def _log_remove_from_clb(steps):
lbs = groupby(lambda s: s.lb_id, steps)
effs = [
cf_msg('convergence-remove-clb-nodes',
lb_id=lb, nodes=sorted(concat(s.node_ids for s in lbsteps)))
for lb, lbsteps in sorted(lbs.iteritems())]
return parallel(effs)
def _log_bulk_rcv3(event, steps):
by_lbs = groupby(lambda s: s[0], concat(s.lb_node_pairs for s in steps))
effs = [
cf_msg(event, lb_id=lb_id, servers=sorted(p[1] for p in pairs))
for lb_id, pairs in sorted(by_lbs.iteritems())
]
return parallel(effs)
def test_cf_msg(self):
"""
`cf_msg` returns Effect with `Log` intent with cloud_feed=True
"""
seq = [(Log('message', dict(cloud_feed=True, a=2,
b=3)), lambda _: 'logged')]
self.assertEqual(perform_sequence(seq, cf_msg('message', a=2, b=3)),
'logged')
def _log_remove_from_clb(steps):
lbs = groupby(lambda s: s.lb_id, steps)
effs = [
cf_msg('convergence-remove-clb-nodes',
lb_id=lb,
nodes=sorted(concat(s.node_ids for s in lbsteps)))
for lb, lbsteps in sorted(lbs.iteritems())
]
return parallel(effs)
def _log_bulk_rcv3(event, steps):
by_lbs = groupby(lambda s: s[0], concat(s.lb_node_pairs for s in steps))
effs = [
cf_msg(event,
lb_id=lb_id,
servers=sorted(p[1] for p in pairs))
for lb_id, pairs in sorted(by_lbs.iteritems())
]
return parallel(effs)
def test_cf_msg(self):
"""
`cf_msg` returns Effect with `Log` intent with cloud_feed=True
"""
seq = [
(Log('message', dict(cloud_feed=True, a=2, b=3)),
lambda _: 'logged')
]
self.assertEqual(perform_sequence(seq, cf_msg('message', a=2, b=3)),
'logged')
def _log_set_metadata(steps):
by_kv = groupby(lambda s: (s.key, s.value), steps)
effs = [
cf_msg('convergence-set-server-metadata',
servers=sorted(s.server_id for s in kvsteps),
key=key,
value=value)
for (key, value), kvsteps in sorted(by_kv.iteritems())
]
return parallel(effs)
def _(steps):
by_cfg = groupby(lambda s: s.server_config, steps)
effs = [
cf_msg(
'convergence-create-servers',
num_servers=len(cfg_steps),
server_config=dict(cfg))
# We sort the items with `thaw` because PMap does not support
# comparison
for cfg, cfg_steps in sorted(by_cfg.iteritems(), key=thaw)]
return parallel(effs)
def _(steps):
by_cfg = groupby(lambda s: s.server_config, steps)
effs = [
cf_msg('convergence-create-servers',
num_servers=len(cfg_steps),
server_config=dict(cfg))
# We sort the items with `thaw` because PMap does not support
# comparison
for cfg, cfg_steps in sorted(by_cfg.iteritems(), key=thaw)
]
return parallel(effs)
def _log_set_metadata(steps):
by_kv = groupby(lambda s: (s.key, s.value), steps)
effs = [
cf_msg(
'convergence-set-server-metadata',
servers=sorted(s.server_id for s in kvsteps),
key=key, value=value
)
for (key, value), kvsteps in sorted(by_kv.iteritems())
]
return parallel(effs)
def _log_add_nodes_clb(steps):
lbs = defaultdict(list)
for step in steps:
for (address, config) in step.address_configs:
lbs[step.lb_id].append('%s:%s' % (address, config.port))
effs = [
cf_msg('convergence-add-clb-nodes',
lb_id=lb_id, addresses=sorted(addresses))
for lb_id, addresses in sorted(lbs.iteritems())
]
return parallel(effs)
def _log_add_nodes_clb(steps):
lbs = defaultdict(list)
for step in steps:
for (address, config) in step.address_configs:
lbs[step.lb_id].append('%s:%s' % (address, config.port))
effs = [
cf_msg('convergence-add-clb-nodes',
lb_id=lb_id,
addresses=sorted(addresses))
for lb_id, addresses in sorted(lbs.iteritems())
]
return parallel(effs)
def _log_change_clb_node(steps):
lbs = groupby(lambda s: (s.lb_id, s.condition, s.weight, s.type),
steps)
effs = [
cf_msg('convergence-change-clb-nodes',
lb_id=lb,
nodes=sorted([s.node_id for s in grouped_steps]),
condition=condition.name, weight=weight, type=node_type.name)
for (lb, condition, weight, node_type), grouped_steps
in sorted(lbs.iteritems())
]
return parallel(effs)
def _log_change_clb_node(steps):
lbs = groupby(lambda s: (s.lb_id, s.condition, s.weight, s.type), steps)
effs = [
cf_msg('convergence-change-clb-nodes',
lb_id=lb,
nodes=sorted([s.node_id for s in grouped_steps]),
condition=condition.name,
weight=weight,
type=node_type.name)
for (lb, condition, weight,
node_type), grouped_steps in sorted(lbs.iteritems())
]
return parallel(effs)
def convergence_succeeded(executor, scaling_group, group_state, resources,
now):
"""
Handle convergence success
"""
if group_state.status == ScalingGroupStatus.DELETING:
# servers have been deleted. Delete the group for real
yield Effect(DeleteGroup(tenant_id=scaling_group.tenant_id,
group_id=scaling_group.uuid))
yield do_return(ConvergenceIterationStatus.GroupDeleted())
elif group_state.status == ScalingGroupStatus.ERROR:
yield Effect(UpdateGroupStatus(scaling_group=scaling_group,
status=ScalingGroupStatus.ACTIVE))
yield cf_msg('group-status-active',
status=ScalingGroupStatus.ACTIVE.name)
# update servers cache with latest servers
yield executor.update_cache(scaling_group, now, include_deleted=False,
**resources)
yield do_return(ConvergenceIterationStatus.Stop())
def convergence_succeeded(executor, scaling_group, group_state, resources):
"""
Handle convergence success
"""
if group_state.status == ScalingGroupStatus.DELETING:
# servers have been deleted. Delete the group for real
yield Effect(DeleteGroup(tenant_id=scaling_group.tenant_id,
group_id=scaling_group.uuid))
yield do_return(ConvergenceIterationStatus.GroupDeleted())
elif group_state.status == ScalingGroupStatus.ERROR:
yield Effect(UpdateGroupStatus(scaling_group=scaling_group,
status=ScalingGroupStatus.ACTIVE))
yield cf_msg('group-status-active',
status=ScalingGroupStatus.ACTIVE.name)
# update servers cache with latest servers.
# See [Convergence servers cache] comment on top of the file.
now = yield Effect(Func(datetime.utcnow))
yield executor.update_cache(scaling_group, now, include_deleted=False,
**resources)
yield do_return(ConvergenceIterationStatus.Stop())
def _log_delete_servers(steps):
return cf_msg('convergence-delete-servers',
servers=sorted([s.server_id for s in steps]))
def _log_delete_servers(steps):
return cf_msg(
'convergence-delete-servers',
servers=sorted([s.server_id for s in steps]))
