Using Dagster with DuckDB#

This tutorial focuses on creating and interacting with DuckDB tables using Dagster's Software-defined Assets (SDAs).

The dagster-duckdb library provides two ways to interact with DuckDB tables. The resource allows you to directly run SQL queries against tables within an asset's compute function. The I/O manager transfers the responsibility of storing and loading DataFrames as DuckDB tables to Dagster.

This tutorial is divided in two parts. Both parts will create the same assets, but how the data is stored in DuckDB will differ. In Part 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 Part 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

Part 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#

Create DuckDB tables in Dagster#

Using the DuckDB resource, you can create DuckDB tables using the DuckDB Python API:


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")

In this example, you're defining an asset that fetches the Iris dataset as a Pandas DataFrame and renames the columns. Then, using the DuckDB resource, the DataFrame is stored in DuckDB as the iris.iris_dataset table.

Make existing tables available in Dagster#

If you already have tables in DuckDB, you may want to have other assets in Dagster depend on those tables. You can accomplish this by creating source assets for these tables.


from dagster import SourceAsset

iris_harvest_data = SourceAsset(key="iris_harvest_data")

In this example, you're creating a SourceAsset for a pre-existing table called 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 or source 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 Definitions, SourceAsset, asset

iris_harvest_data = SourceAsset(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",
        )
    },
)

Part 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. 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.

Store a Dagster asset as a table in DuckDB#

To store data in DuckDB using the DuckDB I/O manager, you can simply return a Pandas DataFrame from your asset. Dagster will handle storing and loading your assets in DuckDB.


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",
        ],
    )

In this example, you're defining an asset that fetches the Iris dataset as a Pandas DataFrame, renames the columns, then returns the DataFrame. The type signature of the function tells the I/O manager what data type it is working with, so it is important to include the return type pd.DataFrame.

When Dagster materializes the iris_dataset asset using the configuration from Step 1: Configure the DuckDB I/O manager, the DuckDB I/O manager will create the table IRIS.IRIS_DATASET if it does not exist and replace the contents of the table with the value returned from the iris_dataset asset.

Make an existing table available in Dagster#

If you already have tables in DuckDB, you may want to make them available to other Dagster assets. You can accomplish this by using source assets for these tables. By creating a source asset for the existing table, you tell Dagster how to find the table so it can be fetched for downstream assets.


from dagster import SourceAsset

iris_harvest_data = SourceAsset(key="iris_harvest_data")

In this example, you're creating a SourceAsset for a pre-existing table containing iris harvests data. To make the data available to other Dagster assets, you need to tell the DuckDB I/O manager how to find the data.

Because you already supplied the database and schema in the I/O manager configuration in Step 1: Configure the DuckDB I/O manager, you only need to provide the table name. This is done with the key parameter in SourceAsset. When the I/O manager needs to load the iris_harvest_data in a downstream asset, it will select the data in the IRIS.IRIS_HARVEST_DATA table as a Pandas DataFrame and provide it to the downstream asset.

Step 3: Load DuckDB tables in downstream assets#

Once you have created an asset or source 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 Definitions, SourceAsset, asset

iris_harvest_data = SourceAsset(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",
        )
    },
)

For more DuckDB features, refer to the DuckDB reference.

For more information on Software-defined Assets, refer to the tutorial or the Assets documentation.

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

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Using Dagster with DuckDB#

Prerequisites#

Part 1: Using the DuckDB resource#

Step 1: Configure the DuckDB resource#

Step 2: Create tables in DuckDB#

Create DuckDB tables in Dagster#

Make existing tables available in Dagster#

Step 3: Define downstream assets#

Completed code example#

Part 2: Using the DuckDB I/O manager#

Step 1: Configure the DuckDB I/O manager#

Step 2: Create tables in DuckDB#

Store a Dagster asset as a table in DuckDB#

Make an existing table available in Dagster#

Step 3: Load DuckDB tables in downstream assets#

Completed code example#

Related#