diff --git a/keps/sig-autoscaling/20200926-scale-from-zero.yaml b/keps/sig-autoscaling/20200926-scale-from-zero.yaml new file mode 100644 index 000000000000..d0f33fe83138 --- /dev/null +++ b/keps/sig-autoscaling/20200926-scale-from-zero.yaml @@ -0,0 +1,636 @@ + + +# KEP-2021: HPA supports scaling to/from zero pods for object/external metrics + + + + + + +- [Release Signoff Checklist](#release-signoff-checklist) +- [Summary](#summary) +- [Motivation](#motivation) + - [Goals](#goals) + - [Non-Goals](#non-goals) +- [Proposal](#proposal) + - [User Stories (Optional)](#user-stories-optional) + - [Story 1](#story-1) + - [Story 2](#story-2) + - [Notes/Constraints/Caveats (Optional)](#notesconstraintscaveats-optional) + - [Risks and Mitigations](#risks-and-mitigations) +- [Design Details](#design-details) + - [Test Plan](#test-plan) + - [Graduation Criteria](#graduation-criteria) + - [Upgrade / Downgrade Strategy](#upgrade--downgrade-strategy) + - [Version Skew Strategy](#version-skew-strategy) +- [Production Readiness Review Questionnaire](#production-readiness-review-questionnaire) + - [Feature Enablement and Rollback](#feature-enablement-and-rollback) + - [Rollout, Upgrade and Rollback Planning](#rollout-upgrade-and-rollback-planning) + - [Monitoring Requirements](#monitoring-requirements) + - [Dependencies](#dependencies) + - [Scalability](#scalability) + - [Troubleshooting](#troubleshooting) +- [Implementation History](#implementation-history) +- [Drawbacks](#drawbacks) +- [Alternatives](#alternatives) +- [Infrastructure Needed (Optional)](#infrastructure-needed-optional) + + +## Release Signoff Checklist + + + +Items marked with (R) are required *prior to targeting to a milestone / release*. + +- [ ] (R) Enhancement issue in release milestone, which links to KEP dir in [kubernetes/enhancements] (not the initial KEP PR) +- [ ] (R) KEP approvers have approved the KEP status as `implementable` +- [ ] (R) Design details are appropriately documented +- [ ] (R) Test plan is in place, giving consideration to SIG Architecture and SIG Testing input +- [ ] (R) Graduation criteria is in place +- [ ] (R) Production readiness review completed +- [ ] Production readiness review approved +- [ ] "Implementation History" section is up-to-date for milestone +- [ ] User-facing documentation has been created in [kubernetes/website], for publication to [kubernetes.io] +- [ ] Supporting documentation—e.g., additional design documents, links to mailing list discussions/SIG meetings, relevant PRs/issues, release notes + + + +[kubernetes.io]: https://kubernetes.io/ +[kubernetes/enhancements]: https://git.k8s.io/enhancements +[kubernetes/kubernetes]: https://git.k8s.io/kubernetes +[kubernetes/website]: https://git.k8s.io/website + +## Summary + +[Horizontal Pod Autoscaler][] (HPA) automatically scales the number of pods in any resource which supports the `scale` subresource based on observed CPU utilization +(or, with custom metrics support, on some other application-provided metrics) from one to many replicas. This proposal adds support for scaling from zero to many replicas +for object and external metrics. + +[Horizontal Pod Autoscaler]: https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale/ + + + +## Motivation + + + +With the addition of scaling based on object and external metrics it became possible to automatically adjust the number of running replicas based on an application +provided metric. A typical use-case case for this is scaling the number of queue consumers based on the length of the consumed queue. + +In cases of a frequently idle queue or a less latency sensitive workload there is no need to run one replica at all times and instead you want to dynamically scale +to zero replicas, especially of those replicas have high resource requests. If replicas are scale to 0, HPA also needs the ability to scale up once messages are available. + +### Goals + + + +* Provide scaling to zero replicas for object and external metrics +* Provide scaling from zero replicas for object and external metrics + +### Non-Goals + + + +* Provide scaling to/from zero replicas for resource metrics +* Provide request buffering at the Kubernetes Service level + +## Proposal + + + +### User Stories (Optional) + + + +#### Story 1: Scale a heavy queue consumer on-demand + +As the operator of a video processing pipeline, I would like to reduce costs. While video processing is CPU intensive, it is not a latency sensitive workload. Therefor I want +my video processing workers to only be created if there is actually a video to be processed and terminated afterwards. + + +### Notes/Constraints/Caveats (Optional) + + + +### Risks and Mitigations + + + +## Design Details + + + +### Test Plan + + + +### Graduation Criteria + + + +### Upgrade / Downgrade Strategy + + + +### Version Skew Strategy + + + +## Production Readiness Review Questionnaire + + + +### Feature Enablement and Rollback + +_This section must be completed when targeting alpha to a release._ + +* **How can this feature be enabled / disabled in a live cluster?** + - [x] Feature gate (also fill in values in `kep.yaml`) + - Feature gate name: `HPAScaleToZero` + - Components depending on the feature gate: + - [ ] Other + - Describe the mechanism: + When HPAScaleToZero feature gate is enabled HPA supports scaling to zero pods based on object or external metrics. HPA remains active as long as at least one metric value available. + + + - Will enabling / disabling the feature require downtime of the control + plane? + No + - Will enabling / disabling the feature require downtime or reprovisioning + of a node? (Do not assume `Dynamic Kubelet Config` feature is enabled). + No + +* **Does enabling the feature change any default behavior?** + Any change of default behavior may be surprising to users or break existing + automations, so be extremely careful here. + + HPA creation with `minReplicas: 0` is no longer rejected. + +* **Can the feature be disabled once it has been enabled (i.e. can we roll back + the enablement)?** + Also set `disable-supported` to `true` or `false` in `kep.yaml`. + Describe the consequences on existing workloads (e.g., if this is a runtime + feature, can it break the existing applications?). + + Yes. To downgrade the cluster to version that does not support scale-to-zero feature: + + 1. make sure there are no hpa objects with minReplicas=0. Here is a oneliner to update it to 1: + $ kubectl get hpa --all-namespaces --no-headers=true | awk '{if($6==0) printf "kubectl patch hpa/%s --namespace=%s -p \"{\\\"spec\\\":{\\\"minReplicas\\\":1}}\"\n", $2, $1 }' | sh + 2. disable HPAScaleToZero feature gate + +* **What happens if we reenable the feature if it was previously rolled back?** + + Nothing, the feature can be re-enabled without problems. + +* **Are there any tests for feature enablement/disablement?** + The e2e framework does not currently support enabling or disabling feature + gates. However, unit tests in each component dealing with managing data, created + with and without the feature, are necessary. At the very least, think about + conversion tests if API types are being modified. + +### Rollout, Upgrade and Rollback Planning + +_This section must be completed when targeting beta graduation to a release._ + +* **How can a rollout fail? Can it impact already running workloads?** + Try to be as paranoid as possible - e.g., what if some components will restart + mid-rollout? + +* **What specific metrics should inform a rollback?** + +* **Were upgrade and rollback tested? Was the upgrade->downgrade->upgrade path tested?** + Describe manual testing that was done and the outcomes. + Longer term, we may want to require automated upgrade/rollback tests, but we + are missing a bunch of machinery and tooling and can't do that now. + +* **Is the rollout accompanied by any deprecations and/or removals of features, APIs, +fields of API types, flags, etc.?** + Even if applying deprecation policies, they may still surprise some users. + +### Monitoring Requirements + +_This section must be completed when targeting beta graduation to a release._ + +* **How can an operator determine if the feature is in use by workloads?** + Ideally, this should be a metric. Operations against the Kubernetes API (e.g., + checking if there are objects with field X set) may be a last resort. Avoid + logs or events for this purpose. + +* **What are the SLIs (Service Level Indicators) an operator can use to determine +the health of the service?** + - [ ] Metrics + - Metric name: + - [Optional] Aggregation method: + - Components exposing the metric: + - [ ] Other (treat as last resort) + - Details: + +* **What are the reasonable SLOs (Service Level Objectives) for the above SLIs?** + At a high level, this usually will be in the form of "high percentile of SLI + per day <= X". It's impossible to provide comprehensive guidance, but at the very + high level (needs more precise definitions) those may be things like: + - per-day percentage of API calls finishing with 5XX errors <= 1% + - 99% percentile over day of absolute value from (job creation time minus expected + job creation time) for cron job <= 10% + - 99,9% of /health requests per day finish with 200 code + +* **Are there any missing metrics that would be useful to have to improve observability +of this feature?** + Describe the metrics themselves and the reasons why they weren't added (e.g., cost, + implementation difficulties, etc.). + +### Dependencies + +_This section must be completed when targeting beta graduation to a release._ + +* **Does this feature depend on any specific services running in the cluster?** + Think about both cluster-level services (e.g. metrics-server) as well + as node-level agents (e.g. specific version of CRI). Focus on external or + optional services that are needed. For example, if this feature depends on + a cloud provider API, or upon an external software-defined storage or network + control plane. + + For each of these, fill in the following—thinking about running existing user workloads + and creating new ones, as well as about cluster-level services (e.g. DNS): + - [Dependency name] + - Usage description: + - Impact of its outage on the feature: + - Impact of its degraded performance or high-error rates on the feature: + + +### Scalability + +_For alpha, this section is encouraged: reviewers should consider these questions +and attempt to answer them._ + +_For beta, this section is required: reviewers must answer these questions._ + +_For GA, this section is required: approvers should be able to confirm the +previous answers based on experience in the field._ + +* **Will enabling / using this feature result in any new API calls?** + Describe them, providing: + - API call type (e.g. PATCH pods) + - estimated throughput + - originating component(s) (e.g. Kubelet, Feature-X-controller) + focusing mostly on: + - components listing and/or watching resources they didn't before + - API calls that may be triggered by changes of some Kubernetes resources + (e.g. update of object X triggers new updates of object Y) + - periodic API calls to reconcile state (e.g. periodic fetching state, + heartbeats, leader election, etc.) + +* **Will enabling / using this feature result in introducing new API types?** + Describe them, providing: + - API type + - Supported number of objects per cluster + - Supported number of objects per namespace (for namespace-scoped objects) + +* **Will enabling / using this feature result in any new calls to the cloud +provider?** + +* **Will enabling / using this feature result in increasing size or count of +the existing API objects?** + Describe them, providing: + - API type(s): + - Estimated increase in size: (e.g., new annotation of size 32B) + - Estimated amount of new objects: (e.g., new Object X for every existing Pod) + +* **Will enabling / using this feature result in increasing time taken by any +operations covered by [existing SLIs/SLOs]?** + Think about adding additional work or introducing new steps in between + (e.g. need to do X to start a container), etc. Please describe the details. + +* **Will enabling / using this feature result in non-negligible increase of +resource usage (CPU, RAM, disk, IO, ...) in any components?** + Things to keep in mind include: additional in-memory state, additional + non-trivial computations, excessive access to disks (including increased log + volume), significant amount of data sent and/or received over network, etc. + This through this both in small and large cases, again with respect to the + [supported limits]. + +### Troubleshooting + +The Troubleshooting section currently serves the `Playbook` role. We may consider +splitting it into a dedicated `Playbook` document (potentially with some monitoring +details). For now, we leave it here. + +_This section must be completed when targeting beta graduation to a release._ + +* **How does this feature react if the API server and/or etcd is unavailable?** + +* **What are other known failure modes?** + For each of them, fill in the following information by copying the below template: + - [Failure mode brief description] + - Detection: How can it be detected via metrics? Stated another way: + how can an operator troubleshoot without logging into a master or worker node? + - Mitigations: What can be done to stop the bleeding, especially for already + running user workloads? + - Diagnostics: What are the useful log messages and their required logging + levels that could help debug the issue? + Not required until feature graduated to beta. + - Testing: Are there any tests for failure mode? If not, describe why. + +* **What steps should be taken if SLOs are not being met to determine the problem?** + +[supported limits]: https://git.k8s.io/community//sig-scalability/configs-and-limits/thresholds.md +[existing SLIs/SLOs]: https://git.k8s.io/community/sig-scalability/slos/slos.md#kubernetes-slisslos + +## Implementation History + + + +- (2019/02/25) Original design doc: https://github.com/kubernetes/kubernetes/issues/69687#issuecomment-467082733 +- (2019/07/16) Alpha implementation (https://github.com/kubernetes/kubernetes/pull/74526) merged for Kubernetes 1.16 + +## Drawbacks + + + +## Alternatives + + + +## Infrastructure Needed (Optional) + +