Kafka 的 Confluent Schema Registry安装与使用教程

1 、Confluent Schema Registry 安装教程

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

#
# Copyright 2018 Confluent Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#

# The address the socket server listens on.
#   FORMAT:
#     listeners = listener_name://host_name:port
#   EXAMPLE:
#     listeners = PLAINTEXT://your.host.name:9092
# 注册服务器的监听地址及其端口号
listeners=http://0.0.0.0:8081

# Zookeeper connection string for the Zookeeper cluster used by your Kafka cluster
# (see zookeeper docs for details).
# This is a comma separated host:port pairs, each corresponding to a zk
# server. e.g. "127.0.0.1:3000,127.0.0.1:3001,127.0.0.1:3002".
# 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

# Alternatively, Schema Registry can now operate without Zookeeper, handling all coordination via
# Kafka brokers. Use this setting to specify the bootstrap servers for your Kafka cluster and it
# will be used both for selecting the master schema registry instance and for storing the data for
# registered schemas.
# (Note that you cannot mix the two modes; use this mode only on new deployments or by shutting down
# all instances, switching to the new configuration, and then starting the schema registry
# instances again.)
#kafkastore.bootstrap.servers=PLAINTEXT://localhost:9092

# 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
6. 注册服务器的API接口

# 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、Confluent Schema Registry 使用教程

1. 创建java工程的pom依赖

<project xmlns="http://maven.apache.org/POM/4.0.0"
         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
    <modelVersion>4.0.0modelVersion>

    <groupId>com.yssgroupId>
    <artifactId>KafkaartifactId>
    <version>1.0-SNAPSHOTversion>

    <dependencies>
        <dependency>
            <groupId>org.apache.kafkagroupId>
            <artifactId>kafka-clientsartifactId>
            <version>2.3.0version>
        dependency>
        
        
        <dependency>
            <groupId>io.confluentgroupId>
            <artifactId>kafka-avro-serializerartifactId>
            <version>5.3.2version>
        dependency>
    dependencies>

project>

2. 生产者示例：

package com.registry;


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;

/**
 * @description:
 * @author: wangshuai
 * @create: 2020-01-03 14:17
 **/
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", "192.168.101.42:9092");
        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://192.168.101.42:8081");
        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);

            producer.send(record);
            Thread.sleep(1000);
        }

        producer.close();
    }
}

3. 消费者示例：

package com.registry;

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;

/**
 * @description:
 * @author: wangshuai
 * @create: 2020-01-03 14:34
 **/
public class ConfluentConsumer {


    public static void main(String[] args) throws Exception {

        Properties props = new Properties();
        props.put("bootstrap.servers", "192.168.101.42:9092");
        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://192.168.101.42:8081");
        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();
        }
    }
}

4. 消费者消费结果示例。