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Mixing Causal Consistency and Asynchronous Replication for Large Neo4j Clusters

YOW! 2017 Sydney

In this talk we’ll explore the new Causal clustering architecture for Neo4j. We’ll see 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.

We’ll discuss the cluster architecture’s new causal consistency model. Causal consistency is 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 we’ll show how despite the mixture of concensus protocols and asynchronous replication, that Neo4j allows users to read their own writes straightforwardly and discuss why this is such a difficult achievement in distributed systems.

For the application developer, we’ll show how Neo4j’s Causal Clustering optimised drivers makes it easy to write applications that scale smoothly from a single server to a large, distributed cluster: a practical motivation for the distributed systems enthusiast.

Jim Webber

Chief Scientist


United Kingdom

Dr. Jim Webber is Chief Scientist at the popular open source graph database Neo4j, where he where he works on R&D for highly scalable graph databases and writes open source software. Jim has written two books on integration and distributed systems: “Developing Enterprise Web Services” on XML Web Services and “REST in Practice” on using the Web for building large-scale systems. His latest book is “Graph Databases” which focuses on the Neo4j database.