本文的前提条件: SparkStreaming in Java
参考地址:Spark Streaming + Kafka Integration Guide (Kafka broker version 0.10.0 or higher)
1.添加POM依赖
<dependency>
<groupId>com.fasterxml.jackson.core</groupId>
<artifactId>jackson-core</artifactId>
<version>2.16.1</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming-kafka-0-10_2.13</artifactId>
<version>3.5.0</version>
</dependency>
2.使用
package cn.coreqi;
import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.spark.SparkConf;
import org.apache.spark.streaming.Durations;
import org.apache.spark.streaming.api.java.*;
import org.apache.spark.streaming.kafka010.ConsumerStrategies;
import org.apache.spark.streaming.kafka010.KafkaUtils;
import org.apache.spark.streaming.kafka010.LocationStrategies;
import org.apache.kafka.common.serialization.StringDeserializer;
import scala.Tuple2;
import java.util.Arrays;
import java.util.Collection;
import java.util.HashMap;
import java.util.Map;
public class Main {
public static void main(String[] args) throws InterruptedException {
// 创建SparkConf对象
SparkConf sparkConf = new SparkConf()
.setMaster("local[*]")
.setAppName("sparkSql");
// 第一个参数表示环境配置,第二个参数表示批量处理的周期(采集周期)
JavaStreamingContext ssc = new JavaStreamingContext(sparkConf, Durations.seconds(3));
// 定义Kafka参数
Map<String, Object> kafkaParams = new HashMap<String, Object>();
kafkaParams.put("bootstrap.servers", "192.168.58.130:9092,192.168.58.131:9092,192.168.58.132:9092");
kafkaParams.put("key.deserializer", StringDeserializer.class);
kafkaParams.put("value.deserializer", StringDeserializer.class);
kafkaParams.put("group.id", "coreqi"); // 配置消费者组
kafkaParams.put("auto.offset.reset", "latest");
kafkaParams.put("enable.auto.commit", false);
// 配置 kafka主题
Collection<String> topics = Arrays.asList("topicA", "topicB");
//读取kafka数据创建DStream
// kafka传入的K,V均为string类型
JavaInputDStream<ConsumerRecord<String, String>> kafkaDataDS =
KafkaUtils.createDirectStream(ssc,
LocationStrategies.PreferConsistent(), //位置策略,采集节点和计算节点如何做匹配,此值为'由框架自行匹配'
ConsumerStrategies.<String, String>Subscribe(topics, kafkaParams)); //消费者策略,订阅
JavaPairDStream<String, String> mapToPair = kafkaDataDS.mapToPair(record -> new Tuple2<>(record.key(), record.value()));
mapToPair.print();
// 由于SparkStreaming采集器是长期执行的任务,所以不能直接关闭
// 如果main方法执行完毕,应用程序也会自动结束,所以不能让main执行完毕
ssc.start(); // 启动采集器
ssc.awaitTermination(); // 等待采集器的关闭
}
}
标签:kafka,数据源,kafkaParams,SparkStreaming,org,apache,import,spark,Kafka
From: https://www.cnblogs.com/fanqisoft/p/17966384