一、pom依赖

测试案例中，pom依赖如下，根据需要自行删减。

4.0.0com.testExamples1.0-SNAPSHOT1.81.8UTF-82.11.82.112.6.01.14.52.0.01.2.00.12.0org.apache.flinkflink-java${flink.version}org.apache.flinkflink-streaming-java_${scala.binary.version}${flink.version}org.apache.flinkflink-clients_${scala.binary.version}${flink.version}org.apache.flinkflink-connector-kafka_${scala.binary.version}${flink.version}org.apache.flinkflink-statebackend-rocksdb_${scala.binary.version}${flink.version}org.apache.flinkflink-runtime-web_${scala.binary.version}${flink.version}org.apache.flinkflink-table-api-java-bridge_2.11${flink.version}org.apache.flinkflink-table-planner_2.11${flink.version}org.apache.hudihudi-flink1.14-bundle${hudi.version}org.apache.hivehive-execcore2.3.1org.apache.hadoophadoop-common${hadoop.version}slf4j-log4j12org.slf4jlog4jlog4jorg.apache.hadoophadoop-client${hadoop.version}org.apache.hadoophadoop-hdfs${hadoop.version}slf4j-log4j12org.slf4jlog4jlog4jorg.apache.logging.log4jlog4j-slf4j-implprovided2.17.1org.apache.logging.log4jlog4j-apiprovided2.17.1org.apache.logging.log4jlog4j-coreprovided2.17.1com.alibaba.fastjson2fastjson22.0.16org.springframework.bootspring-boot-starter-data-rest2.7.0org.springframework.bootspring-boot-starter-loggingcom.typesafeconfig1.2.1${pom.artifactId}-${pom.version}org.apache.maven.pluginsmaven-compiler-plugin3.31.81.8maven-assembly-pluginjar-with-dependenciescom.test.main.Examplesmake-assemblypackagesinglesrc/main/resourcesenvironment/dev/*environment/test/*environment/smoke/*environment/pre/*environment/online/*application.propertiessrc/main/resources/environment/${environment}.devdevtruetesttestsmokesmokeonlineonline

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);//millisecondsconf.setCheckpointTimeout(10 * 60 * 1000);//millisecondsconf.setMinPauseBetweenCheckpoints(2 * 1000);//相邻两次checkpoint之间的时间间隔conf.setCheckpointStorage(checkPointPath);// 3.准备数据DataStreamSource 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 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 studentRowDataDS = studentDS.map(new MapFunction() {@Overridepublic 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;}@Overridepublic 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：

 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 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 readstudentOptions.put(FlinkOptions.READ_START_COMMIT.key(), "16811748000000");// specifies the start commit instant timestudentOptions.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 studentRowDataDS = studentBuilder.source(env);// 3. 数据转换与输出DataStream studentDS = studentRowDataDS.map(new MapFunction() {@Overridepublic 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;}@Overridepublic String toString() {return "Student{" +"id=" + id +", name='" + name + '\'' +", age=" + age +", hobby='" + hobby + '\'' +", rowKind='" + rowKind + '\'' +'}';}}
}

输出结果：

 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);//millisecondsconf.setCheckpointTimeout(10 * 60 * 1000);//millisecondsconf.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 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;}@Overridepublic 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：

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);//millisecondsconf.setCheckpointTimeout(10 * 60 * 1000);//millisecondsconf.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： 

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：

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

本文来自互联网用户投稿，文章观点仅代表作者本人，不代表本站立场，不承担相关法律责任。如若转载，请注明出处。 如若内容造成侵权/违法违规/事实不符，请点击【内容举报】进行投诉反馈！