In this post, I'll illustrate how a web service is created using FastAPI framework where tasks are sent to multiple workers. The workers are built with Celery and Rserve. Redis is used as a message broker/result backend for Celery and a key-value store for Rserve. Demos can be run in both Docker Compose and Kubernetes.

In this post, it'll be demonstrated how to implement the async feature of Shiny. Then its limitation will be discussed with an alternative app, which is built by JavaScript for the frontend and RServe for the backend.

In part I, it is discussed how to serve an R function with plumber, Rserve and rApache. In this post, the APIs are deployed in a Docker container and, after showing example requests, their performance is compared.

API is an effective way of distributing analysis outputs to external clients. When it comes to API development with R, however, there are not many choices. In this post, serving an R function with plumber, Rserve and rApache is discussed.