目录
一.环境准备
1.pom文件
<dependencies>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-core_2.12</artifactId>
<version>3.0.0</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming_2.12</artifactId>
<version>3.0.0</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming-kafka-0-10_2.12</artifactId>
<version>3.0.0</version>
</dependency>
<!-- https://mvnrepository.com/artifact/com.alibaba/druid -->
<dependency>
<groupId>com.alibaba</groupId>
<artifactId>druid</artifactId>
<version>1.1.10</version>
</dependency>
<dependency>
<groupId>mysql</groupId>
<artifactId>mysql-connector-java</artifactId>
<version>5.1.27</version>
</dependency>
<dependency>
<groupId>com.fasterxml.jackson.core</groupId>
<artifactId>jackson-core</artifactId>
<version>2.10.1</version>
</dependency>
</dependencies>
<build>
<plugins>
<!-- 该插件用于将Scala代码编译成class文件 -->
<plugin>
<groupId>net.alchim31.maven</groupId>
<artifactId>scala-maven-plugin</artifactId>
<version>3.2.2</version>
<executions>
<execution>
<!-- 声明绑定到maven的compile阶段 -->
<goals>
<goal>compile</goal>
</goals>
</execution>
</executions>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-assembly-plugin</artifactId>
<version>3.0.0</version>
<configuration>
<descriptorRefs>
<descriptorRef>jar-with-dependencies</descriptorRef>
</descriptorRefs>
</configuration>
<executions>
<execution>
<id>make-assembly</id>
<phase>package</phase>
<goals>
<goal>single</goal>
</goals>
</execution>
</executions>
</plugin>
</plugins>
</build>
2.bean
import java.text.SimpleDateFormat
import java.util.Date
//数据格式：1597148289569，华北，北京，102,4，2020-08-11，11:12
case class AdsInfo(ts: Long,
area: String,
city: String,
userId: String,
adsId: String,
var dayString: String = null, // yyyy-MM-dd
var hmString: String = null) { // hh:mm
val date = new Date(ts)
dayString = new SimpleDateFormat("yyyy-MM-dd").format(date)
hmString = new SimpleDateFormat("HH:mm").format(date)
}
3.工具类
JDBCUtils
object JDBCUtil {
// 创建连接池对象
var dataSource:DataSource = init()
// 连接池的初始化
def init():DataSource = {
val paramMap = new java.util.HashMap[String, String]()
paramMap.put("driverClassName", PropertiesUtil.getValue("jdbc.driver.name"))
paramMap.put("url", PropertiesUtil.getValue("jdbc.url"))
paramMap.put("username", PropertiesUtil.getValue("jdbc.user"))
paramMap.put("password", PropertiesUtil.getValue("jdbc.password"))
paramMap.put("maxActive", PropertiesUtil.getValue("jdbc.datasource.size"))
// 使用Druid连接池对象
DruidDataSourceFactory.createDataSource(paramMap)
}
// 从连接池中获取连接对象
def getConnection(): Connection = {
dataSource.getConnection
}
def main(args: Array[String]): Unit = {
println(getConnection())
}
}
Properties工具类
/**
* project.properties文件
*/
#jdbc配置
jdbc.datasource.size=10
jdbc.url=jdbc:mysql://hadoop102:3306/steamingproject?useUnicode=true&characterEncoding=utf8&rewriteBatchedStatements=true
jdbc.user=root
jdbc.password=root
jdbc.driver.name=com.mysql.jdbc.Driver
# Kafka配置
kafka.broker.list=hadoop102:9092,hadoop103:9092,hadoop104:9092
kafka.topic=mytest
kafka.group.id=cg1
import java.util.ResourceBundle
/**
* Properties文件工具类
*/
object PropertiesUtil {
// 绑定配置文件
// ResourceBundle专门用于读取配置文件，所以读取时，不需要增加扩展名
// 国际化 = I18N => Properties
val summer: ResourceBundle = ResourceBundle.getBundle("project")
def getValue( key : String ): String = {
summer.getString(key)
}
def main(args: Array[String]): Unit = {
println(getValue("jdbc.user"))
}
}
3.创建BaseApp
/**
* @description: 基础类
* @author: HaoWu
* @create: 2020年08月11日
*/
abstract class BaseApp {
val conf: SparkConf = new SparkConf().setMaster("local[*]").setAppName("myAPP")
val ssc: StreamingContext = new StreamingContext(conf, Seconds(3))
//设置消费kafka的参数，可以参考kafka.consumer.ConsumerConfig类中配置说明
val kafkaParams: Map[String, Object] = Map[String, Object](
"bootstrap.servers" -> "hadoop102:9092,hadoop103:9092,hadoop104:9092", //zookeeper的host，port
"group.id" -> "g3", //消费者组
"enable.auto.commit" -> "true", //是否自动提交
"auto.commit.interval.ms" -> "500", //500ms自动提交offset
"key.deserializer" -> "org.apache.kafka.common.serialization.StringDeserializer",
"value.deserializer" -> "org.apache.kafka.common.serialization.StringDeserializer",
"auto.offset.reset" -> "earliest" //第一次运行，从最初始偏移量开始消费数据
)
//消费kafka的mytest主题生成DStream
val ds: InputDStream[ConsumerRecord[String, String]] = KafkaUtils.createDirectStream[String, String](
ssc,
LocationStrategies.PreferConsistent,
//订阅主题
ConsumerStrategies.Subscribe[String, String](List("mytest"),
kafkaParams))
/**
* 将输入流InputDStream[ConsumerRecord[String, String]]=>stream[对象]
* @param ds
* @return
*/
def getAllBeans(ds: InputDStream[ConsumerRecord[String, String]]): DStream[AdsInfo] = {
val result: DStream[AdsInfo] = ds.map(
record => {
val arr: Array[String] = record.value().split(",")
AdsInfo(arr(0).toLong, arr(1), arr(2), arr(3), arr(4))
}
)
result
}
/**
* 处理逻辑
* @param opt
*/
def runApp(opt: => Unit): Unit = {
try {
//处理逻辑
opt
//执行程序
ssc.start()
ssc.awaitTermination()
} catch {
case e: Exception => e.getMessage
}
}
}
需求一：动态添加黑名单
实现实时的动态黑名单机制：将每天对某个广告点击超过 100 次的用户拉黑。
注：黑名单保存到MySQL中。
思路分析
1）读取Kafka数据之后，并对MySQL中存储的黑名单数据做校验；
2）校验通过则对给用户点击广告次数累加一并存入MySQL；
3）在存入MySQL之后对数据做校验，如果单日超过100次则将该用户加入黑名单。
准备工作
1）存放黑名单用户的表
CREATE TABLE black_list (userid CHAR(2) PRIMARY KEY);
2）存放单日各用户点击每个广告的次数
CREATE TABLE user_ad_count (
dt date,
userid CHAR (2),
adid CHAR (2),
count BIGINT,
PRIMARY KEY (dt, userid, adid)
);
/**
* @description: 需求一：动态添加黑名单
* 说明：实现实时的动态黑名单机制：将每天对某个广告点击超过 100 次的用户拉黑
* (用户，广告id，时间，次数)
* 注：黑名单保存到MySQL中
* @author: HaoWu
* @create: 2020年08月12日
*/
object ProjectDemo_1 extends BaseApp {
def main(args: Array[String]): Unit = {
runApp {
val asdInfo: DStream[AdsInfo] = getAllBeans(ds)
/**
* 校验数据是否在黑名单中
*/
def isBlackList(userid: String, connection: Connection): Boolean = {
var flag: Boolean = true
val sql =
"""
|select * from black_list where userid = ?
|""".stripMargin
val ps: PreparedStatement = connection.prepareStatement(sql)
ps.setString(1, userid)
val result: ResultSet = ps.executeQuery()
if (result != null) {
flag = false
}
flag
}
//1.聚合当前批次数据((timestamp,userid,adsid),count)
val countDS: DStream[((String, String, String), Long)] = asdInfo.map {
//((2020-08-11,102,1),1)
case adsInfo: AdsInfo => ((adsInfo.dayString, adsInfo.userId, adsInfo.adsId), 1L)
}.reduceByKey(_ + _)
countDS.foreachRDD(
rdd => rdd.foreachPartition {
iter => {
//2.向mysql插入数据,准备插入sql和连接
val connection: Connection = JDBCUtil.getConnection()
val sql =
"""
|insert into user_ad_count values(?,?,?,?)
|ON DUPLICATE KEY UPDATE COUNT= count + ?
|""".stripMargin
val ps: PreparedStatement = connection.prepareStatement(sql)
//2.过滤出在名单中的数据
iter.filter {
case ((_, userid, _), _) => val falg = isBlackList(userid, connection); falg
}
//往mysql重插入更新数据
.foreach {
case ((date, userid, adsid), count) => {
ps.setString(1, date)
ps.setString(2, userid)
ps.setString(3, adsid)
ps.setLong(4, count)
ps.setLong(5, count)
ps.executeUpdate()
}
}
//关闭
ps.close()
//3.插入成功之后，查询对应得userid点击广告此时是否 > 100？
val sql2 =
"""
|select userid from user_ad_count where count > 20
|""".stripMargin
val ps2: PreparedStatement = connection.prepareStatement(sql2)
val resultSet: ResultSet = ps2.executeQuery()
//封装查询出的黑名单列表
val block_list = new mutable.HashSet[String]()
while (resultSet.next()) {
val userid: String = resultSet.getString("userid")
block_list + userid
}
//关闭resulteSet,PreparedStatement
resultSet.close()
ps2.close()
//4.将block_list数据依次插入黑名单表,没有就插入，有就更新
val sql3: String =
"""
|INSERT INTO black_list VALUES (?)
|ON DUPLICATE KEY UPDATE userid=?
|""".stripMargin
val ps3: PreparedStatement = connection.prepareStatement(sql3)
for (userid <- block_list) {
ps3.setString(1, userid)
ps3.setString(2, userid)
ps3.executeUpdate()
}
ps3.close()
connection.close()
}
}
)
}
}
}
需求二：广告点击量实时统计
描述：实时统计每天各地区各城市各广告的点击总流量，并将其存入MySQL
步骤：①updateStateByKey有状态累加计算 ②向mysql执行插入更新操作
Mysql表
CREATE TABLE area_city_ad_count (
dt date,
area CHAR(4),
city CHAR(4),
adid CHAR(2),
count BIGINT,
PRIMARY KEY (dt,area,city,adid) --联合主键
);
代码实现
import java.sql.{Connection, PreparedStatement}
import com.spark.streaming_need.bean.AdsInfo
import com.spark.streaming_need.utils.JDBCUtil
import org.apache.spark.streaming.dstream.DStream
/**
* @description: 需求二：广告点击量实时统计
* 描述：实时统计每天各地区各城市各广告的点击总流量，并将其存入MySQL
* @author: HaoWu
* @create: 2020年08月11日
*/
object ProjectDemo_2 extends BaseApp {
def main(args: Array[String]): Unit = {
runApp {
//updateStateByKey算子有状态，需要checkpoint
ssc.checkpoint("function2")
//1.单个批次内对数据进行按照天维度的聚合统计
//数据格式：1597148289569,华北,北京,102,4
val DsAds: DStream[AdsInfo] = getAllBeans(ds)
val kvDS: DStream[((String, String, String, String), Int)] = DsAds.map {
case (adsInfo) => {
((adsInfo.dayString, adsInfo.area, adsInfo.city, adsInfo.adsId), 1)
}
}
//2.结合MySQL数据跟当前批次数据更新原有的数据
//计算当前批次和之前的数据累加结果
val result: DStream[((String, String, String, String), Int)] = kvDS.updateStateByKey {
case (seq, opt) => {
var sum: Int = seq.sum
val value = opt.getOrElse(0)
sum += value
Some(sum)
}
}
//3.将结果写入Mysql
result.foreachRDD(
rdd => {
rdd.foreachPartition {
iter => {
//每个分区创建一个Connection连接
val connection: Connection = JDBCUtil.getConnection()
//准备sql，实现mysql的upsert操作
val sql =
"""
|insert into area_city_ad_count values (?,?,?,?,?)
|on duplicate key update count=?
|""".stripMargin
//PreparedStatement
val ps: PreparedStatement = connection.prepareStatement(sql)
//RDD分区中的每个数据都执行写出
iter.foreach {
case ((dayString, area, city, adsId), count) => {
//填充占位符
ps.setString(1, dayString)
ps.setString(2, area)
ps.setString(3, city)
ps.setString(4, adsId)
ps.setInt(5, count)
ps.setInt(6, count)
//执行写入
ps.executeUpdate()
}
}
//关闭资源
ps.close()
connection.close()
}
}
}
)
}
}
}
需求三：最近一小时广告点击量
需求说明
求最近1h的广告点击量，要求按照以下结果显示
结果展示：
1：List [15:50->10,15:51->25,15:52->30]
2：List [15:50->10,15:51->25,15:52->30]
3：List [15:50->10,15:51->25,15:52->30]
思路分析
1）开窗确定时间范围；
2）在窗口内将数据转换数据结构为((adid,hm),count);
3）按照广告id进行分组处理，组内按照时分排序。
代码实现
import org.apache.spark.streaming.{Minutes, Seconds}
import org.apache.spark.streaming.dstream.DStream
/**
* @description: 需求三：最近一小时广告点击量,3秒更新一次
* @author:
* 结果展示：
* 1：List [15:50->10,15:51->25,15:52->30]
* 2：List [15:50->10,15:51->25,15:52->30]
* 3：List [15:50->10,15:51->25,15:52->30]
* @create: 2020年08月12日
*/
object ProjectDemo_3 extends BaseApp {
def main(args: Array[String]): Unit = {
//运行app
runApp {
val AdsDStream: DStream[((String, String), Int)] = getAllBeans(ds).map {
case adsInfo => ((adsInfo.adsId, adsInfo.hmString), 1)
}
val result: DStream[(String, List[(String, Int)])] = AdsDStream
//窗口内聚合
.reduceByKeyAndWindow((a: Int, b: Int) => {
a + b
}, Minutes(60), Seconds(3))
.map { case ((adsId, ahmString), count) => (adsId, (ahmString, count)) }
//按照广告id分组
.groupByKey()
//组内按时间升序
.mapValues {
case iter => iter.toList.sortBy(_._1)
}
result.print(10)
}
}
}
结果
-------------------------------------------
Time: 1597234032000 ms
-------------------------------------------
(1,List((20:01,12), (20:02,112), (20:03,98), (20:04,95), (20:05,104), (20:06,96), (20:07,13)))
(2,List((20:01,24), (20:02,97), (20:03,99), (20:04,103), (20:05,95), (20:06,105), (20:07,6)))
(3,List((20:01,30), (20:02,87), (20:03,92), (20:04,108), (20:05,117), (20:06,88), (20:07,22)))
(4,List((20:01,15), (20:02,101), (20:03,100), (20:04,99), (20:05,84), (20:06,112), (20:07,22)))
(5,List((20:01,19), (20:02,103), (20:03,111), (20:04,95), (20:05,100), (20:06,99), (20:07,10)))
-------------------------------------------
Time: 1597234035000 ms
-------------------------------------------
(1,List((20:01,12), (20:02,112), (20:03,98), (20:04,95), (20:05,104), (20:06,96), (20:07,20)))
(2,List((20:01,24), (20:02,97), (20:03,99), (20:04,103), (20:05,95), (20:06,105), (20:07,13)))
(3,List((20:01,30), (20:02,87), (20:03,92), (20:04,108), (20:05,117), (20:06,88), (20:07,26)))
(4,List((20:01,15), (20:02,101), (20:03,100), (20:04,99), (20:05,84), (20:06,112), (20:07,26)))
(5,List((20:01,19), (20:02,103), (20:03,111), (20:04,95), (20:05,100), (20:06,99), (20:07,15)))
-------------------------------------------
Time: 1597234038000 ms
-------------------------------------------
(1,List((20:01,12), (20:02,112), (20:03,98), (20:04,95), (20:05,104), (20:06,96), (20:07,23)))
(2,List((20:01,24), (20:02,97), (20:03,99), (20:04,103), (20:05,95), (20:06,105), (20:07,16)))
(3,List((20:01,30), (20:02,87), (20:03,92), (20:04,108), (20:05,117), (20:06,88), (20:07,34)))
(4,List((20:01,15), (20:02,101), (20:03,100), (20:04,99), (20:05,84), (20:06,112), (20:07,30)))
(5,List((20:01,19), (20:02,103), (20:03,111), (20:04,95), (20:05,100), (20:06,99), (20:07,20)))