大数据-148 Apache Kudu 从 Flink 下沉数据到 Kudu

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章节内容

上节我们完成了如下的内容：

Kudu Java API
增删改查编写案例测试

在这里插入图片描述

实现思路

将数据从 Flink 下沉到 Kudu 的基本思路如下：

环境准备：确保 Flink 和 Kudu 环境正常运行，并配置好相关依赖。
创建 Kudu 表：在 Kudu 中定义要存储的数据表，包括主键和列类型。
数据流设计：使用 Flink 的 DataStream API 读取输入数据流，进行必要的数据处理和转换。
写入 Kudu：通过 Kudu 的连接器将处理后的数据写入 Kudu 表。需要配置 Kudu 客户端和表的相关信息。
执行作业：启动 Flink 作业，实时将数据流中的数据写入 Kudu，便于后续查询和分析。

添加依赖

<?xml version="1.0" encoding="UTF-8"?>
<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>4.0.0</modelVersion>

    <groupId>org.example</groupId>
    <artifactId>flink-test</artifactId>
    <version>1.0-SNAPSHOT</version>

    <properties>
        <maven.compiler.source>11</maven.compiler.source>
        <maven.compiler.target>11</maven.compiler.target>
        <flink.version>1.11.1</flink.version>
        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
    </properties>

    <dependencies>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-java</artifactId>
            <version>${flink.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-streaming-java_2.12</artifactId>
            <version>${flink.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-clients_2.12</artifactId>
            <version>${flink.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.kudu</groupId>
            <artifactId>kudu-client</artifactId>
            <version>1.17.0</version>
        </dependency>

    </dependencies>
</project>

数据源

new UserInfo("001", "Jack", 18),
new UserInfo("002", "Rose", 20),
new UserInfo("003", "Cris", 22),
new UserInfo("004", "Lily", 19),
new UserInfo("005", "Lucy", 21),
new UserInfo("006", "Json", 24),

自定义下沉器

package icu.wzk.kudu;

import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.functions.sink.RichSinkFunction;
import org.apache.kudu.Schema;
import org.apache.kudu.Type;
import org.apache.kudu.client.*;
import org.apache.log4j.Logger;

import java.io.ByteArrayOutputStream;
import java.io.ObjectOutputStream;
import java.util.Map;


public class MyFlinkSinkToKudu extends RichSinkFunction<Map<String, Object>> {

    private final static Logger logger = Logger.getLogger("MyFlinkSinkToKudu");

    private KuduClient kuduClient;
    private KuduTable kuduTable;

    private String kuduMasterAddr;
    private String tableName;
    private Schema schema;
    private KuduSession kuduSession;
    private ByteArrayOutputStream out;
    private ObjectOutputStream os;

    public MyFlinkSinkToKudu(String kuduMasterAddr, String tableName) {
        this.kuduMasterAddr = kuduMasterAddr;
        this.tableName = tableName;
    }

    @Override
    public void open(Configuration parameters) throws Exception {
        out = new ByteArrayOutputStream();
        os = new ObjectOutputStream(out);
        kuduClient = new KuduClient.KuduClientBuilder(kuduMasterAddr).build();
        kuduTable = kuduClient.openTable(tableName);
        schema = kuduTable.getSchema();
        kuduSession = kuduClient.newSession();
        kuduSession.setFlushMode(KuduSession.FlushMode.AUTO_FLUSH_BACKGROUND);
    }

    @Override
    public void invoke(Map<String, Object> map, Context context) throws Exception {
        if (null == map) {
            return;
        }
        try {
            int columnCount = schema.getColumnCount();
            Insert insert = kuduTable.newInsert();
            PartialRow row = insert.getRow();
            for (int i = 0; i < columnCount; i ++) {
                Object value = map.get(schema.getColumnByIndex(i).getName());
                insertData(row, schema.getColumnByIndex(i).getType(), schema.getColumnByIndex(i).getName(), value);
                OperationResponse response = kuduSession.apply(insert);
                if (null != response) {
                    logger.error(response.getRowError().toString());
                }
            }
        } catch (Exception e) {
            logger.error(e);
        }
    }

    @Override
    public void close() throws Exception {
        try {
            kuduSession.close();
            kuduClient.close();
            os.close();
            out.close();
        } catch (Exception e) {
            logger.error(e);
        }
    }

    private void insertData(PartialRow row, Type type, String columnName, Object value) {
        try {
            switch (type) {
                case STRING:
                    row.addString(columnName, value.toString());
                    return;
                case INT32:
                    row.addInt(columnName, Integer.valueOf(value.toString()));
                    return;
                case INT64:
                    row.addLong(columnName, Long.valueOf(value.toString()));
                    return;
                case DOUBLE:
                    row.addDouble(columnName, Double.valueOf(value.toString()));
                    return;
                case BOOL:
                    row.addBoolean(columnName, Boolean.valueOf(value.toString()));
                    return;
                case BINARY:
                    os.writeObject(value);
                    row.addBinary(columnName, out.toByteArray());
                    return;
                case FLOAT:
                    row.addFloat(columnName, Float.valueOf(value.toString()));
                default:
                    throw new UnsupportedOperationException("Unknown Type: " + type);
            }

        } catch (Exception e) {
            logger.error("插入数据异常: " + e);
        }
    }
}

编写实体

package icu.wzk.kudu;

public class UserInfo {

    private String id;

    private String name;

    private Integer age;

    public UserInfo(String id, String name, Integer age) {
        this.id = id;
        this.name = name;
        this.age = age;
    }

    public String getId() {
        return id;
    }

    public void setId(String id) {
        this.id = id;
    }

    public String getName() {
        return name;
    }

    public void setName(String name) {
        this.name = name;
    }

    public Integer getAge() {
        return age;
    }

    public void setAge(Integer age) {
        this.age = age;
    }
}

执行建表

package icu.wzk.kudu;

import org.apache.kudu.ColumnSchema;
import org.apache.kudu.Schema;
import org.apache.kudu.Type;
import org.apache.kudu.client.CreateTableOptions;
import org.apache.kudu.client.KuduClient;
import org.apache.kudu.client.KuduException;

import java.util.ArrayList;
import java.util.List;

public class KuduCreateTable {

    public static void main(String[] args) throws KuduException {
        String masterAddress = "localhost:7051,localhost:7151,localhost:7251";
        KuduClient.KuduClientBuilder kuduClientBuilder = new KuduClient.KuduClientBuilder(masterAddress);
        KuduClient kuduClient = kuduClientBuilder.build();

        String tableName = "user";
        List<ColumnSchema> columnSchemas = new ArrayList<>();
        ColumnSchema id = new ColumnSchema
                .ColumnSchemaBuilder("id", Type.INT32)
                .key(true)
                .build();
        columnSchemas.add(id);
        ColumnSchema name = new ColumnSchema
                .ColumnSchemaBuilder("name", Type.STRING)
                .key(false)
                .build();
        columnSchemas.add(name);
        ColumnSchema age = new ColumnSchema
                .ColumnSchemaBuilder("age", Type.INT32)
                .key(false)
                .build();
        columnSchemas.add(age);

        Schema schema = new Schema(columnSchemas);
        CreateTableOptions options = new CreateTableOptions();
        // 副本数量为1
        options.setNumReplicas(1);
        List<String> colrule = new ArrayList<>();
        colrule.add("id");
        options.addHashPartitions(colrule, 3);

        kuduClient.createTable(tableName, schema, options);
        kuduClient.close();
    }

}

主逻辑代码

package icu.wzk.kudu;

import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;

import java.util.HashMap;
import java.util.Map;
import java.util.stream.Stream;

public class SinkToKuduTest {

    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        DataStreamSource<UserInfo> dataSource = env.fromElements(
                new UserInfo("001", "Jack", 18),
                new UserInfo("002", "Rose", 20),
                new UserInfo("003", "Cris", 22),
                new UserInfo("004", "Lily", 19),
                new UserInfo("005", "Lucy", 21),
                new UserInfo("006", "Json", 24)
        );
        SingleOutputStreamOperator<Map<String, Object>> mapSource = dataSource
                .map(new MapFunction<UserInfo, Map<String, Object>>() {
                    @Override
                    public Map<String, Object> map(UserInfo value) throws Exception {
                        Map<String, Object> map = new HashMap<>();
                        map.put("id", value.getId());
                        map.put("name", value.getName());
                        map.put("age", value.getAge());
                        return map;
                    }
                });

        String kuduMasterAddr = "localhost:7051,localhost:7151,localhost:7251";
        String tableInfo = "user";
        mapSource.addSink(new MyFlinkSinkToKudu(kuduMasterAddr, tableInfo));

        env.execute("SinkToKuduTest");
    }

}

解释分析

环境设置

StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();：初始化 Flink 的执行环境，这是 Flink 应用的入口。

数据源创建

DataStreamSource dataSource = env.fromElements(…)：创建了一个包含多个 UserInfo 对象的数据源，模拟了一个输入流。

数据转换

SingleOutputStreamOperator<Map<String, Object>> mapSource = dataSource.map(…)：使用 map 函数将 UserInfo 对象转换为 Map<String, Object>，便于后续处理和写入 Kudu。每个 UserInfo 的属性都被放入一个 HashMap 中。