Flowable business processing from Kafka events
summary
Flowable directly ingests Kafka streams using a new event registry, channel adapters and inbound/outbound pipelines so modeled BPMN/CMMN elements start and correlate on real messages; the demo wires Spring Boot + Spring WebFlux + Kafka + Postgres and calls AWS Comprehend for sentiment, spawning one case per customer via a correlation hash and creating tasks for negative reviews. Race conditions use a triggerable service task (post-commit async send). The end-to-end demo runs and scales under simulated “frantic Friday” load.
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chapters (51)
- 0:00Introduction
- 0:22Who are you
- 0:51Agenda
- 1:46Facts and highlights
- 2:45Open standards
- 4:16Standalone
- 7:01Model centric
- 7:47Runtime overview
- 9:02Flowable architecture
- 10:52REST API
- 11:26Spring Boot Starter
- 12:53MyMan
- 16:09Eventdriven architectures
- 17:11Design environment
- 19:45Concepts
- 22:36Use case
- 23:04Customer reviews
- 23:26Customer case
- 24:14Bad review
- 24:27Architecture
- 24:55Demo
- 25:45Architecture overview
- 27:26Model overview
- 27:46Case model
- 28:08Review event
- 28:48Active subscription
- 30:17Handling support requests
- 30:43Starting the process
- 31:19Running the process
- 31:40Running the process in Kafka
- 32:19Review events
- 32:38Review sentiment analysis
- 33:42Review code
- 34:15Push Kafka architecture
- 34:26Spring Boot application
- 34:39Spring Wet Flux endpoint
- 35:04Customer demo
- 35:15Customer case application
- 35:27Dependency
- 36:56Kafka API
- 38:32Send Receive
- 38:57Race Condition
- 39:17Triggerable Service Tasks
- 40:01Making Kafka events reactive
- 40:45Calling AWS comprehend
- 41:08Application overview
- 41:52Cheating
- 42:14Sentiment
- 43:06Experiments
- 44:07Create task
- 44:23Send to Twitter
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