Membaca dari Apache Iceberg ke Dataflow

Untuk membaca dari Apache Iceberg ke Dataflow, gunakan konektor I/O terkelola.

Dependensi

Tambahkan dependensi berikut ke project Anda:

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>

Konfigurasi

Untuk Apache Iceberg, I/O Terkelola menggunakan parameter konfigurasi berikut:

Nama	Jenis data	Deskripsi
table	string	ID tabel Apache Iceberg. Contoh: "db.table1".
catalog_name	string	Nama katalog. Contoh: "local".
catalog_properties	map	Peta properti konfigurasi untuk katalog Apache Iceberg. Properti yang diperlukan bergantung pada katalog. Untuk informasi selengkapnya, lihat CatalogUtil dalam dokumentasi Apache Iceberg.
config_properties	map	Kumpulan properti konfigurasi Hadoop opsional. Untuk selengkapnya informasi, lihat CatalogUtil dalam dokumentasi Apache Iceberg.

Contoh

Contoh berikut membaca dari tabel Apache Iceberg dan menulis data ke file teks.

Java

Untuk melakukan autentikasi ke Dataflow, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, baca Menyiapkan autentikasi untuk lingkungan pengembangan lokal.

import com.google.common.collect.ImmutableMap;
import java.util.Map;
import org.apache.beam.sdk.Pipeline;
import org.apache.beam.sdk.io.TextIO;
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.transforms.MapElements;
import org.apache.beam.sdk.values.PCollectionRowTuple;
import org.apache.beam.sdk.values.TypeDescriptors;

public class ApacheIcebergRead {

  static final String CATALOG_TYPE = "hadoop";

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

    void setWarehouseLocation(String value);

    @Description("Path to write the output file")
    String getOutputPath();

    void setOutputPath(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= $TABLE_NAME --outputPath=$OUTPUT_FILE
    // 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(Managed.read(Managed.ICEBERG).withConfig(config))
        .getSinglePCollection()
        // Format each record as a string with the format 'id:name'.
        .apply(MapElements
            .into(TypeDescriptors.strings())
            .via((row -> {
              return String.format("%d:%s",
                  row.getInt64("id"),
                  row.getString("name"));
            })))
        // Write to a text file.
        .apply(
            TextIO.write()
                .to(options.getOutputPath())
                .withNumShards(1)
                .withSuffix(".txt"));

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