InfoQ Homepage Clustering & Caching Content on InfoQ
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Kubernetes: Crossing the Chasm
Ian Crosby covers the fundamental concepts and features of Kubernetes, best practices and anti-patterns running apps is such an environment, setting up a production ready Kubernetes cluster.
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Containers, Kubernetes and Google Cloud
Mete Atamel shows how to build a system, starting with a microservice, containerize it using Docker, and scale it to a cluster of resilient microservices managed by Kubernetes.
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Caching for Microservices - Introduction to Pivotal Cloud Cache
Pulkit Chandra discusses how to use Pivotal Cloud Cache and its performance under load, demoing a Spring Boot app which uses Spring Data Geode to talk to a Pivotal Cloud Cache cluster.
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In-Memory Caching: Curb Tail Latency with Pelikan
Yao Yue introduces Pelikan - a framework to implement distributed caches such as Memcached and Redis. She discusses the system aspects that are important to the performance of such services.
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Causal Consistency for Large Neo4j Clusters
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.
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Scaling Dropbox
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.
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In-Memory Caching: Curb Tail Latency with Pelikan
Yao Yue introduces Pelikan, a framework to implement distributed caches such as Memcached and Redis.
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Leadership Election with Spring Cloud Cluster
Dave Syer shows how Spring Cloud Cluster provides a simple abstraction for leader election and how it is implemented using Zookeeper, Hazelcast and etc.
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The Human Side of Microservices
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.
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Effortless Eventual Consistency with Weave Mesh
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.
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How Comcast Uses Data Science and ML to Improve the Customer Experience
Jan Neumann presents how Comcast uses machine learning and big data processing to facilitate search for users, for capacity planning, and predictive caching.
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How to Have Your Causality and Wall Clocks Too
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.
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