Design

Promenade is a Kubernetes cluster deployment tool with the following goals:

  • Resiliency in the face of node loss and full cluster reboot.

  • Bare metal node support without external runtime dependencies.

  • Providing a fully functional single-node cluster to allow cluster-hosted tooling to provision the remaining cluster nodes.

  • Helm chart managed component life-cycle.

  • API-managed cluster life-cycle.

Cluster Bootstrapping

The cluster is bootstrapped on a single node, called the genesis node. This node goes through a short-lived bootstrapping phase driven by static pod manifests consumed by kubelet, then quickly moves to chart-managed infrastructure, driven by Armada.

During the bootstrapping phase, the following temporary components are run as static pods which are configured directly from Promenade’s configuration documents:

With these components up, it is possible to leverage Armada to deploy Helm charts to manage these components (and additional components) going forward.

Though completely configurable, a typical Armada manifest should specify charts for:

Once these charts are deployed, the cluster is validated (currently, validation is limited to resolving DNS queries and verifying basic Kubernetes functionality including Pod scheduling log collection), and then the genesis process is complete. Additional nodes can be added to the cluster using day 2 procedures.

After additional master nodes are added to the cluster, it is possible to remove the genesis node from the cluster so that it can be fully re-provisioned using the same process as for all the other nodes.

Life-cycle Management

There are two sets of resources that require life-cycle management: cluster nodes and Kubernetes control plane components. These two sets of resources are managed differently.

Node Life-Cycle Management

Node life-cycle management tools are provided via an API to be consumed by other tools like Drydock and Shipyard.

The life-cycle operations for nodes are:

  1. Adding a node to the cluster

  2. Removing a node from the cluster

  3. Adding and removing node labels.

Adding a node to the cluster

Adding a node to the cluster is done by running a shell script on the node that installs the kubelet and configures it to find and join the cluster. This script can either be generated up front via the CLI, or it can be obtained via the join-scripts endpoint of the API (development of this API is in-progress).

Nodes can only be joined assuming all the proper configuration documents are available, including required certificates for Kubelet.

Removing a node from the cluster

This is currently possible by leveraging the promenade-teardown script placed on each host. API support for this function is planned, but not yet implemented.

Adding and removing node labels

Promenade provides node-labels API for updating node labels. For more information about updating node labels, please reference the Promenade API Documentation.

It through relabeling nodes that key day 2 operations functionality like moving a master node are achieved.

Control-Plane Component Life-Cycle Management

With the exception of the Docker daemon and the kubelet, life-cycle management of control plane components is handled via Helm chart updates, which are orchestrated by Armada.

The Docker daemon is managed as an APT package, with configuration installed at the time the node is configured to join the cluster.

The kubelet is directly installed and configured at the time nodes join the cluster. Work is in progress to improve the upgradability of kubelet via either a system package or a chart.

Resiliency

The two primary failure scenarios Promenade is designed to be resilient against are node loss and full cluster restart.

Kubernetes has a well-defined High Availability pattern, which deals well with node loss.

However, this pattern requires an external load balancer for apiserver discovery. Since it is a goal of this project for the cluster to be able to operate without ongoing external dependencies, we must avoid that requirement.

Additionally, in the event of full cluster restart, we cannot rely on any response from the apiserver to give any kubelet direction on what processes to run. That means, each master node must be self-sufficient, so that once a quorum of Etcd members is achieved the cluster may resume normal operation.

The solution approach is two-pronged:

  1. Deploy a local discovery mechanism for the apiserver processes on each node so that core components can always find the apiservers when their nodes reboot.

  2. Apply the Anchor pattern described below to ensure that essential components on master nodes restart even when the apiservers are not available.

Currently, the discovery mechanism for the apiserver processes is provided by CoreDNS via a zone file written to disk on each node. This approach has some drawbacks, which might be addressed in future work by leveraging a HAProxy for discovery instead.

Anchor Pattern

The anchor pattern provides a way to manage process life-cycle using Helm charts in a way that allows them to be restarted immediately in the event of a node restart – even when the Kubernetes apiserver is unreachable.

In this pattern, a DaemonSet called the anchor that runs on selected nodes and is responsible for managing the life-cycle of assets deployed onto the node file system. In particular, these assets include a Kubernetes Pod manifest to be consumed by kubelet and it manages the processes specified by the Pod. That management continues even when the node reboots, since static pods like this are run by the kubelet even when the apiserver is not available.

Cleanup of these resources is managed by the anchor pods’ preStop life-cycle hooks. This is usually simply removing the files originally placed on the nodes’ file systems, but, e.g. in the case of Etcd, can actually be used to manage more complex cleanup like removal from cluster membership.

Pod Checkpointer

Before moving to the Anchor pattern above, the pod-checkpointer approach pioneered by the Bootkube project was implemented. While this is an appealing approach, it unfortunately suffers from race conditions during full cluster reboot.

During cluster reboot, the checkpointer copies essential static manifests into place for the kubelet to run, which allows those components to start and become available. Once the apiserver and etcd cluster are functional, kubelet is able to register the failure of its workloads, and delete those pods via the API. This is where the race begins.

Once those pods are deleted from the apiserver, the pod checkpointer notices that the flagged pods are no longer scheduled to run on its node and then deletes the static manifests for those pods. Concurrently, the controller-manager and scheduler notice that new pods need to be created and scheduled (sequentially) and begin that work.

If the new pods are created, scheduled and started on the node before pod checkpointers on other nodes delete their critical services, then the cluster may remain healthy after the reboot. If enough nodes running the critical services fail to start the newly created pods before too many are removed, then the cluster does not recover from hard reboot.

The severity of this race is exacerbated by:

  1. The sequence of events required to successfully replace these pods is long (controller-manager must create pods, then scheduler can schedule pods, then kubelet can start pods).

  2. The controller-manager and scheduler may need to perform leader election during the race, because the leader might have been killed early.

  3. The failure to recover any one of the core sets of processes can cause the entire cluster to fail. This is somewhat trajectory-dependent, e.g. if at least one controller-manager is scheduled before the controller-manager processes are all killed, then assuming the other processes are correctly restarted, then the controller-manager will also recover.

  4. etcd is somewhat more sensitive to this race, because it requires two successfully restarted pods (assuming a 3 node cluster) rather than just one as the other components.

This race condition was the motivation for the construction and use of the Anchor pattern. In future versions of Kubernetes, it may be possible to use built-in checkpointing from the kubelet.

Alternatives