Nicolas Spiegelberg discusses Facebook Messages built on top of HBase, the systems involved and the scaling challenges for handling 500TB of new data per month.
Raffi Krikorian explains the architecture used by Twitter to deal with thousands of events per sec - tweets, social graph mutations, and direct messages-.
Guilherme Ottoni presents the design, implementation, and an evaluation of the HipHop compiler for PHP.
Jim Coplien believes that we have done OOP the wrong way for 40 years, and suggests an approach to reflection based on the DCI paradigm and influenced by the human society.
Chuck Rossi unveils some of the tools and processes used by Facebook for pushing new updates every day.
Leo A. Meyerovich explains how social adoption patterns can help language designers make new languages that are inherently attractive and desirable by developers.
David Mortenson details how Facebook maintained efficiency while increasing the number of engineers by reducing the n00b time sink, keeping development fast and avoiding unintended consequences.
Johan Oskarsson explains how Twitter is using Zipkin to trace a pages in order to see their execution path and to determine the time spent for loading for performance monitoring and analysis.
Craig Walls explains how Spring Social can be used to create social applications or connect to existing ones using their APIs.
Dmitriy Ryaboy shares some of the lessons learned scaling Twitter’s analytics infrastructure: Data loves a schema, Make data sources discoverable, and Make costs visible.
Nathan Marz introduces Twitter Storm, outlining its architecture and use cases, and takes a look at future features to be made available.
Dhruba Borthakur discusses the different types of data used by Facebook and how they are stored, including graph data, semi-OLTP data, immutable data for pictures, and Hadoop/Hive for analytics.