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.

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:

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

.

@Scout can I yield twice from within a multi_asset to create two assets?

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:

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.

@Scout can I yield two MaterializeResult objects?

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:

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.

@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

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 =

@Scout how to structure a modular code that handles condition, datetime, and asset at different functions

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:

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