Cache Eviction Policies
The eviction policy directly affects cache hit rate. A poor policy evicts useful data, causing more cache misses and higher latency.
When You Need Cache Eviction Policies
The eviction policy directly affects cache hit rate. A poor policy evicts useful data, causing more cache misses and higher latency. The right policy keeps hot data in cache.
What It Is
Cache eviction policies determine which items to remove when the cache is full and new items need to be stored. Common policies: LRU (Least Recently Used), LFU (Least Frequently Used), FIFO (First In First Out), Random, and TTL-based.
How It Works
Redis supports multiple eviction policies: volatile-lru (LRU among keys with TTL), allkeys-lru (LRU among all keys), volatile-ttl (evict keys closest to expiration), noeviction (return error when full). Choose based on your workload.
LRU is typically implemented with a hash map (O(1) lookup) + doubly linked list (O(1) move to front/remove from back). On access, move the item to the front. On eviction, remove from the back.
The Decision Framework
- LRU (Least Recently Used): Evicts the item that has not been accessed for the longest time. Most common policy. Good for workloads with temporal locality.
- LFU (Least Frequently Used): Evicts the item accessed the fewest times. Better for stable access patterns. But new popular items start with low frequency.
- FIFO: Evicts the oldest item. Simple but ignores access patterns.
- TTL (Time to Live): Items expire after a set time. Prevents stale data. Often combined with LRU.
- W-TinyLFU (Caffeine): Modern policy that combines recency and frequency. Used in Java's Caffeine cache. Near-optimal hit rates.
What the Industry Uses
Redis defaults to noeviction but allkeys-lru is recommended for caches.
Memcached uses LRU eviction within each slab class.
CDNs typically use a combination of LRU and TTL to manage cached content.
Performance and Tradeoffs
- LRU vs LFU: LRU works well for temporal locality; LFU works for stable popularity.
- Simplicity vs Optimality: FIFO is simplest but worst hit rate. W-TinyLFU is near-optimal but complex.
- TTL: Short vs Long: Short TTL keeps data fresh; long TTL maximizes hit rate.
Mistakes Engineers Make
- Using noeviction — causes errors when cache is full instead of gracefully evicting
- Not monitoring eviction rate — high eviction means the cache is too small
- Using FIFO when access patterns have clear locality
Practice These Interview Questions
- What cache eviction policies do you know?
- How is LRU implemented? What is its time complexity?
- When would you use LFU over LRU?
- How does Redis handle cache eviction?
Practical Implementation for .NET Developers
In a .NET application, you would typically implement this pattern using the following approach:
ASP.NET Core setup: Create a service class that encapsulates the logic, register it with dependency injection, and inject it into your controllers or minimal API endpoints. The built-in DI container handles lifecycle management.
Entity Framework Core: For database interactions, EF Core provides the ORM layer. Use migrations for schema management and raw SQL for performance-critical queries. Consider Dapper for read-heavy paths where EF Core's overhead matters.
Azure integration: If deploying to Azure, leverage managed services — Azure Cache for Redis, Azure SQL, Azure Service Bus, Azure Cosmos DB. These eliminate operational overhead and provide built-in monitoring through Application Insights.
Testing: Use xUnit with Testcontainers for integration tests that spin up real databases in Docker. Mock external dependencies with NSubstitute. The WebApplicationFactory class lets you test your entire HTTP pipeline in-process.
Monitoring: Add Application Insights telemetry to track request latency, dependency calls, and custom metrics. Use structured logging with Serilog to make production debugging possible:
Log.Information("Processing order {OrderId} for {CustomerId}", orderId, customerId);
This gives you searchable, structured logs in Azure Monitor or Seq.
Further Reading
The Real-World Incident That Made This Famous
Understanding Cache Eviction Policies became critical after multiple high-profile production incidents at major tech companies. When systems handle millions of users, even small misunderstandings about Cache Eviction Policies can lead to cascading failures that cost millions in lost revenue and erode user trust. Companies like Netflix, Google, Amazon, and Meta have all invested heavily in mastering Cache Eviction Policies because they learned the hard way that ignoring it leads to outages.
The key lesson from these incidents: Cache Eviction Policies is not just a theoretical concept — it is a practical skill that separates engineers who build resilient systems from those who build fragile ones.
How Senior Engineers Think About This
Senior engineers approach Cache Eviction Policies differently from textbook definitions. Instead of memorizing rules, they build mental models. They ask: "What problem does Cache Eviction Policies solve? When does it fail? What are the alternatives?" This problem-first thinking leads to better design decisions because every system has unique constraints.
When evaluating Cache Eviction Policies in a system design context, experienced engineers consider the failure modes first. What happens when this component goes down? How does the system degrade? Is the degradation graceful or catastrophic? These questions reveal more about your understanding than any textbook definition.
Common Interview Mistakes
Mistake 1: Giving a textbook definition without context. Interviewers want to see you connect Cache Eviction Policies to real systems and real problems.
Mistake 2: Not discussing trade-offs. Every design decision involving Cache Eviction Policies has trade-offs. Discuss what you gain and what you give up.
Mistake 3: Overcomplicating the solution. Start with the simplest approach to Cache Eviction Policies that meets the requirements, then add complexity only when justified.
Production Checklist
- Define clear metrics for measuring the effectiveness of your Cache Eviction Policies implementation
- Set up monitoring and alerting that specifically tracks Cache Eviction Policies-related failures
- Document your Cache Eviction Policies design decisions in Architecture Decision Records (ADRs)
- Test failure scenarios related to Cache Eviction Policies in staging before production deployment
- Review and update your Cache Eviction Policies implementation quarterly as system requirements evolve
- Train new team members on the specific Cache Eviction Policies patterns used in your system
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