Caching

The fundamental technique for dramatically improving application speed by storing frequently accessed data in a fast, temporary storage layer.

What is Caching?

Caching is the process of storing copies of files or data in a temporary, high-speed storage location—a cache—so that they can be accessed more quickly. Think of it like keeping your favorite book on your bedside table instead of walking to the library every time you want to read it. The table is your cache; the library is the database.

In system design, a cache sits between your application and your main data store. When your application needs data, it first checks the cache. If the data is there (a cache hit), it's returned immediately, which is extremely fast. If it's not there (a cache miss), the application fetches the data from the slower database, stores a copy in the cache for next time, and then returns it.

Why is Caching Important?

Improves Performance: Reduces latency by serving data from a faster, closer data store, leading to a much better user experience.
Reduces Load on Backend: By handling a significant portion of read requests, the cache shields your primary database from being overwhelmed, especially during traffic spikes.
Reduces Cost: Fewer database reads can translate directly into lower operational costs, especially in cloud environments where you pay per operation.

Caching Visualizer (Cache-Aside Strategy)

See how a cache speeds up data retrieval and reduces database load.

Data Key to Fetch

Client

Cache (LRU, Size: 3)

Database

Client

Cache (LRU, Size: 3)

Database

Log

Enter a key and click "Fetch Data".

Result

Common Caching Strategies

Cache-Aside (Lazy Loading)

This is the most common strategy and the one demonstrated in the visualizer. The application code is responsible for checking the cache first. On a miss, the application reads from the database and then writes the data to the cache.

Read-Through

Similar to cache-aside, but the cache library itself handles fetching from the database on a miss. The application code treats the cache as the main data source, simplifying the application logic.

Write-Through

Data is written to both the cache and the database at the same time. This ensures data consistency (the cache is never stale) but adds latency to write operations.

Write-Back (Write-Behind)

Data is written only to the cache, which then asynchronously writes it to the database after a delay. This is very fast for writes but risks data loss if the cache fails before the data is persisted.

Cache Eviction Policies

When a cache is full, how does it decide what to remove? This is crucial for cache effectiveness.

LRU (Least Recently Used)

: Removes the item that hasn't been accessed for the longest time. A good general-purpose choice, and the one simulated in the visualizer above.

LFU (Least Frequently Used)

: Removes the item that has been accessed the fewest times. Useful for when you want to keep popular items cached, even if they are not accessed very recently.

FIFO (First-In, First-Out)

: Removes the oldest item, regardless of how often or recently it was accessed. Simple to implement but often less effective than LRU.

Coding Challenge

Select a Problem

Design a data structure that follows the constraints of a Least Recently Used (LRU) cache. Implement the `LRUCache` class which supports `get` and `put` operations.

Deepen Your Understanding

For a comprehensive overview of caching and other system design concepts, "System Design Interview – An Insider's Guide" by Alex Xu is an industry-standard resource.

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J

Dafin Edison J

Creator & Developer of Jaconir

I build things for the web. From immersive games to practical tools, my goal is to create products that people love to use and to share the knowledge I've gained along the way.