why doesn't graph_assets support io_manager_key?

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.

@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

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.

does the graph_asset function support context?

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.

@how do I read the partitiondef from a graph_asset function

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:

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.

@Scout I think I did that but I'm still getting dagster._check.CheckError: Failure condition: The output has no asset partitions

There was an error processing your request. Please try again.

@Scout I think I did that but I'm still getting dagster._check.CheckError: Failure condition: The output has no asset partitions

There was an error processing your request. Please try again.

@Scout dagster._check.CheckError: Failure condition: The output has no asset partitions

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`:

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.

@Scout didnt work for me. this did though context.partition_key

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:

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.