⚡ .NET Core Performance Best Practices — Manager Playbook

.NET Core performance engineering from the perspective of an Engineering Manager who needs repeatable, measurable, and team-friendly practices. This isn’t just “write async code” — it’s about designing for throughput, low latency, and predictable scalability from day one.

1. Design for Asynchronous, Non-Blocking Execution

Why: ASP.NET Core’s request pipeline is optimized for async; blocking threads causes thread pool starvation.

How:

• Make the entire call chain async — from controller to data access.
• Avoid .Result or .Wait() — they block threads.
• Use IAsyncEnumerable<T> for streaming large datasets.
• Offload long-running work to background queues (IHostedService, Azure Service Bus).

2. Cache Aggressively, but Intelligently

Why: Caching reduces load on databases and services, but stale or oversized caches can hurt.

How:

• Use MemoryCache for small datasets; Redis for multi-instance apps.
• Apply sliding and absolute expirations to avoid stale data.
• Cache serialized DTOs instead of EF entities.
• Use cache-aside pattern for expensive computations.

3. Optimize Hot Code Paths

Why: 80% of performance issues often come from 20% of the code.

How:

• Profile with PerfView, dotnet-trace, or Application Insights.
• Inline small, frequently called methods where sensible.
• Avoid unnecessary LINQ in tight loops.
• Minimize allocations (reuse buffers, use Span<T>).

4. Database Access Efficiency

Why: ORM misuse is a top cause of latency.

How:

• Use eager loading (Include) only when needed.
• Batch queries to reduce round trips.
• Use Dapper for read-heavy endpoints.
• Index frequently used columns.

5. Memory Management & GC Tuning

Why: Excessive allocations trigger frequent garbage collections.

How:

• Use using to dispose unmanaged resources.
• Avoid Large Object Heap allocations.
• Use ArrayPool<T> or MemoryPool<T> for reusable buffers.
• Enable Server GC mode for high-load services.

6. Reduce Payload Size & Serialization Overhead

Why: Large payloads slow down network and serialization.

How:

• Use System.Text.Json with tuned JsonSerializerOptions.
• Enable gzip or brotli compression.
• Paginate large result sets.

7. Minimize Middleware & Pipeline Overhead

Why: Every middleware adds latency.

How:

• Keep middleware lightweight.
• Short-circuit early for invalid requests.
• Use endpoint routing efficiently.

8. Static Content & CDN Offloading

Why: Serving static files from the app wastes CPU cycles.

How:

• Serve static assets via CDN or reverse proxy.
• Enable response caching for static resources.

9. Measure, Don’t Guess

Why: Premature optimization wastes effort; real data guides the right fixes.

How:

• Use BenchmarkDotNet for micro-benchmarks.
• Monitor latency, throughput, and error rates.
• Track DORA metrics to measure improvement impact.

📊 Manager’s Implementation Strategy

• Baseline First: Run load tests before changes.
• One Change at a Time: Measure impact per tweak.
• Automate Checks: Add regression tests in CI.
• Educate the Team: Share profiling results.
• Document Patterns: Add performance playbook to wiki.

Operational Notes & Checklist

• Run periodic load tests mirroring production traffic.
• Track p95/p99 latency, not just averages.
• Set SLOs and error budgets for key endpoints.
• Quarterly reviews to retire outdated patterns.
• Maintain rollback playbooks for regressions.

Manager’s Tip: Automate measurement, improve incrementally, and treat performance as a product requirement.