When interviewers ask you to design a scalable system, the difference between a mid-level and senior answer lies in how you handle edge cases in caching.
### Essential Caching Patterns to Articulate 1. Cache-Aside (Lazy Loading): Application reads from cache; if miss, reads from DB and writes to cache. Great for read-heavy workloads. 2. Write-Through: Application writes to cache, which synchronously writes to DB. Ensures high consistency at the cost of higher write latency. 3. Write-Behind (Write-Back): Application writes to cache, which asynchronously batches writes to DB. Blazing fast, but risks data loss on cache node failure.
Always proactively discuss cache eviction policies (LRU vs. LFU) and cache invalidation strategies (TTL expiration vs. event-driven CDC invalidation).
### Today's Market Pulse & Hiring Telemetry - Remote Availability: 27% of active listings - High-Demand Roles: Principal Architect, Platform Engineer, Distributed Systems Specialist - Top Geographic Hiring Hubs: Bengaluru, London & San Francisco - Hot Skill of the Day: Distributed Caching (Redis, Memcached) & CDC Pipelines
### Action Item for Today (10 Minutes) 👉 Sketch out a cache-aside versus write-through architecture diagram and identify failure modes for each.
### Practice Interview Prompt 💡 Question: "How would you prevent a cache stampede (thundering herd) when a high-traffic cache key expires?"
🎯 Recommended Strategy: Discuss mutex locking (single flight pattern), probabilistic early expiration (XFetch algorithm), pre-warming background crons, and tiered local in-memory caching.
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