SparkStreaming读取Kafka数据源：使用Direct方式
一、前提工作
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安装了zookeeper
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安装了Kafka
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实验环境：kafka + zookeeper + spark
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实验流程
二、实验内容
实验要求：实现的从kafka读取实现wordcount程序
启动zookeeper
zk.sh start# zk.sh脚本 参考教程 https://blog.csdn.net/pblh123/article/details/134730738?spm=1001.2014.3001.5502
启动Kafka
kf.sh start# kf.sh 参照教程 https://blog.csdn.net/pblh123/article/details/134730738?spm=1001.2014.3001.5502
（测试用，实验不做）创建Kafka主题，如test，可参考：Kafka的安装与基本操作
--topic 定义topic名
--replication-factor 定义副本数
--partitions 定义分区数
--bootstrap-server 连接的Kafka Broker主机名称和端口号
--create 创建主题
--describe 查看主题详细描述
# 创建kafka主题测试
/opt/module/kafka_2.12-3.0.0/bin/kafka-topics.sh --create --bootstrap-server hd1:9092 --replication-factor 3 --partitions 1 --topic gnutest2# 再次查看first主题的详情
/opt/module/kafka_2.12-3.0.0/bin/kafka-topics.sh --bootstrap-server hd1:9092 --describe --topic gnutest2
启动Kafka控制台生产者，可参考：Kafka的安装与基本操作
# 创建kafka生产者
/opt/module/kafka_2.12-3.0.0/bin/kafka-console-producer.sh --bootstrap-server hd1:9092 --topic gnutest2
创建maven项目
添加kafka依赖
<!--- 添加streaming依赖 ---><dependency><groupId>org.apache.spark</groupId><artifactId>spark-streaming_2.13</artifactId><version>${spark.version}</version></dependency><!--- 添加streaming kafka依赖 ---><dependency><groupId>org.apache.spark</groupId><artifactId>spark-streaming-kafka-0-10_2.13</artifactId><version>3.4.1</version></dependency>编写程序，如下所示：
package examsimport org.apache.kafka.common.serialization.StringDeserializer
import org.apache.spark.SparkContext
import org.apache.spark.sql.SparkSession
import org.apache.spark.streaming.dstream.DStream
import org.apache.spark.streaming.kafka010._
import org.apache.spark.streaming.kafka010.LocationStrategies.PreferConsistent
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.streaming.kafka010.ConsumerStrategies.Subscribeimport java.lang/*** @projectName SparkLearning2023 * @package exams * @className exams.SparkStreamingReadKafka * @description ${description} * @author pblh123* @date 2023/12/1 15:19* @version 1.0**/object SparkStreamingReadKafka {def main(args: Array[String]): Unit = {// 1. 创建spark,sc对象if (args.length != 2) {println("您需要输入一个参数")System.exit(5)}val musrl: String = args(0)val spark: SparkSession = new SparkSession.Builder().appName(s"${this.getClass.getSimpleName}").master(musrl).getOrCreate()val sc: SparkContext = spark.sparkContext// 生成streamingContext对象val ssc: StreamingContext = new StreamingContext(sc, Seconds(5))// 2. 代码主体val bststrapServers = args(1)val kafkaParms: Map[String, Object] = Map[String, Object]("bootstrap.servers" -> bststrapServers, //kafka列表"key.deserializer" -> classOf[StringDeserializer], k和v 的序列化类型"value.deserializer" -> classOf[StringDeserializer],"group.id" -> "use_a_separate_group_id_for_each_stream", //消费者组"auto.offset.reset" -> "latest", //如果没有记录偏移量，第一次从最开始读，有偏移量，接着偏移量读"enable.auto.commit" -> (true: java.lang.Boolean) // 消费者不自动提交偏移量)val topics = Array("gnutest2", "t100")// createDirectStream: 主动拉取数据val stream = KafkaUtils.createDirectStream[String, String](ssc,PreferConsistent,Subscribe[String, String](topics, kafkaParms))val mapDStream: DStream[(String, String)] = stream.map(record => (record.key, record.value))//kafka 是一个key value 格式的， 默认key 为null ,一般用不上val resultRDD: DStream[(String, Int)] = mapDStream.flatMap(_._2.split(" ")).map((_, 1)).reduceByKey(_ + _)// 打印resultRDD.print()// 3. 关闭sc,spark对象ssc.start()ssc.awaitTermination()ssc.stop()sc.stop()spark.stop()}
}
配置输入参数
生产者追加数据
