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#ask-ai
Title
# ask-ai
j

Jason Gofford

02/22/2024, 7:37 PM
If I have an asset that is loading data from an API and uploading it to GCS how can I pass the location of the uploaded file (gs://bucket/filename) to a downstream asset so that it can be loaded into BigQuery directly from GCS? I would prefer to not need to load the data back into the second asset for upload.
s

Scout

02/22/2024, 7:37 PM
To pass the location of the uploaded file from GCS to a downstream asset for direct loading into BigQuery, you can use metadata to store the file location and then access this metadata in the downstream asset. Here's a step-by-step approach based on the documentation: 1. Upstream Asset: In the upstream asset that uploads the data to GCS, you would include logic to generate the GCS file path (`gs://bucket/filename`) and store this path in the asset's metadata. This can be done by using the
context.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:
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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.
j

Jason Gofford

02/22/2024, 7:42 PM
@Scout Is MaterializeResult an alternative mechanism through achieving this?
s

Scout

02/22/2024, 7:43 PM
Yes,
MaterializeResult
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:
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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
.
j

Jason Gofford

02/22/2024, 7:48 PM
@Scout OK, so that works. Now lets say that I wanted to extend this to account for a date-based partition. What is the idiomatic way to retrieve the date being considered in a partitioned run such that it can be appended to the filename uploaded to GCS?
s

Scout

02/22/2024, 7:48 PM
In Dagster, when working with partitioned assets, you can access the partition key directly from the asset's execution context. The partition key is a string that uniquely identifies the partition being processed. You can use this partition key to append a date or any other partition-specific information to the filename that you're uploading to GCS. Here's an example of how you might do this:
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python 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.
j

Jason Gofford

02/22/2024, 7:49 PM
@Scout can I similarly retrieve the name or asset key of the asset itself from the context?
s

Scout

02/22/2024, 7:49 PM
Yes, you can retrieve the name or asset key of the asset itself from the context. In Dagster, each asset is associated with an
AssetKey
, 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:
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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.
j

Jason Gofford

02/22/2024, 8:13 PM
@Scout If my ultimate desire is to have raw data from an API in BigQuery is it better "best practice" to do this as two (or more) separate assets/ops (where one pulls the data from the API and loads it into GCS and another picking it up and loading it to bigquery, as I am doing currently), or as a single asset/op that pulls data from the API, loads to GCS and uploads to big query?
s

Scout

02/22/2024, 8:13 PM
The best practice for organizing your data pipeline in Dagster—whether to use separate assets/ops or a single asset/op for the process of pulling data from an API and loading it into BigQuery—depends on several factors, including modularity, reusability, maintainability, and the complexity of your workflow. Here are some considerations to help you decide: 1. Modularity: Breaking down the workflow into separate assets/ops can increase modularity. Each asset/op does one thing and does it well. This separation of concerns makes it easier to understand, test, and maintain each part of the workflow. 2. Reusability: If you anticipate needing to reuse the functionality of pulling data from the API or loading data to GCS in other parts of your system, having them as separate assets/ops would be beneficial. 3. Maintainability: Separate assets/ops can make the pipeline more maintainable. If the API's schema changes, you only need to update the asset/op responsible for pulling data from the API. Similarly, if the loading mechanism to BigQuery changes, you only need to update the asset/op that handles the loading. 4. Error Handling: With separate assets/ops, you can handle errors more gracefully. If the process of loading data to BigQuery fails, you don't need to re-run the API data pull, which might be time-consuming or have API rate limits. 5. Observability: Separate assets/ops can improve observability. You can monitor each step independently and set up alerts for specific failures. 6. Performance: If pulling data from the API and loading it to BigQuery are time-consuming processes, breaking them into separate assets/ops allows for parallel execution if they are independent of each other. 7. Atomicity: If your workflow requires atomicity where either both the pull from the API and the load to BigQuery succeed or neither does, then combining them into a single asset/op might make sense. 8. Resource Utilization: If the intermediate storage in GCS is not necessary and you can stream data directly from the API to BigQuery, you might reduce costs and complexity by using a single asset/op. In general, if the steps are logically distinct and might be used independently, or if you want to optimize for maintainability and error handling, it's a good practice to separate them into different assets/ops. However, if the steps are always used together and the intermediate storage in GCS is not required for other purposes, a single asset/op could be more efficient. Ultimately, the "best" approach depends on the specific requirements and constraints of your project. It's important to consider the trade-offs and choose the structure that best aligns with your goals.
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