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Celery Flower Tutorial
Celery Flower is a tool for monitoring and administrating Celery clusters.
When you run Celery background tasks you want to get some observability on how they perform, how long does it take to run a task, how spot failures and debug their behaviour.
In this tutorial we will talk about running flower locally in Docker Compose as a service and in staging/production.
We will also see that it works great with both RabbitMQ and Redis as Broker and how to connect to Redis as the result backend.
Celery Flower in Docker
For local development consider we are running our Django project in docker-compose and we have the following list of services in
version: '3.3' services: redis: image: redis ports: - "6379:6379" rabbitmq: image: rabbitmq environment: - RABBITMQ_DEFAULT_USER=djangito - RABBITMQ_DEFAULT_PASS=djangito - RABBITMQ_DEFAULT_VHOST=djangito ports: - "21001:5672" - "21002:15672" db: image: postgres environment: - POSTGRES_USER=djangito - POSTGRES_PASSWORD=djangito - POSTGRES_DB=djangito ports: - "21003:5432" web: build: . restart: always command: python manage.py runserver 0.0.0.0:8060 env_file: - .env ports: - "127.0.0.1:8060:8060" volumes: - .:/code links: - db - redis - rabbitmq depends_on: - db - redis - rabbitmq tailwind: build: . restart: always command: python manage.py tailwind start env_file: - .env ports: - "127.0.0.1:8383:8383" volumes: - .:/code links: - db - redis - rabbitmq depends_on: - db - redis - rabbitmq celery: build: . restart: always command: celery -A project.celeryapp:app worker -Q default -n djangitos.%%h --loglevel=INFO --max-memory-per-child=512000 --concurrency=1 env_file: - .env volumes: - .:/code links: - db - redis - rabbitmq depends_on: - db - redis - rabbitmq celery-beat: build: . restart: always command: celery -A project.celeryapp:app beat -S redbeat.RedBeatScheduler --loglevel=DEBUG --pidfile /tmp/celerybeat.pid env_file: - .env volumes: - .:/code links: - db - redis - rabbitmq depends_on: - db - redis - rabbitmq
As you can see we have 2 celery services:
celery-beat. Beat is needed for sending scheduled tasks and
celery is actually a worker that executes those tasks.
What else you can see is that both of them depend on
db service which is our PostgreSQL database,
redis which is used as result backed and
rabbitmq as the broker.
Let's add our
flower service here.
celery-flower: build: . restart: always command: celery -A project.celeryapp:app flower --loglevel=DEBUG --port=9090 ports: - "127.0.0.1:9090:9090" env_file: - .env volumes: - .:/code links: - db - redis - rabbitmq depends_on: - db - redis - rabbitmq
This instructs docker compose to run our flower on port 9090 and open the port for us to access it.
Run this command to start your project.
When all services are up you can open http://127.0.0.1:9090/ and see celery flower interface.
Celery Flower Authentication
Right now our Flower can be accessed by anyone without a password.
The proble is that anyone can come and manipulate your Celery cluster. This can disrupt the work of your project.
But even worse is the fact that anyone can see the data sent as arguments to the tasks and results of our Celery tasks. Those can contain highly sensitive data and we want to prevent leaking of it.
Go back to
docker-compose.yml and edit the
celery-flower. We will add
The whole service will look like this:
celery-flower: build: . restart: always command: celery -A project.celeryapp:app flower --loglevel=DEBUG --port=9090 --basic_auth=djangitos:testpassword ports: - "127.0.0.1:9090:9090" env_file: - .env volumes: - .:/code links: - db - redis - rabbitmq depends_on: - db - redis - rabbitmq
Our login will be
djangitos and password
Go back to terminal where you have
docker-compose running and press CTRL-C to stop it. Wait for it to stop all containers and start
docker-compose up again.
When all services are up – open the Celery Flower web interface again. You will see the login and password prompt.
Celery Flower in Production
While it is easy to spin Flower in local development, it requires additional effort to run Flower in production environment.
One of the important things is having Flower not only with password authentication, but also accessible only via HTTPS.
Appliku makes it easy to since every app, having a
web worker gets SSL certificate and accessible only via HTTPS.
But every app can have only one
web worker, so in order to have Celery Flower we need to create another app where we'll run only flower.
Fork this GitHub repo https://github.com/appliku/flowermonitor
In Appliku dashboard create an application with that forked repository.
In application settings specify the follwing environment variables:
- Add environment variable BROKER_URL pointing to RabbitMQ or Redis instance
- Add environment variable RESULT_BACKEND pointing to Redis instance
- Add environment variable FLOWER_BASIC_AUTH in format USERNAME:PASSWORD (login and password, separated by a colon). This will be used to authenticate Celery Flower web interface
On the Processes tab enable the web worker.
When deployment is finished – click "Open App" link in navigation and you will see the password prompt. Use login and password from FLOWER_BASIC_AUTH environment variable.