ofelia VS Airflow

To anybody that is familiar with Ofelia: how do you use different log files for different jobs?

I've been trying to figure out how to run cron jobs in my docker containers. Most of the solutions I see are some version of "make a separate container to run your crons". One version of this solution is ofelia, which people seem to like a lot.

If you want to run periodic commands on your containers, have you considered using a cron replacement (ofelia chadburn or maybe docker-crontab?

I found Ofelia but from the quick glance I took at their documentation it seems it don't allow to set scheduling rules from the docker compose.

I'm in the exact same situation. I'm currently trying to get mcuadros/ofelia installed to run as a scheduler within Docker. Here's a sample file for Ofelia to run the photoprism import (jobs.ini): https://dl.photoprism.org/docker/scheduler/

Not exactly using the OS' crontab but if you're using Docker to deploy your Django, you might actually find it cleaner to use something like Ofelia to run commands in your Django container.

If you'd like to have this schedule as part of your docker setup as well look into Ofelia https://github.com/mcuadros/ofelia You can run commands - local (on the host or on the docker container Ofelia runs in - by 'docker exec' in an existing container - by spinning up a new container

# See https://github.com/mcuadros/ofelia/blob/master/docs/jobs.md for job settings!

I'm trying to push some Information to an API on a scheduled basis. I figured ofelia is exactly what I need. A Test with "echo test" as command worked just fine, I could see the output in the logs.No if I try to run a curl command I get the following error:2022-01-27T11:30:09.008Z common.go:121 ▶ ERROR [Job "dyndns-kasm" (b5d84c412e5f)] Finished in "1.014721ms", failed: true, skipped: false, error: exec: "curl": executable file not found in $PATH

I am setting up some Wagtail and my custom management commands to be run periodically on my project's Docker container using Ofelia.

Contributing to Apache Airflow's open-source project immersed me in collaborative coding. Experienced maintainers rigorously reviewed my contributions, providing constructive feedback. This ongoing dialogue refined the codebase and honed my understanding of best practices.

In week Two, I contributed to the Apache Airflow repository.

Apache Airflow is an open-source platform to programmatically author, schedule, and monitor workflows. The platform features a web-based user interface and a command-line interface for managing and triggering workflows.

Airflow is the most widely used and well-known tool for orchestrating data workflows. It allows for efficient pipeline construction, scheduling, and monitoring.

AIRFLOW This is more of a library in my opinion, but Airflow has become an essential tool for scheduling in my work. All our ML training pipelines are ordered and scheduled with Airflow and it works seamlessly. The dashboard provided is also fantastic!

I agree there are many options in this space. Two others to consider:

- https://airflow.apache.org/

- https://github.com/spotify/luigi

There are also many Kubernetes based options out there. For the specific use case you specified, you might even consider a plain old Makefile and incrond if you expect these all to run on a single host and be triggered by a new file showing up in a directory…

You can try and contribute some new connectors/operators for workflow managers like Airflow or Airbyte