- namespace separations between resources
- optimized resource utilization and container scheduling
These are the biggest 3 for the smaller orgs I’ve been a part of
Not to be flippant, but any case where things need to connect to other things. At scale they usually need to connect to other things through some sort of load balancer. You get that out of the box with kubernetes for services hosted inside the cluster, and there are straightforward solutions to ingress for clients outside it. Another important feature is pod scheduling. Yes you could wire up a few machines using docker compose and any of a few different networking approaches, but if one of your VMs dies are those workloads going to move to a healthy instance by themselves?
But you don't need containers either.
Service discovery just makes it easier to link up services if your architecture is truly microservice. We used to have an incredible amount of config that tightly coupled our API's.. now we use a combination of service discovery and an API gateway (Ambassador) to decouple the services, cut down on the number of random endpoints in our config, and we also get the added benefit of load balancing, rate limiting, and additional logging.
There's always a tradeoff with scale. If you have four servers than obviously all of this stuff is overkill.
I disagree, actually. I have four servers at home, and have some pods that have been running little tinkery things, and a bunch of open source software, with ridiculous uptime and little or no effort, even when I reboot one of those "servers" to do some gaming on Windows.
Now, do I need that uptime for all of those services? Not really. But for some of them, I want it, and it'd be annoying if I had to go figure out why they'd stopped running. The reality is, things just keep ticking without me worrying when they're on k8s.
These skills transfer into very in-demand job skills as well, and if I ever build anything that gains traction, I already have all the tools, configs, and knowledge to deploy that app across 500 generic cloud servers.
I wouldn't recommend Kubernetes if you only have one application you run in production. Just rsync your production image to production whenever you remember to do a release. But if you have more than 1 thing, it's time to start thinking about it, because the "do whatever" that works great with 1 thing starts to break down when 1 becomes 2. That is not Kubernetes's fault. That's just the nature of the beast.
And it just works. Simply and intuitively. With everything. Because it's been an IETF standard since 1983.
In saying that, I've just performed a reasonably significant migration to k8s, and despite the significant time investment, I am quite happy with it.
However, prior to this we spent 5 years on Dokku[1], which is freaking fantastic project and was for the most part more than enough to meet our needs. It solves pretty much all the same issues start-ups use k8s for, with a lot less overhead.
The reason for the migration was simply that we've reached the point where our clients demand improved reliability (redundancy) and somewhat coincidentally we'd outgrown some other infrastructure; which on its own would require a large migration. So we moved to k8s at the same time as rejigging our infrastructure, for redundancy and future-proofing purposes.
Even if we had a monolith instead, and no plans for an on-prem offering, ultimately I still think that a managed Kubernetes offering makes sense. Efficient resource utilization is all the more important for small companies, and once you have more than a handful of servers, Kubernetes makes it much easier to right-size your fleet by handling all of the scheduling for you.
If you have a couple of servers and that's it, then sure, Kubernetes isn't giving you much. But if you're building a professional offering then you're likely to outgrow that couple of servers pretty quickly.
Yeah, I'm pretty sure we can't simply run it all from a single server.
* DNS. This lets all the various components talk to each other across nodes (Airflow, Spark, Zeppelin, etc.). I can also, via VPN, connect to things to look at what's going on.
* Load-balancing. I create pods and then they run on the node where there's capacity without me thinking about it.
* Auto-scaling. I spin up pods and nodes get created to handle the load.
* Helm charts for things so I don't have to figure out how to run them myself.
* Built-in support in the tools I use. Airflow will spin up k8s pods to run tasks. Spark will spin up pods to run a job. Etc.
-Rolling deployments
-auto scaling pods and nodes
-health checks and self healing
If you use a shell script to handle a cluster with a dozen docker images and nodes, with intermittent crashes, out-of-memory issues, running out of disk, or network failures, your shell script will be so complex that you will basically need to recreate kubernetes from scratch.
Load balancing and auto-scaling are some of the more basic and popular services that kubernetes provides. If your platform already provides them for you, it makes the case for kubernetes weaker. That said, kubernetes can be run on multiple cloud providers, and provides far more features.
Once the complexity of the cluster grows, and vendor lock-in issues increase, kubernetes makes more and more sense.
- being able to force better design when engineers have to work with multiple services depending on each other (using start-up containers, readiness and liveliness, etc)
Those of you who use Kubernetes: What is a functionality it brings to the table you would miss if you automated your docker handling without it?