Go vs Node.js: Building a High-Performance SSE Server

Background

Our client needed a real-time notification system to push updates to thousands of connected clients simultaneously. The system had to handle Server-Sent Events (SSE) connections efficiently while maintaining low latency and high throughput.

Initially, we built the solution using Node.js with Express, leveraging JavaScript's event-driven architecture. While it worked for initial testing, as we scaled to support more concurrent connections, we started noticing performance bottlenecks and increased memory consumption.

This led us to explore alternative solutions, and we decided to rebuild the SSE server using Go to see if we could achieve better performance and resource utilization.

The Challenge

SSE (Server-Sent Events) is a protocol that allows servers to push real-time updates to clients over HTTP. Unlike WebSockets, SSE is unidirectional (server to client) and works over standard HTTP, making it simpler to implement and firewall-friendly.

However, maintaining thousands of open SSE connections presents unique challenges:

• Each connection must remain open indefinitely, consuming memory and resources
• The server must efficiently broadcast messages to all connected clients
• Connection management (handling disconnects, reconnects) must be robust
• Memory usage must remain stable under high connection loads
• CPU usage should stay low even when broadcasting to thousands of clients

Node.js Limitations

Our Node.js implementation faced several performance issues at scale:

• Single-threaded execution: JavaScript's single-threaded nature meant all operations competed for CPU time on one core
• High memory consumption: Each connection created multiple JavaScript objects and closures, increasing memory overhead
• Garbage collection pauses: Frequent GC cycles caused noticeable latency spikes during message broadcasts
• Connection limit: We struggled to maintain more than 5,000 concurrent connections without significant performance degradation

Why We Chose Go

Go's design philosophy and runtime characteristics made it an ideal candidate for building high-performance network servers:

Goroutines and Concurrency

Go's goroutines are lightweight threads managed by the Go runtime. Unlike OS threads, goroutines have minimal overhead (around 2KB of stack space) and can scale to hundreds of thousands on a single machine. This made it perfect for handling many concurrent SSE connections.

Efficient Memory Management

Go's memory allocator and garbage collector are optimized for low-latency server applications. The GC is concurrent and doesn't stop-the-world for extended periods, resulting in more predictable performance under load.

Built-in Concurrency Primitives

Go's channels and select statements provide elegant ways to coordinate between goroutines. This made implementing the pub/sub pattern for broadcasting messages to thousands of clients straightforward and efficient.

The Solution

We rebuilt the SSE server in Go with a focus on performance and scalability. The architecture was simple but highly effective:

Connection Management

Each SSE connection runs in its own goroutine, listening on a dedicated channel for messages. When a client connects, we create a new goroutine and register it with the central hub.

type Client struct {
    id       string
    messages chan []byte
    done     chan bool
}

type Hub struct {
    clients    map[string]*Client
    broadcast  chan []byte
    register   chan *Client
    unregister chan *Client
    mu         sync.RWMutex
}

Broadcast Mechanism

The hub maintains a central goroutine that handles all broadcasts. When a message needs to be sent to all connected clients, it's published to the broadcast channel. The hub then iterates through all registered clients and sends the message to each client's individual channel.

func (h *Hub) Run() {
    for {
        select {
        case client := <-h.register:
            h.mu.Lock()
            h.clients[client.id] = client
            h.mu.Unlock()
            
        case client := <-h.unregister:
            h.mu.Lock()
            delete(h.clients, client.id)
            h.mu.Unlock()
            close(client.messages)
            
        case message := <-h.broadcast:
            h.mu.RLock()
            for _, client := range h.clients {
                select {
                case client.messages <- message:
                default:
                    // Client is slow, skip
                }
            }
            h.mu.RUnlock()
        }
    }
}

Non-blocking Writes

One critical optimization was using non-blocking writes to client channels. If a client is slow to consume messages, we skip them rather than blocking the entire broadcast. This prevents one slow client from affecting others.

Performance Comparison

We conducted extensive load testing to compare the Node.js and Go implementations. The results were striking:

Concurrent Connections

Memory Usage Under Load

At 10,000 concurrent connections, the memory footprint told a clear story:

• Node.js: 4.2 GB RAM with frequent GC spikes
• Go: 180 MB RAM with stable, predictable memory usage

This 23x reduction in memory usage meant we could run the Go server on much smaller instances, significantly reducing infrastructure costs.

Broadcast Performance

We measured how long it took to broadcast a message to all connected clients:

• 1,000 connections: Node.js 45ms, Go 2ms
• 5,000 connections: Node.js 280ms, Go 8ms
• 10,000 connections: Node.js 650ms (with errors), Go 15ms
• 50,000 connections: Not achievable with Node.js, Go 68ms

CPU Efficiency

Go's efficient use of multiple CPU cores was evident. While Node.js pegged a single core at 100% with 5,000 connections, Go distributed the load across all available cores and maintained low overall CPU usage even with 50,000 connections.

Results

The migration to Go delivered transformative improvements:

Scale and Capacity

• Increased concurrent connection capacity from 5,000 to 50,000+ per server
• Reduced server count from 8 instances to 1 for the same load
• Eliminated the need for complex load balancing across multiple Node.js processes

Performance and Reliability

• P99 latency improved from 340ms to 15ms
• Eliminated GC-related latency spikes
• Achieved consistent, predictable performance under varying loads
• Zero connection drops during broadcasts

Cost Savings

• 87% reduction in infrastructure costs (8 instances → 1 instance)
• Lower instance size requirements due to reduced memory footprint
• Reduced monitoring and maintenance overhead

Developer Experience

While JavaScript has a larger ecosystem, Go's simplicity and excellent standard library made the codebase easier to reason about. The strong typing caught bugs at compile time, and the straightforward concurrency model made the code more maintainable.

Tech Stack

The Go implementation used minimal dependencies:

• Go 1.21 with the standard library's net/http package
• Gorilla Mux for HTTP routing (optional, could use stdlib)

Previous Node.js Stack

• Node.js 18 with Express.js
• Cluster module to utilize multiple CPU cores

When to Choose Go Over Node.js

Based on this experience, we recommend Go over Node.js for:

• High-concurrency servers: When you need to handle thousands of concurrent connections
• Real-time systems: WebSocket, SSE, or gRPC servers with strict latency requirements
• CPU-intensive workloads: When you need to efficiently utilize multiple CPU cores
• Memory-constrained environments: When resource efficiency is critical
• Long-running services: When you need stable, predictable performance over time

When Node.js Still Makes Sense

That said, Node.js remains an excellent choice for:

• I/O-bound APIs: RESTful APIs with moderate concurrency
• Rapid prototyping: When development speed is the priority
• Full-stack JavaScript: When sharing code between frontend and backend is valuable
• Rich ecosystem needs: When you need access to npm's vast package library

Conclusion

Migrating our SSE server from Node.js to Go was one of the most impactful performance improvements we've made. The combination of Go's lightweight concurrency model, efficient memory management, and excellent standard library resulted in a server that could handle 10x more connections while using a fraction of the resources.

While Node.js served us well in the early stages, Go's design philosophy of simplicity and efficiency proved to be the right choice for a high-performance, real-time system. The migration paid for itself in reduced infrastructure costs within the first month, and the improved performance significantly enhanced the user experience.

This case study demonstrates that choosing the right tool for the job matters. While both languages are excellent, understanding their strengths and limitations helps you make informed decisions that can dramatically impact your system's performance and costs.