Caching Strategies PMs Should Understand

I am managing a performance optimization project for a national restaurant chain. The core challenge is menu API response times under peak load. The solution involves caching, and understanding caching at a conceptual level has made me a significantly better PM on this project.

The Basics

A cache stores frequently accessed data closer to the consumer so you do not hit the database every time. Simple concept, complex execution. The two questions that matter are: what do you cache, and when do you invalidate it.

For our menu API, we cache menu items, prices, and availability. This data changes infrequently but is requested thousands of times per minute during dinner rush. Without caching, every request hits the database. With caching, most requests are served from memory in milliseconds.

Invalidation Is the Hard Part

The classic computer science joke is that the two hardest problems are cache invalidation and naming things. Having lived through this project, I can confirm the first part. When a restaurant updates a menu item price, how quickly does that change reflect in the cached data? If it takes ten minutes, a customer might order at the old price. If you invalidate too aggressively, you lose the performance benefits.

We settled on a time-based expiration with event-driven invalidation. Menu data expires every five minutes automatically, but a price change event triggers immediate invalidation for the affected items.

Why PMs Need This

When your engineering team says "we need to add a caching layer," you should understand what tradeoff they are making. Caching trades data freshness for performance. Your job as PM is to define the acceptable staleness window based on business requirements. "How stale can this data be before it hurts the customer experience?" is a product question, not a technical one. Own it.