✦TL;DR

Writes data to Apache Iceberg tables using daft. Supports table creation, schema inference, and multiple write modes.

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IcebergOutput writes data to Apache Iceberg tables using daft. It supports table creation, schema inference, and multiple write modes. It inherits from the base Output class and provides specialized functionality for writing to Iceberg table format.

IcebergOutput

Class

📁application_sdk.outputs.iceberg

Inheritance chain:

Output

Writes data to Apache Iceberg tables using daft. Supports table creation, schema inference, and multiple write modes.

Methods3

▸

__init__

__init__(self, iceberg_catalog: Catalog, iceberg_namespace: str, iceberg_table: Union[str, Table], mode: str = 'append', total_record_count: int = 0, chunk_count: int = 0, retain_local_copy: bool = False)

Initialize IcebergOutput with PyIceberg catalog, namespace, and table. Accepts either a table name (string) or existing Table object.

Parameters

iceberg_catalogCatalog

Required

PyIceberg Catalog object

iceberg_namespacestr

Required

Iceberg namespace (database name)

iceberg_tableUnion[str, Table]

Required

Table name or PyIceberg Table object

modestr

Optional

Write mode: 'append' or 'overwrite' (default: 'append')

total_record_countint

Optional

Initial total record count (default: 0)

chunk_countint

Optional

Initial chunk count (default: 0)

retain_local_copybool

Optional

Whether to retain local copy (default: False)

▸

write_dataframe

async

async write_dataframe(self, dataframe: pd.DataFrame) -> None

Writes a pandas DataFrame to an Iceberg table. Internally converts to daft DataFrame, calls write_daft_dataframe() internally, records metrics for successful writes, and skips empty DataFrames.

Parameters

dataframepd.DataFrame

Required

The pandas DataFrame to write

▸

write_daft_dataframe

async

async write_daft_dataframe(self, dataframe: daft.DataFrame) -> None

Writes a daft DataFrame to an Iceberg table with automatic table creation if needed. If table name is provided (string), creates table if it doesn't exist using DataFrame schema. If table object is provided, uses existing table. Writes using Daft's write_iceberg() method, updates chunk_count and total_record_count, records metrics for monitoring, and skips empty DataFrames. Automatically infers schema from DataFrame and uses create_table_if_not_exists() to avoid errors if table already exists.

Parameters

dataframedaft.DataFrame

Required

The daft DataFrame to write

Usage Examples

Initialize with catalog and table name

Create IcebergOutput with catalog and table name - table is created if it doesn't exist

from application_sdk.outputs import IcebergOutput
from pyiceberg.catalog import Catalog

iceberg_output = IcebergOutput(
  iceberg_catalog=catalog,
  iceberg_namespace="default",
  iceberg_table="my_table",
  mode="append"
)

await iceberg_output.write_dataframe(df)

Initialize with existing table object

Use existing PyIceberg Table object

from pyiceberg.table import Table

table = catalog.load_table("default.my_table")
iceberg_output = IcebergOutput(
  iceberg_catalog=catalog,
  iceberg_namespace="default",
  iceberg_table=table,
  mode="overwrite"
)

Usage patterns

For detailed usage patterns including writing to Iceberg tables, automatic table creation, using existing table objects, write modes, and other IcebergOutput-specific features, see Output usage patterns and select the IcebergOutput tab.

See also

• Outputs: Base Output class and common usage patterns for all output types
• ParquetOutput: Write data to Parquet files with chunking and Hive partitioning
• JsonOutput: Write data to JSON files in JSONL format