Spark读取kafka数据

朱雀 2023-10-06 23:25 14阅读 0赞

#### Spark 读取kafka数据 ####

版本： sprak3.0

##### 1.sprak获取kafka连接 #####

object MyKafkaUtil {
        
    
          //1.创建配置信息对象
          private val properties: Properties = PropertiesUtil.load("config.properties")
    
          //2.用于初始化链接到集群的地址
          val broker_list: String = properties.getProperty("kafka.broker.list")
    
          //3.kafka 消费者配置
          val kafkaParam = Map(
    
            "bootstrap.servers" -> broker_list, "key.deserializer" -> classOf[StringDeserializer], "value.deserializer" -> classOf[StringDeserializer],
    
            //消费者组
            "group.id" -> "commerce-consumer-group",
    
            //如果没有初始化偏移量或者当前的偏移量不存在任何服务器上，可以使用这个配置属性
            //可以使用这个配置，latest 自动重置偏移量为最新的偏移量
            "auto.offset.reset" -> "latest",
    
            //如果是 true，则这个消费者的偏移量会在后台自动提交,但是 kafka 宕机容易丢失数据
            //如果是 false，会需要手动维护 kafka 偏移量
            "enable.auto.commit" -> (true: java.lang.Boolean)
          )
    
          // 创建 DStream，返回接收到的输入数据
          // LocationStrategies：根据给定的主题和集群地址创建 consumer
          // LocationStrategies.PreferConsistent：持续的在所有 Executor 之间分配分区
          // ConsumerStrategies：选择如何在 Driver 和 Executor 上创建和配置 Kafka Consumer
          // ConsumerStrategies.Subscribe：订阅一系列主题
          def getKafkaStream(topic: String, ssc: StreamingContext): InputDStream[ConsumerRecord[String, String]] = {
        
            val dStream: InputDStream[ConsumerRecord[String, String]] = KafkaUtils.createDirectStream[String, String](ssc, LocationStrategies.PreferConsistent, ConsumerStrategies.Subscribe[String, String](Array(topic), kafkaParam))
            dStream
          }
        }

##### 方式二 #####

import org.apache.kafka.common.serialization.StringDeserializer
    import org.apache.spark.SparkConf
    import org.apache.spark.streaming._
    import org.apache.spark.streaming.kafka010._
    
    object SparkKafkaDemo {
        
      def main(args: Array[String]) {
        
        if (args.length < 2) {
        
          System.err.println(s"""
                                |Usage: SparkKafkaDemo <brokers> <topics>
                                |  <brokers> is a list of one or more Kafka brokers
                                |  <topics> is a list of one or more kafka topics to consume from
                                |  <interval>
            """.stripMargin)
          System.exit(1)
        }
        val Array(brokers, topics, interval) = args
    
        val sparkConf = new SparkConf().setAppName("E-MapReduce Demo 9: Spark Kafka Demo (Scala)")
        val ssc = new StreamingContext(sparkConf, Seconds(interval.toInt))
    
        val kafkaParams = Map[String, Object](
          "bootstrap.servers" -> brokers,
          "key.deserializer" -> classOf[StringDeserializer],
          "value.deserializer" -> classOf[StringDeserializer],
          "group.id" -> "mugen1",
          "auto.offset.reset" -> "earliest",
          "enable.auto.commit" -> (false: java.lang.Boolean),
          "security.protocol" -> "SASL_PLAINTEXT",
          "sasl.mechanism" -> "GSSAPI",
          "sasl.kerberos.service.name" -> "kafka"
        )
    
        val messages = KafkaUtils.createDirectStream[String, String](
          ssc,
          LocationStrategies.PreferConsistent,
          ConsumerStrategies.Subscribe[String, String](Array(topics), kafkaParams)
        )
    
        // Get the lines, split them into words, count the words and print
        val lines = messages.map(_.value)
        val words = lines.flatMap(_.split(" "))
        val wordCounts = words.map(x => (x, 1L)).reduceByKey(_ + _)
        wordCounts.print()
    
        // Start the computation
        ssc.start()
        ssc.awaitTermination()
      }
    }