Nebula comparision to other orchestration platforms

A common question is why need another orchestrator when there are many other popular options?

The answer for that is that while the popular container orchestrators are great they have a few limitations which Nebula avoids:

  1. Scale - even though this issue keep improving with all orchestrators Nebula approach allows for far greater scalability (alongside Mesos)
  2. Multi region clusters - orchestrators tend to be latency sensitive, Nebula unique architecture makes it ideal to managing distributed apps (like CDN or IOT), while still maintaining agility
  3. Managing clients appliances\IoT devices - this option is not covered by any of the current orchestrators as it's outside their use case scope
  4. Config management - while Puppet\Chef\Ansible are great for config management and orchestrators are great for scaling containers Nebula can also be thought of as a docker config management system

Attached below is a table comparision between Nebula and the popular options, info in it is correct to the time of writing.


The tables slides to to side for viewing it's full information (not clear do to the rtfd theme)

Nebula Mesos+Marathon\DC/OS Kubernetes Swarm
Optimal use case Distributed systems orchestration and\or IoT deployments orchestration DataCenter orchestration DataCenter orchestration DataCenter orchestration IoT deployments orchestration
Stateless masters yes yes yes no - data stored in local master raft consensus unknown
Worker nodes Scale limit tens of thousands tens of thousands 1000-5000 depending on version unknown unknown
Containers Scale limit millions millions 120000 unknown unknown
Health-check limits unlimited - uses docker builtin health checks depending on check type - unknown unlimited - uses docker builtin health checks unknown
Multi region\DC workers yes possible but not recommended according to latest DC/OS docs possible via multiple clusters controlled via an ubernetes cluster yes unknown
Multi region\DC masters yes - each master is only contacted when an API call is made possible but extremely not recommended according to latest DC/OS docs not possible - even with ubernetes each region masters only manage it's own regions possible but not recommended do to raft consensus unknown
Designed for huge scales yes yes if you consider 1000-5000 instances huge unknown unknown
Full REST API yes yes yes partial - by default no outside endpoint is available yes
Self healing yes yes yes yes yes
Simple worker scaling up & down yes yes yes partial - scaling down cleanly requires an api call rather then just shutting down the server like the rest semi - requires flushing a custom OS onto the device
cron job management support yes yes yes no no
CLI yes yes yes yes yes
GUI WIP yes yes no on managed service version
Load Balancing yes- supports bringing your own HAProxy\Nginx\etc in a 2 step process (the first between instances & the 2nd between containers on that instance) yes - HTTP only for outside connections - marathon-lb, supports bringing your own otherwise yes yes - auto routes inside the cluster but you still need to LB between cluster nodes from the outside no - IoT devices only
Simple masters scaling up & down yes - master is fully stateless no no partial - simple as long as quorum remains in the process unknown
Simple containers scaling up & down yes - single api call and\or adding\removing worker nodes yes - single api call yes - single api call yes - single api call unknown
Modular design (or plugin support) yes - every part does 1 thing only yes - 2 step orchestrator yes yes unknown
Backend DB's MongoDB Zookeeper EtcD internal in masters Postgres & Redis & S3
Multiple apps share worker node yes yes yes yes yes
Distributed apps support yes no no no yes
Dynamic allocation of work containers no yes yes yes no
Changes processing time scalability extremely short, each worker is unaffected from the rest longish - must wait for an offer matching it requirements first which at complex clusters can take a bit short - listens to EtcD for changes which is fast but the masters don't scale when the load does longish - gossip protocol will get there in the end but might take the scenic route unknown
Type of containers supported docker with possible future extension docker, mesos universal container docker with possible future extension docker only docker only - requires a custom OS on devices
Key trade-offs allows scales of Mesos with full health checks with very rapid deployments\changes while being unlimited to a single region\DC while being stable at the cost of changing apps per node require an envvar change & having lots of moving parts battle proven orchestrator that's damn near impossible to break at the cost of speed of changes & sticking to a single zone (for clusters with requests counts smaller then a million in a single region this is an amazing solution) the buzzword choice, very popular (so support & updates are great) but kinda forces you to do things the Google way & not nearly as scalable as some other choices comes prebuilt with the docker engine so easy to set up but is kinda of a black box, also using GOSSIP only ensures eventual consistency so who knows when a requested change takes affect designed for IoT so it shares a similar set of trade-offs to Nebula
Recommended deployment method run a docker container of it complex and varies depending on your design complex and varies depending on your design prebuilt in docker engine so just a couple of commands run the managed service of it
Usual pricing strategy FOSS - pay for infra only FOSS - pay for infra only managed - pay for infra + masters overhead management FOSS - pay for infra only managed - pay per device