The Monkey's Paw of Scheduling
Why is it so hard to automate your work?
The Monkey's Paw is a short story where wishes come true — but with unforeseen negative consequences. Scheduling your code is a lot like that.
Python has become ubiquitous, and countless developers want to automate their work through scheduling. Back in 2011, I tried to automate scraping gambling odds using Windows Task Scheduler. It repeatedly failed due to rate limiting, API changes, and infrastructure issues.
The Hidden Costs
Scheduling introduces several unexpected challenges:
Infrastructure complexity — Running scripts remotely requires containerization and infrastructure configuration. Your laptop can't stay open forever.
Continuous change — APIs, schemas, and dependencies evolve constantly, breaking scheduled tasks. What worked yesterday may not work tomorrow.
Data variability — Unexpected payload formats and edge cases cause failures. The data you expect isn't always the data you get.
Visibility gaps — Determining why scheduled jobs fail becomes extremely difficult. Did it run? Did it succeed? Where did it break?
The Solution
Orchestration tools address these unavoidable problems. Scheduling is a hard problem because of everything else that it takes to make something run, not because of the schedule itself.
Rather than asking "can I schedule this?", developers should ask whether they can make it bulletproof and visible. That's where orchestration comes in.