Top 5 Caching Strategies Explained

Caching is a critical component in modern system design, enabling faster data retrieval and improved performance. Below, we explore the top 5 caching strategies, their use cases, and practical implementations.

1. Read Through

How it works: Data is loaded into the cache on a miss, with the cache handling database reads.
Best for: Read-heavy applications like CDNs, social media feeds, and user profiles.

You Should Know:

To implement a Read Through cache, you can use Redis or Memcached. Here’s an example using Redis in Python:

import redis

cache = redis.Redis(host='localhost', port=6379, db=0)

def get_data(key):
data = cache.get(key)
if not data:
data = db_query(key) # Fetch from database
cache.set(key, data) # Store in cache
return data

2. Cache Aside (Lazy Loading)

How it works: The application checks the cache first. On a miss, it fetches data from the database and stores it in the cache.
Best for: Systems with a high read-to-write ratio, like e-commerce sites.

You Should Know:

Here’s how you can implement Cache Aside in a Node.js application:
[javascript]
const redis = require(‘redis’);
const client = redis.createClient();

async function getProductDetails(productId) {
let product = await client.get(product:${productId});
if (!product) {
product = await db.query(‘SELECT * FROM products WHERE id = ?’, [productId]);
client.set(product:${productId}, JSON.stringify(product));
}
return product;
}
[/javascript]

3. Write Through

How it works: Writes are made to both the cache and the database simultaneously, ensuring data consistency.

Best for: Consistency-critical systems, such as financial applications.

You Should Know:

In a Java application, you can use Spring Cache with a Write Through strategy:

@CachePut(value = "users", key = "#user.id")
public User updateUser(User user) {
userRepository.save(user); // Write to database
return user; // Write to cache
}

4. Write Around

How it works: Writes go directly to the database, bypassing the cache. The cache is updated only on a read.
Best for: Write-heavy systems where data isn’t immediately needed, like logging systems.

You Should Know:

For logging systems, you can use a Write Around strategy with Elasticsearch and Redis:

<h1>Write to Elasticsearch</h1>

curl -X POST "localhost:9200/logs/_doc" -H 'Content-Type: application/json' -d '{"message": "Error occurred"}'

<h1>On read, update Redis</h1>

redis-cli SET log:12345 '{"message": "Error occurred"}'

5. Write Back

How it works: Data is first written to the cache and then asynchronously to the database, minimizing write latency.
Best for: High-write throughput systems, such as social media feeds.

You Should Know:

Here’s an example of Write Back in a Go application:
[go]
func writeData(key string, value string) {
cache.Set(key, value) // Write to cache
go func() {
db.Write(key, value) // Asynchronously write to database
}()
}
[/go]

What Undercode Say

Caching is a powerful tool to optimize system performance, but choosing the right strategy depends on your application’s requirements. For read-heavy systems, Read Through and Cache Aside are ideal. For write-heavy systems, Write Around and Write Back work best. Consistency-critical systems benefit from Write Through.

Here are some additional Linux and Windows commands to manage caching:
– Linux:

<h1>Clear RAM cache</h1>

sudo sync; sudo sysctl -w vm.drop_caches=3

<h1>Monitor cache usage</h1>

free -h

• Windows:

  </li>
  </ul>
  
  <h1>Clear DNS cache</h1>
  
  ipconfig /flushdns
  
  <h1>Monitor cache usage</h1>
  
  Get-Counter -Counter "\Memory\Cache Bytes"
  

  For further reading, check out these resources:

  • Mastering Caching in Distributed Applications
  • Caching Strategies Explained

  Expected Output:

  A well-optimized system with reduced latency, improved performance, and efficient resource utilization.

  References:

  Reported By: Ashishps1 Top – Hackers Feeds
  Extra Hub: Undercode MoN
  Basic Verification: Pass ✅

  Join Our Cyber World:

  💬 Whatsapp | 💬 Telegram