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Title
# ask-ai
m

Manuel Fideles

02/15/2024, 1:09 PM
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
?
s

Scout

02/15/2024, 1:09 PM
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:
Copy code
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
ach to generate the
ins
dictionary, possibly using reflection or filesystem operations to discover the asset names. However, the example provided here should give you a starting point for implementing a fixed fan-in pattern with a known set of assets. AssetFanIn and AssetGroupFilter button Orchestrating Python and dbt with Dagste... button Thinking in Assets | Dagster Blog button Graph-Backed Assets | Dagster button Thank you for your feedback!
m

Manuel Fideles

02/15/2024, 1:26 PM
@Scout What if I only want to load the label asset whose name corresponds to the one passed into a Dagster Config?
s

Scout

02/15/2024, 1:27 PM
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:
Copy code
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:
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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.
m

Manuel Fideles

02/15/2024, 1:34 PM
@Scout Can I replace the
process_labels
op with a
graph_asset
?
s

Scout

02/15/2024, 1:34 PM
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:
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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.
m

Manuel Fideles

02/15/2024, 1:38 PM
@Scout Can I skip the processing_graph graph definition entirely and just use the graph_asset definition in the
process_specific_labels
job?
s

Scout

02/15/2024, 1:38 PM
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:
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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!
m

Manuel Fideles

02/15/2024, 1:46 PM
@Scout how can I materialize another asset called training_data that depends on the selected_asset?
s

Scout

02/15/2024, 1:46 PM
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:
Copy code
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.
m

Manuel Fideles

02/15/2024, 1:52 PM
@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
s

Scout

02/15/2024, 1:52 PM
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:
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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.
m

Manuel Fideles

02/15/2024, 1:56 PM
@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?
s

Scout

02/15/2024, 1:56 PM
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:
Copy code
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.
m

Manuel Fideles

02/15/2024, 2:01 PM
@Scout Can I define an alias for an asset name?
s

Scout

02/15/2024, 2:01 PM
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:
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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:
Copy code
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.
m

Manuel Fideles

02/15/2024, 2:44 PM
@Scout In the multi-asset scenario that you initially suggested, can I generate the ins dictionary directly using dict comprehension?
s

Scout

02/15/2024, 2:44 PM
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:
Copy code
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.
m

Manuel Fideles

02/15/2024, 2:49 PM
@Scout Can I not pass the names of the assets in the multi-asset function arguments?
s

Scout

02/15/2024, 2:49 PM
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:
Copy code
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.
m

Manuel Fideles

02/15/2024, 2:52 PM
@Scout Yeah but you're still passing the sourceN_labels args in the function def. What if I don't know what those are?
s

Scout

02/15/2024, 2:52 PM
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:
Copy code
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.
m

Manuel Fideles

02/15/2024, 2:58 PM
@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?
s

Scout

02/15/2024, 2:58 PM
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:
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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.
m

Manuel Fideles

02/15/2024, 3:04 PM
@Scout I didn't understand why we changed the outs dict. Can you break it down?
s

Scout

02/15/2024, 3:04 PM
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:
Copy code
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.
m

Manuel Fideles

02/15/2024, 3:07 PM
@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
s

Scout

02/15/2024, 3:07 PM
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:
Copy code
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.
m

Manuel Fideles

02/15/2024, 3:16 PM
@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
s

Scout

02/15/2024, 3:16 PM
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:
Copy code
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.
m

Manuel Fideles

02/15/2024, 3:18 PM
@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
s

Scout

02/15/2024, 3:18 PM
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:
Copy code
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:
Copy code
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
.
m

Manuel Fideles

02/15/2024, 3:29 PM
@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?
s

Scout

02/15/2024, 3:29 PM
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`:
Copy code
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:
Copy code
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:
Copy code
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.
m

Manuel Fideles

02/15/2024, 3:44 PM
@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?
s

Scout

02/15/2024, 3:44 PM
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:
Copy code
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:
Copy code
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:
Copy code
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.
m

Manuel Fideles

02/15/2024, 3:49 PM
@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.
s

Scout

02/15/2024, 3:50 PM
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:
Copy code
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:
Copy code
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.
m

Manuel Fideles

02/15/2024, 3:55 PM
@Scout Can I define an asset job, while defining a run config in dagster's launchpad?
s

Scout

02/15/2024, 3:56 PM
There was an error processing your request. Please try again.
There was an error processing your request. Please try again.
m

Manuel Fideles

02/15/2024, 8:14 PM
@Scout how can I send an email to my gmail account when a materialization on my local webserver instance ends?
s

Scout

02/15/2024, 8:14 PM
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`:
Copy code
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:
Copy code
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:
Copy code
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:
Copy code
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
formation. Also, be aware that sending emails via SMTP may require additional configuration, such as allowing less secure apps to access your Gmail account (if not using an App Password), which is not recommended for security reasons. This example demonstrates how to send an email after a successful materialization. If you want to send an email only when certain conditions are met, you can add the necessary logic within the
send_email
op to check for those conditions before sending the email.
To send an email to your Gmail account when a materialization on your local webserver instance ends, you can use Dagster's built-in alerting utilities to create an email sensor or hook. However, as of my knowledge cutoff date, Dagster does not provide a built-in utility specifically for sending emails on successful materialization. Instead, you would need to implement custom logic using hooks or sensors. 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 a hook to trigger the email sending op after a successful materialization. First, you'll need to set up your email sending function. Here's an example using Python's `smtplib`:
Copy code
python
import smtplib
from email.mime.text import MIMEText
from dagster import op, success_hook

# Define a success hook that sends an email
@success_hook(required_resource_keys={'smtp'})
def email_on_success(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.')

# Example op that you want to monitor for successful materialization
@op
def my_materialization_op(context):
    # Your materialization logic here
    <http://context.log.info|context.log.info>('Materialization completed.')
Next, define the SMTP resource that will hold your email configuration:
Copy code
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 and attaches the hook:
Copy code
python
from dagster import job

@job(resource_defs={'smtp': smtp_resource})
def my_materialization_job():
    my_materialization_op.with_hooks({email_on_success})()
Finally, provide the SMTP configuration when you launch the job:
Copy code
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 scrip
t 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 information. Also, be aware that sending emails via SMTP may require additional configuration, such as allowing less secure apps to access your Gmail account (if not using an App Password), which is not recommended for security reasons. This example demonstrates how to send an email after a successful materialization using a success hook. If you want to send an email only when certain conditions are met, you can add the necessary logic within the
email_on_success
hook to check for those conditions before sending the email.
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