Cameron Purdy explains how a data grid functions by using a partition topology for data access, update, recovery and local storage, accessing data using read/write-through and write behind, and invoking operations through Observable, QueryMap and InvocableMap interfaces. He also offers some examples of data grids solving complex problems and introduces Coherence, Oracle’s data grid solution.
In this presentation, Cameron Purdy discusses Java scaling. Topics include performance improvement versus scaling improvement, serial bottlenecks, queue theory, rewriting existing frameworks, avoiding the database, single points of failure, avoiding abstractions, disaster recovery, one-size-fits-all architecture, large JVM heaps, network failures, and trusting product claims.
In this presentation, recorded at Javapolis, Cameron Purdy shows how to improve application performance & scalability via caching architectures to reduce load on the database tier and & clustered caching to provide transparent fail-over by reliably sharing live data among clustered JVMs.
What is Data Grid computing? What makes it different from a database? Is a data grid always scalable? Is the cloud the next step? Cameron Purdy answered these questions and others during an InfoQ interview, and also gave some hints on how to build scalable grids and how to avoid horror stories.
