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#ask-ai
Title
# ask-ai
d

David Gasquez

02/14/2024, 8:05 AM
I'm using a DuckDB resource. How can I configure it before running the assets? Should I create my own resource with the custom configuration?
s

Scout

02/14/2024, 8:05 AM
To configure the DuckDB resource before running your assets, you do not need to create your own custom resource. Instead, you can configure the existing
DuckDBResource
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:
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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:
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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.
d

David Gasquez

02/14/2024, 8:11 AM
@Scout, the configuration I need to do involves running some SQL.
s

Scout

02/14/2024, 8:11 AM
If you need to run custom SQL as part of the configuration for your DuckDB resource, you would typically handle this by creating a custom resource that extends the functionality of the existing
DuckDBResource
. 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:
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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.
d

David Gasquez

02/14/2024, 8:23 AM
@Scout what is the best way to also add some extra logic (e.g: materialize tables as parquet)
s

Scout

02/14/2024, 8:23 AM
To add extra logic such as materializing tables as Parquet files, you would typically define an asset or a solid (in the context of a job) that encapsulates this logic. This asset or solid would be responsible for executing the SQL queries to fetch the data from DuckDB and then writing the data out as Parquet files. Here's a high-level outline of how you might define an asset to materialize a DuckDB table as a Parquet file: 1. Define an asset that depends on the DuckDB resource. 2. In the asset's compute function, write the logic to execute the SQL query to fetch data from DuckDB. 3. Use a library like
pandas
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:
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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.
d

David Gasquez

02/14/2024, 8:24 AM
@Scout, what if I want to do that (
my_table_as_parquet
) but for all the assets within a group?
s

Scout

02/14/2024, 8:24 AM
To materialize all assets within a group as Parquet files, you can leverage the concept of asset groups in Dagster. An asset group is a collection of assets that are logically grouped together and can be materialized together. You can define a group name for your assets and then create a job that materializes all assets within that group. Here's a high-level approach to achieve this: 1. Define each asset with a
group_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:
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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.
d

David Gasquez

02/14/2024, 8:28 AM
@Scout, is there a way to dynamically generate the new parquet assets in the
my_parquet_group
group by iterating assets on the
final
group?
s

Scout

02/14/2024, 8:28 AM
As of my knowledge cutoff date in early 2023, Dagster does not natively support dynamically generating new assets based on existing assets within a group during runtime. The asset definitions, including their groupings, are typically static and need to be defined upfront in the code. However, you can write a script or function that generates asset definitions programmatically based on a predefined pattern or naming convention. This script can then be run as part of your development process to create new asset definitions that are part of a group. These definitions would then be used to create the corresponding Parquet files when the assets are materialized. Here's a conceptual approach to generating asset definitions programmatically: 1. Define a function that creates an asset definition for materializing a table as a Parquet file. 2. Use a loop or another mechanism to iterate over the assets in the
final
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:
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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 re
trieve the table names from your metadata store or another source of truth. Keep in mind that the code provided is a conceptual 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.
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