Kafka Schema-Registry-阿里云开发者社区

一、为什么需要Schema-Registry

1.1、注册表

无论是使用传统的Avro API自定义序列化类和反序列化类还是使用Twitter的Bijection类库实现Avro的序列化与反序列化，这两种方法都有一个缺点：在每条Kafka记录里都嵌入了schema，这会让记录的大小成倍地增加。但是不管怎样，在读取记录时仍然需要用到整个 schema，所以要先找到 schema。有没有什么方法可以让数据共用一个schema？

我们遵循通用的结构模式并使用"schema注册表"来达到目的。"schema注册表"的原理如下：

1.把所有写入数据需要用到的 schema 保存在注册表里，然后在记录里引用 schema ID。

2.负责读取数据的应用程序使用 ID 从注册表里拉取 schema 来反序列化记录。

3.序列化器和反序列化器分别负责处理 schema 的注册和拉取。

schema注册表并不属于Kafka，现在已经有一些开源的schema 注册表实现。比如本文要讨论的Confluent Schema Registry。

1.2、为什么使用 Avro

Avro 序列化相比常见的序列化（比如 json）会更快，序列化的数据会更小。相比 protobuf ，它可以支持实时编译，不需要像 protobuf 那样先定义好数据格式文件，编译之后才能使用。

1.3、Confluent Schema-Registry

Confluent公司为了能让 Kafka 支持 Avro 序列化，创建了 Kafka Schema Registry 项目，项目地址为 https://github.com/confluentinc/schema-registry 。对于存储大量数据的 kafka 来说，使用 Avro 序列化，可以减少数据的存储空间提高了存储量，减少了序列化时间提高了性能。 Kafka 有多个topic，里面存储了不同种类的数据，每种数据都对应着一个 Avro schema 来描述这种格式。Registry 服务支持方便的管理这些 topic 的schema，它还对外提供了多个 `restful 接口，用于存储和查找。

二、Confluent Schema-Registry 安装与使用

2.1、安装

1.Schema Registry的各个发现行版本的下载链接

2.上传到linux系统进行解压安装。

3.本教程使用外部以安装好的Kafka集群不使用内部默认的。

4.修改confluent-5.3.1/etc/schema-registry/schema-registry.properties配置文件

# 注册服务器的监听地址及其端口号
listeners=http://0.0.0.0:8081
# 有关连接外部集群的地址有两种方式：1 通过zk连接 2 通过kafka的控制器 。 本教程采用zk连接
kafkastore.connection.url=henghe-042:2181
# The name of the topic to store schemas in
kafkastore.topic=_schemas
# If true, API requests that fail will include extra debugging information, including stack traces
debug=false

5.注册服务器的启动…/…/bin/schema-registry-start -daemon …/…/etc/schema-registry/schema-registry.properties

2.2、RestAPI使用

# Register a new version of a schema under the subject "Kafka-key"
$ curl -X POST -H "Content-Type: application/vnd.schemaregistry.v1+json" \
    --data '{"schema": "{\"type\": \"string\"}"}' \
    http://localhost:8081/subjects/Kafka-key/versions
  {"id":1}
# Register a new version of a schema under the subject "Kafka-value"
$ curl -X POST -H "Content-Type: application/vnd.schemaregistry.v1+json" \
    --data '{"schema": "{\"type\": \"string\"}"}' \
     http://localhost:8081/subjects/Kafka-value/versions
  {"id":1}
# List all subjects
$ curl -X GET http://localhost:8081/subjects
  ["Kafka-value","Kafka-key"]
# List all schema versions registered under the subject "Kafka-value"
$ curl -X GET http://localhost:8081/subjects/Kafka-value/versions
  [1]
# Fetch a schema by globally unique id 1
$ curl -X GET http://localhost:8081/schemas/ids/1
  {"schema":"\"string\""}
# Fetch version 1 of the schema registered under subject "Kafka-value"
$ curl -X GET http://localhost:8081/subjects/Kafka-value/versions/1
  {"subject":"Kafka-value","version":1,"id":1,"schema":"\"string\""}
# Fetch the most recently registered schema under subject "Kafka-value"
$ curl -X GET http://localhost:8081/subjects/Kafka-value/versions/latest
  {"subject":"Kafka-value","version":1,"id":1,"schema":"\"string\""}
# Delete version 3 of the schema registered under subject "Kafka-value"
$ curl -X DELETE http://localhost:8081/subjects/Kafka-value/versions/3
  3
# Delete all versions of the schema registered under subject "Kafka-value"
$ curl -X DELETE http://localhost:8081/subjects/Kafka-value
  [1, 2, 3, 4, 5]
# Check whether a schema has been registered under subject "Kafka-key"
$ curl -X POST -H "Content-Type: application/vnd.schemaregistry.v1+json" \
    --data '{"schema": "{\"type\": \"string\"}"}' \
    http://localhost:8081/subjects/Kafka-key
  {"subject":"Kafka-key","version":1,"id":1,"schema":"\"string\""}
# Test compatibility of a schema with the latest schema under subject "Kafka-value"
$ curl -X POST -H "Content-Type: application/vnd.schemaregistry.v1+json" \
    --data '{"schema": "{\"type\": \"string\"}"}' \
    http://localhost:8081/compatibility/subjects/Kafka-value/versions/latest
  {"is_compatible":true}
# Get top level config
$ curl -X GET http://localhost:8081/config
  {"compatibilityLevel":"BACKWARD"}
# Update compatibility requirements globally
$ curl -X PUT -H "Content-Type: application/vnd.schemaregistry.v1+json" \
    --data '{"compatibility": "NONE"}' \
    http://localhost:8081/config
  {"compatibility":"NONE"}
# Update compatibility requirements under the subject "Kafka-value"
$ curl -X PUT -H "Content-Type: application/vnd.schemaregistry.v1+json" \
    --data '{"compatibility": "BACKWARD"}' \
    http://localhost:8081/config/Kafka-value
  {"compatibility":"BACKWARD"}

2.2、Java 代码

2.2.0、注册

curl -X POST -H "Content-Type: application/vnd.schemaregistry.v1+json" \
--data '{"schema": "{\"type\": \"record\", \"name\": \"User\", \"fields\": [{\"name\": \"id\", \"type\": \"int\"}, {\"name\": \"name\",  \"type\": \"string\"}, {\"name\": \"age\", \"type\": \"int\"}]}"}' \
http://localhost:8081/subjects/chb_test/versions

2.2.1、添加依赖

我们需要 confluent-common 目录下的common-config-4.1.1.jar、common-utils-4.1.1.jar和全部以jackson开头的 jar 包以及 kafka-serde-tools 目录下的kafka-schema-registry-client-4.1.1.jar和kafka-avro-serializer-4.1.1.jar

将本地jar包导入到本地仓库

mvn install:install-file -Dfile=G:\迅雷下载\kafka-avro-serializer-6.2.0.jar -DgroupId=io.confluent -DartifactId=kafka-avro-serializer -Dversion=6.2.0  -Dpackaging=jar

添加到pom.xml

<dependency>
      <groupId>org.apache.kafka</groupId>
      <artifactId>kafka-clients</artifactId>
      <version>2.3.0</version>
    </dependency>
    <!--此依赖是通过本地依赖库导入的，有关如何把jar放入本地依赖库自行搜索-->
    <!--本人的jar文件是在编译源码时自动到依赖库中的所以直接引用-->
    <dependency>
      <groupId>io.confluent</groupId>
      <artifactId>kafka-avro-serializer</artifactId>
      <version>5.3.2</version>
    </dependency>
    <dependency>
      <groupId>io.confluent</groupId>
      <artifactId>kafka-schema-registry-client</artifactId>
      <version>5.3.2</version>
    </dependency>
    <dependency>
      <groupId>io.confluent</groupId>
      <artifactId>common-config</artifactId>
      <version>5.3.2</version>
    </dependency>
    <dependency>
      <groupId>io.confluent</groupId>
      <artifactId>common-utils</artifactId>
      <version>5.3.2</version>
    </dependency>
    <dependency>
      <groupId>org.apache.avro</groupId>
      <artifactId>avro</artifactId>
      <version>1.8.2</version>
    </dependency>
    <!-- jaskson start -->
    <dependency>
      <groupId>com.fasterxml.jackson.core</groupId>
      <artifactId>jackson-core</artifactId>
      <version>2.9.10</version>
    </dependency>
    <dependency>
      <groupId>com.fasterxml.jackson.core</groupId>
      <artifactId>jackson-databind</artifactId>
      <version>2.9.10</version>
    </dependency>
    <!-- jaskson end -->

2.2.2、Kafka 客户端使用原理

Kafka Schema Registry 提供了 KafkaAvroSerializer 和 KafkaAvroDeserializer 两个类。Kafka 如果要使用 Avro 序列化，在实例化 KafkaProducer 和 KafkaConsumer 时，指定序列化或反序列化的配置。

客户端发送数据的流程图如下所示：

2.2.2.1、KafkaProducer

package com.chb.common.kafka.schema;
import java.util.Properties;
import java.util.Random;
import org.apache.avro.Schema;
import org.apache.avro.generic.GenericData;
import org.apache.avro.generic.GenericRecord;
import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.Producer;
import org.apache.kafka.clients.producer.ProducerRecord;
public class ConfluentProducer {
    public static final String USER_SCHEMA = "{\"type\": \"record\", \"name\": \"User\", " +
            "\"fields\": [{\"name\": \"id\", \"type\": \"int\"}, " +
            "{\"name\": \"name\",  \"type\": \"string\"}, {\"name\": \"age\", \"type\": \"int\"}]}";
    public static void main(String[] args) throws InterruptedException {
        Properties props = new Properties();
        props.put("bootstrap.servers", "localhost:6667");
        props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
        // 使用Confluent实现的KafkaAvroSerializer
        props.put("value.serializer", "io.confluent.kafka.serializers.KafkaAvroSerializer");
        // 添加schema服务的地址，用于获取schema
        props.put("schema.registry.url", "http://localhost:8081");
        Producer<String, GenericRecord> producer = new KafkaProducer<>(props);
        Schema.Parser parser = new Schema.Parser();
        Schema schema = parser.parse(USER_SCHEMA);
        Random rand = new Random();
        int id = 0;
        while (id < 100) {
            id++;
            String name = "name" + id;
            int age = rand.nextInt(40) + 1;
            GenericRecord user = new GenericData.Record(schema);
            user.put("id", id);
            user.put("name", name);
            user.put("age", age);
            ProducerRecord<String, GenericRecord> record = new ProducerRecord<>("test-topic", user);
            System.out.println(user);
            producer.send(record);
            Thread.sleep(1000);
        }
        producer.close();
    }
}

2.2.2.2、KafkaConsumer

package com.chb.common.kafka.schema;
import org.apache.avro.generic.GenericRecord;
import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.kafka.clients.consumer.ConsumerRecords;
import org.apache.kafka.clients.consumer.KafkaConsumer;
import java.time.Duration;
import java.util.Collections;
import java.util.Properties;
public class ConfluentConsumer {
    public static void main(String[] args) throws Exception {
        Properties props = new Properties();
        props.put("bootstrap.servers", "localhost:6667");
        props.put("group.id", "test1");
        props.put("enable.auto.commit", "false");
        // 配置禁止自动提交，每次从头消费供测试使用
        props.put("auto.offset.reset", "earliest");
        props.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
        // 使用Confluent实现的KafkaAvroDeserializer
        props.put("value.deserializer", "io.confluent.kafka.serializers.KafkaAvroDeserializer");
        // 添加schema服务的地址，用于获取schema
        props.put("schema.registry.url", "http://localhost:8081");
        KafkaConsumer<String, GenericRecord> consumer = new KafkaConsumer<>(props);
        consumer.subscribe(Collections.singletonList("test-topic"));
        try {
            while (true) {
                ConsumerRecords<String, GenericRecord> records = consumer.poll(Duration.ofMillis(1000));
                for (ConsumerRecord<String, GenericRecord> record : records) {
                    GenericRecord user = record.value();
                    System.out.println("value = [user.id = " + user.get("id") + ", " + "user.name = "
                            + user.get("name") + ", " + "user.age = " + user.get("age") + "], "
                            + "partition = " + record.partition() + ", " + "offset = " + record.offset());
                }
            }
        } finally {
            consumer.close();
        }
    }
}

2.2.2.3、Consumer的消费结果

value = [user.id = 98, user.name = name98, user.age = 38], partition = 0, offset = 97
value = [user.id = 99, user.name = name99, user.age = 30], partition = 0, offset = 98
value = [user.id = 100, user.name = name100, user.age = 39], partition = 0, offset = 99

Kafka Schema-Registry