Background Callbacks | Dash for Python Documentation (2024)

Support for background callbacks on @dash.callback was introduced in Dash 2.6.
If you’re using an earlier version of Dash 2.x, you can use long_callback for long-running callbacks.

To get the most out of this page, make sure you’ve read about
Basic Callbacks in the Dash Fundamentals.

Most web servers have a 30 second timeout by default, which is an issue for callbacks that take longer to complete.
While you can increase the timeout on the web server, you risk allowing long-running callbacks to use all of your app’s
workers, preventing other requests from going through. Background callbacks offer a scalable solution for using long-running
callbacks by running them in a separate background queue. In the background queue, the callbacks are executed one-by-one
in the order that they came in by dedicated queue worker(s).

You can configure a callback to run in the background by setting background=True on the callback.
Callbacks with background=True use a backend configured by you to run the callback logic. There are currently two options:

• A DiskCache backend that runs callback logic in a separate
  process and stores the results to disk using the diskcache library. This is the easiest backend to use for local
  development, but is not recommended for production.

• A Celery backend that runs callback
  logic in a Celery worker and returns results to the Dash app through a Celery broker
  like Redis.
  This is recommended for production as, unlike Disk Cache, it queues the background callbacks, running them one-by-one in the order that they were received by dedicated Celery worker(s). Celery is a widely adopted, production-ready job queue library.
  For further information on the benefits of job queues, see the Why Job Queues? section below.

Dash Enterprise makes it easy to deploy Celery and Redis for using background callbacks in production. Get Pricing or see Dash in action at our next demo session.

Getting Started

The following examples use the diskcache manager when running locally. Install with:

pip install dash[diskcache]

When these examples are deployed to Dash Enterprise, they use celery.

pip install dash[celery]

Basic Steps

To use a background callback, you first need to configure a manager using your chosen backend.
The @dash.callback decorator requires this manager instance.
You can provide the manager instance to the dash.Dash app constructor as the background_callback_manager keyword argument,
or as the manager argument to the @dash.callback decorator.

In the next five examples, we’ll discuss in more detail how to implement background callbacks.

Simple Example

Here is a simple example of a background callback that updates an html.P
element with the number of times that a button has been clicked.
The callback uses time.sleep to simulate a long-running operation.

Disable Button While Callback Is Running

Notice how in the previous example, there is no visual indication that the background callback is running.
A user might click the “Run Job!” button multiple times before the original job can complete.
You can also disable the button while the callback is running and re-enable it when the callback completes.

To do this, use the running argument to @dash.callback. This argument accepts a list of 3-element
tuples. The first element of each tuple must be an Output dependency object referencing a property of a component in
the app layout. The second element is the value that the property should be set to while the callback is running, and
the third element is the value the property should be set to when the callback completes.

This example uses running to set the disabled property of the button to True while the callback is running,
and False when it completes.

Note: In this example, the background_callback_manager is provided to the dash.Dash app constructor instead of
the @dash.callback decorator.

There is a known issue where using running with a multi-pages app doesn’t work as expected when a user changes page when the callback is running.

Cancelable Callback

This example builds on the previous example, adding support for canceling a long-running callback using
the cancel argument to the @dash.callback decorator. We set the cancel argument to a list
of Input dependency objects that reference a property of a component in the app’s layout.
When the value of this property changes while a callback is running, the callback is canceled.
Note that the value of the property is not significant — any change in value cancels the running job (if any).

Progress Bar

This example uses the progress argument to the @dash.callback decorator to update a progress bar while the
callback is running. We set the progress argument to an Output dependency grouping that references properties
of components in the app’s layout.

When a dependency grouping is assigned to the progress argument of @dash.callback, the decorated function
is called with a new special argument as the first argument to the function.
This special argument, named set_progress in the example below, is a function handle that the decorated function
calls in order to provide updates to the app on its current progress. The set_progress function accepts a single
argument, which corresponds to the grouping of properties specified in the Output dependency grouping passed to
the progress argument of @dash.callback.

Progress Bar Chart Graph

The progress argument to the @dash.callback decorator can be used to update arbitrary component properties.
This example creates and updates a Plotly bar graph to display the current calculation status.

This example also uses the progress_default argument to specify a grouping of values that
should be assigned to the components specified by the progress argument when the callback is not in progress.
If progress_default is not provided, all the dependency properties specified in progress are set to None
when the callback is not running. In this case, progress_default is set to a figure with a zero width bar.

Using set_props Within a Callback

New in 2.17

By using set_props inside a callback, you can update a component property that isn’t included as an output of the callback. Updates using set_props inside a background callback take place immediately. In the following example, we update a Dash AG Grid’s rowData using set_props every two seconds, gradually adding more data.

In the above example, set_props works similarly to using progress, but by using set_props, we don’t have to add the component-properties we are updating up front when defining the callback. This means, we could also use set_props to update several different component-property pairs within our callback, instead of one set of component-property pairs allowed with the progress parameter.

Limitations

• Component properties updated using set_props won’t appear in the callback graph for debugging.
• Component properties updated using set_props won’t appear as loading when they are wrapped with a dcc.Loading component.
• set_props doesn’t validate the id or property names provided, so no error will be displayed if they contain typos. This can make apps that use set_props harder to debug.
• Using set_props with chained callbacks may lead to unexpected results.

Why Job Queues?

When your app is deployed in production, a finite number of CPUs serve requests for that app.
Callbacks that take longer than 30 seconds often experience timeouts when deployed in production.
And even callbacks that take less than 30 seconds can tie up all available server resources when multiple
users access your app at the same time. When all CPUs are processing callbacks, new visitors to your app see a
blank screen and eventually a “Server Timed Out” message.

Job queues are a solution to these timeout issues. Like the web processes serving your Dash app, job queues run
with a dedicated number of CPU workers. These workers go through the jobs one at a time and aren’t subject to timeouts.
While the job queue workers are processing the data, the web processes serving the Dash app and the regular callbacks
display informative loading screens, progress bars, and the results of the job queues.
End users never see a timeout and always see a responsive app.

Running in Dash Enterprise Workspaces

To run an app that uses background callbacks with a celery backend in a Dash Enterprise workspace:

1. Add a Redis database to your app.
2. Install Dash with celery in the workspace with pip install dash[celery].
3. In the workspace terminal, run your app with python app.py
4. In a separate workspace terminal, run a celery worker. In the code examples above, we declared a celery instance with celery_app = Celery(__name__...) in an app.py file. We reference this in the command to run the worker with app:celery_app and also set the number of worker processes with --concurrency.
   bash celery -A app:celery_app worker --loglevel=INFO --concurrency=2

If you make changes to your app’s code, you’ll need to restart the celery worker process in the workspace terminal for those changes to apply.

Deploying to Dash Enterprise

To deploy an app that uses background callbacks with a celery backend to Dash Enterprise:

1. Add a Redis database to your app.
2. Update your app’s requirements.txt file to include celery when installing Dash.
   dash[celery]==2.17.0

3. Add a line to your Procfile to run a celery worker. In the code examples above, we declared a celery instance with celery_app = Celery(__name__...) in an app.py file. We reference this in the Procfile with app:celery_app and set the number of worker processes with --concurrency.
   ```
   web: gunicorn app:server –workers 4
   queue: celery -A app:celery_app worker –loglevel=INFO –concurrency=2

   1. *If you're deploying your app to Dash Enterprise 4.x*, include a DOKKU_SCALE file with the `celery` process:
   web=1
   queue=1
   ```
   1. Deploy your app.

Number of workers

In production apps, you can tune the number of workers you want to process your web requests versus process background
jobs in the queue using command line flags in Gunicorn and Celery.

Here is an example of a Procfile with 4 CPUs dedicated to regular Dash callbacks and 2 CPUs dedicated to
processing background callbacks in a queue.

web: gunicorn app:server --workers 4queue: celery -A app:celery_app worker --loglevel=INFO --concurrency=2

The ratio of Gunicorn web workers to Celery queue workers will depend on your app.
You’ll want enough web workers that your app remains responsive to new users opening your app and enough
background queue workers so tasks don’t wait too long in the queue.

If your regular callbacks respond quickly (less than 500ms), consider configuring fewer web gunicorn workers.

Background Callbacks vs Long Callbacks

Background callbacks address the following limitations of long callbacks:

• You can’t access previous results returned by a long callback.
  This functionality is available in Dash Enterprise’s Snapshot Engine.
• Pattern-matching dependencies are not supported with
  long callbacks. An error is raised if a callback ID contains ALL, MATCH, or ALL_SMALLER.
• dash.callback_context is not supported.
• If the Input/State/Output dependencies do not exist when the app starts (if they reference components that are
  generated by another callback), suppress_callback_exceptions=True does not prevent Dash from raising callback
  validation errors.

It was not possible to fix these issues without introducing backwards incompatible changes to long_callback.
So, this feature was re-architected in a way that fixed these limitations without changing and
breaking long_callback.

Additional Resources

• Background Callback Caching

• Introduction to Celery

• Redis documentation

• DiskCache tutorial