Spring XD today and tomorrow
summary
Overview of what Spring XD adds and where it’s going: production XD 1.x gains native Kafka integration (benchmarked to millions of messages/sec), Reactor/RxJava functional stream processing, Spark connectors for in-memory batch and micro-batch streams, and Python model execution via shell processors for live inference. New Flo UI provides drag‑drop stream authoring and metrics. Runtime shifts to a cloud‑native model: modules as Spring Boot microservices and a deployer SPI targeting Lattice/YARN/Cloud Foundry. Demos show partitioning (odds/evens), Spark word‑count, and working Python inference (with a few live-demo glitches).
AI-written summary · about
chapters (13)
- 0:00Introduction to Spring XD
- 1:12Spring XD 1.0 features
- 4:25Flo UI for stream authoring
- 13:55Python integration in streams
- 18:41Apache Kafka deep dive
- 21:38Kafka demo with partitions
- 31:25Reactive processing with RxJava
- 36:34Apache Spark integration
- 42:10Spring Cloud Data Flow introduction
- 49:58Local Lattice setup demo
- 55:28Spring Cloud Stream programming model
- 1:00:07Lattice deployment on AWS
- 1:05:12Project resources and Q&A
related talks
53mSpring and Messaging: The next development generation
01h 06mOrchestrating Data Microservices with Spring Cloud Data Flow - Mark Pollack
01h 12mCloud Event Driven Architectures with Spring Cloud Stream 2.0
43mEvent Streams & Data Rapids: Harnessing the Full Power of Messaging Platforms.
45mWhy Spring Belongs In Your Data Stream (From Edge to Multi Cloud)
49mReactive Spring
50mWalking Through Spring Cloud Data Flow
49mDDD Strategic Design with Spring Boot