Dash Redis Demo

This app demonstrates how to:

Connect to a Redis instance from Dash.
Use Celery for asynchronous (periodic or user-triggered) tasks.

Redis

It works out of the box with the Redis server built in to Dash On Premise but could be adapted to work with other servers such as Heroku Redis or your local Redis server.

For debugging convenince, we install the redis-tools package, which offers the redis-cli command. This can be removed for faster app push times.

To enable Redis in Dash On Premise navigate to the settings page of the Server Manager. Under Special Options & Customizations select Enable Dash Customizations and then select Enable Redis Databases for Dash Apps.

Celery

This app also includes the Celery task queue and a sample periodic update task. (More information on Celery Periodic Tasks here)

In this sample app, the task update_data (in tasks.py) creates a dataframe with sample data, then saves it in Redis so that the Dash app, running on a separate process (or a separate container), can have access to it, and read it.

Using the function setup_periodic_tasks (also in tasks.py), we add the task above, update_data, to the list of periodic tasks, with an interval of 15, so this task runs every 15 seconds.

Deployment

In order to do this, you need to run:

Locally:

A Redis instance, a simple way to do this, if you have docker locally, is to run a Redis container: docker run --name local-redis -d redis

Then: docker inspect local-redis | grep IPAddress to get your Redis instance's IP address

If you don't have docker, alternatively, see the Redis documentation: https://redis.io/documentation to download Redis and set up a local instance.

The next commands need to be running from the root of your Dash App (Where the files app.py and tasks.py are)

The scheduler: REDIS_URL=redis://<your-redis-instance-ip>:6379 celery -A tasks beat --loglevel=DEBUG
The worker that will actually run the tasks: REDIS_URL=redis://<your-redis-instance-ip>:6379 celery -A tasks worker --loglevel=DEBUG

The two command above can be shortened into only one command with the --beat tag as follows: REDIS_URL=redis://<your-redis-instance-ip>:6379 celery -A tasks worker --beat --loglevel=DEBUG But please only do this in development, not in a production environment.

The dash app: REDIS_URL=redis://<your-redis-instance-ip>:6379 python app.py

On your On-Prem server

In order to have this setup when deploying your dash app to your Dash Deployment Server, your Procfile has to execute these three processes (celery worker, celery beat and the dash app)

In order to run Celery tasks On your Dash Deployment Server, your processes workers have to be running and linked to your dash app. To do this, scale up your app's workers processes (Using the names your have in your Procfile) - Note that this command is not necessary if you have a DOKKU_SCALE file that reflects how many workers are assigned to each process, like in this repository:

ssh dokku@YOUR_DASH_SERVER ps:scale APP-NAME worker-default=1 worker-beat=1

Just like in the local setup, you need a Redis instance to be running and to be linked to your Dash App:

To create and link a Redis database in Dash On Premise:

ssh dokku@YOUR_DASH_SERVER redis:create SERVICE-NAME
ssh dokku@YOUR_DASH_SERVER redis:link SERVICE-NAME APP-NAME

If you want to link an existing Redis instance/service, you can simply use the second command, using the SERVICE-NAME of the existing Redis instance.

In the commands above, replace:

YOUR_DASH_SERVER with the name of your Dash server (same as when you run git remote add)
SERVICE-NAME with the name you want for your Redis service
APP-NAME with the name of your app (as specified in the Dash App Manager).

kcompher / dash-redis-celery-periodic-updates