Scheduler duplicate email 8 times [duplicate] - flask

Question

We have a web app made with pyramid and served through gunicorn+nginx. It works with 8 worker threads/processes
We needed to jobs, we have chosen apscheduler. here is how we launch it
from apscheduler.events import EVENT_JOB_EXECUTED, EVENT_JOB_ERROR
from apscheduler.scheduler import Scheduler
rerun_monitor = Scheduler()
rerun_monitor.start()
rerun_monitor.add_interval_job(job_to_be_run,\
seconds=JOB_INTERVAL)
The issue is that all the worker processes of gunicorn pick the scheduler up. We tried implementing a file lock but it does not seem like a good enough solution. What would be the best way to make sure at any given time only one of the worker process picks the scheduled event up and no other thread picks it up till next JOB_INTERVAL?
The solution needs to work even with mod_wsgi in case we decide to switch to apache2+modwsgi later. It needs to work with single process development server which is waitress.
Update from the bounty sponsor
I'm facing the same issue described by the OP, just with a Django app. I'm mostly sure adding this detail won't change much if the original question. For this reason, and to gain a bit more of visibility, I also tagged this question with django.

Answer 1

Because Gunicorn is starting with 8 workers (in your example), this forks the app 8 times into 8 processes. These 8 processes are forked from the Master process, which monitors each of their status & has the ability to add/remove workers.
Each process gets a copy of your APScheduler object, which initially is an exact copy of your Master processes' APScheduler. This results in each "nth" worker (process) executing each job a total of "n" times.
A hack around this is to run gunicorn with the following options:
env/bin/gunicorn module_containing_app:app -b 0.0.0.0:8080 --workers 3 --preload
The --preload flag tells Gunicorn to "load the app before forking the worker processes". By doing so, each worker is "given a copy of the app, already instantiated by the Master, rather than instantiating the app itself". This means the following code only executes once in the Master process:
rerun_monitor = Scheduler()
rerun_monitor.start()
rerun_monitor.add_interval_job(job_to_be_run,\
seconds=JOB_INTERVAL)
Additionally, we need to set the jobstore to be anything other than :memory:.This way, although each worker is its own independent process unable of communicating with the other 7, by using a local database (rather then memory) we guarantee a single-point-of-truth for CRUD operations on the jobstore.
from apscheduler.schedulers.background import BackgroundScheduler
from apscheduler.jobstores.sqlalchemy import SQLAlchemyJobStore
rerun_monitor = Scheduler(
jobstores={'default': SQLAlchemyJobStore(url='sqlite:///jobs.sqlite')})
rerun_monitor.start()
rerun_monitor.add_interval_job(job_to_be_run,\
seconds=JOB_INTERVAL)
Lastly, we want to use the BackgroundScheduler because of its implementation of start(). When we call start() in the BackgroundScheduler, a new thread is spun up in the background, which is responsible for scheduling/executing jobs. This is significant because remember in step (1), due to our --preload flag we only execute the start() function once, in the Master Gunicorn process. By definition, forked processes do not inherit the threads of their Parent, so each worker doesn't run the BackgroundScheduler thread.
from apscheduler.jobstores.sqlalchemy import SQLAlchemyJobStore
rerun_monitor = BackgroundScheduler(
jobstores={'default': SQLAlchemyJobStore(url='sqlite:///jobs.sqlite')})
rerun_monitor.start()
rerun_monitor.add_interval_job(job_to_be_run,\
seconds=JOB_INTERVAL)
As a result of all of this, every Gunicorn worker has an APScheduler that has been tricked into a "STARTED" state, but actually isn't running because it drops the threads of it's parent! Each instance is also capable of updating the jobstore database, just not executing any jobs!
Check out flask-APScheduler for a quick way to run APScheduler in a web-server (like Gunicorn), and enable CRUD operations for each job.

Answer 2

I found a fix that worked with a Django project having a very similar issue. I simply bind a TCP socket the first time the scheduler starts and check against it subsequently. I think the following code can work for you as well with minor tweaks.
import sys, socket
try:
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
sock.bind(("127.0.0.1", 47200))
except socket.error:
print "!!!scheduler already started, DO NOTHING"
else:
from apscheduler.schedulers.background import BackgroundScheduler
scheduler = BackgroundScheduler()
scheduler.start()
print "scheduler started"

Answer 3

Short answer: You can't do it properly without consequences.
I'm using Gunicorn as an example, but it is essentially the same for uWSGI. There are various hacks when running multiple processes, to name a few:
use --preload option
use on_starting hook to start the APScheduler background scheduler
use when_ready hook to start the APScheduler background scheduler
They work to some extent but may get the following errors:
worker timing out frequently
scheduler hanging when there are no jobs https://github.com/agronholm/apscheduler/issues/305
APScheduler is designed to run in a single process where it has complete control over the process of adding jobs to job stores. It uses threading.Event's wait() and set() methods to coordinate. If they are run by different processes, the coordination wouldn't work.
It is possible to run it in Gunicorn in a single process.
use only one worker process
use the post_worker_init hook to start the scheduler, this will make sure the scheduler is run only in the worker process but not the master process
The author also pointed out sharing the job store amount multiple processes isn't possible. https://apscheduler.readthedocs.io/en/stable/faq.html#how-do-i-share-a-single-job-store-among-one-or-more-worker-processes He also provided a solution using RPyC.
While it's entirely doable to wrap APScheduler with a REST interface. You might want to consider serving it as a standalone app with one worker. In another word, if you have others endpoints, put them in another app where you can use multiple workers.