What is the best way to materialize all assets within a group multiple times (in different systems)?

The best way to materialize all assets within a group multiple times across different systems would typically involve setting up your assets and asset groups in a way that aligns with your organizational and operational needs. However, the documentation does not provide a specific step-by-step guide for materializing asset groups multiple times in different systems. From the documentation, we know that assets can be organized into groups to simplify tracking and management in the UI. Each asset is assigned to a single group, which by default is called "default". You can assign assets to groups either by specifying a group name on an individual asset or by using the

load_assets_from_package_module

function with the

group_name

argument to import all assets in a module and apply a grouping. For materializing assets, jobs that target assets can materialize a fixed selection of assets each time they run and be placed on schedules and sensors. You can launch materializations of individual partitions and view the materialization history by partition in the Dagster UI. If you need to materialize all assets within a group across different systems, you might consider the following approaches based on the documentation and discussions: 1. Partitioning: If the assets are partitioned, you can launch runs that materialize the same partition across each asset within a group. This is useful if you want to materialize assets based on a time window or other dimensions such as region or customer. 2. Jobs and Schedules: Define jobs that include all the assets you want to materialize and schedule them to run as needed. This can be done for each system where the assets need to be materialized. 3. Programmatic Materialization: Use Dagster's Python API to programmatically trigger materializations of asset groups. This could involve writing a script or function that invokes the necessary jobs or operations to materialize the assets in each system. 4. Cross-Location Visibility: If you are dealing with multiple Dagster repositories or instances, you might encounter challenges in visualizing and managing asset groups across these boundaries. There is an open issue discussing the need for support for asset groups across locations/repos, which suggests this is an area of active development and interest within the community. 5. Graph-Backed Assets: For more complex scenarios, you might use graph-backed assets, which allow you to define multiple assets computed using the same graph of ops. This can be useful for managing dependencies and ensuring consistent materialization across systems. It's important to note that the specific implementation details and best practices may vary depending on the complexity of your systems, the nature of your assets,

@Scout, show me an example of how to materialize a sample asset that's just a DuckDB table.

To materialize a sample asset that's just a DuckDB table using Dagster, you would define an asset that represents the table and then materialize it using a job. Below is an example based on the documentation: First, you would define an asset function that creates a DuckDB table. Here's a simple example of an asset that creates a table from a Pandas DataFrame:

python from dagster import asset import pandas as pd import duckdb  # Sample data for the asset sample_data = {     'id': [1, 2, 3],     'name': ['Alice', 'Bob', 'Carol'] } sample_df = pd.DataFrame(sample_data)  @asset def my_duckdb_table():     # Connect to DuckDB     with duckdb.connect(database=':memory:', read_only=False) as conn:         # Create a table from the DataFrame         conn.execute("CREATE TABLE my_table AS SELECT * FROM ?", (sample_df,))         # Optionally, you can perform additional SQL operations here     # The asset function does not need to return anything

Next, you would define a job that materializes this asset:

python from dagster import job  # Define a job that materializes the `my_duckdb_table` asset @job def materialize_my_duckdb_table():     my_duckdb_table()

Finally, you would run the job to materialize the asset:

python if __name__ == "__main__":     result = materialize_my_duckdb_table.execute_in_process()

When you run this script, it will execute the job

materialize_my_duckdb_table

, which in turn materializes the

my_duckdb_table

asset by creating a table in DuckDB with the sample data. Please note that this example uses an in-memory DuckDB database for simplicity. In a production scenario, you would likely connect to a persistent DuckDB database file by specifying the path to the database file in the

duckdb.connect

function. Also, remember that this is a simplified example for illustrative purposes. Depending on your use case, you may need to handle more complex logic, such as managing database connections and sessions, error handling, and integrating with other systems or data sources.

@Scout can I replace the job with other asset?

In Dagster, assets are the building blocks that represent computations or data transformations that produce a data asset, while jobs are the executable entities that orchestrate the execution of these assets. You cannot directly replace a job with an asset because they serve different purposes within the Dagster framework. However, you can define multiple assets and have them depend on each other, creating a graph of assets. When you execute a job that includes these assets, Dagster will materialize them in the correct order based on their dependencies. If you want to create a new asset that depends on the

my_duckdb_table

asset, you would define it as follows:

python from dagster import asset  @asset(required_resource_keys={"duckdb"}) def new_asset(context):     # Use the DuckDB connection from resources to perform operations     with context.resources.duckdb.connect() as conn:         # Perform operations that depend on `my_duckdb_table`         # For example, you might query the table or insert new data         pass

In this case,

new_asset

is an asset that depends on the DuckDB resource, and you can define it to perform operations that logically follow the creation of

my_duckdb_table

. You would then include both

my_duckdb_table

and

new_asset

in a job to materialize them together:

python from dagster import job  @job def materialize_assets():     my_duckdb_table()     new_asset()

When you execute the

materialize_assets

job, Dagster will materialize

my_duckdb_table

first, followed by

new_asset

, respecting the implicit dependency between them. Remember that in a real-world scenario, you would configure resources such as the DuckDB connection in your repository so that they can be accessed by assets during execution. The example above assumes that such a resource configuration exists and is passed to the assets via the

context

parameter.

@Scout I want to materialize all the assets that belong to a group

To materialize all assets that belong to a group in Dagster, you can use the

AssetSelection

API to select the assets within a specific group and then materialize them using a job. Here's an example of how you can do this: First, ensure that your assets are assigned to a group. You can assign a group name to an asset using the

group_name

parameter in the

@asset

decorator:

python from dagster import asset  @asset(group_name="my_asset_group") def asset_one():     # Your asset logic here     pass  @asset(group_name="my_asset_group") def asset_two():     # Your asset logic here     pass

Next, you can define a job that selects all assets within the group "my_asset_group" and materializes them:

python from dagster import job, AssetSelection  @job def materialize_my_asset_group():     AssetSelection.groups("my_asset_group").materialize()

When you execute the

materialize_my_asset_group

job, Dagster will materialize all assets that are part of the "my_asset_group" group. To execute the job, you can use the Dagster CLI or trigger it programmatically:

bash # Using the Dagster CLI dagster job execute materialize_my_asset_group

Or programmatically:

python if __name__ == "__main__":     result = materialize_my_asset_group.execute_in_process()

Please note that the

AssetSelection

API allows you to select assets in various ways, including by group, which is useful when you want to operate on a subset of your asset graph. The example provided assumes that the

AssetSelection.groups

method and the

materialize

method are available and work as described, which is based on the conceptual understanding of the Dagster asset model. Always refer to the latest Dagster documentation for the most accurate and up-to-date information on how to use these APIs.

@Scout, how do I specify job asset dependencies?

In Dagster, you can specify dependencies between assets by using the

@asset

decorator to define assets and then expressing dependencies directly in the asset functions. When you define a job that includes these assets, Dagster will automatically infer the dependencies based on the asset definitions. Here's an example of how you might define assets with dependencies:

python from dagster import asset  @asset def asset_a():     # Logic for asset A     return "data from A"  @asset def asset_b(asset_a):     # Logic for asset B that depends on asset A     return f"data from B and {asset_a}"

In this example,

asset_b

depends on

asset_a

. When you define a job that includes both assets, Dagster will ensure that

asset_a

is materialized before

asset_b

. To define a job that materializes these assets, you can use the

define_asset_job

function:

python from dagster import define_asset_job  # Define a job that materializes both assets, respecting their dependencies asset_job = define_asset_job(name="asset_job")

When you execute

asset_job

, Dagster will materialize

asset_a

first, followed by

asset_b

, because

asset_b

has a declared dependency on

asset_a

. If you have a more complex scenario with multiple assets and dependencies, you can define those relationships in the same way—by passing the required assets as arguments to the dependent asset functions. Dagster will analyze the dependencies and execute the assets in the correct order. Remember that the job's execution plan is determined by the dependencies between the assets. If you need to customize the execution beyond the inferred dependencies, you can use the

dependencies

parameter of the

define_asset_job

function to manually specify the execution order. However, this is typically not necessary for asset-based workflows, as Dagster is designed to handle this automatically based on the asset definitions. For the most accurate guidance and examples, please refer to the latest Dagster documentation on defining assets and jobs.

@Scout, I don't want an asset job. I need a custom job that depends on some existing assets.

To create a custom job that depends on existing assets in Dagster, you can use the

@job

decorator along with the

@asset

decorator to define the dependencies between your job and the assets. Here's a step-by-step guide on how to do this: 1. Define your assets using the

@asset

decorator, as you normally would:

python from dagster import asset  @asset def my_first_asset():     # Logic for the first asset     return "data from my_first_asset"  @asset def my_second_asset(my_first_asset):     # Logic for the second asset that depends on the first asset     return f"data from my_second_asset and {my_first_asset}"

2. Define your custom job using the

@job

decorator. Within the job, you can reference the assets you've defined. Dagster will automatically infer the dependencies based on the asset function signatures:

python from dagster import job  @job def my_custom_job():     # The job orchestrates the materialization of assets     my_second_asset()  # This will also materialize `my_first_asset` due to the dependency

When you execute

my_custom_job

, Dagster will materialize

my_first_asset

before

my_second_asset

because

my_second_asset

has a declared dependency on

my_first_asset

. 3. Execute your custom job:

python if __name__ == "__main__":     result = my_custom_job.execute_in_process()

By using the

@job

decorator, you can create a custom job that includes specific assets and respects their dependencies. Dagster will ensure that the assets are materialized in the correct order based on the dependencies you've defined in the asset functions. If you need to include additional ops or logic that are not part of the assets, you can define those ops separately and include them in your job definition:

python from dagster import op  @op def my_custom_op():     # Custom logic that is not part of an asset     pass  @job def my_custom_job_with_op():     my_custom_op()     my_second_asset()  # This will also materialize `my_first_asset` due to the dependency

In this updated job definition,

my_custom_op

will execute first, followed by the materialization of

my_first_asset

and

my_second_asset

. Please refer to the latest Dagster documentation for more details on defining jobs and assets, as well as executing jobs that include assets with dependencies.