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Storm: Distributed and Fault-tolerant Real-time Computation

Posted by Nathan Marz  on  Jul 25, 2012

Nathan Marz discusses Storm concepts –streams, spouts, bolts, topologies-, explaining how to use Storms’ Clojure DSL for real-time stream processing, distributed RPS and continuous computations.

Timelines @ Twitter

Posted by Arya Asemanfar  on  Apr 24, 2012 2

Arya Asemanfar presents Twitter’s timeline architecture, the entire sequence of steps a tweet goes through until it reaches the timeline of each user following the person who tweeted.

Everything I Ever Learned about JVM Performance Tuning @twitter

Posted by Attila Szegedi  on  Apr 12, 2012 1

Attila Szegedi shares lessons learned tuning the JVM at Twitter, spending most of his talk discussing memory tuning, CPU usage tuning, and lock contention tuning.

Storm: Distributed and Fault-tolerant Real-time Computation

Posted by Nathan Marz  on  Oct 21, 2011 1

Nathan Marz explain Storm, a distributed fault-tolerant and real-time computational system currently used by Twitter to keep statistics on user clicks for every URL and domain.

Big Data in Real Time at Twitter

Posted by Nick Kallen  on  Aug 03, 2011 2

Nick Kallen discusses how Twitter handles large amounts of data in real time by creating 4 data types and query patterns -tweets, timelines, social graphs, search indices-, and the DBs storing them.

NoSQL at Twitter

Posted by Ryan King  on  Jan 21, 2011

Ryan King presents how Twitter uses NoSQL technologies - Gizzard, Cassandra, Hadoop, Redis - to deal with increasing data amounts forcing them to scale out beyond what the traditional SQL has to offer

NoSQL at Twitter

Posted by Kevin Weil  on  Dec 23, 2010 6

Kevin Weil presents how Twitter does data analysis using Scribe for logging, base analysis with Pig/Hadoop, and specialized data analysis with HBase, Cassandra, and FlockDB.

Abstractions at Scale–Our Experiences at Twitter

Posted by Marius Eriksen  on  Dec 14, 2010 1

Marius Eriksen considers that leaky abstractions lead to scalability issues, while those providing narrow access to explicit resources - map-reduce, shared-nothing web apps, big table - scale better.

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