Caching Strategies for System Design

Top 5 Caching Strategies Explained

Caching is a game-changer for reducing latency and boosting system performance. Whether you're optimizing for read-heavy workloads, write-heavy operations, or data consistency, choosing the right caching strategy is critical. In this post, we'll dive into the five most common caching strategies used in real-world applications, complete with flowcharts to make them crystal clear.

1. Read Through

The Read Through strategy lets the cache act as an intermediary between the application and the database.

• How it works: The application checks the cache for data. On a cache hit, data is returned instantly. On a cache miss, the cache fetches data from the database, stores it, and returns it to the application.
• Pros: Simplifies application logic; low latency for cache hits; minimizes unnecessary cache data.
• Cons: Higher latency on cache misses due to database queries.
• Use Case: Read-heavy apps like CDNs, social media feeds, or user profiles.
• TTL: Use time-to-live (TTL) to prevent stale data.

Application ----Request Data----> Cache
   ^                                 |
   |                                 |
   |                            Cache Hit
   |                                 |
   |                                 v
Return Data <------------------- Return Data
   ^                                 |
   |                            Cache Miss
   |                                 |
   |                                 v
   |                          Fetch from Database
   |                                 |
   |                           Store Data
   |                                 |
   +---------------------------------+

1. Cache Aside (Lazy Loading) In Cache Aside, the application manages cache-database interactions, loading data into the cache only when needed.

How it works: The application checks the cache. On a hit, it returns data. On a miss, it fetches from the database and updates the cache.
Pros: Only caches frequently accessed data; flexible for high read-to-write ratios.
Cons: Application handles cache logic; risk of stale data without TTL.
Use Case: E-commerce sites with product data (e.g., prices, stock status).
TTL: Set TTL to avoid stale data.

1. Write Through Write Through ensures cache and database are updated synchronously during write operations.

How it works: Every write updates both the cache and database simultaneously.
Pros: Strong data consistency; low-latency reads from cache.
Cons: Higher write latency due to dual updates.
Use Case: Consistency-critical systems like financial apps or transaction processing.
TTL: Optional for memory management.

1. Write Around In Write Around, writes go directly to the database, bypassing the cache.

How it works: Data is written to the database. The cache is updated only during reads (using Cache Aside).
Pros: Faster writes; keeps cache clean by avoiding unnecessary data.
Cons: Cache misses on initial reads; requires Cache Aside for read updates.
Use Case: Write-heavy systems like logging.
TTL: Use to manage cache data.

1. Write Back Write Back prioritizes fast writes by updating the cache first and syncing to the database later.

How it works: Writes go to the cache, with asynchronous database updates in the background.
Pros: Low write latency; ideal for write-heavy workloads.
Cons: Risk of data loss if cache fails before sync; requires persistent caching (e.g., Redis AOF).
Use Case: Write-heavy systems like logging or social media feeds.
TTL: Not typically needed, as cache is the source of truth.

Choosing the Right Strategy
The best caching strategy depends on your system's needs:

• Read-heavy? Try Read Through or Cache Aside.

• Write-heavy? Consider Write Around or Write Back.

• Consistency-critical? Go with Write Through.

Table Summarizing Caching Strategies

Here’s a markdown table summarizing the five caching strategies, as no image was provided:

markdown
| Strategy       | Description                                                                 | Use Case                              | Pros                                      | Cons                                      |
|----------------|-----------------------------------------------------------------------------|---------------------------------------|-------------------------------------------|-------------------------------------------|
| Read Through   | Cache fetches data from database on miss, returns to app                     | CDNs, social media feeds, user profiles | Simplifies app logic, low-latency reads   | Higher latency on cache misses            |
| Cache Aside    | App manages cache, loads data on miss                                       | E-commerce product data               | Flexible, caches only needed data         | App handles cache logic, stale data risk  |
| Write Through  | Writes update cache and database synchronously                              | Financial apps, transaction systems   | Strong consistency, low-latency reads     | Higher write latency                      |
| Write Around   | Writes go to database, cache updated on read (via Cache Aside)               | Logging systems                       | Faster writes, clean cache                | Cache misses on initial reads             |
| Write Back     | Writes to cache, async database updates                                     | Logging, social media feeds           | Low write latency                         | Data loss risk if cache fails before sync |