Cache invalidation strategies in HLD - Scalability & System Analysis

Scalability Analysis - Cache invalidation strategies

Growth Table: Cache Invalidation Strategies

Users / Requests	Cache Size	Invalidation Frequency	Complexity	Latency Impact
100 users	Small (MBs)	Manual or simple TTL	Low	Minimal
10,000 users	Medium (GBs)	TTL + Event-based invalidation	Moderate	Low
1,000,000 users	Large (10s GBs)	Complex event-driven + selective invalidation	High	Moderate
100,000,000 users	Very Large (100s GBs+)	Distributed cache with coordinated invalidation	Very High	Critical to optimize

First Bottleneck

The first bottleneck is cache consistency and stale data. As user scale grows, invalidation delays cause outdated data to be served, harming user experience and correctness. Also, the overhead of invalidation messages or TTL expiration increases, leading to higher latency and resource use.

Scaling Solutions

• TTL (Time To Live): Simple expiration to limit stale data duration.
• Write-through / Write-back Caches: Update cache on data writes to keep consistency.
• Event-based Invalidation: Use messaging or pub/sub to notify cache nodes of changes.
• Selective Invalidation: Invalidate only affected keys, not entire cache.
• Distributed Cache Coordination: Use consensus or coordination protocols to sync invalidations across nodes.
• Cache Versioning: Use version tags to detect stale cache and refresh selectively.
• Hybrid Approaches: Combine TTL with event-based invalidation for balance.

Back-of-Envelope Cost Analysis

• Requests per second (RPS): 1000 users ~ 100-500 RPS; 1M users ~ 10K-50K RPS.
• Cache storage: depends on data size; e.g., 1M keys * 1KB = ~1GB memory.
• Invalidation messages: event-based invalidation adds network overhead proportional to writes.
• Bandwidth: Invalidation traffic can be 1-5% of total traffic depending on write frequency.
• CPU: Processing invalidation events and cache updates adds load on cache servers.

Interview Tip

Start by explaining why cache invalidation is hard and important. Discuss simple TTL first, then event-driven invalidation. Mention trade-offs between stale data and overhead. Show awareness of distributed system challenges like coordination and consistency. Use examples like social media feeds or product catalogs to illustrate.

Self Check Question

Your cache system handles 1000 invalidation events per second. Traffic grows 10x. What do you do first?

Answer: Implement selective invalidation and event batching to reduce invalidation overhead. Consider adding distributed coordination to scale invalidation across cache nodes efficiently.

Key Result

Cache invalidation becomes the first bottleneck as user scale grows due to stale data and invalidation overhead; solutions include TTL, event-driven selective invalidation, and distributed coordination to maintain cache consistency efficiently.