nestjs-performance

name	nestjs-performance
description	Optimize NestJS throughput with Fastify adapter, singleton scope enforcement, compression, and query projections. Use when switching to Fastify, diagnosing request-scoped bottlenecks, or profiling API overhead.
license	MIT
version	1.0.0
author	[Filippo De Silva](https://github.com/FilippoDeSilva)
tags	["nestjs","performance","fastify","compression","singleton","request-scope"]
metadata	{"triggers":{"files":["main.ts"],"keywords":["FastifyAdapter","compression","SINGLETON","REQUEST scope"]}}

Performance Tuning

Priority: P1 (OPERATIONAL)

Workflow: Performance Audit

1. Switch to Fastify — Replace Express with FastifyAdapter for ~2x throughput.
2. Enable compression — Add Gzip/Brotli middleware.
3. Audit provider scopes — Ensure no unintended REQUEST scope chains.
4. Add query projections — Use select: [] on all repository queries.
5. Profile overhead — Benchmark Total Duration, DB Execution, and API Overhead.

Fastify + Compression Setup

See implementation examples

• 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: 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 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 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

• API Overhead vs DB Execution: Use "Execution Bucket" strategy to continuously benchmark Total Duration, DB Execution Time, and API Overhead.
• Total Baseline: Excellent (< 50ms), Acceptable (< 200ms), Poor (> 500ms). Exception: Authentication routes (e.g. bcrypt/argon2) should take 300-500ms intentionally.
• DB Execution Baseline: Excellent (< 5ms), Acceptable (< 30ms), Poor (> 100ms - implies missing index or N+1 problem).
• API Overhead Baseline: Excellent (< 20ms), Poor (> 100ms - implies heavy synchronous processing or serialization blocking Node's event loop).
• 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.

Anti-Patterns

• No REQUEST scope without evaluation: One REQUEST-scoped provider makes entire chain request-scoped.
• No CPU tasks in HTTP handler: Offload image/crypto work to worker_threads or BullMQ.
• No unprojected queries: Always select: [] needed columns to avoid serializing unused data.