前言

之前在Flink Hudi DataStream API代码示例中总结了Hudi Flink DataStream API的代码及本地调试，并且在文中提到其实大家用Table API更多一些，但是我感觉Table API调试源码可能会比较难一点，因为可能会涉及到SQL解析，不清楚Table API的入口在哪里。
但是在我总结的上篇文章Flink用户自定义连接器（Table API Connectors）学习总结中知道了其实Flink Table API读写Hudi是通过自定义实现了一个Hudi的Table API Connectors（‘connector’ = ‘hudi’）,相关类为HoodieTableFactory、HoodieTableSink和HoodieTableSource，入口为HoodieTableFactory。

代码

GitHub地址：https://github.com/dongkelun/hudi-demo/tree/master/hudi0.13_flink1.15

package com.dkl.hudi.flink;

import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.EnvironmentSettings;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.hudi.configuration.FlinkOptions;

import static org.apache.hudi.examples.quickstart.utils.QuickstartConfigurations.sql;

public class HudiFlinkSQL {

    public static void main(String[] args) {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env, EnvironmentSettings.inBatchMode());
        String tableName = "t1";
        if (args.length > 0) {
            tableName = args[0];
        }
        String tablePath = "/tmp/flink/hudi/" + tableName;
        String hoodieTableDDL = sql(tableName)
                .field("id int")
                .field("name string")
                .field("price double")
                .field("ts bigint")
                .field("dt string")
                .option(FlinkOptions.PATH, tablePath)
//                .option(FlinkOptions.READ_AS_STREAMING, true)
//                .option(FlinkOptions.TABLE_TYPE, "COPY_ON_WRITE")
                .partitionField("dt")
                .pkField("id")
                .end();
        tableEnv.executeSql(hoodieTableDDL);
        tableEnv.executeSql(String.format("insert into %s values (1,'hudi',10,100,'2023-05-28')", tableName));
        tableEnv.executeSql(String.format("select * from %s", tableName)).print();
    }
}

其实就是通过tableEnv.executeSql执行Flink SQL,其中创建Hudi表的SQL语句是利用Hudi源码中模块hudi-examples-flink里面的sql方法生成的，它会根据参数返回对应的创建Hudi表的SQL语句，示例：

        String tableName = "t1";
        String tablePath = "/tmp/flink/hudi/" + tableName;
        String hoodieTableDDL = sql(tableName)
                .option(FlinkOptions.PATH, tablePath)
                .option(FlinkOptions.READ_AS_STREAMING, true)
                .option(FlinkOptions.TABLE_TYPE, "COPY_ON_WRITE")
                .end();
        System.out.println(hoodieTableDDL);

输出

create table t1(
  `uuid` VARCHAR(20),
  `name` VARCHAR(10),
  `age` INT,
  `ts` TIMESTAMP(3),
  `partition` VARCHAR(10),
  PRIMARY KEY(uuid) NOT ENFORCED
)
PARTITIONED BY (`partition`)
with (
  'connector' = 'hudi',
  'read.streaming.enabled' = 'true',
  'path' = '/tmp/flink/hudi/t1',
  'connector' = 'hudi',
  'table.type' = 'COPY_ON_WRITE'
)

如果不指定字段的话，会有官方文档示例中几个默认字段，默认主键为uuid，默认为分区表，分区字段为partition

        hoodieTableDDL = sql(tableName)
                .field("id int")
                .field("name string")
                .field("price double")
                .field("ts bigint")
                .field("dt string")
                .option(FlinkOptions.PATH, tablePath)
                .noPartition()
                .pkField("id")
                .end();
        System.out.println(hoodieTableDDL);

可以通过field方法指定表的字段，pkField指定表的主键，noPartition设置表为非分区表，partitionField指定表的分区字段

create table t1(
  id int,
  name string,
  price double,
  ts bigint,
  dt string,
  PRIMARY KEY(id) NOT ENFORCED
)
with (
  'connector' = 'hudi',
  'path' = '/tmp/flink/hudi/t1',
  'connector' = 'hudi'
)

本地运行调试

运行结果

调试

我们根据开头提到的文章Flink用户自定义连接器（Table API Connectors）学习总结可知入口为HoodieTableFactory，其中sink的入口为createDynamicTableSink,source的入口为createDynamicTableSource

本地调试源码的时候可能会遇到在Idea中源码下载不下来的情况，我们可以直接去官网下载对应的源码jar包，然后放到自己本地的仓库中，方便我们调试的时候阅读源码。比如hudi-flink对应的源码的下载地址：https://repo1.maven.org/maven2/org/apache/hudi/hudi-flink/0.13.0/hudi-flink-0.13.0-sources.jar,然后将hudi-flink-0.13.0-sources.jar放到路径 m2\repository\org\apache\hudi\hudi-flink\0.13.0 中就可以了

我们发现Table API的入口和DataStream API的入口差不多，DataStream API的入口是在HoodiePipeline的sink和source方法里，而这两个方法也是也是分别调用了HoodieTableFactory的createDynamicTableSink和createDynamicTableSource

  /**
   * Returns the data stream sink with given catalog table.
   *
   * @param input        The input datastream
   * @param tablePath    The table path to the hoodie table in the catalog
   * @param catalogTable The hoodie catalog table
   * @param isBounded    A flag indicating whether the input data stream is bounded
   */
  private static DataStreamSink<?> sink(DataStream<RowData> input, ObjectIdentifier tablePath, ResolvedCatalogTable catalogTable, boolean isBounded) {
    FactoryUtil.DefaultDynamicTableContext context = Utils.getTableContext(tablePath, catalogTable, Configuration.fromMap(catalogTable.getOptions()));
    HoodieTableFactory hoodieTableFactory = new HoodieTableFactory();
    return ((DataStreamSinkProvider) hoodieTableFactory.createDynamicTableSink(context)
        .getSinkRuntimeProvider(new SinkRuntimeProviderContext(isBounded)))
        .consumeDataStream(input);
  }

  /**
   * Returns the data stream source with given catalog table.
   *
   * @param execEnv      The execution environment
   * @param tablePath    The table path to the hoodie table in the catalog
   * @param catalogTable The hoodie catalog table
   */
    private static DataStream<RowData> source(StreamExecutionEnvironment execEnv, ObjectIdentifier tablePath, ResolvedCatalogTable catalogTable) {
    FactoryUtil.DefaultDynamicTableContext context = Utils.getTableContext(tablePath, catalogTable, Configuration.fromMap(catalogTable.getOptions()));
    HoodieTableFactory hoodieTableFactory = new HoodieTableFactory();
    DataStreamScanProvider dataStreamScanProvider = (DataStreamScanProvider) ((ScanTableSource) hoodieTableFactory
        .createDynamicTableSource(context))
        .getScanRuntimeProvider(new ScanRuntimeProviderContext());
    return dataStreamScanProvider.produceDataStream(execEnv);
  }

源码调试

我们还可以直接在源码里进行调试，这样方便我们直接看到修改源码后的效果。直接在源码里进行调试可能配置环境会比较麻烦一点，每个版本也不太一样。比如我在hudi 0.13.0源码中模块hudi-examples-flink里进行调试，需要做如下修改:

详细的pom修改可以看我提到github上的commit:https://github.com/dongkelun/hudi/commit/558910d4cab189d0cbfa9c69332f3e4e74e56b41

这样我们就可以本地直接运行源码中的类：HoodieFlinkQuickstart，也可以直接修改源码查看效果：

源码里的demo和测试用例比较全，我们可以多看一看多调试一下，利于提升对源码的理解。