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How I Rebuilt a Three-Layer Cache System in Java — Redis, L1, and MongoDB Done Right
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How I Rebuilt a Three-Layer Cache System in Java — Redis, L1, and MongoDB Done Right

DEV Community·Mustafa Bingül·6 months ago
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I've been working on Nexus , a backend infrastructure project, and recently hit a point where the data synchronization layer needed a serious rethink. What looked like a working cache system turned out to have a broken hierarchy, silent data loss paths, race conditions, and a latent deadlock waiting to happen.

This post walks through every problem I found in the original code and exactly how I fixed each one. Code comparisons included throughout.

The Architecture: What We're Building

The system manages data across three layers:

┌─────────────────────────────────┐ │ Redis Cache (MASTER) │ ← single source of truth └────────────┬──────────┬─────────┘ │ │ pull 10s flush 15s │ │ ┌────────────▼──┐ ┌────▼──────────────┐ │ L1 Cache │ │ MongoDB │ │ (in-memory) │ │ (persistent DB) │ └───────────────┘ └────────────────────┘

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The rules are simple:

Redis is always master. No other layer can override it.

L1 is an in-memory mirror of Redis.…

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