Anaconda Enterprise: Documentation

# Running a Bokeh Server

## Purpose

The architecture of Bokeh is such that high-level “model objects” (representing things like plots, ranges, axes, glyphs, etc.) are created in Python, and then converted to a JSON format that is consumed by the client library, BokehJS. (See Defining Key Concepts for a more detailed discussion.) By itself, this flexible and decoupled design offers advantages, for instance it is easy to have other languages (R, Scala, Lua, ...) drive the exact same Bokeh plots and visualizations in the browser.

However, if it were possible to keep the “model objects” in python and in the browser in sync with one another, then more additional and powerful possibilities immediately open up:

- respond to UI and tool events generated in a browser with computations or queries using the full power of python
- automatically push server-side updates to the UI (i.e. widgets or plots in a browser)
- use periodic, timeout, and asynchronous callbacks to drive streaming updates

This capability to synchronize between python and the browser is the main purpose of the Bokeh Server.

The simple example below, embedded from demo.bokehplots.com, illustrates this.

When the controls are manipulated, their new values are automatically synced in the Bokeh server. Callbacks are triggered that also update the date for the plot in the server. These changes are automatically synced back to the browser, and the plot updates.

## Use Case Scenarios

Now that we know what the Bokeh server is for, and what it is capable of doing, it’s worth considering a few different scenarios when you might want to use a Bokeh Server.

### Local or Individual Use

One way that you might want to use the Bokeh server is during exploratory data analysis, possibly in a Jupyter notebook. Alternatively, you might want to create a small app that you can run locally, or that you can send to colleagues to run locally. The Bokeh server is very useful and easy to use in this scenario. Both of the methods here below can be used effectively:

- Connecting with bokeh.client
- Building Bokeh Applications

For the most flexible approach, that could transition most directly to a deployable application, it is suggested to follow the techniques in Building Bokeh Applications.

### Creating Deployable Applications

Another way that you might want to use the Bokeh server is to publish interactive data visualizations and applications that can be viewed and used by a wider audience (perhaps on the internet, or perhaps on an internal company network). The Bokeh Server is also well-suited to this usage, and you will want to first consult the sections:

- Building Bokeh Applications - how to create Bokeh Applications, and then refer to the section
- Deployment Scenarios - how to deploy the Bokeh server with your application.

### Shared Publishing

Both of the scenarios above involve a single creator making applications on the server, either for their own local use, or for consumption by a larger audience. Another scenario is the case where a group of several creators all want publish different applications to the same server. This is not a good use-case for single Bokeh server. Because it is possible to create applications that execute arbitrary python code, process isolation and security concerns make this kind of shared tenancy prohibitive.

In order to support this kind of multi-creator, multi-application environment, one approach is to build up infrastructure that can run as many Bokeh servers as-needed, either on a per-app, or at least a per-user basis. It is possible that we may create a public service to enable just this kind of usage in the future, and it would also certainly be possible for third parties to build their own private infrastructure to do so as well, but that is beyond the scope of this User’s Guide.

Another possibility is to have a single centrally created app (perhaps by an organization), that can access data or 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 a Bokeh server with other web application frameworks. See a complete example at https://github.com/bokeh/bokeh-demos/tree/master/happiness

## Building Bokeh Applications

By far the most flexible way to create interactive data visualizations using the Bokeh server is to create Bokeh Applications, and serve them with the `bokeh serve` command. In this scenario, a Bokeh server uses the application code to create sessions and documents for all browsers that connect:

A Bokeh server (left) uses Application code to create Bokeh Documents. Every new connection from a browser (right) results in the Bokeh server creating a new document, just for that session.

The application code is executed in the Bokeh server every time a new connection is made, to create the new Bokeh `Document` that will be synced to the browser. The application code also sets up any callbacks that should be run whenever properties such as widget values are changes.

There are a few different ways to provide the application code.

### Single module format

Let’s look again at a complete example and then examine some specific parts in more detail:

    # myapp.py

    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 our plot (no data yet)
    r = p.text(x=[], y=[], text=[], text_color=[], text_font_size="20pt",
               text_baseline="middle", text_align="center")

    i = 0

    ds = r.data_source

    # create a callback that will add 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'] = ds.data['x'] + [random()*70 + 15]
        new_data['y'] = ds.data['y'] + [random()*70 + 15]
        new_data['text_color'] = ds.data['text_color'] + [RdYlBu3[i%3]]
        new_data['text'] = ds.data['text'] + [str(i)]
        ds.data = new_data

        i = i + 1

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

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

Notice that we have not specified an output or connection method anywhere in this code. It is a simple script that creates and updates objects. The flexibility of the `bokeh` command line tool means that we can defer output options until the end. We could, e.g., run `bokeh json myapp.py` to get a JSON serialized version of the the application. But in this case, we would like to run the app on a Bokeh server, so we execute:

    bokeh serve --show myapp.py

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

    http://localhost:5006/myapp

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

    http://localhost:5006/

This index can be disabled with the `--disable-index` option, and the redirect behavior can be disabled with the `--disable-index-redirect` option.

In addition to creating Bokeh applications from single python files, it is also possible to create applications from directories.

### Directory format

Bokeh applications may also be created by creating and populating a filesystem directory with the appropriate files. To start a directory application in a directory `myapp`, execute `bokeh serve` with the name of the directory, for instance:

    bokeh serve --show myapp

At a minimum, the directory must contain a `main.py` that constructs a Document for the Bokeh Server to serve:

    myapp
       |
       +---main.py

The full set of files that Bokeh server knows about is:

    myapp
       |
       +---main.py
       +---server_lifecycle.py
       +---static
       +---theme.yaml
       +---templates
            +---index.html

The optional components are

- A `server_lifecycle.py` file that allows optional callbacks to be triggered at different stages of application creation, as descriped in Lifecycle Hooks.
- A `static` subdirectory that can be used to serve static resources associated with this application.
- A `theme.yaml` file that declaratively defines default attributes to be applied to Bokeh model types.
- A `templates` subdirectory with `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.

Custom variables can be passed to the template via the `curdoc().template_variables` dictionary in place:

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

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

When executing your `main.py` Bokeh server ensures that the standard `__file__` module attribute works as you would expect. So it is possible to include data files or custom user defined models in your directory however you like. Additionally, the application directory is also added to `sys.path` so that python modules in the application directory may be easily imported.

An example might be:

    myapp
       |
       +---data
       |    +---things.csv
       |
       +---helpers.py
       +---main.py
       |---models
       |    +---custom.js
       |
       +---server_lifecycle.py
       +---static
       |    +---css
       |    |    +---special.css
       |    |
       |    +---images
       |    |    +---foo.png
       |    |    +---bar.png
       |    |
       |    +---js
       |        +---special.js
       |
       |---templates
       |    +---index.html
       |
       +---theme.yaml

In this case you might have code similar to:

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

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

And similar code to load the JavaScript implementation for a custom model from `models/custom.js`

### Accessing the HTTP Request

When a session is created for a Bokeh application, the session context is made 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`. The request object has a number of fields, such as `arguments`, `cookies`, `protocol`, etc. See the documentation for HTTPServerRequest for full details.

As an example, the following code will access the request `arguments` to set a value for a variable `N` (perhaps controlling the number of points in a plot):

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

    args = curdoc().session_context.request.arguments

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

Warning

The request object is provided so that values such as `arguments` may be easily inspected. Calling any of the Tornado methods such as `finish()` or writing directly to `request.connection` is unsupported and will result in undefined behavior.

### Callbacks and Events

Before jumping in to 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

Regardless of whether there is a Bokeh Server involved, it is possible to create callbacks that execute in the browser, using `CustomJS` and other methods. See JavaScript Callbacks for more detailed information and examples.

It is critical to note that no python code is ever executed when a CustomJS callback is used. This is true even when the call back is supplied as python code to be translated to JavaScript. A `CustomJS` callback is only executed inside the browser’s JavaScript interpreter, and thus can only directly interact with JavaScript data and functions (e.g., BokehJS Backbone models).

#### Python Callbacks with Jupyter Interactors

If you are working in the Jupyter Notebook, it is possible to use Jupyter interactors to quickly create simple GUI forms automatically. Updates to the widgets in the GUI can trigger python callback functions that execute in the Jupyter Python kernel. It is often useful to have these callbacks call `push_notebook()` to push updates to displayed plots. For more detailed information, see Jupyter Interactors.

Note

It is currently possible to push udpates from python, to BokehJS (i.e., to update plots, etc.) using `push_notebook()`. It is not currently possible to get events or updates from the other direction (e.g. to have a range or selection update trigger a python callback) without using a Bokeh Server as described in the next section. Adding the capability for two-way Python<–>JS synchronization through Jupyter comms is a planned future addition.

#### Updating From Threads

If the app needs to perform blocking computation, it is possible to perform that work in a separate thread. However, updates to the Document must be scheduled via a next-tick callback. The callback will execute as soon as possible on the next iteration of the Tornado event loop, and will automatically acquire necessary locks to update the document state safely.

Warning

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

It is important to emphasize that the document update must be scheduled in a “next tick callback”. Any usage that directly updates the document state from another thread, either by calling other document methods, or by setting properties on Bokeh models, risks data and protocol corruption.

It is also important to save a local copy of `curdoc()` so that all threads have access to the same document. This is illustrated in the example below:

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

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

    from tornado import gen

    # this must only be modified from a Bokeh session allback
    source = ColumnDataSource(data=dict(x=[0], y=[0]))

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

    @gen.coroutine
    def update(x, y):
        source.stream(dict(x=[x], y=[y]))

    def blocking_task():
        while True:
            # do some blocking computation
            time.sleep(0.1)
            x, y = random(), random()

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

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

    doc.add_root(p)

    thread = Thread(target=blocking_task)
    thread.start()

To see this example in action, save it to a python file, e.g. `testapp.py` and then execute

    bokeh serve --show testapp.py

Warning

There is currently no locking around adding next tick callbacks to documents. It is recommended that at most one thread add callbacks to the document. It is planned to add more fine grained locking to callback methods in the future.

#### 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 can work, but requires the Bokeh provided `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, by yielding to a blocking function that runs on the thread pool executor and updates by using a next-tick callback. The example also updates the state simply from a standard locked session callback on a different update rate.

    from functools import partial
    import time

    from concurrent.futures import ThreadPoolExecutor
    from tornado import gen

    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):
        time.sleep(1)
        return i

    # the unlocked callback uses this locked callback to safely update
    @gen.coroutine
    def locked_update(i):
        source.stream(dict(x=[source.data['x'][-1]+1], y=[i], color=["blue"]))

    # this unclocked callback will not prevent other session callbacks from
    # executing while it is in flight
    @gen.coroutine
    @without_document_lock
    def unlocked_task():
        global i
        i += 1
        res = yield executor.submit(blocking_task, i)
        doc.add_next_tick_callback(partial(locked_update, i=res))

    @gen.coroutine
    def update():
        source.stream(dict(x=[source.data['x'][-1]+1], y=[i], color=["red"]))

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

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

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

### Lifecycle Hooks

Sometimes it is desirable to have code execute at specific times in a server or session lifetime. For instance, if you are using a Bokeh Server along side a Django server, you would need to call `django.setup()` once, as each Bokeh server starts, to initialize Django properly for use by Bokeh application code.

Bokeh provides this capability through a set of Lifecycle Hooks. To use these hooks, you must create your application in Directory format, and include a designated file called `server_lifecycle.py` 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 is called when the server first starts. '''
        pass

    def on_server_unloaded(server_context):
        ''' If present, this function is called when the server shuts down. '''
        pass

    def on_session_created(session_context):
        ''' If present, this function is called when a session is created. '''
        pass

    def on_session_destroyed(session_context):
        ''' If present, this function is called when a session is closed. '''
        pass

## Embedding Bokeh Server as a Library

It can be useful to embed the Bokeh Server in a larger Tornado application, or the Jupyter notebook, and use the already existing Tornado `IOloop`. Here is the basis of how to integrate Bokeh in such a scenario:

    from bokeh.server.server import Server

    server = Server(
        bokeh_applications,  # list of Bokeh applications
        io_loop=loop,        # Tornado IOLoop
        **server_kwargs      # port, num_procs, etc.
    )

    # start timers and services and immediately return
    server.start()

It is also possible to create and control an `IOLoop` directly. This can be useful to create standalone “normal” python scripts that serve Bokeh apps, or to embed a Bokeh application into a framework like Flask or Django without having to run a separate Bokeh server process. Some examples of this technique can be found in the examples directory:

- examples/howto/server_embed/flask_embed.py
- examples/howto/server_embed/notebook_embed.ipynb
- examples/howto/server_embed/standalone_embed.py
- examples/howto/server_embed/tornado_embed.py

## Connecting with `bokeh.client`

With the new Tornado and websocket-based server introduced in Bokeh 0.11, there is also a proper client API for interacting directly with a Bokeh Server. This client API can be used to trigger updates to the plots and widgets in the browser, either in response to UI events from the browser or as a results of periodic or asynchronous callbacks.

Typically web browsers make connections to a Bokeh server, but it is possible to connect from python by using the `bokeh.client` module.

There are some important difference to note when using `bokeh.client` instead of running Bokeh apps directly on a Bokeh server:

- Callbacks execute in the separate python script, not in the Bokeh server. In for callbacks to function, the script must stay running indefinitely. Typically this is achieved by making a blocking call to `session.loop_until_closed()` at the end of the script. If the python script terminates, callbacks for widgets, etc. will no longer function.
- In this scenario the Bokeh server is merely an intermediary that facilitates the synchronization between the `Document` in the python script, and the one in the browser. As a result, the required network traffic is doubled.
- To open sessions to apps created this way, browsers must navigate to URLs that explicitly refer to a specific session. If multiple browsers open the URL, they will all share the exact same application state. That is, there will be a “Google Docs” style of operation: scrubbing a slider in one browser will also update the slider in any other browser opened to the same session. This may or may not be desirable.
- Apps created in this way are typically more difficult to scale compared to apps run with `bokeh serve app.py` where we can simply run more servers behind a load balancer. If you need to scale out a Bokeh application, running them directly on a Bokeh server is preferable.

As before, the first step is to start a Bokeh Server. However in this case it started without specifying any app script:

    bokeh serve

Next, let’s look at a complete example, and then examine a few key lines individually:

    import numpy as np
    from numpy import pi

    from bokeh.client import push_session
    from bokeh.driving import cosine
    from bokeh.plotting import figure, curdoc

    x = np.linspace(0, 4*pi, 80)
    y = np.sin(x)

    p = figure()
    r1 = p.line([0, 4*pi], [-1, 1], color="firebrick")
    r2 = p.line(x, y, color="navy", line_width=4)

    # open a session to keep our local document in sync with server
    session = push_session(curdoc())

    @cosine(w=0.03)
    def update(step):
        # updating a single column of the the *same length* is OK
        r2.data_source.data["y"] = y * step
        r2.glyph.line_alpha = 1 - 0.8 * abs(step)

    curdoc().add_periodic_callback(update, 50)

    session.show(p) # open the document in a browser

    session.loop_until_closed() # run forever

If you run this script, you will see a plot with an animated line appear in a new browser tab. The first half of the script is like most any script that uses the `bokeh.plotting` interface. The first interesting line is:

    session = push_session(curdoc())

This line opens a new session with the Bokeh Server, initializing it with our current Document. This local Document will be automatically kept in sync with the server. The next few lines define and add a periodic callback to be run every 50 milliseconds:

    @cosine(w=0.03)
    def update(step):
        # updating a single column of the the *same length* is OK
        r2.data_source.data["y"] = y * step
        r2.glyph.line_alpha = 1 - 0.8 * abs(step)

    curdoc().add_periodic_callback(update, 50)

Next, analogous to `bokeh.io.show()`, there is this a `show()` on session objects that will automatically open a browser tab to display the synced Document.

Finally, we need to tell the session to loop forever, so that the periodic callbacks happen:

    session.loop_until_closed() # run forever

It may be undesirable or not possible to run a blocking function on the main thread, e.g. when using `bokeh.client` to create sessions from a web framework such as Django or Flask. In these cases calling `loop_until_closed()` from another thread may be an option. Another possibility is to not use `bokeh.client` at all. See the section Embedding Bokeh Server as a Library for some alternatives.

## Deployment Scenarios

With an application we are developing, we can run it locally any time we want to interact with it. To share it with other people who are able to install the required python stack, we can share the application with them, and let them run it locally themselves in the same manner. However, we might also want to deploy the application in a way that other people can access it as a service: * without having to install all of the prerequisites * without needing to have the source code * like any other webpage

This section describes some of the considerations that arise when deploying Bokeh server applications as a service for others to use.

### Standalone Bokeh Server

First, it is possible to simply run the Bokeh server on a network for users to interact with directly. Depending on the computational burden of your application code, the number of users, the power of the machine used to run on, etc., this could be a simple and immediate option for deployment an internal network.

However, it is often the case that there are needs around authentication, scaling, and uptime. In these cases, more sophisticated deployment configurations are needed. In the following sections we discuss some of these considerations.

#### SSH Tunnels

It may be convenient or necessary to run a standalone instance of the Bokeh server on a host to which direct access cannot be allowed. In such cases, SSH can be used to “tunnel” to the server.

In the simplest scenario, the Bokeh server will run on one host and will be accessed from another location, e.g., a laptop, with no intermediary machines.

Run the server as usual on the remote host:

    bokeh server

Next, issue the following command on the local machine to establish an SSH tunnel to the remote host:

    ssh -NfL localhost:5006:localhost:5006 user@remote.host

Replace user with your username on the remote host and remote.host with the hostname/IP address of the system hosting the Bokeh server. You may be prompted for login credentials for the remote system. After the connection is set up you will be able to navigate to `localhost:5006` as though the Bokeh server were running on the local machine.

The second, slightly more complicated case occurs when there is a gateway between the server and the local machine. In that situation a reverse tunnel must be estabished from the server to the gateway. Additionally the tunnel from the local machine will also point to the gateway.

Issue the following commands on the remote host where the Bokeh server will run:

    nohup bokeh server &
    ssh -NfR 5006:localhost:5006 user@gateway.host

Replace user with your username on the gateway and gateway.host with the hostname/IP address of the gateway. You may be prompted for login credentials for the gateway.

Now set up the other half of the tunnel, from the local machine to the gateway. On the local machine:

    ssh -NfL localhost:5006:localhost:5006 user@gateway.host

Again, replace user with your username on the gateway and gateway.host with the hostname/IP address of the gateway. You should now be able to access the Bokeh server from the local machine by navigating to `localhost:5006` on the local machine, as if the Bokeh server were running on the local machine. You can even set up client connections from a Jupyter notebook running on the local machine.

Note

We intend to expand this section with more guidance for other tools and configurations. If have experience with other web deployment scenarios and wish to contribute your knowledge here, please contact us on the mailing list.

### Basic Reverse Proxy Setup

If the goal is to serve an web application to the general Internet, it is often desirable to host the application on an internal network, and proxy connections to it through some dedicated HTTP server. This sections provides guidance for basic configuration behind some common reverse proxies.

#### Nginx

One very common HTTP and reverse-proxying server is Nginx. A sample server confuguration block is shown below:

    server {
        listen 80 default_server;
        server_name _;

        access_log  /tmp/bokeh.access.log;
        error_log   /tmp/bokeh.error.log debug;

        location / {
            proxy_pass http://127.0.0.1:5100;
            proxy_set_header Upgrade $http_upgrade;
            proxy_set_header Connection "upgrade";
            proxy_http_version 1.1;
            proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
            proxy_set_header Host $host:$server_port;
            proxy_buffering off;
        }

    }

The above `server` block sets up Nginx to to proxy incoming connections to `127.0.0.1` on port 80 to `127.0.0.1:5100` internally. To work in this configuration, we will need to use some of the command line options to configure the Bokeh Server. In particular we need to use `--port` to specify that the Bokeh Server should listen itself on port 5100.

    bokeh serve myapp.py --port 5100

Note that in the basic server block above we have not configured any special handling for static resources, e.g., the Bokeh JS and CSS files. This means that these files are served directly by the Bokeh server itself. While this works, it places an unnecessary additional load on the Bokeh server, since Nginx has a fast static asset handler. To utilize Nginx to serve Bokeh’s static assets, you can add a new stanza inside the server block above, similar to this:

    location /static {
        alias /path/to/bokeh/server/static;
    }

Be careful that the file permissions of the Bokeh resources are accessible to whatever user account is running the Nginx server process. Alternatively, you can copy the resources to a global static directory during your deployment process. See A Full Example with Automation for a demonstration of this.

#### Apache

Another common HTTP server and proxy is Apache. Here is sample confuguration for running a Bokeh server behind Apache:

    <VirtualHost *:80>
        ServerName localhost

        CustomLog "/path/to/logs/access_log" combined
        ErrorLog "/path/to/logs/error_log"

        ProxyPreserveHost On
        ProxyPass /myapp/ws ws://127.0.0.1:5100/myapp/ws
        ProxyPassReverse /myapp/ws ws://127.0.0.1:5100/myapp/ws

        ProxyPass /myapp http://127.0.0.1:5100/myapp/
        ProxyPassReverse /myapp http://127.0.0.1:5100/myapp/

        <Directory />
            Require all granted
            Options -Indexes
        </Directory>

        Alias /static /path/to/bokeh/server/static
        <Directory /path/to/bokeh/server/static>
            # directives to effect the static directory
            Options +Indexes
        </Directory>

    </VirtualHost>

The above configuration aliases /static to the location of the Bokeh static resources directory, however it is also possible (and probably preferable) to copy the Bokeh static resources to whatever standard static files location is configured for Apache as part of the deployment.

Note that you may also need to enable some modules for the above configuration:

    a2enmod proxy
    a2enmod http_proxy
    a2enmod proxy_wstunnel
    apache2ctl restart

These might need to be run with `sudo`, depending on your system.

As before, you would run the Bokeh server with the command:

    bokeh serve myapp.py --port 5100

### Reverse Proxying with Nginx and SSL

If you would like to deploy a Bokeh Server behind an SSL-terminated Nginx proxy, then a few additional customizations are needed. In particular, the Bokeh server must be configured with the `--use-xheaders` flag:

    bokeh serve myapp.py --port 5100 --use-xheaders

The `--use-xheaders` option causes Bokeh to override the remote IP and URI scheme/protocol for all requests with `X-Real-Ip`, `X-Forwarded-For`, `X-Scheme`, `X-Forwarded-Proto` headers when they are available.

You must also customize Nginx. In particular, you must configure Nginx to send the `X-Forwarded-Proto` header, as well as configure Nginx for SSL termination. Optionally, you may want to redirect all HTTP traffic to HTTPS. The complete details of this configuration (e.g. how and where to install SSL certificates and keys) will vary by platform, but a reference `nginx.conf` is provided below:

    # redirect HTTP traffic to HTTPS (optional)
    server {
        listen      80;
        server_name foo.com;
        return      301 https://$server_name$request_uri;
    }

    server {
        listen      443 default_server;
        server_name foo.com;

        # add Strict-Transport-Security to prevent man in the middle attacks
        add_header Strict-Transport-Security "max-age=31536000";

        ssl on;

        # SSL installation details will vary by platform
        ssl_certificate /etc/ssl/certs/my-ssl-bundle.crt;
        ssl_certificate_key /etc/ssl/private/my_ssl.key;

        # enables all versions of TLS, but not SSLv2 or v3 which are deprecated.
        ssl_protocols TLSv1 TLSv1.1 TLSv1.2;

        # disables all weak ciphers
        ssl_ciphers "ECDHE-RSA-AES256-GCM-SHA384:ECDHE-RSA-AES128-GCM-SHA256:DHE-RSA-AES256-GCM-SHA384:DHE-RSA-AES128-GCM-SHA256:ECDHE-RSA-AES256-SHA384:ECDHE-RSA-AES128-SHA256:ECDHE-RSA-AES256-SHA:ECDHE-RSA-AES128-SHA:DHE-RSA-AES256-SHA256:DHE-RSA-AES128-SHA256:DHE-RSA-AES256-SHA:DHE-RSA-AES128-SHA:ECDHE-RSA-DES-CBC3-SHA:EDH-RSA-DES-CBC3-SHA:AES256-GCM-SHA384:AES128-GCM-SHA256:AES256-SHA256:AES128-SHA256:AES256-SHA:AES128-SHA:DES-CBC3-SHA:HIGH:!aNULL:!eNULL:!EXPORT:!DES:!MD5:!PSK:!RC4";

        ssl_prefer_server_ciphers on;

        location / {
            proxy_pass http://127.0.0.1:5100;
            proxy_set_header Upgrade $http_upgrade;
            proxy_set_header Connection "upgrade";
            proxy_http_version 1.1;
            proxy_set_header X-Forwarded-Proto $scheme;
            proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
            proxy_set_header Host $host:$server_port;
            proxy_buffering off;
        }

    }

This configuration will proxy all incoming HTTPS connections to `foo.com` to a Bokeh server running internally on `http://127.0.0.1:5100`.

### Load Balancing with Nginx

The architecture of the Bokeh server is specifically designed to be scalable—by and large, if you need more capacity, you simply run additional servers. Often in this situation it is desired to run all the Bokeh server instances behind a load balancer, so that new connections are distributed amongst the individual servers.

The Bokeh server is horizontally scalable. To add more capacity, more servers can be run behind a load balancer.

Nginx offers a load balancing capability. We will describe some of the basics of one possible configuration, but please also refer to the Nginx load balancer documentation. For instance, there are various different strategies available for choosing what server to connect to next.

First we need to add an `upstream` stanza to our NGinx configuration, typically above the `server` stanza. This section looks something like:

    upstream myapp {
        least_conn;                 # Use Least Connections strategy
        server 127.0.0.1:5100;      # Bokeh Server 0
        server 127.0.0.1:5101;      # Bokeh Server 1
        server 127.0.0.1:5102;      # Bokeh Server 2
        server 127.0.0.1:5103;      # Bokeh Server 3
        server 127.0.0.1:5104;      # Bokeh Server 4
        server 127.0.0.1:5105;      # Bokeh Server 5
    }

We have labeled this `upstream` stanza as `myapp`. We will use this name below. Additionally, we have listed the internal connection information for six different Bokeh server instances (each running on a different port) inside the stanza. You can run and list as many Bokeh servers as you need.

You would run the Bokeh servers with commands similar to:

    serve myapp.py --port 5100
    serve myapp.py --port 5101
    ...

Next, in the `location` stanza for our Bokeh server, change the `proxy_pass` value to refer to the `upstream` stanza we created above. In this case we use `proxy_pass http://myapp;` as shown here:

    server {

        location / {
            proxy_pass http://myapp;

            # all other settings unchanged
            proxy_set_header Upgrade $http_upgrade;
            proxy_set_header Connection "upgrade";
            proxy_http_version 1.1;
            proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
            proxy_set_header Host $host:$server_port;
            proxy_buffering off;
        }

    }

### Process Control with Supervisord

It is often desired to use process control and monitoring tools when deploying web applications. One popular such tool is Supervisor, which can automatically start and stop process, as well as re-start processes if they terminate unexpectedly. Supervisor is configured using INI style config files. A sample file that might be used to start a single Bokeh Server app is below:

    ; supervisor config file

    [unix_http_server]
    file=/tmp/supervisor.sock   ; (the path to the socket file)
    chmod=0700                  ; sockef file mode (default 0700)

    [supervisord]
    logfile=/var/log/supervisord.log ; (main log file; default $CWD/supervisord.log)
    pidfile=/var/run/supervisord.pid ; (supervisord pidfile; default $CWD/supervisord.pid)
    childlogdir=/var/log/supervisor  ; ('AUTO' child log dir, default $TEMP)

    ; The section below must be in the present for the RPC (supervisorctl/web)
    ; interface in to function.
    [rpcinterface:supervisor]
    supervisor.rpcinterface_factory = supervisor.rpcinterface:make_main_rpcinterface

    [supervisorctl]
    serverurl=unix:///tmp/supervisor.sock ; use a unix:// URL for a unix socket

    [program:myapp]
    command=/path/to/bokeh serve myapp.py
    directory=/path/to/workdir
    autostart=false
    autorestart=true
    startretries=3
    numprocs=4
    process_name=%(program_name)s_%(process_num)02d
    stderr_logfile=/var/log/myapp.err.log
    stdout_logfile=/var/log/myapp.out.log
    user=someuser
    environment=USER="someuser",HOME="/home/someuser"

The standard location for the supervisor config file varies from system to system. Consult the Supervisor configuration documentation for more details. It is also possible to specify a config file explicity. To do this, execute:

    supervisord -c /path/to/supervisord.conf

to start the Supervisor process. Then to control processes execute `supervisorctl` commands. For instance to start all processes, run:

    supervisorctl -c /path/to/supervisord.conf start all

To stop all processes run:

    supervisorctl -c /path/to/supervisord.conf start all

And to update the process control after editing the config file, run:

    supervisorctl -c /path/to/supervisord.conf update

### Scaling the server

You can fork multiple server processes with the num-procs option. For example, to fork 3 processes:

    bokeh serve --num-procs 3

Note that the forking operation happens in the underlying Tornado Server, see notes in the Tornado docs.

### A Full Example with Automation

To deploy the demo site at http://demo.bokehplots.com we combine all of the above techniques. Additionally, we used SaltStack to automate many aspects of the deployment.

Note

Other devops automation tools include Puppet, Ansible, and Chef. We would like to provide specific guidance where ever we can, so if you have experience with these tools and would be interested in contributing your knowledge, please contact us on the mailing list.

You can see all the code for deploying the site at the public GitHub repository here:

https://github.com/bokeh/demo.bokehplots.com

You can modify or deploy your own version of this site on an Amazon Linux instance by simply running the `deploy.sh` script at the top level. With minor modifications, this machinery should work on many linux variants.

## Further Reading

Now that you are familiar with the concepts of Running a Bokeh Server, you may be interested in learning more about the internals of the Bokeh server in Server Architecture