Server introduction#



To make this guide easier to follow, consider familiarizing yourself with some of the core concepts of Bokeh in the section Introduction.

Bokeh server makes it easy to create interactive web applications that connect front-end UI events to running Python code.

Bokeh creates high-level Python models, such as plots, ranges, axes, and glyphs, and then converts these objects to JSON to pass them to its client library, BokehJS. For more information on the latter, see Contributing to BokehJS.

This flexible and decoupled design offers some advantages. For instance, it is easy to have other languages, such as R or Scala, drive Bokeh plots and visualizations in the browser.

However, keeping these models in sync between the Python environment and the browser would provide further powerful capabilities:

  • respond to UI and tool events in the browser with computations or queries using the full power of Python

  • automatically push server-side updates to the UI elements such as widgets or plots in the browser

  • use periodic, timeout, and asynchronous callbacks to drive streaming updates

This is where the Bokeh server comes into play:

The primary purpose of the Bokeh server is to synchronize data between the underlying Python environment and the BokehJS library running in the browser.

Here’s a simple example from that illustrates this behavior:

Manipulating the UI controls communicates new values to the backend via Bokeh server. This also triggers callbacks that update the plots with the input in real time.

Use case scenarios#

Consider a few different scenarios when you might want to use the Bokeh server.

Local or individual use#

You might want to use the Bokeh server for exploratory data analysis, possibly in a Jupyter notebook, or for a small app that you and your colleagues can run locally.

The Bokeh server is very convenient here, allowing for quick and simple deployment through effective use of Bokeh server applications. For more detail, see Building applications.

Creating deployable applications#

You might also want to use the Bokeh server to publish interactive data visualizations and applications to a wider audience, say, on the internet or an internal company network. The Bokeh server also suits this usage well, but you might want to first consult the following:

Shared publishing#

Both of the scenarios above involve one person making applications on the server, either for personal use or for consumption by a larger audience.

While it is possible for several people to publish different applications to the same server, this does not make for a good use case because hosted applications can execute arbitrary Python code. This raises process isolation and security concerns and makes this kind of shared tenancy prohibitive.

One way to support this kind of multi-application environment with multiple users is to build up infrastructure that can run a Bokeh server for each app or at least for each user. The Bokeh project or a third party might create a public service for this kind of usage in the future but such developments are beyond the scope this documentation.

Another possibility is to have one app that can access data and other artifacts published by many different people, possibly with access controls. This sort of scenario is possible with the Bokeh server, but often involves integrating it with other web application frameworks.