Are We Overusing Python? How One Line of SQL Beat a 13-Hour Data Load

In tech, we often rely on Python to solve problems, especially when it comes to data. It’s a versatile tool but sometimes we overuse it, missing out on more efficient solutions that are already built into our databases. With 20+ years in cloud and database operations, I’ve learned that knowing when to lean on the database instead of Python can make all the difference.
Recently, I encountered a perfect example of this lesson: a 13-hour data load that could’ve been completed in just seconds with a single SQL command. Here’s the story of how I recognized the inefficiency, made a change, and what it says about our approach to solving problems in tech.

The Problem: A Slow Python-Based Data Load

We were working with an RDS MySQL instance, aiming to load anywhere between 100k and 250k rows of data. Initially, a Python script was handling the insertion row by row, using executemany() to batch the inserts. While this approach was faster than inserting each row individually, it was still incredibly slow and far from optimal.
At first, I thought it was purely a Python issue, that there was a better way to handle bulk inserts. I suggested optimizing the Python code further, but after watching the job stretch to over 13 hours, I began to question whether Python was even the right tool for this task. 

The Switch: Thinking Like a DBA

Over the years, I’ve worn many hats and in this case, thinking like a DBA made all the difference. I didn’t want to just fix the Python code, I wanted to tackle the problem from a database-centric perspective. So I asked myself:

• Why are we using Python when MySQL has built-in tools designed specifically for bulk data loads?
• How can we leverage the database to do what it does best — efficiently load large volumes of data?

That’s when it hit me: I remembered how Oracle’s SQL*Loader, a trusted and widely used tool for bulk loading data handles these operations with exceptional speed. MySQL offers a similar, highly efficient command: LOAD DATA INFILE.

The Solution: LOAD DATA INFILE 

After a quick look through the MySQL documentation, I confirmed that LOAD DATA INFILE was exactly what we needed. It’s a highly optimized command for loading data directly from a file into a MySQL table.
I quickly tested it with a local example, making adjustments for the security settings and file permissions on our RDS instance. The results were astounding; what had been taking over 13 hours with Python now completed in just a few seconds.

The Command:
LOAD DATA INFILE '/path/to/data.csv'
INTO TABLE my_table
FIELDS TERMINATED BY ','
LINES TERMINATED BY '\n'
IGNORE 1 LINES;

The beauty of LOAD DATA INFILE is its ability to handle bulk inserts much more efficiently than Python. The database performs the load without having to loop through each row in code, providing a significant performance boost. 

The Lesson: Don’t Overuse Python for Bulk Loads

This experience was a strong reminder that:

• Databases are built for bulk operations, and using them effectively often means relying on native features, not custom code.
•  Python isn’t always the best choice for tasks like bulk inserts, especially when the database has more efficient ways of handling them.
• Thinking from a database-first perspective can save hours of development and execution time.

While Python is powerful and flexible, there are times when it’s not the right tool. In this case, it was slowing us down unnecessarily, and the database had a solution all along. 

The Value of Perspective in Operations Leadership

This experience wasn’t just about fixing a slow data load — it reinforced the importance of perspective in leadership.

Over the years, I’ve learned that leadership is about more than just technical expertise; it’s about recognizing when to step back and reassess the problem. This experience taught me:

• Understanding the tools at your disposal allows you to spot inefficiencies early. While the team was focused on optimizing Python, I realized the database had a built-in solution.
• Shifting from a Python-centric mindset to a database-first approach was critical for solving the problem efficiently. Leadership here meant fostering a mindset of challenging assumptions and exploring better solutions.
• The goal wasn’t just to improve one task but to promote a culture of continuous improvement. It’s my responsibility to help the team find smarter, more efficient ways to work, even if that means moving away from familiar tools. 

Conclusion: Let the Database Handle the Heavy Lifting

If there’s one takeaway from this experience, it’s that Python shouldn’t be the default solution for everything. There are times when Python is the right tool, but when it comes to bulk data loading, let the database do what it does best.

In this case, one line of SQL in MySQL’s LOAD DATA INFILE was all it took to reduce a 13+ hour load job to just a few seconds. The lesson? Don’t forget that the tools built into your database are often the most powerful and efficient. 

Thanks for reading. Let’s keep the conversation going — connect with me on LinkedIn and share your thoughts.