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How do celery workers communicate in Heroku

I have some celery workers in a Heroku app. My app is using python3.6and django, these are the relevant dependencies and their versions:
celery==3.1.26.post2
redis==2.10.3
django-celery==3.2.2
I do not know if the are useful to this question, but just in case. On Heroku we are running the Heroku-18 stack.
As it's usual, we have our workers declared in a Procfile, with the following content:
web: ... our django app ....
celeryd: python manage.py celery worker -Q celery --loglevel=INFO -O fair
one_type_of_worker: python manage.py celery worker -Q ... --maxtasksperchild=3 --loglevel=INFO -O fair
another_type: python manage.py celery worker -Q ... --maxtasksperchild=3 --loglevel=INFO -O fair
So, my current understanding of this process is the following:
Our celery queues run on multiple workers, each worker runs as a dyno on Heroku (not a server, but a “worker process” kind of thing, since servers aren’t a concept on Heroku). We also have multiple dynos running the same celery worker with the same queue, which results in multiple parallel “threads” for that queue to run more tasks simultaneously (scalability).
The web workers, celery workers, and celery queues can talk to each other because celery manages the orchestration between them. I think it's specifically the broker that handles this responsibility. But for example, this lets our web workers schedule a celery task on a specific queue and it is routed to the correct queue/worker, or a task running in one queue/worker can schedule a task on a different queue/worker.
Now here is when comes my question, so does the worker communicate? Do they use an API endpoint in localhost with a port? RCP? Do they use the broker url? Magic?
I'm asking this because I'm trying to replicate this setup in ECS and I need to know how to set it up for celery.

Here you go to know how celery works at heroku: https://devcenter.heroku.com/articles/celery-heroku
You can't run celery on Heroku without getting a Heroku dyno for celery. Also, make sure you have Redis configured on your Django celery settings.
to run the celery on Heroku, you just add this line to your Procfile
worker: celery -A YOUR-PROJECT_NAME worker -l info -B
Note: above celery commands will run both celery worker and celery beat
If you want to run it separately, you can use separate commands but one command is recommended

Airflow celery worker keeps trying to run completed tasks

I recently added a new machine to my Airflow celery cluster (one that is listening on a separate queue).
Everything seemed to be running fine BUT the new worker keeps picking up the same couple of (completed) tasks over and over again. This is invisible from the airflow web interface, which just shows the old tasks as complete and no new tasks being picked up by the worker.
Checking the old task logs gives me messages like the following:
[2018-04-15 04:13:15,374] {base_task_runner.py:95} INFO - Subtask:
[2018-04-15 04:13:15,373] {models.py:1120} INFO - Dependencies not met
for <TaskInstance: my_task 2018-04-13 03:05:00 [success]>, dependency
'Task Instance State' FAILED: Task is in the 'success' state which is
not a valid state for execution. The task must be cleared in order to
be run.
over and over again
I've checked the metadata database and the tasks do show up as 'done'. I've tried restarting Celery, the scheduler, the worker and the servers themselves to no avail. Both the worker and the scheduler are running on UTC timezone as intended.
setup info:
EC2 cluster on AWS
MySQL Celery backend
Airflow 1.8.0
Has anyone ever run into anything like this?

Gracefully stopping celery in docker container

I have celery running in a docker container processing tasks from rabbitmq. I am trying to stop and remove the celery container, while allowing the current running tasks to complete. The docs suggest that sending the TERM or INT signals to the main process should warm shutdown celery, although I am finding that the child processes are just being killed.
When I send TERM the running processes it throws:
WorkerLostError('Worker exited prematurely: signal 15 (SIGTERM).',)
When I send INT the running process just exits with no error, although it too doesn't allow the tasks to finish as the docs suggest.
I am starting the docker container with the command:
su -m celery_user -c "python manage.py celery worker -Q queue-name"
Any thoughts on why this might be happening? Could it be that the signal is terminating the container as well as the celery process?
I am sending the signal with:
docker kill --signal="TERM" containerid
or docker exec containerid kill -15 1

docker kill will kill the container. What you need to do is to send the signal only to the main celery process.
Personally I use supservisord inside the docker container to manage the celery worker. By default supervisord will send SIGTERM to stop the process.
Here's a sample supervisor config for celery
[program:celery]
command=celery worker -A my.proj.tasks --loglevel=DEBUG -Ofair --hostname celery.host.domain.com --queues=celery
environment=PYTHONPATH=/etc/foo/celeryconfig:/bar/Source,PATH=/foo/custom/bin:/usr/kerberos/bin
user=celery-user
autostart=true
stdout_logfile=/var/log/supervisor/celery.log
redirect_stderr=true

Python background worker for RabbitMQ

I am building a Python 3 application that will consume messages from RabbitMQ. Is there some Python background job library that can make this easy? I am looking for something similar to Sneakers in Ruby. I would like library to have:
easy way to define tasks that process RabbitMQ messages (I have a separate non-Python producer application that will create messages and put them into RabbitMQ)
configure number of worker processes that run
tasks
run workers as daemonized processes

I believe you're looking for Celery
You'll define task as follows
#task
def mytask(param):
return 1 + 1
It will be put in message broker (for example mentioned RabbitMQ), and then consumed and executed from celery
You can configure number of workers
celery worker --concurrency=10
And yes, it can be demonized

To consume task of RabbitMq you have to define worker, but to run worker in a daemonized mode you have to create a supervisor for that worker
command to start worker
celery worker --concurrency=10 -Ofair --loglevel=DEBUG -A file_name_without_extension -Q queue_name
steps to create supervisor
https://thomassileo.name/blog/2012/08/20/how-to-keep-celery-running-with-supervisor/
http://python-rq.org/patterns/supervisor/

Celery cannot detect nodes

I've started a celery3 worker (Redis backend) on dev machine with a command like:
celery -A tasks worker --loglevel=info -E
(and the celery screen says that events are enabled)
Then I try to get stats for this working with command:
celery status
which results in
Error: No nodes replied within time constraint
What can be a possible cause for this?
I've already tried restarting the working and the machine.