Duraid Wadie

Head of M&A Architecture

Medium Article · 3 min read · Oct 25, 2017

We Thought Caching Would Save Us-But It Just Hid the Real Problems

ArchitectureObservabilitySystem Design ConceptsOrganizational CultureAI

Article summary

We Thought Caching Would Save Us-But It Just Hid the Real Problems The first time we added caching, it felt like magic. Queries dropped from 800ms to 20ms. Load on our SQL Server instance dropped. Everyone was happy-until the incidents started rolling in. Stale data. Conflicting writes. Unclear ownership of invalidation logic. And worst of all: engineers stopped optimizing queries because “it’s cached.” This is the story of how caching masked the symptoms, but never solved the root causes-and how we learned to make SQL performance the foundation, not the fallback. What Went Wrong 1. We Cached Around Broken Queries Instead of fixing N+1 lookups or missing indexes, we just cached their outputs. The queries stayed bad-but we masked it with Redis. 2. We Didn’t Design for Invalidation Cache invalidation wasn’t coordinated.

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Practical takeaway

The main idea behind We Thought Caching Would Save Us-But It Just Hid the Real Problems is to help teams move from broad theory to clear, repeatable decision making. When teams apply this thinking, they reduce ambiguity and focus on improvements that deliver measurable momentum.

Example scenario

Imagine a team facing competing priorities. By applying the ideas in We Thought Caching Would Save Us-But It Just Hid the Real Problems, they can map dependencies, identify risks and choose the next move that produces progress without destabilizing their system.

Common mistakes to avoid

How to apply this in real work

Start by identifying where We Thought Caching Would Save Us-But It Just Hid the Real Problems already shows up in your architecture or delivery flow. Then pick one area where clarity would reduce friction. Apply the idea, measure its effect and share the learning.

Signs you are doing it correctly

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