Building applications#

By far the most flexible way to create interactive data visualizations with the Bokeh server is to create Bokeh applications and serve them with the bokeh serve command. The Bokeh server then uses the application code to create sessions and documents for all connecting browsers.


The Bokeh server (left) uses the application code to create Bokeh documents. Every new connection from a browser (right) results in the server creating a new document just for that session.#

The Bokeh server executes the application code with every new connection and creates a new Bokeh document, syncing it to the browser. The application code also sets up the callbacks that should run whenever properties, such as widget values, change.

You can provide the application code in several ways.

Single module format#

Consider the following complete example.


from random import random

from bokeh.layouts import column
from bokeh.models import Button
from bokeh.palettes import RdYlBu3
from bokeh.plotting import figure, curdoc

# create a plot and style its properties
p = figure(x_range=(0, 100), y_range=(0, 100), toolbar_location=None)
p.border_fill_color = 'black'
p.background_fill_color = 'black'
p.outline_line_color = None
p.grid.grid_line_color = None

# add a text renderer to the plot (no data yet)
r = p.text(x=[], y=[], text=[], text_color=[], text_font_size="26px",
           text_baseline="middle", text_align="center")

i = 0

ds = r.data_source

# create a callback that adds a number in a random location
def callback():
    global i

    # BEST PRACTICE --- update .data in one step with a new dict
    new_data = dict()
    new_data['x'] =['x'] + [random()*70 + 15]
    new_data['y'] =['y'] + [random()*70 + 15]
    new_data['text_color'] =['text_color'] + [RdYlBu3[i%3]]
    new_data['text'] =['text'] + [str(i)] = new_data

    i = i + 1

# add a button widget and configure with the call back
button = Button(label="Press Me")
button.on_event('button_click', callback)

# put the button and plot in a layout and add to the document
curdoc().add_root(column(button, p))

The code above doesn’t specify any output or connection method. It is a simple script that creates and updates objects. The bokeh command line tool lets you specify output options after processing your data. You could, for example, run bokeh json to get a JSON-serialized version of the app. However, to run the app on a Bokeh server, use the following command:

bokeh serve --show

The --show option will cause your default browser to open a new tab at the address of the running application, which in this case is:


If you have only one application, the server root will redirect to it. Otherwise, you will see an index of all applications running on the server root:


You can disable this index with the --disable-index option. Likewise, you can disable redirecting with the --disable-index-redirect option.

In addition to creating Bokeh applications from single Python files, you can also create applications from directories.

Directory format#

You can create Bokeh apps by creating and populating a filesystem directory with application files. To start an application in a directory named myapp, you could execute bokeh serve as follows:

bokeh serve --show myapp

This directory must contain a file that constructs a document for the Bokeh server to serve:


The following is the directory app structure that the Bokeh server is familiar with:


Some of the files and subdirectories above are optional.

  • An file that marks this directory as a package. You can make imports relative to the package, such as from . import mymod and from .mymod import func.

  • A file that lets you declare an optional function to process HTTP requests and return a dictionary of items that the session token includes as described in Request handler hooks.

  • A file that lets you trigger optional callbacks at different stages of application execution as described in Lifecycle hooks and Request handler hooks.

  • A static subdirectory that you can use to serve static resources associated with this application.

  • A theme.yaml file where you can declare default attributes for Bokeh to apply to model types.

  • A templates subdirectory with an index.html Jinja template file. The directory may contain additional Jinja templates for index.html to refer to. The template should have the same parameters as the FILE template. For more information, see Customizing the application’s Jinja template.

When executing your, the Bokeh server ensures that the standard __file__ module attribute works as you would expect. So you can include data files or custom user-defined models in your directory however you like.

Bokeh also adds the application directory sys.path to facilitate importing of Python modules in the application directory. However, if an is in the directory, you can use the app as a package as well as make standard package-relative imports.

Here’s an example of a more developed directory tree:

   |    +---things.csv
   |    +---custom.js
   |    +---css
   |    |    +---special.css
   |    |
   |    +---images
   |    |    +---foo.png
   |    |    +---bar.png
   |    |
   |    +---js
   |        +---special.js
   |    +---index.html

In this case, your code might be similar to the following:

from os.path import dirname, join
from .helpers import load_data

load_data(join(dirname(__file__), 'data', 'things.csv'))

The code to load a JavaScript implementation for a custom model from models/custom.js is also similar.

Customizing the application’s Jinja template#

The Directory format section mentions that you can override the default Jinja template, which the Bokeh server uses to generate user-facing HTML.

This lets you use CSS and JavaScript to tweak the way the application appears in the browser.

For more details on how Jinja templating works, see the Jinja project documentation.

Embedding figures in the template#

To reference a Bokeh figure in the templated code, you need to set its name attribute and add the figure to the current document root in the main thread of your Bokeh app, that is

from bokeh.plotting import curdoc

# templates can refer to a configured name value
plot = figure(name="bokeh_jinja_figure")


You can then use that name in the corresponding Jinja template to reference the figure via the roots template parameter as follows:

{% extends base %}

{% block contents %}
    {{ embed(roots.bokeh_jinja_figure) }}
{% endblock %}

Defining custom variables#

You can pass custom variables to the template with the curdoc().template_variables dictionary as follows:

# set a new single key/value pair
curdoc().template_variables["user_id"] = user_id

# or update multiple pairs at once
curdoc().template_variables.update(first_name="Mary", last_name="Jones")

You can then reference the variables in the corresponding Jinja template.

{% extends base %}

{% block contents %}
    <p> Hello {{ user_id }}, AKA '{{ last_name }}, {{ first_name }}'! </p>
{% endblock %}

Accessing HTTP requests#

When creating a session for an application, Bokeh makes the session context available as curdoc().session_context. The most useful function of the session context is to make the Tornado HTTP request object available to the application as session_context.request. HTTP requests are not available directly because of an incompatibility with --num-procs. Instead, only the arguments attribute is available in full and only a subset of cookies and headers allowed by the --include-headers, --exclude-headers, --include-cookies, and --exclude-cookies parameters is available. Attempting to access any other attribute on a request results in an error.

You can enable additional request attributes as described in Request handler hooks.

The following code accesses the request arguments to provide a value for the variable N that could, for example, control the number of plot points.

# request.arguments is a dict that maps argument names to lists of strings,
# for example, the query string ?N=10 results in {'N': [b'10']}

args = curdoc().session_context.request.arguments

  N = int(args.get('N')[0])
  N = 200


The request object makes inspecting values, such as arguments, easy. However, calling any of the Tornado methods, such as finish(), or writing directly to request.connection is unsupported and results in undefined behavior.

Request handler hooks#

To provide additional information where full Tornado HTTP requests may not be available, you can define a custom handler hook.

To do so, create an app in directory format and include a file called in the directory. This file must include a process_request function.

def process_request(request):
    '''If present, this function executes when an HTTP request arrives.'''
    return {}

The process then passes Tornado HTTP requests to the handler, which returns a dictionary for curdoc().session_context.token_payload. This lets you work around some of the --num-procs issues and provide additional information.

Callbacks and events#

Before jumping into callbacks and events specifically in the context of the Bokeh server, it’s worth discussing different use cases for callbacks in general.

JavaScript callbacks in the browser#

Whether you are using the Bokeh server or not, you can create callbacks that execute in the browser with CustomJS and other methods. For more information and examples, see JavaScript callbacks.

CustomJS callbacks never execute Python code, not even if you convert a Python callback into JavaScript. CustomJS callbacks only execute inside the browser’s JavaScript interpreter, which means that they can only interact with JavaScript data and functions, such as BokehJS models.

Python callbacks with Jupyter interactors#

When working with Jupyter notebooks, you can use Jupyter interactors to quickly create simple GUI forms. Updates to GUI widgets trigger Python callbacks that execute in the Python kernel of Jupyter. It is often useful to have these callbacks call push_notebook() to push updates to displayed plots. For more information, see Jupyter interactors.


You can push plot updates from Python to BokehJS with push_notebook(). For two-way communication, embed a Bokeh server in the notebook. For example, this lets range and selection updates trigger Python callbacks. For further details, see examples/howto/server_embed/notebook_embed.ipynb

Updating from threads#

You can make blocking computations in separate threads. However, you must schedule document updates via a next tick callback. This callback executes as soon as possible with the next iteration of the Tornado event loop and automatically acquires necessary locks to safely update the document state.


The ONLY safe operations to perform on a document from a different thread are add_next_tick_callback() and remove_next_tick_callback()

Remember, direct updates to the document state issuing from another thread, whether through other document methods or setting of Bokeh model properties, risk data and protocol corruption.

To allow all threads access to the same document, save a local copy of curdoc(). The example below illustrates this process.

import time
from functools import partial
from random import random
from threading import Thread

from bokeh.models import ColumnDataSource
from bokeh.plotting import curdoc, figure

# only modify from a Bokeh session callback
source = ColumnDataSource(data=dict(x=[0], y=[0]))

# This is important! Save curdoc() to make sure all threads
# see the same document.
doc = curdoc()

async def update(x, y):[x], y=[y]))

def blocking_task():
    while True:
        # do some blocking computation
        x, y = random(), random()

        # but update the document from a callback
        doc.add_next_tick_callback(partial(update, x=x, y=y))

p = figure(x_range=[0, 1], y_range=[0,1])
l ='x', y='y', source=source)


thread = Thread(target=blocking_task)

To see this example in action, save the above code to a Python file, for example,, and then execute the following command:

bokeh serve --show


There is currently no locking around adding next tick callbacks to documents. Bokeh should have a more fine-grained locking for callback methods in the future, but for now it is best to have each thread add no more than one callback to the document.

Updating from unlocked callbacks#

Normally Bokeh session callbacks recursively lock the document until all future work they initiate is completed. However, you may want to drive blocking computations from callbacks using Tornado’s ThreadPoolExecutor in an asynchronous callback. This requires that you use the without_document_lock() decorator to suppress the normal locking behavior.

As with the thread example above, all actions that update document state must go through a next tick callback.

The following example demonstrates an application that drives a blocking computation from one unlocked Bokeh session callback. It yields to a blocking function that runs on the thread pool executor and then updates with a next tick callback. The example also updates the state simply from a standard locked session callback with a different update rate.

import asyncio
import time
from concurrent.futures import ThreadPoolExecutor
from functools import partial

from bokeh.document import without_document_lock
from bokeh.models import ColumnDataSource
from bokeh.plotting import curdoc, figure

source = ColumnDataSource(data=dict(x=[0], y=[0], color=["blue"]))

i = 0

doc = curdoc()

executor = ThreadPoolExecutor(max_workers=2)

def blocking_task(i):
    return i

# the unlocked callback uses this locked callback to safely update
async def locked_update(i):[['x'][-1]+1], y=[i], color=["blue"]))

# this unlocked callback will not prevent other session callbacks from
# executing while it is running
async def unlocked_task():
    global i
    i += 1
    res = await asyncio.wrap_future(executor.submit(blocking_task, i), loop=None)
    doc.add_next_tick_callback(partial(locked_update, i=res))

async def update():[['x'][-1]+1], y=[i], color=["red"]))

p = figure(x_range=[0, 100], y_range=[0, 20])
l ='x', y='y', color='color', source=source)

doc.add_periodic_callback(unlocked_task, 1000)
doc.add_periodic_callback(update, 200)

As before, you can run this example by saving to a Python file and running bokeh serve on it.

Lifecycle hooks#

You may want to execute code at specific points of server or session runtime. Bokeh enables this through a set of lifecycle hooks. To use these hooks, create your application in directory format and include a designated file called in the directory. In this file you can include any or all of the following conventionally named functions:

def on_server_loaded(server_context):
    # If present, this function executes when the server starts.

def on_server_unloaded(server_context):
    # If present, this function executes when the server shuts down.

def on_session_created(session_context):
    # If present, this function executes when the server creates a session.

def on_session_destroyed(session_context):
    # If present, this function executes when the server closes a session.

You can also define on_session_destroyed lifecycle hooks directly on the Document being served. This makes it easy to clean up after a user closes a session by performing such actions as database connection shutdown without the need to bundle a separate file. To declare such a callback, define a function and register it with the Document.on_session_destroyed method:

doc = Document()

def cleanup_session(session_context):
    # This function executes when the user closes the session.


Besides the lifecycle hooks above, you may also define request hooks to access the HTTP requests your users make. For further information, see Request handler hooks.