Spark Streaming 读取Kafka数据写入Elasticsearch

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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。

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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>

下面是Spring boot搭建的项目结构：

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)

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();

这里只是记录了操作的流程和开发中遇到的一些问题，我觉得重难点在于RDD的各种转换逻辑处理操作。这里没有细化下去，太广了。记录好了配置及处理流程，以后需要用时再去复习一下就能够快速熟悉，从而继续高效开发。

好记性不如烂笔头，主要还是为了方便自己以后查看。可能记录得有些简单了。如有疑问可私信沟通。