class UpdateComponentDependenciesTraitTransformer(TraitTransformer):
name = 'update_component_deps'
def __init__(self, trait, *args, **kwargs):
self.trait = trait
super().__init__(*args, **kwargs)
@classmethod
def order_dependencies(cls):
return {'component_descriptor'}
@classmethod
def dependencies(cls):
return {'component_descriptor'}
def inject_steps(self):
if self.trait.set_dependency_version_script_container_image():
privilege_mode = PrivilegeMode.PRIVILEGED
else:
privilege_mode = PrivilegeMode.UNPRIVILEGED
# declare no dependencies --> run asap, but do not block other steps
self.update_component_deps_step = PipelineStep(
name='update_component_dependencies',
raw_dict={
'privilege_mode': privilege_mode,
},
is_synthetic=True,
pull_request_notification_policy=PullRequestNotificationPolicy.
NO_NOTIFICATION,
injecting_trait_name=self.name,
script_type=ScriptType.PYTHON3)
self.update_component_deps_step.add_input(
name=concourse.model.traits.component_descriptor.DIR_NAME,
variable_name=concourse.model.traits.component_descriptor.
ENV_VAR_NAME,
)
self.update_component_deps_step.set_timeout(duration_string='30m')
for name, value in self.trait.vars().items():
self.update_component_deps_step.variables()[name] = value
yield self.update_component_deps_step
def process_pipeline_args(self, pipeline_args: JobVariant):
# our step depends on dependendency descriptor step
component_descriptor_step = pipeline_args.step(
concourse.model.traits.component_descriptor.
DEFAULT_COMPONENT_DESCRIPTOR_STEP_NAME)
self.update_component_deps_step._add_dependency(
component_descriptor_step)
upstream_component_name = self.trait.upstream_component_name()
if upstream_component_name:
self.update_component_deps_step.variables(
)['UPSTREAM_COMPONENT_NAME'] = '"{cn}"'.format(
cn=upstream_component_name, )
class UpdateComponentDependenciesTraitTransformer(TraitTransformer):
name = 'update_component_deps'
def __init__(self, trait, *args, **kwargs):
self.trait = trait
super().__init__(*args, **kwargs)
@classmethod
def order_dependencies(cls):
return {'component_descriptor'}
@classmethod
def dependencies(cls):
return {'component_descriptor'}
def inject_steps(self):
# declare no dependencies --> run asap, but do not block other steps
self.update_component_deps_step = PipelineStep(
name='update_component_dependencies',
raw_dict={},
is_synthetic=True,
notification_policy=StepNotificationPolicy.NO_NOTIFICATION,
script_type=ScriptType.PYTHON3
)
self.update_component_deps_step.add_input(
name=concourse.model.traits.component_descriptor.DIR_NAME,
variable_name=concourse.model.traits.component_descriptor.ENV_VAR_NAME,
)
self.update_component_deps_step.set_timeout(duration_string='30m')
for name, value in self.trait.vars().items():
self.update_component_deps_step.variables()[name] = value
yield self.update_component_deps_step
def process_pipeline_args(self, pipeline_args: JobVariant):
# our step depends on dependendency descriptor step
component_descriptor_step = pipeline_args.step('component_descriptor')
self.update_component_deps_step._add_dependency(component_descriptor_step)
upstream_component_name = self.trait.upstream_component_name()
if upstream_component_name:
self.update_component_deps_step.variables()['UPSTREAM_COMPONENT_NAME'] = '"{cn}"'.format(
cn=upstream_component_name,
)
