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Module: Load Balancing

Load Balancers

Load Balancers: System Design Fundamentals

Load balancing is a critical component of scalable and highly available systems. It distributes incoming network traffic across multiple servers to ensure no single server is overwhelmed. This improves responsiveness, prevents overload, and enhances overall reliability. Here's a breakdown of load balancers, covering types, algorithms, considerations, and more.

What is a Load Balancer?

A load balancer acts as a reverse proxy, sitting in front of a pool of servers (often called a "backend" or "upstream" pool). Clients connect to the load balancer, which then intelligently routes requests to one of the available servers.

Key Benefits:

  • Increased Availability: If one server fails, the load balancer redirects traffic to the remaining healthy servers.
  • Improved Scalability: Easily add or remove servers to the backend pool to handle fluctuating traffic demands.
  • Enhanced Performance: Distributing load reduces response times and improves user experience.
  • Reduced Downtime: Allows for maintenance and updates on servers without impacting users.
  • Security: Can provide SSL termination and protect backend servers from direct exposure.

Types of Load Balancers

Load balancers are categorized based on the OSI model layer they operate on.

  • Layer 4 (Transport Layer) Load Balancers:

    • Operate at the TCP/UDP level.
    • Make routing decisions based on IP addresses and port numbers.
    • Faster and simpler than Layer 7 load balancers.
    • Examples: HAProxy, Nginx (can also do L7), AWS Network Load Balancer (NLB).
    • Use Cases: Suitable for handling high volumes of TCP/UDP traffic, like gaming, streaming, and database connections.

  • Layer 7 (Application Layer) Load Balancers:

    • Operate at the HTTP/HTTPS level.
    • Can make routing decisions based on application-specific data like cookies, headers, URL paths, and content.
    • More flexible and feature-rich than Layer 4 load balancers.
    • Examples: Nginx, Apache, AWS Application Load Balancer (ALB), Google Cloud Load Balancing.
    • Use Cases: Ideal for web applications, APIs, and microservices where content-based routing is required. Can also handle SSL termination, compression, and caching.

  • Hardware Load Balancers:

    • Dedicated physical appliances.
    • Offer high performance and reliability.
    • Typically more expensive than software-based solutions.
    • Examples: F5 BIG-IP, Citrix ADC.

  • Software Load Balancers:

    • Run as software on standard servers or virtual machines.
    • More flexible and cost-effective than hardware load balancers.
    • Examples: Nginx, HAProxy, Keepalived.

  • Cloud Load Balancers:

    • Offered as a service by cloud providers (AWS, Azure, Google Cloud).
    • Highly scalable and managed.
    • Pay-as-you-go pricing.
    • Examples: AWS ALB, NLB, CLB; Azure Load Balancer; Google Cloud Load Balancing.

Load Balancing Algorithms

These algorithms determine how the load balancer distributes traffic to the backend servers.

  • Round Robin: Distributes requests sequentially to each server in the pool. Simple but doesn't account for server load.
  • Weighted Round Robin: Assigns weights to servers based on their capacity. Servers with higher weights receive more requests.
  • Least Connections: Sends requests to the server with the fewest active connections. Good for handling varying request processing times.
  • Weighted Least Connections: Combines weights with the least connections algorithm.
  • IP Hash: Uses the client's IP address to consistently route requests to the same server. Useful for session persistence (sticky sessions).
  • URL Hash: Uses the URL to consistently route requests to the same server.
  • Least Response Time: Routes requests to the server with the lowest average response time. Requires monitoring of server response times.
  • Random: Randomly selects a server from the pool. Simple but can lead to uneven load distribution.
  • Consistent Hashing: Minimizes the impact of adding or removing servers on the cache hit rate. Important for caching layers.

Key Considerations & Features

  • Health Checks: Load balancers periodically check the health of backend servers. Unhealthy servers are removed from the pool until they recover. Health checks can be simple (ping) or more complex (checking application-specific endpoints).
  • Session Persistence (Sticky Sessions): Ensures that requests from the same client are consistently routed to the same server. Important for applications that rely on session state. Can be implemented using cookies, IP addresses, or other identifiers.
  • SSL Termination: Decrypts SSL/TLS traffic at the load balancer, reducing the load on backend servers.
  • Content Switching: Routes requests based on the content of the request (e.g., URL path, headers).
  • Connection Draining: Gracefully removes servers from the pool during maintenance or updates by allowing existing connections to complete.
  • Auto-Scaling Integration: Automatically adjusts the number of backend servers based on traffic demand.
  • Monitoring & Logging: Provides insights into load balancer performance and traffic patterns.
  • High Availability: Load balancers themselves should be highly available, often deployed in active-passive or active-active configurations.
  • Geographic Load Balancing (Global Server Load Balancing - GSLB): Distributes traffic across multiple geographically distributed data centers.

Load Balancer Architectures

  • Active-Passive: One load balancer is active, and the other is on standby. If the active load balancer fails, the passive one takes over.
  • Active-Active: Both load balancers are active and distribute traffic simultaneously. Provides higher availability and scalability.
  • Clustered: Multiple load balancers work together as a single logical unit.

Example Scenario: Web Application

Imagine a web application with three backend servers.

  1. A user sends a request to the web application's domain name.
  2. The DNS resolves the domain name to the load balancer's IP address.
  3. The load balancer receives the request and, using a chosen algorithm (e.g., Round Robin), selects one of the three backend servers.
  4. The load balancer forwards the request to the selected server.
  5. The server processes the request and sends a response back to the load balancer.
  6. The load balancer forwards the response back to the user.

This process repeats for each incoming request, ensuring that the load is distributed evenly across the backend servers.

Resources

This overview provides a solid foundation for understanding load balancers and their role in building scalable and reliable systems. The specific choice of load balancer and algorithm will depend on the specific requirements of your application.