[Django]-Check if celery beat is up and running

Make a task to HTTP requests to a Ping URL at regular intervals. When the URL is not pinged on time, the URL monitor will send you an alert.

import requests
from yourapp.celery_config import app

@app.task
def ping():
    print '[healthcheck] pinging alive status...'
    # healthchecks.io works for me:
    requests.post("https://hchk.io/6466681c-7708-4423-adf0-XXXXXXXXX")

This celery periodic task is scheduled to run every minute, if it doesn’t hit the ping, your beat service is down*, the monitor will kick in your mail (or webhook so you can zapier it to get mobile push notifications as well).

celery -A yourapp.celery_config beat -S djcelery.schedulers.DatabaseScheduler

*or overwhelmed, you should track tasks saturation, this is a nightmare with Celery and should be detected and addressed properly, happens frequently when the workers are busy with blocking tasks that would need optimization

If you have daemonized celery following the tutorial of the celery doc, checking if it’s running or not can be done through

sudo /etc/init.d/celeryd status
sudo /etc/init.d/celerybeat status

You can use the return of such commands in a python module.

Are you use upstart or supervison or something else to run celery workers + celery beat as a background tasks? In production you should use one of them to run celery workers + celery beat in background.

Simplest way to check celery beat is running: ps aux | grep -i '[c]elerybeat'. If you get text string with pid it’s running. Also you can make output of this command more pretty: ps aux | grep -i '[c]elerybeat' | awk '{print $2}'. If you get number – it’s working, if you get nothing – it’s not working.

Also you can check celery workers status: celery -A projectname status.

If you intrested in advanced celery monitoring you can read official documentation monitoring guide.

You can probably look up supervisor.
It provides a celerybeat conf which logs everything related to beat in /var/log/celery/beat.log.

Another way of going about this is to use Flower. You can set it up for your server (make sure its password protected), it somewhat becomes easier to notice in the GUI the tasks which are being queued and what time they are queued thus verifying if your beat is running fine.

I have recently used a solution similar to what @panchicore suggested, for the same problem.

Problem in my workplace was an important system working with celery beat, and once in a while, either due to RabbitMQ outage, or some connectivity issue between our servers and RabbitMQ server, due to which celery beat just stopped triggering crons anymore, unless restarted.

As we didn’t have any tool handy, to monitor keep alive calls sent over HTTP, we have used statsd for the same purpose. There’s a counter incremented on statsd server every minute(done by a celery task), and then we setup email & slack channel alerts on the grafana metrics. (no updates for 10 minutes == outage)

I understand it’s not purely a programatic approach, but any production level monitoring/alerting isn’t complete without a separate monitoring entity.

The programming part is as simple as it can be. A tiny celery task running every minute.

@periodic_task(run_every=timedelta(minutes=1))
def update_keep_alive(self):
    logger.info("running keep alive task")
    statsd.incr(statsd_tags.CELERY_BEAT_ALIVE)

A problem that I have faced with this approach, is due to STATSD packet losses over UDP. So use TCP connection to STATSD for this purpose, if possible.

The goal of liveness for celery beat/scheduler is to check if the celery beat/scheduler is able to send the job to the message broker so that it can be picked up by the respective consumer. [Is it still working or in a hung state]. The celery worker and celery scheduler/beat may or may not be running in the same pod or instance.

To handle such scenarios, we can create a method update_scheduler_liveness with decorator @after_task_publish.connect which will be called every time when the scheduler successfully publishes the message/task to the message broker.
The method update_scheduler_liveness will update the current timestamp to a file every time when the task is published successfully.
In Liveness probe, we need to check the last updated timestamp of the file either using:
stat --printf="%Y" celery_beat_schedule_liveness.stat command
or we can explicitly try to read the file (read mode) and extract the timestamp and compare if the the timestamp is recent or not based on the liveness probe criteria.
In this approach, the more minute liveness criteria you need, the more frequent a job must be triggered from the celery beat. So, for those cases, where the frequency between jobs is pretty huge, a custom/dedicated liveness heartbeat job can be scheduled every 2-5 mins and the consumer can just process it. @after_task_publish.connect decorator provides multiple arguments that can be also used for filtering of liveness specific job that were triggered

If we don’t want to go for file based approach, then we can rely on Redis like data-source with instance specific redis key as well which needs to be implemented on the same lines.

You can check scheduler running or not by the following command

python manage.py celery worker --beat

While working on a project recently, I used this:

HEALTHCHECK CMD ["stat celerybeat.pid || exit 1"]

Essentially, the beat process writes a pid file under some location (usually the home location), all you have to do is to get some stats to check if the file is there.

Note: This worked while launching a standalone celery beta process in a Docker container