• spark集成kafka数据源


    1、spark集成的KafkaUtils.createStream已经过期,这个是Spark Integration For Kafka 0.8里集成的。

          替代的是Spark Integration For Kafka 0.10,已经没有createStream函数,采用createDirectStream,

        区别是直连kafka服务器,而不是连接zookeeper。

    2、依赖

            <dependency>
                <groupId>org.apache.spark</groupId>
                <artifactId>spark-streaming-kafka-0-10_2.11</artifactId>
                <version>2.4.4</version>
            </dependency>

    3、code

    package com.home.spark.streaming
    
    import org.apache.kafka.common.serialization.StringDeserializer
    import org.apache.spark.SparkConf
    import org.apache.spark.rdd.RDD
    import org.apache.spark.streaming.dstream.DStream
    import org.apache.spark.streaming.kafka010.{ConsumerStrategies, HasOffsetRanges, KafkaUtils, LocationStrategies}
    import org.apache.spark.streaming.{Seconds, StreamingContext}
    
    object Ex_kafkaSource {
      def main(args: Array[String]): Unit = {
        val conf = new SparkConf(true).setMaster("local[*]").setAppName("spark streaming wordcount")
        
        conf.set("spark.streaming.stopGracefullyOnShutdown", "true")
    
        //环境对象,设置采集周期
        val scc: StreamingContext = new StreamingContext(conf, Seconds(30))
    
        val kafkaParams = Map[String, Object](
          "bootstrap.servers" -> "192.168.44.10:9092",
          "key.deserializer" -> classOf[StringDeserializer],
          "value.deserializer" -> classOf[StringDeserializer],
          "group.id" -> "use_a_separate_group_id_for_each_stream",
          "auto.offset.reset" -> "latest",
          "enable.auto.commit" -> (true: java.lang.Boolean)
        )
    
        val topics = Array("test")
    
        val kafkaStream = KafkaUtils.createDirectStream[String, String](
          scc,
          LocationStrategies.PreferConsistent,
          ConsumerStrategies.Subscribe[String,String](
            topics,
            kafkaParams
          )
        )
    
        kafkaStream.foreachRDD(rdd => {
          val offsetRange = rdd.asInstanceOf[HasOffsetRanges].offsetRanges
          val maped: RDD[(String, String)] = rdd.map(record => (record.key,record.value))
          //计算逻辑
          maped.foreach(println)
          //循环输出
          for(o <- offsetRange){
            println(s"${o.topic}  ${o.partition} ${o.fromOffset} ${o.untilOffset}")
          }
        })
    
        val words: DStream[String] = kafkaStream.flatMap(t=>t.value().split(" "))
    
    //    val words: DStream[String] = socketStream.flatMap(_.split(" "))
    
        val pairs = words.map(word => (word, 1))
    
        val wordCounts: DStream[(String, Int)] = pairs.reduceByKey(_ + _)
    
        // Print the first ten elements of each RDD generated in this DStream to the console
        wordCounts.print
    
        // Start the computation
        // 通过 streamingContext.start()来启动消息采集和处理
        scc.start()
    
        // Wait for the computation to terminate
        // 通过streamingContext.stop()来手动终止处理程序
        scc.awaitTermination()
      }
    }

    4、kafka测试环境

         https://www.cnblogs.com/asker009/p/9958240.html

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  • 原文地址:https://www.cnblogs.com/asker009/p/12096249.html
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