Using Dagster with DuckDB#

This tutorial focuses on creating and interacting with DuckDB tables using Dagster's asset definitions.

The dagster-duckdb library provides two ways to interact with DuckDB tables:

• Resource: The resource allows you to directly run SQL queries against tables within an asset's compute function. Available resources: DuckDBResource.
• I/O manager: The I/O manager transfers the responsibility of storing and loading DataFrames as DuckdB tables to Dagster. Available I/O managers: DuckDBPandasIOManager, DuckDBPySparkIOManager, DuckDBPolarsIOManager.

This tutorial is divided into two sections to demonstrate the differences between the DuckDB resource and the DuckDB I/O manager. Each section will create the same assets, but the first section will use the DuckDB resource to store data in DuckDB, whereas the second section will use the DuckDB I/O manager. When writing your own assets, you may choose one or the other (or both) approaches depending on your storage requirements. See When to use I/O managers to learn more about when to use I/O managers and when to use resources.

In Option 1 you will:

• Set up and configure the DuckDB resource.
• Use the DuckDB resource to execute a SQL query to create a table.
• Use the DuckDB resource to execute a SQL query to interact with the table.

In Option 2 you will:

• Set up and configure the DuckDB I/O manager.
• Use Pandas to create a DataFrame, then delegate responsibility creating a table to the DuckDB I/O manager.
• Use the DuckDB I/O manager to load the table into memory so that you can interact with it using the Pandas library.

When writing your own assets, you may choose one or the other (or both) approaches depending on your storage requirements. See When to use I/O managers to learn more.

By the end of the tutorial, you will:

• Understand how to interact with a DuckDB database using the DuckDB resource.
• Understand how to use the DuckDB I/O manager to store and load DataFrames as DuckDB tables.
• Understand how to define dependencies between assets corresponding to tables in a DuckDB database.

Prerequisites#

To complete this tutorial, you'll need:

• To install the dagster-duckdb and dagster-duckdb-pandas libraries:

  pip install dagster-duckdb dagster-duckdb-pandas
  

Option 1: Using the DuckDB resource#

Step 1: Configure the DuckDB resource#

To use the DuckDB resource, you'll need to add it to your Definitions object. The DuckDB resource requires some configuration. You must set a path to a DuckDB database as the database configuration value. If the database does not already exist, it will be created for you:

from dagster_duckdb import DuckDBResource

from dagster import Definitions

defs = Definitions(
    assets=[iris_dataset],
    resources={
        "duckdb": DuckDBResource(
            database="path/to/my_duckdb_database.duckdb",  # required
        )
    },
)

Step 2: Create tables in DuckDB#

import pandas as pd
from dagster_duckdb import DuckDBResource

from dagster import asset


@asset
def iris_dataset(duckdb: DuckDBResource) -> None:
    iris_df = pd.read_csv(
        "https://docs.dagster.io/assets/iris.csv",
        names=[
            "sepal_length_cm",
            "sepal_width_cm",
            "petal_length_cm",
            "petal_width_cm",
            "species",
        ],
    )

    with duckdb.get_connection() as conn:
        conn.execute("CREATE TABLE iris.iris_dataset AS SELECT * FROM iris_df")

from dagster import AssetSpec

iris_harvest_data = AssetSpec(key="iris_harvest_data")

Now you can run dagster dev and materialize the iris_dataset asset from the Dagster UI.

Step 3: Define downstream assets#

Once you have created an asset that represents a table in DuckDB, you will likely want to create additional assets that work with the data.

from dagster import asset

# this example uses the iris_dataset asset from Step 1


@asset(deps=[iris_dataset])
def iris_setosa(duckdb: DuckDBResource) -> None:
    with duckdb.get_connection() as conn:
        conn.execute(
            "CREATE TABLE iris.iris_setosa AS SELECT * FROM iris.iris_dataset WHERE"
            " species = 'Iris-setosa'"
        )

In this asset, you're creating second table that only contains the data for the Iris Setosa species. This asset has a dependency on the iris_dataset asset. To define this dependency, you provide the iris_dataset asset as the deps parameter to the iris_setosa asset. You can then run the SQL query to create the table of Iris Setosa data.

Completed code example#

When finished, your code should look like the following:

import pandas as pd
from dagster_duckdb import DuckDBResource

from dagster import AssetSpec, Definitions, asset

iris_harvest_data = AssetSpec(key="iris_harvest_data")


@asset
def iris_dataset(duckdb: DuckDBResource) -> None:
    iris_df = pd.read_csv(
        "https://docs.dagster.io/assets/iris.csv",
        names=[
            "sepal_length_cm",
            "sepal_width_cm",
            "petal_length_cm",
            "petal_width_cm",
            "species",
        ],
    )

    with duckdb.get_connection() as conn:
        conn.execute("CREATE TABLE iris.iris_dataset AS SELECT * FROM iris_df")


@asset(deps=[iris_dataset])
def iris_setosa(duckdb: DuckDBResource) -> None:
    with duckdb.get_connection() as conn:
        conn.execute(
            "CREATE TABLE iris.iris_setosa AS SELECT * FROM iris.iris_dataset WHERE"
            " species = 'Iris-setosa'"
        )


defs = Definitions(
    assets=[iris_dataset],
    resources={
        "duckdb": DuckDBResource(
            database="path/to/my_duckdb_database.duckdb",
        )
    },
)

Option 2: Using the DuckDB I/O manager#

You may want to use an I/O manager to handle storing DataFrames as tables in DuckDB and loading DuckDB tables as DataFrames in downstream assets. You may want to use an I/O manager if:

• You want your data to be loaded in memory so that you can interact with it using Python.
• You'd like to have Dagster manage how you store the data and load it as an input in downstream assets.

Using an I/O manager is not required, and you can reference When to use I/O managers to learn more.

This section of the guide focuses on storing and loading Pandas DataFrames in DuckDB, but Dagster also supports using PySpark and Polars DataFrames with DuckDB. The concepts from this guide apply to working with PySpark and Polars DataFrames, and you can learn more about setting up and using the DuckDB I/O manager with PySpark and Polars DataFrames in the reference guide.

Step 1: Configure the DuckDB I/O manager#

To use the DuckDB I/O, you'll need to add it to your Definitions object. The DuckDB I/O manager requires some configuration to connect to your database. You must provide a path where a DuckDB database will be created. Additionally, you can specify a schema where the DuckDB I/O manager will create tables.

from dagster_duckdb_pandas import DuckDBPandasIOManager

from dagster import Definitions

defs = Definitions(
    assets=[iris_dataset],
    resources={
        "io_manager": DuckDBPandasIOManager(
            database="path/to/my_duckdb_database.duckdb",  # required
            schema="IRIS",  # optional, defaults to PUBLIC
        )
    },
)

Step 2: Create tables in DuckDB#

The DuckDB I/O manager can create and update tables for your Dagster-defined assets, but you can also make existing DuckDB tables available to Dagster.

import pandas as pd

from dagster import asset


@asset
def iris_dataset() -> pd.DataFrame:
    return pd.read_csv(
        "https://docs.dagster.io/assets/iris.csv",
        names=[
            "sepal_length_cm",
            "sepal_width_cm",
            "petal_length_cm",
            "petal_width_cm",
            "species",
        ],
    )

from dagster import AssetSpec

iris_harvest_data = AssetSpec(key="iris_harvest_data")

Step 3: Load DuckDB tables in downstream assets#

Once you have created an asset that represents a table in DuckDB, you will likely want to create additional assets that work with the data. Dagster and the DuckDB I/O manager allow you to load the data stored in DuckDB tables into downstream assets.

import pandas as pd

from dagster import asset

# this example uses the iris_dataset asset from Step 2


@asset
def iris_setosa(iris_dataset: pd.DataFrame) -> pd.DataFrame:
    return iris_dataset[iris_dataset["species"] == "Iris-setosa"]

In this asset, you're providing the iris_dataset asset as a dependency to iris_setosa. By supplying iris_dataset as a parameter to iris_setosa, Dagster knows to use the DuckDBPandasIOManager to load this asset into memory as a Pandas DataFrame and pass it as an argument to iris_setosa. Next, a DataFrame that only contains the data for the Iris Setosa species is created and returned. Then the DuckDBPandasIOManager will store the DataFrame as the IRIS.IRIS_SETOSA table in DuckDB.

Completed code example#

When finished, your code should look like the following:

import pandas as pd
from dagster_duckdb_pandas import DuckDBPandasIOManager

from dagster import AssetSpec, Definitions, asset

iris_harvest_data = AssetSpec(key="iris_harvest_data")


@asset
def iris_dataset() -> pd.DataFrame:
    return pd.read_csv(
        "https://docs.dagster.io/assets/iris.csv",
        names=[
            "sepal_length_cm",
            "sepal_width_cm",
            "petal_length_cm",
            "petal_width_cm",
            "species",
        ],
    )


@asset
def iris_setosa(iris_dataset: pd.DataFrame) -> pd.DataFrame:
    return iris_dataset[iris_dataset["species"] == "Iris-setosa"]


defs = Definitions(
    assets=[iris_dataset, iris_harvest_data, iris_setosa],
    resources={
        "io_manager": DuckDBPandasIOManager(
            database="path/to/my_duckdb_database.duckdb",
            schema="IRIS",
        )
    },
)

Related#

For more DuckDB features, refer to the DuckDB reference.

For more information on asset definitions, refer to the tutorial or the Assets documentation.

For more information on I/O managers, refer to the I/O manager documentation.