@Scout: How i can define an upstream asset

To define an upstream asset in Dagster, you would typically use the

@asset

decorator to create software-defined assets and specify their dependencies. An upstream asset is essentially an asset that other assets depend on. Here's a general approach to defining an upstream asset: 1. Use the

@asset

decorator to define your asset function. 2. If your asset depends on other assets, you can specify these dependencies using the

ins

parameter of the

@asset

decorator. Here's an example of how you might define an upstream asset:

python from dagster import asset  @asset def upstream_asset():     # Your logic to produce the asset goes here     return some_data

And then you can define a downstream asset that depends on this upstream asset like so:

python from dagster import asset  @asset(ins={"upstream_input": AssetIn(key="upstream_asset")}) def downstream_asset(upstream_input):     # Your logic that uses the upstream asset's data     processed_data = some_transformation(upstream_input)     return processed_data

In this example,

downstream_asset

is defined with an input that specifies a dependency on

upstream_asset

. When you materialize

downstream_asset

, Dagster will ensure that

upstream_asset

is materialized first. Remember that the actual implementation of your assets will depend on your specific use case and the data processing logic you need to apply. The above code is a simplified example to illustrate the concept of defining upstream and downstream dependencies between assets in Dagster.

@Scout: Can i define the upstream asset in the asset which has the upstream

Yes, you can define the upstream asset directly within the asset that depends on it by using the

@asset

decorator and specifying the dependencies. In Dagster, when you define an asset, you can declare its upstream dependencies by specifying the inputs to the asset. Each input corresponds to an upstream asset that must be materialized before the current asset. Here's an example of how you might define an asset with an upstream dependency:

python from dagster import asset, AssetIn  # Define the upstream asset @asset def upstream_asset():     # Logic to produce the upstream asset     return some_upstream_data  # Define the downstream asset, which has an upstream dependency @asset(ins={"upstream_input": AssetIn(key="upstream_asset")}) def downstream_asset(upstream_input):     # Logic that uses the upstream asset's data     processed_data = some_transformation(upstream_input)     return processed_data

In this code snippet,

downstream_asset

has a declared input named

upstream_input

, which is an

AssetIn

object with the key set to

"upstream_asset"

. This key corresponds to the

upstream_asset

that we defined above. When you materialize

downstream_asset

, Dagster will automatically recognize that

upstream_asset

needs to be materialized first due to this declared dependency. This way of defining assets makes it clear what the dependencies are and allows Dagster to manage the execution order of assets to respect these dependencies.

@Scout: Can i define an asset an say that this asset ist the upstream asset from

In Dagster, when you define an asset, you don't explicitly state that "this asset is the upstream asset of another." Instead, you define each asset separately and specify dependencies by indicating which assets are inputs (upstream) to others (downstream). The downstream assets declare their upstream dependencies through their inputs. Here's an example to illustrate this:

python from dagster import asset, AssetIn  # Define the upstream asset @asset def upstream_asset():     # Logic to produce the upstream asset     return some_upstream_data  # Define the downstream asset, which depends on the upstream asset @asset(ins={"upstream_input": AssetIn(key="upstream_asset")}) def downstream_asset(upstream_input):     # Logic that uses the upstream asset's data     processed_data = some_transformation(upstream_input)     return processed_data

In this example,

upstream_asset

doesn't know about

downstream_asset

. It's the

downstream_asset

that declares it has an upstream dependency by specifying

upstream_input

as an input with

AssetIn(key="upstream_asset")

. This tells Dagster that

downstream_asset

depends on

upstream_asset

. If you want to define a new asset and declare that

upstream_asset

is its upstream, you would do it in a similar way:

python @asset(ins={"another_upstream_input": AssetIn(key="upstream_asset")}) def another_downstream_asset(another_upstream_input):     # Logic that uses the upstream asset's data     another_processed_data = another_transformation(another_upstream_input)     return another_processed_data

In this new asset,

another_downstream_asset

, we again declare that

upstream_asset

is an upstream dependency by using

AssetIn(key="upstream_asset")

in the inputs. To summarize, in Dagster, you define upstream relationships by specifying inputs to downstream assets. The upstream assets themselves do not declare their downstream dependents.

@Scout: It is possible to define in a assets themselves their downstream dependents.

No, in Dagster, assets themselves do not define their downstream dependents. The dependency relationship is defined from the perspective of the downstream asset. Each downstream asset specifies its upstream dependencies through its inputs, using the

AssetIn

object to reference the keys of the upstream assets it depends on. The design philosophy in Dagster is that upstream assets should not need to know about their consumers. This keeps the definition of each asset clean and focused on its own logic, without needing to be aware of the broader dependency graph. The downstream assets are responsible for declaring what they depend on, which allows Dagster to construct the asset dependency graph and ensure that assets are materialized in the correct order. Here's a quick recap of how you define an asset with an upstream dependency:

python from dagster import asset, AssetIn  # Define the upstream asset @asset def upstream_asset():     # Logic to produce the upstream asset     return some_upstream_data  # Define the downstream asset, which depends on the upstream asset @asset(ins={"upstream_input": AssetIn(key="upstream_asset")}) def downstream_asset(upstream_input):     # Logic that uses the upstream asset's data     processed_data = some_transformation(upstream_input)     return processed_data

In this example,

downstream_asset

declares that it depends on

upstream_asset

by specifying it in the

ins

parameter. The

upstream_asset

does not and should not have any information about which assets depend on it.

@Scout: can i build a upstream dependency betweeen assets in jobs

In Dagster, when you define assets, you can create dependencies between them by specifying inputs to the assets. These dependencies are then respected when you build jobs that materialize those assets. A job in Dagster is a collection of assets that can be materialized together, and it will respect the dependencies that have been defined between those assets. Here's an example of how you might define a job that includes assets with upstream dependencies:

python from dagster import asset, AssetIn, AssetGroup, repository  # Define the upstream asset @asset def upstream_asset():     # Logic to produce the upstream asset     return some_upstream_data  # Define the downstream asset, which depends on the upstream asset @asset(ins={"upstream_input": AssetIn(key="upstream_asset")}) def downstream_asset(upstream_input):     # Logic that uses the upstream asset's data     processed_data = some_transformation(upstream_input)     return processed_data  # Group the assets together my_assets = AssetGroup(assets=[upstream_asset, downstream_asset])  # Build a job that materializes the assets my_job = my_assets.build_job(name="my_job")  # Define the repository @repository def my_repo():     return [my_job]

In this example,

my_job

is a job that materializes both

upstream_asset

and

downstream_asset

. When you execute

my_job

, Dagster will first materialize

upstream_asset

because

downstream_asset

has declared it as an upstream dependency. The job will ensure that the assets are materialized in the correct order based on their dependencies. The dependencies between assets are defined at the asset level, not at the job level. The job simply includes the assets, and Dagster uses the dependencies that have been defined between the assets to determine the execution order when the job is run.

@Scout: but is there another way?

Yes, there is another way to define dependencies between assets within a job using Dagster's op-based APIs. While we prefer to use the asset-based APIs for data dependencies, you can also define dependencies using ops (operations) and build a job that orchestrates these ops while respecting their dependencies. Here's an example of how you might define a job with ops that have upstream dependencies:

python from dagster import op, job, Out, In  # Define the op that acts as the upstream dependency @op(out=Out()) def upstream_op():     # Logic to produce the data     return some_upstream_data  # Define the downstream op, which depends on the upstream op @op(ins={"upstream_data": In()}) def downstream_op(upstream_data):     # Logic that uses the upstream op's data     processed_data = some_transformation(upstream_data)     return processed_data  # Build a job that orchestrates the ops @job def my_job():     downstream_op(upstream_op())

In this example,

upstream_op

is an op that produces some data, and

downstream_op

is an op that takes that data as input and processes it. The

my_job

job defines the execution graph where

downstream_op

depends on the output of

upstream_op

. When you execute

my_job

, Dagster will first execute

upstream_op

and then pass its output to

downstream_op

. The job effectively defines the execution order of the ops based on their dependencies. While ops are a valid way to define dependencies and orchestrate execution, we encourage the use of assets for data-oriented workflows because assets provide a higher-level abstraction that is more suited for modeling data dependencies and lineage. Assets also integrate with Dagster's asset catalog, giving you visibility into the history and relationships of your data. However, the choice between using assets or ops depends on the specific needs and design of your system.