The performance of the app will largely depend on the analytic code that you are running inside the Dash callback functions. A few things to note:
- The dash docs (https://plot.ly/dash) are itself a Dash app. They're getting a lot of traffic today with the launch and they're holding up OK to hundreds of active users.
- The state of Dash apps is entirely in the front-end (in JS in the web browser). The Dash app backend (the python part) is really lightweight - it's a flask server (that you run with an application server like gunicorn) that dispatches to the functions that you decorate. If your functions are really resource-intensive (in memory or if they block for a long period of time), then the app's performance will suffer as part of that. However, since this analytic code is scoped inside a function, the memory will free up after the request is done.
- Since Dash's callbacks are functional, you can pretty easily add caching. Caching will store the previously computed values and serve them if the input arguments are the same. There is some more info in the "performance" section of the docs: https://plot.ly/dash/performance
The state of Dash apps is entirely in the front-end
Cool! I develop APIs using Flask so that's good news for me. I want to keep the API as lean as possible. I also try to cache everything I can or simply render stuff into static data if possible. I.e., some data are updated on a regular basis but do not change so rapidly and thus I can just use a Jinja2 template with some {% raw %} blocks to render my jinja2 templates with much less stuff to do, or even static html when I can.
- The dash docs (https://plot.ly/dash) are itself a Dash app. They're getting a lot of traffic today with the launch and they're holding up OK to hundreds of active users.
- The state of Dash apps is entirely in the front-end (in JS in the web browser). The Dash app backend (the python part) is really lightweight - it's a flask server (that you run with an application server like gunicorn) that dispatches to the functions that you decorate. If your functions are really resource-intensive (in memory or if they block for a long period of time), then the app's performance will suffer as part of that. However, since this analytic code is scoped inside a function, the memory will free up after the request is done.
- Since Dash's callbacks are functional, you can pretty easily add caching. Caching will store the previously computed values and serve them if the input arguments are the same. There is some more info in the "performance" section of the docs: https://plot.ly/dash/performance