How Can Caching Improve ASP.NET Core Application Performance?

For this discussion, I would make the reply educational first and promotional only indirectly. Since it is being posted in a Microsoft community context, avoid turning the discussion reply into a course advertisement or repeated CTA. The expertise itself should create credibility and encourage people to engage with you. Expert-Level Reply Caching is one of the simplest ways to improve ASP.NET Core performance, but the real challenge is not adding a cache — it is deciding what should be cached, where it should live, how long it should remain valid, and how it should be invalidated. A practical way to think about caching is: Request → Check Cache → Cache Hit? → Return Data ↓ No Fetch from DB/API → Store in Cache → Return Data 1. Start by identifying the expensive operation Before introducing caching, measure where the application is actually spending time. Typical candidates include: - Frequently executed database queries - Expensive joins or aggregations - External REST API calls - Configuration/reference data - Computed results that are expensive to generate - Data that changes infrequently but is requested frequently Caching something that is already inexpensive can actually add unnecessary complexity. 2. Choose the right caching strategy In-memory caching (IMemoryCache) Useful when: - The application runs on a single instance - Data is local to that application instance - Very fast access is required - Cached data can be recreated easily The major limitation is scalability. If your application runs on multiple instances, each instance can have a different cache. Distributed caching A distributed cache such as Redis becomes more appropriate when: - Multiple application instances are running - Requests can reach different servers - Cached state needs to be shared - The application is deployed in a scalable/cloud environment The important architectural point is that the cache becomes an infrastructure dependency, so availability, network latency, serialization, monitoring, and failure behaviour must also be considered. HTTP/response caching This is a different level of caching. Instead of only caching an internal object or database result, the application can allow responses to be reused under appropriate HTTP caching rules. This can reduce application processing and network traffic, but it requires careful consideration of cacheability, headers, user-specific data, and invalidation. 3. Cache the result, not the entire problem For example, suppose an application repeatedly executes: Database ↓ Complex Query ↓ Business Logic ↓ DTO ↓ API Response If the underlying data changes infrequently, caching the final DTO or suitable response may eliminate the database and computation cost entirely. However, don't blindly cache user-specific or security-sensitive results. A cache key must correctly represent the context that determines the result. 4. Cache keys are more important than they appear Consider: /products/100 and: /products/101 These clearly require different cache entries. But imagine a query such as: /customer/orders?customerId=100&status=Open The cache key may need to include the customer, status, tenant, locale, permissions, or other factors that affect the result. A poorly designed cache key can create a serious problem: returning valid data to the wrong consumer. That makes cache-key design both a performance and security consideration. 5. Expiration is not invalidation This is one of the most important concepts for developers to understand. Expiration means: "Don't use this cached value after a certain period." Invalidation means: "This value is no longer valid because the underlying data changed." For example, caching product information for 10 minutes may be perfectly acceptable for a catalogue. But if an administrator changes the product price, waiting another 9 minutes for expiration may be unacceptable. This is where applications need to decide between: - Absolute expiration - Sliding expiration - Explicit invalidation - Short TTL - Event-driven invalidation - A combination of these approaches There is no universal expiration value. 6. The cache-aside pattern is a useful starting point A common approach is: Application | v Check Cache | +---- Hit ----> Return cached value | +---- Miss | v Query Database | v Store in Cache | v Return value The application controls when data is loaded and when it is placed into the cache. For many ASP.NET Core applications, this is a straightforward pattern because the database remains the system of record and the cache is treated as an optimisation layer. 7. Don't allow the cache to become the source of truth A useful architectural principle is: Database/System of Record → authoritative data Cache → performance optimisation If the application cannot function correctly when the cache is empty, you should question whether the cache has accidentally become part of your primary data architecture. A cache miss should normally be an expected condition, not an application failure. 8. Consider cache stampede problems Imagine a cached value expires at 10:00 AM. At 10:00:01, 5,000 requests arrive simultaneously. If every request sees a cache miss and independently queries the database, you can suddenly create thousands of database calls. This is commonly referred to as a cache stampede or thundering herd problem. For high-traffic applications, consider techniques such as: - Request coalescing - Locking around cache population - Background refresh - Staggered expiration - Appropriate TTL strategies The correct approach depends on the workload. 9. Caching can make performance worse Caching isn't automatically faster. Potential costs include: - Serialization/deserialization - Network calls to a distributed cache - Memory consumption - Cache misses - Cache management logic - Invalidations - Synchronisation - Additional infrastructure For example, making a network call to a distributed cache to retrieve a tiny piece of data that could have been calculated locally may not improve performance. Always measure. 10. A good enterprise caching decision A useful decision framework is: Question Consideration Does the data get requested frequently? Good caching candidate Is generating it expensive? Strong candidate Does it change frequently? Requires careful invalidation Is it user/tenant specific? Cache-key design becomes critical Is the app multi-instance? Consider distributed caching Can stale data cause business impact? Use appropriate consistency strategy What happens when cache is unavailable? Application should have a defined fallback Can we measure the improvement? Benchmark before and after The key takeaway is that caching is not simply a performance switch. It is a consistency, scalability, security, and architecture decision. For anyone learning ASP.NET Core, a good practical exercise is to take one database-heavy endpoint, measure its baseline response time, implement cache-aside caching, test cache hit/miss behaviour, then deliberately introduce stale data and determine how your invalidation strategy should handle it. For experienced developers, the more interesting question is not "How do I add caching?" but: "What consistency model does this business process actually require, and what caching strategy satisfies it without compromising correctness?" That is where caching moves from a coding technique to an architecture decision.