Tips to Improve Performance in .Net

Async/Await Mistakes That Are Killing Your API Performance Writing async code in C# and ASP.NET Core seems easy, but subtle mistakes can silently tank your app’s performance. Here are 8 mistakes that quietly destroy throughput, increase latency, and frustrate users: 🔹 1. The ConfigureAwait(false) Myth In ASP.NET Core, ConfigureAwait(false) is usually unnecessary. There's no SynchronizationContext, so you gain nothing and add clutter. Use it in libraries - not APIs. 🔹 2. Sync-over-Async: The Thread Pool Killer Calling .Result or .Wait() blocks threads. That’s death for scalability and a shortcut to deadlocks. 🔹 3. async void: The Silent Crasher Only use async void in event handlers. Anywhere else, exceptions go uncaught - and your app can crash without a trace. 🔹 4. Sequential Awaits in Loops Awaiting in a loop = slow. Use Task.WhenAll() to parallelize async operations and boost performance. 🔹 5. ValueTask Misuse ValueTask reduces allocations only when results are often sync (like cache). Otherwise, prefer Task to avoid overhead. 🔹 6. Async in Constructors Constructors can't be async. Blocking with .Result hurts startup time. Defer async initialization until needed. 🔹 7. Exceptions for Flow Control Catching exceptions is expensive. Don’t use try/catch for expected conditions - use guard clauses and result models instead. 🔹 8. Misusing Task.Run in APIs You're already on a thread pool. Wrapping logic in Task.Run just adds overhead - use async all the way for I/O. 🎯 Pro Tip: Profile Everything Performance bugs in async code are invisible until they blow up. Use tools like: ▪️ Application Insights ▪️ BenchmarkDotNet ▪️ PerfView ▪️ Custom metrics ✅ Key Takeaways • Avoid sync-over-async • Use Task.WhenAll for parallelism • Reserve ValueTask for hot paths • Handle expected failures without try/catch • Never assume async = fast - measure it Writing fast, scalable async code is a skill. Master it — and your API will thank you. 👉 Which of these mistakes have you seen in real projects? #dotnet #aspnetcore #csharp #performance #asyncawait #programmingtips #webapi #devtips

A sluggish API isn't just a technical hiccup – it's the difference between retaining and losing users to competitors. Let me share some battle-tested strategies that have helped many  achieve 10x performance improvements: 1. 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗖𝗮𝗰𝗵𝗶𝗻𝗴 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 Not just any caching – but strategic implementation. Think Redis or Memcached for frequently accessed data. The key is identifying what to cache and for how long. We've seen response times drop from seconds to milliseconds by implementing smart cache invalidation patterns and cache-aside strategies. 2. 𝗦𝗺𝗮𝗿𝘁 𝗣𝗮𝗴𝗶𝗻𝗮𝘁𝗶𝗼𝗻 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 Large datasets need careful handling. Whether you're using cursor-based or offset pagination, the secret lies in optimizing page sizes and implementing infinite scroll efficiently. Pro tip: Always include total count and metadata in your pagination response for better frontend handling. 3. 𝗝𝗦𝗢𝗡 𝗦𝗲𝗿𝗶𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 This is often overlooked, but crucial. Using efficient serializers (like MessagePack or Protocol Buffers as alternatives), removing unnecessary fields, and implementing partial response patterns can significantly reduce payload size. I've seen API response sizes shrink by 60% through careful serialization optimization. 4. 𝗧𝗵𝗲 𝗡+𝟭 𝗤𝘂𝗲𝗿𝘆 𝗞𝗶𝗹𝗹𝗲𝗿 This is the silent performance killer in many APIs. Using eager loading, implementing GraphQL for flexible data fetching, or utilizing batch loading techniques (like DataLoader pattern) can transform your API's database interaction patterns. 5. 𝗖𝗼𝗺𝗽𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 GZIP or Brotli compression isn't just about smaller payloads – it's about finding the right balance between CPU usage and transfer size. Modern compression algorithms can reduce payload size by up to 70% with minimal CPU overhead. 6. 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝗼𝗻 𝗣𝗼𝗼𝗹 A well-configured connection pool is your API's best friend. Whether it's database connections or HTTP clients, maintaining an optimal pool size based on your infrastructure capabilities can prevent connection bottlenecks and reduce latency spikes. 7. 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗟𝗼𝗮𝗱 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 Beyond simple round-robin – implement adaptive load balancing that considers server health, current load, and geographical proximity. Tools like Kubernetes horizontal pod autoscaling can help automatically adjust resources based on real-time demand. In my experience, implementing these techniques reduces average response times from 800ms to under 100ms and helps handle 10x more traffic with the same infrastructure. Which of these techniques made the most significant impact on your API optimization journey?

The single biggest performance mistake I see in .NET code. Calling .ToList() too early on EF Core queries. Here's what happens: You have a query like this in your repository: Get all orders → Filter by status → Get only the ones over $1000 → Take the first 10 Looks innocent. But there's a hidden problem. If your repository returns IEnumerable<Order>, EF Core loads ALL orders from the database into memory FIRST. Then your filtering happens in C#. If you have 1 million orders, you just loaded 1 million records to find 10. The fix? Return IQueryable<Order> from your repository. Then EF Core builds the SQL query from your filters and only loads the 10 records you actually need. The difference: → IEnumerable: 1,000,000 records loaded → filter in memory → 10 records → IQueryable: SQL with WHERE and TOP 10 → 10 records loaded Same code on the surface. 100,000x difference in data transfer. How to spot the problem in your code: • Repositories that return IEnumerable, ICollection, or List<T> • Calls to .ToList() or .ToArray() inside the repository • AutoMapper projections before filtering • Any method that materializes data before all filters are applied The rule I follow: → Repositories return IQueryable<T> for compositional queries → Service layer applies filters and projections → Materialize ONLY at the end with ToListAsync() When IEnumerable IS correct: • Working with in-memory collections • When you need LINQ to Objects (not LINQ to SQL) • Small datasets where the difference doesn't matter • When you're done with database operations But for any database-backed query — if you're filtering, ordering, or paginating — keep it IQueryable until the very last moment. This single change has fixed more performance bugs in my projects than any other optimization. Have you been bitten by this one? #dotnet #csharp #efcore #performance #linq #softwareengineering

⚡ 𝗬𝗼𝘂𝗿 .𝗡𝗘𝗧 𝗮𝗽𝗽 𝗺𝗶𝗴𝗵𝘁 𝗯𝗲 𝗯𝗹𝗲𝗲𝗱𝗶𝗻𝗴 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 — 𝗮𝗻𝗱 𝘆𝗼𝘂 𝘄𝗼𝗻’𝘁 𝘀𝗲𝗲 𝗶𝘁 𝘂𝗻𝘁𝗶𝗹 𝗶𝘁’𝘀 𝘁𝗼𝗼 𝗹𝗮𝘁𝗲. I’ve rounded up 6 invisible .NET performance killers that silently drain CPU, memory, and throughput — plus the quick fixes that stop them in their tracks 1️⃣ 𝗟𝗜𝗡𝗤 – 𝗦𝗶𝗹𝗲𝗻𝘁 𝗖𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆 𝗠𝘂𝗹𝘁𝗶𝗽𝗹𝗶𝗲𝗿 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: Deferred execution and intermediate enumerables create CPU & memory overhead at scale. 𝗙𝗶𝘅: Push heavy queries to SQL, use indexes, and avoid LINQ-to-Objects for massive datasets. 2️⃣ 𝗔𝘀𝘆𝗻𝗰/𝗔𝘄𝗮𝗶𝘁 – 𝗛𝗶𝗱𝗱𝗲𝗻 𝗧𝗵𝗿𝗲𝗮𝗱 𝗕𝗹𝗼𝗰𝗸𝗲𝗿 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: Blocking calls (.Result, .Wait()) and excessive context switches cause thread pool starvation. 𝗙𝗶𝘅: Never block async methods, use ValueTask for high-frequency calls, profile thread pool usage. 3️⃣ 𝗟𝗼𝗴𝗴𝗶𝗻𝗴 – 𝗛𝗶𝗱𝗱𝗲𝗻 𝗟𝗮𝘁𝗲𝗻𝗰𝘆 𝗜𝗻𝗷𝗲𝗰𝘁𝗼𝗿 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: Synchronous, verbose logs block threads and spike CPU under load. 𝗙𝗶𝘅: Use structured logging with async sinks (Serilog), log only business-critical events, separate debug vs prod logs. 4️⃣ 𝗗𝗲𝗽𝗲𝗻𝗱𝗲𝗻𝗰𝘆 𝗜𝗻𝗷𝗲𝗰𝘁𝗶𝗼𝗻 – 𝗦𝘁𝗮𝗿𝘁𝘂𝗽 𝗦𝗹𝘂𝗴 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: Too many scoped services or reflection-heavy lifetimes slow startup and per-request resolution. 𝗙𝗶𝘅: Minimize scoped dependencies, pre-compile container graphs, modularize DI setup. 5️⃣ 𝗝𝗦𝗢𝗡 𝗦𝗲𝗿𝗶𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 – 𝗠𝗲𝗺𝗼𝗿𝘆 𝗠𝗲𝗹𝘁𝗲𝗿 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: Large or deep object graphs cause massive allocations and GC pressure. 𝗙𝗶𝘅: Use streaming serialization (Utf8JsonWriter), paginate large collections, measure payload sizes. 6️⃣ 𝗘𝗻𝘁𝗶𝘁𝘆 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 – 𝗡+𝟭 𝗤𝘂𝗲𝗿𝘆 𝗡𝗶𝗴𝗵𝘁𝗺𝗮𝗿𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: Lazy loading and poor query design generate multiple queries, exploding database calls. 𝗙𝗶𝘅: Use .Include() for eager loading, batch load when possible, analyze EF logs for hidden queries. 📌 Full breakdown Article link in First comment: Read the full deep dive #DotNet #DotNetCore #CSharp #DotNetPerformance #PerformanceTuning #CleanArchitecture #DevTips #EntityFramework #LINQ #AsyncAwait #Logging #DependencyInjection #JSONSerialization #EFCore #CodeOptimization #SoftwareEngineering #BackendDeveloper #Microservices

Most .NET developers are still optimizing SQL queries… …while ignoring a built-in performance boost already sitting inside .NET 8 👀 ⚡ Output Caching Middleware (not the old ResponseCaching) With almost zero refactoring, you can: ✅ Store the response  ✅ Skip controller execution  ✅ Skip service logic  ✅ Skip database calls  ✅ Return cached output instantly  ✅ Reduce API load during peak traffic Setup? builder.Services.AddOutputCache(); app.UseOutputCache(); app.MapGet("/products", GetProducts)   .CacheOutput(p => p.Expire(TimeSpan.FromSeconds(30))); That’s it. For the next 30 seconds: 🚀 No DB hits  🚀 No heavy processing  🚀 Just fast responses Where this shines: * Product listings  • Dashboard metrics  • Lookup/master data  • Public read-heavy APIs  • Expensive aggregation queries The real lesson? Performance tuning isn’t always about: Rewriting LINQ Adding more indexes Refactoring stored procedures Sometimes it’s about knowing what the framework already gives you. Modern .NET isn’t just about writing APIs.  It’s about leveraging built-in primitives intelligently. Tiny change.  Huge performance win. #dotnet #aspnetcore #performance #backend #softwarearchitecture #webapi