项目需求：
ip.txt:包含ip起始地址，ip结束地址，ip所属省份
access.txt:包含ip地址和各种访问数据
需求：两表联合查询每个省份的ip数量
SparkCore
使用广播，将小表广播到executor.对大表的每条数据都到小表中进行查找。
package day07
import java.sql.DriverManager
import org.apache.log4j.{Level, Logger}
import org.apache.spark.broadcast.Broadcast
import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkConf, SparkContext}
object IPLocation {
val ipFile = "d:\data\spark\ip.txt"
val acessFile = "d:\data\spark\access.log"
def main(args: Array[String]): Unit = {
Logger.getLogger("org.apache.spark").setLevel(Level.OFF)
val conf = new SparkConf().setAppName("IpLocation").setMaster("local[3]")
val sc = new SparkContext(conf)
//1.读取IP规则资源库
val lines = sc.textFile(ipFile)
//2.整理Ip规则
val ipRules = lines.map(x => {
val splited = x.split("[|]")
val startNum = splited(2).toLong
val endNum = splited(3).toLong
val province = splited(6)
(startNum,endNum,province)
})
//println(ipRules.collect().toBuffer)
//3.将Ip收集起来
val ipDriver: Array[(Long, Long, String)] = ipRules.collect()
//4.将IP通过广播的方式发送到executor
//广播之后，在Driver端获取了广播变量的引用（如果没有广播完，就不往下走）
val broadcastRef: Broadcast[Array[(Long, Long, String)]] = sc.broadcast(ipDriver)
//5.读取访问日志
val access = sc.textFile(acessFile)
//6.整理访问日志
val provinces = access.map(x => {
val fields = x.split("[|]")
val ip = fields(1)
val ipNum = MyUtils.ip2Long(ip)
//通过广播获取所有ip规则，然后进行匹配
val allIpRulesExecutor = broadcastRef.value
//根据规则查找，二分查找
var province = "未知"
val index = MyUtils.binarySearch(allIpRulesExecutor,ipNum)
if(index != -1){
province = allIpRulesExecutor(index)._3
}
(province,1)
})
//7.按照省份进行计数
val reduceRDD: RDD[(String, Int)] = provinces.reduceByKey(_+_)
//8.打印结果
//reduceRDD.foreach(println)
//9.将数据存储到mysql中
/**
* reduceRDD.foreach(x => {
*
* val conn = DriverManager.getConnection("jdbc:mysql://localhost:3306/test?characterEncoding=utf-8&useSSL=true","root","123456")
* val pstm = conn.prepareStatement("insert into access_log values (?,?)")
* pstm.setString(1,x._1)
* pstm.setInt(2,x._2)
* pstm.execute()
* pstm.close()
* conn.close()
* })
*/
//MyUtils.data2MySQL(reduceRDD.collect().toIterator)
reduceRDD.foreachPartition(MyUtils.data2MySQL(_))
sc.stop()
}
}
SparkSql
1.将两张表的数据提取出来，转换成DataFrame，创建两个view。实现join查询
import org.apache.log4j.{Level, Logger}
import org.apache.spark.sql.{Dataset, SparkSession}
object IPDemo {
Logger.getLogger("org.apache.spark").setLevel(Level.OFF)
val ipFile = ("d:\data\spark\ip.txt")
val acessFile = "d:\data\spark\access.log"
def main(args: Array[String]): Unit = {
val spark = SparkSession.builder().appName("Ip").master("local[*]").getOrCreate()
import spark.implicits._
//读取ip文件
val ipFile = spark.read.textFile("d:\data\spark\ip.txt")
//整理ip文件
val ipRules: Dataset[(Long, Long, String)] = ipFile.map(line => {
val splited = line.split("[|]")
val startNum = splited(2).toLong
val endNum = splited(3).toLong
val province = splited(6)
(startNum,endNum,province)
})
//加入元数据
val ipDF = ipRules.toDF("start_num","end_num","province")
//将ip注册成view
ipDF.createTempView("t_ip")
//读取访问日志文件
val access_file = spark.read.textFile(acessFile)
import day07.MyUtils
val accessDF = access_file.map(line =>{
val fields = line.split("[|]")
val ip = fields(1)
MyUtils.ip2Long(ip)
}).toDF("ip")
//将访问日志整理成视图
accessDF.createTempView("t_access")
//sql语句 关联两张表
val result = spark.sql("SELECT province,count(*) counts FROM t_ip JOIN t_access ON ip>=start_num and ip<=end_num GROUP BY province ORDER BY counts DESC")
result.show();
spark.stop()
}
}
2.改进方法
两表join，如果数据量太大，就会导致运行速度变慢。所以将ip的数据以广播的方式发送到Executor。构建一个自定义方法，进行查询。
import day07.MyUtils
import org.apache.spark.sql.{Dataset, SparkSession}
object IpLocation {
val ipFile = "d:\data\spark\ip.txt"
val acessFile = "d:\data\spark\access.log"
def main(args: Array[String]): Unit = {
val spark = SparkSession.builder().appName("SQLIPLocation").master("local[*]").getOrCreate()
//隐式转换
import spark.implicits._
//读取ip文件
val ipFile = spark.read.textFile("d:\data\spark\ip.txt")
//整理ip文件
val ipRules: Dataset[(Long, Long, String)] = ipFile.map(line => {
val splited = line.split("[|]")
val startNum = splited(2).toLong
val endNum = splited(3).toLong
val province = splited(6)
(startNum,endNum,province)
})
//加入元数据
//val ipDF = ipRules.toDF("start_num","end_num","province")
//将全部的IP规则收集到Driver端
val ipRulesDriver = ipRules.collect()
//广播 阻塞的方法 没有广播完，就不会向下
val broadcastRef = spark.sparkContext.broadcast(ipRulesDriver)
//读取web日志
val accessLogLines = spark.read.textFile(acessFile)
val ips = accessLogLines.map(line => {
val Fields = line.split("[|]")
val ip = Fields(1)
MyUtils.ip2Long(ip)
}).toDF("ip_num")
//将访问日志数据注册成视图
ips.createTempView("access_ip")
//定义并注册自定义函数
//自定义函数在哪里定义的? (Driver) 业务逻辑在Executor执行
spark.udf.register("ip_num2Province",(ip_num:Long)=>{
//获取广播到Driver
//根据Driver端的广播变量引用，在发送task时，会将Driver端的引用伴随着发送到Executor
val rulesExecute: Array[(Long, Long, String)] = broadcastRef.value
val index = MyUtils.binarySearch(rulesExecute,ip_num)
var province = "未知"
if(index != -1){
province = rulesExecute(index)._3
}
province
})
val result = spark.sql("select ip_num2Province(ip_num) province,count(*) counts from access_ip group by province order by counts desc")
result.show()
spark.stop()
}
}
三、用到的工具包代码如下：
import java.sql.{Connection, DriverManager, PreparedStatement}
/**
* Created by zx on 2017/12/12.
*/
object MyUtils {
//将ip转换成数字类型
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
}
//查找某个ip所属的省份
def binarySearch(lines: Array[(Long,Long,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) && (ip<=lines(middle)._2))
return middle
if(ip < lines(middle)._1)
high=middle -1
else{
low =middle +1
}
}
-1
}
//连接mysql 插入数据
def data2MySQL(iter:Iterator[(String,Int)])={
val conn = DriverManager.getConnection("jdbc:mysql://localhost:3306/test","root","123456")
val ps = conn.prepareStatement("insert into access_log values (?,?)")
iter.foreach(x =>{
ps.setString(1,x._1)
ps.setInt(2,x._2)
ps.executeUpdate()
})
if(conn!=null){
conn.close()
}
if(ps!=null){
ps.close()
}
}
}
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版权声明：本文为CSDN博主「曼路」的原创文章，遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接及本声明。
原文链接：https://blog.csdn.net/qq_32539825/article/details/82906911