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Setting up a task scheduler in Flask

The first thing that comes to mind while considering a task scheduler is a cron job. As most of the today’s servers are hosted on linux machines, setting a cron job for periodic task might seem like a good option for many. However in production having a crontab is nothing but a pain in the a**. It can be a bit tricky to configure different timezones depending upon the location of the server.

The biggest problem with this approach is when the application is scaled into multiple web servers. In that case instead of running one we could be running multiple cron jobs which might lead to race conditions. Also it’s hard to debug if something goes wrong with the task.

With Flask there are multiple ways to address third problem and Celery is one of the most popular ones. Celery addresses the above problems quite gracefully. It uses same timezones of pytz which helps in calculating timezones and setting the scheduler timings accurately.

Celery uses a backend message broker (redis or RabbitMQ) to save the state of the schedule which acts as a centralized database server for multiple celery workers running on different web servers.The message broker ensures that the task is run only once as per the schedule, hence eliminating the race condition.

Monitoring real time events is also supported by Celery. It includes a beautiful built-in terminal interface that shows all the current events.A nice standalone project Flower provides a web based tool to administer Celery workers and tasks.It also supports asynchronous task execution which comes in handy for long running tasks.

Let’s go hacking

Here we will be using a dockerized environment. Now the installation of redis and celery can be different from system to system and docker environments are pretty common now a days to do such kind of exercises without worrying so much about local dev infrastructure.

  │  app.py
  │  docker-compose.yml
  │  Dockerfile
  │  entrypoint.sh
  │  requirements.txt

Let’s start with the Dockerfile

  FROM python:3.7

  # Create a directory named flask
  RUN mkdir flask

  # Copy everything to flask folder
  COPY . /flask/

  # Make flask as working directory
  WORKDIR /flask

  # Install the Python libraries
  RUN pip3 install --no-cache-dir -r requirements.txt

  EXPOSE 5000

  # Run the entrypoint script
  CMD ["bash", "entrypoint.sh"]

The packages required for this application are mentioned in the requirement.txt file.


The entry point script goes here.


  flask run --host= --port 5000

Celery uses a message broker to pass messages between the web app and celery workers. Here we will setup a Redis container which will be used as the message broker.

  version: "3.7"


      container_name: redis_dev_container
      image: redis
        - "6379:6379"

      container_name: flask_dev_container
      restart: always
      image: flask
        context: ./
        dockerfile: Dockerfile
          - redis
        - "5000:5000"
        - ./:/flask
          - FLASK_DEBUG=1

Now we are all set to start our little experiment. We have a redis container running on port 6379 and a flask container running on localhost:5000. Let’s add a simple api to test whether our tiny web application works.

  from flask import Flask

  app = Flask(__name__)

  def index_view():
      return "Flask-celery task scheduler!"

  if __name__ == "__main__":

And voila!

Now we will be building a simple timer application which will show the elapsed time since the application has started. We need to configure celery with the Redis server URL and also we will be using another Redis database to store the time.

  from flask import Flask
  from celery import Celery
  import redis

  app = Flask(__name__)

  # Add Redis URL configurations
  app.config["CELERY_BROKER_URL"] = "redis://redis:6379/0"
  app.config["CELERY_RESULT_BACKEND"] = "redis://redis:6379/0"

  # Connect Redis db
  redis_db = redis.Redis(
      host="redis", port="6379", db=1, charset="utf-8", decode_responses=True

  # Initialize timer in Redis
  redis_db.mset({"minute": 0, "second": 0})

  # Add periodic tasks
  celery_beat_schedule = {
      "time_scheduler": {
          "task": "app.timer",
          # Run every second
          "schedule": 1.0,

# Initialize Celery and update its config
celery = Celery(app.name)

def index_view():
    return "Flask-celery task scheduler!"

def timer_view():
    time_counter = redis_db.mget(["minute", "second"])
    return f"Minute: {time_counter[0]}, Second: {time_counter[1]}"

def timer():
    second_counter = int(redis_db.get("second")) + 1
    if second_counter >= 59:
        # Reset the counter
        redis_db.set("second", 0)
        # Increment the minute
        redis_db.set("minute", int(redis_db.get("minute")) + 1)
        # Increment the second
        redis_db.set("second", second_counter)

if __name__ == "__main__":

Let’s update the entrypoint.js to run both Celery worker and beat server as background processes.


  # Run Celery worker
  celery -A app.celery worker --loglevel=INFO --detach --pidfile=''

  # Run Celery Beat
  celery -A app.celery beat --loglevel=INFO --detach --pidfile=''

  flask run --host= --port 5000

Our very own timer

The application is only for demonstration purpose. The counter won’t be accurate as the task processing time is not taken into account while calculating time.

Monitoring events

Celery has a rich support for monitoring various statistics for tasks, workers and events. We need to log into the container to enable and monitor events.

  docker exec -it flask_dev_container bash

Enable and list all events

  celery -A app.celery control enable_events

  celery -A app.celery events

This spins up a nice interactive terminal ui listing all the details of the scheduled tasks.


In this post I have used Celery as an better alternative to crontabs even though the primary purpose of Celery is processing tasks queues. Both Celery worker and beat server can be run on different containers as running background processes on the web container is not regarded as best practice.

Unless you are creating a stupid timer application.

The above mentioned code can be found here. repo