Kubernetes is a system for managing containerized applications across multiple hosts, providing basic mechanisms for deployment, maintenance, and scaling of applications. Its APIs are intended to serve as the foundation for an open ecosystem of tools, automation systems, and higher-level API layers.
Kubernetes uses Docker to package, instantiate, and run containerized applications.
Is Kubernetes, then, a Docker "orchestration" system? Yes and no.
Kubernetes establishes robust declarative primitives for maintaining the desired state requested by the user. We see these primitives as the main value added by Kubernetes. Self-healing mechanisms, such as auto-restarting, re-scheduling, and replicating containers require active controllers, not just imperative orchestration.
Kubernetes is primarily targeted at applications composed of multiple containers, such as elastic, distributed micro-services. It is also designed to facilitate migration of non-containerized application stacks to Kubernetes. It therefore includes abstractions for grouping containers in both loosely coupled and tightly coupled formations, and provides ways for containers to find and communicate with each other in relatively familiar ways.
Kubernetes enables users to ask a cluster to run a set of containers. The system automatically chooses hosts to run those containers on. While Kubernetes's scheduler is currently very simple, we expect it to grow in sophistication over time. Scheduling is a policy-rich, topology-aware, workload-specific function that significantly impacts availability, performance, and capacity. The scheduler needs to take into account individual and collective resource requirements, quality of service requirements, hardware/software/policy constraints, affinity and anti-affinity specifications, data locality, inter-workload interference, deadlines, and so on. Workload-specific requirements will be exposed through the API as necessary.
Kubernetes is intended to run on a number of cloud providers, as well as on physical hosts.
A single Kubernetes cluster is not intended to span multiple availability zones. Instead, we recommend building a higher-level layer to replicate complete deployments of highly available applications across multiple zones (see the availability doc for more details).
Kubernetes is not currently suitable for use by multiple users -- see Cluster Security, below.
Finally, Kubernetes aspires to be an extensible, pluggable, building-block OSS platform and toolkit. Therefore, architecturally, we want Kubernetes to be built as a collection of pluggable components and layers, with the ability to use alternative schedulers, controllers, storage systems, and distribution mechanisms, and we're evolving its current code in that direction. Furthermore, we want others to be able to extend Kubernetes functionality, such as with higher-level PaaS functionality or multi-cluster layers, without modification of core Kubernetes source. Therefore, its API isn't just (or even necessarily mainly) targeted at end users, but at tool and extension developers. Consequently, there are no "internal" inter-component APIs. All APIs are visible and available, including the APIs used by the scheduler, the node controller, the replication-controller manager, Kubelet's API, etc. There's no glass to break -- in order to handle more complex use cases, one can just access the lower-level APIs in a fully transparent, composable manner.
A running Kubernetes cluster contains node agents (kubelet) and master components (APIs, scheduler, etc), on top of a distributed storage solution. This diagram shows our desired eventual state, though we're still working on a few things, like making kubelet itself (all our components, really) run within docker, and making the scheduler 100% pluggable.
While Docker itself works with individual containers, Kubernetes provides higher-level organizational constructs in support of common cluster-level usage patterns, currently focused on service applications, but which could also be expanded to batch and test workloads in the future.
A pod (as in a pod of whales or pea pod) is a relatively tightly coupled group of containers that are scheduled onto the same host. It models an application-specific "virtual host" in a containerized environment. Pods serve as units of scheduling, deployment, and horizontal scaling/replication, share fate, and share some resources, such as storage volumes and IP addresses.
Loosely coupled cooperating pods are organized using key/value labels.
Individual labels are used to specify identifying metadata, and to convey the semantic purposes/roles of pods of containers. Examples of typical pod label keys include service, environment (e.g., with values dev, qa, or production), tier (e.g., with values frontend or backend), and track (e.g., with values daily or weekly), but you are free to develop your own conventions.
Via a label selector the user can identify a set of pods. The label selector is the core grouping primitive in Kubernetes. It could be used to identify service replicas or shards, worker pool members, or peers in a distributed application.
Kubernetes currently supports two objects that use label selectors to keep track of their members, services and replicationControllers:
service: A service is a configuration unit for the proxies that run on every worker node. It is named and points to one or more pods.replicationController: A replication controller takes a template and ensures that there is a specified number of "replicas" of that template running at any one time. If there are too many, it'll kill some. If there are too few, it'll start more.
The set of pods that a service targets is defined with a label selector. Similarly, the population of pods that a replicationController is monitoring is also defined with a label selector.
For management convenience and consistency, services and replicationControllers may themselves have labels and would generally carry the labels their corresponding pods have in common.
When looking at the architecture of the system, we'll break it down to services that run on the worker node and services that compose the cluster-level control plane.
The Kubernetes node has the services necessary to run Docker containers and be managed from the master systems.
The Kubernetes node design is an extension of the Container-optimized Google Compute Engine image. Over time the plan is for these images/nodes to merge and be the same thing used in different ways. It has the services necessary to run Docker containers and be managed from the master systems.
Each node runs Docker, of course. Docker takes care of the details of downloading images and running containers.
The second component on the node is called the kubelet. The Kubelet is the logical successor (and rewritten in go) of the Container Agent that is part of the Compute Engine image.
The Kubelet works in terms of a container manifest. A container manifest (defined here) is a YAML file that describes a pod. The Kubelet takes a set of manifests that are provided in various mechanisms and ensures that the containers described in those manifests are started and continue running.
There are 4 ways that a container manifest can be provided to the Kubelet:
- File Path passed as a flag on the command line. This file is rechecked every 20 seconds (configurable with a flag).
- HTTP endpoint HTTP endpoint passed as a parameter on the command line. This endpoint is checked every 20 seconds (also configurable with a flag.)
- etcd server The Kubelet will reach out and do a
watchon an etcd server. The etcd path that is watched is/registry/hosts/$(hostname -f). As this is a watch, changes are noticed and acted upon very quickly. - HTTP server The kubelet can also listen for HTTP and respond to a simple API (underspec'd currently) to submit a new manifest.
Each node also runs a simple network proxy. This reflects services (see here for more details) as defined in the Kubernetes API on each node and can do simple TCP and UDP stream forwarding (round robin) across a set of backends.
Service endpoints are currently found through environment variables (both Docker-links-compatible and Kubernetes {FOO}_SERVICE_HOST and {FOO}_SERVICE_PORT variables are supported). These variables resolve to ports managed by the service proxy.
The Kubernetes control plane is split into a set of components, but they all run on a single master node. These work together to provide a unified view of the cluster.
All persistent master state is stored in an instance of etcd. This provides a great way to store configuration data reliably. With watch support, coordinating components can be notified very quickly of changes.
This server serves up the main Kubernetes API.
It validates and configures data for 3 types of objects: pods, services, and replicationControllers.
Beyond just servicing REST operations, validating them and storing them in etcd, the API Server does two other things:
- Schedules pods to worker nodes. Right now the scheduler is very simple.
- Synchronize pod information (where they are, what ports they are exposing) with the service configuration.
The replicationController type described above isn't strictly necessary for Kubernetes to be useful. It is really a service that is layered on top of the simple pod API. To enforce this layering, the logic for the replicationController is actually broken out into another server. This server watches etcd for changes to replicationController objects and then uses the public Kubernetes API to implement the replication algorithm.
The scripts and data in the cluster/ directory automates creating a set of Google Compute Engine VMs and installing all of the Kubernetes components. There is a single master node and a set of worker (called minion) nodes.
config-default.sh has a set of tweakable definitions/parameters for the cluster.
The heavy lifting of configuring the VMs is done by SaltStack.
The bootstrapping works like this:
- The
kube-up.shscript uses the GCEstartup-scriptmechanism for both the master node and the minion nodes.
- For the minion, this simply configures and installs SaltStack. The network range that this minion is assigned is baked into the startup-script for that minion (see the networking doc for more details).
- For the master, the release files are staged and then downloaded from GCS and unpacked. Various parts (specifically the SaltStack configuration) are installed in the right places. Binaries are included in these tar files.
- SaltStack then installs the necessary servers on each node.
- The custom networking bridge is configured on each minion before Docker is installed.
- Configuration (like telling the
apiserverthe hostnames of the minions) is dynamically created during the saltstack install.
- After the VMs are started, the
kube-up.shscript will callcurlevery 2 seconds until theapiserverstarts responding.
kube-down.sh can be used to tear the entire cluster down. If you build a new release and want to update your cluster, you can use kube-push.sh to update and apply (highstate in salt parlance) the salt config.
As there is no security currently built into the apiserver, the salt configuration will install nginx. nginx is configured to serve HTTPS with a self signed certificate. HTTP basic auth is used from the client to nginx. nginx then forwards the request on to the apiserver over plain old HTTP. As part of cluster spin up, ssh is used to download both the public cert for the server and a client cert pair. These are used for mutual authentication to nginx.
All communication within the cluster (worker nodes to the master, for instance) occurs on the internal virtual network and should be safe from eavesdropping.
The password is generated randomly as part of the kube-up.sh script and stored in ~/.kubernetes_auth.