Skip to content
This repository has been archived by the owner on Dec 19, 2022. It is now read-only.

Latest commit

 

History

History
49 lines (31 loc) · 4.38 KB

README.md

File metadata and controls

49 lines (31 loc) · 4.38 KB
Raw

This is not currently being updated.

See https://cocalc.com/pricing/onprem

cocalc-kubernetes

This was a free open source AGPL licensed slightly modified version of cocalc-docker, but for running CoCalc on a Kubernetes cluster. There is one pod for the server and one pod for each project.

STATUS

  • This is not currently being updated.

  • We sell a much better scalable Kubernetes based version of CoCalc, but it's not open source. It contains some proprietary extensions we developed and battle tested for https://cocalc.com. It's based on flexible HELM charts (a way to configure services in a kubernetes cluster) and it's currently working with GKE (Google Compute Engine's managed kubernetes cluster) and bare-metal Kubernetes clusters. It shouldn't be too hard to make it work on other Kubernetes clusters. The pricing is higher than cocalc-docker, depending on the installation and is proportional to the number of users. Contact us at [email protected] for more information.

Installation

See server/README.md to get going!

LICENSE AND SUPPORT

  • Much of this code is licensed under the AGPL. If you would instead like a business-friendly MIT license, please contact [email protected], and we will sell you a 1-year license for $1499. This also includes some support, though with no guarantees (that costs more).
  • Join the CoCalc Docker mailing list for news, updates and more.
  • Use the CoCalc mailing list for general community support.

SECURITY STATUS

  • If you setup everything as explained in server/README.md, including appropriate network restrictions on the project pods, then there are no known security vulnerabilities. In particular, this is much safer to run than cocalc-docker, if you are going to expose this to untrusted (or uncareful) users.

Discussion

cocalc-kubernetes is an open version of CoCalc that can be run on an existing generic Kubernetes cluster.

It is as similar as possible to cocalc-docker, with the following changes:

  1. It runs in a Kubernetes cluster rather than a plain Docker install, and
  2. Projects run as separate pods on the cluster rather than all in the same Docker container.

The benefits of this architecture include:

  • Projects can have individual resource limitations imposed by Kubernetes.
  • The security issues with cocalc-docker (involving projects connecting to other project services on localhost) can be addressed by blocking outgoing network connections from projects.
  • Projects run across a cluster, so the number of projects that one can run at once is a function of the number (and size) of nodes in the cluster, rather than cocalc-docker host.
  • This can be done with just a slight modification and addition to the entirely open source (AGPL) codebase that cocalc-docker uses.

The drawbacks of this architecture over a more complicated architecture like the closed-source KuCalc (what https://cocalc.com uses) include:

  • It is unclear to what extent it can handle a large number of simultaneous users, since the entire server component (the database, hub, NFS file server, etc.) are all served from a single pod.
  • Project storage is just a single NFS server, so disk iops may be lower for client projects, which may or may not be an issue depending on the network and use of projects.
  • Filesystem level snapshots and other backups have to be handled outside CoCalc. This is the responsibility of the admin and is not part of cocalc-kubernetes at present. However, the TimeTravel functionality, which records every version of a file or notebook while you work on it, and lets you browse all past versions, does fully work in cocalc-kubernetes.
  • The project image is much more minimal than the one provided by https://cocalc.com -- it has to be small enough to reasonably run in a normal way without pull taking too long. The image in KuCalc is hundreds of gigabytes but mounts quickly using some sophisticated tricks.