def _start_workflow(blueprint_id, deployment_id, execution_id, op_name, sm):
workflow_id = "hosts_software_" + op_name
new_execution = models.Execution(
id=execution_id,
status=models.Execution.PENDING,
created_at=str(datetime.now()),
blueprint_id=blueprint_id,
workflow_id=workflow_id,
deployment_id=deployment_id,
error="",
parameters=[],
is_system_workflow=False,
)
sm.put_execution(new_execution.id, new_execution)
workflow = {"operation": "software_replacement_workflow.replace_host_software"}
workflow_client().execute_workflow(
workflow_id,
workflow,
blueprint_id=blueprint_id,
deployment_id=deployment_id,
execution_id=execution_id,
execution_parameters={"op_name": op_name},
)
def _start_workflow(blueprint_id, deployment_id, execution_id, op_name, sm):
workflow_id = 'hosts_software_' + op_name
new_execution = models.Execution(id=execution_id,
status=models.Execution.PENDING,
created_at=str(datetime.now()),
blueprint_id=blueprint_id,
workflow_id=workflow_id,
deployment_id=deployment_id,
error='',
parameters=[],
is_system_workflow=False)
sm.put_execution(new_execution.id, new_execution)
workflow = {
'operation': 'software_replacement_workflow.replace_host_software'
}
workflow_client().execute_workflow(
workflow_id,
workflow,
blueprint_id=blueprint_id,
deployment_id=deployment_id,
execution_id=execution_id,
execution_parameters={'op_name': op_name})
def execute_workflow(self,
deployment_id,
workflow_id,
parameters=None,
allow_custom_parameters=False,
force=False):
deployment = self.get_deployment(deployment_id)
if workflow_id not in deployment.workflows:
raise manager_exceptions.NonexistentWorkflowError(
'Workflow {0} does not exist in deployment {1}'.format(
workflow_id, deployment_id))
workflow = deployment.workflows[workflow_id]
self._verify_deployment_environment_created_successfully(deployment_id)
# validate no execution is currently in progress
if not force:
executions = get_storage_manager().executions_list(
deployment_id=deployment_id)
running = [
e.id for e in executions
if get_storage_manager().get_execution(e.id).status not in
models.Execution.END_STATES
]
if len(running) > 0:
raise manager_exceptions.ExistingRunningExecutionError(
'The following executions are currently running for this '
'deployment: {0}. To execute this workflow anyway, pass '
'"force=true" as a query parameter to this request'.format(
running))
execution_parameters = \
BlueprintsManager._merge_and_validate_execution_parameters(
workflow, workflow_id, parameters, allow_custom_parameters)
execution_id = str(uuid.uuid4())
new_execution = models.Execution(
id=execution_id,
status=models.Execution.PENDING,
created_at=str(datetime.now()),
blueprint_id=deployment.blueprint_id,
workflow_id=workflow_id,
deployment_id=deployment_id,
error='',
parameters=self._get_only_user_execution_parameters(
execution_parameters))
get_storage_manager().put_execution(new_execution.id, new_execution)
workflow_client().execute_workflow(
workflow_id,
workflow,
blueprint_id=deployment.blueprint_id,
deployment_id=deployment_id,
execution_id=execution_id,
execution_parameters=execution_parameters)
return new_execution
def execute_workflow(self, deployment_id, workflow_id,
parameters=None,
allow_custom_parameters=False, force=False):
deployment = self.get_deployment(deployment_id)
if workflow_id not in deployment.workflows:
raise manager_exceptions.NonexistentWorkflowError(
'Workflow {0} does not exist in deployment {1}'.format(
workflow_id, deployment_id))
workflow = deployment.workflows[workflow_id]
self._verify_deployment_environment_created_successfully(deployment_id)
# validate no execution is currently in progress
if not force:
executions = get_storage_manager().executions_list(
deployment_id=deployment_id)
running = [
e.id for e in executions if
get_storage_manager().get_execution(e.id).status
not in models.Execution.END_STATES]
if len(running) > 0:
raise manager_exceptions.ExistingRunningExecutionError(
'The following executions are currently running for this '
'deployment: {0}. To execute this workflow anyway, pass '
'"force=true" as a query parameter to this request'.format(
running))
execution_parameters = \
BlueprintsManager._merge_and_validate_execution_parameters(
workflow, workflow_id, parameters, allow_custom_parameters)
execution_id = str(uuid.uuid4())
new_execution = models.Execution(
id=execution_id,
status=models.Execution.PENDING,
created_at=str(datetime.now()),
blueprint_id=deployment.blueprint_id,
workflow_id=workflow_id,
deployment_id=deployment_id,
error='',
parameters=self._get_only_user_execution_parameters(
execution_parameters))
get_storage_manager().put_execution(new_execution.id, new_execution)
workflow_client().execute_workflow(
workflow_id,
workflow,
blueprint_id=deployment.blueprint_id,
deployment_id=deployment_id,
execution_id=execution_id,
execution_parameters=execution_parameters)
return new_execution
def execute_workflow(self, deployment_id, workflow_id):
deployment = self.get_deployment(deployment_id)
if workflow_id not in deployment.plan['workflows']:
raise manager_exceptions.NonexistentWorkflowError(
'Workflow {0} does not exist in deployment {1}'.format(
workflow_id, deployment_id))
workflow = deployment.plan['workflows'][workflow_id]
plan = deployment.plan
execution_id = str(uuid.uuid4())
response = workflow_client().execute_workflow(
workflow_id,
workflow, plan,
blueprint_id=deployment.blueprint_id,
deployment_id=deployment_id,
execution_id=execution_id)
# TODO raise error if there is error in response
new_execution = models.Execution(
id=execution_id,
status=response['state'],
internal_workflow_id=response['id'],
created_at=str(response['created']),
blueprint_id=deployment.blueprint_id,
workflow_id=workflow_id,
deployment_id=deployment_id,
error='None')
self.sm.put_execution(new_execution.id, new_execution)
return new_execution
def validate_blueprint(self, blueprint_id):
blueprint = self.get_blueprint(blueprint_id)
plan = blueprint.plan
response = workflow_client().validate_workflows(plan)
# TODO raise error if error
return responses.BlueprintValidationStatus(
blueprint_id=blueprint_id, status=response['status'])
def _execute_system_workflow(self, deployment, wf_id, task_mapping,
execution_parameters=None, timeout=0,
created_at=None):
"""
:param deployment: deployment for workflow execution
:param wf_id: workflow id
:param task_mapping: mapping to the system workflow
:param execution_parameters: parameters for the system workflow
:param timeout: 0 will return immediately; any positive value will
cause this method to wait for the given timeout for the task to
complete, and verify it finished successfully before returning
:param created_at: creation time for the workflow execution object.
if omitted, a value will be generated by this method.
:return: async task object
"""
execution_id = str(uuid.uuid4()) # will also serve as the task id
execution_parameters = execution_parameters or {}
# currently, deployment env creation/deletion are not set as
# system workflows
is_system_workflow = wf_id not in (
'create_deployment_environment', 'delete_deployment_environment')
execution = models.Execution(
id=execution_id,
status=models.Execution.PENDING,
created_at=created_at or str(datetime.now()),
blueprint_id=deployment.blueprint_id,
workflow_id=wf_id,
deployment_id=deployment.id,
error='',
parameters=self._get_only_user_execution_parameters(
execution_parameters),
is_system_workflow=is_system_workflow)
self.sm.put_execution(execution.id, execution)
async_task = workflow_client().execute_system_workflow(
deployment, wf_id, execution_id, task_mapping,
execution_parameters)
if timeout > 0:
try:
# wait for the workflow execution to complete
async_task.get(timeout=timeout, propagate=True)
except Exception as e:
# error message for the user
error_msg =\
'Error occurred while executing the {0} system workflow '\
'for deployment {1}: {2} - {3}'.format(
wf_id, deployment.id, type(e).__name__, str(e))
# adding traceback to the log error message
tb = StringIO()
traceback.print_exc(file=tb)
log_error_msg = '{0}; traceback: {1}'.format(
error_msg, tb.getvalue())
current_app.logger.error(log_error_msg)
raise RuntimeError(error_msg)
# verify the execution completed successfully
execution = self.sm.get_execution(async_task.id)
if execution.status != models.Execution.TERMINATED:
raise RuntimeError(
'Failed executing the {0} system workflow for deployment '
'{1}: Execution did not complete successfully before '
'timeout ({2} seconds)'.format(
wf_id, deployment.id, timeout))
return async_task
def execute_workflow(self, deployment_id, workflow_id,
parameters=None,
allow_custom_parameters=False, force=False):
deployment = self.get_deployment(deployment_id)
if workflow_id not in deployment.workflows:
raise manager_exceptions.NonexistentWorkflowError(
'Workflow {0} does not exist in deployment {1}'.format(
workflow_id, deployment_id))
workflow = deployment.workflows[workflow_id]
self._verify_deployment_environment_created_successfully(deployment_id)
transient_workers_config =\
self._get_transient_deployment_workers_mode_config()
is_transient_workers_enabled = transient_workers_config['enabled']
self._check_for_active_executions(deployment_id, force,
transient_workers_config)
execution_parameters = \
BlueprintsManager._merge_and_validate_execution_parameters(
workflow, workflow_id, parameters, allow_custom_parameters)
if is_transient_workers_enabled:
# in this mode, we push the user execution object to storage
# before executing the "_start_deployment_environment" system
# workflow, to prevent from other executions to start running in
# between the system workflow and the user workflow execution.
# to keep correct chronological order, the system workflow's
# "created_at" field is generated here.
start_deployment_env_created_at_time = str(datetime.now())
execution_id = str(uuid.uuid4())
new_execution = models.Execution(
id=execution_id,
status=models.Execution.PENDING,
created_at=str(datetime.now()),
blueprint_id=deployment.blueprint_id,
workflow_id=workflow_id,
deployment_id=deployment_id,
error='',
parameters=self._get_only_user_execution_parameters(
execution_parameters),
is_system_workflow=False)
self.sm.put_execution(new_execution.id, new_execution)
if is_transient_workers_enabled:
# initiating a workflow to start deployment workers
wf_id = '_start_deployment_environment'
deployment_env_start_task_name = \
'cloudify_system_workflows.deployment_environment.start'
self._execute_system_workflow(
deployment, wf_id, deployment_env_start_task_name, timeout=300,
created_at=start_deployment_env_created_at_time)
# executing the user workflow
workflow_client().execute_workflow(
workflow_id,
workflow,
blueprint_id=deployment.blueprint_id,
deployment_id=deployment_id,
execution_id=execution_id,
execution_parameters=execution_parameters)
return new_execution
def _execute_system_workflow(self,
deployment,
wf_id,
task_mapping,
execution_parameters=None,
timeout=0,
created_at=None):
"""
:param deployment: deployment for workflow execution
:param wf_id: workflow id
:param task_mapping: mapping to the system workflow
:param execution_parameters: parameters for the system workflow
:param timeout: 0 will return immediately; any positive value will
cause this method to wait for the given timeout for the task to
complete, and verify it finished successfully before returning
:param created_at: creation time for the workflow execution object.
if omitted, a value will be generated by this method.
:return: async task object
"""
execution_id = str(uuid.uuid4()) # will also serve as the task id
execution_parameters = execution_parameters or {}
# currently, deployment env creation/deletion are not set as
# system workflows
is_system_workflow = wf_id not in ('create_deployment_environment',
'delete_deployment_environment')
execution = models.Execution(
id=execution_id,
status=models.Execution.PENDING,
created_at=created_at or str(datetime.now()),
blueprint_id=deployment.blueprint_id,
workflow_id=wf_id,
deployment_id=deployment.id,
error='',
parameters=self._get_only_user_execution_parameters(
execution_parameters),
is_system_workflow=is_system_workflow)
self.sm.put_execution(execution.id, execution)
async_task = workflow_client().execute_system_workflow(
deployment, wf_id, execution_id, task_mapping,
execution_parameters)
if timeout > 0:
try:
# wait for the workflow execution to complete
async_task.get(timeout=timeout, propagate=True)
except Exception as e:
# error message for the user
error_msg =\
'Error occurred while executing the {0} system workflow '\
'for deployment {1}: {2} - {3}'.format(
wf_id, deployment.id, type(e).__name__, str(e))
# adding traceback to the log error message
tb = StringIO()
traceback.print_exc(file=tb)
log_error_msg = '{0}; traceback: {1}'.format(
error_msg, tb.getvalue())
current_app.logger.error(log_error_msg)
raise RuntimeError(error_msg)
# verify the execution completed successfully
execution = self.sm.get_execution(async_task.id)
if execution.status != models.Execution.TERMINATED:
raise RuntimeError(
'Failed executing the {0} system workflow for deployment '
'{1}: Execution did not complete successfully before '
'timeout ({2} seconds)'.format(wf_id, deployment.id,
timeout))
return async_task
def execute_workflow(self,
deployment_id,
workflow_id,
parameters=None,
allow_custom_parameters=False,
force=False):
deployment = self.get_deployment(deployment_id)
if workflow_id not in deployment.workflows:
raise manager_exceptions.NonexistentWorkflowError(
'Workflow {0} does not exist in deployment {1}'.format(
workflow_id, deployment_id))
workflow = deployment.workflows[workflow_id]
self._verify_deployment_environment_created_successfully(deployment_id)
transient_workers_config =\
self._get_transient_deployment_workers_mode_config()
is_transient_workers_enabled = transient_workers_config['enabled']
self._check_for_active_executions(deployment_id, force,
transient_workers_config)
execution_parameters = \
BlueprintsManager._merge_and_validate_execution_parameters(
workflow, workflow_id, parameters, allow_custom_parameters)
if is_transient_workers_enabled:
# in this mode, we push the user execution object to storage
# before executing the "_start_deployment_environment" system
# workflow, to prevent from other executions to start running in
# between the system workflow and the user workflow execution.
# to keep correct chronological order, the system workflow's
# "created_at" field is generated here.
start_deployment_env_created_at_time = str(datetime.now())
execution_id = str(uuid.uuid4())
new_execution = models.Execution(
id=execution_id,
status=models.Execution.PENDING,
created_at=str(datetime.now()),
blueprint_id=deployment.blueprint_id,
workflow_id=workflow_id,
deployment_id=deployment_id,
error='',
parameters=self._get_only_user_execution_parameters(
execution_parameters),
is_system_workflow=False)
self.sm.put_execution(new_execution.id, new_execution)
if is_transient_workers_enabled:
# initiating a workflow to start deployment workers
wf_id = '_start_deployment_environment'
deployment_env_start_task_name = \
'cloudify_system_workflows.deployment_environment.start'
self._execute_system_workflow(
deployment,
wf_id,
deployment_env_start_task_name,
timeout=300,
created_at=start_deployment_env_created_at_time)
# executing the user workflow
workflow_client().execute_workflow(
workflow_id,
workflow,
blueprint_id=deployment.blueprint_id,
deployment_id=deployment_id,
execution_id=execution_id,
execution_parameters=execution_parameters)
return new_execution
def cancel_workflow(self, execution_id):
execution = self.get_execution(execution_id)
workflow_client().cancel_workflow(
execution.internal_workflow_id
)
return execution
def get_workflows_states_by_internal_workflows_ids(self,
internal_wfs_ids):
return workflow_client().get_workflows_statuses(internal_wfs_ids)
