InfoQ Homepage Social Networking Content on InfoQ
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Real-Time Delivery Architecture at Twitter
Raffi Krikorian details Twitter’s timeline architecture, its “write path” and “read path”, making it possible to deliver 300k tweets/sec.
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Mobile Social Apps, A Natural Fit
Aryeh Selekman discusses current trends in the mobile space, some of the technologies useful to integrate Facebook functionality into mobile applications and the latest W3C mobile standards under dev.
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Storm: Distributed and Fault-tolerant Real-time Computation
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.
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Big Data Architectures at Facebook
Ashish Thusoo presents the data scalability issues at Facebook and the data architecture evolution from EDW to Hadoop to Puma.
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Timelines @ Twitter
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.
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Everything I Ever Learned about JVM Performance Tuning @twitter
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.
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Spring Social: For the New Web of APIs
Craig Walls discusses the need for adding social features to applications, how to secure such applications and how Spring Social can help.
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Storm: Distributed and Fault-tolerant Real-time Computation
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.
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HBase @ Facebook
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.
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Big Data in Real Time at Twitter
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.
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Evolution of Code Design at Facebook
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.
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Scaling the Social Graph: Infrastructure at Facebook
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.
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