Batch Processing and Integration on Cloud Foundry
summary
Shows how Spring Batch and Spring Integration run on Cloud Foundry worker processes using RabbitMQ for routing and AMQP. Builds a stock‑lookup pipeline: a JDBC cursor reader pulls ~5 tickers, an item processor calls web APIs, a JDBC batch writer stores results, scheduled every 10 seconds. Uses a manifest and Maven plugin, binds a RabbitMQ service, deploys a web client and a worker, inspects Spring Batch tables via MC tunnel, and scales by adding instances — the demo runs end‑to‑end.
AI-written summary · about
chapters (23)
- 0:00Introduction to Josh Long
- 1:30Cloud Foundry overview
- 2:36Worker processes and manifests
- 6:22RabbitMQ and AMQP basics
- 11:48AMQP versus JMS
- 13:55RabbitMQ features and routing
- 17:36Cloud messaging comparisons
- 21:15Spring JMS implementation
- 22:50RabbitMQ exchanges and routing
- 25:53Spring AMQP implementation
- 29:22Cloud-native messaging architecture
- 32:52Introduction to Spring Batch
- 37:36Batch processing components
- 43:27Batch scaling and partitioning
- 48:03Demo: Cloud batch processing
- 52:16Configuring batch data sources
- 58:20Batch job infrastructure setup
- 1:02:00Demo: Advanced batch reader/writer
- 1:11:17Spring Batch scoping
- 1:15:11Demo: Running the cloud batch job
- 1:18:33Enterprise application integration
- 1:22:03Spring Integration implementation
- 1:25:00Conclusion and Q&A
related talks
32mCase Study of Batch Processing With Spring Cloud Data Flow Server in Cloud Foundry
01h 31mRunning Your Spring Apps in the Cloud
50mMastering Challenges of Cloud-Native Architectures With Spring
30mBanking on Cloud Foundry Platform: A Case Study of Digital Business Banking Applications
19mThe Taming of the Queue
26mMulti-Cloud Native Data with Spring and VMware Data Solutions
01h 09mContinuous Deployment to the Cloud
09mRunning Java and Spring on Azure with Cloud Foundry