Peter Bourgon and Matthias Radestock explain the theory behind Weave Mesh, some of the important key features, and demonstrate some exciting use cases, like distributed caching and state replication.
Alan Ngai and Premal Shah discuss best practices on monitoring distributed real-time data processing frameworks and how DevOps can gain control and visibility over these data pipelines.
Peter Bourgon presents some of the idioms, design patterns, and practices that have proven themselves developing successful, scalable, and sustainable code using Go.
Brian Goetz explores tools and techniques involved in parallelism, and how to analyze a computation for potential parallelism, with specific attention to the parallel stream library in Java 8.
Neha Narkhede explains how Apache Kafka was designed to support capturing and processing distributed data streams by building up the basic primitives needed for a stream processing system.
Stefan Xenos and Sergey Prigogin present how the JDT new index was made to be an order of magnitude faster than what it was before.
Jim Sproch describes how reconciliation works within React, and how to use it to enhance both performance and user experience.
Jamshid Mahdavi explains how WhatsApp has developed their server components, the deployment processes, and how they monitor, alert, and repair the inevitable failures in a billion-users service.
Luca Marturana covers the current state of the art for container monitoring and visibility, including real use cases with pros/cons of each and focuses on advanced container visibility techniques.
David Riddoch talks about the technologies that make high performance networking possible on commodity servers, with a special focus on direct access to the network adapter by bypassing the kernel.
Runar Bjarnason presents how to get started with the Scalaz-Stream library, shows some examples, and how we can combine functional streams into large distributed systems.
Manuel Fahndrich describes how they tackled one particular resource allocation aspect of Google Cloud Dataflow pipelines - horizontal scaling of worker pools as a function of pipeline input rate.