David Gasquez
02/14/2024, 8:05 AMScout
02/14/2024, 8:05 AMDuckDBResource
provided by Dagster. According to the documentation, you can set up and configure the DuckDB resource by specifying the path to your DuckDB database in the database
configuration value. Here's how you can do it: 1. Install the necessary libraries if you haven't already: pip install dagster-duckdb dagster-duckdb-pandas
2. Configure the DuckDB resource by adding it to your Definitions
object and setting the database
configuration value to the path of your DuckDB database. If the database does not exist, it will be created for you. Here is an example of how to configure the DuckDB resource: python from dagster_duckdb import DuckDBResource from dagster import Definitions defs = Definitions( assets=[...], # your assets go here resources={ "duckdb": DuckDBResource( database="path/to/my_duckdb_database.duckdb", # required ) }, )
By configuring the DuckDBResource
in this way, you are providing the necessary settings for Dagster to interact with your DuckDB database when running your assets. There is no need to create a custom resource unless you require additional functionality beyond what DuckDBResource
offers.David Gasquez
02/14/2024, 8:11 AMScout
02/14/2024, 8:11 AMDuckDBResource
. This custom resource can then execute the necessary SQL commands during its initialization. Here's a general outline of how you might create a custom resource that runs SQL on initialization: 1. Define a new resource class that inherits from DuckDBResource
. 2. Override the __init__
method to perform additional SQL configuration. 3. Use this custom resource in your Dagster job or asset definitions. Below is a hypothetical example of what this might look like. Please note that this is a conceptual example and may not run as-is. You should adjust the code to fit your specific use case and ensure that it aligns with the current capabilities and API of Dagster: python from dagster import resource, Field, StringSource from dagster_duckdb import DuckDBResource @resource({ "database": Field(StringSource), "custom_sql": Field(StringSource, is_required=False), }) def CustomDuckDBResource(context): # Initialize the base DuckDBResource with the provided database path duckdb_resource = DuckDBResource(database=context.resource_config["database"]) # If custom SQL is provided, execute it if "custom_sql" in context.resource_config: custom_sql = context.resource_config["custom_sql"] with duckdb_resource.get_connection() as conn: conn.execute(custom_sql) # Return the initialized DuckDBResource return duckdb_resource # Use the custom resource in your job or asset definitions
In this example, the CustomDuckDBResource
resource takes an additional configuration parameter custom_sql
, which is the SQL command you want to run during initialization. If provided, the resource will execute this SQL command against the DuckDB database. To use this custom resource, you would include it in your job or asset definitions and provide the necessary configuration in your resources
dictionary, including the path to your DuckDB database and any custom SQL you need to run. Remember to test your custom resource thoroughly to ensure that it behaves as expected and handles any potential errors or edge cases appropriately.David Gasquez
02/14/2024, 8:23 AMScout
02/14/2024, 8:23 AMpandas
to read the SQL query results into a DataFrame. 4. Write the DataFrame to a Parquet file using pandas
or pyarrow
. Below is a conceptual example of an asset that materializes a DuckDB table as a Parquet file: python from dagster import asset import pandas as pd @asset(required_resource_keys={"duckdb"}) def my_table_as_parquet(context): # Fetch data from DuckDB with context.resources.duckdb.get_connection() as conn: df = pd.read_sql("SELECT * FROM my_table", conn) # Write data to Parquet file parquet_path = "path/to/my_table.parquet" df.to_parquet(parquet_path) # Return the path to the Parquet file as an output for downstream assets or for logging return parquet_path
In this example, my_table_as_parquet
is an asset that uses the DuckDB resource to connect to the database, fetches data from a table called my_table
, and writes it out as a Parquet file to the specified path. When defining your assets or solids, you can include any additional logic you need, such as transforming the data before writing it out, handling different data types, or managing output paths dynamically. Remember to include the necessary Python libraries in your environment (such as pandas
and pyarrow
or fastparquet
for Parquet file handling) and to test your data processing logic to ensure it works as expected.David Gasquez
02/14/2024, 8:24 AMmy_table_as_parquet
) but for all the assets within a group?Scout
02/14/2024, 8:24 AMgroup_name
property, indicating that they belong to the same group. 2. For each asset, implement the logic to materialize the data as a Parquet file, similar to the my_table_as_parquet
example provided earlier. 3. Create a job that includes all assets within the group. 4. Materialize the job, which will, in turn, materialize all assets within the group. Here's a conceptual example: python from dagster import asset, AssetGroup # Define your assets with a group_name @asset(group_name="my_parquet_group") def asset_one(context): # Logic to materialize this asset as a Parquet file ... @asset(group_name="my_parquet_group") def asset_two(context): # Logic to materialize this asset as a Parquet file ... # ... define more assets as needed ... # Create an asset group my_parquet_group = AssetGroup( assets=[asset_one, asset_two], # Include all assets that belong to the group resource_defs={"duckdb": ...} # Define the resources used by the assets ) # Create a job to materialize the asset group my_parquet_job = my_parquet_group.build_job(name="my_parquet_job") # When you run my_parquet_job, all assets in the group will be materialized
By defining a group_name
for your assets and creating an asset group, you can easily manage and materialize all related assets together. When you execute the job associated with the asset group, Dagster will materialize each asset in the group, which includes the logic to write the data as Parquet files. Please note that the code provided is a high-level example and may need to be adapted to fit the specifics of your use case and the current API of Dagster. Make sure to test your implementation to ensure that it works as expected.David Gasquez
02/14/2024, 8:28 AMmy_parquet_group
group by iterating assets on the final
group?Scout
02/14/2024, 8:28 AMfinal
group.
3. For each asset in the final
group, call the function from step 1 to create a new asset definition in the my_parquet_group
group.
Here's a simplified example of how you might define such a function:
python
from dagster import asset, AssetGroup, AssetOut
def create_parquet_asset(table_name, group_name):
@asset(name=f"{table_name}_as_parquet", group_name=group_name)
def table_as_parquet(context):
# Logic to materialize this table as a Parquet file
...
return table_as_parquet
# Assume you have a list of table names in the `final` group
final_group_table_names = ["table1", "table2", "table3"]
# Create a list to hold your new asset definitions
parquet_assets = []
# Iterate over the table names and create new asset definitions
for table_name in final_group_table_names:
parquet_asset = create_parquet_asset(table_name, "my_parquet_group")
parquet_assets.append(parquet_asset)
# Create an asset group with the new asset definitions
my_parquet_group = AssetGroup(
assets=parquet_assets,
resource_defs={"duckdb": ...} # Define the resources used by the assets
)
# Create a job to materialize the asset group
my_parquet_job = my_parquet_group.build_job(name="my_parquet_job")
In this example, create_parquet_asset
is a function that generates a new asset definition for materializing a table as a Parquet file. The final_group_table_names
list contains the names of the tables you want to materialize, which you would need to define based on your final
group. The parquet_assets
list holds the new asset definitions, which are then used to create the my_parquet_group
.
Please note that this approach requires you to know the names of the tables in the final
group ahead of time. If you need to discover these names dynamically at runtime, you would need to implement additional logic to reScout
02/14/2024, 8:29 AM