Caching

Why this is important

– Speed & responsiveness: Cached responses can be delivered incredibly fast, sometimes in microseconds or milliseconds, compared to seconds it might take to get data from a database or another service. This can turn a slow 3-second response into a super-fast 30-millisecond one.
– Reliability: If the main systems that hold your data temporarily go offline, cached information can still be served. This makes your service more robust and dependable.
– Scalability & cost savings: Caching reduces the workload on your servers and databases. This allows your API to handle many more users without you having to spend a lot of money on expensive upgrades to your infrastructure.

Where to use caching

To get the best performance, scalability, and user experience, caching should be used in different parts of your system. Here’s a look at each level:

Client-side (Browser & Apps)

How it works
API responses are saved directly on the user’s device (like in their web browser’s cache, local storage, or a Service Worker). When the user asks for the same information again, it’s pulled directly from their device instead of from the internet.

Key techniques
– Using special HTTP headers like Cache-Control (which tells the browser how long to store something) and ETag (a unique ID for a piece of content).
– Using Service Workers for more advanced caching, like making an app work offline.

Advantages
Makes things incredibly fast for the user and uses much less internet data. It also allows for offline experiences.

Things to watch out for
You need to be careful about making sure the saved data isn’t old or “stale.” Also, it’s limited by how much storage the user’s device has.

CDN / Edge caching

– How it works: API responses are saved on servers that are geographically close to your users. These are part of a Content Delivery Network (CDN) like Cloudflare or Amazon CloudFront.
– Advantages: Reduces delays for users around the world, takes work off your main servers, and makes your service more available (even if your main server has issues).
– Best for: Content that doesn’t change very often (static) or changes sometimes (semi-static). Works well for both small and global audiences.

 Server-side caching

How it works
Your main application servers temporarily store API response data very close to where your application code runs. This uses:

– In-memory caches: Super fast storage like Redis or Memcached.
– Disk-based caches: Storing on hard drives, or using tools like Varnish or NGINX as “reverse proxies” to serve cached content.

Advantages
Speeds up response times by avoiding repeated calculations or database lookups. This type of cache can be shared by many users.

Things to watch out for
Adds more complexity to your system (like deciding when to remove old data from the cache) and uses your server’s resources.

Middleware / Database layer caching

How it works
Caching happens directly where your application talks to the database. It stores the results of database queries or calculations so you don’t have to hit the database every single time for the same request.

Advantages
Reduces the workload on your database, especially for very large or complex queries. Helps maintain performance even when many users are asking for data.

Things to watch out for
Requires smart ways to make sure the cached data stays in sync with the main data in the database and isn’t old

Caching patterns

Choosing the right caching strategy is crucial for balancing speed, consistency (making sure data is always up-to-date), and how complex your system becomes. Here’s a look at the most common ways to cache data:

Cache-aside (Lazy loading)

How it works
– Your application first checks if the data it needs is in the cache.
– If it’s not in the cache (a “cache miss”), your application then gets the data from the main database or service.
– It then stores this newly fetched data in the cache before sending it back to the user.

Pros
– Simple to set up.
– Gives you precise control over what gets cached.
– Only caches data that is actually requested.

Cons
– The first time data is requested, it will be slow because it’s not in the cache yet.
– You need to manually figure out when cached data becomes old and remove it (invalidate it).

Best usage: Systems that mostly read data and don’t change data very often (read-heavy workloads).

Read-through

How it works
The cache itself handles getting the data. If the data isn’t in the cache, the cache automatically goes to the main data source (like a database) to fetch it, store it, and then return it, all without your application needing to do those steps.

Pros
– It takes some logic out of your main application.
– Simplifies how your application uses the cache.

Cons
– The cache becomes a critical part of your system (a dependency).
– Still, if the data isn’t in the cache, you’ll experience a delay while it’s fetched from the main source.

Write-through

How it works
When your application writes (saves) data, that data is sent to both the cache and the main database at the same time.

Pros
Ensures that the cache and the main database are always perfectly in sync.

Cons
– Can make writing (saving) data slower because it has to go to two places.
– Uses more memory/storage because data is duplicated in the cache.

Best usage: Systems that need very strong consistency, where data must always be up-to-date everywhere (like banking systems).

Write-back (Write-behind)

How it works

When your application writes data, it’s first saved only to the cache. The cache then updates the main database later, in the background, either when the cached data is removed or at scheduled times.

Pros
– Makes writing (saving) data very fast.
– Can group many small writes into one bigger update to the database (batching).

Cons
Risk of losing data if the cache crashes before it has a chance to save everything to the main database.

Best for: Systems that do a lot of writing and can tolerate data being slightly out of sync for a short period (eventual consistency).

Write-around

How it works
When your application writes data, it completely bypasses the cache and goes directly to the main storage (database). The cache is only updated when the data is read later on.

Pros
Prevents the cache from being filled with data that is rarely read (avoids “cache pollution”).

Cons
The very first time data is read after it’s been written, it will still be a “cache miss” and therefore slower, as it hasn’t been put into the cache yet.

Challenges with caching

While caching can make your system much faster, it also adds complexity and potential risks. Here are common issues and how to deal with them.

Old & inconsistent data (Stale data)

The problem
If you don’t have a good plan to update or remove old data from the cache, users might see outdated or incorrect information, which can lead to mistakes in your business.

How to fix it

– Use TTLs (Time-To-Live) wisely: Set a time limit for how long data stays in the cache. Make it long enough to help performance, but short enough so data doesn’t get too old.
– Invalidate based on events: When data changes in your main system (like your database), immediately tell the cache to remove or update that specific entry.
– “Stale-while-revalidate”: Serve the old (stale) content right away to the user, but in the background, quietly get the fresh, updated version to put in the cache for next time.

Security & privacy risks

Sensitive data leaks
If caching layers are not set up correctly, they can accidentally expose private information like user tokens, session details, or personalised content to the wrong people.

Cache poisoning
Attackers might try to put bad or malicious responses into your shared caches, which then get served to legitimate users.

Side-channel leaks
Sometimes, even how long it takes for a cached response to be delivered can unintentionally reveal sensitive information, especially in advanced AI systems.

How to prevent
– Don’t cache sensitive data: Avoid caching private or personal information. Use specific instructions that tell caches not to store certain data.
– Clean and validate: Always check and clean both the data going into the cache and the data coming out of it.
– Separate caches: Use different caches for each user or session so personalised data isn’t mixed.
– Monitor and be clear: Keep an eye out for security weaknesses related to timing (side-channels) and make sure your caching rules are clear and open.

Misconfigured cache policies

– What can go wrong: Incorrect settings or conflicting rules across your system can make your cache behave unpredictably or serve outdated content.
– Solution: Put all your cache rules (like HTTP headers) in one central place in your code. Regularly check your cache settings to make sure they are correct.

Cache stampedes & thundering herds

The issue
If many cached items all expire at the exact same time, a sudden flood of identical requests can hit your main servers, overwhelming them.

How to defend
– Use locks: Put a “lock” around the process of refreshing cached data. This ensures only one request goes to the main server to refresh the data, while others wait for it.
– Randomise expiry: Make cached items expire at slightly random times, spreading out the load of refreshes.

Over-caching & wasting resources

Symptoms
Caching data that is rarely used can take up valuable memory or storage space without providing much performance benefit.

What to do
– Look at how often different data is used.
– Prioritise caching data that is requested very frequently or that takes a long time to get from the main source.
– Make sure your cache size is appropriate and monitor how often data is removed from the cache.

Cache coherence in distributed systems

Challenge

In systems with many servers or multiple layers of caching, it’s hard to make sure that when data changes, all copies of that data in all caches are updated at the same time. This can lead to different parts of your system having different versions of the data.

Approaches
– Distributed cache: Use a special type of cache (like a Redis cluster) that can share and synchronise data across multiple servers.
– Broadcast invalidation: When data changes, quickly send out messages to all relevant caches telling them to remove or update that old data.

Sportmonks & caching

At Sportmonks, we provide high-volume sports data APIs and explicitly recommend caching to optimise performance and manage rate limits:

Best practices guidance
We advise caching on regularly used or complex search results, such as historical statistics or standings, to reduce redundant calls and improve app responsiveness.

Client libraries with cache support
For instance, the official Python wrapper caches certain static resources (e.g., leagues, seasons) automatically for an hour, minimizing HTTP traffic and latency.

Why this matters

– Sportmonks’ rate limits (e.g., 3,000 calls/hour/entity) make caching essential to avoid hitting quotas..
– By caching semi-static data client-side or on the backend, developers benefit from faster responses, reduced load on Sportmonks servers, and improved reliability, especially during high-traffic events.

Speed up your football app with smart caching

From live match events to season standings, not all data needs to be fetched repeatedly. Sportmonks encourages developers to cache high-traffic or slow-changing data, like leagues, player stats, and fixtures, to improve speed and reduce wasted calls. Whether you’re using our API in a fantasy app, betting platform, or sports dashboard, caching helps you serve users faster while staying under rate limits.

Don’t let redundant requests slow you down. Implement caching with Sportmonks and keep your football experience fast, efficient, and scalable.