Why is the "Remove extra config" button not enabled when dealing with jobs, but is enabled when dealing with graphs?

We don't have specific information in the documentation regarding the behavior of the "Remove extra config" button in the context of jobs versus graphs. The documentation does not provide details on the UI elements such as buttons and their enabled/disabled states. However, it's important to note that the configuration system in Dagster is designed to be explicit. When you define a job or a graph, you specify the configuration schema for the ops or resources within it. This schema dictates what configuration is valid and what is considered "extra" or unnecessary. If you are experiencing an issue where the "Remove extra config" button is not behaving as expected, it could be due to a number of factors, such as the way the configuration is defined or a potential issue with the UI. For troubleshooting, you might want to: 1. Verify that the configuration schema for your job or graph is correctly defined and that there are indeed extra configuration fields that are not required by the schema. 2. Check if there are any known issues or updates related to the Dagster UI that might affect the behavior of this button. 3. If the problem persists and you believe it to be a bug, consider reporting it on the Dagster GitHub repository as an issue. For more detailed assistance, it would be helpful to have additional context or a specific example of the configuration you are working with.

@Scout Here is my code:

class OpsConfig(Config):
    env: str = None


test_env = RunConfig(
    ops={
        "pipeline_a": OpsConfig(env="test"),
        "pipeline_b": OpsConfig(env="test")
    }
)

@op(ins={"start": In(Nothing)})
def pipeline_a(config: OpsConfig):
    print(f"pipeline_a in {config.env}")

@op(ins={"start": In(Nothing)})
def pipeline_b(config: OpsConfig):
    print(f"pipeline_b in {config.env}")

@job(name="Testing_Scenario", config=test_env)
def testing_scenario():
    a = pipeline_a()
    b = pipeline_b()


test_env_graph = RunConfig(
    ops={
        "test_graph": {
            "ops": {
                "pipeline_a": OpsConfig(env="test"),
                "pipeline_b": OpsConfig(env="test")
            }
        }
    }
)

@graph
def test_graph():
    a = pipeline_a()
    b = pipeline_b()


@job(name="Testing_Scenario_Graph", config=test_env_graph)
def testing_scenario_graph():
    test_graph()

When I select a specific pipeline in the job "Testing_Scenario", the launchpad does not update it self, neither the remove extra config button works. It only works when using the job "Testing_Scenario_Graph" that has build using a graph.

The code you've provided defines two jobs,

testing_scenario

and

testing_scenario_graph

, with the latter being constructed from a graph

test_graph

. Both jobs are configured using

RunConfig

, but they are structured differently. For

testing_scenario

, the

RunConfig

is applied directly to the ops within the job:

python test_env = RunConfig(     ops={         "pipeline_a": OpsConfig(env="test"),         "pipeline_b": OpsConfig(env="test")     } )

For

testing_scenario_graph

, the

RunConfig

is nested within the graph name:

python test_env_graph = RunConfig(     ops={         "test_graph": {             "ops": {                 "pipeline_a": OpsConfig(env="test"),                 "pipeline_b": OpsConfig(env="test")             }         }     } )

The behavior you're experiencing with the "Remove extra config" button not being enabled for

testing_scenario

could be related to how the configuration is being applied to the job versus the graph. In Dagster, the configuration for a job or graph is expected to match the structure of the job or graph itself. If there's a mismatch, the UI may not recognize which parts of the configuration are "extra" and should be removed. Here are a few steps you can take to troubleshoot the issue: 1. Ensure that the configuration schema for your ops (

pipeline_a

and

pipeline_b

) matches the structure of the

RunConfig

you are providing. If there's a discrepancy, the UI may not be able to determine what is considered extra configuration. 2. Check if the nesting of the configuration for the graph within

test_env_graph

is necessary and correct. The

RunConfig

for a graph should typically not need to specify the graph name as a key, unless the graph itself is nested within another graph. 3. Verify that the

RunConfig

is being interpreted correctly by Dagster when you launch the job. You can do this by running the job in a test environment and checking the logs to see if the configuration is being applied as expected. 4. Review the Dagster documentation and any relevant release notes for updates or changes to the configuration system that might affect the behavior of the "Remove extra config" button. 5. If you suspect there might be a bug in the UI, consider reaching out to the Dagster community or filing an issue on the Dagster GitHub repository with details of your configuration and the behavior you're observing. Remember that the configuration system in Dagster is explicit, and the structure of the configuration must align with the structure of the job or graph for it to be interpreted correctly by the system. If the structure is off, it could lead to unexpected behavior in the UI.

@Scout show me an example of a job with two ops, where op_b depends on op_a, also add a runconfig() with some dummy variable for each op.

Certainly! Below is an example of a Dagster job with two ops,

op_a

and

op_b

, where

op_b

depends on

op_a

. Additionally, I've included a

RunConfig

with dummy variables for each op.

python
from dagster import job, op, In, Out, Nothing, RunConfig

# Define op_a which does not depend on any inputs and produces an output
@op(out=Out(Nothing))
def op_a(context):
    <http://context.log.info|context.log.info>("Running op_a")
    # Perform some action here
    return "result_from_op_a"

# Define op_b which depends on the output of op_a
@op(ins={"start": In(Nothing)})
def op_b(context, start):
    <http://context.log.info|context.log.info>(f"Running op_b after {start}")
    # Perform some action here

# Define the job with op_a and op_b, where op_b depends on op_a
@job
def my_job():
    result = op_a()
    op_b(start=result)

# Define the RunConfig with dummy variables for each op
my_run_config = RunConfig(
    ops={
        "op_a": {"config": {"dummy_variable_a": "value_a"}},
        "op_b": {"config": {"dummy_variable_b": "value_b"}},
    }
)

# Now you can execute the job with the run config
if __name__ == "__main__":
    result = my_job.execute_in_process(run_config=my_run_config)

In this example,

op_a

is an op that does some work and produces an output of type

Nothing

, which is a special type in Dagster used to indicate that an op is only used for its side effects and does not produce meaningful output. The

op_b

op takes an input of type

Nothing

, which is provided by

op_a

, indicating that

op_b

should run after

op_a

. The

RunConfig

is used to provide configuration for each op. In this case, we have dummy variables

dummy_variable_a

and

dummy_variable_b

for

op_a

and

op_b

, respectively. These are just placeholders and would be replaced with actual configuration relevant to your ops. Please note that the

RunConfig

class and its usage in this example are illustrative and may not directly correspond to the actual API provided by Dagster. The actual way to provide runtime configuration for a job execution may differ based on the version of Dagster you are using. Always refer to the official Dagster documentation for the most accurate and up-to-date information. Reuse op within a job with different con... button Op jobs | Dagster button Thank you for your feedback!

@Scout show me an example of a job with a single graph that has two ops, where op_b depends on op_a, also add a runconfig() with some dummy variable for each op.

Certainly! Below is an example of a Dagster job that includes a single graph with two ops,

op_a

and

op_b

, where

op_b

depends on

op_a

. Additionally, I've included a

RunConfig

with dummy variables for each op.

python
from dagster import job, op, graph, In, Nothing, RunConfig

# Define op_a which does not depend on any inputs and produces an output
@op
def op_a(context):
    <http://context.log.info|context.log.info>("Running op_a")
    # Perform some action here
    return "result_from_op_a"

# Define op_b which depends on the output of op_a
@op
def op_b(context, input_from_a):
    <http://context.log.info|context.log.info>(f"Running op_b with input: {input_from_a}")
    # Perform some action here

# Define the graph that includes op_a and op_b
@graph
def my_graph():
    result = op_a()
    op_b(input_from_a=result)

# Convert the graph to a job
my_job = my_graph.to_job()

# Define the RunConfig with dummy variables for each op
my_run_config = RunConfig(
    ops={
        "op_a": {"config": {"dummy_variable_a": "value_a"}},
        "op_b": {"config": {"dummy_variable_b": "value_b"}},
    }
)

# Now you can execute the job with the run config
if __name__ == "__main__":
    result = my_job.execute_in_process(run_config=my_run_config)

In this example,

op_a

is an op that does some work and produces an output, which is then passed as an input to

op_b

. The

my_graph

function uses the

@graph

decorator to define the dependency between

op_a

and

op_b

. The

my_job

variable is created by calling the

to_job()

method on the

my_graph

graph, which converts the graph into a job that can be executed. The

RunConfig

is used to provide configuration for each op within the graph. In this case, we have dummy variables

dummy_variable_a

and

dummy_variable_b

for

op_a

and

op_b

, respectively. These are placeholders and would be replaced with actual configuration relevant to your ops. Please note that the

RunConfig

class and its usage in this example are illustrative and may not directly correspond to the actual API provided by Dagster. The actual way to provide runtime configuration for a job execution may differ based on the version of Dagster you are using. Always refer to the official Dagster documentation for the most accurate and up-to-date information. Testing | Dagster button Wonky op graph displays when creating As... button Thank you for your feedback!

@Scout Whats the difference between running a job without a graph versus running a job with a single graph - both having the same ops?

In Dagster, both jobs and graphs are used to define execution workflows, but they serve slightly different purposes and offer different levels of abstraction. Running a Job without a Graph: When you run a job without a graph, you are directly connecting ops within the job definition. This approach is straightforward and can be suitable for simple workflows where you don't need to reuse the same set of ops in multiple jobs or contexts. Here's an example of a job without a graph:

python from dagster import job, op  @op def op_a():     # logic for op_a  @op def op_b():     # logic for op_b  @job def my_job():     op_b(op_a())

Running a Job with a Single Graph: When you run a job with a single graph, you are encapsulating the ops within a graph abstraction. This allows you to reuse the graph in multiple jobs or even nest it within other graphs. It also provides a clear separation between the definition of your computation (the graph) and the orchestration of that computation (the job). Here's an example of a job with a single graph:

python from dagster import job, op, graph  @op def op_a():     # logic for op_a  @op def op_b():     # logic for op_b  @graph def my_graph():     op_b(op_a())  @job def my_job():     my_graph()

Key Differences: 1. Reusability: Graphs can be reused across multiple jobs or nested within other graphs. This is not possible when you define ops directly within a job. 2. Abstraction: Graphs provide a level of abstraction that separates the definition of the computation from its orchestration. This can lead to cleaner code, especially for complex workflows. 3. Testing: Graphs can be tested independently from the job, which can simplify testing workflows. 4. Flexibility: When using graphs, you can create multiple jobs from the same graph with different configurations or resources, providing more flexibility in how you execute the same computation under different conditions. 5. UI Representation: In the Dagster UI (Dagit), graphs can provide a more organized and visually clear representation of the computation, which can be helpful for understanding and monitoring workflows. In summary, while you can achieve the same end result with both approaches, using graphs provides more flexibility, reusability, and clarity, especially as the complexity of your workflows increases. The documentation suggests using graphs when you anticipate the need to reuse computation patterns or when you want to maintain a clear separation between the definition of your computation and its orchestration.

@Scout but without graphs, the dagster launchpad does not remove extra configurations when you select a especific op in the UI.

The behavior you're describing, where the Dagster Launchpad does not remove extra configurations when selecting a specific op in the UI for a job without a graph, could be related to how Dagster's UI handles configuration for jobs versus graphs. In the case of a job defined with a graph, the UI might be more capable of understanding the structure of the job and the relationships between the ops, which could enable it to more accurately determine which configurations are unnecessary and can be removed. This is because the graph abstraction provides a clear definition of the computation and its dependencies. On the other hand, when a job is defined without a graph, the UI might not have the same level of insight into the job's structure, making it less effective at identifying and removing extra configurations for individual ops. This could be due to the more direct and less abstracted way that the ops are connected within the job definition. If you are experiencing this issue, it could be a limitation or a bug in the UI. The documentation does not explicitly state the expected behavior of the "Remove extra config" button in these scenarios, so if this is causing problems for your workflow, it would be appropriate to reach out to the Dagster community for support or to file an issue on the Dagster GitHub repository. The maintainers and contributors can then investigate the issue further and provide a fix or an explanation for the behavior you're observing.