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Lightweight Python Data Pipelining With Huey (to Replace Scheduler)

I replaced my existing scheduling system with Huey, a lightweight Python data pipelining tool. I followed the Huey documentation and wrote a configuration file, and after putting it in place, it worked as expected! I'm sharing my experience of setting up Huey, including how I used the huey_consumer.py script and a systemd service to monitor it.

Replacing My Scheduling System With Huey: My Experience With Setting Up and Monitoring a Lightweight Python Data Pipelining Tool

By Michael Levin

Thursday, August 4, 2022

Time to finally do the Huey work!

Get to know Huey, from scratch. Set it up from documentation. https://huey.readthedocs.io/en/latest/guide.html

This is in addition to the PyPI/Github documentation with the repo. https://pypi.org/project/huey/

These documents each give slightly different insights as to a system like this. While it has many similarities to the scheduling system I just set up over on the Munchkin container, there are differences.

Foremost, there can be tasks that are not necessarily scheduled that are just sort of sitting there awaiting further instructions. The example they give is this:

# demo.py
from huey import SqliteHuey

huey = SqliteHuey(filename='/tmp/demo.db')

def add(a, b):
    return a + b

Okay, so the file is named demo.py. There is something called the Huey Consumer, whose file is huey_consumper.py. And it apparently gets invoked as:

$ huey_consumer.py demo.huey

I have to try that. That makes me wonder. I guess huey is installed in such a way as that script file can be invoked as a program. I know such tricks exist as is used all over nbdev, but usually they’re sbin-like and don’t use file extensions. Either way, it has to be tried to be understood. Does the command-line lock-up until a Ctrl+C? This looks like in ideal candidate to be called through systemd. Such things should be services. Perfect.

Okay, so the next step is clear.

Start a video. Capture the experience. Follow the Huey tutorial.

periodic_task(validate_datetime, retries=0, retry_delay=0, priority=None,
context=False, name=None, expires=None, **kwargs)

huey_consumer.py demo.huey

Notice this is not called the same way as scheduler.py. Huey gets called through huey_consumer.py, and in the example they don’t give a path leading to it. This is typical when importing pip installed packages from within Python code. But when it’s on the command-line, it means stuff has been added to the path or put in the existing path.

To figure out what’s really going on here, we have to list the contents of the path variable in the Linux container’s environment variables with printenv. Oh! It’s important to note that we get a different path variable based on whether we have our Python virtualenv activated or not. Here is our default path without source ~/py310/bin/activate:


And here it is with my py310 venv activated:


And then we simple replace colons with line returns, and here they are:


Okay, that won’t take long to figure out. Not many choices. And since it was something pip installed with venv isolation, the money is on it being in the first entry. I guess it’s also worth noting that once we had the Python venv virtual environment activated, we could have also used the whereis command to get the path to the executable:

(py310) ubuntu@Huey:/etc/systemd/system$ whereis huey_consumer.py
huey_consumer: /home/ubuntu/py310/bin/huey_consumer.py /home/ubuntu/py310/bin/huey_consumer

But it’s good to know what your complete path is, so printenv is a bit more important than whereis, but knowing both is great in situations like this.

Okay, so the path is tested. I can launch huey_consumer.py demo.py from the command-line and even funnel it through the GNU screen terminal server, and so success is assured in turning Huey into a monitor-able Linux daemon .service file. And here it is:

Description=Run Python script to handle scheduling

ExecStart=/usr/bin/screen -dmS huey /home/ubuntu/py310/bin/huey_consumer.py demo.huey


And in fact it worked!