Spark Streaming项目实战 项目一每天每地区热门广告Top3 项目二最近12s广告点击量实时统计

目录

• ​​项目描述​​
• ​​项目数据生成方式​​
• ​​项目数据格式​​
• ​​项目准备​​
• ​​项目需求一：每天每地区热门广告Top3​​
• ​​项目需求二：最近12s广告点击量实时统计​​

项目描述

本实战项目​​实时分析处理用户对广告点击的行为​​数据。

项目数据生成方式

​​使用代码的方式持续的生成数据，然后写入到kafka中，然后从kafka消费数据，​​并对数据根据需求进行分析。

项目数据格式

时间戳,    地区,   城市,   用户id， 广告id
1566035129449,  华南,   深圳,   101,    2

项目准备

1. 步骤1: 开启集群

启动 zookeeper 和 Kafka

2. 步骤2: 创建 Topic

#查看kakfa都有哪些主题
 bin/kafka-topics.sh --list --zookeeper hadoop201:2181
#创建主题 指定3个分区，2个副本
bin/kafka-topics.sh --zookeeper hadoop201:2181 --create --topic my-ads-bak --partitions 3 --replication-factor 2

3. 步骤3: 创建项目spark-realtime

4) 拷贝依赖

<dependencies>
    <dependency>
        <groupId>org.apache.spark</groupId>
        <artifactId>spark-core_2.11</artifactId>
        <version>2.1.1</version>
    </dependency>
    <dependency>
        <groupId>mysql</groupId>
        <artifactId>mysql-connector-java</artifactId>
        <version>5.1.27</version>
    </dependency>

    <dependency>
        <groupId>org.apache.spark</groupId>
        <artifactId>spark-hive_2.11</artifactId>
        <version>2.1.1</version>
    </dependency>

    <dependency>
        <groupId>org.apache.hive</groupId>
        <artifactId>hive-exec</artifactId>
        <version>1.2.1</version>
    </dependency>

    <dependency>
        <groupId>org.apache.spark</groupId>
        <artifactId>spark-sql_2.11</artifactId>
        <version>2.1.1</version>
    </dependency>

    <dependency>
        <groupId>org.apache.spark</groupId>
        <artifactId>spark-streaming_2.11</artifactId>
        <version>2.1.1</version>
    </dependency>

    <dependency>
        <groupId>org.apache.spark</groupId>
        <artifactId>spark-streaming-kafka-0-8_2.11</artifactId>
        <version>2.1.1</version>
    </dependency>

    <dependency>
        <groupId>com.alibaba</groupId>
        <artifactId>fastjson</artifactId>
        <version>1.2.47</version>
    </dependency>
    <dependency>
        <groupId>net.sf.json-lib</groupId>
        <artifactId>json-lib</artifactId>
        <version>2.4</version>
        <classifier>jdk15</classifier>
    </dependency>
    <!-- https://mvnrepository.com/artifact/org.json4s/json4s-native -->
    <dependency>
        <groupId>org.json4s</groupId>
        <artifactId>json4s-native_2.11</artifactId>
        <version>3.2.11</version>
    </dependency>

    <!-- https://mvnrepository.com/artifact/org.json4s/json4s-jackson -->
    <dependency>
        <groupId>org.json4s</groupId>
        <artifactId>json4s-jackson_2.11</artifactId>
        <version>3.2.11</version>
    </dependency>
    <!-- https://mvnrepository.com/artifact/redis.clients/jedis -->
    <dependency>
        <groupId>redis.clients</groupId>
        <artifactId>jedis</artifactId>
        <version>2.9.0</version>
    </dependency>

    <!-- https://mvnrepository.com/artifact/com.alibaba/druid -->
    <dependency>
        <groupId>com.alibaba</groupId>
        <artifactId>druid</artifactId>
        <version>1.1.10</version>
    </dependency>
</dependencies>

5. 添加框架支持（scala）
7. 新建scala目录，然后标记为源码目录



8. 7） 拷贝如下类到要编写代码的目录

AdsInfo，样例类
CityInfo，样例类 
RandomNumUtil，用于生成随机数
RandomOptions，用于生成带有比重的随机选项
MockRealTime，生成模拟数据
RealtimeApp，测试从kafka读取数据（直接拷贝Kafka数据源高级API即可）

​​类下载链接​​

eg: 我要编写的代码目录

项目需求一：每天每地区热门广告Top3

package com.xcu.spark

import java.text.SimpleDateFormat
import java.util.Date

import kafka.serializer.StringDecoder
import org.apache.kafka.clients.consumer.ConsumerConfig
import org.apache.spark.SparkConf
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.streaming.dstream.{DStream, InputDStream}
import org.apache.spark.streaming.kafka.KafkaUtils


/**
 * @Package : com.xcu.spark
 * @Author :
 * @Date : 2020 11月 星期一
 * @Desc :
 */
object RealTime_req1 {
  def main(args: Array[String]): Unit = {
    //创建配置文件
    val conf: SparkConf = new SparkConf().setAppName("RealTime_req1").setMaster("local[*]")
    //创建SparkStreaming执行上下文
    val ssc: StreamingContext = new StreamingContext(conf, Seconds(3))
    //设置检测点目录
    ssc.sparkContext.setCheckpointDir("./cp")
    //kafka参数声明
    val brokers = "hadoop201:9092,hadoop202:9092,hadoop203:9092"
    val topic = "my-ads-bak"
    val group = "bigdata"
    val deserialization = "org.apache.kafka.common.serialization.StringDeserializer"
    val kafkaParams = Map(
      ConsumerConfig.GROUP_ID_CONFIG -> group,
      ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG -> brokers,
      ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG -> deserialization,
      ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG -> deserialization
    )
    //创建DS
    val kafkaDS: InputDStream[(String, String)] = KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder](
      ssc, kafkaParams, Set(topic)
    )
    //从Kafka的kv值中取value msg = 1590136353874,华北,北京,103,1
    val dataDS: DStream[String] = kafkaDS.map(_._2)
    //将原始数据进行转换  ==> (天_地区_广告,1)
    val mapDS: DStream[(String, Int)] = dataDS.map {
      line => {
        val fields: Array[String] = line.split(",")
        //获取时间戳
        val timeStamp: Long = fields(0).toLong
        //根据时间戳创建日期对象
        val day: Date = new Date(timeStamp)
        //创建SimpleDateFormat,对日期对象进行转换
        val sdf: SimpleDateFormat = new SimpleDateFormat("yyyy-MM-dd")
        //将日期对象转换为字符串
        val str: String = sdf.format(day)
        //获取地区
        val area: String = fields(1)
        //获取广告
        val adv: String = fields(4)
        //封装元组
        (str + "_" + area + "_" + adv, 1)
      }
    }
    //对每天每地区广告点击数进行聚合处理 (天_地区_广告,sum)
    //注意：这里要统计的是一天的数据，所以要将每一个采集周期的数据，进行统计。所以需要传递状态，所以要用
    val updateDS: DStream[(String, Int)] = mapDS.updateStateByKey(
      (seq: Seq[Int], buffer: Option[Int]) => {
        Option(seq.sum + buffer.getOrElse(0))
      }
    )
    //再次对结构进行转换
    val mapDS1: DStream[(String, (String, Int))] = updateDS.map {
      //(天_地区_广告,sum)
      case (k, sum) => {
        val fields: Array[String] = k.split("_")
        //天_地区,(广告,sum))
        (fields(0) + "_" + fields(1), (fields(2), sum))
      }
    }
    //将相同的天和地区放到一组
    val groupDS: DStream[(String, Iterable[(String, Int)])] = mapDS1.groupByKey()
    //对分组后的数据进行排序
    val resDS: DStream[(String, List[(String, Int)])] = groupDS.mapValues {
      datas => {
        datas.toList.sortBy(-_._2).take(3)
      }
    }
    //打印输出结果
    resDS.print()
    //启动采集器
    ssc.start()
    //等待线程结束，关闭采集器
    ssc.awaitTermination()
  }
}

项目需求二：最近12s广告点击量实时统计

package com.xcu.spark

import java.text.SimpleDateFormat
import java.util.Date

import kafka.serializer.StringDecoder
import org.apache.kafka.clients.consumer.ConsumerConfig
import org.apache.spark.SparkConf
import org.apache.spark.streaming.dstream.{DStream, InputDStream}
import org.apache.spark.streaming.kafka.KafkaUtils
import org.apache.spark.streaming.{Seconds, StreamingContext}


/**
 * @Package : com.xcu.spark
 * @Author :
 * @Date : 2020 11月 星期一
 * @Desc :
 */
object RealTime_req2 {
  def main(args: Array[String]): Unit = {
    //创建配置文件
    val conf: SparkConf = new SparkConf().setAppName("RealTime_req2").setMaster("local[*]")
    //创建SparkStreaming执行上下文
    val ssc: StreamingContext = new StreamingContext(conf, Seconds(3))
    //设置检测点目录
    ssc.sparkContext.setCheckpointDir("./cp")
    //kafka参数声明
    val brokers = "hadoop201:9092,hadoop202:9092,hadoop203:9092"
    val topic = "my-ads-bak"
    val group = "bigdata"
    val deserialization = "org.apache.kafka.common.serialization.StringDeserializer"
    val kafkaParams = Map(
      ConsumerConfig.GROUP_ID_CONFIG -> group,
      ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG -> brokers,
      ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG -> deserialization,
      ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG -> deserialization
    )
    //创建DS
    val kafkaDS: InputDStream[(String, String)] = KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder](
      ssc, kafkaParams, Set(topic)
    )
    //从Kafka的kv值中取value msg = 1590136353874,华北,北京,103,1
    val dataDS: DStream[String] = kafkaDS.map(_._2)
    //定义窗口的大小以及滑动的步长
    val windowDS: DStream[String] = dataDS.window(Seconds(12), Seconds(3))
    //对结构进行转换
    val mapDS: DStream[(String, Int)] = windowDS.map {
      line => {
        val fields: Array[String] = line.split(",")
        val timeStmp: Long = fields(0).toLong
        val day: Date = new Date(timeStmp)
        //
        val sdf = new SimpleDateFormat("mm:ss")
        val time: String = sdf.format(day)
        (fields(4) + "_" + time, 1)
      }
    }
    //对数据进行聚合
    val resDS: DStream[(String, Int)] = mapDS.reduceByKey(_ + _)
    //打印输出
    resDS.print()
    //启动采集器
    ssc.start()
    //等待线程结束，关闭采集器
    ssc.awaitTermination()
  }
}