Great Stack to Doesn't Work #3 — Redis: "99% Cache Hit Ratio, System Down"

Mehmet TURAÇ Posted on May 30 Great Stack to Doesn't Work #3 — Redis: "99% Cache Hit Ratio, System Down" # redis # backend # devops # discuss Great Stack to Doesn't Work (4 Part Series) 1 Great Stack to Doesn't Work #1 — PostgreSQL: "I Added an Index and It Got Slower" 2 Great Stack to Doesn't Work #2 — Kafka: "Where Did My Messages Go?" 3 Great Stack to Doesn't Work Bonus: SQL vs NoSQL: Which One in 2026? 4 Great Stack to Doesn't Work #3 — Redis: "99% Cache Hit Ratio, System Down" A survival guide for when everything goes wrong in production. Your Redis dashboard looks perfect. Hit ratio: 99.2%. Latency: sub-millisecond. Memory usage: 60% of available. Every metric says healthy. Then at 2:47 PM, your API starts returning 500s. Response times spike to 30 seconds. Users can't log in. The dashboard still shows 99% hit ratio because the cache is working — it's serving cached errors to everyone equally fast. Redis is doing exactly what you told it to do. The problem is what you told it to do. Why Single-Threaded Is Fast (Until It Isn't) Redis processes commands on a single thread. No locks. No context switching. No synchronization overhead. One CPU core, fully utilized, can handle 100K+ operations per second because it never waits for another thread to release a lock. The event loop model (similar to Node.js) multiplexes thousands of client connections on a single thread using non-blocking I/O. Read a request, process it, write the response, move to the next. When your commands are simple — GET, SET, INCR — each one takes microseconds. The trap: slow commands block everything. KEYS * on a million-key database? That's a full keyspace scan on the main thread. While it runs, every other client waits. SORT on a large set? Same. LRANGE on a list with 10 million elements? Same. Redis 6.0 introduced I/O threading ( io-threads config) for reading and writing network data on multiple threads, but command execution i