数据湖（六）：Hudi与Flink整合

​Hudi与Flink整合

Hudi0.8.0版本与Flink1.12.x之上版本兼容，目前经过测试，Hudi0.8.0版本开始支持Flink，通过Flink写数据到Hudi时，必须开启checkpoint，至少有5次checkpoint后才能看到对应hudi中的数据。

但是应该是有一些问题，目前问题如下：

• 在本地执行Flink代码向Flink写数据时，存在“java.lang.AbstractMethodError: Method org/apache/hudi/sink/StreamWriteOperatorCoordinator.notifyCheckpointComplete(J)V is abstract”错误信息，预计是hudi版本支持问题。
• 写入到Flink中的数据，如果使用Flink读取出来，会有对应的错误：“Exception in thread "main" org.apache.hudi.exception.HoodieException: Get table avro schema error”，这个错误主要是由于上一个错误导致Hudi中没有commit信息，在内部读取时，读取不到Commit信息导致。

一、maven pom.xml导入如下包

<properties>
    <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
    <maven.compiler.source>1.8</maven.compiler.source>
    <maven.compiler.target>1.8</maven.compiler.target>
    <flink.version>1.12.1</flink.version>
</properties>

<dependencies>
    <!-- Flink操作Hudi需要的包-->
    <dependency>
        <groupId>org.apache.hudi</groupId>
        <artifactId>hudi-flink-bundle_2.11</artifactId>
        <version>0.8.0</version>
    </dependency>

    <dependency>
        <groupId>org.apache.flink</groupId>
        <artifactId>flink-clients_2.11</artifactId>
        <version>${flink.version}</version>
    </dependency>

    <!-- java 开发Flink所需依赖 -->
    <dependency>
        <groupId>org.apache.flink</groupId>
        <artifactId>flink-java</artifactId>
        <version>${flink.version}</version>
    </dependency>
    <dependency>
        <groupId>org.apache.flink</groupId>
        <artifactId>flink-streaming-java_2.11</artifactId>
        <version>${flink.version}</version>
    </dependency>

    <!-- Flink 开发Scala需要导入以下依赖 -->
    <dependency>
        <groupId>org.apache.flink</groupId>
        <artifactId>flink-scala_2.11</artifactId>
        <version>${flink.version}</version>
    </dependency>
    <dependency>
        <groupId>org.apache.flink</groupId>
        <artifactId>flink-streaming-scala_2.11</artifactId>
        <version>${flink.version}</version>
    </dependency>

    <!-- 读取hdfs文件需要jar包-->
    <dependency>
    <groupId>org.apache.hadoop</groupId>
    <artifactId>hadoop-client</artifactId>
    <version>2.9.2</version>
    </dependency>
    <!-- Flink 状态管理 RocksDB 依赖 -->
    <dependency>
        <groupId>org.apache.flink</groupId>
        <artifactId>flink-statebackend-rocksdb_2.11</artifactId>
        <version>${flink.version}</version>
    </dependency>

    <!-- Flink Kafka连接器的依赖 -->
    <dependency>
        <groupId>org.apache.flink</groupId>
        <artifactId>flink-connector-kafka_2.11</artifactId>
        <version>${flink.version}</version>
    </dependency>

    <dependency>
        <groupId>org.apache.flink</groupId>
        <artifactId>flink-csv</artifactId>
        <version>1.12.1</version>
    </dependency>

    <!-- Flink SQL & Table-->
    <dependency>
        <groupId>org.apache.flink</groupId>
        <artifactId>flink-table-planner_2.11</artifactId>
        <version>${flink.version}</version>
    </dependency>
    <dependency>
        <groupId>org.apache.flink</groupId>
        <artifactId>flink-table-api-scala-bridge_2.11</artifactId>
        <version>${flink.version}</version>
    </dependency>

    <!-- Flink SQL中使用Blink 需要导入的包-->
    <dependency>
        <groupId>org.apache.flink</groupId>
        <artifactId>flink-table-planner-blink_2.11</artifactId>
        <version>${flink.version}</version>
    </dependency>
</dependencies>

​

二、Flink 写入数据到Hudi代码

//1.创建对象
    val env: StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment
    val tableEnv: StreamTableEnvironment = StreamTableEnvironment.create(env,EnvironmentSettings.newInstance()
    .useBlinkPlanner().inStreamingMode().build())

    import org.apache.flink.streaming.api.scala._

    //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。
    env.enableCheckpointing(2000)
//    env.setStateBackend(new RocksDBStateBackend("hdfs://mycluster/flinkstate"))

    //3.设置并行度
    env.setParallelism(1)

    //4.读取Kakfa 中的数据
    tableEnv.executeSql(
      """
        | create table kafkaInputTable(
        |  id varchar,
        |  name varchar,
        |  age int,
        |  ts varchar,
        |  loc varchar
        | ) with (
        |  'connector' = 'kafka',
        |  'topic' = 'test_tp',
        |  'properties.bootstrap.servers'='node1:9092,node2:9092,node3:9092',
        |  'scan.startup.mode'='latest-offset',
        |  'properties.group.id' = 'testgroup',
        |  'format' = 'csv'
        | )
      """.stripMargin)

    val table: Table = tableEnv.from("kafkaInputTable")

    //5.创建Flink 对应的hudi表
    tableEnv.executeSql(
      """
        |CREATE TABLE t1(
        |  id VARCHAR(20) PRIMARY KEY NOT ENFORCED,--默认主键列为uuid,这里可以后面跟上“PRIMARY KEY NOT ENFORCED”指定为主键列
        |  name VARCHAR(10),
        |  age INT,
        |  ts VARCHAR(20),
        |  loc VARCHAR(20)
        |)
        |PARTITIONED BY (loc)
        |WITH (
        |  'connector' = 'hudi',
        |  'path' = '/flink_hudi_data',
        |  'write.tasks' = '1', -- default is 4 ,required more resource
        |  'compaction.tasks' = '1', -- default is 10 ,required more resource
        |  'table.type' = 'COPY_ON_WRITE' -- this creates a MERGE_ON_READ table, by default is COPY_ON_WRITE
        |)
      """.stripMargin)

    //6.向表中插入数据
    tableEnv.executeSql(
      s"""
         | insert into t1 select id,name,age,ts,loc from ${table}
      """.stripMargin)

    env.execute()

​

以上代码需要注意“PRIMARY KEY NOT ENFORCED”可以不指定，如果不指定hudi对应的主键列默认是“uuid”，指定后可以使用自定义的列名当做主键。

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