Joshua Suereth designs a scalable distributed search service with Akka and Scala using actors, and covering practical aspects of how to scale out with Akka’s clustering API.
Michael Hausenblas introduces Apache Drill, a distributed system for interactive analysis of large-scale datasets, including its architecture and typical use cases.
Peter Boros discusses a MySQL architecture useful for the majority of projects, backup, online schema changes, reliability and scalability issues, and basics of sharding.
Marton Anka shares lessons learned and technical details scaling LogMeIn over a decade.
Sid Anand uses examples from LinkedIn, Netflix, and eBay to discuss some common causes of outages and scaling issues. He also discusses modern practices in availability and scaling in web sites today.
Jesper Richter-Reichhelm shares lessons learned from failures while scaling Wooga games to millions of daily users.
Robin Johnson discusses using a data management model for games that can be scaled, and the bottlenecks and challenges met by OMGPOP scaling to millions of users.
Rajiv Eranki shares the pains and lessons learned scaling up Dropbox from a few thousands to tens of millions of users.
Theo Schlossnagle keynotes on the role of open source software and the breaking up of silos in the enterprise in creating scalable systems.
Jeremy Edberg shares some of the lessons learned scaling Reddit, advising on pitfalls to avoid.
Marc Pacheco tells how Songkick made radical changes to increase the performance of the site while retaining a productive development team.
Jay Kreps discusses the evolution of LinkedIn's architecture and lessons learned scaling from a monolithic application to a distributed set of services, from one database to distributed data stores.