Determine optimal cache sizing based on working set analysis, calculate Average Memory Access Time (AMAT), and analyze cache hit rates for Redis, Memcached, CDN, or application caching. Compare different cache types and eviction policies.
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Cache sizing is critical for application performance. Too small and you suffer excessive cache misses; too large and you waste resources. Our calculator uses working set analysis and the AMAT (Average Memory Access Time) formula to help you find the optimal cache size for your specific workload, whether you're using Redis, Memcached, CDN, or application-level caching.
Cache effectiveness is measured by hit rate (percentage of requests served from cache) and AMAT (Average Memory Access Time). The working set represents the subset of data actively accessed—typically following the 80/20 rule where 20% of data serves 80% of requests. Proper sizing ensures your cache can hold the working set while accounting for eviction policy overhead.
AMAT Formula
AMAT = Hit Time + (Miss Rate × Miss Penalty)Properly sized caches can reduce latency by 10-100x compared to database queries, dramatically improving user experience and application responsiveness.
Cache memory (especially Redis/Memcached clusters) is expensive. Right-sizing prevents over-provisioning while ensuring adequate performance.
Understand how much cache memory you need as your data grows, helping you plan infrastructure scaling and budget allocation.
Many applications have latency SLAs. Cache sizing directly impacts your ability to meet p99 latency requirements under load.
Size your Redis or Memcached cluster to cache API responses, reducing database load and improving response times for frequently accessed data.
Estimate CDN cache requirements for static assets, images, and edge-cached API responses to optimize delivery costs and performance.
Size application-level caches for database query results, reducing read load on your primary database and improving query latency.
Calculate Redis memory requirements for session storage based on active user count, session size, and TTL settings.
AMAT (Average Memory Access Time) combines hit time (latency for cache hits) and miss penalty (latency for cache misses) weighted by their probabilities. Lower AMAT means better overall performance. The formula is: AMAT = Hit Time + (Miss Rate × Miss Penalty).
For read-heavy applications, aim for 90-99% hit rates. Below 80% suggests your cache is undersized or your access patterns don't benefit from caching. Hit rates above 99% are excellent but may indicate over-provisioning.
Different eviction policies have different memory overhead: LRU requires tracking access times (20% overhead), LFU needs frequency counters (25% overhead), FIFO is simplest (10% overhead). Choose based on your access patterns.
The working set is the subset of data actively accessed within a time window. Most applications follow the 80/20 rule: 20% of data serves 80% of requests. Your cache should be sized to hold at least the working set.
Redis offers more features (persistence, data structures, pub/sub) with slightly higher overhead (~0.5ms). Memcached is simpler and slightly faster (~0.3ms) for pure key-value caching. Choose based on your feature requirements.
Monitor cache hit/miss ratios, memory utilization, and eviction rates. Tools like Redis INFO command, Memcached stats, or APM solutions (Datadog, New Relic) provide these metrics. High eviction rates indicate undersizing.