Hi @Scout, i have an asset which returns two dataframes, lets call them return df1, df2. i now want to use both dataframes in a downstream asset. how to?

To use multiple dataframes produced by an upstream asset in a downstream asset, you can define a single asset that represents multiple assets using the

@multi_asset

decorator. This allows you to specify multiple outputs for a single asset function, and each output can be consumed by downstream assets. Here's an example from the documentation that illustrates how you might define a multi-asset:

python from dagster import multi_asset, AssetOut  @multi_asset(     outs={         "df1": AssetOut(),         "df2": AssetOut(),     } ) def my_multi_asset():     # Your logic to create df1 and df2     df1 = ...     df2 = ...     return {"df1": df1, "df2": df2}

In this example,

my_multi_asset

is an asset that produces two separate dataframes,

df1

and

df2

. Each dataframe is associated with its own

AssetOut

in the

outs

dictionary. Now, to consume these dataframes in a downstream asset, you would define the downstream asset with dependencies on the outputs of `my_multi_asset`:

python from dagster import asset  @asset def downstream_asset(df1, df2):     # Your logic that uses df1 and df2     ...

In this downstream asset,

df1

and

df2

are the inputs that correspond to the outputs of the

my_multi_asset

. Dagster will automatically wire up the dependencies based on the names of the outputs and the input parameters of the downstream asset. Remember to replace the placeholder logic with your actual code for creating and using the dataframes.

upstream asset does not return two outs with assetOut(), but instead just returns two seperate dataframes like this: return df1, df2 @Scout

If your upstream asset returns two separate dataframes without using

AssetOut

, you can still consume them in a downstream asset by using the

@multi_asset

decorator to define the upstream asset and then referencing the outputs in the downstream asset. Here's how you can define the upstream asset using `@multi_asset`:

python from dagster import multi_asset, AssetOut, Output  @multi_asset(     outs={         "df1": AssetOut(),         "df2": AssetOut(),     } ) def upstream_asset():     # Your logic to create df1 and df2     df1 = ...     df2 = ...     yield Output(df1, "df1")     yield Output(df2, "df2")

In this example,

upstream_asset

is a multi-asset that yields two separate outputs,

df1

and

df2

. Each output is associated with its own

AssetOut

in the

outs

dictionary, and you use the

yield

statement to output each dataframe with the corresponding output name. Now, to consume these dataframes in a downstream asset, you would define the downstream asset with dependencies on the outputs of `upstream_asset`:

python from dagster import asset  @asset def downstream_asset(df1, df2):     # Your logic that uses df1 and df2     ...

In the downstream asset,

df1

and

df2

are the input parameters that correspond to the outputs of the

upstream_asset

. Dagster will automatically wire up the dependencies based on the names of the outputs and the input parameters of the downstream asset. This approach allows you to define explicit outputs for the upstream asset and then use those outputs in downstream assets.

@Scout is it possible to use only the asset decorator and return multiple results (like dataframes) which can be used be seperate downstream assets?

In Dagster, the

@asset

decorator is typically used to define a single asset. However, if you want to return multiple results from an asset and have each result be used by separate downstream assets, you would need to use the

@multi_asset

decorator instead, as it allows you to define multiple outputs from a single computation. The

@asset

decorator does not natively support defining multiple outputs that can be individually consumed by downstream assets. Each

@asset

-decorated function is expected to return a single result, which represents the materialization of that asset. If you have a function that naturally produces multiple dataframes and you want to treat each as a separate asset, you would need to split this function into multiple

@asset

-decorated functions, each producing a single dataframe, or use the

@multi_asset

decorator to define multiple outputs. Here's an example of how you might split a function into multiple

@asset

-decorated functions:

python from dagster import asset  # Original function that produces two dataframes def produce_dataframes():     df1 = ...  # Logic to create df1     df2 = ...  # Logic to create df2     return df1, df2  # Separate assets for each dataframe @asset def df1_asset():     df1, _ = produce_dataframes()     return df1  @asset def df2_asset():     _, df2 = produce_dataframes()     return df2

In this example, we have a helper function

produce_dataframes

that produces two dataframes. We then define two separate assets,

df1_asset

and

df2_asset

, each calling

produce_dataframes

and returning one of the dataframes. Each of these assets can then be consumed by downstream assets independently. However, this approach is not efficient because

produce_dataframes

is called twice, once for each asset, which could be computationally expensive if the function is doing significant work. The preferred way to handle multiple outputs is to use the

@multi_asset

decorator, as shown in the previous examples. This allows you to define multiple outputs that can be individually consumed by downstream assets while only running the computation once.

@Scout so lets say i fetch data from an api and use different payloads. whats best practise on how to handle this in dagster? each api call with each unique payload is a seperate unique asset?

In Dagster, best practice for handling data fetched from an API with different payloads would depend on how you want to track and manage the data within your system. Here are a few considerations to help you decide the best approach: 1. Granularity of Assets: If each API call with a unique payload results in a dataset that you want to track independently, then treating each call as a separate asset could make sense. This allows you to track the lineage, materializations, and dependencies of each dataset individually. 2. Reusability: If the logic for calling the API is the same and only the payload changes, you might want to create a single asset with a parameterized function that takes the payload as an input. This way, you can reuse the same logic for different payloads without duplicating code. 3. Partitioning: If the datasets are logically part of a larger collection (e.g., daily snapshots of data, different segments of a market, etc.), you might consider using partitioned assets. Partitions allow you to represent a collection of datasets that are all computed in the same way but vary based on a partition key (such as date or payload identifier). 4. Asset Groups: If you have a set of assets that are related (e.g., they all come from the same API but with different payloads), you might group them together using asset groups for better organization and visualization in Dagit, Dagster's UI. Here's an example of how you might define an asset that fetches data from an API with different payloads:

python
from dagster import asset, Field, String

@asset(config_schema={"payload": Field(String)})
def fetch_data_from_api(context):
    payload = context.op_config["payload"]
    data = call_api(payload=payload)
    return data

def call_api(payload):
    # Logic to call the API with the given payload
    ...

In this example,

fetch_data_from_api

is a parameterized asset that takes a payload from its configuration. You can then materialize this asset with different payloads by providing different configurations when you run your pipeline or job. If you decide that each API call should be a separate asset, you could define multiple assets, each with its own payload:

python
@asset(name="asset_with_payload_1")
def fetch_data_with_payload_1():
    payload = "payload_1"
    return call_api(payload=payload)

@asset(name="asset_with_payload_2")
def fetch_data_with_payload_2():
    payload = "payload_2"
    return call_api(payload=payload)

# Additional assets for other payloads...

Each of these assets can then be independently tracked and managed within Dagster. Ultimately, the best practice will depend on your specific use case, how you want to manage dependencies between assets, and how you want to visualize and track the d

can you show me an example of the whole python file where i see the combination of a parameterized asset which takes a playload from its configuration and then also define multiple assets each with its own payload? And also how to to this in init.py @Scout

Certainly! Below is an example of how you might structure a Python file that includes both a parameterized asset that takes a payload from its configuration and multiple assets each with their own payload. This example assumes that you have a function

call_api

that takes a payload and returns data from an API.

python # api_assets.py  from dagster import asset, Field, String  # A generic function to call an API with a given payload def call_api(payload):     # Replace with actual API call logic     print(f"Calling API with payload: {payload}")     return f"Data for payload {payload}"  # A parameterized asset that takes a payload from its configuration @asset(config_schema={"payload": Field(String)}) def fetch_data_from_api(context):     payload = context.op_config["payload"]     data = call_api(payload=payload)     return data  # Assets with specific payloads @asset(name="asset_with_payload_1") def fetch_data_with_payload_1():     payload = "payload_1"     return call_api(payload=payload)  @asset(name="asset_with_payload_2") def fetch_data_with_payload_2():     payload = "payload_2"     return call_api(payload=payload)  # Additional assets for other payloads...

Now, to use these assets in your

__init__.py

file, you would import them and include them in a

Definitions

object. Here's how you might do that:

python # __init__.py  from dagster import Definitions from .api_assets import fetch_data_from_api, fetch_data_with_payload_1, fetch_data_with_payload_2  # Assuming you have other assets, jobs, schedules, etc., you would include them here as well defs = Definitions(     assets=[         fetch_data_from_api,         fetch_data_with_payload_1,         fetch_data_with_payload_2,         # ... other assets     ],     # ... other definitions like jobs, schedules, sensors, resources, etc. )

In this

__init__.py

file, you're creating a

Definitions

object that includes all the assets you've defined in

api_assets.py

. This

Definitions

object is used by Dagster to discover and load your assets and other definitions. When you want to materialize the parameterized asset

fetch_data_from_api

with a specific payload, you would provide a configuration when you run your pipeline or job. Here's an example of how you might do that:

yaml # run_config.yaml  ops:   fetch_data_from_api:     config:       payload: "dynamic_payload_value"

You would then use this configuration file when running your job to specify the payload for the

fetch_data_from_api

asset. The other assets,

fetch_data_with_payload_1

and

fetch_data_with_payload_2

, have hardcoded payloads and do not require additional configuration to run.

@Scout is there a simpler way to share variables like path strings between assets then going the multi asset route?

Yes, there are simpler ways to share variables like path strings between assets without using the

multi_asset

route. Here are a few methods you can use: 1. Configurations: You can define configurations at the job or repository level that can be accessed by all assets. This allows you to share common variables like path strings.

python
from dagster import asset, repository, job

@asset(required_resource_keys={"config"})
def asset_one(context):
    path = context.resources.config["path"]
    # Use the path for your logic

@asset(required_resource_keys={"config"})
def asset_two(context):
    path = context.resources.config["path"]
    # Use the path for your logic

@job(resource_defs={"config": {"path": "shared/path/string"}})
def my_job():
    asset_one()
    asset_two()

@repository
def my_repo():
    return [my_job]

2. Resources: Define a resource that provides the shared variable and include it in the required resources for each asset.

python
from dagster import asset, resource, build_init_resource_context

@resource(config_schema={"path": str})
def path_resource(context):
    return context.resource_config["path"]

@asset(required_resource_keys={"path_resource"})
def asset_one(context):
    path = context.resources.path_resource
    # Use the path for your logic

@asset(required_resource_keys={"path_resource"})
def asset_two(context):
    path = context.resources.path_resource
    # Use the path for your logic

# When you run your job, you would provide the configuration for the path_resource.

3. Module-level Variables: If the shared variable is constant and does not need to be configured at runtime, you can define it at the module level.

python
# shared.py
SHARED_PATH = "shared/path/string"

# assets.py
from dagster import asset
from shared import SHARED_PATH

@asset
def asset_one():
    path = SHARED_PATH
    # Use the path for your logic

@asset
def asset_two():
    path = SHARED_PATH
    # Use the path for your logic

4. Environment Variables: Use environment variables to share configuration between assets. This is useful when you have environment-specific paths.

python
import os
from dagster import asset

@asset
def asset_one():
    path = os.getenv("SHARED_PATH")
    # Use the path for your logic

@asset
def asset_two():
    path = os.getenv("SHARED_PATH")
    # Use the path for your logic

# Set the environment variable SHARED_PATH before running your job.

Each of these methods has its own use cases and trade-offs. Configurations and resources provide more flexibility and are suitable for runtime-configurable variables. Module-level variables and environment variables are simpler but less flexible, as they are not easily configurable at runtime. Choose the method that best fits your use c

@Scout i basically define a dynamic cloud storage path in asset1 and want to pass the path to asset2. whats the best way to do this?

If you need to define a dynamic cloud storage path in

asset1

and pass it to

asset2

, you can use an I/O manager to handle the passing of the path between assets. An I/O manager in Dagster is a resource that handles storing and loading assets. You can configure an I/O manager to write the path to a location where

asset2

can access it. Here's a simplified example of how you might implement this:

python from dagster import asset, IOManager, io_manager, Output, InputDefinition  class PathIOManager(IOManager):     def handle_output(self, context, obj):         # Here you would write the path to a location, e.g., a file, environment variable, etc.         # For this example, let's assume we're writing to a file         with open("/tmp/shared_path.txt", "w") as f:             f.write(obj)      def load_input(self, context):         # Here you would read the path from the location         with open("/tmp/shared_path.txt", "r") as f:             return f.read()  @io_manager def path_io_manager(_):     return PathIOManager()  @asset(io_manager_key="path_io_manager") def asset1():     # Define your dynamic cloud storage path     dynamic_path = "<s3://my-bucket/dynamic/path>"     # Output the path using an IOManager     return Output(dynamic_path)  @asset(ins={"input_path": InputDefinition(name="input_path", io_manager_key="path_io_manager")}) def asset2(input_path):     # Use the dynamic path in your logic     # The 'input_path' parameter contains the path produced by asset1     pass  # When you run your job, you would provide the configuration for the path_io_manager.

In this example,

PathIOManager

is a custom I/O manager that writes the output of

asset1

(the dynamic path) to a file and reads it back in for

asset2

. The

@io_manager

decorator is used to create an instance of the I/O manager.

asset1

is defined with

io_manager_key="path_io_manager"

, which tells Dagster to use the custom I/O manager for handling its output. It returns an

Output

object containing the dynamic path.

asset2

has an input definition that specifies the same I/O manager key, which tells Dagster to use the

PathIOManager

to load the input. The dynamic path produced by

asset1

is then available in

asset2

as the

input_path

parameter. This approach allows you to pass dynamic information between assets without having to use environment variables or other global state, and it keeps the logic encapsulated within the Dagster framework.

different way to use it without I/O Managers? @Scout

If you prefer not to use I/O managers, you can pass data between assets by leveraging Dagster's in-memory object passing. This is done by defining dependencies between assets, where the output of one asset is directly used as the input to another asset. Here's an example of how you can do this:

python from dagster import asset  # Asset 1 computes a dynamic path and returns it @asset def asset1():     # Compute the dynamic cloud storage path     dynamic_path = "<s3://my-bucket/dynamic/path>"     return dynamic_path  # Asset 2 takes the output of asset1 (the dynamic path) as an input @asset def asset2(dynamic_path):     # Use the dynamic path in your logic     # The 'dynamic_path' parameter contains the path produced by asset1     pass

In this example,

asset1

returns a dynamic cloud storage path.

asset2

declares a dependency on

asset1

by taking

dynamic_path

as an input parameter. When you execute these assets within a job, Dagster will automatically pass the output of

asset1

to

asset2

. Here's how you would define a job that includes both assets:

python from dagster import job  @job def my_job():     asset2(asset1())

When

my_job

is executed, Dagster will run

asset1

first, capture its output, and then provide it as an input to

asset2

. This approach is straightforward and works well when the data being passed between assets can be held in memory. However, if the data is too large to fit in memory or if you want to have more control over how data is stored and retrieved (for example, if you want to store intermediate results for later use or for other jobs), then using I/O managers would be the recommended approach.

@Scout i want to use this but currently i already return 3 variables in total like this:

def asset1():
    # Compute the dynamic cloud storage path
    dynamic_path = "<s3://my-bucket/dynamic/path>"
    blob_name = "xyz"
    bucket_name = "asdasd"
    return dynamic_path, blob_name, bucket_name

How can i reference dynamic_path, blob_name and bucket_name downstream asset?

If

asset1

returns multiple values and you want to use them in a downstream asset, you can unpack the returned tuple in the downstream asset. Here's an example of how you can define

asset1

to return multiple values and use them in `asset2`:

python from dagster import asset  # Asset 1 computes a dynamic path and other values, then returns them @asset def asset1():     # Compute the dynamic cloud storage path and other values     dynamic_path = "<s3://my-bucket/dynamic/path>"     blob_name = "xyz"     bucket_name = "asdasd"     return dynamic_path, blob_name, bucket_name  # Asset 2 takes the outputs of asset1 as inputs @asset def asset2(asset1_result):     # Unpack the tuple returned by asset1     dynamic_path, blob_name, bucket_name = asset1_result     # Use the values in your logic     pass

In this example,

asset1

returns a tuple containing

dynamic_path

,

blob_name

, and

bucket_name

.

asset2

takes a single input parameter,

asset1_result

, which is the tuple returned by

asset1

. Inside

asset2

, you unpack the tuple to get the individual values. When defining a job that includes these assets, you would wire them up like this:

python from dagster import job  @job def my_job():     asset2(asset1())

When

my_job

is executed, Dagster will run

asset1

, capture its output as a tuple, and then provide it as an input to

asset2

, where it is unpacked into the individual variables. This approach allows you to pass multiple values between assets without needing to use I/O managers or other more complex mechanisms. It's a simple and effective way to handle multiple return values from an asset in Dagster.

@Scout i get this error ValueError: too many values to unpack (expected 3) upstream return looks like this: return gcs_path_for_upload, bucket_name, blob_name downstream asset looks like this: def gcs_csv_to_bq(context: AssetExecutionContext, all_ranking_data_to_gcs: str): gcs_path_for_upload, bucket_name, blob_name = all_ranking_data_to_gcs print(gcs_path_for_upload) print(bucket_name) print(blob_name)

The error you're encountering,

ValueError: too many values to unpack (expected 3)

, suggests that the downstream asset is expecting a single input that can be unpacked into three variables, but it's receiving something different. In Dagster, when you return multiple values from an asset and want to use them in a downstream asset, you should ensure that the downstream asset is correctly configured to accept and unpack the tuple of values. Here's how you can adjust your downstream asset to correctly unpack the values:

python from dagster import asset, AssetExecutionContext  # Assuming this is your upstream asset @asset def asset1():     gcs_path_for_upload = "<gcs_path_for_upload>"     bucket_name = "<bucket_name>"     blob_name = "<blob_name>"     return gcs_path_for_upload, bucket_name, blob_name  # Your downstream asset should accept a single parameter that is a tuple of three elements @asset def asset2(context: AssetExecutionContext, all_ranking_data_to_gcs):     # Unpack the tuple into three separate variables     gcs_path_for_upload, bucket_name, blob_name = all_ranking_data_to_gcs     print(gcs_path_for_upload)     print(bucket_name)     print(blob_name)

Make sure that when you define the job, you wire the assets correctly:

python from dagster import job  @job def my_job():     asset2(asset1())

This setup should work without the

ValueError

you mentioned, as long as

asset1

is indeed returning a tuple with exactly three elements. If

asset1

is returning more than three elements, or if the downstream asset is being provided with additional inputs, you would need to adjust the unpacking to match the actual number of elements returned. If you continue to encounter issues, double-check the output of

asset1

to ensure it matches the expected structure and that no additional inputs are being passed to

asset2

inadvertently.

appearently the tuple is not passed to downstream asset. why is that? @Scout

If the tuple is not being passed to the downstream asset as expected, there could be several reasons for this issue. Let's go through some common troubleshooting steps to identify the problem: 1. Check Asset Definitions: Ensure that both assets are defined correctly and that the upstream asset is indeed returning a tuple of values. 2. Check Job Definition: Verify that the job is composed correctly, with the downstream asset depending on the upstream asset. 3. Check Execution Plan: Look at the execution plan in Dagit, the Dagster UI, to confirm that the assets are wired up as expected. The execution plan should show

asset1

as an upstream dependency of

asset2

. 4. Check Asset Materialization: Ensure that

asset1

is actually being executed and materialized before

asset2

. If

asset1

is not being run,

asset2

will not receive the expected input. 5. Check for Errors: Look for any errors in the execution logs that might indicate why

asset1

is not passing its output to

asset2

. 6. Check Asset Dependencies: Make sure that

asset2

is declared with the correct dependency on

asset1

. In the job definition,

asset2

should be called with

asset1()

as an argument. 7. Check Input Definitions: If

asset2

has explicit input definitions, ensure that they are expecting a single input that matches the output structure of

asset1

. Here's an example of how the job should be defined:

python from dagster import job  @job def my_job():     asset2(asset1())

If you've checked all the above and the issue persists, it could be a more subtle problem related to the Dagster system's handling of assets and dependencies. In that case, you might want to: - Update Dagster: Ensure you are using the latest version of Dagster, as there may have been bug fixes or improvements related to asset dependencies. - Consult the Documentation: Review the Dagster documentation for any updates or changes to the way assets and dependencies are handled. - Seek Support: If you're still stuck, consider reaching out to the Dagster community or support channels for assistance. You can provide details about your assets, job definition, and any logs or error messages you're seeing to get more targeted help. Remember that without seeing the full code and execution context, it's challenging to diagnose the exact issue. The steps above are general troubleshooting tips that should help you narrow down the problem.