You can’t live without a cache, but it’s more dangerous if it’s wrong (Distributed Cache Verification Prompt)

초코송이단 2025년 06월 11일

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If you're a developer, you've probably experienced the feeling of despair when your database becomes critically overloaded due to a rapidly growing number of users. We experienced this last year when our traffic grew 10x and response times were taking seconds. We implemented a simple memory cache to put out the fire, but it was the beginning of another problem.

Different servers had different cache contents, so the same request would get different results, and even when data was updated, users would see the wrong information because of outdated cache. "The cache is making things more complicated," my teammates would say.

That's when I realized that while caching itself is simple, creating a cache system that works well in a distributed environment is a whole other ballgame. It's a complex architectural design that requires not only performance, but also consistency and availability.

Prompt.

복사

# Distributed cache architecture expert

System Status:

- Service size: [number of concurrent users/volume of requests per day].

- Current architecture: [server configuration and database environment].

- Performance requirements: [target response time and throughput].

- Data characteristics: [Percentage of frequently changing data vs. static data].

Distributed cache design strategy:

▲ Step 1: Establish a cache strategy

- Choose a cache pattern (Cache-aside vs Write-through vs Write-behind)

- Design a TTL (time-to-live) policy that fits [our data characteristics].

- Apply Circuit Breaker to prevent DB load in case of cache misses

Step 2: Select a distributed cache solution

- Redis Cluster vs Hazelcast vs Memcached Comparative Analysis

- Determine consistency vs. availability tradeoffs based on CAP theorem

- Design a sharding strategy and data distribution algorithm

Step 3: Consistency Guarantee Mechanisms

- Design a cache invalidation strategy

- How to control concurrency through distributed locks

- Event-driven

Based on this systematic design, we built a distributed cache system based on Redis Cluster over a period of six months, and we saw dramatic improvements, the most notable being a reduction in average response time from 3 seconds to 200ms.

The key was to go beyond just improving speed and focus on ensuring system-wide reliability. For example, we replicated hot data such as popular product information across multiple cache nodes to avoid concentrating the load on a particular node, and built an automatic failover mechanism to ensure that the service would not be interrupted if a cache server went down.

The intelligent cache invalidation system was particularly impressive: instead of relying solely on TTLs, we created an event-driven system that instantly refreshes all relevant caches when data actually changes, virtually eliminating data inconsistency issues.

It was also a big change for the development team, because we were always wondering, "Is the cache causing the bug?" and now we could trust the cache and focus on developing more complex features.

After a year of operation, we've reduced our database load by more than 70%, saved a lot of money on server costs, and most importantly, user satisfaction has improved so much that we've gotten a lot of positive feedback from people asking, "Why is it so much faster?"

If you're a development team struggling to handle large amounts of traffic, why not start by systematically designing a cache architecture for your distributed environment instead of just adding cache? It may seem complicated at first, but once you get it right, you'll be able to improve the stability and performance of your entire system at the same time!

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