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Spark Streaming 项目实战 (2) | 从 Kafka中消费数据

大南瓜鸭 2022-02-06 阅读 47



  大家好,我是不温卜火,是一名计算机学院大数据专业大二的学生,昵称来源于成语—​​不温不火​​​,本意是​​希望自己性情温和​​​。作为一名互联网行业的小白,博主写博客一方面是为了记录自己的学习过程,另一方面是总结自己所犯的错误希望能够帮助到很多和自己一样处于起步阶段的萌新。但由于水平有限,博客中难免会有一些错误出现,有纰漏之处恳请各位大佬不吝赐教!暂时只有csdn这一个平台​


  本片博文为大家带来的是Spark Streaming 项目实战 (2) | 从 Kafka中消费数据。

Spark Streaming 项目实战 (2) | 从 Kafka中消费数据_spark


目录


一. 测试是否能够从Kafka消费到数据二. 完整程序源码3. 运行结果

  编写​​App​​, 从 kafka 读取数据

  新建一个Maven项目:​​spark-streaming-project​

  在依赖选择上​​spark-streaming-kafka​​​此次选用​​0-10_2.11​​​而非​​0-08_2.11​

<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming-kafka-0-10_2.11</artifactId>
<version>2.1.1</version>
</dependency>

Spark Streaming 项目实战 (2) | 从 Kafka中消费数据_spark_03

一. 测试是否能够从Kafka消费到数据

  • 1. 新建APP(Trait)
package com.buwenbuhuo.streaming.project.app


import org.apache.kafka.common.serialization.StringDeserializer
import org.apache.spark.SparkConf
import org.apache.spark.streaming.dstream.{DStream, InputDStream}
import org.apache.spark.streaming.kafka010.KafkaUtils
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.streaming.kafka010.LocationStrategies.PreferConsistent
import org.apache.spark.streaming.kafka010.ConsumerStrategies.Subscribe
/**
*
* @author 不温卜火
* @create 2020-08-14 13:41
* MyCSDN : https://buwenbuhuo.blog.csdn.net/
*
*/
trait App {
def main(args: Array[String]): Unit = {
val conf = new SparkConf().setAppName("App").setMaster("local[*]")
val ssc = new StreamingContext(conf, Seconds(3))

val kafkaParams = Map[String, Object](
"bootstrap.servers" -> "hadoop002:9092,hadoop003:9092,hadoop004:9092",
"key.deserializer" -> classOf[StringDeserializer],
"value.deserializer" -> classOf[StringDeserializer],
"group.id" -> "bigdata0814",
"auto.offset.reset" -> "latest",
// 自动提交管理
"enable.auto.commit" -> (true: java.lang.Boolean)
)
val topics = Array("ads_log0814")

val sourceStream: DStream[String] = KafkaUtils.createDirectStream[String, String](
ssc,
PreferConsistent, // 标配
Subscribe[String, String](topics, kafkaParams)
).map(_.value())
sourceStream.print(1000)


ssc.start()
ssc.awaitTermination()
}
}
  • 2. 新建AreaTopAPP
package com.buwenbuhuo.streaming.project.app

/**
*
* @author 不温卜火
* @create 2020-08-14 13:41
* MyCSDN : https://buwenbuhuo.blog.csdn.net/
*
*/
object AreaTopAPP extends App {

}
  • 3. 运行AreaTopAPP

Spark Streaming 项目实战 (2) | 从 Kafka中消费数据_apache_04

  上述即为测试 ,但是其实在这个app内,有一部分可以专门封装成一个新的样例类

  测试能够成功得到所想要的结果,下面给出完善最终的程序源码

二. 完整程序源码

编写App, 从 kafka 读取数据

  • bean 类 ​​AdsInfo​
package com.buwenbuhuo.streaming.project.bean
import java.sql.Timestamp
import java.text.SimpleDateFormat
import java.util.Date

/**
*
* @author 不温卜火
* @create 2020-08-14 17:45
* MyCSDN : https://buwenbuhuo.blog.csdn.net/
*
*/
case class AdsInfo(ts: Long,
area: String,
city: String,
userId: String,
adsId: String,
var timestamp: Timestamp = null,
var dayString: String = null, // 2012-8-14
var hmString: String = null) { // 11:20

timestamp = new Timestamp(ts)

val date = new Date(ts)
dayString = new SimpleDateFormat("yyyy-MM-dd").format(date)
hmString = new SimpleDateFormat("HH:mm").format(date)
}
  • 2. 工具类类 ​​MyKafkaUtils​
package com.buwenbuhuo.streaming.project.util

import org.apache.kafka.common.serialization.StringDeserializer
import org.apache.spark.streaming.StreamingContext
import org.apache.spark.streaming.dstream.DStream
import org.apache.spark.streaming.kafka010.ConsumerStrategies.Subscribe
import org.apache.spark.streaming.kafka010.KafkaUtils
import org.apache.spark.streaming.kafka010.LocationStrategies.PreferConsistent

/**
*
* @author 不温卜火
* @create 2020-08-14 15:20
* MyCSDN : https://buwenbuhuo.blog.csdn.net/
*
*/
object MyKafkaUtils {

val kafkaParams = Map[String, Object](
"bootstrap.servers" -> "hadoop002:9092,hadoop003:9092,hadoop004:9092",
"key.deserializer" -> classOf[StringDeserializer],
"value.deserializer" -> classOf[StringDeserializer],
"group.id" -> "bigdata0814",
"auto.offset.reset" -> "latest",
// 自动提交管理
"enable.auto.commit" -> (true: java.lang.Boolean)
)

/*
* 根据传入的参数,返回从kafka得到的流
* @param ssc
* @param topic
* @return
*/
def getKafkaSteam(ssc:StreamingContext,topics:String*): DStream[String] =

KafkaUtils.createDirectStream[String, String](
ssc,
PreferConsistent, // 标配
Subscribe[String, String](topics.toIterable, kafkaParams)
).map(_.value())
}
  • 3. 从kafka消费数据(APP)
package com.buwenbuhuo.streaming.project.app


import com.buwenbuhuo.streaming.project.bean.AdsInfo
import com.buwenbuhuo.streaming.project.util.MyKafkaUtils
import org.apache.spark.SparkConf
import org.apache.spark.streaming.dstream.DStream
import org.apache.spark.streaming.{Seconds, StreamingContext}

/**
*
* @author 不温卜火
* @create 2020-08-14 13:41
* MyCSDN : https://buwenbuhuo.blog.csdn.net/
*
*/
trait App {
def main(args: Array[String]): Unit = {
val conf = new SparkConf().setAppName("App").setMaster("local[*]")
val ssc = new StreamingContext(conf, Seconds(3))
// 得到最原始的流
val sourceStream: DStream[String] = MyKafkaUtils.getKafkaSteam(ssc,"ads_log0814")

val adsInfoStream: DStream[AdsInfo] = sourceStream.map(s => {
val spilt: Array[String] = s.split(",")
AdsInfo(spilt(0).toLong, spilt(1), spilt(2), spilt(3), spilt(4))
})

adsInfoStream.print(1000)

ssc.start()
ssc.awaitTermination()
}
}
  • 4. AreaTopAPP
package com.buwenbuhuo.streaming.project.app

/**
*
* @author 不温卜火
* @create 2020-08-14 13:41
* MyCSDN : https://buwenbuhuo.blog.csdn.net/
*
*/
object AreaTopAPP extends App {

}

3. 运行结果

同时运行​​MockRealtimeData​​(数据生产者)和​​AreaTopAPP​​(数据消费者)

Spark Streaming 项目实战 (2) | 从 Kafka中消费数据_apache_05

Spark Streaming 项目实战 (2) | 从 Kafka中消费数据_kafka_06

  本次的分享就到这里了,


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Spark Streaming 项目实战 (2) | 从 Kafka中消费数据_kafka_08



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