We describe an application of CEP using a microservice-based streaming architecture. We use Drools business rule engine to apply rules in real time to an event stream from IoT traffic sensor data.
It’s just not true ML solves all problems. ML seeks to make predictions, which is very useful. But most business processes don’t need prediction every step of the way, they are rather more like a series of steps with conditionals arranged in a DAG
Rules need to be:
Independent
Easily Updated (Add, Change, Delete)
Rules apply to only minimum set of relevant data
Allow business domain experts to contribute
Integrate Flink/Spark Streaming with DroolsPerformance and Scalability TestingFlink brings “for free” lots of benefits:State is saved automatically by checkpointsFault-recovery for Drools state is simplifiedRecord-at-a-time processing is a good model to add data to KieSession