@Scout what is the usage of asset_context's function

asset_partition_key_for_output()

, how is it supposed to be used and how is it different from concept of

asset_partition_key_for_input()

function

The

asset_partition_key_for_output()

function is used within the context of a Dagster asset to obtain the partition key that corresponds to a particular output, which is associated with a specific asset. This function is particularly useful when dealing with partitioned assets, where each partition represents a slice or subset of the data that the asset is responsible for materializing. The

asset_partition_key_for_output()

function is used when you want to know the partition key for the output being produced by an asset. This can be important for naming files, creating database entries, or any other operation where the partition key needs to be included or considered. On the other hand, the

asset_partition_key_for_input()

function is used to obtain the partition key for an input that an asset is consuming. This is relevant when an asset depends on other partitioned assets, and you need to know which partition of the upstream asset is being used as input for the current asset. The difference between these two functions lies in their usage context: one is for outputs (materializations) of the current asset, and the other is for inputs (dependencies) from upstream assets. They both provide the partition key, but for different parts of the asset's execution context. Here's a brief example to illustrate the usage:

python @asset def my_asset(context):     # Use asset_partition_key_for_output to get the partition key for the output     output_partition_key = context.asset_partition_key_for_output()     # Perform operations using the output_partition_key     # ...  @asset def downstream_asset(context, upstream_asset):     # Use asset_partition_key_for_input to get the partition key for the input     input_partition_key = context.asset_partition_key_for_input("upstream_asset")     # Perform operations using the input_partition_key     # ...

In this example,

my_asset

uses

asset_partition_key_for_output()

to get the partition key for its output, while

downstream_asset

uses

asset_partition_key_for_input()

to get the partition key for the input it receives from

upstream_asset

. Please note that the code example provided is for illustrative purposes based on the general usage of these functions as described in the documentation. The actual implementation in your Dagster project may vary based on the specific details of your assets and partitioning scheme.

@Scout can you explain this concept using MultiPartition example ?

As of my knowledge cutoff date in early 2023, the Dagster documentation does not provide explicit examples or detailed explanations of

asset_partition_key_for_output()

or

asset_partition_key_for_input()

functions in the context of multi-partitioned assets. These functions are conceptual and would be used in the context of assets that have multiple dimensions of partitioning, but without specific references in the documentation, I cannot provide a concrete example. However, I can explain the concept using a hypothetical multi-partitioned asset scenario. In a multi-partitioned asset, each asset could be partitioned along multiple dimensions. For example, you might have an asset that is partitioned by both date and region. Here's a conceptual example to illustrate how you might think about these functions in a multi-partitioned context:

python
from dagster import asset, MultiPartitionsDefinition, StaticPartitionsDefinition

# Define multi-partition definitions for date and region
date_partitions = StaticPartitionsDefinition(["2023-01-01", "2023-01-02"])
region_partitions = StaticPartitionsDefinition(["us-west", "us-east"])

# Define a multi-partitioned asset using the date and region partitions
@asset(partitions_def=MultiPartitionsDefinition({"date": date_partitions, "region": region_partitions}))
def multi_partitioned_asset(context):
    # Use asset_partition_key_for_output to get the partition keys for the output
    output_partition_keys = context.asset_partition_key_for_output()
    date_key = output_partition_keys["date"]
    region_key = output_partition_keys["region"]
    # Perform operations using the date_key and region_key
    # ...

# Assume we have another asset that depends on the multi-partitioned asset
@asset
def downstream_asset(context, multi_partitioned_asset):
    # Use asset_partition_key_for_input to get the partition keys for the input
    input_partition_keys = context.asset_partition_key_for_input("multi_partitioned_asset")
    date_key = input_partition_keys["date"]
    region_key = input_partition_keys["region"]
    # Perform operations using the date_key and region_key from the upstream asset
    # ...

In this hypothetical example,

multi_partitioned_asset

is partitioned by both date and region. When materializing this asset, you would use

asset_partition_key_for_output()

to get the partition keys for the output, which includes keys for both the date and region dimensions. For

downstream_asset

, which depends on

multi_partitioned_asset

, you would use

asset_partition_key_for_input()

to get the partition keys for the input it receives from

multi_partitioned_asset

. This would also include keys for both the date and region dimensions. Please note that this is a conceptual explanation, and the actual usag

@Scout When returning the values from a Partioned Asset, using multi partition, how is the return value bound to the output keys, do i need to do anything specific to bind it

In Dagster, when you're working with partitioned assets, especially multi-partitioned assets, the return values from an asset computation are automatically bound to the output keys defined by the asset's

AssetOut

specifications. You don't need to do anything specific to bind the return values to the output keys; Dagster handles this association for you based on the asset definition and the partitioning scheme. Here's a conceptual example to illustrate how you might define a multi-partitioned asset and return values:

python from dagster import asset, MultiPartitionsDefinition, StaticPartitionsDefinition, AssetOut  # Define multi-partition definitions for date and region date_partitions = StaticPartitionsDefinition(["2023-01-01", "2023-01-02"]) region_partitions = StaticPartitionsDefinition(["us-west", "us-east"])  # Define a multi-partitioned asset using the date and region partitions @asset(     partitions_def=MultiPartitionsDefinition({"date": date_partitions, "region": region_partitions}),     outs={"date_region_data": AssetOut()} ) def multi_partitioned_asset(context):     # Perform some computation to generate data for each partition     date_region_data = compute_data_for_date_and_region(context)          # Return the data, which will be automatically associated with the "date_region_data" output key     return {"date_region_data": date_region_data}  def compute_data_for_date_and_region(context):     # Use context to access partition keys if needed     output_partition_keys = context.asset_partition_key_for_output()     date_key = output_partition_keys["date"]     region_key = output_partition_keys["region"]          # Perform your computation here     # ...          # Return the computed data     return computed_data

In this example,

multi_partitioned_asset

is defined with a single output named

date_region_data

. When the asset computation function

multi_partitioned_asset

returns a dictionary with the key

date_region_data

, Dagster knows to bind the returned data to the

date_region_data

output of the asset. Dagster's asset materialization process will take care of persisting the returned data in the appropriate storage location, as defined by the I/O manager associated with the asset. The partition keys are used to determine the correct storage paths or database partitions, ensuring that the data is stored in a way that corresponds to the asset's partitioning scheme. Remember to consult the latest Dagster documentation for the most accurate guidance on working with partitioned assets, as APIs and best practices may evolve over time.

@Scout how would Asset Checks work with MultiPartition Assets

As of my knowledge cutoff date, Dagster supports asset checks, which are a way to define expectations for your assets and ensure data quality. However, the documentation indicates that while Dagster supports per-asset checks, it does not yet support per-partition checks for multi-partitioned assets. This means that you can define checks that apply to the asset as a whole, but not individual checks that apply to each partition of a multi-partitioned asset. When you define an asset check for a multi-partitioned asset, the check will need to summarize the state of all partitions into one pass/fail result for the asset. Here's a conceptual example based on the discussions in the Dagster community:

python from dagster import asset, asset_check, AssetCheckResult, StaticPartitionsDefinition  @asset(partitions_def=StaticPartitionsDefinition(["2023-01-01", "2023-01-02"])) def multi_partitioned_asset(_):     # Your asset computation logic here     pass  @asset_check(asset=multi_partitioned_asset) def check_on_multi_partitioned_asset(context, multi_partitioned_asset_output):     # The check logic here needs to consider all partitions of the asset     # For example, you might want to verify that all partitions meet a certain condition     all_partitions_pass = True     for partition_key, output in multi_partitioned_asset_output.items():         if not is_output_valid(output):             all_partitions_pass = False             break      return AssetCheckResult(         passed=all_partitions_pass,         metadata={"details": "All partitions passed the check" if all_partitions_pass else "Some partitions failed the check"}     )  def is_output_valid(output):     # Implement your validation logic here     pass

In this example, the

check_on_multi_partitioned_asset

function is an asset check that applies to the

multi_partitioned_asset

. The check function receives the output of the asset, which includes all partitions, and it must determine a single pass/fail result that applies to the entire asset. The Dagster team has indicated that support for per-partition checks is planned for future releases, which would allow you to define checks that apply to individual partitions of a multi-partitioned asset. This feature would enable more granular data quality checks and validations. For the most current information on asset checks and their capabilities with multi-partitioned assets, please refer to the latest Dagster documentation or check for updates on the Dagster GitHub repository.