David Greenberg discusses how Two Sigma was able to scale up their research to harness tens of thousands of CPUs and the challenges faced.
Matt Ranney talks about Uber’s growth and how they’ve embraced microservices. This has led to an explosion of new services, crossing over 1,000 production services in early March 2016.
Tony Grout and Chris Matts share stories from their several year multi-company journey towards scaled agile, showing how to look at Agile from an organizational perspective and not through tools.
Matt Warren takes a look at how to measure, what to measure and how get the best performance from .NET code, considering examples from the Roslyn codebase and StackOverflow (the product).
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.