# Caching Strategies

Caching is the process of storing copies of files or data in a temporary storage location (cache) so that they can be accessed more quickly. It is one of the most effective ways to improve the performance and scalability of a system.

## 1. Where to Cache?

Caching can happen at multiple layers of an application stack.

### Client-Side Caching (Browser)
Browsers store static assets (images, CSS, JS) locally to avoid downloading them on every page visit. Controlled via HTTP headers like `Cache-Control` and `ETag`.

### CDN (Content Delivery Network)
A CDN is a network of geographically distributed servers. It caches static content closer to the user to reduce latency.
*   **Example:** Cloudflare, AWS CloudFront.
*   **Use Case:** Serving images, videos, and frontend bundles.

### Application Server Caching
Storing the result of expensive computations or database queries in memory (RAM).
*   **Tools:** Redis, Memcached.
*   **Use Case:** Storing user sessions, API responses, or frequently accessed database records.

### Database Caching
Databases often have their own internal buffers to cache frequently accessed rows or index pages in memory.

## 2. Caching Patterns

### Cache-Aside (Lazy Loading)
The application code is responsible for loading data into the cache.
1.  App checks Cache.
2.  If **Hit**: Return data.
3.  If **Miss**: App queries Database -> Updates Cache -> Returns data.

*   **Pros:** Only requested data is cached.
*   **Cons:** First request is slow (cache miss).

### Write-Through
Data is written to the cache and the database simultaneously.
*   **Pros:** Data in cache is always fresh.
*   **Cons:** Slower writes; cache might contain data that is never read.

### Write-Back (Write-Behind)
Data is written only to the cache initially, and then asynchronously written to the database later.
*   **Pros:** Very fast writes.
*   **Cons:** Risk of data loss if the cache crashes before syncing to the DB.

## 3. Cache Eviction Policies

When the cache is full, how do we decide what to delete?

*   **LRU (Least Recently Used):** Discard items that haven't been used for the longest time. (Most common).
*   **LFU (Least Frequently Used):** Discard items that are used least often.
*   **FIFO (First In First Out):** Discard the oldest items first.
*   **TTL (Time To Live):** Items expire automatically after a set time (e.g., 1 hour).

## 4. The Hardest Problem: Invalidation

> "There are only two hard things in Computer Science: cache invalidation and naming things." — Phil Karlton

Deciding *when* to remove or update data in the cache is difficult. If you update a user's profile in the DB but forget to update the cache, the user sees old data.

[[programming/distributed-systems]]
[[programming/database-basics]]
[[programming/load-balancing]]