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Title
# ask-ai
e

elgreco

02/24/2024, 1:23 PM
@Scout when I use the input manager how do I specify the input config on an asset so that the input manager can use it?
s

Scout

02/24/2024, 1:23 PM
When using an input manager with assets in Dagster, you can specify the input configuration that the input manager will use by defining an
input_config_schema
on the input manager. This schema defines the expected configuration structure that the input manager will receive. You can then access this configuration within the
load_input
function of the input manager through the
InputContext
object. Here's a step-by-step guide based on the documentation: 1. Define your input manager with an
input_config_schema
. This schema specifies the configuration parameters that your input manager expects. 2. Implement the
load_input
method in your input manager class. Within this method, you can access the configuration via
context.config
. 3. When executing a job that uses this input manager, you can pass the per-input configuration in the
run_config
. Here is an example from the documentation:
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python class MyConfigurableInputLoader(InputManager):     def load_input(self, context: InputContext):         return read_dataframe_from_table(name=context.config["table"])  @input_manager(input_config_schema={"table": str}) def my_configurable_input_loader():     return MyConfigurableInputLoader()  # or  @input_manager(input_config_schema={"table": str}) def my_other_configurable_input_loader(context):     return read_dataframe_from_table(name=context.config["table"])
Then, when executing a job, you can pass in the per-input config like this:
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python load_table_job.execute_in_process(     run_config={"ops": {"my_op": {"inputs": {"dataframe": {"table": "table_1"}}}}}, )
In this example,
load_table_job
is the job that you're executing,
my_op
is the operation within the job that has an input named
dataframe
, and
table_1
is the value for the
table
parameter that you're passing to the input manager. Remember to replace
my_op
,
dataframe
, and
table_1
with the actual names used in your job definition and the specific configuration you want to pass to your input manager.
e

elgreco

02/24/2024, 1:43 PM
@Scout can I also have the table location as part of the source asset metadata or config, so that it's always part of the asset. Then the input manager should pick up the location and pass it into the load function
s

Scout

02/24/2024, 1:43 PM
Yes, you can include metadata as part of the source asset definition, and then access this metadata within your input manager. The metadata for an asset can be defined statically when the asset is declared, and it can be accessed by the input manager when loading the asset as an input to an operation. Here's how you can do it: 1. Define your source asset with metadata that includes the table location:
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python from dagster import SourceAsset, MetadataValue  source_asset = SourceAsset(     key=AssetKey("my_table"),     metadata={         "table": MetadataValue.text("my_table_name"),         "schema": MetadataValue.text("my_schema_name"),     } )
2. In your input manager, you can access the metadata of the upstream output that produced the object you're loading. This is done through the
InputContext.upstream_output
property:
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python class MyIOManager(IOManager):     def load_input(self, context: InputContext):         if context.upstream_output and context.upstream_output.metadata:             table_name = context.upstream_output.metadata["table"]             schema = context.upstream_output.metadata["schema"]             return read_dataframe_from_table(name=table_name, schema=schema)         else:             raise Exception("Upstream output doesn't have schema and metadata set")
3. When you define your asset that depends on the source asset, you can use the input manager that you've defined:
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python from dagster import asset  @asset(io_manager_key="my_io_manager") def my_asset(my_table):     # The my_table parameter is loaded by MyIOManager with the metadata from source_asset     # Perform your operations with my_table here     pass
In this example,
my_io_manager
is the key associated with the
MyIOManager
instance that you've registered in your repository. When
my_asset
is executed, Dagster will use
MyIOManager
to load
my_table
, and the input manager will retrieve the table name and schema from the metadata of the source asset. Make sure to replace
"my_table_name"
,
"my_schema_name"
, and other placeholders with the actual values you intend to use in your asset definitions.
e

elgreco

02/24/2024, 1:57 PM
@Scout how do I permanently add the input config on an asset?
s

Scout

02/24/2024, 1:57 PM
In Dagster, you can permanently add configuration to an asset by defining a
config_schema
on the asset itself. This allows you to specify configuration that should be provided when materializing the asset. The configuration can include any parameters that are required for the computation or for loading the asset's inputs. Here's how you can define an asset with a `config_schema`:
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python from dagster import asset, Field  @asset(config_schema={"table_name": Field(str, is_required=True)}) def my_asset(context):     table_name = context.op_config["table_name"]     # Use the table_name in your asset computation     ...
In this example,
my_asset
requires a configuration field
table_name
which must be provided when materializing the asset. The
Field
class is used to define the type of the configuration field and whether it is required. When you materialize this asset, you will need to provide the configuration in the `run_config`:
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python from dagster import materialize  result = materialize(     [my_asset],     run_config={         "ops": {             "my_asset": {                 "config": {                     "table_name": "my_table"                 }             }         }     } )
The
run_config
specifies the value for
table_name
that the asset will use during execution. If you want to set a default configuration for the asset that can be overridden when needed, you can use the
default_value
parameter of the `Field`:
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python @asset(config_schema={"table_name": Field(str, default_value="default_table_name")}) def my_asset_with_default(context):     table_name = context.op_config["table_name"]     # Use the table_name in your asset computation     ...
In this case, if you do not provide a
table_name
in the
run_config
, the asset will use the default value
"default_table_name"
. Remember that the configuration specified in
config_schema
is static and part of the asset's definition. It is not meant to change frequently and is typically used for parameters that are essential to the asset's computation. If you need to change the configuration, you will need to do so in the code where the asset is defined or by providing a different
run_config
when materializing the asset.
e

elgreco

02/24/2024, 2:01 PM
@Scout the observable_source_asset doesn't have the option to add a config_schema in the decarator, how do I achieve this now?
s

Scout

02/24/2024, 2:01 PM
As of my knowledge cutoff date in early 2023, the
observable_source_asset
decorator in Dagster does not directly support a
config_schema
parameter. This is because source assets are typically used to represent external data sources and are not computed within Dagster; therefore, they don't have computation logic that would require configuration. However, if you need to associate configuration with a source asset for the purpose of loading it with an input manager or IO manager, you can use the following approaches: 1. Use Metadata: You can attach metadata to the source asset that includes the necessary configuration information. This metadata can then be accessed by the input manager or IO manager when loading the asset.
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python
from dagster import SourceAsset, MetadataValue

my_source_asset = SourceAsset(
    key="my_source_asset_key",
    metadata={
        "table_name": MetadataValue.text("my_table_name"),
        # Add other configuration metadata as needed
    }
)
2. Use a Custom Input Manager: You can create a custom input manager that knows how to load the source asset based on predefined logic or external configuration files.
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python
from dagster import IOManager, InputContext, io_manager

class MyCustomInputManager(IOManager):
    def load_input(self, context: InputContext):
        # Here you can access the metadata if needed
        table_name = context.upstream_output.metadata.get("table_name")
        # Implement the logic to load the data based on the table_name or other config
        return load_data(table_name)

@io_manager
def my_custom_input_manager():
    return MyCustomInputManager()
3. Use Resources: If the configuration is related to the environment or setup rather than the asset itself, you can define a resource with a
config_schema
and then use this resource within your input manager or IO manager.
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python
from dagster import resource, Field

@resource(config_schema={"database_url": Field(str)})
def my_database_resource(init_context):
    return {
        "database_url": init_context.resource_config["database_url"]
    }

# Then, in your IOManager, you can access this resource
class MyIOManager(IOManager):
    def load_input(self, context: InputContext):
        database_url = context.resources.my_database_resource["database_url"]
        # Use the database_url to load the data
        return load_data_from_database(database_url)
When you define your repository, you can then configure the resource with the necessary configuration: ```python from dagster import repository, define_asset_job @repository def my_repository(): return [ my_source_asset, define_asset_job("my_job", selection="*"), my_custom_input_manager, my_database_resource.configured(
{"database_url": "my_database_url"}), ] ``` In this example,
my_database_resource
is configured with the
database_url
when defining the repository, and this configuration is used by
MyIOManager
when loading inputs. Please note that the specific implementation details may vary based on the version of Dagster you are using and the exact requirements of your use case. If the functionality has changed after my knowledge cutoff date, I recommend checking the latest Dagster documentation or reaching out to the Dagster community for the most up-to-date guidance.
e

elgreco

02/24/2024, 2:11 PM
@Scout can you show an example on how to access the metadata within the observable function of the observable_source_asset
s

Scout

02/24/2024, 2:11 PM
As of my knowledge cutoff date, the
observable_source_asset
decorator is used to define source assets with observation functions in Dagster. However, the observation function itself does not directly access the metadata of the source asset it is observing. Instead, the observation function is typically used to check for changes in the external data source and return a
DataVersion
object that represents the current version of the data. If you want to access metadata within the observation function, you would typically define that metadata outside of the observation function and access it within the function. Here's an example of how you might do this:
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python from dagster import observable_source_asset, DataVersion, MetadataValue  # Define metadata that you want to access in your observation function source_asset_metadata = {     "bucket": MetadataValue.text("my_bucket"),     "key": MetadataValue.text("my_data_key"), }  @observable_source_asset(metadata=source_asset_metadata) def my_source_asset(context):     # Access the metadata from the context     bucket = context.metadata["bucket"].value     key = context.metadata["key"].value      # Use the metadata to perform your observation logic     # For example, check if a file in a cloud storage bucket has changed     data_version = check_for_changes(bucket, key)      # Return a DataVersion object based on the results of your observation     return DataVersion(data_version)
In this example,
check_for_changes
would be a custom function you define that checks the specified bucket and key for changes and returns a string representing the data version. The
DataVersion
object is then created with this string and returned from the observation function. The
context
parameter in the observation function provides access to the metadata defined on the source asset. You can access the metadata values using the
context.metadata
dictionary, where each key corresponds to a metadata entry defined on the source asset. Please note that the specific APIs and capabilities of Dagster may have evolved since my knowledge cutoff date, so I recommend checking the latest Dagster documentation for any updates or changes to the
observable_source_asset
functionality.
e

elgreco

02/24/2024, 2:19 PM
@Scout why can't I provide an input manager as IO manager key for an observable source asset: dagster._core.errors.DagsterInvalidDefinitionError: io manager with key 'input_uc_reader' required by SourceAsset with key ["feat_my_feature_update", "source_asset"], but received <class 'dagster._core.storage.input_manager.InputManagerDefinition'>.
s

Scout

02/24/2024, 2:19 PM
The error message you're encountering indicates that you're trying to provide an
InputManagerDefinition
where an
IOManagerDefinition
is expected. In Dagster,
InputManager
and
IOManager
serve different purposes, and they are not interchangeable. - InputManager: An
InputManager
is responsible for loading inputs for ops (operations) or assets. It defines how to retrieve data that an op or asset depends on. - IOManager: An
IOManager
is responsible for handling both the storage of outputs and the retrieval of inputs for ops or assets. It defines how to store outputs and how to retrieve them later as inputs to other ops or assets. When defining a
SourceAsset
, you need to specify an
IOManager
, not an
InputManager
. The
IOManager
is used to load the contents of the asset when it's used as an input to other assets within a job. Here's an example of how you might define a
SourceAsset
with an `IOManager`:
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python from dagster import SourceAsset, io_manager, IOManager  class MyIOManager(IOManager):     def handle_output(self, context, obj):         # Logic to store the output         pass      def load_input(self, context):         # Logic to load the input         pass  @io_manager def my_io_manager():     return MyIOManager()  my_source_asset = SourceAsset(     key="my_source_asset_key",     io_manager_key="my_io_manager_key", )
In this example,
MyIOManager
is an
IOManager
that can handle both outputs and inputs. The
my_io_manager
function is a factory that creates instances of
MyIOManager
, and it's decorated with
@io_manager
to indicate that it provides an
IOManager
. When you define your
SourceAsset
, you should use the key associated with the
IOManager
(
"my_io_manager_key"
in this case), not an
InputManager
. If you encounter this error, you should check the following: 1. Ensure that you have defined an
IOManager
and not an
InputManager
for use with your
SourceAsset
. 2. Make sure that the key you provide in the
io_manager_key
argument of the
SourceAsset
corresponds to an
IOManager
registered in your repository. If you have an
InputManager
that you want to use specifically for loading source assets, you will need to convert it to an
IOManager
or wrap it in an
IOManager
that delegates to the
InputManager
for the loading logic. Remember that an
IOManager
must implement both
handle_output
and
load_input
methods, even if the
handle_output
method is a no-op for source assets. Please check the latest Dagster documentation or reach out to the Dagster community for the most up-to-date guidance if there have been changes after my knowledge cutoff date.
e

elgreco

02/24/2024, 4:34 PM
@Scout how do I read the metadata on a source_observable_asset?
s

Scout

02/24/2024, 4:34 PM
In Dagster, when you define an
observable_source_asset
, you can attach metadata to it. However, the observation function (the function you decorate with
@observable_source_asset
) does not receive an
AssetExecutionContext
that would allow direct access to the asset's metadata. Instead, it receives an
OpExecutionContext
, which does not have a direct reference to the asset's metadata. If you need to access metadata within the observation function, you would typically define that metadata outside of the observation function and access it within the function. Here's an example of how you might do this:
Copy code
python from dagster import observable_source_asset, DataVersion, MetadataValue  # Define metadata that you want to access in your observation function source_asset_metadata = {     "bucket": MetadataValue.text("my_bucket"),     "key": MetadataValue.text("my_data_key"), }  @observable_source_asset(metadata=source_asset_metadata) def my_source_asset(context):     # Access the metadata from the context     bucket = context.metadata["bucket"].value     key = context.metadata["key"].value      # Use the metadata to perform your observation logic     # For example, check if a file in a cloud storage bucket has changed     data_version = check_for_changes(bucket, key)      # Return a DataVersion object based on the results of your observation     return DataVersion(data_version)
In this example,
check_for_changes
would be a custom function you define that checks the specified bucket and key for changes and returns a string representing the data version. The
DataVersion
object is then created with this string and returned from the observation function. The
context
parameter in the observation function provides access to the metadata defined on the source asset. You can access the metadata values using the
context.metadata
dictionary, where each key corresponds to a metadata entry defined on the source asset. Please note that the specific APIs and capabilities of Dagster may have evolved since my knowledge cutoff date, so I recommend checking the latest Dagster documentation for any updates or changes to the
observable_source_asset
functionality.
e

elgreco

02/24/2024, 4:39 PM
@Scout OpExecutionContext cannot access the asset metadata... AttributeError: 'OpExecutionContext' object has no attribute 'metadata'
s

Scout

02/24/2024, 4:39 PM
I apologize for the confusion. You are correct that
OpExecutionContext
does not have a
metadata
attribute. In Dagster, metadata associated with an asset is not directly accessible within the
OpExecutionContext
of an
observable_source_asset
. If you need to use specific metadata within the observation function of an
observable_source_asset
, you would typically have to manage this metadata outside of the Dagster context and access it within the function using your own mechanisms, such as environment variables, configuration files, or other external sources. Here's a simplified example of how you might manage metadata externally:
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python import os from dagster import observable_source_asset, DataVersion  # Assume metadata is set as environment variables bucket = os.getenv("MY_BUCKET") key = os.getenv("MY_DATA_KEY")  @observable_source_asset def my_source_asset(_):     # Use the metadata to perform your observation logic     data_version = check_for_changes(bucket, key)      # Return a DataVersion object based on the results of your observation     return DataVersion(data_version)
In this example,
check_for_changes
is a hypothetical function that checks for changes in the specified bucket and key, and
MY_BUCKET
and
MY_DATA_KEY
are environment variables that you have set with the necessary metadata. Please note that this is a workaround, and the best practice for managing metadata in Dagster may vary depending on your specific use case and the version of Dagster you are using. Always refer to the latest documentation for the most accurate and up-to-date information.
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