def _record_autoscaling_decision(
marathon_service_config: MarathonServiceConfig,
autoscaling_params: AutoscalingParamsDict,
utilization: float,
log_utilization_data: Mapping[str, str],
error: float,
current_instances: int,
num_healthy_instances: int,
new_instance_count: int,
safe_downscaling_threshold: int,
task_data_insufficient: bool,
) -> None:
"""
Based on the calculations made, perform observability side effects.
Log messages, generate time series, send any alerts, etc.
"""
write_to_log(
config=marathon_service_config,
line=json.dumps(
dict(
timestamp=time.time(),
paasta_cluster=marathon_service_config.get_cluster(),
paasta_service=marathon_service_config.get_service(),
paasta_instance=marathon_service_config.get_instance(),
autoscaling_params=autoscaling_params,
utilization=utilization,
error=error,
current_instances=current_instances,
num_healthy_instances=num_healthy_instances,
new_instance_count=new_instance_count,
safe_downscaling_threshold=safe_downscaling_threshold,
task_data_insufficient=task_data_insufficient,
)
),
level="debug",
)
meteorite_dims = {
"paasta_service": marathon_service_config.service,
"paasta_cluster": marathon_service_config.cluster,
"paasta_instance": marathon_service_config.instance,
"paasta_pool": marathon_service_config.get_pool(),
"decision_policy": autoscaling_params[DECISION_POLICY_KEY], # type: ignore
}
if yelp_meteorite:
gauge = yelp_meteorite.create_gauge("paasta.service.instances", meteorite_dims)
gauge.set(new_instance_count)
gauge = yelp_meteorite.create_gauge(
"paasta.service.max_instances", meteorite_dims
)
gauge.set(marathon_service_config.get_max_instances())
gauge = yelp_meteorite.create_gauge(
"paasta.service.min_instances", meteorite_dims
)
gauge.set(marathon_service_config.get_min_instances())
def autoscale_marathon_instance(
marathon_service_config: MarathonServiceConfig,
system_paasta_config: SystemPaastaConfig,
marathon_tasks: Sequence[MarathonTask],
mesos_tasks: Sequence[Task],
) -> None:
try:
with create_autoscaling_lock(marathon_service_config.service, marathon_service_config.instance):
current_instances = marathon_service_config.get_instances()
task_data_insufficient = is_task_data_insufficient(
marathon_service_config=marathon_service_config,
marathon_tasks=marathon_tasks,
current_instances=current_instances,
)
autoscaling_params = marathon_service_config.get_autoscaling_params()
log_utilization_data: Mapping = {}
utilization = get_utilization(
marathon_service_config=marathon_service_config,
system_paasta_config=system_paasta_config,
autoscaling_params=autoscaling_params,
log_utilization_data=log_utilization_data,
marathon_tasks=marathon_tasks,
mesos_tasks=mesos_tasks,
)
error = get_error_from_utilization(
utilization=utilization,
setpoint=autoscaling_params['setpoint'],
current_instances=current_instances,
)
new_instance_count = get_new_instance_count(
utilization=utilization,
error=error,
autoscaling_params=autoscaling_params,
current_instances=current_instances,
marathon_service_config=marathon_service_config,
num_healthy_instances=len(marathon_tasks),
)
safe_downscaling_threshold = int(current_instances * 0.7)
if new_instance_count != current_instances:
if new_instance_count < current_instances and task_data_insufficient:
write_to_log(
config=marathon_service_config,
line='Delaying scaling *down* as we found too few healthy tasks running in marathon. '
'This can happen because tasks are delayed/waiting/unhealthy or because we are '
'waiting for tasks to be killed. Will wait for sufficient healthy tasks before '
'we make a decision to scale down.',
)
return
if new_instance_count == safe_downscaling_threshold:
write_to_log(
config=marathon_service_config,
line='Autoscaler clamped: %s' % str(log_utilization_data),
level='debug',
)
write_to_log(
config=marathon_service_config,
line='Scaling from %d to %d instances (%s)' % (
current_instances, new_instance_count, humanize_error(error),
),
)
set_instances_for_marathon_service(
service=marathon_service_config.service,
instance=marathon_service_config.instance,
instance_count=new_instance_count,
)
else:
write_to_log(
config=marathon_service_config,
line='Staying at %d instances (%s)' % (current_instances, humanize_error(error)),
level='debug',
)
meteorite_dims = {
'service_name': marathon_service_config.service,
'decision_policy': autoscaling_params[DECISION_POLICY_KEY], # type: ignore
'paasta_cluster': marathon_service_config.cluster,
'instance_name': marathon_service_config.instance,
}
if yelp_meteorite:
gauge = yelp_meteorite.create_gauge('paasta.service.instances', meteorite_dims)
gauge.set(new_instance_count)
gauge = yelp_meteorite.create_gauge('paasta.service.max_instances', meteorite_dims)
gauge.set(marathon_service_config.get_max_instances())
gauge = yelp_meteorite.create_gauge('paasta.service.min_instances', meteorite_dims)
gauge.set(marathon_service_config.get_min_instances())
except LockHeldException:
log.warning("Skipping autoscaling run for {service}.{instance} because the lock is held".format(
service=marathon_service_config.service,
instance=marathon_service_config.instance,
))
