Caching Explained: What It Means, When to Use It, and How to Add it to Your Tech Stack for Maximum Performance
What Caching Actually Is
Caching is the process of storing data in a faster layer than its original source.
Examples
- Dashboard summaries: Instead of recomputing a dashboard summary, store the result in Redis
- External API calls: Instead of calling an external API, store the response for 5 minutes
- User profiles: Instead of regenerating a user profile, keep the JSON in memory
- Database queries: Instead of querying the database, store the result at the CDN edge
What Caching Reduces
CPU load, database queries, network calls, disk I/O, and user-perceived latency. It exists everywhere in computing, from CPU hardware to CDNs.
Why We Cache: The Real Reasons
1. Performance
Caching eliminates slow, repeated work. For example: DB query takes 20-80ms, while Redis cache hit takes 0.2-1ms.
2. Cost Reduction
If 80% of your reads come from cache, you can downsize your servers.
3. Stability Under Load
Caching absorbs traffic spikes and prevents your DB from collapsing.
4. Speed for End Users
Faster pages lead to better UX, higher conversion, and better SEO.
5. Predictable System Behaviour
Cache makes read loads more stable, even when write activity fluctuates.
Where Caching Fits in Your Tech Stack
Caching can happen in multiple layers. Each level has different trade-offs in speed, consistency, and cost.
- Browser Cache: Static assets (JS, CSS, images)
- CDN / Edge Cache: Cloudflare, Fastly, Akamai for HTML, APIs, images, fully rendered pages
- Application Cache: Redis, Memcached for query results, precomputed objects, session data
- Database Cache: Query cache, index cache, buffer cache
- CPU Cache: Hardware-level caching of instructions and data (L1/L2/L3)
- Local In-Memory Cache: Per-process or per-service caching (FastAPI/Laravel/Symfony/Node)
Caching Technologies and When to Use Them
Redis: The Industry Standard
Use Redis when you need shared cache across servers, millisecond lookups, complex data structures (lists, hashes, sorted sets), and features like rate limiting, sessions, or leaderboards. For a comprehensive deep dive into Redis implementation, explore our guide on how Redis transforms application performance with distributed caching.
Best for: API responses, pre-computed dashboard objects, permissions maps, authentication sessions, and caching database joins.
Memcached: Ultra-Simple, Ultra-Fast
Use Memcached when you need key-value only storage, extremely low latency, and massive horizontal scale.
Best for: High-throughput, ephemeral caching, heavy traffic APIs, and temporary objects.
CDN Edge Caching for Global User Bases
Use Cloudflare, Fastly, or Akamai when you need HTML caching, static asset acceleration, and geo-distributed delivery.
Best for: Marketing sites, public dashboards, e-commerce landing pages, and API caching at the edge.
Local In-Memory Cache
Use local memory when you need the fastest possible reads and data that does not need to be shared between servers. The speed difference from CPU to RAM to disk is enormous (nanoseconds to milliseconds).
Database-Level Caching
Postgres and MySQL have buffer pools, query planner cache, and index cache. You don't control these directly, but you benefit from them automatically when queries repeat.
To maximize database cache effectiveness, ensure your queries are optimized. Learn advanced techniques for optimizing complex PostgreSQL joins to reduce query execution time and improve cache hit rates.
How Much Faster Can Caching Make Your System?
Realistic improvements from implementing caching:
| Action | Latency Without Cache | Latency With Cache | Improvement |
|---|---|---|---|
| DB query → Redis cache | 30-120 ms | 0.5-2 ms | 20×-200× |
| External API call → Cache | 200-1000 ms | 0.5-2 ms | 200×-1000× |
| Expensive computation → Cache | 500-3000 ms | 0.5-2 ms | 100×-2000× |
| Page HTML → CDN | 100-300 ms | < 20 ms | 5×-15× |
Caching is the easiest way to achieve sub-100ms systems.
For high-traffic platforms handling thousands of concurrent users, combining caching with database optimization delivers exceptional results. Explore our comprehensive guide on how project management platforms achieve sub-second page loading times at scale.
When You Should Not Use Caching
Caching is powerful but dangerous when misused. Avoid caching when:
- Data changes extremely frequently: Cache invalidation becomes more expensive than direct reads
- Strong transactional consistency is required: Financial transactions, inventory systems need real-time accuracy
- Race conditions may cause stale state: Multiple systems updating the same data can create inconsistencies
- Large objects may exceed cache memory: Caching multi-gigabyte objects may not be practical
- Cache invalidation is too complex: If invalidation logic is harder to maintain than the original query
The golden rule: Cache the things that are expensive to compute but cheap to store.
Frequently Asked Questions
For organizations looking to optimize both their caching strategy and database performance, understanding how to optimize complex PostgreSQL joins can complement your caching implementation. Additionally, choosing the right database architecture is crucial-learn about SQL vs NoSQL databases and when to use each for your specific caching and data storage needs. For analytical workloads on large datasets, consider ClickHouse as a lightning-fast OLAP database.
What is an example of a cache?
Browser cache storing images, Redis storing precomputed dashboard summaries, Cloudflare caching HTML at the edge, and CPU caching instructions at L1/L2/L3. A cache is simply temporary fast storage for frequently accessed data.
What is the main purpose of cache memory?
To reduce the time it takes to access data. Cache memory serves as a fast-access layer so your system doesn't recompute or re-fetch the same information repeatedly.
What are the pros and cons of caching?
Pros: Huge performance gains, lower infrastructure load, faster page loads, smooth performance under concurrency, and lower cloud cost. Cons: Risk of stale data, requires invalidation logic, higher memory cost, and debugging can be harder.
What is L1, L2, L3 and L4 caching?
These are CPU cache levels: L1 Cache is fastest, smallest, and closest to the CPU core. L2 Cache is larger and slower than L1. L3 Cache is shared across cores, bigger but slower. L4 Cache is rare; a larger off-chip cache used in high-end systems. These hardware caches dramatically speed up CPU operations.
When should I use caching?
Use caching when data does not change frequently, the computation or query is expensive, you expect high read volume, data can safely be slightly stale, and you want to reduce load on your database or API.
What is a multipoint data storage engine?
A system that stores and retrieves data from multiple coordinated storage layers, such as local memory, distributed in-memory cache, disk storage, and cloud-based storage. It optimises where data lives to maximise speed and minimise cost.
Is caching expensive?
Caching costs memory, which can be more expensive than disk or CPU. However, because caching reduces database load and compute usage, it typically reduces total hosting cost, especially under heavy traffic.
Need Help Designing a Caching Strategy?
We build caching architectures that deliver sub-100ms APIs, massively reduced server load, and predictable high-concurrency performance.
Caching done properly
Caching is one of the highest-leverage performance wins, when done right. We design caching layers that are fast, predictable and easy to invalidate.
See: SaaS Infrastructure OptimisationRelated Resources
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