Automate Nginx Config Audits: Python Script to Export Structured Excel Reports

Script Overview

This script parses Nginx configuration files, extracts upstream, server, and location blocks, and outputs a structured Excel (.xlsx) report for auditing, analysis, and sharing.

Key Features

Parse Nginx configuration : extract upstream and server information.

Structure data : convert parsing results to a Pandas DataFrame.

Export to Excel : save as nginx_config_table.xlsx for easy viewing and collaboration.

Implementation Details

1. Parse Nginx configuration content

Use regular expressions to match all upstream blocks and extract backend server addresses.

Split all server blocks and parse each for:

Listening ports (supports listen 80; or listen 443 ssl;).

Domain names ( server_name, multiple supported).

Each location block:

Route path (e.g., /api/).

Forward target ( proxy_pass) or static path ( root / alias).

Associated upstream for load balancing.

Other configuration lines and comments are retained as remarks.

2. Generate structured table

Each location becomes a row with fields: Domain/Service, Listening Port, Route‑Location, Target/Path, Load Balancing, and Parameters/Remarks.

3. Export to Excel file

Use pandas.to_excel() to save the DataFrame as nginx_config_table.xlsx, preserving the full structure and supporting filtering, sorting, and sharing.

Full Python Script

import re
import pandas as pd
import sys

def parse_nginx_config(config_content):
    """Parse Nginx configuration, extract upstream and server info."""
    servers = []
    upstreams = {}

    # Extract all upstream blocks
    upstream_blocks = re.findall(r'upstream\s+(\w+)\s*\{\s*([^}]*?)\s*\}', config_content, re.DOTALL)
    for upstream_name, upstream_body in upstream_blocks:
        upstream_servers = re.findall(r'server\s+([^\s;]+);', upstream_body)
        upstreams[upstream_name] = ', '.join(upstream_servers)

    # Split all server blocks
    server_blocks = re.split(r'
\s*server\s*\{', config_content)
    for block in server_blocks:
        if not block.strip():
            continue
        server_info = {}

        # Extract listening ports
        listen_matches = re.findall(r'listen\s+(\d+)(?:\s+ssl)?;', block)
        server_info['监听端口'] = ', '.join(listen_matches) if listen_matches else None

        # Extract server_name
        server_name_match = re.search(r'server_name\s+([^\s;]+(?:\s+[^\s;]+)*);', block)
        if server_name_match:
            server_info['域名/服务'] = ', '.join(server_name_match.group(1).split())
        else:
            server_info['域名/服务'] = "无"

        # Extract location blocks
        locations = []
        location_blocks = re.findall(r'location\s+([^\s{]+)\s*\{\s*([^}]*?)\s*\}', block, re.DOTALL)
        for loc_path, loc_content in location_blocks:
            location_info = {
                '路由-location': loc_path,
                '目标/路径': None,
                '负载均衡': None,
                '参数与备注': []
            }

            # proxy_pass
            proxy_pass_match = re.search(r'proxy_pass\s+(http://[^\s;/]+)[^\s;]*;', loc_content)
            if proxy_pass_match:
                proxy_target = proxy_pass_match.group(1)
                location_info['目标/路径'] = proxy_target
                for upstream_name, servers_list in upstreams.items():
                    if proxy_target.endswith(f"/{upstream_name}"):
                        location_info['负载均衡'] = servers_list
                        break

            # static paths
            if not location_info['目标/路径']:
                root_match = re.search(r'root\s+([^\s;]+);', loc_content)
                if root_match:
                    location_info['目标/路径'] = root_match.group(1)
                else:
                    alias_match = re.search(r'alias\s+([^\s;]+);', loc_content)
                    if alias_match:
                        location_info['目标/路径'] = alias_match.group(1)

            if not location_info['目标/路径']:
                location_info['目标/路径'] = "无"

            # retain original lines as remarks
            lines = [line.strip() for line in loc_content.strip().split('
') if line.strip()]
            location_info['参数与备注'] = '
'.join(lines)

            locations.append(location_info)

        server_info['locations'] = locations
        servers.append(server_info)

    return servers

def generate_table(servers):
    """Convert parsing results to Pandas DataFrame."""
    rows = []
    for server in servers:
        for loc in server.get('locations', []):
            row = {
                '域名/服务': server.get('域名/服务'),
                '监听端口': server.get('监听端口'),
                '路由-location': loc.get('路由-location'),
                '目标/路径': loc.get('目标/路径'),
                '负载均衡': loc.get('负载均衡'),
                '参数与备注': loc.get('参数与备注')
            }
            rows.append(row)
    return pd.DataFrame(rows)

def main():
    try:
        import pandas as pd
    except ImportError:
        print("错误: 未找到pandas库，请先安装。可以使用命令：pip install pandas")
        return

    if len(sys.argv) != 2:
        print("用法: python3 parse_nginx.py <nginx.conf>")
        return

    file_path = sys.argv[1]
    try:
        with open(file_path, 'r', encoding='utf-8') as f:
            config_content = f.read()
    except FileNotFoundError:
        print(f"错误: 文件 {file_path} 未找到，请检查路径是否正确。")
        return
    except Exception as e:
        print(f"发生错误: {e}")
        return

    servers = parse_nginx_config(config_content)
    table = generate_table(servers)

    print(table)

    excel_file_path = 'nginx_config_table.xlsx'
    table.to_excel(excel_file_path, index=False)
    print(f"Nginx配置解析结果已保存至 {excel_file_path}")

if __name__ == '__main__':
    main()

Usage

Install dependencies: pip install pandas (pandas will also install openpyxl for .xlsx output).

Run the script: python3 parse_nginx.py nginx.conf.

Application Scenarios

Configuration audit: quickly list all service routes and backend dependencies.

Migration/refactoring: export configuration structure to assist architectural changes.

Team collaboration: transform complex configs into readable Excel tables.

Security checks: identify unauthorized exposed paths or misrouted traffic.

Conclusion

This lightweight Python script enables operations engineers to visualize complex Nginx configurations efficiently, greatly improving configuration management productivity. The script relies on regular‑expression parsing and works for most standard Nginx setups.