def __init__(self,
regions,
aws_access_key,
aws_secret_key,
kubeconfig,
pod_namespace,
idle_threshold,
type_idle_threshold,
instance_init_time,
cluster_name,
notifier,
scale_up=True,
maintainance=True,
datadog_api_key=None,
over_provision=5,
dry_run=False):
if kubeconfig:
# for using locally
logger.debug('Using kubeconfig %s', kubeconfig)
self.api = pykube.HTTPClient(
pykube.KubeConfig.from_file(kubeconfig))
else:
# for using on kube
logger.debug('Using kube service account')
self.api = pykube.HTTPClient(
pykube.KubeConfig.from_service_account())
if pod_namespace is None:
self.pod_namespace = pykube.all
else:
self.pod_namespace = pod_namespace
self._drained = {}
self.session = boto3.session.Session(
aws_access_key_id=aws_access_key,
aws_secret_access_key=aws_secret_key,
region_name=regions[0]) # provide a default region
self.autoscaling_groups = autoscaling_groups.AutoScalingGroups(
session=self.session, regions=regions, cluster_name=cluster_name)
self.autoscaling_timeouts = autoscaling_groups.AutoScalingTimeouts(
self.session)
# config
self.regions = regions
self.idle_threshold = idle_threshold
self.instance_init_time = instance_init_time
self.type_idle_threshold = type_idle_threshold
self.over_provision = over_provision
self.scale_up = scale_up
self.maintainance = maintainance
self.notifier = notifier
if datadog_api_key:
datadog.initialize(api_key=datadog_api_key)
logger.info('Datadog initialized')
self.stats = datadog.ThreadStats()
self.stats.start()
self.dry_run = dry_run
def __init__(self, aws_regions, aws_access_key, aws_secret_key,
azure_client_id, azure_client_secret, azure_subscription_id, azure_tenant_id,
azure_resource_group_names, azure_slow_scale_classes, kubeconfig,
idle_threshold, type_idle_threshold,
instance_init_time, cluster_name, notifier,
max_scale_in_fraction=0.1,
scale_up=True, maintainance=True,
datadog_api_key=None,
over_provision=5, dry_run=False):
if kubeconfig:
# for using locally
logger.debug('Using kubeconfig %s', kubeconfig)
self.api = pykube.HTTPClient(
pykube.KubeConfig.from_file(kubeconfig))
else:
# for using on kube
logger.debug('Using kube service account')
self.api = pykube.HTTPClient(
pykube.KubeConfig.from_service_account())
self.max_scale_in_fraction = max_scale_in_fraction
self._drained = {}
self.session = None
if aws_access_key and aws_secret_key:
self.session = boto3.session.Session(
aws_access_key_id=aws_access_key,
aws_secret_access_key=aws_secret_key,
region_name=aws_regions[0]) # provide a default region
self.autoscaling_groups = autoscaling_groups.AutoScalingGroups(
session=self.session, regions=aws_regions,
cluster_name=cluster_name)
self.autoscaling_timeouts = autoscaling_groups.AutoScalingTimeouts(
self.session)
azure_regions = []
resource_groups = []
self.azure_client = None
if azure_client_id:
azure_credentials = ServicePrincipalCredentials(
client_id=azure_client_id,
secret=azure_client_secret,
tenant=azure_tenant_id
)
# Setup the Azure client
resource_client = ResourceManagementClient(azure_credentials, azure_subscription_id)
resource_client.providers.register('Microsoft.Compute')
resource_client.providers.register('Microsoft.Network')
resource_client.providers.register('Microsoft.Insights')
region_map = {}
for resource_group_name in azure_resource_group_names:
resource_group = resource_client.resource_groups.get(resource_group_name)
location = resource_group.location
if location in region_map:
logger.fatal("{} and {} are both in {}. May only have one resource group per region".format(
resource_group_name, region_map[location], location
))
region_map[location] = resource_group_name
azure_regions.append(location)
resource_groups.append(resource_group)
compute_client = ComputeManagementClient(azure_credentials, azure_subscription_id)
compute_client.config.retry_policy.policy = azure.AzureBoundedRetry.from_retry(compute_client.config.retry_policy.policy)
monitor_client = MonitorClient(azure_credentials, azure_subscription_id)
monitor_client.config.retry_policy.policy = azure.AzureBoundedRetry.from_retry(monitor_client.config.retry_policy.policy)
self.azure_client = AzureWriteThroughCachedApi(AzureWrapper(compute_client, monitor_client))
self.azure_groups = azure.AzureGroups(resource_groups, azure_slow_scale_classes, self.azure_client)
# config
self.azure_resource_group_names = azure_resource_group_names
self.azure_regions = azure_regions
self.aws_regions = aws_regions
self.idle_threshold = idle_threshold
self.instance_init_time = instance_init_time
self.type_idle_threshold = type_idle_threshold
self.over_provision = over_provision
self.scale_up = scale_up
self.maintainance = maintainance
self.notifier = notifier
if datadog_api_key:
datadog.initialize(api_key=datadog_api_key)
logger.info('Datadog initialized')
self.stats = datadog.ThreadStats()
self.stats.start()
self.dry_run = dry_run
def __init__(self,
regions,
aws_access_key,
aws_secret_key,
kubeconfig,
pod_namespace,
idle_threshold,
type_idle_threshold,
instance_init_time,
cluster_name,
notifier,
scale_up=True,
maintainance=True,
datadog_api_key=None,
over_provision=5,
dry_run=False,
drainable_labels={},
scale_label=None,
instance_type_priorities={}):
if kubeconfig:
# for using locally
logger.debug('Using kubeconfig %s', kubeconfig)
self.api = pykube.HTTPClient(
pykube.KubeConfig.from_file(kubeconfig))
else:
# for using on kube
logger.debug('Using kube service account')
self.api = pykube.HTTPClient(
pykube.KubeConfig.from_service_account())
if pod_namespace is None:
self.pod_namespace = pykube.all
else:
self.pod_namespace = pod_namespace
self._drained = {}
self.session = boto3.session.Session(
aws_access_key_id=aws_access_key,
aws_secret_access_key=aws_secret_key,
region_name=regions[0]) # provide a default region
self.autoscaling_groups = autoscaling_groups.AutoScalingGroups(
session=self.session, regions=regions, cluster_name=cluster_name)
self.autoscaling_timeouts = autoscaling_groups.AutoScalingTimeouts(
self.session)
# config
self.regions = regions
self.idle_threshold = idle_threshold
self.instance_init_time = instance_init_time
self.type_idle_threshold = type_idle_threshold
self.over_provision = over_provision
self.scale_up = scale_up
self.maintainance = maintainance
self.notifier = notifier
if datadog_api_key:
datadog.initialize(api_key=datadog_api_key)
logger.info('Datadog initialized')
self.stats = datadog.ThreadStats()
self.stats.start()
self.dry_run = dry_run
self.drainable_labels = drainable_labels
self.scale_label = scale_label
if not instance_type_priorities:
self.instance_type_priorities = self._GROUP_PRIORITIES
else:
multiple_priorities = len(
filter(lambda x: len(instance_type_priorities[x]) > 1,
instance_type_priorities.keys()))
if multiple_priorities > 0:
raise ValueError(
'You have specified more than one priority for %d instance types. Please specify a single priority for each instance type that you care about.'
% multiple_priorities)
self.instance_type_priorities = {
instance: min([int(value) for value in values])
for instance, values in instance_type_priorities.items()
}