Apache YuniKorn Core

Clone this repo:
  1. d5edb7a ADDENDUM [YUNIKORN-2772] Scheduler restart does not preserve app start time (#1019) by Peter Bacsko · 2 days ago master
  2. bf04bd4 [YUNIKORN-2772] Scheduler restart does not preserve app start time (#1018) by Peter Bacsko · 8 weeks ago
  3. c56c288 [YUNIKORN-2592] Remove spurious warning when setting queue resources (#1016) by Craig Condit · 3 months ago
  4. 1e58dfa [YUNIKORN-3041] Core: Update dependencies for CVE fixes (#1015) by Craig Condit · 3 months ago
  5. 9eee423 [YUNIKORN-3036] fix race prevention regression (#1014) by Wilfred Spiegelenburg · 3 months ago

Apache YuniKorn - A Universal Scheduler

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Apache YuniKorn is a light-weight, universal resource scheduler for container orchestrator systems. It is created to achieve fine-grained resource sharing for various workloads efficiently on a large scale, multi-tenant, and cloud-native environment. YuniKorn brings a unified, cross-platform, scheduling experience for mixed workloads that consist of stateless batch workloads and stateful services.

YuniKorn now supports K8s and can be deployed as a custom K8s scheduler. YuniKorn's architecture design also allows adding different shim layer and adopt to different ResourceManager implementation including Apache Hadoop YARN, or any other systems.

Get Started

See how to get started with running YuniKorn on Kubernetes, please read the documentation on yunikorn.apache.org.

Want to know more about the value of the YuniKorn project, and what YuniKorn can do? Here are some session recordings and demos.

Get Involved

Please read get involved document if you want to discuss issues, contribute your ideas, explore use cases, or participate the development.

If you want to contribute code to this repo, please read the developer doc. All the design docs are available here.

Code Structure

Apache YuniKorn project has the following git repositories:

The yunikorn-core is the brain of the scheduler, which makes placement decisions (allocate container X on node Y) according to the builtin rich scheduling policies. Scheduler core implementation is agnostic to the underneath resource manager system.