@Scout how to create pre-hook

The documentation does not provide information about a specific feature called "pre-hook" in Dagster. Hooks in Dagster are generally used to perform actions in response to events that occur during pipeline execution, such as the success or failure of an operation (op). The documentation describes

@success_hook

and

@failure_hook

decorators that can be used to define hooks that run after an op has completed successfully or failed, respectively. If you are looking to perform actions before an op runs, you might consider using other mechanisms provided by Dagster, such as custom logic within the op itself or using resources to set up necessary preconditions before the op execution. For any functionality that is not explicitly covered in the documentation, it's possible that it may not be supported directly by Dagster. If you have a specific use case or requirement for a pre-hook feature, you could consider reaching out to the Dagster community or checking the GitHub issues and discussions for any updates or workarounds that other users might have shared.

@Scout how can i create custom hooks?

To create custom hooks in Dagster, you can use the

@success_hook

and

@failure_hook

decorators provided by Dagster. These decorators allow you to define hooks that will be triggered on the success or failure of ops within a job. Here's a step-by-step guide on how to create custom hooks based on the documentation: 1. Define the Hook Function: You start by defining a Python function that will act as your hook. This function will take a single argument,

context

, which is an instance of

HookContext

. The

HookContext

provides information about the op, job, and resources available to the hook. 2. Decorate the Function: You then decorate this function with either

@success_hook

or

@failure_hook

, depending on when you want the hook to be triggered. 3. Specify Required Resources: If your hook requires any resources (like a Slack client to send messages), you can specify these using the

required_resource_keys

parameter in the decorator. Here's an example of how you might define a custom hook that sends a message to Slack when an op succeeds:

python from dagster import HookContext, success_hook, ResourceDefinition  # Define a resource for Slack def slack_resource(_):     # Here you would create and return your Slack client     pass  # Define the hook function @success_hook(required_resource_keys={"slack"}) def slack_message_on_success(context: HookContext):     message = f"Op {context.op.name} finished successfully"     context.resources.slack.chat_postMessage(channel="#foo", text=message)

And here's an example of a custom hook that sends a message when an op fails:

python from dagster import HookContext, failure_hook  # Define the hook function @failure_hook(required_resource_keys={"slack"}) def slack_message_on_failure(context: HookContext):     message = f"Op {context.op.name} failed"     context.resources.slack.chat_postMessage(channel="#foo", text=message)

4. Apply the Hook to Ops or Jobs: After defining your hooks, you can apply them to individual ops using the

.with_hooks

method or to an entire job by passing a set of hook definitions to the

hooks

parameter of the

@job

decorator. Here's how you might apply the

slack_message_on_success

hook to a job:

python from dagster import job  @job(resource_defs={"slack": slack_resource}, hooks={slack_message_on_success}) def my_job():     # Define your ops here     pass

Remember to provide the necessary configuration for any resources your hooks use when you execute the job. This is a basic example, and your actual implementation might vary depending on the specifics of your use case and the resources you're interacting with. Always refer to the latest Dagster documentation for the most up-to-date guidance on creating and using hooks.

@Scout i would like to run a hook BEFORE a pipeline run

In Dagster, hooks are designed to respond to events that occur during or after pipeline execution, such as the success or failure of an operation (op). There isn't a built-in hook decorator for actions that should occur before an op or pipeline run starts, similar to a "pre-hook." However, you can achieve pre-run logic by using other mechanisms in Dagster: 1. Resources: Initialize resources that are required for your pipeline. Resources can be used to set up necessary preconditions for your ops. The resource initialization code runs before any ops are executed. 2. Custom Op Logic: Include the pre-run logic at the beginning of your op. This ensures that the logic is executed before the main body of the op. 3. Job Lifecycle Hooks: While not directly supported as pre-hooks, you can implement custom logic in the

run_launcher

or

executor

to perform actions before the pipeline run starts. This is more advanced and requires a deeper integration with Dagster's execution system. 4. Sensors: Sensors in Dagster are used to trigger pipeline runs in response to external events. While they are not pre-hooks, you can use a sensor to perform checks or set up conditions before triggering a pipeline run. If none of these options suit your needs and you specifically require a pre-run hook, you might consider reaching out to the Dagster community for advice or to propose a feature request. The community and maintainers could provide insights into whether such a feature is on the roadmap or suggest alternative approaches. For now, the recommended approach is to use the existing mechanisms within Dagster to perform any necessary actions before a pipeline run. Always refer to the latest Dagster documentation for the most up-to-date guidance and best practices.

@Scout how do i run a hook during a pipeline run?

To run a hook during a pipeline run in Dagster, you can define hooks using the

@success_hook

and

@failure_hook

decorators and then attach them to your ops or jobs. These hooks will be triggered during the pipeline run when the corresponding events occur (i.e., when an op succeeds or fails). Here's how you can define and use hooks in a Dagster pipeline: 1. Define the Hook: Create a Python function and decorate it with either

@success_hook

or

@failure_hook

, depending on when you want the hook to be triggered.

python from dagster import success_hook, failure_hook, HookContext  @success_hook def my_success_hook(context: HookContext):     # Logic to run when an op succeeds     pass  @failure_hook def my_failure_hook(context: HookContext):     # Logic to run when an op fails     pass

2. Attach the Hook to Ops: Use the

.with_hooks

method to attach the hook to individual ops within your job.

python from dagster import op, job  @op def my_op(context):     # Your op logic here     pass  @job def my_job():     my_op.with_hooks(hook_defs={my_success_hook})

3. Attach the Hook to Jobs: Alternatively, you can attach hooks to the entire job by passing a set of hook definitions to the

hooks

parameter of the

@job

decorator.

python @job(hooks={my_success_hook, my_failure_hook}) def my_job():     my_op()

When you execute the job, Dagster will run the hooks at the appropriate times during the pipeline execution: -

my_success_hook

will be executed after any op within the job completes successfully. -

my_failure_hook

will be executed after any op within the job fails. Remember that hooks are a powerful way to add custom behavior to your pipeline runs, such as notifications, metrics collection, or cleanup tasks. Always refer to the latest Dagster documentation for the most up-to-date guidance on creating and using hooks.

@Scout is there a way to create an op that wont show in the UI such as a dummy op that i will use at the begining of the pipeline and create a hook for it and use it as pipeline start?

As of my knowledge cutoff date in early 2023, Dagster does not provide a built-in feature to create "invisible" ops or dummy ops that do not appear in the UI. All ops that are part of a job's execution plan are typically visible in the Dagster UI, as they represent the computational steps that the job is performing. However, if you want to create a dummy op that serves as a starting point for your pipeline and triggers a hook, you can define a minimal op that does very little or nothing and attach a hook to it. This op will still be visible in the UI, but it can serve the purpose of a pipeline start signal. Here's an example of how you might define a dummy op and a hook that acts as a pipeline start:

python from dagster import op, job, HookContext, success_hook  # Define a dummy op @op def start_pipeline_op():     pass  # This op does nothing  # Define a hook that will run after the dummy op succeeds @success_hook def pipeline_start_hook(context: HookContext):     # Logic to run at the start of the pipeline     pass  # Attach the hook to the dummy op @job def my_pipeline():     start_pipeline_op.with_hooks(hook_defs={pipeline_start_hook})     # Rest of your pipeline ops

In this example,

start_pipeline_op

is a dummy op that does nothing, and

pipeline_start_hook

is a hook that contains the logic you want to run at the start of the pipeline. When you execute

my_pipeline

, the

start_pipeline_op

will run, and upon its success, the

pipeline_start_hook

will be triggered. While this approach does not hide the dummy op from the UI, it allows you to execute custom logic at the beginning of your pipeline run. If you have a specific requirement for ops not to be shown in the UI, you might consider reaching out to the Dagster community or checking the GitHub issues and discussions for any updates or workarounds that other users might have shared.

@Scout hooks in dagster run AFTER the op? or is it possible during the op?

In Dagster, hooks are designed to run after an op has completed. Specifically,

@success_hook

is triggered after an op completes successfully, and

@failure_hook

is triggered if an op fails. There is no built-in mechanism for hooks to run during the execution of an op. Here's a brief overview of how hooks work in Dagster: - Success Hook: A success hook is executed after an op completes without any errors. It allows you to define logic that should run only when the op is successful. - Failure Hook: A failure hook is executed if an op raises an exception during its execution. This allows you to handle errors or perform cleanup actions. Both types of hooks are defined as functions and decorated with

@success_hook

or

@failure_hook

. They receive a

HookContext

object, which provides information about the op and the job. If you need to perform actions during the execution of an op, you should include that logic within the body of the op itself. Hooks are not intended for use during an op's execution; they are meant to respond to the op's outcome. For example, if you want to log something or perform a check in the middle of an op's execution, you would do so directly within the op's function:

python from dagster import op  @op def my_op(context):     # Logic before the check     ...      # Perform some check or action during the op     <http://context.log.info|context.log.info>("Performing a check")     if some_condition:         # Handle the condition         ...      # Logic after the check     ...

In this example, the check is performed as part of the op's execution flow, not as a separate hook. If you have a use case that requires monitoring or interacting with an op during its execution, you would need to implement that logic within the op itself.

@Scout if i create a function inside an op and decorate it as success will it trigger the hook DURING the op?

No, decorating a function inside an op with

@success_hook

will not trigger the hook during the op's execution. In Dagster, hooks are not designed to be nested within ops, and the

@success_hook

decorator is intended to be used at the top level to define hooks that respond to the completion of ops. Hooks in Dagster are triggered by the Dagster framework after an op has finished executing. They are not called like regular functions within the op's code. Instead, they are registered with the framework, and the framework calls them when the appropriate event (success or failure of an op) occurs. If you want to execute some logic during an op's execution, you should simply include that logic as part of the op's function body. Here's an example of how to include additional logic within an op:

python from dagster import op  @op def my_op(context):     # Your op logic here     do_something()      # Additional logic you want to run during the op     do_additional_thing()      # More op logic     do_something_else()

In this example,

do_additional_thing()

is a regular function call that occurs as part of the op's execution. It is not a hook and does not need to be decorated with

@success_hook

. To use hooks properly, you define them outside of ops and then attach them to ops or jobs using the

@job

decorator or the

.with_hooks

method on ops. Here's an example of defining and using a success hook:

python from dagster import op, success_hook, HookContext, job  # Define a success hook @success_hook def my_success_hook(context: HookContext):     # Logic to run after an op succeeds     <http://context.log.info|context.log.info>(f"Op {context.op.name} succeeded.")  # Define an op @op def my_op():     # Op logic     pass  # Attach the hook to the job @job(hooks={my_success_hook}) def my_job():     my_op()

In this example,

my_success_hook

will be triggered after

my_op

completes successfully when

my_job

is executed. The hook is not part of the op's internal logic; it is a separate function that responds to the op's success event.

@Scout what if i do something like this:

from dagster._core.definitions.decorators.hook_decorator import _validate_hook_fn_params,event_list_hook
from dagster._core.definitions.events import HookExecutionResult
import dagster._check as check


StartHookFn = Callable[["HookContext"], Any]

@overload
def start_hook(hook_fn: StartHookFn) -> HookDefinition:
    ...


@overload
def start_hook(
    *,
    name: Optional[str] = ...,
    required_resource_keys: Optional[AbstractSet[str]] = ...,
) -> Callable[[StartHookFn], HookDefinition]:
    ...


def start_hook(
    hook_fn: Optional[StartHookFn] = None,
    *,
    name: Optional[str] = None,
    required_resource_keys: Optional[AbstractSet[str]] = None,
) -> Union[HookDefinition, Callable[[StartHookFn], HookDefinition]]:
    """Create a hook on step success events with the specified parameters from the decorated function.

    Args:
        name (Optional[str]): The name of this hook.
        required_resource_keys (Optional[AbstractSet[str]]): Keys for the resources required by the
            hook.

    Examples:
        .. code-block:: python

            @start_hook(required_resource_keys={'slack'})
            def slack_message_on_success(context):
                message = 'op {} succeeded'.format(context.op.name)
                context.resources.slack.send_message(message)

            @start_hook
            def do_something_on_success(context):
                do_something()


    """

    def wrapper(fn: StartHookFn) -> HookDefinition:
        check.callable_param(fn, "fn")

        expected_positionals = ["context"]
        _validate_hook_fn_params(fn, expected_positionals)

        if name is None or callable(name):
            _name = fn.__name__
        else:
            _name = name

        @event_list_hook(name=_name, required_resource_keys=required_resource_keys, decorated_fn=fn)
        def _start_hook(
            context: "HookContext", event_list: Sequence["DagsterEvent"]
        ) -> HookExecutionResult:
            for event in event_list:
                if event.is_step_start:
                    fn(context)
                    return HookExecutionResult(hook_name=_name, is_skipped=False)

            # hook is skipped when fn didn't run
            return HookExecutionResult(hook_name=_name, is_skipped=True)

        return _start_hook

    # This case is for when decorator is used bare, without arguments, i.e. @start_hook
    if hook_fn is not None:
        check.invariant(required_resource_keys is None)
        return wrapper(hook_fn)

    return wrapper

The code snippet you've provided appears to be an attempt to create a custom hook decorator in Dagster that triggers at the start of an op's execution. However, modifying the internal Dagster code or creating custom decorators that rely on internal APIs is not recommended and is not supported by the official Dagster documentation. Dagster's public API provides

@success_hook

and

@failure_hook

decorators to create hooks that trigger after an op has completed successfully or failed, respectively. There is no public API support for a "start hook" that triggers before an op's execution. Using internal APIs, as shown in your snippet, can lead to unexpected behavior and compatibility issues, especially when upgrading Dagster to newer versions, as internal implementations may change without notice. If you need to run logic at the start of an op, the recommended approach is to include that logic within the op itself. If you need to run logic at the start of a pipeline, consider using resources or custom initialization logic in your job definition. For any functionality that is not explicitly covered in the official documentation, you might consider reaching out to the Dagster community for advice or to propose a feature request. The community and maintainers could provide insights into whether such a feature is on the roadmap or suggest alternative approaches. It's important to follow the practices and patterns recommended by the official Dagster documentation to ensure that your pipelines are stable, maintainable, and compatible with future Dagster releases.

@Scout you said

consider using resources or custom initialization logic in your job definition.

please elaborate

Certainly! In Dagster, resources are a way to manage external dependencies and shared logic. They are often used to initialize connections to databases, configure logging, or set up other external services before a job runs. You can use resources to perform setup tasks that need to happen before your pipeline logic begins. Here's how you can use resources for custom initialization logic: 1. Define a Resource: Create a resource by defining a function that returns the resource object. This function can contain any initialization logic you need.

python from dagster import resource  @resource def my_resource(init_context):     # Perform initialization logic here     # For example, set up a database connection     db_connection = connect_to_database(init_context.resource_config['connection_string'])     return db_connection

2. Attach the Resource to a Job: When defining your job, you can attach the resource using the

resource_defs

argument. This will ensure that the resource is initialized when the job starts.

python from dagster import job  @job(resource_defs={'my_resource': my_resource}) def my_job():     # Define your ops here     pass

3. Use the Resource in Ops: Within your ops, you can access the resource via the context object. This allows you to use the initialized resource as part of your op logic.

python from dagster import op  @op(required_resource_keys={'my_resource'}) def my_op(context):     # Use the initialized resource     db_connection = context.resources.my_resource     # Perform operations using the resource     ...

When

my_job

is executed, Dagster will first initialize the

my_resource

resource, running any setup logic you've defined. Then, it will proceed to execute the ops within the job. This approach allows you to perform initialization logic at the start of your job, ensuring that all necessary preconditions are met before your ops begin executing. It's a clean and idiomatic way to manage setup tasks in Dagster.

@Scout dagster._core.errors.DagsterInvariantViolationError: In @graph pipeline_init, attempted to call hook method for InvokedNodeOutputHandle. This object represents the output "result" from the already invoked op 'pipeline_start'. Consider checking the location of parentheses.

The error message you're seeing indicates that there's an issue with how a hook is being used in conjunction with an op within a graph in Dagster. Specifically, it suggests that there's an attempt to call a hook method on an

InvokedNodeOutputHandle

, which is not valid. An

InvokedNodeOutputHandle

represents the output of an op that has already been executed, and hooks cannot be directly applied to these outputs. This error often arises from a misunderstanding of how to apply hooks within a graph. Hooks should be applied to ops or to the entire job, not to the outputs of ops. Here's an example of how to correctly apply a hook to an op within a graph:

python from dagster import graph, op, success_hook, HookContext  # Define a success hook @success_hook def my_success_hook(context: HookContext):     # Logic to run after an op succeeds     pass  # Define an op @op def my_op():     # Op logic     pass  # Define a graph @graph def my_graph():     # Apply the hook to the op within the graph     my_op.with_hooks(hook_defs={my_success_hook})  # Convert the graph to a job my_job = my_graph.to_job()

In this example, the

my_success_hook

is applied to

my_op

within the definition of

my_graph

. When

my_job

is executed, the hook will be triggered after

my_op

completes successfully. If you're encountering this error, review your graph and job definitions to ensure that hooks are being applied correctly. Make sure that you're not trying to attach hooks to the outputs of ops, but rather to the ops themselves or to the entire job. If you need further assistance, please provide the relevant portion of your code where you're defining your graph and applying hooks, and I can help you identify the specific issue.

@Scout While in @graph context 'pipeline_exit', received an uninvoked op 'pipeline_end'. Provided hook definitions: ['slack_message_hook_on_success', 'slack_message_hook_on_start']. warnings.warn(warning_message.strip())

The warning message you're encountering suggests that there's an issue with how an op is being used within a graph context in Dagster. Specifically, it indicates that an op named

pipeline_end

is being referenced without being invoked, and that there are hook definitions provided (

slack_message_hook_on_success

,

slack_message_hook_on_start

) that may be related to this issue. In Dagster, when you define a graph and reference ops within it, you need to invoke the ops by calling them as functions. If you reference an op without invoking it (i.e., without calling it with parentheses

()

), you'll receive a warning like the one you've described. Here's an example of how to correctly invoke an op within a graph:

python from dagster import graph, op  @op def pipeline_start():     # Logic for starting the pipeline     pass  @op def pipeline_end():     # Logic for ending the pipeline     pass  @graph def pipeline_exit():     # Invoke the ops by calling them with parentheses     pipeline_start()     pipeline_end()  # Convert the graph to a job pipeline_exit_job = pipeline_exit.to_job()

In this example, both

pipeline_start

and

pipeline_end

are invoked within the

pipeline_exit

graph by calling them with parentheses. This is the correct way to include ops within a graph. If you're seeing the warning message, it's likely that you have something like this in your code:

python @graph def pipeline_exit():     # Incorrectly referencing an op without invoking it     pipeline_end  # Missing parentheses

To resolve the warning, ensure that you invoke all ops within your graph by calling them with parentheses. If you have hooks that you want to apply to specific ops, you can do so using the

.with_hooks

method on the op when you invoke it:

python @graph def pipeline_exit():     pipeline_start.with_hooks(hook_defs={slack_message_hook_on_start})()     pipeline_end.with_hooks(hook_defs={slack_message_hook_on_success})()

Review your graph definition and make sure that all ops are correctly invoked, and that hooks are applied to the ops, not to the graph itself. If you continue to experience issues, please provide the relevant code snippet, and I can offer more specific guidance.

@Scout i've created an empty operator that returns None since i have a hook for it so when that operator starts it means the pipeline has started (i've create the logic needed for dynamically knowing this and triggering only for the start of the pipeline) i have 5 ops at the beginning of the pipeline and once of them is that dummy op - i would like to create a dependency from the dummy op to the other 4 - how can i do it without passing any argument?

In Dagster, if you want to create an explicit ordering dependency between ops without passing any data, you can use the

Nothing

type. The

Nothing

type is a special Dagster type that indicates an op has a dependency on another op without needing to pass any data between them. Here's how you can create a dependency from your dummy op to the other four ops using the

Nothing

type: 1. Define your dummy op to return

Nothing

.

python from dagster import op, Nothing, Out  @op(out=Out(Nothing)) def start_pipeline():     # This op does nothing but starts the pipeline     pass

2. Define the other ops to have an input of type

Nothing

. This input will be used to establish the dependency.

python from dagster import op, In  @op(ins={"start": In(Nothing)}) def op1(start):     # Logic for op1     pass  @op(ins={"start": In(Nothing)}) def op2(start):     # Logic for op2     pass  @op(ins={"start": In(Nothing)}) def op3(start):     # Logic for op3     pass  @op(ins={"start": In(Nothing)}) def op4(start):     # Logic for op4     pass

3. In your graph or job, connect the dummy op to the other ops using the

Nothing

dependency.

python from dagster import graph  @graph def my_pipeline():     start = start_pipeline()     op1(start=start)     op2(start=start)     op3(start=start)     op4(start=start)

When you execute

my_pipeline

, Dagster will first run

start_pipeline

, and then it will run

op1

,

op2

,

op3

, and

op4

in any order since they all depend on the completion of

start_pipeline

but do not depend on each other's outputs. This setup ensures that

start_pipeline

runs before the other four ops, and since it returns

Nothing

, it does not pass any data to them. The other ops wait for

start_pipeline

to complete before they begin execution.

@Scout i wanted to avoid even doing this

start = start_pipeline()
    op1(start=start)

If you want to avoid explicitly passing the

start

dependency in the graph, you can use the

after

parameter in the

@op

decorator to specify dependencies. This parameter allows you to declare that an op should run after certain other ops have completed, without needing to pass the

Nothing

type between them. Here's how you can define the dependency using the

after

parameter: 1. Define your dummy op without any outputs:

python from dagster import op  @op def start_pipeline():     # This op does nothing but starts the pipeline     pass

2. Define the other ops with the

after

parameter to indicate they should run after `start_pipeline`:

python from dagster import op  @op def op1():     # Logic for op1     pass  @op def op2():     # Logic for op2     pass  @op def op3():     # Logic for op3     pass  @op def op4():     # Logic for op4     pass

3. In your graph or job, use the

after

parameter when invoking the ops:

python from dagster import graph  @graph def my_pipeline():     start = start_pipeline()     op1(after=[start])     op2(after=[start])     op3(after=[start])     op4(after=[start])

By using the

after

parameter, you're telling Dagster that

op1

,

op2

,

op3

, and

op4

should not start until after

start_pipeline

has completed. This way, you don't need to pass the

start

output explicitly when constructing the graph. Please note that the

after

parameter is used within the graph or job definition when you're invoking the ops, not in the

@op

decorator itself. This allows you to establish execution order dependencies without passing data.

@Scout i also have an pipeline exit dummy op - i would like to run it after few ops at the end since they have no dependency between them\

To ensure that your pipeline exit dummy op runs after several other ops that have no dependencies between them, you can use the same

after

parameter when invoking the exit op within your graph or job definition. This parameter allows you to specify that the exit op should run after the completion of the specified ops. Here's how you can set up the pipeline exit dummy op to run after a few independent ops: 1. Define your independent ops and the pipeline exit dummy op:

python from dagster import op  @op def independent_op1():     # Logic for independent_op1     pass  @op def independent_op2():     # Logic for independent_op2     pass  @op def independent_op3():     # Logic for independent_op3     pass  @op def pipeline_exit():     # This op does nothing but signifies the pipeline exit     pass

2. In your graph or job, invoke the independent ops and then the pipeline exit dummy op with the

after

parameter, listing all the independent ops:

python from dagster import graph  @graph def my_pipeline():     op1 = independent_op1()     op2 = independent_op2()     op3 = independent_op3()          # Specify that pipeline_exit should run after op1, op2, and op3 have completed     pipeline_exit(after=[op1, op2, op3])

By using the

after

parameter when invoking

pipeline_exit

, you're telling Dagster that

pipeline_exit

should not start until after

independent_op1

,

independent_op2

, and

independent_op3

have completed. This ensures that

pipeline_exit

runs at the end of your pipeline, after the specified independent ops, even though they do not have data dependencies between them. Remember to convert your graph to a job if you want to execute it as a pipeline:

python my_pipeline_job = my_pipeline.to_job()

This setup will maintain the desired execution order within your Dagster pipeline.

@Scout what if i have a graph that runs another graph?

In Dagster, you can compose graphs by running one graph within another. This is useful when you want to modularize your pipeline logic or reuse certain parts of your pipeline across different jobs. When you have a graph that runs another graph, you can still manage dependencies and execution order in a similar way as you would with ops. Here's an example of how you can define a graph that runs another graph and manage the execution order: 1. Define your inner graph:

python from dagster import graph, op  @op def inner_op1():     # Logic for inner_op1     pass  @op def inner_op2():     # Logic for inner_op2     pass  @graph def inner_graph():     inner_op1()     inner_op2()

2. Define your outer graph that includes the inner graph:

python from dagster import graph, op  @op def outer_op1():     # Logic for outer_op1     pass  @op def outer_op2():     # Logic for outer_op2     pass  @graph def outer_graph():     # Invoke the inner graph     inner_graph()      # Define additional ops or graphs     outer_op1()     outer_op2()

3. If you want to run an op after the inner graph has completed, you can capture the outputs of the inner graph's ops (if any) and use them as dependencies for the subsequent ops:

python @graph def outer_graph():     # Invoke the inner graph and capture its outputs if needed     inner_results = inner_graph()          # Use the outputs of the inner graph as dependencies for outer ops     outer_op1(after=[inner_results])     outer_op2(after=[inner_results])

4. If the inner graph does not return any outputs, or if you simply want to ensure that certain ops run after the inner graph without considering outputs, you can use the

after

parameter as before:

python @graph def outer_graph():     # Invoke the inner graph     inner = inner_graph()          # Specify that outer ops should run after the inner graph has completed     outer_op1(after=[inner])     outer_op2(after=[inner])

5. Convert the outer graph to a job to execute it as a pipeline:

python outer_pipeline_job = outer_graph.to_job()

By structuring your graphs and jobs in this way, you can create complex pipelines with nested graphs while still maintaining clear execution order and dependencies.

@Scout let me rephrase i have this

start = pipeline_init()

the next step is 4 graphs that run in parallel, i would like to run them after the start

To run four graphs in parallel after a start op, you can use the

after

parameter when invoking the graphs within your outer graph. This parameter allows you to specify that the graphs should run after the

start

op has completed. Here's an example of how you can set up your outer graph to run four inner graphs in parallel after the

start

op: 1. Define your start op:

python from dagster import op  @op def pipeline_init():     # Logic for pipeline initialization     pass

2. Define your inner graphs:

python from dagster import graph  @graph def graph1():     # Define ops for graph1     pass  @graph def graph2():     # Define ops for graph2     pass  @graph def graph3():     # Define ops for graph3     pass  @graph def graph4():     # Define ops for graph4     pass

3. Define your outer graph and use the

after

parameter to ensure the inner graphs run after the

start

op:

python from dagster import graph  @graph def outer_graph():     start = pipeline_init()          # Invoke the inner graphs with the `after` parameter     graph1(after=[start])     graph2(after=[start])     graph3(after=[start])     graph4(after=[start])

4. Convert the outer graph to a job:

python outer_pipeline_job = outer_graph.to_job()

When you execute

outer_pipeline_job

, Dagster will first run the

pipeline_init

op. After it completes, it will run

graph1

,

graph2

,

graph3

, and

graph4

in parallel, since they do not have dependencies on each other and only depend on the completion of the

start

op. This setup ensures that your initialization logic runs first, followed by the parallel execution of your four graphs.

@Scout doesnt work received a list containing an invalid type at index 0 for input "after" (passed by keyword) in graph invocation

I apologize for the confusion. The

after

parameter is not used in Dagster to specify execution order for graphs. Instead, you should use the

after

parameter within the definitions of the ops inside the graphs if you want to specify execution order dependencies between ops. To run four graphs in parallel after a start op, you can simply invoke the graphs within your outer graph after the start op, without any explicit dependency. Since there is no data dependency between the start op and the graphs, Dagster will not enforce any execution order, and the graphs will run as soon as they are ready. Here's how you can structure your outer graph:

python from dagster import graph  @graph def outer_graph():     start = pipeline_init()          # Invoke the inner graphs without specifying any dependencies     graph1()     graph2()     graph3()     graph4()

In this setup,

start

is not used to enforce any execution order, and the four graphs are free to run in parallel. If you want to ensure that the graphs run only after the

start

op has completed, you would need to include a dummy op with a

Nothing

dependency in each graph that is triggered by the

start

op. Here's an example of how you can modify one of the inner graphs to include a dummy op with a

Nothing

dependency:

python from dagster import graph, op, Nothing, In  @op def dummy_op(start: Nothing):     pass  @graph def graph1():     start = pipeline_init()     dummy_op(start=start)     # Rest of the ops for graph1

Repeat this pattern for

graph2

,

graph3

, and

graph4

, ensuring that each graph has a dummy op that depends on the

start

op. Then, in your outer graph, you can invoke these graphs without any additional parameters:

python @graph def outer_graph():     start = pipeline_init()          # Invoke the modified inner graphs     graph1(start=start)     graph2(start=start)     graph3(start=start)     graph4(start=start)

Now, when you execute

outer_graph

, Dagster will run the

start

op first, followed by the dummy ops in each graph, which will enforce the execution order you desire.

@Scout its not clear what you said

I apologize for any confusion. Let's clarify the steps to ensure that four graphs run in parallel after a start op within an outer graph in Dagster. 1. Define your start op: This is the op that you want to run before anything else in your pipeline.

python from dagster import op  @op def pipeline_init():     # Logic for pipeline initialization     pass

2. Define your inner graphs: These are the graphs that you want to run in parallel after the start op.

python from dagster import graph  @graph def graph1():     # Define ops for graph1     pass  @graph def graph2():     # Define ops for graph2     pass  @graph def graph3():     # Define ops for graph3     pass  @graph def graph4():     # Define ops for graph4     pass

3. Define your outer graph: This graph will include the start op and the four inner graphs.

python from dagster import graph  @graph def outer_graph():     # First, invoke the start op     pipeline_init()          # Then, invoke the inner graphs. These will run in parallel after the start op.     graph1()     graph2()     graph3()     graph4()

4. Convert the outer graph to a job: This allows you to execute the entire pipeline.

python outer_pipeline_job = outer_graph.to_job()

When you run

outer_pipeline_job

, Dagster will execute the

pipeline_init

op first. After

pipeline_init

completes, Dagster will proceed to execute

graph1

,

graph2

,

graph3

, and

graph4

. Since there are no data dependencies between

pipeline_init

and the inner graphs, Dagster will run the inner graphs in parallel by default. If you want to enforce that the inner graphs only start after

pipeline_init

has completed, you would need to create a dependency from

pipeline_init

to each of the inner graphs. However, as of my knowledge cutoff in early 2023, Dagster does not support direct dependencies between graphs. Instead, you would need to establish dependencies at the op level within each graph, potentially using a

Nothing

dependency as I described earlier. If the behavior you're looking for is not supported by the current version of Dagster, I recommend reaching out to the Dagster community for assistance or to check if there have been any updates that provide this functionality.

@Scout this is incorrect

from dagster import graph

@graph
def outer_graph():
    # First, invoke the start op
    pipeline_init()
    
    # Then, invoke the inner graphs. These will run in parallel after the start op.
    graph1()
    graph2()
    graph3()
    graph4()

it runs all 5 graphs in parallel but i want to run first pipeline_init graph and then the other 4

I apologize for the confusion. To ensure that

pipeline_init

runs before the other four graphs, you can use the

@graph

decorator to define a new graph that encapsulates the logic of running

pipeline_init

followed by the other graphs. However, you will need to establish dependencies between the ops within

pipeline_init

and the ops within the other graphs. Here's a revised approach: 1. Define your

pipeline_init

graph and ensure it has an op that represents the completion of the initialization:

python from dagster import graph, op, Nothing, Out  @op(out=Out(Nothing)) def start_op():     # Logic for pipeline initialization     pass  @graph def pipeline_init():     start_op()

2. Define your four graphs that should run after

pipeline_init

. Each graph should have an initial op that depends on a

Nothing

input, which will be used to establish the dependency on `pipeline_init`:

python from dagster import graph, op, In  @op(ins={"start": In(Nothing)}) def graph1_start(start):     # Logic for the start of graph1     pass  @graph def graph1():     graph1_start()  # Repeat for graph2, graph3, and graph4

3. Define your outer graph that first invokes

pipeline_init

and then invokes the other graphs, passing the output of

pipeline_init

to the initial op of each graph:

python from dagster import graph  @graph def outer_graph():     # Invoke the pipeline_init graph and capture its output     start = pipeline_init()          # Pass the output of pipeline_init to the initial ops of the other graphs     graph1(start=start)     graph2(start=start)     graph3(start=start)     graph4(start=start)

4. Convert the outer graph to a job:

python outer_pipeline_job = outer_graph.to_job()

When you run

outer_pipeline_job

, Dagster will execute the

pipeline_init

graph first. The output of

pipeline_init

(which is of type

Nothing

) will then be passed to the initial ops of

graph1

,

graph2

,

graph3

, and

graph4

, enforcing that they run after

pipeline_init

has completed. This approach ensures that

pipeline_init

runs first, followed by the other four graphs in parallel, while respecting the dependency established by the

Nothing

type.

@Scout dagster._core.errors.DagsterInvalidDefinitionError: @graph 'read_data' has unmapped input 'start'. Remove it or pass it to the appropriate op/graph invocation.

The error message you're encountering,

DagsterInvalidDefinitionError

, indicates that there is an unmapped input 'start' in the

@graph

'read_data'. This means that the graph

read_data

expects an input named 'start', but it is not being provided or passed to any op or graph within the

read_data

graph. To resolve this issue, you need to ensure that the 'start' input is correctly passed to the ops within the

read_data

graph that require it. If the 'start' input is of type

Nothing

, it is used to establish execution order rather than to pass data. Here's an example of how you might define a graph that requires a 'start' input:

python from dagster import graph, op, In, Nothing  @op(ins={"start": In(Nothing)}) def my_op(start):     # Your op logic here  @graph def read_data(start):     my_op(start=start)

When you invoke the

read_data

graph from another graph or job, you need to provide the 'start' input:

python from dagster import graph, op, Nothing, Out  @op(out=Out(Nothing)) def start_op():     # Logic for pipeline initialization  @graph def outer_graph():     start = start_op()     read_data(start=start)     # ... invoke other graphs similarly, passing 'start' as needed

In this example,

start_op

is an op that returns

Nothing

, and

read_data

is a graph that takes a 'start' input of type

Nothing

. When

outer_graph

is invoked, it calls

start_op

and then passes its output to

read_data

as the 'start' input. Make sure that all graphs and ops that require the 'start' input are correctly receiving it when invoked. If an op or graph does not require the 'start' input, you should not include it in the definition or invocation. If you continue to experience issues, please check the definitions of your graphs and ops to ensure that inputs are correctly mapped and provided where necessary. If you need further assistance, please provide the relevant code snippets, and I can offer more specific guidance.

@Scout if i pass a var into a graph i have to pass it to the ops under that same graph?

Yes, in Dagster, if you pass an input to a graph, you must map that input to the ops within the graph that require it. Graphs in Dagster are essentially a way to organize and reuse a collection of ops, and they define the flow of data between those ops. When you provide an input to a graph, you need to specify how that input is used by the ops inside the graph. Here's an example to illustrate this:

python from dagster import graph, op, In  # Define an op that requires an input @op def process_data(data):     # Perform some processing on the data     pass  # Define a graph that takes an input and passes it to the op @graph def my_graph(input_data):     process_data(input_data)  # When invoking the graph, you pass the input to it result = my_graph(input_data="some data")

In this example,

my_graph

takes an input called

input_data

and passes it to the

process_data

op within the graph. When you invoke

my_graph

, you must provide the

input_data

argument, which is then used by the

process_data

op. If your graph contains multiple ops that require the same input, you must pass the input to each op that needs it:

python @op def op1(data):     pass  @op def op2(data):     pass  @graph def my_graph(input_data):     op1(input_data)     op2(input_data)

If an op within the graph does not require the input, you do not need to pass it to that op. Inputs are only passed to the ops that have explicitly defined them as parameters. Remember that the names of the inputs in the ops must match the names used when passing the inputs within the graph. If the names do not match, you will encounter an error indicating that an input is unmapped or missing.

@Scout is there a way to create a graph of graphs when one of the sub graph wont show in the UI?

As of my knowledge cutoff date, all graphs and sub-graphs that are part of a job's execution plan in Dagster are visible in the Dagit UI. There is no built-in feature to hide specific sub-graphs from the UI while they are part of the execution plan. The Dagit UI is designed to provide a complete visualization of the execution plan, including all graphs, sub-graphs, and ops, to help users understand the structure and dependencies of their pipelines. Hiding parts of the execution plan would go against this design principle, as it could lead to confusion and make it harder to debug issues. If you have a specific reason for wanting to hide a sub-graph from the UI, you might consider reorganizing your job structure or abstracting the logic in a different way that does not require the sub-graph to be part of the execution plan visualized in Dagit. For example, if the logic within the sub-graph you want to hide is purely setup or teardown that does not need to be visualized, you could potentially move that logic into resources that are initialized or cleaned up outside of the graph execution. If there have been updates to Dagster after my knowledge cutoff date that provide new features or options for controlling the visibility of sub-graphs in the UI, I recommend checking the latest Dagster documentation or reaching out to the Dagster community for the most current information.

@Scout how can i access the EVENT TYPE STEP_FAILURE?

In Dagster, you can access the event type

STEP_FAILURE

through the event log, which records all the events that occur during pipeline execution. The event log includes various event types, such as

STEP_START

,

STEP_SUCCESS

, and

STEP_FAILURE

, among others. To access

STEP_FAILURE

events, you can query the event log for a specific pipeline run. Here's an example of how you might do this programmatically:

python from dagster import DagsterEventType  def get_step_failure_events(run_id, instance):     """     Retrieve all step failure events for a given run ID.      Args:         run_id (str): The ID of the pipeline run.         instance (DagsterInstance): The instance of the Dagster pipeline.      Returns:         List[EventLogEntry]: A list of EventLogEntry objects representing step failure events.     """     # Fetch the event log entries for the given run ID     event_records = instance.all_logs(run_id)      # Filter the event log entries to only include step failure events     step_failure_events = [         event_record         for event_record in event_records         if event_record.dagster_event and event_record.dagster_event.event_type == DagsterEventType.STEP_FAILURE     ]      return step_failure_events

In this function,

run_id

is the ID of the pipeline run you're interested in, and

instance

is an instance of

DagsterInstance

that represents the Dagster pipeline's runtime environment. You can obtain the

run_id

from the context of a pipeline run, and you typically have access to the

instance

object within the context of your Dagster system. Please note that the exact method of accessing the event log and filtering for

STEP_FAILURE

events may vary depending on the version of Dagster you are using and the context in which you are trying to access these events. Always refer to the latest Dagster documentation for the most up-to-date guidance on interacting with the event log and handling events.

@Scout how to convert the EVENT TYPE STEP_FAILURE to string? or can i just str() it?

In Dagster, event types such as

STEP_FAILURE

are represented by an enumeration, specifically the

DagsterEventType

enum. To convert an event type to a string, you can access the

value

attribute of the enum member, which will give you the string representation of the event type. You do not need to use the

str()

function directly on the enum member. Here's an example of how to get the string representation of the

STEP_FAILURE

event type:

python from dagster import DagsterEventType  # Get the string representation of the STEP_FAILURE event type step_failure_str = DagsterEventType.STEP_FAILURE.value  print(step_failure_str)  # Output: "STEP_FAILURE"

In this example,

DagsterEventType.STEP_FAILURE.value

will give you the string

"STEP_FAILURE"

, which is the string representation of the event type. If you have an

EventLogEntry

object that represents a step failure event, you can also convert the event type to a string as follows:

python from dagster import DagsterInstance  # Assuming you have a run_id and a DagsterInstance run_id = "your_run_id_here" instance = DagsterInstance.get()  # Get the event log entries for the given run ID event_records = instance.all_logs(run_id)  # Find the first STEP_FAILURE event and convert its type to a string for event_record in event_records:     if event_record.dagster_event and event_record.dagster_event.event_type == DagsterEventType.STEP_FAILURE:         step_failure_str = event_record.dagster_event.event_type.value         print(step_failure_str)  # Output: "STEP_FAILURE"         break

In this code snippet, we iterate over the event log entries, check if an entry is a step failure event, and then print the string representation of the event type. Remember to always refer to the latest Dagster documentation for the most accurate and up-to-date information on handling events and their properties.