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Cache Hit Rate Simulator
Visualize performance impact of different caching strategies, eviction policies, and cache sizes for your specific workload patterns.
Cache Configuration
64MB16GB
None24h
SingleL1+L2+L3
Workload Pattern
Hit Rate
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Latency
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Throughput
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Cost Savings
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Eviction Policy Details
LRUCurrent
Least Recently Used - Good for temporal locality
Pros
- •Simple to implement
- •Good for recent access patterns
- •Predictable behavior
Cons
- •Vulnerable to scan attacks
- •High metadata overhead
LFU
Least Frequently Used - Good for frequency-based access
Pros
- •Resistant to scan attacks
- •Good for skewed access patterns
- •Long-term optimization
Cons
- •Complex implementation
- •Slow to adapt
- •High memory overhead
FIFO
First In, First Out - Simple but less effective
Pros
- •Very simple
- •Low overhead
- •Fast operations
Cons
- •Poor hit rates
- •No locality awareness
- •Unpredictable performance
Random
Random eviction - Baseline comparison
Pros
- •Simplest implementation
- •No metadata needed
- •Predictable worst-case
Cons
- •Poor performance
- •No optimization
- •Wasteful
💡 Optimization Recommendations
Multi-level caching: Use L1 (Redis) + L2 (application) for optimal performance
Monitor cache metrics: Track hit rate, eviction rate, and memory usage in production