What Is Cache Hit Ratio & Why It Matters?

Cache Hit Ratio

Caching has long been recognized as a key contributor in speed and reliability - often making the difference between an optimal user experience and a frustratingly slow one. Within this concept lies a vital metric called Cache Hit Ratio (CHR) that is used to determine the quality of a caching system.

In today’s world, Cache Hit Ratio has become an integral component of a Content Delivery Network (CDN), and measures the effectiveness of a CDN caching mechanism.

In this article, we’re going to dive deep into this vital metric, and decode its definition, calculation, importance, and more to have a comprehensive understanding of this concept.

What is Cache Hit Ratio?

Cache Hit Ratio (CHR) is a measurement that monitors the efficiency of a cache system. This term quantifies the proportion of requests a cache is able to fulfill successfully out of the total requests it receives.

In other words, it tells us how often the data a user needs is available in the cache, thus indicating the cache’s performance and efficacy.

While CHR applies to any cache system, it also becomes a critical benchmark for measuring the performance of a Content Delivery Network (CDN).

Most CDN services, given their pivotal role in speeding up content delivery by bringing content closer to geographically dispersed end users at the edge, provide CDN Cache Hit Ratio, and other data within their user interface or dashboard.

‍

Calculating Cache Hit Ratio

Cache Hit Ratio is calculated using a simple formula. To derive the ratio, the number of cache hits is divided by the total number of requests (hits and misses). If we express it as a percentage, the result is multiplied by 100.

Cache Hit Ratio Formula: {Cache Hits / (Cache Hits + Cache Misses)} * 100%

Let’s consider an example to understand how to calculate cache hit ratio using the formula above:

If a CDN registers 39 cache hits and 2 cache misses in a given timeframe, then the CHR is 39 divided by 41, or 0.951. As a percentage, this would be a cache hit ratio of 95.1%.

This indicates that the CDN was able to deliver requested data from its cache 95.1% of the time.

The Importance of Cache Hit Ratio

Cache Hit Ratio offers an insight into the effectiveness of a cache. A good CHR indicates a high-performing cache, which is able to fulfill the majority of user data requests without passing the request to the origin server.

But what constitutes a good cache ratio and a bad cache ratio?

Good Cache Ratio

Typically a website primarily composed of static content could exhibit a CHR in the 85-89% range. However, websites rich in dynamic content may have a lower CHR, due to the fact that dynamic content is non-cacheable, and has 0% desired CHR.

Bad Cache Ratio

There’s no proper metric for a bad cache ratio since it's usually dependent on the kind of content you’re trying to serve. However, your CHR for static content - even as a bare minimum - should be above 70%.

It’s a soft metric, but a testament for the CDN service to provide effective value for your website. If you’re someone affected by a less than 70% CHR, then it’s high time you try switching CDN providers.

The Caveat

Here’s the catch though: While aiming for a high CHR is a worthy goal, it should not be at the expense of other performance aspects. After all, the primary objective of a CDN extends beyond just caching. Its principal purpose is to make web properties faster and more reliable.

For example, if content is not being served from the CDN server which is closest to the end user, the CDN’s performance will suffer, regardless of a stellar CHR.

Strategies to Improve Cache Hit Ratio

Enhancing your Cache Hit Ratio isn’t just a numbers game. You need to strategically refine your caching tactics. The best approach to solving this problem is as follows:

Implement Proper Cache Control Directives: Your headers are an important aspect of your cache control strategy. The more optimized they are for your site, the higher your Cache Hit Ratio will be.
Adjust Time-To-Live (TTL) Settings: This metric defines the period a piece of content stays in the cache before it’s considered outdated.

Each website is unique, and therefore, your specific requirements and objectives will always be unique. Therefore, an iterative approach to experimentation is always better for tuning such settings.

CHR in API Caching & Microservices

Unlike traditional content caching, API caching deals with structured responses that may include dynamic or personalized data. Unlike images or static pages, API responses often need to be fresh and user-specific, which affects Cache Hit Ratio (CHR).

Here’s where API caching gets tricky:

Frequent Data Changes: APIs often deliver real-time data, making caching difficult.
User-Specific Responses: Caching entire responses may cause privacy and data inconsistency issues.
Query String Variability: API requests with different parameters (?user=123 vs. ?user=456) can reduce CHR.

To improve Cache Hit Ratio in this context, you would:

Use Response Fragmentation – Cache parts of an API response instead of the whole request (GraphQL works well for this).
Set Proper Cache Control Headers – Cache-Control: public, max-age=300 for static API responses.
Enable Stale-While-Revalidate – Serve stale data while fetching fresh data in the background.
Implement Edge API Caching – Use CDN-based API gateways (e.g., Cloudflare, Fastly, AWS API Gateway) to reduce origin requests.
Cache GraphQL & REST Responses Differently – For GraphQL, cache at the resolver level instead of full queries.

CHR and Edge Computing

Edge Computing brings processing closer to users, significantly impacting cache hit efficiency. Instead of relying on centralized CDN PoPs, edge nodes handle localized caching and request resolution.

Main differences are:

Feature	CDN Caching	Edge Caching
Location	Regional PoPs	Distributed micro-caches (5G nodes, IoT devices)
Latency Reduction	Moderate	Ultra-low latency
CHR Optimization	Dependent on TTL & PoP availability	Higher due to localized caching
Best For	Static content, websites, APIs	Real-time applications, IoT, AR/VR

To improve CHR with Edge Computing, you would:

Use Tiered Caching: Cache frequently accessed content at Edge locations and less popular content in higher-tier CDN caches.
Leverage Compute at the Edge: Use serverless functions (e.g., AWS Lambda@Edge, Cloudflare Workers) to pre-process and cache responses.
Implement AI-Driven Caching: Predict popular content patterns and proactively cache content at the edge.

Reduce Cache Fragmentation: Normalize query parameters and minimize unnecessary cache variations.

Conclusion

So, what’s the bottom line? A high CHR does not necessarily guarantee the optimal effectiveness of a CDN. Other factors, such as the proximity of the CDN server to the end user also significantly impact the performance.

These factors, in conjunction with CHR, should be considered a holistic evaluation of CDN performance. The key is to strike the right balance between achieving an impressive CHR and maintaining overall CDN performance!

FAQs

1. What factors affect Hit Rate Calculation accuracy?

Several factors can skew Cache Hit Rate (CHR) accuracy, including bot traffic, cache bypass rules, cache invalidations, and request variations (e.g., different query parameters). Monitoring raw cache logs and removing irrelevant traffic sources improves accuracy.

2. Can a high Cache Hit Rate reduce bandwidth costs?

Yes! A higher CHR means fewer requests reaching the origin server, reducing bandwidth usage. CDN caching, optimized TTL settings, and compressing cached content further reduce costs by minimizing data transfer from the origin.

3. What is the difference between Cache Hit Rate and Miss Rate?

Cache Hit Rate (CHR) is the percentage of requests served from cache (hits / (hits + misses) * 100).

Cache Miss Rate is the opposite: requests not found in cache (misses / (hits + misses) * 100).A high CHR means fewer cache misses, improving performance and reducing server load.

Published on:

February 27, 2025