https://jaehyeon.me/tags/rserve/Recent content in RServe on Jaehyeon KimHugo -- gohugo.ioenCopyright © 2023-2026 Jaehyeon Kim. All Rights Reserved.Fri, 15 Nov 2019 00:00:00 +0000https://jaehyeon.me/blog/2019-11-15-task-queue/Fri, 15 Nov 2019 00:00:00 +0000https://jaehyeon.me/blog/2019-11-15-task-queue/While I’m looking into Apache Airflow, a workflow management tool, I thought it would be beneficial to get some understanding of how Celery works. To do so, I built a simple web service that sends tasks to Celery workers and collects the results from them. FastAPI is used for developing the web service and Redis is used for the message broker and result backend. During the development, I thought it would be possible to implement similar functionality in R with Rserve.https://jaehyeon.me/blog/2018-05-19-asyn-shiny-and-its-limitation/Sat, 19 May 2018 00:00:00 +0000https://jaehyeon.me/blog/2018-05-19-asyn-shiny-and-its-limitation/A Shiny app is served by one (single-threaded blocking) process by Open Source Shiny Server. This causes a scalability issue because all requests are handled one by one in a queue. Recently the creator of Shiny introduced the promises package, which brings asynchronous programming capabilities to R. This is a remarkable step forward to web development in R. In this post, it’ll be demonstrated how to implement the async feature of Shiny.https://jaehyeon.me/blog/2017-11-19-api-development-with-r-2/Sun, 19 Nov 2017 00:00:00 +0000https://jaehyeon.me/blog/2017-11-19-api-development-with-r-2/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. The rocker/r-ver:3.4 is used as the base image and each of the APIs is added to it. For simplicity, the APIs are served by Supervisor. For performance testing, Locust is used. The source of this post can be found in this GitHub repository.https://jaehyeon.me/blog/2017-11-18-api-development-with-r-1/Sat, 18 Nov 2017 00:00:00 +0000https://jaehyeon.me/blog/2017-11-18-api-development-with-r-1/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. Probably development would be made with plumber, Rserve, rApache or OpenCPU if a client or bridge layer to R is not considered. This is 2 part series in relation to API development with R. In this post, serving an R function with plumber, Rserve and rApache is discussed.