Martijn Verburg discusses his new start-up jClarity, which offers performance tooling for the Cloud. He also provides an update on the Adopt a JSR and Adopt OpenJDK programs.
Jim Hirschauer describes the application monitoring tool landscape, KPIs and metrics to consider when monitoring, and compares monitoring traditional vs. cloud-based applications. He talks about performance considerations when instrumenting code, how organizations can be 'Smarter' about their Big Data, and looks at what's new in AppDynamics 3.7.
In this InfoQ interview, Michael Nygard explores some of the available loopholes in the CAP theorem helping architects to engineer distributed systems that meet their needs. He also discusses new patterns he’s observed since his book, Realease IT and shares his thoughts on continuous delivery, DevOps and ALM.
Ken Little talks about scaling Tumblr to keep up with their blogging users: scaling the data model, sharding, their PHP frontend and the Scala backend, and much more.
Serkan Piantino explains how Facebook has managed to scale up, what types of errors occur in an architecture that size and how to handle them, RAM vs disk, and much more.
Rich Hickey and Justin Sheehy talk about scalability and transactionability of datastores. They explain tradeoffs for achieving read and/or write scalability on top of Datomic and Riak.
Hive co-creator Ashish Thusoo describes the Big Data challenges Facebook faced and presents solutions in 2 areas: Reduction in the data footprint and CPU utilization. Generating 300 to 400 terabytes per day, they store RC files as blocks, but store as columns within a block to get better compression. He also talks about the current Big Data ecosystem and trends for companies going forward.
Bryan talks about the challenges of operating Node.js in real production environments and the experiences he had working with it at Joyent. He also talks about DTrace, SmartOS, V8 and compares with other platforms.
Attila Szegedi talks about performance tuning Java and Scala programs at Twitter: how to approach GC problems, the importance of asynchronous I/O, when to use MySQL/Cassandra/Redis, and much more.
John Nolan shows the state of hardware acceleration with GPUs and FPGAs, why it's hard to write efficient code for them, and why to favor polymorphism over if statements for performance.