Principle and implementation of fair algorithm in Nginx load balancing

The principle and implementation of the fair algorithm in Nginx load balancing requires specific code examples

In a distributed system, load balancing is a very important part. As a high-performance web server and reverse proxy server, Nginx has a load balancing function. Nginx provides a variety of load balancing algorithms, among which the fair algorithm is a commonly used algorithm.

1. Principle of fair algorithm
   The fair algorithm is a weight-based load balancing algorithm. Its core idea is to distribute requests to different servers according to the weight of the server to achieve the goal of each server. The load is relatively balanced.

The specific implementation process is as follows:
1) Construct a server list based on different server weights.
2) When a request arrives, traverse the server list and select the server with the highest weight for processing.
3) After processing the request, subtract the greatest common divisor of the total weight from the weight of the server.
4) Save the updated server list for next request.

In this way, the fair algorithm can allocate requests according to the weight of the server to achieve a more balanced load distribution.

2. Specific implementation of the fair algorithm

In Nginx, the fair algorithm is not directly provided, but we can implement it through custom instructions. The following is an example of an Nginx configuration file that implements the fair algorithm:

http {
    upstream backend {
        fair;
        server backend1.example.com weight=3;
        server backend2.example.com weight=2;
        server backend3.example.com weight=1;
    }

    server {
        listen 80;
        server_name example.com;

        location / {
            proxy_pass http://backend;
        }
    }
}

In this configuration, we define the backend through the upstream directive and enable the fair algorithm through the fair directive. Then a virtual host with listening port 80 is configured through the server directive, and the request is forwarded to the backend.

In the server list, we set the weight of different servers through the weight parameter. The weight setting is determined based on server performance and load. Servers with higher weights will handle more requests.

3. Code implementation of fair algorithm

In order to better understand the implementation principle of fair algorithm, the following is a simple Python code example:

import math

servers = [
    {'name': 'backend1.example.com', 'weight': 3},
    {'name': 'backend2.example.com', 'weight': 2},
    {'name': 'backend3.example.com', 'weight': 1},
]

total_weight = sum(server['weight'] for server in servers)
gcd = math.gcd(*[server['weight'] for server in servers])

for server in servers:
    server['current_weight'] = server['weight']
    
while True:
    best_server = None
    best_weight = 0
    
    for server in servers:
        server['current_weight'] += server['weight']
        if server['current_weight'] > best_weight:
            best_server = server
            best_weight = server['current_weight']
    
    best_server['current_weight'] -= total_weight
    
    print('Request sent to: %s' % best_server['name'])

In this code, we first define the server list and calculate the total weight and the greatest common divisor of the weights.

Then, every time a request arrives, the most suitable server is selected for processing based on the current weight, and the current weight in the server list is updated.

Finally, by printing out the server to which the request is sent, we can see the load balancing effect of the fair algorithm.

Summary:
The fair algorithm in Nginx implements weight-based load balancing, and achieves a more balanced load distribution by reasonably allocating requests to each server. Through custom instructions and code examples, we can better understand and apply the fair algorithm. In practical applications, setting weights based on server performance and load conditions can improve system performance and stability.

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