Update thresholds document
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## Background
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Since 1.6 release Kubernetes officially supports 5000-node clusters. However,
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the question is what that actually means. As of early Q3 2017 we are in the
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process of defining set of performance-related SLIs ([Service Level Indicators])
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and SLOs ([Service Level Objectives]).
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As described in [How we define scalability] document, it is impossible
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to provide guarantees in a generic situation. One of the prerequisites
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for SLOs being satisfied is keeping the load in the cluster within
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recommended limits. This document is trying to explicitly summarize
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dimensions and limits themselves.
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However, no matter what SLIs and SLOs we have, there will always be some users
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coming and saying that their cluster is not meeting the SLOs. And in most cases
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it appears that the reason behind is that we (as developers) have silently
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assumed something (e.g. there will be no more than 10000 services in the
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cluster) and users were not aware of that.
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This document is trying to explicitly summarize limits for the number of objects
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in the system that we are aware of and state if we will try to relax them in the
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future or not.
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[How we define scalability]: https://github.com/kubernetes/community/blob/master/sig-scalability/slos/slos.md#how-we-define-scalability
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## Kubernetes thresholds
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We start with explicit definition of quantities and thresholds we assume are
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satisfied in the cluster. This is followed by an explanation for some of those.
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Important notes about the numbers:
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1. In most cases, exceeding these thresholds doesn’t mean that the cluster
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fails over - it just means that its overall performance degrades.
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1. **Some thresholds below (e.g. total number of all objects, or total number of
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pods or namespaces) are given for the largest possible cluster. For smaller
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clusters, the limits are proportionally lower.**
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1. The thresholds obviously differ between different Kubernetes releases
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(hopefully each of them is non-decreasing). The numbers we present are for
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the current release (Kubernetes 1.7 release).
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1. There are a lot of factors that influence the thresholds, e.g. etcd version
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or storage data format. For each of those we assume the default from the
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release to avoid providing numbers for huge number of combinations of those.
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1. The “Head threshold” is representing the status of Kubernetes head. This
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column should be snapshotted at every release to produce per-release
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thresholds (and dedicated column for each release should then be added).
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Scalability dimensions and thresholds are very complex topic. In fact,
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configurations that Kubernetes supports create `Scalability Envelope`:
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| Quantity | Head threshold | 1.8 release | Long term goal |
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|-------------------------------------|----------------|-------------|----------------|
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| Total number of all objects | 250000 | | 1000000 |
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| Number of nodes | 5000 | | 5000 |
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| Number of pods | 150000 | | 500000 |
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| Number of pods per node<sup>1</sup> | 110 | | 500 |
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| Number of pods per core<sup>1</sup> | 10 | | 10 |
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| Number of namespaces (ns) | 10000 | | 100000 |
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| Number of pods per ns | 3000 | | 50000 |
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| Number of services | 10000 | | 100000 |
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| Number of services per ns | 5000 | | 5000 |
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| Number of all services backends | TBD | | 500000 |
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| Number of backends per service | 250 | | 5000 |
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| Number of deployments per ns | 2000 | | 10000 |
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| Number of pods per deployment | TBD | | 10000 |
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| Number of jobs per ns | TBD | | 1000 |
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| Number of daemon sets per ns | TBD | | 100 |
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| Number of stateful sets per ns | TBD | | 100 |
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| Number of secrets per ns | TBD | | TBD |
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| Number of secrets per pod | TBD | | TBD |
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| Number of config maps per ns | TBD | | TBD |
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| Number of config maps per pod | TBD | | TBD |
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| Number of storageclasses | TBD | | TBD |
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| Number of roles and rolebindings | TBD | | TBD |
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There are also thresholds for other types, but for those the numbers depend
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also on the environment (bare metal or which cloud provider) the cluster is
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running in. These include:
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Some the properties of the envelope:
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1. It's NOT a kube, because dimensions are sometimes not independent.
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1. It's NOT convex.
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1. As you move farther along one dimension, your cross-section wrt other
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dimensions gets smaller.
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1. It's bounded.
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1. It's decomposable into smaller envelopes.
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| Quantity | Head threshold | 1.8 release | Long term goal |
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|-------------------------------------------|----------------|-------------|----------------|
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| Number of ingresses | TBD | | TBD |
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| Number of PersistentVolumes | TBD | | TBD |
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| Number of PersistentVolumeClaims per ns | TBD | | TBD |
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| Number of PersistentVolumeClaims per node | TBD | | TBD |
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You can learn more about it in this [Kubecon talk] (or [Kubecon slides]).
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There are couple caveats to the thresholds we are presenting below:
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1. In majority of cases, thresholds are NOT hard limits - crossing
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the limit results in degraded performance and doesn't mean cluster
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immediately fails over.
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1. **Many of the tresholds (for cluster scope) are given for the largest
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possible cluster. For smaller clusters, the limits are proportionally
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lower.**
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1. The thresholds may differ (hopefully be non-decreasing) across Kubernetes
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releases. The threshold below are given for Kubernetes head. <br/>
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**TODO:** We are planning to start versioning the table below, but we
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are not there yet.
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1. Given that configuration influences thresholds, we are assuming vanilla
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Kubernetes setup.
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The table below is **NOT exhaustive** - more content is coming soon.
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| Quantity | Threshold scope=namespace | Threshold: scope=cluster |
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|------------------------|---------------------------|--------------------------|
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| #Nodes | n/a | 5000 |
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| #Namespaces | n/a | 10000 |
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| #Pods | 3000 | 150000 |
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| #Pods per node | min(110, 10*#cores) | min(110, 10*#cores) |
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| #Services | 5000 | 10000 |
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| #All service endpoints | TBD | TBD |
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| #Endpoints per service | 250 | n/a |
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| #Secrets | TBD | TBD |
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| #ConfigMaps | TBD | TBD |
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| #Deployments | 2000 | TBD |
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| #DaemonSets | TBD | TBD |
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| #Jobs | TBD | TBD |
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| #StatefulSets | TBD | TBD |
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There are also thresholds that depend on environment/cloud provider. The **NOT
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exhaustive** list includes:
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| Quantity | Threshold scope=namespace | Threshold: scope=cluster |
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|----------------------------------|---------------------------|--------------------------|
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| #Ingresses | TBD | TBD |
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| #PersistentVolumes | n/a | TBD |
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| #PersistentVolumeClaims | TBD | TBD |
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| #PersistentVolumeClaims per node | TBD | TBD |
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The rationale for some of those numbers:
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1. Total number of objects <br/>
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There is a limitation on the total number of objects on the system, as this
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affects among others etcd and its resource consumption.
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1. Number of nodes <br/>
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We believe that having clusters with more than 5000 nodes is not the best
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option and users should consider splitting into multiple clusters. However,
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we may consider bumping the long term goal at some time in the future.
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1. Number of services and endpoints <br/>
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Each service port and each service backend has a corresponding entry in
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iptables. Number of backends of a given service impact the size of the
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`Endpoints` objects, which impacts size of data that is being sent all over
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the system.
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1. Number of objects of a given type per namespace <br/>
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This holds for different objects (pods, secrets, deployments, ...). There are
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a number of control loops in the system that need to iterate over all objects
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in a given namespace as a reaction to some changes in state. Having large
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number of objects of a given type in a single namespace can make those loops
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expensive and slow down processing given state changes.
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---
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<sup>1</sup> The limit for number of pods on a given node is in fact minimum from the “pod per node” and “pods per core times number of cores of a node”.
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[Service Level Indicators]: https://en.wikipedia.org/wiki/Service_level_indicator
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[Service Level Objectives]: https://en.wikipedia.org/wiki/Service_level_objective
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[Kubecon slides]: https://docs.google.com/presentation/d/1aWjxpY4YJ4KJQUTqaVHdR4sbhwqDiW30EF4_hGCc-gI
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[Kubecon talk]: https://www.youtube.com/watch?v=t_Ww6ELKl4Q
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