The Black Friday Story and the System Crash Nightmare
About six months ago, I was tasked with building a tracking system for an e-commerce platform. Initially, things were quite simple. The team opted for a “slow and steady” approach: every time a user clicked on a product, a Python script would write a record directly into PostgreSQL.
Everything was smooth until Black Friday. Traffic spiked to 5,000 requests per second. The database started screaming with Too many connections errors. Latency jumped from 50ms to 15 seconds. Eventually, the system froze, causing the team to lose log data for thousands of orders. That’s when I realized: Synchronous data writing when handling massive event volumes is a fatal mistake.
Why Your Python Scripts Often Fall Short?
After dissecting the system, I identified three core issues that caused the old script to fail:
- Database Bottlenecks: Postgres or MySQL aren’t built to swallow 10,000 records per second without a buffer.
- The Domino Effect: If the database goes down for even one minute for maintenance, the entire upstream tracking script dies because there’s nowhere to push the data.
- Language Barriers: The pure-Python
kafka-pythonlibrary is easy to install. However, it is hindered by the Global Interpreter Lock (GIL), making heavy I/O processing extremely slow under high load.
Evaluating the Options: What is the Optimal Choice?
I spent a week benchmarking the three most popular approaches today.
1. Using Redis as a Queue
