Read from Apache Iceberg to Dataflow

You might need to ingest and transform data for downstream analytics at scale that's stored in Apache Iceberg open table formats. Ingesting Apache Iceberg data into Dataflow using the managed I/O connector lets you run distributed, parallel batch pipelines to read and process high-volume datasets across Hadoop, Hive, REST catalogs, and the BigQuery metastore.

If you work with BigQuery tables for Apache Iceberg, use the BigQueryIO connector with the BigQuery Storage API instead. The target table must already exist, as dynamic table creation isn't supported.

Capabilities

Managed I/O supports the following capabilities for Apache Iceberg:

Catalogs	Hadoop Hive REST-based catalogs BigQuery metastore (requires Apache Beam SDK 2.62.0 or later if not using the Portable Runner)
Read capabilities	Batch read
Write capabilities	Batch write Streaming write Dynamic destinations Dynamic table creation

For BigQuery tables for Apache Iceberg, use the BigQueryIO connector with BigQuery Storage API. The table must already exist; dynamic table creation is not supported.

Dependencies

Add the following dependencies to your project:

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>

Example

The following example reads from an Apache Iceberg table and writes the data to text files.

Java

To authenticate to Dataflow, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

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();
  }
}

What's next

• Write to Apache Iceberg.
• Streaming Write to Apache Iceberg with Lakehouse REST Catalog.
• Learn more about Managed I/O.