[2026] Python Task Scheduling | Automate Jobs with schedule, APScheduler, and cron

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[2026] Python Task Scheduling | Automate Jobs with schedule, APScheduler, and cron

이 글의 핵심

Schedule recurring Python jobs: schedule library, APScheduler cron and intervals, cron on Linux/Mac, Windows Task Scheduler, plus backup and scraping examples.

Introduction

For batch scripts or long-running services, pairing periodic work with a scheduler library or the OS scheduler is usually safer than a bare while loop. By the end of this article you can compare options for recurring execution in Python and adapt the examples to your own projects.

“Run jobs automatically”

Task scheduling means running work at defined times or intervals without manual intervention.

1. schedule library

Install

pip install schedule

Basics

다음은 python를 활용한 상세한 구현 코드입니다. 필요한 모듈을 import하고, 함수를 통해 로직을 구현합니다. 각 부분의 역할을 이해하면서 코드를 살펴보시기 바랍니다.

import schedule
import time
def job():
    print("Job running!")
# Every 10 seconds
schedule.every(10).seconds.do(job)
# Every minute
schedule.every(1).minutes.do(job)
# Daily at 9:00 AM
schedule.every().day.at("09:00").do(job)
# Every Monday at 10:00 AM
schedule.every().monday.at("10:00").do(job)
# Run loop
while True:
    schedule.run_pending()
    time.sleep(1)

2. Real-world example

Automated backup

다음은 python를 활용한 상세한 구현 코드입니다. 필요한 모듈을 import하고, 함수를 통해 로직을 구현합니다. 각 부분의 역할을 이해하면서 코드를 살펴보시기 바랍니다.

import schedule
import time
from datetime import datetime
import shutil
from pathlib import Path
def backup_files():
    """Copy a data folder to backups."""
    timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
    backup_name = f"backup_{timestamp}"
    
    source = Path('./data')
    backup_dir = Path('./backups')
    backup_dir.mkdir(exist_ok=True)
    
    backup_path = backup_dir / backup_name
    shutil.copytree(source, backup_path)
    
    print(f"[{datetime.now()}] Backup complete: {backup_name}")
# Daily at midnight
schedule.every().day.at("00:00").do(backup_files)
# Every Sunday at 11:00 PM
schedule.every().sunday.at("23:00").do(backup_files)
print("Backup scheduler started...")
while True:
    schedule.run_pending()
    time.sleep(60)

3. APScheduler

Install

pip install apscheduler

Advanced scheduling

다음은 python를 활용한 상세한 구현 코드입니다. 필요한 모듈을 import하고, 함수를 통해 로직을 구현합니다. 각 부분의 역할을 이해하면서 코드를 살펴보시기 바랍니다.

from apscheduler.schedulers.blocking import BlockingScheduler
from datetime import datetime
scheduler = BlockingScheduler()
def job1():
    print(f"[{datetime.now()}] Job 1")
def job2():
    print(f"[{datetime.now()}] Job 2")
# Cron-style
scheduler.add_job(job1, 'cron', hour=9, minute=0)  # daily at 9:00
# Interval
scheduler.add_job(job2, 'interval', minutes=30)  # every 30 minutes
# Weekdays at 9:00
scheduler.add_job(
    job1,
    'cron',
    day_of_week='mon-fri',
    hour=9,
    minute=0
)
scheduler.start()

4. Automating web scraping

Periodic data collection

다음은 python를 활용한 상세한 구현 코드입니다. 필요한 모듈을 import하고, 함수를 통해 로직을 구현합니다, 에러 처리를 통해 안정성을 확보합니다. 각 부분의 역할을 이해하면서 코드를 살펴보시기 바랍니다.

import schedule
import requests
from bs4 import BeautifulSoup
import pandas as pd
from datetime import datetime
def scrape_prices():
    """Collect product prices."""
    urls = [
        'https://shop1.com/product/123',
        'https://shop2.com/product/456'
    ]
    
    prices = []
    
    for url in urls:
        try:
            response = requests.get(url, timeout=10)
            soup = BeautifulSoup(response.text, 'html.parser')
            
            price = soup.select_one('.price').text
            price = int(price.replace(',', ').replace('원', '))
            
            prices.append({
                'url': url,
                'price': price,
                'timestamp': datetime.now()
            })
        except Exception as e:
            print(f"Error: {url} - {e}")
    
    df = pd.DataFrame(prices)
    df.to_csv('price_history.csv', mode='a', header=False, index=False)
    print(f"[{datetime.now()}] Price scrape finished")
# Every hour
schedule.every(1).hours.do(scrape_prices)
while True:
    schedule.run_pending()
    time.sleep(60)

5. Email notifications

Daily report email

다음은 python를 활용한 상세한 구현 코드입니다. 필요한 모듈을 import하고, 함수를 통해 로직을 구현합니다, 에러 처리를 통해 안정성을 확보합니다. 각 부분의 역할을 이해하면서 코드를 살펴보시기 바랍니다.

import smtplib
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart
from datetime import datetime
import schedule
def send_daily_report():
    """Send a daily HTML report by email."""
    report = generate_report()
    
    msg = MIMEMultipart()
    msg['From'] = 'sender@example.com'
    msg['To'] = 'recipient@example.com'
    msg['Subject'] = f"Daily report - {datetime.now().strftime('%Y-%m-%d')}"
    
    msg.attach(MIMEText(report, 'html'))
    
    try:
        server = smtplib.SMTP('smtp.gmail.com', 587)
        server.starttls()
        server.login('sender@example.com', 'password')
        server.send_message(msg)
        server.quit()
        print("Report sent")
    except Exception as e:
        print(f"Send failed: {e}")
def generate_report():
    """Build simple HTML for the report."""
    return """
    <html>
        <body>
            <h1>Daily report</h1>
            <p>Total revenue: 1,000,000 KRW</p>
            <p>New customers: 50</p>
        </body>
    </html>
    """
# Every day at 8:00 AM
schedule.every().day.at("08:00").do(send_daily_report)

6. Running in the background

Windows Task Scheduler

아래 코드는 bash를 사용한 구현 예제입니다. 코드를 직접 실행해보면서 동작을 확인해보세요.

# Register a Python script with Windows Task Scheduler
# 1. Open Task Scheduler
# 2. Create Basic Task
# 3. Program: python.exe
# 4. Arguments: C:\path\to\script.py

Linux cron

아래 코드는 bash를 사용한 구현 예제입니다. 각 부분의 역할을 이해하면서 코드를 살펴보시기 바랍니다.

# Edit crontab
crontab -e
# Daily at 9:00 AM
0 9 * * * /usr/bin/python3 /path/to/script.py
# Every hour
0 * * * * /usr/bin/python3 /path/to/script.py
# Every Monday at 10:00 AM
0 10 * * 1 /usr/bin/python3 /path/to/script.py

Practical tips

Scheduling checklist

다음은 python를 활용한 상세한 구현 코드입니다. 필요한 모듈을 import하고, 함수를 통해 로직을 구현합니다, 에러 처리를 통해 안정성을 확보합니다. 각 부분의 역할을 이해하면서 코드를 살펴보시기 바랍니다.

# ✅ Logging
import logging
logging.basicConfig(
    filename='scheduler.log',
    level=logging.INFO,
    format='%(asctime)s - %(message)s'
)
def job():
    logging.info("Job start")
    # work here
    logging.info("Job done")
# ✅ Wrap errors
def safe_job():
    try:
        job()
    except Exception as e:
        logging.error(f"Error: {e}")
# ✅ Timeouts (Unix)
import signal
def timeout_handler(signum, frame):
    raise TimeoutError("Job timed out")
signal.signal(signal.SIGALRM, timeout_handler)
signal.alarm(300)  # 5 minute cap

Summary

Key takeaways

  1. schedule: simple, readable timers
  2. APScheduler: cron-like and interval jobs in process
  3. cron: system scheduler on Linux and macOS
  4. Automation: backups, scraping, reports
  5. Operations: logging and error handling

Common use cases

  • Automated backups
  • Price monitoring
  • Data collection
  • Report generation
  • Alerts and notifications
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