Skip to main content

Edge Cache Hit Rate Estimator

Calculate CDN edge cache hit rates with TTL analysis and optimization recommendations. Enter values for instant results with step-by-step formulas.

Share this calculator

Formula

Hit Rate = Base Rate Γ— Asset Coverage Γ— TTL Factor + Cacheability Bonus

Base rate (85% industry average) is adjusted by cache size coverage of unique assets, TTL effectiveness (normalized to 1 hour), and percentage of cacheable content.

Worked Examples

Example 1: E-commerce Product Images

Problem: CDN serving 10M monthly requests for 50,000 product images. 80% cacheable, 2-hour TTL, 50GB edge cache, 150ms origin.

Solution: Cacheable: 10M Γ— 80% = 8M requests\nCache size: 50GB / 50KB avg = 1M assets (covers all)\nTTL factor: 120min/60 = 1.0 (optimal)\n\nBase hit rate: 85% Γ— 1.0 Γ— 1.0 = 85%\nWith cacheability bonus: 85% + 8% = 93%\n\nCache hits: 8M Γ— 93% = 7.44M\nCache misses: 560K\nBypass: 2M\n\nLatency: (7.44MΓ—20 + 2.56MΓ—150)/10M = 53ms avg\nOrigin load reduction: 74.4%

Result: 93% hit rate | 53ms avg latency | 74% origin reduction

Example 2: News Site Articles

Problem: Media site: 5M daily requests, 10,000 articles, 60% cacheable (40% personalized), 15-min TTL for freshness, 5GB cache.

Solution: Cacheable: 5M Γ— 60% = 3M requests\nCache capacity: 5GB / 100KB = 50K articles (covers all)\nTTL factor: 15/60 = 0.25 (short TTL hurts)\n\nBase hit rate: 85% Γ— 1.0 Γ— 0.25 = 21.25%\nWith cacheability: 21.25% + 6% = 27.25%\n\nThis is LOW due to short TTL.\n\nRecommendation: Use stale-while-revalidate\nWith SWR (simulated 4hr effective): 85% Γ— 1.0 Γ— 1.0 = 85%+\n\nTrade-off: freshness vs performance

Result: 27% hit rate (too low) | Implement SWR for 85%+ | Balance freshness

Example 3: SaaS Static Assets

Problem: SaaS app: 50M monthly requests, 2,000 JS/CSS/font files, 95% cacheable, 7-day TTL, 20GB cache, immutable versioning.

Solution: Cacheable: 50M Γ— 95% = 47.5M requests\nCache capacity: 20GB / 200KB = 100K files (50Γ— coverage)\nTTL: 7 days >> 1 hour, factor = 1.0\n\nBase hit rate: 85% Γ— 1.0 Γ— 1.0 = 85%\nWith high cacheability: 85% + 9.5% = 94.5%\nWith immutable versioning: +3% = 97.5%\n\nCache hits: 47.5M Γ— 97.5% = 46.3M\nBypass: 2.5M\n\nBandwidth saved: 46.3M Γ— 200KB = 9.26TB/month\nCost savings: ~$463/month at $0.00001/origin request

Result: 97.5% hit rate | 9.26TB bandwidth saved | $463/mo savings

Frequently Asked Questions

What is cache hit rate?

Cache hit rate is the percentage of requests served from cache versus origin. A 90% hit rate means 90 of 100 requests are served from edge cache without contacting origin servers. Higher rates mean faster responses and lower origin load.

What is a good edge cache hit rate?

Industry benchmarks: 90%+ is excellent, 80-90% is good, 60-80% is fair, below 60% needs optimization. CDNs like Cloudflare report average hit rates of 85-95% for optimized sites. Static sites can achieve 99%+.

How does TTL affect cache hit rate?

Longer TTLs increase hit rates by keeping content cached longer. Short TTLs (minutes) suit dynamic content but reduce hits. Static assets should use long TTLs (days/weeks). Balance freshness vs performance based on content type.

What content should be cached at the edge?

Cache: static assets (JS, CSS, images, fonts), API responses that rarely change, HTML for static pages. Don't cache: personalized content, real-time data, authenticated responses, POST requests, content with Set-Cookie headers.

How do I improve cache hit rate?

Strategies: increase TTLs, normalize URLs (remove tracking params), use consistent cache keys, increase cache size, implement stale-while-revalidate, reduce asset variations, use cache-control headers properly, and pre-warm popular content.

What is cache eviction and why does it matter?

Cache eviction removes old content when cache is full. LRU (Least Recently Used) is common. Small caches with many assets cause frequent evictions, lowering hit rates. Size cache appropriately for your asset count.

Background & Theory

The Edge Cache Hit Rate Estimator applies the following established principles and formulas. Structural and construction engineering is governed by fundamental load analysis, material science, and regulatory standards that ensure the safety and durability of built structures. The primary distinction in load analysis is between dead loads β€” the permanent self-weight of structural elements, finishes, and fixed equipment β€” and live loads, which represent variable occupancy, furniture, and environmental forces such as wind and snow. These are combined using factored load equations, such as the ASCE 7 formula U = 1.2D + 1.6L, where D is dead load and L is live load. Concrete mix design is governed by the water-cement (w/c) ratio, which is the primary determinant of compressive strength and durability. A w/c ratio of 0.40–0.45 typically yields concrete with 28-day compressive strengths of 30–40 MPa. Common mix ratios by weight for structural concrete are approximately 1 part cement : 1.5–2 parts sand : 3 parts coarse aggregate. Structural steel is characterized by its yield strength (the stress at which permanent deformation begins, typically 250–350 MPa for mild steel) and ultimate tensile strength (typically 400–500 MPa). Mid-span deflection of a simply supported beam under a central point load is given by Ξ΄ = FLΒ³ / (48EI), where F is force, L is span length, E is Young's modulus, and I is the second moment of area. Building insulation is rated by R-value, a measure of thermal resistance in units of mΒ²Β·K/W (SI) or ftΒ²Β·Β°FΒ·h/BTU (imperial). Higher R-values indicate greater resistance to heat flow. Foundation design depends on the allowable bearing capacity of the underlying soil, which ranges from approximately 75 kPa for soft clay to over 10,000 kPa for bedrock. Drainage gradients for surface water are typically specified as a minimum of 1–2% slope away from building foundations to prevent hydrostatic pressure and water infiltration.

History

The history behind the Edge Cache Hit Rate Estimator traces back through the following developments. The history of construction engineering spans thousands of years of accumulated empirical knowledge and, more recently, rigorous scientific analysis. The ancient Egyptians built the Great Pyramid of Giza around 2560 BCE using an estimated 2.3 million stone blocks, demonstrating sophisticated logistics, geometry, and workforce organization. Roman engineers advanced the field dramatically through the use of pozzolanic concrete β€” a mixture of volcanic ash, lime, and seawater β€” enabling the construction of the Pantheon dome (43.3 m diameter, completed around 125 CE) and a vast network of aqueducts and roads across the empire. Cast iron emerged as a structural material during the Industrial Revolution, first used prominently in the Iron Bridge at Coalbrookdale, England, completed in 1779. Wrought iron and later steel allowed far greater spans and heights. The Eiffel Tower, completed in 1889, demonstrated the structural possibilities of wrought iron at scale and influenced the development of steel-frame skyscraper construction in Chicago and New York. Reinforced concrete was systematically developed by Joseph Monier, a French gardener, who patented iron-reinforced concrete pots and panels in the 1860s, and later by engineers including FranΓ§ois Hennebique who created the first comprehensive reinforced concrete framing system in the 1890s. The 1906 San Francisco earthquake caused widespread devastation and galvanized the engineering profession to develop seismic design provisions. Subsequent earthquakes β€” including the 1971 San Fernando and 1994 Northridge events β€” drove successive improvements in seismic codes, base isolation technology, and ductile detailing of reinforced concrete and steel frames. Building codes became increasingly standardized in the twentieth century, with the International Building Code (IBC) first published in 2000 providing a unified model code adopted across much of the United States. Building Information Modeling (BIM) emerged in the 2000s as a digital workflow integrating architectural, structural, and MEP design into a unified three-dimensional model, fundamentally changing coordination practices across the industry.

References