Ashish Thusoo presents the data scalability issues at Facebook and the data architecture evolution from EDW to Hadoop to Puma.
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.
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.
Craig Walls discusses the need for adding social features to applications, how to secure such applications and how Spring Social can help.
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.
Kannan Muthukkaruppan overviews HBase, explaining what Facebook Messages is and why they chose HBase to implement it, their contribution to HBase, and what they plan to use it for in the future.
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.
Nick Schrock presents how Facebook’s code evolved over time, explaining some new constructs – fbobjects, Preparables, Ents - introduced to address the complexities of a large social graph.
Jason Sobel presents the evolution of Facebook’s infrastructure over time, from the original LAMP stack to the present multi-datacenter configuration, the challenges faced and plans for the future.
Tyler Close considers that the old client-server security model is no longer viable and a new security web model is needed, presenting tools and techniques to secure the social web apps of today.
Craig Walls discusses social web applications, how to integrate them, how to provide social data in a RESTful and secure way, introducing Spring Social, a framework for developing social web apps.
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.