create new Filter to authenticate incoming requests! val authFilter = Filter.mk[Req, AuthReq, Res, Res] { (req, svc) =>! authSvc.authenticate(req) flatMap svc(_)! }! ! // #3 Create a service to convert an authenticated timeline request to a json response! val apiService = Service.mk[AuthReq, Res] { req =>! timelineSvc(req.userId) flatMap {tl =>! val tweets = tl map tweetSvc.getById(_)! Future.collect(tweets) map tweetsToJson(_) }! }! }! ! // #4 Start a new HTTP server on port 80 using the authenticating filter and our service! Http.serve(":80", authFilter andThen apiService)!
use behind the scenes when we tweet? Tweets need to be stored somewhere (via a Finagle-based core service) TBird: persistent storage for tweets Built originally on Gizzard: https://github.com/twitter/gizzard Tweets stored in sharded and replicated MySQL TFlock: track relations between users and tweets Built originally on FlockDB: https://github.com/twitter/flockdb tweet fanout write search batch fin
use behind the scenes when we tweet? When a tweet is generated it needs to be written to all relevant timelines Timelines are essentially a list of tweet ids (heavily cached) Fanout is the process where tweets are delivered to timelines For caching we rely on the open source project Redis https://github.com/antirez/redis tweet fanout write search batch fin
more! Added custom logging, data structures We are working to upstream some changes… @thinkingfish gave a fantastic talk on this: https://www.youtube.com/watch?v=rP9EKvWt0zo ! Open Source Proxy for Redis: Twemproxy https://github.com/twitter/twemproxy Used by Vine, Pinterest, Wikimedia, Snapchat etc…
use behind the scenes when we tweet? Everyone searches for tweets: https://dev.twitter.com/rest/public/search In fact, one of the most heavily trafficked search engines in the world Back in the day, Twitter search was built on MySQL Today, Twitter search is an optimized real-time search/indexing technology Powered by Apache Lucene: http://lucene.apache.org ! ! tweet fanout write search batch fin
built on top of Apache Lucene ! We optimized Lucene (cut corners) to handle tweets only since that’s all we do e.g., less space: 140 characters only need 8 bits ! Read about Blender, our search front-end https://blog.twitter.com/2011/twitter-search-now-3x-faster *http://www.umiacs.umd.edu/~jimmylin/publications/Busch_etal_ICDE2012.pdf
use behind the scenes when we tweet? tweet fanout write search batch Hadoop is used for many things at Twitter, like counting words :) scribe logs, batch processing, recommendations, trends, user modeling and more! 10,000+ hadoop servers, 100,000+ daily hadoop jobs,10M+ daily hadoop tasks Parquet is a columnar storage format for Hadoop https://parquet.incubator.apache.org Scalding is our Scala DSL for writing Hadoop jobs https://github.com/twitter/scalding ! fin
a collaboration between Twitter/Cloudera Inspired by Google Dremel paper** Now at Apache: http://parquet.incubator.apache.org/ ! Scalding built on top of Scala and Cascading https://github.com/Cascading/cascading Makes it easier* to write Hadoop jobs (using Scala) *https://blog.twitter.com/2013/announcing-parquet-10-columnar-storage-for-hadoop
improve them! https://github.com/twitterdev (t.co/code) ! Also, later today check out the lightning talk by Andrew Noonan later about the “Twitter’s developer toolbox” !
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