flink重温笔记（十四）： flink 高级特性和新特性（3）—

Flink学习笔记

前言：今天是学习 flink 的第 14 天啦！学习了 flink 高级特性和新特性之数据类型及 avro 序列化，主要是解决大数据领域数据规范化写入和规范化读取的问题，avro 数据结构可以节约存储空间，本文中结合企业真实应用场景，即 kafka 的读取和写入采用自定义序列化，结合自己实验猜想和代码实践，总结了很多自己的理解和想法，希望和大家多多交流！

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文章目录

Flink学习笔记
- 四、Flink 高级特性和新特性
- - 4. 数据类型及序列化
  - - 4.1 数据类型
    - 4.2 POJO 类型细节
    - 4.3 Avro优点介绍
    - 4.4 定义Avro Json格式
    - 4.5 使用 Java 自定义序列化到 Kfaka
    - - 4.5.1 准备数据
      - 4.5.2 自定义Avro 序列化和反序列化
      - 4.5.3 创建生产者工具类
      - 4.5.4 创建消费者工具类
      - 4.5.5 运行程序
    - 4.6 使用 Flink 自定义序列化到 Kafka
    - - 4.6.1 准备数据
      - 4.6.2 自定义Avro 序列化和反序列化
      - 4.6.3 创建 Flink-source 类
      - 4.6.4 创建 Flink-sink 类
      - 4.6.5 运行程序

四、Flink 高级特性和新特性

4. 数据类型及序列化

4.1 数据类型

flink 支持的数据类型：七种

4.2 POJO 类型细节

注意事项：

该类需要有 public 修饰
该类需要有 public 修饰的无参构造函数
该类的所有（no-static）、（no-transient）字段必须是 public，如果不是 public 则必须是有标准的 getter 和 setter
该类的所有字段都必须是 flink 支持的数据类型

4.3 Avro优点介绍

Avro 是数据序列化系统，支持大批量数据交换的应用。
支持二进制序列化方式，性能好 / 效率高，使用 JSON 描述。
动态语言友好，RPC 远程调用，支持同步和异步通信。

4.4 定义Avro Json格式

namespace：要生成的目录
type：类型 avro 需要指定 record
name：会自动生成的对象
fields：要指定的字段

注意: 创建的文件后缀名一定要叫 avsc，而不是 avro 后缀，使用 idea 生成 Order 对象

{
    "namespace": "cn.itcast.beans",
    "type": "record",
    "name": "OrderModel",
    "fields": [
        {"name": "userId", "type": "string"},
        {"name": "timestamp",  "type": "long"},
        {"name": "money",  "type": "double"},
        {"name": "category", "type": "string"}
    ]
}

注意：由于在导入 pom 依赖的时候，需要注意插件冲突，注释掉以下依赖，不然会一直爆错！

<!--        这个会和 avro 冲突，所以先注释一下-->
<!--        <dependency>-->
<!--            <groupId>org.apache.hive</groupId>-->
<!--            <artifactId>hive-exec</artifactId>-->
<!--            <version>2.1.0</version>-->
<!--        </dependency>-->

快看一！导入需要的依赖到 pom 文件中：

<dependency>
    <groupId>org.apache.avro</groupId>
    <artifactId>avro</artifactId>
    <version>1.8.2</version>
</dependency>
<!-- https://mvnrepository.com/artifact/org.apache.flink/flink-avro -->
<dependency>
    <groupId>org.apache.flink</groupId>
    <artifactId>flink-avro</artifactId>
    <version>${flink.version}</version>
</dependency>

<!-- https://mvnrepository.com/artifact/org.apache.kafka/kafka-clients -->
<dependency>
    <groupId>org.apache.kafka</groupId>
    <artifactId>kafka-clients</artifactId>
    <version>2.5.1</version>
</dependency>
<dependency>
    <groupId>org.apache.kafka</groupId>
    <artifactId>kafka-streams</artifactId>
    <version>2.5.1</version>
</dependency>

快看二！导入需要的插件到 pom 文件中：

<!-- avro编译插件 -->
<plugin>
    <groupId>org.apache.avro</groupId>
    <artifactId>avro-maven-plugin</artifactId>
    <version>1.8.2</version>
    <executions>
        <execution>
            <phase>generate-sources</phase>
            <goals>
                <goal>schema</goal>
            </goals>
            <configuration>
                <sourceDirectory>${project.basedir}/src/main/avro/</sourceDirectory>
                <outputDirectory>${project.basedir}/src/main/java/</outputDirectory>
            </configuration>
        </execution>
    </executions>
</plugin>

4.5 使用 Java 自定义序列化到 Kfaka

4.5.1 准备数据

order.csv

user_001,1621718199,10.1,电脑
user_001,1621718201,14.1,手机
user_002,1621718202,82.5,手机
user_001,1621718205,15.6,电脑
user_004,1621718207,10.2,家电
user_001,1621718208,15.8,电脑
user_005,1621718212,56.1,电脑
user_002,1621718260,40.3,家电
user_001,1621718580,11.5,家居
user_001,1621718860,61.6,家居

4.5.2 自定义Avro 序列化和反序列化

首先需要实现2个接口分别为 Serializer 和 Deserializer 分别是序列化和反序列化

package cn.itcast.day14.serialization_java;

/**
 * @author lql
 * @time 2024-03-10 16:29:49
 * @description TODO
 */

import cn.itcast.day14.beans.OrderModel;
import org.apache.avro.io.BinaryDecoder;
import org.apache.avro.io.BinaryEncoder;
import org.apache.avro.io.DecoderFactory;
import org.apache.avro.io.EncoderFactory;
import org.apache.avro.specific.SpecificDatumReader;
import org.apache.avro.specific.SpecificDatumWriter;

import java.io.ByteArrayInputStream;
import java.io.ByteArrayOutputStream;
import java.io.IOException;
import java.util.Map;
import org.apache.kafka.common.serialization.*;

/**
 *  自定义序列化和反序列化
 */
public class SimpleAvroSchemaJava implements Serializer<OrderModel>, Deserializer<OrderModel> {
    @Override
    public void configure(Map<String, ?> configs, boolean isKey) {

    }

    @Override
    public void close() {

    }

    @Override
    public byte[] serialize(String s, OrderModel order) {
        // 创建序列化执行器
        SpecificDatumWriter<OrderModel> writer = new SpecificDatumWriter<OrderModel>(order.getSchema());
        // 创建一个流 用存储序列化后的二进制文件
        ByteArrayOutputStream out = new ByteArrayOutputStream();
        // 创建二进制编码器
        BinaryEncoder encoder = EncoderFactory.get().directBinaryEncoder(out, null);
        try {
            // 数据入都流中
            writer.write(order, encoder);
        } catch (IOException e) {
            e.printStackTrace();
        }

        return out.toByteArray();
    }

    @Override
    public OrderModel deserialize(String s, byte[] bytes) {
        // 用来保存结果数据
        OrderModel order = new OrderModel();
        // 创建输入流用来读取二进制文件
        ByteArrayInputStream arrayInputStream = new ByteArrayInputStream(bytes);
        // 创建输入序列化执行器
        SpecificDatumReader<OrderModel> stockSpecificDatumReader = new SpecificDatumReader<OrderModel>(order.getSchema());
        //创建二进制解码器
        BinaryDecoder binaryDecoder = DecoderFactory.get().directBinaryDecoder(arrayInputStream, null);
        try {
            // 数据读取
            order= stockSpecificDatumReader.read(null, binaryDecoder);
        } catch (IOException e) {
            e.printStackTrace();
        }
        // 结果返回
        return order;
    }
}

4.5.3 创建生产者工具类

package cn.itcast.day14.serialization_java;

import cn.itcast.day14.beans.OrderModel;
import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.ProducerRecord;

import java.io.BufferedReader;
import java.io.File;
import java.io.FileReader;
import java.util.ArrayList;
import java.util.List;
import java.util.Properties;
/**
 * @author lql
 * @time 2024-03-10 16:33:31
 * @description TODO
 */
public class OrderProducerJava {
    public static void main(String[] args) {
        // 获取数据
        List<OrderModel> data = getData();
        System.out.println(data);
        try {
            // 创建配置文件
            Properties props = new Properties();
            props.setProperty("bootstrap.servers", "node1:9092");
            // 这里的健：还是 string 序列化
            props.setProperty("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
            // 这里的值：需要指向自定义的序列化
            props.setProperty("value.serializer", "cn.itcast.day14.serialization_java.SimpleAvroSchemaJava");
            // 创建kafka的生产者
            KafkaProducer<String, OrderModel> userBehaviorProducer = new KafkaProducer<String, OrderModel>(props);
            // 循环遍历数据
            for (OrderModel orderModel : data) {
                ProducerRecord<String, OrderModel> producerRecord 
                        = new ProducerRecord<String, OrderModel>("order", orderModel);
                userBehaviorProducer.send(producerRecord);
                System.out.println("数据写入成功"+data);
                Thread.sleep(1000);
            }
        } catch (InterruptedException e) {
            e.printStackTrace();
        }
    }

    public static List<OrderModel> getData() {
        ArrayList<OrderModel> orderModels = new ArrayList<OrderModel>();
        try {
            BufferedReader br = new BufferedReader(
                    new FileReader(
                            new File("D:\\IDEA_Project\\BigData_Java\\flinkbase_pro\\data\\input\\order.csv")
                    )
            );
            String line = "";
            while ((line = br.readLine()) != null) {
                String[] fields = line.split(",");
                orderModels.add(new OrderModel(fields[0], 
                        Long.parseLong(fields[1]), 
                        Double.parseDouble(fields[2]), 
                        fields[3]));
            }
        } catch (Exception e) {
            e.printStackTrace();
        }
        return orderModels;
    }
}

4.5.4 创建消费者工具类

package cn.itcast.day14.serialization_java;

import cn.itcast.day14.beans.OrderModel;
import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.kafka.clients.consumer.ConsumerRecords;
import org.apache.kafka.clients.consumer.KafkaConsumer;

import java.util.Arrays;
import java.util.Properties;
/**
 * @author lql
 * @time 2024-03-10 16:38:29
 * @description TODO
 */
public class OrderConsumerJava {
    public static void main(String[] args) {
        Properties prop = new Properties();
        prop.put("bootstrap.servers", "node1:9092");
        prop.put("group.id", "order");
        prop.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
        
        // 设置反序列化类为自定义的avro反序列化类
        prop.put("value.deserializer", "cn.itcast.day14.serialization_java.SimpleAvroSchemaJava");
        
        KafkaConsumer<String, OrderModel> consumer = new KafkaConsumer<String, OrderModel>(prop);
        
        consumer.subscribe(Arrays.asList("order"));
        while (true) {
            // poll 方法用于从 kafka 中拉取数据
            ConsumerRecords<String, OrderModel> poll = consumer.poll(1000);
            for (ConsumerRecord<String, OrderModel> stringStockConsumerRecord : poll) {
                System.out.println(stringStockConsumerRecord.value());
            }
        }
    }
}

4.5.5 运行程序

# 首先启动zookeeper

# 启动 kafka，记得后台启动
后台:
	cd /export/servers/kafka_2.11-0.10.0.0
	nohup bin/kafka-server-start.sh config/server.properties 2>&1 &
	停止:
	cd /export/servers/kafka_2.11-0.10.0.0
	bin/kafka-server-stop.sh
	
# 创建topic
bin/kafka-topics.sh --create --zookeeper node1:2181 --replication-factor 1 --partitions 1 --topic order
# 模拟消费者
bin/kafka-console-consumer.sh --zookeeper node1:2181 --from-beginning --topic order

结果：

生产者打印：

[{"userId": "user_001", "timestamp": 1621718199, "money": 10.1, "category": "电脑"}, {"userId": "user_001", "timestamp": 1621718201, "money": 14.1, "category": "手机"}, {"userId": "user_002", "timestamp": 1621718202, "money": 82.5, "category": "手机"}, {"userId": "user_001", "timestamp": 1621718205, "money": 15.6, "category": "电脑"}, {"userId": "user_004", "timestamp": 1621718207, "money": 10.2, "category": "家电"}, {"userId": "user_001", "timestamp": 1621718208, "money": 15.8, "category": "电脑"}, {"userId": "user_005", "timestamp": 1621718212, "money": 56.1, "category": "电脑"}, {"userId": "user_002", "timestamp": 1621718260, "money": 40.3, "category": "家电"}, {"userId": "user_001", "timestamp": 1621718580, "money": 11.5, "category": "家居"}, {"userId": "user_001", "timestamp": 1621718860, "money": 61.6, "category": "家居"}]
数据写入成功

消费者打印：

{"userId": "user_001", "timestamp": 1621718199, "money": 10.1, "category": "电脑"}
{"userId": "user_001", "timestamp": 1621718201, "money": 14.1, "category": "手机"}
{"userId": "user_002", "timestamp": 1621718202, "money": 82.5, "category": "手机"}
{"userId": "user_001", "timestamp": 1621718205, "money": 15.6, "category": "电脑"}
{"userId": "user_004", "timestamp": 1621718207, "money": 10.2, "category": "家电"}
{"userId": "user_001", "timestamp": 1621718208, "money": 15.8, "category": "电脑"}
{"userId": "user_005", "timestamp": 1621718212, "money": 56.1, "category": "电脑"}
{"userId": "user_002", "timestamp": 1621718260, "money": 40.3, "category": "家电"}
{"userId": "user_001", "timestamp": 1621718580, "money": 11.5, "category": "家居"}
{"userId": "user_001", "timestamp": 1621718860, "money": 61.6, "category": "家居"}

总结：值的序列化需要指定自己定义的序列化。

4.6 使用 Flink 自定义序列化到 Kafka

4.6.1 准备数据

order.csv

user_001,1621718199,10.1,电脑
user_001,1621718201,14.1,手机
user_002,1621718202,82.5,手机
user_001,1621718205,15.6,电脑
user_004,1621718207,10.2,家电
user_001,1621718208,15.8,电脑
user_005,1621718212,56.1,电脑
user_002,1621718260,40.3,家电
user_001,1621718580,11.5,家居
user_001,1621718860,61.6,家居

4.6.2 自定义Avro 序列化和反序列化

首先需要实现2个接口分别为 SerializationSchema 和 DeserializationSchema 分别是序列化和反序列化

package cn.itcast.day14.serialization_flink;

/**
 * @author lql
 * @time 2024-03-10 17:35:09
 * @description TODO
 */
import cn.itcast.day14.beans.OrderModel;
import org.apache.avro.io.BinaryDecoder;
import org.apache.avro.io.BinaryEncoder;
import org.apache.avro.io.DecoderFactory;
import org.apache.avro.io.EncoderFactory;
import org.apache.avro.specific.SpecificDatumReader;
import org.apache.avro.specific.SpecificDatumWriter;
import org.apache.flink.api.common.serialization.DeserializationSchema;
import org.apache.flink.api.common.serialization.SerializationSchema;
import org.apache.flink.api.common.typeinfo.TypeInformation;

import java.io.ByteArrayInputStream;
import java.io.ByteArrayOutputStream;
import java.io.IOException;

/**
 *  自定义序列化和反序列化
 */
public class SimpleAvroSchemaFlink implements DeserializationSchema<OrderModel>, SerializationSchema<OrderModel> {

    @Override
    public byte[] serialize(OrderModel order) {
        // 创建序列化执行器
        SpecificDatumWriter<OrderModel> writer = new SpecificDatumWriter<OrderModel>(order.getSchema());
        // 创建一个流 用存储序列化后的二进制文件
        ByteArrayOutputStream out = new ByteArrayOutputStream();
        // 创建二进制编码器
        BinaryEncoder encoder = EncoderFactory.get().directBinaryEncoder(out, null);
        try {
            // 数据入都流中
            writer.write(order, encoder);
        } catch (IOException e) {
            e.printStackTrace();
        }

        return out.toByteArray();
    }

    @Override
    public TypeInformation<OrderModel> getProducedType() {
        return TypeInformation.of(OrderModel.class);
    }

    @Override
    public OrderModel deserialize(byte[] bytes) throws IOException {
        // 用来保存结果数据
        OrderModel userBehavior = new OrderModel();
        // 创建输入流用来读取二进制文件
        ByteArrayInputStream arrayInputStream = new ByteArrayInputStream(bytes);
        // 创建输入序列化执行器
        SpecificDatumReader<OrderModel> stockSpecificDatumReader = new SpecificDatumReader<OrderModel>(userBehavior.getSchema());
        //创建二进制解码器
        BinaryDecoder binaryDecoder = DecoderFactory.get().directBinaryDecoder(arrayInputStream, null);
        try {
            // 数据读取
            userBehavior=stockSpecificDatumReader.read(null, binaryDecoder);
        } catch (IOException e) {
            e.printStackTrace();
        }
        // 结果返回
        return userBehavior;
    }

    @Override
    public boolean isEndOfStream(OrderModel userBehavior) {
        return false;
    }
}

4.6.3 创建 Flink-source 类

package cn.itcast.day14.serialization_flink;

/**
 * @author lql
 * @time 2024-03-10 17:37:15
 * @description TODO
 */
import cn.itcast.day14.beans.OrderModel;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaProducer;

import java.util.Properties;

public class OrderProducerFlink {
    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        DataStreamSource<String> value = env.readTextFile("D:\\IDEA_Project\\BigData_Java\\flinkbase_pro\\data\\input\\order.csv");

        DataStream<OrderModel> orderModelDataStream = value.map(row -> {
            String[] fields = row.split(",");
            return new OrderModel(fields[0], Long.parseLong(fields[1]), Double.parseDouble(fields[2]), fields[3]);
        });

        Properties prop = new Properties();
        prop.setProperty("bootstrap.servers", "node1:9092");
        //4.连接Kafka
        FlinkKafkaProducer<OrderModel> producer = new FlinkKafkaProducer<>(
                "order",
                new SimpleAvroSchemaFlink(),
                prop
        );

        //5.将数据打入kafka
        orderModelDataStream.addSink(producer);

        //6.执行任务
        env.execute();
    }
}

4.6.4 创建 Flink-sink 类

package cn.itcast.day14.serialization_flink;

/**
 * @author lql
 * @time 2024-03-10 17:40:04
 * @description TODO
 */
import cn.itcast.day14.beans.OrderModel;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer;

import java.util.Properties;

public class OrderConsumerFlink {
    public static void main(String[] args) throws Exception {
        //1.构建流处理运行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1); // 设置并行度1 方便后面测试
        // 2.设置kafka 配置信息
        Properties prop = new Properties();
        prop.put("bootstrap.servers", "node1:9092");
        prop.put("group.id", "UserBehavior");
        prop.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");

        // 下面有提及：SimpleAvroSchemaFlink，上面就不需要指定了！
        // 3.构建Kafka 连接器
        FlinkKafkaConsumer kafka = new FlinkKafkaConsumer<OrderModel>("order", new SimpleAvroSchemaFlink(), prop);

        //4.设置Flink层最新的数据开始消费
        kafka.setStartFromLatest();
        //5.基于kafka构建数据源
        DataStream<OrderModel> data = env.addSource(kafka);
        //6.结果打印
        data.print();
        env.execute();
    }
}

4.6.5 运行程序

这里运用 Kafka-Tool 2.0.7 可视化工具，工具包放在资源处啦，大家感兴趣可以观看我上传的资源哟！

总结：📚 刚开始学习这个知识点时，我真是感觉有些吃力，觉得它太抽象、太难以理解了。但想到这是企业工作环境中必须掌握的技术，能够为企业节省资源、提高数据存储效率，我就鼓起勇气，决定迎难而上。💪

于是，我根据例子，一个字母一个字母地敲下代码，反复调试、尝试。终于，当程序运行成功的那一刻，我感到了前所未有的成就感！

🎉原来，成功真的就是坚持的结果。只有当你坚持不懈地努力，才能有机会看到胜利的曙光。

🌈明天，我也要继续努力学习，不断挑战自己，迎接更多的成功！🚀 加油！💪