Integration of Nginx Proxy Manager and distributed storage system: solving massive data access problems

Integration of Nginx Proxy Manager and distributed storage system: To solve the problem of massive data access, specific code examples are needed

Introduction:
With the era of big data Now, many businesses are faced with the challenge of handling massive amounts of data. Traditional single-node storage systems cannot meet the needs of highly concurrent data requests and real-time data processing. In order to solve this problem, many companies have begun to adopt distributed storage systems to process massive data. This article will introduce how to integrate Nginx Proxy Manager with a distributed storage system to solve the problem of massive data access.

1. Introduction to Nginx Proxy Manager
Nginx Proxy Manager is a reverse proxy manager based on Nginx. It provides a user-friendly web interface to manage proxy services. Nginx Proxy Manager can easily configure and manage proxy rules, and supports automatic load balancing, reverse proxy caching and other functions. It is a powerful and easy-to-use tool that greatly simplifies the configuration and management of proxy services.

2. Selection of distributed storage system
Before choosing a distributed storage system, we need to clarify our needs. According to different application scenarios, we can choose different distributed storage systems, such as Hadoop, HBase, Cassandra, etc. Here we take Hadoop as an example. Hadoop is an open source distributed storage and computing platform that can build large-scale data storage and processing systems on cheap hardware.

3. Steps to integrate Nginx Proxy Manager with Hadoop

1. Installation and configuration of Nginx Proxy Manager
   First, we need to install and configure Nginx Proxy Manager on the server. For specific installation and configuration steps, please refer to the official documentation of Nginx Proxy Manager.
2. Install Hadoop cluster
   Next, we need to build a Hadoop cluster. In this example, we assume that we have 3 servers, namely namenode, datanode1 and datanode2. Among them, namenode is the main node of Hadoop, responsible for storing file metadata and controlling the operation of the entire cluster; datanode1 and datanode2 are the working nodes of Hadoop, responsible for storing and processing actual data.
3. Configure reverse proxy rules of Nginx Proxy Manager
   In the web interface of Nginx Proxy Manager, we can configure reverse proxy rules. We can configure multiple proxy rules as needed, and each proxy rule corresponds to a node of the Hadoop cluster. The specific configuration steps are as follows:
   (1) In the "Proxy Hostnames" field, enter the node IP address and port number of the Hadoop cluster.
   (2) In the "Remote Hostname" field, enter the node IP address and port number inside the cluster.
   (3) Click the "Save" button to save the proxy rules.
4. Configuring Hadoop access permissions
   In order to access the nodes of the Hadoop cluster, we need to configure the corresponding access permissions. The specific configuration steps are as follows:
   (1) Edit Hadoop's core-site.xml configuration file and add the IP address and port number of Nginx Proxy Manager to the fs.defaultFS attribute.
   (2) Edit the hdfs-site.xml configuration file of Hadoop and add the IP address and port number of the Nginx Proxy Manager to the dfs.namenode.secondary.http-address attribute.
   (3) Restart the Hadoop cluster to make the configuration take effect.

So far, we have completed the integration of Nginx Proxy Manager and Hadoop cluster. Now, we can access the nodes of the Hadoop cluster by accessing the Nginx Proxy Manager.

4. Code Example
The following is a simple Python code example that demonstrates how to use Nginx Proxy Manager to access the nodes of the Hadoop cluster:

import requests

# 设置Nginx Proxy Manager的URL
url = "http://nginx-proxy-manager-ip:port"

# 设置访问Hadoop的节点路径
path = "/hadoop-node-path"

# 发起GET请求
response = requests.get(url + path)

# 输出响应内容
print(response.text)

Through the above example code, we can use Python Send a GET request to access the nodes of the Hadoop cluster.

Summary:
By integrating Nginx Proxy Manager with a distributed storage system, we can easily access and process massive data. In this article, we use Hadoop as an example to introduce how to integrate Nginx Proxy Manager with a distributed storage system, and provide a simple Python code example. I hope this article will be helpful in solving the problem of massive data access.

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