Spring Cloud Stream Kafka应用程序未使用正确的Avro模式生成消息

Question

我有一个应用程序(spring-boot-shipping-service)，其中包含一个KStream，它获取由外部生产者（spring-boot-order-service）生成的OrderCreatedEvent消息。此生成器使用以下架构：

Order-Created-Event.avsc

{
  "namespace" : "com.codependent.statetransfer.order",
  "type" : "record",
  "name" : "OrderCreatedEvent",
  "fields" : [
    {"name":"id","type":"int"},
    {"name":"productId","type":"int"},
    {"name":"customerId","type":"int"}
  ]
}

我的kstream 与ktable 联接，并向order主题发布一种新的消息:Ordershippedevent。

Order-Shiped-Event.avsc

{
  "namespace" : "com.codependent.statetransfer.order",
  "type" : "record",
  "name" : "OrderShippedEvent",
  "fields" : [
    {"name":"id","type":"int"},
    {"name":"productId","type":"int"},
    {"name":"customerName","type":"string"},
    {"name":"customerAddress","type":"string"}
  ]
}

出于某种原因，新的OrderShippedEvent消息生成时没有标头application/vnd.OrderShippedEvent.v1+avro，而是标头application/vnd.orderCreatedEvent.v1+avro。

这是order主题中的原始OrderCreatedEvent：

Key (4 bytes): +
  Value (4 bytes): V?
  Timestamp: 1555943926163
  Partition: 0
  Offset: 34
  Headers: contentType="application/vnd.ordercreatedevent.v1+avro",spring_json_header_types={"contentType":"java.lang.String"}

生成的OrderShippedEvent的模式不正确：

Key (4 bytes): +
  Value (26 bytes): V?
JamesHill Street
  Timestamp: 1555943926163
  Partition: 0
  Offset: 35
  Headers: contentType="application/vnd.ordercreatedevent.v1+avro",spring_json_header_types={"contentType":"java.lang.String"}

我已经检查了Confluent Schema注册表内容，其中有Order-Shipped-Event.avsc模式：

为什么它不在生成的消息中使用正确的shema？

application.yml

server:
  port: 8085

spring:
  application:
    name: spring-boot-shipping-service
  cloud:
    stream:
      kafka:
        streams:
          binder:
            configuration:
              default:
                key:
                  serde: org.apache.kafka.common.serialization.Serdes$IntegerSerde
      bindings:
        input:
          destination: customer
          contentType: application/*+avro
        order:
          destination: order
          contentType: application/*+avro
        output:
          destination: order
          contentType: application/*+avro
      schema-registry-client:
        endpoint: http://localhost:8081

ShippingkStreamProcessor

interface ShippingKStreamProcessor {

    @Input("input")
    fun input(): KStream<Int, Customer>

    @Input("order")
    fun order(): KStream<String, OrderCreatedEvent>

    @Output("output")
    fun output(): KStream<String, OrderShippedEvent>

ShippingkStreamConfiguration

    @StreamListener
    @SendTo("output")
    fun process(@Input("input") input: KStream<Int, Customer>, @Input("order") orderEvent: KStream<Int, OrderCreatedEvent>): KStream<Int, OrderShippedEvent> {

        val serdeConfig = mapOf(
                AbstractKafkaAvroSerDeConfig.SCHEMA_REGISTRY_URL_CONFIG to "http://localhost:8081")

        val intSerde = Serdes.IntegerSerde()
        val customerSerde = SpecificAvroSerde<Customer>()
        customerSerde.configure(serdeConfig, true)
        val orderCreatedSerde = SpecificAvroSerde<OrderCreatedEvent>()
        orderCreatedSerde.configure(serdeConfig, true)
        val orderShippedSerde = SpecificAvroSerde<OrderShippedEvent>()
        orderShippedSerde.configure(serdeConfig, true)


        val stateStore: Materialized<Int, Customer, KeyValueStore<Bytes, ByteArray>> =
                Materialized.`as`<Int, Customer, KeyValueStore<Bytes, ByteArray>>("customer-store")
                        .withKeySerde(intSerde)
                        .withValueSerde(customerSerde)

        val customerTable: KTable<Int, Customer> = input.groupByKey(Serialized.with(intSerde, customerSerde))
                .reduce({ _, y -> y }, stateStore)

        return (orderEvent.filter { _, value -> value is OrderCreatedEvent && value.id != 0 }
                .selectKey { _, value -> value.customerId } as KStream<Int, OrderCreatedEvent>)
                .join(customerTable, { orderIt, customer ->
                    OrderShippedEvent(orderIt.id, orderIt.productId, customer.name, customer.address)
                }, Joined.with(intSerde, orderCreatedSerde, customerSerde))
                .selectKey { _, value -> value.id }
    }

2019-04-22 23:40:39.953 DEBUG 46039 --- [-StreamThread-1] o.s.web.client.RestTemplate              : HTTP GET http://localhost:8081/subjects/ordercreatedevent/versions/1
2019-04-22 23:40:39.971 DEBUG 46039 --- [-StreamThread-1] o.s.web.client.RestTemplate              : Accept=[application/json, application/*+json]
2019-04-22 23:40:39.972 DEBUG 46039 --- [-StreamThread-1] o.s.web.client.RestTemplate              : Writing [] as "application/vnd.schemaregistry.v1+json"
2019-04-22 23:40:39.984 DEBUG 46039 --- [-StreamThread-1] o.s.web.client.RestTemplate              : Response 200 OK
2019-04-22 23:40:39.985 DEBUG 46039 --- [-StreamThread-1] o.s.web.client.RestTemplate              : Reading to [java.util.Map<?, ?>]
2019-04-22 23:40:40.186  INFO 46039 --- [read-1-producer] org.apache.kafka.clients.Metadata        : Cluster ID: 5Sw6sBD0TFOaximF3Or-dQ
2019-04-22 23:40:40.318 DEBUG 46039 --- [-StreamThread-1] AvroSchemaRegistryClientMessageConverter : Obtaining schema for class class com.codependent.statetransfer.order.OrderShippedEvent
2019-04-22 23:40:40.318 DEBUG 46039 --- [-StreamThread-1] AvroSchemaRegistryClientMessageConverter : Avro type detected, using schema from object
2019-04-22 23:40:40.342 DEBUG 46039 --- [-StreamThread-1] o.s.web.client.RestTemplate              : HTTP POST http://localhost:8081/subjects/ordershippedevent/versions
2019-04-22 23:40:40.342 DEBUG 46039 --- [-StreamThread-1] o.s.web.client.RestTemplate              : Accept=[application/json, application/*+json]
2019-04-22 23:40:40.342 DEBUG 46039 --- [-StreamThread-1] o.s.web.client.RestTemplate              : Writing [{"schema":"{\"type\":\"record\",\"name\":\"OrderShippedEvent\",\"namespace\":\"com.codependent.statetransfer.order\",\"fields\":[{\"name\":\"id\",\"type\":\"int\"},{\"name\":\"productId\",\"type\":\"int\"},{\"name\":\"customerName\",\"type\":{\"type\":\"string\",\"avro.java.string\":\"String\"}},{\"name\":\"customerAddress\",\"type\":{\"type\":\"string\",\"avro.java.string\":\"String\"}}]}"}] as "application/json"
2019-04-22 23:40:40.348 DEBUG 46039 --- [-StreamThread-1] o.s.web.client.RestTemplate              : Response 200 OK
2019-04-22 23:40:40.348 DEBUG 46039 --- [-StreamThread-1] o.s.web.client.RestTemplate              : Reading to [java.util.Map<?, ?>]
2019-04-22 23:40:40.349 DEBUG 46039 --- [-StreamThread-1] o.s.web.client.RestTemplate              : HTTP POST http://localhost:8081/subjects/ordershippedevent
2019-04-22 23:40:40.349 DEBUG 46039 --- [-StreamThread-1] o.s.web.client.RestTemplate              : Accept=[application/json, application/*+json]
2019-04-22 23:40:40.349 DEBUG 46039 --- [-StreamThread-1] o.s.web.client.RestTemplate              : Writing [{"schema":"{\"type\":\"record\",\"name\":\"OrderShippedEvent\",\"namespace\":\"com.codependent.statetransfer.order\",\"fields\":[{\"name\":\"id\",\"type\":\"int\"},{\"name\":\"productId\",\"type\":\"int\"},{\"name\":\"customerName\",\"type\":{\"type\":\"string\",\"avro.java.string\":\"String\"}},{\"name\":\"customerAddress\",\"type\":{\"type\":\"string\",\"avro.java.string\":\"String\"}}]}"}] as "application/json"
2019-04-22 23:40:40.361 DEBUG 46039 --- [-StreamThread-1] o.s.web.client.RestTemplate              : Response 200 OK
2019-04-22 23:40:40.362 DEBUG 46039 --- [-StreamThread-1] o.s.web.client.RestTemplate              : Reading to [java.util.Map<?, ?>]
2019-04-22 23:40:40.362 DEBUG 46039 --- [-StreamThread-1] AvroSchemaRegistryClientMessageConverter : Finding correct DatumWriter for type com.codependent.statetransfer.order.OrderShippedEvent

return
                    messageConverter.toMessage(message.getPayload(),
                            messageHeaders).getPayload();

collector.send(topic, key, value, context.headers(), timestamp, keySerializer, valSerializer, partitioner);

更新3：

复制的步骤：

>

下载并开始合流5.2.1合流开始

您可以检查添加到order主题的新创建的OrderShippedEvent消息是否有错误的标题。这可以在合流控制中心(localhost:9092->topics->order)或运行kafkacat中看到：

$> kafkacat -b localhost:9092 -t order -C \
  -f '\nKey (%K bytes): %k   
  Value (%S bytes): %s
  Timestamp: %T
  Partition: %p
  Offset: %o
  Headers: %h\n'

Answer 1

@相互依赖这确实是一个我们需要在活页夹中解决的问题，我们很快就会解决。同时，作为一种变通方法，您可以让处理器不返回kstream，而是在方法本身中执行发送。您可以对当前返回的KStream调用to(TopicNameExtractor)。TopicNameExtractor将使您能够访问记录上下文，您可以使用该上下文手动设置内容类型。