每日一题 为了工作 2020 0502 第六十一题

//使用kafka+sparkStreaming进行数据处理

//从kafka拉取数据

package com.swust.predict


import org.apache.kafka.clients.consumer.ConsumerRecord
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.ConsumerStrategies.Subscribe
import org.apache.spark.streaming.kafka010.{CanCommitOffsets, HasOffsetRanges, KafkaUtils, OffsetRange}
import org.apache.spark.streaming.kafka010.LocationStrategies.PreferConsistent
import org.apache.spark.streaming.{Seconds, StreamingContext}


object GetDataFromKafka {

  def main(args: Array[String]): Unit = {
    val conf = new SparkConf().setAppName("GetDataFromKafka").setMaster("local[*]")
    conf.set("spark.streaming.kafka.consumer.cache.enabled","false")

    val ssc = new StreamingContext(conf,Seconds(5))
    val topics = Set("car_events")
    val brokers = "data001:9092,data003:9092,data004:9092"
    val kafkaParams = Map[String,Object](
      "bootstrap.servers" -> brokers,
      "key.deserializer" -> classOf[StringDeserializer],
      "value.deserializer" -> classOf[StringDeserializer],
      "group.id" -> "predictGroup",//
      "auto.offset.reset" -> "latest",
      "enable.auto.commit" -> (false: java.lang.Boolean)//默认是true
    )
    val index = 1
    //创建Dstream
    val kafkaDstream: InputDStream[ConsumerRecord[String, String]] = KafkaUtils.createDirectStream(
      ssc,
      PreferConsistent,
      Subscribe[String, String](topics, kafkaParams)
    )

    val events: DStream[String] = kafkaDstream.map(line => {
      val value: String = line.value().toString
      value
    })
    val show: Unit = events.foreachRDD(rdd => {
      rdd.foreachPartition(data => {
        //data.take(200)
        data.foreach(one => {
          println(one)
        })

      })
    })
//    events.foreachRDD(rdd =>{
//      val ranges: Array[OffsetRange] = rdd.asInstanceOf[HasOffsetRanges].offsetRanges
//      events.asInstanceOf[CanCommitOffsets].commitAsync(ranges)
//    })
    ssc.start()
    ssc.awaitTermination()
  }

}

　　

//向kafka推送数据

package com.traffic.streaming

import java.util.Properties

import net.sf.json.JSONObject
import org.apache.kafka.clients.producer.{KafkaProducer, ProducerRecord}
import org.apache.spark.{SparkConf, SparkContext}

//向kafka car_events中生产数据
object KafkaEventProducer {
  def main(args: Array[String]): Unit = {

    //设置需要写入数据的消息队列
    val topic = "car_events"
    //设置配置属性信息
    val props = new Properties()
    props.put("bootstrap.servers", "data001:9092,data003:9092,data004:9092")
    props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer")
    props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer")

    //创建kafka消息队列的生产者对象
    val producer = new KafkaProducer[String,String](props)

    val sparkConf = new SparkConf().setAppName("traffic data").setMaster("local[4]")
    val sc = new SparkContext(sparkConf)

    val records: Array[Array[String]] = sc.textFile("F:\code\AnyMaven\data\carFlow_all_column_test.txt")
      .filter(!_.startsWith(";")) //过滤掉不以;开头的数据
      .map(_.split(",")).collect()


    for (i <- 1 to 1000) {
      for (record <- records) {
        // prepare event data
        val event = new JSONObject()
        event.put("camera_id", record(0))
        event.put("car_id", record(2))
        event.put("event_time", record(4))
        event.put("speed", record(6))
        event.put("road_id", record(13))
        // produce event message
        //向kafka中输入数据
        producer.send(new ProducerRecord[String, String](topic, event.toString))
//        println("Message sent: " + event)
        Thread.sleep(200)
      }
    }
    sc.stop
  }
}

　　

//运行结果

//向kafka集群拖送数据

 //从kafka集群拉取数据