I'm trying to implement the fixed fan-in pattern. I have a bunch of assets from different files in the

assets

folder, and all of those return a Dataframe. Any of these assets can be the first step of the pipeline. How would I go about it (code-wise), given that all of the assets have the following naming convention:

<source>_labels

?

To implement a fixed fan-in pattern in Dagster where you have multiple assets that can serve as the first step of the pipeline, you would typically define a job that brings together the outputs of these assets into a single computation step. Since all your assets return a DataFrame and follow the naming convention

<source>_labels

, you can use the

multi_asset

decorator to define a computation that depends on all of these assets. Here's a step-by-step guide on how you might approach this: 1. Define your assets in the

assets

folder, ensuring each asset is decorated with the

@asset

decorator and follows the naming convention

<source>_labels

. 2. Create a new Python file where you will define your fan-in logic. Import the necessary Dagster modules and the assets you've defined. 3. Use the

multi_asset

decorator to create a new asset that depends on all the assets you want to fan in. You can use the

AssetIn

object to specify dependencies with a particular naming pattern. 4. Inside the function decorated with

multi_asset

, implement the logic to combine the DataFrames from the upstream assets. Here's an example of what the code might look like:

python
from dagster import asset, multi_asset, AssetIn, AssetKey
import pandas as pd

# Import your assets here
from assets import source1_labels, source2_labels, source3_labels

# Define a new asset that depends on all `<source>_labels` assets
@multi_asset(
    outs={"combined_dataframe": AssetOut()},
    ins={
        "source1_labels": AssetIn(key=AssetKey("source1_labels")),
        "source2_labels": AssetIn(key=AssetKey("source2_labels")),
        "source3_labels": AssetIn(key=AssetKey("source3_labels")),
        # Add more dependencies as needed
    }
)
def combine_dataframes(source1_labels, source2_labels, source3_labels):
    # Implement your logic to combine the DataFrames here
    # For example, you might concatenate them along a particular axis
    combined_df = pd.concat([source1_labels, source2_labels, source3_labels])
    return combined_df

# Now you can use `combine_dataframes` as the first step in your pipeline

Please note that the actual implementation may vary based on your specific requirements and the structure of your DataFrames. The example above assumes that you want to concatenate the DataFrames, but you might need to perform other operations such as merging or joining, depending on your use case. Remember to replace the

source1_labels

,

source2_labels

,

source3_labels

, etc., with the actual asset functions you have defined in your

assets

folder. The

AssetKey

should match the naming convention of your assets. If you have a large number of assets and you want to dynamically include them based on the naming pattern, you might need to use a more dynamic appro

@Scout What if I only want to load the label asset whose name corresponds to the one passed into a Dagster Config?

If you want to load a specific asset based on a name passed into a Dagster config, you can use the

AssetSelection

API to dynamically select the asset at runtime based on the config value. Here's how you can set this up: 1. Define a job that takes a config value to determine which asset to load. 2. Use

AssetSelection

to select the asset whose name matches the config value. 3. Define an op or asset that depends on the dynamically selected asset. Here's an example of what the code might look like:

python from dagster import job, op, AssetSelection, asset, AssetKey, AssetOut, ConfigSchema, String  # Assume you have assets defined like this: # @asset # def source1_labels(): #     ...  # Define an op that takes a DataFrame and does something with it @op(out=AssetOut()) def process_labels(df):     # Process the DataFrame     return df  # Define a job that selects a specific asset based on a config value @job(config_schema={"label_source": ConfigSchema(String)}) def process_specific_labels(context):     label_source = context.op_config["label_source"]     # Use AssetSelection to dynamically select the asset based on the config value     selected_asset_key = AssetKey([label_source + "_labels"])     selected_asset = AssetSelection.keys(selected_asset_key).with_deps(process_labels)          # Materialize the selected asset     selected_asset.materialize()

In this example, the

process_specific_labels

job takes a config value

label_source

that specifies which label asset to load. The

AssetSelection.keys

method is used to create a selection of the asset with the corresponding name. The

with_deps

method is then used to specify that the

process_labels

op should be run with the output of the selected asset. When you execute the job, you would pass the config value to specify which asset to load:

yaml # Example run config resources:   io_manager:     config:       base_dir: "/path/to/storage" ops:   process_labels:     config:       label_source: "source1"  # This will load the `source1_labels` asset

Please adjust the example to fit your actual asset definitions and processing logic. The key concept here is using the

AssetSelection

API to dynamically select assets based on a config value.

@Scout Can I replace the

process_labels

op with a

graph_asset

?

Yes, you can replace the

process_labels

op with a

graph_asset

. A

graph_asset

is a way to define an asset that is computed by a graph of ops, rather than a single op. This is useful when the computation of an asset involves multiple steps. Here's how you can define a

graph_asset

and use it within your job:

python from dagster import graph, op, graph_asset, AssetSelection, AssetKey, AssetOut, ConfigSchema, String  # Assume you have assets defined like this: # @asset # def source1_labels(): #     ...  # Define ops that will be part of your graph @op def step_one(df):     # Process the DataFrame     return df  @op def step_two(df):     # Further process the DataFrame     return df  # Define a graph that composes your ops @graph def processing_graph(df):     return step_two(step_one(df))  # Define a graph_asset that uses the processing_graph @graph_asset def process_labels_graph(df):     return processing_graph(df)  # Define a job that selects a specific asset based on a config value @job(config_schema={"label_source": ConfigSchema(String)}) def process_specific_labels(context):     label_source = context.op_config["label_source"]     # Use AssetSelection to dynamically select the asset based on the config value     selected_asset_key = AssetKey([label_source + "_labels"])     selected_asset = AssetSelection.keys(selected_asset_key).with_deps(process_labels_graph)          # Materialize the selected asset     selected_asset.materialize()

In this example,

processing_graph

is a graph composed of multiple ops (

step_one

and

step_two

). The

process_labels_graph

is a

graph_asset

that uses the

processing_graph

to process the data. The

process_specific_labels

job then uses

AssetSelection

to dynamically select the asset based on the config value and materializes the selected asset using the

process_labels_graph

. When you execute the job, you would still pass the config value to specify which asset to load, and the

graph_asset

will be used to process the data from the selected asset. Please note that the actual implementation may vary based on your specific requirements and the structure of your data processing pipeline. The key concept here is that you can use

graph_asset

in a similar way as you would use an op, but for more complex computations that involve multiple steps.

@Scout Can I skip the processing_graph graph definition entirely and just use the graph_asset definition in the

process_specific_labels

job?

In Dagster, a

graph_asset

is a way to define an asset that is computed by a graph of ops. However, the

graph_asset

decorator itself is not used to define the graph; it is used to define the asset that the graph computes. Therefore, you cannot skip the definition of the graph when using

graph_asset

. The

graph_asset

decorator is applied to a function that returns the result of invoking a graph. This function is what Dagster uses to create the asset. The graph itself must be defined separately using the

@graph

decorator, and it is composed of individual ops that define the computation steps. Here's an example of how you would define a graph and then use

graph_asset

to create an asset from that graph:

python
from dagster import graph, op, graph_asset

# Define ops that will be part of your graph
@op
def step_one(df):
    # Process the DataFrame
    return df

@op
def step_two(df):
    # Further process the DataFrame
    return df

# Define a graph that composes your ops
@graph
def processing_graph(df):
    return step_two(step_one(df))

# Define a graph_asset that uses the processing_graph
@graph_asset
def process_labels_graph(df):
    return processing_graph(df)

In this example,

processing_graph

is the graph that defines the computation, and

process_labels_graph

is the

graph_asset

that represents the asset computed by the graph. If you want to use the

graph_asset

within a job, you would still need to define the graph separately and then invoke it within the

graph_asset

function. The job would then use the

graph_asset

to materialize the asset as part of its execution. In summary, you cannot skip the graph definition when using

graph_asset

. The graph provides the computational logic, and the

graph_asset

defines how that logic is used to compute an asset. Understanding how software-defined asset... button Dagster Docs button Dagster Docs button Graphs with Inputs button Thank you for your feedback!

@Scout how can I materialize another asset called training_data that depends on the selected_asset?

To materialize another asset called

training_data

that depends on the dynamically selected asset

selected_asset

, you would define

training_data

as an asset that takes the output of

selected_asset

as an input. Here's how you can set this up: 1. Define the

training_data

asset with an input that corresponds to the output of the

selected_asset

. 2. In the job that materializes

selected_asset

, add the logic to also materialize

training_data

. Here's an example of what the code might look like:

python from dagster import asset, job, op, AssetSelection, AssetKey, AssetOut, ConfigSchema, String  # Assume you have assets defined like this: # @asset # def source1_labels(): #     ...  # Define an asset that represents the training data @asset def training_data(selected_asset_output):     # Process the selected_asset_output to create training data     # For example, you might perform some transformations on the DataFrame     processed_data = some_transformation(selected_asset_output)     return processed_data  # Define a job that selects a specific asset based on a config value @job(config_schema={"label_source": ConfigSchema(String)}) def process_specific_labels(context):     label_source = context.op_config["label_source"]     # Use AssetSelection to dynamically select the asset based on the config value     selected_asset_key = AssetKey([label_source + "_labels"])     selected_asset = AssetSelection.keys(selected_asset_key)          # Materialize the selected asset and the training_data asset     selected_asset.with_deps(training_data).materialize()

In this example,

training_data

is an asset that depends on the output of the dynamically selected asset

selected_asset

. The

process_specific_labels

job uses

AssetSelection

to dynamically select the asset based on the config value and then uses

with_deps

to specify that

training_data

should be materialized after

selected_asset

. When you execute the job, you would pass the config value to specify which asset to load, and both

selected_asset

and

training_data

will be materialized in the correct order. Please adjust the example to fit your actual asset definitions and processing logic. The key concept here is that you can chain asset dependencies so that when one asset is materialized, the dependent assets are also materialized in the correct order.

@Scout I defined the

pdf_data

graph_asset

. This asset depends on the

labels

asset, that follows the before-mentioned

<source>_labels

naming convention. However, when reloading the definitions, I got the following error:

Input asset '["labels"]' for asset '["pdf_data"]' is not produced by any of the provided asset ops and is not one of the provided sources

The error message you're encountering indicates that Dagster cannot find an asset named

labels

that

pdf_data

depends on. This could be due to a few reasons, such as a mismatch in asset keys or a missing asset definition. Here are some steps to troubleshoot and resolve the issue: 1. Check Asset Key: Ensure that the asset key you're using in the

pdf_data

graph_asset matches the actual key of the

labels

asset. If your

labels

asset is named following a

<source>_labels

pattern, you need to make sure that the

pdf_data

asset is looking for the correct asset key, including the

<source>

prefix. 2. Define Asset Dependencies Correctly: When defining a

graph_asset

, you need to specify its dependencies correctly using the

AssetIn

object. The key provided to

AssetIn

should match the full key of the

labels

asset. 3. Ensure Asset Definitions Are Available: Make sure that the asset definitions for both

pdf_data

and the

labels

asset are available in the repository and that there are no typos or errors in the asset keys. Here's an example of how you might define a

graph_asset

that depends on a

labels

asset with a dynamic source:

python from dagster import graph_asset, AssetIn, AssetKey  # Example of a labels asset definition # @asset # def source1_labels(): #     ...  # Define a graph_asset that depends on a labels asset @graph_asset(     ins={"labels_asset": AssetIn(key=AssetKey("source1_labels"))} ) def pdf_data(labels_asset):     # Process the labels_asset to create pdf_data     ...

In this example,

pdf_data

is defined with an input

labels_asset

that depends on an asset with the key

source1_labels

. You need to replace

"source1_labels"

with the actual source prefix that matches the

labels

asset you want to depend on. If you want to dynamically select which

labels

asset to use based on a config value, you would need to handle that logic within the job that materializes the

pdf_data

asset, similar to the previous example where we used

AssetSelection

based on a config value. If you continue to encounter issues, please ensure that all asset definitions are correctly registered in your repository and that there are no mismatches between the asset keys you are using in your

graph_asset

definitions and the actual asset keys.

@Scout In the previous answer, you said that: "If you want to dynamically select which

labels

asset to use based on a config value, you would need to handle that logic within the job that materializes the

pdf_data

asset, similar to the previous example where we used

AssetSelection

based on a config value." How would I go about that?

To dynamically select which

labels

asset to use based on a config value and then use that selected asset to materialize the

pdf_data

asset, you can follow these steps: 1. Define a job that accepts a config value specifying which

labels

asset to use. 2. Use

AssetSelection

to dynamically select the

labels

asset based on the provided config value. 3. Define the

pdf_data

asset to depend on the dynamically selected

labels

asset. 4. Materialize the

pdf_data

asset within the job, ensuring that the selected

labels

asset is provided as an input. Here's an example of how you might set this up:

python from dagster import job, op, AssetSelection, AssetKey, AssetOut, ConfigSchema, String  # Define your `pdf_data` asset as an op @op(out=AssetOut()) def pdf_data_op(labels_asset):     # Process the labels_asset to create pdf_data     ...  # Define a job that selects a specific `labels` asset and materializes `pdf_data` @job(config_schema={"label_source": ConfigSchema(String)}) def materialize_pdf_data(context):     label_source = context.op_config["label_source"]     # Dynamically select the `labels` asset based on the config value     labels_asset_key = AssetKey([f"{label_source}_labels"])     labels_asset_selection = AssetSelection.keys(labels_asset_key)      # Materialize the `pdf_data` asset using the selected `labels` asset     labels_asset_selection.with_deps(pdf_data_op).materialize()

In this example, the

materialize_pdf_data

job takes a config value

label_source

that specifies which

labels

asset to use. The

AssetSelection.keys

method is used to create a selection of the

labels

asset with the corresponding name. The

with_deps

method is then used to specify that the

pdf_data_op

should be run with the output of the selected

labels

asset. When you execute the job, you would pass the config value to specify which

labels

asset to load, and the

pdf_data

asset will be materialized using the selected

labels

asset as an input. Please adjust the example to fit your actual asset definitions and processing logic. The key concept here is that you can use

AssetSelection

to dynamically select assets based on a config value and then use those selected assets as inputs to other assets or ops within a job.

@Scout Can I define an alias for an asset name?

As of my knowledge cutoff date in early 2023, Dagster does not provide a built-in feature specifically for aliasing asset names directly within asset definitions. However, you can achieve a similar effect by defining a new asset that simply passes through the data from another asset, effectively creating an alias. Here's an example of how you might create an alias for an asset:

python from dagster import asset  # Original asset definition @asset def original_asset():     # Some computation to produce data     return data  # Alias asset definition @asset def alias_for_original_asset(original_asset):     # This asset acts as an alias and simply passes through the data     return original_asset

In this example,

alias_for_original_asset

acts as an alias for

original_asset

. When you materialize

alias_for_original_asset

, it will automatically materialize

original_asset

if it hasn't been materialized yet, and then pass through its data. If you want to refer to an asset by a different name within a job or graph, you can also use local variable assignment as a form of aliasing:

python from dagster import job, op  # Assume original_asset_op is an op that produces an asset @op def original_asset_op():     ...  # Define a job where you want to use an alias for the original_asset_op @job def my_job():     # Alias the original_asset_op with a local variable     alias_for_original_asset = original_asset_op     # Now you can use alias_for_original_asset as if it were original_asset_op     alias_for_original_asset()

In this job definition,

alias_for_original_asset

is used as an alias for

original_asset_op

. This is purely a Python variable assignment and does not create a new asset in the Dagster asset catalog. Remember that these approaches are workarounds and do not create a true alias at the asset catalog level. If you need to rename an asset while preserving its materialization history, this is a different concern and would require a different approach, such as using database migrations or similar strategies to update the asset catalog entries.

@Scout In the multi-asset scenario that you initially suggested, can I generate the ins dictionary directly using dict comprehension?

Yes, you can generate the

ins

dictionary for a multi-asset directly using dict comprehension in Python. This can be particularly useful when you have a pattern or a list of asset names and you want to create input dependencies dynamically based on that list. Here's an example of how you might use dict comprehension to generate the

ins

dictionary for a multi-asset:

python from dagster import multi_asset, AssetIn, AssetKey  # Assume you have a list of asset names following a pattern asset_names = ["source1", "source2", "source3"]  # Use dict comprehension to generate the `ins` dictionary @multi_asset(     ins={         f"{name}_labels": AssetIn(key=AssetKey([f"{name}_labels"]))         for name in asset_names     } ) def combine_dataframes(**dataframes):     # dataframes is a dict with keys like "source1_labels", "source2_labels", etc.     # Implement your logic to combine the DataFrames here     ...

In this example,

asset_names

is a list of asset name prefixes. The dict comprehension inside the

@multi_asset

decorator generates the

ins

dictionary, where each key is the name of the input (e.g.,

"source1_labels"

) and the value is an

AssetIn

object with the corresponding

AssetKey

. The

combine_dataframes

function will receive a dictionary called

dataframes

where the keys are the names of the inputs and the values are the actual data passed from the upstream assets. You can then implement the logic to combine these DataFrames inside the function. Please note that the actual implementation of the combination logic will depend on your specific use case and how you want to combine the DataFrames. The example provided here is a template to show how dict comprehension can be used to generate the

ins

dictionary for a multi-asset in Dagster.

@Scout Can I not pass the names of the assets in the multi-asset function arguments?

In a multi-asset definition, you do not need to explicitly pass the names of the assets as function arguments. Instead, you define the inputs to the multi-asset using the

ins

parameter, which maps input names to

AssetIn

objects. The function that is decorated with

@multi_asset

will then have parameters that correspond to these input names, and Dagster will provide the values of these inputs when the function is executed. Here's an example to illustrate this:

python from dagster import multi_asset, AssetIn, AssetKey  # Define the `ins` dictionary using dict comprehension asset_names = ["source1", "source2", "source3"] ins_dict = {     f"{name}_labels": AssetIn(key=AssetKey([f"{name}_labels"]))     for name in asset_names }  # Define a multi-asset that combines data from multiple label assets @multi_asset(ins=ins_dict) def combine_dataframes(source1_labels, source2_labels, source3_labels):     # Implement your logic to combine the DataFrames here     ...

In this example, the

ins_dict

is created using dict comprehension, and it maps the input names (e.g.,

"source1_labels"

) to

AssetIn

objects with the corresponding

AssetKey

. The

combine_dataframes

function then has parameters named

source1_labels

,

source2_labels

, and

source3_labels

, which correspond to the keys in the

ins_dict

. Dagster will automatically pass the assets associated with these keys as arguments to the function when the multi-asset is executed. The function parameters are used to access the data from the upstream assets within the function body. You do not need to manually pass the asset names when calling the function because Dagster handles the wiring of inputs based on the

ins

definition.

@Scout Yeah but you're still passing the sourceN_labels args in the function def. What if I don't know what those are?

If you don't know the exact names of the

sourceN_labels

assets ahead of time, you can use the

**kwargs

syntax in your

@multi_asset

function definition to capture all incoming keyword arguments without explicitly naming them. This allows you to handle a dynamic set of inputs where the exact names are not known until runtime. Here's an example of how you might define a

@multi_asset

function that uses

**kwargs

to accept any number of label assets:

python from dagster import multi_asset, AssetIn, AssetKey  # Assume you have a list of asset names that match a certain pattern # For example, this list could be dynamically generated based on some criteria dynamic_asset_names = ["source1", "source2", "source3"]  # This could be generated dynamically  # Use dict comprehension to generate the `ins` dictionary ins_dict = {     f"{name}_labels": AssetIn(key=AssetKey([f"{name}_labels"]))     for name in dynamic_asset_names }  # Define a multi-asset that combines data from a dynamic set of label assets @multi_asset(ins=ins_dict) def combine_dataframes(**kwargs):     # kwargs is a dictionary where the keys are the input names (e.g., "source1_labels")     # and the values are the data provided by the upstream assets      # Implement your logic to combine the DataFrames here     # For example, you might concatenate them along a particular axis     combined_df = some_combination_logic(kwargs.values())     return combined_df

In this example,

combine_dataframes

uses

**kwargs

to accept any number of inputs without explicitly listing them as function arguments. The

ins_dict

is still generated using dict comprehension based on the

dynamic_asset_names

list, which could be dynamically generated based on your application's logic. The

kwargs

dictionary inside the function will contain all the inputs passed to the multi-asset, with the input names as keys and the actual data as values. You can then process these inputs as needed without having to know their exact names in the function signature. This approach allows you to define a multi-asset function that is flexible and can handle a varying number of inputs, which is useful when the exact inputs are not known until runtime.

@Scout Ok, now say I only want to output the labels asset whose name is compatible with the "source" field that I configured in the materialize_pdf_data job config_schema. I.e. the multi-asset receives all asset definitions, but only outputs one (i.e. the one that's configured). How could I do that?

To output only the

labels

asset whose name is compatible with the "source" field configured in the

materialize_pdf_data

job's

config_schema

, you can use the

@multi_asset

decorator with conditional logic inside the function to determine which asset to yield based on the provided config. You can also use the

is_required

parameter on

AssetOut

to indicate that only one of the assets will be materialized conditionally. Here's an example of how you might set this up:

python from dagster import multi_asset, AssetOut, AssetIn, AssetKey, Output, job, ConfigSchema, String  # Assume you have a list of asset names that match a certain pattern # This list could be dynamically generated based on some criteria dynamic_asset_names = ["source1", "source2", "source3"]  # Use dict comprehension to generate the `ins` dictionary ins_dict = {     f"{name}_labels": AssetIn(key=AssetKey([f"{name}_labels"]))     for name in dynamic_asset_names }  # Define a multi-asset that outputs only the asset specified in the job config @multi_asset(     ins=ins_dict,     outs={         f"{name}_labels": AssetOut(is_required=False)         for name in dynamic_asset_names     } ) def conditional_labels_asset(context, **kwargs):     # Get the source from the config     source = context.op_config["source"]     asset_key = f"{source}_labels"      # Yield only the asset that matches the source in the config     if asset_key in kwargs:         yield Output(kwargs[asset_key], output_name=asset_key)  # Define a job that selects a specific `labels` asset based on a config value @job(config_schema={"source": ConfigSchema(String)}) def materialize_pdf_data(context):     # The job logic goes here     # The conditional_labels_asset will only output the asset specified in the config     conditional_labels_asset()

In this example, the

conditional_labels_asset

multi-asset function takes a context and

**kwargs

as arguments. The

**kwargs

captures all the incoming label assets, and the function uses the

context.op_config["source"]

to determine which asset to yield. The

outs

dictionary in the

@multi_asset

decorator specifies that each possible output is not required by default (

is_required=False

), allowing the function to yield only the asset that matches the configured source. When you execute the

materialize_pdf_data

job, you would pass the config value to specify which

labels

asset to output, and only that asset will be materialized. Please note that this example assumes that the

source

field in the job config matches one of the keys in

dynamic_asset_names

. Adjust the example as needed to fit your actual asset definitions and processing logic.

@Scout I didn't understand why we changed the outs dict. Can you break it down?

I apologize for the confusion. The

outs

dictionary in the

@multi_asset

decorator is used to define the outputs of the multi-asset. In the previous example, I mistakenly included an

outs

dictionary with

is_required=False

for each potential output, which is not necessary for your use case and may have caused confusion. In your scenario, where you want to output only one specific asset based on a configuration value, you don't need to define multiple outputs with

is_required=False

. Instead, you can define a single output for the multi-asset and then conditionally yield the correct asset based on the configuration. Here's a simplified example that should clarify the approach:

python from dagster import multi_asset, AssetOut, AssetIn, AssetKey, Output, job, ConfigSchema, String  # Assume you have a list of asset names that match a certain pattern # This list could be dynamically generated based on some criteria dynamic_asset_names = ["source1", "source2", "source3"]  # Use dict comprehension to generate the `ins` dictionary ins_dict = {     f"{name}_labels": AssetIn(key=AssetKey([f"{name}_labels"]))     for name in dynamic_asset_names }  # Define a multi-asset that outputs only the asset specified in the job config @multi_asset(     ins=ins_dict,     outs={"selected_labels": AssetOut()}  # Define a single output for the selected asset ) def conditional_labels_asset(context, **kwargs):     # Get the source from the config     source = context.op_config["source"]     asset_key = f"{source}_labels"      # Yield only the asset that matches the source in the config     if asset_key in kwargs:         yield Output(kwargs[asset_key], output_name="selected_labels")  # Define a job that selects a specific `labels` asset based on a config value @job(config_schema={"source": ConfigSchema(String)}) def materialize_pdf_data(context):     # The job logic goes here     # The conditional_labels_asset will only output the asset specified in the config     conditional_labels_asset()

In this corrected example, the

conditional_labels_asset

multi-asset function has a single output named

selected_labels

. The function checks the

source

specified in the job's config and yields the corresponding asset using the

Output

object with the

output_name

set to "selected_labels". This way, regardless of which asset is selected based on the configuration, the output will always be named

selected_labels

. When you execute the

materialize_pdf_data

job, you would pass the config value to specify which

labels

asset to output, and the multi-asset will yield only the selected asset as

selected_labels

. This approach simplifies the output definition and aligns with your requirement to output only one specific asset based on the job configuration.

@Scout Nice, that sounds good! How would I go about updating the job, where the order of materialization should be

labels -> pdf_data -> training_data

To update the job so that the order of materialization is

labels -> pdf_data -> training_data

, you would define dependencies between these assets. In Dagster, you can set up these dependencies by using the

AssetOut

and

AssetIn

decorators to specify the upstream assets that an asset depends on. Here's how you can define these dependencies: 1. Define the

labels

asset with a dynamic key based on the job config. 2. Define the

pdf_data

asset to depend on the

labels

asset. 3. Define the

training_data

asset to depend on the

pdf_data

asset. Here's an example of how you might set this up:

python from dagster import asset, AssetKey, AssetOut, AssetIn, job, ConfigSchema, String  # Define the `labels` asset with a dynamic key based on the job config @asset(required_resource_keys={"config"}) def labels(context):     source = context.resources.config["source"]     asset_key = f"{source}_labels"     # Load and return the data for the labels asset     ...  # Define the `pdf_data` asset to depend on the `labels` asset @asset(ins={"labels": AssetIn()}) def pdf_data(labels):     # Process the labels data to create pdf_data     ...  # Define the `training_data` asset to depend on the `pdf_data` asset @asset(ins={"pdf_data": AssetIn()}) def training_data(pdf_data):     # Process the pdf_data to create training_data     ...  # Define a job that materializes the assets in the correct order @job(config_schema={"source": ConfigSchema(String)}, resource_defs={"config": config_resource}) def materialize_data_pipeline():     labels()     pdf_data()     training_data()

In this example, the

labels

asset is defined with a dynamic key that is determined by the job's config. The

pdf_data

asset is defined with an input that depends on the

labels

asset, and the

training_data

asset is defined with an input that depends on the

pdf_data

asset. The

materialize_data_pipeline

job is responsible for materializing the assets in the correct order. When you run this job, you will provide the

source

config value, which determines which

labels

asset to materialize. The

pdf_data

asset will then be materialized using the output from the

labels

asset, and the

training_data

asset will be materialized using the output from the

pdf_data

asset. Please note that the

config_resource

is a placeholder for a resource that provides the job's config to the

labels

asset. You will need to define this resource to pass the config to the

labels

asset. This setup ensures that the assets are materialized in the specified order:

labels -> pdf_data -> training_data

. Each asset will only be materialized after its dependencies have been materialized, maintaining the correct order of operations.

@Scout Can I just call the asset function inside a job definition? That's what you did in the previous answer, I find that strange

In Dagster, you can indeed call asset functions directly within a job definition. When you define assets using the

@asset

decorator, these assets can be invoked like functions within a job to specify the order in which they should be materialized. This is a common pattern when you want to create a job that materializes a specific set of assets in a particular order. Here's a simplified example to illustrate this:

python from dagster import asset, job  # Define an asset @asset def my_asset():     # Compute and return some data     ...  # Define a job that materializes the asset @job def my_job():     # Call the asset function to include it in the job's execution plan     my_asset()

In this example,

my_asset

is an asset defined with the

@asset

decorator. The

my_job

job includes a call to

my_asset()

, which means that when

my_job

is executed, it will materialize

my_asset

. When you define a job in this way, Dagster automatically infers the dependencies between assets based on their input and output definitions. If an asset function takes another asset as an input, Dagster will ensure that the upstream asset is materialized first. This approach allows you to define complex workflows where the materialization of assets is orchestrated by a job, and the dependencies between assets dictate the order of execution. It's important to note that when you call an asset function within a job, you're not invoking the asset's computation at that moment. Instead, you're building an execution plan that Dagster will use to determine the correct order of operations when the job is run. The actual computations and materializations happen when you execute the job, either through the Dagster UI, the command line, or programmatically.

@Scout Ok, that makes sense. I'm getting the following error in the labels multi-asset definition:

dagster._core.errors.DagsterInvalidDefinitionError: Key 'midwest_ent_labels' in provided ins dict does not correspond to any of the names of the arguments to the decorated function

The error message you're encountering indicates that there's a mismatch between the keys specified in the

ins

dictionary of the

@multi_asset

decorator and the arguments of the decorated function. In Dagster, when you define a multi-asset, the keys in the

ins

dictionary must correspond to the names of the parameters in the function signature. Here's a general structure of how to define a multi-asset:

python from dagster import multi_asset, AssetIn, AssetKey  @multi_asset(     ins={         "input1": AssetIn(key=AssetKey("input1_asset")),         "input2": AssetIn(key=AssetKey("input2_asset")),         # ... other inputs     } ) def my_multi_asset(input1, input2, **kwargs):     # Function body that uses input1, input2, and any other dynamic inputs from kwargs     ...

In this structure, the

ins

dictionary has keys

"input1"

and

"input2"

that correspond to the function parameters

input1

and

input2

. If you have additional dynamic inputs, you can use

**kwargs

to capture them without explicitly listing them in the function signature. To resolve the error you're encountering, you need to ensure that the keys in the

ins

dictionary match the function's parameter names. If you're using

**kwargs

to capture dynamic inputs, you won't list those inputs explicitly in the function signature, but you still need to define them in the

ins

dictionary. Here's an example that might resemble your situation:

python from dagster import multi_asset, AssetIn, AssetKey  # Assume you have a dynamic list of asset names dynamic_asset_names = ["midwest_ent_labels", "other_source_labels"]  # Use dict comprehension to generate the `ins` dictionary ins_dict = {     f"{name}": AssetIn(key=AssetKey([name]))     for name in dynamic_asset_names }  # Define a multi-asset that combines data from a dynamic set of label assets @multi_asset(ins=ins_dict) def conditional_labels_asset(**kwargs):     # kwargs is a dictionary where the keys are the input names     # and the values are the data provided by the upstream assets      # Implement your logic to combine or select the DataFrames here     ...

In this example, the

ins_dict

is generated using dict comprehension based on the

dynamic_asset_names

list. The

conditional_labels_asset

function uses

**kwargs

to accept any number of inputs without explicitly listing them as function arguments. Make sure that the keys in the

ins_dict

do not include any extra prefixes or suffixes that do not match the actual asset keys. If the error persists, double-check the asset keys and ensure they are correctly defined in the repository and match the keys used in the

ins_dict

.

@Scout In a previous example, you mentioned a config_resource that's simply a placeholder for a resource that provides the job's config to the

labels

asset. How would I define that resource?

To define a

config_resource

that provides the job's configuration to the

labels

asset, you would create a resource that can access the job's configuration and make it available to assets or ops that require it. In Dagster, resources are a way to make external resources (like configurations, database connections, etc.) available to ops and assets during job execution. Here's an example of how you might define a `config_resource`:

python from dagster import resource, initialize_resource, ResourceDefinition  # Define a resource class that holds the configuration class ConfigResource:     def __init__(self, config):         self.config = config  # Define a resource function that creates an instance of the resource class @resource def config_resource(init_context):     # Access the job's configuration from the init_context     job_config = init_context.resource_config     # Return an instance of the ConfigResource with the job's configuration     return ConfigResource(config=job_config)  # Define a resource definition for the config_resource config_resource_def = ResourceDefinition(resource_fn=config_resource)

In this example,

ConfigResource

is a simple class that holds the configuration. The

config_resource

function is decorated with

@resource

and is responsible for creating an instance of

ConfigResource

using the job's configuration, which is accessed via

init_context.resource_config

. When you define a job, you can include this resource definition in the

resource_defs

argument to make the configuration available to assets or ops within the job:

python from dagster import job  # Define a job that uses the config_resource @job(resource_defs={"config": config_resource_def}) def my_job():     # Job definition that uses assets or ops requiring the config resource     ...

Now, any asset or op that requires the

config

resource can access it by including it in the

required_resource_keys

set:

python from dagster import asset  # Define an asset that uses the config resource @asset(required_resource_keys={"config"}) def labels(context):     # Access the configuration from the config resource     source = context.resources.config.config["source"]     # Load and return the data for the labels asset     ...

In this asset definition,

context.resources.config

is used to access the

ConfigResource

instance, and

context.resources.config.config

is used to access the actual configuration dictionary that was provided to the job. This setup allows you to pass configuration to assets through resources, which can be particularly useful when you have configuration that needs to be shared across multiple assets or ops within a job.

@Scout If all i'm doing with the config is passing a string that holds the source name, wouldn't it be the same if I just created a config inheriting from dagster's Config class, say ETLPipelineConfig, and that config would be passed to the labels multi-asset?

Yes, if you are only passing a simple string that holds the source name, you can define a config class that inherits from Dagster's

Config

class and use it to pass the configuration directly to your multi-asset. This approach simplifies the process by allowing you to define a config schema directly on the asset. Here's an example of how you might define such a config class and use it with your multi-asset:

python from dagster import Config, multi_asset  # Define a config class that inherits from Dagster's Config class ETLPipelineConfig(Config):     source: str  # Define a multi-asset that uses the ETLPipelineConfig @multi_asset(config_schema=ETLPipelineConfig) def labels(config):     # Access the source name from the config     source_name = config.source     # Load and return the data for the labels asset     ...

In this example,

ETLPipelineConfig

is a config class that specifies a single string field named

source

. The

labels

multi-asset is then defined with

config_schema=ETLPipelineConfig

, which means it expects to receive an instance of

ETLPipelineConfig

as its configuration. When you run a job that includes this multi-asset, you would provide the configuration for the

source

field, and the

labels

multi-asset will use that configuration to determine which source to load. Here's how you might define a job that uses this multi-asset and provides the configuration:

python from dagster import job  # Define a job that provides the configuration for the labels multi-asset @job def my_etl_job():     labels()

And here's an example of the run configuration you would provide when executing the job:

yaml # Example run config ops:   labels:     config:       source: "my_source_name"

This configuration specifies that the

source

field for the

labels

multi-asset should be set to

"my_source_name"

. Dagster will validate the run configuration against the schema defined in

ETLPipelineConfig

and proceed only if validation is successful. This approach is more straightforward when you have simple configuration needs and is especially suitable when the configuration is specific to the asset and does not need to be shared with other parts of the job.

@Scout I got the error

Param "config_schema" is not one of ['Mapping']. Got <class 'aiml_pipeline.assets.etl_pipeline.ETLPipelineConfig'> which is type <class 'dagster._config.pythonic_config.typing_utils.BaseConfigMeta'>. Only dicts are supported for asset config_schema.

The error message you're encountering suggests that Dagster is expecting a dictionary for the

config_schema

parameter, but it received a class that inherits from

BaseConfigMeta

instead. In Dagster, the

config_schema

parameter for assets is used to define the shape of the configuration data that the asset expects. This is typically done using Python primitive types, Dagster config types, or a dictionary that describes the structure of the configuration. To resolve this issue, you should define the

config_schema

using a dictionary or a Dagster config type that describes the expected configuration. Here's an example of how you can define the

config_schema

using a dictionary:

python from dagster import asset, Field, String  # Define the `config_schema` using a dictionary @asset(config_schema={"source": Field(String)}) def labels(context):     # Access the source name from the config     source_name = context.op_config["source"]     # Load and return the data for the labels asset     ...

In this example, the

config_schema

is defined as a dictionary with a single key

"source"

, which is associated with a

Field

of type

String

. This tells Dagster that the

labels

asset expects a configuration with a string field named

source

. When you run a job that includes this asset, you would provide the configuration for the

source

field as shown in the previous example:

yaml # Example run config ops:   labels:     config:       source: "my_source_name"

This configuration will be validated against the

config_schema

dictionary, and if it passes validation, the

labels

asset will use the provided

source

to perform its computation. Using a dictionary for

config_schema

is the recommended way to define the configuration schema for assets in Dagster, as it allows for clear and explicit validation of the configuration data.

@Scout Can I define an asset job, while defining a run config in dagster's launchpad?

There was an error processing your request. Please try again.

@Scout how can I send an email to my gmail account when a materialization on my local webserver instance ends?

To send an email to your Gmail account when a materialization ends, you can use Dagster's built-in alerting utilities to create an email sensor or hook. Dagster provides a utility function

make_email_on_run_failure_sensor

to send emails on job failure, but for sending emails on successful materialization, you would need to implement custom logic. Here's a general approach to send an email upon successful materialization: 1. Create a custom op that sends an email using an SMTP server. 2. Use this op at the end of your job to send an email after materialization. First, you'll need to set up your email sending function. Here's an example using Python's `smtplib`:

python
import smtplib
from email.mime.text import MIMEText
from dagster import op

@op(required_resource_keys={'smtp'})
def send_email(context):
    source = context.resources.smtp
    msg = MIMEText('Materialization has completed successfully.')
    msg['Subject'] = 'Dagster Materialization Success'
    msg['From'] = source['sender']
    msg['To'] = source['recipient']

    with smtplib.SMTP_SSL(source['host'], source['port']) as server:
        server.login(source['sender'], source['password'])
        server.send_message(msg)

    <http://context.log.info|context.log.info>('Email sent successfully.')

Next, define the SMTP resource that will hold your email configuration:

python
from dagster import resource

@resource(config_schema={
    'host': str,
    'port': int,
    'sender': str,
    'password': str,
    'recipient': str,
})
def smtp_resource(init_context):
    return {
        'host': init_context.resource_config['host'],
        'port': init_context.resource_config['port'],
        'sender': init_context.resource_config['sender'],
        'password': init_context.resource_config['password'],
        'recipient': init_context.resource_config['recipient'],
    }

Now, you can define a job that uses this op after materialization:

python
from dagster import job

@job(resource_defs={'smtp': smtp_resource})
def my_materialization_job():
    # ... your materialization logic here ...
    send_email()  # This will send an email after materialization

Finally, provide the SMTP configuration when you launch the job:

yaml
# Example run config
resources:
  smtp:
    config:
      host: '<http://smtp.gmail.com|smtp.gmail.com>'
      port: 465
      sender: '<mailto:your-gmail-address@gmail.com|your-gmail-address@gmail.com>'
      password: 'your-gmail-password'
      recipient: '<mailto:recipient-address@gmail.com|recipient-address@gmail.com>'

Please note that using your Gmail password directly in the script is not recommended for security reasons. Instead, you should use an [App Password](https://support.google.com/accounts/answer/185833) if you have 2-Step Verification enabled on your Google account, or consider using environment variables or a secret manager to store sensitive in