Building Highly-Scalable Spring Applications with In-Memory, Distributed Data Grids

Recorded at SpringOne2GX 2015. Speakers: John Blum & Luke Shannon Data/Integration Track Slides: http://www.slideshare.net/SpringCentr... Building highly scalable, low latency applications while simultaneously preserving consistency, high availability and resiliency requires a new breed of technology. In this presentation we introduce Pivotal GemFire along with the open source offering, Apache Geode. Apache Geode is a proven, distributed, in-memory database with ACID properties that can handle large volumes of transactional data under heavy load. Apache Geode gives Spring-based applications the edge they require as demand changes without sacrificing integrity or the end-user's experience. Using Spring Boot and Spring Data GemFire, we demonstrate how to effectively build highly scalable applications with Pivotal GemFire/Apache Geode starting with configuration and setup, then moving into persisting and accessing data with Spring Data Commons Repositories, OQL with proper Indexing, and Spring Data GemFire's annotation-based, data-aware Function executions, based on the familiar Map-Reduce pattern of bringing business logic to your data. Next, we expand on these fundamental, foundational features with advanced topics on Partitioning, Collocation, Write-Through/Write-Behind, Register Interests/CQs, PDX and Cache as well as Global, JTA-based Transactions. Finally, we show how Pivotal GemFire/Apache Geode can be used to seamlessly address other application concerns from caching with Spring's core Cache Abstraction to session management using Spring Session. And most importantly, we cover techniques for properly testing applications built on Pivotal GemFire along with techniques to manage and monitor your cluster. By sessions end, attendees should feel comfortable building highly-scalable applications effectively and productively.