rktnetes brings rkt container engine to Kubernetes
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"We find CoreOS’s rkt a compelling container engine in Kubernetes because of how rkt is composed with the underlying systemd,” said Mark Petrovic, senior MTS and architect at Xoom, a PayPal service. “The rkt runtime assumes only the responsibility it needs to, then delegates to other system services where appropriate. This separation of concerns is important to us.” rktnetes is the nickname given to the code that enables Kubernetes nodes to execute application containers with the rkt container engine, rather than with Docker. This change adds new abilities to Kubernetes, for instance running containers under flexible levels of isolation. rkt explores an alternative approach to container runtime architecture, aimed to reflect the Unix philosophy of cleanly separated, modular tools. Work done to support rktnetes also opens up future possibilities for Kubernetes, such as multiple container image format support, and the integration of other container runtimes tailored for specific use cases or platforms. rktnetes is about more than just rkt. It’s also about refining and exercising Kubernetes interfaces, and paving the way for other modular runtimes in the future. While the Docker container engine is well known, and is currently the default Kubernetes container runtime, a number of benefits derive from pluggable container environments. Some clusters may call for very specific container engine implementations, for example, and ensuring the Kubernetes design is flexible enough to support alternate runtimes, starting with rkt, helps keep the interfaces between components clean and simple. The current container runtime used by Kubernetes imposes a number of design decisions. Experimenting with other container execution architectures is worthwhile in such a rapidly evolving space. Today, when Kubernetes sends a request to a node to start running a pod, it communicates through the kubelet on each node with the default container runtime’s central daemon, responsible for managing all of the node’s containers. rkt does not implement a monolithic container management daemon. (It is worth noting that the
The task of building container images is abstracted away from the container runtime core in rkt, and implemented by an independent utility. The same approach is taken to ongoing container lifecycle management. A single binary, rkt, configures the environment and prepares container images for execution, then sets the container application and its isolation environment running. At this point, the rkt program has done its “one job”, and the container isolator takes over. The API for querying container engine and pod state, used by Kubernetes to track cluster work on each node, is implemented in a separate service, isolating coordination and orchestration features from the core container runtime. While the API service does not fully implement all the API features of the current default container engine, it already helps isolate containers from failures and upgrades in the core runtime, and provides the read-only parts of the expected API for querying container metadata. With rkt managing container execution, Kubernetes can take advantage of the CoreOS container engine’s modular stage1 isolation mechanism. The typical container runs under rkt in a software-isolated environment constructed from Linux kernel namespaces, cgroups, and other facilities. Containers isolated in this common way nevertheless share a single kernel with all the other containers on a system, making for lightweight isolation of running apps. However, rkt features pluggable isolation environments, referred to as stage1s, to change how containers are executed and isolated. For example, the
Currently, rktnetes can use the KVM stage1 to execute all containers on a node with VM isolation by setting the kubelet’s --rkt-stage1-image option. Experimental work exists to choose the stage1 isolation regime on a per-pod basis with a Kubernetes annotation declaring the pod’s appropriate stage1. KVM containers and standard Linux containers can be mixed together in the same cluster. Kubernetes today talks to the default container engine over an API provided by the Docker daemon. rktnetes communicates with rkt a little bit differently. First, there is a distinction between how Kubernetes changes the state of a node’s containers – how it starts and stops pods, or reschedules them for failover or scaling – and how the orchestrator queries pod metadata for regular, read-only bookkeeping. Two different facilities implement these two different cases. The kubelet on each cluster node communicates with rkt to
A discrete
Pod logs, having been written to journal files, are made available for kubectl logs and other forensic subcommands by the API service as well, which reads from log files to provide pod log data to the kubelet for answering control plane requests. This dual interface to the container environment is an area of very active development, and plans are for the API service to expand to provide methods for the pod manipulation commands. The underlying mechanism will continue to keep separation of concerns in mind, but will hide more of this from the kubelet. The methods the kubelet uses to control the rktnetes container engine will grow less different from the default container runtime interface over time. So what can you do with rktnetes today? Currently, rktnetes passes all of
Developers and early adopters can follow the known issues in the rktnetes notes to get an idea of the wrinkles and bumps test-drivers can expect to encounter. This list groups the high-level pieces required to bring rktnetes to feature parity with the existing container runtime and API. We hope you’ll try out rktnetes in your Kubernetes clusters, too. The introductory guide Running Kubernetes on rkt walks through the steps to spin up a rktnetes cluster, from kubelet --container-runtime=rkt to networking and starting pods. This intro also sketches the configuration you’ll need to start a cluster on GCE with the Kubernetes kube-up.sh script. Recent work aims to make rktnetes cluster creation much easier, too. While not yet merged, an
We’re excited to see the experiments the wider Kubernetes and CoreOS communities devise to put rktnetes to the test, and we welcome your input – and pull requests!What’s rktnetes?
Why does Kubernetes need rktnetes?
Separation of concerns: Decomposing the monolithic container daemon
Modular container isolation levels
How rkt works with Kubernetes
Managing microservice lifecycles
The API service for reading pod data
Try rktnetes
Use rkt with Kubernetes Today