Adam Johnson

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Time to Move on From Cron


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I was recently tasked with keeping the various repeating jobs running for our data scientists at YPlan. They have a number of nightly or weekly jobs to be run, such as creating summary tables of the day’s various activity logs, pulling in data from third party services, and so on.

The jobs were already running on a couple of brittle hand-configured EC2 instances via cron, and it was my job to bring them all together on one instance configured using Ansible. During the re-build, I came to the conclusion that cron, the unix staple, just wasn’t cutting it, and I’d have to find a replacement.

But, not use cron? Why not? It has been relied upon by sysadmins for decades to keep such jobs running to schedule. What’s wrong with it?

Well, let’s be honest… it’s horrible to use. The syntax for when jobs run is esoteric (quick - what does */5 * 2 * * mean again?), and it’s famously hard to match environment between your shell and it. Logging is normally a matter of redirecting into files with 2>&1 >>myfile.log, and often you end up prefacing every job with a cd into its directory. I know when I’ve used cron in the past I’ve ended up writing a wrapper script to do the working directory/environment/logging stuff and then put the wrapper script around each job in the crontab… by which time, I could’ve investigated and used a better tool.

Exhibit A: the internet is abound in crontab testing webtools to turn to in frustration when you realize you’ve already spent an hour debugging why that job didn’t run when you said it should. (CRON tester) CRON tester

A job scheduler should be easily integrated with the rest of your code, and simple enough to read when the jobs run. I’d been blessed prior to this that the main job scheduler I’d been working with was the one built in to the celery task queue. I’ve also recently wrestled with launchctl, the convoluted XML-based Mac OS X scheduler which is worse than cron.

For my data scientists though, celery would have been overkill and too much to explain, so I set out looking for a simple job scheduler that was still better than cron. Because most of the data science code is already in Python, finding a library that would do it was my first idea. Through my googling, I came across the simple schedule library (as I mentioned in my last python post) and realized that it would do everything I wanted - and no more. It’s based upon a similarly simple ruby library Clockwork, which was dreamt up by Adam Wiggins, another cron-hater.

I installed the library and within minutes had a script ready to run (here gutted to just an example job, but you get the idea):

#!/usr/bin/env python
import schedule

from configuration import logging  # ensures that logging module is configured

logger = logging.getLogger('jobs')

def log_job(job_func):"Running `{}`".format(job_func.__name__))

def example():"I'm an example job!")

schedule.every(2), example)

def main():'Starting schedule loop')
    while True:

if __name__ == '__main__':

The simple DSL syntax that schedule offers was nice and clear to me, and because it was running in a python process I had all the flexibility of being tied into the existing code in the repository, such as the logging configuration. The log_job helper I added was there because, to begin with, most of the jobs gave no output when run.

With that all sorted, I had to think about keeping the jobs running, and continuing should one through an exception or otherwise crash the python interpreter. This is where I turned to another tool that I’ve been using for a long time - Supervisord.

Supervisor is just focused around keeping processes running, according to the specification you give it. I typed up this simple supervisor configuration (templated by Ansible) which keeps running no matter what, and captures all its output to a log file (to be replaced by logstash soon):

directory = {{ repo_path }}
user = {{ username }}
autorestart = true
environment = PYTHONPATH={{ repo_path }}:{{ libraries_path }}
command = {{ repo_path }}
stdout_logfile = {{ repo_path }}jobs.out.log
redirect_stderr = True

The jobs themselves are mostly python scripts that can be run from the command line, in which case to run them as jobs I simply import the module and run its main on schedule, e.g.:

schedule.every()"07:00").do(log_job, database.rebuild_summary_tables.main)

There are some jobs implemented in R though - but use of the sh library it is easy to run them as easily as python functions:

import sys
from sh import Rscript
from configuration import sh_file_logger

def process_stats():
    Rscript('process_stats.R', _out=sh_file_logger)

Here, the sh_file_logger is a special file-like-logger-wrapper, based on this recipe, that replaces stdout so the R script’s output is directed through the python logging module, keeping all the logs in the same place.

In conclusion, the data scientists are happy and so am I. Jobs are running and logged properly, and the syntax is straightforward so it’s simple for any team member to add a new one, without having to understand any more.

Next time you’re looking at scheduling a few jobs on a single machine, don’t automatically turn to cron. There are much more sensible alternatives out there!

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Tags: python