flink 读取 kafka 每天汇总后保存myswl

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mob64ca13ffd0f1 2024-11-30 12:08:29

文章标签 flink java apache hadoop 文章分类 架构后端开发

一、pom依赖

测试案例中，pom依赖如下，根据需要自行删减。

<?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>com.test</groupId>
    <artifactId>Examples</artifactId>
    <version>1.0-SNAPSHOT</version>

    <properties>
        <maven.compiler.source>1.8</maven.compiler.source>
        <maven.compiler.target>1.8</maven.compiler.target>
        <encoding>UTF-8</encoding>
        <scala.version>2.11.8</scala.version>
        <scala.binary.version>2.11</scala.binary.version>
        <hadoop.version>2.6.0</hadoop.version>
        <flink.version>1.14.5</flink.version>
        <kafka.version>2.0.0</kafka.version>
        <hbase.version>1.2.0</hbase.version>
        <hudi.version>0.12.0</hudi.version>
    </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_${scala.binary.version}</artifactId>
            <version>${flink.version}</version>
        </dependency>

        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-clients_${scala.binary.version}</artifactId>
            <version>${flink.version}</version>
        </dependency>

        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-connector-kafka_${scala.binary.version}</artifactId>
            <version>${flink.version}</version>
        </dependency>

        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-statebackend-rocksdb_${scala.binary.version}</artifactId>
            <version>${flink.version}</version>
        </dependency>

        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-runtime-web_${scala.binary.version}</artifactId>
            <version>${flink.version}</version>
        </dependency>

        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-table-api-java-bridge_2.11</artifactId>
            <version>${flink.version}</version>
        </dependency>

        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-table-planner_2.11</artifactId>
            <version>${flink.version}</version>
        </dependency>

        <dependency>
            <groupId>org.apache.hudi</groupId>
            <artifactId>hudi-flink1.14-bundle</artifactId>
            <version>${hudi.version}</version>
        </dependency>

        <dependency>
            <groupId>org.apache.hive</groupId>
            <artifactId>hive-exec</artifactId>
            <classifier>core</classifier>
            <version>2.3.1</version>
        </dependency>

        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-common</artifactId>
            <version>${hadoop.version}</version>
            <exclusions>
                <exclusion>
                    <artifactId>slf4j-log4j12</artifactId>
                    <groupId>org.slf4j</groupId>
                </exclusion>
                <exclusion>
                    <artifactId>log4j</artifactId>
                    <groupId>log4j</groupId>
                </exclusion>
            </exclusions>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>${hadoop.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-hdfs</artifactId>
            <version>${hadoop.version}</version>
            <exclusions>
                <exclusion>
                    <artifactId>slf4j-log4j12</artifactId>
                    <groupId>org.slf4j</groupId>
                </exclusion>
                <exclusion>
                    <artifactId>log4j</artifactId>
                    <groupId>log4j</groupId>
                </exclusion>
            </exclusions>
        </dependency>

        <!--下面是打印日志的，可以不加-->
        <dependency>
            <groupId>org.apache.logging.log4j</groupId>
            <artifactId>log4j-slf4j-impl</artifactId>
            <scope>provided</scope>
            <version>2.17.1</version>
        </dependency>

        <dependency>
            <groupId>org.apache.logging.log4j</groupId>
            <artifactId>log4j-api</artifactId>
            <scope>provided</scope>
            <version>2.17.1</version>
        </dependency>

        <dependency>
            <groupId>org.apache.logging.log4j</groupId>
            <artifactId>log4j-core</artifactId>
            <scope>provided</scope>
            <version>2.17.1</version>
        </dependency>

        <!-- https://mvnrepository.com/artifact/com.alibaba.fastjson2/fastjson2 -->
        <dependency>
            <groupId>com.alibaba.fastjson2</groupId>
            <artifactId>fastjson2</artifactId>
            <version>2.0.16</version>
        </dependency>

        <!--restTemplate启动器-->
        <!-- https://mvnrepository.com/artifact/org.springframework.boot/spring-boot-starter-data-rest -->
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-data-rest</artifactId>
            <version>2.7.0</version>
            <exclusions>
                <exclusion>
                    <groupId>org.springframework.boot</groupId>
                    <artifactId>spring-boot-starter-logging</artifactId>
                </exclusion>
            </exclusions>
        </dependency>

        <!-- 配置管理 -->
        <dependency>
            <groupId>com.typesafe</groupId>
            <artifactId>config</artifactId>
            <version>1.2.1</version>
        </dependency>
    </dependencies>

    <build>
        <finalName>${pom.artifactId}-${pom.version}</finalName>
        <plugins>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-compiler-plugin</artifactId>
                <version>3.3</version>
                <configuration>
                    <source>1.8</source>
                    <target>1.8</target>
                </configuration>
            </plugin>

            <plugin>
                <artifactId>maven-assembly-plugin</artifactId>
                <configuration>
                    <descriptorRefs>
                        <descriptorRef>jar-with-dependencies</descriptorRef>
                    </descriptorRefs>
                    <archive>
                        <manifest>
                            <mainClass>com.test.main.Examples</mainClass>
                        </manifest>
                    </archive>
                </configuration>
                <executions>
                    <execution>
                        <id>make-assembly</id>
                        <phase>package</phase>
                        <goals>
                            <goal>single</goal>
                        </goals>
                    </execution>
                </executions>
            </plugin>
        </plugins>

        <resources>
            <resource>
                <directory>src/main/resources</directory>
                <excludes>
                    <exclude>environment/dev/*</exclude>
                    <exclude>environment/test/*</exclude>
                    <exclude>environment/smoke/*</exclude>
                    <exclude>environment/pre/*</exclude>
                    <exclude>environment/online/*</exclude>
                    <exclude>application.properties</exclude>
                </excludes>
            </resource>
            <resource>
                <directory>src/main/resources/environment/${environment}</directory>
                <targetPath>.</targetPath>
            </resource>
        </resources>
    </build>

    <profiles>
        <profile>
            <!-- 开发环境 -->
            <id>dev</id>
            <properties>
                <environment>dev</environment>
            </properties>
            <activation>
                <activeByDefault>true</activeByDefault>
            </activation>
        </profile>
        <profile>
            <!-- 测试环境 -->
            <id>test</id>
            <properties>
                <environment>test</environment>
            </properties>
        </profile>
        <profile>
            <!-- 冒烟环境 -->
            <id>smoke</id>
            <properties>
                <environment>smoke</environment>
            </properties>
        </profile>
        <profile>
            <!-- 生产环境 -->
            <id>online</id>
            <properties>
                <environment>online</environment>
            </properties>
        </profile>
    </profiles>
</project>

Hudi官网文档链接：

Flink Guide | Apache Hudi

二、DataStream API方式读写Hudi

2.1 写Hudi

package com.test.hudi;

import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.contrib.streaming.state.EmbeddedRocksDBStateBackend;
import org.apache.flink.runtime.state.StateBackend;
import org.apache.flink.streaming.api.CheckpointingMode;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.environment.CheckpointConfig;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.data.GenericRowData;
import org.apache.flink.table.data.RowData;
import org.apache.flink.table.data.StringData;
import org.apache.hudi.common.model.HoodieTableType;
import org.apache.hudi.configuration.FlinkOptions;
import org.apache.hudi.util.HoodiePipeline;

import java.util.HashMap;
import java.util.Map;

public class FlinkDataStreamWrite2HudiTest {
    public static void main(String[] args) throws Exception {
        // 1.创建执行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        // 2.必须开启checkpoint 默认有5个checkpoint后，hudi目录下才会有数据；不然只有一个.hoodie目录
        String checkPointPath = "hdfs://hw-cdh-test02:8020/flinkinfo/meta/savepoints/FlinkDataStreamWrite2HudiTest";
        StateBackend backend = new EmbeddedRocksDBStateBackend(true);
        env.setStateBackend(backend);
        CheckpointConfig conf = env.getCheckpointConfig();
        // 任务流取消和故障应保留检查点
        conf.enableExternalizedCheckpoints(CheckpointConfig.ExternalizedCheckpointCleanup.RETAIN_ON_CANCELLATION);
        conf.setCheckpointingMode(CheckpointingMode.EXACTLY_ONCE);
        conf.setCheckpointInterval(1000);//milliseconds
        conf.setCheckpointTimeout(10 * 60 * 1000);//milliseconds
        conf.setMinPauseBetweenCheckpoints(2 * 1000);//相邻两次checkpoint之间的时间间隔
        conf.setCheckpointStorage(checkPointPath);

        // 3.准备数据
        DataStreamSource<Student> studentDS = env.fromElements(
                new Student(101L, "Johnson", 17L, "swimming"),
                new Student(102L, "Lin", 15L, "shopping"),
                new Student(103L, "Tom", 5L, "play"));

        // 4.创建Hudi数据流
        // 4.1 Hudi表名和路径
        String studentHudiTable = "ods_student_table";
        String studentHudiTablePath = "hdfs://hw-cdh-test02:8020/user/hive/warehouse/lake/" + studentHudiTable;
        Map<String, String> studentOptions = new HashMap<>();
        studentOptions.put(FlinkOptions.PATH.key(), studentHudiTablePath);
        studentOptions.put(FlinkOptions.TABLE_TYPE.key(), HoodieTableType.MERGE_ON_READ.name());

        HoodiePipeline.Builder studentBuilder = HoodiePipeline.builder(studentHudiTable)
                .column("id BIGINT")
                .column("name STRING")
                .column("age BIGINT")
                .column("hobby STRING")
                .pk("id")
//                .pk("id,age")// 可以设置联合主键，用逗号分隔
                .options(studentOptions);

        // 5.转成RowData流
        DataStream<RowData> studentRowDataDS = studentDS.map(new MapFunction<Student, RowData>() {
            @Override
            public RowData map(Student value) throws Exception {
                try {
                    Long id = value.id;
                    String name = value.name;
                    Long age = value.age;
                    String hobby = value.hobby;

                    GenericRowData row = new GenericRowData(4);
                    row.setField(0, Long.valueOf(id));
                    row.setField(1, StringData.fromString(name));
                    row.setField(2, Long.valueOf(age));
                    row.setField(3, StringData.fromString(hobby));

                    return row;
                } catch (Exception e) {
                    e.printStackTrace();
                    return null;
                }
            }
        });

        studentBuilder.sink(studentRowDataDS, false);

        env.execute("FlinkDataStreamWrite2HudiTest");
    }

    public static class Student{
        public Long id;
        public String name;
        public Long age;
        public String hobby;

        public Student() {
        }

        public Student(Long id, String name, Long age, String hobby) {
            this.id = id;
            this.name = name;
            this.age = age;
            this.hobby = hobby;
        }

        public Long getId() {
            return id;
        }

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

        public String getName() {
            return name;
        }

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

        public Long getAge() {
            return age;
        }

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

        public String getHobby() {
            return hobby;
        }

        public void setHobby(String hobby) {
            this.hobby = hobby;
        }

        @Override
        public String toString() {
            return "Student{" +
                    "id=" + id +
                    ", name='" + name + '\'' +
                    ", age=" + age +
                    ", hobby='" + hobby + '\'' +
                    '}';
        }
    }
}

案例中，通过env.fromElements造三条数据写入Hudi，通过查询，可证明3条数据写入成功：

flink 读取 kafka 每天汇总后保存myswl_hadoop

在实际开发中，需要切换数据源，比如从kafka读取数据，写入Hudi，将上面的数据源进行替换，并完成RowData转换即可。（切记，一定要开启checkpoint，否则只有一个,hoodie目录。本人在这里踩过坑，调了一个下午，数据都没有写入成功，只有一个hoodie目录，后来经过研究才知道需要设置checkpoint。本案例中，由于是造的三条数据，跑完之后程序就停了，不设置checkpoint，数据也会写入hudi表；但是如果正在的流计算，从kafka读数据，写入hudi，如果不设置checkpoint，数据最终无法写入hudi表）。

2.2 读Hudi

package com.test.hudi;

import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.data.RowData;
import org.apache.hudi.common.model.HoodieTableType;
import org.apache.hudi.configuration.FlinkOptions;
import org.apache.hudi.util.HoodiePipeline;

import java.util.HashMap;
import java.util.Map;

public class FlinkDataStreamReadFromHudiTest {
    public static void main(String[] args) throws Exception {
        // 1. 创建执行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        // 2.创建Hudi数据流
        String studentHudiTable = "ods_student_table";
        String studentHudiTablePath = "hdfs://hw-cdh-test02:8020/user/hive/warehouse/lake/" + studentHudiTable;
        Map<String, String> studentOptions = new HashMap<>();
        studentOptions.put(FlinkOptions.PATH.key(), studentHudiTablePath);
        studentOptions.put(FlinkOptions.TABLE_TYPE.key(), HoodieTableType.MERGE_ON_READ.name());
        studentOptions.put(FlinkOptions.READ_AS_STREAMING.key(), "true");// this option enable the streaming read
        studentOptions.put(FlinkOptions.READ_START_COMMIT.key(), "16811748000000");// specifies the start commit instant time
        studentOptions.put(FlinkOptions.READ_STREAMING_CHECK_INTERVAL.key(), "4");//
        studentOptions.put(FlinkOptions.CHANGELOG_ENABLED.key(), "true");//
        HoodiePipeline.Builder studentBuilder = HoodiePipeline.builder(studentHudiTable)
                .column("id BIGINT")
                .column("name STRING")
                .column("age BIGINT")
                .column("hobby STRING")
                .pk("id")
                .options(studentOptions);
        DataStream<RowData> studentRowDataDS = studentBuilder.source(env);

        // 3. 数据转换与输出
        DataStream<Student> studentDS = studentRowDataDS.map(new MapFunction<RowData, Student>() {
            @Override
            public Student map(RowData value) throws Exception {
                try {
                    String rowKind = value.getRowKind().name();
                    Long id = value.getLong(0);
                    String name = value.getString(1).toString();
                    Long age = value.getLong(2);
                    String hobby = value.getString(3).toString();

                    Student student = new Student(id, name, age, hobby, rowKind);
                    return student;
                } catch (Exception e) {
                    e.printStackTrace();
                    return null;
                }
            }
        });
        studentDS.print();

        env.execute("FlinkDataStreamReadFromHudiTest");
    }

    public static class Student{
        public Long id;
        public String name;
        public Long age;
        public String hobby;
        public String rowKind;

        public Student() {
        }

        public Student(Long id, String name, Long age, String hobby, String rowKind) {
            this.id = id;
            this.name = name;
            this.age = age;
            this.hobby = hobby;
            this.rowKind = rowKind;
        }

        public Long getId() {
            return id;
        }

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

        public String getName() {
            return name;
        }

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

        public Long getAge() {
            return age;
        }

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

        public String getHobby() {
            return hobby;
        }

        public void setHobby(String hobby) {
            this.hobby = hobby;
        }

        public String getRowKind() {
            return rowKind;
        }

        public void setRowKind(String rowKind) {
            this.rowKind = rowKind;
        }

        @Override
        public String toString() {
            return "Student{" +
                    "id=" + id +
                    ", name='" + name + '\'' +
                    ", age=" + age +
                    ", hobby='" + hobby + '\'' +
                    ", rowKind='" + rowKind + '\'' +
                    '}';
        }
    }
}

输出结果：

flink 读取 kafka 每天汇总后保存myswl_hadoop_02

其中，rowKind，是对行的描述，有 INSERT, UPDATE_BEFORE, UPDATE_AFTER, DELETE，分别对应op的 +I, -U, +U, -D，表示插入、更新前、更新后、删除操作。

三、Table API方式读写Hudi

3.1 写Hudi

3.1.1 数据来自DataStream

package com.test.hudi;

import org.apache.flink.contrib.streaming.state.EmbeddedRocksDBStateBackend;
import org.apache.flink.runtime.state.StateBackend;
import org.apache.flink.streaming.api.CheckpointingMode;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.environment.CheckpointConfig;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;

public class FlinkDataStreamSqlWrite2HudiTest {
    public static void main(String[] args) throws Exception {
        // 1.创建执行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        StreamTableEnvironment tabEnv = StreamTableEnvironment.create(env);

        // 2.必须开启checkpoint 默认有5个checkpoint后，hudi目录下才会有数据；不然只有一个.hoodie目录
        String checkPointPath = "hdfs://hw-cdh-test02:8020/flinkinfo/meta/savepoints/FlinkDataStreamWrite2HudiTest";
        StateBackend backend = new EmbeddedRocksDBStateBackend(true);
        env.setStateBackend(backend);
        CheckpointConfig conf = env.getCheckpointConfig();
        // 任务流取消和故障应保留检查点
        conf.enableExternalizedCheckpoints(CheckpointConfig.ExternalizedCheckpointCleanup.RETAIN_ON_CANCELLATION);
        conf.setCheckpointingMode(CheckpointingMode.EXACTLY_ONCE);
        conf.setCheckpointInterval(1000);//milliseconds
        conf.setCheckpointTimeout(10 * 60 * 1000);//milliseconds
        conf.setMinPauseBetweenCheckpoints(2 * 1000);//相邻两次checkpoint之间的时间间隔
        conf.setCheckpointStorage(checkPointPath);

        // 3.准备数据，真实环境中，这里可以替换成从kafka读取数据
        DataStreamSource<Student> studentDS = env.fromElements(
                new Student(201L, "zhangsan", 117L, "eat"),
                new Student(202L, "lisi", 115L, "drink"),
                new Student(203L, "wangwu", 105L, "sleep"));
        // 由于后续没有DataStream的执行算子，可以会报错：
        // Exception in thread "main" java.lang.IllegalStateException: No operators defined in streaming topology. Cannot execute.
        // 不过不影响数据写入Hudi
        // 当然，也可以加一步DataStream的执行算子，比如 print
//        studentDS.print("DataStream: ");

        // 4.通过DataStream创建表
        // 4.1 第一个参数：表名；第二个参数：DataStream；第三个可选参数：指定列名，可以指定DataStream中的元素名和列名的匹配关系，比如 "userId as user_id, name, age, hobby"
        tabEnv.registerDataStream("tmp_student_table", studentDS, "id, name, age, hobby");

        // 5.准备Hudi表的数据流，并将数据写入Hudi表
        tabEnv.executeSql("" +
                "CREATE TABLE out_ods_student_table(\n" +
                "    id BIGINT COMMENT '学号',\n" +
                "    name STRING\t COMMENT '姓名',\n" +
                "    age BIGINT  COMMENT '年龄',\n" +
                "    hobby STRING    COMMENT '爱好',\n" +
                "    PRIMARY KEY (id) NOT ENFORCED\n" +
                ")\n" +
                "WITH(\n" +
                "    'connector' = 'hudi',\n" +
                "    'path' = 'hdfs://hw-cdh-test02:8020/user/hive/warehouse/lake/ods_student_table',\n" +
                "    'table.type' = 'MERGE_ON_READ',\n" +
                "    'compaction.async.enabled' = 'true',\n" +
                "    'compaction.tasks' = '1',\n" +
                "    'compaction.trigger.strategy' = 'num_commits',\n" +
                "    'compaction.delta_commits' = '3',\n" +
                "    'hoodie.cleaner.policy'='KEEP_LATEST_COMMITS',\n" +
                "    'hoodie.cleaner.commits.retained'='30',\n" +
                "    'hoodie.keep.min.commits'='35' ,\n" +
                "    'hoodie.keep.max.commits'='40'\n" +
                ")");
        tabEnv.executeSql("insert into out_ods_student_table select id,name,age,hobby from tmp_student_table");


        env.execute("FlinkDataStreamSqlWrite2HudiTest");
    }

    public static class Student{
        public Long id;
        public String name;
        public Long age;
        public String hobby;

        public Student() {
        }

        public Student(Long id, String name, Long age, String hobby) {
            this.id = id;
            this.name = name;
            this.age = age;
            this.hobby = hobby;
        }

        public Long getId() {
            return id;
        }

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

        public String getName() {
            return name;
        }

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

        public Long getAge() {
            return age;
        }

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

        public String getHobby() {
            return hobby;
        }

        public void setHobby(String hobby) {
            this.hobby = hobby;
        }

        @Override
        public String toString() {
            return "Student{" +
                    "id=" + id +
                    ", name='" + name + '\'' +
                    ", age=" + age +
                    ", hobby='" + hobby + '\'' +
                    '}';
        }
    }
}

通过查看Hudi表，证明3条数据写入成功：

flink 读取 kafka 每天汇总后保存myswl_hadoop_03

3.1.2 数据来自Table

package com.test.hudi;

import org.apache.flink.contrib.streaming.state.EmbeddedRocksDBStateBackend;
import org.apache.flink.runtime.state.StateBackend;
import org.apache.flink.streaming.api.CheckpointingMode;
import org.apache.flink.streaming.api.environment.CheckpointConfig;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;

public class FlinkValuesSqlWrite2HudiTest {
    public static void main(String[] args) throws Exception {
        // 1. 创建执行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        StreamTableEnvironment tabEnv = StreamTableEnvironment.create(env);

        // 2.必须开启checkpoint 默认有5个checkpoint后，hudi目录下才会有数据；不然只有一个.hoodie目录
        String checkPointPath = "hdfs://hw-cdh-test02:8020/flinkinfo/meta/savepoints/FlinkDataStreamWrite2HudiTest";
        StateBackend backend = new EmbeddedRocksDBStateBackend(true);
        env.setStateBackend(backend);
        CheckpointConfig conf = env.getCheckpointConfig();
        // 任务流取消和故障应保留检查点
        conf.enableExternalizedCheckpoints(CheckpointConfig.ExternalizedCheckpointCleanup.RETAIN_ON_CANCELLATION);
        conf.setCheckpointingMode(CheckpointingMode.EXACTLY_ONCE);
        conf.setCheckpointInterval(1000);//milliseconds
        conf.setCheckpointTimeout(10 * 60 * 1000);//milliseconds
        conf.setMinPauseBetweenCheckpoints(2 * 1000);//相邻两次checkpoint之间的时间间隔
        conf.setCheckpointStorage(checkPointPath);

        // 3.准备Hudi表的数据流，并将数据写入Hudi表
        tabEnv.executeSql("" +
                "CREATE TABLE out_ods_student_table(\n" +
                "    id BIGINT COMMENT '学号',\n" +
                "    name STRING\t COMMENT '姓名',\n" +
                "    age BIGINT  COMMENT '年龄',\n" +
                "    hobby STRING    COMMENT '爱好',\n" +
                "    PRIMARY KEY (id) NOT ENFORCED\n" +
                ")\n" +
                "WITH(\n" +
                "    'connector' = 'hudi',\n" +
                "    'path' = 'hdfs://hw-cdh-test02:8020/user/hive/warehouse/lake/ods_student_table',\n" +
                "    'table.type' = 'MERGE_ON_READ',\n" +
                "    'compaction.async.enabled' = 'true',\n" +
                "    'compaction.tasks' = '1',\n" +
                "    'compaction.trigger.strategy' = 'num_commits',\n" +
                "    'compaction.delta_commits' = '3',\n" +
                "    'hoodie.cleaner.policy'='KEEP_LATEST_COMMITS',\n" +
                "    'hoodie.cleaner.commits.retained'='30',\n" +
                "    'hoodie.keep.min.commits'='35' ,\n" +
                "    'hoodie.keep.max.commits'='40'\n" +
                ")");
        tabEnv.executeSql("" +
                "insert into out_ods_student_table values\n" +
                "    (301, 'xiaoming', 201, 'read'),\n" +
                "    (302, 'xiaohong', 202, 'write'),\n" +
                "    (303, 'xiaogang', 203, 'sing')");

        env.execute("FlinkValuesSqlWrite2HudiTest");
    }
}

通过查看Hudi表，证明3条数据写入成功：

flink 读取 kafka 每天汇总后保存myswl_flink_04

3.2 读Hudi

package com.test.hudi;

import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;

public class FlinkSqlReadFromHudiTest {
    public static void main(String[] args) throws Exception {
        // 1.创建执行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        StreamTableEnvironment tabEnv = StreamTableEnvironment.create(env);

        // 2.准备Hudi表的数据流，并从Hudi表读取数据
        tabEnv.executeSql("" +
                "CREATE TABLE out_ods_student_table(\n" +
                "    id BIGINT COMMENT '学号',\n" +
                "    name STRING\t COMMENT '姓名',\n" +
                "    age BIGINT  COMMENT '年龄',\n" +
                "    hobby STRING    COMMENT '爱好',\n" +
                "    PRIMARY KEY (id) NOT ENFORCED\n" +
                ")\n" +
                "WITH(\n" +
                "    'connector' = 'hudi',\n" +
                "    'path' = 'hdfs://hw-cdh-test02:8020/user/hive/warehouse/lake/ods_student_table',\n" +
                "    'table.type' = 'MERGE_ON_READ',\n" +
                "    'compaction.async.enabled' = 'true',\n" +
                "    'compaction.tasks' = '1',\n" +
                "    'compaction.trigger.strategy' = 'num_commits',\n" +
                "    'compaction.delta_commits' = '3',\n" +
                "    'hoodie.cleaner.policy'='KEEP_LATEST_COMMITS',\n" +
                "    'hoodie.cleaner.commits.retained'='30',\n" +
                "    'hoodie.keep.min.commits'='35' ,\n" +
                "    'hoodie.keep.max.commits'='40'\n" +
                ")");
        tabEnv.executeSql("select id,name,age,hobby from out_ods_student_table").print();

        env.execute("FlinkSqlReadFromHudiTest");
    }
}

输出结果：

flink 读取 kafka 每天汇总后保存myswl_flink_05

四、补充

4.1 联合主键

在Flink Table操作Hudi的时候，可能会涉及到联合组件，可以在SQL中加入联合主键。比如：

tabEnv.executeSql("" +
        "CREATE TABLE out_ods_userinfo_table_test(\n" +
        "    province_id BIGINT COMMENT '省份编号',\n" +
        "    user_id BIGINT COMMENT '用户编号',\n" +
        "    name STRING\t COMMENT '姓名',\n" +
        "    age BIGINT COMMENT '年龄',\n" +
        "    hobby STRING COMMENT '爱好',\n" +
        "    PRIMARY KEY (province_id,user_id) NOT ENFORCED\n" +
        ")\n" +
        "WITH(\n" +
        "    'connector' = 'hudi',\n" +
        "    'path' = 'hdfs://hw-cdh-test02:8020/user/hive/warehouse/lake/ods_userinfo_table_test',\n" +
        "    'table.type' = 'MERGE_ON_READ',\n" +
        "    'hoodie.datasource.write.keygenerator.class'='org.apache.hudi.keygen.ComplexKeyGenerator',\n" +
        "    'hoodie.datasource.write.recordkey.field'= 'province_id,user_id',\n" +
        "    'compaction.async.enabled' = 'true',\n" +
        "    'compaction.tasks' = '1',\n" +
        "    'compaction.trigger.strategy' = 'num_commits',\n" +
        "    'compaction.delta_commits' = '3',\n" +
        "    'hoodie.cleaner.policy'='KEEP_LATEST_COMMITS',\n" +
        "    'hoodie.cleaner.commits.retained'='30',\n" +
        "    'hoodie.keep.min.commits'='35' ,\n" +
        "    'hoodie.keep.max.commits'='40'\n" +
        ")");

4.2 读取指定时间之后的数据

根据官方文档说明，可以读取指定提交时间之后的数据。比如指定时间20230318130057，那么读取到的都是提交时间在 2023-03-18 13:00:57 之后的数据，之前的数据读取不到。

4.2.1 DataStream API

options.put(FlinkOptions.READ_START_COMMIT.key(), "'20230318130057'"); // specifies the start commit instant time

4.2.2 Table API

'read.start-commit' = '20230318130057', -- specifies the start commit instant time

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