how to release a resource after each run?

To release a resource after each run in Dagster, you can utilize the

teardown_after_execution

lifecycle hook provided by the resource definition. According to the documentation, once a resource is no longer needed at the end of a run, the

teardown_after_execution

method is called. This method is passed the same context object as

setup_for_execution

and can be used for cleaning up any state that was initialized in

setup_for_execution

. Here's a brief overview of how you can implement this: 1. Define your resource class and include the

teardown_after_execution

method. 2. In the

teardown_after_execution

method, implement the logic to release or clean up the resource. 3. Ensure that your resource is configured correctly in your pipeline/job so that it is used during the run. The

teardown_after_execution

method will be called once per run, per process. This means that if you are using the in-process executor, it will be called once per run. If you are using the multiprocess executor, each process's instance of the resource will have its

teardown_after_execution

method called. Here is an example from the documentation that illustrates how to set up a resource with lifecycle hooks:

python from dagster import resource, InitResourceContext  class MyResource:     def __init__(self, init_context: InitResourceContext):         # Initialization logic here         pass      def setup_for_execution(self, init_context: InitResourceContext):         # Setup logic here         pass      def teardown_after_execution(self, context: InitResourceContext):         # Cleanup logic here         pass  # Usage in a pipeline/job @resource def my_resource(init_context: InitResourceContext):     resource_instance = MyResource(init_context)     resource_instance.setup_for_execution(init_context)     try:         yield resource_instance     finally:         resource_instance.teardown_after_execution(init_context)

In this example,

my_resource

is a resource definition that yields an instance of

MyResource

. The

setup_for_execution

method is called to set up any necessary state, and the

teardown_after_execution

method is responsible for cleaning up the state after the run is completed.

@Scout how to switch to in-process executor? do I update dagster.yaml?

To switch to the in-process executor in Dagster, you can specify this in your pipeline/job configuration. While you can set defaults in the

dagster.yaml

file, the most direct way to switch executors for a specific pipeline/job is to include the appropriate configuration when you launch a run. Here's how you can specify the in-process executor using the configuration:

yaml execution:   in_process: {}

You can include this configuration snippet in your run configuration file or directly in the Dagit UI when you launch a run. If you're using the Python API to execute a job, you can pass this configuration as a Python dictionary to the

execute_in_process

method or when you build the run config programmatically. For example, if you're using the Python API, you could do something like this:

python result = my_pipeline.execute_in_process(     run_config={         "execution": {             "in_process": {}         }     } )

If you want to set the in-process executor as the default for all runs, you can update your

dagster.yaml

with the following configuration:

yaml execution:   default:     config:       in_process: {}

This configuration in the

dagster.yaml

file sets the in-process executor as the default execution mode for all pipelines/jobs in the instance. However, please note that this will apply to all pipelines/jobs unless overridden by a specific run configuration.

if I set this in dagster.yaml, does it mean that all runs will be executed sequentially? @Scout

Setting the in-process executor in the

dagster.yaml

file does not inherently mean that all runs will be executed sequentially. The in-process executor means that the execution of a pipeline/job will happen within the same process as the one that launched the run. This is in contrast to executors that may run each step in a separate process or even on different machines, such as the multiprocess or celery executors. However, the in-process executor does not manage the concurrency of multiple pipeline/job runs. The concurrency of runs is managed by the run coordinator and the run launcher configured in your Dagster instance. If you want to ensure that runs are executed sequentially, you would need to configure the run coordinator to enforce that behavior. By default, without additional concurrency limits or prioritization rules, runs are executed in the order they are submitted to the queue. If you want to enforce that only one run can execute at a time, you would need to set up appropriate concurrency limits in your Dagster instance configuration. For example, you could set a global concurrency limit in your

dagster.yaml

to ensure that only one run can execute at a time:

yaml run_coordinator:   module: dagster.core.run_coordinator   class: QueuedRunCoordinator   config:     max_concurrent_runs: 1

This configuration would ensure that only one run is executed at a time, regardless of the executor used. If a run is in progress, subsequent runs will be queued until the current run is completed.

no I actually want different runs to execute their own processes, but the steps in each run can run in the same process. @Scout

If you want different runs to execute in their own processes, but have the steps within each run execute in the same process, you can indeed use the in-process executor. This executor will run all steps of a single pipeline/job run within the same process, which is what you're looking for. To configure your Dagster instance to use the in-process executor by default, you can set it in the

dagster.yaml

file like this:

yaml execution:   default:     config:       in_process: {}

With this configuration, each run will be executed in its own process, but all steps within that run will execute in the same process as the run itself. This allows for shared memory and resources within a run, which can be beneficial for certain workloads. Please note that the concurrency of runs (i.e., whether multiple runs can execute simultaneously) is still managed by the run coordinator and run launcher. The in-process executor configuration only affects how the steps within a run are executed relative to each other. If you have not set any concurrency limits, Dagster will execute runs concurrently as long as resources are available and according to the rules of the run coordinator and launcher you have configured.

how do I tell if a run is already configured to use in-process executor? @Scout

To determine if a run is already configured to use the in-process executor, you can check the run configuration that was used to launch the run. The run configuration specifies the executor and other settings for the run. Here are a few ways to check the executor configuration for a run: 1. Dagit UI: If you are using the Dagit web interface, navigate to the specific run's page. There, you can view the run's configuration (often in YAML format) under the "Config" tab. Look for the

execution

key to see if it specifies the in-process executor:

yaml     execution:       in_process: {}

2. Dagster API: If you have access to the Dagster Python API, you can programmatically retrieve the run configuration for a specific run using the

DagsterInstance

object. Here's an example of how you might do that:

python     from dagster import DagsterInstance      # Assuming you have the run_id for the run you want to check     run_id = "example_run_id"     instance = DagsterInstance.get()     run = instance.get_run_by_id(run_id)      if run:         run_config = run.run_config         # Check if the execution config specifies in_process         if run_config.get("execution", {}).get("in_process") is not None:             print("The run is configured to use the in-process executor.")         else:             print("The run is not configured to use the in-process executor.")

3. Run Logs: If you have access to the logs for the run, either through Dagit or through your logging system, you can look for log entries that indicate which executor was used. The logs might include information about the executor configuration at the start of the run. 4. Run Storage: If you are storing run configurations in a database or file system, you can directly inspect the stored configuration for the run in question. Remember that if no executor is specified in the run configuration, Dagster will use the default executor specified in the

dagster.yaml

file or the default in-process executor if no default is set.