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Other Components

Other components included in the Gardener project

1 - Dependency Watchdog

A watchdog which actively looks out for disruption and recovery of critical services

Dependency Watchdog

REUSE status CI Build status Unit Tests Go Report Card GoDoc

Overview

A watchdog which actively looks out for disruption and recovery of critical services. If there is a disruption then it will prevent cascading failure by conservatively scaling down dependent configured resources and if a critical service has just recovered then it will expedite the recovery of dependent services/pods.

Avoiding cascading failure is handled by Prober component and expediting recovery of dependent services/pods is handled by Weeder component. These are separately deployed as individual pods.

Current Limitation & Future Scope

Although in the current offering the Prober is tailored to handle one such use case of kube-apiserver connectivity, but the usage of prober can be extended to solve similar needs for other scenarios where the components involved might be different.

Start using or developing the Dependency Watchdog

See our documentation in the /docs repository, please find the index here.

Feedback and Support

We always look forward to active community engagement.

Please report bugs or suggestions on how we can enhance dependency-watchdog to address additional recovery scenarios on GitHub issues

1.1 - Concepts

1.1.1 - Prober

Prober

Overview

Prober starts asynchronous and periodic probes for every shoot cluster. The first probe is the api-server probe which checks the reachability of the API Server from the control plane. The second probe is the lease probe which is done after the api server probe is successful and checks if the number of expired node leases is below a certain threshold. If the lease probe fails, it will scale down the dependent kubernetes resources. Once the connectivity to kube-apiserver is reestablished and the number of expired node leases are within the accepted threshold, the prober will then proactively scale up the dependent kubernetes resources it had scaled down earlier. The failure threshold fraction for lease probe and dependent kubernetes resources are defined in configuration that is passed to the prober.

Origin

In a shoot cluster (a.k.a data plane) each node runs a kubelet which periodically renewes its lease. Leases serve as heartbeats informing Kube Controller Manager that the node is alive. The connectivity between the kubelet and the Kube ApiServer can break for different reasons and not recover in time.

As an example, consider a large shoot cluster with several hundred nodes. There is an issue with a NAT gateway on the shoot cluster which prevents the Kubelet from any node in the shoot cluster to reach its control plane Kube ApiServer. As a consequence, Kube Controller Manager transitioned the nodes of this shoot cluster to Unknown state.

Machine Controller Manager which also runs in the shoot control plane reacts to any changes to the Node status and then takes action to recover backing VMs/machine(s). It waits for a grace period and then it will begin to replace the unhealthy machine(s) with new ones.

This replacement of healthy machines due to a broken connectivity between the worker nodes and the control plane Kube ApiServer results in undesired downtimes for customer workloads that were running on these otherwise healthy nodes. It is therefore required that there be an actor which detects the connectivity loss between the the kubelet and shoot cluster’s Kube ApiServer and proactively scales down components in the shoot control namespace which could exacerbate the availability of nodes in the shoot cluster.

Dependency Watchdog Prober in Gardener

Prober is a central component which is deployed in the garden namespace in the seed cluster. Control plane components for a shoot are deployed in a dedicated shoot namespace for the shoot within the seed cluster.

NOTE: If you are not familiar with what gardener components like seed, shoot then please see the appendix for links.

Prober periodically probes Kube ApiServer via two separate probes:

  1. API Server Probe: Local cluster DNS name which resolves to the ClusterIP of the Kube Apiserver
  2. Lease Probe: Checks for number of expired leases to be within the specified threshold. The threshold defines the limit after which DWD can say that the kubelets are not able to reach the API server.

Behind the scene

For all active shoot clusters (which have not been hibernated or deleted or moved to another seed via control-plane-migration), prober will schedule a probe to run periodically. During each run of a probe it will do the following:

  1. Checks if the Kube ApiServer is reachable via local cluster DNS. This should always succeed and will fail only when the Kube ApiServer has gone down. If the Kube ApiServer is down then there can be no further damage to the existing shoot cluster (barring new requests to the Kube Api Server).
  2. Only if the probe is able to reach the Kube ApiServer via local cluster DNS, will it attempt to check the number of expired node leases in the shoot. The node lease renewal is done by the Kubelet, and so we can say that the lease probe is checking if the kubelet is able to reach the API server. If the number of expired node leases reaches the threshold, then the probe fails.
  3. If and when a lease probe fails, then it will initiate a scale-down operation for dependent resources as defined in the prober configuration.
  4. In subsequent runs it will keep performing the lease probe. If it is successful, then it will start the scale-up operation for dependent resources as defined in the configuration.

Prober lifecycle

A reconciler is registered to listen to all events for Cluster resource.

When a Reconciler receives a request for a Cluster change, it will query the extension kube-api server to get the Cluster resource.

In the following cases it will either remove an existing probe for this cluster or skip creating a new probe:

  1. Cluster is marked for deletion.
  2. Hibernation has been enabled for the cluster.
  3. There is an ongoing seed migration for this cluster.
  4. If a new cluster is created with no workers.
  5. If an update is made to the cluster by removing all workers (in other words making it worker-less).

If none of the above conditions are true and there is no existing probe for this cluster then a new probe will be created, registered and started.

Probe failure identification

DWD probe can either be a success or it could return an error. If the API server probe fails, the lease probe is not done and the probes will be retried. If the error is a TooManyRequests error due to requests to the Kube-API-Server being throttled, then the probes are retried after a backOff of backOffDurationForThrottledRequests.

If the lease probe fails, then the error could be due to failure in listing the leases. In this case, no scaling operations are performed. If the error in listing the leases is a TooManyRequests error due to requests to the Kube-API-Server being throttled, then the probes are retried after a backOff of backOffDurationForThrottledRequests.

If there is no error in listing the leases, then the Lease probe fails if the number of expired leases reaches the threshold fraction specified in the configuration. A lease is considered expired in the following scenario:-

	time.Now() >= lease.Spec.RenewTime + (p.config.KCMNodeMonitorGraceDuration.Duration * expiryBufferFraction)

Here, lease.Spec.RenewTime is the time when current holder of a lease has last updated the lease. config is the probe config generated from the configuration and KCMNodeMonitorGraceDuration is amount of time which KCM allows a running Node to be unresponsive before marking it unhealthy (See ref) . expiryBufferFraction is a hard coded value of 0.75. Using this fraction allows the prober to intervene before KCM marks a node as unknown, but at the same time allowing kubelet sufficient retries to renew the node lease (Kubelet renews the lease every 10s See ref).

Appendix

1.1.2 - Weeder

Weeder

Overview

Weeder watches for an update to service endpoints and on receiving such an event it will create a time-bound watch for all configured dependent pods that need to be actively recovered in case they have not yet recovered from CrashLoopBackoff state. In a nutshell it accelerates recovery of pods when an upstream service recovers.

An interference in automatic recovery for dependent pods is required because kubernetes pod restarts a container with an exponential backoff when the pod is in CrashLoopBackOff state. This backoff could become quite large if the service stays down for long. Presence of weeder would not let that happen as it’ll restart the pod.

Prerequisites

Before we understand how Weeder works, we need to be familiar with kubernetes services & endpoints.

NOTE: If a kubernetes service is created with selectors then kubernetes will create corresponding endpoint resource which will have the same name as that of the service. In weeder implementation service and endpoint name is used interchangeably.

Config

Weeder can be configured via command line arguments and a weeder configuration. See configure weeder.

Internals

Weeder keeps a watch on the events for the specified endpoints in the config. For every endpoints a list of podSelectors can be specified. It cretes a weeder object per endpoints resource when it receives a satisfactory Create or Update event. Then for every podSelector it creates a goroutine. This goroutine keeps a watch on the pods with labels as per the podSelector and kills any pod which turn into CrashLoopBackOff. Each weeder lives for watchDuration interval which has a default value of 5 mins if not explicitly set.

To understand the actions taken by the weeder lets use the following diagram as a reference. Let us also assume the following configuration for the weeder:

watchDuration: 2m0s
servicesAndDependantSelectors:
  etcd-main-client: # name of the service/endpoint for etcd statefulset that weeder will receive events for.
    podSelectors: # all pods matching the label selector are direct dependencies for etcd service
      - matchExpressions:
          - key: gardener.cloud/role
            operator: In
            values:
              - controlplane
          - key: role
            operator: In
            values:
              - apiserver
  kube-apiserver: # name of the service/endpoint for kube-api-server pods that weeder will receive events for. 
    podSelectors: # all pods matching the label selector are direct dependencies for kube-api-server service
      - matchExpressions:
          - key: gardener.cloud/role
            operator: In
            values:
              - controlplane
          - key: role
            operator: NotIn
            values:
              - main
              - apiserver

Only for the sake of demonstration lets pick the first service -> dependent pods tuple (etcd-main-client as the service endpoint).

  1. Assume that there are 3 replicas for etcd statefulset.
  2. Time here is just for showing the series of events
  • t=0 -> all etcd pods go down
  • t=10 -> kube-api-server pods transition to CrashLoopBackOff
  • t=100 -> all etcd pods recover together
  • t=101 -> Weeder sees Update event for etcd-main-client endpoints resource
  • t=102 -> go routine created to keep watch on kube-api-server pods
  • t=103 -> Since kube-api-server pods are still in CrashLoopBackOff, weeder deletes the pods to accelerate the recovery.
  • t=104 -> new kube-api-server pod created by replica-set controller in kube-controller-manager

Points to Note

  • Weeder only respond on Update events where a notReady endpoints resource turn to Ready. Thats why there was no weeder action at time t=10 in the example above.
    • notReady -> no backing pod is Ready
    • Ready -> atleast one backing pod is Ready
  • Weeder doesn’t respond on Delete events
  • Weeder will always wait for the entire watchDuration. If the dependent pods transition to CrashLoopBackOff after the watch duration or even after repeated deletion of these pods they do not recover then weeder will exit. Quality of service offered via a weeder is only Best-Effort.

1.2 - Deployment

1.2.1 - Configure

Configure Dependency Watchdog Components

Prober

Dependency watchdog prober command takes command-line-flags which are meant to fine-tune the prober. In addition a ConfigMap is also mounted to the container which provides tuning knobs for the all probes that the prober starts.

Command line arguments

Prober can be configured via the following flags:

Flag NameTypeRequiredDefault ValueDescription
kube-api-burstintNo10Burst to use while talking with kubernetes API server. The number must be >= 0. If it is 0 then a default value of 10 will be used
kube-api-qpsfloatNo5.0Maximum QPS (queries per second) allowed when talking with kubernetes API server. The number must be >= 0. If it is 0 then a default value of 5.0 will be used
concurrent-reconcilesintNo1Maximum number of concurrent reconciles
config-filestringYesNAPath of the config file containing the configuration to be used for all probes
metrics-bind-addrstringNo“:9643”The TCP address that the controller should bind to for serving prometheus metrics
health-bind-addrstringNo“:9644”The TCP address that the controller should bind to for serving health probes
enable-leader-electionboolNofalseIn case prober deployment has more than 1 replica for high availability, then it will be setup in a active-passive mode. Out of many replicas one will become the leader and the rest will be passive followers waiting to acquire leadership in case the leader dies.
leader-election-namespacestringNo“garden”Namespace in which leader election resource will be created. It should be the same namespace where DWD pods are deployed
leader-elect-lease-durationtime.DurationNo15sThe duration that non-leader candidates will wait after observing a leadership renewal until attempting to acquire leadership of a led but unrenewed leader slot. This is effectively the maximum duration that a leader can be stopped before it is replaced by another candidate. This is only applicable if leader election is enabled.
leader-elect-renew-deadlinetime.DurationNo10sThe interval between attempts by the acting master to renew a leadership slot before it stops leading. This must be less than or equal to the lease duration. This is only applicable if leader election is enabled.
leader-elect-retry-periodtime.DurationNo2sThe duration the clients should wait between attempting acquisition and renewal of a leadership. This is only applicable if leader election is enabled.

You can view an example kubernetes prober deployment YAML to see how these command line args are configured.

Prober Configuration

A probe configuration is mounted as ConfigMap to the container. The path to the config file is configured via config-file command line argument as mentioned above. Prober will start one probe per Shoot control plane hosted within the Seed cluster. Each such probe will run asynchronously and will periodically connect to the Kube ApiServer of the Shoot. Configuration below will influence each such probe.

You can view an example YAML configuration provided as data in a ConfigMap here.

NameTypeRequiredDefault ValueDescription
kubeConfigSecretNamestringYesNAName of the kubernetes Secret which has the encoded KubeConfig required to connect to the Shoot control plane Kube ApiServer via an internal domain. This typically uses the local cluster DNS.
probeIntervalmetav1.DurationNo10sInterval with which each probe will run.
initialDelaymetav1.DurationNo30sInitial delay for the probe to become active. Only applicable when the probe is created for the first time.
probeTimeoutmetav1.DurationNo30sIn each run of the probe it will attempt to connect to the Shoot Kube ApiServer. probeTimeout defines the timeout after which a single run of the probe will fail.
backoffJitterFactorfloat64No0.2Jitter with which a probe is run.
dependentResourceInfos[]prober.DependentResourceInfoYesNADetailed below.
kcmNodeMonitorGraceDurationmetav1.DurationYesNAIt is the node-monitor-grace-period set in the kcm flags. Used to determine whether a node lease can be considered expired.
nodeLeaseFailureFractionfloat64No0.6is used to determine the maximum number of leases that can be expired for a lease probe to succeed.

DependentResourceInfo

If a lease probe fails, then it scales down the dependent resources defined by this property. Similarly, if the lease probe is now successful, then it scales up the dependent resources defined by this property.

Each dependent resource info has the following properties:

NameTypeRequiredDefault ValueDescription
refautoscalingv1.CrossVersionObjectReferenceYesNAIt is a collection of ApiVersion, Kind and Name for a kubernetes resource thus serving as an identifier.
optionalboolYesNAIt is possible that a dependent resource is optional for a Shoot control plane. This property enables a probe to determine the correct behavior in case it is unable to find the resource identified via ref.
scaleUpprober.ScaleInfoNoCaptures the configuration to scale up this resource. Detailed below.
scaleDownprober.ScaleInfoNoCaptures the configuration to scale down this resource. Detailed below.

NOTE: Since each dependent resource is a target for scale up/down, therefore it is mandatory that the resource reference points a kubernetes resource which has a scale subresource.

ScaleInfo

How to scale a DependentResourceInfo is captured in ScaleInfo. It has the following properties:

NameTypeRequiredDefault ValueDescription
levelintYesNADetailed below.
initialDelaymetav1.DurationNo0s (No initial delay)Once a decision is taken to scale a resource then via this property a delay can be induced before triggering the scale of the dependent resource.
timeoutmetav1.DurationNo30sDefines the timeout for the scale operation to finish for a dependent resource.

Determining target replicas

Prober cannot assume any target replicas during a scale-up operation for the following reasons:

  1. Kubernetes resources could be set to provide highly availability and the number of replicas could wary from one shoot control plane to the other. In gardener the number of replicas of pods in shoot namespace are controlled by the shoot control plane configuration.
  2. If Horizontal Pod Autoscaler has been configured for a kubernetes dependent resource then it could potentially change the spec.replicas for a deployment/statefulset.

Given the above constraint lets look at how prober determines the target replicas during scale-down or scale-up operations.

  1. Scale-Up: Primary responsibility of a probe while performing a scale-up is to restore the replicas of a kubernetes dependent resource prior to scale-down. In order to do that it updates the following for each dependent resource that requires a scale-up:

    1. spec.replicas: Checks if dependency-watchdog.gardener.cloud/replicas is set. If it is, then it will take the value stored against this key as the target replicas. To be a valid value it should always be greater than 0.
    2. If dependency-watchdog.gardener.cloud/replicas annotation is not present then it falls back to the hard coded default value for scale-up which is set to 1.
    3. Removes the annotation dependency-watchdog.gardener.cloud/replicas if it exists.
  2. Scale-Down: To scale down a dependent kubernetes resource it does the following:

    1. Adds an annotation dependency-watchdog.gardener.cloud/replicas and sets its value to the current value of spec.replicas.
    2. Updates spec.replicas to 0.

Level

Each dependent resource that should be scaled up or down is associated to a level. Levels are ordered and processed in ascending order (starting with 0 assigning it the highest priority). Consider the following configuration:

dependentResourceInfos:
  - ref: 
      kind: "Deployment"
      name: "kube-controller-manager"
      apiVersion: "apps/v1"
    scaleUp: 
      level: 1 
    scaleDown: 
      level: 0 
  - ref:
      kind: "Deployment"
      name: "machine-controller-manager"
      apiVersion: "apps/v1"
    scaleUp:
      level: 1
    scaleDown:
      level: 1
  - ref:
      kind: "Deployment"
      name: "cluster-autoscaler"
      apiVersion: "apps/v1"
    scaleUp:
      level: 0
    scaleDown:
      level: 2

Let us order the dependent resources by their respective levels for both scale-up and scale-down. We get the following order:

Scale Up Operation

Order of scale up will be:

  1. cluster-autoscaler
  2. kube-controller-manager and machine-controller-manager will be scaled up concurrently after cluster-autoscaler has been scaled up.

Scale Down Operation

Order of scale down will be:

  1. kube-controller-manager
  2. machine-controller-manager after (1) has been scaled down.
  3. cluster-autoscaler after (2) has been scaled down.

Disable/Ignore Scaling

A probe can be configured to ignore scaling of configured dependent kubernetes resources. To do that one must set dependency-watchdog.gardener.cloud/ignore-scaling annotation to true on the scalable resource for which scaling should be ignored.

Weeder

Dependency watchdog weeder command also (just like the prober command) takes command-line-flags which are meant to fine-tune the weeder. In addition a ConfigMap is also mounted to the container which helps in defining the dependency of pods on endpoints.

Command Line Arguments

Weeder can be configured with the same flags as that for prober described under command-line-arguments section You can find an example weeder deployment YAML to see how these command line args are configured.

Weeder Configuration

Weeder configuration is mounted as ConfigMap to the container. The path to the config file is configured via config-file command line argument as mentioned above. Weeder will start one go routine per podSelector per endpoint on an endpoint event as described in weeder internal concepts.

You can view the example YAML configuration provided as data in a ConfigMap here.

NameTypeRequiredDefault ValueDescription
watchDuration*metav1.DurationNo5m0sThe time duration for which watch is kept on dependent pods to see if anyone turns to CrashLoopBackoff
servicesAndDependantSelectorsmap[string]DependantSelectorsYesNAEndpoint name and its corresponding dependent pods. More info below.

DependantSelectors

If the service recovers from downtime, then weeder starts to watch for CrashLoopBackOff pods. These pods are identified by info stored in this property.

NameTypeRequiredDefault ValueDescription
podSelectors[]*metav1.LabelSelectorYesNAThis is a list of Label selector

1.2.2 - Monitor

Monitoring

Work In Progress

We will be introducing metrics for Dependency-Watchdog-Prober and Dependency-Watchdog-Weeder. These metrics will be pushed to prometheus. Once that is completed we will provide details on all the metrics that will be supported here.

1.3 - Contribution

How to contribute?

Contributions are always welcome!

In order to contribute ensure that you have the development environment setup and you familiarize yourself with required steps to build, verify-quality and test.

Setting up development environment

Installing Go

Minimum Golang version required: 1.18. On MacOS run:

brew install go

For other OS, follow the installation instructions.

Installing Git

Git is used as version control for dependency-watchdog. On MacOS run:

brew install git

If you do not have git installed already then please follow the installation instructions.

Installing Docker

In order to test dependency-watchdog containers you will need a local kubernetes setup. Easiest way is to first install Docker. This becomes a pre-requisite to setting up either a vanilla KIND/minikube cluster or a local Gardener cluster.

On MacOS run:

brew install -cash docker

For other OS, follow the installation instructions.

Installing Kubectl

To interact with the local Kubernetes cluster you will need kubectl. On MacOS run:

brew install kubernetes-cli

For other IS, follow the installation instructions.

Get the sources

Clone the repository from Github:

git clone https://github.com/gardener/dependency-watchdog.git

Using Makefile

For every change following make targets are recommended to run.

# build the code changes
> make build
# ensure that all required checks pass
> make verify # this will check formatting, linting and will run unit tests
# if you do not wish to run tests then you can use the following make target.
> make check

All tests should be run and the test coverage should ideally not reduce. Please ensure that you have read testing guidelines.

Before raising a pull request ensure that if you are introducing any new file then you must add licesence header to all new files. To add license header you can run this make target:

> make add-license-headers
# This will add license headers to any file which does not already have it.

NOTE: Also have a look at the Makefile as it has other targets that are not mentioned here.

Raising a Pull Request

To raise a pull request do the following:

  1. Create a fork of dependency-watchdog
  2. Add dependency-watchdog as upstream remote via
   git remote add upstream https://github.com/gardener/dependency-watchdog
  1. It is recommended that you create a git branch and push all your changes for the pull-request.
  2. Ensure that while you work on your pull-request, you continue to rebase the changes from upstream to your branch. To do that execute the following command:
   git pull --rebase upstream master
  1. We prefer clean commits. If you have multiple commits in the pull-request, then squash the commits to a single commit. You can do this via interactive git rebase command. For example if your PR branch is ahead of remote origin HEAD by 5 commits then you can execute the following command and pick the first commit and squash the remaining commits.
   git rebase -i HEAD~5 #actual number from the head will depend upon how many commits your branch is ahead of remote origin master

1.5 - Dwd Using Local Garden

Dependency Watchdog with Local Garden Cluster

Setting up Local Garden cluster

A convenient way to test local dependency-watchdog changes is to use a local garden cluster. To setup a local garden cluster you can follow the setup-guide.

Dependency Watchdog resources

As part of the local garden installation, a local seed will be available.

Dependency Watchdog resources created in the seed

Namespaced resources

In the garden namespace of the seed cluster, following resources will be created:

Resource (GVK)Name
{apiVersion: v1, Kind: ServiceAccount}dependency-watchdog-prober
{apiVersion: v1, Kind: ServiceAccount}dependency-watchdog-weeder
{apiVersion: apps/v1, Kind: Deployment}dependency-watchdog-prober
{apiVersion: apps/v1, Kind: Deployment}dependency-watchdog-weeder
{apiVersion: v1, Kind: ConfigMap}dependency-watchdog-prober-*
{apiVersion: v1, Kind: ConfigMap}dependency-watchdog-weeder-*
{apiVersion: rbac.authorization.k8s.io/v1, Kind: Role}gardener.cloud:dependency-watchdog-prober:role
{apiVersion: rbac.authorization.k8s.io/v1, Kind: Role}gardener.cloud:dependency-watchdog-weeder:role
{apiVersion: rbac.authorization.k8s.io/v1, Kind: RoleBinding}gardener.cloud:dependency-watchdog-prober:role-binding
{apiVersion: rbac.authorization.k8s.io/v1, Kind: RoleBinding}gardener.cloud:dependency-watchdog-weeder:role-binding
{apiVersion: resources.gardener.cloud/v1alpha1, Kind: ManagedResource}dependency-watchdog-prober
{apiVersion: resources.gardener.cloud/v1alpha1, Kind: ManagedResource}dependency-watchdog-weeder
{apiVersion: v1, Kind: Secret}managedresource-dependency-watchdog-weeder
{apiVersion: v1, Kind: Secret}managedresource-dependency-watchdog-prober

Cluster resources

Resource (GVK)Name
{apiVersion: rbac.authorization.k8s.io/v1, Kind: ClusterRole}gardener.cloud:dependency-watchdog-prober:cluster-role
{apiVersion: rbac.authorization.k8s.io/v1, Kind: ClusterRole}gardener.cloud:dependency-watchdog-weeder:cluster-role
{apiVersion: rbac.authorization.k8s.io/v1, Kind: ClusterRoleBinding}gardener.cloud:dependency-watchdog-prober:cluster-role-binding
{apiVersion: rbac.authorization.k8s.io/v1, Kind: ClusterRoleBinding}gardener.cloud:dependency-watchdog-weeder:cluster-role-binding

Dependency Watchdog resources created in Shoot control namespace

Resource (GVK)Name
{apiVersion: v1, Kind: Secret}dependency-watchdog-prober
{apiVersion: resources.gardener.cloud/v1alpha1, Kind: ManagedResource}shoot-core-dependency-watchdog

Dependency Watchdog resources created in the kube-node-lease namespace of the shoot

Resource (GVK)Name
{apiVersion: rbac.authorization.k8s.io/v1, Kind: Role}gardener.cloud:target:dependency-watchdog
{apiVersion: rbac.authorization.k8s.io/v1, Kind: RoleBinding}gardener.cloud:target:dependency-watchdog

These will be created by the GRM and will have a managed resource named shoot-core-dependency-watchdog in the shoot namespace in the seed.

Update Gardener with custom Dependency Watchdog Docker images

Build, Tag and Push docker images

To build dependency watchdog docker images run the following make target:

> make docker-build

Local gardener hosts a docker registry which can be access at localhost:5001. To enable local gardener to be able to access the custom docker images you need to tag and push these images to the embedded docker registry. To do that execute the following commands:

> docker images
# Get the IMAGE ID of the dependency watchdog images that were built using docker-build make target.
> docker tag <IMAGE-ID> localhost:5001/europe-docker.pkg.dev/gardener-project/public/gardener/dependency-watchdog-prober:<TAGNAME>
> docker push localhost:5001/europe-docker.pkg.dev/gardener-project/public/gardener/dependency-watchdog-prober:<TAGNAME>

Update ManagedResource

Garden resource manager will revert back any changes that are done to the kubernetes deployment for dependency watchdog. This is quite useful in live landscapes where only tested and qualified images are used for all gardener managed components. Any change therefore is automatically reverted.

However, during development and testing you will need to use custom docker images. To prevent garden resource manager from reverting the changes done to the kubernetes deployment for dependency watchdog components you must update the respective managed resources first.

# List the managed resources
> kubectl get mr -n garden | grep dependency
# Sample response
dependency-watchdog-weeder            seed    True      True      False         26h
dependency-watchdog-prober            seed    True      True      False         26h
# Lets assume that you are currently testing prober and would like to use a custom docker image
> kubectl edit mr dependency-watchdog-prober -n garden
# This will open the resource YAML for editing. Add the annotation resources.gardener.cloud/ignore=true
# Reference: https://github.com/gardener/gardener/blob/master/docs/concepts/resource-manager.md
# Save the YAML file.

When you are done with your testing then you can again edit the ManagedResource and remove the annotation. Garden resource manager will revert back to the image with which gardener was initially built and started.

Update Kubernetes Deployment

Find and update the kubernetes deployment for dependency watchdog.

> kubectl get deploy -n garden | grep dependency
# Sample response
dependency-watchdog-weeder            1/1     1            1           26h
dependency-watchdog-prober            1/1     1            1           26h

# Lets assume that you are currently testing prober and would like to use a custom docker image
> kubectl edit deploy dependency-watchdog-prober -n garden
# This will open the resource YAML for editing. Change the image or any other changes and save.

1.6 - Testing

Testing Strategy and Developer Guideline

Intent of this document is to introduce you (the developer) to the following:

  • Category of tests that exists.
  • Libraries that are used to write tests.
  • Best practices to write tests that are correct, stable, fast and maintainable.
  • How to run each category of tests.

For any new contributions tests are a strict requirement. Boy Scouts Rule is followed: If you touch a code for which either no tests exist or coverage is insufficient then it is expected that you will add relevant tests.

Tools Used for Writing Tests

These are the following tools that were used to write all the tests (unit + envtest + vanilla kind cluster tests), it is preferred not to introduce any additional tools / test frameworks for writing tests:

Gomega

We use gomega as our matcher or assertion library. Refer to Gomega’s official documentation for details regarding its installation and application in tests.

Testing Package from Standard Library

We use the Testing package provided by the standard library in golang for writing all our tests. Refer to its official documentation to learn how to write tests using Testing package. You can also refer to this example.

Writing Tests

Common for All Kinds

  • For naming the individual tests (TestXxx and testXxx methods) and helper methods, make sure that the name describes the implementation of the method. For eg: testScalingWhenMandatoryResourceNotFound tests the behaviour of the scaler when a mandatory resource (KCM deployment) is not present.
  • Maintain proper logging in tests. Use t.log() method to add appropriate messages wherever necessary to describe the flow of the test. See this for examples.
  • Make use of the testdata directory for storing arbitrary sample data needed by tests (YAML manifests, etc.). See this package for examples.

Table-driven tests

We need a tabular structure in two cases:

  • When we have multiple tests which require the same kind of setup:- In this case we have a TestXxxSuite method which will do the setup and run all the tests. We have a slice of test struct which holds all the tests (typically a title and run method). We use a for loop to run all the tests one by one. See this for examples.
  • When we have the same code path and multiple possible values to check:- In this case we have the arguments and expectations in a struct. We iterate through the slice of all such structs, passing the arguments to appropriate methods and checking if the expectation is met. See this for examples.

Env Tests

Env tests in Dependency Watchdog use the sigs.k8s.io/controller-runtime/pkg/envtest package. It sets up a temporary control plane (etcd + kube-apiserver) and runs the test against it. The code to set up and teardown the environment can be checked out here.

These are the points to be followed while writing tests that use envtest setup:

  • All tests should be divided into two top level partitions:

    1. tests with common environment (testXxxCommonEnvTests)
    2. tests which need a dedicated environment for each one. (testXxxDedicatedEnvTests)

    They should be contained within the TestXxxSuite method. See this for examples. If all tests are of one kind then this is not needed.

  • Create a method named setUpXxxTest for performing setup tasks before all/each test. It should either return a method or have a separate method to perform teardown tasks. See this for examples.

  • The tests run by the suite can be table-driven as well.

  • Use the envtest setup when there is a need of an environment close to an actual setup. Eg: start controllers against a real Kubernetes control plane to catch bugs that can only happen when talking to a real API server.

NOTE: It is currently not possible to bring up more than one envtest environments. See issue#1363. We enforce running serial execution of test suites each of which uses a different envtest environments. See hack/test.sh.

Vanilla Kind Cluster Tests

There are some tests where we need a vanilla kind cluster setup, for eg:- The scaler.go code in the prober package uses the scale subresource to scale the deployments mentioned in the prober config. But the envtest setup does not support the scale subresource as of now. So we need this setup to test if the deployments are scaled as per the config or not. You can check out the code for this setup here. You can add utility methods for different kubernetes and custom resources in there.

These are the points to be followed while writing tests that use Vanilla Kind Cluster setup:

  • Use this setup only if there is a need of an actual Kubernetes cluster(api server + control plane + etcd) to write the tests. (Because this is slower than your normal envTest setup)
  • Create setUpXxxTest similar to the one in envTest. Follow the same structural pattern used in envTest for writing these tests. See this for examples.

Run Tests

To run unit tests, use the following Makefile target

make test

To run KIND cluster based tests, use the following Makefile target

make kind-tests # these tests will be slower as it brings up a vanilla KIND cluster

To view coverage after running the tests, run :

go tool cover -html=cover.out

Flaky tests

If you see that a test is flaky then you can use make stress target which internally uses stress tool

make stress test-package=<test-package> test-func=<test-func> tool-params="<tool-params>"

An example invocation:

make stress test-package=./internal/util test-func=TestRetryUntilPredicateWithBackgroundContext tool-params="-p 10"

The make target will do the following:

  1. It will create a test binary for the package specified via test-package at /tmp/pkg-stress.test directory.
  2. It will run stress tool passing the tool-params and targets the function test-func.

2 - Machine Controller Manager

Declarative way of managing machines for Kubernetes cluster

machine-controller-manager

REUSE status CI Build status Go Report Card

Note One can add support for a new cloud provider by following Adding support for new provider.

Overview

Machine Controller Manager aka MCM is a group of cooperative controllers that manage the lifecycle of the worker machines. It is inspired by the design of Kube Controller Manager in which various sub controllers manage their respective Kubernetes Clients. MCM gives you the following benefits:

  • seamlessly manage machines/nodes with a declarative API (of course, across different cloud providers)
  • integrate generically with the cluster autoscaler
  • plugin with tools such as the node-problem-detector
  • transport the immutability design principle to machine/nodes
  • implement e.g. rolling upgrades of machines/nodes

MCM supports following providers. These provider code is maintained externally (out-of-tree), and the links for the same are linked below:

It can easily be extended to support other cloud providers as well.

Example of managing machine:

kubectl create/get/delete machine vm1

Key terminologies

Nodes/Machines/VMs are different terminologies used to represent similar things. We use these terms in the following way

  1. VM: A virtual machine running on any cloud provider. It could also refer to a physical machine (PM) in case of a bare metal setup.
  2. Node: Native kubernetes node objects. The objects you get to see when you do a “kubectl get nodes”. Although nodes can be either physical/virtual machines, for the purposes of our discussions it refers to a VM.
  3. Machine: A VM that is provisioned/managed by the Machine Controller Manager.

Design of Machine Controller Manager

The design of the Machine Controller Manager is influenced by the Kube Controller Manager, where-in multiple sub-controllers are used to manage the Kubernetes clients.

Design Principles

It’s designed to run in the master plane of a Kubernetes cluster. It follows the best principles and practices of writing controllers, including, but not limited to:

  • Reusing code from kube-controller-manager
  • leader election to allow HA deployments of the controller
  • workqueues and multiple thread-workers
  • SharedInformers that limit to minimum network calls, de-serialization and provide helpful create/update/delete events for resources
  • rate-limiting to allow back-off in case of network outages and general instability of other cluster components
  • sending events to respected resources for easy debugging and overview
  • Prometheus metrics, health and (optional) profiling endpoints

Objects of Machine Controller Manager

Machine Controller Manager reconciles a set of Custom Resources namely MachineDeployment, MachineSet and Machines which are managed & monitored by their controllers MachineDeployment Controller, MachineSet Controller, Machine Controller respectively along with another cooperative controller called the Safety Controller.

Machine Controller Manager makes use of 4 CRD objects and 1 Kubernetes secret object to manage machines. They are as follows:

Custom ResourceObjectDescription
MachineClassA MachineClass represents a template that contains cloud provider specific details used to create machines.
MachineA Machine represents a VM which is backed by the cloud provider.
MachineSetA MachineSet ensures that the specified number of Machine replicas are running at a given point of time.
MachineDeploymentA MachineDeployment provides a declarative update for MachineSet and Machines.
SecretA Secret here is a Kubernetes secret that stores cloudconfig (initialization scripts used to create VMs) and cloud specific credentials.

See here for CRD API Documentation

Components of Machine Controller Manager

ControllerDescription
MachineDeployment controllerMachine Deployment controller reconciles the MachineDeployment objects and manages the lifecycle of MachineSet objects. MachineDeployment consumes provider specific MachineClass in its spec.template.spec which is the template of the VM spec that would be spawned on the cloud by MCM.
MachineSet controllerMachineSet controller reconciles the MachineSet objects and manages the lifecycle of Machine objects.
Safety controllerThere is a Safety Controller responsible for handling the unidentified or unknown behaviours from the cloud providers. Safety Controller:
  • freezes the MachineDeployment controller and MachineSet controller if the number of Machine objects goes beyond a certain threshold on top of Spec.replicas. It can be configured by the flag --safety-up or --safety-down and also --machine-safety-overshooting-period`.
  • freezes the functionality of the MCM if either of the target-apiserver or the control-apiserver is not reachable.
  • unfreezes the MCM automatically once situation is resolved to normal. A freeze label is applied on MachineDeployment/MachineSet to enforce the freeze condition.

Along with the above Custom Controllers and Resources, MCM requires the MachineClass to use K8s Secret that stores cloudconfig (initialization scripts used to create VMs) and cloud specific credentials. All these controllers work in an co-operative manner. They form a parent-child relationship with MachineDeployment Controller being the grandparent, MachineSet Controller being the parent, and Machine Controller being the child.

Development

To start using or developing the Machine Controller Manager, see the documentation in the /docs repository.

FAQ

An FAQ is available here.

cluster-api Implementation

2.1 - Documents

2.1.1 - Apis

Specification

ProviderSpec Schema


Machine

Machine is the representation of a physical or virtual machine.

FieldTypeDescription
apiVersionstringmachine.sapcloud.io/v1alpha1
kindstringMachine
metadataKubernetes meta/v1.ObjectMeta

ObjectMeta for machine object

Refer to the Kubernetes API documentation for the fields of the metadata field.
specMachineSpec

Spec contains the specification of the machine



classClassSpec(Optional)

Class contains the machineclass attributes of a machine

providerIDstring(Optional)

ProviderID represents the provider’s unique ID given to a machine

nodeTemplateNodeTemplateSpec(Optional)

NodeTemplateSpec describes the data a node should have when created from a template

MachineConfigurationMachineConfiguration

(Members of MachineConfiguration are embedded into this type.)

(Optional)

Configuration for the machine-controller.

statusMachineStatus

Status contains fields depicting the status


MachineClass

MachineClass can be used to templatize and re-use provider configuration across multiple Machines / MachineSets / MachineDeployments.

FieldTypeDescription
apiVersionstringmachine.sapcloud.io/v1alpha1
kindstringMachineClass
metadataKubernetes meta/v1.ObjectMeta(Optional) Refer to the Kubernetes API documentation for the fields of the metadata field.
nodeTemplateNodeTemplate(Optional)

NodeTemplate contains subfields to track all node resources and other node info required to scale nodegroup from zero

credentialsSecretRefKubernetes core/v1.SecretReference

CredentialsSecretRef can optionally store the credentials (in this case the SecretRef does not need to store them). This might be useful if multiple machine classes with the same credentials but different user-datas are used.

providerSpeck8s.io/apimachinery/pkg/runtime.RawExtension

Provider-specific configuration to use during node creation.

providerstring

Provider is the combination of name and location of cloud-specific drivers.

secretRefKubernetes core/v1.SecretReference

SecretRef stores the necessary secrets such as credentials or userdata.


MachineDeployment

MachineDeployment enables declarative updates for machines and MachineSets.

FieldTypeDescription
apiVersionstringmachine.sapcloud.io/v1alpha1
kindstringMachineDeployment
metadataKubernetes meta/v1.ObjectMeta(Optional)

Standard object metadata.

Refer to the Kubernetes API documentation for the fields of the metadata field.
specMachineDeploymentSpec(Optional)

Specification of the desired behavior of the MachineDeployment.



replicasint32(Optional)

Number of desired machines. This is a pointer to distinguish between explicit zero and not specified. Defaults to 0.

selectorKubernetes meta/v1.LabelSelector(Optional)

Label selector for machines. Existing MachineSets whose machines are selected by this will be the ones affected by this MachineDeployment.

templateMachineTemplateSpec

Template describes the machines that will be created.

strategyMachineDeploymentStrategy(Optional)

The MachineDeployment strategy to use to replace existing machines with new ones.

minReadySecondsint32(Optional)

Minimum number of seconds for which a newly created machine should be ready without any of its container crashing, for it to be considered available. Defaults to 0 (machine will be considered available as soon as it is ready)

revisionHistoryLimit*int32(Optional)

The number of old MachineSets to retain to allow rollback. This is a pointer to distinguish between explicit zero and not specified.

pausedbool(Optional)

Indicates that the MachineDeployment is paused and will not be processed by the MachineDeployment controller.

rollbackToRollbackConfig(Optional)

DEPRECATED. The config this MachineDeployment is rolling back to. Will be cleared after rollback is done.

progressDeadlineSeconds*int32(Optional)

The maximum time in seconds for a MachineDeployment to make progress before it is considered to be failed. The MachineDeployment controller will continue to process failed MachineDeployments and a condition with a ProgressDeadlineExceeded reason will be surfaced in the MachineDeployment status. Note that progress will not be estimated during the time a MachineDeployment is paused. This is not set by default, which is treated as infinite deadline.

statusMachineDeploymentStatus(Optional)

Most recently observed status of the MachineDeployment.


MachineSet

MachineSet TODO

FieldTypeDescription
apiVersionstringmachine.sapcloud.io/v1alpha1
kindstringMachineSet
metadataKubernetes meta/v1.ObjectMeta(Optional) Refer to the Kubernetes API documentation for the fields of the metadata field.
specMachineSetSpec(Optional)

replicasint32(Optional)
selectorKubernetes meta/v1.LabelSelector(Optional)
machineClassClassSpec(Optional)
templateMachineTemplateSpec(Optional)
minReadySecondsint32(Optional)
statusMachineSetStatus(Optional)

ClassSpec

(Appears on: MachineSetSpec, MachineSpec)

ClassSpec is the class specification of machine

FieldTypeDescription
apiGroupstring

API group to which it belongs

kindstring

Kind for machine class

namestring

Name of machine class


ConditionStatus (string alias)

(Appears on: MachineDeploymentCondition, MachineSetCondition)


CurrentStatus

(Appears on: MachineStatus)

CurrentStatus contains information about the current status of Machine.

FieldTypeDescription
phaseMachinePhase
timeoutActivebool
lastUpdateTimeKubernetes meta/v1.Time

Last update time of current status


LastOperation

(Appears on: MachineSetStatus, MachineStatus, MachineSummary)

LastOperation suggests the last operation performed on the object

FieldTypeDescription
descriptionstring

Description of the current operation

errorCodestring(Optional)

ErrorCode of the current operation if any

lastUpdateTimeKubernetes meta/v1.Time

Last update time of current operation

stateMachineState

State of operation

typeMachineOperationType

Type of operation


MachineConfiguration

(Appears on: MachineSpec)

MachineConfiguration describes the configurations useful for the machine-controller.

FieldTypeDescription
drainTimeoutKubernetes meta/v1.Duration(Optional)

MachineDraintimeout is the timeout after which machine is forcefully deleted.

healthTimeoutKubernetes meta/v1.Duration(Optional)

MachineHealthTimeout is the timeout after which machine is declared unhealhty/failed.

creationTimeoutKubernetes meta/v1.Duration(Optional)

MachineCreationTimeout is the timeout after which machinie creation is declared failed.

maxEvictRetries*int32(Optional)

MaxEvictRetries is the number of retries that will be attempted while draining the node.

nodeConditions*string(Optional)

NodeConditions are the set of conditions if set to true for MachineHealthTimeOut, machine will be declared failed.


MachineDeploymentCondition

(Appears on: MachineDeploymentStatus)

MachineDeploymentCondition describes the state of a MachineDeployment at a certain point.

FieldTypeDescription
typeMachineDeploymentConditionType

Type of MachineDeployment condition.

statusConditionStatus

Status of the condition, one of True, False, Unknown.

lastUpdateTimeKubernetes meta/v1.Time

The last time this condition was updated.

lastTransitionTimeKubernetes meta/v1.Time

Last time the condition transitioned from one status to another.

reasonstring

The reason for the condition’s last transition.

messagestring

A human readable message indicating details about the transition.


MachineDeploymentConditionType (string alias)

(Appears on: MachineDeploymentCondition)


MachineDeploymentSpec

(Appears on: MachineDeployment)

MachineDeploymentSpec is the specification of the desired behavior of the MachineDeployment.

FieldTypeDescription
replicasint32(Optional)

Number of desired machines. This is a pointer to distinguish between explicit zero and not specified. Defaults to 0.

selectorKubernetes meta/v1.LabelSelector(Optional)

Label selector for machines. Existing MachineSets whose machines are selected by this will be the ones affected by this MachineDeployment.

templateMachineTemplateSpec

Template describes the machines that will be created.

strategyMachineDeploymentStrategy(Optional)

The MachineDeployment strategy to use to replace existing machines with new ones.

minReadySecondsint32(Optional)

Minimum number of seconds for which a newly created machine should be ready without any of its container crashing, for it to be considered available. Defaults to 0 (machine will be considered available as soon as it is ready)

revisionHistoryLimit*int32(Optional)

The number of old MachineSets to retain to allow rollback. This is a pointer to distinguish between explicit zero and not specified.

pausedbool(Optional)

Indicates that the MachineDeployment is paused and will not be processed by the MachineDeployment controller.

rollbackToRollbackConfig(Optional)

DEPRECATED. The config this MachineDeployment is rolling back to. Will be cleared after rollback is done.

progressDeadlineSeconds*int32(Optional)

The maximum time in seconds for a MachineDeployment to make progress before it is considered to be failed. The MachineDeployment controller will continue to process failed MachineDeployments and a condition with a ProgressDeadlineExceeded reason will be surfaced in the MachineDeployment status. Note that progress will not be estimated during the time a MachineDeployment is paused. This is not set by default, which is treated as infinite deadline.


MachineDeploymentStatus

(Appears on: MachineDeployment)

MachineDeploymentStatus is the most recently observed status of the MachineDeployment.

FieldTypeDescription
observedGenerationint64(Optional)

The generation observed by the MachineDeployment controller.

replicasint32(Optional)

Total number of non-terminated machines targeted by this MachineDeployment (their labels match the selector).

updatedReplicasint32(Optional)

Total number of non-terminated machines targeted by this MachineDeployment that have the desired template spec.

readyReplicasint32(Optional)

Total number of ready machines targeted by this MachineDeployment.

availableReplicasint32(Optional)

Total number of available machines (ready for at least minReadySeconds) targeted by this MachineDeployment.

unavailableReplicasint32(Optional)

Total number of unavailable machines targeted by this MachineDeployment. This is the total number of machines that are still required for the MachineDeployment to have 100% available capacity. They may either be machines that are running but not yet available or machines that still have not been created.

conditions[]MachineDeploymentCondition

Represents the latest available observations of a MachineDeployment’s current state.

collisionCount*int32(Optional)

Count of hash collisions for the MachineDeployment. The MachineDeployment controller uses this field as a collision avoidance mechanism when it needs to create the name for the newest MachineSet.

failedMachines[]*github.com/gardener/machine-controller-manager/pkg/apis/machine/v1alpha1.MachineSummary(Optional)

FailedMachines has summary of machines on which lastOperation Failed


MachineDeploymentStrategy

(Appears on: MachineDeploymentSpec)

MachineDeploymentStrategy describes how to replace existing machines with new ones.

FieldTypeDescription
typeMachineDeploymentStrategyType(Optional)

Type of MachineDeployment. Can be “Recreate” or “RollingUpdate”. Default is RollingUpdate.

rollingUpdateRollingUpdateMachineDeployment(Optional)

Rolling update config params. Present only if MachineDeploymentStrategyType =

RollingUpdate.

TODO: Update this to follow our convention for oneOf, whatever we decide it to be.


MachineDeploymentStrategyType (string alias)

(Appears on: MachineDeploymentStrategy)


MachineOperationType (string alias)

(Appears on: LastOperation)

MachineOperationType is a label for the operation performed on a machine object.


MachinePhase (string alias)

(Appears on: CurrentStatus)

MachinePhase is a label for the condition of a machine at the current time.


MachineSetCondition

(Appears on: MachineSetStatus)

MachineSetCondition describes the state of a machine set at a certain point.

FieldTypeDescription
typeMachineSetConditionType

Type of machine set condition.

statusConditionStatus

Status of the condition, one of True, False, Unknown.

lastTransitionTimeKubernetes meta/v1.Time(Optional)

The last time the condition transitioned from one status to another.

reasonstring(Optional)

The reason for the condition’s last transition.

messagestring(Optional)

A human readable message indicating details about the transition.


MachineSetConditionType (string alias)

(Appears on: MachineSetCondition)

MachineSetConditionType is the condition on machineset object


MachineSetSpec

(Appears on: MachineSet)

MachineSetSpec is the specification of a MachineSet.

FieldTypeDescription
replicasint32(Optional)
selectorKubernetes meta/v1.LabelSelector(Optional)
machineClassClassSpec(Optional)
templateMachineTemplateSpec(Optional)
minReadySecondsint32(Optional)

MachineSetStatus

(Appears on: MachineSet)

MachineSetStatus holds the most recently observed status of MachineSet.

FieldTypeDescription
replicasint32

Replicas is the number of actual replicas.

fullyLabeledReplicasint32(Optional)

The number of pods that have labels matching the labels of the pod template of the replicaset.

readyReplicasint32(Optional)

The number of ready replicas for this replica set.

availableReplicasint32(Optional)

The number of available replicas (ready for at least minReadySeconds) for this replica set.

observedGenerationint64(Optional)

ObservedGeneration is the most recent generation observed by the controller.

machineSetCondition[]MachineSetCondition(Optional)

Represents the latest available observations of a replica set’s current state.

lastOperationLastOperation

LastOperation performed

failedMachines[]github.com/gardener/machine-controller-manager/pkg/apis/machine/v1alpha1.MachineSummary(Optional)

FailedMachines has summary of machines on which lastOperation Failed


MachineSpec

(Appears on: Machine, MachineTemplateSpec)

MachineSpec is the specification of a Machine.

FieldTypeDescription
classClassSpec(Optional)

Class contains the machineclass attributes of a machine

providerIDstring(Optional)

ProviderID represents the provider’s unique ID given to a machine

nodeTemplateNodeTemplateSpec(Optional)

NodeTemplateSpec describes the data a node should have when created from a template

MachineConfigurationMachineConfiguration

(Members of MachineConfiguration are embedded into this type.)

(Optional)

Configuration for the machine-controller.


MachineState (string alias)

(Appears on: LastOperation)

MachineState is a current state of the operation.


MachineStatus

(Appears on: Machine)

MachineStatus holds the most recently observed status of Machine.

FieldTypeDescription
conditions[]Kubernetes core/v1.NodeCondition

Conditions of this machine, same as node

lastOperationLastOperation

Last operation refers to the status of the last operation performed

currentStatusCurrentStatus

Current status of the machine object

lastKnownStatestring(Optional)

LastKnownState can store details of the last known state of the VM by the plugins. It can be used by future operation calls to determine current infrastucture state


MachineSummary

MachineSummary store the summary of machine.

FieldTypeDescription
namestring

Name of the machine object

providerIDstring

ProviderID represents the provider’s unique ID given to a machine

lastOperationLastOperation

Last operation refers to the status of the last operation performed

ownerRefstring

OwnerRef


MachineTemplateSpec

(Appears on: MachineDeploymentSpec, MachineSetSpec)

MachineTemplateSpec describes the data a machine should have when created from a template

FieldTypeDescription
metadataKubernetes meta/v1.ObjectMeta(Optional)

Standard object’s metadata. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#metadata

Refer to the Kubernetes API documentation for the fields of the metadata field.
specMachineSpec(Optional)

Specification of the desired behavior of the machine. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#spec-and-status



classClassSpec(Optional)

Class contains the machineclass attributes of a machine

providerIDstring(Optional)

ProviderID represents the provider’s unique ID given to a machine

nodeTemplateNodeTemplateSpec(Optional)

NodeTemplateSpec describes the data a node should have when created from a template

MachineConfigurationMachineConfiguration

(Members of MachineConfiguration are embedded into this type.)

(Optional)

Configuration for the machine-controller.


NodeTemplate

(Appears on: MachineClass)

NodeTemplate contains subfields to track all node resources and other node info required to scale nodegroup from zero

FieldTypeDescription
capacityKubernetes core/v1.ResourceList

Capacity contains subfields to track all node resources required to scale nodegroup from zero

instanceTypestring

Instance type of the node belonging to nodeGroup

regionstring

Region of the expected node belonging to nodeGroup

zonestring

Zone of the expected node belonging to nodeGroup

architecture*string(Optional)

CPU Architecture of the node belonging to nodeGroup


NodeTemplateSpec

(Appears on: MachineSpec)

NodeTemplateSpec describes the data a node should have when created from a template

FieldTypeDescription
metadataKubernetes meta/v1.ObjectMeta(Optional) Refer to the Kubernetes API documentation for the fields of the metadata field.
specKubernetes core/v1.NodeSpec(Optional)

NodeSpec describes the attributes that a node is created with.



podCIDRstring(Optional)

PodCIDR represents the pod IP range assigned to the node.

podCIDRs[]string(Optional)

podCIDRs represents the IP ranges assigned to the node for usage by Pods on that node. If this field is specified, the 0th entry must match the podCIDR field. It may contain at most 1 value for each of IPv4 and IPv6.

providerIDstring(Optional)

ID of the node assigned by the cloud provider in the format: ://

unschedulablebool(Optional)

Unschedulable controls node schedulability of new pods. By default, node is schedulable. More info: https://kubernetes.io/docs/concepts/nodes/node/#manual-node-administration

taints[]Kubernetes core/v1.Taint(Optional)

If specified, the node’s taints.

configSourceKubernetes core/v1.NodeConfigSource(Optional)

Deprecated: Previously used to specify the source of the node’s configuration for the DynamicKubeletConfig feature. This feature is removed.

externalIDstring(Optional)

Deprecated. Not all kubelets will set this field. Remove field after 1.13. see: https://issues.k8s.io/61966


RollbackConfig

(Appears on: MachineDeploymentSpec)

FieldTypeDescription
revisionint64(Optional)

The revision to rollback to. If set to 0, rollback to the last revision.


RollingUpdateMachineDeployment

(Appears on: MachineDeploymentStrategy)

Spec to control the desired behavior of rolling update.

FieldTypeDescription
maxUnavailablek8s.io/apimachinery/pkg/util/intstr.IntOrString(Optional)

The maximum number of machines that can be unavailable during the update. Value can be an absolute number (ex: 5) or a percentage of desired machines (ex: 10%). Absolute number is calculated from percentage by rounding down. This can not be 0 if MaxSurge is 0. By default, a fixed value of 1 is used. Example: when this is set to 30%, the old MC can be scaled down to 70% of desired machines immediately when the rolling update starts. Once new machines are ready, old MC can be scaled down further, followed by scaling up the new MC, ensuring that the total number of machines available at all times during the update is at least 70% of desired machines.

maxSurgek8s.io/apimachinery/pkg/util/intstr.IntOrString(Optional)

The maximum number of machines that can be scheduled above the desired number of machines. Value can be an absolute number (ex: 5) or a percentage of desired machines (ex: 10%). This can not be 0 if MaxUnavailable is 0. Absolute number is calculated from percentage by rounding up. By default, a value of 1 is used. Example: when this is set to 30%, the new MC can be scaled up immediately when the rolling update starts, such that the total number of old and new machines do not exceed 130% of desired machines. Once old machines have been killed, new MC can be scaled up further, ensuring that total number of machines running at any time during the update is atmost 130% of desired machines.


Generated with gen-crd-api-reference-docs

2.2 - Proposals

2.2.1 - Excess Reserve Capacity

Excess Reserve Capacity

Goal

Currently, autoscaler optimizes the number of machines for a given application-workload. Along with effective resource utilization, this feature brings concern where, many times, when new application instances are created - they don’t find space in existing cluster. This leads the cluster-autoscaler to create new machines via MachineDeployment, which can take from 3-4 minutes to ~10 minutes, for the machine to really come-up and join the cluster. In turn, application-instances have to wait till new machines join the cluster.

One of the promising solutions to this issue is Excess Reserve Capacity. Idea is to keep a certain number of machines or percent of resources[cpu/memory] always available, so that new workload, in general, can be scheduled immediately unless huge spike in the workload. Also, the user should be given enough flexibility to choose how many resources or how many machines should be kept alive and non-utilized as this affects the Cost directly.

Note

  • We decided to go with Approach-4 which is based on low priority pods. Please find more details here: https://github.com/gardener/gardener/issues/254
  • Approach-3 looks more promising in long term, we may decide to adopt that in future based on developments/contributions in autoscaler-community.

Possible Approaches

Following are the possible approaches, we could think of so far.

Approach 1: Enhance Machine-controller-manager to also entertain the excess machines

  • Machine-controller-manager currently takes care of the machines in the shoot cluster starting from creation-deletion-health check to efficient rolling-update of the machines. From the architecture point of view, MachineSet makes sure that X number of machines are always running and healthy. MachineDeployment controller smartly uses this facility to perform rolling-updates.

  • We can expand the scope of MachineDeployment controller to maintain excess number of machines by introducing new parallel independent controller named MachineTaint controller. This will result in MCM to include Machine, MachineSet, MachineDeployment, MachineSafety, MachineTaint controllers. MachineTaint controller does not need to introduce any new CRD - analogy fits where taint-controller also resides into kube-controller-manager.

  • Only Job of MachineTaint controller will be:

    • List all the Machines under each MachineDeployment.
    • Maintain taints of noSchedule and noExecute on X latest MachineObjects.
    • There should be an event-based informer mechanism where MachineTaintController gets to know about any Update/Delete/Create event of MachineObjects - in turn, maintains the noSchedule and noExecute taints on all the latest machines. - Why latest machines? - Whenever autoscaler decides to add new machines - essentially ScaleUp event - taints from the older machines are removed and newer machines get the taints. This way X number of Machines immediately becomes free for new pods to be scheduled. - While ScaleDown event, autoscaler specifically mentions which machines should be deleted, and that should not bring any concerns. Though we will have to put proper label/annotation defined by autoscaler on taintedMachines, so that autoscaler does not consider the taintedMachines for deletion while scale-down. * Annotation on tainted node: "cluster-autoscaler.kubernetes.io/scale-down-disabled": "true"
  • Implementation Details:

    • Expect new optional field ExcessReplicas in MachineDeployment.Spec. MachineDeployment controller now adds both Spec.Replicas and Spec.ExcessReplicas[if provided], and considers that as a standard desiredReplicas. - Current working of MCM will not be affected if ExcessReplicas field is kept nil.
    • MachineController currently reads the NodeObject and sets the MachineConditions in MachineObject. Machine-controller will now also read the taints/labels from the MachineObject - and maintains it on the NodeObject.
  • We expect cluster-autoscaler to intelligently make use of the provided feature from MCM.

    • CA gets the input of min:max:excess from Gardener. CA continues to set the MachineDeployment.Spec.Replicas as usual based on the application-workload.
    • In addition, CA also sets the MachieDeployment.Spec.ExcessReplicas .
    • Corner-case: * CA should decrement the excessReplicas field accordingly when desiredReplicas+excessReplicas on MachineDeployment goes beyond max.

Approach 2: Enhance Cluster-autoscaler by simulating fake pods in it

Approach 3: Enhance cluster-autoscaler to support pluggable scaling-events

  • Forked version of cluster-autoscaler could be improved to plug-in the algorithm for excess-reserve capacity.
  • Needs further discussion around upstream support.
  • Create golang channel to separate the algorithms to trigger scaling (hard-coded in cluster-autoscaler, currently) from the algorithms about how to to achieve the scaling (already pluggable in cluster-autoscaler). This kind of separation can help us introduce/plug-in new algorithms (such as based node resource utilisation) without affecting existing code-base too much while almost completely re-using the code-base for the actual scaling.
  • Also this approach is not specific to our fork of cluster-autoscaler. It can be made upstream eventually as well.

Approach 4: Make intelligent use of Low-priority pods

  • Refer to: pod-priority-preemption
  • TL; DR:
    • High priority pods can preempt the low-priority pods which are already scheduled.
    • Pre-create bunch[equivivalent of X shoot-control-planes] of low-priority pods with priority of zero, then start creating the workload pods with better priority which will reschedule the low-priority pods or otherwise keep them in pending state if the limit for max-machines has reached.
    • This is still alpha feature.

2.2.2 - GRPC Based Implementation of Cloud Providers

GRPC based implementation of Cloud Providers - WIP

Goal:

Currently the Cloud Providers’ (CP) functionalities ( Create(), Delete(), List() ) are part of the Machine Controller Manager’s (MCM)repository. Because of this, adding support for new CPs into MCM requires merging code into MCM which may not be required for core functionalities of MCM itself. Also, for various reasons it may not be feasible for all CPs to merge their code with MCM which is an Open Source project.

Because of these reasons, it was decided that the CP’s code will be moved out in separate repositories so that they can be maintained separately by the respective teams. Idea is to make MCM act as a GRPC server, and CPs as GRPC clients. The CP can register themselves with the MCM using a GRPC service exposed by the MCM. Details of this approach is discussed below.

How it works:

MCM acts as GRPC server and listens on a pre-defined port 5000. It implements below GRPC services. Details of each of these services are mentioned in next section.

  • Register()
  • GetMachineClass()
  • GetSecret()

GRPC services exposed by MCM:

Register()

rpc Register(stream DriverSide) returns (stream MCMside) {}

The CP GRPC client calls this service to register itself with the MCM. The CP passes the kind and the APIVersion which it implements, and MCM maintains an internal map for all the registered clients. A GRPC stream is returned in response which is kept open througout the life of both the processes. MCM uses this stream to communicate with the client for machine operations: Create(), Delete() or List(). The CP client is responsible for reading the incoming messages continuously, and based on the operationType parameter embedded in the message, it is supposed to take the required action. This part is already handled in the package grpc/infraclient. To add a new CP client, import the package, and implement the ExternalDriverProvider interface:

type ExternalDriverProvider interface {
	Create(machineclass *MachineClassMeta, credentials, machineID, machineName string) (string, string, error)
	Delete(machineclass *MachineClassMeta, credentials, machineID string) error
	List(machineclass *MachineClassMeta, credentials, machineID string) (map[string]string, error)
}

GetMachineClass()

rpc GetMachineClass(MachineClassMeta) returns (MachineClass) {}

As part of the message from MCM for various machine operations, the name of the machine class is sent instead of the full machine class spec. The CP client is expected to use this GRPC service to get the full spec of the machine class. This optionally enables the client to cache the machine class spec, and make the call only if the machine calass spec is not already cached.

GetSecret()

rpc GetSecret(SecretMeta) returns (Secret) {}

As part of the message from MCM for various machine operations, the Cloud Config (CC) and CP credentials are not sent. The CP client is expected to use this GRPC service to get the secret which has CC and CP’s credentials from MCM. This enables the client to cache the CC and credentials, and to make the call only if the data is not already cached.

How to add a new Cloud Provider’s support

Import the package grpc/infraclient and grpc/infrapb from MCM (currently in MCM’s “grpc-driver” branch)

  • Implement the interface ExternalDriverProvider
    • Create(): Creates a new machine
    • Delete(): Deletes a machine
    • List(): Lists machines
  • Use the interface MachineClassDataProvider
    • GetMachineClass(): Makes the call to MCM to get machine class spec
    • GetSecret(): Makes the call to MCM to get secret containing Cloud Config and CP’s credentials

Example implementation:

Refer GRPC based implementation for AWS client: https://github.com/ggaurav10/aws-driver-grpc

2.2.3 - Hotupdate Instances

Hot-Update VirtualMachine tags without triggering a rolling-update

Motivation

  • MCM Issue#750 There is a requirement to provide a way for consumers to add tags which can be hot-updated onto VMs. This requirement can be generalized to also offer a convenient way to specify tags which can be applied to VMs, NICs, Devices etc.

  • MCM Issue#635 which in turn points to MCM-Provider-AWS Issue#36 - The issue hints at other fields like enable/disable source/destination checks for NAT instances which needs to be hot-updated on network interfaces.

  • In GCP provider - instance.ServiceAccounts can be updated without the need to roll-over the instance. See

Boundary Condition

All tags that are added via means other than MachineClass.ProviderSpec should be preserved as-is. Only updates done to tags in MachineClass.ProviderSpec should be applied to the infra resources (VM/NIC/Disk).

What is available today?

WorkerPool configuration inside shootYaml provides a way to set labels. As per the definition these labels will be applied on Node resources. Currently these labels are also passed to the VMs as tags. There is no distinction made between Node labels and VM tags.

MachineClass has a field which holds provider specific configuration and one such configuration is tags. Gardener provider extensions updates the tags in MachineClass.

Let us look at an example of MachineClass.ProviderSpec in AWS:

providerSpec:
  ami: ami-02fe00c0afb75bbd3
  tags:
    #[section-1] pool lables added by gardener extension
    #########################################################
    kubernetes.io/arch: amd64
    networking.gardener.cloud/node-local-dns-enabled: "true"
    node.kubernetes.io/role: node
    worker.garden.sapcloud.io/group: worker-ser234
    worker.gardener.cloud/cri-name: containerd
    worker.gardener.cloud/pool: worker-ser234
    worker.gardener.cloud/system-components: "true"

    #[section-2] Tags defined in the gardener-extension-provider-aws
    ###########################################################
    kubernetes.io/cluster/cluster-full-name: "1"
    kubernetes.io/role/node: "1"

    #[section-3]
    ###########################################################
    user-defined-key1: user-defined-val1
    user-defined-key2: user-defined-val2

Refer src for tags defined in section-1. Refer src for tags defined in section-2. Tags in section-3 are defined by the user.

Out of the above three tag categories, MCM depends section-2 tags (mandatory-tags) for its orphan collection and Driver’s DeleteMachineand GetMachineStatus to work.

ProviderSpec.Tags are transported to the provider specific resources as follows:

ProviderResources Tags are set onCode ReferenceComment
AWSInstance(VM), Volume, Network-Interfaceaws-VM-Vol-NICNo distinction is made between tags set on VM, NIC or Volume
AzureInstance(VM), Network-Interfaceazure-VM-parameters & azureNIC-Parameters
GCPInstance(VM), 1 tag: name (denoting the name of the worker) is added to Diskgcp-VM & gcp-DiskIn GCP key-value pairs are called labels while network tags have only keys
AliCloudInstance(VM)aliCloud-VM

What are the problems with the current approach?

There are a few shortcomings in the way tags/labels are handled:

  • Tags can only be set at the time a machine is created.
  • There is no distinction made amongst tags/labels that are added to VM’s, disks or network interfaces. As stated above for AWS same set of tags are added to all. There is a limit defined on the number of tags/labels that can be associated to the devices (disks, VMs, NICs etc). Example: In AWS a max of 50 user created tags are allowed. Similar restrictions are applied on different resources across providers. Therefore adding all tags to all devices even if the subset of tags are not meant for that resource exhausts the total allowed tags/labels for that resource.
  • The only placeholder in shoot yaml as mentioned above is meant to only hold labels that should be applied on primarily on the Node objects. So while you could use the node labels for extended resources, using it also for tags is not clean.
  • There is no provision in the shoot YAML today to add tags only to a subset of resources.

MachineClass Update and its impact

When Worker.ProviderConfig is changed then a worker-hash is computed which includes the raw ProviderConfig. This hash value is then used as a suffix when constructing the name for a MachineClass. See aws-extension-provider as an example. A change in the name of the MachineClass will then in-turn trigger a rolling update of machines. Since tags are provider specific and therefore will be part of ProviderConfig, any update to them will result in a rolling-update of machines.

Proposal

Shoot YAML changes

Provider specific configuration is set via providerConfig section for each worker pool.

Example worker provider config (current):

providerConfig:
   apiVersion: aws.provider.extensions.gardener.cloud/v1alpha1
   kind: WorkerConfig
   volume:
     iops: 10000
   dataVolumes:
   - name: kubelet-dir
     snapshotID: snap-13234
   iamInstanceProfile: # (specify either ARN or name)
     name: my-profile
     arn: my-instance-profile-arn

It is proposed that an additional field be added for tags under providerConfig. Proposed changed YAML:

providerConfig:
   apiVersion: aws.provider.extensions.gardener.cloud/v1alpha1
   kind: WorkerConfig
   volume:
     iops: 10000
   dataVolumes:
   - name: kubelet-dir
     snapshotID: snap-13234
   iamInstanceProfile: # (specify either ARN or name)
     name: my-profile
     arn: my-instance-profile-arn
   tags:
     vm:
       key1: val1
       key2: val2
       ..
     # for GCP network tags are just keys (there is no value associated to them). 
     # What is shown below will work for AWS provider.
     network:
       key3: val3
       key4: val4

Under tags clear distinction is made between tags for VMs, Disks, network interface etc. Each provider has a different allowed-set of characters that it accepts as key names, has different limits on the tags that can be set on a resource (disk, NIC, VM etc.) and also has a different format (GCP network tags are only keys).

TODO:

  • Check if worker.labels are getting added as tags on infra resources. We should continue to support it and double check that these should only be added to VMs and not to other resources.

  • Should we support users adding VM tags as node labels?

Provider specific WorkerConfig API changes

Taking AWS provider extension as an example to show the changes.

WorkerConfig will now have the following changes:

  1. A new field for tags will be introduced.
  2. Additional metadata for struct fields will now be added via struct tags.
type WorkerConfig struct {
    metav1.TypeMeta
    Volume *Volume
    // .. all fields are not mentioned here.
    // Tags are a collection of tags to be set on provider resources (e.g. VMs, Disks, Network Interfaces etc.)
    Tags *Tags `hotupdatable:true`
}

// Tags is a placeholder for all tags that can be set/updated on VMs, Disks and Network Interfaces.
type Tags struct {
    // VM tags set on the VM instances.
    VM map[string]string
    // Network tags set on the network interfaces.
    Network map[string]string
    // Disk tags set on the volumes/disks.
    Disk map[string]string
}

There is a need to distinguish fields within ProviderSpec (which is then mapped to the above WorkerConfig) which can be updated without the need to change the hash suffix for MachineClass and thus trigger a rolling update on machines.

To achieve that we propose to use struct tag hotupdatable whose value indicates if the field can be updated without the need to do a rolling update. To ensure backward compatibility, all fields which do not have this tag or have hotupdatable set to false will be considered as immutable and will require a rolling update to take affect.

Gardener provider extension changes

Taking AWS provider extension as an example. Following changes should be made to all gardener provider extensions

AWS Gardener Extension generates machine config using worker pool configuration. As part of that it also computes the workerPoolHash which is then used to create the name of the MachineClass.

Currently WorkerPoolHash function uses the entire providerConfig to compute the hash. Proposal is to do the following:

  1. Remove the code from function WorkerPoolHash.
  2. Add another function to compute hash using all immutable fields in the provider config struct and then pass that to worker.WorkerPoolHash as additionalData.

The above will ensure that tags and any other field in WorkerConfig which is marked with updatable:true is not considered for hash computation and will therefore not contribute to changing the name of MachineClass object thus preventing a rolling update.

WorkerConfig and therefore the contained tags will be set as ProviderSpec in MachineClass.

If only fields which have updatable:true are changed then it should result in update/patch of MachineClass and not creation.

Driver interface changes

Driver interface which is a facade to provider specific API implementations will have one additional method.

type Driver interface {
    // .. existing methods are not mentioned here for brevity.
    UpdateMachine(context.Context, *UpdateMachineRequest) error
}

// UpdateMachineRequest is the request to update machine tags. 
type UpdateMachineRequest struct {
    ProviderID string
    LastAppliedProviderSpec raw.Extension
    MachineClass *v1alpha1.MachineClass
    Secret *corev1.Secret
}

If any machine-controller-manager-provider-<providername> has not implemented UpdateMachine then updates of tags on Instances/NICs/Disks will not be done. An error message will be logged instead.

Machine Class reconciliation

Current MachineClass reconciliation does not reconcile MachineClass resource updates but it only enqueues associated machines. The reason is that it is assumed that anything that is changed in a MachineClass will result in a creation of a new MachineClass with a different name. This will result in a rolling update of all machines using the MachineClass as a template.

However, it is possible that there is data that all machines in a MachineSet share which do not require a rolling update (e.g. tags), therefore there is a need to reconcile the MachineClass as well.

Reconciliation Changes

In order to ensure that machines get updated eventually with changes to the hot-updatable fields defined in the MachineClass.ProviderConfig as raw.Extension.

We should only fix MCM Issue#751 in the MachineClass reconciliation and let it enqueue the machines as it does today. We additionally propose the following two things:

  1. Introduce a new annotation last-applied-providerspec on every machine resource. This will capture the last successfully applied MachineClass.ProviderSpec on this instance.

  2. Enhance the machine reconciliation to include code to hot-update machine.

In machine-reconciliation there are currently two flows triggerDeletionFlow and triggerCreationFlow. When a machine gets enqueued due to changes in MachineClass then in this method following changes needs to be introduced:

Check if the machine has last-applied-providerspec annotation.

Case 1.1

If the annotation is not present then there can be just 2 possibilities:

  • It is a fresh/new machine and no backing resources (VM/NIC/Disk) exist yet. The current flow checks if the providerID is empty and Status.CurrenStatus.Phase is empty then it enters into the triggerCreationFlow.

  • It is an existing machine which does not yet have this annotation. In this case call Driver.UpdateMachine. If the driver returns no error then add last-applied-providerspec annotation with the value of MachineClass.ProviderSpec to this machine.

Case 1.2

If the annotation is present then compare the last applied provider-spec with the current provider-spec. If there are changes (check their hash values) then call Driver.UpdateMachine. If the driver returns no error then add last-applied-providerspec annotation with the value of MachineClass.ProviderSpec to this machine.

NOTE: It is assumed that if there are changes to the fields which are not marked as hotupdatable then it will result in the change of name for MachineClass resulting in a rolling update of machines. If the name has not changed + machine is enqueued + there is a change in machine-class then it will be change to a hotupdatable fields in the spec.

Trigger update flow can be done after reconcileMachineHealth and syncMachineNodeTemplates in machine-reconciliation.

There are 2 edge cases that needs attention and special handling:

Premise: It is identified that there is an update done to one or more hotupdatable fields in the MachineClass.ProviderSpec.

Edge-Case-1

In the machine reconciliation, an update-machine-flow is triggered which in-turn calls Driver.UpdateMachine. Consider the case where the hot update needs to be done to all VM, NIC and Disk resources. The driver returns an error which indicates a partial-failure. As we have mentioned above only when Driver.UpdateMachine returns no error will last-applied-providerspec be updated. In case of partial failure the annotation will not be updated. This event will be re-queued for a re-attempt. However consider a case where before the item is re-queued, another update is done to MachineClass reverting back the changes to the original spec.

At T1At T2 (T2 > T1)At T3 (T3> T2)
last-applied-providerspec=S1
MachineClass.ProviderSpec = S1
last-applied-providerspec=S1
MachineClass.ProviderSpec = S2
 Another update to MachineClass.ProviderConfig = S3 is enqueue (S3 == S1)
last-applied-providerspec=S1
Driver.UpdateMachine for S1-S2 update - returns partial failure
Machine-Key is requeued

At T4 (T4> T3) when a machine is reconciled then it checks that last-applied-providerspec is S1 and current MachineClass.ProviderSpec = S3 and since S3 is same as S1, no update is done. At T2 Driver.UpdateMachine was called to update the machine with S2 but it partially failed. So now you will have resources which are partially updated with S2 and no further updates will be attempted.

Edge-Case-2

The above situation can also happen when Driver.UpdateMachine is in the process of updating resources. It has hot-updated lets say 1 resource. But now MCM crashes. By the time it comes up another update to MachineClass.ProviderSpec is done essentially reverting back the previous change (same case as above). In this case reconciliation loop never got a chance to get any response from the driver.

To handle the above edge cases there are 2 options:

Option #1

Introduce a new annotation inflight-providerspec-hash . The value of this annotation will be the hash value of the MachineClass.ProviderSpec that is in the process of getting applied on this machine. The machine will be updated with this annotation just before calling Driver.UpdateMachine (in the trigger-update-machine-flow). If the driver returns no error then (in a single update):

  1. last-applied-providerspec will be updated

  2. inflight-providerspec-hash annotation will be removed.

Option #2 - Preferred

Leverage Machine.Status.LastOperation with Type set to MachineOperationUpdate and State set to MachineStateProcessing This status will be updated just before calling Driver.UpdateMachine.

Semantically LastOperation captures the details of the operation post-operation and not pre-operation. So this solution would be a divergence from the norm.

2.2.4 - Initialize Machine

Post-Create Initialization of Machine Instance

Background

Today the driver.Driver facade represents the boundary between the the machine-controller and its various provider specific implementations.

We have abstract operations for creation/deletion and listing of machines (actually compute instances) but we do not correctly handle post-creation initialization logic. Nor do we provide an abstract operation to represent the hot update of an instance after creation.

We have found this to be necessary for several use cases. Today in the MCM AWS Provider, we already misuse driver.GetMachineStatus which is supposed to be a read-only operation obtaining the status of an instance.

  1. Each AWS EC2 instance performs source/destination checks by default. For EC2 NAT instances these should be disabled. This is done by issuing a ModifyInstanceAttribute request with the SourceDestCheck set to false. The MCM AWS Provider, decodes the AWSProviderSpec, reads providerSpec.SrcAndDstChecksEnabled and correspondingly issues the call to modify the already launched instance. However, this should be done as an action after creating the instance and should not be part of the VM status retrieval.

  2. Similarly, there is a pending PR to add the Ipv6AddessCount and Ipv6PrefixCount to enable the assignment of an ipv6 address and an ipv6 prefix to instances. This requires constructing and issuing an AssignIpv6Addresses request after the EC2 instance is available.

  3. We have other uses-cases such as MCM Issue#750 where there is a requirement to provide a way for consumers to add tags which can be hot-updated onto instances. This requirement can be generalized to also offer a convenient way to specify tags which can be applied to VMs, NICs, Devices etc.

  4. We have a need for “machine-instance-not-ready” taint as described in MCM#740 which should only get removed once the post creation updates are finished.

Objectives

We will split the fulfilment of this overall need into 2 stages of implementation.

  1. Stage-A: Support post-VM creation initialization logic of the instance suing a proposed Driver.InitializeMachine by permitting provider implementors to add initialization logic after VM creation, return with special new error code codes.Initialization for initialization errors and correspondingly support a new machine operation stage InstanceInitialization which will be updated in the machine LastOperation. The triggerCreationFlow - a reconciliation sub-flow of the MCM responsible for orchestrating instance creation and updating machine status will be changed to support this behaviour.

  2. Stage-B: Introduction of Driver.UpdateMachine and enhancing the MCM, MCM providers and gardener extension providers to support hot update of instances through Driver.UpdateMachine. The MCM triggerUpdationFlow - a reconciliation sub-flow of the MCM which is supposed to be responsible for orchestrating instance update - but currently not used, will be updated to invoke the provider Driver.UpdateMachine on hot-updates to to the Machine object

Stage-A Proposal

Current MCM triggerCreationFlow

Today, reconcileClusterMachine which is the main routine for the Machine object reconciliation invokes triggerCreationFlow at the end when the machine.Spec.ProviderID is empty or if the machine.Status.CurrentStatus.Phase is empty or in CrashLoopBackOff

%%{ init: {
    'themeVariables':
        { 'fontSize': '12px'}
} }%%
flowchart LR

other["..."]
-->chk{"machine ProviderID empty
OR
Phase empty or CrashLoopBackOff ?
"}--yes-->triggerCreationFlow
chk--noo-->LongRetry["return machineutils.LongRetry"]

Today, the triggerCreationFlow is illustrated below with some minor details omitted/compressed for brevity

NOTES

  • The lastop below is an abbreviation for machine.Status.LastOperation. This, along with the machine phase is generally updated on the Machine object just before returning from the method.
  • regarding phase=CrashLoopBackOff|Failed. the machine phase may either be CrashLoopBackOff or move to Failed if the difference between current time and the machine.CreationTimestamp has exceeded the configured MachineCreationTimeout.
%%{ init: {
    'themeVariables':
        { 'fontSize': '12px'}
} }%%
flowchart TD


end1(("end"))
begin((" "))
medretry["return MediumRetry, err"]
shortretry["return ShortRetry, err"]
medretry-->end1
shortretry-->end1

begin-->AddBootstrapTokenToUserData
-->gms["statusResp,statusErr=driver.GetMachineStatus(...)"]
-->chkstatuserr{"Check statusErr"}
chkstatuserr--notFound-->chknodelbl{"Chk Node Label"}
chkstatuserr--else-->createFailed["lastop.Type=Create,lastop.state=Failed,phase=CrashLoopBackOff|Failed"]-->medretry
chkstatuserr--nil-->initnodename["nodeName = statusResp.NodeName"]-->setnodename


chknodelbl--notset-->createmachine["createResp, createErr=driver.CreateMachine(...)"]-->chkCreateErr{"Check createErr"}

chkCreateErr--notnil-->createFailed

chkCreateErr--nil-->getnodename["nodeName = createResp.NodeName"]
-->chkstalenode{"nodeName != machine.Name\n//chk stale node"}
chkstalenode--false-->setnodename["if unset machine.Labels['node']= nodeName"]
-->machinepending["if empty/crashloopbackoff lastop.type=Create,lastop.State=Processing,phase=Pending"]
-->shortretry

chkstalenode--true-->delmachine["driver.DeleteMachine(...)"]
-->permafail["lastop.type=Create,lastop.state=Failed,Phase=Failed"]
-->shortretry

subgraph noteA [" "]
    permafail -.- note1(["VM was referring to stale node obj"])
end
style noteA opacity:0


subgraph noteB [" "]
    setnodename-.- note2(["Proposal: Introduce Driver.InitializeMachine after this"])
end

Enhancement of MCM triggerCreationFlow

Relevant Observations on Current Flow

  1. Observe that we always perform a call to Driver.GetMachineStatus and only then conditionally perform a call to Driver.CreateMachine if there was was no machine found.
  2. Observe that after the call to a successful Driver.CreateMachine, the machine phase is set to Pending, the LastOperation.Type is currently set to Create and the LastOperation.State set to Processing before returning with a ShortRetry. The LastOperation.Description is (unfortunately) set to the fixed message: Creating machine on cloud provider.
  3. Observe that after an erroneous call to Driver.CreateMachine, the machine phase is set to CrashLoopBackOff or Failed (in case of creation timeout).

The following changes are proposed with a view towards minimal impact on current code and no introduction of a new Machine Phase.

MCM Changes

  1. We propose introducing a new machine operation Driver.InitializeMachine with the following signature
    type Driver interface {
        // .. existing methods are omitted for brevity.
    
        // InitializeMachine call is responsible for post-create initialization of the provider instance.
        InitializeMachine(context.Context, *InitializeMachineRequest) error
    }
    
    // InitializeMachineRequest is the initialization request for machine instance initialization
    type InitializeMachineRequest struct {
        // Machine object whose VM instance should be initialized 
        Machine *v1alpha1.Machine
    
        // MachineClass backing the machine object
        MachineClass *v1alpha1.MachineClass
    
        // Secret backing the machineClass object
        Secret *corev1.Secret
    }
    
  2. We propose introducing a new MC error code codes.Initialization indicating that the VM Instance was created but there was an error in initialization after VM creation. The implementor of Driver.InitializeMachine can return this error code, indicating that InitializeMachine needs to be called again. The Machine Controller will change the phase to CrashLoopBackOff as usual when encountering a codes.Initialization error.
  3. We will introduce a new machine operation stage InstanceInitialization. In case of an codes.Initialization error
    1. the machine.Status.LastOperation.Description will be set to InstanceInitialization,
    2. machine.Status.LastOperation.ErrorCode will be set to codes.Initialization
    3. the LastOperation.Type will be set to Create
    4. the LastOperation.State set to Failed before returning with a ShortRetry
  4. The semantics of Driver.GetMachineStatus will be changed. If the instance associated with machine exists, but the instance was not initialized as expected, the provider implementations of GetMachineStatus should return an error: status.Error(codes.Initialization).
  5. If Driver.GetMachineStatus returned an error encapsulating codes.Initialization then Driver.InitializeMachine will be invoked again in the triggerCreationFlow.
  6. As according to the usual logic, the main machine controller reconciliation loop will now re-invoke the triggerCreationFlow again if the machine phase is CrashLoopBackOff.

Illustration

Enhanced triggerCreationFlow

AWS Provider Changes

Driver.InitializeMachine

The implementation for the AWS Provider will look something like:

  1. After the VM instance is available, check providerSpec.SrcAndDstChecksEnabled, construct ModifyInstanceAttributeInput and call ModifyInstanceAttribute. In case of an error return codes.Initialization instead of the current codes.Internal
  2. Check providerSpec.NetworkInterfaces and if Ipv6PrefixCount is not nil, then construct AssignIpv6AddressesInput and call AssignIpv6Addresses. In case of an error return codes.Initialization. Don’t use the generic codes.Internal

The existing Ipv6 PR will need modifications.

Driver.GetMachineStatus
  1. If providerSpec.SrcAndDstChecksEnabled is false, check ec2.Instance.SourceDestCheck. If it does not match then return status.Error(codes.Initialization)
  2. Check providerSpec.NetworkInterfaces and if Ipv6PrefixCount is not nil, check ec2.Instance.NetworkInterfaces and check if InstanceNetworkInterface.Ipv6Addresses has a non-nil slice. If this is not the case then return status.Error(codes.Initialization)

Instance Not Ready Taint

  • Due to the fact that creation flow for machines will now be enhanced to correctly support post-creation startup logic, we should not scheduled workload until this startup logic is complete. Even without this feature we have a need for such a taint as described in MCM#740
  • We propose a new taint node.machine.sapcloud.io/instance-not-ready which will be added as a node startup taint in gardener core KubeletConfiguration.RegisterWithTaints
  • The will will then removed by MCM in health check reconciliation, once the machine becomes fully ready. (when moving to Running phase)
  • We will add this taint as part of --ignore-taint in CA
  • We will introduce a disclaimer / prerequisite in the MCM FAQ, to add this taint as part of kubelet config under --register-with-taints, otherwise workload could get scheduled , before machine beomes Running

Stage-B Proposal

Enhancement of Driver Interface for Hot Updation

Kindly refer to the Hot-Update Instances design which provides elaborate detail.

2.3 - ToDo

2.3.1 - Outline

Machine Controller Manager

CORE – ./machine-controller-manager(provider independent) Out of tree : Machine controller (provider specific) MCM is a set controllers:

  • Machine Deployment Controller

  • Machine Set Controller

  • Machine Controller

  • Machine Safety Controller

Questions and refactoring Suggestions

Refactoring

StatementFilePathStatus
ConcurrentNodeSyncs” bad name - nothing to do with node syncs actually.
If its value is ’10’ then it will start 10 goroutines (workers) per resource type (machine, machinist, machinedeployment, provider-specific-class, node - study the different resource types.
cmd/machine-controller-manager/app/options/options.gopending
LeaderElectionConfiguration is very similar to the one present in “client-go/tools/leaderelection/leaderelection.go” - can we simply used the one in client-go instead of defining again?pkg/options/types.go - MachineControllerManagerConfigurationpending
Have all userAgents as constant. Right now there is just one.cmd/app/controllermanager.gopending
Shouldn’t run function be defined on MCMServer struct itself?cmd/app/controllermanager.gopending
clientcmd.BuildConfigFromFlags fallsback to inClusterConfig which will surely not work as that is not the target. Should it not check and exit early?cmd/app/controllermanager.go - run Functionpending
A more direct way to create an in cluster config is using k8s.io/client-go/rest -> rest.InClusterConfig instead of using clientcmd.BuildConfigFromFlags passing empty arguments and depending upon the implementation to fallback to creating a inClusterConfig. If they change the implementation that you get affected.cmd/app/controllermanager.go - run Functionpending
Introduce a method on MCMServer which gets a target KubeConfig and controlKubeConfig or alternatively which creates respective clients.cmd/app/controllermanager.go - run Functionpending
Why can’t we use Kubernetes.NewConfigOrDie also for kubeClientControl?cmd/app/controllermanager.go - run Functionpending
I do not see any benefit of client builders actually. All you need to do is pass in a config and then directly use client-go functions to create a client.cmd/app/controllermanager.go - run Functionpending
Function: getAvailableResources - rename this to getApiServerResourcescmd/app/controllermanager.gopending
Move the method which waits for API server to up and ready to a separate method which returns a discoveryClient when the API server is ready.cmd/app/controllermanager.go - getAvailableResources functionpending
Many methods in client-go used are now deprecated. Switch to the ones that are now recommended to be used instead.cmd/app/controllermanager.go - startControllerspending
This method needs a general overhaulcmd/app/controllermanager.go - startControllerspending
If the design is influenced/copied from KCM then its very different. There are different controller structs defined for deployment, replicaset etc which makes the code much more clearer. You can see “kubernetes/cmd/kube-controller-manager/apps.go” and then follow the trail from there. - agreed needs to be changed in future (if time permits)pkg/controller/controller.gopending
I am not sure why “MachineSetControlInterface”, “RevisionControlInterface”, “MachineControlInterface”, “FakeMachineControl” are defined in this file?pkg/controller/controller_util.gopending
IsMachineActive - combine the first 2 conditions into one with OR.pkg/controller/controller_util.gopending
Minor change - correct the comment, first word should always be the method name. Currently none of the comments have correct names.pkg/controller/controller_util.gopending
There are too many deep copies made. What is the need to make another deep copy in this method? You are not really changing anything here.pkg/controller/deployment.go - updateMachineDeploymentFinalizerspending
Why can’t these validations be done as part of a validating webhook?pkg/controller/machineset.go - reconcileClusterMachineSetpending
Small change to the following if condition. else if is not required a simple else is sufficient. Code1
pkg/controller/machineset.go - reconcileClusterMachineSetpending
Why call these inactiveMachines, these are live and running and therefore active.pkg/controller/machineset.go - terminateMachinespending

Clarification

StatementFilePathStatus
Why are there 2 versions - internal and external versions?Generalpending
Safety controller freezes MCM controllers in the following cases:
* Num replicas go beyond a threshold (above the defined replicas)
* Target API service is not reachable
There seems to be an overlap between DWD and MCM Safety controller. In the meltdown scenario why is MCM being added to DWD, you could have used Safety controller for that.
Generalpending
All machine resources are v1alpha1 - should we not promote it to beta. V1alpha1 has a different semantic and does not give any confidence to the consumers.cmd/app/controllermanager.gopending
Shouldn’t controller manager use context.Context instead of creating a stop channel? - Check if signals (os.Interrupt and SIGTERM are handled properly. Do not see code where this is handled currently.)cmd/app/controllermanager.gopending
What is the rationale behind a timeout of 10s? If the API server is not up, should this not just block as it can anyways not do anything. Also, if there is an error returned then you exit the MCM which does not make much sense actually as it will be started again and you will again do the poll for the API server to come back up. Forcing an exit of MCM will not have any impact on the reachability of the API server in anyway so why exit?cmd/app/controllermanager.go - getAvailableResourcespending
There is a very weird check - availableResources[machineGVR] || availableResources[machineSetGVR] || availableResources[machineDeploymentGVR]
Shouldn’t this be conjunction instead of disjunction?
* What happens if you do not find one or all of these resources?
Currently an error log is printed and nothing else is done. MCM can be used outside gardener context where consumers can directly create MachineClass and Machine and not create MachineSet / Maching Deployment. There is no distinction made between context (gardener or outside-gardener).
cmd/app/controllermanager.go - StartControllerspending
Instead of having an empty select {} to block forever, isn’t it better to wait on the stop channel?cmd/app/controllermanager.go - StartControllerspending
Do we need provider specific queues and syncs and listerspkg/controller/controller.gopending
Why are resource types prefixed with “Cluster”? - not sure , check PRpkg/controller/controller.gopending
When will forgetAfterSuccess be false and why? - as per the current code this is never the case. - Himanshu will checkcmd/app/controllermanager.go - createWorkerpending
What is the use of “ExpectationsInterface” and “UIDTrackingContExpectations”?
* All expectations related code should be in its own file “expectations.go” and not in this file.
pkg/controller/controller_util.gopending
Why do we not use lister but directly use the controlMachingClient to get the deployment? Is it because you want to avoid any potential delays caused by update of the local cache held by the informer and accessed by the lister? What is the load on API server due to this?pkg/controller/deployment.go - reconcileClusterMachineDeploymentpending
Why is this conversion needed? code2pkg/controller/deployment.go - reconcileClusterMachineDeploymentpending
A deep copy of machineDeployment is already passed and within the function another deepCopy is made. Any reason for it?pkg/controller/deployment.go - addMachineDeploymentFinalizerspending
What is an Status.ObservedGeneration?
*Read more about generations and observedGeneration at:
https://github.com/kubernetes/community/blob/master/contributors/devel/sig-architecture/api-conventions.md#metadata
https://alenkacz.medium.com/kubernetes-operator-best-practices-implementing-observedgeneration-250728868792
Ideally the update to the ObservedGeneration should only be made after successful reconciliation and not before. I see that this is just copied from deployment_controller.go as is
pkg/controller/deployment.go - reconcileClusterMachineDeploymentpending
Why and when will a MachineDeployment be marked as frozen and when will it be un-frozen?pkg/controller/deployment.go - reconcileClusterMachineDeploymentpending
Shoudn’t the validation of the machine deployment be done during the creation via a validating webhook instead of allowing it to be stored in etcd and then failing the validation during sync? I saw the checks and these can be done via validation webhook.pkg/controller/deployment.go - reconcileClusterMachineDeploymentpending
RollbackTo has been marked as deprecated. What is the replacement? code3pkg/controller/deployment.go - reconcileClusterMachineDeploymentpending
What is the max machineSet deletions that you could process in a single run? The reason for asking this question is that for every machineSetDeletion a new goroutine spawned.
* Is the Delete call a synchrounous call? Which means it blocks till the machineset deletion is triggered which then also deletes the machines (due to cascade-delete and blockOwnerDeletion= true)?
pkg/controller/deployment.go - terminateMachineSetspending
If there are validation errors or error when creating label selector then a nil is returned. In the worker reconcile loop if the return value is nil then it will remove it from the queue (forget + done). What is the way to see any errors? Typically when we describe a resource the errors are displayed. Will these be displayed when we discribe a MachineDeployment?pkg/controller/deployment.go - reconcileClusterMachineSetpending
If an error is returned by updateMachineSetStatus and it is IsNotFound error then returning an error will again queue the MachineSet. Is this desired as IsNotFound indicates the MachineSet has been deleted and is no longer there?pkg/controller/deployment.go - reconcileClusterMachineSetpending
is machineControl.DeleteMachine a synchronous operation which will wait till the machine has been deleted? Also where is the DeletionTimestamp set on the Machine? Will it be automatically done by the API server?pkg/controller/deployment.go - prepareMachineForDeletionpending

Bugs/Enhancements

Statement + TODOFilePathStatus
This defines QPS and Burst for its requests to the KAPI. Check if it would make sense to explicitly define a FlowSchema and PriorityLevelConfiguration to ensure that the requests from this controller are given a well-defined preference. What is the rational behind deciding these values?pkg/options/types.go - MachineControllerManagerConfigurationpending
In function “validateMachineSpec” fldPath func parameter is never used.pkg/apis/machine/validation/machine.gopending
If there is an update failure then this method recursively calls itself without any sort of delays which could lead to a LOT of load on the API server. (opened: https://github.com/gardener/machine-controller-manager/issues/686)pkg/controller/deployment.go - updateMachineDeploymentFinalizerspending
We are updating filteredMachines by invoking syncMachinesNodeTemplates, syncMachinesConfig and syncMachinesClassKind but we do not create any deepCopy here. Everywhere else the general principle is when you mutate always make a deepCopy and then mutate the copy instead of the original as a lister is used and that changes the cached copy.
Fix: SatisfiedExpectations check has been commented and there is a TODO there to fix it. Is there a PR for this?
pkg/controller/machineset.go - reconcileClusterMachineSetpending

Code references

1.1 code1

       if machineSet.DeletionTimestamp == nil {
        
        		// manageReplicas is the core machineSet method where scale up/down occurs
        
        		// It is not called when deletion timestamp is set
        
        		manageReplicasErr = c.manageReplicas(ctx, filteredMachines, machineSet)
        
        
        
        	} else if machineSet.DeletionTimestamp != nil { 
        
            //FIX: change this to simple else without the if

1.2 code2

    defer dc.enqueueMachineDeploymentAfter(deployment, 10*time.Minute)
    
    *  `Clarification`:  Why  is  this  conversion  needed?
    
    err = v1alpha1.Convert_v1alpha1_MachineDeployment_To_machine_MachineDeployment(deployment, internalMachineDeployment, nil)

1.3 code3


// rollback is not re-entrant in case the underlying machine sets are updated with a new

	// revision so we should ensure that we won't proceed to update machine sets until we

	// make sure that the deployment has cleaned up its rollback spec in subsequent enqueues.

	if d.Spec.RollbackTo != nil {

		return dc.rollback(ctx, d, machineSets, machineMap)

	}

2.4 - FAQ

Frequently Asked Questions

Frequently Asked Questions

The answers in this FAQ apply to the newest (HEAD) version of Machine Controller Manager. If you’re using an older version of MCM please refer to corresponding version of this document. Few of the answers assume that the MCM being used is in conjuction with cluster-autoscaler:

Table of Contents:

Basics

What is Machine Controller Manager?

Machine Controller Manager aka MCM is a bunch of controllers used for the lifecycle management of the worker machines. It reconciles a set of CRDs such as Machine, MachineSet, MachineDeployment which depicts the functionality of Pod, Replicaset, Deployment of the core Kubernetes respectively. Read more about it at README.

  • Gardener uses MCM to manage its Kubernetes nodes of the shoot cluster. However, by design, MCM can be used independent of Gardener.

Why is my machine deleted?

A machine is deleted by MCM generally for 2 reasons-

  • Machine is unhealthy for at least MachineHealthTimeout period. The default MachineHealthTimeout is 10 minutes.

    • By default, a machine is considered unhealthy if any of the following node conditions - DiskPressure, KernelDeadlock, FileSystem, Readonly is set to true, or KubeletReady is set to false. However, this is something that is configurable using the following flag.
  • Machine is scaled down by the MachineDeployment resource.

    • This is very usual when an external controller cluster-autoscaler (aka CA) is used with MCM. CA deletes the under-utilized machines by scaling down the MachineDeployment. Read more about cluster-autoscaler’s scale down behavior here.

What are the different sub-controllers in MCM?

MCM mainly contains the following sub-controllers:

  • MachineDeployment Controller: Responsible for reconciling the MachineDeployment objects. It manages the lifecycle of the MachineSet objects.
  • MachineSet Controller: Responsible for reconciling the MachineSet objects. It manages the lifecycle of the Machine objects.
  • Machine Controller: responsible for reconciling the Machine objects. It manages the lifecycle of the actual VMs/machines created in cloud/on-prem. This controller has been moved out of tree. Please refer an AWS machine controller for more info - link.
  • Safety-controller: Responsible for handling the unidentified/unknown behaviors from the cloud providers. Please read more about its functionality below.

What is Safety Controller in MCM?

Safety Controller contains following functions:

  • Orphan VM handler:
    • It lists all the VMs in the cloud matching the tag of given cluster name and maps the VMs with the machine objects using the ProviderID field. VMs without any backing machine objects are logged and deleted after confirmation.
    • This handler runs every 30 minutes and is configurable via machine-safety-orphan-vms-period flag.
  • Freeze mechanism:
    • Safety Controller freezes the MachineDeployment and MachineSet controller if the number of machine objects goes beyond a certain threshold on top of Spec.Replicas. It can be configured by the flag –safety-up or –safety-down and also machine-safety-overshooting-period.
    • Safety Controller freezes the functionality of the MCM if either of the target-apiserver or the control-apiserver is not reachable.
    • Safety Controller unfreezes the MCM automatically once situation is resolved to normal. A freeze label is applied on MachineDeployment/MachineSet to enforce the freeze condition.

How to?

How to install MCM in a Kubernetes cluster?

MCM can be installed in a cluster with following steps:

  • Apply all the CRDs from here

  • Apply all the deployment, role-related objects from here.

    • Control cluster is the one where the machine-* objects are stored. Target cluster is where all the node objects are registered.

How to better control the rollout process of the worker nodes?

MCM allows configuring the rollout of the worker machines using maxSurge and maxUnavailable fields. These fields are applicable only during the rollout process and means nothing in general scale up/down scenarios. The overall process is very similar to how the Deployment Controller manages pods during RollingUpdate.

  • maxSurge refers to the number of additional machines that can be added on top of the Spec.Replicas of MachineDeployment during rollout process.
  • maxUnavailable refers to the number of machines that can be deleted from Spec.Replicas field of the MachineDeployment during rollout process.

How to scale down MachineDeployment by selective deletion of machines?

During scale down, triggered via MachineDeployment/MachineSet, MCM prefers to delete the machine/s which have the least priority set. Each machine object has an annotation machinepriority.machine.sapcloud.io set to 3 by default. Admin can reduce the priority of the given machines by changing the annotation value to 1. The next scale down by MachineDeployment shall delete the machines with the least priority first.

How to force delete a machine?

A machine can be force deleted by adding the label force-deletion: "True" on the machine object before executing the actual delete command. During force deletion, MCM skips the drain function and simply triggers the deletion of the machine. This label should be used with caution as it can violate the PDBs for pods running on the machine.

How to pause the ongoing rolling-update of the machinedeployment?

An ongoing rolling-update of the machine-deployment can be paused by using spec.paused field. See the example below:

apiVersion: machine.sapcloud.io/v1alpha1
kind: MachineDeployment
metadata:
  name: test-machine-deployment
spec:
  paused: true

It can be unpaused again by removing the Paused field from the machine-deployment.

How to delete machine object immedietly if I don’t have access to it?

If the user doesn’t have access to the machine objects (like in case of Gardener clusters) and they would like to replace a node immedietly then they can place the annotation node.machine.sapcloud.io/trigger-deletion-by-mcm: "true" on their node. This will start the replacement of the machine with a new node.

On the other hand if the user deletes the node object immedietly then replacement will start only after MachineHealthTimeout.

This annotation can also be used if the user wants to expedite the replacement of unhealthy nodes

NOTE:

  • node.machine.sapcloud.io/trigger-deletion-by-mcm: "false" annotation is NOT acted upon by MCM , neither does it mean that MCM will not replace this machine.
  • this annotation would delete the desired machine but another machine would be created to maintain desired replicas specified for the machineDeployment/machineSet. Currently if the user doesn’t have access to machineDeployment/machineSet then they cannot remove a machine without replacement.

How to avoid garbage collection of your node?

MCM provides an in-built safety mechanism to garbage collect VMs which have no corresponding machine object. This is done to save costs and is one of the key features of MCM. However, sometimes users might like to add nodes directly to the cluster without the help of MCM and would prefer MCM to not garbage collect such VMs. To do so they should remove/not-use tags on their VMs containing the following strings:

  1. kubernetes.io/cluster/
  2. kubernetes.io/role/
  3. kubernetes-io-cluster-
  4. kubernetes-io-role-

How to trigger rolling update of a machinedeployment?

Rolling update can be triggered for a machineDeployment by updating one of the following:

  • .spec.template.annotations
  • .spec.template.spec.class.name

Internals

What is the high level design of MCM?

Please refer the following document.

What are the different configuration options in MCM?

MCM allows configuring many knobs to fine-tune its behavior according to the user’s need. Please refer to the link to check the exact configuration options.

What are the different timeouts/configurations in a machine’s lifecycle?

A machine’s lifecycle is governed by mainly following timeouts, which can be configured here.

  • MachineDrainTimeout: Amount of time after which drain times out and the machine is force deleted. Default ~2 hours.
  • MachineHealthTimeout: Amount of time after which an unhealthy machine is declared Failed and the machine is replaced by MachineSet controller.
  • MachineCreationTimeout: Amount of time after which a machine creation is declared Failed and the machine is replaced by the MachineSet controller.
  • NodeConditions: List of node conditions which if set to true for MachineHealthTimeout period, the machine is declared Failed and replaced by MachineSet controller.
  • MaxEvictRetries: An integer number depicting the number of times a failed eviction should be retried on a pod during drain process. A pod is deleted after max-retries.

How is the drain of a machine implemented?

MCM imports the functionality from the upstream Kubernetes-drain library. Although, few parts have been modified to make it work best in the context of MCM. Drain is executed before machine deletion for graceful migration of the applications. Drain internally uses the EvictionAPI to evict the pods and triggers the Deletion of pods after MachineDrainTimeout. Please note:

  • Stateless pods are evicted in parallel.
  • Stateful applications (with PVCs) are serially evicted. Please find more info in this answer below.

How are the stateful applications drained during machine deletion?

Drain function serially evicts the stateful-pods. It is observed that serial eviction of stateful pods yields better overall availability of pods as the underlying cloud in most cases detaches and reattaches disks serially anyways. It is implemented in the following manner:

  • Drain lists all the pods with attached volumes. It evicts very first stateful-pod and waits for its related entry in Node object’s .status.volumesAttached to be removed by KCM. It does the same for all the stateful-pods.
  • It waits for PvDetachTimeout (default 2 minutes) for a given pod’s PVC to be removed, else moves forward.

How does maxEvictRetries configuration work with drainTimeout configuration?

It is recommended to only set MachineDrainTimeout. It satisfies the related requirements. MaxEvictRetries is auto-calculated based on MachineDrainTimeout, if maxEvictRetries is not provided. Following will be the overall behavior of both configurations together:

  • If maxEvictRetries isn’t set and only maxDrainTimeout is set:
    • MCM auto calculates the maxEvictRetries based on the drainTimeout.
  • If drainTimeout isn’t set and only maxEvictRetries is set:
    • Default drainTimeout and user provided maxEvictRetries for each pod is considered.
  • If both maxEvictRetries and drainTimoeut are set:
    • Then both will be respected.
  • If none are set:
    • Defaults are respected.

What are the different phases of a machine?

A phase of a machine can be identified with Machine.Status.CurrentStatus.Phase. Following are the possible phases of a machine object:

  • Pending: Machine creation call has succeeded. MCM is waiting for machine to join the cluster.

  • CrashLoopBackOff: Machine creation call has failed. MCM will retry the operation after a minor delay.

  • Running: Machine creation call has succeeded. Machine has joined the cluster successfully and corresponding node doesn’t have node.gardener.cloud/critical-components-not-ready taint.

  • Unknown: Machine health checks are failing, eg kubelet has stopped posting the status.

  • Failed: Machine health checks have failed for a prolonged time. Hence it is declared failed by Machine controller in a rate limited fashion. Failed machines get replaced immediately.

  • Terminating: Machine is being terminated. Terminating state is set immediately when the deletion is triggered for the machine object. It also includes time when it’s being drained.

NOTE: No phase means the machine is being created on the cloud-provider.

Below is a simple phase transition diagram: image

What health checks are performed on a machine?

Health check performed on a machine are:

  • Existense of corresponding node obj
  • Status of certain user-configurable node conditions.
    • These conditions can be specified using the flag --node-conditions for OOT MCM provider or can be specified per machine object.
    • The default user configurable node conditions can be found here
  • True status of NodeReady condition . This condition shows kubelet’s status

If any of the above checks fails , the machine turns to Unknown phase.

Currently MCM replaces only 1 Unkown machine at a time per machinedeployment. This means until the particular Unknown machine get terminated and its replacement joins, no other Unknown machine would be removed.

The above is achieved by enabling Machine controller to turn machine from Unknown -> Failed only if the above condition is met. MachineSet controller on the other hand marks Failed machine as Terminating immediately.

One reason for this rate limited replacement was to ensure that in case of network failures , where node’s kubelet can’t reach out to kube-apiserver , all nodes are not removed together i.e. meltdown protection. In gardener context however, DWD is deployed to deal with this scenario, but to stay protected from corner cases , this mechanism has been introduced in MCM.

NOTE: Rate limiting replacement is not yet configurable

How MCM responds when scale-out/scale-in is done during rolling update of a machinedeployment?

Machinedeployment controller executes the logic of scaling BEFORE logic of rollout. It identifies scaling by comparing the deployment.kubernetes.io/desired-replicas of each machineset under the machinedeployment with machinedeployment’s .spec.replicas. If the difference is found for any machineSet, a scaling event is detected.

Case scale-out -> ONLY New machineSet is scaled out
Case scale-in -> ALL machineSets(new or old) are scaled in , in proportion to their replica count , any leftover is adjusted in the largest machineSet.

During update for scaling event, a machineSet is updated if any of the below is true for it:

  • .spec.Replicas needs update
  • deployment.kubernetes.io/desired-replicas needs update

Once scaling is achieved, rollout continues.

How does MCM prioritize the machines for deletion on scale-down of machinedeployment?

There could be many machines under a machinedeployment with different phases, creationTimestamp. When a scale down is triggered, MCM decides to remove the machine using the following logic:

  • Machine with least value of machinepriority.machine.sapcloud.io annotation is picked up.
  • If all machines have equal priorities, then following precedence is followed:
    • Terminating > Failed > CrashloopBackoff > Unknown > Pending > Available > Running
  • If still there is no match, the machine with oldest creation time (.i.e. creationTimestamp) is picked up.

How some unhealthy machines are drained quickly ?

If a node is unhealthy for more than the machine-health-timeout specified for the machine-controller, the controller health-check moves the machine phase to Failed. By default, the machine-health-timeout is 10` minutes.

Failed machines have their deletion timestamp set and the machine then moves to the Terminating phase. The node drain process is initiated. The drain process is invoked either gracefully or forcefully.

The usual drain process is graceful. Pods are evicted from the node and the drain process waits until any existing attached volumes are mounted on new node. However, if the node Ready is False or the ReadonlyFilesystem is True for greater than 5 minutes (non-configurable), then a forceful drain is initiated. In a forceful drain, pods are deleted and VolumeAttachment objects associated with the old node are also marked for deletion. This is followed by the deletion of the cloud provider VM associated with the Machine and then finally ending with the Node object deletion.

During the deletion of the VM we only delete the local data disks and boot disks associated with the VM. The disks associated with persistent volumes are left un-touched as their attach/de-detach, mount/unmount processes are handled by k8s attach-detach controller in conjunction with the CSI driver.

Troubleshooting

My machine is stuck in deletion for 1 hr, why?

In most cases, the Machine.Status.LastOperation provides information around why a machine can’t be deleted. Though following could be the reasons but not limited to:

  • Pod/s with misconfigured PDBs block the drain operation. PDBs with maxUnavailable set to 0, doesn’t allow the eviction of the pods. Hence, drain/eviction is retried till MachineDrainTimeout. Default MachineDrainTimeout could be as large as ~2hours. Hence, blocking the machine deletion.
    • Short term: User can manually delete the pod in the question, with caution.
    • Long term: Please set more appropriate PDBs which allow disruption of at least one pod.
  • Expired cloud credentials can block the deletion of the machine from infrastructure.
  • Cloud provider can’t delete the machine due to internal errors. Such situations are best debugged by using cloud provider specific CLI or cloud console.

My machine is not joining the cluster, why?

In most cases, the Machine.Status.LastOperation provides information around why a machine can’t be created. It could possibly be debugged with following steps:

  • Firstly make sure all the relevant controllers like kube-controller-manager , cloud-controller-manager are running.
  • Verify if the machine is actually created in the cloud. User can use the Machine.Spec.ProviderId to query the machine in cloud.
  • A Kubernetes node is generally bootstrapped with the cloud-config. Please verify, if MachineDeployment is pointing the correct MachineClass, and MachineClass is pointing to the correct Secret. The secret object contains the actual cloud-config in base64 format which will be used to boot the machine.
  • User must also check the logs of the MCM pod to understand any broken logical flow of reconciliation.

My rolling update is stuck , why?

The following can be the reason:

  • Insufficient capacity for the new instance type the machineClass mentions.
  • Old machines are stuck in deletion
  • If you are using Gardener for setting up kubernetes cluster, then machine object won’t turn to Running state until node-critical-components are ready. Refer this for more details.

Developer

How should I test my code before submitting a PR?

  • Developer can locally setup the MCM using following guide
  • Developer must also enhance the unit tests related to the incoming changes.
  • Developer can locally run the unit test by executing:
make test-unit
  • Developer can locally run integration tests to ensure basic functionality of MCM is not altered.

Developer should add/update the API fields at both of the following places:

Once API changes are done, auto-generate the code using following command:

make generate

Please ignore the API-violation errors for now.

How can I update the dependencies of MCM?

MCM uses gomod for depedency management. Developer should add/udpate depedency in the go.mod file. Please run following command to automatically tidy the dependencies.

make tidy

In the context of Gardener

How can I configure MCM using Shoot resource?

All of the knobs of MCM can be configured by the workers section of the shoot resource.

  • Gardener creates a MachineDeployment per zone for each worker-pool under workers section.
  • workers.dataVolumes allows to attach multiple disks to a machine during creation. Refer the link.
  • workers.machineControllerManager allows configuration of multiple knobs of the MachineDeployment from the shoot resource.

How is my worker-pool spread across zones?

Shoot resource allows the worker-pool to spread across multiple zones using the field workers.zones. Refer link.

  • Gardener creates one MachineDeployment per zone. Each MachineDeployment is initiated with the following replica:
MachineDeployment.Spec.Replicas = (Workers.Minimum)/(Number of availibility zones)

2.5 - Adding Support for a Cloud Provider

Adding support for a new provider

Steps to be followed while implementing a new (hyperscale) provider are mentioned below. This is the easiest way to add new provider support using a blueprint code.

However, you may also develop your machine controller from scratch, which would provide you with more flexibility. First, however, make sure that your custom machine controller adheres to the Machine.Status struct defined in the MachineAPIs. This will make sure the MCM can act with higher-level controllers like MachineSet and MachineDeployment controller. The key is the Machine.Status.CurrentStatus.Phase key that indicates the status of the machine object.

Our strong recommendation would be to follow the steps below. This provides the most flexibility required to support machine management for adding new providers. And if you feel to extend the functionality, feel free to update our machine controller libraries.

Setting up your repository

  1. Create a new empty repository named machine-controller-manager-provider-{provider-name} on GitHub username/project. Do not initialize this repository with a README.
  2. Copy the remote repository URL (HTTPS/SSH) to this repository displayed once you create this repository.
  3. Now, on your local system, create directories as required. {your-github-username} given below could also be {github-project} depending on where you have created the new repository.
    mkdir -p $GOPATH/src/github.com/{your-github-username}
    
  4. Navigate to this created directory.
    cd $GOPATH/src/github.com/{your-github-username}
    
  5. Clone this repository on your local machine.
    git clone git@github.com:gardener/machine-controller-manager-provider-sampleprovider.git
    
  6. Rename the directory from machine-controller-manager-provider-sampleprovider to machine-controller-manager-provider-{provider-name}.
    mv machine-controller-manager-provider-sampleprovider machine-controller-manager-provider-{provider-name}
    
  7. Navigate into the newly-created directory.
    cd machine-controller-manager-provider-{provider-name}
    
  8. Update the remote origin URL to the newly created repository’s URL you had copied above.
    git remote set-url origin git@github.com:{your-github-username}/machine-controller-manager-provider-{provider-name}.git
    
  9. Rename GitHub project from gardener to {github-org/your-github-username} wherever you have cloned the repository above. Also, edit all occurrences of the word sampleprovider to {provider-name} in the code. Then, use the hack script given below to do the same.
    make rename-project PROJECT_NAME={github-org/your-github-username} PROVIDER_NAME={provider-name}
    eg:
        make rename-project PROJECT_NAME=gardener PROVIDER_NAME=AmazonWebServices (or)
        make rename-project PROJECT_NAME=githubusername PROVIDER_NAME=AWS
    
  10. Now, commit your changes and push them upstream.
    git add -A
    git commit -m "Renamed SampleProvide to {provider-name}"
    git push origin master
    

Code changes required

The contract between the Machine Controller Manager (MCM) and the Machine Controller (MC) AKA driver has been documented here and the machine error codes can be found here. You may refer to them for any queries.

⚠️

  • Keep in mind that there should be a unique way to map between machine objects and VMs. This can be done by mapping machine object names with VM-Name/ tags/ other metadata.
  • Optionally, there should also be a unique way to map a VM to its machine class object. This can be done by tagging VM objects with tags/resource groups associated with the machine class.

Steps to integrate

  1. Update the pkg/provider/apis/provider_spec.go specification file to reflect the structure of the ProviderSpec blob. It typically contains the machine template details in the MachineClass object. Follow the sample spec provided already in the file. A sample provider specification can be found here.
  2. Fill in the methods described at pkg/provider/core.go to manage VMs on your cloud provider. Comments are provided above each method to help you fill them up with desired REQUEST and RESPONSE parameters.
    • A sample provider implementation for these methods can be found here.
    • Fill in the required methods CreateMachine(), and DeleteMachine() methods.
    • Optionally fill in methods like GetMachineStatus(), InitializeMachine, ListMachines(), and GetVolumeIDs(). You may choose to fill these once the working of the required methods seems to be working.
    • GetVolumeIDs() expects VolumeIDs to be decoded from the volumeSpec based on the cloud provider.
    • There is also an OPTIONAL method GenerateMachineClassForMigration() that helps in migration of {ProviderSpecific}MachineClass to MachineClass CR (custom resource). This only makes sense if you have an existing implementation (in-tree) acting on different CRD types. You would like to migrate this. If not, you MUST return an error (machine error UNIMPLEMENTED) to avoid processing this step.
  3. Perform validation of APIs that you have described and make it a part of your methods as required at each request.
  4. Write unit tests to make it work with your implementation by running make test.
    make test
    
  5. Tidy the go dependencies.
    make tidy
    
  6. Update the sample YAML files on the kubernetes/ directory to provide sample files through which the working of the machine controller can be tested.
  7. Update README.md to reflect any additional changes

Testing your code changes

Make sure $TARGET_KUBECONFIG points to the cluster where you wish to manage machines. Likewise, $CONTROL_NAMESPACE represents the namespaces where MCM is looking for machine CR objects, and $CONTROL_KUBECONFIG points to the cluster that holds these machine CRs.

  1. On the first terminal running at $GOPATH/src/github.com/{github-org/your-github-username}/machine-controller-manager-provider-{provider-name},
    • Run the machine controller (driver) using the command below.
      make start
      
  2. On the second terminal pointing to $GOPATH/src/github.com/gardener,
    • Clone the latest MCM code
      git clone git@github.com:gardener/machine-controller-manager.git
      
    • Navigate to the newly-created directory.
      cd machine-controller-manager
      
    • Deploy the required CRDs from the machine-controller-manager repo,
      kubectl apply -f kubernetes/crds
      
    • Run the machine-controller-manager in the master branch
      make start
      
  3. On the third terminal pointing to $GOPATH/src/github.com/{github-org/your-github-username}/machine-controller-manager-provider-{provider-name}
    • Fill in the object files given below and deploy them as described below.
    • Deploy the machine-class
      kubectl apply -f kubernetes/machine-class.yaml
      
    • Deploy the kubernetes secret if required.
      kubectl apply -f kubernetes/secret.yaml
      
    • Deploy the machine object and make sure it joins the cluster successfully.
      kubectl apply -f kubernetes/machine.yaml
      
    • Once the machine joins, you can test by deploying a machine-deployment.
    • Deploy the machine-deployment object and make sure it joins the cluster successfully.
      kubectl apply -f kubernetes/machine-deployment.yaml
      
    • Make sure to delete both the machine and machine-deployment objects after use.
      kubectl delete -f kubernetes/machine.yaml
      kubectl delete -f kubernetes/machine-deployment.yaml
      

Releasing your docker image

  1. Make sure you have logged into gcloud/docker using the CLI.
  2. To release your docker image, run the following.
    make release IMAGE_REPOSITORY=<link-to-image-repo>
  1. A sample kubernetes deploy file can be found at kubernetes/deployment.yaml. Update the same (with your desired MCM and MC images) to deploy your MCM pod.

2.6 - Deployment

Deploying the Machine Controller Manager into a Kubernetes cluster

As already mentioned, the Machine Controller Manager is designed to run as controller in a Kubernetes cluster. The existing source code can be compiled and tested on a local machine as described in Setting up a local development environment. You can deploy the Machine Controller Manager using the steps described below.

Prepare the cluster

  • Connect to the remote kubernetes cluster where you plan to deploy the Machine Controller Manager using the kubectl. Set the environment variable KUBECONFIG to the path of the yaml file containing the cluster info.
  • Now, create the required CRDs on the remote cluster using the following command,
$ kubectl apply -f kubernetes/crds

Build the Docker image

⚠️ Modify the Makefile to refer to your own registry.

  • Run the build which generates the binary to bin/machine-controller-manager
$ make build
  • Build docker image from latest compiled binary
$ make docker-image
  • Push the last created docker image onto the online docker registry.
$ make push
  • Now you can deploy this docker image to your cluster. A sample development file is provided. By default, the deployment manages the cluster it is running in. Optionally, the kubeconfig could also be passed as a flag as described in /kubernetes/deployment/out-of-tree/deployment.yaml. This is done when you want your controller running outside the cluster to be managed from.
$ kubectl apply -f kubernetes/deployment/out-of-tree/deployment.yaml
  • Also deploy the required clusterRole and clusterRoleBindings
$ kubectl apply -f kubernetes/deployment/out-of-tree/clusterrole.yaml
$ kubectl apply -f kubernetes/deployment/out-of-tree/clusterrolebinding.yaml

Configuring optional parameters while deploying

Machine-controller-manager supports several configurable parameters while deploying. Refer to the following lines, to know how each parameter can be configured, and what it’s purpose is for.

Usage

To start using Machine Controller Manager, follow the links given at usage here.

2.7 - Integration Tests

Integration tests

Usage

General setup & configurations

Integration tests for machine-controller-manager-provider-{provider-name} can be executed manually by following below steps.

  1. Clone the repository machine-controller-manager-provider-{provider-name} on the local system.
  2. Navigate to machine-controller-manager-provider-{provider-name} directory and create a dev sub-directory in it.
  3. If the tags on instances & associated resources on the provider are of String type (for example, GCP tags on its instances are of type String and not key-value pair) then add TAGS_ARE_STRINGS := true in the Makefile and export it. For GCP this has already been hard coded in the Makefile.

Running the tests

  1. There is a rule test-integration in the Makefile of the provider repository, which can be used to start the integration test:
    $ make test-integration 
    
  2. This will ask for additional inputs. Most of them are self explanatory except:
  • The script assumes that both the control and target clusters are already being created.
  • In case of non-gardener setup (control cluster is not a gardener seed), the name of the machineclass must be test-mc-v1 and the value of providerSpec.secretRef.name should be test-mc-secret.
  • In case of azure, TARGET_CLUSTER_NAME must be same as the name of the Azure ResourceGroup for the cluster.
  • If you are deploying the secret manually, a Secret named test-mc-secret (that contains the provider secret and cloud-config) in the default namespace of the Control Cluster should be created.
  1. The controllers log files (mcm_process.log and mc_process.log) are stored in .ci/controllers-test/logs repo and can be used later.

Adding Integration Tests for new providers

For a new provider, Running Integration tests works with no changes. But for the orphan resource test cases to work correctly, the provider-specific API calls and the Resource Tracker Interface (RTI) should be implemented. Please check machine-controller-manager-provider-aws for reference.

Extending integration tests

  • Update ControllerTests to be extend the testcases for all providers. Common testcases for machine|machineDeployment creation|deletion|scaling are packaged into ControllerTests.
  • To extend the provider specfic test cases, the changes should be done in the machine-controller-manager-provider-{provider-name} repository. For example, to extended the testcases for machine-controller-manager-provider-aws, make changes to test/integration/controller/controller_test.go inside the machine-controller-manager-provider-aws repository. commons contains the Cluster and Clientset objects that makes it easy to extend the tests.

2.8 - Local Setup

Preparing the Local Development Setup (Mac OS X)

Conceptionally, the Machine Controller Manager is designed to run in a container within a Pod inside a Kubernetes cluster. For development purposes, you can run the Machine Controller Manager as a Go process on your local machine. This process connects to your remote cluster to manage VMs for that cluster. That means that the Machine Controller Manager runs outside a Kubernetes cluster which requires providing a Kubeconfig in your local filesystem and point the Machine Controller Manager to it when running it (see below).

Although the following installation instructions are for Mac OS X, similar alternate commands could be found for any Linux distribution.

Installing Golang environment

Install the latest version of Golang (at least v1.8.3 is required) by using Homebrew:

$ brew install golang

In order to perform linting on the Go source code, install Golint:

$ go get -u golang.org/x/lint/golint

Installing Docker (Optional)

In case you want to build Docker images for the Machine Controller Manager you have to install Docker itself. We recommend using Docker for Mac OS X which can be downloaded from here.

Setup Docker Hub account (Optional)

Create a Docker hub account at Docker Hub if you don’t already have one.

Local development

⚠️ Before you start developing, please ensure to comply with the following requirements:

  1. You have understood the principles of Kubernetes, and its components, what their purpose is and how they interact with each other.
  2. You have understood the architecture of the Machine Controller Manager

The development of the Machine Controller Manager could happen by targeting any cluster. You basically need a Kubernetes cluster running on a set of machines. You just need the Kubeconfig file with the required access permissions attached to it.

Installing the Machine Controller Manager locally

Clone the repository from GitHub.

$ git clone git@github.com:gardener/machine-controller-manager.git
$ cd machine-controller-manager

Prepare the cluster

  • Connect to the remote kubernetes cluster where you plan to deploy the Machine Controller Manager using kubectl. Set the environment variable KUBECONFIG to the path of the yaml file containing your cluster info
  • Now, create the required CRDs on the remote cluster using the following command,
$ kubectl apply -f kubernetes/crds.yaml

Getting started

Setup and Restore with Gardener

Setup

In gardener access to static kubeconfig files is no longer supported due to security reasons. One needs to generate short-lived (max TTL = 1 day) admin kube configs for target and control clusters. A convenience script/Makefile target has been provided to do the required initial setup which includes:

  • Creating a temporary directory where target and control kubeconfigs will be stored.
  • Create a request to generate the short lived admin kubeconfigs. These are downloaded and stored in the temporary folder created above.
  • In gardener clusters DWD (Dependency Watchdog) runs as an additional component which can interfere when MCM/CA is scaled down. To prevent that an annotation dependency-watchdog.gardener.cloud/ignore-scaling is added to machine-controller-manager deployment which prevents DWD from scaling up the deployment replicas.
  • Scales down machine-controller-manager deployment in the control cluster to 0 replica.
  • Creates the required .env file and populates required environment variables which are then used by the Makefile in both machine-controller-manager and in machine-controller-manager-provider-<provider-name> projects.
  • Copies the generated and downloaded kubeconfig files for the target and control clusters to machine-controller-manager-provider-<provider-name> project as well.

To do the above you can either invoke make gardener-setup or you can directly invoke the script ./hack/gardener_local_setup.sh. If you invoke the script with -h or --help option then it will give you all CLI options that one can pass.

Restore

Once the testing is over you can invoke a convenience script/Makefile target which does the following:

  • Removes all generated admin kubeconfig files from both machine-controller-manager and in machine-controller-manager-provider-<provider-name> projects.
  • Removes the .env file that was generated as part of the setup from both machine-controller-manager and in machine-controller-manager-provider-<provider-name> projects.
  • Scales up machine-controller-manager deployment in the control cluster back to 1 replica.
  • Removes the annotation dependency-watchdog.gardener.cloud/ignore-scaling that was added to prevent DWD to scale up MCM.

To do the above you can either invoke make gardener-restore or you can directly invoke the script ./hack/gardener_local_restore.sh. If you invoke the script with -h or --help option then it will give you all CLI options that one can pass.

Setup and Restore without Gardener

Setup

If you are not running MCM components in a gardener cluster, then it is assumed that there is not going to be any DWD (Dependency Watchdog) component. A convenience script/Makefile target has been provided to the required initial setup which includes:

  • Copies the provided control and target kubeconfig files to machine-controller-manager-provider-<provider-name> project.
  • Scales down machine-controller-manager deployment in the control cluster to 0 replica.
  • Creates the required .env file and populates required environment variables which are then used by the Makefile in both machine-controller-manager and in machine-controller-manager-provider-<provider-name> projects.

To do the above you can either invoke make non-gardener-setup or you can directly invoke the script ./hack/non_gardener_local_setup.sh. If you invoke the script with -h or --help option then it will give you all CLI options that one can pass.

Restore

Once the testing is over you can invoke a convenience script/Makefile target which does the following:

  • Removes all provided kubeconfig files from both machine-controller-manager and in machine-controller-manager-provider-<provider-name> projects.
  • Removes the .env file that was generated as part of the setup from both machine-controller-manager and in machine-controller-manager-provider-<provider-name> projects.
  • Scales up machine-controller-manager deployment in the control cluster back to 1 replica.

To do the above you can either invoke make non-gardener-restore or you can directly invoke the script ./hack/non_gardener_local_restore.sh. If you invoke the script with -h or --help option then it will give you all CLI options that one can pass.

Once the setup is done then you can start the machine-controller-manager as a local process using the following Makefile target:

$ make start
I1227 11:08:19.963638   55523 controllermanager.go:204] Starting shared informers
I1227 11:08:20.766085   55523 controller.go:247] Starting machine-controller-manager

⚠️ The file dev/target-kubeconfig.yaml points to the cluster whose nodes you want to manage. dev/control-kubeconfig.yaml points to the cluster from where you want to manage the nodes from. However, dev/control-kubeconfig.yaml is optional.

The Machine Controller Manager should now be ready to manage the VMs in your kubernetes cluster.

⚠️ This is assuming that your MCM is built to manage machines for any in-tree supported providers. There is a new way to deploy and manage out of tree (external) support for providers whose development can be found here

Testing Machine Classes

To test the creation/deletion of a single instance for one particular machine class you can use the managevm cli. The corresponding INFRASTRUCTURE-machine-class.yaml and the INFRASTRUCTURE-secret.yaml need to be defined upfront. To build and run it

GO111MODULE=on go build -o managevm cmd/machine-controller-manager-cli/main.go
# create machine
./managevm --secret PATH_TO/INFRASTRUCTURE-secret.yaml --machineclass PATH_TO/INFRASTRUCTURE-machine-class.yaml --classkind INFRASTRUCTURE --machinename test
# delete machine
./managevm --secret PATH_TO/INFRASTRUCTURE-secret.yaml --machineclass PATH_TO/INFRASTRUCTURE-machine-class.yaml --classkind INFRASTRUCTURE --machinename test --machineid INFRASTRUCTURE:///REGION/INSTANCE_ID

Usage

To start using Machine Controller Manager, follow the links given at usage here.

2.9 - Machine

Creating/Deleting machines (VM)

Setting up your usage environment

Important :

Make sure that the kubernetes/machine_objects/machine.yaml points to the same class name as the kubernetes/machine_classes/aws-machine-class.yaml.

Similarly kubernetes/machine_objects/aws-machine-class.yaml secret name and namespace should be same as that mentioned in kubernetes/secrets/aws-secret.yaml

Creating machine

  • Modify kubernetes/machine_objects/machine.yaml as per your requirement and create the VM as shown below:
$ kubectl apply -f kubernetes/machine_objects/machine.yaml

You should notice that the Machine Controller Manager has immediately picked up your manifest and started to create a new machine by talking to the cloud provider.

  • Check Machine Controller Manager machines in the cluster
$ kubectl get machine
NAME           STATUS    AGE
test-machine   Running   5m

A new machine is created with the name provided in the kubernetes/machine_objects/machine.yaml file.

  • After a few minutes (~3 minutes for AWS), you should notice a new node joining the cluster. You can verify this by running:
$ kubectl get nodes
NAME                                         STATUS     AGE     VERSION
ip-10-250-14-52.eu-east-1.compute.internal.  Ready      1m      v1.8.0

This shows that a new node has successfully joined the cluster.

Inspect status of machine

To inspect the status of any created machine, run the command given below.

$ kubectl get machine test-machine -o yaml
apiVersion: machine.sapcloud.io/v1alpha1
kind: Machine
metadata:
  annotations:
    kubectl.kubernetes.io/last-applied-configuration: |
            {"apiVersion":"machine.sapcloud.io/v1alpha1","kind":"Machine","metadata":{"annotations":{},"labels":{"test-label":"test-label"},"name":"test-machine","namespace":""},"spec":{"class":{"kind":"AWSMachineClass","name":"test-aws"}}}
  clusterName: ""
  creationTimestamp: 2017-12-27T06:58:21Z
  finalizers:
  - machine.sapcloud.io/operator
  generation: 0
  initializers: null
  labels:
    node: ip-10-250-14-52.eu-east-1.compute.internal
    test-label: test-label
  name: test-machine
  namespace: ""
  resourceVersion: "12616948"
  selfLink: /apis/machine.sapcloud.io/v1alpha1/test-machine
  uid: 535e596c-ead3-11e7-a6c0-828f843e4186
spec:
  class:
    kind: AWSMachineClass
    name: test-aws
  providerID: aws:///eu-east-1/i-00bef3f2618ffef23
status:
  conditions:
  - lastHeartbeatTime: 2017-12-27T07:00:46Z
    lastTransitionTime: 2017-12-27T06:59:16Z
    message: kubelet has sufficient disk space available
    reason: KubeletHasSufficientDisk
    status: "False"
    type: OutOfDisk
  - lastHeartbeatTime: 2017-12-27T07:00:46Z
    lastTransitionTime: 2017-12-27T06:59:16Z
    message: kubelet has sufficient memory available
    reason: KubeletHasSufficientMemory
    status: "False"
    type: MemoryPressure
  - lastHeartbeatTime: 2017-12-27T07:00:46Z
    lastTransitionTime: 2017-12-27T06:59:16Z
    message: kubelet has no disk pressure
    reason: KubeletHasNoDiskPressure
    status: "False"
    type: DiskPressure
  - lastHeartbeatTime: 2017-12-27T07:00:46Z
    lastTransitionTime: 2017-12-27T07:00:06Z
    message: kubelet is posting ready status
    reason: KubeletReady
    status: "True"
    type: Ready
  currentStatus:
    lastUpdateTime: 2017-12-27T07:00:06Z
    phase: Running
  lastOperation:
    description: Machine is now ready
    lastUpdateTime: 2017-12-27T07:00:06Z
    state: Successful
    type: Create
  node: ip-10-250-14-52.eu-west-1.compute.internal

Delete machine

To delete the VM using the kubernetes/machine_objects/machine.yaml as shown below

$ kubectl delete -f kubernetes/machine_objects/machine.yaml

Now the Machine Controller Manager picks up the manifest immediately and starts to delete the existing VM by talking to the cloud provider. The node should be detached from the cluster in a few minutes (~1min for AWS).

2.10 - Machine Deployment

Maintaining machine replicas using machines-deployments

Setting up your usage environment

Follow the steps described here

Important ⚠️

Make sure that the kubernetes/machine_objects/machine-deployment.yaml points to the same class name as the kubernetes/machine_classes/aws-machine-class.yaml.

Similarly kubernetes/machine_classes/aws-machine-class.yaml secret name and namespace should be same as that mentioned in kubernetes/secrets/aws-secret.yaml

Creating machine-deployment

  • Modify kubernetes/machine_objects/machine-deployment.yaml as per your requirement. Modify the number of replicas to the desired number of machines. Then, create an machine-deployment.
$ kubectl apply -f kubernetes/machine_objects/machine-deployment.yaml

Now the Machine Controller Manager picks up the manifest immediately and starts to create a new machines based on the number of replicas you have provided in the manifest.

  • Check Machine Controller Manager machine-deployments in the cluster
$ kubectl get machinedeployment
NAME                      READY   DESIRED   UP-TO-DATE   AVAILABLE   AGE
test-machine-deployment   3       3         3            0           10m

You will notice a new machine-deployment with your given name

  • Check Machine Controller Manager machine-sets in the cluster
$ kubectl get machineset
NAME                                 DESIRED   CURRENT   READY   AGE
test-machine-deployment-5bc6dd7c8f   3         3         0       10m

You will notice a new machine-set backing your machine-deployment

  • Check Machine Controller Manager machines in the cluster
$ kubectl get machine
NAME                                       STATUS    AGE
test-machine-deployment-5bc6dd7c8f-5d24b   Pending   5m
test-machine-deployment-5bc6dd7c8f-6mpn4   Pending   5m
test-machine-deployment-5bc6dd7c8f-dpt2q   Pending   5m

Now you will notice N (number of replicas specified in the manifest) new machines whose name are prefixed with the machine-deployment object name that you created.

  • After a few minutes (~3 minutes for AWS), you would see that new nodes have joined the cluster. You can see this using
$  kubectl get nodes
NAME                                          STATUS    AGE       VERSION
ip-10-250-20-19.eu-west-1.compute.internal    Ready     1m        v1.8.0
ip-10-250-27-123.eu-west-1.compute.internal   Ready     1m        v1.8.0
ip-10-250-31-80.eu-west-1.compute.internal    Ready     1m        v1.8.0

This shows how new nodes have joined your cluster

Inspect status of machine-deployment

To inspect the status of any created machine-deployment run the command below,

$ kubectl get machinedeployment test-machine-deployment -o yaml

You should get the following output.

apiVersion: machine.sapcloud.io/v1alpha1
kind: MachineDeployment
metadata:
  annotations:
    deployment.kubernetes.io/revision: "1"
    kubectl.kubernetes.io/last-applied-configuration: |
            {"apiVersion":"machine.sapcloud.io/v1alpha1","kind":"MachineDeployment","metadata":{"annotations":{},"name":"test-machine-deployment","namespace":""},"spec":{"minReadySeconds":200,"replicas":3,"selector":{"matchLabels":{"test-label":"test-label"}},"strategy":{"rollingUpdate":{"maxSurge":1,"maxUnavailable":1},"type":"RollingUpdate"},"template":{"metadata":{"labels":{"test-label":"test-label"}},"spec":{"class":{"kind":"AWSMachineClass","name":"test-aws"}}}}}
  clusterName: ""
  creationTimestamp: 2017-12-27T08:55:56Z
  generation: 0
  initializers: null
  name: test-machine-deployment
  namespace: ""
  resourceVersion: "12634168"
  selfLink: /apis/machine.sapcloud.io/v1alpha1/test-machine-deployment
  uid: c0b488f7-eae3-11e7-a6c0-828f843e4186
spec:
  minReadySeconds: 200
  replicas: 3
  selector:
    matchLabels:
      test-label: test-label
  strategy:
    rollingUpdate:
      maxSurge: 1
      maxUnavailable: 1
    type: RollingUpdate
  template:
    metadata:
      creationTimestamp: null
      labels:
        test-label: test-label
    spec:
      class:
        kind: AWSMachineClass
        name: test-aws
status:
  availableReplicas: 3
  conditions:
  - lastTransitionTime: 2017-12-27T08:57:22Z
    lastUpdateTime: 2017-12-27T08:57:22Z
    message: Deployment has minimum availability.
    reason: MinimumReplicasAvailable
    status: "True"
    type: Available
  readyReplicas: 3
  replicas: 3
  updatedReplicas: 3

Health monitoring

Health monitor is also applied similar to how it’s described for machine-sets

Update your machines

Let us consider the scenario where you wish to update all nodes of your cluster from t2.xlarge machines to m5.xlarge machines. Assume that your current test-aws has its spec.machineType: t2.xlarge and your deployment test-machine-deployment points to this AWSMachineClass.

Inspect existing cluster configuration

  • Check Nodes present in the cluster
$ kubectl get nodes
NAME                                          STATUS    AGE       VERSION
ip-10-250-20-19.eu-west-1.compute.internal    Ready     1m        v1.8.0
ip-10-250-27-123.eu-west-1.compute.internal   Ready     1m        v1.8.0
ip-10-250-31-80.eu-west-1.compute.internal    Ready     1m        v1.8.0
  • Check Machine Controller Manager machine-sets in the cluster. You will notice one machine-set backing your machine-deployment
$ kubectl get machineset
NAME                                 DESIRED   CURRENT   READY   AGE
test-machine-deployment-5bc6dd7c8f   3         3         3       10m
  • Login to your cloud provider (AWS). In the VM management console, you will find N VMs created of type t2.xlarge.

Perform a rolling update

To update this machine-deployment VMs to m5.xlarge, we would do the following:

  • Copy your existing aws-machine-class.yaml
cp kubernetes/machine_classes/aws-machine-class.yaml kubernetes/machine_classes/aws-machine-class-new.yaml
  • Modify aws-machine-class-new.yaml, and update its metadata.name: test-aws2 and spec.machineType: m5.xlarge
  • Now create this modified MachineClass
kubectl apply -f kubernetes/machine_classes/aws-machine-class-new.yaml
  • Edit your existing machine-deployment
kubectl edit machinedeployment test-machine-deployment
  • Update from spec.template.spec.class.name: test-aws to spec.template.spec.class.name: test-aws2

Re-check cluster configuration

After a few minutes (~3mins)

  • Check nodes present in cluster now. They are different nodes.
$ kubectl get nodes
NAME                                          STATUS    AGE       VERSION
ip-10-250-11-171.eu-west-1.compute.internal   Ready     4m        v1.8.0
ip-10-250-17-213.eu-west-1.compute.internal   Ready     5m        v1.8.0
ip-10-250-31-81.eu-west-1.compute.internal    Ready     5m        v1.8.0
  • Check Machine Controller Manager machine-sets in the cluster. You will notice two machine-sets backing your machine-deployment
$ kubectl get machineset
NAME                                 DESIRED   CURRENT   READY   AGE
test-machine-deployment-5bc6dd7c8f   0         0         0       1h
test-machine-deployment-86ff45cc5    3         3         3       20m
  • Login to your cloud provider (AWS). In the VM management console, you will find N VMs created of type t2.xlarge in terminated state, and N new VMs of type m5.xlarge in running state.

This shows how a rolling update of a cluster from nodes with t2.xlarge to m5.xlarge went through.

More variants of updates

  • The above demonstration was a simple use case. This could be more complex like - updating the system disk image versions/ kubelet versions/ security patches etc.
  • You can also play around with the maxSurge and maxUnavailable fields in machine-deployment.yaml
  • You can also change the update strategy from rollingupdate to recreate

Undo an update

  • Edit the existing machine-deployment
$ kubectl edit machinedeployment test-machine-deployment
  • Edit the deployment to have this new field of spec.rollbackTo.revision: 0 as shown as comments in kubernetes/machine_objects/machine-deployment.yaml
  • This will undo your update to the previous version.

Pause an update

  • You can also pause the update while update is going on by editing the existing machine-deployment
$ kubectl edit machinedeployment test-machine-deployment
  • Edit the deployment to have this new field of spec.paused: true as shown as comments in kubernetes/machine_objects/machine-deployment.yaml

  • This will pause the rollingUpdate if it’s in process

  • To resume the update, edit the deployment as mentioned above and remove the field spec.paused: true updated earlier

Delete machine-deployment

  • To delete the VM using the kubernetes/machine_objects/machine-deployment.yaml
$ kubectl delete -f kubernetes/machine_objects/machine-deployment.yaml

The Machine Controller Manager picks up the manifest and starts to delete the existing VMs by talking to the cloud provider. The nodes should be detached from the cluster in a few minutes (~1min for AWS).

2.11 - Machine Error Codes

Machine Error code handling

Notational Conventions

The keywords “MUST”, “MUST NOT”, “REQUIRED”, “SHALL”, “SHALL NOT”, “SHOULD”, “SHOULD NOT”, “RECOMMENDED”, “NOT RECOMMENDED”, “MAY”, and “OPTIONAL” are to be interpreted as described in RFC 2119 (Bradner, S., “Key words for use in RFCs to Indicate Requirement Levels”, BCP 14, RFC 2119, March 1997).

The key words “unspecified”, “undefined”, and “implementation-defined” are to be interpreted as described in the rationale for the C99 standard.

An implementation is not compliant if it fails to satisfy one or more of the MUST, REQUIRED, or SHALL requirements for the protocols it implements. An implementation is compliant if it satisfies all the MUST, REQUIRED, and SHALL requirements for the protocols it implements.

Terminology

TermDefinition
CRCustom Resource (CR) is defined by a cluster admin using the Kubernetes Custom Resource Definition primitive.
VMA Virtual Machine (VM) provisioned and managed by a provider. It could also refer to a physical machine in case of a bare metal provider.
MachineMachine refers to a VM that is provisioned/managed by MCM. It typically describes the metadata used to store/represent a Virtual Machine
NodeNative kubernetes Node object. The objects you get to see when you do a “kubectl get nodes”. Although nodes can be either physical/virtual machines, for the purposes of our discussions it refers to a VM.
MCMMachine Controller Manager (MCM) is the controller used to manage higher level Machine Custom Resource (CR) such as machine-set and machine-deployment CRs.
Provider/Driver/MCProvider (or) Driver (or) Machine Controller (MC) is the driver responsible for managing machine objects present in the cluster from whom it manages these machines. A simple example could be creation/deletion of VM on the provider.

Pre-requisite

MachineClass Resources

MCM introduces the CRD MachineClass. This is a blueprint for creating machines that join a certain cluster as nodes in a certain role. The provider only works with MachineClass resources that have the structure described here.

ProviderSpec

The MachineClass resource contains a providerSpec field that is passed in the ProviderSpec request field to CMI methods such as CreateMachine. The ProviderSpec can be thought of as a machine template from which the VM specification must be adopted. It can contain key-value pairs of these specs. An example for these key-value pairs are given below.

ParameterMandatoryTypeDescription
vmPoolYesstringVM pool name, e.g. TEST-WOKER-POOL
sizeYesstringVM size, e.g. xsmall, small, etc. Each size maps to a number of CPUs and memory size.
rootFsSizeNointRoot (/) filesystem size in GB
tagsYesmapTags to be put on the created VM

Most of the ProviderSpec fields are not mandatory. If not specified, the provider passes an empty value in the respective Create VM parameter.

The tags can be used to map a VM to its corresponding machine object’s Name

The ProviderSpec is validated by methods that receive it as a request field for presence of all mandatory parameters and tags, and for validity of all parameters.

Secrets

The MachineClass resource also contains a secretRef field that contains a reference to a secret. The keys of this secret are passed in the Secrets request field to CMI methods.

The secret can contain sensitive data such as

  • cloud-credentials secret data used to authenticate at the provider
  • cloud-init scripts used to initialize a new VM. The cloud-init script is expected to contain scripts to initialize the Kubelet and make it join the cluster.

Identifying Cluster Machines

To implement certain methods, the provider should be able to identify all machines associated with a particular Kubernetes cluster. This can be achieved using one/more of the below mentioned ways:

  • Names of VMs created by the provider are prefixed by the cluster ID specified in the ProviderSpec.
  • VMs created by the provider are tagged with the special tags like kubernetes.io/cluster (for the cluster ID) and kubernetes.io/role (for the role), specified in the ProviderSpec.
  • Mapping Resource Groups to individual cluster.

Error Scheme

All provider API calls defined in this spec MUST return a machine error status, which is very similar to standard machine status.

Machine Provider Interface

  • The provider MUST have a unique way to map a machine object to a VM which triggers the deletion for the corresponding VM backing the machine object.
  • The provider SHOULD have a unique way to map the ProviderSpec of a machine-class to a unique Cluster. This avoids deletion of other machines, not backed by the MCM.

CreateMachine

A Provider is REQUIRED to implement this interface method. This interface method will be called by the MCM to provision a new VM on behalf of the requesting machine object.

  • This call requests the provider to create a VM backing the machine-object.

  • If VM backing the Machine.Name already exists, and is compatible with the specified Machine object in the CreateMachineRequest, the Provider MUST reply 0 OK with the corresponding CreateMachineResponse.

  • The provider can OPTIONALLY make use of the MachineClass supplied in the MachineClass in the CreateMachineRequest to communicate with the provider.

  • The provider can OPTIONALLY make use of the secrets supplied in the Secret in the CreateMachineRequest to communicate with the provider.

  • The provider can OPTIONALLY make use of the Status.LastKnownState in the Machine object to decode the state of the VM operation based on the last known state of the VM. This can be useful to restart/continue an operations which are mean’t to be atomic.

  • The provider MUST have a unique way to map a machine object to a VM. This could be implicitly provided by the provider by letting you set VM-names (or) could be explicitly specified by the provider using appropriate tags to map the same.

  • This operation SHOULD be idempotent.

  • The CreateMachineResponse returned by this method is expected to return

    • ProviderID that uniquely identifys the VM at the provider. This is expected to match with the node.Spec.ProviderID on the node object.
    • NodeName that is the expected name of the machine when it joins the cluster. It must match with the node name.
    • LastKnownState is an OPTIONAL field that can store details of the last known state of the VM. It can be used by future operation calls to determine current infrastucture state. This state is saved on the machine object.
// CreateMachine call is responsible for VM creation on the provider
CreateMachine(context.Context, *CreateMachineRequest) (*CreateMachineResponse, error)

// CreateMachineRequest is the create request for VM creation
type CreateMachineRequest struct {
	// Machine object from whom VM is to be created
	Machine *v1alpha1.Machine

	// MachineClass backing the machine object
	MachineClass *v1alpha1.MachineClass

	//  Secret backing the machineClass object
	Secret *corev1.Secret
}

// CreateMachineResponse is the create response for VM creation
type CreateMachineResponse struct {
	// ProviderID is the unique identification of the VM at the cloud provider.
	// ProviderID typically matches with the node.Spec.ProviderID on the node object.
	// Eg: gce://project-name/region/vm-ID
	ProviderID string

	// NodeName is the name of the node-object registered to kubernetes.
	NodeName string

	// LastKnownState represents the last state of the VM during an creation/deletion error
	LastKnownState string
}
CreateMachine Errors

If the provider is unable to complete the CreateMachine call successfully, it MUST return a non-ok ginterface method code in the machine status. If the conditions defined below are encountered, the provider MUST return the specified machine error code. The MCM MUST implement the specified error recovery behavior when it encounters the machine error code.

machine CodeConditionDescriptionRecovery BehaviorAuto Retry Required
0 OKSuccessfulThe call was successful in creating/adopting a VM that matches supplied creation request. The CreateMachineResponse is returned with desired valuesN
1 CANCELEDCancelledCall was cancelled. Perform any pending clean-up tasks and return the callN
2 UNKNOWNSomething went wrongNot enough information on what went wrongRetry operation after sometimeY
3 INVALID_ARGUMENTRe-check supplied parametersRe-check the supplied Machine.Name and ProviderSpec. Make sure all parameters are in permitted range of values. Exact issue to be given in .messageUpdate providerSpec to fix issues.N
4 DEADLINE_EXCEEDEDTimeoutThe call processing exceeded supplied deadlineRetry operation after sometimeY
6 ALREADY_EXISTSAlready exists but desired parameters doesn’t matchParameters of the existing VM don’t match the ProviderSpecCreate machine with a different nameN
7 PERMISSION_DENIEDInsufficent permissionsThe requestor doesn’t have enough permissions to create an VM and it’s required dependenciesUpdate requestor permissions to grant the sameN
8 RESOURCE_EXHAUSTEDResource limits have been reachedThe requestor doesn’t have enough resource limits to process this creation requestEnhance resource limits associated with the user/account to process thisN
9 PRECONDITION_FAILEDVM is in inconsistent stateThe VM is in a state that is invalid for this operationManual intervention might be needed to fix the state of the VMN
10 ABORTEDOperation is pendingIndicates that there is already an operation pending for the specified machineWait until previous pending operation is processedY
11 OUT_OF_RANGEResources were out of rangeThe requested number of CPUs, memory size, of FS size in ProviderSpec falls outside of the corresponding valid rangeUpdate request paramaters to request valid resource requestsN
12 UNIMPLEMENTEDNot implementedUnimplemented indicates operation is not implemented or not supported/enabled in this service.Retry with an alternate logic or implement this method at the provider. Most methods by default are in this stateN
13 INTERNALMajor errorMeans some invariants expected by underlying system has been broken. If you see one of these errors, something is very broken.Needs manual intervension to fix thisN
14 UNAVAILABLENot AvailableUnavailable indicates the service is currently unavailable.Retry operation after sometimeY
16 UNAUTHENTICATEDMissing provider credentialsRequest does not have valid authentication credentials for the operationFix the provider credentialsN

The status message MUST contain a human readable description of error, if the status code is not OK. This string MAY be surfaced by MCM to end users.

InitializeMachine

Provider can OPTIONALLY implement this driver call. Else should return a UNIMPLEMENTED status in error.
This interface method will be called by the MCM to initialize a new VM just after creation. This can be used to configure network configuration etc.

  • This call requests the provider to initialize a newly created VM backing the machine-object.
  • The InitializeMachineResponse returned by this method is expected to return
    • ProviderID that uniquely identifys the VM at the provider. This is expected to match with the node.Spec.ProviderID on the node object.
    • NodeName that is the expected name of the machine when it joins the cluster. It must match with the node name.
// InitializeMachine call is responsible for VM initialization on the provider.
InitializeMachine(context.Context, *InitializeMachineRequest) (*InitializeMachineResponse, error)

// InitializeMachineRequest encapsulates params for the VM Initialization operation (Driver.InitializeMachine).
type InitializeMachineRequest struct {
	// Machine object representing VM that must be initialized
	Machine *v1alpha1.Machine

	// MachineClass backing the machine object
	MachineClass *v1alpha1.MachineClass

	// Secret backing the machineClass object
	Secret *corev1.Secret
}

// InitializeMachineResponse is the response for VM instance initialization (Driver.InitializeMachine).
type InitializeMachineResponse struct {
	// ProviderID is the unique identification of the VM at the cloud provider.
	// ProviderID typically matches with the node.Spec.ProviderID on the node object.
	// Eg: gce://project-name/region/vm-ID
	ProviderID string

	// NodeName is the name of the node-object registered to kubernetes.
	NodeName string
}
InitializeMachine Errors

If the provider is unable to complete the InitializeMachine call successfully, it MUST return a non-ok machine code in the machine status.

If the conditions defined below are encountered, the provider MUST return the specified machine error code. The MCM MUST implement the specified error recovery behavior when it encounters the machine error code.

machine CodeConditionDescriptionRecovery BehaviorAuto Retry Required
0 OKSuccessfulThe call was successful in initializing a VM that matches supplied initialization request. The InitializeMachineResponse is returned with desired valuesN
5 NOT_FOUNDTimeoutVM Instance for Machine isn’t found at providerSkip Initialization and ContinueN
12 UNIMPLEMENTEDNot implementedUnimplemented indicates operation is not implemented or not supported/enabled in this service.Skip Initialization and continueN
13 INTERNALMajor errorMeans some invariants expected by underlying system has been broken.Needs investigation and possible intervention to fix thisY
17 UNINITIALIZEDFailed InitializationVM Instance could not be initializaedInitialization is reattempted in next reconcile cycleY

The status message MUST contain a human readable description of error, if the status code is not OK. This string MAY be surfaced by MCM to end users.

DeleteMachine

A Provider is REQUIRED to implement this driver call. This driver call will be called by the MCM to deprovision/delete/terminate a VM backed by the requesting machine object.

  • If a VM corresponding to the specified machine-object’s name does not exist or the artifacts associated with the VM do not exist anymore (after deletion), the Provider MUST reply 0 OK.

  • The provider SHALL only act on machines belonging to the cluster-id/cluster-name obtained from the ProviderSpec.

  • The provider can OPTIONALY make use of the secrets supplied in the Secrets map in the DeleteMachineRequest to communicate with the provider.

  • The provider can OPTIONALY make use of the Spec.ProviderID map in the Machine object.

  • The provider can OPTIONALLY make use of the Status.LastKnownState in the Machine object to decode the state of the VM operation based on the last known state of the VM. This can be useful to restart/continue an operations which are mean’t to be atomic.

  • This operation SHOULD be idempotent.

  • The provider must have a unique way to map a machine object to a VM which triggers the deletion for the corresponding VM backing the machine object.

  • The DeleteMachineResponse returned by this method is expected to return

    • LastKnownState is an OPTIONAL field that can store details of the last known state of the VM. It can be used by future operation calls to determine current infrastucture state. This state is saved on the machine object.
// DeleteMachine call is responsible for VM deletion/termination on the provider
DeleteMachine(context.Context, *DeleteMachineRequest) (*DeleteMachineResponse, error)

// DeleteMachineRequest is the delete request for VM deletion
type DeleteMachineRequest struct {
	// Machine object from whom VM is to be deleted
	Machine *v1alpha1.Machine

	// MachineClass backing the machine object
	MachineClass *v1alpha1.MachineClass

	// Secret backing the machineClass object
	Secret *corev1.Secret
}

// DeleteMachineResponse is the delete response for VM deletion
type DeleteMachineResponse struct {
	// LastKnownState represents the last state of the VM during an creation/deletion error
	LastKnownState string
}
DeleteMachine Errors

If the provider is unable to complete the DeleteMachine call successfully, it MUST return a non-ok machine code in the machine status. If the conditions defined below are encountered, the provider MUST return the specified machine error code.

machine CodeConditionDescriptionRecovery BehaviorAuto Retry Required
0 OKSuccessfulThe call was successful in deleting a VM that matches supplied deletion request.N
1 CANCELEDCancelledCall was cancelled. Perform any pending clean-up tasks and return the callN
2 UNKNOWNSomething went wrongNot enough information on what went wrongRetry operation after sometimeY
3 INVALID_ARGUMENTRe-check supplied parametersRe-check the supplied Machine.Name and make sure that it is in the desired format and not a blank value. Exact issue to be given in .messageUpdate Machine.Name to fix issues.N
4 DEADLINE_EXCEEDEDTimeoutThe call processing exceeded supplied deadlineRetry operation after sometimeY
7 PERMISSION_DENIEDInsufficent permissionsThe requestor doesn’t have enough permissions to delete an VM and it’s required dependenciesUpdate requestor permissions to grant the sameN
9 PRECONDITION_FAILEDVM is in inconsistent stateThe VM is in a state that is invalid for this operationManual intervention might be needed to fix the state of the VMN
10 ABORTEDOperation is pendingIndicates that there is already an operation pending for the specified machineWait until previous pending operation is processedY
12 UNIMPLEMENTEDNot implementedUnimplemented indicates operation is not implemented or not supported/enabled in this service.Retry with an alternate logic or implement this method at the provider. Most methods by default are in this stateN
13 INTERNALMajor errorMeans some invariants expected by underlying system has been broken. If you see one of these errors, something is very broken.Needs manual intervension to fix thisN
14 UNAVAILABLENot AvailableUnavailable indicates the service is currently unavailable.Retry operation after sometimeY
16 UNAUTHENTICATEDMissing provider credentialsRequest does not have valid authentication credentials for the operationFix the provider credentialsN

The status message MUST contain a human readable description of error, if the status code is not OK. This string MAY be surfaced by MCM to end users.

GetMachineStatus

A Provider can OPTIONALLY implement this driver call. Else should return a UNIMPLEMENTED status in error. This call will be invoked by the MC to get the status of a machine. This optional driver call helps in optimizing the working of the provider by avoiding unwanted calls to CreateMachine() and DeleteMachine().

  • If a VM corresponding to the specified machine object’s Machine.Name exists on provider the GetMachineStatusResponse fields are to be filled similar to the CreateMachineResponse.
  • The provider SHALL only act on machines belonging to the cluster-id/cluster-name obtained from the ProviderSpec.
  • The provider can OPTIONALY make use of the secrets supplied in the Secrets map in the GetMachineStatusRequest to communicate with the provider.
  • The provider can OPTIONALY make use of the VM unique ID (returned by the provider on machine creation) passed in the ProviderID map in the GetMachineStatusRequest.
  • This operation MUST be idempotent.
// GetMachineStatus call get's the status of the VM backing the machine object on the provider
GetMachineStatus(context.Context, *GetMachineStatusRequest) (*GetMachineStatusResponse, error)

// GetMachineStatusRequest is the get request for VM info
type GetMachineStatusRequest struct {
	// Machine object from whom VM status is to be fetched
	Machine *v1alpha1.Machine

	// MachineClass backing the machine object
	MachineClass *v1alpha1.MachineClass

	//  Secret backing the machineClass object
	Secret *corev1.Secret
}

// GetMachineStatusResponse is the get response for VM info
type GetMachineStatusResponse struct {
	// ProviderID is the unique identification of the VM at the cloud provider.
	// ProviderID typically matches with the node.Spec.ProviderID on the node object.
	// Eg: gce://project-name/region/vm-ID
	ProviderID string

	// NodeName is the name of the node-object registered to kubernetes.
	NodeName string
}
GetMachineStatus Errors

If the provider is unable to complete the GetMachineStatus call successfully, it MUST return a non-ok machine code in the machine status. If the conditions defined below are encountered, the provider MUST return the specified machine error code.

machine CodeConditionDescriptionRecovery BehaviorAuto Retry Required
0 OKSuccessfulThe call was successful in getting machine details for given machine Machine.NameN
1 CANCELEDCancelledCall was cancelled. Perform any pending clean-up tasks and return the callN
2 UNKNOWNSomething went wrongNot enough information on what went wrongRetry operation after sometimeY
3 INVALID_ARGUMENTRe-check supplied parametersRe-check the supplied Machine.Name and make sure that it is in the desired format and not a blank value. Exact issue to be given in .messageUpdate Machine.Name to fix issues.N
4 DEADLINE_EXCEEDEDTimeoutThe call processing exceeded supplied deadlineRetry operation after sometimeY
5 NOT_FOUNDMachine isn’t found at providerThe machine could not be found at providerNot requiredN
7 PERMISSION_DENIEDInsufficent permissionsThe requestor doesn’t have enough permissions to get details for the VM and it’s required dependenciesUpdate requestor permissions to grant the sameN
9 PRECONDITION_FAILEDVM is in inconsistent stateThe VM is in a state that is invalid for this operationManual intervention might be needed to fix the state of the VMN
11 OUT_OF_RANGEMultiple VMs foundMultiple VMs found with matching machine object namesOrphan VM handler to cleanup orphan VMs / Manual intervention maybe required if orphan VM handler isn’t enabled.Y
12 UNIMPLEMENTEDNot implementedUnimplemented indicates operation is not implemented or not supported/enabled in this service.Retry with an alternate logic or implement this method at the provider. Most methods by default are in this stateN
13 INTERNALMajor errorMeans some invariants expected by underlying system has been broken. If you see one of these errors, something is very broken.Needs manual intervension to fix thisN
14 UNAVAILABLENot AvailableUnavailable indicates the service is currently unavailable.Retry operation after sometimeY
16 UNAUTHENTICATEDMissing provider credentialsRequest does not have valid authentication credentials for the operationFix the provider credentialsN
17 UNINITIALIZEDFailed InitializationVM Instance could not be initializaedInitialization is reattempted in next reconcile cycleN

The status message MUST contain a human readable description of error, if the status code is not OK. This string MAY be surfaced by MCM to end users.

ListMachines

A Provider can OPTIONALLY implement this driver call. Else should return a UNIMPLEMENTED status in error. The Provider SHALL return the information about all the machines associated with the MachineClass. Make sure to use appropriate filters to achieve the same to avoid data transfer overheads. This optional driver call helps in cleaning up orphan VMs present in the cluster. If not implemented, any orphan VM that might have been created incorrectly by the MCM/Provider (due to bugs in code/infra) might require manual clean up.

  • If the Provider succeeded in returning a list of Machine.Name with their corresponding ProviderID, then return 0 OK.
  • The ListMachineResponse contains a map of MachineList whose
    • Key is expected to contain the ProviderID &
    • Value is expected to contain the Machine.Name corresponding to it’s kubernetes machine CR object
  • The provider can OPTIONALY make use of the secrets supplied in the Secrets map in the ListMachinesRequest to communicate with the provider.
// ListMachines lists all the machines that might have been created by the supplied machineClass
ListMachines(context.Context, *ListMachinesRequest) (*ListMachinesResponse, error)

// ListMachinesRequest is the request object to get a list of VMs belonging to a machineClass
type ListMachinesRequest struct {
	// MachineClass object
	MachineClass *v1alpha1.MachineClass

	// Secret backing the machineClass object
	Secret *corev1.Secret
}

// ListMachinesResponse is the response object of the list of VMs belonging to a machineClass
type ListMachinesResponse struct {
	// MachineList is the map of list of machines. Format for the map should be <ProviderID, MachineName>.
	MachineList map[string]string
}
ListMachines Errors

If the provider is unable to complete the ListMachines call successfully, it MUST return a non-ok machine code in the machine status. If the conditions defined below are encountered, the provider MUST return the specified machine error code. The MCM MUST implement the specified error recovery behavior when it encounters the machine error code.

machine CodeConditionDescriptionRecovery BehaviorAuto Retry Required
0 OKSuccessfulThe call for listing all VMs associated with ProviderSpec was successful.N
1 CANCELEDCancelledCall was cancelled. Perform any pending clean-up tasks and return the callN
2 UNKNOWNSomething went wrongNot enough information on what went wrongRetry operation after sometimeY
3 INVALID_ARGUMENTRe-check supplied parametersRe-check the supplied ProviderSpec and make sure that all required fields are present in their desired value format. Exact issue to be given in .messageUpdate ProviderSpec to fix issues.N
4 DEADLINE_EXCEEDEDTimeoutThe call processing exceeded supplied deadlineRetry operation after sometimeY
7 PERMISSION_DENIEDInsufficent permissionsThe requestor doesn’t have enough permissions to list VMs and it’s required dependenciesUpdate requestor permissions to grant the sameN
12 UNIMPLEMENTEDNot implementedUnimplemented indicates operation is not implemented or not supported/enabled in this service.Retry with an alternate logic or implement this method at the provider. Most methods by default are in this stateN
13 INTERNALMajor errorMeans some invariants expected by underlying system has been broken. If you see one of these errors, something is very broken.Needs manual intervension to fix thisN
14 UNAVAILABLENot AvailableUnavailable indicates the service is currently unavailable.Retry operation after sometimeY
16 UNAUTHENTICATEDMissing provider credentialsRequest does not have valid authentication credentials for the operationFix the provider credentialsN

The status message MUST contain a human readable description of error, if the status code is not OK. This string MAY be surfaced by MCM to end users.

GetVolumeIDs

A Provider can OPTIONALLY implement this driver call. Else should return a UNIMPLEMENTED status in error. This driver call will be called by the MCM to get the VolumeIDs for the list of PersistentVolumes (PVs) supplied. This OPTIONAL (but recommended) driver call helps in serailzied eviction of pods with PVs while draining of machines. This implies applications backed by PVs would be evicted one by one, leading to shorter application downtimes.

// GetVolumeIDsRequest is the request object to get a list of VolumeIDs for a PVSpec
type GetVolumeIDsRequest struct {
	// PVSpecsList is a list of PV specs for whom volume-IDs are required
	// Plugin should parse this raw data into pre-defined list of PVSpecs
	PVSpecs []*corev1.PersistentVolumeSpec
}

// GetVolumeIDsResponse is the response object of the list of VolumeIDs for a PVSpec
type GetVolumeIDsResponse struct {
	// VolumeIDs is a list of VolumeIDs.
	VolumeIDs []string
}
GetVolumeIDs Errors
machine CodeConditionDescriptionRecovery BehaviorAuto Retry Required
0 OKSuccessfulThe call getting list of VolumeIDs for the list of PersistentVolumes was successful.N
1 CANCELEDCancelledCall was cancelled. Perform any pending clean-up tasks and return the callN
2 UNKNOWNSomething went wrongNot enough information on what went wrongRetry operation after sometimeY
3 INVALID_ARGUMENTRe-check supplied parametersRe-check the supplied PVSpecList and make sure that it is in the desired format. Exact issue to be given in .messageUpdate PVSpecList to fix issues.N
4 DEADLINE_EXCEEDEDTimeoutThe call processing exceeded supplied deadlineRetry operation after sometimeY
12 UNIMPLEMENTEDNot implementedUnimplemented indicates operation is not implemented or not supported/enabled in this service.Retry with an alternate logic or implement this method at the provider. Most methods by default are in this stateN
13 INTERNALMajor errorMeans some invariants expected by underlying system has been broken. If you see one of these errors, something is very broken.Needs manual intervension to fix thisN
14 UNAVAILABLENot AvailableUnavailable indicates the service is currently unavailable.Retry operation after sometimeY

The status message MUST contain a human readable description of error, if the status code is not OK. This string MAY be surfaced by MCM to end users.

GenerateMachineClassForMigration

A Provider SHOULD implement this driver call, else it MUST return a UNIMPLEMENTED status in error. This driver call will be called by the Machine Controller to try to perform a machineClass migration for an unknown machineClass Kind. This helps in migration of one kind of machineClass to another kind. For instance an machineClass custom resource of AWSMachineClass to MachineClass.

  • On successful generation of machine class the Provider MUST reply 0 OK (or) nil error.
  • GenerateMachineClassForMigrationRequest expects the provider-specific machine class (eg. AWSMachineClass) to be supplied as the ProviderSpecificMachineClass. The provider is responsible for unmarshalling the golang struct. It also passes a reference to an existing MachineClass object.
  • The provider is expected to fill in thisMachineClass object based on the conversions.
  • An optional ClassSpec containing the type ClassSpec struct is also provided to decode the provider info.
  • GenerateMachineClassForMigration is only responsible for filling up the passed MachineClass object.
  • The task of creating the new CR of the new kind (MachineClass) with the same name as the previous one and also annotating the old machineClass CR with a migrated annotation and migrating existing references is done by the calling library implicitly.
  • This operation MUST be idempotent.
// GenerateMachineClassForMigrationRequest is the request for generating the generic machineClass
// for the provider specific machine class
type GenerateMachineClassForMigrationRequest struct {
	// ProviderSpecificMachineClass is provider specfic machine class object.
	// E.g. AWSMachineClass
	ProviderSpecificMachineClass interface{}
	// MachineClass is the machine class object generated that is to be filled up
	MachineClass *v1alpha1.MachineClass
	// ClassSpec contains the class spec object to determine the machineClass kind
	ClassSpec *v1alpha1.ClassSpec
}

// GenerateMachineClassForMigrationResponse is the response for generating the generic machineClass
// for the provider specific machine class
type GenerateMachineClassForMigrationResponse struct{}
MigrateMachineClass Errors
machine CodeConditionDescriptionRecovery BehaviorAuto Retry Required
0 OKSuccessfulMigration of provider specific machine class was successfulMachine reconcilation is retried once the new class has been createdY
12 UNIMPLEMENTEDNot implementedUnimplemented indicates operation is not implemented or not supported/enabled in this provider.NoneN
13 INTERNALMajor errorMeans some invariants expected by underlying system has been broken. If you see one of these errors, something is very broken.Might need manual intervension to fix thisY

The status message MUST contain a human readable description of error, if the status code is not OK. This string MAY be surfaced by MCM to end users.

Configuration and Operation

Supervised Lifecycle Management

  • For Providers packaged in software form:
    • Provider Packages SHOULD use a well-documented container image format (e.g., Docker, OCI).
    • The chosen package image format MAY expose configurable Provider properties as environment variables, unless otherwise indicated in the section below. Variables so exposed SHOULD be assigned default values in the image manifest.
    • A Provider Supervisor MAY programmatically evaluate or otherwise scan a Provider Package’s image manifest in order to discover configurable environment variables.
    • A Provider SHALL NOT assume that an operator or Provider Supervisor will scan an image manifest for environment variables.

Environment Variables

  • Variables defined by this specification SHALL be identifiable by their MC_ name prefix.
  • Configuration properties not defined by the MC specification SHALL NOT use the same MC_ name prefix; this prefix is reserved for common configuration properties defined by the MC specification.
  • The Provider Supervisor SHOULD supply all RECOMMENDED MC environment variables to a Provider.
  • The Provider Supervisor SHALL supply all REQUIRED MC environment variables to a Provider.
Logging
  • Providers SHOULD generate log messages to ONLY standard output and/or standard error.
    • In this case the Provider Supervisor SHALL assume responsibility for all log lifecycle management.
  • Provider implementations that deviate from the above recommendation SHALL clearly and unambiguously document the following:
    • Logging configuration flags and/or variables, including working sample configurations.
    • Default log destination(s) (where do the logs go if no configuration is specified?)
    • Log lifecycle management ownership and related guidance (size limits, rate limits, rolling, archiving, expunging, etc.) applicable to the logging mechanism embedded within the Provider.
  • Providers SHOULD NOT write potentially sensitive data to logs (e.g. secrets).
Available Services
  • Provider Packages MAY support all or a subset of CMI services; service combinations MAY be configurable at runtime by the Provider Supervisor.
    • This specification does not dictate the mechanism by which mode of operation MUST be discovered, and instead places that burden upon the VM Provider.
  • Misconfigured provider software SHOULD fail-fast with an OS-appropriate error code.
Linux Capabilities
  • Providers SHOULD clearly document any additionally required capabilities and/or security context.
Cgroup Isolation
  • A Provider MAY be constrained by cgroups.
Resource Requirements
  • VM Providers SHOULD unambiguously document all of a Provider’s resource requirements.

Deploying

  • Recommended: The MCM and Provider are typically expected to run as two containers inside a common Pod.
  • However, for the security reasons they could execute on seperate Pods provided they have a secure way to exchange data between them.

2.12 - Machine Set

Maintaining machine replicas using machines-sets

Setting up your usage environment

Important ⚠️

Make sure that the kubernetes/machines_objects/machine-set.yaml points to the same class name as the kubernetes/machine_classes/aws-machine-class.yaml.

Similarly kubernetes/machine_classes/aws-machine-class.yaml secret name and namespace should be same as that mentioned in kubernetes/secrets/aws-secret.yaml

Creating machine-set

  • Modify kubernetes/machine_objects/machine-set.yaml as per your requirement. You can modify the number of replicas to the desired number of machines. Then, create an machine-set:
$ kubectl apply -f kubernetes/machine_objects/machine-set.yaml

You should notice that the Machine Controller Manager has immediately picked up your manifest and started to create a new machines based on the number of replicas you have provided in the manifest.

  • Check Machine Controller Manager machine-sets in the cluster
$ kubectl get machineset
NAME               DESIRED   CURRENT   READY   AGE
test-machine-set   3         3         0       1m

You will see a new machine-set with your given name

  • Check Machine Controller Manager machines in the cluster:
$ kubectl get machine
NAME                     STATUS    AGE
test-machine-set-b57zs   Pending   5m
test-machine-set-c4bg8   Pending   5m
test-machine-set-kvskg   Pending   5m

Now you will see N (number of replicas specified in the manifest) new machines whose names are prefixed with the machine-set object name that you created.

  • After a few minutes (~3 minutes for AWS), you should notice new nodes joining the cluster. You can verify this by running:
$ kubectl get nodes
NAME                                         STATUS    AGE       VERSION
ip-10-250-0-234.eu-west-1.compute.internal   Ready     3m        v1.8.0
ip-10-250-15-98.eu-west-1.compute.internal   Ready     3m        v1.8.0
ip-10-250-6-21.eu-west-1.compute.internal    Ready     2m        v1.8.0

This shows how new nodes have joined your cluster

Inspect status of machine-set

  • To inspect the status of any created machine-set run the following command:
$ kubectl get machineset test-machine-set -o yaml
apiVersion: machine.sapcloud.io/v1alpha1
kind: MachineSet
metadata:
  annotations:
    kubectl.kubernetes.io/last-applied-configuration: |
            {"apiVersion":"machine.sapcloud.io/v1alpha1","kind":"MachineSet","metadata":{"annotations":{},"name":"test-machine-set","namespace":"","test-label":"test-label"},"spec":{"minReadySeconds":200,"replicas":3,"selector":{"matchLabels":{"test-label":"test-label"}},"template":{"metadata":{"labels":{"test-label":"test-label"}},"spec":{"class":{"kind":"AWSMachineClass","name":"test-aws"}}}}}
  clusterName: ""
  creationTimestamp: 2017-12-27T08:37:42Z
  finalizers:
  - machine.sapcloud.io/operator
  generation: 0
  initializers: null
  name: test-machine-set
  namespace: ""
  resourceVersion: "12630893"
  selfLink: /apis/machine.sapcloud.io/v1alpha1/test-machine-set
  uid: 3469faaa-eae1-11e7-a6c0-828f843e4186
spec:
  machineClass: {}
  minReadySeconds: 200
  replicas: 3
  selector:
    matchLabels:
      test-label: test-label
  template:
    metadata:
      creationTimestamp: null
      labels:
        test-label: test-label
    spec:
      class:
        kind: AWSMachineClass
        name: test-aws
status:
  availableReplicas: 3
  fullyLabeledReplicas: 3
  machineSetCondition: null
  lastOperation:
    lastUpdateTime: null
  observedGeneration: 0
  readyReplicas: 3
  replicas: 3

Health monitoring

  • If you try to delete/terminate any of the machines backing the machine-set by either talking to the Machine Controller Manager or from the cloud provider, the Machine Controller Manager recreates a matching healthy machine to replace the deleted machine.
  • Similarly, if any of your machines are unreachable or in an unhealthy state (kubelet not ready / disk pressure) for longer than the configured timeout (~ 5mins), the Machine Controller Manager recreates the nodes to replace the unhealthy nodes.

Delete machine-set

  • To delete the VM using the kubernetes/machine_objects/machine-set.yaml:
$ kubectl delete -f kubernetes/machine-set.yaml

Now the Machine Controller Manager has immediately picked up your manifest and started to delete the existing VMs by talking to the cloud provider. Your nodes should be detached from the cluster in a few minutes (~1min for AWS).

2.13 - Prerequisite

Setting up the usage environment

Important ⚠️

All paths are relative to the root location of this project repository.

Run the Machine Controller Manager either as described in Setting up a local development environment or Deploying the Machine Controller Manager into a Kubernetes cluster.

Make sure that the following steps are run before managing machines/ machine-sets/ machine-deploys.

Set KUBECONFIG

Using the existing Kubeconfig, open another Terminal panel/window with the KUBECONFIG environment variable pointing to this Kubeconfig file as shown below,

$ export KUBECONFIG=<PATH_TO_REPO>/dev/kubeconfig.yaml

Replace provider credentials and desired VM configurations

Open kubernetes/machine_classes/aws-machine-class.yaml and replace required values there with the desired VM configurations.

Similarily open kubernetes/secrets/aws-secret.yaml and replace - userData, providerAccessKeyId, providerSecretAccessKey with base64 encoded values of cloudconfig file, AWS access key id, and AWS secret access key respectively. Use the following command to get the base64 encoded value of your details

$ echo "sample-cloud-config" | base64
base64-encoded-cloud-config

Do the same for your access key id and secret access key.

Deploy required CRDs and Objects

Create all the required CRDs in the cluster using kubernetes/crds.yaml

$ kubectl apply -f kubernetes/crds.yaml

Create the class template that will be used as an machine template to create VMs using kubernetes/machine_classes/aws-machine-class.yaml

$ kubectl apply -f kubernetes/machine_classes/aws-machine-class.yaml

Create the secret used for the cloud credentials and cloudconfig using kubernetes/secrets/aws-secret.yaml

$ kubectl apply -f kubernetes/secrets/aws-secret.yaml

Check current cluster state

Get to know the current cluster state using the following commands,

  • Checking aws-machine-class in the cluster
$ kubectl get awsmachineclass
NAME       MACHINE TYPE   AMI          AGE
test-aws   t2.large       ami-123456   5m
  • Checking kubernetes secrets in the cluster
$ kubectl get secret
NAME                  TYPE                                  DATA      AGE
test-secret           Opaque                                3         21h
  • Checking kubernetes nodes in the cluster
$ kubectl get nodes

Lists the default set of nodes attached to your cluster

  • Checking Machine Controller Manager machines in the cluster
$ kubectl get machine
No resources found.
  • Checking Machine Controller Manager machine-sets in the cluster
$ kubectl get machineset
No resources found.
  • Checking Machine Controller Manager machine-deploys in the cluster
$ kubectl get machinedeployment
No resources found.

2.14 - Testing And Dependencies

Dependency management

We use golang modules to manage golang dependencies. In order to add a new package dependency to the project, you can perform go get <PACKAGE>@<VERSION> or edit the go.mod file and append the package along with the version you want to use.

Updating dependencies

The Makefile contains a rule called tidy which performs go mod tidy.

go mod tidy makes sure go.mod matches the source code in the module. It adds any missing modules necessary to build the current module’s packages and dependencies, and it removes unused modules that don’t provide any relevant packages.

$ make tidy

The dependencies are installed into the go mod cache folder.

⚠️ Make sure you test the code after you have updated the dependencies!

3 - Etcd Druid

A druid for etcd management in Gardener

ETCD Druid

REUSE status CI Build status Go Report Card

Background

Etcd in the control plane of Kubernetes clusters which are managed by Gardener is deployed as a StatefulSet. The statefulset has replica of a pod containing two containers namely, etcd and backup-restore. The etcd container calls components in etcd-backup-restore via REST api to perform data validation before etcd is started. If this validation fails etcd data is restored from the latest snapshot stored in the cloud-provider’s object store. Once etcd has started, the etcd-backup-restore periodically creates full and delta snapshots. It also performs defragmentation of etcd data periodically.

The etcd-backup-restore needs as input the cloud-provider information comprising of security credentials to access the object store, the object store bucket name and prefix for the directory used to store snapshots. Currently, for operations like migration and validation, the bash script has to be updated to initiate the operation.

Goals

  • Deploy etcd and etcd-backup-restore using an etcd CRD.
  • Support more than one etcd replica.
  • Perform scheduled snapshots.
  • Support operations such as restores, defragmentation and scaling with zero-downtime.
  • Handle cloud-provider specific operation logic.
  • Trigger a full backup on request before volume deletion.
  • Offline compaction of full and delta snapshots stored in object store.

Proposal

The existing method of deploying etcd and backup-sidecar as a StatefulSet alleviates the pain of ensuring the pods are live and ready after node crashes. However, deploying etcd as a Statefulset introduces a plethora of challenges. The etcd controller should be smart enough to handle etcd statefulsets taking into account limitations imposed by statefulsets. The controller shall update the status regarding how to target the K8s objects it has created. This field in the status can be leveraged by HVPA to scale etcd resources eventually.

CRD specification

The etcd CRD should contain the information required to create the etcd and backup-restore sidecar in a pod/statefulset.

apiVersion: druid.gardener.cloud/v1alpha1
kind: Etcd
metadata:
  finalizers:
  - druid.gardener.cloud/etcd
  name: test
  namespace: demo
spec:
  annotations:
    app: etcd-statefulset
    gardener.cloud/role: controlplane
    networking.gardener.cloud/to-dns: allowed
    networking.gardener.cloud/to-private-networks: allowed
    networking.gardener.cloud/to-public-networks: allowed
    role: test
  backup:
    deltaSnapshotMemoryLimit: 1Gi
    deltaSnapshotPeriod: 300s
    fullSnapshotSchedule: 0 */24 * * *
    garbageCollectionPeriod: 43200s
    garbageCollectionPolicy: Exponential
    imageRepository: europe-docker.pkg.dev/gardener-project/public/gardener/etcdbrctl
    imageVersion: v0.25.0
    port: 8080
    resources:
      limits:
        cpu: 500m
        memory: 2Gi
      requests:
        cpu: 23m
        memory: 128Mi
    snapstoreTempDir: /var/etcd/data/temp
  etcd:
    Quota: 8Gi
    clientPort: 2379
    defragmentationSchedule: 0 */24 * * *
    enableTLS: false
    imageRepository: europe-docker.pkg.dev/gardener-project/public/gardener/etcd-wrapper
    imageVersion: v0.1.0
    initialClusterState: new
    initialClusterToken: new
    metrics: basic
    pullPolicy: IfNotPresent
    resources:
      limits:
        cpu: 2500m
        memory: 4Gi
      requests:
        cpu: 500m
        memory: 1000Mi
    serverPort: 2380
    storageCapacity: 80Gi
    storageClass: gardener.cloud-fast
  sharedConfig:
    autoCompactionMode: periodic
    autoCompactionRetention: 30m
  labels:
    app: etcd-statefulset
    gardener.cloud/role: controlplane
    networking.gardener.cloud/to-dns: allowed
    networking.gardener.cloud/to-private-networks: allowed
    networking.gardener.cloud/to-public-networks: allowed
    role: test
  pvcRetentionPolicy: DeleteAll
  replicas: 1
  storageCapacity: 80Gi
  storageClass: gardener.cloud-fast
  store:
    storageContainer: test
    storageProvider: S3
    storePrefix: etcd-test
    storeSecret: etcd-backup
  tlsClientSecret: etcd-client-tls
  tlsServerSecret: etcd-server-tls
status:
  etcd:
    apiVersion: apps/v1
    kind: StatefulSet
    name: etcd-test

Implementation Agenda

As first step implement defragmentation during maintenance windows. Subsequently, we will add zero-downtime upgrades and defragmentation.

Workflow

Deployment workflow

controller-diagram

Defragmentation workflow

defrag-diagram

Local Setup

To setup Etcd-druid locally as a pod running inside a kind cluster, follow this document

3.1 - API Reference

Packages:

druid.gardener.cloud/v1alpha1

Package v1alpha1 is the v1alpha1 version of the etcd-druid API.

Resource Types:

    BackupSpec

    (Appears on: EtcdSpec)

    BackupSpec defines parameters associated with the full and delta snapshots of etcd.

    FieldDescription
    port
    int32
    (Optional)

    Port define the port on which etcd-backup-restore server will be exposed.

    tls
    TLSConfig
    (Optional)
    image
    string
    (Optional)

    Image defines the etcd container image and tag

    store
    StoreSpec
    (Optional)

    Store defines the specification of object store provider for storing backups.

    resources
    Kubernetes core/v1.ResourceRequirements
    (Optional)

    Resources defines compute Resources required by backup-restore container. More info: https://kubernetes.io/docs/concepts/configuration/manage-compute-resources-container/

    compactionResources
    Kubernetes core/v1.ResourceRequirements
    (Optional)

    CompactionResources defines compute Resources required by compaction job. More info: https://kubernetes.io/docs/concepts/configuration/manage-compute-resources-container/

    fullSnapshotSchedule
    string
    (Optional)

    FullSnapshotSchedule defines the cron standard schedule for full snapshots.

    garbageCollectionPolicy
    GarbageCollectionPolicy
    (Optional)

    GarbageCollectionPolicy defines the policy for garbage collecting old backups

    garbageCollectionPeriod
    Kubernetes meta/v1.Duration
    (Optional)

    GarbageCollectionPeriod defines the period for garbage collecting old backups

    deltaSnapshotPeriod
    Kubernetes meta/v1.Duration
    (Optional)

    DeltaSnapshotPeriod defines the period after which delta snapshots will be taken

    deltaSnapshotMemoryLimit
    k8s.io/apimachinery/pkg/api/resource.Quantity
    (Optional)

    DeltaSnapshotMemoryLimit defines the memory limit after which delta snapshots will be taken

    compression
    CompressionSpec
    (Optional)

    SnapshotCompression defines the specification for compression of Snapshots.

    enableProfiling
    bool
    (Optional)

    EnableProfiling defines if profiling should be enabled for the etcd-backup-restore-sidecar

    etcdSnapshotTimeout
    Kubernetes meta/v1.Duration
    (Optional)

    EtcdSnapshotTimeout defines the timeout duration for etcd FullSnapshot operation

    leaderElection
    LeaderElectionSpec
    (Optional)

    LeaderElection defines parameters related to the LeaderElection configuration.

    ClientService

    (Appears on: EtcdConfig)

    ClientService defines the parameters of the client service that a user can specify

    FieldDescription
    annotations
    map[string]string
    (Optional)

    Annotations specify the annotations that should be added to the client service

    labels
    map[string]string
    (Optional)

    Labels specify the labels that should be added to the client service

    CompactionMode (string alias)

    (Appears on: SharedConfig)

    CompactionMode defines the auto-compaction-mode: ‘periodic’ or ‘revision’. ‘periodic’ for duration based retention and ‘revision’ for revision number based retention.

    CompressionPolicy (string alias)

    (Appears on: CompressionSpec)

    CompressionPolicy defines the type of policy for compression of snapshots.

    CompressionSpec

    (Appears on: BackupSpec)

    CompressionSpec defines parameters related to compression of Snapshots(full as well as delta).

    FieldDescription
    enabled
    bool
    (Optional)
    policy
    CompressionPolicy
    (Optional)

    Condition

    (Appears on: EtcdCopyBackupsTaskStatus, EtcdStatus)

    Condition holds the information about the state of a resource.

    FieldDescription
    type
    ConditionType

    Type of the Etcd condition.

    status
    ConditionStatus

    Status of the condition, one of True, False, Unknown.

    lastTransitionTime
    Kubernetes meta/v1.Time

    Last time the condition transitioned from one status to another.

    lastUpdateTime
    Kubernetes meta/v1.Time

    Last time the condition was updated.

    reason
    string

    The reason for the condition’s last transition.

    message
    string

    A human-readable message indicating details about the transition.

    ConditionStatus (string alias)

    (Appears on: Condition)

    ConditionStatus is the status of a condition.

    ConditionType (string alias)

    (Appears on: Condition)

    ConditionType is the type of condition.

    CrossVersionObjectReference

    (Appears on: EtcdStatus)

    CrossVersionObjectReference contains enough information to let you identify the referred resource.

    FieldDescription
    kind
    string

    Kind of the referent

    name
    string

    Name of the referent

    apiVersion
    string
    (Optional)

    API version of the referent

    Etcd

    Etcd is the Schema for the etcds API

    FieldDescription
    metadata
    Kubernetes meta/v1.ObjectMeta
    Refer to the Kubernetes API documentation for the fields of the metadata field.
    spec
    EtcdSpec


    selector
    Kubernetes meta/v1.LabelSelector

    selector is a label query over pods that should match the replica count. It must match the pod template’s labels. More info: https://kubernetes.io/docs/concepts/overview/working-with-objects/labels/#label-selectors

    labels
    map[string]string
    annotations
    map[string]string
    (Optional)
    etcd
    EtcdConfig
    backup
    BackupSpec
    sharedConfig
    SharedConfig
    (Optional)
    schedulingConstraints
    SchedulingConstraints
    (Optional)
    replicas
    int32
    priorityClassName
    string
    (Optional)

    PriorityClassName is the name of a priority class that shall be used for the etcd pods.

    storageClass
    string
    (Optional)

    StorageClass defines the name of the StorageClass required by the claim. More info: https://kubernetes.io/docs/concepts/storage/persistent-volumes#class-1

    storageCapacity
    k8s.io/apimachinery/pkg/api/resource.Quantity
    (Optional)

    StorageCapacity defines the size of persistent volume.

    volumeClaimTemplate
    string
    (Optional)

    VolumeClaimTemplate defines the volume claim template to be created

    status
    EtcdStatus

    EtcdConfig

    (Appears on: EtcdSpec)

    EtcdConfig defines parameters associated etcd deployed

    FieldDescription
    quota
    k8s.io/apimachinery/pkg/api/resource.Quantity
    (Optional)

    Quota defines the etcd DB quota.

    defragmentationSchedule
    string
    (Optional)

    DefragmentationSchedule defines the cron standard schedule for defragmentation of etcd.

    serverPort
    int32
    (Optional)
    clientPort
    int32
    (Optional)
    image
    string
    (Optional)

    Image defines the etcd container image and tag

    authSecretRef
    Kubernetes core/v1.SecretReference
    (Optional)
    metrics
    MetricsLevel
    (Optional)

    Metrics defines the level of detail for exported metrics of etcd, specify ‘extensive’ to include histogram metrics.

    resources
    Kubernetes core/v1.ResourceRequirements
    (Optional)

    Resources defines the compute Resources required by etcd container. More info: https://kubernetes.io/docs/concepts/configuration/manage-compute-resources-container/

    clientUrlTls
    TLSConfig
    (Optional)

    ClientUrlTLS contains the ca, server TLS and client TLS secrets for client communication to ETCD cluster

    peerUrlTls
    TLSConfig
    (Optional)

    PeerUrlTLS contains the ca and server TLS secrets for peer communication within ETCD cluster Currently, PeerUrlTLS does not require client TLS secrets for gardener implementation of ETCD cluster.

    etcdDefragTimeout
    Kubernetes meta/v1.Duration
    (Optional)

    EtcdDefragTimeout defines the timeout duration for etcd defrag call

    heartbeatDuration
    Kubernetes meta/v1.Duration
    (Optional)

    HeartbeatDuration defines the duration for members to send heartbeats. The default value is 10s.

    clientService
    ClientService
    (Optional)

    ClientService defines the parameters of the client service that a user can specify

    EtcdCopyBackupsTask

    EtcdCopyBackupsTask is a task for copying etcd backups from a source to a target store.

    FieldDescription
    metadata
    Kubernetes meta/v1.ObjectMeta
    Refer to the Kubernetes API documentation for the fields of the metadata field.
    spec
    EtcdCopyBackupsTaskSpec


    sourceStore
    StoreSpec

    SourceStore defines the specification of the source object store provider for storing backups.

    targetStore
    StoreSpec

    TargetStore defines the specification of the target object store provider for storing backups.

    maxBackupAge
    uint32
    (Optional)

    MaxBackupAge is the maximum age in days that a backup must have in order to be copied. By default all backups will be copied.

    maxBackups
    uint32
    (Optional)

    MaxBackups is the maximum number of backups that will be copied starting with the most recent ones.

    waitForFinalSnapshot
    WaitForFinalSnapshotSpec
    (Optional)

    WaitForFinalSnapshot defines the parameters for waiting for a final full snapshot before copying backups.

    status
    EtcdCopyBackupsTaskStatus

    EtcdCopyBackupsTaskSpec

    (Appears on: EtcdCopyBackupsTask)

    EtcdCopyBackupsTaskSpec defines the parameters for the copy backups task.

    FieldDescription
    sourceStore
    StoreSpec

    SourceStore defines the specification of the source object store provider for storing backups.

    targetStore
    StoreSpec

    TargetStore defines the specification of the target object store provider for storing backups.

    maxBackupAge
    uint32
    (Optional)

    MaxBackupAge is the maximum age in days that a backup must have in order to be copied. By default all backups will be copied.

    maxBackups
    uint32
    (Optional)

    MaxBackups is the maximum number of backups that will be copied starting with the most recent ones.

    waitForFinalSnapshot
    WaitForFinalSnapshotSpec
    (Optional)

    WaitForFinalSnapshot defines the parameters for waiting for a final full snapshot before copying backups.

    EtcdCopyBackupsTaskStatus

    (Appears on: EtcdCopyBackupsTask)

    EtcdCopyBackupsTaskStatus defines the observed state of the copy backups task.

    FieldDescription
    conditions
    []Condition
    (Optional)

    Conditions represents the latest available observations of an object’s current state.

    observedGeneration
    int64
    (Optional)

    ObservedGeneration is the most recent generation observed for this resource.

    lastError
    string
    (Optional)

    LastError represents the last occurred error.

    EtcdMemberConditionStatus (string alias)

    (Appears on: EtcdMemberStatus)

    EtcdMemberConditionStatus is the status of an etcd cluster member.

    EtcdMemberStatus

    (Appears on: EtcdStatus)

    EtcdMemberStatus holds information about a etcd cluster membership.

    FieldDescription
    name
    string

    Name is the name of the etcd member. It is the name of the backing Pod.

    id
    string
    (Optional)

    ID is the ID of the etcd member.

    role
    EtcdRole
    (Optional)

    Role is the role in the etcd cluster, either Leader or Member.

    status
    EtcdMemberConditionStatus

    Status of the condition, one of True, False, Unknown.

    reason
    string

    The reason for the condition’s last transition.

    lastTransitionTime
    Kubernetes meta/v1.Time

    LastTransitionTime is the last time the condition’s status changed.

    EtcdRole (string alias)

    (Appears on: EtcdMemberStatus)

    EtcdRole is the role of an etcd cluster member.

    EtcdSpec

    (Appears on: Etcd)

    EtcdSpec defines the desired state of Etcd

    FieldDescription
    selector
    Kubernetes meta/v1.LabelSelector

    selector is a label query over pods that should match the replica count. It must match the pod template’s labels. More info: https://kubernetes.io/docs/concepts/overview/working-with-objects/labels/#label-selectors

    labels
    map[string]string
    annotations
    map[string]string
    (Optional)
    etcd
    EtcdConfig
    backup
    BackupSpec
    sharedConfig
    SharedConfig
    (Optional)
    schedulingConstraints
    SchedulingConstraints
    (Optional)
    replicas
    int32
    priorityClassName
    string
    (Optional)

    PriorityClassName is the name of a priority class that shall be used for the etcd pods.

    storageClass
    string
    (Optional)

    StorageClass defines the name of the StorageClass required by the claim. More info: https://kubernetes.io/docs/concepts/storage/persistent-volumes#class-1

    storageCapacity
    k8s.io/apimachinery/pkg/api/resource.Quantity
    (Optional)

    StorageCapacity defines the size of persistent volume.

    volumeClaimTemplate
    string
    (Optional)

    VolumeClaimTemplate defines the volume claim template to be created

    EtcdStatus

    (Appears on: Etcd)

    EtcdStatus defines the observed state of Etcd.

    FieldDescription
    observedGeneration
    int64
    (Optional)

    ObservedGeneration is the most recent generation observed for this resource.

    etcd
    CrossVersionObjectReference
    (Optional)
    conditions
    []Condition
    (Optional)

    Conditions represents the latest available observations of an etcd’s current state.

    serviceName
    string
    (Optional)

    ServiceName is the name of the etcd service.

    lastError
    string
    (Optional)

    LastError represents the last occurred error.

    clusterSize
    int32
    (Optional)

    Cluster size is the size of the etcd cluster.

    currentReplicas
    int32
    (Optional)

    CurrentReplicas is the current replica count for the etcd cluster.

    replicas
    int32
    (Optional)

    Replicas is the replica count of the etcd resource.

    readyReplicas
    int32
    (Optional)

    ReadyReplicas is the count of replicas being ready in the etcd cluster.

    ready
    bool
    (Optional)

    Ready is true if all etcd replicas are ready.

    updatedReplicas
    int32
    (Optional)

    UpdatedReplicas is the count of updated replicas in the etcd cluster.

    labelSelector
    Kubernetes meta/v1.LabelSelector
    (Optional)

    LabelSelector is a label query over pods that should match the replica count. It must match the pod template’s labels.

    members
    []EtcdMemberStatus
    (Optional)

    Members represents the members of the etcd cluster

    peerUrlTLSEnabled
    bool
    (Optional)

    PeerUrlTLSEnabled captures the state of peer url TLS being enabled for the etcd member(s)

    GarbageCollectionPolicy (string alias)

    (Appears on: BackupSpec)

    GarbageCollectionPolicy defines the type of policy for snapshot garbage collection.

    LeaderElectionSpec

    (Appears on: BackupSpec)

    LeaderElectionSpec defines parameters related to the LeaderElection configuration.

    FieldDescription
    reelectionPeriod
    Kubernetes meta/v1.Duration
    (Optional)

    ReelectionPeriod defines the Period after which leadership status of corresponding etcd is checked.

    etcdConnectionTimeout
    Kubernetes meta/v1.Duration
    (Optional)

    EtcdConnectionTimeout defines the timeout duration for etcd client connection during leader election.

    MetricsLevel (string alias)

    (Appears on: EtcdConfig)

    MetricsLevel defines the level ‘basic’ or ‘extensive’.

    SchedulingConstraints

    (Appears on: EtcdSpec)

    SchedulingConstraints defines the different scheduling constraints that must be applied to the pod spec in the etcd statefulset. Currently supported constraints are Affinity and TopologySpreadConstraints.

    FieldDescription
    affinity
    Kubernetes core/v1.Affinity
    (Optional)

    Affinity defines the various affinity and anti-affinity rules for a pod that are honoured by the kube-scheduler.

    topologySpreadConstraints
    []Kubernetes core/v1.TopologySpreadConstraint
    (Optional)

    TopologySpreadConstraints describes how a group of pods ought to spread across topology domains, that are honoured by the kube-scheduler.

    SecretReference

    (Appears on: TLSConfig)

    SecretReference defines a reference to a secret.

    FieldDescription
    SecretReference
    Kubernetes core/v1.SecretReference

    (Members of SecretReference are embedded into this type.)

    dataKey
    string
    (Optional)

    DataKey is the name of the key in the data map containing the credentials.

    SharedConfig

    (Appears on: EtcdSpec)

    SharedConfig defines parameters shared and used by Etcd as well as backup-restore sidecar.

    FieldDescription
    autoCompactionMode
    CompactionMode
    (Optional)

    AutoCompactionMode defines the auto-compaction-mode:‘periodic’ mode or ‘revision’ mode for etcd and embedded-Etcd of backup-restore sidecar.

    autoCompactionRetention
    string
    (Optional)

    AutoCompactionRetention defines the auto-compaction-retention length for etcd as well as for embedded-Etcd of backup-restore sidecar.

    StorageProvider (string alias)

    (Appears on: StoreSpec)

    StorageProvider defines the type of object store provider for storing backups.

    StoreSpec

    (Appears on: BackupSpec, EtcdCopyBackupsTaskSpec)

    StoreSpec defines parameters related to ObjectStore persisting backups

    FieldDescription
    container
    string
    (Optional)

    Container is the name of the container the backup is stored at.

    prefix
    string

    Prefix is the prefix used for the store.

    provider
    StorageProvider
    (Optional)

    Provider is the name of the backup provider.

    secretRef
    Kubernetes core/v1.SecretReference
    (Optional)

    SecretRef is the reference to the secret which used to connect to the backup store.

    TLSConfig

    (Appears on: BackupSpec, EtcdConfig)

    TLSConfig hold the TLS configuration details.

    FieldDescription
    tlsCASecretRef
    SecretReference
    serverTLSSecretRef
    Kubernetes core/v1.SecretReference
    clientTLSSecretRef
    Kubernetes core/v1.SecretReference
    (Optional)

    WaitForFinalSnapshotSpec

    (Appears on: EtcdCopyBackupsTaskSpec)

    WaitForFinalSnapshotSpec defines the parameters for waiting for a final full snapshot before copying backups.

    FieldDescription
    enabled
    bool

    Enabled specifies whether to wait for a final full snapshot before copying backups.

    timeout
    Kubernetes meta/v1.Duration
    (Optional)

    Timeout is the timeout for waiting for a final full snapshot. When this timeout expires, the copying of backups will be performed anyway. No timeout or 0 means wait forever.


    Generated with gen-crd-api-reference-docs

    3.2 - 01 Multi Node Etcd Clusters

    Multi-node etcd cluster instances via etcd-druid

    This document proposes an approach (along with some alternatives) to support provisioning and management of multi-node etcd cluster instances via etcd-druid and etcd-backup-restore.

    Content

    Goal

    • Enhance etcd-druid and etcd-backup-restore to support provisioning and management of multi-node etcd cluster instances within a single Kubernetes cluster.
    • The etcd CRD interface should be simple to use. It should preferably work with just setting the spec.replicas field to the desired value and should not require any more configuration in the CRD than currently required for the single-node etcd instances. The spec.replicas field is part of the scale sub-resource implementation in Etcd CRD.
    • The single-node and multi-node scenarios must be automatically identified and managed by etcd-druid and etcd-backup-restore.
    • The etcd clusters (single-node or multi-node) managed by etcd-druid and etcd-backup-restore must automatically recover from failures (even quorum loss) and disaster (e.g. etcd member persistence/data loss) as much as possible.
    • It must be possible to dynamically scale an etcd cluster horizontally (even between single-node and multi-node scenarios) by simply scaling the Etcd scale sub-resource.
    • It must be possible to (optionally) schedule the individual members of an etcd clusters on different nodes or even infrastructure availability zones (within the hosting Kubernetes cluster).

    Though this proposal tries to cover most aspects related to single-node and multi-node etcd clusters, there are some more points that are not goals for this document but are still in the scope of either etcd-druid/etcd-backup-restore and/or gardener. In such cases, a high-level description of how they can be addressed in the future are mentioned at the end of the document.

    Background and Motivation

    Single-node etcd cluster

    At present, etcd-druid supports only single-node etcd cluster instances. The advantages of this approach are given below.

    • The problem domain is smaller. There are no leader election and quorum related issues to be handled. It is simpler to setup and manage a single-node etcd cluster.
    • Single-node etcd clusters instances have less request latency than multi-node etcd clusters because there is no requirement to replicate the changes to the other members before committing the changes.
    • etcd-druid provisions etcd cluster instances as pods (actually as statefulsets) in a Kubernetes cluster and Kubernetes is quick (<20s) to restart container/pods if they go down.
    • Also, etcd-druid is currently only used by gardener to provision etcd clusters to act as back-ends for Kubernetes control-planes and Kubernetes control-plane components (kube-apiserver, kubelet, kube-controller-manager, kube-scheduler etc.) can tolerate etcd going down and recover when it comes back up.
    • Single-node etcd clusters incur less cost (CPU, memory and storage)
    • It is easy to cut-off client requests if backups fail by using readinessProbe on the etcd-backup-restore healthz endpoint to minimize the gap between the latest revision and the backup revision.

    The disadvantages of using single-node etcd clusters are given below.

    • The database verification step by etcd-backup-restore can introduce additional delays whenever etcd container/pod restarts (in total ~20-25s). This can be much longer if a database restoration is required. Especially, if there are incremental snapshots that need to be replayed (this can be mitigated by compacting the incremental snapshots in the background).
    • Kubernetes control-plane components can go into CrashloopBackoff if etcd is down for some time. This is mitigated by the dependency-watchdog. But Kubernetes control-plane components require a lot of resources and create a lot of load on the etcd cluster and the apiserver when they come out of CrashloopBackoff. Especially, in medium or large sized clusters (> 20 nodes).
    • Maintenance operations such as updates to etcd (and updates to etcd-druid of etcd-backup-restore), rolling updates to the nodes of the underlying Kubernetes cluster and vertical scaling of etcd pods are disruptive because they cause etcd pods to be restarted. The vertical scaling of etcd pods is somewhat mitigated during scale down by doing it only during the target clusters’ maintenance window. But scale up is still disruptive.
    • We currently use some form of elastic storage (via persistentvolumeclaims) for storing which have some upper-bounds on the I/O latency and throughput. This can be potentially be a problem for large clusters (> 220 nodes). Also, some cloud providers (e.g. Azure) take a long time to attach/detach volumes to and from machines which increases the down time to the Kubernetes components that depend on etcd. It is difficult to use ephemeral/local storage (to achieve better latency/throughput as well as to circumvent volume attachment/detachment) for single-node etcd cluster instances.

    Multi-node etcd-cluster

    The advantages of introducing support for multi-node etcd clusters via etcd-druid are below.

    • Multi-node etcd cluster is highly-available. It can tolerate disruption to individual etcd pods as long as the quorum is not lost (i.e. more than half the etcd member pods are healthy and ready).
    • Maintenance operations such as updates to etcd (and updates to etcd-druid of etcd-backup-restore), rolling updates to the nodes of the underlying Kubernetes cluster and vertical scaling of etcd pods can be done non-disruptively by respecting poddisruptionbudgets for the various multi-node etcd cluster instances hosted on that cluster.
    • Kubernetes control-plane components do not see any etcd cluster downtime unless quorum is lost (which is expected to be lot less frequent than current frequency of etcd container/pod restarts).
    • We can consider using ephemeral/local storage for multi-node etcd cluster instances because individual member restarts can afford to take time to restore from backup before (re)joining the etcd cluster because the remaining members serve the requests in the meantime.
    • High-availability across availability zones is also possible by specifying (anti)affinity for the etcd pods (possibly via kupid).

    Some disadvantages of using multi-node etcd clusters due to which it might still be desirable, in some cases, to continue to use single-node etcd cluster instances in the gardener context are given below.

    • Multi-node etcd cluster instances are more complex to manage. The problem domain is larger including the following.
      • Leader election
      • Quorum loss
      • Managing rolling changes
      • Backups to be taken from only the leading member.
      • More complex to cut-off client requests if backups fail to minimize the gap between the latest revision and the backup revision is under control.
    • Multi-node etcd cluster instances incur more cost (CPU, memory and storage).

    Dynamic multi-node etcd cluster

    Though it is not part of this proposal, it is conceivable to convert a single-node etcd cluster into a multi-node etcd cluster temporarily to perform some disruptive operation (etcd, etcd-backup-restore or etcd-druid updates, etcd cluster vertical scaling and perhaps even node rollout) and convert it back to a single-node etcd cluster once the disruptive operation has been completed. This will necessarily still involve a down-time because scaling from a single-node etcd cluster to a three-node etcd cluster will involve etcd pod restarts, it is still probable that it can be managed with a shorter down time than we see at present for single-node etcd clusters (on the other hand, converting a three-node etcd cluster to five node etcd cluster can be non-disruptive).

    This is definitely not to argue in favour of such a dynamic approach in all cases (eventually, if/when dynamic multi-node etcd clusters are supported). On the contrary, it makes sense to make use of static (fixed in size) multi-node etcd clusters for production scenarios because of the high-availability.

    Prior Art

    ETCD Operator from CoreOS

    etcd operator

    Project status: archived

    This project is no longer actively developed or maintained. The project exists here for historical reference. If you are interested in the future of the project and taking over stewardship, please contact etcd-dev@googlegroups.com.

    etcdadm from kubernetes-sigs

    etcdadm is a command-line tool for operating an etcd cluster. It makes it easy to create a new cluster, add a member to, or remove a member from an existing cluster. Its user experience is inspired by kubeadm.

    It is a tool more tailored for manual command-line based management of etcd clusters with no API’s. It also makes no assumptions about the underlying platform on which the etcd clusters are provisioned and hence, doesn’t leverage any capabilities of Kubernetes.

    Etcd Cluster Operator from Improbable-Engineering

    Etcd Cluster Operator

    Etcd Cluster Operator is an Operator for automating the creation and management of etcd inside of Kubernetes. It provides a custom resource definition (CRD) based API to define etcd clusters with Kubernetes resources, and enable management with native Kubernetes tooling._

    Out of all the alternatives listed here, this one seems to be the only possible viable alternative. Parts of its design/implementations are similar to some of the approaches mentioned in this proposal. However, we still don’t propose to use it as -

    1. The project is still in early phase and is not mature enough to be consumed as is in productive scenarios of ours.
    2. The resotration part is completely different which makes it difficult to adopt as-is and requries lot of re-work with the current restoration semantics with etcd-backup-restore making the usage counter-productive.

    General Approach to ETCD Cluster Management

    Bootstrapping

    There are three ways to bootstrap an etcd cluster which are static, etcd discovery and DNS discovery. Out of these, the static way is the simplest (and probably faster to bootstrap the cluster) and has the least external dependencies. Hence, it is preferred in this proposal. But it requires that the initial (during bootstrapping) etcd cluster size (number of members) is already known before bootstrapping and that all of the members are already addressable (DNS,IP,TLS etc.). Such information needs to be passed to the individual members during startup using the following static configuration.

    • ETCD_INITIAL_CLUSTER
      • The list of peer URLs including all the members. This must be the same as the advertised peer URLs configuration. This can also be passed as initial-cluster flag to etcd.
    • ETCD_INITIAL_CLUSTER_STATE
      • This should be set to new while bootstrapping an etcd cluster.
    • ETCD_INITIAL_CLUSTER_TOKEN
      • This is a token to distinguish the etcd cluster from any other etcd cluster in the same network.

    Assumptions

    • ETCD_INITIAL_CLUSTER can use DNS instead of IP addresses. We need to verify this by deleting a pod (as against scaling down the statefulset) to ensure that the pod IP changes and see if the recreated pod (by the statefulset controller) re-joins the cluster automatically.
    • DNS for the individual members is known or computable. This is true in the case of etcd-druid setting up an etcd cluster using a single statefulset. But it may not necessarily be true in other cases (multiple statefulset per etcd cluster or deployments instead of statefulsets or in the case of etcd cluster with members distributed across more than one Kubernetes cluster.

    Adding a new member to an etcd cluster

    A new member can be added to an existing etcd cluster instance using the following steps.

    1. If the latest backup snapshot exists, restore the member’s etcd data to the latest backup snapshot. This can reduce the load on the leader to bring the new member up to date when it joins the cluster.
      1. If the latest backup snapshot doesn’t exist or if the latest backup snapshot is not accessible (please see backup failure) and if the cluster itself is quorate, then the new member can be started with an empty data. But this will will be suboptimal because the new member will fetch all the data from the leading member to get up-to-date.
    2. The cluster is informed that a new member is being added using the MemberAdd API including information like the member name and its advertised peer URLs.
    3. The new etcd member is then started with ETCD_INITIAL_CLUSTER_STATE=existing apart from other required configuration.

    This proposal recommends this approach.

    Note

    • If there are incremental snapshots (taken by etcd-backup-restore), they cannot be applied because that requires the member to be started in isolation without joining the cluster which is not possible. This is acceptable if the amount of incremental snapshots are managed to be relatively small. This adds one more reason to increase the priority of the issue of incremental snapshot compaction.
    • There is a time window, between the MemberAdd call and the new member joining the cluster and getting up to date, where the cluster is vulnerable to leader elections which could be disruptive.

    Alternative

    With v3.4, the new raft learner approach can be used to mitigate some of the possible disruptions mentioned above. Then the steps will be as follows.

    1. If the latest backup snapshot exists, restore the member’s etcd data to the latest backup snapshot. This can reduce the load on the leader to bring the new member up to date when it joins the cluster.
    2. The cluster is informed that a new member is being added using the MemberAddAsLearner API including information like the member name and its advertised peer URLs.
    3. The new etcd member is then started with ETCD_INITIAL_CLUSTER_STATE=existing apart from other required configuration.
    4. Once the new member (learner) is up to date, it can be promoted to a full voting member by using the MemberPromote API

    This approach is new and involves more steps and is not recommended in this proposal. It can be considered in future enhancements.

    Managing Failures

    A multi-node etcd cluster may face failures of diffent kinds during its life-cycle. The actions that need to be taken to manage these failures depend on the failure mode.

    Removing an existing member from an etcd cluster

    If a member of an etcd cluster becomes unhealthy, it must be explicitly removed from the etcd cluster, as soon as possible. This can be done by using the MemberRemove API. This ensures that only healthy members participate as voting members.

    A member of an etcd cluster may be removed not just for managing failures but also for other reasons such as -

    • The etcd cluster is being scaled down. I.e. the cluster size is being reduced
    • An existing member is being replaced by a new one for some reason (e.g. upgrades)

    If the majority of the members of the etcd cluster are healthy and the member that is unhealthy/being removed happens to be the leader at that moment then the etcd cluster will automatically elect a new leader. But if only a minority of etcd clusters are healthy after removing the member then the the cluster will no longer be quorate and will stop accepting write requests. Such an etcd cluster needs to be recovered via some kind of disaster-recovery.

    Restarting an existing member of an etcd cluster

    If the existing member of an etcd cluster restarts and retains an uncorrupted data directory after the restart, then it can simply re-join the cluster as an existing member without any API calls or configuration changes. This is because the relevant metadata (including member ID and cluster ID) are maintained in the write ahead logs. However, if it doesn’t retain an uncorrupted data directory after the restart, then it must first be removed and added as a new member.

    Recovering an etcd cluster from failure of majority of members

    If a majority of members of an etcd cluster fail but if they retain their uncorrupted data directory then they can be simply restarted and they will re-form the existing etcd cluster when they come up. However, if they do not retain their uncorrupted data directory, then the etcd cluster must be recovered from latest snapshot in the backup. This is very similar to bootstrapping with the additional initial step of restoring the latest snapshot in each of the members. However, the same limitation about incremental snapshots, as in the case of adding a new member, applies here. But unlike in the case of adding a new member, not applying incremental snapshots is not acceptable in the case of etcd cluster recovery. Hence, if incremental snapshots are required to be applied, the etcd cluster must be recovered in the following steps.

    1. Restore a new single-member cluster using the latest snapshot.
    2. Apply incremental snapshots on the single-member cluster.
    3. Take a full snapshot which can now be used while adding the remaining members.
    4. Add new members using the latest snapshot created in the step above.

    Kubernetes Context

    • Users will provision an etcd cluster in a Kubernetes cluster by creating an etcd CRD resource instance.
    • A multi-node etcd cluster is indicated if the spec.replicas field is set to any value greater than 1. The etcd-druid will add validation to ensure that the spec.replicas value is an odd number according to the requirements of etcd.
    • The etcd-druid controller will provision a statefulset with the etcd main container and the etcd-backup-restore sidecar container. It will pass on the spec.replicas field from the etcd resource to the statefulset. It will also supply the right pre-computed configuration to both the containers.
    • The statefulset controller will create the pods based on the pod template in the statefulset spec and these individual pods will be the members that form the etcd cluster.

    Component diagram

    This approach makes it possible to satisfy the assumption that the DNS for the individual members of the etcd cluster must be known/computable. This can be achieved by using a headless service (along with the statefulset) for each etcd cluster instance. Then we can address individual pods/etcd members via the predictable DNS name of <statefulset_name>-{0|1|2|3|…|n}.<headless_service_name> from within the Kubernetes namespace (or from outside the Kubernetes namespace by appending .<namespace>.svc.<cluster_domain> suffix). The etcd-druid controller can compute the above configurations automatically based on the spec.replicas in the etcd resource.

    This proposal recommends this approach.

    Alternative

    One statefulset is used for each member (instead of one statefulset for all members). While this approach gives a flexibility to have different pod specifications for the individual members, it makes managing the individual members (e.g. rolling updates) more complicated. Hence, this approach is not recommended.

    ETCD Configuration

    As mentioned in the general approach section, there are differences in the configuration that needs to be passed to individual members of an etcd cluster in different scenarios such as bootstrapping, adding a new member, removing a member, restarting an existing member etc. Managing such differences in configuration for individual pods of a statefulset is tricky in the recommended approach of using a single statefulset to manage all the member pods of an etcd cluster. This is because statefulset uses the same pod template for all its pods.

    The recommendation is for etcd-druid to provision the base configuration template in a ConfigMap which is passed to all the pods via the pod template in the StatefulSet. The initialization flow of etcd-backup-restore (which is invoked every time the etcd container is (re)started) is then enhanced to generate the customized etcd configuration for the corresponding member pod (in a shared volume between etcd and the backup-restore containers) based on the supplied template configuration. This will require that etcd-backup-restore will have to have a mechanism to detect which scenario listed above applies during any given member container/pod restart.

    Alternative

    As mentioned above, one statefulset is used for each member of the etcd cluster. Then different configuration (generated directly by etcd-druid) can be passed in the pod templates of the different statefulsets. Though this approach is advantageous in the context of managing the different configuration, it is not recommended in this proposal because it makes the rest of the management (e.g. rolling updates) more complicated.

    Data Persistence

    The type of persistence used to store etcd data (including the member ID and cluster ID) has an impact on the steps that are needed to be taken when the member pods or containers (minority of them or majority) need to be recovered.

    Persistent

    Like the single-node case, persistentvolumes can be used to persist ETCD data for all the member pods. The individual member pods then get their own persistentvolumes. The advantage is that individual members retain their member ID across pod restarts and even pod deletion/recreation across Kubernetes nodes. This means that member pods that crash (or are unhealthy) can be restarted automatically (by configuring livenessProbe) and they will re-join the etcd cluster using their existing member ID without any need for explicit etcd cluster management).

    The disadvantages of this approach are as follows.

    • The number of persistentvolumes increases linearly with the cluster size which is a cost-related concern.
    • Network-mounted persistentvolumes might eventually become a performance bottleneck under heavy load for a latency-sensitive component like ETCD.
    • Volume attach/detach issues when associated with etcd cluster instances cause downtimes to the target shoot clusters that are backed by those etcd cluster instances.

    Ephemeral

    The ephemeral volumes use-case is considered as an optimization and may be planned as a follow-up action.

    Disk

    Ephemeral persistence can be achieved in Kubernetes by using either emptyDir volumes or local persistentvolumes to persist ETCD data. The advantages of this approach are as follows.

    • Potentially faster disk I/O.
    • The number of persistent volumes does not increase linearly with the cluster size (at least not technically).
    • Issues related volume attachment/detachment can be avoided.

    The main disadvantage of using ephemeral persistence is that the individual members may retain their identity and data across container restarts but not across pod deletion/recreation across Kubernetes nodes. If the data is lost then on restart of the member pod, the older member (represented by the container) has to be removed and a new member has to be added.

    Using emptyDir ephemeral persistence has the disadvantage that the volume doesn’t have its own identity. So, if the member pod is recreated but scheduled on the same node as before then it will not retain the identity as the persistence is lost. But it has the advantage that scheduling of pods is unencumbered especially during pod recreation as they are free to be scheduled anywhere.

    Using local persistentvolumes has the advantage that the volume has its own indentity and hence, a recreated member pod will retain its identity if scheduled on the same node. But it has the disadvantage of tying down the member pod to a node which is a problem if the node becomes unhealthy requiring etcd druid to take additional actions (such as deleting the local persistent volume).

    Based on these constraints, if ephemeral persistence is opted for, it is recommended to use emptyDir ephemeral persistence.

    In-memory

    In-memory ephemeral persistence can be achieved in Kubernetes by using emptyDir with medium: Memory. In this case, a tmpfs (RAM-backed file-system) volume will be used. In addition to the advantages of ephemeral persistence, this approach can achieve the fastest possible disk I/O. Similarly, in addition to the disadvantages of ephemeral persistence, in-memory persistence has the following additional disadvantages.

    How to detect if valid metadata exists in an etcd member

    Since the likelyhood of a member not having valid metadata in the WAL files is much more likely in the ephemeral persistence scenario, one option is to pass the information that ephemeral persistence is being used to the etcd-backup-restore sidecar (say, via command-line flags or environment variables).

    But in principle, it might be better to determine this from the WAL files directly so that the possibility of corrupted WAL files also gets handled correctly. To do this, the wal package has some functions that might be useful.

    Recommendation

    It might be possible that using the wal package for verifying if valid metadata exists might be performance intensive. So, the performance impact needs to be measured. If the performance impact is acceptable (both in terms of resource usage and time), it is recommended to use this way to verify if the member contains valid metadata. Otherwise, alternatives such as a simple check that WAL folder exists coupled with the static information about use of persistent or ephemeral storage might be considered.

    How to detect if valid data exists in an etcd member

    The initialization sequence in etcd-backup-restore already includes database verification. This would suffice to determine if the member has valid data.

    Recommendation

    Though ephemeral persistence has performance and logistics advantages, it is recommended to start with persistent data for the member pods. In addition to the reasons and concerns listed above, there is also the additional concern that in case of backup failure, the risk of additional data loss is a bit higher if ephemeral persistence is used (simultaneous quoram loss is sufficient) when compared to persistent storage (simultaenous quorum loss with majority persistence loss is needed). The risk might still be acceptable but the idea is to gain experience about how frequently member containers/pods get restarted/recreated, how frequently leader election happens among members of an etcd cluster and how frequently etcd clusters lose quorum. Based on this experience, we can move towards using ephemeral (perhaps even in-memory) persistence for the member pods.

    Separating peer and client traffic

    The current single-node ETCD cluster implementation in etcd-druid and etcd-backup-restore uses a single service object to act as the entry point for the client traffic. There is no separation or distinction between the client and peer traffic because there is not much benefit to be had by making that distinction.

    In the multi-node ETCD cluster scenario, it makes sense to distinguish between and separate the peer and client traffic. This can be done by using two services.

    • peer
      • To be used for peer communication. This could be a headless service.
    • client
      • To be used for client communication. This could be a normal ClusterIP service like it is in the single-node case.

    The main advantage of this approach is that it makes it possible (if needed) to allow only peer to peer communication while blocking client communication. Such a thing might be required during some phases of some maintenance tasks (manual or automated).

    Cutting off client requests

    At present, in the single-node ETCD instances, etcd-druid configures the readinessProbe of the etcd main container to probe the healthz endpoint of the etcd-backup-restore sidecar which considers the status of the latest backup upload in addition to the regular checks about etcd and the side car being up and healthy. This has the effect of setting the etcd main container (and hence the etcd pod) as not ready if the latest backup upload failed. This results in the endpoints controller removing the pod IP address from the endpoints list for the service which eventually cuts off ingress traffic coming into the etcd pod via the etcd client service. The rationale for this is to fail early when the backup upload fails rather than continuing to serve requests while the gap between the last backup and the current data increases which might lead to unacceptably large amount of data loss if disaster strikes.

    This approach will not work in the multi-node scenario because we need the individual member pods to be able to talk to each other to maintain the cluster quorum when backup upload fails but need to cut off only client ingress traffic.

    It is recommended to separate the backup health condition tracking taking appropriate remedial actions. With that, the backup health condition tracking is now separated to the BackupReady condition in the Etcd resource status and the cutting off of client traffic (which could now be done for more reasons than failed backups) can be achieved in a different way described below.

    Manipulating Client Service podSelector

    The client traffic can be cut off by updating (manually or automatically by some component) the podSelector of the client service to add an additional label (say, unhealthy or disabled) such that the podSelector no longer matches the member pods created by the statefulset. This will result in the client ingress traffic being cut off. The peer service is left unmodified so that peer communication is always possible.

    Health Check

    The etcd main container and the etcd-backup-restore sidecar containers will be configured with livenessProbe and readinessProbe which will indicate the health of the containers and effectively the corresponding ETCD cluster member pod.

    Backup Failure

    As described above using readinessProbe failures based on latest backup failure is not viable in the multi-node ETCD scenario.

    Though cutting off traffic by manipulating client service podSelector is workable, it may not be desirable.

    It is recommended that on backup failure, the leading etcd-backup-restore sidecar (the one that is responsible for taking backups at that point in time, as explained in the backup section below, updates the BackupReady condition in the Etcd status and raises a high priority alert to the landscape operators but does not cut off the client traffic.

    The reasoning behind this decision to not cut off the client traffic on backup failure is to allow the Kubernetes cluster’s control plane (which relies on the ETCD cluster) to keep functioning as long as possible and to avoid bringing down the control-plane due to a missed backup.

    The risk of this approach is that with a cascaded sequence of failures (on top of the backup failure), there is a chance of more data loss than the frequency of backup would otherwise indicate.

    To be precise, the risk of such an additional data loss manifests only when backup failure as well as a special case of quorum loss (majority of the members are not ready) happen in such a way that the ETCD cluster needs to be re-bootstrapped from the backup. As described here, re-bootstrapping the ETCD cluster requires restoration from the latest backup only when a majority of members no longer have uncorrupted data persistence.

    If persistent storage is used, this will happen only when backup failure as well as a majority of the disks/volumes backing the ETCD cluster members fail simultaneously. This would indeed be rare and might be an acceptable risk.

    If ephemeral storage is used (especially, in-memory), the data loss will happen if a majority of the ETCD cluster members become NotReady (requiring a pod restart) at the same time as the backup failure. This may not be as rare as majority members’ disk/volume failure. The risk can be somewhat mitigated at least for planned maintenance operations by postponing potentially disruptive maintenance operations when BackupReady condition is false (vertical scaling, rolling updates, evictions due to node roll-outs).

    But in practice (when ephemeral storage is used), the current proposal suggests restoring from the latest full backup even when a minority of ETCD members (even a single pod) restart both to speed up the process of the new member catching up to the latest revision but also to avoid load on the leading member which needs to supply the data to bring the new member up-to-date. But as described here, in case of a minority member failure while using ephemeral storage, it is possible to restart the new member with empty data and let it fetch all the data from the leading member (only if backup is not accessible). Though this is suboptimal, it is workable given the constraints and conditions. With this, the risk of additional data loss in the case of ephemeral storage is only if backup failure as well as quorum loss happens. While this is still less rare than the risk of additional data loss in case of persistent storage, the risk might be tolerable. Provided the risk of quorum loss is not too high. This needs to be monitored/evaluated before opting for ephemeral storage.

    Given these constraints, it is better to dynamically avoid/postpone some potentially disruptive operations when BackupReady condition is false. This has the effect of allowing n/2 members to be evicted when the backups are healthy and completely disabling evictions when backups are not healthy.

    1. Skip/postpone potentially disruptive maintenance operations (listed below) when the BackupReady condition is false.
    2. Vertical scaling.
    3. Rolling updates, Basically, any updates to the StatefulSet spec which includes vertical scaling.
    4. Dynamically toggle the minAvailable field of the PodDisruptionBudget between n/2 + 1 and n (where n is the ETCD desired cluster size) whenever the BackupReady condition toggles between true and false.

    This will mean that etcd-backup-restore becomes Kubernetes-aware. But there might be reasons for making etcd-backup-restore Kubernetes-aware anyway (e.g. to update the etcd resource status with latest full snapshot details). This enhancement should keep etcd-backup-restore backward compatible. I.e. it should be possible to use etcd-backup-restore Kubernetes-unaware as before this proposal. This is possible either by auto-detecting the existence of kubeconfig or by an explicit command-line flag (such as --enable-client-service-updates which can be defaulted to false for backward compatibility).

    Alternative

    The alternative is for etcd-druid to implement the above functionality.

    But etcd-druid is centrally deployed in the host Kubernetes cluster and cannot scale well horizontally. So, it can potentially be a bottleneck if it is involved in regular health check mechanism for all the etcd clusters it manages. Also, the recommended approach above is more robust because it can work even if etcd-druid is down when the backup upload of a particular etcd cluster fails.

    Status

    It is desirable (for the etcd-druid and landscape administrators/operators) to maintain/expose status of the etcd cluster instances in the status sub-resource of the Etcd CRD. The proposed structure for maintaining the status is as shown in the example below.

    apiVersion: druid.gardener.cloud/v1alpha1
    kind: Etcd
    metadata:
      name: etcd-main
    spec:
      replicas: 3
      ...
    ...
    status:
      ...
      conditions:
      - type: Ready                 # Condition type for the readiness of the ETCD cluster
        status: "True"              # Indicates of the ETCD Cluster is ready or not
        lastHeartbeatTime:          "2020-11-10T12:48:01Z"
        lastTransitionTime:         "2020-11-10T12:48:01Z"
        reason: Quorate             # Quorate|QuorumLost
      - type: AllMembersReady       # Condition type for the readiness of all the member of the ETCD cluster
        status: "True"              # Indicates if all the members of the ETCD Cluster are ready
        lastHeartbeatTime:          "2020-11-10T12:48:01Z"
        lastTransitionTime:         "2020-11-10T12:48:01Z"
        reason: AllMembersReady     # AllMembersReady|NotAllMembersReady
      - type: BackupReady           # Condition type for the readiness of the backup of the ETCD cluster
        status: "True"              # Indicates if the backup of the ETCD cluster is ready
        lastHeartbeatTime:          "2020-11-10T12:48:01Z"
        lastTransitionTime:         "2020-11-10T12:48:01Z"
        reason: FullBackupSucceeded # FullBackupSucceeded|IncrementalBackupSucceeded|FullBackupFailed|IncrementalBackupFailed
      ...
      clusterSize: 3
      ...
      replicas: 3
      ...
      members:
      - name: etcd-main-0          # member pod name
        id: 272e204152             # member Id
        role: Leader               # Member|Leader
        status: Ready              # Ready|NotReady|Unknown
        lastTransitionTime:        "2020-11-10T12:48:01Z"
        reason: LeaseSucceeded     # LeaseSucceeded|LeaseExpired|UnknownGracePeriodExceeded|PodNotRead
      - name: etcd-main-1          # member pod name
        id: 272e204153             # member Id
        role: Member               # Member|Leader
        status: Ready              # Ready|NotReady|Unknown
        lastTransitionTime:        "2020-11-10T12:48:01Z"
        reason: LeaseSucceeded     # LeaseSucceeded|LeaseExpired|UnknownGracePeriodExceeded|PodNotRead
    

    This proposal recommends that etcd-druid (preferrably, the custodian controller in etcd-druid) maintains most of the information in the status of the Etcd resources described above.

    One exception to this is the BackupReady condition which is recommended to be maintained by the leading etcd-backup-restore sidecar container. This will mean that etcd-backup-restore becomes Kubernetes-aware. But there are other reasons for making etcd-backup-restore Kubernetes-aware anyway (e.g. to maintain health conditions). This enhancement should keep etcd-backup-restore backward compatible. But it should be possible to use etcd-backup-restore Kubernetes-unaware as before this proposal. This is possible either by auto-detecting the existence of kubeconfig or by an explicit command-line flag (such as --enable-etcd-status-updates which can be defaulted to false for backward compatibility).

    Members

    The members section of the status is intended to be maintained by etcd-druid (preferraby, the custodian controller of etcd-druid) based on the leases of the individual members.

    Note

    An earlier design in this proposal was for the individual etcd-backup-restore sidecars to update the corresponding status.members entries themselves. But this was redesigned to use member leases to avoid conflicts rising from frequent updates and the limitations in the support for Server-Side Apply in some versions of Kubernetes.

    The spec.holderIdentity field in the leases is used to communicate the ETCD member id and role between the etcd-backup-restore sidecars and etcd-druid.

    Member name as the key

    In an ETCD cluster, the member id is the unique identifier for a member. However, this proposal recommends using a single StatefulSet whose pods form the members of the ETCD cluster and Pods of a StatefulSet have uniquely indexed names as well as uniquely addressible DNS.

    This proposal recommends that the name of the member (which is the same as the name of the member Pod) be used as the unique key to identify a member in the members array. This can minimise the need to cleanup superfluous entries in the members array after the member pods are gone to some extent because the replacement pods for any member will share the same name and will overwrite the entry with a possibly new member id.

    There is still the possibility of not only superfluous entries in the members array but also superfluous members in the ETCD cluster for which there is no corresponding pod in the StatefulSet anymore.

    For example, if an ETCD cluster is scaled up from 3 to 5 and the new members were failing constantly due to insufficient resources and then if the ETCD client is scaled back down to 3 and failing member pods may not have the chance to clean up their member entries (from the members array as well as from the ETCD cluster) leading to superfluous members in the cluster that may have adverse effect on quorum of the cluster.

    Hence, the superfluous entries in both members array as well as the ETCD cluster need to be cleaned up as appropriate.

    Member Leases

    One Kubernetes lease object per desired ETCD member is maintained by etcd-druid (preferrably, the custodian controller in etcd-druid). The lease objects will be created in the same namespace as their owning Etcd object and will have the same name as the member to which they correspond (which, in turn would be the same as the pod name in which the member ETCD process runs).

    The lease objects are created and deleted only by etcd-druid but are continually renewed within the leaseDurationSeconds by the individual etcd-backup-restore sidecars (corresponding to their members) if the the corresponding ETCD member is ready and is part of the ETCD cluster.

    This will mean that etcd-backup-restore becomes Kubernetes-aware. But there are other reasons for making etcd-backup-restore Kubernetes-aware anyway (e.g. to maintain health conditions). This enhancement should keep etcd-backup-restore backward compatible. But it should be possible to use etcd-backup-restore Kubernetes-unaware as before this proposal. This is possible either by auto-detecting the existence of kubeconfig or by an explicit command-line flag (such as --enable-etcd-lease-renewal which can be defaulted to false for backward compatibility).

    A member entry in the Etcd resource status would be marked as Ready (with reason: LeaseSucceeded) if the corresponding pod is ready and the corresponding lease has not yet expired. The member entry would be marked as NotReady if the corresponding pod is not ready (with reason PodNotReady) or as Unknown if the corresponding lease has expired (with reason: LeaseExpired).

    While renewing the lease, the etcd-backup-restore sidecars also maintain the ETCD member id and their role (Leader or Member) separated by : in the spec.holderIdentity field of the corresponding lease object since this information is only available to the ETCD member processes and the etcd-backup-restore sidecars (e.g. 272e204152:Leader or 272e204153:Member). When the lease objects are created by etcd-druid, the spec.holderIdentity field would be empty.

    The value in spec.holderIdentity in the leases is parsed and copied onto the id and role fields of the corresponding status.members by etcd-druid.

    Conditions

    The conditions section in the status describe the overall condition of the ETCD cluster. The condition type Ready indicates if the ETCD cluster as a whole is ready to serve requests (i.e. the cluster is quorate) even though some minority of the members are not ready. The condition type AllMembersReady indicates of all the members of the ETCD cluster are ready. The distinction between these conditions could be significant for both external consumers of the status as well as etcd-druid itself. Some maintenance operations might be safe to do (e.g. rolling updates) only when all members of the cluster are ready. The condition type BackupReady indicates of the most recent backup upload (full or incremental) succeeded. This information also might be significant because some maintenance operations might be safe to do (e.g. anything that involves re-bootstrapping the ETCD cluster) only when backup is ready.

    The Ready and AllMembersReady conditions can be maintained by etcd-druid based on the status in the members section. The BackupReady condition will be maintained by the leading etcd-backup-restore sidecar that is in charge of taking backups.

    More condition types could be introduced in the future if specific purposes arise.

    ClusterSize

    The clusterSize field contains the current size of the ETCD cluster. It will be actively kept up-to-date by etcd-druid in all scenarios.

    • Before bootstrapping the ETCD cluster (during cluster creation or later bootstrapping because of quorum failure), etcd-druid will clear the status.members array and set status.clusterSize to be equal to spec.replicas.
    • While the ETCD cluster is quorate, etcd-druid will actively set status.clusterSize to be equal to length of the status.members whenever the length of the array changes (say, due to scaling of the ETCD cluster).

    Given that clusterSize reliably represents the size of the ETCD cluster, it can be used to calculate the Ready condition.

    Alternative

    The alternative is for etcd-druid to maintain the status in the Etcd status sub-resource. But etcd-druid is centrally deployed in the host Kubernetes cluster and cannot scale well horizontally. So, it can potentially be a bottleneck if it is involved in regular health check mechanism for all the etcd clusters it manages. Also, the recommended approach above is more robust because it can work even if etcd-druid is down when the backup upload of a particular etcd cluster fails.

    Decision table for etcd-druid based on the status

    The following decision table describes the various criteria etcd-druid takes into consideration to determine the different etcd cluster management scenarios and the corresponding reconciliation actions it must take. The general principle is to detect the scenario and take the minimum action to move the cluster along the path to good health. The path from any one scenario to a state of good health will typically involve going through multiple reconciliation actions which probably take the cluster through many other cluster management scenarios. Especially, it is proposed that individual members auto-heal where possible, even in the case of the failure of a majority of members of the etcd cluster and that etcd-druid takes action only if the auto-healing doesn’t happen for a configured period of time.

    1. Pink of health

    Observed state

    • Cluster Size
      • Desired: n
      • Current: n
    • StatefulSet replicas
      • Desired: n
      • Ready: n
    • Etcd status
      • members
        • Total: n
        • Ready: n
        • Members NotReady for long enough to be evicted, i.e. lastTransitionTime > notReadyGracePeriod: 0
        • Members with readiness status Unknown long enough to be considered NotReady, i.e. lastTransitionTime > unknownGracePeriod: 0
        • Members with expired lease: 0
      • conditions:
        • Ready: true
        • AllMembersReady: true
        • BackupReady: true

    Nothing to do

    2. Member status is out of sync with their leases

    Observed state

    • Cluster Size
      • Desired: n
      • Current: n
    • StatefulSet replicas
      • Desired: n
      • Ready: n
    • Etcd status
      • members
        • Total: n
        • Ready: r
        • Members NotReady for long enough to be evicted, i.e. lastTransitionTime > notReadyGracePeriod: 0
        • Members with readiness status Unknown long enough to be considered NotReady, i.e. lastTransitionTime > unknownGracePeriod: 0
        • Members with expired lease: l
      • conditions:
        • Ready: true
        • AllMembersReady: true
        • BackupReady: true

    Mark the l members corresponding to the expired leases as Unknown with reason LeaseExpired and with id populated from spec.holderIdentity of the lease if they are not already updated so.

    Mark the n - l members corresponding to the active leases as Ready with reason LeaseSucceeded and with id populated from spec.holderIdentity of the lease if they are not already updated so.

    Please refer here for more details.

    3. All members are Ready but AllMembersReady condition is stale

    Observed state

    • Cluster Size
      • Desired: N/A
      • Current: N/A
    • StatefulSet replicas
      • Desired: n
      • Ready: N/A
    • Etcd status
      • members
        • Total: n
        • Ready: n
        • Members NotReady for long enough to be evicted, i.e. lastTransitionTime > notReadyGracePeriod: 0
        • Members with readiness status Unknown long enough to be considered NotReady, i.e. lastTransitionTime > unknownGracePeriod: 0
        • Members with expired lease: 0
      • conditions:
        • Ready: N/A
        • AllMembersReady: false
        • BackupReady: N/A

    Mark the status condition type AllMembersReady to true.

    4. Not all members are Ready but AllMembersReady condition is stale

    Observed state

    • Cluster Size

      • Desired: N/A
      • Current: N/A
    • StatefulSet replicas

      • Desired: n
      • Ready: N/A
    • Etcd status

      • members
        • Total: N/A
        • Ready: r where 0 <= r < n
        • Members NotReady for long enough to be evicted, i.e. lastTransitionTime > notReadyGracePeriod: nr where 0 < nr < n
        • Members with readiness status Unknown long enough to be considered NotReady, i.e. lastTransitionTime > unknownGracePeriod: u where 0 < u < n
        • Members with expired lease: h where 0 < h < n
      • conditions:
        • Ready: N/A
        • AllMembersReady: true
        • BackupReady: N/A

      where (nr + u + h) > 0 or r < n

    Mark the status condition type AllMembersReady to false.

    5. Majority members are Ready but Ready condition is stale

    Observed state

    • Cluster Size

      • Desired: N/A
      • Current: N/A
    • StatefulSet replicas

      • Desired: n
      • Ready: N/A
    • Etcd status

      • members
        • Total: n
        • Ready: r where r > n/2
        • Members NotReady for long enough to be evicted, i.e. lastTransitionTime > notReadyGracePeriod: nr where 0 < nr < n/2
        • Members with readiness status Unknown long enough to be considered NotReady, i.e. lastTransitionTime > unknownGracePeriod: u where 0 < u < n/2
        • Members with expired lease: N/A
      • conditions:
        • Ready: false
        • AllMembersReady: N/A
        • BackupReady: N/A

      where 0 < (nr + u + h) < n/2

    Mark the status condition type Ready to true.

    6. Majority members are NotReady but Ready condition is stale

    Observed state

    • Cluster Size

      • Desired: N/A
      • Current: N/A
    • StatefulSet replicas

      • Desired: n
      • Ready: N/A
    • Etcd status

      • members
        • Total: n
        • Ready: r where 0 < r < n
        • Members NotReady for long enough to be evicted, i.e. lastTransitionTime > notReadyGracePeriod: nr where 0 < nr < n
        • Members with readiness status Unknown long enough to be considered NotReady, i.e. lastTransitionTime > unknownGracePeriod: u where 0 < u < n
        • Members with expired lease: N/A
      • conditions:
        • Ready: true
        • AllMembersReady: N/A
        • BackupReady: N/A

      where (nr + u + h) > n/2 or r < n/2

    Mark the status condition type Ready to false.

    7. Some members have been in Unknown status for a while

    Observed state

    • Cluster Size
      • Desired: N/A
      • Current: n
    • StatefulSet replicas
      • Desired: N/A
      • Ready: N/A
    • Etcd status
      • members
        • Total: N/A
        • Ready: N/A
        • Members NotReady for long enough to be evicted, i.e. lastTransitionTime > notReadyGracePeriod: N/A
        • Members with readiness status Unknown long enough to be considered NotReady, i.e. lastTransitionTime > unknownGracePeriod: u where u <= n
        • Members with expired lease: N/A
      • conditions:
        • Ready: N/A
        • AllMembersReady: N/A
        • BackupReady: N/A

    Mark the u members as NotReady in Etcd status with reason: UnknownGracePeriodExceeded.

    8. Some member pods are not Ready but have not had the chance to update their status

    Observed state

    • Cluster Size
      • Desired: N/A
      • Current: n
    • StatefulSet replicas
      • Desired: n
      • Ready: s where s < n
    • Etcd status
      • members
        • Total: N/A
        • Ready: N/A
        • Members NotReady for long enough to be evicted, i.e. lastTransitionTime > notReadyGracePeriod: N/A
        • Members with readiness status Unknown long enough to be considered NotReady, i.e. lastTransitionTime > unknownGracePeriod: N/A
        • Members with expired lease: N/A
      • conditions:
        • Ready: N/A
        • AllMembersReady: N/A
        • BackupReady: N/A

    Mark the n - s members (corresponding to the pods that are not Ready) as NotReady in Etcd status with reason: PodNotReady

    9. Quorate cluster with a minority of members NotReady

    Observed state

    • Cluster Size
      • Desired: N/A
      • Current: n
    • StatefulSet replicas
      • Desired: N/A
      • Ready: N/A
    • Etcd status
      • members
        • Total: n
        • Ready: n - f
        • Members NotReady for long enough to be evicted, i.e. lastTransitionTime > notReadyGracePeriod: f where f < n/2
        • Members with readiness status Unknown long enough to be considered NotReady, i.e. lastTransitionTime > unknownGracePeriod: 0
        • Members with expired lease: N/A
      • conditions:
        • Ready: true
        • AllMembersReady: false
        • BackupReady: true

    Delete the f NotReady member pods to force restart of the pods if they do not automatically restart via failed livenessProbe. The expectation is that they will either re-join the cluster as an existing member or remove themselves and join as new members on restart of the container or pod and renew their leases.

    10. Quorum lost with a majority of members NotReady

    Observed state

    • Cluster Size
      • Desired: N/A
      • Current: n
    • StatefulSet replicas
      • Desired: N/A
      • Ready: N/A
    • Etcd status
      • members
        • Total: n
        • Ready: n - f
        • Members NotReady for long enough to be evicted, i.e. lastTransitionTime > notReadyGracePeriod: f where f >= n/2
        • Members with readiness status Unknown long enough to be considered NotReady, i.e. lastTransitionTime > unknownGracePeriod: N/A
        • Members with expired lease: N/A
      • conditions:
        • Ready: false
        • AllMembersReady: false
        • BackupReady: true

    Scale down the StatefulSet to replicas: 0. Ensure that all member pods are deleted. Ensure that all the members are removed from Etcd status. Delete and recreate all the member leases. Recover the cluster from loss of quorum as discussed here.

    11. Scale up of a healthy cluster

    Observed state

    • Cluster Size
      • Desired: d
      • Current: n where d > n
    • StatefulSet replicas
      • Desired: N/A
      • Ready: n
    • Etcd status
      • members
        • Total: n
        • Ready: n
        • Members NotReady for long enough to be evicted, i.e. lastTransitionTime > notReadyGracePeriod: 0
        • Members with readiness status Unknown long enough to be considered NotReady, i.e. lastTransitionTime > unknownGracePeriod: 0
        • Members with expired lease: 0
      • conditions:
        • Ready: true
        • AllMembersReady: true
        • BackupReady: true

    Add d - n new members by scaling the StatefulSet to replicas: d. The rest of the StatefulSet spec need not be updated until the next cluster bootstrapping (alternatively, the rest of the StatefulSet spec can be updated pro-actively once the new members join the cluster. This will trigger a rolling update).

    Also, create the additional member leases for the d - n new members.

    12. Scale down of a healthy cluster

    Observed state

    • Cluster Size
      • Desired: d
      • Current: n where d < n
    • StatefulSet replicas
      • Desired: n
      • Ready: n
    • Etcd status
      • members
        • Total: n
        • Ready: n
        • Members NotReady for long enough to be evicted, i.e. lastTransitionTime > notReadyGracePeriod: 0
        • Members with readiness status Unknown long enough to be considered NotReady, i.e. lastTransitionTime > unknownGracePeriod: 0
        • Members with expired lease: 0
      • conditions:
        • Ready: true
        • AllMembersReady: true
        • BackupReady: true

    Remove d - n existing members (numbered d, d + 1n) by scaling the StatefulSet to replicas: d. The StatefulSet spec need not be updated until the next cluster bootstrapping (alternatively, the StatefulSet spec can be updated pro-actively once the superfluous members exit the cluster. This will trigger a rolling update).

    Also, delete the member leases for the d - n members being removed.

    The superfluous entries in the members array will be cleaned up as explained here. The superfluous members in the ETCD cluster will be cleaned up by the leading etcd-backup-restore sidecar.

    13. Superfluous member entries in Etcd status

    Observed state

    • Cluster Size
      • Desired: N/A
      • Current: n
    • StatefulSet replicas
      • Desired: n
      • Ready: n
    • Etcd status
      • members
        • Total: m where m > n
        • Ready: N/A
        • Members NotReady for long enough to be evicted, i.e. lastTransitionTime > notReadyGracePeriod: N/A
        • Members with readiness status Unknown long enough to be considered NotReady, i.e. lastTransitionTime > unknownGracePeriod: N/A
        • Members with expired lease: N/A
      • conditions:
        • Ready: N/A
        • AllMembersReady: N/A
        • BackupReady: N/A

    Remove the superfluous m - n member entries from Etcd status (numbered n, n+1m). Remove the superfluous m - n member leases if they exist. The superfluous members in the ETCD cluster will be cleaned up by the leading etcd-backup-restore sidecar.

    Decision table for etcd-backup-restore during initialization

    As discussed above, the initialization sequence of etcd-backup-restore in a member pod needs to generate suitable etcd configuration for its etcd container. It also might have to handle the etcd database verification and restoration functionality differently in different scenarios.

    The initialization sequence itself is proposed to be as follows. It is an enhancement of the existing initialization sequence. etcd member initialization sequence

    The details of the decisions to be taken during the initialization are given below.

    1. First member during bootstrap of a fresh etcd cluster

    Observed state

    • Cluster Size: n
    • Etcd status members:
      • Total: 0
      • Ready: 0
      • Status contains own member: false
    • Data persistence
      • WAL directory has cluster/ member metadata: false
      • Data directory is valid and up-to-date: false
    • Backup
      • Backup exists: false
      • Backup has incremental snapshots: false

    Generate etcd configuration with n initial cluster peer URLs and initial cluster state new and return success.

    2. Addition of a new following member during bootstrap of a fresh etcd cluster

    Observed state

    • Cluster Size: n
    • Etcd status members:
      • Total: m where 0 < m < n
      • Ready: m
      • Status contains own member: false
    • Data persistence
      • WAL directory has cluster/ member metadata: false
      • Data directory is valid and up-to-date: false
    • Backup
      • Backup exists: false
      • Backup has incremental snapshots: false

    Generate etcd configuration with n initial cluster peer URLs and initial cluster state new and return success.

    3. Restart of an existing member of a quorate cluster with valid metadata and data

    Observed state

    • Cluster Size: n
    • Etcd status members:
      • Total: m where m > n/2
      • Ready: r where r > n/2
      • Status contains own member: true
    • Data persistence
      • WAL directory has cluster/ member metadata: true
      • Data directory is valid and up-to-date: true
    • Backup
      • Backup exists: N/A
      • Backup has incremental snapshots: N/A

    Re-use previously generated etcd configuration and return success.

    4. Restart of an existing member of a quorate cluster with valid metadata but without valid data

    Observed state

    • Cluster Size: n
    • Etcd status members:
      • Total: m where m > n/2
      • Ready: r where r > n/2
      • Status contains own member: true
    • Data persistence
      • WAL directory has cluster/ member metadata: true
      • Data directory is valid and up-to-date: false
    • Backup
      • Backup exists: N/A
      • Backup has incremental snapshots: N/A

    Remove self as a member (old member ID) from the etcd cluster as well as Etcd status. Add self as a new member of the etcd cluster as well as in the Etcd status. If backups do not exist, create an empty data and WAL directory. If backups exist, restore only the latest full snapshot (please see here for the reason for not restoring incremental snapshots). Generate etcd configuration with n initial cluster peer URLs and initial cluster state existing and return success.

    5. Restart of an existing member of a quorate cluster without valid metadata

    Observed state

    • Cluster Size: n
    • Etcd status members:
      • Total: m where m > n/2
      • Ready: r where r > n/2
      • Status contains own member: true
    • Data persistence
      • WAL directory has cluster/ member metadata: false
      • Data directory is valid and up-to-date: N/A
    • Backup
      • Backup exists: N/A
      • Backup has incremental snapshots: N/A

    Remove self as a member (old member ID) from the etcd cluster as well as Etcd status. Add self as a new member of the etcd cluster as well as in the Etcd status. If backups do not exist, create an empty data and WAL directory. If backups exist, restore only the latest full snapshot (please see here for the reason for not restoring incremental snapshots). Generate etcd configuration with n initial cluster peer URLs and initial cluster state existing and return success.

    6. Restart of an existing member of a non-quorate cluster with valid metadata and data

    Observed state

    • Cluster Size: n
    • Etcd status members:
      • Total: m where m < n/2
      • Ready: r where r < n/2
      • Status contains own member: true
    • Data persistence
      • WAL directory has cluster/ member metadata: true
      • Data directory is valid and up-to-date: true
    • Backup
      • Backup exists: N/A
      • Backup has incremental snapshots: N/A

    Re-use previously generated etcd configuration and return success.

    7. Restart of the first member of a non-quorate cluster without valid data

    Observed state

    • Cluster Size: n
    • Etcd status members:
      • Total: 0
      • Ready: 0
      • Status contains own member: false
    • Data persistence
      • WAL directory has cluster/ member metadata: N/A
      • Data directory is valid and up-to-date: false
    • Backup
      • Backup exists: N/A
      • Backup has incremental snapshots: N/A

    If backups do not exist, create an empty data and WAL directory. If backups exist, restore the latest full snapshot. Start a single-node embedded etcd with initial cluster peer URLs containing only own peer URL and initial cluster state new. If incremental snapshots exist, apply them serially (honouring source transactions). Take and upload a full snapshot after incremental snapshots are applied successfully (please see here for more reasons why). Generate etcd configuration with n initial cluster peer URLs and initial cluster state new and return success.

    8. Restart of a following member of a non-quorate cluster without valid data

    Observed state

    • Cluster Size: n
    • Etcd status members:
      • Total: m where 1 < m < n
      • Ready: r where 1 < r < n
      • Status contains own member: false
    • Data persistence
      • WAL directory has cluster/ member metadata: N/A
      • Data directory is valid and up-to-date: false
    • Backup
      • Backup exists: N/A
      • Backup has incremental snapshots: N/A

    If backups do not exist, create an empty data and WAL directory. If backups exist, restore only the latest full snapshot (please see here for the reason for not restoring incremental snapshots). Generate etcd configuration with n initial cluster peer URLs and initial cluster state existing and return success.

    Backup

    Only one of the etcd-backup-restore sidecars among the members are required to take the backup for a given ETCD cluster. This can be called a backup leader. There are two possibilities to ensure this.

    Leading ETCD main container’s sidecar is the backup leader

    The backup-restore sidecar could poll the etcd cluster and/or its own etcd main container to see if it is the leading member in the etcd cluster. This information can be used by the backup-restore sidecars to decide that sidecar of the leading etcd main container is the backup leader (i.e. responsible to for taking/uploading backups regularly).

    The advantages of this approach are as follows.

    • The approach is operationally and conceptually simple. The leading etcd container and backup-restore sidecar are always located in the same pod.
    • Network traffic between the backup container and the etcd cluster will always be local.

    The disadvantage is that this approach may not age well in the future if we think about moving the backup-restore container as a separate pod rather than a sidecar container.

    Independent leader election between backup-restore sidecars

    We could use the etcd lease mechanism to perform leader election among the backup-restore sidecars. For example, using something like go.etcd.io/etcd/clientv3/concurrency.

    The advantage and disadvantages are pretty much the opposite of the approach above. The advantage being that this approach may age well in the future if we think about moving the backup-restore container as a separate pod rather than a sidecar container.

    The disadvantages are as follows.

    • The approach is operationally and conceptually a bit complex. The leading etcd container and backup-restore sidecar might potentially belong to different pods.
    • Network traffic between the backup container and the etcd cluster might potentially be across nodes.

    History Compaction

    This proposal recommends to configure automatic history compaction on the individual members.

    Defragmentation

    Defragmentation is already triggered periodically by etcd-backup-restore. This proposal recommends to enhance this functionality to be performed only by the leading backup-restore container. The defragmentation must be performed only when etcd cluster is in full health and must be done in a rolling manner for each members to avoid disruption. The leading member should be defragmented last after all the rest of the members have been defragmented to minimise potential leadership changes caused by defragmentation. If the etcd cluster is unhealthy when it is time to trigger scheduled defragmentation, the defragmentation must be postponed until the cluster becomes healthy. This check must be done before triggering defragmentation for each member.

    Work-flows in etcd-backup-restore

    There are different work-flows in etcd-backup-restore. Some existing flows like initialization, scheduled backups and defragmentation have been enhanced or modified. Some new work-flows like status updates have been introduced. Some of these work-flows are sensitive to which etcd-backup-restore container is leading and some are not.

    The life-cycle of these work-flows is shown below. etcd-backup-restore work-flows life-cycle

    Work-flows independent of leader election in all members

    • Serve the HTTP API that all members are expected to support currently but some HTTP API call which are used to take out-of-sync delta or full snapshot should delegate the incoming HTTP requests to the leading-sidecar and one of the possible approach to achieve this is via an HTTP reverse proxy.
    • Check the health of the respective etcd member and renew the corresponding member lease.

    Work-flows only on the leading member

    • Take backups (full and incremental) at configured regular intervals
    • Defragment all the members sequentially at configured regular intervals
    • Cleanup superflous members from the ETCD cluster for which there is no corresponding pod (the ordinal in the pod name is greater than the cluster size) at regular intervals (or whenever the Etcd resource status changes by watching it)

    High Availability

    Considering that high-availability is the primary reason for using a multi-node etcd cluster, it makes sense to distribute the individual member pods of the etcd cluster across different physical nodes. If the underlying Kubernetes cluster has nodes from multiple availability zones, it makes sense to also distribute the member pods across nodes from different availability zones.

    One possibility to do this is via SelectorSpreadPriority of kube-scheduler but this is only best-effort and may not always be enforced strictly.

    It is better to use pod anti-affinity to enforce such distribution of member pods.

    Zonal Cluster - Single Availability Zone

    A zonal cluster is configured to consist of nodes belonging to only a single availability zone in a region of the cloud provider. In such a case, we can at best distribute the member pods of a multi-node etcd cluster instance only across different nodes in the configured availability zone.

    This can be done by specifying pod anti-affinity in the specification of the member pods using kubernetes.io/hostname as the topology key.

    apiVersion: apps/v1
    kind: StatefulSet
    ...
    spec:
      ...
      template:
        ...
        spec:
          ...
          affinity:
            podAntiAffinity:
              requiredDuringSchedulingIgnoredDuringExecution:
              - labelSelector: {} # podSelector that matches the member pods of the given etcd cluster instance
                topologyKey: "kubernetes.io/hostname"
          ...
        ...
      ...
    

    The recommendation is to keep etcd-druid agnostic of such topics related scheduling and cluster-topology and to use kupid to orthogonally inject the desired pod anti-affinity.

    Alternative

    Another option is to build the functionality into etcd-druid to include the required pod anti-affinity when it provisions the StatefulSet that manages the member pods. While this has the advantage of avoiding a dependency on an external component like kupid, the disadvantage is that we might need to address development or testing use-cases where it might be desirable to avoid distributing member pods and schedule them on as less number of nodes as possible. Also, as mentioned below, kupid can be used to distribute member pods of an etcd cluster instance across nodes in a single availability zone as well as across nodes in multiple availability zones with very minor variation. This keeps the solution uniform regardless of the topology of the underlying Kubernetes cluster.

    Regional Cluster - Multiple Availability Zones

    A regional cluster is configured to consist of nodes belonging to multiple availability zones (typically, three) in a region of the cloud provider. In such a case, we can distribute the member pods of a multi-node etcd cluster instance across nodes belonging to different availability zones.

    This can be done by specifying pod anti-affinity in the specification of the member pods using topology.kubernetes.io/zone as the topology key. In Kubernetes clusters using Kubernetes release older than 1.17, the older (and now deprecated) failure-domain.beta.kubernetes.io/zone might have to be used as the topology key.

    apiVersion: apps/v1
    kind: StatefulSet
    ...
    spec:
      ...
      template:
        ...
        spec:
          ...
          affinity:
            podAntiAffinity:
              requiredDuringSchedulingIgnoredDuringExecution:
              - labelSelector: {} # podSelector that matches the member pods of the given etcd cluster instance
                topologyKey: "topology.kubernetes.io/zone
          ...
        ...
      ...
    

    The recommendation is to keep etcd-druid agnostic of such topics related scheduling and cluster-topology and to use kupid to orthogonally inject the desired pod anti-affinity.

    Alternative

    Another option is to build the functionality into etcd-druid to include the required pod anti-affinity when it provisions the StatefulSet that manages the member pods. While this has the advantage of avoiding a dependency on an external component like kupid, the disadvantage is that such built-in support necessarily limits what kind of topologies of the underlying cluster will be supported. Hence, it is better to keep etcd-druid altogether agnostic of issues related to scheduling and cluster-topology.

    PodDisruptionBudget

    This proposal recommends that etcd-druid should deploy PodDisruptionBudget (minAvailable set to floor(<cluster size>/2) + 1) for multi-node etcd clusters (if AllMembersReady condition is true) to ensure that any planned disruptive operation can try and honour the disruption budget to ensure high availability of the etcd cluster while making potentially disrupting maintenance operations.

    Also, it is recommended to toggle the minAvailable field between floor(<cluster size>/2) and <number of members with status Ready true> whenever the AllMembersReady condition toggles between true and false. This is to disable eviction of any member pods when not all members are Ready.

    In case of a conflict, the recommendation is to use the highest of the applicable values for minAvailable.

    Rolling updates to etcd members

    Any changes to the Etcd resource spec that might result in a change to StatefulSet spec or otherwise result in a rolling update of member pods should be applied/propagated by etcd-druid only when the etcd cluster is fully healthy to reduce the risk of quorum loss during the updates. This would include vertical autoscaling changes (via, HVPA). If the cluster status unhealthy (i.e. if either AllMembersReady or BackupReady conditions are false), etcd-druid must restore it to full health before proceeding with such operations that lead to rolling updates. This can be further optimized in the future to handle the cases where rolling updates can still be performed on an etcd cluster that is not fully healthy.

    Follow Up

    Ephemeral Volumes

    See section Ephemeral Volumes.

    Shoot Control-Plane Migration

    This proposal adds support for multi-node etcd clusters but it should not have significant impact on shoot control-plane migration any more than what already present in the single-node etcd cluster scenario. But to be sure, this needs to be discussed further.

    Performance impact of multi-node etcd clusters

    Multi-node etcd clusters incur a cost on write performance as compared to single-node etcd clusters. This performance impact needs to be measured and documented. Here, we should compare different persistence option for the multi-nodeetcd clusters so that we have all the information necessary to take the decision balancing the high-availability, performance and costs.

    Metrics, Dashboards and Alerts

    There are already metrics exported by etcd and etcd-backup-restore which are visualized in monitoring dashboards and also used in triggering alerts. These might have hidden assumptions about single-node etcd clusters. These might need to be enhanced and potentially new metrics, dashboards and alerts configured to cover the multi-node etcd cluster scenario.

    Especially, a high priority alert must be raised if BackupReady condition becomes false.

    Costs

    Multi-node etcd clusters will clearly involve higher cost (when compared with single-node etcd clusters) just going by the CPU and memory usage for the additional members. Also, the different options for persistence for etcd data for the members will have different cost implications. Such cost impact needs to be assessed and documented to help navigate the trade offs between high availability, performance and costs.

    Future Work

    Gardener Ring

    Gardener Ring, requires provisioning and management of an etcd cluster with the members distributed across more than one Kubernetes cluster. This cannot be achieved by etcd-druid alone which has only the view of a single Kubernetes cluster. An additional component that has the view of all the Kubernetes clusters involved in setting up the gardener ring will be required to achieve this. However, etcd-druid can be used by such a higher-level component/controller (for example, by supplying the initial cluster configuration) such that individual etcd-druid instances in the individual Kubernetes clusters can manage the corresponding etcd cluster members.

    Autonomous Shoot Clusters

    Autonomous Shoot Clusters also will require a highly availble etcd cluster to back its control-plane and the multi-node support proposed here can be leveraged in that context. However, the current proposal will not meet all the needs of a autonomous shoot cluster. Some additional components will be required that have the overall view of the autonomous shoot cluster and they can use etcd-druid to manage the multi-node etcd cluster. But this scenario may be different from that of Gardener Ring in that the individual etcd members of the cluster may not be hosted on different Kubernetes clusters.

    Optimization of recovery from non-quorate cluster with some member containing valid data

    It might be possible to optimize the actions during the recovery of a non-quorate cluster where some of the members contain valid data and some other don’t. The optimization involves verifying the data of the valid members to determine the data of which member is the most recent (even considering the latest backup) so that the full snapshot can be taken from it before recovering the etcd cluster. Such an optimization can be attempted in the future.

    Optimization of rolling updates to unhealthy etcd clusters

    As mentioned above, optimizations to proceed with rolling updates to unhealthy etcd clusters (without first restoring the cluster to full health) can be pursued in future work.

    3.3 - 02 Snapshot Compaction

    Snapshot Compaction for Etcd

    Current Problem

    To ensure recoverability of Etcd, backups of the database are taken at regular interval. Backups are of two types: Full Snapshots and Incremental Snapshots.

    Full Snapshots

    Full snapshot is a snapshot of the complete database at given point in time.The size of the database keeps changing with time and typically the size is relatively large (measured in 100s of megabytes or even in gigabytes. For this reason, full snapshots are taken after some large intervals.

    Incremental Snapshots

    Incremental Snapshots are collection of events on Etcd database, obtained through running WATCH API Call on Etcd. After some short intervals, all the events that are accumulated through WATCH API Call are saved in a file and named as Incremental Snapshots at relatively short time intervals.

    Recovery from the Snapshots

    Recovery from Full Snapshots

    As the full snapshots are snapshots of the complete database, the whole database can be recovered from a full snapshot in one go. Etcd provides API Call to restore the database from a full snapshot file.

    Recovery from Incremental Snapshots

    Delta snapshots are collection of retrospective Etcd events. So, to restore from Incremental snapshot file, the events from the file are needed to be applied sequentially on Etcd database through Etcd Put/Delete API calls. As it is heavily dependent on Etcd calls sequentially, restoring from Incremental Snapshot files can take long if there are numerous commands captured in Incremental Snapshot files.

    Delta snapshots are applied on top of running Etcd database. So, if there is inconsistency between the state of database at the point of applying and the state of the database when the delta snapshot commands were captured, restoration will fail.

    Currently, in Gardener setup, Etcd is restored from the last full snapshot and then the delta snapshots, which were captured after the last full snapshot.

    The main problem with this is that the complete restoration time can be unacceptably large if the rate of change coming into the etcd database is quite high because there are large number of events in the delta snapshots to be applied sequentially. A secondary problem is that, though auto-compaction is enabled for etcd, it is not quick enough to compact all the changes from the incremental snapshots being re-applied during the relatively short period of time of restoration (as compared to the actual period of time when the incremental snapshots were accumulated). This may lead to the etcd pod (the backup-restore sidecar container, to be precise) to run out of memory and/or storage space even if it is sufficient for normal operations.

    Solution

    Compaction command

    To help with the problem mentioned earlier, our proposal is to introduce compact subcommand with etcdbrctl. On execution of compact command, A separate embedded Etcd process will be started where the Etcd data will be restored from the snapstore (exactly as in the restoration scenario today). Then the new Etcd database will be compacted and defragmented using Etcd API calls. The compaction will strip off the Etcd database of old revisions as per the Etcd auto-compaction configuration. The defragmentation will free up the unused fragment memory space released after compaction. Then a full snapshot of the compacted database will be saved in snapstore which then can be used as the base snapshot during any subsequent restoration (or backup compaction).

    How the solution works

    The newly introduced compact command does not disturb the running Etcd while compacting the backup snapshots. The command is designed to run potentially separately (from the main Etcd process/container/pod). Etcd Druid can be configured to run the newly introduced compact command as a separate job (scheduled periodically) based on total number of Etcd events accumulated after the most recent full snapshot.

    Etcd-druid flags:

    Etcd-druid introduces the following flags to configure the compaction job:

    • --enable-backup-compaction (default false): Set this flag to true to enable the automatic compaction of etcd backups when the threshold value denoted by CLI flag --etcd-events-threshold is exceeded.
    • --compaction-workers (default 3): Number of worker threads of the CompactionJob controller. The controller creates a backup compaction job if a certain etcd event threshold is reached. If compaction is enabled, the value for this flag must be greater than zero.
    • --etcd-events-threshold (default 1000000): Total number of etcd events that can be allowed before a backup compaction job is triggered.
    • --active-deadline-duration (default 3h): Duration after which a running backup compaction job will be terminated.
    • --metrics-scrape-wait-duration (default 0s): Duration to wait for after compaction job is completed, to allow Prometheus metrics to be scraped.

    Points to take care while saving the compacted snapshot:

    As compacted snapshot and the existing periodic full snapshots are taken by different processes running in different pods but accessing same store to save the snapshots, some problems may arise:

    1. When uploading the compacted snapshot to the snapstore, there is the problem of how does the restorer know when to start using the newly compacted snapshot. This communication needs to be atomic.
    2. With a regular schedule for compaction that happens potentially separately from the main etcd pod, is there a need for regular scheduled full snapshots anymore?
    3. We are planning to introduce new directory structure, under v2 prefix, for saving the snapshots (compacted and full), as mentioned in details below. But for backward compatibility, we also need to consider the older directory, which is currently under v1 prefix, during accessing snapshots.

    How to swap full snapshot with compacted snapshot atomically

    Currently, full snapshots and the subsequent delta snapshots are grouped under same prefix path in the snapstore. When a full snapshot is created, it is placed under a prefix/directory with the name comprising of timestamp. Then subsequent delta snapshots are also pushed into the same directory. Thus each prefix/directory contains a single full snapshot and the subsequent delta snapshots. So far, it is the job of ETCDBR to start main Etcd process and snapshotter process which takes full snapshot and delta snapshot periodically. But as per our proposal, compaction will be running as parallel process to main Etcd process and snapshotter process. So we can’t reliably co-ordinate between the processes to achieve switching to the compacted snapshot as the base snapshot atomically.

    Current Directory Structure
    - Backup-192345
        - Full-Snapshot-0-1-192345
        - Incremental-Snapshot-1-100-192355
        - Incremental-Snapshot-100-200-192365
        - Incremental-Snapshot-200-300-192375
    - Backup-192789
        - Full-Snapshot-0-300-192789
        - Incremental-Snapshot-300-400-192799
        - Incremental-Snapshot-400-500-192809
        - Incremental-Snapshot-500-600-192819
    

    To solve the problem, proposal is:

    1. ETCDBR will take the first full snapshot after it starts main Etcd Process and snapshotter process. After taking the first full snapshot, snapshotter will continue taking full snapshots. On the other hand, ETCDBR compactor command will be run as periodic job in a separate pod and use the existing full or compacted snapshots to produce further compacted snapshots. Full snapshots and compacted snapshots will be named after same fashion. So, there is no need of any mechanism to choose which snapshots(among full and compacted snapshot) to consider as base snapshots.
    2. Flatten the directory structure of backup folder. Save all the full snapshots, delta snapshots and compacted snapshots under same directory/prefix. Restorer will restore from full/compacted snapshots and delta snapshots sorted based on the revision numbers in name (or timestamp if the revision numbers are equal).
    Proposed Directory Structure
    Backup :
        - Full-Snapshot-0-1-192355 (Taken by snapshotter)
        - Incremental-Snapshot-revision-1-100-192365
        - Incremental-Snapshot-revision-100-200-192375
        - Full-Snapshot-revision-0-200-192379 (Taken by snapshotter)
        - Incremental-Snapshot-revision-200-300-192385
        - Full-Snapshot-revision-0-300-192386 (Taken by compaction job)
        - Incremental-Snapshot-revision-300-400-192396
        - Incremental-Snapshot-revision-400-500-192406
        - Incremental-Snapshot-revision-500-600-192416
        - Full-Snapshot-revision-0-600-192419 (Taken by snapshotter)
        - Full-Snapshot-revision-0-600-192420 (Taken by compaction job)
    
    What happens to the delta snapshots that were compacted?

    The proposed compaction sub-command in etcdbrctl (and hence, the CronJob provisioned by etcd-druid that will schedule it at a regular interval) would only upload the compacted full snapshot. It will not delete the snapshots (delta or full snapshots) that were compacted. These snapshots which were superseded by a freshly uploaded compacted snapshot would follow the same life-cycle as other older snapshots. I.e. they will be garbage collected according to the configured backup snapshot retention policy. For example, if an exponential retention policy is configured and if compaction is done every 30m then there might be at most 48 additional (compacted) full snapshots (24h * 2) in the backup for the latest day. As time rolls forward to the next day, these additional compacted snapshots (along with the delta snapshots that were compacted into them) will get garbage collected retaining only one full snapshot for the day before according to the retention policy.

    Future work

    In the future, we have plan to stop the snapshotter just after taking the first full snapshot. Then, the compaction job will be solely responsible for taking subsequent full snapshots. The directory structure would be looking like following:

    Backup :
        - Full-Snapshot-0-1-192355 (Taken by snapshotter)
        - Incremental-Snapshot-revision-1-100-192365
        - Incremental-Snapshot-revision-100-200-192375
        - Incremental-Snapshot-revision-200-300-192385
        - Full-Snapshot-revision-0-300-192386 (Taken by compaction job)
        - Incremental-Snapshot-revision-300-400-192396
        - Incremental-Snapshot-revision-400-500-192406
        - Incremental-Snapshot-revision-500-600-192416
        - Full-Snapshot-revision-0-600-192420 (Taken by compaction job)
    

    Backward Compatibility

    1. Restoration : The changes to handle the newly proposed backup directory structure must be backward compatible with older structures at least for restoration because we need have to restore from backups in the older structure. This includes the support for restoring from a backup without a metadata file if that is used in the actual implementation.
    2. Backup : For new snapshots (even on a backup containing the older structure), the new structure may be used. The new structure must be setup automatically including creating the base full snapshot.
    3. Garbage collection : The existing functionality of garbage collection of snapshots (full and incremental) according to the backup retention policy must be compatible with both old and new backup folder structure. I.e. the snapshots in the older backup structure must be retained in their own structure and the snapshots in the proposed backup structure should be retained in the proposed structure. Once all the snapshots in the older backup structure go out of the retention policy and are garbage collected, we can think of removing the support for older backup folder structure.

    Note: Compactor will run parallel to current snapshotter process and work only if there is any full snapshot already present in the store. By current design, a full snapshot will be taken if there is already no full snapshot or the existing full snapshot is older than 24 hours. It is not limitation but a design choice. As per proposed design, the backup storage will contain both periodic full snapshots as well as periodic compacted snapshot. Restorer will pickup the base snapshot whichever is latest one.

    3.4 - 03 Scaling Up An Etcd Cluster

    Scaling-up a single-node to multi-node etcd cluster deployed by etcd-druid

    To mark a cluster for scale-up from single node to multi-node etcd, just patch the etcd custom resource’s .spec.replicas from 1 to 3 (for example).

    Challenges for scale-up

    1. Etcd cluster with single replica don’t have any peers, so no peer communication is required hence peer URL may or may not be TLS enabled. However, while scaling up from single node etcd to multi-node etcd, there will be a requirement to have peer communication between members of the etcd cluster. Peer communication is required for various reasons, for instance for members to sync up cluster state, data, and to perform leader election or any cluster wide operation like removal or addition of a member etc. Hence in a multi-node etcd cluster we need to have TLS enable peer URL for peer communication.
    2. Providing the correct configuration to start new etcd members as it is different from boostrapping a cluster since these new etcd members will join an existing cluster.

    Approach

    We first went through the etcd doc of update-advertise-peer-urls to find out information regarding peer URL updation. Interestingly, etcd doc has mentioned the following:

    To update the advertise peer URLs of a member, first update it explicitly via member command and then restart the member.
    

    But we can’t assume peer URL is not TLS enabled for single-node cluster as it depends on end-user. A user may or may not enable the TLS for peer URL for a single node etcd cluster. So, How do we detect whether peer URL was enabled or not when cluster is marked for scale-up?

    Detecting if peerURL TLS is enabled or not

    For this we use an annotation in member lease object member.etcd.gardener.cloud/tls-enabled set by backup-restore sidecar of etcd. As etcd configuration is provided by backup-restore, so it can find out whether TLS is enabled or not and accordingly set this annotation member.etcd.gardener.cloud/tls-enabled to either true or false in member lease object. And with the help of this annotation and config-map values etcd-druid is able to detect whether there is a change in a peer URL or not.

    Etcd-Druid helps in scaling up etcd cluster

    Now, it is detected whether peer URL was TLS enabled or not for single node etcd cluster. Etcd-druid can now use this information to take action:

    • If peer URL was already TLS enabled then no action is required from etcd-druid side. Etcd-druid can proceed with scaling up the cluster.
    • If peer URL was not TLS enabled then etcd-druid has to intervene and make sure peer URL should be TLS enabled first for the single node before marking the cluster for scale-up.

    Action taken by etcd-druid to enable the peerURL TLS

    1. Etcd-druid will update the etcd-bootstrap config-map with new config like initial-cluster,initial-advertise-peer-urls etc. Backup-restore will detect this change and update the member lease annotation to member.etcd.gardener.cloud/tls-enabled: "true".
    2. In case the peer URL TLS has been changed to enabled: Etcd-druid will add tasks to the deployment flow:
      • Check if peer TLS has been enabled for existing StatefulSet pods, by checking the member leases for the annotation member.etcd.gardener.cloud/tls-enabled.
      • If peer TLS enablement is pending for any of the members, then check and patch the StatefulSet with the peer TLS volume mounts, if not already patched. This will cause a rolling update of the existing StatefulSet pods, which allows etcd-backup-restore to update the member peer URL in the etcd cluster.
      • Requeue this reconciliation flow until peer TLS has been enabled for all the existing etcd members.

    After PeerURL is TLS enabled

    After peer URL TLS enablement for single node etcd cluster, now etcd-druid adds a scale-up annotation: gardener.cloud/scaled-to-multi-node to the etcd statefulset and etcd-druid will patch the statefulsets .spec.replicas to 3(for example). The statefulset controller will then bring up new pods(etcd with backup-restore as a sidecar). Now etcd’s sidecar i.e backup-restore will check whether this member is already a part of a cluster or not and incase it is unable to check (may be due to some network issues) then backup-restore checks presence of this annotation: gardener.cloud/scaled-to-multi-node in etcd statefulset to detect scale-up. If it finds out it is the scale-up case then backup-restore adds new etcd member as a learner first and then starts the etcd learner by providing the correct configuration. Once learner gets in sync with the etcd cluster leader, it will get promoted to a voting member.

    Providing the correct etcd config

    As backup-restore detects that it’s a scale-up scenario, backup-restore sets initial-cluster-state to existing as this member will join an existing cluster and it calculates the rest of the config from the updated config-map provided by etcd-druid.

    Sequence diagram

    Future improvements:

    The need of restarting etcd pods twice will change in the future. please refer: https://github.com/gardener/etcd-backup-restore/issues/538

    3.5 - Cli Flags

    CLI Flags

    Etcd-druid exposes the following CLI flags that allow for configuring its behavior.

    CLI FLagComponentDescriptionDefault
    feature-gatesetcd-druidA set of key=value pairs that describe feature gates for alpha/experimental features. Please check feature-gates for more information.""
    metrics-bind-addresscontroller-managerThe IP address that the metrics endpoint binds to.""
    metrics-portcontroller-managerThe port used for the metrics endpoint.8080
    metrics-addrcontroller-managerThe fully qualified address:port that the metrics endpoint binds to.
    Deprecated: this field will be eventually removed. Please use --metrics-bind-address and –metrics-port instead.
    ":8080"
    webhook-server-bind-addresscontroller-managerThe IP address on which to listen for the HTTPS webhook server.""
    webhook-server-portcontroller-managerThe port on which to listen for the HTTPS webhook server.9443
    webhook-server-tls-server-cert-dircontroller-managerThe path to a directory containing the server’s TLS certificate and key (the files must be named tls.crt and tls.key respectively)."/etc/webhook-server-tls"
    enable-leader-electioncontroller-managerEnable leader election for controller manager. Enabling this will ensure there is only one active controller manager.false
    leader-election-idcontroller-managerName of the resource that leader election will use for holding the leader lock."druid-leader-election"
    leader-election-resource-lockcontroller-managerSpecifies which resource type to use for leader election. Supported options are ’endpoints’, ‘configmaps’, ’leases’, ’endpointsleases’ and ‘configmapsleases’.
    Deprecated. Will be removed in the future in favour of using only leases as the leader election resource lock for the controller manager.
    "leases"
    disable-lease-cachecontroller-managerDisable cache for lease.coordination.k8s.io resources.false
    etcd-workersetcd-controllerNumber of workers spawned for concurrent reconciles of etcd spec and status changes. If not specified then default of 3 is assumed.3
    ignore-operation-annotationetcd-controllerSpecifies whether to ignore or honour the annotation gardener.cloud/operation: reconcile on resources to be reconciled.
    Deprecated: please use --enable-etcd-spec-auto-reconcile instead.
    false
    enable-etcd-spec-auto-reconcileetcd-controllerIf true then automatically reconciles Etcd Spec. If false, waits for explicit annotation gardener.cloud/operation: reconcile to be placed on the Etcd resource to trigger reconcile.false
    disable-etcd-serviceaccount-automountetcd-controllerIf true then .automountServiceAccountToken will be set to false for the ServiceAccount created for etcd StatefulSets.false
    etcd-status-sync-periodetcd-controllerPeriod after which an etcd status sync will be attempted.15s
    etcd-member-notready-thresholdetcd-controllerThreshold after which an etcd member is considered not ready if the status was unknown before.5m
    etcd-member-unknown-thresholdetcd-controllerThreshold after which an etcd member is considered unknown.1m
    enable-backup-compactioncompaction-controllerEnable automatic compaction of etcd backups.false
    compaction-workerscompaction-controllerNumber of worker threads of the CompactionJob controller. The controller creates a backup compaction job if a certain etcd event threshold is reached. If compaction is enabled, the value for this flag must be greater than zero.3
    etcd-events-thresholdcompaction-controllerTotal number of etcd events that can be allowed before a backup compaction job is triggered.1000000
    active-deadline-durationcompaction-controllerDuration after which a running backup compaction job will be terminated.3h
    metrics-scrape-wait-durationcompaction-controllerDuration to wait for after compaction job is completed, to allow Prometheus metrics to be scraped.0s
    etcd-copy-backups-task-workersetcdcopybackupstask-controllerNumber of worker threads for the etcdcopybackupstask controller.3
    secret-workerssecret-controllerNumber of worker threads for the secrets controller.10
    enable-etcd-components-webhooketcdcomponents-webhookEnable EtcdComponents Webhook to prevent unintended changes to resources managed by etcd-druid.false
    reconciler-service-accountetcdcomponents-webhookThe fully qualified name of the service account used by etcd-druid for reconciling etcd resources. If unspecified, the default service account mounted for etcd-druid will be used.<etcd-druid-service-account>
    etcd-components-exempt-service-accountsetcdcomponents-webhookThe comma-separated list of fully qualified names of service accounts that are exempt from EtcdComponents Webhook checks.""

    3.6 - Controllers

    Controllers

    etcd-druid is an operator to manage etcd clusters, and follows the Operator pattern for Kubernetes. It makes use of the Kubebuilder framework which makes it quite easy to define Custom Resources (CRs) such as Etcds and EtcdCopyBackupTasks through Custom Resource Definitions (CRDs), and define controllers for these CRDs. etcd-druid uses Kubebuilder to define the Etcd CR and its corresponding controllers.

    All controllers that are a part of etcd-druid reside in package internal/controller, as sub-packages.

    Etcd-druid currently consists of the following controllers, each having its own responsibility:

    • etcd : responsible for the reconciliation of the Etcd CR spec, which allows users to run etcd clusters within the specified Kubernetes cluster, and also responsible for periodically updating the Etcd CR status with the up-to-date state of the managed etcd cluster.
    • compaction : responsible for snapshot compaction.
    • etcdcopybackupstask : responsible for the reconciliation of the EtcdCopyBackupsTask CR, which helps perform the job of copying snapshot backups from one object store to another.
    • secret : responsible in making sure Secrets being referenced by Etcd resources are not deleted while in use.

    Package Structure

    The typical package structure for the controllers that are part of etcd-druid is shown with the compaction controller:

    internal/controller/compaction
    ├── config.go
    ├── reconciler.go
    └── register.go
    
    • config.go: contains all the logic for the configuration of the controller, including feature gate activations, CLI flag parsing and validations.
    • register.go: contains the logic for registering the controller with the etcd-druid controller manager.
    • reconciler.go: contains the controller reconciliation logic.

    Each controller package also contains auxiliary files which are relevant to that specific controller.

    Controller Manager

    A manager is first created for all controllers that are a part of etcd-druid. The controller manager is responsible for all the controllers that are associated with CRDs. Once the manager is Start()ed, all the controllers that are registered with it are started.

    Each controller is built using a controller builder, configured with details such as the type of object being reconciled, owned objects whose owner object is reconciled, event filters (predicates), etc. Predicates are filters which allow controllers to filter which type of events the controller should respond to and which ones to ignore.

    The logic relevant to the controller manager like the creation of the controller manager and registering each of the controllers with the manager, is contained in internal/manager/manager.go.

    Etcd Controller

    The etcd controller is responsible for the reconciliation of the Etcd resource spec and status. It handles the provisioning and management of the etcd cluster. Different components that are required for the functioning of the cluster like Leases, ConfigMaps, and the Statefulset for the etcd cluster are all deployed and managed by the etcd controller.

    Additionally, etcd controller also periodically updates the Etcd resource status with the latest available information from the etcd cluster, as well as results and errors from the recent-most reconciliation of the Etcd resource spec.

    The etcd controller is essential to the functioning of the etcd cluster and etcd-druid, thus the minimum number of worker threads is 1 (default being 3), controlled by the CLI flag --etcd-workers.

    Etcd Spec Reconciliation

    While building the controller, an event filter is set such that the behavior of the controller, specifically for Etcd update operations, depends on the gardener.cloud/operation: reconcile annotation. This is controlled by the --enable-etcd-spec-auto-reconcile CLI flag, which, if set to false, tells the controller to perform reconciliation only when this annotation is present. If the flag is set to true, the controller will reconcile the etcd cluster anytime the Etcd spec, and thus generation, changes, and the next queued event for it is triggered.

    Note: Creation and deletion of Etcd resources are not affected by the above flag or annotation.

    The reason this filter is present is that any disruption in the Etcd resource due to reconciliation (due to changes in the Etcd spec, for example) while workloads are being run would cause unwanted downtimes to the etcd cluster. Hence, any user who wishes to avoid such disruptions, can choose to set the --enable-etcd-spec-auto-reconcile CLI flag to false. An example of this is Gardener’s gardenlet, which reconciles the Etcd resource only during a shoot cluster’s maintenance window.

    The controller adds a finalizer to the Etcd resource in order to ensure that it does not get deleted until all dependent resources managed by etcd-druid, aka managed components, are properly cleaned up. Only the etcd controller can delete a resource once it adds finalizers to it. This ensures that the proper deletion flow steps are followed while deleting the resource. During deletion flow, managed components are deleted in parallel.

    Etcd Status Updates

    The Etcd resource status is updated periodically by etcd controller, the interval for which is determined by the CLI flag --etcd-status-sync-period.

    Status fields of the Etcd resource such as LastOperation, LastErrors and ObservedGeneration, are updated to reflect the result of the recent reconciliation of the Etcd resource spec.

    • LastOperation holds information about the last operation performed on the etcd cluster, indicated by fields Type, State, Description and LastUpdateTime. Additionally, a field RunID indicates the unique ID assigned to the specific reconciliation run, to allow for better debugging of issues.
    • LastErrors is a slice of errors encountered by the last reconciliation run. Each error consists of fields Code to indicate the custom etcd-druid error code for the error, a human-readable Description, and the ObservedAt time when the error was seen.
    • ObservedGeneration indicates the latest generation of the Etcd resource that etcd-druid has “observed” and consequently reconciled. It helps identify whether a change in the Etcd resource spec was acted upon by druid or not.

    Status fields of the Etcd resource which correspond to the StatefulSet like CurrentReplicas, ReadyReplicas and Replicas are updated to reflect those of the StatefulSet by the controller.

    Status fields related to the etcd cluster itself, such as Members, PeerUrlTLSEnabled and Ready are updated as follows:

    • Cluster Membership: The controller updates the information about etcd cluster membership like Role, Status, Reason, LastTransitionTime and identifying information like the Name and ID. For the Status field, the member is checked for the Ready condition, where the member can be in Ready, NotReady and Unknown statuses.

    Etcd resource conditions are indicated by status field Conditions. The condition checks that are currently performed are:

    • AllMembersReady: indicates readiness of all members of the etcd cluster.
    • Ready: indicates overall readiness of the etcd cluster in serving traffic.
    • BackupReady: indicates health of the etcd backups, i.e., whether etcd backups are being taken regularly as per schedule. This condition is applicable only when backups are enabled for the etcd cluster.
    • DataVolumesReady: indicates health of the persistent volumes containing the etcd data.

    Compaction Controller

    The compaction controller deploys the snapshot compaction job whenever required. To understand the rationale behind this controller, please read snapshot-compaction.md. The controller watches the number of events accumulated as part of delta snapshots in the etcd cluster’s backups, and triggers a snapshot compaction when the number of delta events crosses the set threshold, which is configurable through the --etcd-events-threshold CLI flag (1M events by default).

    The controller watches for changes in snapshot Leases associated with Etcd resources. It checks the full and delta snapshot Leases and calculates the difference in events between the latest delta snapshot and the previous full snapshot, and initiates the compaction job if the event threshold is crossed.

    The number of worker threads for the compaction controller needs to be greater than or equal to 0 (default 3), controlled by the CLI flag --compaction-workers. This is unlike other controllers which need at least one worker thread for the proper functioning of etcd-druid as snapshot compaction is not a core functionality for the etcd clusters to be deployed. The compaction controller should be explicitly enabled by the user, through the --enable-backup-compaction CLI flag.

    EtcdCopyBackupsTask Controller

    The etcdcopybackupstask controller is responsible for deploying the etcdbrctl copy command as a job. This controller reacts to create/update events arising from EtcdCopyBackupsTask resources, and deploys the EtcdCopyBackupsTask job with source and target backup storage providers as arguments, which are derived from source and target bucket secrets referenced by the EtcdCopyBackupsTask resource.

    The number of worker threads for the etcdcopybackupstask controller needs to be greater than or equal to 0 (default being 3), controlled by the CLI flag --etcd-copy-backups-task-workers. This is unlike other controllers who need at least one worker thread for the proper functioning of etcd-druid as EtcdCopyBackupsTask is not a core functionality for the etcd clusters to be deployed.

    Secret Controller

    The secret controller’s primary responsibility is to add a finalizer on Secrets referenced by the Etcd resource. The secret controller is registered for Secrets, and the controller keeps a watch on the Etcd CR. This finalizer is added to ensure that Secrets which are referenced by the Etcd CR aren’t deleted while still being used by the Etcd resource.

    Events arising from the Etcd resource are mapped to a list of Secrets such as backup and TLS secrets that are referenced by the Etcd resource, and are enqueued into the request queue, which the reconciler then acts on.

    The number of worker threads for the secret controller must be at least 1 (default being 10) for this core controller, controlled by the CLI flag --secret-workers, since the referenced TLS and infrastructure access secrets are essential to the proper functioning of the etcd cluster.

    3.7 - DEP Title

    DEP-NN: Your short, descriptive title

    Table of Contents

    Summary

    Motivation

    Goals

    Non-Goals

    Proposal

    Alternatives

    3.8 - etcd Network Latency

    Network Latency analysis: sn-etcd-sz vs mn-etcd-sz vs mn-etcd-mz

    This page captures the etcd cluster latency analysis for below scenarios using the benchmark tool (build from etcd benchmark tool).

    sn-etcd-sz -> single-node etcd single zone (Only single replica of etcd will be running)

    mn-etcd-sz -> multi-node etcd single zone (Multiple replicas of etcd pods will be running across nodes in a single zone)

    mn-etcd-mz -> multi-node etcd multi zone (Multiple replicas of etcd pods will be running across nodes in multiple zones)

    PUT Analysis

    Summary

    • sn-etcd-sz latency is ~20% less than mn-etcd-sz when benchmark tool with single client.
    • mn-etcd-sz latency is less than mn-etcd-mz but the difference is ~+/-5%.
    • Compared to mn-etcd-sz, sn-etcd-sz latency is higher and gradually grows with more clients and larger value size.
    • Compared to mn-etcd-mz, mn-etcd-sz latency is higher and gradually grows with more clients and larger value size.
    • Compared to follower, leader latency is less, when benchmark tool with single client for all cases.
    • Compared to follower, leader latency is high, when benchmark tool with multiple clients for all cases.

    Sample commands:

    # write to leader
    benchmark put --target-leader --conns=1 --clients=1 --precise \
        --sequential-keys --key-starts 0 --val-size=256 --total=10000 \
        --endpoints=$ETCD_HOST 
    
    
    # write to follower
    benchmark put  --conns=1 --clients=1 --precise \
        --sequential-keys --key-starts 0 --val-size=256 --total=10000 \
        --endpoints=$ETCD_FOLLOWER_HOST
    

    Latency analysis during PUT requests to etcd

    • In this case benchmark tool tries to put key with random 256 bytes value.
      • Benchmark tool loads key/value to leader with single client .

        • sn-etcd-sz latency (~0.815ms) is ~50% lesser than mn-etcd-sz (~1.74ms ).
          • mn-etcd-sz latency (~1.74ms ) is slightly lesser than mn-etcd-mz (~1.8ms) but the difference is negligible (within same ms).
        • Number of keysValue sizeNumber of connectionsNumber of clientsTarget etcd serverAverage write QPSAverage latency per requestzoneserver nameTest name
          1000025611leader1220.05200.815mseu-west-1cetcd-main-0sn-etcd-sz
          1000025611leader586.5451.74mseu-west-1aetcd-main-1mn-etcd-sz
          1000025611leader554.01556544426341.8mseu-west-1aetcd-main-1mn-etcd-mz
      • Benchmark tool loads key/value to follower with single client.

        • mn-etcd-sz latency(~2.2ms) is 20% to 30% lesser than mn-etcd-mz(~2.7ms).
        • Compare to follower, leader has lower latency.
        • Number of keysValue sizeNumber of connectionsNumber of clientsTarget etcd serverAverage write QPSAverage latency per requestzoneserver nameTest name
          1000025611follower-1445.7432.23mseu-west-1aetcd-main-0mn-etcd-sz
          1000025611follower-1378.93667476107892.63mseu-west-1cetcd-main-0mn-etcd-mz
          Number of keysValue sizeNumber of connectionsNumber of clientsTarget etcd serverAverage write QPSAverage latency per requestzoneserver nameTest name
          1000025611follower-2457.9672.17mseu-west-1aetcd-main-2mn-etcd-sz
          1000025611follower-2345.65861298257962.89mseu-west-1betcd-main-2mn-etcd-mz
      • Benchmark tool loads key/value to leader with multiple clients.

        • sn-etcd-sz latency(~78.3ms) is ~10% greater than mn-etcd-sz(~71.81ms).
        • mn-etcd-sz latency(~71.81ms) is less than mn-etcd-mz(~72.5ms) but the difference is negligible.
        • Number of keysValue sizeNumber of connectionsNumber of clientsTarget etcd serverAverage write QPSAverage latency per requestzoneserver nameTest name
          1000002561001000leader12638.90578.32mseu-west-1cetcd-main-0sn-etcd-sz
          1000002561001000leader13789.24871.81mseu-west-1aetcd-main-1mn-etcd-sz
          1000002561001000leader13728.44643639522372.5mseu-west-1aetcd-main-1mn-etcd-mz
      • Benchmark tool loads key/value to follower with multiple clients.

        • mn-etcd-sz latency(~69.8ms) is ~5% greater than mn-etcd-mz(~72.6ms).
        • Compare to leader, follower has lower latency.
        • Number of keysValue sizeNumber of connectionsNumber of clientsTarget etcd serverAverage write QPSAverage latency per requestzoneserver nameTest name
          1000002561001000follower-114271.98369.80mseu-west-1aetcd-main-0mn-etcd-sz
          1000002561001000follower-113695.9872.62mseu-west-1aetcd-main-1mn-etcd-mz
          Number of keysValue sizeNumber of connectionsNumber of clientsTarget etcd serverAverage write QPSAverage latency per requestzoneserver nameTest name
          1000002561001000follower-214325.43669.47mseu-west-1aetcd-main-2mn-etcd-sz
          1000002561001000follower-215750.40949040747563.3mseu-west-1betcd-main-2mn-etcd-mz
    • In this case benchmark tool tries to put key with random 1 MB value.
      • Benchmark tool loads key/value to leader with single client.

        • sn-etcd-sz latency(~16.35ms) is ~20% lesser than mn-etcd-sz(~20.64ms).
        • mn-etcd-sz latency(~20.64ms) is less than mn-etcd-mz(~21.08ms) but the difference is negligible..
        • Number of keysValue sizeNumber of connectionsNumber of clientsTarget etcd serverAverage write QPSAverage latency per requestzoneserver nameTest name
          1000100000011leader61.11716.35mseu-west-1cetcd-main-0sn-etcd-sz
          1000100000011leader48.41620.64mseu-west-1aetcd-main-1mn-etcd-sz
          1000100000011leader45.751734166480221.08mseu-west-1aetcd-main-1mn-etcd-mz
      • Benchmark tool loads key/value withto follower single client.

        • mn-etcd-sz latency(~23.10ms) is ~10% greater than mn-etcd-mz(~21.8ms).
        • Compare to follower, leader has lower latency.
        • Number of keysValue sizeNumber of connectionsNumber of clientsTarget etcd serverAverage write QPSAverage latency per requestzoneserver nameTest name
          1000100000011follower-143.26123.10mseu-west-1aetcd-main-0mn-etcd-sz
          1000100000011follower-145.751734166480221.8mseu-west-1cetcd-main-0mn-etcd-mz
          1000100000011follower-145.3322.05mseu-west-1cetcd-main-0mn-etcd-mz
          Number of keysValue sizeNumber of connectionsNumber of clientsTarget etcd serverAverage write QPSAverage latency per requestzoneserver nameTest name
          1000100000011follower-240.051824.95mseu-west-1aetcd-main-2mn-etcd-sz
          1000100000011follower-243.2857315570983823.09mseu-west-1betcd-main-2mn-etcd-mz
          1000100000011follower-245.9221.76mseu-west-1aetcd-main-1mn-etcd-mz
          1000100000011follower-235.570528.1mseu-west-1betcd-main-2mn-etcd-mz
      • Benchmark tool loads key/value to leader with multiple clients.

        • sn-etcd-sz latency(~6.0375secs) is ~30% greater than mn-etcd-sz``~4.000secs).
        • mn-etcd-sz latency(~4.000secs) is less than mn-etcd-mz(~ 4.09secs) but the difference is negligible.
        • Number of keysValue sizeNumber of connectionsNumber of clientsTarget etcd serverAverage write QPSAverage latency per requestzoneserver nameTest name
          10001000000100300leader55.3736.0375secseu-west-1cetcd-main-0sn-etcd-sz
          10001000000100300leader67.3194.000secseu-west-1aetcd-main-1mn-etcd-sz
          10001000000100300leader65.919141679575944.09secseu-west-1aetcd-main-1mn-etcd-mz
      • Benchmark tool loads key/value to follower with multiple clients.

        • mn-etcd-sz latency(~4.04secs) is ~5% greater than mn-etcd-mz(~ 3.90secs).
        • Compare to leader, follower has lower latency.
        • Number of keysValue sizeNumber of connectionsNumber of clientsTarget etcd serverAverage write QPSAverage latency per requestzoneserver nameTest name
          10001000000100300follower-166.5284.0417secseu-west-1aetcd-main-0mn-etcd-sz
          10001000000100300follower-170.64934618563323.90secseu-west-1cetcd-main-0mn-etcd-mz
          10001000000100300follower-171.953.84secseu-west-1cetcd-main-0mn-etcd-mz
          Number of keysValue sizeNumber of connectionsNumber of clientsTarget etcd serverAverage write QPSAverage latency per requestzoneserver nameTest name
          10001000000100300follower-266.4474.0164secseu-west-1aetcd-main-2mn-etcd-sz
          10001000000100300follower-267.530380863694843.87secseu-west-1betcd-main-2mn-etcd-mz
          10001000000100300follower-268.463.92secseu-west-1aetcd-main-1mn-etcd-mz


    Range Analysis

    Sample commands are:

    # Single connection read request with sequential keys
    benchmark range 0 --target-leader --conns=1 --clients=1 --precise \
        --sequential-keys --key-starts 0  --total=10000 \
        --consistency=l \
        --endpoints=$ETCD_HOST 
    # --consistency=s [Serializable]
    benchmark range 0 --target-leader --conns=1 --clients=1 --precise \
        --sequential-keys --key-starts 0  --total=10000 \
        --consistency=s \
        --endpoints=$ETCD_HOST 
    # Each read request with range query matches key 0 9999 and repeats for total number of requests.  
    benchmark range 0 9999 --target-leader --conns=1 --clients=1 --precise \
        --total=10 \
        --consistency=s \
        --endpoints=https://etcd-main-client:2379
    # Read requests with multiple connections
    benchmark range 0 --target-leader --conns=100 --clients=1000 --precise \
        --sequential-keys --key-starts 0  --total=100000 \
        --consistency=l \
        --endpoints=$ETCD_HOST 
    benchmark range 0 --target-leader --conns=100 --clients=1000 --precise \
        --sequential-keys --key-starts 0  --total=100000 \
        --consistency=s \
        --endpoints=$ETCD_HOST 
    

    Latency analysis during Range requests to etcd

    • In this case benchmark tool tries to get specific key with random 256 bytes value.
      • Benchmark tool range requests to leader with single client.

        • sn-etcd-sz latency(~1.24ms) is ~40% greater than mn-etcd-sz(~0.67ms).

        • mn-etcd-sz latency(~0.67ms) is ~20% lesser than mn-etcd-mz(~0.85ms).

        • Number of requestsValue sizeNumber of connectionsNumber of clientssequential-keysConsistencyTarget etcd serverAverage write QPSAverage latency per requestzoneserver nameTest name
          1000025611truelleader800.2721.24mseu-west-1cetcd-main-0sn-etcd-sz
          1000025611truelleader1173.90810.67mseu-west-1aetcd-main-1mn-etcd-sz
          1000025611truelleader999.30201891786930.85mseu-west-1aetcd-main-1mn-etcd-mz
        • Compare to consistency Linearizable, Serializable is ~40% less for all cases

        • Number of requestsValue sizeNumber of connectionsNumber of clientssequential-keysConsistencyTarget etcd serverAverage write QPSAverage latency per requestzoneserver nameTest name
          1000025611truesleader1411.2290.70mseu-west-1cetcd-main-0sn-etcd-sz
          1000025611truesleader2033.1310.35mseu-west-1aetcd-main-1mn-etcd-sz
          1000025611truesleader2100.24263620120250.47mseu-west-1aetcd-main-1mn-etcd-mz
      • Benchmark tool range requests to follower with single client .

        • mn-etcd-sz latency(~1.3ms) is ~20% lesser than mn-etcd-mz(~1.6ms).
        • Compare to follower, leader read request latency is ~50% less for both mn-etcd-sz, mn-etcd-mz
        • Number of requestsValue sizeNumber of connectionsNumber of clientssequential-keysConsistencyTarget etcd serverAverage write QPSAverage latency per requestzoneserver nameTest name
          1000025611truelfollower-1765.3251.3mseu-west-1aetcd-main-0mn-etcd-sz
          1000025611truelfollower-1596.11.6mseu-west-1cetcd-main-0mn-etcd-mz
        • Compare to consistency Linearizable, Serializable is ~50% less for all cases
        • Number of requestsValue sizeNumber of connectionsNumber of clientssequential-keysConsistencyTarget etcd serverAverage write QPSAverage latency per requestzoneserver nameTest name
          1000025611truesfollower-11823.6310.54mseu-west-1aetcd-main-0mn-etcd-sz
          1000025611truesfollower-11442.60.69mseu-west-1cetcd-main-0mn-etcd-mz
          1000025611truesfollower-11416.390.70mseu-west-1cetcd-main-0mn-etcd-mz
          1000025611truesfollower-12077.4490.47mseu-west-1aetcd-main-1mn-etcd-mz
      • Benchmark tool range requests to leader with multiple client.

        • sn-etcd-sz latency(~84.66ms) is ~20% greater than mn-etcd-sz(~73.95ms).

        • mn-etcd-sz latency(~73.95ms) is more or less equal to mn-etcd-mz(~ 73.8ms).

        • Number of requestsValue sizeNumber of connectionsNumber of clientssequential-keysConsistencyTarget etcd serverAverage write QPSAverage latency per requestzoneserver nameTest name
          1000002561001000truelleader11775.72184.66mseu-west-1cetcd-main-0sn-etcd-sz
          1000002561001000truelleader13446.959873.95mseu-west-1aetcd-main-1mn-etcd-sz
          1000002561001000truelleader13527.1981060535373.8mseu-west-1aetcd-main-1mn-etcd-mz
        • Compare to consistency Linearizable, Serializable is ~20% lesser for all cases

        • sn-etcd-sz latency(~69.37ms) is more or less equal to mn-etcd-sz(~69.89ms).

        • mn-etcd-sz latency(~69.89ms) is slightly higher than mn-etcd-mz(~67.63ms).

        • Number of requestsValue sizeNumber of connectionsNumber of clientssequential-keysConsistencyTarget etcd serverAverage write QPSAverage latency per requestzoneserver nameTest name
          1000002561001000truesleader14334.902769.37mseu-west-1cetcd-main-0sn-etcd-sz
          1000002561001000truesleader14270.00869.89mseu-west-1aetcd-main-1mn-etcd-sz
          1000002561001000truesleader14715.28735402386967.63mseu-west-1aetcd-main-1mn-etcd-mz
      • Benchmark tool range requests to follower with multiple client.

        • mn-etcd-sz latency(~60.69ms) is ~20% lesser than mn-etcd-mz(~70.76ms).

        • Compare to leader, follower has lower read request latency.

        • Number of requestsValue sizeNumber of connectionsNumber of clientssequential-keysConsistencyTarget etcd serverAverage write QPSAverage latency per requestzoneserver nameTest name
          1000002561001000truelfollower-111586.03260.69mseu-west-1aetcd-main-0mn-etcd-sz
          1000002561001000truelfollower-114050.570.76mseu-west-1cetcd-main-0mn-etcd-mz
        • mn-etcd-sz latency(~86.09ms) is ~20 higher than mn-etcd-mz(~64.6ms).

          • Compare to mn-etcd-sz consistency Linearizable, Serializable is ~20% higher.*
        • Compare to mn-etcd-mz consistency Linearizable, Serializable is ~slightly less.

        • Number of requestsValue sizeNumber of connectionsNumber of clientssequential-keysConsistencyTarget etcd serverAverage write QPSAverage latency per requestzoneserver nameTest name
          1000002561001000truesfollower-111582.43886.09mseu-west-1aetcd-main-0mn-etcd-sz
          1000002561001000truesfollower-115422.264.6mseu-west-1cetcd-main-0mn-etcd-mz
      • Benchmark tool range requests to leader all keys.

        • sn-etcd-sz latency(~678.77ms) is ~5% slightly lesser than mn-etcd-sz(~697.29ms).

        • mn-etcd-sz latency(~697.29ms) is less than mn-etcd-mz(~701ms) but the difference is negligible.

        • Number of requestsValue sizeNumber of connectionsNumber of clientssequential-keysConsistencyTarget etcd serverAverage write QPSAverage latency per requestzoneserver nameTest name
          2025625falselleader6.8875678.77mseu-west-1cetcd-main-0sn-etcd-sz
          2025625falselleader6.720697.29mseu-west-1aetcd-main-1mn-etcd-sz
          2025625falselleader6.7701mseu-west-1aetcd-main-1mn-etcd-mz
          • Compare to consistency Linearizable, Serializable is ~5% slightly higher for all cases
        • sn-etcd-sz latency(~687.36ms) is less than mn-etcd-sz(~692.68ms) but the difference is negligible.

        • mn-etcd-sz latency(~692.68ms) is ~5% slightly lesser than mn-etcd-mz(~735.7ms).

        • Number of requestsValue sizeNumber of connectionsNumber of clientssequential-keysConsistencyTarget etcd serverAverage write QPSAverage latency per requestzoneserver nameTest name
          2025625falsesleader6.76687.36mseu-west-1cetcd-main-0sn-etcd-sz
          2025625falsesleader6.635692.68mseu-west-1aetcd-main-1mn-etcd-sz
          2025625falsesleader6.3735.7mseu-west-1aetcd-main-1mn-etcd-mz
      • Benchmark tool range requests to follower all keys

        • mn-etcd-sz(~737.68ms) latency is ~5% slightly higher than mn-etcd-mz(~713.7ms).

        • Compare to leader consistency Linearizableread request, follower is ~5% slightly higher.

        • Number of requestsValue sizeNumber of connectionsNumber of clientssequential-keysConsistencyTarget etcd serverAverage write QPSAverage latency per requestzoneserver nameTest name
          2025625falselfollower-16.163737.68mseu-west-1aetcd-main-0mn-etcd-sz
          2025625falselfollower-16.52713.7mseu-west-1cetcd-main-0mn-etcd-mz
        • mn-etcd-sz latency(~757.73ms) is ~10% higher than mn-etcd-mz(~690.4ms).

        • Compare to follower consistency Linearizableread request, follower consistency Serializable is ~3% slightly higher for mn-etcd-sz.

        • Compare to follower consistency Linearizableread request, follower consistency Serializable is ~5% less for mn-etcd-mz.

        • *Compare to leader consistency Serializableread request, follower consistency Serializable is ~5% less for mn-etcd-mz. *

        • Number of requestsValue sizeNumber of connectionsNumber of clientssequential-keysConsistencyTarget etcd serverAverage write QPSAverage latency per requestzoneserver nameTest name
          2025625falsesfollower-16.0295757.73mseu-west-1aetcd-main-0mn-etcd-sz
          2025625falsesfollower-16.87690.4mseu-west-1cetcd-main-0mn-etcd-mz


    • In this case benchmark tool tries to get specific key with random `1MB` value.
      • Benchmark tool range requests to leader with single client.

        • sn-etcd-sz latency(~5.96ms) is ~5% lesser than mn-etcd-sz(~6.28ms).

        • mn-etcd-sz latency(~6.28ms) is ~10% higher than mn-etcd-mz(~5.3ms).

        • Number of requestsValue sizeNumber of connectionsNumber of clientssequential-keysConsistencyTarget etcd serverAverage write QPSAverage latency per requestzoneserver nameTest name
          1000100000011truelleader167.3815.96mseu-west-1cetcd-main-0sn-etcd-sz
          1000100000011truelleader158.8226.28mseu-west-1aetcd-main-1mn-etcd-sz
          1000100000011truelleader187.945.3mseu-west-1aetcd-main-1mn-etcd-mz
        • Compare to consistency Linearizable, Serializable is ~15% less for sn-etcd-sz, mn-etcd-sz, mn-etcd-mz

        • Number of requestsValue sizeNumber of connectionsNumber of clientssequential-keysConsistencyTarget etcd serverAverage write QPSAverage latency per requestzoneserver nameTest name
          1000100000011truesleader184.955.398mseu-west-1cetcd-main-0sn-etcd-sz
          1000100000011truesleader176.9015.64mseu-west-1aetcd-main-1mn-etcd-sz
          1000100000011truesleader209.994.7mseu-west-1aetcd-main-1mn-etcd-mz
      • Benchmark tool range requests to follower with single client.

        • mn-etcd-sz latency(~6.66ms) is ~10% higher than mn-etcd-mz(~6.16ms).

        • Compare to leader, follower read request latency is ~10% high for mn-etcd-sz

        • Compare to leader, follower read request latency is ~20% high for mn-etcd-mz

        • Number of requestsValue sizeNumber of connectionsNumber of clientssequential-keysConsistencyTarget etcd serverAverage write QPSAverage latency per requestzoneserver nameTest name
          1000100000011truelfollower-1150.6806.66mseu-west-1aetcd-main-0mn-etcd-sz
          1000100000011truelfollower-1162.0726.16mseu-west-1cetcd-main-0mn-etcd-mz
        • Compare to consistency Linearizable, Serializable is ~15% less for mn-etcd-sz(~5.84ms), mn-etcd-mz(~5.01ms).

        • Compare to leader, follower read request latency is ~5% slightly high for mn-etcd-sz, mn-etcd-mz

        • Number of requestsValue sizeNumber of connectionsNumber of clientssequential-keysConsistencyTarget etcd serverAverage write QPSAverage latency per requestzoneserver nameTest name
          1000100000011truesfollower-1170.9185.84mseu-west-1aetcd-main-0mn-etcd-sz
          1000100000011truesfollower-1199.015.01mseu-west-1cetcd-main-0mn-etcd-mz
      • Benchmark tool range requests to leader with multiple clients.

        • sn-etcd-sz latency(~1.593secs) is ~20% lesser than mn-etcd-sz(~1.974secs).

        • mn-etcd-sz latency(~1.974secs) is ~5% greater than mn-etcd-mz(~1.81secs).

        • Number of requestsValue sizeNumber of connectionsNumber of clientssequential-keysConsistencyTarget etcd serverAverage write QPSAverage latency per requestzoneserver nameTest name
          10001000000100500truelleader252.1491.593secseu-west-1cetcd-main-0sn-etcd-sz
          10001000000100500truelleader205.5891.974secseu-west-1aetcd-main-1mn-etcd-sz
          10001000000100500truelleader230.421.81secseu-west-1aetcd-main-1mn-etcd-mz
        • Compare to consistency Linearizable, Serializable is more or less same for sn-etcd-sz(~1.57961secs), mn-etcd-mz(~1.8secs) not a big difference

        • Compare to consistency Linearizable, Serializable is ~10% high for mn-etcd-sz(~ 2.277secs).

        • Number of requestsValue sizeNumber of connectionsNumber of clientssequential-keysConsistencyTarget etcd serverAverage write QPSAverage latency per requestzoneserver nameTest name
          10001000000100500truesleader252.4061.57961secseu-west-1cetcd-main-0sn-etcd-sz
          10001000000100500truesleader181.9052.277secseu-west-1aetcd-main-1mn-etcd-sz
          10001000000100500truesleader227.641.8secseu-west-1aetcd-main-1mn-etcd-mz
      • Benchmark tool range requests to follower with multiple client.

        • mn-etcd-sz latency is ~20% less than mn-etcd-mz.

        • Compare to leader consistency Linearizable, follower read request latency is ~15 less for mn-etcd-sz(~1.694secs).

        • Compare to leader consistency Linearizable, follower read request latency is ~10% higher for mn-etcd-sz(~1.977secs).

        • Number of requestsValue sizeNumber of connectionsNumber of clientssequential-keysConsistencyTarget etcd serverAverage write QPSAverage latency per requestzoneserver nameTest name
          10001000000100500truelfollower-1248.4891.694secseu-west-1aetcd-main-0mn-etcd-sz
          10001000000100500truelfollower-1210.221.977secseu-west-1cetcd-main-0mn-etcd-mz
          Number of requestsValue sizeNumber of connectionsNumber of clientssequential-keysConsistencyTarget etcd serverAverage write QPSAverage latency per requestzoneserver nameTest name
          10001000000100500truelfollower-2205.7651.967secseu-west-1aetcd-main-2mn-etcd-sz
          10001000000100500truelfollower-2195.22.159secseu-west-1betcd-main-2mn-etcd-mz
        • Number of requestsValue sizeNumber of connectionsNumber of clientssequential-keysConsistencyTarget etcd serverAverage write QPSAverage latency per requestzoneserver nameTest name
          10001000000100500truesfollower-1231.4581.7413secseu-west-1aetcd-main-0mn-etcd-sz
          10001000000100500truesfollower-1214.801.907secseu-west-1cetcd-main-0mn-etcd-mz
          Number of requestsValue sizeNumber of connectionsNumber of clientssequential-keysConsistencyTarget etcd serverAverage write QPSAverage latency per requestzoneserver nameTest name
          10001000000100500truesfollower-2183.3202.2810secseu-west-1aetcd-main-2mn-etcd-sz
          10001000000100500truesfollower-2195.402.164secseu-west-1betcd-main-2mn-etcd-mz
      • Benchmark tool range requests to leader all keys.

        • sn-etcd-sz latency(~8.993secs) is ~3% slightly lower than mn-etcd-sz(~9.236secs).

        • mn-etcd-sz latency(~9.236secs) is ~2% slightly lower than mn-etcd-mz(~9.100secs).

        • Number of requestsValue sizeNumber of connectionsNumber of clientssequential-keysConsistencyTarget etcd serverAverage write QPSAverage latency per requestzoneserver nameTest name
          20100000025falselleader0.51398.993secseu-west-1cetcd-main-0sn-etcd-sz
          20100000025falselleader0.5069.236secseu-west-1aetcd-main-1mn-etcd-sz
          20100000025falselleader0.5089.100secseu-west-1aetcd-main-1mn-etcd-mz
        • Compare to consistency Linearizableread request, follower for sn-etcd-sz(~9.secs) is a slight difference 10ms.

        • Compare to consistency Linearizableread request, follower for mn-etcd-sz(~9.113secs) is ~1% less, not a big difference.

        • Compare to consistency Linearizableread request, follower for mn-etcd-mz(~8.799secs) is ~3% less, not a big difference.

        • sn-etcd-sz latency(~9.secs) is ~1% slightly less than mn-etcd-sz(~9.113secs).

        • mn-etcd-sz latency(~9.113secs) is ~3% slightly higher than mn-etcd-mz(~8.799secs).

          Number of requestsValue sizeNumber of connectionsNumber of clientssequential-keysConsistencyTarget etcd serverAverage write QPSAverage latency per requestzoneserver nameTest name
          20100000025falsesleader0.511259.0003secseu-west-1cetcd-main-0sn-etcd-sz
          20100000025falsesleader0.49939.113secseu-west-1aetcd-main-1mn-etcd-sz
          20100000025falsesleader0.5228.799secseu-west-1aetcd-main-1mn-etcd-mz
      • Benchmark tool range requests to follower all keys

        • mn-etcd-sz latency(~9.065secs) is ~1% slightly higher than mn-etcd-mz(~9.007secs).

        • Compare to leader consistency Linearizableread request, follower is ~1% slightly higher for both cases mn-etcd-sz, mn-etcd-mz .

        • Number of requestsValue sizeNumber of connectionsNumber of clientssequential-keysConsistencyTarget etcd serverAverage write QPSAverage latency per requestzoneserver nameTest name
          20100000025falselfollower-10.5129.065secseu-west-1aetcd-main-0mn-etcd-sz
          20100000025falselfollower-10.5339.007secseu-west-1cetcd-main-0mn-etcd-mz
        • Compare to consistency Linearizableread request, follower for mn-etcd-sz(~9.553secs) is ~5% high.

        • Compare to consistency Linearizableread request, follower for mn-etcd-mz(~7.7433secs) is ~15% less.

        • mn-etcd-sz(~9.553secs) latency is ~20% higher than mn-etcd-mz(~7.7433secs).

        • Number of requestsValue sizeNumber of connectionsNumber of clientssequential-keysConsistencyTarget etcd serverAverage write QPSAverage latency per requestzoneserver nameTest name
          20100000025falsesfollower-10.47439.553secseu-west-1aetcd-main-0mn-etcd-sz
          20100000025falsesfollower-10.55007.7433secseu-west-1cetcd-main-0mn-etcd-mz





    NOTE: This Network latency analysis is inspired by etcd performance.

    3.9 - EtcdMember Custom Resource

    DEP-04: EtcdMember Custom Resource

    Table of Contents

    Summary

    Today, etcd-druid mainly acts as an etcd cluster provisioner, and seldom takes remediatory actions if the etcd cluster goes into an undesired state that needs to be resolved by a human operator. In other words, etcd-druid cannot perform day-2 operations on etcd clusters in its current form, and hence cannot carry out its full set of responsibilities as a true “operator” of etcd clusters. For etcd-druid to be fully capable of its responsibilities, it must know the latest state of the etcd clusters and their individual members at all times.

    This proposal aims to bridge that gap by introducing EtcdMember custom resource allowing individual etcd cluster members to publish information/state (previously unknown to etcd-druid). This provides etcd-druid a handle to potentially take cluster-scoped remediatory actions.

    Terminology

    • druid: etcd-druid - an operator for etcd clusters.

    • etcd-member: A single etcd pod in an etcd cluster that is realised as a StatefulSet.

    • backup-sidecar: It is the etcd-backup-restore sidecar container in each etcd-member pod.

      NOTE: Term sidecar can now be confused with the latest definition in KEP-73. etcd-backup-restore container is currently not set as an init-container as proposed in the KEP but as a regular container in a multi-container [Pod](Pods | Kubernetes).

    • leading-backup-sidecar: A backup-sidecar that is associated to an etcd leader.

    • restoration: It refers to an individual etcd-member restoring etcd data from an existing backup (comprising of full and delta snapshots). The authors have deliberately chosen to distinguish between restoration and learning. Learning refers to a process where a learner “learns” from an etcd-cluster leader.

    Motivation

    Sharing state of an individual etcd-member with druid is essential for diagnostics, monitoring, cluster-wide-operations and potential remediation. At present, only a subset of etcd-member state is shared with druid using leases. It was always meant as a stopgap arrangement as mentioned in the corresponding issue and is not the best use of leases.

    There is a need to have a clear distinction between an etcd-member state and etcd cluster state since most of an etcd cluster state is often derived by looking at individual etcd-member states. In addition, actors which update each of these states should be clearly identified so as to prevent multiple actors updating a single resource holding the state of either an etcd cluster or an etcd-member. As a consequence, etcd-members should not directly update the Etcd resource status and would therefore need a new custom resource allowing each member to publish detailed information about its latest state.

    Goals

    • Introduce EtcdMember custom resource via which each etcd-member can publish information about its state. This enables druid to deterministically orchestrate out-of-turn operations like compaction, defragmentation, volume management etc.
    • Define and capture states, sub-states and deterministic transitions amongst states of an etcd-member.
    • Today leases are misused to share member-specific information with druid. Their usage to share member state [leader, follower, learner], member-id, snapshot revisions etc should be removed.

    Non-Goals

    • Auto-recovery from quorum loss or cluster-split due to network partitioning.
    • Auto-recovery of an etcd-member due to volume mismatch.
    • Relooking at segregating responsiblities between etcd and backup-sidecar containers.

    Proposal

    This proposal introduces a new custom resource EtcdMember, and in the following sections describes different sets of information that should be captured as part of the new resource.

    Etcd Member Metadata

    Every etcd-member has a unique memberID and it is part of an etcd cluster which has a unique clusterID. In a well-formed etcd cluster every member must have the same clusterID. Publishing this information to druid helps in identifying issues when one or more etcd-members form their own individual clusters, thus resulting in multiple clusters where only one was expected. Issues Issue#419, Canary#4027, Canary#3973 are some such occurrences.

    Today, this information is published by using a member lease. Both these fields are populated in the leases’ Spec.HolderIdentity by the backup-sidecar container.

    The authors propose to publish member metadata information in EtcdMember resource.

    id: <etcd-member id>
    clusterID: <etcd cluster id>
    

    NOTE: Druid would not do any auto-recovery when it finds out that there are more than one clusters being formed. Instead this information today will be used for diagnostic and alerting.

    Etcd Member State Transitions

    Each etcd-member goes through different States during its lifetime. State is a derived high-level summary of where an etcd-member is in its lifecycle. A SubState gives additional information about the state. This proposal extends the concept of states with the notion of a SubState, since State indicates a top-level state of an EtcdMember resource, which can have one or more SubStates.

    While State is sufficient for many human operators, the notion of a SubState provides operators with an insight about the discrete stage of an etcd-member in its lifecycle. For example, consider a top-level State: Starting, which indicates that an etcd-member is starting. Starting is meant to be a transient state for an etcd-member. If an etcd-member remains in this State longer than expected, then an operator would require additional insight, which the authors propose to provide via SubState (in this case, the possible SubStates could be PendingLearner and Learner, which are detailed in the following sections).

    At present, these states are not captured and only the final state is known - i.e the etcd-member either fails to come up (all re-attempts to bring up the pod via the StatefulSet controller has exhausted) or it comes up. Getting an insight into all its state transitions would help in diagnostics.

    The status of an etcd-member at any given point in time can be best categorized as a combination of a top-level State and a SubState. The authors propose to introduce the following states and sub-states:

    States and Sub-States

    NOTE: Abbreviations have been used wherever possible, only to represent sub-states. These representations are chosen only for brevity and will have proper longer names.

    StatesSub-StatesDescription
    New-Every newly created etcd-member will start in this state and is termed as the initial state or the start state.
    InitializingDBV-S (DBValidationSanity)This state denotes that backup-restore container in etcd-member pod has started initialization. Sub-State DBV-S which is an abbreviation for DBValidationSanity denotes that currently sanity etcd DB validation is in progress.
    InitializingDBV-F (DBValidationFull)This state denotes that backup-restore container in etcd-member pod has started initialization. Sub-State DBV-F which is an abbreviation for DBValidationFull denotes that currently full etcd DB validation is in progress.
    InitializingR (Restoration)This state denotes that backup-restore container in etcd-member pod has started initialization. Sub-State R which is an abbreviation for Restoration denotes that DB validation failed and now backup-restore has commenced restoration of etcd DB from the backup (comprising of full snapshot and delta-snapshots). An etcd-member will transition to this sub-state only when it is part of a single-node etcd-cluster.
    Starting (SI)PL (PendingLearner)An etcd-member can transition from Initializing state to PendingLearner state. In this state backup-restore container will optionally delete any existing etcd data directory and then attempts to add its peer etcd-member process as a learner. Since there can be only one learner at a time in an etcd cluster, an etcd-member could be in this state for some time till its request to get added as a learner is accepted.
    Starting (SI)LearnerWhen backup-restore is successfully able to add its peer etcd-member process as a Learner. In this state the etcd-member process will start its DB sync from an etcd leader.
    Started (Sd)FollowerA follower is a voting raft member. A Learner etcd-member will get promoted to a Follower once its DB is in sync with the leader. It could also become a follower if during a re-election it loses leadership and transitions from being a Leader to Follower.
    Started (Sd)LeaderA leader is an etcd-member which will handle all client write requests and linearizable read requests. A member could transition to being a Leader from an existing Follower role due to winning a leader election or for a single node etcd cluster it directly transitions from Initializing state to Leader state as there is no other member.

    In the following sub-sections, the state transitions are categorized into several flows making it easier to grasp the different transitions.

    Top Level State Transitions

    Following DFA represents top level state transitions (without any representation of sub-states). As described in the table above there are 4 top level states:

    • New- this is a start state for all newly created etcd-members

    • Initializing - In this state backup-restore will perform pre-requisite actions before it triggers the start of an etcd process. DB validation and optionally restoration is done in this state. Possible sub-states are: DBValidationSanity, DBValidationFull and Restoration

    • Starting - Once the optional initialization is done backup-restore will trigger the start of an etcd process. It can either directly go to Learner sub-state or wait for getting added as a learner and therefore be in PendingLearner sub-state.

    • Started - In this state the etcd-member is a full voting member. It can either be in Leader or Follower sub-states.

    Starting an Etcd-Member in a Single-Node Etcd Cluster

    Following DFA represents the states, sub-states and transitions of a single etcd-member for a cluster that is bootstrapped from cluster size of 0 -> 1.

    Addition of a New Etcd-Member in a Multi-Node Etcd Cluster

    Following DFA represents the states, sub-states and transitions of an etcd cluster which starts with having a single member (Leader) and then one or more new members are added which represents a scale-up of an etcd cluster from 1 -> n, where n is odd.

    Restart of a Voting Etcd-Member in a Multi-Node Etcd Cluster

    Following DFA represents the states, sub-states and transitions when a voting etcd-member in a multi-node etcd cluster restarts.

    NOTE: If the DB validation fails then data directory of the etcd-member is removed and etcd-member is removed from cluster membership, thus transitioning it to New state. The state transitions from New state are depicted by this section.

    Deterministic Etcd Member Creation/Restart During Scale-Up

    Bootstrap information:

    When an etcd-member starts, then it needs to find out:

    • If it should join an existing cluster or start a new cluster.

    • If it should add itself as a Learner or directly start as a voting member.

    Issue with the current approach:

    At present, this is facilitated by three things:

    1. During scale-up, druid adds an annotation gardener.cloud/scaled-to-multi-node to the StatefulSet. Each etcd-members looks up this annotation.

    2. backup-sidecar attempts to fetch etcd cluster member-list and checks if this etcd-member is already part of the cluster.

    3. Size of the cluster by checking initial-cluster in the etcd config.

    Druid adds an annotation gardener.cloud/scaled-to-multi-node on the StatefulSet which is then shared by all etcd-members irrespective of the starting state of an etcd-member (as Learner or Voting-Member). This especially creates an issue for the current leader (often pod with index 0) during the scale-up of an etcd cluster as described in this issue.

    It has been agreed that the current solution to this issue is a quick and dirty fix and needs to be revisited to be uniformly applied to all etcd-members. The authors propose to provide a more deterministic approach to scale-up using the EtcdMember resource.

    New approach

    Instead of adding an annotation gardener.cloud/scaled-to-multi-node on the StatefulSet, a new annotation druid.gardener.cloud/create-as-learner should be added by druid on an EtcdMember resource. This annotation will only be added to newly created members during scale-up.

    Each etcd-member should look at the following to deterministically compute the bootstrap information specified above:

    • druid.gardener.cloud/create-as-learner annotation on its respective EtcdMember resource. This new annotation will be honored in the following cases:

      • When an etcd-member is created for the very first time.

      • An etcd-member is restarted while it is in Starting state (PendingLearner and Learner sub-states).

    • Etcd-cluster member list. to check if it is already part of the cluster.

    • Existing etcd data directory and its validity.

    NOTE: When the etcd-member gets promoted to a voting-member, then it should remove the annotation on its respective EtcdMember resource.

    TLS Enablement for Peer Communication

    Etcd-members in a cluster use peer URL(s) to communicate amongst each other. If the advertised peer URL(s) for an etcd-member are updated then etcd mandates a restart of the etcd-member.

    Druid only supports toggling the transport level security for the advertised peer URL(s). To indicate that the etcd process within the etcd-member has the updated advertised peer URL(s), an annotation member.etcd.gardener.cloud/tls-enabled is added by backup-sidecar container to the member lease object.

    During the reconciliation run for an Etcd resource in druid, if reconciler detects a change in advertised peer URL(s) TLS configuration then it will watch for the above mentioned annotation on the member lease. If the annotation has a value of false then it will trigger a restart of the etcd-member pod.

    The authors propose to publish member metadata information in EtcdMember resource and not misuse member leases.

    peerTLSEnabled: <bool>
    

    Monitoring Backup Health

    Backup-sidecar takes delta and full snapshot both periodically and threshold based. These backed-up snapshots are essential for restoration operations for bootstrapping an etcd cluster from 0 -> 1 replicas. It is essential that leading-backup-sidecar container which is responsible for taking delta/full snapshots and uploading these snapshots to the configured backup store, publishes this information for druid to consume.

    At present, information about backed-up snapshot (only latest-revision-number) is published by leading-backup-sidecar container by updating Spec.HolderIdentity of the delta-snapshot and full-snapshot leases.

    Druid maintains conditions in the Etcd resource status, which include but are not limited to maintaining information on whether backups being taken for an etcd cluster are healthy (up-to-date) or stale (outdated in context to a configured schedule). Druid computes these conditions using information from full/delta snapshot leases.

    In order to provide a holistic view of the health of backups to human operators, druid requires additional information about the snapshots that are being backed-up. The authors propose to not misuse leases and instead publish the following snapshot information as part EtcdMember custom resource:

    snapshots:
      lastFull:
        timestamp: <time of full snapshot>
        name: <name of the file that is uploaded>
        size: <size of the un-compressed snapshot file uploaded>
        startRevision: <start revision of etcd db captured in the snapshot>
        endRevision: <end revision of etcd db captured in the snapshot>
      lastDelta:
        timestamp: <time of delta snapshot>
        name: <name of the file that is uploaded>
        size: <size of the un-compressed snapshot file uploaded>
        startRevision: <start revision of etcd db captured in the snapshot>
        endRevision: <end revision of etcd db captured in the snapshot>
    

    While this information will primarily help druid compute accurate conditions regarding backup health from snapshot information and publish this to human operators, it could be further utilised by human operators to take remediatory actions (e.g. manually triggering a full or delta snapshot or further restarting the leader if the issue is still not resolved) if backup is unhealthy.

    Enhanced Snapshot Compaction

    Druid can be configured to perform regular snapshot compactions for etcd clusters, to reduce the total number of delta snapshots to be restored if and when a DB restoration for an etcd cluster is required. Druid triggers a snapshot compaction job when the accumulated etcd events in the latest set of delta snapshots (taken after the last full snapshot) crosses a specified threshold.

    As described in Issue#591 scheduling compaction only based on number of accumulated etcd events is not sufficient to ensure a successful compaction. This is specifically targeted for kubernetes clusters where each etcd event is larger in size owing to large spec or status fields or respective resources.

    Druid will now need information regarding snapshot sizes, and more importantly the total size of accumulated delta snapshots since the last full snapshot.

    The authors propose to enhance the proposed snapshots field described in Use Case #3 with the following additional field:

    snapshots:
      accumulatedDeltaSize: <total size of delta snapshots since last full snapshot>
    

    Druid can then use this information in addition to the existing revision information to decide to trigger an early snapshot compaction job. This effectively allows druid to be proactive in performing regular compactions for etcds receiving large events, reducing the probability of a failed snapshot compaction or restoration.

    Enhanced Defragmentation

    Reader is recommended to read Etcd Compaction & Defragmentation in order to understand the following terminology:

    dbSize - total storage space used by the etcd database

    dbSizeInUse - logical storage space used by the etcd database, not accounting for free pages in the DB due to etcd history compaction

    The leading-backup-sidecar performs periodic defragmentations of the DBs of all the etcd-members in the cluster, controlled via a defragmentation cron schedule provided to each backup-sidecar. Defragmentation is a costly maintenance operation and causes a brief downtime to the etcd-member being defragmented, due to which the leading-backup-sidecar defragments each etcd-member sequentially. This ensures that only one etcd-member would be unavailable at any given time, thus avoiding an accidental quorum loss in the etcd cluster.

    The authors propose to move the responsibility of orchestrating these individual defragmentations to druid due to the following reasons:

    • Since each backup-sidecar only has knowledge of the health of its own etcd, it can only determine whether its own etcd can be defragmented or not, based on etcd-member health. Trying to defragment a different healthy etcd-member while another etcd-member is unhealthy would lead to a transient quorum loss.
    • Each backup-sidecar is only a sidecar to its own etcd-member, and by good design principles, it must not be performing any cluster-wide maintenance operations, and this responsibility should remain with the etcd cluster operator.

    Additionally, defragmentation of an etcd DB becomes inevitable if the DB size exceeds the specified DB space quota, since the etcd DB then becomes read-only, ie no write operations on the etcd would be possible unless the etcd DB is defragmented and storage space is freed up. In order to automate this, druid will now need information about the etcd DB size from each member, specifically the leading etcd-member, so that a cluster-wide defragmentation can be triggered if the DB size reaches a certain threshold, as already described by this issue.

    The authors propose to enhance each etcd-member to regularly publish information about the dbSize and dbSizeInUse so that druid may trigger defragmentation for the etcd cluster.

    dbSize: <db-size> # e.g 6Gi
    dbSizeInUse: <db-size-in-use> # e.g 3.5Gi
    

    Difference between dbSize and dbSizeInUse gives a clear indication of how much storage space would be freed up if a defragmentation is performed. If the difference is not significant (based on a configurable threshold provided to druid), then no defragmentation should be performed. This will ensure that druid does not perform frequent defragmentations that do not yield much benefit. Effectively it is to maximise the benefit of defragmentation since this operations involves transient downtime for each etcd-member.

    Monitoring Defragmentations

    As discussed in the previous section, every etcd-member is defragmented periodically, and can also be defragmented based on the DB size reaching a certain threshold. It is beneficial for druid to have knowledge of this data from each etcd-member for the following reasons:

    • [Diagnostics] It is expected that backup-sidecar will push releveant metrics and configure alerts on these metrics.

    • [Operational] Derive status of defragmentation at etcd cluster level. In case of partial failures for a subset of etcd-members druid can potentially re-trigger defragmentation only for those etcd-members.

    The authors propose to capture this information as part of lastDefragmentation section in the EtcdMember resource.

    lastDefragmentation:
      startTime: <start time of defragmentation>
      endTime: <end time of defragmentation>
      status: <Succeeded | Failed>
      message: <success or failure message>
      initialDBSize: <size of etcd DB prior to defragmentation>
      finalDBSize: <size of etcd DB post defragmentation>
    

    NOTE: Defragmentation is a cluster-wide operation, and insights derived from aggregating defragmentation data from individual etcd-members would be captured in the Etcd resource status

    Monitoring Restorations

    Each etcd-member may perform restoration of data multiple times throughout its lifecycle, possibly owing to data corruptions. It would be useful to capture this information as part of an EtcdMember resource, for the following use cases:

    • [Diagnostics] It is expected that backup-sidecar will push a metric indicating failure to restore.

    • [Operational] Restoration from backup-bucket only happens for a single node etcd cluster. If restoration is failing then druid cannot take any remediatory actions since there is no etcd quorum.

    The authors propose to capture this information under lastRestoration section in the EtcdMember resource.

    lastRestoration:
      status: <Failed | Success | In-Progress>
      reason: <reason-code for status>
      message: <human readable message for status>
      startTime: <start time of restoration>
      endTime: <end time of restoration>
    

    Authors have considered the following cases to better understand how errors during restoration will be handled:

    Case #1 - Failure to connect to Provider Object Store

    At present full and delta snapshots are downloaded during restoration. If there is a failure then initialization status transitions to Failed followed by New which forces etcd-wrapper to trigger the initialization again. This in a way forces a retry and currently there is no limit on the number of attempts.

    Authors propose to improve the retry logic but keep the overall behavior of not forcing a container restart the same.

    Case #2 - Read-Only Mounted volume

    If a mounted volume which is used to create the etcd data directory turns read-only then authors propose to capture this state via EtcdMember.

    Authors propose that druid should initiate recovery by deleting the PVC for this etcd-member and letting StatefulSet controller re-create the Pod and the PVC. Removing PVC and deleting the pod is considered safe because:

    • Data directory is present and is the DB is corrupt resulting in an un-usasble etcd.
    • Data directory is not present but any attempt to create a directory structure fails due to read-only FS.

    In both these cases there is no side-effect of deleting the PVC and the Pod.

    Case #3 - Revision mismatch

    There is currently an issue in backup-sidecar which results in a revision mismatch in the snapshots (full/delta) taken by leading the backup-sidecar container. This results in a restoration failure. One occurance of such issue has been captured in Issue#583. This occurence points to a bug which should be fixed however there is a rare possibility that these snapshots (full/delta) get corrupted. In this rare situation, backup-sidecar should only raise an alert.

    Authors propose that druid should not take any remediatory actions as this involves:

    • Inspecting snapshots
    • If the full snapshot is corrupt then a decision needs to be taken to recover from the last full snapshot as the base snapshot. This can result in data loss and therefore needs manual intervention.
    • If a delta snapshot is corrupt, then recovery can be done till the corrupt revision in the delta snapshot. Since this will also result in a loss of data therefore this decision needs to be take by an operator.

    Monitoring Volume Mismatches

    Each etcd-member checks for possible etcd data volume mismatches, based on which it decides whether to start the etcd process or not, but this information is not captured anywhere today. It would be beneficial to capture this information as part of the EtcdMember resource so that a human operator may check this and manually fix the underlying problem with the wrong volume being attached or mounted to an etcd-member pod.

    The authors propose to capture this information under volumeMismatches section in the EtcdMember resource.

    volumeMismatches:
    - identifiedAt: <time at which wrong volume mount was identified>
      fixedAt: <time at which correct volume was mounted>
      volumeID: <volume ID of wrong volume that got mounted>
      numRestarts: <num of etcd-member restarts that were attempted>
    

    Each entry under volumeMismatches will be for a unique volumeID. If there is a pod restart and it results in yet another unexpected volumeID (different from the already captured volumeIDs) then a new entry will get created. numRestarts denotes the number of restarts seen by the etcd-member for a specific volumeID.

    Based on information from the volumeMismatches section, druid may choose to perform rudimentary remediatory actions as simple as restarting the member pod to force a possible rescheduling of the pod to a different node which could potentially force the correct volume to be mounted to the member.

    Custom Resource API

    Spec vs Status

    Information that is captured in the etcd-member custom resource could be represented either as EtcdMember.Status or EtcdMemberState.Spec.

    Gardener has a similar need to capture a shoot state and they have taken the decision to represent it via ShootState resource where the state or status of a shoot is captured as part of the Spec field in the ShootState custom resource.

    The authors wish to instead align themselves with the K8S API conventions and choose to use EtcdMember custom resource and capture the status of each member in Status field of this resource. This has the following advantages:

    • Spec represents a desired state of a resource and what is intended to be captured is the As-Is state of a resource which Status is meant to capture. Therefore, semantically using Status is the correct choice.

    • Not mis-using Spec now to represent As-Is state provides us with a choice to extend the custom resource with any future need for a Spec a.k.a desired state.

    Representing State Transitions

    The authors propose to use a custom representation for states, sub-states and transitions.

    Consider the following representation:

    transitions:
    - state: <name of the state that the etcd-member has transitioned to>
      subState: <name of the sub-state if any>
      reason: <reason code for the transition>
      transitionTime: <time of transition to this state>
      message: <detailed message if any>
    

    As an example, consider the following transitions which represent addition of an etcd-member during scale-up of an etcd cluster, followed by a restart of the etcd-member which detects a corrupt DB:

    status:
      transitions:
      - state: New
        subState: New
        reason: ClusterScaledUp
        transitionTime: "2023-07-17T05:00:00Z"
        message: "New member added due to etcd cluster scale-up"
      - state: Starting
        subState: PendingLearner
        reason: WaitingToJoinAsLearner
        transitionTime: "2023-07-17T05:00:30Z"
        message: "Waiting to join the cluster as a learner"
      - state: Starting
        subState: Learner
        reason: JoinedAsLearner
        transitionTime: "2023-07-17T05:01:20Z"
        message: "Joined the cluster as a learner"
      - state: Started
        subState: Follower
        reason: PromotedAsVotingMember
        transitionTime: "2023-07-17T05:02:00Z"
        message: "Now in sync with leader, promoted as voting member"
      - state: Initializing
        subState: DBValidationFull
        reason: DetectedPreviousUncleanExit
        transitionTime: "2023-07-17T08:00:00Z"
        message: "Detected previous unclean exit, requires full DB validation"
      - state: New
        subState: New
        reason: DBCorruptionDetected
        transitionTime: "2023-07-17T08:01:30Z"
        message: "Detected DB corruption during initialization, removing member from cluster"
      - state: Starting
        subState: PendingLearner
        reason: WaitingToJoinAsLearner
        transitionTime: "2023-07-17T08:02:10Z"
        message: "Waiting to join the cluster as a learner"
      - state: Starting
        subState: Learner
        reason: JoinedAsLearner
        transitionTime: "2023-07-17T08:02:20Z"
        message: "Joined the cluster as a learner"
      - state: Started
        subState: Follower
        reason: PromotedAsVotingMember
        transitionTime: "2023-07-17T08:04:00Z"
        message: "Now in sync with leader, promoted as voting member"
    
    Reason Codes

    The authors propose the following list of possible reason codes for transitions. This list is not exhaustive, and can be further enhanced to capture any new transitions in the future.

    ReasonTransition From State (SubState)Transition To State (SubState)
    ClusterScaledUp | NewSingleNodeClusterCreatednilNew
    DetectedPreviousCleanExitNew | Started (Leader) | Started (Follower)Initializing (DBValidationSanity)
    DetectedPreviousUncleanExitNew | Started (Leader) | Started (Follower)Initializing (DBValidationFull)
    DBValidationFailedInitializing (DBValidationSanity) | Initializing (DBValidationFull)Initializing (Restoration) | New
    DBValidationSucceededInitializing (DBValidationSanity) | Initializing (DBValidationFull)Started (Leader) | Started (Follower)
    Initializing (Restoration)SucceededInitializing (Restoration)Started (Leader)
    WaitingToJoinAsLearnerNewStarting (PendingLearner)
    JoinedAsLearnerStarting (PendingLearner)Starting (Learner)
    PromotedAsVotingMemberStarting (Learner)Started (Follower)
    GainedClusterLeadershipStarted (Follower)Started (Leader)
    LostClusterLeadershipStarted (Leader)Started (Follower)

    API

    EtcdMember

    The authors propose to add the EtcdMember custom resource API to etcd-druid APIs and initially introduce it with v1alpha1 version.

    apiVersion: druid.gardener.cloud/v1alpha1
    kind: EtcdMember
    metadata:
      labels:
        gardener.cloud/owned-by: <name of parent Etcd resource>
      name: <name of the etcd-member>
      namespace: <namespace | will be the same as that of parent Etcd resource>
      ownerReferences:
      - apiVersion: druid.gardener.cloud/v1alpha1
        blockOwnerDeletion: true
        controller: true
        kind: Etcd
        name: <name of the parent Etcd resource>
        uid: <UID of the parent Etcd resource> 
    status:
      id: <etcd-member id>
      clusterID: <etcd cluster id>
      peerTLSEnabled: <bool>
      dbSize: <db-size>
      dbSizeInUse: <db-size-in-use>
      snapshots:
        lastFull:
          timestamp: <time of full snapshot>
          name: <name of the file that is uploaded>
          size: <size of the un-compressed snapshot file uploaded>
          startRevision: <start revision of etcd db captured in the snapshot>
          endRevision: <end revision of etcd db captured in the snapshot>
        lastDelta:
          timestamp: <time of delta snapshot>
          name: <name of the file that is uploaded>
          size: <size of the un-compressed snapshot file uploaded>
          startRevision: <start revision of etcd db captured in the snapshot>
          endRevision: <end revision of etcd db captured in the snapshot>
        accumulatedDeltaSize: <total size of delta snapshots since last full snapshot>
      lastRestoration:
        type: <FromSnapshot | FromLeader>
        status: <Failed | Success | In-Progress>
        startTime: <start time of restoration>
        endTime: <end time of restoration>
      lastDefragmentation:
        startTime: <start time of defragmentation>
        endTime: <end time of defragmentation>
        reason: 
        message:
        initialDBSize: <size of etcd DB prior to defragmentation>
        finalDBSize: <size of etcd DB post defragmentation>
      volumeMismatches:
      - identifiedAt: <time at which wrong volume mount was identified>
        fixedAt: <time at which correct volume was mounted>
        volumeID: <volume ID of wrong volume that got mounted>
        numRestarts: <num of pod restarts that were attempted>
      transitions:
      - state: <name of the state that the etcd-member has transitioned to>
        subState: <name of the sub-state if any>
        reason: <reason code for the transition>
        transitionTime: <time of transition to this state>
        message: <detailed message if any>
    
    Etcd

    Authors propose the following changes to the Etcd API:

    1. In the Etcd.Status resource API, member status is computed and stored. This field will be marked as deprecated and in a later version of druid it will be removed. In its place, the authors propose to introduce the following:
    type EtcdStatus struct {
      // MemberRefs contains references to all existing EtcdMember resources
      MemberRefs []CrossVersionObjectReference
    }
    
    1. In Etcd.Status resource API, PeerUrlTLSEnabled reflects the status of enabling TLS for peer communication across all etcd-members. Currentlty this field is not been used anywhere. In this proposal, the authors have also proposed that each EtcdMember resource should capture the status of TLS enablement of peer URL. The authors propose to relook at the need to have this field under EtcdStatus.

    Lifecycle of an EtcdMember

    Creation

    Druid creates an EtcdMember resource for every replica in etcd.Spec.Replicas during reconciliation of an etcd resource. For a fresh etcd cluster this is done prior to creation of the StatefulSet resource and for an existing cluster which has now been scaled-up, it is done prior to updating the StatefulSet resource.

    Updation

    All fields in EtcdMember.Status are only updated by the corresponding etcd-member. Druid only consumes the information published via EtcdMember resources.

    Deletion

    Druid is responsible for deletion of all existing EtcdMember resources for an etcd cluster. There are three scenarios where an EtcdMember resource will be deleted:

    1. Deletion of etcd resource.

    2. Scale down of an etcd cluster to 0 replicas due to hibernation of the k8s control plane.

    3. Transient scale down of an etcd cluster to 0 replicas to recover from a quorum loss.

    Authors found no reason to retain EtcdMember resources when the etcd cluster is scale down to 0 replicas since the information contained in each EtcdMember resource would no longer represent the current state of each member and would thus be stale. Any controller in druid which acts upon the EtcdMember.Status could potentially take incorrect actions.

    Reconciliation

    Authors propose to introduce a new controller (let’s call it etcd-member-controller) which watches for changes to the EtcdMember resource(s). If a reconciliation of an Etcd resource is required as a result of change in EtcdMember status then this controller should enqueue an event and force a reconciliation via existing etcd-controller, thus preserving the single-actor-principal constraint which ensures deterministic changes to etcd cluster resources.

    NOTE: Further decisions w.r.t responsibility segregation will be taken during implementation and will not be documented in this proposal.

    Stale EtcdMember Status Handling

    It is possible that an etcd-member is unable to update its respective EtcdMember resource. Following can be some of the implications which should be kept in mind while reconciling EtcdMember resource in druid:

    • Druid sees stale state transitions (this assumes that the backup-sidecar attempts to update the state/sub-state in etcdMember.status.transitions with best attempt). There is currently no implication other than an operator seeing a stale state.
    • dbSize and dbSizeInUse could not be updated. A consequence could be that druid continues to see high value for dbSize - dbSizeInUse for a extended amount of time. Druid should ensure that it does not trigger repeated defragmentations.
    • If VolumeMismatches is stale, then druid should no longer attempt to recover by repeatedly restarting the pod.
    • Failed restoration was recorded last and further updates to this array failed. Druid should not repeatedly take full-snapshots.
    • If snapshots.accumulatedDeltaSize could not be updated, then druid should not schedule repeated compaction Jobs.

    Reference

    3.10 - Feature Gates in Etcd-Druid

    Feature Gates in Etcd-Druid

    This page contains an overview of the various feature gates an administrator can specify on etcd-druid.

    Overview

    Feature gates are a set of key=value pairs that describe etcd-druid features. You can turn these features on or off by passing them to the --feature-gates CLI flag in the etcd-druid command.

    The following tables are a summary of the feature gates that you can set on etcd-druid.

    • The “Since” column contains the etcd-druid release when a feature is introduced or its release stage is changed.
    • The “Until” column, if not empty, contains the last etcd-druid release in which you can still use a feature gate.
    • If a feature is in the Alpha or Beta state, you can find the feature listed in the Alpha/Beta feature gate table.
    • If a feature is stable you can find all stages for that feature listed in the Graduated/Deprecated feature gate table.
    • The Graduated/Deprecated feature gate table also lists deprecated and withdrawn features.

    Feature Gates for Alpha or Beta Features

    FeatureDefaultStageSinceUntil
    UseEtcdWrapperfalseAlpha0.190.21
    UseEtcdWrappertrueBeta0.22

    Feature Gates for Graduated or Deprecated Features

    FeatureDefaultStageSinceUntil

    Using a Feature

    A feature can be in Alpha, Beta or GA stage. An Alpha feature means:

    • Disabled by default.
    • Might be buggy. Enabling the feature may expose bugs.
    • Support for feature may be dropped at any time without notice.
    • The API may change in incompatible ways in a later software release without notice.
    • Recommended for use only in short-lived testing clusters, due to increased risk of bugs and lack of long-term support.

    A Beta feature means:

    • Enabled by default.
    • The feature is well tested. Enabling the feature is considered safe.
    • Support for the overall feature will not be dropped, though details may change.
    • The schema and/or semantics of objects may change in incompatible ways in a subsequent beta or stable release. When this happens, we will provide instructions for migrating to the next version. This may require deleting, editing, and re-creating API objects. The editing process may require some thought. This may require downtime for applications that rely on the feature.
    • Recommended for only non-critical uses because of potential for incompatible changes in subsequent releases.

    Please do try Beta features and give feedback on them! After they exit beta, it may not be practical for us to make more changes.

    A General Availability (GA) feature is also referred to as a stable feature. It means:

    • The feature is always enabled; you cannot disable it.
    • The corresponding feature gate is no longer needed.
    • Stable versions of features will appear in released software for many subsequent versions.

    List of Feature Gates

    FeatureDescription
    UseEtcdWrapperEnables the use of etcd-wrapper image and a compatible version of etcd-backup-restore, along with component-specific configuration changes necessary for the usage of the etcd-wrapper image.

    3.11 - Getting Started Locally

    Etcd-Druid Local Setup

    This page aims to provide steps on how to setup Etcd-Druid locally with and without storage providers.

    Clone the etcd-druid github repo

    # clone the repo
    git clone https://github.com/gardener/etcd-druid.git
    # cd into etcd-druid folder
    cd etcd-druid
    

    Note:

    • Etcd-druid uses kind as it’s local Kubernetes engine. The local setup is configured for kind due to its convenience but any other kubernetes setup would also work.
    • To set up etcd-druid with backups enabled on a LocalStack provider, refer this document
    • In the section Annotate Etcd CR with the reconcile annotation, the flag --enable-etcd-spec-auto-reconcile is set to false, which means a special annotation is required on the Etcd CR, for etcd-druid to reconcile it. To disable this behavior and allow auto-reconciliation of the Etcd CR for any change in the Etcd spec, set the controllers.etcd.enableEtcdSpecAutoReconcile value to true in the values.yaml located at charts/druid/values.yaml. Or if etcd-druid is being run as a process, then while starting the process, set the CLI flag --enable-etcd-spec-auto-reconcile=true for it.

    Setting up the kind cluster

    # Create a kind cluster
    make kind-up
    

    This creates a new kind cluster and stores the kubeconfig in the ./hack/e2e-test/infrastructure/kind/kubeconfig file.

    To target this newly created cluster, set the KUBECONFIG environment variable to the kubeconfig file located at ./hack/e2e-test/infrastructure/kind/kubeconfig by using the following

    export KUBECONFIG=$PWD/hack/e2e-test/infrastructure/kind/kubeconfig
    

    Setting up etcd-druid

    Either one of these commands may be used to deploy etcd-druid to the configured k8s cluster.

    1. The following command deploys etcd-druid to the configured k8s cluster:

      make deploy
      
    2. The following command deploys etcd-druid to the configured k8s cluster using Skaffold dev mode, such that changes in the etcd-druid code are automatically picked up and applied to the deployment. This helps with local development and quick iterative changes:

      make deploy-dev
      
    3. The following command deploys etcd-druid to the configured k8s cluster using Skaffold debug mode, so that a debugger can be attached to the running etcd-druid deployment. Please refer to this guide for more information on Skaffold-based debugging:

      make deploy-debug
      

    This generates the Etcd and EtcdCopyBackupsTask CRDs and deploys an etcd-druid pod into the cluster.

    Note: Before calling any of the make deploy* commands, certain environment variables may be set in order to enable/disable certain functionalities of etcd-druid. These are:

    • DRUID_ENABLE_ETCD_COMPONENTS_WEBHOOK=true : enables the etcdcomponents webhook
    • DRUID_E2E_TEST=true : sets specific configuration for etcd-druid for optimal e2e test runs, like a lower sync period for the etcd controller.
    • USE_ETCD_DRUID_FEATURE_GATES=false : enables etcd-druid feature gates.

    Prepare the Etcd CR

    Etcd CR can be configured in 2 ways. Either to take backups to the store or disable them. Follow the appropriate section below based on the requirement.

    The Etcd CR can be found at this location $PWD/config/samples/druid_v1alpha1_etcd.yaml

    • Without Backups enabled

      To set up etcd-druid without backups enabled, make sure the spec.backup.store section of the Etcd CR is commented out.

    • With Backups enabled (On Cloud Provider Object Stores)

      • Prepare the secret

        Create a secret for cloud provider access. Find the secret yaml templates for different cloud providers here.

        Replace the dummy values with the actual configurations and make sure to add a name and a namespace to the secret as intended.

        Note 1: The secret should be applied in the same namespace as druid.

        Note 2: All the values in the data field of secret yaml should be in base64 encoded format.

      • Apply the secret

        kubectl apply -f path/to/secret
        
      • Adapt Etcd resource

        Uncomment the spec.backup.store section of the druid yaml and set the keys to allow backuprestore to take backups by connecting to an object store.

        # Configuration for storage provider
        store:
            secretRef:
                name: etcd-backup-secret-name
            container: object-storage-container-name
            provider: aws # options: aws,azure,gcp,openstack,alicloud,dell,openshift,local
            prefix: etcd-test
        

        Brief explanation of keys:

        • secretRef.name is the name of the secret that was applied as mentioned above
        • store.container is the object storage bucket name
        • store.provider is the bucket provider. Pick from the options mentioned in comment
        • store.prefix is the folder name that you want to use for your snapshots inside the bucket.

    Applying the Etcd CR

    Note: With backups enabled, make sure the bucket is created in corresponding cloud provider before applying the Etcd yaml

    Create the Etcd CR (Custom Resource) by applying the Etcd yaml to the cluster

    # Apply the prepared etcd CR yaml
    kubectl apply -f config/samples/druid_v1alpha1_etcd.yaml
    

    Verify the Etcd cluster

    To obtain information regarding the newly instantiated etcd cluster, perform the following step, which gives details such as the cluster size, readiness status of its members, and various other attributes.

    kubectl get etcd -o=wide
    

    Verify Etcd Member Pods

    To check the etcd member pods, do the following and look out for pods starting with the name etcd-

    kubectl get pods
    

    Verify Etcd Pods’ Functionality

    Verify the working conditions of the etcd pods by putting data through a etcd container and access the db from same/another container depending on single/multi node etcd cluster.

    Ideally, you can exec into the etcd container using kubectl exec -it <etcd_pod> -c etcd -- bash if it utilizes a base image containing a shell. However, note that the etcd-wrapper Docker image employs a distroless image, which lacks a shell. To interact with etcd, use an Ephemeral container as a debug container. Refer to this documentation for building and using an ephemeral container by attaching it to the etcd container.

    # Put a key-value pair into the etcd 
    etcdctl put key1 value1
    # Retrieve all key-value pairs from the etcd db
    etcdctl get --prefix ""
    

    For a multi-node etcd cluster, insert the key-value pair from the etcd container of one etcd member and retrieve it from the etcd container of another member to verify consensus among the multiple etcd members.

    View Etcd Database File

    The Etcd database file is located at var/etcd/data/new.etcd/snap/db inside the backup-restore container. In versions with an alpine base image, you can exec directly into the container. However, in recent versions where the backup-restore docker image started using a distroless image, a debug container is required to communicate with it, as mentioned in the previous section.

    Updating the Etcd CR

    The Etcd spec can be updated with new changes, such as etcd cluster configuration or backup-restore configuration, and etcd-druid will reconcile these changes as expected, under certain conditions:

    1. If the --enable-etcd-spec-auto-reconcile flag is set to true, the spec change is automatically picked up and reconciled by etcd-druid.
    2. If the --enable-etcd-spec-auto-reconcile flag is unset, or set to false, then etcd-druid will expect an additional annotation gardener.cloud/operation: reconcile on the Etcd resource in order to pick it up for reconciliation. Upon successful reconciliation, this annotation is removed by etcd-druid. The annotation can be added as follows:
      # Annotate etcd-test CR to reconcile
      kubectl annotate etcd etcd-test gardener.cloud/operation="reconcile"
      

    Cleaning the setup

    # Delete the cluster
    make kind-down
    

    This cleans up the entire setup as the kind cluster gets deleted. It deletes the created Etcd, all pods that got created along the way and also other resources such as statefulsets, services, PV’s, PVC’s, etc.

    3.12 - Getting Started Locally Localstack

    Getting Started with etcd-druid, LocalStack, and Kind

    This guide provides step-by-step instructions on how to set up etcd-druid with LocalStack and Kind on your local machine. LocalStack emulates AWS services locally, which allows the etcd cluster to interact with AWS S3 without the need for an actual AWS connection. This setup is ideal for local development and testing.

    Prerequisites

    • Docker (installed and running)
    • AWS CLI (version >=1.29.0 or >=2.13.0)

    Environment Setup

    Step 1: Provision the Kind Cluster

    Execute the command below to provision a kind cluster. This command also forwards port 4566 from the kind cluster to your local machine, enabling LocalStack access:

    make kind-up
    

    Step 2: Deploy LocalStack

    Deploy LocalStack onto the Kubernetes cluster using the command below:

    make deploy-localstack
    

    Step 3: Set up an S3 Bucket

    1. Set up the AWS CLI to interact with LocalStack by setting the necessary environment variables. This configuration redirects S3 commands to the LocalStack endpoint and provides the required credentials for authentication:
    export AWS_ENDPOINT_URL_S3="http://localhost:4566"
    export AWS_ACCESS_KEY_ID=ACCESSKEYAWSUSER
    export AWS_SECRET_ACCESS_KEY=sEcreTKey
    export AWS_DEFAULT_REGION=us-east-2
    
    1. Create an S3 bucket for etcd-druid backup purposes:
    aws s3api create-bucket --bucket etcd-bucket --region us-east-2 --create-bucket-configuration LocationConstraint=us-east-2 --acl private
    

    Step 4: Deploy etcd-druid

    Deploy etcd-druid onto the Kind cluster using the command below:

    make deploy
    

    Step 5: Configure etcd with LocalStack Store

    Apply the required Kubernetes manifests to create an etcd custom resource (CR) and a secret for AWS credentials, facilitating LocalStack access:

    export KUBECONFIG=hack/e2e-test/infrastructure/kind/kubeconfig
    kubectl apply -f config/samples/druid_v1alpha1_etcd_localstack.yaml -f config/samples/etcd-secret-localstack.yaml
    

    Step 6: Reconcile the etcd

    Initiate etcd reconciliation by annotating the etcd resource with the gardener.cloud/operation=reconcile annotation:

    kubectl annotate etcd etcd-test gardener.cloud/operation=reconcile
    

    Congratulations! You have successfully configured etcd-druid, LocalStack, and kind on your local machine. Inspect the etcd-druid logs and LocalStack to ensure the setup operates as anticipated.

    To validate the buckets, execute the following command:

    aws s3 ls etcd-bucket/etcd-test/v2/
    

    Cleanup

    To dismantle the setup, execute the following command:

    make kind-down
    unset AWS_ENDPOINT_URL_S3 AWS_ACCESS_KEY_ID AWS_SECRET_ACCESS_KEY AWS_DEFAULT_REGION KUBECONFIG
    

    3.13 - Local e2e Tests

    e2e Test Suite

    Developers can run extended e2e tests, in addition to unit tests, for Etcd-Druid in or from their local environments. This is recommended to verify the desired behavior of several features and to avoid regressions in future releases.

    The very same tests typically run as part of the component’s release job as well as on demand, e.g., when triggered by Etcd-Druid maintainers for open pull requests.

    Testing Etcd-Druid automatically involves a certain test coverage for gardener/etcd-backup-restore which is deployed as a side-car to the actual etcd container.

    Prerequisites

    The e2e test lifecycle is managed with the help of skaffold. Every involved step like setup, deploy, undeploy or cleanup is executed against a Kubernetes cluster which makes it a mandatory prerequisite at the same time. Only skaffold itself with involved docker, helm and kubectl executions as well as the e2e-tests are executed locally. Required binaries are automatically downloaded if you use the corresponding make target, as described in this document.

    It’s expected that especially the deploy step is run against a Kubernetes cluster which doesn’t contain an Druid deployment or any left-overs like druid.gardener.cloud CRDs. The deploy step will likely fail in such scenarios.

    Tip: Create a fresh KinD cluster or a similar one with a small footprint before executing the tests.

    Providers

    The following providers are supported for e2e tests:

    • AWS
    • Azure
    • GCP
    • Local

    Valid credentials need to be provided when tests are executed with mentioned cloud providers.

    Flow

    An e2e test execution involves the following steps:

    StepDescription
    setupCreate a storage bucket which is used for etcd backups (only with cloud providers).
    deployBuild Docker image, upload it to registry (if remote cluster - see Docker build), deploy Helm chart (charts/druid) to Kubernetes cluster.
    testExecute e2e tests as defined in test/e2e.
    undeployRemove the deployed artifacts from Kubernetes cluster.
    cleanupDelete storage bucket and Druid deployment from test cluster.

    Make target

    Executing e2e-tests is as easy as executing the following command with defined Env-Vars as desribed in the following section and as needed for your test scenario.

    make test-e2e
    

    Common Env Variables

    The following environment variables influence how the flow described above is executed:

    • PROVIDERS: Providers used for testing (all, aws, azure, gcp, local). Multiple entries must be comma separated.

      Note: Some tests will use very first entry from env PROVIDERS for e2e testing (ex: multi-node tests). So for multi-node tests to use specific provider, specify that provider as first entry in env PROVIDERS.

    • KUBECONFIG: Kubeconfig pointing to cluster where Etcd-Druid will be deployed (preferably KinD).
    • TEST_ID: Some ID which is used to create assets for and during testing.
    • STEPS: Steps executed by make target (setup, deploy, test, undeploy, cleanup - default: all steps).

    AWS Env Variables

    • AWS_ACCESS_KEY_ID: Key ID of the user.
    • AWS_SECRET_ACCESS_KEY: Access key of the user.
    • AWS_REGION: Region in which the test bucket is created.

    Example:

    make \
      AWS_ACCESS_KEY_ID="abc" \
      AWS_SECRET_ACCESS_KEY="xyz" \
      AWS_REGION="eu-central-1" \
      KUBECONFIG="$HOME/.kube/config" \
      PROVIDERS="aws" \
      TEST_ID="some-test-id" \
      STEPS="setup,deploy,test,undeploy,cleanup" \
    test-e2e
    

    Azure Env Variables

    • STORAGE_ACCOUNT: Storage account used for managing the storage container.
    • STORAGE_KEY: Key of storage account.

    Example:

    make \
      STORAGE_ACCOUNT="abc" \
      STORAGE_KEY="eHl6Cg==" \
      KUBECONFIG="$HOME/.kube/config" \
      PROVIDERS="azure" \
      TEST_ID="some-test-id" \
      STEPS="setup,deploy,test,undeploy,cleanup" \
    test-e2e
    

    GCP Env Variables

    • GCP_SERVICEACCOUNT_JSON_PATH: Path to the service account json file used for this test.
    • GCP_PROJECT_ID: ID of the GCP project.

    Example:

    make \
      GCP_SERVICEACCOUNT_JSON_PATH="/var/lib/secrets/serviceaccount.json" \
      GCP_PROJECT_ID="xyz-project" \
      KUBECONFIG="$HOME/.kube/config" \
      PROVIDERS="gcp" \
      TEST_ID="some-test-id" \
      STEPS="setup,deploy,test,undeploy,cleanup" \
    test-e2e
    

    Local Env Variables

    No special environment variables are required for running e2e tests with Local provider.

    Example:

    make \
      KUBECONFIG="$HOME/.kube/config" \
      PROVIDERS="local" \
      TEST_ID="some-test-id" \
      STEPS="setup,deploy,test,undeploy,cleanup" \
    test-e2e
    

    e2e test with localstack

    The above-mentioned e2e tests need storage from real cloud providers to be setup. But there is a tool named localstack that enables to run e2e test with mock AWS storage. We can also provision KIND cluster for e2e tests. So, together with localstack and KIND cluster, we don’t need to depend on any actual cloud provider infrastructure to be setup to run e2e tests.

    How are the KIND cluster and localstack set up

    KIND or Kubernetes-In-Docker is a kubernetes cluster that is set up inside a docker container. This cluster is with limited capability as it does not have much compute power. But this cluster can easily be setup inside a container and can be tear down easily just by removing a container. That’s why KIND cluster is very easy to use for e2e tests. Makefile command helps to spin up a KIND cluster and use the cluster to run e2e tests.

    There is a docker image for localstack. The image is deployed as pod inside the KIND cluster through hack/e2e-test/infrastructure/localstack/localstack.yaml. Makefile takes care of deploying the yaml file in a KIND cluster.

    The developer needs to run make ci-e2e-kind command. This command in turn runs hack/ci-e2e-kind.sh which spin up the KIND cluster and deploy localstack in it and then run the e2e tests using localstack as mock AWS storage provider. e2e tests are actually run on host machine but deploy the druid controller inside KIND cluster. Druid controller spawns multinode etcd clusters inside KIND cluster. e2e tests verify whether the druid controller performs its jobs correctly or not. Mock localstack storage is cleaned up after every e2e tests. That’s why the e2e tests need to access the localstack pod running inside KIND cluster. The network traffic between host machine and localstack pod is resolved via mapping localstack pod port to host port while setting up the KIND cluster via hack/e2e-test/infrastructure/kind/cluster.yaml

    How to execute e2e tests with localstack and KIND cluster

    Run the following make command to spin up a KinD cluster, deploy localstack and run the e2e tests with provider aws:

    make ci-e2e-kind
    

    3.14 - Metrics

    Monitoring

    etcd-druid uses Prometheus for metrics reporting. The metrics can be used for real-time monitoring and debugging of compaction jobs.

    The simplest way to see the available metrics is to cURL the metrics endpoint /metrics. The format is described here.

    Follow the Prometheus getting started doc to spin up a Prometheus server to collect etcd metrics.

    The naming of metrics follows the suggested Prometheus best practices. All compaction related metrics are put under namespace etcddruid and the respective subsystems.

    Snapshot Compaction

    These metrics provide information about the compaction jobs that run after some interval in shoot control planes. Studying the metrics, we can deduce how many compaction job ran successfully, how many failed, how many delta events compacted etc.

    NameDescriptionType
    etcddruid_compaction_jobs_totalTotal number of compaction jobs initiated by compaction controller.Counter
    etcddruid_compaction_jobs_currentNumber of currently running compaction job.Gauge
    etcddruid_compaction_job_duration_secondsTotal time taken in seconds to finish a running compaction job.Histogram
    etcddruid_compaction_num_delta_eventsTotal number of etcd events to be compacted by a compaction job.Gauge

    There are two labels for etcddruid_compaction_jobs_total metrics. The label succeeded shows how many of the compaction jobs are succeeded and label failed shows how many of compaction jobs are failed.

    There are two labels for etcddruid_compaction_job_duration_seconds metrics. The label succeeded shows how much time taken by a successful job to complete and label failed shows how much time taken by a failed compaction job.

    etcddruid_compaction_jobs_current metric comes with label etcd_namespace that indicates the namespace of the Etcd running in the control plane of a shoot cluster..

    Etcd

    These metrics are exposed by the etcd process that runs in each etcd pod.

    The following list metrics is applicable to clustering of a multi-node etcd cluster. The full list of metrics exposed by etcd is available here.

    No.Metrics NameDescriptionComments
    1etcd_disk_wal_fsync_duration_secondslatency distributions of fsync called by WAL.High disk operation latencies indicate disk issues.
    2etcd_disk_backend_commit_duration_secondslatency distributions of commit called by backend.High disk operation latencies indicate disk issues.
    3etcd_server_has_leaderwhether or not a leader exists. 1: leader exists, 0: leader not exists.To capture quorum loss or to check the availability of etcd cluster.
    4etcd_server_is_leaderwhether or not this member is a leader. 1 if it is, 0 otherwise.
    5etcd_server_leader_changes_seen_totalnumber of leader changes seen.Helpful in fine tuning the zonal cluster like etcd-heartbeat time etc, it can also indicates the etcd load and network issues.
    6etcd_server_is_learnerwhether or not this member is a learner. 1 if it is, 0 otherwise.
    7etcd_server_learner_promote_successestotal number of successful learner promotions while this member is leader.Might be helpful in checking the success of API calls called by backup-restore.
    8etcd_network_client_grpc_received_bytes_totaltotal number of bytes received from grpc clients.Client Traffic In.
    9etcd_network_client_grpc_sent_bytes_totaltotal number of bytes sent to grpc clients.Client Traffic Out.
    10etcd_network_peer_sent_bytes_totaltotal number of bytes sent to peers.Useful for network usage.
    11etcd_network_peer_received_bytes_totaltotal number of bytes received from peers.Useful for network usage.
    12etcd_network_active_peerscurrent number of active peer connections.Might be useful in detecting issues like network partition.
    13etcd_server_proposals_committed_totaltotal number of consensus proposals committed.A consistently large lag between a single member and its leader indicates that member is slow or unhealthy.
    14etcd_server_proposals_pendingcurrent number of pending proposals to commit.Pending proposals suggests there is a high client load or the member cannot commit proposals.
    15etcd_server_proposals_failed_totaltotal number of failed proposals seen.Might indicates downtime caused by a loss of quorum.
    16etcd_server_proposals_applied_totaltotal number of consensus proposals applied.Difference between etcd_server_proposals_committed_total and etcd_server_proposals_applied_total should usually be small.
    17etcd_mvcc_db_total_size_in_bytestotal size of the underlying database physically allocated in bytes.
    18etcd_server_heartbeat_send_failures_totaltotal number of leader heartbeat send failures.Might be helpful in fine-tuning the cluster or detecting slow disk or any network issues.
    19etcd_network_peer_round_trip_time_secondsround-trip-time histogram between peers.Might be helpful in fine-tuning network usage specially for zonal etcd cluster.
    20etcd_server_slow_apply_totaltotal number of slow apply requests.Might indicate overloaded from slow disk.
    21etcd_server_slow_read_indexes_totaltotal number of pending read indexes not in sync with leader’s or timed out read index requests.

    The full list of metrics is available here.

    Etcd-Backup-Restore

    These metrics are exposed by the etcd-backup-restore container in each etcd pod.

    The following list metrics is applicable to clustering of a multi-node etcd cluster. The full list of metrics exposed by etcd-backup-restore is available here.

    No.Metrics NameDescription
    1.etcdbr_cluster_sizeto capture the scale-up/scale-down scenarios.
    2.etcdbr_is_learnerwhether or not this member is a learner. 1 if it is, 0 otherwise.
    3.etcdbr_is_learner_count_totaltotal number times member added as the learner.
    4.etcdbr_restoration_duration_secondstotal latency distribution required to restore the etcd member.
    5.etcdbr_add_learner_duration_secondstotal latency distribution of adding the etcd member as a learner to the cluster.
    6.etcdbr_member_remove_duration_secondstotal latency distribution removing the etcd member from the cluster.
    7.etcdbr_member_promote_duration_secondstotal latency distribution of promoting the learner to the voting member.
    8.etcdbr_defragmentation_duration_secondstotal latency distribution of defragmentation of each etcd cluster member.

    Prometheus supplied metrics

    The Prometheus client library provides a number of metrics under the go and process namespaces.

    3.15 - operator out-of-band tasks

    DEP-05: Operator Out-of-band Tasks

    Table of Contents

    Summary

    This DEP proposes an enhancement to etcd-druid’s capabilities to handle out-of-band tasks, which are presently performed manually or invoked programmatically via suboptimal APIs. The document proposes the establishment of a unified interface by defining a well-structured API to harmonize the initiation of any out-of-band task, monitor its status, and simplify the process of adding new tasks and managing their lifecycles.

    Terminology

    • etcd-druid: etcd-druid is an operator to manage the etcd clusters.

    • backup-sidecar: It is the etcd-backup-restore sidecar container running in each etcd-member pod of etcd cluster.

    • leading-backup-sidecar: A backup-sidecar that is associated to an etcd leader of an etcd cluster.

    • out-of-band task: Any on-demand tasks/operations that can be executed on an etcd cluster without modifying the Etcd custom resource spec (desired state).

    Motivation

    Today, etcd-druid mainly acts as an etcd cluster provisioner (creation, maintenance and deletion). In future, capabilities of etcd-druid will be enhanced via etcd-member proposal by providing it access to much more detailed information about each etcd cluster member. While we enhance the reconciliation and monitoring capabilities of etcd-druid, it still lacks the ability to allow users to invoke out-of-band tasks on an existing etcd cluster.

    There are new learnings while operating etcd clusters at scale. It has been observed that we regularly need capabilities to trigger out-of-band tasks which are outside of the purview of a regular etcd reconciliation run. Many of these tasks are multi-step processes, and performing them manually is error-prone, even if an operator follows a well-written step-by-step guide. Thus, there is a need to automate these tasks. Some examples of an on-demand/out-of-band tasks:

    • Recover from a permanent quorum loss of etcd cluster.
    • Trigger an on-demand full/delta snapshot.
    • Trigger an on-demand snapshot compaction.
    • Trigger an on-demand maintenance of etcd cluster.
    • Copy the backups from one object store to another object store.

    Goals

    • Establish a unified interface for operator tasks by defining a single dedicated custom resource for out-of-band tasks.
    • Define a contract (in terms of prerequisites) which needs to be adhered to by any task implementation.
    • Facilitate the easy addition of new out-of-band task(s) through this custom resource.
    • Provide CLI capabilities to operators, making it easy to invoke supported out-of-band tasks.

    Non-Goals

    • In the current scope, capability to abort/suspend an out-of-band task is not going to be provided. This could be considered as an enhancement based on pull.
    • Ordering (by establishing dependency) of out-of-band tasks submitted for the same etcd cluster has not been considered in the first increment. In a future version based on how operator tasks are used, we will enhance this proposal and the implementation.

    Proposal

    Authors propose creation of a new single dedicated custom resource to represent an out-of-band task. Etcd-druid will be enhanced to process the task requests and update its status which can then be tracked/observed.

    Custom Resource Golang API

    EtcdOperatorTask is the new custom resource that will be introduced. This API will be in v1alpha1 version and will be subject to change. We will be respecting Kubernetes Deprecation Policy.

    // EtcdOperatorTask represents an out-of-band operator task resource.
    type EtcdOperatorTask struct {
      metav1.TypeMeta
      metav1.ObjectMeta
    
      // Spec is the specification of the EtcdOperatorTask resource.
      Spec EtcdOperatorTaskSpec `json:"spec"`
      // Status is most recently observed status of the EtcdOperatorTask resource.
      Status EtcdOperatorTaskStatus `json:"status,omitempty"`
    }
    

    Spec

    The authors propose that the following fields should be specified in the spec (desired state) of the EtcdOperatorTask custom resource.

    • To capture the type of out-of-band operator task to be performed, .spec.type field should be defined. It can have values from all supported out-of-band tasks eg. “OnDemandSnaphotTask”, “QuorumLossRecoveryTask” etc.
    • To capture the configuration specific to each task, a .spec.config field should be defined of type string as each task can have different input configuration.
    // EtcdOperatorTaskSpec is the spec for a EtcdOperatorTask resource.
    type EtcdOperatorTaskSpec struct {
      
      // Type specifies the type of out-of-band operator task to be performed. 
      Type string `json:"type"`
    
      // Config is a task specific configuration.
      Config string `json:"config,omitempty"`
    
      // TTLSecondsAfterFinished is the time-to-live to garbage collect the 
      // related resource(s) of task once it has been completed.
      // +optional
      TTLSecondsAfterFinished *int32 `json:"ttlSecondsAfterFinished,omitempty"`
    
      // OwnerEtcdReference refers to the name and namespace of the corresponding 
      // Etcd owner for which the task has been invoked.
      OwnerEtcdRefrence types.NamespacedName `json:"ownerEtcdRefrence"`
    }
    

    Status

    The authors propose the following fields for the Status (current state) of the EtcdOperatorTask custom resource to monitor the progress of the task.

    // EtcdOperatorTaskStatus is the status for a EtcdOperatorTask resource.
    type EtcdOperatorTaskStatus struct {
      // ObservedGeneration is the most recent generation observed for the resource.
      ObservedGeneration *int64 `json:"observedGeneration,omitempty"`
      // State is the last known state of the task.
      State TaskState `json:"state"`
      // Time at which the task has moved from "pending" state to any other state.
      InitiatedAt metav1.Time `json:"initiatedAt"`
      // LastError represents the errors when processing the task.
      // +optional
      LastErrors []LastError `json:"lastErrors,omitempty"`
      // Captures the last operation status if task involves many stages.
      // +optional
      LastOperation *LastOperation `json:"lastOperation,omitempty"`
    }
    
    type LastOperation struct {
      // Name of the LastOperation.
      Name opsName `json:"name"`
      // Status of the last operation, one of pending, progress, completed, failed.
      State OperationState `json:"state"`
      // LastTransitionTime is the time at which the operation state last transitioned from one state to another.
      LastTransitionTime metav1.Time `json:"lastTransitionTime"`
      // A human readable message indicating details about the last operation.
      Reason string `json:"reason"`
    }
    
    // LastError stores details of the most recent error encountered for the task.
    type LastError struct {
      // Code is an error code that uniquely identifies an error.
      Code ErrorCode `json:"code"`
      // Description is a human-readable message indicating details of the error.
      Description string `json:"description"`
      // ObservedAt is the time at which the error was observed.
      ObservedAt metav1.Time `json:"observedAt"`
    }
    
    // TaskState represents the state of the task.
    type TaskState string
    
    const (
      TaskStateFailed TaskState = "Failed"
      TaskStatePending TaskState = "Pending"
      TaskStateRejected TaskState = "Rejected"
      TaskStateSucceeded TaskState = "Succeeded"
      TaskStateInProgress TaskState = "InProgress"
    )
    
    // OperationState represents the state of last operation.
    type OperationState string
    
    const (
      OperationStateFailed OperationState = "Failed"
      OperationStatePending OperationState = "Pending"
      OperationStateCompleted OperationState = "Completed"
      OperationStateInProgress OperationState = "InProgress"
    )
    

    Custom Resource YAML API

    apiVersion: druid.gardener.cloud/v1alpha1
    kind: EtcdOperatorTask
    metadata:
        name: <name of operator task resource>
        namespace: <cluster namespace>
        generation: <specific generation of the desired state>
    spec:
        type: <type/category of supported out-of-band task>
        ttlSecondsAfterFinished: <time-to-live to garbage collect the custom resource after it has been completed>
        config: <task specific configuration>
        ownerEtcdRefrence: <refer to corresponding etcd owner name and namespace for which task has been invoked>
    status:
        observedGeneration: <specific observedGeneration of the resource>
        state: <last known current state of the out-of-band task>
        initiatedAt: <time at which task move to any other state from "pending" state>
        lastErrors:
        - code: <error-code>
          description: <description of the error>
          observedAt: <time the error was observed>
        lastOperation:
          name: <operation-name>
          state: <task state as seen at the completion of last operation>
          lastTransitionTime: <time of transition to this state>
          reason: <reason/message if any>
    

    Lifecycle

    Creation

    Task(s) can be created by creating an instance of the EtcdOperatorTask custom resource specific to a task.

    Note: In future, either a kubectl extension plugin or a druidctl tool will be introduced. Dedicated sub-commands will be created for each out-of-band task. This will drastically increase the usability for an operator for performing such tasks, as the CLI extension will automatically create relevant instance(s) of EtcdOperatorTask with the provided configuration.

    Execution

    • Authors propose to introduce a new controller which watches for EtcdOperatorTask custom resource.
    • Each out-of-band task may have some task specific configuration defined in .spec.config.
    • The controller needs to parse this task specific config, which comes as a string, according to the schema defined for each task.
    • For every out-of-band task, a set of pre-conditions can be defined. These pre-conditions are evaluated against the current state of the target etcd cluster. Based on the evaluation result (boolean), the task is permitted or denied execution.
    • If multiple tasks are invoked simultaneously or in pending state, then they will be executed in a First-In-First-Out (FIFO) manner.

    Note: Dependent ordering among tasks will be addressed later which will enable concurrent execution of tasks when possible.

    Deletion

    Upon completion of the task, irrespective of its final state, Etcd-druid will ensure the garbage collection of the task custom resource and any other Kubernetes resources created to execute the task. This will be done according to the .spec.ttlSecondsAfterFinished if defined in the spec, or a default expiry time will be assumed.

    Use Cases

    Recovery from permanent quorum loss

    Recovery from permanent quorum loss involves two phases - identification and recovery - both of which are done manually today. This proposal intends to automate the latter. Recovery today is a multi-step process and needs to be performed carefully by a human operator. Automating these steps would be prudent, to make it quicker and error-free. The identification of the permanent quorum loss would remain a manual process, requiring a human operator to investigate and confirm that there is indeed a permanent quorum loss with no possibility of auto-healing.

    Task Config

    We do not need any config for this task. When creating an instance of EtcdOperatorTask for this scenario, .spec.config will be set to nil (unset).

    Pre-Conditions
    • There should be a quorum loss in a multi-member etcd cluster. For a single-member etcd cluster, invoking this task is unnecessary as the restoration of the single member is automatically handled by the backup-restore process.
    • There should not already be a permanent-quorum-loss-recovery-task running for the same etcd cluster.

    Trigger on-demand snapshot compaction

    Etcd-druid provides a configurable etcd-events-threshold flag. When this threshold is breached, then a snapshot compaction is triggered for the etcd cluster. However, there are scenarios where an ad-hoc snapshot compaction may be required.

    Possible scenarios
    • If an operator anticipates a scenario of permanent quorum loss, they can trigger an on-demand snapshot compaction to create a compacted full-snapshot. This can potentially reduce the recovery time from a permanent quorum loss.
    • As an additional benefit, a human operator can leverage the current implementation of snapshot compaction, which internally triggers restoration. Hence, by initiating an on-demand snapshot compaction task, the operator can verify the integrity of etcd cluster backups, particularly in cases of potential backup corruption or re-encryption. The success or failure of this snapshot compaction can offer valuable insights into these scenarios.
    Task Config

    We do not need any config for this task. When creating an instance of EtcdOperatorTask for this scenario, .spec.config will be set to nil (unset).

    Pre-Conditions
    • There should not be a on-demand snapshot compaction task already running for the same etcd cluster.

    Note: on-demand snapshot compaction runs as a separate job in a separate pod, which interacts with the backup bucket and not the etcd cluster itself, hence it doesn’t depend on the health of etcd cluster members.

    Trigger on-demand full/delta snapshot

    Etcd custom resource provides an ability to set FullSnapshotSchedule which currently defaults to run once in 24 hrs. DeltaSnapshotPeriod is also made configurable which defines the duration after which a delta snapshot will be taken. If a human operator does not wish to wait for the scheduled full/delta snapshot, they can trigger an on-demand (out-of-schedule) full/delta snapshot on the etcd cluster, which will be taken by the leading-backup-restore.

    Possible scenarios
    • An on-demand full snapshot can be triggered if scheduled snapshot fails due to any reason.
    • Gardener Shoot Hibernation: Every etcd cluster incurs an inherent cost of preserving the volumes even when a gardener shoot control plane is scaled down, i.e the shoot is in a hibernated state. However, it is possible to save on hyperscaler costs by invoking this task to take a full snapshot before scaling down the etcd cluster, and deleting the etcd data volumes afterwards.
    • Gardener Control Plane Migration: In gardener, a cluster control plane can be moved from one seed cluster to another. This process currently requires the etcd data to be replicated on the target cluster, so a full snapshot of the etcd cluster in the source seed before the migration would allow for faster restoration of the etcd cluster in the target seed.
    Task Config
    // SnapshotType can be full or delta snapshot.
    type SnapshotType string
    
    const (
      SnapshotTypeFull SnapshotType = "full"
      SnapshotTypeDelta SnapshotType = "delta"
    )
    
    type OnDemandSnapshotTaskConfig struct {
      // Type of on-demand snapshot.
      Type SnapshotType `json:"type"`
    }
    
    spec:
      config: |
            type: <type of on-demand snapshot>
    
    Pre-Conditions
    • Etcd cluster should have a quorum.
    • There should not already be a on-demand snapshot task running with the same SnapshotType for the same etcd cluster.

    Trigger on-demand maintenance of etcd cluster

    Operator can trigger on-demand maintenance of etcd cluster which includes operations like etcd compaction, etcd defragmentation etc.

    Possible Scenarios
    • If an etcd cluster is heavily loaded, which is causing performance degradation of an etcd cluster, and the operator does not want to wait for the scheduled maintenance window then an on-demand maintenance task can be triggered which will invoke etcd-compaction, etcd-defragmentation etc. on the target etcd cluster. This will make the etcd cluster lean and clean, thus improving cluster performance.
    Task Config
    type OnDemandMaintenanceTaskConfig struct {
      // MaintenanceType defines the maintenance operations need to be performed on etcd cluster.
      MaintenanceType maintenanceOps `json:"maintenanceType`
    }
    
    type maintenanceOps struct {
      // EtcdCompaction if set to true will trigger an etcd compaction on the target etcd.
      // +optional
      EtcdCompaction bool `json:"etcdCompaction,omitempty"`
      // EtcdDefragmentation if set to true will trigger a etcd defragmentation on the target etcd.
      // +optional
      EtcdDefragmentation bool `json:"etcdDefragmentation,omitempty"`
    }
    
    spec:
      config: |
        maintenanceType:
          etcdCompaction: <true/false>
          etcdDefragmentation: <true/false>    
    
    Pre-Conditions
    • Etcd cluster should have a quorum.
    • There should not already be a duplicate task running with same maintenanceType.

    Copy Backups Task

    Copy the backups(full and delta snapshots) of etcd cluster from one object store(source) to another object store(target).

    Possible Scenarios
    • In Gardener, the Control Plane Migration process utilizes the copy-backups task. This task is responsible for copying backups from one object store to another, typically located in different regions.
    Task Config
    // EtcdCopyBackupsTaskConfig defines the parameters for the copy backups task.
    type EtcdCopyBackupsTaskConfig struct {
      // SourceStore defines the specification of the source object store provider.
      SourceStore StoreSpec `json:"sourceStore"`
    
      // TargetStore defines the specification of the target object store provider for storing backups.
      TargetStore StoreSpec `json:"targetStore"`
    
      // MaxBackupAge is the maximum age in days that a backup must have in order to be copied.
      // By default all backups will be copied.
      // +optional
      MaxBackupAge *uint32 `json:"maxBackupAge,omitempty"`
    
      // MaxBackups is the maximum number of backups that will be copied starting with the most recent ones.
      // +optional
      MaxBackups *uint32 `json:"maxBackups,omitempty"`
    }
    
    spec:
      config: |
        sourceStore: <source object store specification>
        targetStore: <target object store specification>
        maxBackupAge: <maximum age in days that a backup must have in order to be copied>
        maxBackups: <maximum no. of backups that will be copied>    
    

    Note: For detailed object store specification please refer here

    Pre-Conditions
    • There should not already be a copy-backups task running.

    Note: copy-backups-task runs as a separate job, and it operates only on the backup bucket, hence it doesn’t depend on health of etcd cluster members.

    Note: copy-backups-task has already been implemented and it’s currently being used in Control Plane Migration but copy-backups-task will be harmonized with EtcdOperatorTask custom resource.

    Metrics

    Authors proposed to introduce the following metrics:

    • etcddruid_operator_task_duration_seconds : Histogram which captures the runtime for each etcd operator task. Labels:

      • Key: type, Value: all supported tasks
      • Key: state, Value: One-Of {failed, succeeded, rejected}
      • Key: etcd, Value: name of the target etcd resource
      • Key: etcd_namespace, Value: namespace of the target etcd resource
    • etcddruid_operator_tasks_total: Counter which counts the number of etcd operator tasks. Labels:

      • Key: type, Value: all supported tasks
      • Key: state, Value: One-Of {failed, succeeded, rejected}
      • Key: etcd, Value: name of the target etcd resource
      • Key: etcd_namespace, Value: namespace of the target etcd resource

    3.16 - Recovery From Permanent Quorum Loss In Etcd Cluster

    Recovery from Permanent Quorum Loss in an Etcd Cluster

    Quorum loss in Etcd Cluster

    Quorum loss means when the majority of Etcd pods (greater than or equal to n/2 + 1) are down simultaneously for some reason.

    There are two types of quorum loss that can happen to an Etcd multinode cluster:

    1. Transient quorum loss - A quorum loss is called transient when the majority of Etcd pods are down simultaneously for some time. The pods may be down due to network unavailability, high resource usages, etc. When the pods come back after some time, they can re-join the cluster and quorum is recovered automatically without any manual intervention. There should not be a permanent failure for the majority of etcd pods due to hardware failure or disk corruption.

    2. Permanent quorum loss - A quorum loss is called permanent when the majority of Etcd cluster members experience permanent failure, whether due to hardware failure or disk corruption, etc. In that case, the etcd cluster is not going to recover automatically from the quorum loss. A human operator will now need to intervene and execute the following steps to recover the multi-node Etcd cluster.

    If permanent quorum loss occurs to a multinode Etcd cluster, the operator needs to note down the PVCs, configmaps, statefulsets, CRs, etc. related to that Etcd cluster and work on those resources only. The following steps guide a human operator to recover from permanent quorum loss of an etcd cluster. We assume the name of the Etcd CR for the Etcd cluster is etcd-main.

    Etcd cluster in shoot control plane of gardener deployment: There are two Etcd clusters running in the shoot control plane. One is named etcd-events and another is named etcd-main. The operator needs to take care of permanent quorum loss to a specific cluster. If permanent quorum loss occurs to etcd-events cluster, the operator needs to note down the PVCs, configmaps, statefulsets, CRs, etc. related to the etcd-events cluster and work on those resources only.

    ⚠️ Note: Please note that manually restoring etcd can result in data loss. This guide is the last resort to bring an Etcd cluster up and running again.

    If etcd-druid and etcd-backup-restore is being used with gardener, then:

    Target the control plane of affected shoot cluster via kubectl. Alternatively, you can use gardenctl to target the control plane of the affected shoot cluster. You can get the details to target the control plane from the Access tile in the shoot cluster details page on the Gardener dashboard. Ensure that you are targeting the correct namespace.

    1. Add the following annotations to the Etcd resource etcd-main:

      1. kubectl annotate etcd etcd-main druid.gardener.cloud/suspend-etcd-spec-reconcile=

      2. kubectl annotate etcd etcd-main druid.gardener.cloud/disable-resource-protection=

    2. Note down the configmap name that is attached to the etcd-main statefulset. If you describe the statefulset with kubectl describe sts etcd-main, look for the lines similar to following lines to identify attached configmap name. It will be needed at later stages:

      Volumes:
        etcd-config-file:
          Type:      ConfigMap (a volume populated by a ConfigMap)
          Name:      etcd-bootstrap-4785b0
          Optional:  false
      

      Alternatively, the related configmap name can be obtained by executing following command as well:

      kubectl get sts etcd-main -o jsonpath='{.spec.template.spec.volumes[?(@.name=="etcd-config-file")].configMap.name}'

    3. Scale down the etcd-main statefulset replicas to 0:

      kubectl scale sts etcd-main --replicas=0

    4. The PVCs will look like the following on listing them with the command kubectl get pvc:

      main-etcd-etcd-main-0        Bound    pv-shoot--garden--aws-ha-dcb51848-49fa-4501-b2f2-f8d8f1fad111   80Gi       RWO            gardener.cloud-fast   13d
      main-etcd-etcd-main-1        Bound    pv-shoot--garden--aws-ha-b4751b28-c06e-41b7-b08c-6486e03090dd   80Gi       RWO            gardener.cloud-fast   13d
      main-etcd-etcd-main-2        Bound    pv-shoot--garden--aws-ha-ff17323b-d62e-4d5e-a742-9de823621490   80Gi       RWO            gardener.cloud-fast   13d
      

      Delete all PVCs that are attached to etcd-main cluster.

      kubectl delete pvc -l instance=etcd-main

    5. Check the etcd’s member leases. There should be leases starting with etcd-main as many as etcd-main replicas. One of those leases will have holder identity as <etcd-member-id>:Leader and rest of etcd member leases have holder identities as <etcd-member-id>:Member. Please ignore the snapshot leases, i.e., those leases which have the suffix snap.

      etcd-main member leases:

       NAME        HOLDER                  AGE
       etcd-main-0 4c37667312a3912b:Member 1m
       etcd-main-1 75a9b74cfd3077cc:Member 1m
       etcd-main-2 c62ee6af755e890d:Leader 1m
      

      Delete all etcd-main member leases.

    6. Edit the etcd-main cluster’s configmap (ex: etcd-bootstrap-4785b0) as follows:

      Find the initial-cluster field in the configmap. It should look similar to the following:

      # Initial cluster
        initial-cluster: etcd-main-0=https://etcd-main-0.etcd-main-peer.default.svc:2380,etcd-main-1=https://etcd-main-1.etcd-main-peer.default.svc:2380,etcd-main-2=https://etcd-main-2.etcd-main-peer.default.svc:2380
      

      Change the initial-cluster field to have only one member (etcd-main-0) in the string. It should now look like this:

      # Initial cluster
        initial-cluster: etcd-main-0=https://etcd-main-0.etcd-main-peer.default.svc:2380
      
    7. Scale up the etcd-main statefulset replicas to 1:

      kubectl scale sts etcd-main --replicas=1

    8. Wait for the single-member etcd cluster to be completely ready.

      kubectl get pods etcd-main-0 will give the following output when ready:

      NAME          READY   STATUS    RESTARTS   AGE
      etcd-main-0   2/2     Running   0          1m
      
    9. Remove the following annotations from the Etcd resource etcd-main:

      1. kubectl annotate etcd etcd-main druid.gardener.cloud/suspend-etcd-spec-reconcile-

      2. kubectl annotate etcd etcd-main druid.gardener.cloud/disable-resource-protection-

    10. Finally, add the following annotation to the Etcd resource etcd-main:

      kubectl annotate etcd etcd-main gardener.cloud/operation='reconcile'

    11. Verify that the etcd cluster is formed correctly.

      All the etcd-main pods will have outputs similar to following:

      NAME          READY   STATUS    RESTARTS   AGE
      etcd-main-0   2/2     Running   0          5m
      etcd-main-1   2/2     Running   0          1m
      etcd-main-2   2/2     Running   0          1m
      

      Additionally, check if the Etcd CR is ready with kubectl get etcd etcd-main:

      NAME        READY   AGE
      etcd-main   true    13d
      

      Additionally, check the leases for 30 seconds at least. There should be leases starting with etcd-main as many as etcd-main replicas. One of those leases will have holder identity as <etcd-member-id>:Leader and rest of those leases have holder identities as <etcd-member-id>:Member. The AGE of those leases can also be inspected to identify if those leases were updated in conjunction with the restart of the Etcd cluster: Example:

      NAME        HOLDER                  AGE
      etcd-main-0 4c37667312a3912b:Member 1m
      etcd-main-1 75a9b74cfd3077cc:Member 1m
      etcd-main-2 c62ee6af755e890d:Leader 1m
      

    3.17 - Restoring Single Member In Multi Node Etcd Cluster

    Restoration of a single member in multi-node etcd deployed by etcd-druid.

    Note:

    • For a cluster with n members, we are proposing the solution to only single member restoration within a etcd cluster not the quorum loss scenario (when majority of members within a cluster fail).
    • In this proposal we are not targetting the recovery of single member which got separated from cluster due to network partition.

    Motivation

    If a single etcd member within a multi-node etcd cluster goes down due to DB corruption/PVC corruption/Invalid data-dir then it needs to be brought back. Unlike in the single-node case, a minority member of a multi-node cluster can’t be restored from the snapshots present in storage container as you can’t restore from the old snapshots as it contains the metadata information of cluster which leads to memberID mismatch that prevents the new member from coming up as new member is getting its metadata information from db which got restore from old snapshots.

    Solution

    • If a corresponding backup-restore sidecar detects that its corresponding etcd is down due to data-dir corruption or Invalid data-dir
    • Then backup-restore will first remove the failing etcd member from the cluster using the MemberRemove API call and clean the data-dir of failed etcd member.
    • It won’t affect the etcd cluster as quorum is still maintained.
    • After successfully removing failed etcd member from the cluster, backup-restore sidecar will try to add a new etcd member to a cluster to get the same cluster size as before.
    • Backup-restore firstly adds new member as a Learner using the MemberAddAsLearner API call, once learner is added to the cluster and it’s get in sync with leader and becomes up-to-date then promote the learner(non-voting member) to a voting member using MemberPromote API call.
    • So, the failed member first needs to be removed from the cluster and then added as a new member.

    Example:

    1. If a 3 member etcd cluster has 1 downed member(due to invalid data-dir), the cluster can still make forward progress because the quorum is 2.
    2. Etcd downed member get restarted and it’s corresponding backup-restore sidecar receives an initialization request.
    3. Then, backup-restore sidecar checks for data corruption/invalid data-dir.
    4. Backup-restore sidecar detects that data-dir is invalid and its a multi-node etcd cluster.
    5. Then, backup-restore sidecar removed the downed etcd member from cluster.
    6. The number of members in a cluster becomes 2 and the quorum remains at 2, so it won’t affect the etcd cluster.
    7. Clean the data-dir and add a member as a learner(non-voting member).
    8. As soon as learner gets in sync with leader, promote the learner to a voting member, hence increasing number of members in a cluster back to 3.

    3.18 - Supported K8s Versions

    Supported Kubernetes Versions

    We strongly recommend using etcd-druid with the supported kubernetes versions, published in this document. The following is a list of kubernetes versions supported by the respective etcd-druid versions.

    Etcd-druid versionKubernetes version
    >=0.20>=1.21
    >=0.14 && <0.20All versions supported
    <0.14< 1.25

    3.19 - Webhooks

    Webhooks

    The etcd-druid controller-manager registers certain admission webhooks that allow for validation or mutation of requests on resources in the cluster, in order to prevent misconfiguration and restrict access to the etcd cluster resources.

    All webhooks that are a part of etcd-druid reside in package internal/webhook, as sub-packages.

    Package Structure

    The typical package structure for the webhooks that are part of etcd-druid is shown with the EtcdComponents Webhook:

    internal/webhook/etcdcomponents
    ├── config.go
    ├── handler.go
    └── register.go
    
    • config.go: contains all the logic for the configuration of the webhook, including feature gate activations, CLI flag parsing and validations.
    • register.go: contains the logic for registering the webhook with the etcd-druid controller manager.
    • handler.go: contains the webhook admission handler logic.

    Each webhook package may also contain auxiliary files which are relevant to that specific webhook.

    Etcd Components Webhook

    Druid controller-manager registers and runs the etcd controller, which creates and manages various components/resources such as Leases, ConfigMaps, and the Statefulset for the etcd cluster. It is essential for all these resources to contain correct configuration for the proper functioning of the etcd cluster.

    Unintended changes to any of these managed resources can lead to misconfiguration of the etcd cluster, leading to unwanted downtime for etcd traffic. To prevent such unintended changes, a validating webhook called EtcdComponents Webhook guards these managed resources, ensuring that only authorized entities can perform operations on these managed resources.

    EtcdComponents webhook prevents UPDATE and DELETE operations on all resources managed by etcd controller, unless such an operation is performed by druid itself, and during reconciliation of the Etcd resource. Operations are also allowed if performed by one of the authorized entities specified by CLI flag --etcd-components-webhook-exempt-service-accounts, but only if the Etcd resource is not being reconciled by etcd-druid at that time.

    There may be specific cases where a human operator may need to make changes to the managed resources, possibly to test or fix an etcd cluster. An example of this is recovery from permanent quorum loss, where a human operator will need to suspend reconciliation of the Etcd resource, make changes to the underlying managed resources such as StatefulSet and ConfigMap, and then resume reconciliation for the Etcd resource. Such manual interventions will require out-of-band changes to the managed resources. Protection of managed resources for such Etcd resources can be turned off by adding an annotation druid.gardener.cloud/disable-etcd-component-protection on the Etcd resource. This will effectively disable EtcdComponents Webhook protection for all managed resources for the specific Etcd.

    Note: UPDATE operations for Leases by etcd members are always allowed, since these are regularly updated by the etcd-backup-restore sidecar.

    The Etcd Components Webhook is disabled by default, and can be enabled via the CLI flag `–enable-etcd-components-webhook.