def start_workflow(workflow, parameters):
"""Start a workflow."""
def _start_workflow_db(workflow, parameters):
workflow.run_started_at = datetime.now()
workflow.status = WorkflowStatus.running
if parameters:
workflow.input_parameters = parameters.get("input_parameters")
workflow.operational_options = parameters.get("operational_options")
current_db_sessions.add(workflow)
current_db_sessions.commit()
current_db_sessions = Session.object_session(workflow)
kwrm = KubernetesWorkflowRunManager(workflow)
failure_message = (
"Workflow {id_} could not be started because it {verb} " "already {status}."
).format(
id_=workflow.id_,
verb=get_workflow_status_change_verb(workflow.status.name),
status=str(workflow.status.name),
)
if "restart" in parameters.keys():
if parameters["restart"]:
if workflow.status not in [
WorkflowStatus.failed,
WorkflowStatus.finished,
WorkflowStatus.queued,
]:
raise REANAWorkflowControllerError(failure_message)
elif workflow.status not in [WorkflowStatus.created, WorkflowStatus.queued]:
if workflow.status == WorkflowStatus.deleted:
raise REANAWorkflowStatusError(failure_message)
raise REANAWorkflowControllerError(failure_message)
try:
kwrm.start_batch_workflow_run(
overwrite_input_params=parameters.get("input_parameters"),
overwrite_operational_options=parameters.get("operational_options"),
)
_start_workflow_db(workflow, parameters)
except SQLAlchemyError as e:
message = "Database connection failed, please retry."
logging.error(
f"Error while creating {workflow.id_}: {message}\n{e}", exc_info=True
)
# Rollback Kubernetes job creation
kwrm.stop_batch_workflow_run()
logging.error(
f"Stopping Kubernetes jobs associated with workflow " f"{workflow.id_} ..."
)
raise REANAExternalCallError(message)
except ApiException as e:
message = "Kubernetes connection failed, please retry."
logging.error(
f"Error while creating {workflow.id_}: {message}\n{e}", exc_info=True
)
raise REANAExternalCallError(message)
def stop_workflow(workflow):
"""Stop a given workflow."""
if workflow.status == RunStatus.running:
kwrm = KubernetesWorkflowRunManager(workflow)
kwrm.stop_batch_workflow_run()
workflow.status = RunStatus.stopped
Session.add(workflow)
Session.commit()
else:
message = ("Workflow {id_} is not running.").format(id_=workflow.id_)
raise REANAWorkflowControllerError(message)
def stop_workflow(workflow):
"""Stop a given workflow."""
if workflow.status == WorkflowStatus.running:
kwrm = KubernetesWorkflowRunManager(workflow)
workflow.run_stopped_at = datetime.now()
kwrm.stop_batch_workflow_run()
workflow.status = WorkflowStatus.stopped
current_db_sessions = Session.object_session(workflow)
current_db_sessions.add(workflow)
current_db_sessions.commit()
else:
message = ("Workflow {id_} is not running.").format(id_=workflow.id_)
raise REANAWorkflowControllerError(message)
def test_stop_workflow_backend_only_kubernetes(
sample_serial_workflow_in_db, add_kubernetes_jobs_to_workflow):
"""Test deletion of workflows with only Kubernetes based jobs."""
workflow = sample_serial_workflow_in_db
workflow.status = WorkflowStatus.running
workflow_jobs = add_kubernetes_jobs_to_workflow(workflow)
backend_job_ids = [job.backend_job_id for job in workflow_jobs]
with patch("reana_workflow_controller.workflow_run_manager."
"current_k8s_batchv1_api_client") as api_client:
kwrm = KubernetesWorkflowRunManager(workflow)
kwrm.stop_batch_workflow_run()
for delete_call in api_client.delete_namespaced_job.call_args_list:
if delete_call.args[0] in backend_job_ids:
del backend_job_ids[backend_job_ids.index(delete_call.args[0])]
assert not backend_job_ids
