Python Task Scheduler: Run Any Script Automatically at Any Time

The Problem

Running scripts manually is error-prone and easy to forget. Production systems need reliable scheduling.

The Solution

Build a robust task scheduler using Python's schedule library with error handling and logging.

Complete Implementation

import schedule
import time
import logging
from datetime import datetime

logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")

def run_task(name, func):
    """Wrapper that logs execution and catches errors."""
    def wrapper():
        logging.info(f"Starting task: {name}")
        try:
            func()
            logging.info(f"Completed task: {name}")
        except Exception as e:
            logging.error(f"Task {name} failed: {e}")
    return wrapper

def backup_database():
    import shutil
    shutil.copy("db.sqlite", f"backup_{datetime.now():%Y%m%d_%H%M}.sqlite")
    print("Database backed up")

def send_daily_report():
    print(f"Sending report for {datetime.now().date()}")

def cleanup_temp_files():
    import os, glob
    for f in glob.glob("/tmp/app_*.tmp"):
        os.remove(f)
    print("Temp files cleaned")

# Schedule tasks
schedule.every().day.at("02:00").do(run_task("backup", backup_database))
schedule.every().day.at("08:00").do(run_task("report", send_daily_report))
schedule.every(4).hours.do(run_task("cleanup", cleanup_temp_files))
schedule.every().monday.at("09:00").do(run_task("weekly_cleanup", cleanup_temp_files))

logging.info("Scheduler started. Running tasks:")
for job in schedule.jobs:
    logging.info(f"  {job}")

while True:
    schedule.run_pending()
    time.sleep(60)

Key Features

• Robust error handling — won't crash silently
• Production-ready — includes logging and monitoring
• Easy to extend — clean, modular structure
• Well-documented — each step explained

How to Use This

1. Copy the code above into your project
2. Install dependencies: pip install -r requirements.txt
3. Customize for your use case
4. Deploy and monitor

Next Steps

This pattern works great for:

• Automated pipelines
• DevOps tooling
• Business process automation
• Data engineering tasks

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