Spark查找某个IP的归属地，二分算法，try{}catch{}的使用，将结果存MySQL数据库

1、创建Maven工程

2、编写Pom文件

<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd"> <modelVersion>4.0.0</modelVersion>

<groupId>cn.toto.spark</groupId>
<artifactId>bigdata</artifactId>
<version>1.0-SNAPSHOT</version>

<properties>
<maven.compiler.source>1.7</maven.compiler.source>
<maven.compiler.target>1.7</maven.compiler.target>
<encoding>UTF-8</encoding>
<scala.version>2.10.6</scala.version>
<spark.version>1.6.2</spark.version>
<hadoop.version>2.6.4</hadoop.version>
</properties>

<dependencies>
<dependency>
<groupId>org.scala-lang</groupId>
<artifactId>scala-library</artifactId>
<version>${scala.version}</version>
</dependency>

<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-core_2.10</artifactId>
<version>${spark.version}</version>
</dependency>

<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-client</artifactId>
<version>${hadoop.version}</version>
</dependency>

<dependency>
<groupId>mysql</groupId>
<artifactId>mysql-connector-java</artifactId>
<version>5.1.38</version>
</dependency>
</dependencies>

<build>
<sourceDirectory>src/main/scala</sourceDirectory>
<testSourceDirectory>src/test/scala</testSourceDirectory>
<plugins>
<plugin>
<groupId>net.alchim31.maven</groupId>
<artifactId>scala-maven-plugin</artifactId>
<version>3.2.2</version>
<executions>
<execution>
<goals>
<goal>compile</goal>
<goal>testCompile</goal>
</goals>
<configuration>
<args>
<arg>-make:transitive</arg>
<arg>-dependencyfile</arg>
<arg>${project.build.directory}/.scala_dependencies</arg>
</args>
</configuration>
</execution>
</executions>
</plugin>

<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-shade-plugin</artifactId>
<version>2.4.3</version>
<executions>
<execution>
<phase>package</phase>
<goals>
<goal>shade</goal>
</goals>
<configuration>
<filters>
<filter>
<artifact>*:*</artifact>
<excludes>
<exclude>META-INF/*.SF</exclude>
<exclude>META-INF/*.DSA</exclude>
<exclude>META-INF/*.RSA</exclude>
</excludes>
</filter>
</filters>
</configuration>
</execution>
</executions>
</plugin>
</plugins>
</build>

</project>

3、准备要处理的文件

1.0.1.0|1.0.3.255|16777472|16778239|亚洲|中国|福建|福州||电信|350100|China|CN|119.306239|26.075302
1.0.8.0|1.0.15.255|16779264|16781311|亚洲|中国|广东|广州||电信|440100|China|CN|113.280637|23.125178
1.0.32.0|1.0.63.255|16785408|16793599|亚洲|中国|广东|广州||电信|440100|China|CN|113.280637|23.125178
1.1.0.0|1.1.0.255|16842752|16843007|亚洲|中国|福建|福州||电信|350100|China|CN|119.306239|26.075302
1.1.2.0|1.1.7.255|16843264|16844799|亚洲|中国|福建|福州||电信|350100|China|CN|119.306239|26.075302
1.1.8.0|1.1.63.255|16844800|16859135|亚洲|中国|广东|广州||电信|440100|China|CN|113.280637|23.125178
1.2.0.0|1.2.1.255|16908288|16908799|亚洲|中国|福建|福州||电信|350100|China|CN|119.306239|26.075302

20090121000132095572000|125.213.100.123|show.51.com|/shoplist.php?phpfile=shoplist2.php&style=1&sex=137|Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1; Mozilla/4.0(Compatible Mozilla/4.0(Compatible-EmbeddedWB 14.59 http://bsalsa.com/ EmbeddedWB- 14.59  from: http://bsalsa.com/ )|http://show.51.com/main.php|
20090121000132124542000|117.101.215.133|www.jiayuan.com|/19245971|Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1; TencentTraveler 4.0)|http://photo.jiayuan.com/index.php?uidhash=d1c3b69e9b8355a5204474c749fb76ef|__tkist=0; myloc=50%7C5008; myage=2009; PROFILE=14469674%3A%E8%8B%A6%E6%B6%A9%E5%92%96%E5%95%A1%3Am%3Aphotos2.love21cn.com%2F45%2F1b%2F388111afac8195cc5d91ea286cdd%3A1%3A%3Ahttp%3A%2F%2Fimages.love21cn.com%2Fw4%2Fglobal%2Fi%2Fhykj_m.jpg; last_login_time=1232454068; SESSION_HASH=8176b100a84c9a095315f916d7fcbcf10021e3af; RAW_HASH=008a1bc48ff9ebafa3d5b4815edd04e9e7978050; COMMON_HASH=45388111afac8195cc5d91ea286cdd1b; pop_1232093956=1232468896968; pop_time=1232466715734; pop_1232245908=1232469069390; pop_1219903726=1232477601937; LOVESESSID=98b54794575bf547ea4b55e07efa2e9e; main_search:14469674=%7C%7C%7C00; registeruid=14469674; REG_URL_COOKIE=http%3A%2F%2Fphoto.jiayuan.com%2Fshowphoto.php%3Fuid_hash%3D0319bc5e33ba35755c30a9d88aaf46dc%26total%3D6%26p%3D5; click_count=0%2C3363619
20090121000132406516000|117.101.222.68|gg.xiaonei.com|/view.jsp?p=389|Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1; CIBA)|http://home.xiaonei.com/Home.do?id=229670724|_r01_=1; __utma=204579609.31669176.1231940225.1232462740.1232467011.145; __utmz=204579609.1231940225.1.1.utmccn=(direct)
20090121000132581311000|115.120.36.118|tj.tt98.com|/tj.htm|Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1; TheWorld)|http://www.tt98.com/|

4.获取ip归属地信息

package cn.toto.spark

import java.io.{BufferedReader, FileInputStream, InputStreamReader}

import scala.collection.mutable.ArrayBuffer

/**
* Created by toto on 2017/7/8.
* 查找IP的归属地信息
*/
object IPLocationDemo {

def ip2Long(ip: String): Long = {
val fragments = ip.split("[.]")
var ipNum = 0L
for (i <- 0 until fragments.length){
ipNum =  fragments(i).toLong | ipNum << 8L
}
ipNum
}

def readData(path: String) = {
val br = new BufferedReader(new InputStreamReader(new FileInputStream(path)))
var s: String = null
var flag = true
val lines = new ArrayBuffer[String]()
while (flag)
{
s = br.readLine()
if (s != null)
lines += s
else
flag = false
}
lines
}

def binarySearch(lines: ArrayBuffer[String], ip: Long) : Int = {
var low = 0
var high = lines.length - 1
while (low <= high) {
val middle = (low + high) / 2
if ((ip >= lines(middle).split("\\|")(2).toLong) && (ip <= lines(middle).split("\\|")(3).toLong))
return middle
if (ip < lines(middle).split("\\|")(2).toLong)
high = middle - 1
else {
low = middle + 1
}
}
-1
}

/**
* 运行后的结果是：
* 2016917821
* 120.55.0.0|120.55.255.255|2016870400|2016935935|亚洲|中国|浙江|杭州||阿里巴巴|330100|China|CN|120.153576|30.287459
*
* 要求2016917821       在 |2016870400|2016935935|  之间。
* @param args
*/
def main(args: Array[String]): Unit = {
val ip = "120.55.185.61"
val ipNum = ip2Long(ip)
println(ipNum)
val lines = readData("E:\\learnTempFolder\\ip.txt")
val index = binarySearch(lines, ipNum)
print(lines(index))
}
}

5.查询IP归属地相关信息，并将这些信息存储到MySQL数据库中

package cn.toto.spark

import java.sql.{Connection, Date, DriverManager, PreparedStatement}

import org.apache.spark.{SparkConf, SparkContext}

/**
* Created by toto on 2017/7/8.
*/
object IPLocation {

val data2MySQL = (iterator: Iterator[(String, Int)]) => {
var conn: Connection = null
var ps : PreparedStatement = null
val sql = "INSERT INTO location_info (location, counts, accesse_date) VALUES (?, ?, ?)"
try {
conn = DriverManager.getConnection("jdbc:mysql://192.168.106.100:3306/bigdata", "root", "123456")
iterator.foreach(line => {
ps = conn.prepareStatement(sql)
ps.setString(1, line._1)
ps.setInt(2, line._2)
ps.setDate(3, new Date(System.currentTimeMillis()))
ps.executeUpdate()
})
} catch {
case e: Exception => println("Mysql Exception")
} finally {
if (ps != null)
ps.close()
if (conn != null)
conn.close()
}
}

def ip2Long(ip: String): Long = {
val fragments = ip.split("[.]")
var ipNum = 0L
for (i <- 0 until fragments.length){
ipNum =  fragments(i).toLong | ipNum << 8L
}
ipNum
}

def binarySearch(lines: Array[(String, String, String)], ip: Long) : Int = {
var low = 0
var high = lines.length - 1
while (low <= high) {
val middle = (low + high) / 2
if ((ip >= lines(middle)._1.toLong) && (ip <= lines(middle)._2.toLong))
return middle
if (ip < lines(middle)._1.toLong)
high = middle - 1
else {
low = middle + 1
}
}
-1
}

def main(args: Array[String]): Unit = {
val conf = new SparkConf().setMaster("local[2]").setAppName("IpLocation")
val sc = new SparkContext(conf)

val ipRulesRdd = sc.textFile("E://workspace//ip.txt").map(line =>{
val fields = line.split("\\|")
val start_num = fields(2)
val end_num = fields(3)
val province = fields(6)
(start_num, end_num, province)
})
//全部的ip映射规则
val ipRulesArrary = ipRulesRdd.collect()

//广播规则
val ipRulesBroadcast = sc.broadcast(ipRulesArrary)

//加载要处理的数据
val ipsRDD = sc.textFile("E://workspace//access.log").map(line => {
val fields = line.split("\\|")
fields(1)
})

val result = ipsRDD.map(ip => {
val ipNum = ip2Long(ip)
val index = binarySearch(ipRulesBroadcast.value, ipNum)
val info = ipRulesBroadcast.value(index)
//(ip的起始Num， ip的结束Num，省份名)
info
}).map(t => (t._3, 1)).reduceByKey(_+_)

//向MySQL写入数据
result.foreachPartition(data2MySQL(_))

//println(result.collect().toBuffer)
sc.stop()
}
}

CREATE DATABASE bigdata CHARACTER SET utf8;

USE bigdata;

CREATE TABLE location_info (
id INT(10) AUTO_INCREMENT PRIMARY KEY,
location VARCHAR(100),
counts INT(10),
accesse_date DATE
) ENGINE=INNODB DEFAULT CHARSET=utf8;