Building Highly-Scalable Spring Applications with In-Memory, Distributed Data Grids
summary
Shows a working stack: Apache Geode / Pivotal GemFire as the in‑memory distributed store, Spring Boot + Spring Data GemFire for clients and wiring, and PDX serialization plus write‑through to a Derby DB. Demo runs clusters (example: 10 members → 8 servers + 2 locators, 240 GB, 10k entries), client app pushes 25 transactions every 5s, functions run on servers to return aggregated scores, node failure triggers automatic rebalancing, and low‑stock events push websocket alerts — everything operates end to end.
AI-written summary · about
chapters (8)
- 0:00Introduction
- 1:31Apache Geode architecture
- 6:47Geode internals and features
- 21:20Spring Data GemFire overview
- 31:08Basic Geode configuration demo
- 36:47Comprehensive demo application
- 1:07:51Caching with Spring
- 1:17:27New features and conclusion
related talks
01h 25mGetting started with Spring Data and Distributed Database Grids
48mScaling Spring Boot Applications in Real-Time - John Blum, Luke Shannon
01h 23mCase Study: Provisioning a Multi-Site In-Memory Database
45mSimplifying Apache Geode with Spring Data - John Blum
28mThe Past Year in Spring for Apache Geode
59mDistributed applications using Hazelcast
26mMulti-Cloud Native Data with Spring and VMware Data Solutions
05mStartup Showcase: Hazelcast