The Day My First Python Script Caused Data Corruption in My MySQL Database: A Recovery Story

It was a Friday afternoon, the kind where you can practically smell the weekend brewing. I was fresh out of college, brimming with newfound Python skills, and eager to impress my first boss at a small startup. My task? Automate a tedious manual process of updating product prices in our MySQL database. Easy peasy, right? I whipped up a Python script, tested it locally (on a test database, I told myself), and felt like a coding wizard. I hit 'run' on the production server, watched it chug along, and then… silence. Not the good kind of silence. The kind of silence that precedes a digital scream. Suddenly, users started reporting weird prices. Not just wrong, but gibberish. Like "-1000000000000000000" or prices that looked like they were encoded in binary. My stomach dropped faster than a dropped semicolon. I had somehow, in my beginner exuberance, corrupted our entire product catalog. Cue the panic.

TL;DR: My first Python script for updating MySQL prices went sideways, corrupting production data. After a frantic scramble, I managed to recover the data, but not without learning some very hard lessons about production code, data integrity, and the importance of robust error handling.

The "Oops" Moment: When Code Goes Rogue

My script was supposed to read a CSV file, iterate through each product, and execute an UPDATE SQL statement. Simple, elegant, or so I thought. The problem was, I hadn't accounted for a few critical scenarios. First, the CSV file had some weird encoding issues that my script didn't handle gracefully. Instead of skipping or logging these bad rows, it was trying to process them. Second, and this was the kicker, my UPDATE statement had a slight oversight: I wasn't properly sanitizing or validating the input price before inserting it into the database. Imagine trying to shove a square peg into a round hole, but instead of just getting stuck, the hole itself warps and cracks. That’s what happened.

The script was happily chugging along, encountering these malformed price entries, and instead of throwing a clear error and stopping, it was attempting to insert these garbage values. MySQL, bless its resilient heart, tried its best to accommodate, but eventually, consistency went out the window. My once-pristine products table started looking like a digital crime scene.

Here's a simplified (and thankfully, less destructive) version of the kind of SQL statement I was running:

UPDATE products
SET price = 'malformed_price_from_csv'
WHERE product_id = 'some_id';

The malformed_price_from_csv could have been anything from an empty string to a string of nonsensical characters, depending on the CSV row. My Python code was essentially passing these directly to the database without a second thought.

The Frantic Recovery: A Race Against Time

My manager, a saint of a man, walked over with a concerned look. "Everything okay? Users are complaining about prices." My face probably turned the color of a ripe tomato. I mumbled something about "investigating" while my heart pounded like a drum solo.

The first thing I did was kill the rogue script. Then came the real panic: how to fix this? My mind raced through the potential options:

Restore from backup: This is usually the go-to. But the last full backup was from the day before. We had transaction logs, but I wasn't experienced enough to wield them effectively at that moment. Plus, even if I restored, we'd lose about 12 hours of legitimate sales data. Not ideal. Manual correction: Absolutely not. There were thousands of product entries affected. This would take days, and we'd still be guessing which ones were actually corrupted. Write a script to fix it: This felt like the most viable, albeit terrifying, option. I needed to identify the corrupted rows and then find a way to either revert them or impute reasonable values.

I decided to combine elements. I'd write a Python script, but this time, it would be a recovery script. I needed to:

Identify the corrupted rows: I queried the database to find rows with prices that were clearly not valid numbers.

Find the last known good value: This was the trickiest part. I had to go back to a point before the script ran and look at the transaction logs (or in my case, a slightly older export I’d saved locally from before I ran the script). This involved a lot of manual spot-checking and cross-referencing.

Update the corrupted rows: Once I had a strategy for each type of corruption, I'd write another script to apply the fixes.

The query to find some of the corrupted entries looked a bit like this:

SELECT product_id, price
FROM products
WHERE price NOT REGEXP '^[0-9]+(\.[0-9]{1,2})?$'; -- This regex is a simplified example

This query helped me identify rows where the price column didn't conform to a standard numeric format. It was a start, but it didn't catch everything, especially the numeric ones that were just wildly out of bounds (like the negative infinity ones).

For those, I had to look at logs or, in my desperate case, a local CSV export I'd made earlier that day. My recovery script then looked something like this:

```python import mysql.connector import csv

Database connection details (replace with your actual credentials)

db_config = { "host": "your_db_host", "user": "your_db_user", "password": "your_db_password", "database": "your_db_name" }

Load known good prices from a trusted source (e.g., a previous CSV export)

known_good_prices = {} try: with open('trusted_product_prices.csv', mode='r', encoding='utf-8') as infile: reader = csv.DictReader(infile) for row in reader: known_good_prices[row['product_id']] = row['price'] except FileNotFoundError: print("Error: Trusted prices file not found. Cannot proceed with recovery.") exit()

Connect to the database

try: conn = mysql.connector.connect(*db_config) cursor = conn.cursor()

# Identify corrupted rows (example: prices that are not valid numbers or are excessively large/small) # This is a simplified example; a real-world scenario might need more complex checks cursor.execute("SELECT product_id,