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>1.8java.version>
<spark.version>1.6.2spark.version>
<scala.version>2.10.6scala.version>
<elasticsearch.version>5.2.0elasticsearch.version>
<kafka.version>1.0kafka.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.bootgroupId>
        <artifactId>spring-boot-starter-parentartifactId>
        <version>1.5.7.RELEASEversion>
        <relativePath />
    parent>

    <properties>
        <project.build.sourceEncoding>UTF-8project.build.sourceEncoding>
        <project.reporting.outputEncoding>UTF-8project.reporting.outputEncoding>
        <java.version>1.7java.version>
        <spark.version>1.6.2spark.version>
        <scala.version>2.10.6scala.version>
        <elasticsearch.version>5.2.0elasticsearch.version>
    properties>

    <dependencies>
        <dependency>
            <groupId>org.springframework.bootgroupId>
            <artifactId>spring-boot-starterartifactId>
        dependency>

        <dependency>
            <groupId>org.springframework.bootgroupId>
            <artifactId>spring-boot-starter-webartifactId>
        dependency>
        <dependency>
            <groupId>com.google.code.gsongroupId>
            <artifactId>gsonartifactId>
        dependency>

        
        <dependency>
            <groupId>org.scala-langgroupId>
            <artifactId>scala-libraryartifactId>
            <version>${scala.version}version>
        dependency>
        <dependency>
            <groupId>org.apache.sparkgroupId>
            <artifactId>spark-core_2.10artifactId>
            <version>${spark.version}version>
        dependency>
        <dependency>
            <groupId>org.apache.sparkgroupId>
            <artifactId>spark-streaming_2.10artifactId>
            <version>${spark.version}version>
        dependency>
        <dependency>
            <groupId>org.apache.sparkgroupId>
            <artifactId>spark-streaming-kafka_2.10artifactId>
            <version>${spark.version}version>
        dependency>

        
        <dependency>
            <groupId>org.elasticsearchgroupId>
            <artifactId>elasticsearch-spark-13_2.10artifactId>
            <version>${elasticsearch.version}version>
        dependency>
    dependencies>

    <build>
        <plugins>
            <plugin>
                <groupId>org.springframework.bootgroupId>
                <artifactId>spring-boot-maven-pluginartifactId>
            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。