登录
当前位置： 首页 > 知识库问答 >
问题：

使用Kafka主题消息时的反序列化问题

仰英发
2023-03-14

我使用Kafka Consumer API来构建Consumer。为了构建反序列化器，我实现了Deserializer类并提供了必要的实现。我收到此错误“Exception Raisedorg.apache.kafka.Common.Errors.SerializationException：错误反序列化分区staging.DataFeeds.PartnerHotel-0的键/值，偏移量为19205124”

#POJO classes

    public class Change {
    private  Schema schema;
    private  Payload payload;
    //Getters and constructor
    }
    public class Details {
    private List<String> effectedAttributes;
    private List<PartnerHotel> cluster;
    //Getters and contructor
    }
    public class Field {
    private String type;
    private Boolean optional;
    private String field;
    //Getters and constructor
    }
    public class Fields {
    private String type;
    private List<Field> fields;
    private Boolean optional;
    private String name;
    //Getters and contructor
    }
    public class Geom{
    private int srid;
    private String wkb;
    //Getters and contructor
    }
    public class PartnerHotel{
    private int id;
    private int shopId;
    private String partnerHotelId;
    private boolean isOnline;
    private boolean isRemovedByUser;
    private int mappingPriority;
    private int hotelId;
    private String statusHotelId;
    private String name;
    private String street;
    private String zipCode;
    private String city;
    private String sourceCityId;
    private String state;
    private String stateAlpha2;
    private String country;
    private String alpha2;
    private String alpha3;
    private double latitude;
    private double longitude;
    private Geom geomPoint;
    private int countryIdShop;
    private int selectedGeoname;
    private String propertyType;
    private List<String> tags;
    private int stars;
    private String url;
    private int nrRatings;
    private double recommendation;
    private long dateHotelId;
    private long timeStamp;
    private long lastImport;
    //Getters and contructor
    }
    public class Payload {
    private PartnerHotel before;
    private PartnerHotel after;
    private Source source;
    private String op;
    private String ts_ms;
    //Getters and contructor
    }
    public class Schema {
    private String type;
    private Boolean optional;
    private String name;
    private List<Fields> fields;
    //Getters and contructor
    }
    public class Source {
    private String version;
    private String name;
    private String ts_usec;
    private String txId;
    private String lxn;
    private Boolean snapshot;
    private Object lastSnapshotRecord;
    //Getters and contructor
    }

#Deserializer

    public class ChangeDeserializer implements Deserializer<Change> {

    public ChangeDeserializer(){ }

    public void configure(Map<String, ?> map, boolean b) {}

    public Change deserialize(String topic, byte[] data) {
        if(data == null){
            return null;
        }
        try{
            ObjectMapper objectMapper = new ObjectMapper();
            Change change = objectMapper.readValue(data,Change.class);
            return change;
        }
        catch(IOException exception){
            throw new DeserializationException("Unable to deserialize               Change", exception);
        }}

    public void close() {}
    }

#Consumer
    public class KafkaAcnowledger {
        public static void main(String[] args){
        Properties props = new Properties();
        props.put("bootstrap.servers", "someUrl");
        props.put("group.id", "test131");
        props.put("enable.auto.commit", "true");
        props.put("auto.commit.interval.ms", "1000");
        props.put("max.poll.records",1);
        props.put("auto.offset.reset","earliest");
        props.put("key.deserializer",    "org.apache.kafka.common.serialization.LongDeserializer");
        props.put("value.deserializer",    "deserializer.ChangeDeserializer");
        KafkaConsumer<Long, Change> consumer = new KafkaConsumer(props);
        consumer.subscribe(Arrays.asList("staging.datafeeds.PartnerHotel"));
        while (true) {
            try{
            ConsumerRecords<Long, Change> records = consumer.poll(100);
            for (ConsumerRecord<Long, Change> record : records)
                System.out.printf("offset = %d, key = %s, value = %s%n", record.offset(), record.key(), record.value());
        }
        catch(Exception exception){
                System.out.println("Exception raised" + exception);
        }
        }


    }
    }

使用者中的poll（）看起来很好，Enter code here异常我得到了一个序列化异常。我通过kafka-consumer-groups.sh检查了消费者组，这个消费者组就在列表中。

{"schema":{"type":"struct","fields":[{"type":"struct","fields":[{"type":"int32","optional":false,"field":"id"},{"type":"int16","optional":false,"field":"shopId"},{"type":"string","optional":false,"field":"partnerHotelId"},{"type":"boolean","optional":false,"field":"isOnline"},{"type":"boolean","optional":false,"field":"isRemovedByUser"},{"type":"int32","optional":false,"field":"mappingPriority"},{"type":"int32","optional":true,"field":"hotelId"},{"type":"string","optional":true,"field":"statusHotelId"},{"type":"int64","optional":true,"name":"io.debezium.time.MicroTimestamp","version":1,"field":"dateHotelId"},{"type":"int64","optional":false,"name":"io.debezium.time.MicroTimestamp","version":1,"field":"timestamp"},{"type":"int64","optional":false,"name":"io.debezium.time.MicroTimestamp","version":1,"field":"lastImport"},{"type":"string","optional":true,"field":"name"},{"type":"string","optional":true,"field":"street"},{"type":"string","optional":true,"field":"zipcode"},{"type":"string","optional":true,"field":"city"},{"type":"string","optional":true,"field":"sourceCityId"},{"type":"string","optional":true,"field":"state"},{"type":"string","optional":true,"field":"stateAlpha2"},{"type":"string","optional":true,"field":"country"},{"type":"string","optional":true,"field":"alpha2"},{"type":"string","optional":true,"field":"alpha3"},{"type":"double","optional":true,"field":"latitude"},{"type":"double","optional":true,"field":"longitude"},{"type":"struct","fields":[{"type":"bytes","optional":false,"field":"wkb"},{"type":"int32","optional":true,"field":"srid"}],"optional":true,"name":"io.debezium.data.geometry.Geometry","version":1,"doc":"Geometry","field":"geomPoint"},{"type":"array","items":{"type":"int32","optional":true},"optional":false,"field":"proposedGeonames"},{"type":"int32","optional":true,"field":"countryIdShop"},{"type":"int32","optional":true,"field":"selectedGeoname"},{"type":"string","optional":true,"field":"propertyType"},{"type":"array","items":{"type":"string","optional":true},"optional":false,"field":"tags"},{"type":"array","items":{"type":"string","optional":true},"optional":false,"field":"chains"},{"type":"array","items":{"type":"string","optional":true},"optional":false,"field":"creditCards"},{"type":"int32","optional":true,"field":"stars"},{"type":"string","optional":true,"field":"url"},{"type":"int32","optional":true,"field":"nrRatings"},{"type":"double","optional":true,"field":"recommendation"},{"type":"array","items":{"type":"int32","optional":true},"optional":false,"field":"proposedHotels"},{"type":"array","items":{"type":"int32","optional":true},"optional":false,"field":"proposedPartnerHotels"},{"type":"array","items":{"type":"int32","optional":true},"optional":false,"field":"removedFromHotelIds"}],"optional":true,"name":"staging.datafeeds.PartnerHotel.Value","field":"before"},{"type":"struct","fields":[{"type":"int32","optional":false,"field":"id"},{"type":"int16","optional":false,"field":"shopId"},{"type":"string","optional":false,"field":"partnerHotelId"},{"type":"boolean","optional":false,"field":"isOnline"},{"type":"boolean","optional":false,"field":"isRemovedByUser"},{"type":"int32","optional":false,"field":"mappingPriority"},{"type":"int32","optional":true,"field":"hotelId"},{"type":"string","optional":true,"field":"statusHotelId"},{"type":"int64","optional":true,"name":"io.debezium.time.MicroTimestamp","version":1,"field":"dateHotelId"},{"type":"int64","optional":false,"name":"io.debezium.time.MicroTimestamp","version":1,"field":"timestamp"},{"type":"int64","optional":false,"name":"io.debezium.time.MicroTimestamp","version":1,"field":"lastImport"},{"type":"string","optional":true,"field":"name"},{"type":"string","optional":true,"field":"street"},{"type":"string","optional":true,"field":"zipcode"},{"type":"string","optional":true,"field":"city"},{"type":"string","optional":true,"field":"sourceCityId"},{"type":"string","optional":true,"field":"state"},{"type":"string","optional":true,"field":"stateAlpha2"},{"type":"string","optional":true,"field":"country"},{"type":"string","optional":true,"field":"alpha2"},{"type":"string","optional":true,"field":"alpha3"},{"type":"double","optional":true,"field":"latitude"},{"type":"double","optional":true,"field":"longitude"},{"type":"struct","fields":[{"type":"bytes","optional":false,"field":"wkb"},{"type":"int32","optional":true,"field":"srid"}],"optional":true,"name":"io.debezium.data.geometry.Geometry","version":1,"doc":"Geometry","field":"geomPoint"},{"type":"array","items":{"type":"int32","optional":true},"optional":false,"field":"proposedGeonames"},{"type":"int32","optional":true,"field":"countryIdShop"},{"type":"int32","optional":true,"field":"selectedGeoname"},{"type":"string","optional":true,"field":"propertyType"},{"type":"array","items":{"type":"string","optional":true},"optional":false,"field":"tags"},{"type":"array","items":{"type":"string","optional":true},"optional":false,"field":"chains"},{"type":"array","items":{"type":"string","optional":true},"optional":false,"field":"creditCards"},{"type":"int32","optional":true,"field":"stars"},{"type":"string","optional":true,"field":"url"},{"type":"int32","optional":true,"field":"nrRatings"},{"type":"double","optional":true,"field":"recommendation"},{"type":"array","items":{"type":"int32","optional":true},"optional":false,"field":"proposedHotels"},{"type":"array","items":{"type":"int32","optional":true},"optional":false,"field":"proposedPartnerHotels"},{"type":"array","items":{"type":"int32","optional":true},"optional":false,"field":"removedFromHotelIds"}],"optional":true,"name":"staging.datafeeds.PartnerHotel.Value","field":"after"},{"type":"struct","fields":[{"type":"string","optional":true,"field":"version"},{"type":"string","optional":false,"field":"name"},{"type":"string","optional":false,"field":"db"},{"type":"int64","optional":true,"field":"ts_usec"},{"type":"int64","optional":true,"field":"txId"},{"type":"int64","optional":true,"field":"lsn"},{"type":"string","optional":true,"field":"schema"},{"type":"string","optional":true,"field":"table"},{"type":"boolean","optional":true,"default":false,"field":"snapshot"},{"type":"boolean","optional":true,"field":"last_snapshot_record"}],"optional":false,"name":"io.debezium.connector.postgresql.Source","field":"source"},{"type":"string","optional":false,"field":"op"},{"type":"int64","optional":true,"field":"ts_ms"}],"optional":false,"name":"staging.datafeeds.PartnerHotel.Envelope"},"payload":{"before":null,"after":{"id":13893497,"shopId":135,"partnerHotelId":"6-42036","isOnline":false,"isRemovedByUser":false,"mappingPriority":0,"hotelId":null,"statusHotelId":"AUTO","dateHotelId":null,"timestamp":1529334013938327,"lastImport":1503491984188866,"name":"Ferienvermietung Wiedemann","street":"Chausseeberg 3","zipcode":"17429","city":"Mellenthin","sourceCityId":null,"state":null,"stateAlpha2":null,"country":"Deutschland","alpha2":"DE","alpha3":null,"latitude":53.920278,"longitude":14.013333,"geomPoint":{"wkb":"AQEAACDmEAAARuo9ldMGLEA5nWSry/VKQA==","srid":4326},"proposedGeonames":[2872064],"countryIdShop":83,"selectedGeoname":2872064,"propertyType":null,"tags":["77","36","33","34","38","43","41","123","26","29","1","7","6","70","9","1000","58","17","18","15","13","14","20","65","63","46","10","52"],"chains":[],"creditCards":[],"stars":null,"url":"http://www.buchen.travel/onepage-idealo-booking/index.php?room=6-42036","nrRatings":null,"recommendation":null,"proposedHotels":[],"proposedPartnerHotels":[],"removedFromHotelIds":[]},"source":{"version":"0.8.3.Final","name":"staging","db":"geo","ts_usec":1554391067119000,"txId":4757138,"lsn":1139303143104,"schema":"datafeeds","table":"PartnerHotel","snapshot":true,"last_snapshot_record":false},"op":"r","ts_ms":1554391067119}}

共有1个答案

丌官凯康
2023-03-14

您的POJO与您的消息不兼容，jackson无法解析它。至少缺少几个字段，可以发现跟随错误。

Unrecognized field "timestamp" (class  com.example.kafka.Change$PartnerHotel), not marked as ignorable
Unrecognized field "zipcode" (class  com.example.kafka.Change$PartnerHotel), not marked as ignorable
Unrecognized field "proposedGeonames" (class  com.example.kafka.Change$PartnerHotel), not marked as ignorable
Unrecognized field "chains" (class  com.example.kafka.Change$PartnerHotel), not marked as ignorable
Unrecognized field "creditCards" (class  com.example.kafka.Change$PartnerHotel), not marked as ignorable
Unrecognized field "proposedHotels" (class  com.example.kafka.Change$PartnerHotel), not marked as ignorable
Unrecognized field "proposedPartnerHotels" (class  com.example.kafka.Change$PartnerHotel), not marked as ignorable
Unrecognized field "removedFromHotelIds" (class  com.example.kafka.Change$PartnerHotel), not marked as ignorable
Unrecognized field "db" (class  com.example.kafka.Change$Source), not marked as ignorable
Unrecognized field "lsn" (class  com.example.kafka.Change$Source), not marked as ignorable
Unrecognized field "schema" (class  com.example.kafka.Change$Source), not marked as ignorable
Unrecognized field "table" (class  com.example.kafka.Change$Source), not marked as ignorable
Unrecognized field "last_snapshot_record" (class  com.example.kafka.Change$Source), not marked as ignorable

要修复它，您必须将这些字段添加到POJO中，或者禁用fail on Unknown:objectmapper.disable(deserializationfeature.fail_on_unknown_properties)；。更多关于jackson反序列化错误的信息可以在这里找到：jackson Unrecognized field

 类似资料：
  • @kafkalistener未使用消息-反序列化问题

    我试图通过Spring KafkaListener在单独的消费者应用程序中使用这些消息 集装箱工厂配置 在这种配置下，使用者不接收消息（字节）。如果我将Kafka侦听器更改为接受字符串，则会出现以下异常： 集装箱工厂配置 制片人-

  • 使用来自同一kafka主题的字符串和JSON消息-反序列化问题

    JSON消息正在被字符串使用者消费。My Products发送两种类型的消息字符串和序列化JSON 消费者方面我有两个消费者1。正在侦听字符串消息%2。监听json并反序列化为对象 即使是json消息也由String Listener使用。

  • 用Flink反序列化Protobuf kafka消息

    我试图阅读和打印从Kafka使用Apache Flink的原型消息。 我遵循官方文件，但没有成功：https://nightlies.apache.org/flink/flink-docs-master/docs/dev/datastream/fault-tolerance/serialization/third_party_serializers/ Flink消费者代码是： 反序列化器代码是：

  • 使用Apache Beam反序列化Kafka AVRO消息

    主要目标是聚合两个Kafka主题，一个压缩慢速移动数据，另一个每秒接收一次的快速移动数据。 我已经能够在简单的场景中使用消息，例如KV（Long， String），使用如下内容： 但是，当您需要从 AVRO 反序列化时，这似乎不是方法。我有一个KV（字符串，AVRO），我需要消费。 我尝试从AVRO模式生成Java类，然后将它们包含在“应用”中，例如: 但这似乎不是正确的方法。 是否有任何文档/示

  • 反序列化Avro序列化Kafka流的问题

  • kafka消息的节俭序列化-每个结构单主题

    我计划使用kafka作为事件来源的持久日志，目前正在研究不同的序列化选项。我目前的重点是使用thrift对我将存储在Kafka中的消息进行序列化和反序列化。

相关问答

反序列化Avro序列化Kafka流的问题kafka消息的节俭序列化-每个结构单主题Python用avro存储库反序列化kafka消息反序列化问题发送到Kafka主题时反序列化对象时出错

相关文章

Gson和抽象超类：反序列化问题使用Boost序列化和反序列化JSON实施PropertyChangedBase时caliburn.micro序列化问题golang中json反序列化可能遇到的问题js序列化和反序列化的使用讲解

相关阅读

ConsumeMessageOrderlyService顺序消费消息为什么要使用MQ消息中间件?这几个问题必须拿下!一次教会你如何解决RabbitMQ消息丢失问题！消息队列 MQ 简介SLF4J参数化日志消息

相关工具

冇问题主题颜色切换可以切换主题的天气App大块头微信消息stackoverflow Java top 100问题翻译

相关文档

Windows MSMQ 消息队列［初步文档］小米消息队列 EMQ 开发文档小米消息队列 Talos 开发文档Debian GNU/Linux 常见问题Stack Overflow 关于 Python 的部分高分问题
快捷导航： 新手教程 算法原理 架构设计 Java进阶 数据库进阶 大厂专栏 面试经验 编程笔记 编程问答 所有专题 文档资料 工具软件 电子书籍 小牛导航
在线工具： 房贷计算器 个税计算器 Linux命令查询 Json格式化 正则表达式 颜色转换 AES加解密 SHA1加密 MD5加密 毒鸡汤 字数统计 随机密码生成 进制转换 Base64编解码 励志句子
Copyright © 2019-2024 小牛知识库@xnip.cn. All Rights Reserved.