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README
Apache-2.0

Ip2region是什么?

ip2region - 准确率99.9%的离线IP地址定位库,0.0x毫秒级查询,ip2region.db数据库只有数MB,提供了java,php,c,python,nodejs,golang,c#等查询绑定和Binary,B树,内存三种查询算法。

Ip2region特性

99.9%准确率

数据聚合了一些知名ip到地名查询提供商的数据,这些是他们官方的的准确率,经测试着实比经典的纯真IP定位准确一些。
ip2region的数据聚合自以下服务商的开放API或者数据(升级程序每秒请求次数2到4次):
01, >80%, 淘宝IP地址库, http://ip.taobao.com/
02, ≈10%, GeoIP, https://geoip.com/
03, ≈2%, 纯真IP库, http://www.cz88.net/
备注:如果上述开放API或者数据都不给开放数据时ip2region将停止数据的更新服务。

标准化的数据格式

每条ip数据段都固定了格式:

_城市Id|国家|区域|省份|城市|ISP_

只有中国的数据精确到了城市,其他国家有部分数据只能定位到国家,后前的选项全部是0,已经包含了全部你能查到的大大小小的国家(请忽略前面的城市Id,个人项目需求)。

体积小

包含了全部的IP,生成的数据库文件ip2region.db只有几MB,最小的版本只有1.5MB,随着数据的详细度增加数据库的大小也慢慢增大,目前还没超过8MB。

查询速度快

全部的查询客户端单次查询都在0.x毫秒级别,内置了三种查询算法

  1. memory算法:整个数据库全部载入内存,单次查询都在0.1x毫秒内,C语言的客户端单次查询在0.00x毫秒级别。
  2. binary算法:基于二分查找,基于ip2region.db文件,不需要载入内存,单次查询在0.x毫秒级别。
  3. b-tree算法:基于btree算法,基于ip2region.db文件,不需要载入内存,单词查询在0.x毫秒级别,比binary算法更快。

任何客户端b-tree都比binary算法快,当然memory算法固然是最快的!

多查询客户端的支持

已经集成的客户端有:java、C#、php、c、python、nodejs、php扩展(php5和php7)、golang、rust、lua、lua_c, nginx。

binding 描述 开发状态 binary查询耗时 b-tree查询耗时 memory查询耗时
c ANSC c binding 已完成 0.0x毫秒 0.0x毫秒 0.00x毫秒
c# c# binding 已完成 0.x毫秒 0.x毫秒 0.1x毫秒
golang golang binding 已完成 0.x毫秒 0.x毫秒 0.1x毫秒
java java binding 已完成 0.x毫秒 0.x毫秒 0.1x毫秒
lua lua实现的binding 已完成 0.x毫秒 0.x毫秒 0.x毫秒
lua_c lua的c扩展 已完成 0.0x毫秒 0.0x毫秒 0.00x毫秒
nginx nginx的c扩展 已完成 0.0x毫秒 0.0x毫秒 0.00x毫秒
nodejs nodejs 已完成 0.x毫秒 0.x毫秒 0.1x毫秒
php php实现的binding 已完成 0.x毫秒 0.1x毫秒 0.1x毫秒
php5_ext php5的c扩展 已完成 0.0x毫秒 0.0x毫秒 0.00x毫秒
php7_ext php7的c扩展 已完成 0.0毫秒 0.0x毫秒 0.00x毫秒
python python bindng 已完成 0.x毫秒 0.x毫秒 0.x毫秒
rust rust binding 已完成 0.x毫秒 0.x毫秒 0.x毫秒

ip2region快速测试

请参考每个binding下的README说明去运行cli测试程序,例如C语言的demo运行如下:

cd binding/c/
gcc -g -O2 testSearcher.c ip2region.c
./a.out ../../data/ip2region.db

会看到如下cli界面:

initializing  B-tree ... 
+----------------------------------+
| ip2region test script            |
| Author: chenxin619315@gmail.com  |
| Type 'quit' to exit program      |
+----------------------------------+
p2region>> 101.105.35.57
2163|中国|华南|广东省|深圳市|鹏博士 in 0.02295 millseconds

输入IP地址开始测试,第一次会稍微有点慢,在运行命令后面接入binary,memory来尝试其他算法,建议使用b-tree算法,速度和并发需求的可以使用memory算法,具体集成请参考不同binding下的测试源码。

ip2region安装

具体请参考每个binding下的README文档和测试demo,以下是一些可用的快捷安装方式:

maven仓库地址

<dependency>
    <groupId>org.lionsoul</groupId>
    <artifactId>ip2region</artifactId>
    <version>1.7.2</version>
</dependency>

nodejs

npm install node-ip2region --save

nuget安装

Install-Package IP2Region

php composer

# 插件来自:https://github.com/zoujingli/ip2region
composer require zoujingli/ip2region

ip2region 并发使用

  1. 全部binding的各个search接口都不是线程安全的实现,不同线程可以通过创建不同的查询对象来使用,并发量很大的情况下,binary和b-tree算法可能会打开文件数过多的错误,请修改内核的最大允许打开文件数(fs.file-max=一个更高的值),或者使用持久化的memory算法。

  2. memorySearch接口,在发布对象前进行一次预查询(本质上是把ip2region.db文件加载到内存),可以安全用于多线程环境。

ip2region.db的生成

从1.8版本开始,ip2region开源了ip2region.db生成程序的java实现,提供了ant编译支持,编译后会得到以下提到的dbMaker-{version}.jar,对于需要研究生成程序的或者更改自定义生成配置的请参考${ip2region_root}/maker/java内的java源码。

从ip2region 1.2.2版本开始里面提交了一个dbMaker-{version}.jar的可以执行jar文件,用它来完成这个工作:

  1. 确保你安装好了java环境(不玩Java的童鞋就自己谷歌找找拉,临时用一用,几分钟的事情)
  2. cd到${ip2region_root}/maker/java,然后运行如下命令:
java -jar dbMaker-{version}.jar -src 文本数据文件 -region 地域csv文件 [-dst 生成的ip2region.db文件的目录]

# 文本数据文件:db文件的原始文本数据文件路径,自带的ip2region.db文件就是/data/ip.merge.txt生成而来的,你可以换成自己的或者更改/data/ip.merge.txt重新生成
# 地域csv文件:该文件目的是方便配置ip2region进行数据关系的存储,得到的数据包含一个city_id,这个直接使用/data/origin/global_region.csv文件即可
# ip2region.db文件的目录:是可选参数,没有指定的话会在当前目录生成一份./data/ip2region.db文件
  1. 获取生成的ip2region.db文件覆盖原来的ip2region.db文件即可
  2. 默认的ip2region.db文件生成命令:
cd ${ip2region_root}/java/
java -jar dbMaker-1.2.2.jar -src ./data/ip.merge.txt -region ./data/global_region.csv

# 会看到一大片的输出

相关备注

声明

ip2region重点在于研究IP数据的存储设计和各种语言的查询实现,并没有原始IP数据的支撑,数据来源请参考上面的描述,升级数据需要很多IP的支撑并且会对原始平台造成一定量的请求压力,本项目不保证及时的数据更新,没有也不会有商用版本,你可以使用自定义的数据导入ip2region进行自定义查询的实现。

技术交流

  1. 数据库文件的结构和原理请阅读 @冬芽 的blog:“ip2region数据库文件的结构和原理”
  2. ip2region交流分享,微信:lionsoul2014(请备注ip2region),QQ:1187582057(很少关注)
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简介

准确率99.9%的离线IP地址定位库,0.0x毫秒级查询,数据库文件大小只有1.5M,提供了java,php,c,python,nodejs,golang,c#等查询绑定和Binary,B树,内存三种查询算法 展开 收起
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