Scaling Node.js APIs for production requires a combination of architectural decisions, performance optimizations, and operational practices. At VirgoSoft, we've developed a set of patterns that have proven effective across multiple high-traffic applications.
Horizontal Scaling with Load Balancers
Our primary approach is horizontal scaling. We deploy multiple Node.js instances behind a load balancer, typically using Nginx or AWS Application Load Balancer. Each instance runs independently, allowing us to handle traffic spikes by adding more instances. We use PM2 for process management, which provides automatic restarts, clustering, and zero-downtime deployments.
Database Connection Pooling
Database connections are a critical bottleneck. We implement connection pooling using libraries like pg-pool for PostgreSQL or the native MongoDB driver's connection pooling. This ensures we maintain a reusable set of database connections rather than creating new ones for each request. We typically configure pools with 10-20 connections per instance, depending on the database workload.
Caching Strategies
We leverage Redis for caching frequently accessed data. API responses that don't change frequently are cached with appropriate TTLs. We also use in-memory caching for configuration data and session storage. This significantly reduces database load and improves response times, especially for read-heavy workloads.
Async Operations and Queue Management
Long-running tasks are moved to background job queues using Bull or similar queue systems. This prevents blocking the main event loop and ensures API endpoints remain responsive. We process tasks asynchronously, whether it's sending emails, generating reports, or performing data transformations.
Monitoring and Performance Tuning
We use APM tools like New Relic or Datadog to monitor API performance in real-time. This helps us identify bottlenecks, slow queries, and memory leaks. We also implement structured logging with correlation IDs to trace requests across services. Regular performance profiling helps us optimize hot paths and reduce latency.
These patterns have enabled us to scale Node.js APIs to handle millions of requests per day while maintaining sub-100ms response times for most endpoints. The key is starting with a solid foundation and iteratively optimizing based on real-world performance data.