Depending on my client's needs we do it oldschool and just rent a beefy server.
Using your brain to actually assess a situation and decide without any emotional or monetary attachment to a specific solution actually works like a charm most of the time.
I also had customers who run their own cloud based on k8s.
And I heard some people have customers that are on a public cloud ;-)
Choose the right solution for the problem at hand.
Interesting that the mania for over-investment in devops is beginning to abate. Here on Hacker News I was a steady critic of both Docker and Kubernetes, going to at least 2017, but most of these posts were unpopular. I have to go back to 2019 to find one that sparked a conversation:
The stuff I posted about Kubernetes did not draw a conversation, but I was simply documenting what I was seeing: vast over-investment in devops even at tiny startups that were just getting going and could have easily dumped everything on a single server, exactly as we used to do things back in 2005.
The attraction of this stuff is mostly the ability to keep your infrastructure configurations as code. However, I have previously checked in my systemd cofig files for projects and set up a script to pull them on new systems.
It's not clear that docker-compose or even kubernetes* is that much more complicated if you are only running 3 things.
The complexity of k8s comes the moment you need to hold state of some kind. Now instead of one systemd entry, we have to worry about persistent volume claims and other such nonsense. When you are doing things that are completely stateless, it's simpler than systemd.
If you need to care about state with systemd you still have the "nonsense" of persistent volume claims, they are just something you keep in notes somewhere, in my experience usually in heads of the sysadmins or an excel sheet or a text file that tries to track which server has what data connected how.
Understand that in the hypothetical system we are discussing, there are something like 1-2 servers. In that case the "volume claim" is just "it's a file on the obvious filesystem" and does not actually need to be spelled out they way you need to spell it out in k8s. The file path you give in environment variables is where the most up-to-date version of the volume claim is. And that file is free to expand to hundreds of GB without bothering you.
Things get iffier when you start doing things like running multiple instances of something (maybe you're sticking two test environments for your developers), or suddenly you grew a bit or no longer fit on the server and start migrating around.
The complexity of PVCs in my experience isn't really that big compared to this, possibly lower, and I did stuff both ways.
Every generation has to make similar mistakes again and again.
I am sure if we had the opportunity and the hype was there we would've used k8s in 2005 as well.
The same thing is true for e.g. JavaScript on the frontend.
I am currently migrating a project from React to HTMX.
Suddenly there is no build step anymore.
Some people were like: "That's possible?"
Yes, yes it is and it turns out for that project it increases stability and makes everything less complex while adding the exact same business value.
Does that mean that React is always the wrong choice?
Well, yes, React sucks, but solutions like React? No! It depends on what you need, on the project!
Just as a carpenter doesn't use a hammer to saw, we as a profession should strive to use the right tool for the right job. (Albeit it's less clear than for the carpenter, granted)
It's actually kinda hilarious how RSC (React Server Components) is pretty much going back to what PHP was but yeah proves your point as hype moves on people begin to realize why certain things were good vs not
Along those lines, I am building https://github.com/claceio/clace for teams to deploy internal tools. It provides a Cloud Run type interface to run containers, including scaling down to zero. It implements an application server than runs containerized apps.
Since HTMX was mentioned, Clace also makes it easy to build Hypermedia driven apps.
Would you be open to non Python support as well? This tool seems useful, very useful in fact, but I mainly use .NET (which yes can run very well in containers).
Starlark (python like config language) is used to configure Clace. For containerized apps, python frameworks are supported without a Dockerfile being required. All other languages currently require a user provided Dockerfile, the `container` spec can be used.
> Just as a carpenter doesn't use a hammer to saw, we as a profession should strive to use the right tool for the right job
I think this is a gross misunderstanding of the complexity of tools available to carpenters. Use a saw. Sure, electric, hand powered? Bandsaw, chop saw, jigsaw, scrollsaw? What about using CAD to control the saw?
> Suddenly there is no build step anymore
How do you handle making sure the JS you write works on all the browsers you want to support? Likewise for CSS: do you use something like autoprefixer? Or do you just memorize all the vendor prefixes?
As far as browser prefixes go, you know that browser vendors have largely stopped using those? Not even recently, that process started already way back in 2016. Chances are that if you are using prefixes in 2024 you are supporting browsers versions who, by all logic, should no longer have internet access because of all the security implications....
>Just as a carpenter doesn't use a hammer to saw, we as a profession should strive to use the right tool for the right job. (Albeit it's less clear than for the carpenter, granted)
The problem is that most devs don’t view themselves as carpenters. They view themselves as hammer capenters or saw carpenters etc…
It’s not entirely their fault, some of the tools are so complex that you really need to devote most of your time to 1 of them.
I realize that this kind of tool specialization is sometimes required, but I that it’s overused by at the very least an order of magnitude.
The vast majority of companies that are running k8s, react, kafka etc… with a team of 40+, would be better off running rails (or similar) on heroku (or similar), or a VPS, or a couple servers in the basement. Most of these companies could easily replace their enormous teams of hammer carpenters and saw carpenters with 3-4 carpenters.
But devs have their own gravity. The more devs you have the faster you draw in new ones, so it’s unclear to me if a setup like the above is sustainable long term outside of very specific circumstances.
But if it were simpler there wouldn’t be nearly many jobs, so I really shouldn’t complain. And it’s not like every other department isn’t also bloated.
So, let's say you want to deploy server instances. Let's keep it simple and say you want to have 2 instances running. You want to have zero-downtime-deployment. And you want to have these 2 instances be able to access configuration (that contains secrets). You want load balancing, with the option to integrate an external load balancer. And, last, you want to be able to run this setup both locally and also on at least 2 cloud providers. (EDIT: I meant to be able to run it on 2 cloud providers. Meaning, one at a time, not both at the same time. The idea is that it's easy to migrate if necessary)
This is certainly a small subset of what kubernetes offers, but I'm curious, what would be your goto-solution for those requirements?
Do you know of actual (not hypothetical) cases, where you could "flip a switch" and run the exact same Kubernetes setups on 2 different cloud providers?
Yes. I've worked on a number of very large banking and telco Kubernetes platforms.
All used multi-cloud and it was about 95% common code with the other 5% being driver style components for underlying storage, networking, IAM etc. Also using Kind/k3d for local development.
Both EKS (Amazon) and GKE (Google Cloud) run Cilium for the networking part of their managed Kubernetes offerings. That's the only real "hard part". From the users' point of view, the S3 buckets, the network-attached block devices, and compute (CRIO container runtime) are all the same.
You are using some other cloud provider or want uniformity there's https://Talos.dev
I run clusters on OKE, EKS, and GKE. Code overlap is like 99% with the only real differences all around ingress load balancers.
Kubernetes is what has provided us the abstraction layer to do multicloud in our SaaS. Once you are outside the k8s control plane, it is wildly different, but inside is very consistent.
If you are located in germany and run critial IT infrastructure (banks, insurance companies, energy companies) you have to be able to deal with a cloud provider completely going down in 24 houres. Not everyone who has to can really do it, but the big players can.
I think you are proving the point; there are very, very few applications that need to run on two cloud providers. If you do, sure, use Kubernetes if that makes your job easier. For the other 99% of applications, it’s overkill.
Apart from that requirement, all of this is very doable with EC2 instances behind an ALB, each running nginx as a reverse proxy to an application server with hot restarting (e.g. Puma) launched with a systemd unit.
To me that sounds harder than just using EKS. Also, other people are more likely to understand how it works, can run it in other environments (e.g. locally), etc.
Sorry, that was a misunderstanding. I meant that I want to be able to run it on two cloud providers, but one at a time is fine. It just means that it would be easy to migrate/switch over if necessary.
Hmm, let's see, so you've got to know: EC2, ALB, Nginx, Puma, Systemd, then presumably something like Terraform and Ansible to deploy those configs, or write a custom set of bash scripts. And all of that and you're tied to one cloud provider.
Or, instead of reinventing the same wheels for Nth time, I could just use a set of abstractions that work for 99% of network services out there, on any cloud or bare metal. That set of abstractions is k8s.
Why does a startup need zero-downtime-deployment? Who cares if your site is down for 5 seconds? (This is how long it takes to restart my Django instance after updates).
Because it increases development speed. It's maybe okay to be down for 5 seconds. But if I screw up, I might be down until I fix it. With zero-downtime deployment, if I screw up, then the old instances are still running and I can take my time to fix it.
"Imagine you are in a rubber raft, you are surrounded by sharks, and the raft just sprung a massive leak - what do you do?". The answer, of course, is to stop imagining.
Most people on the "just use bash scripts and duct tape" side of things assume that you really don't need these features, that your customers are ok with downtime and generally that the project that you are working on is just your personal cat photo catalog anyway and don't need such features. So, stop pretending that you need anything at all and get a job at the local grocery store.
The bottom line is there are use cases, that involve real customers, with real money that do need to scale, do need uptime guarantees, do require diverse deployment environments, etc.
Yep. I'm one of 2 Devops at an R&D company with about 100 employees. They need these services for development, if an important service goes down you can multiply that downtime by 100, turning hours into man-days and days into man-months. K8 is simply the easiest way to reduce the risk of having to plead for your job.
I guess most businesses are smaller than this, but at what size do you start to need reliability for your internal services?
You know that you can scale servers just as well, you can use good practices with scripts and deployments in bash and having them documented and in version control.
Equating bash scripts and running servers to duct taping and poor engineering vs k8s yaml being „proper engineering„ is well wrong.
My personal goto-solution for those requirements -- well 1 cloud provider, I'll follow up on that in a second -- would be using ECS or an equivalent service. I see the OP was a critic of Docker as well, but for me, ECS hits a sweet spot. I know the compute is at a premium, but at least in my use-cases, it's so far been a sensible trade.
About the 2 cloud providers bit. Is that a common thing? I get wanting migrate away from one for another, but having a need for running on more than 1 cloud simultaneously just seems alien to me.
Actually, I totally agree. ECS (in combination with secret manager) is basically fulfilling all needs, except being not so easy to reproduce/simulate locally and of course with the vendor lock-in.
Last time I checked ECS was even more expensive than using Lambda but without the ability of fast starting your container, so I really don't get the niche it fits into, compared to Lambda on one side and self-hosting docker on minimal EC2 instances on the other side.
I may need to look at Lambda closer! At least way back, I thought it was a no-go since the main runtime I work with is Ruby. As for minimal EC2 instances, definitely, I do that for environments where it makes sense and that's the case fairly often.
That's an interesting set of requirements though. If that is indeed your set of requirements then perhaps Kubernetes is a good choice.
But the set seems somewhat arbitrary. Can you reduce it further? What if you don't require 2 cloud providers? What if you don't need zero-downtime?
Indeed given that you have 4 machines (2 instances, x 2 providers) could a human manage this? Is Kubernetes overkill?
I ask this merely to wonder. Naturally if you are rolling out hundreds of machines you should, and no doubt by then you have significant revenue (and thus able to pay for dedicated staff) , but where is the cross-over?
Because to be honest most startups don't have enough traction to need 2 servers, never mind 4, never mind 100.
I get the aspiration to be large. I get the need to spend that VC cash. But I wonder if Devops is often just premature and that focus would be better spent getting paying customers.
> Can you reduce it further? What if you don't require 2 cloud providers? What if you don't need zero-downtime?
I think the "2 cloud providers" criteria is maybe negotiable. Also, maybe there was a misunderstanding: I didn't mean to say I want to run it on two cloud providers. But rather that I run it on one of them but I could easily migrate to the other one if necessary.
The zero-downtime one isn't. It's not necessarily so much about actually having zero-downtime. It's about that I don't want to think about it. Anything besides zero-downtime actually adds additional complexity to the development process. It has nothing to do with trying to be large actually.
I disagree with that last part. By default, having a few seconds downtime is not complex. The easiest thing you could do to a server is restart it. Its literally just a restart!
It's not. Imagine there is a bug that stops the app from starting. It could be anything, from a configuration error (e.g. against the database) to a problem with warmup (if necessary) or any kind of other bug like an exception that only triggers in production for whatever reasons.
EDIT: and worse, it could be something that just started and would even happen when trying to deploy the old version of the code. Imagine a database configuration change that allows the old connections to stay open until they are closed but prevents new connections from being created. In that case, even an automatic roll back to the previous code version would not resolve the downtime. This is not theory, I had those cases quite a few times in my career.
I managed a few production services like this and it added a lot of overhead to my work. On the one hand I'd get developers asking me why their stuff hasn't been deployed yet. But then I'd also have to think carefully about when to deploy and actually watch it to make sure it came back up again. I would often miss deployment windows because I was doing something else (my real job).
I'm sure there are many solutions but K8s gives us both fully declarative infrastructure configs and zero downtime deployment out of the box (well, assuming you set appropriate readiness probes etc)
So now I (a developer) don't have to worry about server restarts or anything for normal day to day work. We don't have a dedicated DevOps/platforms/SRE team or whatnot. Now if something needs attention, whatever it is, I put my k8s hat on and look at it. Previously it was like "hmm... how does this service deployment work again..?"
For something this simple, multi-cloud seems almost irrelevant to the complexity. If I’m understanding your requirements right, a deployment consists of two instances and a load balancer (which could be another instance or something cloud-soecific). Does this really need to have fancy orchestration to launch everything? It could be done by literally clicking the UI to create the instances on a cloud and by literally running three programs to deploy locally.
I've worked at tiny startups before. Tiny startups don't need zero-downtime-deployment. They don't have enough traffic to need load balancing. Especially when you are running locally, you don't need any of these.
Tiny startups can’t afford to loose customers because they can’t scale though, right? Who is going to invest in a company that isn’t building for scale?
Tiny startups are rarely trying to build projects for small customer bases (eg little scaling required.) They’re trying to be the next unicorn. So they should probably make sure they can easily scale away from tossing everything on the same server
> Tiny startups can’t afford to loose customers because they can’t scale though, right? Who is going to invest in a company that isn’t building for scale?
Having too many (or too big) customers to handle is a nice problem to have, and one you can generally solve when you get there. There are a handful of giant customers that would want you to be giant from day 1, but those customers are very difficult to land and probably not worth the effort.
Startups need product-market fit before they need scale. It’s incredibly hard to come by and most won’t get it. Their number one priority should be to run as many customer acquisition experiments as possible for as little as possible. Every hour they spend on scale before they need it is an hour less of runway.
I'm actually not using kubernetes because I find it too complex. But I'm looking for a solution for that problem and I haven't found one, so I was wondering what OP uses.
Sorry, but I don't want to "click in a UI". And it is certainly not something you can just automate with 30 lines of bash. If you can, please elaborate.
> And it is certainly not something you can just automate with 30 lines of bash. If you can, please elaborate.
Maybe not literally 30.. I didn't bother actually writing it. Also bash was just a single example. It's way less terraform code to do the same thing. You just need an ELB backed by an autoscaling group. That's not all that much to setup. That gets you the two loadbalanced servers and zero downtime deploys. When you want to deploy, you just create a new scaling group and launch configuration and attach to the ELB and ramp down the old one.. Easy peasy. For the secrets, you need at least KMS and maybe secret manager if you're feeling fancy.. That's not much to setup. I know for sure AWS and azure provide nice CLIs that would let you do this in not that many commands. or just use terraform
Personally if I really cared about multi cloud support, I'd go terraform (or whatever it's called now).
> You just need an ELB backed by an autoscaling group
Sure, and then you can neither 1.) test your setup locally nor 2.) easily move to another cloud provider. So that doesn't really fit what I asked.
If they answer is "there is nothing, just accept the vendor lock-in" then fine, but please don't reply with "30 lines of bash" and make me have expectations. :-(
A script that installs some dependencies on an Ubuntu vm. A script that rsyncs the build artifact to the machine. The script can drain connections and restart the service using the new build, then onto the next VM. The cloud load balancer points at those VMs and has a health check. It's very simple. Nothing fancy.
Our small company uses this setup. We migrated from GCP to AWS when our free GCP credits from YC ran out and then we used our free AWS credits. That migration took me about a day of rejiggering scripts and another of stumbling around in the horrible AWS UI and API. Still seems far, far easier than paying the kubernetes tax.
0 downtime. Jesus Christ. Nginx and HAProxy solved this shit decades ago. You can drop out a server or group. Deploy it. Add it back in. With a single telnet command. You don’t need junk containers to solve things like “0 downtime deployments”. That was a solved problem.
You are not wrong, but that only covers a part of what I was asking. How about the rest? How do you actually bring your services to production? I'm curious.
Migrating to another cloud should be quite easy. There are many PaaS solutions.
The hard parts will be things like migrating the data, make sure there's no downtime AND no drift/diff in the underlying data when some clients write to Cloud-A and some write to CLoud-B, etc. But k8 do not fix these problems, so..
Came here to say the same thing: PaaS. Intriguing that none of the other 12 sibling comments mention this… each in their bubble I guess (including me). We use Azure App Service at my day job and it just works. Not multi-cloud obviously, but the other stuff: zero downtime deploys, scale-out with load balancing… and not having to handle OS updates etc. And containers are optional, you can just drop your binaries and it runs.
CPUs are ~300x more powerful and storage offers ~10,000x more IOPS than 2005 hardware. More efficient server code exists today. You can scale very far on one server. If you were bootstrapping a startup, you could probably plan to use a pair of gaming PCs until at least the first 1-10M users.
How many requests do you expect users actually do? Especially if you're serving a B2B market; not everything is centered around addiction/"engagement". My 8 year old PC can do over 10k page requests/second for a reddit or myspace clone (without getting into caching). A modern high end gaming PC should be around 10x more capable (in terms of both CPU and storage IOPS). The limit in terms of needing to upgrade to "unusual" hardware for a PC would likely be the NIC. Networking is one place where typical consumer gear is stuck in 2005.
Webapps might make it hard to tell, but a modern computer (or even an old computer like mine) is mindbogglingly fast.
Just to make it clear: There are a million use cases that don't involve scaling fast.
For example B2B businesses where you have very few but extremely high value customers for specialized use cases.
Another one is building bully hardware. Your software infrastructure does not need to grow any faster than your shop floor is building it.
Whether you want to call that a "startup" is up for debate (and mostly semanticist if you ask me) but at one point they were all a zero employee company and needed to survive their first 5 years.
In general you won't find their products on the app store.
I've worked at a few tiny startups, and I've both manually administered a single server and run small k8s clusters. k8s is way easier. I think I've spent 1, maybe 2 hours on devops this year. It's not a full-time job, it's not a part-time job, it's not even an unpaid internship. Perhaps at a bigger company with more resources and odd requirements...
Yeah, using EKS isn't the same thing as "administering k8s", unless I misread you above. Actual administration is already done for you, it's batteries included, turn-key, and integrated with everything AWS.
A job ago we had our own k8s cluster in our own DC, and it required a couple of teams to keep running and reasonably integrated with everything else in the rest of the company. It was probably cheaper overall than cloud given the compute capacity we had, but also probably not by much given the amount of people dedicated to it.
Even my 3-node k3s at home requires more attention than what you described.
People gravely miss-understand containerization and Docker.
All it lets you do is put shell commands into a text file and be able to run it self-contained anywhere. What is there to hate?
You still use the same local filesystem, the same host networking, still rsync your data dir, still use the same external MySQL server even if you want -- nothing has changed.
You do NOT need a load balancer, a control plane, networked storage, Kubernetes or any of that. You ADD ON those things when you want them like you add on optional heated seats to your car.
Why would you want to run it anywhere. People mostly select an OS and just update that. It may be great when distributing applications for others to host, but not when it’s the only strategy. I have to reverse engineer dockerfiles when the developer wouldn’t provide a proper documentation.
OS upgrades are a pain. Even just package updates could break everything. Having everything in containers makes migrating to another system much easier.
I wish Docker Swarm would get more attention. It could be the perfect Kubernetes lightweight alternative. Instead it seems like it could get deprecated any day now.
K8s always seems like the tool that people choose to avoid cloud vendor lock in but there is something to be said for C k8s lock in as well as the article points out.
If you end up with exotic networking or file system mounts you can just be stuck maintaining k8s forever and some updates aren’t so stable so you have to be more vigilant that windows updates.
I don't think it makes sense to conflate vendor lock-in with taking a dependency on a given technology. Do we then have "Linux lock-in" and "Postgres lock-in"? The term "lock-in" shouldn't be stretched to cover this concept imo.
The "You Probably Don't Either" is a little presumptuous. Many projects probably don't need cloud run either. Certainly, many projects shouldn't even be started in the first place.
I work with Kubernetes enough that I would answer to the title "kubernetes developer" and I would recommend you don't use kubernetes. In the same way I would recommend you don't use a car when you can walk.
Your friend lives 1/8 miles away. You go to see them every day so why wouldn't you drive? Well, cars are expensive and you should avoid them if you don't need them. There are a TON of downsides to driving a car 1/4 of a mile every day. And there are a TON of benefits to using a car to drive 25 miles every day.
I hate to quash a good debate but this all falls under the predictable but often forgotten "it depends". Usually do you need kubernetes == do you have a lot of shit to run.
That's pretty much it. I think the main issue nowadays is that companies think full stack engineering means OG(FE BE DB) + CICD + Infra + security compliance + SRE.
If a team of 5-10 SWEs have to do all of that while only graded on feature releases, k8s would massively suck.
I also agree that experienced platform/infra engineers tend to whine less about k8s.
Nah the difference between managing k8 and the system it was based on is VASTLY different. K8 is much harder than it needs to be because there wasn't tooling for a long time to manage it well. Going from google internal to K8 is incredibly painful.
I think this is only true if the original k8s cluster you're operating against was written by an expert and laid out as such.
If you're entering into k8s land with someone else's very complicated mess across hundreds of files, you're going to be in for a bad time.
A big problem, I feel, is that if you don't have an expert design the k8s system from the start, it's just going to be a horrible time; and, many people, when they're asked to set up a k8s setup for their startup or whatever, aren't already experts, so the thing that's produced is not maintainable.
Thanks to kubernetes "flattening" that mess into somewhat flat object map (I like to call it a blackboard system :D) it can be reasonably easy to figure out what's the desired state and what's the current state for given cluster, even if the files are a mess.
However...
Talking with people who started using kubernetes later than me[1], it seems like a lot of confusion starts by trying to start with somewhat complete example like using a Deployment + Ingress + Services to deploy, well, a typical web application. The stuff that would be trivial to run in typical PaaS.
The problem is that then you do not know what a lot of those magic incantations mean, and the actually very, very simple mechanism of how things work in k8s are lost, and you can't find your way in a running cluster.
[1] I started learning around 1.0, went with dev deployment with 1.3, graduated it to prod with 1.4. Smooth sailing since[2]
[2] The worst issues since involved dealing with what was actually global GCP networking outage that we were extra sensitive to due to extensive DNS use in kubernetes, and once naively assuming that the people before me set sensible sizes for various nodes, only to find a combination of too small to live EC2 instances choking till control plane death, and outdated etcd (because the rest of the company twas too conservative in updating) getting into rare but possible bug that corrupted data which was triggered by the flapping caused by too small instances. Neither I count as k8s issue, would have killed anything else I could setup given the same constraints.
You can cobble together your own unique special combination of services to run apps on! It's an open ended adventure into itself!
I'm all for folks doing less, if it makes sense! But there's basically nothing except slapping together the bits yourself & convincing yourself your unique home-made system is fine. You'll be going it alone, & figuring out on the fly, all to save yourself from getting good at the one effort that has a broad community, practitioners, and massive extensibility via CRD & operators.
Except that all of this subjects you and all of your workloads to warrantless US government surveillance due to running in the big public clouds.
I personally don’t want the federal government being able to peek into my files and data at any time, even though I’ve done nothing wrong. It’s the innocent people who have most to lose from government intrusion.
It seems insane to me to just throw up one’s hands and store private data in Google or Amazon clouds, especially when not doing so is so much cheaper.
People don’t want solutions, they want holistic experiences. Kubernetes feels bad and a pile of scripts feels good. Proudly declaring how much you buy into feelings feels even better!
Those pile of scripts are the solutions on which Kubernetes was built on. And the fact that they never break is why greybeards still use them over the complexity of shiny new things.
The script break so hard is why one of my first times i felt a "grey beard moment" (with actual grey beard even) was telling a junior the "running ansible from their laptop is not acceptable solution"
>> Kubernetes is feature-rich, yet these “enterprise” capabilities turned even simple tasks into protracted processes.
I don't agree. After learning the basics I would never go back. It doesn't turn simple tasks into a long process. It massively simplifies a ton functionality. And you really only need to learn 4 or 5 new concepts to get it off the ground.
If you have a simple website you don't need Kubernetes, but 99% of devs are working in medium sized shops where they have multiple teams working across multiple functionalities and Kubernetes helps this out.
Karpenter is not hard to set up at all. It solves the problem about over-provisioning out of the box and has for almost 5 years.
It's like writing an article: "I didn't need redis, and you probably don't either" and then talking about how Redis isn't good for relational data.
People talk about Kubernetes as container orchestration, but I think that's kind of backwards.
Kubernetes is a tool for creating computer clusters. Hence the name "Borg" (Kubernetes's grandpa) referring to assimilating heterogeneous hardware into a collective entity. Containers are an implementation detail.
Do you need a computer cluster? If so k8s is pretty great. If you don't care about redundancy and can get all the compute you need out of a single machine, then you may not need a cluster.
Once you're using containers on a bunch of VMs in different geographical regions, then you effectively have hacked together a virtual cluster. You can get by without k8s. You just have to write a lot of glue code to manage VMs, networking, load balancing, etc on the cloud provider you use. The overhead of that is probably larger than just learning Kubernetes in the long run, but it's reasonable to take on that technical debt if you're just trying to move fast and aren't concerned about the long run.
This has got to be the most out there k8s take I've read in a while. k8s doesn't save you from learning your cloud providers infrastructure, you have to learn k8s in addition to your cloud provider's infrastructure. It's all ALBs, ASGs, Security Groups, EBS Volumbes and IAM policy underneath and k8s, while very clever, isn't so clever as to abstract much of any of it away from you. On EKS you get to enjoy more odd limitations with your nodes than EC2 would give you on its own.
You're already building on a cluster, your cloud provider's hypervisor. They'll literally build virtual compute of any size and shape for you on demand out of heterogeneous hardware and the security guarantees are much stronger than colocated containers on k8s nodes.
There are quite a few steps between single server and k8s.
The same argument can be made about Borg. Someone at Google needs to know about things like Juniper switches and hard drive bays. Someone needs to repair or replace defective compute nodes and power switches. Before they had software load balancing, someone had to manage the hardware load balancers.
But the idea of Borg is that all of that's abstracted away for the typical developer. It's the same with k8s. The infrastructure team in your org needs to understand the implementation details, but really only a few.
You can also configure load balancers, IAM groups & policy etc as k8s CRDs. So all that stuff can be in one place in the code base alongside the rest of your infrastructure. So in that sense it does abstract those concepts. You still need to know something about them, but you don't have to configure them programmatically yourself since k8s will do that.
K8s doesn't help you solve your geographical region problem, because the geographical region problem is not running appserver instances in multiple regions. Almost any PaaS will do that for you out of the box, with way less fuss than k8s. The hard part is distributing your data.
Less overhead than writing your own glue code, less overhead than learning Kubernetes, is just use a PaaS like Google App Engine, Amazon Elastic Beanstalk, Digital Ocean App Platform, or Heroku. You have access to the same distributed databases you would with k8s.
Cloud Run is PaaS for people that like Docker. If you don't even want to climb that learning curve, try one of the others.
PaaS get such a bad rap from devs in my experience, even though they would solve so many problems. They'd rather keep their k8s clusters scaled to max traffic and spend their nights dealing with odd networking and configuration issues than just throw their app on Cloud Run and call it a day.
> just use a PaaS like Google App Engine, Amazon Elastic Beanstalk, Digital Ocean App Platform, or Heroku.
This is the right way for web most of the time, but most places will choose k8s anyway. It’s perplexing until you come to terms with the dirty secret of resume driven development, which is that it’s not just junior engs but lots of seniors too and some management that’s all conspiring to basically defraud business owners. I think the unspoken agreement is that Hard work sucks, but easy work that helps you learn no transferable skills might be worse. The way you evaluate this tradeoff predictably depends how close you are to retirement age. Still, since engineers are often disrespected/discarded by business owners and have no job security, oaths of office, professional guilds, or fiduciary responsibility.. it’s no wonder things are pretty mercenary out there.
Pipelines are as important as web these days but of course there are many options for pipelines as a service also.
K8s is the obviously correct choice for teams that really must build new kinds of platforms that have many diverse kinds of components, or have lots of components with unique requirements for coupling (like say “scale this thing based on that other thing”, but where you’d have real perf penalties for leaving the k8s ecosystem to parse events or whatever).
The often mentioned concern about platform lock in is going to happen to you no matter what, and switching clouds completely rarely happens anyway. If you do switch, it will be hard and time consuming no matter what.
To be fair, k8s also enables brand new architectural possibilities that may or may not be beautiful. But it’s engineering, not art, and beautiful is not the same as cheap, easy, maintainable, etc.
If that's practical experience that you need, I remember reading some studies comparing the reliability and efficiency of nomad vs k8s under load (spoiler: they do not look very good for k8s).
If that's popularity that you need, then sure, nobody ever got fired for choosing kubernetes.
Yep, it's a cluster OS. If you need to run a cluster, you need to explore and understand the trade offs of k8s versus other approaches. Personally I run a small cluster on k3s, for internal tools, and love it. Yes it's a load of new abstractions to learn, but once learnt if really helps in designing large scalable systems. I manage lots of pet machines and VMs for clients, and it would be soooo much easier on k8.
I'm not sure google cloud run can be considered a fair comparison to Kubernetes. It would be like saying AWS Lambda is a lot easier to use than EC2. I've used both Kubernetes and GCR at the current company I cofounded, and theres pros and cons to both. (Team of about 10 engineers)
GCR was simple to run simple workloads, but, an out of the box Postgres database can't just handle unlimited connections and so connecting to it from GCR without having a DB connection proxy like PG bouncer risks exhausting the connection pool. For a traditional web app at any moderate scale, you typically need some fine grained control over per process, per server and per DB connection pools, which you'd lose with GCR.
Also, to take advantage of GCR's fine grained CPU pricing, you'd have to have an app that boots extremely quickly, so it can be turned off during periods of inactivity, and rescheduled when a request comes in.
Most of our heaviest workloads run on Kubernetes for those reasons.
The other thing thats changed since this author probably explored Kubernetes is that there are a ton of providers now that offer a Kubernetes control plane for no cost. The ones that I know of are Digital Ocean and Linode, where the pricing for a Kubernetes cluster is the same as their droplet pricing for the same resources. That didn't use to be the case. [1] The cheapest you can get is a $12 / month, fully featured cluster on Linode.
I've been building, in my spare time, a platform that tries to make Kubernetes more usable for single developers: https://canine.sh, based on my learnings that the good parts of Kubernetes are actually quite simple to use and manage.
GCR assumes it's workload is HTTP, or a "job" (container that exits once it's task has completed). It scales on request volume and CPU, and the load balancer is integrated into the service. It's not obvious to me how you'd even run a "raw" TCP service like pgbouncer on it.
> an out of the box Postgres database can't just handle unlimited connections and so connecting to it from GCR without having a DB connection proxy like PG bouncer risks exhausting the connection pool.
But even 1000 seems ok? 1 per container, so 1000 running containers? Quite a lot in my world, especially since they can be quite beefy. Would be very worried about the cost way before 1000 simultaneously running containers :)
This assumes your Postgres has a an infinite amount of memory which would also be very expensive. I think you'd probably want to assume each connection takes ~10-20MB of memory.
Docker swarm would have worked for 98.5% of all users (how k8s won over swarm should be a case study). And kamal, or some thing like it, would work for 88.25% of all users.
> Kubernetes comes with substantial infrastructure costs that go beyond DevOps and management time. The high cost arises from needing to provision a bare-bones cluster with redundant management nodes.
That's your problem right there. You really don't want to be setting up and managing a cluster from scratch for anything less than a datacenter-scale operation. If you are already on a cloud provider just use their managed Kubernetes offering instead. It will come with a free control plane and abstract away most of the painful parts for you (like etcd, networking, load balancing, ACLs, node provisioning, kubelets, proxies). That way you just bring your own nodes/VMs and can still enjoy the deployment standardization and other powerful features without the operational burden.
Even for on-prem scenario, I'd rather maintain a K8S control plane and let developer teams manage their own apps deployment in their own little namespace, than provisioning a bunch of new VMs each time a team need some services deployed.
I can imagine. Do you have complete automation setup around maintaining the cluster?
We are now on-prem using “pet” clusters with namespace as a service automated on it. This causes all kinds of issues with different workloads with different performance characteristics and requirements. They also share ingress and egress nodes so impact on those has a large blast radius. This leads to more rules and requirements.
Having dedicated and managed clusters where everyone can determine their sizing and granularity of workloads to deploy to which cluster is paradise compared to that.
> This causes all kinds of issues with different workloads with different performance characteristics and requirements.
Most of these issues can be fixed by setting resource requests equal to limits and using integer CPU values to guarantee QoS. You should also have an interface with developers explaining which nodes in your datacenter have which characteristics, using node labels and taints, and force developers to pick specific node groups as such by specifying node affinity and tolerations, by not bringing online nodes without taints.
> They also share ingress and egress nodes so impact on those has a large blast radius.
This is true regardless of whether or not you use Kubernetes.
For the different workloads it is more that all the nodes in the cluster are the same and mixing memory with cpu intensive or io intensive workloads is hard to schedule or to get to a proper utilisation rate. Next to that when indeed setting request and limit properly it means that our Java apps use/reserve multiple cores even when handling little traffic and basically idling. Golang apps scale better there, especially towards 0.
When running on “bare” VMs each VM is its own member in the network. The pods in the cluster use an overlay network and egress is limited to egress nodes which are now shared by all workloads.
Having dedicated K8s clusters would reduce the sharing of network ingress and egress as well as choose the vm size for my workloads.
Even as a K8s hater, this is a pretty salient point.
If you are serious about minimizing ops work, you can make sure people are deploying things in very simple ways, and in that world you are looking at _very easy_ deployment strategies relative to having to wire up VMs over and over again.
Just feels like lots of devs will take whatever random configs they find online and throw them over the fence, so now you just have a big tangled mess for your CRUD app.
> Just feels like lots of devs will take whatever random configs they find online
Well it usually isn't a mystery. Requiring a developer team to learn k8s likely with no resources, time, or help is not a recipe for success. You might have minimised someone else's ops work, but at what cost?
I am partly sympathetic to that (and am a person who does this) but I think too many devs are very nihilistic and use this as an excuse to stop thinking. Everyone in a company is busy doing stuff!
There's a lot of nuance here. I think ops teams are comfortable with what I consider "config spaghetti". Some companies are incentivised to ship stuff that's hard to configure manually. And a lot of other dynamics are involved.
But at the end of the day if a dev copy-pastes some config into a file, taking a quick look over and asking yourself "how much of this can I actually remove?" is a valuable skill.
Really you want the ops team to be absorbing this as well, but this is where constant atomization of teams makes things worse! Extra coordination costs + a loss of a holistic view of the system means that the iteration cycles become too high.
But there are plenty of things where (especially if you are the one integrating something!) you should be able to look over a thing and see, like, an if statement that will always be false for your case and just remove it. So many modern ops tools are garbage and don't accept the idea of running something on your machine, but an if statement is an if statement is an if statement.
> Just feels like lots of devs will take whatever random configs they find online and throw them over the fence, so now you just have a big tangled mess for your CRUD app.
Agree.
To reduce the chance a dev pull some random configs out of nowhere, we maintain a Helm template that can be used to deploy almost all of our services in a sane way, just replace the container image and ports. The deployment is probably not optimal, but further tuning can be done after the service is up and we have gathered enough metrics.
We've also put all our configs in one place, since we found that devs tend to copy from existing configs in the repo before searching the internet.
> than provisioning a bunch of new VMs each time a team need some services deployed.
Back in the old days before cloud providers this was the only option. I started my career in early 2010s and got the tailend of this, it was not fun.
I remember my IT department refusing to set up git for us (we were using SVN before) so we just asked a VM and set up a git repo in there ourselves to host our code.
This for me is THE reason for using container management. Without containers, you end up with hundreds of VMs. Then, when the time comes that you have to upgrade to a new OS, you have to go through the dance, for every service:
- set up new VMs
- deploy software on new VMs
- have the team responsible give their ok
It takes forever, and in my experience, often never completes because some snowflake exists somewhere, or something needs a lib that doesn't exist on the new OS. VMs decouple the OS from the hardware, but you should still decouple the service from the OS. So that means containers. But then managing hundreds of containers still sucks.
GCP has $74 free credit for Zonal cluster, so you effectively have the first cluster for free.
And even $70 is cheap, considering that a cluster should be shared by all the services from all the teams in the same environment, bar very few exceptions.
If you do find yourself wanting to create a cluster by hand, it's probably because you don't actually need lots of machines in the "cluster". In my experience it's super handy to run tests on a single-node "cluster", and then k3s is super simple. It takes something like 8 seconds to install k3s on a bare CI/CD instance, and then you can install your YAML and see that it works.
Once you're used to it, the high-level abstractions of k8 are wonderful. I run k3s on raspberry pi's because it takes care of all sorts of stuff for you, and it's easy to port code and design patterns from the big backend service to a little home project.
For most small setups, the cost of running an empty kubernetes cluster (managed) is typically higher than setting up a db, a couple of vms and a loadbalancer, which goes a long way for running a simple service. Add some buckets, a CDN and you are pretty much good to go.
If you need dedicated people just to stay on top of running your services, you have a problem that's costing you hundreds of thousands per year. There's a lot of fun and easy stuff you can do with that kind of money. This is a pattern I see with a lot of teams that get sucked into using Kubernetes, micro services, terraform, etc. Once you need a few people just to stay on top of the complexity that comes from that, you are already spending a lot. I tend to keep things simple on my own projects because any amount of time I spend on that, I'm not spending on more valuable work like adding features, fixing bugs, etc.
Of course it's not black and white and there's always a trade off between over and under engineering. But a lot of teams default to over engineering simply by using Kubernetes from day one. You don't actually need to. There's nothing wrong with a monolith running on two simple vms with a load balancer in front of it. Worked fine twenty years ago and it is still perfectly valid. And it's dead easy to setup and manage in most popular cloud environments. If you use some kind of scaling group, it will scale just fine.
> For most small setups, the cost of running an empty kubernetes cluster (managed) is typically higher than setting up a db, a couple of vms and a loadbalancer, which goes a long way for running a simple service.
Not really, the cost of an empty EKS cluster is the management fee of $0.1/hour, or roughly the price of a small EC2 instance.
Though if you are only spending $350 monthly on VM, Database and Load Balancer, you can probably count resource instances by hand, and don't need a K8S cluster yet.
If you're looking to run a few containers you may also want to look at docker swarm itself. You h get some of the benefits of orchestration and a small manageable overhead. And it's just part of docker.
I don't understand how these posts exist when much of my consulting and career for the last few years has been on companies that have basically set up a bare bones out of the manual EKS/GCP solution and just essentially let it sit for 3+ years untouched until it got to a crisis. That to me as a systems engineer is nuts and a testament to how good this stuff is when you get it even kind of right. Of course, I'm referring to managed systems. Doing Kubernetes from scratch I would not dream of doing.
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[ 4.2 ms ] story [ 332 ms ] threadDepending on my client's needs we do it oldschool and just rent a beefy server.
Using your brain to actually assess a situation and decide without any emotional or monetary attachment to a specific solution actually works like a charm most of the time.
I also had customers who run their own cloud based on k8s.
And I heard some people have customers that are on a public cloud ;-)
Choose the right solution for the problem at hand.
https://news.ycombinator.com/item?id=20371961
The stuff I posted about Kubernetes did not draw a conversation, but I was simply documenting what I was seeing: vast over-investment in devops even at tiny startups that were just getting going and could have easily dumped everything on a single server, exactly as we used to do things back in 2005.
It's not clear that docker-compose or even kubernetes* is that much more complicated if you are only running 3 things.
* if you are an experienced user
The complexity of PVCs in my experience isn't really that big compared to this, possibly lower, and I did stuff both ways.
Every generation has to make similar mistakes again and again.
I am sure if we had the opportunity and the hype was there we would've used k8s in 2005 as well.
The same thing is true for e.g. JavaScript on the frontend.
I am currently migrating a project from React to HTMX.
Suddenly there is no build step anymore.
Some people were like: "That's possible?"
Yes, yes it is and it turns out for that project it increases stability and makes everything less complex while adding the exact same business value.
Does that mean that React is always the wrong choice?
Well, yes, React sucks, but solutions like React? No! It depends on what you need, on the project!
Just as a carpenter doesn't use a hammer to saw, we as a profession should strive to use the right tool for the right job. (Albeit it's less clear than for the carpenter, granted)
Since HTMX was mentioned, Clace also makes it easy to build Hypermedia driven apps.
I do plan to add specs for other languages. New specs have to be added here https://github.com/claceio/appspecs. New specs can be created locally also in the config, see https://clace.io/docs/develop/#building-apps-from-spec
I think this is a gross misunderstanding of the complexity of tools available to carpenters. Use a saw. Sure, electric, hand powered? Bandsaw, chop saw, jigsaw, scrollsaw? What about using CAD to control the saw?
> Suddenly there is no build step anymore
How do you handle making sure the JS you write works on all the browsers you want to support? Likewise for CSS: do you use something like autoprefixer? Or do you just memorize all the vendor prefixes?
I don't use any prefixed CSS and haven't for many years.
Last time I did knowingly and voluntarily was about a decade ago.
The problem is that most devs don’t view themselves as carpenters. They view themselves as hammer capenters or saw carpenters etc…
It’s not entirely their fault, some of the tools are so complex that you really need to devote most of your time to 1 of them.
I realize that this kind of tool specialization is sometimes required, but I that it’s overused by at the very least an order of magnitude.
The vast majority of companies that are running k8s, react, kafka etc… with a team of 40+, would be better off running rails (or similar) on heroku (or similar), or a VPS, or a couple servers in the basement. Most of these companies could easily replace their enormous teams of hammer carpenters and saw carpenters with 3-4 carpenters.
But devs have their own gravity. The more devs you have the faster you draw in new ones, so it’s unclear to me if a setup like the above is sustainable long term outside of very specific circumstances.
But if it were simpler there wouldn’t be nearly many jobs, so I really shouldn’t complain. And it’s not like every other department isn’t also bloated.
This is certainly a small subset of what kubernetes offers, but I'm curious, what would be your goto-solution for those requirements?
All used multi-cloud and it was about 95% common code with the other 5% being driver style components for underlying storage, networking, IAM etc. Also using Kind/k3d for local development.
You are using some other cloud provider or want uniformity there's https://Talos.dev
Kubernetes is what has provided us the abstraction layer to do multicloud in our SaaS. Once you are outside the k8s control plane, it is wildly different, but inside is very consistent.
Effectively using Google and Azure managed K8s. (Full GKE > GKE Autopilot > Google Cloud Run). The same containers will run locally, in Azure, or AWS.
It's fantastic for projects but and small. The free monthly grant makes it perfect for weekend projects.
Apart from that requirement, all of this is very doable with EC2 instances behind an ALB, each running nginx as a reverse proxy to an application server with hot restarting (e.g. Puma) launched with a systemd unit.
Or, instead of reinventing the same wheels for Nth time, I could just use a set of abstractions that work for 99% of network services out there, on any cloud or bare metal. That set of abstractions is k8s.
So you need seamless deployments.
Most people on the "just use bash scripts and duct tape" side of things assume that you really don't need these features, that your customers are ok with downtime and generally that the project that you are working on is just your personal cat photo catalog anyway and don't need such features. So, stop pretending that you need anything at all and get a job at the local grocery store.
The bottom line is there are use cases, that involve real customers, with real money that do need to scale, do need uptime guarantees, do require diverse deployment environments, etc.
I guess most businesses are smaller than this, but at what size do you start to need reliability for your internal services?
Equating bash scripts and running servers to duct taping and poor engineering vs k8s yaml being „proper engineering„ is well wrong.
About the 2 cloud providers bit. Is that a common thing? I get wanting migrate away from one for another, but having a need for running on more than 1 cloud simultaneously just seems alien to me.
But the set seems somewhat arbitrary. Can you reduce it further? What if you don't require 2 cloud providers? What if you don't need zero-downtime?
Indeed given that you have 4 machines (2 instances, x 2 providers) could a human manage this? Is Kubernetes overkill?
I ask this merely to wonder. Naturally if you are rolling out hundreds of machines you should, and no doubt by then you have significant revenue (and thus able to pay for dedicated staff) , but where is the cross-over?
Because to be honest most startups don't have enough traction to need 2 servers, never mind 4, never mind 100.
I get the aspiration to be large. I get the need to spend that VC cash. But I wonder if Devops is often just premature and that focus would be better spent getting paying customers.
I think the "2 cloud providers" criteria is maybe negotiable. Also, maybe there was a misunderstanding: I didn't mean to say I want to run it on two cloud providers. But rather that I run it on one of them but I could easily migrate to the other one if necessary.
The zero-downtime one isn't. It's not necessarily so much about actually having zero-downtime. It's about that I don't want to think about it. Anything besides zero-downtime actually adds additional complexity to the development process. It has nothing to do with trying to be large actually.
EDIT: and worse, it could be something that just started and would even happen when trying to deploy the old version of the code. Imagine a database configuration change that allows the old connections to stay open until they are closed but prevents new connections from being created. In that case, even an automatic roll back to the previous code version would not resolve the downtime. This is not theory, I had those cases quite a few times in my career.
I'm sure there are many solutions but K8s gives us both fully declarative infrastructure configs and zero downtime deployment out of the box (well, assuming you set appropriate readiness probes etc)
So now I (a developer) don't have to worry about server restarts or anything for normal day to day work. We don't have a dedicated DevOps/platforms/SRE team or whatnot. Now if something needs attention, whatever it is, I put my k8s hat on and look at it. Previously it was like "hmm... how does this service deployment work again..?"
Tiny startups are rarely trying to build projects for small customer bases (eg little scaling required.) They’re trying to be the next unicorn. So they should probably make sure they can easily scale away from tossing everything on the same server
Having too many (or too big) customers to handle is a nice problem to have, and one you can generally solve when you get there. There are a handful of giant customers that would want you to be giant from day 1, but those customers are very difficult to land and probably not worth the effort.
Admittedly, if you don't know k8s, it might be non-starter... but if you some knowledge, k3s plus cheap server is a wonderful combo
There’s shitloads of solutions.
It’s like minutes of clicking in a ui of any cloud provider to do any of that. So doing it multiple times is a non issue.
Or automate it with like 30 lines of bash. Or chef. Or puppet. Or salt. Or ansible. Or terraform. Or or or or or.
Kubernetes brings in a lot of nonsense that isn’t worth the tradeoff for most software.
If you feel it makes your life better, then great!
But there’s way simpler solutions that work for most things
Sorry, but I don't want to "click in a UI". And it is certainly not something you can just automate with 30 lines of bash. If you can, please elaborate.
Maybe not literally 30.. I didn't bother actually writing it. Also bash was just a single example. It's way less terraform code to do the same thing. You just need an ELB backed by an autoscaling group. That's not all that much to setup. That gets you the two loadbalanced servers and zero downtime deploys. When you want to deploy, you just create a new scaling group and launch configuration and attach to the ELB and ramp down the old one.. Easy peasy. For the secrets, you need at least KMS and maybe secret manager if you're feeling fancy.. That's not much to setup. I know for sure AWS and azure provide nice CLIs that would let you do this in not that many commands. or just use terraform
Personally if I really cared about multi cloud support, I'd go terraform (or whatever it's called now).
Sure, and then you can neither 1.) test your setup locally nor 2.) easily move to another cloud provider. So that doesn't really fit what I asked.
If they answer is "there is nothing, just accept the vendor lock-in" then fine, but please don't reply with "30 lines of bash" and make me have expectations. :-(
Our small company uses this setup. We migrated from GCP to AWS when our free GCP credits from YC ran out and then we used our free AWS credits. That migration took me about a day of rejiggering scripts and another of stumbling around in the horrible AWS UI and API. Still seems far, far easier than paying the kubernetes tax.
You are not wrong, but that only covers a part of what I was asking. How about the rest? How do you actually bring your services to production? I'm curious.
And, PS, I don't use k8s. Just saying.
Migrating to another cloud should be quite easy. There are many PaaS solutions. The hard parts will be things like migrating the data, make sure there's no downtime AND no drift/diff in the underlying data when some clients write to Cloud-A and some write to CLoud-B, etc. But k8 do not fix these problems, so..
How have you been going since 2005 and still not understand the economics of software?
Webapps might make it hard to tell, but a modern computer (or even an old computer like mine) is mindbogglingly fast.
For example B2B businesses where you have very few but extremely high value customers for specialized use cases.
Another one is building bully hardware. Your software infrastructure does not need to grow any faster than your shop floor is building it.
Whether you want to call that a "startup" is up for debate (and mostly semanticist if you ask me) but at one point they were all a zero employee company and needed to survive their first 5 years.
In general you won't find their products on the app store.
Negligible for me personally, it's much less than either our EC2 or RDS costs.
A job ago we had our own k8s cluster in our own DC, and it required a couple of teams to keep running and reasonably integrated with everything else in the rest of the company. It was probably cheaper overall than cloud given the compute capacity we had, but also probably not by much given the amount of people dedicated to it.
Even my 3-node k3s at home requires more attention than what you described.
The amount of work/cost of using k8s for handling them in comparison to doing it "old style" is probably negative by now.
All it lets you do is put shell commands into a text file and be able to run it self-contained anywhere. What is there to hate?
You still use the same local filesystem, the same host networking, still rsync your data dir, still use the same external MySQL server even if you want -- nothing has changed.
You do NOT need a load balancer, a control plane, networked storage, Kubernetes or any of that. You ADD ON those things when you want them like you add on optional heated seats to your car.
Anybody who doesn't have the money, time or engineering resources will jump on whatever appear as a decent alternative.
My intuition is that alternative already exist but I can't see it...
A bit like Spring emerged as an alternative to J2EE or what HTMX is to React & co.
Is it k3s or something more radical?
Is it on a chinese Github?
If you end up with exotic networking or file system mounts you can just be stuck maintaining k8s forever and some updates aren’t so stable so you have to be more vigilant that windows updates.
You can have Postgres lock in as much as Wordpress has MySql lock in.
I agree that you have less Linux lock in but Docker still requires a Linux kernel everywhere it goes. BSD need not apply.
Your friend lives 1/8 miles away. You go to see them every day so why wouldn't you drive? Well, cars are expensive and you should avoid them if you don't need them. There are a TON of downsides to driving a car 1/4 of a mile every day. And there are a TON of benefits to using a car to drive 25 miles every day.
I hate to quash a good debate but this all falls under the predictable but often forgotten "it depends". Usually do you need kubernetes == do you have a lot of shit to run.
If you're used to managing platforms e.g. networking, load balancers, security etc. then it's intuitive and easy.
If you're used to everything being managed for you then it will feel steep.
If a team of 5-10 SWEs have to do all of that while only graded on feature releases, k8s would massively suck.
I also agree that experienced platform/infra engineers tend to whine less about k8s.
If you're entering into k8s land with someone else's very complicated mess across hundreds of files, you're going to be in for a bad time.
A big problem, I feel, is that if you don't have an expert design the k8s system from the start, it's just going to be a horrible time; and, many people, when they're asked to set up a k8s setup for their startup or whatever, aren't already experts, so the thing that's produced is not maintainable.
And then everyone is cursed.
At least Kubernetes is all YAML, consistent and can be tested locally.
However...
Talking with people who started using kubernetes later than me[1], it seems like a lot of confusion starts by trying to start with somewhat complete example like using a Deployment + Ingress + Services to deploy, well, a typical web application. The stuff that would be trivial to run in typical PaaS.
The problem is that then you do not know what a lot of those magic incantations mean, and the actually very, very simple mechanism of how things work in k8s are lost, and you can't find your way in a running cluster.
[1] I started learning around 1.0, went with dev deployment with 1.3, graduated it to prod with 1.4. Smooth sailing since[2]
[2] The worst issues since involved dealing with what was actually global GCP networking outage that we were extra sensitive to due to extensive DNS use in kubernetes, and once naively assuming that the people before me set sensible sizes for various nodes, only to find a combination of too small to live EC2 instances choking till control plane death, and outdated etcd (because the rest of the company twas too conservative in updating) getting into rare but possible bug that corrupted data which was triggered by the flapping caused by too small instances. Neither I count as k8s issue, would have killed anything else I could setup given the same constraints.
You can cobble together your own unique special combination of services to run apps on! It's an open ended adventure into itself!
I'm all for folks doing less, if it makes sense! But there's basically nothing except slapping together the bits yourself & convincing yourself your unique home-made system is fine. You'll be going it alone, & figuring out on the fly, all to save yourself from getting good at the one effort that has a broad community, practitioners, and massive extensibility via CRD & operators.
I personally don’t want the federal government being able to peek into my files and data at any time, even though I’ve done nothing wrong. It’s the innocent people who have most to lose from government intrusion.
It seems insane to me to just throw up one’s hands and store private data in Google or Amazon clouds, especially when not doing so is so much cheaper.
Dear friend, you have built a Kubernetes
https://news.ycombinator.com/item?id=42226005
>> Kubernetes is feature-rich, yet these “enterprise” capabilities turned even simple tasks into protracted processes.
I don't agree. After learning the basics I would never go back. It doesn't turn simple tasks into a long process. It massively simplifies a ton functionality. And you really only need to learn 4 or 5 new concepts to get it off the ground.
If you have a simple website you don't need Kubernetes, but 99% of devs are working in medium sized shops where they have multiple teams working across multiple functionalities and Kubernetes helps this out.
Karpenter is not hard to set up at all. It solves the problem about over-provisioning out of the box and has for almost 5 years.
It's like writing an article: "I didn't need redis, and you probably don't either" and then talking about how Redis isn't good for relational data.
Google CloudRun, Database, PubSub, Cloud Storage, VPC, IAM, Artifact Registry etc lock-in: good.
This is not always true.
Kubernetes is a tool for creating computer clusters. Hence the name "Borg" (Kubernetes's grandpa) referring to assimilating heterogeneous hardware into a collective entity. Containers are an implementation detail.
Do you need a computer cluster? If so k8s is pretty great. If you don't care about redundancy and can get all the compute you need out of a single machine, then you may not need a cluster.
Once you're using containers on a bunch of VMs in different geographical regions, then you effectively have hacked together a virtual cluster. You can get by without k8s. You just have to write a lot of glue code to manage VMs, networking, load balancing, etc on the cloud provider you use. The overhead of that is probably larger than just learning Kubernetes in the long run, but it's reasonable to take on that technical debt if you're just trying to move fast and aren't concerned about the long run.
You're already building on a cluster, your cloud provider's hypervisor. They'll literally build virtual compute of any size and shape for you on demand out of heterogeneous hardware and the security guarantees are much stronger than colocated containers on k8s nodes.
There are quite a few steps between single server and k8s.
The cloud extensions were always just a convenience.
But the idea of Borg is that all of that's abstracted away for the typical developer. It's the same with k8s. The infrastructure team in your org needs to understand the implementation details, but really only a few.
You can also configure load balancers, IAM groups & policy etc as k8s CRDs. So all that stuff can be in one place in the code base alongside the rest of your infrastructure. So in that sense it does abstract those concepts. You still need to know something about them, but you don't have to configure them programmatically yourself since k8s will do that.
Less overhead than writing your own glue code, less overhead than learning Kubernetes, is just use a PaaS like Google App Engine, Amazon Elastic Beanstalk, Digital Ocean App Platform, or Heroku. You have access to the same distributed databases you would with k8s.
Cloud Run is PaaS for people that like Docker. If you don't even want to climb that learning curve, try one of the others.
This is the right way for web most of the time, but most places will choose k8s anyway. It’s perplexing until you come to terms with the dirty secret of resume driven development, which is that it’s not just junior engs but lots of seniors too and some management that’s all conspiring to basically defraud business owners. I think the unspoken agreement is that Hard work sucks, but easy work that helps you learn no transferable skills might be worse. The way you evaluate this tradeoff predictably depends how close you are to retirement age. Still, since engineers are often disrespected/discarded by business owners and have no job security, oaths of office, professional guilds, or fiduciary responsibility.. it’s no wonder things are pretty mercenary out there.
Pipelines are as important as web these days but of course there are many options for pipelines as a service also.
K8s is the obviously correct choice for teams that really must build new kinds of platforms that have many diverse kinds of components, or have lots of components with unique requirements for coupling (like say “scale this thing based on that other thing”, but where you’d have real perf penalties for leaving the k8s ecosystem to parse events or whatever).
The often mentioned concern about platform lock in is going to happen to you no matter what, and switching clouds completely rarely happens anyway. If you do switch, it will be hard and time consuming no matter what.
To be fair, k8s also enables brand new architectural possibilities that may or may not be beautiful. But it’s engineering, not art, and beautiful is not the same as cheap, easy, maintainable, etc.
Seems pretty active per its commit activity: https://github.com/hashicorp/nomad/graphs/commit-activity
But the fact that I hadn't heard of it before makes it sound not very popular, at least not for the bubble I live in :).
Does anyone have any practical experiences to share about it?
You'll certainly want to combine it with Consul and use Consul templates and service discovery though.
I'd say the difficulty and complexity level is between Kubernetes and Docker Swarm, not having to use YML too is a big benefit imho.
If that's popularity that you need, then sure, nobody ever got fired for choosing kubernetes.
K8s tries to abstract away individual "servers" and gives you an API to interact with all the compute/storage in the cluster.
They really aren't.
Personally I have a big Nix derivation to deploy my (heterogeneous) cluster to bare metal.
None of the k8s concepts or ideas apply here.
GCR was simple to run simple workloads, but, an out of the box Postgres database can't just handle unlimited connections and so connecting to it from GCR without having a DB connection proxy like PG bouncer risks exhausting the connection pool. For a traditional web app at any moderate scale, you typically need some fine grained control over per process, per server and per DB connection pools, which you'd lose with GCR.
Also, to take advantage of GCR's fine grained CPU pricing, you'd have to have an app that boots extremely quickly, so it can be turned off during periods of inactivity, and rescheduled when a request comes in.
Most of our heaviest workloads run on Kubernetes for those reasons.
The other thing thats changed since this author probably explored Kubernetes is that there are a ton of providers now that offer a Kubernetes control plane for no cost. The ones that I know of are Digital Ocean and Linode, where the pricing for a Kubernetes cluster is the same as their droplet pricing for the same resources. That didn't use to be the case. [1] The cheapest you can get is a $12 / month, fully featured cluster on Linode.
I've been building, in my spare time, a platform that tries to make Kubernetes more usable for single developers: https://canine.sh, based on my learnings that the good parts of Kubernetes are actually quite simple to use and manage.
[1] Digital oceans pricing page references its free control plane offering https://www.digitalocean.com/pricing
4x(Web processes) -> 1x(pgbouncer) -> database
This ensures that the pgbouncer instance is effectively multiplexing all the connections across your whole fleet.
In each individual web process, you can have another shared connection pool.
This is how we set it up
Good point. How many connections can it handle? Seems like it's up to 262142 in theory? Or am I reading this wrong: https://cloud.google.com/sql/docs/postgres/flags#postgres-m ??
But even 1000 seems ok? 1 per container, so 1000 running containers? Quite a lot in my world, especially since they can be quite beefy. Would be very worried about the cost way before 1000 simultaneously running containers :)
That's your problem right there. You really don't want to be setting up and managing a cluster from scratch for anything less than a datacenter-scale operation. If you are already on a cloud provider just use their managed Kubernetes offering instead. It will come with a free control plane and abstract away most of the painful parts for you (like etcd, networking, load balancing, ACLs, node provisioning, kubelets, proxies). That way you just bring your own nodes/VMs and can still enjoy the deployment standardization and other powerful features without the operational burden.
We are now on-prem using “pet” clusters with namespace as a service automated on it. This causes all kinds of issues with different workloads with different performance characteristics and requirements. They also share ingress and egress nodes so impact on those has a large blast radius. This leads to more rules and requirements.
Having dedicated and managed clusters where everyone can determine their sizing and granularity of workloads to deploy to which cluster is paradise compared to that.
Most of these issues can be fixed by setting resource requests equal to limits and using integer CPU values to guarantee QoS. You should also have an interface with developers explaining which nodes in your datacenter have which characteristics, using node labels and taints, and force developers to pick specific node groups as such by specifying node affinity and tolerations, by not bringing online nodes without taints.
> They also share ingress and egress nodes so impact on those has a large blast radius.
This is true regardless of whether or not you use Kubernetes.
When running on “bare” VMs each VM is its own member in the network. The pods in the cluster use an overlay network and egress is limited to egress nodes which are now shared by all workloads.
Having dedicated K8s clusters would reduce the sharing of network ingress and egress as well as choose the vm size for my workloads.
If you are serious about minimizing ops work, you can make sure people are deploying things in very simple ways, and in that world you are looking at _very easy_ deployment strategies relative to having to wire up VMs over and over again.
Just feels like lots of devs will take whatever random configs they find online and throw them over the fence, so now you just have a big tangled mess for your CRUD app.
Well it usually isn't a mystery. Requiring a developer team to learn k8s likely with no resources, time, or help is not a recipe for success. You might have minimised someone else's ops work, but at what cost?
There's a lot of nuance here. I think ops teams are comfortable with what I consider "config spaghetti". Some companies are incentivised to ship stuff that's hard to configure manually. And a lot of other dynamics are involved.
But at the end of the day if a dev copy-pastes some config into a file, taking a quick look over and asking yourself "how much of this can I actually remove?" is a valuable skill.
Really you want the ops team to be absorbing this as well, but this is where constant atomization of teams makes things worse! Extra coordination costs + a loss of a holistic view of the system means that the iteration cycles become too high.
But there are plenty of things where (especially if you are the one integrating something!) you should be able to look over a thing and see, like, an if statement that will always be false for your case and just remove it. So many modern ops tools are garbage and don't accept the idea of running something on your machine, but an if statement is an if statement is an if statement.
Agree.
To reduce the chance a dev pull some random configs out of nowhere, we maintain a Helm template that can be used to deploy almost all of our services in a sane way, just replace the container image and ports. The deployment is probably not optimal, but further tuning can be done after the service is up and we have gathered enough metrics.
We've also put all our configs in one place, since we found that devs tend to copy from existing configs in the repo before searching the internet.
Back in the old days before cloud providers this was the only option. I started my career in early 2010s and got the tailend of this, it was not fun.
I remember my IT department refusing to set up git for us (we were using SVN before) so we just asked a VM and set up a git repo in there ourselves to host our code.
- set up new VMs
- deploy software on new VMs
- have the team responsible give their ok
It takes forever, and in my experience, often never completes because some snowflake exists somewhere, or something needs a lib that doesn't exist on the new OS. VMs decouple the OS from the hardware, but you should still decouple the service from the OS. So that means containers. But then managing hundreds of containers still sucks.
With container management, I just
- add x new nodes to cluster
- drain x old nodes and delete them
Azure has a free tier with control plane completely free (but no SLA) - great deal for test clusters and testing infra.
If you are that worried about costs, then public cloud may not be for you at all, or you should look at ECS/App containers or serverless.
And even $70 is cheap, considering that a cluster should be shared by all the services from all the teams in the same environment, bar very few exceptions.
Once you're used to it, the high-level abstractions of k8 are wonderful. I run k3s on raspberry pi's because it takes care of all sorts of stuff for you, and it's easy to port code and design patterns from the big backend service to a little home project.
If you need dedicated people just to stay on top of running your services, you have a problem that's costing you hundreds of thousands per year. There's a lot of fun and easy stuff you can do with that kind of money. This is a pattern I see with a lot of teams that get sucked into using Kubernetes, micro services, terraform, etc. Once you need a few people just to stay on top of the complexity that comes from that, you are already spending a lot. I tend to keep things simple on my own projects because any amount of time I spend on that, I'm not spending on more valuable work like adding features, fixing bugs, etc.
Of course it's not black and white and there's always a trade off between over and under engineering. But a lot of teams default to over engineering simply by using Kubernetes from day one. You don't actually need to. There's nothing wrong with a monolith running on two simple vms with a load balancer in front of it. Worked fine twenty years ago and it is still perfectly valid. And it's dead easy to setup and manage in most popular cloud environments. If you use some kind of scaling group, it will scale just fine.
Not really, the cost of an empty EKS cluster is the management fee of $0.1/hour, or roughly the price of a small EC2 instance.
That's about 2x our monthly cloud expenses. That's not a small VM. You can buy a mac mini for that.
Though if you are only spending $350 monthly on VM, Database and Load Balancer, you can probably count resource instances by hand, and don't need a K8S cluster yet.