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#ask-ai
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# ask-ai
j

Justin Bricker

02/07/2024, 5:28 PM
@Scout What is the use case for a
in_process_executor
?
s

Scout

02/07/2024, 5:28 PM
The
in_process_executor
is an executor in Dagster that executes all steps of a job within a single process. The use case for the
in_process_executor
includes scenarios where you want to: 1. Simplify debugging by running the entire job within a single process, which can make it easier to trace and understand the flow of execution and troubleshoot issues. 2. Avoid the overhead associated with starting multiple processes, which can be beneficial for jobs with short-running steps where the overhead of process creation might outweigh the benefits of parallel execution. 3. Work with data or compute steps that are not easily serializable across process boundaries, as the
in_process_executor
does not require serialization of data between steps. 4. Run jobs in environments where multiprocessing is not desired or supported, such as certain restricted or constrained execution environments. The documentation also notes that when using the
execute_in_process
method to execute a job, the
executor_def
on the job will be ignored and replaced with the in-process executor. This means that even if the job is defined with a different executor, using
execute_in_process
will ensure that the job runs within a single process. Additionally, when specifying the
in_process
configuration for the default
multi_or_in_process_executor
, you can switch the execution mode of the job to run in-process rather than in multiprocess mode. This can be done via the job's run configuration. In summary, the
in_process_executor
is useful for scenarios where single-process execution is preferred for simplicity, debugging, performance considerations, or compatibility with the execution environment.
j

Justin Bricker

02/07/2024, 5:32 PM
@Scout If I need the ops defined in the job below to operate in sequence, do I need to use an in_process_executor?
Copy code
@job(
    name='deploy_job',
    resource_defs=job_resources(),
    executor_def=in_process_executor,
    config=RunConfig(
        ops={'load_op': app_db_load_config,
             'assemble_op': app_db_load_config,
             'promote_op': app_db_load_config, }
    ),
)
def utilizations_app_deploy_job():
    load_data = load_op()
    assemble_data = assemble_op(upstream=load_data)
    promote_data = promote_op(upstream=assemble_data)
    refresh_partitions = refresh_partitions_op(upstream=promote_data)
    refresh_partitions = refresh_partitions_op(upstream=refresh_partitions)
    post_deploy_tasks_op(upstream=refresh_partitions)
s

Scout

02/07/2024, 5:32 PM
In Dagster, the execution order of ops within a job is determined by their dependencies, not by the executor used. The
in_process_executor
is not required to run ops in sequence; it is the dependencies between ops that enforce the execution order. In the job definition you provided, the ops are already set up to run in sequence due to the
upstream
arguments, which create dependencies between the ops: 1.
load_data
will run first because it has no dependencies. 2.
assemble_data
will run after
load_data
because it depends on
load_data
. 3.
promote_data
will run after
assemble_data
because it depends on
assemble_data
. 4.
refresh_partitions
will run after
promote_data
because it depends on
promote_data
. 5.
post_deploy_tasks_op
will run last because it depends on
refresh_partitions
. The
executor_def=in_process_executor
in the job definition is specifying that the job should use the in-process executor, but this is not what enforces the sequential execution. It is the dependencies that you have defined between the ops that ensure they will run in the specified order. If you want to ensure that the ops run in the same process for reasons such as shared memory or debugging, then specifying
in_process_executor
is appropriate. However, if your only goal is to have the ops run in sequence, then you simply need to define the dependencies correctly, as you have done, and you can use the default executor or any other executor that respects the dependencies.
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