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Spark(十六)「SparkStreaming需求练习」

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Spark(十六)「SparkStreaming需求练习」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;

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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>

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2.bean

欢迎大家来到IT世界,在知识的湖畔探索吧!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

欢迎大家来到IT世界,在知识的湖畔探索吧!/**
 * 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)))

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