This talk presents Hazelcast, an open-source distributed Java in-memory container that allows multiple processes to share data using standard Java APIs such as Maps, Sets and Lists.
Ines Sombre and Caitie McCaffrey offer a guided tour of papers from past and present research that have reshaped the way we think about building large scale distributed systems.
Anil Madhavapeddy introduces the Irmin library by means of a functional queue, shows how the Git mirroring works, and then demonstrates some more complex applications.
Natalia Chechina outlines features of actor and functional programming models, and the reason these models attract so much interest in parallel, concurrent, and scaling world.
Crista Lopes demos writing the same program using multiple styles, showcasing the richness of human computational thought and the need to avoid being stuck with one or two styles for life.
Matt Heath discusses how circuit breakers and other similar patterns can be used to increase reliability in distributed systems such as Go-based microservice platforms.
Vaclav Petricek discusses how to train models, architect and build a scalable system powered by Storm, Hadoop, Spark, Spring Boot and Vowpal Wabbit that meets SLAs measured in tens of milliseconds.
Michael Brunton-Spall shows how DevOps-like patterns can be applied on microservices to give the development teams more responsibility for their choices, and much more.
Diptanu Choudhury discusses the design of Netflix’ distributed scheduler based on Mesos and Titan, focusing on bin packing algorithms, scaling in and out of clusters, fault tolerance, and redundancy.
Small sessions on: Deterministic testing in a non-deterministic world. Hash Spreads and Probe Functions. Typesafe Config on Steroids. Real-Time Distributed Event-Driven Computing at Credit Suisse.
Benjamin Hindman discusses Apache Mesos, focusing on the Mesos API and how the primitives provided by Mesos can make it easier to build new stateful services and frameworks.
Tal Weiss explores five crucial Java techniques for distributed debugging and some of the pitfalls that make bug resolution much harder, and can even lead to downtime.