How would you set up a sensor, asset_job and an asset so that the sensor cursor value is passed through to the asset's op config?

To set up a sensor, an asset job, and an asset so that the sensor's cursor value is passed through to the asset's op config, you would follow these steps: 1. Define the Asset: Create a software-defined asset using the

@asset

decorator. This asset will be the target of the asset job. 2. Define the Asset Job: Use

define_asset_job

to create a job that will run computations on your asset. This job will be triggered by the sensor. 3. Define the Sensor: Create an asset sensor using the

@asset_sensor

decorator. In the sensor function, you will create a

RunRequest

that includes the cursor value in the

run_config

that will be passed to the asset job. Here's an example of how you might set this up, based on the documentation:

python from dagster import asset, define_asset_job, asset_sensor, RunRequest, SensorEvaluationContext, AssetKey  # Define your asset @asset def my_asset():     # Asset computation logic here     pass  # Define your asset job my_asset_job = define_asset_job(name="my_asset_job", selection="my_asset")  # Define your asset sensor @asset_sensor(asset_key=AssetKey("my_asset"), job=my_asset_job) def my_asset_sensor(context: SensorEvaluationContext):     # Logic to determine if a RunRequest should be created     # For example, checking for new materializations or other conditions     # ...      # Create a RunRequest with the cursor value in the run_config     return RunRequest(         run_key=context.cursor,  # Use the sensor's cursor as the run key         run_config={             "ops": {                 "my_asset": {                     "config": {                         "param": context.cursor  # Pass the cursor value to the asset's op config                     }                 }             }         },         job_name="my_asset_job"     )

In this example, the

my_asset_sensor

function checks for certain conditions (like new materializations) and creates a

RunRequest

when those conditions are met. The

RunRequest

includes the cursor value in the

run_config

under the

ops

dictionary, which corresponds to the asset's op config. This way, when the asset job is triggered by the sensor, it receives the cursor value as part of its configuration. Please note that the actual logic for when to trigger a run and how to handle the cursor will depend on your specific use case. The cursor is typically used to keep track of the sensor's state between invocations, ensuring that each run processes new data since the last run.

@Scout why is my sensor's cursor not updating?

If your sensor's cursor is not updating, there could be several reasons based on the documentation: 1. Manual Cursor Management: If you are using a

multi_asset_sensor

or a custom sensor, you may need to manually manage the cursor using

context.update_cursor(...)

within the sensor function. If you forget to update the cursor or if the update is not done correctly, the cursor will not change. 2. Sensor Evaluation Errors: If there are errors during the sensor evaluation that prevent the sensor function from completing successfully, the cursor may not be updated. Check for any exceptions or errors in your sensor code. 3. SensorResult Issues: As mentioned in a GitHub issue, a

MultiAssetSensor

definition that returns a

SensorResult

object does not update the sensor cursor if not explicitly included in the response, even though the evaluation context has advanced the cursor. This behavior is expected when run requests are returned, but if you're using

SensorResult

, you may need to ensure the cursor is being managed correctly. 4. Cursor Overwriting: There was an issue raised where the asset sensor would silently overwrite the user-provided cursor, leading to confusing results. If you are setting the cursor in an

@asset_sensor

-decorated function, it might be getting overwritten by the system. 5. Sensor Configuration: Ensure that your sensor is configured correctly in your repository. If there is a misconfiguration, the sensor may not be running as expected, which could affect cursor updates. 6. Dagster Version: There might be a bug in the version of Dagster you are using that affects cursor behavior. Check the Dagster release notes and GitHub issues for any known bugs related to sensors and cursors. 7. Dagster Instance Issues: If there are issues with the Dagster instance, such as database connectivity problems or permission issues, it might affect the sensor's ability to update its cursor. To troubleshoot the issue, you can: - Check the sensor logs for any errors or warnings that might indicate why the cursor is not updating. - Ensure that you are using the latest version of Dagster or a version where any known bugs related to your issue have been resolved. - Review your sensor code to make sure the cursor is being updated correctly within the sensor evaluation function. - If you are using

SensorResult

, ensure that the cursor is managed as expected and not being inadvertently overwritten. - Check the Dagster instance and its connectivity to the underlying metadata database to ensure there are no systemic issues affecting sensor execution. If you continue to experience issues, you may want to consult the Dagster community or support channels with specific details about your sensor code and the behavior you'r

@Scout can i store plots as asset output?

As of my knowledge cutoff date in early 2023, Dagster does not natively support storing plots directly as asset outputs in the same way it handles data frames or other serializable data types. However, you can work around this limitation by saving your plots to files (such as PNG, JPEG, or PDF) and then managing these files as assets using Dagster's I/O management system. Here's a general approach to storing plots as asset outputs: 1. Generate the Plot: Use your preferred plotting library (e.g., matplotlib, seaborn, plotly) to create a plot. 2. Save the Plot to a File: Save the generated plot to a file on disk. 3. Create an Asset to Manage the File: Use Dagster's asset management system to track the file as an asset. You can use an I/O manager to handle the storage and retrieval of the plot file. 4. Log AssetMaterialization: Optionally, you can log an

AssetMaterialization

event to record metadata about the plot, such as its file path, in the Dagster asset catalog. Here's an example of how you might implement this in code:

python from dagster import asset, AssetMaterialization, Output import matplotlib.pyplot as plt  # Function to save the plot to a file def save_plot(data, filename):     fig, ax = plt.subplots()     ax.plot(data)     fig.savefig(filename)     plt.close(fig)  # Asset that generates and saves a plot @asset def my_plot_asset():     data = [1, 2, 3, 4]  # Replace with your actual data     filename = "my_plot.png"  # Replace with your desired file path     save_plot(data, filename)          # Log the materialization event for the plot file     yield AssetMaterialization(asset_key="my_plot_asset", description="Plot saved to file.", metadata={"path": filename})          # Return the file path as the output of the asset     yield Output(filename)

In this example,

my_plot_asset

is an asset that generates a plot, saves it to a file, logs the materialization event, and outputs the file path. The file itself can be stored and managed using an I/O manager, and the file path can be used by downstream assets or processes that need to access the plot. Remember that the actual implementation details may vary depending on your specific use case, the plotting library you're using, and how you want to manage the plot files within your Dagster repository.

@Scout can i use a dbt cli resource to get the schema from a specific data model?

Yes, you can use a dbt CLI resource in Dagster to interact with dbt and retrieve information about your dbt models, including the schema. The dbt CLI resource allows you to execute dbt commands programmatically from within Dagster and access the results, which can include metadata about your dbt project and its models. To get the schema from a specific data model, you would typically run a dbt command that generates or accesses the

manifest.json

file, which contains metadata about all the models in your dbt project. The

manifest.json

file includes details such as the schema and name for each model. Here's a general approach to using a dbt CLI resource to get the schema from a specific data model: 1. Set Up the Dbt CLI Resource: Define a dbt CLI resource in your Dagster repository. This resource will be configured with the necessary dbt project directory and profiles directory. 2. Invoke Dbt Commands: Use the dbt CLI resource to run dbt commands that generate or use the

manifest.json

file. 3. Parse the Manifest File: After running the dbt command, parse the

manifest.json

file to extract the schema for the specific model you're interested in. Here's an example of how you might set up and use the dbt CLI resource in Dagster:

python
from dagster import resource, op, Out
from dagster_dbt import dbt_cli_resource
import json

# Define the dbt CLI resource
@resource(config_schema={"project_dir": str, "profiles_dir": str})
def my_dbt_cli_resource(context):
    return dbt_cli_resource.configured({
        "project_dir": context.resource_config["project_dir"],
        "profiles_dir": context.resource_config["profiles_dir"],
    })

# Op that uses the dbt CLI resource to get the schema of a specific model
@op(required_resource_keys={"dbt"}, out=Out(str))
def get_dbt_model_schema(context, model_name):
    # Run the dbt command that generates the manifest.json file
    context.resources.dbt.run(["deps"])
    context.resources.dbt.run(["compile"])
    
    # Load the manifest.json file
    manifest_path = context.resources.dbt.config["project_dir"] + "/target/manifest.json"
    with open(manifest_path) as f:
        manifest = json.load(f)
    
    # Extract the schema for the specific model
    model_key = f"model.{context.resources.dbt.config['project_name']}.{model_name}"
    schema = manifest['nodes'][model_key]['schema']
    
    return schema

# Example usage in a job
@job(resource_defs={"dbt": my_dbt_cli_resource})
def my_dbt_job():
    get_dbt_model_schema()

In this example, the

my_dbt_cli_resource

resource is defined to configure the dbt CLI with the necessary project and profiles directories. The

get_dbt_model_schema

op uses this resource to run dbt commands and then parses the

manifest.json

file to get the schema

@Scout can I output a pandas dataframe in an asset MetadataValue? Or some other way where users can download the dataframe from dagster cloud?

In Dagster, you can attach metadata to an asset materialization, which can include various types of information about the asset. However, a Pandas DataFrame itself cannot be directly attached as a

MetadataValue

due to its potentially large size and complex structure. Instead, you can include metadata that provides a reference to the DataFrame, such as a file path or a URL where the DataFrame has been saved. To make a Pandas DataFrame available for download from Dagster Cloud, you would typically write the DataFrame to a file in a supported format (like CSV or Parquet) and store that file in a location accessible to users, such as a cloud storage bucket. You can then log an

AssetMaterialization

event with a

MetadataValue

that includes the path or URL to the file. Here's an example of how you might do this:

python from dagster import asset, AssetMaterialization, MetadataValue import pandas as pd  # Function to save the DataFrame to a file and return the file path def save_dataframe(df, filename):     df.to_csv(filename, index=False)     return filename  # Asset that generates a DataFrame and saves it to a file @asset def my_asset():     # Generate or load your DataFrame     df = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})          # Save the DataFrame to a CSV file     file_path = save_dataframe(df, 'my_dataframe.csv')          # Log the materialization event with the file path     yield AssetMaterialization(         description="DataFrame saved as CSV.",         metadata={             "file_path": MetadataValue.path(file_path),             # If the file is stored in a location with a URL, you can use MetadataValue.url             # "file_url": MetadataValue.url("<https://example.com/path/to/my_dataframe.csv>")         }     )          # Return the DataFrame as the output of the asset     yield Output(df)

In this example, the

my_asset

function generates a DataFrame, saves it to a CSV file, and logs an

AssetMaterialization

event with the file path. Users can then download the DataFrame from the provided file path or URL. If you're using Dagster Cloud, you'll need to ensure that the file storage location you're using (such as AWS S3, Google Cloud Storage, or Azure Blob Storage) is accessible to your users and that appropriate permissions are set up to allow for file downloads. Remember to always check the latest Dagster documentation for the most current best practices and APIs for working with assets and metadata.

@Scout can i attach metadata to an asset Output before it fails?

In Dagster, you can attach metadata to an asset's output before it fails by using the

AssetMaterialization

event. However, once a failure occurs, the execution of the asset is halted, and any subsequent code, including the emission of metadata, will not be executed. Therefore, you need to attach the metadata before raising the failure. Here's an example of how you might attach metadata to an asset's output before intentionally failing:

python
from dagster import asset, AssetMaterialization, MetadataValue, Failure

@asset
def my_asset():
    # Some logic that may lead to failure
    try:
        # Your asset's computation logic here
        pass
    except Exception as e:
        # Log the materialization event with metadata before failing
        yield AssetMaterialization(
            description="An error occurred before failing.",
            metadata={
                "error_message": MetadataValue.text(str(e)),
                # Include any other relevant metadata here
            }
        )
        # Raise a Failure event to halt execution and mark the asset as failed
        raise Failure(description="Intentional failure after logging metadata.")

    # If no failure occurs, continue with normal execution
    # ...

In this example, if an exception is caught, the asset logs an

AssetMaterialization

event with metadata about the error before raising a

Failure

event to halt execution. This allows you to capture and record metadata about the failure before the asset's execution is stopped. It's important to note that once a

Failure

event is raised, the asset will not produce any further output or metadata. Therefore, any metadata you wish to attach must be done prior to the failure. As always, please refer to the latest Dagster documentation for the most up-to-date guidance on working with assets, metadata, and handling failures. Display metadata from a Failure event on... button Metadata for the Failure event using Ass... button How to add metadata for each asset withi... button Dagster Docs button Thank you for your feedback!

@Scout what would be the return type of this function?

python
from dagster import asset, AssetMaterialization, MetadataValue, Failure

@asset
def my_asset():
    # Some logic that may lead to failure
    try:
        # Your asset's computation logic here
        pass
    except Exception as e:
        # Log the materialization event with metadata before failing
        yield AssetMaterialization(
            description="An error occurred before failing.",
            metadata={
                "error_message": MetadataValue.text(str(e)),
                # Include any other relevant metadata here
            }
        )
        # Raise a Failure event to halt execution and mark the asset as failed
        raise Failure(description="Intentional failure after logging metadata.")

    # If no failure occurs, continue with normal execution
    # ...

In Dagster, when you use the

@asset

decorator, the function is expected to return an output that corresponds to the asset being computed. However, in the provided code snippet, the function

my_asset

uses

yield

to emit an

AssetMaterialization

event, and it may raise a

Failure

exception. The use of

yield

suggests that the function could be a generator. When a function decorated with

@asset

yields

AssetMaterialization

events, it is treated as a generator that can yield multiple events. The return type of such a function is implicitly a generator, and it does not return a value in the traditional sense. Instead, it yields events that are consumed by the Dagster framework. If the function successfully completes its computation without encountering an exception, it would typically yield an output value corresponding to the asset. If an exception occurs and a

Failure

is raised, the function halts execution and does not yield further values. Therefore, the return type of the function

my_asset

is a generator that yields

AssetMaterialization

events, and potentially raises a

Failure

exception. There is no explicit return type specified in the function signature, but if you were to type-annotate this function, you might use

Generator[AssetMaterialization, None, None]

to indicate that it is a generator that yields

AssetMaterialization

events and does not return any value. However, since Dagster handles these decorators and types internally, you typically do not need to provide such annotations in your Dagster assets.

@Scout how can I pass the current asset's AssetKey to the AssetMaterialization

In Dagster, when you create an asset using the

@asset

decorator, the asset key is automatically derived from the asset's function name or can be explicitly provided. If you want to pass the current asset's

AssetKey

to the

AssetMaterialization

, you can do so by referencing the asset's name directly or by constructing the

AssetKey

if you have set a custom one. Here's an example of how you might pass the current asset's

AssetKey

to the `AssetMaterialization`:

python from dagster import asset, AssetMaterialization, AssetKey  @asset def my_asset():     # Your asset's computation logic here     result = compute_something()      # Create an AssetKey for the current asset     # If you're using the default asset key (derived from the function name), you can do this:     asset_key = AssetKey("my_asset")     # If you have a custom asset key, construct it accordingly:     # asset_key = AssetKey(["my_custom_namespace", "my_asset"])      # Emit an AssetMaterialization event with the asset key     yield AssetMaterialization(         asset_key=asset_key,         description="Description of the materialization event",         # Include any other relevant metadata here     )      # Return the result of the asset computation     return result

In this example,

my_asset

is the name of the asset, and the

AssetKey

is constructed using that name. If you have a custom asset key structure, you would construct the

AssetKey

with the appropriate namespace and name. When you yield an

AssetMaterialization

within an asset, Dagster automatically associates it with the asset being computed, so explicitly passing the asset key is often not necessary unless you have a specific reason to customize the key. Remember to refer to the latest Dagster documentation for the most current best practices and APIs for working with assets and materializations.

@Scout can you show me the correct way to return an AssetMaterialization from an asset?

yield AssetMaterialization

isn't picking up the current asset's

asset_key

In Dagster, when you define an asset using the

@asset

decorator and want to emit an

AssetMaterialization

event, you typically use

yield

to emit the event within the asset function. However, if

yield AssetMaterialization

isn't picking up the current asset's

AssetKey

, you might need to explicitly specify the

AssetKey

when you create the

AssetMaterialization

object. Here's an example of how to do this:

python from dagster import asset, AssetMaterialization, AssetKey  @asset def my_asset():     # Your asset's computation logic here     result = compute_something()      # Emit an AssetMaterialization event with the explicit asset key     yield AssetMaterialization(         asset_key=AssetKey("my_asset"),  # Replace with your asset's name or custom asset key         description="Description of the materialization event",         # Include any other relevant metadata here     )      # Return the result of the asset computation     return result

In this example, the

AssetMaterialization

event is emitted with an explicit

AssetKey

. If you have a custom asset key structure, you would construct the

AssetKey

with the appropriate namespace and name. However, if you are still experiencing issues with the asset key not being picked up, it's possible that there may be a bug or a version-specific issue with Dagster. In such cases, it's recommended to check the following: 1. Ensure you are using the latest version of Dagster, as there may have been bug fixes or changes to the behavior of asset materialization. 2. Review the Dagster documentation for any updates or changes to the recommended practices for emitting

AssetMaterialization

events. 3. If the problem persists, consider reaching out to the Dagster community or support channels for further assistance. Please note that the behavior of asset materialization and the handling of asset keys can evolve, so always refer to the latest Dagster documentation for the most accurate and up-to-date guidance.

@Scout Can you help me resolve this error?

dagster._core.errors.DagsterStepOutputNotFoundError: Core compute for op "match_da_reporting_product_vw_column_names" did not return an output for non-optional output "result"

@asset
def match_da_reporting_product_vw_column_names(
    da_reporting_product_vw_sds: pd.DataFrame,
    da_reporting_product_vw_swiftload: pd.DataFrame,
):
    source_columns = set(da_reporting_product_vw_sds.columns)
    destination_columns = set(da_reporting_product_vw_swiftload.columns)
    if source_columns != destination_columns:
        # get only the columns which do not match
        columns_only_in_source = source_columns.difference(destination_columns)
        columns_only_in_destination = destination_columns.difference(source_columns)
        yield AssetMaterialization(
            asset_key="match_da_reporting_product_vw_column_names",
            description="Column names do not match.",
            metadata={
                "columns_only_in_source": MetadataValue.text(
                    str(columns_only_in_source)
                ),
                "columns_only_in_destination": MetadataValue.text(
                    str(columns_only_in_destination)
                ),
            },
        )
        raise Failure(description="Column names do not match.")
    return Output(
        value=True,
        metadata={
            "source_column_names": MetadataValue.text(str(source_columns)),
            "destination_column_names": MetadataValue.text(str(destination_columns)),
        },
    )

The error message you're encountering indicates that the asset

match_da_reporting_product_vw_column_names

is expected to yield an output named "result", but it did not. In Dagster, when an asset function completes without yielding an

Output

, and the asset is not marked with

output_required=False

, Dagster expects a non-optional output named "result" by default. Looking at your code, there are a couple of issues that need to be addressed: 1. The use of

yield

with

AssetMaterialization

is correct, but you need to ensure that an

Output

is always yielded from the asset function, regardless of whether the columns match or not. If the columns do not match, you should still yield an

Output

after the

AssetMaterialization

and before raising a

Failure

. 2. The

Output

object is used in the context of ops, not assets. For assets, you simply return the value. Here's a revised version of your asset function that ensures an output is always yielded:

python
from dagster import asset, AssetMaterialization, MetadataValue, Failure
import pandas as pd

@asset
def match_da_reporting_product_vw_column_names(
    da_reporting_product_vw_sds: pd.DataFrame,
    da_reporting_product_vw_swiftload: pd.DataFrame,
):
    source_columns = set(da_reporting_product_vw_sds.columns)
    destination_columns = set(da_reporting_product_vw_swiftload.columns)
    if source_columns != destination_columns:
        # get only the columns which do not match
        columns_only_in_source = source_columns.difference(destination_columns)
        columns_only_in_destination = destination_columns.difference(source_columns)
        yield AssetMaterialization(
            description="Column names do not match.",
            metadata={
                "columns_only_in_source": MetadataValue.text(
                    str(columns_only_in_source)
                ),
                "columns_only_in_destination": MetadataValue.text(
                    str(columns_only_in_destination)
                ),
            },
        )
        # Yield an output even in the case of failure to satisfy Dagster's output expectations
        yield True  # This represents a dummy output to satisfy Dagster's requirement for an output
        raise Failure(description="Column names do not match.")
    else:
        # If the columns match, return True or some other meaningful result
        return True

In this revised version, the asset function yields a dummy

True

value before raising a

Failure

when the columns do not match. This ensures that Dagster's expectation for an output is met. If the columns match, the function simply returns

True

. Please note that the

AssetMaterialization

does not need an explicit

asset_key

argument because Dagster automatically associates the materiali

@Scout can I use

deps

in an asset decorator to get programmatically get values from upstream assets?

Yes, you can use the

deps

parameter in the

@asset

decorator to specify dependencies on upstream assets. This allows you to programmatically get values from upstream assets in Dagster. When you define an asset with dependencies, Dagster will ensure that the upstream assets are computed before the downstream asset, and it will pass the values of the upstream assets as arguments to the downstream asset function. Here's an example of how you might define assets with dependencies:

python from dagster import asset  @asset def upstream_asset():     # Compute and return the value for the upstream asset     return "upstream_value"  @asset(deps={"upstream_asset_dep": upstream_asset}) def downstream_asset(upstream_asset_dep):     # Use the value from the upstream asset in the downstream asset's computation     return f"downstream_value based on {upstream_asset_dep}"

In this example,

downstream_asset

depends on

upstream_asset

. The

deps

parameter in the

@asset

decorator for

downstream_asset

specifies that there is a dependency named

upstream_asset_dep

which corresponds to the

upstream_asset

function. When

downstream_asset

is computed, Dagster will automatically invoke

upstream_asset

first and pass its return value as the argument

upstream_asset_dep

to

downstream_asset

. The

deps

parameter is a dictionary where the keys are the names of the parameters in the downstream asset function, and the values are the upstream asset functions or asset keys that they depend on. Remember to refer to the latest Dagster documentation for the most current best practices and APIs for working with assets and dependencies.

@Scout can you give me an example of a dagster cloud workspace file with multiple code locations sourcing from different modules within the same python package?

In Dagster Cloud, a workspace file (

workspace.yaml

) is used to define code locations. If you have multiple code locations within the same Python package, you can specify each one using the

module_name

property under

load_from

. Each code location corresponds to a Python module that contains your Dagster definitions, such as assets, jobs, resources, schedules, and sensors. Here's an example of a

workspace.yaml

file that defines multiple code locations sourcing from different modules within the same Python package:

yaml load_from:   - python_module: my_package.module_one   - python_module: my_package.module_two   - python_module: my_package.subpackage.module_three

In this example,

my_package

is the name of your Python package, and

module_one

,

module_two

, and

module_three

are different modules within that package. The

subpackage

is a subpackage within

my_package

. Each module specified here would be loaded as a separate code location in Dagster Cloud. Make sure that your Python package is installed in the Python environment where Dagster Cloud is running, and that the modules specified in the

workspace.yaml

file are accessible from that environment. Please refer to the latest Dagster documentation for the most accurate and up-to-date guidance on configuring workspaces and code locations in Dagster Cloud.

@Scout can you show me the proper way to enable optional config for assets with a pre-populated default value?

To enable optional config for assets with a pre-populated default value in Dagster, you can define a

config_schema

on the asset with a default value specified. This allows you to set a default configuration that will be used if no runtime config is provided. Here's an example of how to define an asset with optional config and a default value:

python
from dagster import asset, Field

@asset(config_schema={"param": Field(str, is_required=False, default_value="default_value")})
def my_asset_with_default_config(context):
    # Access the config value, which will be the default value if not overridden
    param_value = context.op_config.get("param")
    # Your asset logic here using the param_value
    return f"Parameter value is: {param_value}"

In this example, the asset

my_asset_with_default_config

has an optional config parameter

param

with a default value of

"default_value"

. The

Field

constructor is used to define the config schema for

param

, marking it as not required (

is_required=False

) and specifying the default value (

default_value="default_value"

). When this asset is materialized, if no config is provided for

param

, the default value will be used. If you want to override the default value, you can provide a runtime config like so:

yaml
ops:
  my_asset_with_default_config:
    config:
      param: "overridden_value"

This runtime config would set the

param

value to

"overridden_value"

instead of the default value. Remember to refer to the latest Dagster documentation for the most current best practices and APIs for working with asset configuration. Track the config used to bind

with_reso... button How to define default values for config ... button Allow assets to accept config via

.conf... button Make it possible to specify config when ... button Thank you for your feedback!

@Scout can I format my MetadataValue.text as a code block?

As of my last update, Dagster's

MetadataValue.text

does not support formatting the text as a code block directly. The

MetadataValue.text

is intended for plain text metadata entries. However, if you want to display text as a code block in the Dagster UI, you can use

MetadataValue.md

to create a markdown-formatted string that renders as a code block. Here's an example of how you might use markdown to format a string as a code block in an `AssetMaterialization`:

python from dagster import asset, AssetMaterialization, MetadataValue  @asset def my_asset():     code_snippet = "def example_function():\n    return 'Hello, World!'"     markdown_code_block = f"```\n{code_snippet}\n```"  # Format as a markdown code block      yield AssetMaterialization(         description="Example of code block formatting",         metadata={             "code_snippet": MetadataValue.md(markdown_code_block)         },     )     # Your asset logic here     return "Asset result"

In this example, the

code_snippet

string is formatted as a markdown code block using triple backticks (```) before and after the code snippet. When you use

MetadataValue.md

with this markdown-formatted string, it will render as a code block in the Dagster UI. Please refer to the latest Dagster documentation for any updates or changes to the metadata entry system that might allow for new ways to format text as code blocks or other rich text formats.