Performance is an inevitable concern for all successful applications. With success comes greater adoption. This widespread adoption leads to issues from lack of processing power and memory to network failures. Questions begin circulating like “Why is your app so slow?” or “How do you scale Shiny to support more users?”. Scaling at the infrastructure level can be a cost-friendly option to help alleviate problems with your Shiny app’s performance.
At Appsilon, we’ve developed Shiny enterprise apps that scale for hundreds of concurrent users. You can throw money at options like improving memory to existing servers, or adding more servers. And in some cases, these options are the correct route to take. However, there are other areas of improvement if you focus efforts on the Shiny application itself.
Want to learn more? Watch our video on how we scaled a Shiny App to hundreds users.
In the presentation below, Pedro Silva discusses Appsilon’s approach to performance on an infrastructure level. Although the talk is geared towards developers with a higher skill level, Pedro’s talking points are valuable to all devs looking to create a faster, successful app.
For a Data Scientist, Shiny can be an amazing tool for creating fast and powerful prototypes and dashboards. But what to do when your application becomes TOO popular and more and more people want to use it?
As the number of users grows, keeping up with the demand of a Shiny application can be tricky, and there is only so much you can do to improve performance at the code level.
This presentation gives an overview of our custom approach to improving Shiny dashboard performance on an infrastructure level. It also includes tips for scaling Shiny dashboards to hundreds of concurrent users while keeping your budget under control.
Need help scaling with RStudio Connect? Appsilon is a proud RStudio Full Service Certified Partner.
Appsilon provides innovative data analytics, machine learning, and managed services solutions for Fortune 500 companies, NGOs, and non-profit organizations. We deliver the world’s most advanced R Shiny applications, with a unique ability to rapidly develop and scale enterprise Shiny dashboards. Our proprietary machine learning frameworks allow us to deliver Computer Vision, NLP, and fraud detection prototypes in as little as one week.
Appsilon Tech Team Members regularly contribute to open-source packages. This is part of our commitment to positively impact the world through technology. Consider dropping a star on your favorite shiny packages at our Github to let us know we’re on the right track.
Do you have any comments or questions? Swing by our feedback threads, like the discussion at our new shiny.fluent package. We love to hear from the R community. If you spot an issue or room for improvement, don’t hesitate to send a pull request.