Implementing Distributed Caching in ASP.NET Core: Redis, Cache Invalidation, and Strategies
A practical guide to Redis, cache invalidation, and scalable caching strategies in ASP.NET Core
As applications grow, performance becomes just as important as functionality. One of the most effective ways to improve performance in ASP.NET Core is by using distributed caching.
In this guide, we’ll explore how to implement caching with Redis, understand cache invalidation, and design strategies that keep your applications fast and scalable.
Why Caching Becomes Essential
In the early stages of building an application, performance often feels “good enough.” A request comes in, the application queries the database, and a response is returned.
But as usage increases, cracks start to appear.
You begin to notice:
Slow response times
Increased database load
Repeated queries for the same data
The problem is simple. Your system is doing the same work over and over again.
Caching solves this by remembering results so they don’t have to be recomputed.
Instead of asking the database every time, your application can return a cached result instantly.
This becomes especially important in systems that already process large volumes of data, such as the pipelines we explored previously.
What Is Distributed Caching?
Caching can be done in two ways.
In-memory caching stores data inside a single application instance. It’s fast but limited. Once the app restarts, the cache is gone, and it doesn’t work well across multiple servers.
Distributed caching, on the other hand, stores cached data in a shared external system.
This means:
Multiple application instances share the same cache
Data persists beyond a single process
The system scales more easily
Microsoft provides official guidance on distributed caching in ASP.NET Core:
https://learn.microsoft.com/aspnet/core/performance/caching/distributed
Why Redis Is the Go-To Choice
Redis is one of the most popular distributed caching systems.
Official documentation:
https://redis.io/docs/
It’s:
Extremely fast (in-memory)
Simple to use
Designed for high throughput
Widely supported
Redis stores data as key-value pairs, making it perfect for caching.
Setting Up Redis in ASP.NET Core
To use Redis, install the package:
dotnet add package Microsoft.Extensions.Caching.StackExchangeRedisConfigure Redis
In Program.cs:
builder.Services.AddStackExchangeRedisCache(options =>
{
options.Configuration = “localhost:6379”;
options.InstanceName = “MyAppCache”;
});Using the Cache
public class ProductService
{
private readonly IDistributedCache _cache;
public ProductService(IDistributedCache cache)
{
_cache = cache;
}
public async Task<string> GetProductAsync(string id)
{
var cacheKey = $”product:{id}”;
var cached = await _cache.GetStringAsync(cacheKey);
if (cached != null)
{
return cached;
}
var product = await FetchFromDatabase(id);
await _cache.SetStringAsync(cacheKey, product,
new DistributedCacheEntryOptions
{
AbsoluteExpirationRelativeToNow = TimeSpan.FromMinutes(10)
});
return product;
}
}This pattern is simple and effective.
Understanding Cache Invalidation
Caching is powerful, but it introduces a new problem.
What happens when data changes?
If your cache still holds old data, users will see outdated information.
This is called cache invalidation, and it’s one of the hardest problems in software.
Common Invalidation Strategies
There are a few common approaches.
Time-Based Expiration
AbsoluteExpirationRelativeToNow = TimeSpan.FromMinutes(10)The cache expires after a fixed time.
Simple, but not always accurate.
Sliding Expiration
The cache resets its lifetime every time it’s accessed.
Good for frequently used data.
Manual Invalidation
await _cache.RemoveAsync(cacheKey);You explicitly remove cache when data changes.
This is more accurate but requires discipline.
Cache Strategies That Actually Work
Caching is not just about storing data. It’s about how and when you use it.
Cache-Aside (Lazy Loading)
This is the most common pattern.
Check cache first
If not found, fetch from database
Store in cache
This is what we used earlier.
Write-Through
Data is written to both the database and cache at the same time.
Ensures consistency but adds complexity.
Write-Behind
Data is written to cache first, then persisted later.
Improves performance but introduces risk.
Choosing the Right Strategy
Most ASP.NET Core applications start with cache-aside because it’s simple and reliable.
As systems grow, more advanced strategies can be introduced.
Distributed Caching in Real Systems
In real-world applications, caching is used everywhere.
Example: Product Catalog
Instead of querying the database for every request:
Cache product details
Cache category lists
Cache frequently viewed items
This reduces database load significantly.
Example: API Responses
Entire API responses can be cached.
This is especially useful for read-heavy endpoints.
Caching and Data Pipelines
Caching plays a critical role in data pipelines.
Instead of recalculating results repeatedly, intermediate results can be cached.
This improves performance and reduces system load.
If you recall the pipeline concepts from the previous blog, caching acts as a shortcut in the flow.
Caching and NoSQL Systems
Caching works well alongside NoSQL databases.
Since NoSQL systems often handle large volumes of data, caching frequently accessed data reduces pressure on the database.
This ties into our earlier NoSQL discussion:
https://www.asptoday.com/p/working-with-nosql-databases-in-aspnet-core
Performance Considerations
Caching improves performance, but misuse can cause issues.
Be careful of:
Over-caching large objects
Storing unnecessary data
Using long expiration times
Also, serialization matters. Storing large JSON objects can impact performance.
Performance tuning remains important:
Handling Cache Failures
Your system should not depend entirely on the cache.
If Redis goes down:
The application should still work
It should fall back to the database
Caching is an optimization, not a requirement.
Real-World Example: Order Dashboard
Imagine a dashboard showing recent orders.
Without caching:
Every request hits the database
With caching:
Results are stored for a short time
Multiple users see fast responses
This significantly improves scalability.
Common Mistakes to Avoid
One common mistake is caching everything.
Not all data benefits from caching.
Another mistake is ignoring invalidation.
Stale data can cause serious issues.
Finally, avoid tightly coupling cache logic to business logic. Keep caching as a separate concern.
Final Thoughts
Distributed caching is one of the simplest and most effective ways to improve application performance.
By using Redis and applying the right strategies, you can reduce load, improve response times, and scale your application more efficiently.
Start with simple patterns like cache-aside, understand invalidation, and evolve your approach as your system grows.
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