It proved its power, and that’s why it is the tool of choice for many data scientists. R language was created to make data analysis faster. It was designed for convenient data analysis, not for webapps. JavaScript promises are different thanks to the event loop mechanism, which features fully asynchronous I/O and worker threads. Also, a promises package can be used to improve responsiveness, but it is still fundamentally different than promises in JavaScript. You have to use workarounds to provide it.įor example, you can do this by serving multiple instances of the app.
Multithreading allows for application responsiveness, and by design, R doesn’t. This means all users connected to one R process will block each other. Why is scaling Shiny tricky? R is single-threaded Regardless of this, the main aspect you should focus on how you serve the application. There are many ways to optimize a Shiny app like using promises for non-blocking access, profvis for finding bottlenecks, and shiny modules for well-structured code, etc. From that point this app becomes a tool used on production by many people, that should be reliable and work fast for many concurrent users.
When a data science team creates a Shiny app, sometimes it becomes very popular. Shiny is a great tool for fast prototyping.
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