Load balancing is crucial for distributing incoming requests across multiple servers to ensure optimal resource utilization, minimize response time, and avoid overload on any single server. Let's explore different load balancing algorithms and their trade-offs.

Round Robin

The simplest load balancing algorithm that distributes requests sequentially across all available servers. Each server gets an equal share of requests over time.

Python round_robin.py 
class RoundRobinBalancer:
    def __init__(self, servers):
        self.servers = servers
        self.current = 0
    
    def get_server(self):
        server = self.servers[self.current]
        self.current = (self.current + 1) % len(self.servers)
        return server

Weighted Round Robin

Assigns different weights to servers based on their capacity. Servers with higher weights receive more requests proportionally.

Least Connections

Routes requests to the server with the fewest active connections. This is effective when requests have varying processing times.

$ nginx -t && nginx -s reload
nginx: the configuration file /etc/nginx/nginx.conf syntax is ok nginx: configuration file /etc/nginx/nginx.conf test is successful

Consistent Hashing

Uses a hash function to map requests to servers. This ensures that the same request always goes to the same server, which is useful for session affinity and caching.

Geographic Load Balancing

Routes requests based on the geographic location of the client to the nearest server, reducing latency and improving user experience.

Health Checks and Failover

Modern load balancers continuously monitor server health and automatically remove unhealthy servers from the rotation, ensuring high availability.

YAML nginx-config.yml 
upstream backend {
    least_conn;
    server backend1.example.com weight=3;
    server backend2.example.com weight=2;
    server backend3.example.com backup;
}

Conclusion

Choosing the right load balancing strategy depends on your specific requirements: request patterns, server capacities, session requirements, and geographic distribution. Many modern systems combine multiple strategies for optimal performance.