Jim Webber explores the new causal clustering architecture for Neo4j, detailing how Neo4j uses the Raft protocol for a robust underlay for intensive write operations and how the asynchronous new scale-out mechanism provides enormous capacity for very demanding graph workloads. Jim focuses on the cluster architecture’s new causal consistency model—a big leap forward compared to the commonplace eventual consistency—which makes it convenient to write applications that use the full capacity of the cluster. In particular, he demonstrates that despite the mixture of consensus protocols and asynchronous replication, Neo4j allows users to read their own writes straightforwardly. (He also explains why this is such a difficult achievement in distributed systems.) You’ll also learn how Neo4j’s causal clustering optimized drivers make it easy to write applications that scale smoothly from a single server to a large distributed cluster.
Jim Webber is chief scientist at Neo Technology, where he works on next-generation solutions for massively scaling graph data. Previously, Jim was a professional services director with ThoughtWorks, where he worked on large-scale computing systems in finance and telecoms. Jim holds a PhD in computing science from Newcastle University in the UK.
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