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hudi with flink1.14验证

时间：2022-11-22 17:57:30浏览次数：77  
标签：hudi val 验证 import flink org apache flink1.14

hudi with flink datastream by java

版本

  • flink1.14
  • hudi0.12.1

pom依赖

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

        <!-- Flink Hudi run extra -->
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>2.6.0</version>
            <scope>provided</scope>
            <exclusions>
                <exclusion>
                    <groupId>org.slf4j</groupId>
                    <artifactId>slf4j-log4j12</artifactId>
                </exclusion>
            </exclusions>
        </dependency>

        <!-- Flink web -->
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-runtime-web_2.11</artifactId>
            <version>${flink.version}</version>
            <scope>provided</scope>
        </dependency>

        <!-- Table ecosystem -->
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-table-api-java-bridge_2.11</artifactId>
            <version>${flink.version}</version>
            <scope>provided</scope>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-table-runtime_2.11</artifactId>
            <version>${flink.version}</version>
            <scope>provided</scope>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-table-planner_2.11</artifactId>
            <version>${flink.version}</version>
            <scope>provided</scope>
        </dependency>

        <!-- Flink core -->
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-streaming-java_2.11</artifactId>
            <version>${flink.version}</version>
            <scope>provided</scope>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-clients_2.11</artifactId>
            <version>${flink.version}</version>
            <scope>provided</scope>
        </dependency>

        <!-- Flink connector -->
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-connector-kafka_2.11</artifactId>
            <version>${flink.version}</version>
            <scope>provided</scope>
        </dependency>

        <!-- Flink statebackend -->
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-statebackend-rocksdb_2.11</artifactId>
            <version>${flink.version}</version>
            <scope>provided</scope>
        </dependency>

        <dependency>
            <groupId>org.slf4j</groupId>
            <artifactId>slf4j-simple</artifactId>
            <version>1.7.25</version>
            <scope>compile</scope>
        </dependency>
    </dependencies>
View Code

write

消费kafka，通过flink datastream持续写入hudi

package com.liangji.hudi0121.flink114;

import org.apache.flink.api.common.eventtime.WatermarkStrategy;
import org.apache.flink.api.java.utils.ParameterTool;
import org.apache.flink.connector.kafka.source.KafkaSource;
import org.apache.flink.connector.kafka.source.enumerator.initializer.OffsetsInitializer;
import org.apache.flink.contrib.streaming.state.EmbeddedRocksDBStateBackend;
import org.apache.flink.formats.common.TimestampFormat;
import org.apache.flink.formats.json.JsonRowDataDeserializationSchema;
import org.apache.flink.streaming.api.CheckpointingMode;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.CheckpointConfig;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.DataTypes;
import org.apache.flink.table.data.RowData;
import org.apache.flink.table.runtime.typeutils.InternalTypeInfo;
import org.apache.flink.table.types.logical.RowType;
import org.apache.hudi.common.model.HoodieTableType;
import org.apache.hudi.configuration.FlinkOptions;
import org.apache.hudi.util.HoodiePipeline;
import org.apache.kafka.clients.CommonClientConfigs;
import org.apache.kafka.clients.consumer.ConsumerConfig;
import org.apache.kafka.common.config.SaslConfigs;

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

public class JavaTestInsert {
    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        String targetTable = "t1";
        String basePath = "/project/ccr_ai_upgrade/ccr_ai_upgrade/aiui/flink/demo/t1";
        String checkpointDir= "/project/ccr_ai_upgrade/ccr_ai_upgrade/aiui/flink/chk";

        ParameterTool parameterTool = ParameterTool.fromArgs(args);
        //设置并行度
        String sourceTopic = parameterTool.get("sourceTopic","odeon_test_liangji_114_test");
        String cg = parameterTool.get("cg","CG_odeon_test_liangji_114_test");
        String cgpwd = parameterTool.get("cgPwd","252287");

        EmbeddedRocksDBStateBackend backend = new EmbeddedRocksDBStateBackend(true);
        env.setStateBackend(backend);
        env.enableCheckpointing(60000);
        CheckpointConfig config = env.getCheckpointConfig();
        config.setCheckpointStorage(checkpointDir);
        config.enableExternalizedCheckpoints(CheckpointConfig.ExternalizedCheckpointCleanup.RETAIN_ON_CANCELLATION);
        config.setCheckpointingMode(CheckpointingMode.AT_LEAST_ONCE);
        config.setCheckpointTimeout(60000);

        Properties prop = new Properties();
        prop.setProperty(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, "172.31.162.72:9093,172.31.162.73:9093,172.31.162.74:9093");
        prop.setProperty(CommonClientConfigs.SECURITY_PROTOCOL_CONFIG,"SASL_PLAINTEXT");
        prop.setProperty(SaslConfigs.SASL_MECHANISM,"SCRAM-SHA-256");
        prop.setProperty(ConsumerConfig.GROUP_ID_CONFIG,cg);
        prop.setProperty(SaslConfigs.SASL_JAAS_CONFIG,"org.apache.kafka.common.security.scram.ScramLoginModule required username=\""+cg+"\"\n" +"password=\""+cgpwd+"\";");
        prop.setProperty(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG,"true");
        prop.setProperty(ConsumerConfig.AUTO_COMMIT_INTERVAL_MS_CONFIG,"1000");
        prop.setProperty(ConsumerConfig.SESSION_TIMEOUT_MS_CONFIG,"30000");

        RowType rowType = createJsonRowType();
        KafkaSource<RowData> source = KafkaSource.<RowData>builder()
                .setBootstrapServers("bootstrap server")
                .setTopics(sourceTopic)
                .setGroupId(cg)
                .setStartingOffsets(OffsetsInitializer.latest())
                .setValueOnlyDeserializer(new JsonRowDataDeserializationSchema(rowType, InternalTypeInfo.of(rowType), false, false, TimestampFormat.ISO_8601))
                .setProperties(prop)
                .build();

        DataStream<RowData> dataStream = env.fromSource(source, WatermarkStrategy.noWatermarks(), "Kafka Source");

        Map<String, String> options = new HashMap<>();
        options.put(FlinkOptions.PATH.key(), basePath);
        options.put(FlinkOptions.TABLE_TYPE.key(), HoodieTableType.MERGE_ON_READ.name());
        options.put(FlinkOptions.PRECOMBINE_FIELD.key(), "ts");

        HoodiePipeline.Builder builder = HoodiePipeline.builder(targetTable)
                .column("uuid VARCHAR(200)")
                .column("name VARCHAR(100)")
                .column("age INT")
                .column("ts TIMESTAMP(0)")
                .column("`partition` VARCHAR(20)")
                .pk("uuid")
                .partition("partition")
                .options(options);

        builder.sink(dataStream, false); // The second parameter indicating whether the input data stream is bounded
        env.execute("Api_Sink");
    }

    private static RowType createJsonRowType() {
        return (RowType) DataTypes.ROW(
                DataTypes.FIELD("uuid", DataTypes.STRING()),
                DataTypes.FIELD("name", DataTypes.STRING()),
                DataTypes.FIELD("age", DataTypes.INT()),
                DataTypes.FIELD("ts", DataTypes.TIMESTAMP(0)),
                DataTypes.FIELD("partition", DataTypes.STRING())
                ).getLogicalType();
    }
}
View Code

kafka消息发送示例如下：

package com.liangji.kafka;

import org.apache.kafka.clients.CommonClientConfigs;
import org.apache.kafka.clients.producer.Producer;
import org.apache.kafka.clients.producer.ProducerConfig;
import org.apache.kafka.clients.producer.ProducerRecord;
import org.apache.kafka.clients.producer.RecordMetadata;
import org.apache.kafka.common.config.SaslConfigs;

import java.util.Properties;
import java.util.UUID;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.Future;

/**
 * @author liangji
 * @Description TODO
 * @Date 2021/5/7 19:17
 */
public class KafkaProducerFrom1 {

    public static void main(String[] args) throws ExecutionException, InterruptedException {
        Properties props = new Properties();
        props.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, "bootstrap server");
        props.put(CommonClientConfigs.SECURITY_PROTOCOL_CONFIG,"SASL_PLAINTEXT");
        props.put(SaslConfigs.SASL_MECHANISM,"SCRAM-SHA-256");
        props.put(SaslConfigs.SASL_JAAS_CONFIG,"org.apache.kafka.common.security.scram.ScramLoginModule required username=\"PG_odeon_test_liangji_114_test\" password=\"889448\";");
        props.put(ProducerConfig.ACKS_CONFIG, "all");
        props.put(ProducerConfig.RETRIES_CONFIG, 3);
        props.put(ProducerConfig.RETRY_BACKOFF_MS_CONFIG, 1000);
        props.put(ProducerConfig.MAX_IN_FLIGHT_REQUESTS_PER_CONNECTION, 1);
        props.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.StringSerializer");
        props.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.StringSerializer");

        Producer producer = new org.apache.kafka.clients.producer.KafkaProducer<>(props);

        while (true) {
            String uuid = UUID.randomUUID().toString().replace("-", "").toLowerCase();
            ProducerRecord producerRecord = new ProducerRecord<>("odeon_test_liangji_114_test","k", "{" +
                    "\"uuid\":" + "\"" + uuid + "\"," +
                    "\"name\":\"liangji-" + uuid + "\"," +
                    "\"age\":\"18\"" + "," +
                    "\"ts\":\"2022-11-17T14:00:00\"" + "," +
                    "\"partition\":\"2022-11-17\"" +
                    "}");
            Future<RecordMetadata> future = producer.send(producerRecord);
            future.get();
            System.out.println("success");
            Thread.sleep(1000);
        }
    }
}
View Code

query

通过flink datastream持续读取hudi

package com.liangji.hudi0121.flink114;

import org.apache.commons.lang3.StringUtils;
import org.apache.flink.configuration.Configuration;
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.common.table.HoodieTableMetaClient;
import org.apache.hudi.common.table.timeline.HoodieInstant;
import org.apache.hudi.configuration.FlinkOptions;
import org.apache.hudi.configuration.HadoopConfigurations;
import org.apache.hudi.util.HoodiePipeline;

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

public class JavaTestQuery {
    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        String targetTable = "t1";
        String basePath = "/project/ccr_ai_upgrade/ccr_ai_upgrade/aiui/flink/demo/t1";

        String startCommit = "20221117164814122";
        if (StringUtils.isEmpty(startCommit)) {
            HoodieTableMetaClient metaClient = HoodieTableMetaClient.builder()
                    .setConf(HadoopConfigurations.getHadoopConf(new Configuration())).setBasePath(basePath).build();
            startCommit = metaClient.getCommitsAndCompactionTimeline().filterCompletedInstants().firstInstant()
                    .map(HoodieInstant::getTimestamp).orElse(null);
        }

        Map<String, String> options = new HashMap<>();
        options.put(FlinkOptions.PATH.key(), basePath);
        options.put(FlinkOptions.TABLE_TYPE.key(), HoodieTableType.MERGE_ON_READ.name());
        options.put(FlinkOptions.READ_AS_STREAMING.key(), "true");
        options.put(FlinkOptions.READ_START_COMMIT.key(), startCommit);

        HoodiePipeline.Builder builder = HoodiePipeline.builder(targetTable)
                .column("uuid VARCHAR(200)")
                .column("name VARCHAR(100)")
                .column("age INT")
                .column("ts TIMESTAMP(0)")
                .column("`partition` VARCHAR(20)")
                .pk("uuid")
                .partition("partition")
                .options(options);

        DataStream<RowData> rowDataDataStream = builder.source(env);
        rowDataDataStream.print();
        env.execute("Api_Source");
    }
}
View Code

hudi with flink sql & hudi on metastore by scala

pom依赖

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

        <!-- Flink Hudi run extra -->
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>2.6.0</version>
            <scope>provided</scope>
            <exclusions>
                <exclusion>
                    <groupId>org.slf4j</groupId>
                    <artifactId>slf4j-log4j12</artifactId>
                </exclusion>
                <exclusion>
                    <groupId>org.apache.commons</groupId>
                    <artifactId>commons-math3</artifactId>
                </exclusion>
            </exclusions>
        </dependency>

        <!-- Flink web -->
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-runtime-web_2.11</artifactId>
            <version>${flink.version}</version>
            <scope>provided</scope>
        </dependency>

        <!-- Table ecosystem -->
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-table-api-scala-bridge_2.11</artifactId>
            <version>${flink.version}</version>
            <scope>provided</scope>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-table-planner_2.11</artifactId>
            <version>${flink.version}</version>
            <scope>provided</scope>
        </dependency>

        <!-- Flink core -->
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-clients_2.11</artifactId>
            <version>${flink.version}</version>
            <scope>provided</scope>
        </dependency>

        <!-- Flink connector -->
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-connector-kafka_2.11</artifactId>
            <version>${flink.version}</version>
            <scope>provided</scope>
        </dependency>

        <!-- Flink statebackend -->
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-statebackend-rocksdb_2.11</artifactId>
            <version>${flink.version}</version>
            <scope>provided</scope>
        </dependency>

        <dependency>
            <groupId>org.slf4j</groupId>
            <artifactId>slf4j-simple</artifactId>
            <version>1.7.25</version>
            <scope>compile</scope>
        </dependency>
    </dependencies>
View Code

ddl

通过flink sql创建表，并将hudi表元数据托管在hive metastore中

package com.liangji.hudi0121.flink114.sql.ddl

import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment
import org.apache.flink.table.api.bridge.scala.StreamTableEnvironment

import java.net.URL

object CreateTable {
  def main(args: Array[String]): Unit = {
    System.setProperty("HADOOP_USER_NAME","hueuser")

    val env = StreamExecutionEnvironment.getExecutionEnvironment
    val tableEnv = StreamTableEnvironment.create(env)

    val createCatalogSql =
      s"""
        |CREATE CATALOG hudi_catalog WITH (
        |    'type' = 'hudi',
        |    'mode' = 'hms',
        |    'default-database' = 'default',
        |    'hive.conf.dir' = 'E:\\mine\\hudi0121-demo-maven\\flink114-scala\\src\\main\\resources'
        |)
        |""".stripMargin

    val createDbSql = """create database if not exists hudi_catalog.ccr_ai_upgrade""".stripMargin

    val changeDbSql = """use hudi_catalog.ccr_ai_upgrade""".stripMargin

    val createTbSql =
      """
        |CREATE TABLE if not exists test_hudi_flink_mor (
        |  uuid VARCHAR(200) PRIMARY KEY NOT ENFORCED,
        |  name VARCHAR(100),
        |  age INT,
        |  ts TIMESTAMP(0),
        |  `partition` VARCHAR(20)
        |)
        |PARTITIONED BY (`partition`
        |)
        |WITH (
        |  'connector' = 'hudi',
        |  'path' = '/project/ccr_ai_upgrade/ccr_ai_upgrade/aiui/flink/demo/test_hudi_flink_mor',
        |  'table.type' = 'MERGE_ON_READ',
        |  'hoodie.datasource.write.keygenerator.class' = 'org.apache.hudi.keygen.ComplexAvroKeyGenerator',
        |  'hoodie.datasource.write.recordkey.field' = 'uuid',
        |  'hoodie.datasource.write.hive_style_partitioning' = 'true'
        |)
        |""".stripMargin

    tableEnv.executeSql(createCatalogSql)
    tableEnv.executeSql(createDbSql)
    tableEnv.executeSql(changeDbSql)
    tableEnv.executeSql(createTbSql).print()
  }
}
View Code

batch

write

通过flink sql写入hudi表，如下是通过batch方式写入

package com.liangji.hudi0121.flink114.sql.write

import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment
import org.apache.flink.table.api.bridge.scala.StreamTableEnvironment

object BatchWrite {
  def main(args: Array[String]): Unit = {
    System.setProperty("HADOOP_USER_NAME","hueuser")

    val env = StreamExecutionEnvironment.getExecutionEnvironment
    val tableEnv = StreamTableEnvironment.create(env)

    val createCatalogSql =
      s"""
        |CREATE CATALOG hudi_catalog WITH (
        |    'type' = 'hudi',
        |    'mode' = 'hms',
        |    'default-database' = 'default',
        |    'hive.conf.dir' = 'E:\\mine\\hudi0121-demo-maven\\flink114-scala\\src\\main\\resources'
        |)
        |""".stripMargin

    val changeDbSql = """use hudi_catalog.ccr_ai_upgrade""".stripMargin

    tableEnv.executeSql(createCatalogSql)
    tableEnv.executeSql(changeDbSql)
    tableEnv.executeSql("insert into test_hudi_flink_mor values ('b','liangji-1',19,TIMESTAMP '2022-11-18 18:00:00','2022-11-18')").print()
  }
}
View Code

query

通过flink sql查询hudi表，如下是通过batch方式查询

package com.liangji.hudi0121.flink114.sql.query

import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment
import org.apache.flink.table.api.bridge.scala.StreamTableEnvironment

object BatchQuery {
  def main(args: Array[String]): Unit = {
    System.setProperty("HADOOP_USER_NAME","hueuser")

    val env = StreamExecutionEnvironment.getExecutionEnvironment
    val tableEnv = StreamTableEnvironment.create(env)

    val createCatalogSql =
      s"""
        |CREATE CATALOG hudi_catalog WITH (
        |    'type' = 'hudi',
        |    'mode' = 'hms',
        |    'default-database' = 'default',
        |    'hive.conf.dir' = 'E:\\mine\\hudi0121-demo-maven\\flink114-scala\\src\\main\\resources'
        |)
        |""".stripMargin

    val changeDbSql = """use hudi_catalog.ccr_ai_upgrade""".stripMargin

    tableEnv.executeSql(createCatalogSql)
    tableEnv.executeSql(changeDbSql)
    tableEnv.executeSql("select * from test_hudi_flink_mor").print()
  }
}
View Code

streaming

write

通过flink sql消费hudi表，流式写入hudi表，如下是通过streaming方式写入

package com.liangji.hudi0121.flink114.sql.streaming.write

import org.apache.flink.contrib.streaming.state.EmbeddedRocksDBStateBackend
import org.apache.flink.streaming.api.CheckpointingMode
import org.apache.flink.streaming.api.environment.CheckpointConfig
import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment
import org.apache.flink.table.api.bridge.scala.StreamTableEnvironment

object StreamingWrite {
  def main(args: Array[String]): Unit = {
    System.setProperty("HADOOP_USER_NAME","hueuser")

    val env = StreamExecutionEnvironment.getExecutionEnvironment
    val tableEnv = StreamTableEnvironment.create(env)

    val checkpointDir = "hdfs://ns-tmp/project/ccr_ai_upgrade/ccr_ai_upgrade/aiui/flink/chk"
    val backend = new EmbeddedRocksDBStateBackend(true)
    env.setStateBackend(backend)
    env.enableCheckpointing(60000)
    val config = env.getCheckpointConfig
    config.setCheckpointStorage(checkpointDir)
    config.enableExternalizedCheckpoints(CheckpointConfig.ExternalizedCheckpointCleanup.RETAIN_ON_CANCELLATION)
    config.setCheckpointingMode(CheckpointingMode.AT_LEAST_ONCE)
    config.setCheckpointTimeout(60000)

    val createCatalogSql =
      s"""
        |CREATE CATALOG hudi_catalog WITH (
        |    'type' = 'hudi',
        |    'mode' = 'hms',
        |    'default-database' = 'default',
        |    'hive.conf.dir' = 'E:\\mine\\hudi0121-demo-maven\\flink114-scala\\src\\main\\resources'
        |)
        |""".stripMargin

    val changeDbSql = """use hudi_catalog.ccr_ai_upgrade""".stripMargin

    tableEnv.executeSql(createCatalogSql)
    tableEnv.executeSql(changeDbSql)

    val table = tableEnv.sqlQuery("select * from test_hudi_flink_mor/*+ OPTIONS('read.streaming.enabled'='true')*/")

    // table=>datastream
    val stream = tableEnv.toDataStream(table)

    // do anything u want

    // datastream=>table
    val inputTable = tableEnv.fromDataStream(stream)
    // create temp view
    tableEnv.createTemporaryView("InputTable", inputTable)

    tableEnv.executeSql("insert into test_hudi_flink_mor_streaming select * from InputTable")
  }
}
View Code

注：

  1. 需开启chk，否则数据不会提交
  2. 可自由转换table和datastream进行逻辑处理

query

通过flink sql流式读取hudi表，如下是通过streaming方式读取

package com.liangji.hudi0121.flink114.sql.streaming.query

import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment
import org.apache.flink.table.api.bridge.scala.StreamTableEnvironment

object StreamingQuery {
  def main(args: Array[String]): Unit = {
    System.setProperty("HADOOP_USER_NAME","hueuser")

    val env = StreamExecutionEnvironment.getExecutionEnvironment
    val tableEnv = StreamTableEnvironment.create(env)

    val createCatalogSql =
      s"""
        |CREATE CATALOG hudi_catalog WITH (
        |    'type' = 'hudi',
        |    'mode' = 'hms',
        |    'default-database' = 'default',
        |    'hive.conf.dir' = 'E:\\mine\\hudi0121-demo-maven\\flink114-scala\\src\\main\\resources'
        |)
        |""".stripMargin

    val changeDbSql = """use hudi_catalog.ccr_ai_upgrade""".stripMargin

    tableEnv.executeSql(createCatalogSql)
    tableEnv.executeSql(changeDbSql)

    tableEnv.executeSql("select * from test_hudi_flink_mor/*+ OPTIONS('read.streaming.enabled'='true')*/").print()
  }
}
View Code

 

注：需在sql添加hint：'read.streaming.enabled'='true'

参考

  1. https://github.com/apache/hudi/blob/master/hudi-examples/hudi-examples-flink/src/main/java/org/apache/hudi/examples/quickstart/HoodieFlinkQuickstart.java
  2. Flink SQL通过Hudi HMS Catalog读写Hudi并同步Hive表（强烈推荐这种方式）
  3. Static methods in interface require -target:jvm-1.8_好色仙人的徒弟的博客-CSDN博客
  4. Flink 使用之操作 Hudi 表
  5. 使用 Flink Hudi 构建流式数据湖-阿里云开发者社区
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标签：hudi,val,验证,import,flink,org,apache,flink1.14
From： https://www.cnblogs.com/just-JL/p/16915935.html

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