Python Kubernetes Downscaler

This is a fork of hjacobs/kube-downscaler which is no longer maintained.

Scale down / "pause" Kubernetes workload (Deployments, StatefulSets, and/or HorizontalPodAutoscalers and CronJobs too !) during non-work hours.

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Python Kubernetes Downscaler

Concepts

📝 Deployments are interchangeable by any kind of supported workload for this whole guide unless explicitly stated otherwise.

The complete list of supported workload is defined here.

Algorithm

py-kube-downscaler will scale down the deployment's replicas if all of the following conditions are met:

current time is not part of the "uptime" schedule or is part of the "downtime" schedule.

If true, the schedules are evaluated in the following order:
- downscaler/downscale-period or downscaler/downtime annotation on the workload definition
- downscaler/upscale-period or downscaler/uptime annotation on the workload definition
- downscaler/downscale-period or downscaler/downtime annotation on the workload's namespace
- downscaler/upscale-period or downscaler/uptime annotation on the workload's namespace
- --upscale-period or --default-uptime CLI argument
- --downscale-period or --default-downtime CLI argument
- UPSCALE_PERIOD or DEFAULT_UPTIME environment variable
- DOWNSCALE_PERIOD or DEFAULT_DOWNTIME environment variable
The workload's namespace is not part of the exclusion list:
- If you provide an exclusion list, it will be used in place of the default (which includes only kube-system).
The workload's label does not match the labels list.
The workload's name is not part of the exclusion list
The workload is not marked for exclusion (annotation downscaler/exclude: "true" or downscaler/exclude-until: "2024-04-05")
There are no active pods that force the whole cluster into uptime (annotation downscaler/force-uptime: "true")

Minimum replicas

The deployment, by default, will be scaled down to zero replicas. This can be configured with a deployment or its namespace's annotation of downscaler/downtime-replicas or via CLI with --downtime-replicas.

Ex: downscaler/downtime-replicas: "1"

Specific workload

In case of HorizontalPodAutoscalers, the minReplicas field cannot be set to zero and thus downscaler/downtime-replicas should be at least 1.

-> See later in #Usage notes

Regarding CronJobs, their state will be defined to suspend: true as you might expect.

Example use cases

Deploy the downscaler to a test (non-prod) cluster with a default uptime or downtime time range to scale down all deployments during the night and weekend.
Deploy the downscaler to a production cluster without any default uptime/downtime setting and scale down specific deployments by setting the downscaler/uptime (or downscaler/downtime) annotation. This might be useful for internal tooling frontends which are only needed during work time.

You need to combine the downscaler with an elastic cluster autoscaler to actually save cloud costs. The official cluster autoscaler and the kube-aws-autoscaler were tested to work fine with the downscaler.

Usage

Helm Chart

For detailed information on deploying the py-kube-downscaler using our Helm chart, please refer to the Helm Chart README in the chart directory.

Example configuration

The example configuration uses the --dry-run as a safety flag to prevent downscaling --- remove it to enable the downscaler, e.g. by editing the deployment:

$ kubectl edit deploy py-kube-downscaler

The example deployment manifests come with a configured uptime (deploy/config.yaml sets it to "Mon-Fri 07:30-20:30 CET"), you can overwrite this per namespace or deployment, e.g.:

$ kubectl run nginx --image=nginx
$ kubectl annotate deploy nginx 'downscaler/uptime=Mon-Fri 09:00-17:00 America/Buenos_Aires'

Notes

Note that the default grace period of 15 minutes applies to the new nginx deployment, i.e.

if the current time is not within Mon-Fri 9-17 (Buenos Aires timezone), it will downscale not immediately, but after 15 minutes. The downscaler will eventually log something like:

    INFO: Scaling down Deployment default/nginx from 1 to 0 replicas (uptime: Mon-Fri 09:00-17:00 America/Buenos_Aires, downtime: never)

Note that in cases where a HorizontalPodAutoscaler (HPA) is used along with Deployments, consider the following:

If downscale to 0 replicas is desired, the annotation should be applied on the Deployment. This is a special case, since minReplicas of 0 on HPA is not allowed. Setting Deployment replicas to 0 essentially disables the HPA. In such a case, the HPA will emit events like failed to get memory utilization: unable to get metrics for resource memory: no metrics returned from resource metrics API as there is no Pod to retrieve metrics from.
If downscale greater than 0 is desired, the annotation should be applied on the HPA. This allows for dynamic scaling of the Pods even during downtime based upon the external traffic as well as maintain a lower minReplicas during downtime if there is no/low traffic. If the Deployment is annotated instead of the HPA, it leads to a race condition where py-kube-downscaler scales down the Deployment and HPA upscales it as its minReplicas is higher.

To enable Downscaler on HPA with --downtime-replicas=1, ensure to add the following annotations to Deployment and HPA.

$ kubectl annotate deploy nginx 'downscaler/exclude=true'
$ kubectl annotate hpa nginx 'downscaler/downtime-replicas=1'
$ kubectl annotate hpa nginx 'downscaler/uptime=Mon-Fri 09:00-17:00 America/Buenos_Aires'

Configuration

Uptime / downtime spec

The downscaler is configured via command line args, environment variables and/or Kubernetes annotations.

Time definitions (e.g. DEFAULT_UPTIME) accept a comma separated list of specifications, e.g. the following configuration would downscale all deployments for non-work hours:

DEFAULT_UPTIME="Mon-Fri 07:30-20:30 Europe/Berlin"

To only downscale during the weekend and Friday after 20:00:

DEFAULT_DOWNTIME="Sat-Sun 00:00-24:00 CET,Fri-Fri 20:00-24:00 CET'

Each time specification can be in one of two formats:

Recurring specifications have the format <WEEKDAY-FROM>-<WEEKDAY-TO-INCLUSIVE> <HH>:<MM>-<HH>:<MM> <TIMEZONE>. The timezone value can be any Olson timezone, e.g. "US/Eastern", "PST" or "UTC".
Absolute specifications have the format <TIME_FROM>-<TIME_TO> where each <TIME> is an ISO 8601 date and time of the format <YYYY>-<MM>-<DD>T<HH>:<MM>:<SS>[+-]<TZHH>:<TZMM>.

Alternative logic, based on periods

Instead of strict uptimes or downtimes, you can chose time periods for upscaling or downscaling. The time definitions are the same. In this case, the upscale or downscale happens only on time periods, rest of times will be ignored.

If upscale or downscale periods are configured, uptime and downtime will be ignored. This means that some options are mutually exclusive, e.g. you can either use --downscale-period or --default-downtime, but not both.

This definition will downscale your cluster between 19:00 and 20:00. If you upscale your cluster manually, it won't be scaled down until next day 19:00-20:00.

DOWNSCALE_PERIOD="Mon-Sun 19:00-20:00 Europe/Berlin"

Command Line Options

Available command line options:

--dry-run

: Dry run mode: do not change anything, just print what would be done

--debug

: Debug mode: print more information

--once

: Run loop only once and exit

--interval

: Loop interval (default: 30s)

--namespace

: Restrict the downscaler to work only in a single namespace (default: all namespaces). This is mainly useful for deployment scenarios where the deployer of py-kube-downscaler only has access to a given namespace (instead of cluster access). If used simultaneously with --exclude-namespaces, none is applied.

--include-resources

: Downscale resources of this kind as comma separated list. [deployments, statefulsets, stacks, horizontalpodautoscalers, cronjobs, daemonsets, rollouts, scaledobjects] (default: deployments)

--grace-period

: Grace period in seconds for new deployments before scaling them down (default: 15min). The grace period counts from time of creation of the deployment, i.e. updated deployments will immediately be scaled down regardless of the grace period.

--upscale-period

: Alternative logic to scale up only in given period of time (default: never), can also be configured via environment variable UPSCALE_PERIOD or via the annotation downscaler/upscale-period on each deployment

--downscale-period

: Alternative logic to scale down only in given period of time (default: never), can also be configured via environment variable DOWNSCALE_PERIOD or via the annotation downscaler/downscale-period on each deployment

--default-uptime

: Default time range to scale up for (default: always), can also be configured via environment variable DEFAULT_UPTIME or via the annotation downscaler/uptime on each deployment

--default-downtime

: Default time range to scale down for (default: never), can also be configured via environment variable DEFAULT_DOWNTIME or via the annotation downscaler/downtime on each deployment

--exclude-namespaces

: Exclude namespaces from downscaling (list of regex patterns, default: kube-system), can also be configured via environment variable EXCLUDE_NAMESPACES. If used simultaneously with --namespace, none is applied.

--exclude-deployments

: Exclude specific deployments/statefulsets/cronjobs from downscaling (default: py-kube-downscaler, downscaler), can also be configured via environment variable EXCLUDE_DEPLOYMENTS. Despite its name, this option will match the name of any included resource type (Deployment, StatefulSet, CronJob, ..).

--downtime-replicas

: Default value of replicas to downscale to, the annotation downscaler/downtime-replicas takes precedence over this value.

--deployment-time-annotation

: Optional: name of the annotation that would be used instead of the creation timestamp of the resource. This option should be used if you want the resources to be kept scaled up during a grace period (--grace-period) after a deployment. The format of the annotation's timestamp value must be exactly the same as for Kubernetes' creationTimestamp: %Y-%m-%dT%H:%M:%SZ. Recommended: set this annotation by your deployment tooling automatically.

--matching-labels

: Optional: list of workload's labels which are covered by the py-kube-downscaler scope. All workloads whose labels don't match any in the list are ignored. For backwards compatibility, if this argument is not specified, py-kube-downscaler will apply to all resources.

Namespace Defaults

DEFAULT_UPTIME, DEFAULT_DOWNTIME, FORCE_UPTIME and exclusion can also be configured using Namespace annotations. Where configured these values supersede the other global default values.

apiVersion: v1
kind: Namespace
metadata:
    name: foo
    labels:
        name: foo
    annotations:
        downscaler/uptime: Mon-Sun 07:30-18:00 CET

The following annotations are supported on the Namespace level:

downscaler/upscale-period
downscaler/downscale-period
downscaler/uptime: set "uptime" for all resources in this namespace
downscaler/downtime: set "downtime" for all resources in this namespace
downscaler/force-downtime: force scaling down all resources in this namespace - can be true/false or a period
downscaler/force-uptime: force scaling up all resources in this namespace - can be true/false or a period
downscaler/exclude: set to true to exclude all resources in the namespace
downscaler/exclude-until: temporarily exclude all resources in the namespace until the given timestamp
downscaler/downtime-replicas: overwrite the default target replicas to scale down to (default: zero)

Contributing

Easiest way to contribute is to provide feedback! We would love to hear what you like and what you think is missing. Create an issue or ping try_except_ on Twitter.

PRs are welcome.

License

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.

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