从 Dataflow 写入 Apache Iceberg

如需从 Dataflow 写入 Apache Iceberg，请使用托管式 I/O 连接器。

托管式 I/O 支持 Apache Iceberg 的以下功能：

目录	Hadoop Hive 基于 REST 的目录 BigQuery Metastore（如果不使用 Runner v2，则需要使用 Apache Beam SDK 2.62.0 或更高版本）
读取功能	批处理读取
写入功能	批处理写入 流式写入 动态目标 动态表格创建

对于 Apache Iceberg 的 BigQuery 表，请将 BigQueryIO 连接器与 BigQuery Storage API 搭配使用。该表必须已经存在；不支持动态表格创建。

依赖项

将以下依赖项添加到项目中：

Java

<dependency>
  <groupId>org.apache.beam</groupId>
  <artifactId>beam-sdks-java-managed</artifactId>
  <version>${beam.version}</version>
</dependency>

<dependency>
  <groupId>org.apache.beam</groupId>
  <artifactId>beam-sdks-java-io-iceberg</artifactId>
  <version>${beam.version}</version>
</dependency>

动态目标

适用于 Apache Iceberg 的托管式 I/O 支持动态目标。连接器可以根据传入记录中的字段值动态选择目标表，而不是写入单个固定表。

如需使用动态目标，请为 table 配置参数提供模板。如需了解详情，请参阅动态目标。

示例

以下示例展示了如何使用托管式 I/O 写入 Apache Iceberg。

将数据写入 Apache Iceberg 表

以下示例将内存中的 JSON 数据写入 Apache Iceberg 表。

Java

如需向 Dataflow 进行身份验证，请设置应用默认凭证。如需了解详情，请参阅为本地开发环境设置身份验证。

import com.google.common.collect.ImmutableMap;
import java.util.Arrays;
import java.util.List;
import java.util.Map;
import org.apache.beam.sdk.Pipeline;
import org.apache.beam.sdk.managed.Managed;
import org.apache.beam.sdk.options.Description;
import org.apache.beam.sdk.options.PipelineOptions;
import org.apache.beam.sdk.options.PipelineOptionsFactory;
import org.apache.beam.sdk.schemas.Schema;
import org.apache.beam.sdk.transforms.Create;
import org.apache.beam.sdk.transforms.JsonToRow;
import org.apache.beam.sdk.values.PCollectionRowTuple;

public class ApacheIcebergWrite {
  static final List<String> TABLE_ROWS = Arrays.asList(
      "{\"id\":0, \"name\":\"Alice\"}",
      "{\"id\":1, \"name\":\"Bob\"}",
      "{\"id\":2, \"name\":\"Charles\"}"
  );

  static final String CATALOG_TYPE = "hadoop";

  // The schema for the table rows.
  public static final Schema SCHEMA = new Schema.Builder()
      .addStringField("name")
      .addInt64Field("id")
      .build();

  public interface Options extends PipelineOptions {
    @Description("The URI of the Apache Iceberg warehouse location")
    String getWarehouseLocation();

    void setWarehouseLocation(String value);

    @Description("The name of the Apache Iceberg catalog")
    String getCatalogName();

    void setCatalogName(String value);

    @Description("The name of the table to write to")
    String getTableName();

    void setTableName(String value);
  }

  public static void main(String[] args) {

    // Parse the pipeline options passed into the application. Example:
    //   --runner=DirectRunner --warehouseLocation=$LOCATION --catalogName=$CATALOG \
    //   --tableName= $TABLE_NAME
    // For more information, see https://beam.apache.org/documentation/programming-guide/#configuring-pipeline-options
    Options options = PipelineOptionsFactory.fromArgs(args).withValidation().as(Options.class);
    Pipeline pipeline = Pipeline.create(options);

    // Configure the Iceberg source I/O
    Map catalogConfig = ImmutableMap.<String, Object>builder()
        .put("warehouse", options.getWarehouseLocation())
        .put("type", CATALOG_TYPE)
        .build();

    ImmutableMap<String, Object> config = ImmutableMap.<String, Object>builder()
        .put("table", options.getTableName())
        .put("catalog_name", options.getCatalogName())
        .put("catalog_properties", catalogConfig)
        .build();

    // Build the pipeline.
    pipeline.apply(Create.of(TABLE_ROWS))
        .apply(JsonToRow.withSchema(SCHEMA))
        .apply(Managed.write(Managed.ICEBERG).withConfig(config));

    pipeline.run().waitUntilFinish();
  }
}

使用动态目标写入数据

以下示例根据输入数据中的某个字段将数据写入不同的 Apache Iceberg 表。

Java

如需向 Dataflow 进行身份验证，请设置应用默认凭证。如需了解详情，请参阅为本地开发环境设置身份验证。

import com.google.common.collect.ImmutableMap;
import java.util.Arrays;
import java.util.List;
import java.util.Map;
import org.apache.beam.sdk.Pipeline;
import org.apache.beam.sdk.PipelineResult;
import org.apache.beam.sdk.managed.Managed;
import org.apache.beam.sdk.options.Description;
import org.apache.beam.sdk.options.PipelineOptions;
import org.apache.beam.sdk.options.PipelineOptionsFactory;
import org.apache.beam.sdk.schemas.Schema;
import org.apache.beam.sdk.transforms.Create;
import org.apache.beam.sdk.transforms.JsonToRow;

public class ApacheIcebergDynamicDestinations {

  // The schema for the table rows.
  public static final Schema SCHEMA = new Schema.Builder()
      .addInt64Field("id")
      .addStringField("name")
      .addStringField("airport")
      .build();

  // The data to write to table, formatted as JSON strings.
  static final List<String> TABLE_ROWS = List.of(
      "{\"id\":0, \"name\":\"Alice\", \"airport\": \"ORD\" }",
      "{\"id\":1, \"name\":\"Bob\", \"airport\": \"SYD\" }",
      "{\"id\":2, \"name\":\"Charles\", \"airport\": \"ORD\" }"
  );

  public interface Options extends PipelineOptions {
    @Description("The URI of the Apache Iceberg warehouse location")
    String getWarehouseLocation();

    void setWarehouseLocation(String value);

    @Description("The name of the Apache Iceberg catalog")
    String getCatalogName();

    void setCatalogName(String value);
  }

  // Write JSON data to Apache Iceberg, using dynamic destinations to determine the Iceberg table
  // where Dataflow writes each record. The JSON data contains a field named "airport". The
  // Dataflow pipeline writes to Iceberg tables with the naming pattern "flights-{airport}".
  public static void main(String[] args) {
    // Parse the pipeline options passed into the application. Example:
    //   --runner=DirectRunner --warehouseLocation=$LOCATION --catalogName=$CATALOG \
    // For more information, see https://beam.apache.org/documentation/programming-guide/#configuring-pipeline-options
    Options options = PipelineOptionsFactory.fromArgs(args).withValidation().as(Options.class);
    Pipeline pipeline = Pipeline.create(options);

    // Configure the Iceberg source I/O
    Map catalogConfig = ImmutableMap.<String, Object>builder()
        .put("warehouse", options.getWarehouseLocation())
        .put("type", "hadoop")
        .build();

    ImmutableMap<String, Object> config = ImmutableMap.<String, Object>builder()
        .put("catalog_name", options.getCatalogName())
        .put("catalog_properties", catalogConfig)
        // Route the incoming records based on the value of the "airport" field.
        .put("table", "flights-{airport}")
        // Specify which fields to keep from the input data.
        .put("keep", Arrays.asList("name", "id"))
        .build();

    // Build the pipeline.
    pipeline
        // Read in-memory JSON data.
        .apply(Create.of(TABLE_ROWS))
        // Convert the JSON records to Row objects.
        .apply(JsonToRow.withSchema(SCHEMA))
        // Write each Row to Apache Iceberg.
        .apply(Managed.write(Managed.ICEBERG).withConfig(config));

    // Run the pipeline.
    pipeline.run().waitUntilFinish();
  }
}

后续步骤

• 从 Apache Iceberg 读取数据。
• 使用 BigLake REST 目录向 Apache Iceberg 进行流式写入。
• 详细了解托管式 I/O。