@Scout how i can restart a faulere Job from outside of dagster

To restart a failed job from outside of Dagster, you can use Dagster's Python APIs to programmatically trigger a reexecution. Here's a step-by-step guide based on the documentation: 1. Identify the failed job: You need to have the

run_id

of the failed job that you want to restart. 2. Create a DagsterInstance: You need an instance of

DagsterInstance

to interact with the Dagster system. If you are running this script outside of the Dagster environment, you can create an ephemeral instance or connect to a persistent one.

python
    from dagster import DagsterInstance
    instance = DagsterInstance.ephemeral()  # For ephemeral instance
    # OR
    instance = DagsterInstance.get()  # To connect to a persistent instance

3. Set up ReexecutionOptions: Use the

ReexecutionOptions

class to specify how you want to reexecute the job. If you want to restart from the point of failure, you can use the

from_failure

method.

python
    from dagster import ReexecutionOptions
    options = ReexecutionOptions.from_failure(run_id, instance)

4. Execute the job: Use the

execute_job

function to trigger the reexecution. You will need to provide the job as a reconstructable target, the instance, and the reexecution options.

python
    from dagster import execute_job, reconstructable
    # Assuming 'my_failed_job' is the name of the job you want to restart
    result = execute_job(
        reconstructable(my_failed_job),
        instance=instance,
        reexecution_options=options
    )

Please note that the above code snippets are based on the documentation and assume that you have a job defined as

my_failed_job

and that you have the

run_id

of the failed job. Adjust the code to fit your specific job and environment. If you need to trigger this from a completely external system, you might need to set up a service or an API endpoint that can receive a request to restart a job and then run the above code. This could be done using a web framework like Flask or FastAPI, or by using a task queue system like Celery that can trigger the job restart based on external events. Re-execution | Dagster button Resume failed Backfill option button Re executing a job run with dbt assets f... button GraphQL Python Client | Dagster button Thank you for your feedback!

@Scout how does DagsterInstance.ephemeral() works? How i give the Connection Information?

DagsterInstance.ephemeral()

creates a temporary, in-memory instance of a DagsterInstance that is suitable for ephemeral execution, such as in testing contexts. This instance does not persist any information to disk and is discarded once the Python process exits. It uses in-memory components for things like run and event log storage. Here's how you can use `DagsterInstance.ephemeral()`:

python
from dagster import DagsterInstance

# Create an ephemeral DagsterInstance
instance = DagsterInstance.ephemeral()

Regarding connection information, since an ephemeral instance is designed to be temporary and in-memory, it does not require connection information for a database or other persistent storage. It is self-contained and does not connect to an external database. If you need to provide connection information because you want to interact with external systems or resources (like databases, object stores, etc.), you would typically do this through resource configurations in your job definitions, not through the

DagsterInstance

itself. For example, if you have an op that interacts with a database, you would define a resource for the database connection and pass the connection information through the resource configuration when you set up your job:

python
from dagster import job, op, resource, DagsterInstance

# Define a resource for the database connection
@resource(config_schema={"connection_string": str})
def db_resource(context):
    # Here you would create a database connection using the provided connection string
    return create_db_connection(context.resource_config["connection_string"])

# Define an op that uses the db_resource
@op(required_resource_keys={"db"})
def my_op(context):
    db_conn = context.resources.db
    # Perform operations using the db_conn

# Define a job that uses the db_resource
@job(resource_defs={"db": db_resource})
def my_job():
    my_op()

# Create an ephemeral DagsterInstance
instance = DagsterInstance.ephemeral()

# Execute the job with the resource configuration
result = my_job.execute_in_process(
    run_config={
        "resources": {
            "db": {
                "config": {
                    "connection_string": "your_connection_string_here"
                }
            }
        }
    },
    instance=instance
)

In this example, the

db_resource

is configured with a connection string when the job is executed. The

execute_in_process

method allows you to pass

run_config

and an instance, which can be an ephemeral instance for testing purposes. Dagster Docs button Cannot start backfills when gRPC server ... button Dagster Docs button Using the Python DagsterGraphQLClient ag... button Thank you for your feedback!

@Scout how can i restart a failed job in dagster Cloud with an api call

In Dagster Cloud, you can use the GraphQL API to programmatically restart a failed job. To do this, you would typically send a GraphQL mutation to the Dagster Cloud endpoint that triggers a new run based on the previous failed run. Here's a high-level overview of the steps you would take: 1. Obtain API Access: Ensure you have API access to your Dagster Cloud instance. You will need an API token to authenticate your requests. 2. Identify the Failed Run: You need the

runId

of the failed job that you want to restart. 3. Construct the GraphQL Mutation: Create a GraphQL mutation that uses the

launchPipelineReexecution

mutation, providing the necessary parameters such as the

runId

of the failed run. 4. Send the API Request: Use an HTTP client to send the GraphQL mutation to the Dagster Cloud endpoint, including the API token for authentication. Here is an example of what the GraphQL mutation might look like:

graphql
mutation($runId: String!) {
  launchPipelineReexecution(
    executionParams: {
      runConfigData: {}
      selector: {
        pipelineName: "your_pipeline_name"
      }
      mode: "default"
      executionMetadata: {
        parentRunId: $runId
        rootRunId: $runId
      }
    }
  ) {
    __typename
    ... on LaunchRunSuccess {
      run {
        runId
        pipelineName
        status
      }
    }
    ... on PythonError {
      message
      stack
    }
  }
}

You would replace

"your_pipeline_name"

with the name of the pipeline you want to re-execute and

$runId

with the ID of the failed run. To send the request, you can use a tool like

curl

or a programming language with an HTTP client library. Here's an example using `curl`:

bash
curl -X POST \
  -H "Authorization: Bearer YOUR_API_TOKEN" \
  -H "Content-Type: application/json" \
  --data '{
    "query": "mutation($runId: String!) { launchPipelineReexecution(executionParams: { runConfigData: {} selector: { pipelineName: \"your_pipeline_name\" } mode: \"default\" executionMetadata: { parentRunId: $runId rootRunId: $runId } }) { __typename ... on LaunchRunSuccess { run { runId pipelineName status } } ... on PythonError { message stack } } }",
    "variables": {
      "runId": "your_failed_run_id"
    }
  }' \
  <https://your-dagster-cloud-endpoint/graphql>

Make sure to replace

YOUR_API_TOKEN

with your actual API token,

your_pipeline_name

with the name of your pipeline,

your_failed_run_id

with the ID of the failed run, and

<https://your-dagster-cloud-endpoint/graphql>

with the actual endpoint for your Dagster Cloud instance. Please note that the exact fields and mutation name may vary based on the version of Dagster Cloud you are using, so you should consult the Dagster Cloud GraphQL API documentation for the most accurate an

@Scout is there another way without Graph ql query

As of my knowledge cutoff date in early 2023, the primary way to programmatically interact with Dagster Cloud and perform actions such as restarting a failed job is through the GraphQL API. Dagster Cloud is designed with GraphQL as the interface for such operations, and it provides a powerful and flexible way to interact with the system. If you are looking for a way to restart a job without directly writing GraphQL queries, you might consider using the Dagster Python Client, which provides Python bindings to interact with Dagster's GraphQL API. This client abstracts away the need to write GraphQL queries by providing a more Pythonic interface. However, as of my last update, the Dagster Python Client had limited functionality and might not support all operations available through the GraphQL API. You would need to check the latest documentation or the client's source code to see if it includes a method for restarting failed jobs in Dagster Cloud. If the functionality you need is not available in the Dagster Python Client, then using the GraphQL API directly is the recommended approach. If you find writing GraphQL queries challenging, you might consider creating helper functions or scripts that encapsulate the GraphQL logic, making it easier to perform common tasks like restarting jobs. For any updates or new features that may have been introduced after my knowledge cutoff date, please refer to the latest Dagster documentation or reach out to the Dagster community for assistance.