spark-streaming（二）

DStream创建（kafka数据源）

1.在idea中的 pom.xml 中添加依赖

<dependency><groupId>org.apache.spark</groupId><artifactId>spark-streaming-kafka-0-10_2.12</artifactId><version>3.0.0</version>
</dependency>

2.创建一个新的object，并写入以下代码

import org.apache.kafka.clients.consumer.ConsumerConfig
import org.apache.kafka.common.serialization.StringDeserializer
import org.apache.spark.SparkConf
import org.apache.spark.streaming.dstream.InputDStream
import org.apache.spark.streaming.kafka010.{ConsumerStrategies, KafkaUtils, LocationStrategies}
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.kafka.clients.consumer.ConsumerRecord/*** 通过 DirectAPI 0 - 10 消费 Kafka 数据* 消费的 offset 保存在 _consumer_offsets 主题中*/
object DirectAPI {def main(args: Array[String]): Unit = {val sparkConf = new SparkConf().setMaster("local[*]").setAppName("direct")val ssc = new StreamingContext(sparkConf, Seconds(3))// 定义 Kafka 相关参数val kafkaPara: Map[String, Object] = Map[String, Object](ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG -> "node01:9092,node02:9092,node03:9092",ConsumerConfig.GROUP_ID_CONFIG -> "kafka","key.deserializer" -> classOf[StringDeserializer],"value.deserializer" -> classOf[StringDeserializer])// 通过读取 Kafka 数据，创建 DStreamval kafkaDStream: InputDStream[ConsumerRecord[String, String]] = KafkaUtils.createDirectStream[String, String](ssc,LocationStrategies.PreferConsistent,ConsumerStrategies.Subscribe[String, String](Set("kafka"), kafkaPara))// 提取出数据中的 value 部分val valueDStream = kafkaDStream.map(record => record.value())// WordCount 计算逻辑valueDStream.flatMap(_.split(" ")).map((_, 1)).reduceByKey(_ + _).print()ssc.start()ssc.awaitTermination()}
}

3.在虚拟机中，开启kafka、zookeeper、yarn、dfs集群