I think every architect or developer knows about the consumer and producer pattern. This pattern is used to create jobs that can be processed in the background asynchronously. In that context the Producer creates the jobs that must be processed by the Consumer. To store the job description, the Producer typically uses a message queue. In a Cloud environment there are a lot of different Q techniques available. RabbitMQ, Redis or Azure ServiceBus Queue to name just a few. Normally as a architect or developer you choose one technique and use the appropriate integration library in your code. You have to know how the integration library works and you have to ensure that the library is available for your development platform. With a change to another Q technology, the code must also always be adapted. Getting used to an integration library can sometimes be hard and requires additional work for your developers.
dapr is an event-driven, portable runtime for building microservices on cloud and edge. dapr changes the way how you build event-driven microservices.
In dapr you can use output and input bindings to send message to and receive messages from a queue. When you decide on a queue technique like Redis, RabbitMQ or Azure ServiceBus Queues, you usually have to use integration libraries for binding in your code.With dapr you can integrate input and output bindings on a higher abstraction level and you don’t need to know how the integration library works.
With dapr you can integrate queues independent from the underlying technology. But how about scaling. Sometimes you want to scale out the consumer depending on the number of messages in the queue. To achieve this you can use KEDA in Kubernetes.
„KEDA allows for fine grained autoscaling (including to/from zero) for event driven Kubernetes workloads. KEDA serves as a Kubernetes Metrics Server and allows users to define autoscaling rules using a dedicated Kubernetes custom resource definition.„
See it in action
To see Dapr and KEDA in action I have created a GitHub repository that guides you through setting up the described architecture above.