DuckDB + PySpark (dagster-duckdb-pyspark)¶

This library provides an integration with the DuckDB database and PySpark data processing library.

Related guides:

• Using Dagster with DuckDB guide

• DuckDB I/O manager reference

dagster_duckdb_pyspark.DuckDBPySparkIOManager IOManagerDefinition[source]¶

Config Schema:

database (dagster.StringSource):

Path to the DuckDB database.

schema (Union[dagster.StringSource, None], optional):

Name of the schema to use.

An I/O manager definition that reads inputs from and writes PySpark DataFrames to DuckDB. When using the DuckDBPySparkIOManager, any inputs and outputs without type annotations will be loaded as PySpark DataFrames.

Returns:

IOManagerDefinition

Examples

from dagster_duckdb_pyspark import DuckDBPySparkIOManager

@asset(
    key_prefix=["my_schema"]  # will be used as the schema in DuckDB
)
def my_table() -> pyspark.sql.DataFrame:  # the name of the asset will be the table name
    ...

defs = Definitions(
    assets=[my_table],
    resources={"io_manager": DuckDBPySparkIOManager(database="my_db.duckdb")}
)

If you do not provide a schema, Dagster will determine a schema based on the assets and ops using the I/O Manager. For assets, the schema will be determined from the asset key, as in the above example. For ops, the schema can be specified by including a “schema” entry in output metadata. If “schema” is not provided via config or on the asset/op, “public” will be used for the schema.

@op(
    out={"my_table": Out(metadata={"schema": "my_schema"})}
)
def make_my_table() -> pyspark.sql.DataFrame:
    # the returned value will be stored at my_schema.my_table
    ...

To only use specific columns of a table as input to a downstream op or asset, add the metadata “columns” to the In or AssetIn.

@asset(
    ins={"my_table": AssetIn("my_table", metadata={"columns": ["a"]})}
)
def my_table_a(my_table: pyspark.sql.DataFrame) -> pyspark.sql.DataFrame:
    # my_table will just contain the data from column "a"
    ...

dagster_duckdb_pyspark.duckdb_pyspark_io_manager IOManagerDefinition¶

Config Schema:

database (dagster.StringSource):

Path to the DuckDB database.

schema (Union[dagster.StringSource, None], optional):

Name of the schema to use.

An I/O manager definition that reads inputs from and writes PySpark DataFrames to DuckDB. When using the duckdb_pyspark_io_manager, any inputs and outputs without type annotations will be loaded as PySpark DataFrames.

Returns:

IOManagerDefinition

Examples

from dagster_duckdb_pyspark import duckdb_pyspark_io_manager

@asset(
    key_prefix=["my_schema"]  # will be used as the schema in DuckDB
)
def my_table() -> pyspark.sql.DataFrame:  # the name of the asset will be the table name
    ...

@repository
def my_repo():
    return with_resources(
        [my_table],
        {"io_manager": duckdb_pyspark_io_manager.configured({"database": "my_db.duckdb"})}
    )

If you do not provide a schema, Dagster will determine a schema based on the assets and ops using the I/O Manager. For assets, the schema will be determined from the asset key. For ops, the schema can be specified by including a “schema” entry in output metadata. If “schema” is not provided via config or on the asset/op, “public” will be used for the schema.

@op(
    out={"my_table": Out(metadata={"schema": "my_schema"})}
)
def make_my_table() -> pyspark.sql.DataFrame:
    # the returned value will be stored at my_schema.my_table
    ...

To only use specific columns of a table as input to a downstream op or asset, add the metadata “columns” to the In or AssetIn.

@asset(
    ins={"my_table": AssetIn("my_table", metadata={"columns": ["a"]})}
)
def my_table_a(my_table: pyspark.sql.DataFrame) -> pyspark.sql.DataFrame:
    # my_table will just contain the data from column "a"
    ...

class dagster_duckdb_pyspark.DuckDBPySparkTypeHandler[source]¶

Stores PySpark DataFrames in DuckDB.

To use this type handler, return it from the type_handlers` method of an I/O manager that inherits from ``DuckDBIOManager.

Example

from dagster_duckdb import DuckDBIOManager
from dagster_duckdb_pyspark import DuckDBPySparkTypeHandler

class MyDuckDBIOManager(DuckDBIOManager):
    @staticmethod
    def type_handlers() -> Sequence[DbTypeHandler]:
        return [DuckDBPySparkTypeHandler()]

@asset(
    key_prefix=["my_schema"]  # will be used as the schema in duckdb
)
def my_table() -> pyspark.sql.DataFrame:  # the name of the asset will be the table name
    ...

defs = Definitions(
    assets=[my_table],
    resources={"io_manager": MyDuckDBIOManager(database="my_db.duckdb")}
)