API Caching — Optimizing Speed and Efficiency Without Compromising Data Freshness

Caching is an incredibly powerful tool in API design, often being the determining factor between an API response taking 30 milliseconds or 3 seconds. It substantially reduces database load, enhances scalability, and makes even convoluted systems appear instantaneous. However, for many developers, caching can seem daunting and risky. Concerns often revolve around the possibility of serving stale data, displaying incorrect user information, or experiencing ineffective cache invalidation.

This article seeks to provide a thorough understanding of caching for modern APIs. We will delve into caching strategies at various levels — from HTTP headers and browser caches, to CDN edge nodes, server-side memory, and data-layer optimizations. Regardless of whether you're working with REST, GraphQL, or edge functions, understanding caching is crucial to making fast the default.

The Importance of Caching

The primary rule of caching is: don't recompute what you already know.

Modern applications call APIs hundreds of times per session — frequently fetching the same user profiles, pricing data, product descriptions or settings. Each of these round-trip requests hits servers, runs database queries, and consumes CPU resources.

Intelligent caching allows you to:

• Eliminate redundant work
• Reduce latency
• Smooth out traffic spikes
• Save money on compute and bandwidth
• Deliver instant user experiences

Exploring the Layers of Caching

1. Browser Cache

Each API response can direct the browser to cache it using headers such as:

Cache-Control: max-age=60, public

Browsers, following these instructions, will reuse the cached value until the expiration time. This is especially beneficial for:

• Static API responses
• Logged-out content (e.g., product catalog)

However, it should be avoided for:

• User-specific or sensitive data (unless private is set)

2. CDN / Edge Cache

Content Delivery Networks (CDNs) like Cloudflare, Akamai, and Fastly cache API responses at the edge with headers like:

Cache-Control: s-maxage=300

In this context, max-age refers to client/browser cache duration, while s-maxage pertains to CDN/shared cache duration.

Edge caching is ideal for:

• High-traffic, read-heavy APIs
• Unauthenticated requests
• APIs with infrequent data changes

When combined with cache keys and query normalization, CDNs can handle millions of requests per second with minimal backend hits.

3. API Gateway / Reverse Proxy Cache

API Gateways like Varnish, NGINX, or custom middlewares can cache specific routes, as shown in the following NGINX configuration:

location /api/products {
  proxy_cache my_cache;
  proxy_cache_valid 200 10m;
}

This technique is particularly useful for:

• Caching expensive aggregations
• Bypassing downstream pressure
• Creating API staging layers

4. Server Memory / In-Memory Store

Utilizing tools like Redis or in-process caches (e.g., lru-cache, NodeCache) allows for caching of recent results:

const result = cache.get(key) || fetchAndCache(key);

This approach offers:

• Ultra-fast access (microseconds)
• Ideal conditions for per-request memoization
• Utility for rate limits, session lookups, feature flags

However, a downside is that it's volatile (RAM-only) and doesn't scale across nodes without clustering.

5. Database and ORM-Level Caching

Many Object-Relational Mappers (ORMs), such as Prisma, Sequelize, and TypeORM, support query-level caching or memoization of joins.

Tools for this include:

• Redis for caching SQL results
• Dataloader for batching/resolving GraphQL queries
• Materialized views for precomputing database joins

Database-level caching is suitable for:

• Expensive joins or aggregations
• Read-heavy dashboards
• High-concurrency data access

Understanding HTTP Caching Headers

Cache-Control: public, max-age=300, s-maxage=900
ETag: "abc123"
Expires: Wed, 21 Oct 2025 07:28:00 GMT

• Cache-Control: defines caching rules
  • public or private
  • max-age: the browser cache duration
  • s-maxage: the shared cache (CDN) duration
• ETag: a content hash used for revalidation
• Expires: a header that has been deprecated in favor of max-age

The ETag can be used in conjunction with If-None-Match for validation:

GET /api/products
If-None-Match: "abc123"

If the data remains unchanged, the server responds with:

304 Not Modified

This results in a fast response with no body.

Caching in GraphQL

Caching in GraphQL can be more complex because:

• Requests typically go over POST by default
• Each query can request different data structures
• Identifiers aren’t always predictable

But, caching is still possible by:

• Employing Query hashing (like Apollo Persisted Queries)
• Using Response normalization and caching (like Apollo and Relay)
• Creating a CDN layer with key-based caching on hashed payloads

Using fragments and field-based cache hints can further improve data reuse and prefetching.

Invalidation — The Hardest Part

While caching is straightforward, invalidating a cache when data changes is challenging.

Strategies include:

• Time-To-Live (TTL) based expiration (easy, safe, eventually stale)
• Manual purge (triggered on mutation)
• Tag-based invalidation (e.g., purge all keys tagged with product:123)
• Stale-while-revalidate (serve stale data, update in the background)

Well-designed APIs emit webhooks or events when mutations occur — enabling real-time purging of cache keys or refreshing background workers.

Real-World Caching Systems

Stripe

• Caches static configurations (products, prices) at CDN and edge
• Authenticated data has a short lifespan and is not cacheable
• Uses internal TTLs for consistent freshness

GitHub

• Extensively uses HTTP caching for public content (repositories, gists)
• Sends ETag headers for diff-based fetching
• GraphQL v4 supports cache hints via directives

Shopify

• Heavily caches product catalogs, assets, and media
• Real-time updates are pushed via webhooks
• CDN TTLs are tuned by traffic type and change frequency

Anti-Patterns and Gotchas

• Caching user-specific data without scoping can lead to data leaks
• Not varying the cache key by query parameter can cause caching issues
• Mixing private and public headers can lead to security risks
• Caching error responses (e.g., 404 or 500) can lead to incorrect error handling
• Using long TTLs without a purging logic can lead to stale data

Remember, it's crucial to validate and test cache behavior — never just assume.

Monitoring and Observability

Keep track of cache:

• Hit/miss ratio
• Eviction rate
• Stale requests
• Time-to-live stats
• Backend bypass rate

Remember to log cache headers in both staging and production environments.

Use metrics to fine-tune cache TTLs and identify over or under-caching instances.

Conclusion: Cache as a Feature, Not a Hack

Caching isn't a shortcut — it's an essential part of engineering. It's a way to provide users with speed, consistency, and reliability without the pain of scaling.

The best APIs don’t just compute fast — they cache smart. From HTTP to Redis to CDN edges, every layer can contribute to better performance.

Build with a cache-first mindset. Design for invalidation. Monitor, tune, and test.

Fast is not just a feature. It should be the default.