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#ask-ai
Title
# ask-ai
m

Mikael Frosthage

02/08/2024, 3:52 PM
why doesn't graph_assets support io_manager_key?
s

Scout

02/08/2024, 3:52 PM
The documentation does not explicitly state why
graph_assets
do not support the
io_manager_key
argument. However, it is important to understand that
graph_assets
are a higher-level abstraction that encapsulate a graph of operations (ops), and the management of inputs and outputs is typically handled at the individual op or asset level within the graph. In Dagster, I/O managers are used to handle the storage and retrieval of data that is produced or consumed by ops or assets. When you define a
graph_asset
, you are essentially creating a composition of multiple ops or assets, and the I/O management is expected to be defined for those individual components rather than the graph as a whole. If you need to specify different I/O behavior for different parts of your graph, you would typically do this by setting the
io_manager_key
on the individual assets or ops within the graph. This allows you to control the storage and retrieval behavior for each piece of data that flows through the graph. If you have a specific use case or requirement for setting an
io_manager_key
directly on a
graph_asset
, and you find that this functionality is not currently supported, it may be worth discussing with the Dagster community or checking if there are any open issues or discussions on the Dagster GitHub repository that address this need.
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Mikael Frosthage

02/08/2024, 3:59 PM
@Scout I only have ops apart from the graph_asset. I get the impression that I need to create an asset and the assign the io_manager to that asset
s

Scout

02/08/2024, 3:59 PM
In Dagster, when you're working with
graph_asset
, you typically define the I/O behavior at the level of the individual ops that make up the graph. If you have a set of ops and you're using a
graph_asset
to compose them, you would indeed manage I/O at the op level, potentially using an I/O manager. If you want to manage I/O for the entire graph, you would need to create a software-defined asset that represents the output of the graph. You can then assign an I/O manager to that asset, which will handle the storage and retrieval of the data produced by the graph. Here's a general approach to how you might structure this: 1. Define your ops, each potentially with its own I/O behavior if needed. 2. Compose these ops into a graph using the
@graph
decorator. 3. Create a software-defined asset that represents the output of the graph. 4. Assign an I/O manager to the software-defined asset to manage its output. Remember that the
io_manager_key
is used to reference a specific I/O manager defined in your resource configuration. When you define a software-defined asset, you can specify the
io_manager_key
to indicate which I/O manager should be used for that asset. If you need further assistance on how to implement this in your specific case, you might want to refer to the Dagster documentation for examples of defining assets and assigning I/O managers, or reach out to the Dagster community for more tailored support.
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Mikael Frosthage

02/08/2024, 4:03 PM
does the graph_asset function support context?
@Scout does the graph_asset function support context?
s

Scout

02/08/2024, 4:03 PM
As of my knowledge cutoff date in early 2023, the
graph_asset
function in Dagster does not support a
context
parameter directly. The
graph_asset
decorator is used to define an asset that's computed using a graph of ops, and the dependencies between the ops are specified inside the body of the decorated function. In Dagster, the
context
object is typically available within the execution of an op and provides access to resources, logging, and configuration for that op. When you're working with a
graph_asset
, the individual ops that make up the graph will have access to their own context when they execute. If you need to pass information to the ops within a
graph_asset
, you would do so by defining resources and making them available to the ops through the resource keys, or by passing data through the inputs and outputs of the ops. For more detailed guidance on how to work with
graph_asset
and context within ops, you should refer to the latest Dagster documentation or seek assistance from the Dagster community. The documentation and community discussions can provide up-to-date information and examples that reflect the most current capabilities of Dagster.
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Mikael Frosthage

02/08/2024, 8:32 PM
@how do I read the partitiondef from a graph_asset function
@Scout how do I read the partitiondef from a graph_asset function
s

Scout

02/08/2024, 8:32 PM
In Dagster, when you define a
graph_asset
, you're creating an asset that is computed using a graph of ops. If your
graph_asset
is partitioned, you might want to access the partition information within the function that defines the graph. As of my last update, to access the partition definition from within a
graph_asset
function, you would typically do so by using the
AssetExecutionContext
that is available to the ops within the graph. Each op can access the partition information through its context when it is executed. Here's an example of how you might define an op within a
graph_asset
that accesses partition information:
Copy code
python from dagster import op, graph_asset, DailyPartitionsDefinition  @op def my_op(context):     # Access partition information from the op's context     partition_def = context.asset_partitions_def_for_output()     <http://context.log.info|context.log.info>(f"Partition definition: {partition_def}")  @graph_asset(partitions_def=DailyPartitionsDefinition(start_date="2023-01-01")) def my_graph_asset():     my_op()
In this example, the
my_op
op accesses the partition definition for its output using the
context.asset_partitions_def_for_output()
method. This method is available on the
OpExecutionContext
object, which is provided to the op as the
context
parameter. Please note that the exact method to access the partition definition may vary depending on the version of Dagster you are using and the specific use case. Always refer to the latest Dagster documentation for the most accurate and up-to-date information on how to work with partitioned assets and access partition information within your Dagster assets and ops.
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Mikael Frosthage

02/08/2024, 8:40 PM
@Scout I think I did that but I'm still getting dagster._check.CheckError: Failure condition: The output has no asset partitions
s

Scout

02/08/2024, 8:40 PM
There was an error processing your request. Please try again.
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Mikael Frosthage

02/08/2024, 8:40 PM
@Scout I think I did that but I'm still getting dagster._check.CheckError: Failure condition: The output has no asset partitions
s

Scout

02/08/2024, 8:40 PM
There was an error processing your request. Please try again.
m

Mikael Frosthage

02/08/2024, 8:40 PM
@Scout dagster._check.CheckError: Failure condition: The output has no asset partitions
s

Scout

02/08/2024, 8:40 PM
The error message you're encountering,
dagster._check.CheckError: Failure condition: The output has no asset partitions
, suggests that there's an attempt to access partition information for an output that is not associated with a partitioned asset. This can happen when the code expects an asset to be partitioned, but the asset is not actually defined with a
PartitionsDefinition
. To resolve this issue, you should ensure that: 1. The asset is indeed defined with a
PartitionsDefinition
. If you're using a
graph_asset
, make sure that the
partitions_def
argument is provided and correctly configured. 2. When accessing partition information within an op, ensure that the op is part of a partitioned asset computation. If the op is not part of such a computation, attempting to access partition information will result in an error. Here's an example of how you might define a partitioned asset using `graph_asset`:
Copy code
python from dagster import op, graph_asset, DailyPartitionsDefinition  @op def my_partitioned_op(context):     # Access partition key from the op's context     partition_key = context.get_mapping_key()     <http://context.log.info|context.log.info>(f"Partition key: {partition_key}")  @graph_asset(partitions_def=DailyPartitionsDefinition(start_date="2023-01-01")) def my_partitioned_graph_asset():     my_partitioned_op()
In this example,
my_partitioned_graph_asset
is defined with a
DailyPartitionsDefinition
, which means that
my_partitioned_op
is expected to be executed within the context of a partitioned asset computation. The op can then access the partition key using
context.get_mapping_key()
. If you're still encountering issues, please check the following: - Verify that the
PartitionsDefinition
is correctly applied to the asset. - Ensure that the partition information is being accessed in the correct context (within a partitioned asset computation). - Review the stack trace provided with the error message to pinpoint where the incorrect partition access is occurring. If the problem persists, it may be helpful to provide more context or code snippets, so that the issue can be diagnosed more accurately. Additionally, you can check the Dagster GitHub issues to see if others have encountered similar problems or if there are any known bugs related to partitioned assets.
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Mikael Frosthage

02/08/2024, 9:09 PM
@Scout didnt work for me. this did though context.partition_key
s

Scout

02/08/2024, 9:09 PM
I apologize for any confusion. If
context.partition_key
worked for you, it means that you are correctly accessing the partition key within the context of a partitioned asset or op. The
partition_key
property on the
context
object provides the partition key for the current run when dealing with partitioned assets or ops. Here's an example of how you might use
context.partition_key
within an op that is part of a partitioned asset:
Copy code
python from dagster import op, graph_asset, DailyPartitionsDefinition  @op def my_partitioned_op(context):     # Access partition key from the op's context     partition_key = context.partition_key     <http://context.log.info|context.log.info>(f"Partition key: {partition_key}")  @graph_asset(partitions_def=DailyPartitionsDefinition(start_date="2023-01-01")) def my_partitioned_graph_asset():     my_partitioned_op()
In this example,
my_partitioned_op
accesses the partition key using
context.partition_key
, which is the correct way to access the partition key for the current run if the asset is partitioned. It's important to note that
context.partition_key
should only be used within the execution of a partitioned asset or op, and it will raise an error if the current run is not a partitioned run. If you have any further questions or need additional assistance, please feel free to ask. We're here to help ensure that you have a smooth experience with Dagster.
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