Celery Dashboard

This package allows you to have access to a dashboard for your celery application. It was built with two main goals:

Have a persistent and complete view of how your tasks and queues are behaving.
Have a simple codebase in order for it to be easily extensible.

Advantages over Flower

A simple codebase: We use celery's signals api to register all tasks state from being queued to being ran or revoked.
No single point of failure: Flower spawns a thread that listens to all events. Using the signals api allows us to delegate the work: each worker is responsible of keeping track of its tasks.
A modern UI (Vue.js + Vuetify)
Data is stored in a PostgreSQL database, making it persistent and available from anywhere even if your dashboard's webserver happens to crash.
Allow keeping track of the progress of long running tasks

Screenshots

Requirements & compatibility

A PostgreSQL database >= 9.5 is required. The code has been tested for celery version 3.1 and 4.1 running under both python 2.7 and python 3.5.

Getting started

The first thing to do after installing the package is to update the python file where your celery application is created as follows:

from celery import Celery

from celery_dashboard import init

celery_app = Celery('test_app', broker='redis://localhost', backend='redis://localhost')

init(celery_app,
     "YOU POSTGRES DATABASE URI (e.g postgresql://docker:docker@localhost:5432/docker)",
     "YOUR DASHBOARD USERNAME", "YOUR DASHBOARD PASSWORD")

@celery_app.task(name="divide")
def div(x, y):
    return x / y

That's it ! your app is all setup. Now if you want to checkout the dashboard you just need to run celery -A <your_app> dashboard And the dashboard will be running on localhost:5000. If you want to specify an other port you can add --port=<your_port> to the command line

Database cleaning

In order to keep you database size under control, the init() function will automatically setup some cleaning tasks in your beat schedule.

By default these tasks will clean:

your SUCCESS tasks that are 4 hours old
your STARTED tasks that are 1 hour old. In case a worker crashes (killed with a SIGKILL for example) it will not be able to update its running task's status. So this task will stay in a STARTED status forever. In order to clean these tasks, we assume that if a task is in STARTED status for more that an hour, it should be cleaned.

You can of course override this settings by passing the cleaning_thresholds paramater to the init function. cleaning_thresholds is a dict with statuses as keys and thresholds as values (in seconds), such as:

{"STARTED": 3600, "SUCCESS": 3600 * 4}

All these tasks will be routed to the queue celery_dashboard.