Jim Webber explores the new Causal clustering architecture for Neo4j, how it allows users to read writes straightforwardly, explaining why this is difficult to achieve in distributed systems.
Preslav Le talks about how Dropbox’s infrastructure evolved over the years, how it looks today, as well the challenges and lessons learned on the way.
Yao Yue introduces Pelikan, a framework to implement distributed caches such as Memcached and Redis.
Dave Syer shows how Spring Cloud Cluster provides a simple abstraction for leader election and how it is implemented using Zookeeper, Hazelcast and etc.
John Billings talks about winning over those skeptical about the benefits of microservices along with tips on caching, failure, interface changes, etc. for building a distributed system architecture.
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.
Jan Neumann presents how Comcast uses machine learning and big data processing to facilitate search for users, for capacity planning, and predictive caching.
Jon Moore talks about distributed monotonic clocks (DMC) whose timestamps can reflect causality but which have a component that stays close to wall clock time.
Yongsheng Wu talks about how to build highly-resilient systems at scale. Wu presents also failure cases that prompted engineers at Pinterest to build such systems, and how they test these systems.
Chris Dennis and Alex Snaps discuss introducing caching into a Spring application to solve real world problems.
Jason McCreary takes a look at using background job processes, messaging queues, and cache to help an application scale.
Jessie Frazelle talks about which customer cases drove Docker clustering and describes the key technical decisions and code in the implementation.