Understanding Queue-Based Systems: A Deep Dive into Data Consistency and Failure Handling

My "Aha!" Moment with Queue-Based Systems

The Initial Puzzle

A year ago, I was watching this video from Uber about the Cadence framework. I got stuck on understanding why putting things in a queue solves the issue of failure. At that time, I only associated queues with:

1. Async/Event Driven pipelines
2. Scaling systems to handle burst traffic

The Realization

Today, while working on a nearline pipeline, I had a breakthrough moment. The system required updating both a cache and data storage after polling an event from a Kafka topic. This led me to question:

"How do I ensure data consistency between both data stores?"

The challenge was clear: you could have scenarios where:

• The cache update succeeds but the database fails
• Other service-related failures occur

After some thought, I realized the solution:

"It's fine as long as I commit the offset after both operations are done, right?"

(Assuming those data storage operations are all idempotent)

Suddenly, everything clicked! I remembered that Uber video, and the concept of queue-based systems finally made perfect sense. The queue wasn't just about handling async operations or scaling - it was a crucial tool for maintaining data consistency and handling failures in distributed systems.