@Scout how to access dynamically the job run status? did it fail or not

To dynamically access the job run status and determine if it failed or not, you can use the properties and convenience functions provided by Dagster. According to the documentation, there are several properties that you can check to determine the status of a run: - `_is_failure_or_canceled`: This property indicates if the run has either failed or was canceled. - `_is_finished`: This property indicates if the run has completely finished execution. - `_is_success`: This property indicates if the run has successfully finished executing. Additionally, Dagster provides convenience functions for retrieving

DagsterRunStatus.SUCCESS

and

DagsterRunStatus.FAILURE

events. For example, you can use

ExecuteInProcessResult.get_job_success_event

and

ExecuteInProcessResult.get_job_failure_event

to retrieve these specific events after a job execution. If you have a run status sensor triggered on another status, you can retrieve all events from the

result

and filter based on your event type. Here is an example from the documentation on how to use a run failure sensor:

python @run_failure_sensor def my_email_failure_sensor(context: RunFailureSensorContext):     message = (         f'Job "{context.dagster_run.job_name}" failed. Error:'         f" {context.failure_event.message}"     )     email_alert(message)

In this example, the sensor is triggered when a job fails, and it sends an email alert with the job name and error message. Remember that these properties and functions are part of the internal API and should be used accordingly. If you need to access the run status dynamically during execution, you would typically do this within the context of a sensor or within your job's logic by inspecting the result of the execution.

@Scout i have a success hook and failure hook - based on the job status i want to trigger the hook dynamically

In Dagster, hooks are used to perform actions in response to different events within a job's lifecycle, such as success or failure of an operation (op). Hooks are defined and then attached to ops or entire jobs to be triggered automatically when the associated event occurs. To dynamically trigger a hook based on the job status, you would typically define both a success hook and a failure hook, and then attach them to the job. When the job runs, Dagster will automatically trigger the appropriate hook based on whether the job succeeded or failed. Here's an example of how you might define and use success and failure hooks:

python
from dagster import success_hook, failure_hook, HookContext, job, op

@success_hook(required_resource_keys={"slack"})
def slack_message_on_success(context: HookContext):
    message = f"Job {context.job_name} succeeded."
    context.resources.slack.send_message(message)

@failure_hook(required_resource_keys={"slack"})
def slack_message_on_failure(context: HookContext):
    message = f"Job {context.job_name} failed."
    context.resources.slack.send_message(message)

@op
def my_op(context):
    # Your op logic here

@job(hooks={slack_message_on_success, slack_message_on_failure})
def my_job():
    my_op()

In this example,

slack_message_on_success

is a success hook that sends a message to Slack when the job succeeds, and

slack_message_on_failure

is a failure hook that sends a message to Slack when the job fails. Both hooks are attached to

my_job

, so they will be triggered automatically by Dagster when the job completes, depending on the outcome. If you want to trigger hooks manually or conditionally based on more complex logic, you would need to implement that logic within your job or op and then use the

context

to interact with the resources or systems you want to notify. However, this would be a departure from the typical use of hooks, which are designed to be declarative and automatically managed by Dagster based on the job's execution events. Dagster Docs button Op Hooks | Dagster button Dagster Docs button Enable using hooks with software-defined... button Thank you for your feedback!

@Scout please elaborate on how to use DagsterRunStatus dynamically without specifying DagsterRunStatus.SUCCESS or DagsterRunStatus.FAILURE

In Dagster,

DagsterRunStatus

is an enum that represents the status of a pipeline run, such as

SUCCESS

,

FAILURE

,

STARTED

, etc. If you want to react to run statuses dynamically without specifying a particular status like

DagsterRunStatus.SUCCESS

or

DagsterRunStatus.FAILURE

, you would typically use a sensor. A sensor in Dagster is a function that runs periodically and checks for certain conditions. When those conditions are met, the sensor can trigger a run or perform other actions. You can use a sensor to dynamically check the status of runs and then perform actions accordingly. Here's an example of how you might set up a sensor to check for any run status dynamically:

python from dagster import sensor, RunRequest, DagsterInstance, PipelineRunStatus, RunStatusSensorContext  @sensor def dynamic_run_status_sensor(context: RunStatusSensorContext):     instance = DagsterInstance.get()     runs = instance.get_runs(filters=PipelineRunStatus.RUNNING)      for run in runs:         run_status = run.status          # Perform different actions based on the run status         if run_status == PipelineRunStatus.SUCCESS:             # Do something when a run succeeds             pass         elif run_status == PipelineRunStatus.FAILURE:             # Do something when a run fails             pass         # Add more conditions for other statuses as needed      # Return a RunRequest if you want to trigger a new pipeline run     # based on the conditions checked above     return RunRequest(run_key="optional_unique_key", run_config={})

In this example,

dynamic_run_status_sensor

is a sensor that checks the status of all running pipeline runs. It uses the

DagsterInstance

to query the current runs and their statuses. Based on the status of each run, you can then perform different actions. Please note that this is a general example. The actual implementation may vary based on your specific use case, and you may need to adjust the logic to fit the conditions you want to check for. Additionally, the sensor does not need to return a

RunRequest

unless you want to trigger a new pipeline run based on the sensor's logic. Remember that sensors are typically used to observe external systems or conditions and then trigger runs or perform actions based on those observations. If you want to perform actions within a pipeline run based on the status of ops or the run itself, you would use hooks as described in the previous messages.

@Scout please elaborate on this ExecuteInProcessResult

The

ExecuteInProcessResult

object in Dagster is a result object returned by in-process testing APIs. It is used for retrieving run success, events, and outputs from execution methods that return this object. This object is particularly useful when you want to execute a job or graph within the same process as your test or script, often for testing purposes. Here are some key properties and methods of `ExecuteInProcessResult`: - `_all_events`: This property contains all Dagster events emitted during execution. It is a list of

DagsterEvent

objects that you can inspect to understand what happened during the run. - `_dagster_run`: This property holds the

DagsterRun

object that was executed. It contains metadata about the run, such as the run ID and the run config. - `_job_def`: This property references the

JobDefinition

that was executed. - `output_for_node(node_str, output_name='result')`: This method retrieves the output value with a particular name from the run of the job. You can specify the name of the op/graph whose output should be retrieved and the name of the output on the op/graph. - `output_value(output_name='result')`: This method retrieves the output of the top-level job if an output is returned. It defaults to retrieving the 'result' output. Here's an example of how you might use `ExecuteInProcessResult`:

python from dagster import job, op, execute_in_process  @op def my_op():     return "Hello, Dagster!"  @job def my_job():     my_op()  # Execute the job in process and get the result result = my_job.execute_in_process()  # Access the success status of the run run_success = result.success  # Access all events emitted during the run all_events = result.all_events  # Access the output of `my_op` output_value = result.output_for_node("my_op")

In this example,

my_job

is executed in-process using the

execute_in_process

method. The

result

object is an instance of

ExecuteInProcessResult

, which allows you to check if the run was successful, access all events, and retrieve the output of

my_op

. This is particularly useful for testing and debugging, as it allows you to execute your job or graph in a controlled environment and inspect the results immediately after execution.

@Scout can i access inside an op the job run status?

Inside an op, you generally don't have direct access to the overall job run status because ops are designed to be isolated units of computation that don't have knowledge of the job's execution context. The philosophy behind this is to keep ops pure and focused on their specific task without being concerned about the broader job state. However, you can access certain context-related information within an op through the

context

parameter, which provides access to logging, resources, and configuration, but not to the job's run status. If you need to perform actions based on the job's run status, you would typically handle this outside of the op, using hooks or sensors. Hooks can be used to perform actions on the success or failure of ops or the entire job, while sensors can monitor the job's run status and trigger runs or other actions based on changes in external systems or conditions. For example, you can use a

success_hook

or

failure_hook

to perform actions after an op completes:

python from dagster import op, success_hook, failure_hook, HookContext  @success_hook def my_success_hook(context: HookContext):     # Perform action on success     pass  @failure_hook def my_failure_hook(context: HookContext):     # Perform action on failure     pass  @op def my_op(context):     # Op logic here     pass  # Attach hooks to the op my_op.with_hooks(hook_defs={my_success_hook, my_failure_hook})

If you need to access the run status within an op for some reason, you would have to pass it explicitly as an input to the op, which would require knowing the status ahead of time. This is not a common pattern and would likely indicate that there's a need to rethink the job's design to better align with Dagster's execution model. In summary, while you can't directly access the job run status within an op, you can use Dagster's constructs like hooks and sensors to react to the job's run status in a way that is consistent with the system's design principles.

@Scout what if i want something like this:

python
from dagster import op, success_hook, failure_hook, HookContext

def create_run_status(ExecuteInProcessResult):
 @success_hook
 def my_success_hook(context: HookContext):
    # Perform action on success
    pass

 @failure_hook
 def my_failure_hook(context: HookContext):
    # Perform action on failure
    pass

  return my_success_hook if ExecuteInProcessResult.success else my_failure_hook

@op
def my_op(context):
    # Op logic here
    pass

# Attach hooks to the op
my_op.with_hooks(hook_defs={my_success_hook, my_failure_hook})

is it possible to do something like that?

The code snippet you've provided seems to be attempting to dynamically choose which hook to attach to an op based on the success or failure of an

ExecuteInProcessResult

. However, this isn't how hooks are intended to be used in Dagster, and the code as written will not work for several reasons: 1. Hooks are static definitions that are attached to ops or jobs and are triggered by the Dagster framework based on the execution events. They are not dynamically selected at runtime. 2. The

ExecuteInProcessResult

object is the result of executing a job or graph in-process, typically used in testing scenarios. It is not available at the time you are defining your op and its hooks. 3. The

create_run_status

function is trying to return a hook based on the success or failure of an execution result, but this is not how hooks are attached. Hooks are defined separately and attached to ops or jobs without conditional logic at the time of definition. Here's how you can correctly use success and failure hooks with an op:

python from dagster import op, success_hook, failure_hook, HookContext  @success_hook def my_success_hook(context: HookContext):     # Perform action on success     pass  @failure_hook def my_failure_hook(context: HookContext):     # Perform action on failure     pass  @op def my_op(context):     # Op logic here     pass  # Attach hooks to the op my_op = my_op.with_hooks(hook_defs={my_success_hook, my_failure_hook})

In this corrected example, both

my_success_hook

and

my_failure_hook

are attached to

my_op

. Dagster will automatically trigger the appropriate hook based on whether the op execution succeeds or fails. There is no need for conditional logic to select the hook; Dagster handles this internally. If you want to perform specific actions based on the success or failure of a job within the same Python process, you would typically execute the job in-process and then inspect the

ExecuteInProcessResult

after execution:

python result = my_job.execute_in_process()  if result.success:     # Perform action on success else:     # Perform action on failure

This pattern is generally used in testing or scripting scenarios, not in the definition of ops and hooks within a Dagster job.

@Scout how about creating a sensor that will look for the job or op status and will send the status to the create_run_status function like below and based on the status it will trigger the hook?

python
from dagster import op, success_hook, failure_hook, HookContext

def create_run_status(ExecuteInProcessResult):
 @success_hook
 def my_success_hook(context: HookContext):
    # Perform action on success
    pass

 @failure_hook
 def my_failure_hook(context: HookContext):
    # Perform action on failure
    pass

  return my_success_hook if ExecuteInProcessResult.success else my_failure_hook

@op
def my_op(context):
    # Op logic here
    pass

# Attach hooks to the op
my_op.with_hooks(hook_defs={my_success_hook, my_failure_hook})

The concept of dynamically selecting and triggering hooks based on the status of a job or op execution is not supported in the way you've described. In Dagster, hooks are statically defined and attached to ops or jobs, and they are triggered automatically by the Dagster framework based on the execution events. You cannot pass an

ExecuteInProcessResult

to a function to dynamically determine which hook to use. However, you can create a sensor that monitors the status of job runs and performs actions based on that status. The sensor itself would not directly trigger hooks, but it could perform similar actions to what you might do in a hook. Here's an example of how you might create a sensor that performs actions based on the success or failure of job runs:

python from dagster import sensor, RunRequest, DagsterInstance, PipelineRunStatus, RunStatusSensorContext  @sensor def job_status_sensor(context: RunStatusSensorContext):     instance = DagsterInstance.get()     runs = instance.get_runs(filters=PipelineRunStatus.RUNNING)      for run in runs:         if run.status == PipelineRunStatus.SUCCESS:             # Perform action on success             pass         elif run.status == PipelineRunStatus.FAILURE:             # Perform action on failure             pass         # Add more conditions for other statuses as needed      # Optionally, return a RunRequest if you want to trigger a new pipeline run     # based on the conditions checked above     return RunRequest(run_key="optional_unique_key", run_config={})

In this example,

job_status_sensor

is a sensor that checks the status of all running pipeline runs and performs actions based on the status of each run. This sensor is not directly triggering hooks but is instead performing actions within the sensor itself. If you want to perform actions similar to hooks but based on the results of a job execution within the same Python process, you would execute the job in-process and then inspect the

ExecuteInProcessResult

after execution, as mentioned in the previous response. Remember, the sensor is not part of the job's execution path; it's a separate process that periodically checks for conditions and then performs actions or triggers runs. Hooks, on the other hand, are part of the job's execution path and are triggered by specific events during the execution of ops or the job itself.

@Scout write me the docs for chat_postMessage

I'm sorry for any confusion, but as a Dagster AI BOT, I don't have access to external documentation such as Slack API documentation for methods like

chat_postMessage

. My expertise is focused on Dagster and its ecosystem. If you're looking for documentation on the

chat_postMessage

method, which is typically part of the Slack API, you would need to refer to the Slack API documentation provided by Slack. The

chat_postMessage

method is used to send a message to a channel in Slack, and the documentation for it would include details on the required and optional parameters, the expected response, and any special considerations or permissions needed to use the method. For accurate and up-to-date information on

chat_postMessage

, please visit the official Slack API documentation on their website or consult their API reference guide.

@Scout how do i get the run status of an job?

In Dagster, the status of individual ops within a job run is not directly exposed to other ops during execution. Ops are designed to be isolated units of computation, and they do not have built-in mechanisms to query the status of other ops. However, if you need to track the status of ops for monitoring or logging purposes, you can use the following approaches: 1. Event Logs: Dagster emits events for each significant step in the lifecycle of an op, including start, success, failure, and skipped events. You can access these events through the Dagster event log. While you cannot access these events from within another op during execution, you can query the event log after the job has run to determine the status of each op. 2. Hooks: You can use hooks to perform actions based on the success or failure of ops. For example, you can define a

success_hook

and a

failure_hook

and attach them to your ops. These hooks can log the status or perform other actions when the op succeeds or fails. Here's an example of how you might use hooks:

python from dagster import op, success_hook, failure_hook, HookContext  @success_hook def op_success_hook(context: HookContext):     <http://context.log.info|context.log.info>(f"Op {context.op.name} succeeded.")  @failure_hook def op_failure_hook(context: HookContext):     context.log.error(f"Op {context.op.name} failed.")  @op def my_op(context):     # Op logic here     pass  my_op = my_op.with_hooks(hook_defs={op_success_hook, op_failure_hook})

3. Sensors: While sensors are typically used to monitor external systems or conditions, you could potentially use a sensor to check the status of job runs and perform actions based on the status of specific ops by querying the event log. 4. Asset Materializations: If your ops are associated with assets, you can use asset materializations to track the success of ops. When an op successfully processes and materializes an asset, it can emit an asset materialization event, which can be used to infer the success of the op. Remember that the above methods are for after-the-fact analysis or for triggering side effects based on op status. If you need to make runtime decisions within an op based on the status of another op, you may need to restructure your job to pass data explicitly between ops or to use custom logic to manage dependencies and execution order.

@Scout i want to get react dynamically based on the status but i dont want to hard code the status like this:

@run_status_sensor(run_status=DagsterRunStatus.SUCCESS)
def my_run_success_sensor(context: RunStatusSensorContext):
    run_id = context.dagster_run.run_id
    # Perform actions based on the successful run
    pass

instead i would like to trigger a hook dynamically but i want one function to handle the entire logic of those hooks since i could have 10 different hooks for different statuses - and since im hooking the entire job it self and not ops them selves, since i have more than 50 ops - i would like to just hook to the job and trigger a function that will get the current status what ever it is and trigger the correct hook

In Dagster, hooks are static by design and are meant to be declared explicitly for specific events such as success or failure. They are not intended to be triggered dynamically based on the job status within the execution of the job itself. However, you can achieve similar behavior by using a sensor to monitor job statuses and then perform actions based on those statuses. Here's how you can create a sensor that checks the status of jobs and then performs actions accordingly, without hardcoding the status:

python from dagster import sensor, DagsterInstance, PipelineRunStatus, RunRequest  @sensor def dynamic_status_sensor(context):     instance = DagsterInstance.get()     runs = instance.get_runs()      for run in runs:         if run.status == PipelineRunStatus.SUCCESS:             # Perform action for success             pass         elif run.status == PipelineRunStatus.FAILURE:             # Perform action for failure             pass         # Add more conditions for other statuses as needed

In this example,

dynamic_status_sensor

checks the status of all runs and performs actions based on the status of each run. This sensor is not directly triggering hooks but is instead performing actions within the sensor itself. If you want to centralize the logic for handling different statuses, you could define a function that encapsulates the logic and call it from within the sensor:

python def handle_run_status(run):     if run.status == PipelineRunStatus.SUCCESS:         # Handle success         pass     elif run.status == PipelineRunStatus.FAILURE:         # Handle failure         pass     # Add more conditions for other statuses as needed  @sensor def dynamic_status_sensor(context):     instance = DagsterInstance.get()     runs = instance.get_runs()      for run in runs:         handle_run_status(run)

This approach allows you to have one function,

handle_run_status

, that contains the logic for handling all the different run statuses. Please note that sensors are typically used to observe external systems or conditions and then trigger runs or perform actions based on those observations. They are not part of the job's execution path; they are a separate process that periodically checks for conditions. If you need to perform actions within a job based on the status of ops or the job itself, you would use hooks, but as mentioned earlier, hooks in Dagster are not designed to be triggered dynamically in the way you described.

@Scout you are doing a for loop here:

for run in runs:
        handle_run_status(run)

does it get only the latest run or all runs in general since i want only the latest run

The loop in the provided example iterates over all runs returned by

instance.get_runs()

, which by default retrieves all runs from the Dagster instance. If you want to get only the latest run, you can modify the query to sort the runs by their start time and limit the result to only one run. Here's how you can modify the sensor to fetch only the latest run:

python from dagster import sensor, DagsterInstance, RunsFilter  @sensor def latest_run_status_sensor(context):     instance = DagsterInstance.get()     # Get the latest run by ordering by start time and limiting to one run     latest_run = instance.get_runs(filters=RunsFilter(), order_by="start_time", limit=1, ascending=False)      if latest_run:         # There will be only one run in the list, so we take the first         handle_run_status(latest_run[0])

In this updated example,

latest_run_status_sensor

retrieves only the latest run by specifying

order_by="start_time"

and

limit=1

, and setting

ascending=False

to ensure that the runs are ordered from newest to oldest. The

handle_run_status

function is then called with the latest run. Please note that the

handle_run_status

function should be defined to contain the logic for handling different run statuses, as shown in the previous example.

@Scout how would you handle it in the handle_run_status function ? since handle_run_status is deprecated https://docs.dagster.io/migration#removed-api-list

The

handle_run_status

function mentioned in the previous example is not a Dagster API; it was used as a hypothetical example of a user-defined function that you could create to encapsulate the logic for handling different run statuses. This function is not part of Dagster's API and therefore is not subject to deprecation. To clarify, here's an example of how you might define your own

handle_run_status

function to perform different actions based on the status of the latest run:

python from dagster import DagsterInstance, PipelineRunStatus  def handle_run_status(run):     # Check the status of the run and perform actions accordingly     if run.status == PipelineRunStatus.SUCCESS:         # Handle success         print(f"Run {run.run_id} succeeded.")     elif run.status == PipelineRunStatus.FAILURE:         # Handle failure         print(f"Run {run.run_id} failed.")     # Add more conditions for other statuses as needed  # Example usage within a sensor @sensor def latest_run_status_sensor(context):     instance = DagsterInstance.get()     # Get the latest run by ordering by start time and limiting to one run     latest_run = instance.get_runs(filters=RunsFilter(), order_by="start_time", limit=1, ascending=False)      if latest_run:         # There will be only one run in the list, so we take the first         handle_run_status(latest_run[0])

In this example,

handle_run_status

is a custom function that you would define in your codebase to handle the logic associated with different run statuses. It is not a Dagster API function and is not affected by any deprecations in the Dagster framework. The

latest_run_status_sensor

sensor checks for the latest run and then calls

handle_run_status

with that run. The

handle_run_status

function then performs actions based on the status of the run, such as logging a message. You can replace the print statements with your own logic to handle each run status as needed.