What is Caching? | How Caching Works

Caching stores frequently accessed data in a fast, temporary storage layer so it can be returned without repeating the original fetch or computation. It reduces response time and backend load by turning repeated reads into cache hits instead of origin requests.

When to use caching

Use caching when you need to:

Reduce latency for repeated requests (same content requested many times).
Protect databases/origins from high read load (traffic spikes, thundering herds).
Improve scalability without proportionally scaling backend compute.
Lower costs by reducing origin egress, database queries, and compute cycles.
Serve content closer to users (edge/CDN caching for global audiences).

When not to use caching

Avoid or limit caching when:

Data must be strongly consistent and changes must appear immediately everywhere.
Responses are highly personalized per-user and you can’t vary safely (risk of data leaks).
The working set is too large or too random (low hit ratio → wasted complexity).
Writes dominate reads (caching helps less; invalidation overhead can exceed benefit).
You can’t define correct expiration/invalidation rules (staleness risk is unacceptable).

Signals you need caching

P95/P99 latency rises sharply during traffic peaks.
Backend/database CPU is high while many requests return identical responses.
Slow endpoints are “read-heavy” (same query executed repeatedly).
Increased error rates/timeouts under load even though code is correct.
High origin egress and bandwidth costs for static or semi-static assets.
Users far from your primary region see consistently slower performance.

The Importance of Caching in Modern Computing

In today’s digital landscape, where users expect lightning-fast responses and seamless experiences, caching plays a pivotal role in meeting these demands. Here are some key reasons why caching is more important than ever:

Performance Benefits: By storing frequently accessed data closer to where it’s needed, caching dramatically reduces latency and improves response times. This is particularly crucial for web applications, where even milliseconds of delay can impact user experience and conversion rates.
Cost-effectiveness: Caching helps reduce the load on primary storage systems and databases, which are often more expensive to scale. By serving frequently requested data from cache, organizations can optimize their resource utilization and reduce infrastructure costs.
Scalability Advantages: As applications grow and user bases expand, caching becomes an essential tool for maintaining performance at scale. It helps distribute the load across systems and reduces the strain on backend resources.

How Caching Works

First request: system fetches data from the origin (database/service/storage) and stores a copy in cache.
Subsequent requests: system checks cache first.
- Cache hit: return cached response (fast path).
- Cache miss: fetch from origin, store it, then return it.
Expiration/invalidation: cached items are removed or refreshed based on TTLs, purges, or revalidation rules.
Eviction policy: when cache is full, a policy (e.g., LRU) decides what to remove.

Two primary caching strategies are used:

Write-through Caching: Data is written to both the cache and the primary storage simultaneously, ensuring consistency but potentially slowing down write operations.
Write-back Caching: Data is initially written only to the cache and later synchronized with the primary storage, improving write performance but risking data loss in case of system failures.

Types of Caching

Caching can be implemented at various levels of a computing system, each serving different purposes:

Hardware Caching

CPU Cache: Modern processors include multiple levels of cache (L1, L2, L3) to reduce the time it takes to access data from main memory. L1 cache is the smallest but fastest, while L3 is larger but slightly slower.
RAM Caching: Operating systems often use unused RAM to cache disk data, significantly speeding up file access times.
Disk Caching: Both HDDs and SSDs employ caching mechanisms to improve read and write performance.

Software Caching

Web Browser Caching: Browsers store static assets like images, CSS, and JavaScript files locally, reducing load times for frequently visited websites.
Database Caching: Database systems use caching to store query results, reducing the need to repeatedly execute complex queries.
Application-level Caching: Developers can implement caching within their applications to store computed results or frequently accessed data.

Network Caching

Content Delivery Networks (CDNs): CDNs cache content across geographically distributed servers, reducing latency for users worldwide.
DNS Caching: DNS resolvers cache domain name lookups, speeding up subsequent requests to the same domain.
Proxy Server Caching: Proxy servers can cache web content, reducing bandwidth usage and improving response times for users behind the proxy.

Caching Strategies and Algorithms

Effective caching relies on intelligent strategies for managing cached data. Some popular caching algorithms include:

Least Recently Used (LRU): Removes the least recently accessed items when the cache is full.
First In, First Out (FIFO): Evicts the oldest items in the cache.
Least Frequently Used (LFU): Removes items that are accessed least frequently.

Time-based expiration is another common strategy, where cached items are invalidated after a set period to ensure data freshness.

Cache invalidation techniques are crucial for maintaining data consistency. These may include:

Purge: Removing specific items from the cache.
Refresh: Updating cached items with fresh data from the primary source.
Bulk invalidation: Clearing entire sections of the cache at once.

Challenges in Caching

While caching offers numerous benefits, it also presents several challenges:

Cache Coherence: Ensuring that all copies of data across different caches remain consistent can be complex, especially in distributed systems.
Cache Thrashing: When the working set of an application is larger than the cache, it can lead to frequent cache misses and evictions, degrading performance.
Cache Pollution: Less useful data occupying cache space can reduce the effectiveness of the cache for more critical data.
Stale Data: Cached data can become outdated if not properly managed, leading to inconsistencies and potential errors.

Best Practices for Implementing Caching

To maximize the benefits of caching while mitigating its challenges, consider these best practices:

Choose the Right Cache Size: Balance between having enough cache to improve performance and not wasting resources on unnecessary caching.
Optimize Cache Eviction Policies: Select and fine-tune eviction algorithms based on your specific use case and access patterns.
Implement Proper Cache Invalidation: Develop a robust strategy for keeping cached data fresh and consistent with the primary data source.
Monitor and Measure Cache Performance: Regularly analyze cache hit rates, miss rates, and overall system performance to identify areas for improvement.

Case Studies: Caching in Action

Real-world examples demonstrate the power of effective caching:

Netflix: Uses a multi-tiered caching system to deliver smooth streaming experiences to millions of users worldwide. Their Open Connect appliances cache content at ISP locations, reducing bandwidth costs and improving playback quality.
Facebook: Developed and open-sourced Memcached, a distributed memory caching system, to handle the massive scale of their social network. This system significantly reduces database load and improves response times for user requests.
Google: Employs sophisticated caching techniques in its search engine, storing frequently accessed search results and web page snippets to deliver near-instantaneous results to users.

With data volumes exploding and user expectations for speed and responsiveness continue to rise, caching remain a critical skill for developers, system architects, and IT professionals to optimize performance and scale applications.

Common mistakes (and fixes)

Mistake: Caching personalized responses without varying correctly Fix: use Vary headers, separate keys per user/session, or avoid caching private data.
Mistake: TTLs chosen arbitrarily Fix: set TTLs based on update frequency, business tolerance for staleness, and observed hit ratio.
Mistake: No invalidation plan (“cache is forever”) Fix: implement purge/ban/tag-based invalidation or versioned URLs for assets.
Mistake: Cache stampede on expiry Fix: request coalescing, locking, early refresh, or stale-while-revalidate.
Mistake: Assuming hit ratio is the only KPI Fix: also track correctness, tail latency, origin load, and eviction/churn.
Mistake: Caching errors unintentionally (e.g., 500s) Fix: define explicit rules for what status codes are cacheable and for how long.

Mini FAQ

What is caching in simple terms? Caching is storing a reusable copy of data in a faster place so repeated requests can be answered quickly without recomputing or refetching.

What’s the difference between a cache hit and a cache miss? A cache hit means the data was found in the cache and returned quickly; a miss means the system had to fetch from the original source and optionally store it.

How do I choose a TTL? Pick a TTL based on how often the data changes and how much staleness your users can tolerate; validate using hit ratio, latency, and stale-data incidents.

Is caching the same as a CDN? No. A CDN is a distributed network that often provides edge caching, but caching can also happen in browsers, apps, databases, and proxies.

How can caching break correctness? If you cache data that changes frequently, cache per-user data incorrectly, or invalidate poorly, users can receive stale or wrong responses.

Join our community