Google I / O 2011: App Engine fireside chat with the team Tube. Duration : 61.42 Mins.
Max Ross, Max, a software engineer on the App Engine team, where he led the development of data storage and occasionally tinkers with the Java runtime environment. He is also the founder of Hibernate Shards project. Alon Levi, Sean Lynch, Greg Dalesandre, Guido van Rossum, Brett Slatkin, Peter Magnusson, Mickey Kataria, Peter McKenzie fireside chat with the App Engine team
Little engine (s) to be drawn: Scaling online social networks Video Clips. Duration : 63.97 Mins.
Google Tech Talk (see below) June 17, 2010 by Josep M. Pujol. ABSTRACT The difficulty of separating the social graph has brought new challenges of designing systems for the scale of online social networks (OSN). The vertical scale for the use full replication can be an expensive proposition. Horizontal scalability by partitioning and distributing data across multiple servers using, for example key-value store with DHTs can suffer costly inter-server communication and provide differentTopics. These challenges are often led to costly re-architecting efforts to OSNs popular as Twitter and Facebook. We design, implement and evaluate SPAR, a socio-partitioning and replication middleware that mediates between application and database-level OSN. Spar used to partition the underlying social graph data structure, the user and selectively replicated to ensure users that their neighbors have user data on your machine co-located. The profits of these are multi-fold:Application developers can expect the local semantics, ie to develop, as for a single machine, scalability is achieved with the addition of commodity machines with low memory requirements and network I / O, and N + K redundancy reached at a fraction of the cost. We offer a complete design system, a comprehensive analysis of data from Twitter, Orkut and Facebook, and implementation work. We have shown that SPAR works well in terms of reducing overhead costs and dealing with high dynamic...
Google Tech Talk (see below) June 17, 2010 Presented by Josep M. Pujol. SUMMARY The social graph partitioning problem has new challenges of the planning system for the scale of online social networks (OSN). vertical scale for the use of full replication can be a costly affair. The scale can horizontally by partitioning and distributing data across multiple servers, using as key-value is stored using DHT suffer from communications between servers and expensive to provide differentArguments. Such challenges have often resulted in costly efforts to re-architecture for OSN popular as Twitter and Facebook. We design, implement and evaluate SPAR, partitioning, online replication and middleware that mediates between application and database layer of OSN. SPAR uses the underlying structure to guarantee their social graph to partition data and selectively replicate user that the user their neighbors, co-located data on your computer. The profits of these are multi-fold:Application developers can expect local semantics, namely the development as a single machine, scalability is achieved with the addition of commodity machines with low memory requirements and network I / O, and N + K redundancy becomes reached a fraction of the cost. We offer a complete design of the system, overall evaluation of records from Twitter, Facebook and Orkut, and an implementation of the work. We show that SPAR also leads in terms of reducing overhead costs and dealing with high dynamic...
