Performance Tuning

Priority: P1 (OPERATIONAL)

High-performance patterns and optimization techniques for NestJS applications.

• Adapter: Use FastifyAdapter instead of Express (2x throughput).
• Compression: Enable Gzip/Brotli compression.

typescript // main.ts app.use(compression());

• Keep-Alive: Configure http.Agent keep-alive settings to reuse TCP connections for upstream services.

Scope & Dependency Injection

• Default Scope: Adhere to SINGLETON scope (default).
• Request Scope: AVOID REQUEST scope unless absolutely necessary.
• Pro Tip: A single request-scoped service makes its entire injection chain request-scoped.
• Solution: Use Durable Providers (durable: true) for multi-tenancy.
• Lazy Loading: Use LazyModuleLoader for heavyweight modules (e.g., Admin panels).

Caching Strategy

• Application Cache: Use @nestjs/cache-manager for computation results.
• Deep Dive: See Caching & Redis for L1/L2 strategies and Invalidation patterns.
• HTTP Cache: Set Cache-Control headers for client-side caching (CDN/Browser).
• Distributed: In microservices, use Redis store, not memory store.

Queues & Async Processing

• Offloading: Never block the HTTP request for long-running tasks (Emails, Reports, webhooks).
• Tool: Use @nestjs/bull (BullMQ) or RabbitMQ (@nestjs/microservices).
• Pattern: Producer (Controller) -> Queue -> Consumer (Processor).

Serialization

• Warning: class-transformer is CPU expensive.
• Optimization: For high-throughput READ endpoints, consider manual mapping or using fast-json-stringify (built-in fastify serialization) instead of interceptors.

Database Tuning

• Projections: Always use select: [] to fetch only needed columns.
• N+1: Prevent N+1 queries by using relations carefully or DataLoader for Graph/Field resolvers.
• Connection Pooling: Configure pool size (e.g., pool: { min: 2, max: 10 }) in config to match DB limits.

Profiling & Scaling

• Offloading: Move CPU-heavy tasks (Image processing, Crypto) to worker_threads.
• Clustering: For non-containerized environments, use ClusterModule to utilize all CPU cores. In K8s, prefer ReplicaSets.