def generic_scale(_ctx, delta, modification, graph):
try:
_ctx.logger.info('Deployment modification started. '
'[modification_id={0}]'.format(modification.id))
if delta > 0:
added_and_related = set(modification.added.node_instances)
added = set(i for i in added_and_related
if i.modification == 'added')
related = added_and_related - added
try:
lifecycle.install_node_instances(graph=graph,
node_instances=added,
related_nodes=related)
except:
_ctx.logger.error('Scale out failed, scaling back in.')
for task in graph.tasks_iter():
graph.remove_task(task)
lifecycle.uninstall_node_instances(graph=graph,
node_instances=added,
related_nodes=related)
raise
else:
removed_and_related = set(modification.removed.node_instances)
removed = set(i for i in removed_and_related
if i.modification == 'removed')
related = removed_and_related - removed
lifecycle.uninstall_node_instances(graph=graph,
node_instances=removed,
related_nodes=related)
except:
_ctx.logger.warn('Rolling back deployment modification. '
'[modification_id={0}]'.format(modification.id))
try:
modification.rollback()
except:
_ctx.logger.warn('Deployment modification rollback failed. The '
'deployment model is most likely in some '
'corrupted state.'
'[modification_id={0}]'.format(modification.id))
raise
raise
else:
try:
modification.finish()
except:
_ctx.logger.warn('Deployment modification finish failed. The '
'deployment model is most likely in some '
'corrupted state.'
'[modification_id={0}]'.format(modification.id))
raise
def _install_nodes():
if skip_install:
return
# Adding nodes or node instances should be based on modified
# instances
lifecycle.install_node_instances(
graph=graph,
node_instances=to_install,
related_nodes=set(
instances_by_change['added_target_instances_ids'][1]))
# This one as well.
lifecycle.execute_establish_relationships(
graph=graph,
node_instances=set(
instances_by_change['extended_and_target_instances'][1]),
modified_relationship_ids=modified_entity_ids['relationship'])
def scale_entity(ctx,
scalable_entity_name,
delta,
scale_compute,
ignore_failure=False,
**kwargs):
"""Scales in/out the subgraph of node_or_group_name.
If a node name is passed, and `scale_compute` is set to false, the
subgraph will consist of all the nodes that are contained in the node and
the node itself.
If a node name is passed, and `scale_compute` is set to true, the subgraph
will consist of all nodes that are contained in the compute node that
contains the node and the compute node itself.
If a group name or a node that is not contained in a compute
node, is passed, this property is ignored.
`delta` is used to specify the scale factor.
For `delta > 0`: If current number of instances is `N`, scale out to
`N + delta`.
For `delta < 0`: If current number of instances is `N`, scale in to
`N - |delta|`.
:param ctx: cloudify context
:param scalable_entity_name: the node or group name to scale
:param delta: scale in/out factor
:param scale_compute: should scale apply on compute node containing
the specified node
:param ignore_failure: ignore operations failures in uninstall workflow
"""
if isinstance(delta, basestring):
try:
delta = int(delta)
except ValueError:
raise ValueError('The delta parameter must be a number. Got: {0}'
.format(delta))
if delta == 0:
ctx.logger.info('delta parameter is 0, so no scaling will take place.')
return
scaling_group = ctx.deployment.scaling_groups.get(scalable_entity_name)
if scaling_group:
curr_num_instances = scaling_group['properties']['current_instances']
planned_num_instances = curr_num_instances + delta
scale_id = scalable_entity_name
else:
node = ctx.get_node(scalable_entity_name)
if not node:
raise ValueError("No scalable entity named {0} was found".format(
scalable_entity_name))
host_node = node.host_node
scaled_node = host_node if (scale_compute and host_node) else node
curr_num_instances = scaled_node.number_of_instances
planned_num_instances = curr_num_instances + delta
scale_id = scaled_node.id
if planned_num_instances < 0:
raise ValueError('Provided delta: {0} is illegal. current number of '
'instances of entity {1} is {2}'
.format(delta,
scalable_entity_name,
curr_num_instances))
modification = ctx.deployment.start_modification({
scale_id: {
'instances': planned_num_instances
# These following parameters are not exposed at the moment,
# but should be used to control which node instances get scaled in
# (when scaling in).
# They are mentioned here, because currently, the modification API
# is not very documented.
# Special care should be taken because if `scale_compute == True`
# (which is the default), then these ids should be the compute node
# instance ids which are not necessarily instances of the node
# specified by `scalable_entity_name`.
# Node instances denoted by these instance ids should be *kept* if
# possible.
# 'removed_ids_exclude_hint': [],
# Node instances denoted by these instance ids should be *removed*
# if possible.
# 'removed_ids_include_hint': []
}
})
graph = ctx.graph_mode()
try:
ctx.logger.info('Deployment modification started. '
'[modification_id={0}]'.format(modification.id))
if delta > 0:
added_and_related = set(modification.added.node_instances)
added = set(i for i in added_and_related
if i.modification == 'added')
related = added_and_related - added
try:
lifecycle.install_node_instances(
graph=graph,
node_instances=added,
related_nodes=related)
except Exception:
ctx.logger.error('Scale out failed, scaling back in.')
for task in graph.tasks_iter():
graph.remove_task(task)
lifecycle.uninstall_node_instances(
graph=graph,
node_instances=added,
ignore_failure=ignore_failure,
related_nodes=related)
raise
else:
removed_and_related = set(modification.removed.node_instances)
removed = set(i for i in removed_and_related
if i.modification == 'removed')
related = removed_and_related - removed
lifecycle.uninstall_node_instances(
graph=graph,
node_instances=removed,
ignore_failure=ignore_failure,
related_nodes=related)
except Exception:
ctx.logger.warn('Rolling back deployment modification. '
'[modification_id={0}]'.format(modification.id))
try:
modification.rollback()
except Exception:
ctx.logger.warn('Deployment modification rollback failed. The '
'deployment model is most likely in some corrupted'
' state.'
'[modification_id={0}]'.format(modification.id))
raise
raise
else:
try:
modification.finish()
except Exception:
ctx.logger.warn('Deployment modification finish failed. The '
'deployment model is most likely in some corrupted'
' state.'
'[modification_id={0}]'.format(modification.id))
raise
def update(ctx,
update_id,
added_instance_ids,
added_target_instances_ids,
removed_instance_ids,
remove_target_instance_ids,
modified_entity_ids,
extended_instance_ids,
extend_target_instance_ids,
reduced_instance_ids,
reduce_target_instance_ids,
skip_install,
skip_uninstall,
ignore_failure=False):
instances_by_change = {
'added_instances': (added_instance_ids, []),
'added_target_instances_ids': (added_target_instances_ids, []),
'removed_instances': (removed_instance_ids, []),
'remove_target_instance_ids': (remove_target_instance_ids, []),
'extended_and_target_instances':
(extended_instance_ids + extend_target_instance_ids, []),
'reduced_and_target_instances':
(reduced_instance_ids + reduce_target_instance_ids, []),
}
for instance in ctx.node_instances:
instance_holders = \
[instance_holder
for _, (changed_ids, instance_holder)
in instances_by_change.iteritems()
if instance.id in changed_ids]
for instance_holder in instance_holders:
instance_holder.append(instance)
if not skip_install:
graph = ctx.graph_mode()
# Adding nodes or node instances should be based on modified instances
lifecycle.install_node_instances(
graph=graph,
node_instances=set(instances_by_change['added_instances'][1]),
related_nodes=set(instances_by_change['added_target_instances_ids']
[1]))
# This one as well.
lifecycle.execute_establish_relationships(
graph=ctx.graph_mode(),
node_instances=set(
instances_by_change['extended_and_target_instances'][1]),
modified_relationship_ids=modified_entity_ids['relationship']
)
if not skip_uninstall:
graph = ctx.graph_mode()
lifecycle.execute_unlink_relationships(
graph=graph,
node_instances=set(
instances_by_change['reduced_and_target_instances'][1]),
modified_relationship_ids=modified_entity_ids['relationship']
)
lifecycle.uninstall_node_instances(
graph=graph,
node_instances=set(instances_by_change['removed_instances'][1]),
ignore_failure=ignore_failure,
related_nodes=set(
instances_by_change['remove_target_instance_ids'][1])
)
# Finalize the commit (i.e. remove relationships or nodes)
client = get_rest_client()
client.deployment_updates.finalize_commit(update_id)
def install(ctx, **kwargs):
"""Default install workflow"""
lifecycle.install_node_instances(
graph=ctx.graph_mode(),
node_instances=set(ctx.node_instances))
def install(ctx, **kwargs):
"""Default install workflow"""
lifecycle.install_node_instances(graph=ctx.graph_mode(),
node_instances=set(ctx.node_instances))
def scale_entity(ctx,
scalable_entity_name,
delta,
scale_compute,
ignore_failure=False,
include_instances=None,
exclude_instances=None,
rollback_if_failed=True,
**kwargs):
"""Scales in/out the subgraph of node_or_group_name.
If a node name is passed, and `scale_compute` is set to false, the
subgraph will consist of all the nodes that are contained in the node and
the node itself.
If a node name is passed, and `scale_compute` is set to true, the subgraph
will consist of all nodes that are contained in the compute node that
contains the node and the compute node itself.
If a group name or a node that is not contained in a compute
node, is passed, this property is ignored.
`delta` is used to specify the scale factor.
For `delta > 0`: If current number of instances is `N`, scale out to
`N + delta`.
For `delta < 0`: If current number of instances is `N`, scale in to
`N - |delta|`.
:param ctx: cloudify context
:param scalable_entity_name: the node or group name to scale
:param delta: scale in/out factor
:param scale_compute: should scale apply on compute node containing
the specified node
:param ignore_failure: ignore operations failures in uninstall workflow
:param include_instances: Instances to include when scaling down
:param exclude_instances: Instances to exclude when scaling down
:param rollback_if_failed: when False, no rollback will be triggered.
"""
include_instances = include_instances or []
exclude_instances = exclude_instances or []
if isinstance(include_instances, basestring):
include_instances = [include_instances]
if isinstance(exclude_instances, basestring):
exclude_instances = [exclude_instances]
include_instances = [str(inst) for inst in include_instances]
exclude_instances = [str(inst) for inst in exclude_instances]
if isinstance(delta, basestring):
try:
delta = int(delta)
except ValueError:
raise ValueError(
'The delta parameter must be a number. Got: {0}'.format(delta))
if delta == 0:
ctx.logger.info('delta parameter is 0, so no scaling will take place.')
return
if delta > 0 and (include_instances or exclude_instances):
raise ValueError(
'Instances cannot be included or excluded when scaling up.')
scaling_group = ctx.deployment.scaling_groups.get(scalable_entity_name)
if scaling_group:
groups_members = get_groups_with_members(ctx)
# Available instances for checking inclusions/exclusions needs to
# include all groups and their members
available_instances = set(chain.from_iterable(
groups_members.values())).union(groups_members)
validate_inclusions_and_exclusions(
include_instances,
exclude_instances,
available_instances=available_instances,
delta=delta,
scale_compute=scale_compute,
groups_members=groups_members,
)
curr_num_instances = scaling_group['properties']['current_instances']
planned_num_instances = curr_num_instances + delta
scale_id = scalable_entity_name
else:
node = ctx.get_node(scalable_entity_name)
if not node:
raise ValueError("No scalable entity named {0} was found".format(
scalable_entity_name))
validate_inclusions_and_exclusions(
include_instances,
exclude_instances,
available_instances=[instance.id for instance in node.instances],
delta=delta,
scale_compute=scale_compute,
)
host_node = node.host_node
scaled_node = host_node if (scale_compute and host_node) else node
curr_num_instances = scaled_node.number_of_instances
planned_num_instances = curr_num_instances + delta
scale_id = scaled_node.id
if planned_num_instances < 0:
raise ValueError('Provided delta: {0} is illegal. current number of '
'instances of entity {1} is {2}'.format(
delta, scalable_entity_name, curr_num_instances))
modification = ctx.deployment.start_modification({
scale_id: {
'instances': planned_num_instances,
'removed_ids_exclude_hint': exclude_instances,
'removed_ids_include_hint': include_instances,
# While these parameters are now exposed, this comment is being
# kept as it provides useful insight into the hints
# These following parameters are not exposed at the moment,
# but should be used to control which node instances get scaled in
# (when scaling in).
# They are mentioned here, because currently, the modification API
# is not very documented.
# Special care should be taken because if `scale_compute == True`
# (which is the default), then these ids should be the compute node
# instance ids which are not necessarily instances of the node
# specified by `scalable_entity_name`.
# Node instances denoted by these instance ids should be *kept* if
# possible.
# 'removed_ids_exclude_hint': [],
# Node instances denoted by these instance ids should be *removed*
# if possible.
# 'removed_ids_include_hint': []
}
})
# refresh node instances so that workflow_ctx.get_node_instance() can
# return the new node instance that were just created
ctx.refresh_node_instances()
graph = ctx.graph_mode()
try:
ctx.logger.info('Deployment modification started. '
'[modification_id={0}]'.format(modification.id))
if delta > 0:
added_and_related = set(modification.added.node_instances)
added = set(i for i in added_and_related
if i.modification == 'added')
related = added_and_related - added
try:
lifecycle.install_node_instances(graph=graph,
node_instances=added,
related_nodes=related)
except Exception:
if not rollback_if_failed:
ctx.logger.error('Scale out failed.')
raise
ctx.logger.error('Scale out failed, scaling back in.')
for task in graph.tasks_iter():
graph.remove_task(task)
lifecycle.uninstall_node_instances(
graph=graph,
node_instances=added,
ignore_failure=ignore_failure,
related_nodes=related)
raise
else:
removed_and_related = set(modification.removed.node_instances)
removed = set(i for i in removed_and_related
if i.modification == 'removed')
related = removed_and_related - removed
lifecycle.uninstall_node_instances(graph=graph,
node_instances=removed,
ignore_failure=ignore_failure,
related_nodes=related)
except Exception:
if not rollback_if_failed:
raise
ctx.logger.warn('Rolling back deployment modification. '
'[modification_id={0}]'.format(modification.id))
try:
modification.rollback()
except Exception:
ctx.logger.warn('Deployment modification rollback failed. The '
'deployment model is most likely in some corrupted'
' state.'
'[modification_id={0}]'.format(modification.id))
raise
raise
else:
try:
modification.finish()
except Exception:
ctx.logger.warn('Deployment modification finish failed. The '
'deployment model is most likely in some corrupted'
' state.'
'[modification_id={0}]'.format(modification.id))
raise
def scale(ctx, node_id, delta, scale_compute, **kwargs):
"""Scales in/out the subgraph of node_id.
If `scale_compute` is set to false, the subgraph will consist of all
the nodes that are contained in `node_id` and `node_id` itself.
If `scale_compute` is set to true, the subgraph will consist of all
nodes that are contained in the compute node that contains `node_id`
and the compute node itself.
If `node_id` is not contained in a compute node and is not a compute node,
this property is ignored.
`delta` is used to specify the scale factor.
For `delta > 0`: If current number of instances is `N`, scale out to
`N + delta`.
For `delta < 0`: If current number of instances is `N`, scale in to
`N - |delta|`.
:param ctx: cloudify context
:param node_id: the node_id to scale
:param delta: scale in/out factor
:param scale_compute: should scale apply on compute node containing
'node_id'
"""
graph = ctx.graph_mode()
node = ctx.get_node(node_id)
if not node:
raise ValueError("Node {0} doesn't exist".format(node_id))
if delta == 0:
ctx.logger.info('delta parameter is 0, so no scaling will take place.')
return
host_node = node.host_node
scaled_node = host_node if (scale_compute and host_node) else node
curr_num_instances = scaled_node.number_of_instances
planned_num_instances = curr_num_instances + delta
if planned_num_instances < 0:
raise ValueError('Provided delta: {0} is illegal. current number of'
'instances of node {1} is {2}'
.format(delta, node_id, curr_num_instances))
modification = ctx.deployment.start_modification({
scaled_node.id: {
'instances': planned_num_instances
# These following parameters are not exposed at the moment,
# but should be used to control which node instances get scaled in
# (when scaling in).
# They are mentioned here, because currently, the modification API
# is not very documented.
# Special care should be taken because if `scale_compute == True`
# (which is the default), then these ids should be the compute node
# instance ids which are not necessarily instances of the node
# specified by `node_id`.
# Node instances denoted by these instance ids should be *kept* if
# possible.
# 'removed_ids_exclude_hint': [],
# Node instances denoted by these instance ids should be *removed*
# if possible.
# 'removed_ids_include_hint': []
}
})
try:
ctx.logger.info('Deployment modification started. '
'[modification_id={0}]'.format(modification.id))
if delta > 0:
added_and_related = set(modification.added.node_instances)
added = set(i for i in added_and_related
if i.modification == 'added')
related = added_and_related - added
try:
lifecycle.install_node_instances(
graph=graph,
node_instances=added,
intact_nodes=related)
except:
ctx.logger.error('Scale out failed, scaling back in.')
for task in graph.tasks_iter():
graph.remove_task(task)
lifecycle.uninstall_node_instances(
graph=graph,
node_instances=added,
intact_nodes=related)
raise
else:
removed_and_related = set(modification.removed.node_instances)
removed = set(i for i in removed_and_related
if i.modification == 'removed')
related = removed_and_related - removed
lifecycle.uninstall_node_instances(
graph=graph,
node_instances=removed,
intact_nodes=related)
except:
ctx.logger.warn('Rolling back deployment modification. '
'[modification_id={0}]'.format(modification.id))
try:
modification.rollback()
except:
ctx.logger.warn('Deployment modification rollback failed. The '
'deployment model is most likely in some corrupted'
' state.'
'[modification_id={0}]'.format(modification.id))
raise
raise
else:
try:
modification.finish()
except:
ctx.logger.warn('Deployment modification finish failed. The '
'deployment model is most likely in some corrupted'
' state.'
'[modification_id={0}]'.format(modification.id))
raise
def scale(ctx, node_id, delta, scale_compute, **kwargs):
"""Scales in/out the subgraph of node_id.
If `scale_compute` is set to false, the subgraph will consist of all
the nodes that are contained in `node_id` and `node_id` itself.
If `scale_compute` is set to true, the subgraph will consist of all
nodes that are contained in the compute node that contains `node_id`
and the compute node itself.
If `node_id` is not contained in a compute node and is not a compute node,
this property is ignored.
`delta` is used to specify the scale factor.
For `delta > 0`: If current number of instances is `N`, scale out to
`N + delta`.
For `delta < 0`: If current number of instances is `N`, scale in to
`N - |delta|`.
:param ctx: cloudify context
:param node_id: the node_id to scale
:param delta: scale in/out factor
:param scale_compute: should scale apply on compute node containing
'node_id'
"""
graph = ctx.graph_mode()
node = ctx.get_node(node_id)
if not node:
raise ValueError("Node {0} doesn't exist".format(node_id))
if delta == 0:
ctx.logger.info('delta parameter is 0, so no scaling will take place.')
return
host_node = node.host_node
scaled_node = host_node if (scale_compute and host_node) else node
curr_num_instances = scaled_node.number_of_instances
planned_num_instances = curr_num_instances + delta
if planned_num_instances < 0:
raise ValueError('Provided delta: {0} is illegal. current number of'
'instances of node {1} is {2}'.format(
delta, node_id, curr_num_instances))
modification = ctx.deployment.start_modification({
scaled_node.id: {
'instances': planned_num_instances
# These following parameters are not exposed at the moment,
# but should be used to control which node instances get scaled in
# (when scaling in).
# They are mentioned here, because currently, the modification API
# is not very documented.
# Special care should be taken because if `scale_compute == True`
# (which is the default), then these ids should be the compute node
# instance ids which are not necessarily instances of the node
# specified by `node_id`.
# Node instances denoted by these instance ids should be *kept* if
# possible.
# 'removed_ids_exclude_hint': [],
# Node instances denoted by these instance ids should be *removed*
# if possible.
# 'removed_ids_include_hint': []
}
})
try:
ctx.logger.info('Deployment modification started. '
'[modification_id={0}]'.format(modification.id))
if delta > 0:
added_and_related = set(modification.added.node_instances)
added = set(i for i in added_and_related
if i.modification == 'added')
related = added_and_related - added
try:
lifecycle.install_node_instances(graph=graph,
node_instances=added,
intact_nodes=related)
except:
ctx.logger.error('Scale out failed, scaling back in.')
for task in graph.tasks_iter():
graph.remove_task(task)
lifecycle.uninstall_node_instances(graph=graph,
node_instances=added,
intact_nodes=related)
raise
else:
removed_and_related = set(modification.removed.node_instances)
removed = set(i for i in removed_and_related
if i.modification == 'removed')
related = removed_and_related - removed
lifecycle.uninstall_node_instances(graph=graph,
node_instances=removed,
intact_nodes=related)
except:
ctx.logger.warn('Rolling back deployment modification. '
'[modification_id={0}]'.format(modification.id))
try:
modification.rollback()
except:
ctx.logger.warn('Deployment modification rollback failed. The '
'deployment model is most likely in some corrupted'
' state.'
'[modification_id={0}]'.format(modification.id))
raise
raise
else:
try:
modification.finish()
except:
ctx.logger.warn('Deployment modification finish failed. The '
'deployment model is most likely in some corrupted'
' state.'
'[modification_id={0}]'.format(modification.id))
raise
def _run_scale_settings(ctx,
scale_settings,
scalable_entity_properties,
scale_transaction_field=None,
scale_transaction_value=None,
ignore_failure=False,
ignore_rollback_failure=True,
instances_remove_ids=None,
node_sequence=None):
modification = ctx.deployment.start_modification(scale_settings)
graph = ctx.graph_mode()
try:
ctx.logger.info('Deployment modification started. '
'[modification_id={0}]'.format(modification.id))
if len(set(modification.added.node_instances)):
ctx.logger.info('Added: {}'.format(
repr([
node_instance._node_instance.id
for node_instance in modification.added.node_instances
if node_instance.modification == 'added'
])))
added_and_related = set(modification.added.node_instances)
added = set(i for i in added_and_related
if i.modification == 'added')
related = added_and_related - added
try:
for node_instance in added:
properties_updates = scalable_entity_properties.get(
node_instance._node_instance.node_id, {})
# save properties updates
properties = {}
if properties_updates:
# pop one dict for runtime properties
properties.update(properties_updates.pop())
# save transaction list
if scale_transaction_field:
# save original set of instances in scale up.
if scale_transaction_value:
properties.update({
scale_transaction_field:
scale_transaction_value
})
else:
properties.update(
{scale_transaction_field: modification.id})
# check properties to update
if properties:
ctx.logger.debug(
"{}: Updating {} runtime properties by {}".format(
node_instance._node_instance.node_id,
node_instance._node_instance.id,
repr(properties)))
_update_runtime_properties(
ctx, node_instance._node_instance.id, properties)
if node_sequence:
subgraph_func = lifecycle.install_node_instance_subgraph
_process_node_instances(
ctx=ctx,
graph=graph,
node_instances=added,
ignore_failure=ignore_failure,
node_instance_subgraph_func=subgraph_func,
node_sequence=node_sequence)
else:
lifecycle.install_node_instances(graph=graph,
node_instances=added,
related_nodes=related)
except Exception as ex:
ctx.logger.error(
'Scale out failed, scaling back in. {}'.format(repr(ex)))
_uninstall_instances(ctx=ctx,
graph=graph,
removed=added,
related=related,
ignore_failure=ignore_rollback_failure,
node_sequence=node_sequence)
raise ex
if len(set(modification.removed.node_instances)):
ctx.logger.info('Removed: {}'.format(
repr([
node_instance._node_instance.id
for node_instance in modification.removed.node_instances
if node_instance.modification == 'removed'
])))
removed_and_related = set(modification.removed.node_instances)
removed = set(i for i in removed_and_related
if i.modification == 'removed')
ctx.logger.info('Proposed: {}'.format(repr(instances_remove_ids)))
if instances_remove_ids:
for instance in removed:
if instance._node_instance.id not in instances_remove_ids:
raise Exception(
"Instance {} not in proposed list {}.".format(
repr(instance._node_instance.id),
repr(instances_remove_ids)))
related = removed_and_related - removed
_uninstall_instances(ctx=ctx,
graph=graph,
removed=removed,
ignore_failure=ignore_failure,
related=related,
node_sequence=node_sequence)
except Exception as ex:
ctx.logger.warn('Rolling back deployment modification. '
'[modification_id={0}]: {1}'.format(
modification.id, repr(ex)))
try:
deadline = time.time() + ctx.wait_after_fail
except AttributeError:
deadline = time.time() + 1800
while deadline > time.time():
if graph._is_execution_cancelled():
raise api.ExecutionCancelled()
for task in graph._terminated_tasks():
graph._handle_terminated_task(task)
if not any(task.get_state() == tasks.TASK_SENT
for task in graph.tasks_iter()):
break
else:
time.sleep(0.1)
modification.rollback()
raise ex
else:
modification.finish()
def _run_scale_settings(ctx, scale_settings, scalable_entity_properties,
scale_transaction_field=None,
scale_transaction_value=None,
ignore_failure=False,
instances_remove_ids=None):
modification = ctx.deployment.start_modification(scale_settings)
graph = ctx.graph_mode()
try:
ctx.logger.info('Deployment modification started. '
'[modification_id={0}]'.format(modification.id))
if len(set(modification.added.node_instances)):
ctx.logger.info('Added: {}'.format(repr([
node_instance._node_instance.id
for node_instance in modification.added.node_instances
if node_instance.modification == 'added'
])))
added_and_related = set(modification.added.node_instances)
added = set(i for i in added_and_related
if i.modification == 'added')
related = added_and_related - added
try:
for node_instance in added:
properties_updates = scalable_entity_properties.get(
node_instance._node_instance.node_id, {})
# save properties updates
properties = {}
if properties_updates:
# pop one dict for runtime properties
properties.update(properties_updates.pop())
# save transaction list
if scale_transaction_field:
# save original set of instances in scale up.
if scale_transaction_value:
properties.update({
scale_transaction_field:
scale_transaction_value
})
else:
properties.update({
scale_transaction_field: modification.id
})
# check properties to update
if properties:
ctx.logger.debug(
"{}: Updating {} runtime properties by {}".format(
node_instance._node_instance.node_id,
node_instance._node_instance.id,
repr(properties)))
_update_runtime_properties(
ctx, node_instance._node_instance.id, properties)
lifecycle.install_node_instances(
graph=graph,
node_instances=added,
related_nodes=related)
except Exception as ex:
ctx.logger.error('Scale out failed, scaling back in. {}'
.format(repr(ex)))
_uninstall_instances(
ctx, graph, [
node_instance._node_instance.id
for node_instance in added
], [
node_instance._node_instance.id
for node_instance in related
], ignore_failure)
raise ex
if len(set(modification.removed.node_instances)):
ctx.logger.info('Removed: {}'.format(repr([
node_instance._node_instance.id
for node_instance in modification.removed.node_instances
if node_instance.modification == 'removed'
])))
removed_and_related = set(modification.removed.node_instances)
removed = set(i for i in removed_and_related
if i.modification == 'removed')
ctx.logger.info('Proposed: {}'
.format(repr(instances_remove_ids)))
if instances_remove_ids:
for instance in removed:
if instance._node_instance.id not in instances_remove_ids:
raise Exception(
"Instance {} not in proposed list {}.".format(
repr(instance._node_instance.id),
repr(instances_remove_ids)
)
)
related = removed_and_related - removed
lifecycle.uninstall_node_instances(
graph=graph,
node_instances=removed,
ignore_failure=ignore_failure,
related_nodes=related)
except Exception as ex:
ctx.logger.warn('Rolling back deployment modification. '
'[modification_id={0}]: {1}'
.format(modification.id, repr(ex)))
modification.rollback()
raise ex
else:
modification.finish()
def scale_entity(ctx,
scalable_entity_name,
delta,
scale_compute,
ignore_failure=False,
**kwargs):
"""Scales in/out the subgraph of node_or_group_name.
If a node name is passed, and `scale_compute` is set to false, the
subgraph will consist of all the nodes that are contained in the node and
the node itself.
If a node name is passed, and `scale_compute` is set to true, the subgraph
will consist of all nodes that are contained in the compute node that
contains the node and the compute node itself.
If a group name or a node that is not contained in a compute
node, is passed, this property is ignored.
`delta` is used to specify the scale factor.
For `delta > 0`: If current number of instances is `N`, scale out to
`N + delta`.
For `delta < 0`: If current number of instances is `N`, scale in to
`N - |delta|`.
:param ctx: cloudify context
:param scalable_entity_name: the node or group name to scale
:param delta: scale in/out factor
:param scale_compute: should scale apply on compute node containing
the specified node
:param ignore_failure: ignore operations failures in uninstall workflow
"""
if isinstance(delta, basestring):
try:
delta = int(delta)
except ValueError:
raise ValueError(
'The delta parameter must be a number. Got: {0}'.format(delta))
if delta == 0:
ctx.logger.info('delta parameter is 0, so no scaling will take place.')
return
scaling_group = ctx.deployment.scaling_groups.get(scalable_entity_name)
if scaling_group:
curr_num_instances = scaling_group['properties']['current_instances']
planned_num_instances = curr_num_instances + delta
scale_id = scalable_entity_name
else:
node = ctx.get_node(scalable_entity_name)
if not node:
raise ValueError("No scalable entity named {0} was found".format(
scalable_entity_name))
host_node = node.host_node
scaled_node = host_node if (scale_compute and host_node) else node
curr_num_instances = scaled_node.number_of_instances
planned_num_instances = curr_num_instances + delta
scale_id = scaled_node.id
if planned_num_instances < 0:
raise ValueError('Provided delta: {0} is illegal. current number of '
'instances of entity {1} is {2}'.format(
delta, scalable_entity_name, curr_num_instances))
modification = ctx.deployment.start_modification({
scale_id: {
'instances': planned_num_instances
# These following parameters are not exposed at the moment,
# but should be used to control which node instances get scaled in
# (when scaling in).
# They are mentioned here, because currently, the modification API
# is not very documented.
# Special care should be taken because if `scale_compute == True`
# (which is the default), then these ids should be the compute node
# instance ids which are not necessarily instances of the node
# specified by `scalable_entity_name`.
# Node instances denoted by these instance ids should be *kept* if
# possible.
# 'removed_ids_exclude_hint': [],
# Node instances denoted by these instance ids should be *removed*
# if possible.
# 'removed_ids_include_hint': []
}
})
graph = ctx.graph_mode()
try:
ctx.logger.info('Deployment modification started. '
'[modification_id={0}]'.format(modification.id))
if delta > 0:
added_and_related = set(modification.added.node_instances)
added = set(i for i in added_and_related
if i.modification == 'added')
related = added_and_related - added
try:
lifecycle.install_node_instances(graph=graph,
node_instances=added,
related_nodes=related)
except Exception:
ctx.logger.error('Scale out failed, scaling back in.')
for task in graph.tasks_iter():
graph.remove_task(task)
lifecycle.uninstall_node_instances(
graph=graph,
node_instances=added,
ignore_failure=ignore_failure,
related_nodes=related)
raise
else:
removed_and_related = set(modification.removed.node_instances)
removed = set(i for i in removed_and_related
if i.modification == 'removed')
related = removed_and_related - removed
lifecycle.uninstall_node_instances(graph=graph,
node_instances=removed,
ignore_failure=ignore_failure,
related_nodes=related)
except Exception:
ctx.logger.warn('Rolling back deployment modification. '
'[modification_id={0}]'.format(modification.id))
try:
modification.rollback()
except Exception:
ctx.logger.warn('Deployment modification rollback failed. The '
'deployment model is most likely in some corrupted'
' state.'
'[modification_id={0}]'.format(modification.id))
raise
raise
else:
try:
modification.finish()
except Exception:
ctx.logger.warn('Deployment modification finish failed. The '
'deployment model is most likely in some corrupted'
' state.'
'[modification_id={0}]'.format(modification.id))
raise
def update(ctx,
update_id,
added_instance_ids,
added_target_instances_ids,
removed_instance_ids,
remove_target_instance_ids,
modified_entity_ids,
extended_instance_ids,
extend_target_instance_ids,
reduced_instance_ids,
reduce_target_instance_ids,
skip_install,
skip_uninstall,
ignore_failure=False):
instances_by_change = {
'added_instances': (added_instance_ids, []),
'added_target_instances_ids': (added_target_instances_ids, []),
'removed_instances': (removed_instance_ids, []),
'remove_target_instance_ids': (remove_target_instance_ids, []),
'extended_and_target_instances':
(extended_instance_ids + extend_target_instance_ids, []),
'reduced_and_target_instances':
(reduced_instance_ids + reduce_target_instance_ids, []),
}
for instance in ctx.node_instances:
instance_holders = \
[instance_holder
for _, (changed_ids, instance_holder)
in instances_by_change.iteritems()
if instance.id in changed_ids]
for instance_holder in instance_holders:
instance_holder.append(instance)
if not skip_install:
graph = ctx.graph_mode()
# Adding nodes or node instances should be based on modified instances
lifecycle.install_node_instances(
graph=graph,
node_instances=set(instances_by_change['added_instances'][1]),
related_nodes=set(instances_by_change['added_target_instances_ids']
[1]))
# This one as well.
lifecycle.execute_establish_relationships(
graph=ctx.graph_mode(),
node_instances=set(
instances_by_change['extended_and_target_instances'][1]),
modified_relationship_ids=modified_entity_ids['relationship']
)
if not skip_uninstall:
graph = ctx.graph_mode()
lifecycle.execute_unlink_relationships(
graph=graph,
node_instances=set(
instances_by_change['reduced_and_target_instances'][1]),
modified_relationship_ids=modified_entity_ids['relationship']
)
lifecycle.uninstall_node_instances(
graph=graph,
node_instances=set(instances_by_change['removed_instances'][1]),
ignore_failure=ignore_failure,
related_nodes=set(
instances_by_change['remove_target_instance_ids'][1])
)
# Finalize the commit (i.e. remove relationships or nodes)
client = get_rest_client()
client.deployment_updates.finalize_commit(update_id)
