Spark实战（二）：Kafka-SparkStreaming-Elasticsearch

本文介绍saprk实时部分----spark-streaming。spark-streaming可以实现实时批处理功能，实际上还是相当于小的批处理，但是是7*24工作，可以近实时但需要维护成本。本文里的用java写的demo，实现功能是将kafka作为spark-streaming的输入源，Elasticsearch作为输出源来实现实时处理架构。

还是先上代码
maven

	org.apache.spark
	spark-streaming-kafka-0-10_2.11
	2.3.1


	org.apache.spark
	spark-core_2.11
	2.3.1
	provided


	org.apache.spark
	spark-streaming_2.11
	2.3.1
	provided



	org.apache.spark
	spark-sql_2.11
	2.3.1
	provided


	org.elasticsearch
	elasticsearch-hadoop
	6.3.0

代码

import org.apache.spark.streaming.api.java.JavaStreamingContext;
import org.apache.spark.streaming.kafka010.ConsumerStrategies;
import org.apache.spark.streaming.kafka010.KafkaUtils;
import org.apache.spark.streaming.kafka010.LocationStrategies;

import org.apache.spark.streaming.api.java.JavaDStream;
import org.apache.spark.streaming.api.java.JavaInputDStream;

import java.util.Arrays;
import java.util.HashMap;
import java.util.HashSet;
import java.util.Map;
import java.util.Set;
import java.util.regex.Pattern;

import org.apache.kafka.clients.consumer.ConsumerConfig;
import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.kafka.common.serialization.StringDeserializer;
import org.apache.spark.SparkConf;

import org.apache.spark.streaming.Durations;
import org.elasticsearch.spark.rdd.api.java.JavaEsSpark;
import org.elasticsearch.spark.streaming.api.java.JavaEsSparkStreaming;

public static void main(String[] args) throws Exception {
	// 从SparkConf创建StreamingContext并指定1秒钟的批处理大小
	String master = "spark://192.168.7.51:7077";
	//	 String master = "local[2]";
	
	SparkConf conf = new SparkConf().setMaster(master).setAppName("StreamingTest")
			// .set("spark.executor.memory", "1g")
			// .set("spark.dynamicAllocation.enabled", "false")
		      .set("spark.es.nodes", "192.168.7.51")//指定es地址
		      .set("spark.es.port", "9200")//指定es端口号
			.setJars(new String[] {//如果是集群模式要指定jar包，整个项目打包
					"/Users/username/eclipse-workspace/spark-streaming/target/spark-streaming-0.0.1-SNAPSHOT.jar" });
	
	//指定1秒获取一次kafka数据
	JavaStreamingContext jssc = new JavaStreamingContext(conf, Durations.seconds(1));
	
	String brokers = "kafka1:9092";//指定kafka地址
	String groupId = "kafka";//指定消费者组id
	String topics = "test1";//指定topic
	Set topicsSet = new HashSet<>(Arrays.asList(topics.split(",")));
	Map kafkaParams = new HashMap<>();
	kafkaParams.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, brokers);
	kafkaParams.put(ConsumerConfig.GROUP_ID_CONFIG, groupId);
	kafkaParams.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);
	kafkaParams.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);
	
	//取出1秒内的数据转成rddstream
	JavaInputDStream> messages = KafkaUtils.createDirectStream(jssc,
			LocationStrategies.PreferConsistent(), ConsumerStrategies.Subscribe(topicsSet, kafkaParams));
	
	//取出每条message中的value
	JavaDStream lines = messages.map(record -> record.value());
	
	//拼成可以插入Elasticsearch的格式
	JavaDStream out = lines.map(str -> "{\"test\":\""+str+"\"}");
	
	//打印
	out.print();
	
	//写入Elasticsearch
	JavaEsSparkStreaming.saveJsonToEs(out, "/spark/doc");

	//启动streaming
	jssc.start();
	
	// 等待作业完成
	jssc.awaitTermination();
	jssc.stop();
}

spark-streaming接收kafka数据

spark-streaming接收kafka数据使用spark-streaming-kafka-0-10_2.11包。

参考官网：http://spark.apache.org/docs/latest/streaming-kafka-0-10-integration.html

maven

	org.apache.spark
	spark-streaming-kafka-0-10_2.11
	2.3.1

scala

val kafkaParams = Map[String, Object](
  "bootstrap.servers" -> "localhost:9092,anotherhost: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" -> (false: java.lang.Boolean)
)

val topics = Array("topicA", "topicB")
val stream = KafkaUtils.createDirectStream[String, String](
  streamingContext,
  PreferConsistent,
  Subscribe[String, String](topics, kafkaParams)
)

stream.map(record => (record.key, record.value))

java

Map kafkaParams = new HashMap<>();
kafkaParams.put("bootstrap.servers", "localhost:9092,anotherhost:9092");
kafkaParams.put("key.deserializer", StringDeserializer.class);
kafkaParams.put("value.deserializer", StringDeserializer.class);
kafkaParams.put("group.id", "use_a_separate_group_id_for_each_stream");
kafkaParams.put("auto.offset.reset", "latest");
kafkaParams.put("enable.auto.commit", false);

Collection topics = Arrays.asList("topicA", "topicB");

JavaInputDStream> stream =
  KafkaUtils.createDirectStream(
    streamingContext,
    LocationStrategies.PreferConsistent(),
    ConsumerStrategies.Subscribe(topics, kafkaParams)
  );

stream.mapToPair(record -> new Tuple2<>(record.key(), record.value()));

spark-streaming输出结果到Elasticsearch

maven

	org.elasticsearch
	elasticsearch-hadoop
	6.3.0

scala

import org.elasticsearch.spark.streaming.EsSparkStreaming;

java

import org.elasticsearch.spark.streaming.api.java.JavaEsSparkStreaming;

需要指定Elasticsearch，可以写在配置文件里，也可以在程序里指定。

• 配置文件：spark-default.conf

spark.es.nodes　　  eshosts
spark.es.port　　   9200

• 写在程序里

SparkConf conf = new SparkConf().setMaster(master).setAppName("StreamingTest")
		// .set("spark.executor.memory", "1g")
		// .set("spark.dynamicAllocation.enabled", "false")
	      .set("spark.es.nodes", "192.168.7.51")
	      .set("spark.es.port", "9200");

写入es

JavaEsSparkStreaming.saveJsonToEs(out, "/spark/doc");

---

更多：Spark专栏
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作者：桃花惜春风
转载请标明出处，原文地址：
https://blog.csdn.net/xiaoyu_BD/article/details/82777869
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