Jason Gofford
02/22/2024, 7:37 PMScout
02/22/2024, 7:37 PMcontext.add_output_metadata
method to add the file path to the metadata. 2. Downstream Asset: In the downstream asset that loads the data into BigQuery, you would access the metadata from the upstream asset to retrieve the GCS file path. This can be done by using the AssetIn
object to specify the upstream asset dependency and access the metadata. Here's an example of how you might define these assets: python from dagster import asset, AssetIn, AssetOut, MetadataEntry @asset def upstream_asset(context): # Logic to load data from API and upload to GCS # ... gcs_file_path = "<gs://bucket/filename>" # Store the GCS file path in the metadata context.add_output_metadata({"gcs_file_path": MetadataEntry.string(gcs_file_path)}) return gcs_file_path @asset(ins={"upstream_data": AssetIn()}) def downstream_asset(context, upstream_data): # Access the GCS file path from the upstream asset's metadata gcs_file_path = context.get_input_metadata("upstream_data")["gcs_file_path"] # Logic to load the data into BigQuery directly from GCS using the file path # ...
In this example, upstream_asset
uploads data to GCS and stores the file path in the metadata. downstream_asset
then accesses this file path from the metadata of upstream_data
(which is the output of upstream_asset
) and uses it to load the data into BigQuery. Please note that the actual implementation may vary depending on the specifics of your environment and the libraries you are using to interact with GCS and BigQuery. The provided example is a conceptual illustration based on the documentation's guidance on using metadata with assets.Jason Gofford
02/22/2024, 7:42 PMScout
02/22/2024, 7:43 PMMaterializeResult
is an alternative mechanism that you can use to pass metadata or values between assets in Dagster. MaterializeResult
allows you to report metadata, code version, and other information about the asset's computation without having to rely on an IO manager or the output system. When you return a MaterializeResult
from an asset, you can include metadata entries that downstream assets can access. This is particularly useful when your assets are producing results that are larger than memory or are stored externally, such as in a file in cloud storage or as a database table. Here's an example of how you might use MaterializeResult
to pass the GCS file path from an upstream asset to a downstream asset: python from dagster import asset, MaterializeResult, MetadataEntry @asset def upstream_asset(): # Logic to load data from API and upload to GCS # ... gcs_file_path = "<gs://bucket/filename>" # Return a MaterializeResult with the GCS file path in the metadata return MaterializeResult(metadata={"gcs_file_path": MetadataEntry.string(gcs_file_path)}) @asset def downstream_asset(context): # Access the GCS file path from the upstream asset's MaterializeResult metadata gcs_file_path = context.get_materialize_result_value(upstream_asset)["gcs_file_path"] # Logic to load the data into BigQuery directly from GCS using the file path # ...
In this example, upstream_asset
returns a MaterializeResult
with metadata that includes the GCS file path. downstream_asset
then accesses this metadata to retrieve the GCS file path and uses it to load the data into BigQuery. Please note that the context.get_materialize_result_value
function and the exact way of accessing the metadata from MaterializeResult
in the downstream asset are conceptual and based on the documentation's guidance. The actual implementation details may vary, and you should refer to the latest Dagster documentation or release notes for the most up-to-date information on using MaterializeResult
.Jason Gofford
02/22/2024, 7:48 PMScout
02/22/2024, 7:48 PMpython from dagster import asset, MaterializeResult, MetadataEntry @asset def upstream_asset(context): # Access the partition key from the context partition_key = context.partition_key # Use the partition key in the filename gcs_file_path = f"<gs://bucket/filename_{partition_key}>" # Logic to load data from API and upload to GCS with the partitioned filename # ... # Return a MaterializeResult with the GCS file path in the metadata return MaterializeResult(metadata={"gcs_file_path": MetadataEntry.string(gcs_file_path)}) @asset def downstream_asset(context): # Access the GCS file path from the upstream asset's MaterializeResult metadata gcs_file_path = context.get_materialize_result_value(upstream_asset)["gcs_file_path"] # Logic to load the data into BigQuery directly from GCS using the file path # ...
In this example, upstream_asset
uses the context.partition_key
to construct a partition-specific GCS file path. This file path is then included in the metadata of the MaterializeResult
. The downstream_asset
can then access this file path and use it to load data into BigQuery. The partition key is typically a string representation of the date or time range that the partition represents. If you have a specific date format you want to use, you can format the partition key accordingly before appending it to the filename. Please ensure that your partitioned assets are configured with the appropriate partitioning scheme that matches your data's partitioning logic. The partition key will be generated based on this configuration and will be available in the asset's execution context during a partitioned run.Jason Gofford
02/22/2024, 7:49 PMScout
02/22/2024, 7:49 PMAssetKey
, which is a unique identifier for the asset within the Dagster instance. The AssetKey
can be a simple string or a list of strings representing a hierarchical path. To access the AssetKey
from within an asset's execution context, you can use the context.asset_key
property. This property returns the AssetKey
associated with the asset being executed. Here's an example of how you might access the AssetKey
and its name within an asset: python from dagster import asset @asset def my_asset(context): # Access the AssetKey for the current asset asset_key = context.asset_key # The name of the asset is the last component of the AssetKey asset_name = asset_key.path[-1] # Use the asset name in your logic, for example, to create a filename filename = f"{asset_name}_data.csv" # Your asset logic here # ... # Optionally, you can log the asset name or key <http://context.log.info|context.log.info>(f"Asset name: {asset_name}") <http://context.log.info|context.log.info>(f"Asset key: {asset_key}") return filename
In this example, my_asset
retrieves its own AssetKey
and extracts the asset name from it. The asset name is then used to construct a filename, which could be used for saving data to a file or uploading to a storage service like GCS. Please note that the `AssetKey`'s path
attribute is a list of strings that represents the hierarchical path of the asset key. If you have defined a multi-component asset key using the key_prefix
argument, the path
will include those components as well. The last element in the path
list is typically the name of the asset as defined in the @asset
decorator or the function name if no explicit name is given.Jason Gofford
02/22/2024, 8:13 PMScout
02/22/2024, 8:13 PM