Spark Streaming 读取Kafka数据写入ES

时间：2023-01-15 20:46:04

简介： 
目前项目中已有多个渠道到Kafka的数据处理，本文主要记录通过Spark Streaming 读取Kafka中的数据，写入到Elasticsearch，达到一个实时（严格来说，是近实时，刷新时间间隔可以自定义）数据刷新的效果。

应用场景： 
业务库系统做多维分析的时候，数据来源各不相同。很多历史数据都是每天定时跑批生成。但是做分析产品，对于T+0日的数据， 则不好取。对于T+0日的数据，目前我采取的解决方案就是Spark Streaming 读取Kafka写入到Elasticsearch，业务系统通过查询历史数据和T+0日数据，得到一个数据实时展示的效果。

先介绍一下内容涉及的几个版本：

<java.version>1.8</java.version> <spark.version>1.6.2</spark.version> <scala.version>2.10.6</scala.version> <elasticsearch.version>5.2.0</elasticsearch.version> <kafka.version>1.0</kafka.version>
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下面是Spring boot搭建的项目结构：

Spark Streaming 读取Kafka数据写入ES

之前学习的时候，参考的spark版本1.6.2，kafka版本是0.8的，但是后面自己做项目的kafka版本是1.0的。我把对应的kafka_2.10-0.8.2.1.jar改成kafka_2.10-0.10.0.0.jar 但是遇到了下面的这个异常：

Exception in thread "main" java.lang.ClassCastException: kafka.cluster.BrokerEndPoint cannot be cast to kafka.cluster.Broker at org.apache.spark.streaming.kafka.KafkaCluster$$anonfun$2$$anonfun$3$$anonfun$apply$6$$anonfun$apply$7.apply(KafkaCluster.scala:90) at scala.Option.map(Option.scala:145) at org.apache.spark.streaming.kafka.KafkaCluster$$anonfun$2$$anonfun$3$$anonfun$apply$6.apply(KafkaCluster.scala:90) at org.apache.spark.streaming.kafka.KafkaCluster$$anonfun$2$$anonfun$3$$anonfun$apply$6.apply(KafkaCluster.scala:87) at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:251) at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:251) at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33) at scala.collection.mutable.WrappedArray.foreach(WrappedArray.scala:34) at scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:251) at scala.collection.AbstractTraversable.flatMap(Traversable.scala:105) at org.apache.spark.streaming.kafka.KafkaCluster$$anonfun$2$$anonfun$3.apply(KafkaCluster.scala:87) at org.apache.spark.streaming.kafka.KafkaCluster$$anonfun$2$$anonfun$3.apply(KafkaCluster.scala:86) at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:251) at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:251) at scala.collection.immutable.Set$Set1.foreach(Set.scala:74) at scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:251) at scala.collection.AbstractTraversable.flatMap(Traversable.scala:105) at org.apache.spark.streaming.kafka.KafkaCluster$$anonfun$2.apply(KafkaCluster.scala:86) at org.apache.spark.streaming.kafka.KafkaCluster$$anonfun$2.apply(KafkaCluster.scala:85) at scala.util.Either$RightProjection.flatMap(Either.scala:523) at org.apache.spark.streaming.kafka.KafkaCluster.findLeaders(KafkaCluster.scala:85) at org.apache.spark.streaming.kafka.KafkaCluster.getLeaderOffsets(KafkaCluster.scala:179) at org.apache.spark.streaming.kafka.KafkaCluster.getLeaderOffsets(KafkaCluster.scala:161) at org.apache.spark.streaming.kafka.KafkaCluster.getEarliestLeaderOffsets(KafkaCluster.scala:155) at org.apache.spark.streaming.kafka.KafkaUtils$$anonfun$5.apply(KafkaUtils.scala:213) at org.apache.spark.streaming.kafka.KafkaUtils$$anonfun$5.apply(KafkaUtils.scala:211) at scala.util.Either$RightProjection.flatMap(Either.scala:523) at org.apache.spark.streaming.kafka.KafkaUtils$.getFromOffsets(KafkaUtils.scala:211) at org.apache.spark.streaming.kafka.KafkaUtils$.createDirectStream(KafkaUtils.scala:484) at org.apache.spark.streaming.kafka.KafkaUtils$.createDirectStream(KafkaUtils.scala:607) at org.apache.spark.streaming.kafka.KafkaUtils.createDirectStream(KafkaUtils.scala) at com.midea.magiccube.spark.LoanInfoStatistic.getActionDStream(LoanInfoStatistic.java:210) at com.midea.magiccube.spark.LoanInfoStatistic.main(LoanInfoStatistic.java:69)
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主要内容是：java.lang.ClassCastException: kafka.cluster.BrokerEndPoint cannot be cast to kafka.cluster.Broker，经过一番了解后，初步估计是kafka版本和spark版本不兼容，于是我又将版本回退，发现能够跑通。

pom.xml内容如下：

    <parent> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-parent</artifactId> <version>1.5.7.RELEASE</version> <relativePath /> </parent> <properties> <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding> <project.reporting.outputEncoding>UTF-8</project.reporting.outputEncoding> <java.version>1.7</java.version> <spark.version>1.6.2</spark.version> <scala.version>2.10.6</scala.version> <elasticsearch.version>5.2.0</elasticsearch.version> </properties> <dependencies> <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter</artifactId> </dependency> <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-web</artifactId> </dependency> <dependency> <groupId>com.google.code.gson</groupId> <artifactId>gson</artifactId> </dependency> <!-- spark 相关 --> <dependency> <groupId>org.scala-lang</groupId> <artifactId>scala-library</artifactId> <version>${scala.version}</version> </dependency> <dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-core_2.10</artifactId> <version>${spark.version}</version> </dependency> <dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-streaming_2.10</artifactId> <version>${spark.version}</version> </dependency> <dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-streaming-kafka_2.10</artifactId> <version>${spark.version}</version> </dependency> <!-- spark to es 相关支持 --> <dependency> <groupId>org.elasticsearch</groupId> <artifactId>elasticsearch-spark-13_2.10</artifactId> <version>${elasticsearch.version}</version> </dependency> </dependencies> <build> <plugins> <plugin> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-maven-plugin</artifactId> </plugin> </plugins> </build>

接下来就是开发具体的Spark Streaming 读写的代码了。

  1. 配置SparkConf对象并初始化es配置参数。

    SparkConf sc = new SparkConf();
sc.setAppName("Name").setMaster("local[2]");
sc.set("es.nodes", IP);
sc.set("es.index.auto.create", "true");
sc.set("es.mapping.id", "id");
sc.set("es.port", PORT);

  2. 绑定sc参数，并设置循环取数时间间隔为5s

    JavaStreamingContext jssc = new JavaStreamingContext(sc, Durations.seconds(5));
jssc.checkpoint("E:/checkpoint");

  3. 设置kafka配置信息，KafkaUtils.createDirectStream(）方法读取信息得到 JavaPairDStream< String,String>对象dStream。

  4. dStream.mapToPair()解析kafka数据并封装成JavaPairDStream< String, 自定义实体> entityDStream对象。

  5. entityDStream.transform()将数据转化为JavaDStream dataDStream方便写入ES。

  6. 接着将数据写入ES，JavaEsSparkStreaming.saveToEs(dataDStream, “索引名”);

  7. 最后启动和关闭对象JavaStreamingContext jssc

    jssc.start();
jssc.awaitTermination();
jssc.close();
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