Kafka 消费数据时序列化失败

如果出现以下异常

java.io.NotSerializableException: org.apache.kafka.clients.consumer.ConsumerRecord
Serialization stack:
    - object not serializable (class: org.apache.kafka.clients.consumer.ConsumerRecord, value: ConsumerRecord(topic = dec_message, partition = 0, leaderEpoch = 0, offset = 0, 
    - element of array (index: 0)
    - array (class [Lorg.apache.kafka.clients.consumer.ConsumerRecord;, size 11)
    at org.apache.spark.serializer.SerializationDebugger$.improveException(SerializationDebugger.scala:41)
    at org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:46)
    at org.apache.spark.serializer.JavaSerializerInstance.serialize(JavaSerializer.scala:100)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:456)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
    at java.lang.Thread.run(Thread.java:748)
20/05/20 11:29:00 ERROR TaskSetManager: Task 0.0 in stage 0.0 (TID 0) had a not serializable result: org.apache.kafka.clients.consumer.ConsumerRecord
Serialization stack:
    - object not serializable (class: org.apache.kafka.clients.consumer.ConsumerRecord, 
    - element of array (index: 0)
    - array (class [Lorg.apache.kafka.clients.consumer.ConsumerRecord;, size 11); not retrying
20/05/20 11:29:00 INFO TaskSchedulerImpl: Removed TaskSet 0.0, whose tasks have all completed, from pool 
20/05/20 11:29:00 INFO TaskSchedulerImpl: Cancelling stage 0
20/05/20 11:29:00 INFO TaskSchedulerImpl: Killing all running tasks in stage 0: Stage cancelled
20/05/20 11:29:00 INFO DAGScheduler: ResultStage 0 (print at DemoMain.java:58) failed in 0.419 s due to Job aborted due to stage failure: Task 0.0 in stage 0.0 (TID 0) had a not serializable result: org.apache.kafka.clients.consumer.ConsumerRecord
Serialization stack:
    - object not serializable (class: org.apache.kafka.clients.consumer.ConsumerRecord, value: ConsumerRecord(topic = dec_message, partition = 0, leaderEpoch = 0, offset = 0, 
    - element of array (index: 0)
    - array (class [Lorg.apache.kafka.clients.consumer.ConsumerRecord;, size 11)
20/05/20 11:29:00 INFO DAGScheduler: Job 0 failed: print at DemoMain.java:58, took 0.461578 s
20/05/20 11:29:00 INFO JobScheduler: Finished job streaming job 1589945340000 ms.0 from job set of time 1589945340000 ms
20/05/20 11:29:00 ERROR JobScheduler: Error running job streaming job 1589945340000 ms.0
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0.0 in stage 0.0 (TID 0) had a not serializable result: org.apache.kafka.clients.consumer.ConsumerRecord
Serialization stack:
    
    - element of array (index: 0)
    - array (class [Lorg.apache.kafka.clients.consumer.ConsumerRecord;, size 11)
    at org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:1889)
    at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:1877)
    at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:1876)
    at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
    at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
    at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1876)
    at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:926)
    at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:926)
    at scala.Option.foreach(Option.scala:274)
    at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:926)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2110)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2059)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2048)
    at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
    at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:737)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2061)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2082)
    at org.apache.spark.streaming.kafka010.KafkaRDD.take(KafkaRDD.scala:139)
    at org.apache.spark.streaming.kafka010.KafkaRDD.take(KafkaRDD.scala:48)
    at org.apache.spark.streaming.dstream.DStream.$anonfun$print$3(DStream.scala:735)
    at org.apache.spark.streaming.dstream.DStream.$anonfun$print$3$adapted(DStream.scala:734)
    at org.apache.spark.streaming.dstream.ForEachDStream.$anonfun$generateJob$2(ForEachDStream.scala:51)
    at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
    at org.apache.spark.streaming.dstream.DStream.createRDDWithLocalProperties(DStream.scala:416)
    at org.apache.spark.streaming.dstream.ForEachDStream.$anonfun$generateJob$1(ForEachDStream.scala:51)
    at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
    at scala.util.Try$.apply(Try.scala:213)
    at org.apache.spark.streaming.scheduler.Job.run(Job.scala:39)
    at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler.$anonfun$run$1(JobScheduler.scala:257)
    at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
    at scala.util.DynamicVariable.withValue(DynamicVariable.scala:62)
    at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler.run(JobScheduler.scala:257)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
    at java.lang.Thread.run(Thread.java:748)
Exception in thread "main" org.apache.spark.SparkException: Job aborted due to stage failure: Task 0.0 in stage 0.0 (TID 0) had a not serializable result: org.apache.kafka.clients.consumer.ConsumerRecord
Serialization stack:
    - object not serializable (class: org.apache.kafka.clients.consumer.ConsumerRecord, 
    - element of array (index: 0)
    - array (class [Lorg.apache.kafka.clients.consumer.ConsumerRecord;, size 11)
    at org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:1889)
    at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:1877)
    at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:1876)
    at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
    at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
    at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1876)
    at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:926)
    at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:926)
    at scala.Option.foreach(Option.scala:274)
    at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:926)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2110)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2059)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2048)
    at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
    at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:737)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2061)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2082)
    at org.apache.spark.streaming.kafka010.KafkaRDD.take(KafkaRDD.scala:139)
    at org.apache.spark.streaming.kafka010.KafkaRDD.take(KafkaRDD.scala:48)
    at org.apache.spark.streaming.dstream.DStream.$anonfun$print$3(DStream.scala:735)
    at org.apache.spark.streaming.dstream.DStream.$anonfun$print$3$adapted(DStream.scala:734)
    at org.apache.spark.streaming.dstream.ForEachDStream.$anonfun$generateJob$2(ForEachDStream.scala:51)
    at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
    at org.apache.spark.streaming.dstream.DStream.createRDDWithLocalProperties(DStream.scala:416)
    at org.apache.spark.streaming.dstream.ForEachDStream.$anonfun$generateJob$1(ForEachDStream.scala:51)
    at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
    at scala.util.Try$.apply(Try.scala:213)
    at org.apache.spark.streaming.scheduler.Job.run(Job.scala:39)
    at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler.$anonfun$run$1(JobScheduler.scala:257)
    at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
    at scala.util.DynamicVariable.withValue(DynamicVariable.scala:62)
    at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler.run(JobScheduler.scala:257)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
    at java.lang.Thread.run(Thread.java:748)
20/05/20 11:29:00 INFO StreamingContext: Invoking stop(stopGracefully=false) from shutdown hook

解决办法

将ConsumerRecord类注册为使用Kyro序列化

    public SparkConf getSparkConf() {
        SparkConf sparkConf = new SparkConf()
                .setAppName("local_spark_statistics")
                .setMaster("local")
                .set("spark.serializer","org.apache.spark.serializer.KryoSerializer");
        sparkConf.registerKryoClasses((Class<?>[]) Arrays.asList(ConsumerRecord.class).toArray());
        return sparkConf;
    }

序列化在分布式系统中扮演着重要的角色，优化Spark程序时，首当其冲的就是对序列化方式的优化。Spark为使用者提供两种序列化方式：
 
Java serialization: 默认的序列化方式。
 
Kryo serialization: 相较于 Java serialization 的方式，速度更快，空间占用更小，但并不支持所有的序列化格式，同时使用的时候需要注册class。spark-sql中默认使用的是kyro的序列化方式。
 


一、如果需要多个类都使用Kyro序列化，可以自定义一个注册类，同时进行多个类的注册，如下
    主要的使用过程就三步：
    1.设置序列化使用的库
        conf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer");  //使用Kryo序列化库
    2.在该库中注册用户定义的类型
        conf.set("spark.kryo.registrator", toKryoRegistrator.class.getName());       //在Kryo序列化库中注册自定义的类集合
    3.在自定义类中实现KryoRegistrator接口的registerClasses方法.要求自定义类实现Serializable，即下面的temp1、temp2类
        public static class toKryoRegistrator implements KryoRegistrator {
            public void registerClasses(Kryo kryo) {
                kryo.register(tmp1.class, new FieldSerializer(kryo, tmp1.class));  //在Kryo序列化库中注册自定义的类
                kryo.register(tmp2.class, new FieldSerializer(kryo, tmp2.class));  //在Kryo序列化库中注册自定义的类
            }
        }
        
二、如果只是注册一个类使用Kyro序列化，直接使用如下即可
    1.设置序列化使用的库
        conf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer");  //使用Kryo序列化库
    2.注册序列化类
        conf.registerKryoClasses((Class<?>[]) Arrays.asList(ConsumerRecord.class).toArray());
©著作权归作者所有,转载或内容合作请联系作者
  • 人面猴
    序言：七十年代末，一起剥皮案震惊了整个滨河市，随后出现的几起案子，更是在滨河造成了极大的恐慌，老刑警刘岩，带你破解...
    沈念sama阅读 212,294评论 6赞 493
  • 死咒
    序言：滨河连续发生了三起死亡事件，死亡现场离奇诡异，居然都是意外死亡，警方通过查阅死者的电脑和手机，发现死者居然都...
    沈念sama阅读 90,493评论 3赞 385
  • 救了他两次的神仙让他今天三更去死
    文/潘晓璐 我一进店门，熙熙楼的掌柜王于贵愁眉苦脸地迎上来，“玉大人，你说我怎么就摊上这事。” “怎么了？”我有些...
    开封第一讲书人阅读 157,790评论 0赞 348
  • 道士缉凶录：失踪的卖姜人
    文/不坏的土叔 我叫张陵，是天一观的道长。 经常有香客问我，道长，这世上最难降的妖魔是什么？ 我笑而不...
    开封第一讲书人阅读 56,595评论 1赞 284
  • 港岛之恋（遗憾婚礼）
    正文 为了忘掉前任，我火速办了婚礼，结果婚礼上，老公的妹妹穿的比我还像新娘。我一直安慰自己，他们只是感情好，可当我...
    茶点故事阅读 65,718评论 6赞 386
  • 恶毒庶女顶嫁案：这布局不是一般人想出来的
    文/花漫 我一把揭开白布。 她就那样静静地躺着，像睡着了一般。 火红的嫁衣衬着肌肤如雪。 梳的纹丝不乱的头发上，一...
    开封第一讲书人阅读 49,906评论 1赞 290
  • 城市分裂传说
    那天，我揣着相机与录音，去河边找鬼。 笑死，一个胖子当着我的面吹牛，可吹牛的内容都是我干的。 我是一名探鬼主播，决...
    沈念sama阅读 39,053评论 3赞 410
  • 双鸳鸯连环套：你想象不到人心有多黑
    文/苍兰香墨 我猛地睁开眼，长吁一口气：“原来是场噩梦啊……” “哼！你这毒妇竟也来了？” 一声冷哼从身侧响起，我...
    开封第一讲书人阅读 37,797评论 0赞 268
  • 万荣杀人案实录
    序言：老挝万荣一对情侣失踪，失踪者是张志新（化名）和其女友刘颖，没想到半个月后，有当地人在树林里发现了一具尸体，经...
    沈念sama阅读 44,250评论 1赞 303
  • 护林员之死
    正文 独居荒郊野岭守林人离奇死亡，尸身上长有42处带血的脓包…… 初始之章·张勋 以下内容为张勋视角 年9月15日...
    茶点故事阅读 36,570评论 2赞 327
  • 白月光启示录
    正文 我和宋清朗相恋三年，在试婚纱的时候发现自己被绿了。 大学时的朋友给我发了我未婚夫和他白月光在一起吃饭的照片。...
    茶点故事阅读 38,711评论 1赞 341
  • 活死人
    序言：一个原本活蹦乱跳的男人离奇死亡，死状恐怖，灵堂内的尸体忽然破棺而出，到底是诈尸还是另有隐情，我是刑警宁泽，带...
    沈念sama阅读 34,388评论 4赞 332
  • 日本核电站爆炸内幕
    正文 年R本政府宣布，位于F岛的核电站，受9级特大地震影响，放射性物质发生泄漏。R本人自食恶果不足惜，却给世界环境...
    茶点故事阅读 40,018评论 3赞 316
  • 男人毒药：我在死后第九天来索命
    文/蒙蒙 一、第九天 我趴在偏房一处隐蔽的房顶上张望。 院中可真热闹，春花似锦、人声如沸。这庄子的主人今日做“春日...
    开封第一讲书人阅读 30,796评论 0赞 21
  • 一桩弑父案，背后竟有这般阴谋
    文/苍兰香墨 我抬头看了看天上的太阳。三九已至，却和暖如春，着一层夹袄步出监牢的瞬间，已是汗流浃背。 一阵脚步声响...
    开封第一讲书人阅读 32,023评论 1赞 266
  • 情欲美人皮
    我被黑心中介骗来泰国打工， 没想到刚下飞机就差点儿被人妖公主榨干…… 1. 我叫王不留，地道东北人。 一个月前我还...
    沈念sama阅读 46,461评论 2赞 360
  • 代替公主和亲
    正文 我出身青楼，却偏偏与公主长得像，于是被迫代替她去往敌国和亲。 传闻我的和亲对象是个残疾皇子，可洞房花烛夜当晚...
    茶点故事阅读 43,595评论 2赞 350