一、pom依赖
测试案例中，pom依赖如下，根据需要自行删减。
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">
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
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.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
@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：
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.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
// 3. 数据转换与输出
DataStream
@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：
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
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：
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：
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
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