How to assign different weights to backend servers?
Jun 17, 2025 am 09:28 AM
To assign different weights to the backend server, you must first configure weight parameters in the load balancer, such as Nginx, HAProxy or load balancing functions provided by cloud services. 1. The weight determines the traffic allocation ratio. The higher the value, the more allocation requests are, but not the percentage, but the relative value. 2. The weights take effect differently under different algorithms. The polling algorithm is allocated by the number of times, and the minimum connection algorithm affects priority. 3. Verify whether the weights are effective. You can observe the traffic through accessing log statistics, monitoring tools or using test tools to simulate traffic. 4. Note that some platforms such as Kubernetes do not directly support weights, and need to be implemented with the help of other strategies. Correct understanding of the weighting mechanism and scheduling algorithm of the load balancer used is the key to ensuring the effective configuration.
When building a load balancing system, it is a very common requirement to assign different weights to the backend server. Simply put, weight determines the proportion of traffic distribution among multiple servers. If you want some servers with stronger performance or lower load to bear more requests, you can do this by setting different weights.
General method: Use load balancer to configure weights
Most modern load balancing schemes support setting weight parameters for each backend node. For example, load balancing services provided by Nginx, HAProxy, Envoy or cloud vendors (such as AWS ALB, Alibaba Cloud SLB), etc.
Taking Nginx as an example, you can write it like this in the configuration file:
upstream backend {
server 10.0.0.1 weight=3;
server 10.0.0.2 weight=1;
} The example above shows that 10.0.0.1 has three times the weight of 10.0.0.2 , so it handles three-quarters of the request.
Different load balancers may have slightly different understandings of "weights". Some are based on polling mechanisms, and some are based on the minimum number of connections. It is best to check the document to confirm the specific behavior before use.
Common misunderstandings and precautions
- The weight is not a percentage , but a relative value. For example, the traffic ratio of two servers with weight=2 and weight=4 is actually 1:2.
- If you use the Round Robin algorithm , the weights are effective by rotation by number of times; if it is the Least Connections algorithm , the weights will affect the scheduling priority, but the strict proportion is not guaranteed.
- Some platforms (such as Service in Kubernetes) do not directly support weight configuration and need to be implemented with the help of an Ingress controller or custom policies.
How to verify whether the weight is effective?
You can verify whether the actual traffic is distributed according to the expected proportion in the following ways:
- Record access logs on each backend server and count the number of requests.
- Use monitoring tools such as Prometheus Grafana to observe the request volume chart for each node.
- Initiate a batch of test requests and simulate traffic with scripts or command-line tools (such as ab, wrk, curl loops).
For example, use the ab tool to initiate 100 concurrent request tests:
ab -n 100 -c 10 http://your-loadbalancer.com/
Then check the number of requests received by the backend server to see if it meets the weight ratio you set.
summary
The core of assigning different weights to the backend server is understanding how the load balancer you are using supports this feature and ensuring that the scheduling algorithm you choose matches the weight logic. The configuration process is usually not complicated, but it is easy to misunderstand the meaning of weights and cause the effect to be inconsistent with expectations. Just pay attention to these details and you can control the flow distribution more flexibly.
The above is the detailed content of How to assign different weights to backend servers?. For more information, please follow other related articles on the PHP Chinese website!
Hot AI Tools
Undress AI Tool
Undress images for free
Undresser.AI Undress
AI-powered app for creating realistic nude photos
AI Clothes Remover
Online AI tool for removing clothes from photos.
Clothoff.io
AI clothes remover
Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!
Hot Article
Hot Tools
Notepad++7.3.1
Easy-to-use and free code editor
SublimeText3 Chinese version
Chinese version, very easy to use
Zend Studio 13.0.1
Powerful PHP integrated development environment
Dreamweaver CS6
Visual web development tools
SublimeText3 Mac version
God-level code editing software (SublimeText3)
Hot Topics
How to optimize TCP/IP performance and network performance of Linux systems
Nov 07, 2023 am 11:15 AM
In the field of modern computers, the TCP/IP protocol is the basis for network communication. As an open source operating system, Linux has become the preferred operating system used by many businesses and organizations. However, as network applications and services become more and more critical components of business, administrators often need to optimize network performance to ensure fast and reliable data transfer. This article will introduce how to improve the network transmission speed of Linux systems by optimizing TCP/IP performance and network performance of Linux systems. This article will discuss a
Failover and recovery mechanism in Nginx load balancing solution
Oct 15, 2023 am 11:14 AM
Introduction to the failover and recovery mechanism in the Nginx load balancing solution: For high-load websites, the use of load balancing is one of the important means to ensure high availability of the website and improve performance. As a powerful open source web server, Nginx's load balancing function has been widely used. In load balancing, how to implement failover and recovery mechanisms is an important issue that needs to be considered. This article will introduce the failover and recovery mechanism in Nginx load balancing and give specific code examples. 1. Failover mechanism
Using Nginx Proxy Manager to implement reverse proxy load balancing strategy
Sep 26, 2023 pm 12:05 PM
Use NginxProxyManager to implement reverse proxy load balancing strategy NginxProxyManager is an Nginx-based proxy management tool that can help us easily implement reverse proxy and load balancing. By configuring NginxProxyManager, we can distribute requests to multiple backend servers to achieve load balancing and improve system availability and performance. 1. Install and configure NginxProxyManager
Building a high-availability load balancing system: Best practices for Nginx Proxy Manager
Sep 27, 2023 am 08:22 AM
Building a high-availability load balancing system: Best practices for NginxProxyManager Introduction: In the development of Internet applications, the load balancing system is one of the essential components. It can achieve high concurrency and high availability services by distributing requests to multiple servers. NginxProxyManager is a commonly used load balancing software. This article will introduce how to use NginxProxyManager to build a high-availability load balancing system and provide
Dynamic failure detection and load weight adjustment strategy in Nginx load balancing solution
Oct 15, 2023 pm 03:54 PM
Dynamic failure detection and load weight adjustment strategies in the Nginx load balancing solution require specific code examples. Introduction In high-concurrency network environments, load balancing is a common solution that can effectively improve the availability and performance of the website. Nginx is an open source, high-performance web server that provides powerful load balancing capabilities. This article will introduce two important features in Nginx load balancing, dynamic failure detection and load weight adjustment strategy, and provide specific code examples. 1. Dynamic failure detection Dynamic failure detection
High availability and disaster recovery solution for Nginx load balancing solution
Oct 15, 2023 am 11:43 AM
High Availability and Disaster Recovery Solution of Nginx Load Balancing Solution With the rapid development of the Internet, the high availability of Web services has become a key requirement. In order to achieve high availability and disaster tolerance, Nginx has always been one of the most commonly used and reliable load balancers. In this article, we will introduce Nginx’s high availability and disaster recovery solutions and provide specific code examples. High availability of Nginx is mainly achieved through the use of multiple servers. As a load balancer, Nginx can distribute traffic to multiple backend servers to
How to use Workerman to build a high-availability load balancing system
Nov 07, 2023 pm 01:16 PM
How to use Workerman to build a high-availability load balancing system requires specific code examples. In the field of modern technology, with the rapid development of the Internet, more and more websites and applications need to handle a large number of concurrent requests. In order to achieve high availability and high performance, the load balancing system has become one of the essential components. This article will introduce how to use the PHP open source framework Workerman to build a high-availability load balancing system and provide specific code examples. 1. Introduction to Workerman Worke
Application of load balancing strategy in Java framework performance optimization
May 31, 2024 pm 08:02 PM
Load balancing strategies are crucial in Java frameworks for efficient distribution of requests. Depending on the concurrency situation, different strategies have different performance: Polling method: stable performance under low concurrency. Weighted polling method: The performance is similar to the polling method under low concurrency. Least number of connections method: best performance under high concurrency. Random method: simple but poor performance. Consistent Hashing: Balancing server load. Combined with practical cases, this article explains how to choose appropriate strategies based on performance data to significantly improve application performance.
