Before proposing caching in a design interview, what should you do?

Before proposing caching in a design interview, what should you do?

• A. Identify a concrete bottleneck and quantify it.
• B. Immediately suggest Redis caching for everything.
• C. Assume the database will scale indefinitely.
• D. Skip to performance testing without discussion.

Answer: (B)

The key idea is to ground caching decisions in data about real bottlenecks. In a design interview, you should first identify where latency or load is actually coming from and quantify it—how often a path is hit, how long it takes, and how it affects throughput and user experience. This concrete understanding lets you justify caching for the parts that matter, choose where to place the cache, and decide appropriate eviction and invalidation strategies. It also keeps the design scalable and maintainable by avoiding unnecessary complexity. Jumping to caching for everything without evidence, or assuming the database will scale indefinitely, or skipping discussion and diving straight into performance tests, all miss the chance to reason about data access patterns, consistency, and cost. Those approaches can lead to wasted effort, caching the wrong data, or introducing new failure modes without real benefits.

The key idea is to ground caching decisions in data about real bottlenecks. In a design interview, you should first identify where latency or load is actually coming from and quantify it—how often a path is hit, how long it takes, and how it affects throughput and user experience. This concrete understanding lets you justify caching for the parts that matter, choose where to place the cache, and decide appropriate eviction and invalidation strategies. It also keeps the design scalable and maintainable by avoiding unnecessary complexity.

Jumping to caching for everything without evidence, or assuming the database will scale indefinitely, or skipping discussion and diving straight into performance tests, all miss the chance to reason about data access patterns, consistency, and cost. Those approaches can lead to wasted effort, caching the wrong data, or introducing new failure modes without real benefits.