.NET

Mastering Caching Strategies: Cache-Aside, Write-Through, Read-Through, and More!

Let’s face it — caching can make or break your app’s performance. Whether you’re building a real-time dashboard, an e-commerce site, or a microservices backend, caching is one of the most powerful performance boosters you can use.

But with great power comes great architecture decisions!

There are multiple caching strategies available — each with its pros, cons, and ideal use cases.

In this blog, we’ll demystify:

  • Cache-Aside
  • Write-Through
  • Read-Through
  • Write-Back
  • Write-Around

And how to implement them (especially in .NET).

What is Caching?

Caching is storing frequently-accessed data in a faster and closer storage layer (like memory) so that future reads are quicker and less expensive.

Think of it like keeping your most-used apps on your phone’s home screen — instead of digging through folders every time.

Cache-Aside (Lazy Loading)

How does it work?

  • The application checks the cache first.
  • If the data isn’t there (cache miss), it’s loaded from the database, returned to the caller, and added to the cache.

Real-world analogy

You don’t remember a friend’s number. You look it up once, save it in contacts (cache), and reuse it next time.

Pros

  • Simple and flexible
  • Cache only stores what’s needed
  • Easy to implement

Cons

  • Cache miss penalty on the first request
  • Stale data if the DB is updated elsewhere
public async Task<Product> GetProduct(int id)
{
    var cacheKey = $"product:{id}";
    var cached = await _cache.GetStringAsync(cacheKey);

    if (!string.IsNullOrEmpty(cached))
        return JsonSerializer.Deserialize<Product>(cached);

    var product = await _db.Products.FindAsync(id);
    if (product != null)
    {
        await _cache.SetStringAsync(cacheKey, JsonSerializer.Serialize(product));
    }

    return product;
}

Write-Through

How does it work?

  • Write to the cache and the database simultaneously.
  • The cache is always updated whenever the DB is updated.

Real-world analogy

Every time you update your address, you update both your driving license (database) and your phone’s contacts (cache).

Pros

  • Cache always has fresh data
  • Simple read logic (just use the cache)

Cons

  • Slightly slower writes (due to double write)
  • Not ideal for write-heavy systems
public async Task SaveProduct(Product product)
{
    await _db.Products.UpdateAsync(product); // DB write
    await _cache.SetStringAsync($"product:{product.Id}", JsonSerializer.Serialize(product)); // Cache write
}

Read-Through

How does it work?

  • The application never directly accesses the database.
  • The cache is configured to load the data from the DB on cache misses automatically.

Real-world analogy

You ask your assistant for a file. If it’s not already in their drawer (cache), they fetch it from storage and give it to you (and remember it for next time).

Pros

  • Centralized control
  • Great for reducing boilerplate in apps

Cons

  • Cache engine must support DB integration
  • Less flexible at the application level

Implementation tip

You’ll typically need to wrap your cache layer or use a library like Redis Gears or integrate it at the repository level.

Write-Back (Write-Behind)

How does it work?

  • You write to the cache only.
  • The cache asynchronously writes to the database later (in batches or after TTL).

Real-world analogy

You draft emails in your outbox, and they’re sent later when the connection is stable.

Pros

  • Extremely fast writes
  • Ideal for systems with write bursts

Cons

  • Risk of data loss if the cache crashes before the DB write
  • More complex logic and background syncing are required

.NET Consideration

You’ll need to implement a queue or background service that flushes the cache changes into the database periodically.

Write-Around

How does it work?

  • Writes go only to the DB.
  • Cache is updated only when data is read again (via cache-aside).

Real-world analogy

You restock your store shelves (cache) only when a customer asks and the item isn’t there.

Pros

  • Keeps the cache clean
  • Avoids polluting the cache with rarely-read data

Cons

  • First read after a write = cache miss
  • Potential for outdated cache

.NET Consideration

Simply avoid caching on write, but use cache-aside on read.

Tools You Can Use in .NET

  • IMemoryCache (in-memory)
  • IDistributedCache (Redis/SQL cache)
  • StackExchange.Redis for advanced features
  • LazyCache or CacheManager libraries for wrappers
  • Hangfire for background flushing (write-back)
  • Polly for caching with fallback and resilience

Conclusion

Caching is both an art and a science. The wrong strategy can give you stale data, write inconsistencies, or even app crashes. The right one? It’ll make your app fly.

Whether you’re working with APIs, distributed systems, or monoliths, understanding these caching strategies gives you the confidence to design apps that are fast, resilient, and scalable.
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