https://dagster.io/ logo
#ask-ai
Title
# ask-ai
j

Jason Gofford

03/13/2024, 1:13 PM
Show me a succinct example of defining a multi_asset using yield. The yielded multi_assets should be generated by sub-items of a list.
s

Scout

03/13/2024, 1:13 PM
Certainly! Here's a succinct example of defining a
multi_asset
where the yielded assets are generated by sub-items of a list, as described in the documentation:
Copy code
python from dagster import AssetOut, Output, multi_asset  @multi_asset(     outs={         "asset_a": AssetOut(is_required=False),         "asset_b": AssetOut(is_required=False),     },     can_subset=True, ) def generate_assets_from_list(context):     data_list = ["item_a", "item_b"]     for item in data_list:         if item == "item_a":             yield Output(value="value_for_a", output_name="asset_a")         elif item == "item_b":             yield Output(value="value_for_b", output_name="asset_b")
In this example,
generate_assets_from_list
is a
multi_asset
function that iterates over a list of items (
data_list
). Depending on the value of each item, it yields an
Output
with a specific value and output name that corresponds to one of the defined assets (
asset_a
or
asset_b
). The
is_required=False
attribute for each
AssetOut
indicates that the assets are optional, and
can_subset=True
allows for computing an arbitrary subset of the assets associated with the
multi_asset
.
j

Jason Gofford

03/13/2024, 1:14 PM
@Scout can I yield twice from within a multi_asset to create two assets?
s

Scout

03/13/2024, 1:14 PM
Yes, within a
multi_asset
function, you can yield multiple times to create multiple assets. Each
yield
statement should produce an
Output
object corresponding to one of the defined output assets. Here's an example based on the documentation:
Copy code
python from dagster import AssetOut, Output, multi_asset  @multi_asset(     outs={         "asset1": AssetOut(),         "asset2": AssetOut(),     } ) def my_multi_asset():     # Yielding the first asset     yield Output(value="data_for_asset1", output_name="asset1")          # Yielding the second asset     yield Output(value="data_for_asset2", output_name="asset2")
In this example,
my_multi_asset
is a
multi_asset
function that yields two outputs, each corresponding to a different asset (
asset1
and
asset2
). Each
yield
statement creates one of the assets with the specified value.
j

Jason Gofford

03/13/2024, 1:15 PM
@Scout can I yield two MaterializeResult objects?
s

Scout

03/13/2024, 1:15 PM
As of my knowledge cutoff date in early 2023, the
MaterializeResult
object is not a part of the Dagster API. Instead, Dagster uses
Output
objects to yield results from within a
multi_asset
function, and
AssetMaterialization
events to represent the materialization of an asset. If you want to yield materialization events within a Dagster
multi_asset
function, you would use
AssetMaterialization
and
Output
objects. Here's an example of how you might yield two
AssetMaterialization
events within a
multi_asset
function:
Copy code
python from dagster import AssetOut, Output, multi_asset, AssetMaterialization  @multi_asset(     outs={         "asset1": AssetOut(),         "asset2": AssetOut(),     } ) def my_multi_asset():     # Yielding an AssetMaterialization event for the first asset     yield AssetMaterialization(asset_key="asset1", description="Materialized asset 1")     yield Output(value="data_for_asset1", output_name="asset1")          # Yielding an AssetMaterialization event for the second asset     yield AssetMaterialization(asset_key="asset2", description="Materialized asset 2")     yield Output(value="data_for_asset2", output_name="asset2")
In this example,
my_multi_asset
yields
AssetMaterialization
events to indicate the materialization of two assets (
asset1
and
asset2
). Each
AssetMaterialization
is followed by an
Output
that provides the actual data for the asset. Please note that the actual implementation and usage might differ based on the version of Dagster you are using, and you should refer to the latest documentation or release notes for the most up-to-date information.
f

Francoise Belmi

03/16/2024, 3:01 PM
@Scout how are multiple scripts defined in dagster, should you define it as a whole project or should I split it into different project files if I am using it only as an orchestration tool to replace task scheduler
s

Scout

03/16/2024, 3:01 PM
In Dagster, you can organize your code in various ways depending on the complexity of your project, the level of isolation you want between different parts of your codebase, and how you want to manage dependencies. Here are some strategies for managing multiple scripts or assets in Dagster: 1. Single Project with Multiple Files: If your scripts are related and share common dependencies, you might choose to keep them in a single Dagster project repository. You can organize your code into multiple Python files or modules within the same project. This approach is simple to implement and facilitates code sharing between assets. 2. Multiple Directories in a Single Repository: For a bit more isolation while still keeping things in a single repository, you can use multiple directories within the repository, each representing a different logical part of your project. This can help manage complexity and divide work into areas of responsibility. 3. Multiple Repositories: For stronger isolation, you can use multiple repositories to manage different parts of your project. Each repository can have its own set of dependencies, CI/CD pipeline, and can be deployed independently. This approach is useful if your scripts are unrelated or if different teams are responsible for different scripts. When using Dagster as an orchestration tool to replace a task scheduler, you should consider the following: - Dependencies: If your scripts have overlapping dependencies or need to communicate with each other, it might be easier to keep them in the same project to manage these interactions. - Team Structure: If different teams are responsible for different scripts, it might make sense to split them into separate repositories for better access control and project management. - Deployment: Consider how you want to deploy your scripts. If they can be deployed together, a single project might suffice. If they need to be deployed independently, separate projects or repositories might be better. - Scale: As your project grows, what starts as a manageable single project might become unwieldy. At that point, it might be time to consider splitting it into multiple projects or repositories. Regardless of the approach you choose, Dagster provides the
Definitions
object to combine different definitions (such as assets, jobs, sensors, and schedules) and have them aware of each other. This allows you to manage your Dagster elements in a structured way. Here's an example of how you might define multiple assets in a single project using the
Definitions
object: ```python from dagster import Definitions, load_assets_from_modules from . import assets_module1, assets_module2 all_assets = load_assets_from_modules([assets_module1, assets_module2]) defs =
Definitions( assets=all_assets, ) ``` In this example,
assets_module1
and
assets_module2
are Python modules that contain asset definitions. The
load_assets_from_modules
function loads all assets from these modules, and the
Definitions
object combines them into a single project. Ultimately, the best approach depends on your specific use case and how you want to manage your codebase. It's important to choose a structure that aligns with your project's needs and allows for scalable and maintainable development.
f

Francoise Belmi

03/16/2024, 5:35 PM
@Scout how to structure a modular code that handles condition, datetime, and asset at different functions
s

Scout

03/16/2024, 5:35 PM
Structuring a modular codebase in Dagster that handles conditions, datetime operations, and assets across different functions involves organizing your code into logical units that can be composed and reused. Here's a step-by-step guide on how to approach this: 1. Define Assets: Start by defining your assets using the
@asset
decorator. Each asset function should encapsulate the logic required to compute that asset. If you need to perform datetime operations as part of the asset computation, you can include that logic within the asset function or call a helper function. 2. Helper Functions: Create helper functions for common operations, such as datetime manipulation or conditional logic. These functions can be imported and used within your asset functions to keep your code DRY (Don't Repeat Yourself) and maintainable. 3. Conditional Logic: If you have conditional logic that determines whether an asset should be computed or not, you can implement this logic within the asset function itself or use sensors to trigger asset computations based on certain conditions. 4. Organize Code: Organize your code into modules based on functionality or domain. For example, you might have a
datetime_helpers.py
module for datetime operations, a
conditions.py
module for conditional logic, and separate modules for different groups of assets. 5. Use Dependencies: If an asset depends on the output of another asset or on the result of a condition, you can express this dependency using the
deps
parameter of the
@asset
decorator. This ensures that assets are computed in the correct order. 6. Asset Groups: Group related assets together using asset groups. This can help organize your asset graph and make it easier to manage and visualize dependencies between assets. Here's an example of how you might structure your code:
Copy code
python
# datetime_helpers.py
from datetime import datetime, timedelta

def get_previous_day(date):
    return date - timedelta(days=1)

# conditions.py
def should_compute_asset(date):
    # Implement your conditional logic here
    return True

# assets.py
from dagster import asset
from .datetime_helpers import get_previous_day
from .conditions import should_compute_asset

@asset
def daily_asset(context):
    date = context.op_config["date"]
    if should_compute_asset(date):
        # Perform computation for the asset
        return f"Asset computed for {date}"
    else:
        <http://context.log.info|context.log.info>(f"No computation needed for {date}")
        return None

# repository.py
from dagster import repository
from .assets import daily_asset

@repository
def my_repository():
    return [daily_asset]
In this example,
daily_asset
is an asset that uses helper functions from
datetime_helpers.py
and
conditions.py
to perform
datetime manipulation and conditional logic. The
my_repository
function collects all assets into a repository. By structuring your code in this way, you can create a modular and maintainable codebase that clearly separates concerns and allows for easy testing and reuse of common functionality.
10 Views