Paul Herzog
02/20/2024, 6:43 PMScout
02/20/2024, 6:43 PMDefinitions
in one __init__.py
file. The Definitions
object in Dagster is a utility that allows you to combine various definitions such as assets, schedules, jobs, and sensors, and have them be aware of each other. This is particularly useful as your project grows and you have a variety of definitions to manage. If you want to schedule each asset differently, you can define multiple schedules and include them in your Definitions
object. Here's an example from the documentation that shows how to add a schedule to the `Definitions`: python from dagster import ( Definitions, ScheduleDefinition, define_asset_job, load_assets_from_modules, ) from . import assets all_assets = load_assets_from_modules([assets]) # Define a job that will materialize the assets hackernews_job = define_asset_job("hackernews_job", selection=AssetSelection.all()) # Addition: a ScheduleDefinition the job it should run and a cron schedule of how frequently to run it hackernews_schedule = ScheduleDefinition( job=hackernews_job, cron_schedule="0 * * * *", # every hour ) defs = Definitions( assets=all_assets, schedules=[hackernews_schedule], )
In this example, a ScheduleDefinition
is created for the hackernews_job
and added to the Definitions
object. If you have multiple assets that you want to schedule differently, you can create a ScheduleDefinition
for each job associated with those assets and include them all in your Definitions
object. Remember that each asset can be associated with its own job, and each job can have its own schedule. This way, you can have fine-grained control over when and how each asset is materialized.Scout
02/20/2024, 6:43 PMDefinitions
in one __init__.py
file. The Definitions
object in Dagster is a utility that allows you to combine various definitions such as assets, schedules, jobs, and sensors, and have them be aware of each other. This is particularly useful as your project grows and you have a variety of definitions to manage. If you want to schedule each asset differently, you can define separate schedules for each asset and include them in your Definitions
object. Here's an example from the documentation that shows how to add a schedule to the Definitions
object: python from dagster import ( Definitions, ScheduleDefinition, define_asset_job, load_assets_from_modules, ) from . import assets all_assets = load_assets_from_modules([assets]) # Define a job that will materialize the assets hackernews_job = define_asset_job("hackernews_job", selection=AssetSelection.all()) # Addition: a ScheduleDefinition the job it should run and a cron schedule of how frequently to run it hackernews_schedule = ScheduleDefinition( job=hackernews_job, cron_schedule="0 * * * *", # every hour ) defs = Definitions( assets=all_assets, schedules=[hackernews_schedule], )
In this example, a ScheduleDefinition
is created for the hackernews_job
and added to the Definitions
object. You can create multiple schedules for different jobs or assets and include them all in the Definitions
object. This way, you can manage the scheduling of each asset individually while keeping all definitions organized within a single Definitions
object in your __init__.py
file.Paul Herzog
02/20/2024, 6:45 PMScout
02/20/2024, 6:45 PMDefinitions
objects with different sets of assets, but you're using the same variable name defs
for each one. This will result in the previous Definitions
object being overwritten each time you assign a new one to defs
. In Python, variable names are references to objects, and if you assign a new object to the same name, the old reference is lost. If you want to maintain separate Definitions
for each set of assets, you should use different variable names for each one. Here's an example of how you could adjust your code: python from dagster import Definitions # Assuming accuranker_assets, gsc_assets, and teamtailor_assets are defined elsewhere # and resources_by_env is a dictionary with environment-specific resources accuranker_defs = Definitions( assets=accuranker_assets, resources=resources_by_env[os.getenv("ENV", "local")] ) gsc_defs = Definitions( assets=gsc_assets, resources=resources_by_env[os.getenv("ENV", "local")] ) teamtailor_defs = Definitions( assets=teamtailor_assets, resources=resources_by_env[os.getenv("ENV", "local")] )
Now, each set of assets has its own Definitions
object, and you can refer to them separately as accuranker_defs
, gsc_defs
, and teamtailor_defs
. However, if your intention is to have a single Definitions
object that includes all the assets and resources together, you should combine them into a single Definitions
call: python from dagster import Definitions # Combine all assets into a single list all_assets = accuranker_assets + gsc_assets + teamtailor_assets # Create a single Definitions object with all assets all_defs = Definitions( assets=all_assets, resources=resources_by_env[os.getenv("ENV", "local")] )
This way, you have one Definitions
object named all_defs
that includes all the assets and the resources. Remember that the Definitions
object is meant to be a container for all related definitions in your Dagster project, so typically you would have one Definitions
object per repository or project scope.Paul Herzog
02/20/2024, 6:47 PMScout
02/20/2024, 6:47 PMDefinitions
object. Here's an example of how you might do that: python from dagster import Definitions, ScheduleDefinition, define_asset_job, load_assets_from_modules import os # Assuming you have different asset groups defined in separate modules from . import accuranker_assets, gsc_assets, teamtailor_assets # Load assets from each module accuranker_all_assets = load_assets_from_modules([accuranker_assets]) gsc_all_assets = load_assets_from_modules([gsc_assets]) teamtailor_all_assets = load_assets_from_modules([teamtailor_assets]) # Define jobs for each asset group accuranker_job = define_asset_job("accuranker_job", selection=AssetSelection.all()) gsc_job = define_asset_job("gsc_job", selection=AssetSelection.all()) teamtailor_job = define_asset_job("teamtailor_job", selection=AssetSelection.all()) # Define schedules for each job accuranker_schedule = ScheduleDefinition( job=accuranker_job, cron_schedule="0 8 * * *", # every day at 8 AM ) gsc_schedule = ScheduleDefinition( job=gsc_job, cron_schedule="0 12 * * *", # every day at 12 PM ) teamtailor_schedule = ScheduleDefinition( job=teamtailor_job, cron_schedule="0 16 * * *", # every day at 4 PM ) # Combine everything into a single Definitions object defs = Definitions( assets=accuranker_all_assets + gsc_all_assets + teamtailor_all_assets, schedules=[accuranker_schedule, gsc_schedule, teamtailor_schedule], resources=resources_by_env[os.getenv("ENV", "local")] )
In this example, we're creating separate jobs for each asset group and then defining a schedule for each job with different cron schedules. Finally, we combine all the assets and schedules into a single Definitions
object. This way, each asset group will be scheduled to run at different times, as specified by their respective schedules. The Definitions
object defs
will contain all the assets and schedules, and you can use this object to manage your Dagster repository.Paul Herzog
02/20/2024, 6:48 PMScout
02/20/2024, 6:48 PMScout
02/20/2024, 6:49 PMPaul Herzog
02/20/2024, 6:56 PMScout
02/20/2024, 6:56 PMAssetSelection
class directly, which is an abstract class in Dagster. The AssetSelection
class is used to select a subset of assets for a job, and it provides several class methods for creating instances, such as AssetSelection.all()
, AssetSelection.keys()
, AssetSelection.groups()
, and AssetSelection.key_prefixes()
. You should use these class methods rather than trying to instantiate AssetSelection
directly. Here's an example of how you might use AssetSelection
correctly: python from dagster import define_asset_job, AssetSelection # Define a job that will materialize all assets all_assets_job = define_asset_job("all_assets_job", selection=AssetSelection.all()) # Define a job that will materialize assets with specific keys specific_assets_job = define_asset_job( "specific_assets_job", selection=AssetSelection.keys(["asset_key1", "asset_key2"]) )
In this example, AssetSelection.all()
and AssetSelection.keys(...)
are used to create selections of assets for jobs. If you're encountering this error in a different context, please ensure that you're using the AssetSelection
class methods correctly and not trying to create an instance of AssetSelection
directly. If you continue to experience issues, please provide more context or code snippets, and I'll do my best to assist you further.Paul Herzog
02/20/2024, 7:00 PMScout
02/20/2024, 7:00 PMAssetSelection.all()
method selects all assets within the repository. If you want to schedule each asset group differently, you need to use a more specific selection for each job. For example, you can use AssetSelection.keys(...)
or AssetSelection.groups(...)
to select specific assets or groups of assets.
2. Asset Loading: The load_assets_from_modules
function expects a list of modules, not a list of asset functions. You should pass the module that contains your assets, not the assets themselves.
3. Asset Grouping: You are using the group_name
parameter in load_assets_from_modules
, which is not a valid parameter for this function. If you want to group assets, you should define them within a module and then load that module using load_assets_from_modules
.
Here's how you might adjust your code:
python
# Assuming you have modules named `accuranker`, `gsc`, and `teamtailor` that contain your asset definitions
from .jt_assets import accuranker, gsc, teamtailor
# Load assets from each module
accuranker_assets = load_assets_from_modules([accuranker])
gsc_assets = load_assets_from_modules([gsc])
teamtailor_assets = load_assets_from_modules([teamtailor])
# Define jobs for each asset group using specific selections
accuranker_job = define_asset_job("accuranker_job", selection=AssetSelection.groups("Accuranker"))
gsc_job = define_asset_job("gsc_job", selection=AssetSelection.groups("GSC"))
teamtailor_job = define_asset_job("teamtailor_job", selection=AssetSelection.groups("TeamTailor_API"))
# Define schedules for each job with specific cron schedules
accuranker_schedule = ScheduleDefinition(job=accuranker_job, cron_schedule="0 6 * * *")
gsc_schedule = ScheduleDefinition(job=gsc_job, cron_schedule="0 7 * * *")
teamtailor_schedule = ScheduleDefinition(job=teamtailor_job, cron_schedule="0 8 * * *")
# Combine everything into a single Definitions object
defs = Definitions(
assets=accuranker_assets + gsc_assets + teamtailor_assets,
schedules=[accuranker_schedule, gsc_schedule, teamtailor_schedule],
resources=resources_by_env[os.getenv("ENV", "local")]
)
In this adjusted code, we're assuming that accuranker
, gsc
, and teamtailor
are modules that contain your asset definitions. We load assets from these modules and then define jobs with specific asset selections based on groups. Each job is then scheduled with a different cron schedule.
Please ensure that your asset definitions are organized into modules and that you're using the correct selection methods to target the assets you want to include in eScout
02/20/2024, 7:01 PM