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#ask-ai
Title
# ask-ai
b

Brian Stroh

02/25/2024, 4:20 AM
If I have metadata in an asset decorator, can i add to that metadata during the asset execution?
s

Scout

02/25/2024, 4:20 AM
Yes, you can add to the metadata of an asset during its execution. The documentation provides an example of how to enrich software-defined assets with different types of metadata during execution. Here's a step-by-step explanation based on the documentation: 1. Define the Asset: Initially, you define your asset using the
@asset
decorator. You can attach definition metadata to the asset using the
metadata
parameter within the decorator. 2. Enrich Metadata During Execution: During the execution of the asset, you can add or enrich the metadata by returning a
MaterializeResult
object from your asset function. The
MaterializeResult
allows you to include metadata that describes the contents produced by a particular run. 3. Use
MaterializeResult
: To add metadata during execution, you replace the return type annotation from
None
to
MaterializeResult
and return a
MaterializeResult
object with the desired metadata. Here is an example from the documentation that demonstrates how to add a row count and a preview of the asset as metadata during execution:
Copy code
python from dagster import AssetExecutionContext, MetadataValue, asset, MaterializeResult import pandas as pd import json import requests  @asset def topstories(context: AssetExecutionContext) -> MaterializeResult:     with open("data/topstory_ids.json", "r") as f:         topstory_ids = json.load(f)      results = []     for item_id in topstory_ids:         item = requests.get(             f"<https://hacker-news.firebaseio.com/v0/item/{item_id}.json>"         ).json()         results.append(item)          if len(results) % 20 == 0:             <http://context.log.info|context.log.info>(f"Got {len(results)} items so far.")      df = pd.DataFrame(results)     df.to_csv("data/topstories.csv")      return MaterializeResult(         metadata={             "num_records": len(df),  # Metadata can be any key-value pair             "preview": MetadataValue.md(df.head().to_markdown()),             # The `MetadataValue` class has useful static methods to build Metadata         }     )
In this example, the
topstories
asset function returns a
MaterializeResult
with metadata that includes the number of records and a markdown preview of the data. This metadata is added during the execution of the asset and is not part of the initial asset definition. Remember that the metadata you add during execution is specific to the materialization of the asset and can vary from run to run, depending on the data processed or any other dynamic context.
b

Brian Stroh

02/28/2024, 5:27 AM
@Scout how can i resolve this error with my sensor evaluation? dagster._core.errors.DagsterInvalidConfigError: Error in config mapping Error 1: Received unexpected config entry "last_run_time" at path rootopsmy_op. Expected: "{ config?: { last_run_time?: (String | { env: String })? } }".
s

Scout

02/28/2024, 5:27 AM
The error message you're encountering indicates that there is an issue with the configuration provided to an operation (
my_op
) within your Dagster job. Specifically, the error is saying that an unexpected configuration entry
last_run_time
was provided, and it does not match the expected schema. Here's how you can resolve this error: 1. Review the Configuration Schema: Check the configuration schema for
my_op
to understand what configuration it expects. The error message suggests that
last_run_time
should be nested within a
config
block and can either be a string or an environment variable reference. 2. Correct the Configuration: Update the configuration you are passing to the sensor to match the expected schema. If
last_run_time
is intended to be part of the configuration for
my_op
, it should be nested correctly. Here's an example of what the corrected configuration might look like:
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yaml ops:   my_op:     config:       last_run_time: "2021-01-01T00:00:00"  # Example of a string value
Or, if you are using an environment variable:
Copy code
yaml ops:   my_op:     config:       last_run_time:         env: "MY_ENV_VARIABLE"  # Reference to an environment variable
3. Test the Configuration: After updating the configuration, test it to ensure that it is now valid. You can do this by running the sensor or job with the updated configuration and checking if the error persists. 4. Review Sensor Code: If the
last_run_time
configuration is being generated or manipulated within the sensor code, review the code to ensure that it constructs the configuration dictionary correctly according to the schema expected by
my_op
. 5. Validate Configuration: Optionally, you can use Dagster's configuration validation utilities to programmatically check if the configuration is valid before running the job. This can help catch errors early. If you continue to face issues, you may want to provide more context or the specific code that is causing the error, as that would help in giving a more precise solution.