The Many Content Caching Strategies and How to Use Them

Imagine you run a global software company with a central server storing all the files for installing and updating the software. Your users are spread worldwide and want to download the software as fast as possible and update it as needed. In the current architecture, every request is sent to the central server, causing server overload and long response times.

To overcome this, you can establish a distributed network of regional servers that each store a copy of the software files and handle user requests. This process of copying frequently accessed data to storage located closer to users is known as caching.

The main objective of caching is to improve data retrieval time by reducing the need to access the underlying storage layer. As a result, caching decreases network latency, resource strain and response time. It also minimizes the costs associated with bandwidth, databases and compute servers.

This article explores different types of content caching, including multilevel caching, cache invalidation and versioning, intelligent cache purging and geo-distributed caching. By the end of this guide, you'll have a solid understanding of how to use content caching to boost your website's performance and provide a better user experience.

More on caching:

• How to Tune Redis to Handle Big Spikes in Traffic

Multilevel Caching

Modern applications serving millions of users across the world need multiple layers of caching to provide optimal performance and user satisfaction while keeping operational costs low. If your  use case involves high traffic and diverse content—including streaming platforms, e-commerce sites and social media—you’ll need a combination of caching strategies, or multilevel caching.

The following sections explore some important caching strategies that can be combined to form a multilayered caching architecture.

Caching at CDN Edge Servers

A content delivery network (CDN) consists of multiple distributed servers strategically placed across the globe to deliver content to users nearest to their location. CDN caches generally serve static content, like images, files and stylesheets.

Advances in caching now allow you to run scripts on the cached data instead of on an origin server where all the data resides. This makes it possible to serve dynamic data, such as user profiles and personalizations. When a user sends a content request, it's sent to the nearest edge server. An edge server typically sits at the entry point of a network, located as close as possible to the requesting user. The edge server checks its local cache for the content. If it's available, it's returned directly to the user. If not, a request is sent to the origin server, the requested content is copied to the cache and a copy is delivered to the user.

Using Reverse Proxy Servers for Caching

A reverse proxy is a web server located between the internet and the application's backend servers. Traditionally, the role of a proxy server has been to intercept requests between the outside world and the application, handle routing and policy management and protect the client's identity. Now, proxy servers are also being used to optimize content delivery. When a request comes in from the internet, the proxy intercepts it, checks its cache for the data, routes it to the appropriate backend server, caches a copy of the response and returns it to the user. This process reduces the load on the backend servers, leading to faster response times.

Nginx is a well-known web server and reverse proxy widely used for caching. Application-Level Caching Application-level caching involves caching API responses, static files, HTML pages, page fragments or calculated results in the application code itself. This requires developers to manage the cache since they understand the application's internal processes.

For instance, let's say you have an API serving a list of music categories. Since the list does not change over a long period, it can be cached for fast access. Redis and memcached are popular in-memory caching solutions. Browser Caching When a user accesses a website, the browser stores resources such as images, JavaScript files, HTML files and CSS in its cache. When the website is accessed again, those resources are loaded from the cache. The browser's policy controls the caching via headers like Cache-Control, which determines the period until the response is cached, and Etag, which checks if the response has been updated.

Query Result Caching

Query result caching allows you to store the results of frequently executed database queries, reducing database load and latency. If a user runs a query that has been run previously and the underlying table data is the same, the cached results are returned.

Query result caching is particularly beneficial for resource-intensive queries that run on a large data set or span over extended periods. Imagine running an aggregation query over several months of data each time there's a request; this would cause a significant bottleneck. It also minimizes the need for additional compute servers, saving data center costs.

Distributed Caching

Consider an e-commerce application serving users via a CDN. If the application only relies on local caching as it expands, such as application-level or browser caching, the servers will be heavily loaded with a significant increase in traffic, causing slow responses and performance issues. Distributed caching spreads the cache across multiple servers to enable the efficient distribution and retrieval of content. Data is replicated among servers, ensuring high availability and fault tolerance.

Geo-Distributed Caching Strategies

The following sections cover strategies for caching in applications with a geographically diverse userbase.

Geographic Hashing

Geographic hashing, or geohashing, is a technique for distributing and retrieving data based on geographic coordinates. This process involves partitioning the Earth's surface into a grid of cells, each identified by a unique alphanumeric hash value. The hashed values serve as keys for indexing data.

Consider a location-based application that recommends nearby restaurants. When a query is made for data near a particular location, the system computes the hash value for that location and retrieves cache entries associated with nearby hash values, ensuring fast retrieval. As user locations change, corresponding cache entries are updated or removed based on their hash values.

Anycast Routing

Anycast routing is a network addressing and routing method where multiple servers share the same IP address. These servers, often acting as cache nodes, are distributed globally. When a user makes a request, the Anycast Domain Name System (DNS) routes it to the closest cache node, determined by latency and hop count. If the requested content is available, the node delivers it directly to the user. If the node is down, the DNS reroutes the request to the next nearest node, guaranteeing reliability.

One example use case are IoT platforms, which use Anycast routing to optimize communication between devices and cloud servers.

Data Center Replication

In a geo-distributed system, servers are positioned in various remote locations, causing slow data access due to the distance. Data replication addresses this issue by copying data to multiple servers across regions, making it highly available; if one cached node fails, the data can still be accessed from another node.

For example, in a social media app, popular user profiles are replicated across all sites, while less popular profiles are limited to fewer locations. Balancing replication and storage is essential to avoid replicating unnecessary or infrequently accessed data, preventing server overload and additional costs.

Cloud service providers use data center replication to offer storage and computing services.

Content Localization

Content localization is defined as the process of "adapting a product or service to a specific locale" (or market). Localized content ensures users get relevant information for their region, language and culture.

For example, a news website can provide region-specific coverage, while an e-commerce platform can highlight local products. Statista reports that out of approximately 5 billion internet users, fewer than 20 percent speak English. Restricting your website to English-only may result in significant revenue loss, making content localization a priority.

Latency-Based Routing

As the name suggests, latency-based routing directs user requests to regions with the lowest latency. You can maintain latency records for each region, allowing the DNS to select those with the lowest latency. The latency values can fluctuate due to variations in network connectivity and routing. By studying these values over time, you can make informed decisions about the implementation.

Latency-based routing significantly reduces how long it takes for content to reach the user's device, improving system performance.

Online gaming platforms use latency-based routing to ensure that players are connected to the nearest game server to minimize lag.

Regional Cache Fill

The process of content transfer to a cache is called cache fill. In geo-distributed applications, content specific to a particular region is intended to be stored on cache servers nearest to that region. Less popular data is periodically purged from the cache to accommodate more region-specific content. For example, Amazon CloudFront has nine Regional Edge Caches that are designed to be larger than other individual caches to include a wide variety of regional content and serve nearby areas with low latency.

Global Load Balancing

Global load balancing distributes incoming user requests across multiple servers around the world, optimizing resource utilization and maintaining system availability even if a server fails. Through policy management, you can route user requests to healthy cache nodes. You can track load balancers and cache node health with monitoring and analytics tools that offer visibility into cache usage patterns. This data helps decide which content to keep and purge so that the cache stays relevant to user needs.

Large-scale web applications like social media platforms use global load balancing to manage high traffic.

Cache Invalidation and Purging Strategies

When information is cached, it essentially creates a snapshot in time. As the underlying data changes, the cached copy becomes outdated. The disparity between the cache and the source can result in incorrect data and misinformation, which is where cache invalidation comes in.

Cache invalidation is the process of updating or removing cached entries when the original data changes. The following sections explore several cache invalidation and purging strategies.

Time-Based Expiration

A time-based expiration strategy invalidates the cache after a predetermined interval. To implement this strategy, add a timeout value to the cache's configuration, representing the time in seconds after which the cache entry expires.

Applications of time-based expiration include weather forecasting, stock market trading and travel-related websites, where data changes after specific periods. It's also useful when minor delays are tolerated, like when a user modifies profile information and sees it reflected after a few minutes when the cache is updated.

Change Detection Invalidation

In change detection invalidation, the cache is invalidated when there's an indication that the underlying data has changed. You need a well-built event tracking and notification/messaging system to propagate the change to the cache.

Change data capture is a software design pattern that tracks changes in the infrastructure and sends notifications so that the cache is updated accordingly. For example, when a user deletes a post in a social media application, the change data capture mechanism signals the cache of the change, triggering invalidation.

Manual Invalidation

Manual invalidation involves developers or administrators manually invalidating the cache. While this gives complete control of the cached content, it can be error-prone and requires careful coordination. For example, when a blogger updates a post on a blog, the developer must manually invalidate the cache to get the updated post.

Tag-Based Invalidation

In tag-based invalidation, cached entries are assigned unique tags. When the source data is updated, tags are updated, and the related cache is invalidated accordingly. This is useful in applications with seasonal or event-based data that can be grouped with tags. Once the event or period is over, the tagged cache can be invalidated simultaneously. Some examples include websites featuring news stories, retail promotions and sporting events.

Version Numbering

Like tag-based invalidation, version-based invalidation assigns version numbers to cached entries. The version number is typically included in the response URLs. When the response changes, the associated version number is modified (usually incremented).

You should use versioning when consistency is of prime importance.

Content Hashing

In a content hashing approach, a distinct hash is created for every cached item and stored with it. A hash is a unique identifier generated by standard hashing algorithms such as MD5, SHA-1 and SHA-256. This hash value acts as a key for the content, mapping directly to a location in the cache where the content is stored. When a request is made, a hash of the requested data is generated and used to check if the corresponding content is in the cache. If the hash matches an entry, the cached content is retrieved. This method ensures speedy retrieval, reduces access time and avoids storing duplicate content.

Intelligent Cache Purging Strategies

As an application expands its userbase, popularity and reach, it needs to be able to handle additional concerns, including protecting sensitive data, handling high traffic and ensuring user retention. Let's explore some intelligent cache-purging strategies that address these challenges.

Real-Time Traffic Monitoring

Monitoring tools provide valuable insights into cache performance, including cache hits, misses and invalidations. These metrics help administrators understand how well the cache is performing. Traffic monitoring also helps identify frequently requested and obsolete content, bandwidth usage, latency and CDN costs so that the caching strategy can be improved to maximize application efficiency.

Popular monitoring tools include Datadog, Grafana and Prometheus.

Popularity Metrics Analysis

Caching popular content decreases resource strain and related expenses and aims to give users a seamless experience. One indicator of popularity is when content is requested multiple times until it exceeds a predetermined threshold.

You can use metrics such as view counts, likes, shares, ratings and downloads to determine popularity scores and decide if content should be stored or purged from the cache. For instance, social media platforms like TikTok or Instagram may choose to cache trending reels and videos.

Sensitivity and Security-Level Assessment

Caching in applications that handle sensitive information, such as those in banking and healthcare, poses significant security concerns due to the risk of mishandling data.

One way to mitigate the risk is to use the Cache-Control header, which has directives to control caching. The "no-store" directive indicates that caches should not store the response. Each time a user sends a request, it's sent directly to the origin server, ensuring that the information is not stored locally or in shared caches. Similarly, the "private" directive specifies that the response can only be cached in a private cache, such as a browser cache. This is useful for caching personalized content that shouldn't be accessible via shared caches. You also need to purge the cache promptly when it's no longer needed to maintain security and compliance with regulations.

User Engagement Tracking

Caching based on user engagement includes analyzing metrics like page views, clicks, time spent on pages, frequency of visits and other user actions. For example, cache entries for users who haven't interacted with the application for a long time or whose engagement scores fall below a certain threshold should be purged. These metrics, combined with recommender systems, are also used to make personalized recommendations (such as suggesting highly rated shows or the latest releases based on user interests).

Predictive Behavior Modeling and Automatic Cache Management Using AI/ML

Data models based on machine learning (ML) and artificial intelligence (AI) are powerful tools for CDNs to boost user satisfaction. Their predictive algorithms analyze historical patterns by operating on large, varying data sets and anticipate future requests.

Preemptive caching is one of the techniques used to prefetch data into the cache. The prefetched data can include trending shows, songs or anything that is in high demand.

These data models are continuously retrained with new inputs, improving their accuracy and adaptability to changing patterns. Monitoring cache performance and model predictions is also important, as it allows for continuous feedback to refine the models.

Automated Purging Triggers

Cache invalidation can be automated by configuring triggers. When data changes, you can create a trigger and publish it to the cache, signaling invalidation. Common triggers include database modifications like insert, update and delete; scheduled jobs configured to run at regular intervals; file content changes and message queue notifications. You can also create custom notification mechanisms. AWS's solution includes creating a Lambda function, assigning required permissions and configuring a Simple Storage Service (S3) trigger to be used for invalidation.

Adaptive TTL Adjustments

Invalidation based on adaptive time to live (TTL) is an advanced time-based expiration technique in which the TTL value is based on specific rules. For example, AWS uses soft and hard TTLs. The client first tries to refresh cached items according to the soft TTL. However, if the server is unavailable or fails to respond, the current cache data is used until the hard TTL is reached.

Conclusion

As data grows in volume and complexity, caching continues to be critical for maximizing web performance. In this article, you learned that you can use a combination of caching strategies to incorporate intelligent caching techniques.

As a next step in this direction, consider Equinix dedicated cloud, a powerful global infrastructure platform for building customized content caching solutions. Deploy single-tenant bare metal compute and storage in more than 30 densely populated metros around the world; connect it privately to any major public cloud, network operator, ISP or local eyeball network; manage this infrastructure remotely and programmatically and pay as you go.