It's claiming that there's something better that isn't discovered. Probably 10 years in the future.
I will be really surprised if anyone really thinks that Kubernetes and even AWS is going to be the state of the art in 2031.
(Good recent blog post and line of research I like about compositional cloud programming, from a totally different angle: https://medium.com/riselab/the-state-of-the-serverless-art-7...)
FWIW I worked with Borg for 8 years on many applications (and at Google for over a decade), so this isn't coming from nowhere. The author of the post I quoted worked with it even more: https://news.ycombinator.com/item?id=25243159
I was never an SRE, but I have written and deployed code to every data center at Google, as well as helping dozens of people like data scientists and machine learning researchers use it, etc. It's hard to use.
I gave this post a modest title since I'm not doing anything about this right now, but I'm glad @genericlemon24 gave it some more visibility :)
I’m curious if you’ve taken a look at Nomad and the other HashiCorp tools? They appear focussed and compositional, as you say, and this is why we are probably going to adopt them instead of K8s - they seem to be in a strong position to replace the core of K8s with something simpler.
Our team tried to migrate to AWS ECS a few times and found it much harder to abstract stuff from devs / create self-service patterns.
That said it's not a walk in the park. You will need to scratch your head a little bit to setup consul + nomad + vault + a load balancer correctly.
This is thing I like about the HashiCorp tools. You don't have to eat the whole cake in a single sitting.
To avoid the pain of managing a CA and passing out certificates for TLS between services, I use a wireguard mesh and bind nomad, consul and vault to these wg interfaces. This includes all the chatter of these components, as well as the services I deploy with nomad. It's configured such that any job can join the "private" wireguard network or "public" internet gateway.
It takes a few days to set up, but it's very easy to manage.
I’m a freelancer that hosts client stuff and I need something between “SSH into server” and “kubernetes.”
No, I never did buy the docker hype. Seem to be doing okay.
I've been wondering, would it make sense to try to package all that into a single, hopefully simple and easily configurable, Linux image? And if it might be, why hasn't anyone done that yet?
I will say that I looked at HCL and it looks very nice:
https://github.com/hashicorp/hcl
But somehow it's not as popular as a mess of YAML and Go Templates? That genuinely leaves me scratching my head. I guess it's because people pick platforms and not languages? (BTW, in 2009 I designed and implemented the template language that Go templates are based on, and I find their common application pretty bizarre, e.g. in some Helm charts I looked at from this thread)
Oil is growing a config dialect that looks a lot like HCL (although it's convergent evolution; I've never used it.) I think there is a lot of room for mixing declarative and imperative; as far as I can see HCL is mostly declarative (defining data structures).
Anyway I'd be interested in reading about HashiCorp stuff but for some reason in my neck of the woods I don't hear too much about it. Maybe that's because they're paid services and the open source Kubernetes seems attractive by comparison? Or is it more of a VM vs. container thing?
I think it's safe to say that if the next 10 years are anywhere near as disruptive as the last 10 we will surely be doing a lot of things very differently.
What made Docker spark was being abke to use commodity hardware and ush to production with the same exact environment and behavior.
You could have done the same with Solaris, if you developed on a Sun Ultra5 workstation and published the application in a zone in the server. But 2003 was a different world and not everyone had a Spark box to develop nearby.
If you’re explaining, your losing.
Plus at the very least, I'd be very careful about putting type systems into the same basket as Kubernetes. One is a basic language feature used offline and before deploying. The other is a highly complex interwoven web of tools that might take your systems offline if used incorrectly.
Without Kubernetes, you need Debian and it's Apache and MySQL packages. It's called a LAMP stack and for many production deployments, that's good enough. Because without all that "cloud magic", a $50 per month sever running a bare metal OS is beyond overpowered for most web apps, so you can skip all the scaling exercises. And with a redundant PSU and a redundant network port, 99.99% uptime is achievable. A feat so difficult, I'd like to mention, that Amazon Web Services or Heruko rarely manage to...
Complexity has high costs. Just because you don't see Kubernetes' complexity now, doesn't mean you won't pay for it through reduced performance, increased bug surface, increased downtime, or additional configuration nightmares.
> Complexity has high costs
Complexity management is the central theme of building any large, valuable system. We would probably find that the more complex (and correct) a system, the more valuable it becomes on a relative basis to other competing solutions. The US tax code is a pretty damn good example of complexity intentionally taken to the extreme (for purposes of total market capture). We shouldn't be surprised to find other technology vendors framing problems & marketing their wares under similar pretenses.
The best way to deal with complexity is to eliminate it or the conditions under which it must exist. For example, we made the engineering & product choice that says we do not ever intend to scale an instance of our application beyond the capabilities of a single server. Consider the implications of this constraint when reviewing how many engineers we actually need to hire, or if Kubernetes even makes sense.
I think one of the biggest failings in software development is a lack of respect for the nature and impact of complexity. If we are serious about reducing or eliminating modes of complexity, we have to be willing to dig really deep and consider dramatic changes to the ways in which we architect these systems.
I know its been posted to death on HN over the last ~48 hours, but Out of the Tar Pit is the best survey of complexity that I have seen in my career so far:
They’re always asking if you’ve worked with the latest tech stacks, never if you can set up a really well optimized nginx server.
I worked for one startup lead by a Java Architecture Astronaut, and working with the byzantine patterns and build systems made adding even simple features a morale-draining slog. It killed the product.
It's really tempting to believe that with the right hardware, we can put everything on one powerful and inexpensive box. A couple of problems with that:
1. What happens when you have to reboot to apply a kernel update?
2. The geographic location of that single box is itself a gap in redundancy. This is one thing I like about AWS and the other hyperscalers, with their regions that each have multiple data centers connected by a private network, with load balancers and other things spanning the region.
My conclusions so far are this:
Single node application systems are by far the most reliable and manageable from a business logic standpoint. At no point does spreading a problem across more than 1 computer make that problem easier to solve.
If you are concerned about latency, you need to get really abstract with your problem and ask what is even possible in information theoretic terms. If you are truly constrained to 1 serialized, synchronous context (i.e. a competitive counterstrike match or a stock exchange), there is little you can do to alleviate the root problem as your users get further from the server. You can certainly look at using some consensus protocol like multi-paxos, but then your transaction latency goes from microseconds (if you were clever) to milliseconds, representing orders of magnitude slowdown in the typical case.
The best solution I can come up with is a synchronously-replicated append-only log store which is utilized in a primary/sync-witness/async-witness/... configuration. The first tier of resilience would be synchronous and provided by a set of witness nodes which must ack as a majority to progress primary. These nodes would ideally be within 1-2ms of the primary. The async witnesses could be in orbit and/or on mars. These are more about extreme geological disaster recovery. The witness nodes would also use a separate consensus protocol to decide when the primary needs to be taken down and replaced with a sync (or god forbid async) witness. They would be able to elect an emergency leader separate from the primary who would be authorized to stop the bad primary in the hypervisor, and edit any relevant DNS records to ensure traffic stops hitting the bad system.
For the customers I work with, it is wayyy easier to build & sell a system that operates on a single box with sync replication + manual failover. Our customers are tolerant to production having a brief outage for a few minutes during the business day. Especially considering the fact that I have still never had to do this exercise in a production setting. The hardware we run this stuff on is so ridiculously stable.
Manageable? Maybe. Reliable? No. Most companies don't need it but if a pipe bursts above the server room and now all of your medical records for your hospital are unavailable you're going to have a bad time.
> At no point does spreading a problem across more than 1 computer make that problem easier to solve.
I don't think anyone claimed it was easier (yet). The main thing people strive for is:
1. Dynamically reshaping your compute needs to match any needs.
2. Surviving failures of nodes or crashes in your application.
3. Managing all of the BS that goes with obtaining this (logs, etc).
> Especially considering the fact that I have still never had to do this exercise in a production setting. The hardware we run this stuff on is so ridiculously stable.
I hope this isn't the case but this sounds like quite the death flag.
That's not actually something to brag about, as it implies they weren't receiving important kernel updates, particularly security updates.
That said. We communicate directly with other systems on the same premises that probably haven't been rebooted in over a decade.
Building something that can run 10 years without downtime is not happenstance. It is very intentional and deliberate engineering.
Allows you to patch the kernel without reboot.
> The best solution I can come up with is a synchronously-replicated append-only log store which is utilized in a primary/sync-witness/async-witness/... configuration. The first tier of resilience would be synchronous and provided by a set of witness nodes which must ack as a majority to progress primary. These nodes would ideally be within 1-2ms of the primary. The async witnesses could be in orbit and/or on mars. These are more about extreme geological disaster recovery. The witness nodes would also use a separate consensus protocol to decide when the primary needs to be taken down and replaced with a sync (or god forbid async) witness. They would be able to elect an emergency leader separate from the primary who would be authorized to stop the bad primary in the hypervisor, and edit any relevant DNS records to ensure traffic stops hitting the bad system.
This part was what I felt deserved a counter-point though. Consensus is indeed at the core of the issue once you want distributed fault tolerance. However, I think you'll quickly hit two things with your approach: 1-2ms of latency, I fear that it may come with highly correlated failures on the "first tier of resilience". Moreover, the "second tier" being much farther, keeping them in consensus will imply harsh trade-offs. If you use synchronous consensus protocols, you'll slow down drastically the "first tier" (assuming you want consistency), if you go for the asynchronous replication (not consensus, this matters...) then the second tier can't really intervene on leader election or failover without risking a partition on a false positive (and if you try to be conservative there your RPO will suffer).
If you're into these (fundamental) issues, I'd recommend Leslie Lamport's work (i.e. https://www.college-de-france.fr/site/en-martin-abadi/semina... or http://www.lamport.org/), the paper pointing a disappointing impossibility: https://dl.acm.org/doi/abs/10.1145/3149.214121 and its generalization (https://dl.acm.org/doi/abs/10.1145/167088.167119).
Where your datacenters are geographically located is usually a big first step in even starting these types of conversations. The nature of "maybe sync replication represents a liability or is feasible" might be a conversation about the geography of a region and statistical likelihood of certain disasters impacting multiple sites simultaneously.
Some customers cant ever afford to lose a single transaction no matter what, some just need it to be reasonably stable but incredibly fast (e.g. gaming vs banking).
Will definitely be spending some time reviewing Lamport's works again. Establishing the notion of stable time between all participants is a fascinating way to solve a lot of problems in distributed systems.
If needed, I can choose to apply the update in the middle of the night in the timezone of most of my visitors.
If it really is a sensitive app and I can't afford any uptime, I just add another inexpensive box, put a load balancer in front (a Cloudflare load balancer would work fine). And since I now have 2 servers, I need a way to manage them without having to manually log in to each of them each time. Enter Ansible. And that's it.
2. Now that I have two cheap boxes, nothing prevents me from having them in two separate data centers and two separate providers.
This is what I have. 2 boxes only. One on my own premises in Canada and another on Hetzner in Europe. Each one can go down, no sweat. Also I do not strive for "live uninterrupted updates". All my applications and processes allow for short disruptions not to interfere with the main course.
Also I write native servers in C++ for backend and they process thousands of requests per sec without breaking much sweat. Way more than my business would ever need.
Same situations with my clients as well with the difference that for legal reason they rent computers or whatever goes as such from Amazon or Azure.
2. The latency within AWS is easily as high as US-EU backbone latency. Also, most web apps these days need 2000ms to load all the tracking and advertising crap, so 100ms location latency are negligible in comparison. Plus for most real companies, you'll have one website per country anyway. One server for .com and one server for .eu
No one should or is using Kubernetes to run a simple LAMP stack.
But if you have dozens of containers and want them to be manager in a consistent, secure, observable and maintainable way then Kubernetes is going to be a better solution than anything you build yourself.
Yes they are. Some developer got all excited about the capabilities of k8s, and had an initial larger scope for a project, so they set it up with GKE or EKS, and it managed to provide just enough business value to barrow in like a tick that won't be going away for years.
Developers get all excited for new shiny tools and chuck it into production all the time, particularly at smaller orgs.
1. Start with basic LAMP app in git that's manually deployed to an EC2 instance 2. Add in CI / CD + CodeDeploy 3. Create a staging environment 4. Dockerize local environment to keep dev environments in sync and onboard easier (really, this part's a gamechanger for a small company) 5. Ok so now we have Docker for local dev environments but stage and prod are managed separately. Can we just run our Docker containers in stage / production?
When I researched step 5, the options were basically k8s or Docker swarm but Docker swarm didn't seem battle tested (for prod). k8s was clearly a nightmare for a small team to maintain so we started looking into GKE / EKS -- but EKS was still in beta. Thus we punted. We've actually started using ECS for a newer project and I'd likely go that route for step 5 instead.
So they weren't trying to run a simple LAMP stack then.
I recently asked a friend who still works there if anything else has been pushed to k8s since I left (6 months ago). The answer: no.
Au contraire! This is very common. Probably some combo of resume-driven devops and "new shiny" excitement.
i started by simple wrapper around docker 8 or so years ago. over the years we’ve moved to k8s b/c it provided essentially the same api we had home grown. this reduced the LoC and moved the complexity of something like dynamic reverse proxy via nginx downstream into kubernetes ingress abstraction backed by nginx-ingress-controller.
Two groups of people shouting past each other.
And 99% (rhetoric number of course) of needed solutions would be served by a single real server (rent one on Hetzner or wherever) with 0 need to ever upgrade.
But sometimes with the big microservice/kubernetes solution, the configuration space is so big that miscommunications/mistakes could potentially take you down for more hours/year than the downsides of being single-hosted would. So now you invested all of that effort and for what?
One company I worked for wrote a distributed scheduled jobs system that was immune to single-machine failures, it went down like 4 times in a year, messing up my team each time. I was like "guys, if we just provisioned one machine with cron and no failover, it would have better reliability".
Besides if it warm the cockles of your heart so much you can always take any of my products and shove it in the container and run it under k8.
Or, to channel Fred Brooks, essential and inessential complexity.
Which items fall in this category?
Yes, it is complex, but there are lots of use cases where it is the most elegant and least complex solution. Yes, it definitely does not make sense to use it for a lamp stack deployed to one server, but there are use cases where it's a huge improvement (e.g. Spark on Hadoop is extremely complex and clunky when compared to spark on kubernetes).
The issue is that most people nowadays have never worked with a bare metal Lamp server, so they grossly underestimate how large their company can grow before needing any distributed HA solution. I'd wager 90% of startups go bankrupt or are acquired before outgrowing a single Lamp server.
- Install software onto your machines
Package managers, thousands of them.
- Start services
SysVinit, and if shell is too complicated for you, you can write totally not-complicated unit files for SystemD. For most services, they already exist.
- Configure your virtual machines to listen on specific ports
Chef, Puppet, Ansible, other configuration tools, literally hundreds of them etc.
- have a load balancer directing traffic to and watching the health of those ports
Any commercial load balancer.
- a system to re-start processes when they exit
Any good init system will do this.
- something to take the logs of your systems and ship them to a centralized place so you can analyze them.
Syslog has had this functionality for decades.
- A place to store secrets and provide those secrets to your services.
A problem that is unique to kubernetes and serverless. Remember the days of assuming that your box was secure without having to do 10123 layers of abstraction?
- A system to replace outdated services with newer versions ( for either security updates, or feature updates ).
Package managers.
- A system to direct traffic to allow your services to communicate with one another. ( Service discovery )
This is called an internal load balancer.
- A way to add additional instances to a running service and tell the load balancer about them
Most load balancers have built up processes for these.
- A way to remove instances when they are no longer needed due to decreased load.
maybe the only thing you may need to activelly configure, again in your load balancer.
None of this really needs to be written itself, and these assumptions come from a very specific type of application architecture, which, no matter how much people try to make it, is not a one-size-fits-all solution.
Ingests are not much more complicated than an nginx config, services are literally 5 lines each pod, and the deployments are roughly as complicated as a 15 line docker file.
- Run our own load balancing machine and manage it (as said, we don't want this)
- Use AWS/GCP/Azure, setup Load Balancer (and rest of the project) manually or with Terraform/CloudFormation/whatever scripts
- Use AWS/GCP/Azure and Kubernetes, define Load Balancer in YAML, let K8S and the platform handle all the boring stuff
This is the simplest setup and already I will always go for Kubernetes, as it's the fastest and simplest, as well as the most easily maintainable. I can also easily slap on new services, upgrade stuff, etc. Being able to define the whole architecture in a declarative way, without actually having to manually do the changes, is a huge time-saver. Especially in our case, where we have more projects than developers - switching context from one project to another is much easier. Not to mention that I can just start a development environment with all the needed services using the same (or very similar) manifests, creating a near-prod environment.
Much like Multics was "simple" for the users, but not for the sysadmins.
The central problem of most companies has been finding / affording people who can maintain their stuff.
If Amazon / MS / Google can make it simple enough that skilled people can be quickly cross trained, and then have enough architecture knowledge to be productive, that's a huge win over "require everyone to spend 6 months muddling through and learning our stack we built ourselves and partially documented."
> Being able to define the whole architecture in a declarative way
With k8s (and other 'cloud' stuff) you seem to need to know a whole mess of a lot of the tool's stuff up front, vs a "progressive enhancement" way of doing one thing, getting it working, doing something else, getting it working, etc.
And I mean, I don't even like k8s. I typically go for the AWS suite of stuff when building out systems infrastructure. But this assertion is bonkers.
I can’t imagine anyone with root access to a kubernetes server is any less dangerous that root on a simple webserver.
I haven't "managed a server" outside of my house for a few years now, and I quite like it. I theoretically have had root to ECS clusters, but I've never logged into them. Why would I? Amazon is going to be better at it than I am. Not only do I have more important things to be doing, but I'll do a worse job of it than they will. And to be clear: I consider myself pretty kinda really good at this stuff. But not good enough to make it a competitive advantage unless it's what I want to sell, and I sure as heck don't.
And the article's point, that whatever comes next will probably be better and might even be The Real Thing--I think that is wise.
Until it's not. Then suddenly you're trying to decipher cryptic cloud provider error messages in a service that made a false promise to you that it's abstraction was so air-tight that you'd never have to learn the underlying technology at all.
Then suddenly, you do need to know the underlying implementation, and quickly.
I haven't needed to do something like that in production, as opposed to pre-production deployment suss-out, since (and I went and checked my enough to be sure) 2017. Though, to be fair, I've been working in devrel since last August, so call it four years of rooting around in the trenches, not five. ;)
No, you have to write or adopt tools for each of these things. They don't just magically happen.
Then you have to maintain, secure, integrate.
k8s solves a broad class of problems in an elegant way. Since other people have adopted it, it gets patched and improved. And you can easily hire for the skillset.
How do you do failover?
Sharing servers to save on costs?
Orchestrate CI/CD pipelines, preferably on the fly?
Infrastructure as Code?
Eventually you a point where the abstraction wins. Most people will say "but AWS...", but the reality is quicker, easier to use, and runs via multiple providers, so I think it's going to keep doing well personally.
We aren't really comparing apples and oranges in all cases that have been talked about in the larger thread. Some of the comparisons seem to be between "self hosted LAMP stack" vs. "kubernetes as a service on AWS". These are vastly different things. We should compare "self hosted LAMP stack" vs. "hosted in cloud LAMP stack" for example or "self-hosted kubernetes" vs. "self-administered kubernetes on EC2" vs "kubernetes as a service on AWS". All of these will have vastly different characteristics, pros and cons depending on your company and teams' realities.
Failover is something that a load balancer does automatically for you. Your services just need to provide a health check. Now where you actually run those nodes is a different thing. These might be slow to procure servers hosted at your provider. Or these might be manually set up EC2 instances or terraformed EC2 instances. Dunno what everyone uses as load balancers nowadays but a previous place for example had F5s and we had our own vsphere farm.
Sharing servers: I don't think this is a good idea at all except if you mean internally and if you do that then there's good and bad ways (see above on vsphere farm. If one project caused another to starve performance wise because of what was running on the same physical machines it was easy to resolve. If this was virtual servers at a traditional hoster, good luck. AWS is probably somewhere in between with EC2 and especially their storage.
Dedicated CI/CD pipelines: This is an awesome one to have and can cost an arm and a leg. I enjoy this very much at my current place w/ EC2 CI agents that scale with the number of devs currently working and dedicated "complete copy of Prod" dev environments (basically a kubernetes namespace for each dev/QA person/e2e test run to play with as they like).
Infra as code: Does not require kubernetes at all but can be implemented with kubernetes. If you already used docker to run stuff anyway for example and you can "abstract away" the kubernetes complexities to your SRE team and/or AWS, go ahead and use kubernetes. But be aware that if nobody at your place actually know kubernetes because you just relied on the hosted version of it, you're at the whim of their support people when something blows up in Production. You may not be big enough to have your own SRE team to take care of this but then you might also just not really benefit enough from kubernetes complexity and a simpler arrangement could have been easier for the people you do have to actually understand.
Essentially if you work back from the desired state of having IaC, CI/CD, test environments per MR, you likely see something like k8s as a framework that helps you achieve that.
Of course, if you start from "I just need a LAMP stack" you might have a very different conclusion. But when you reach the same endgame ( actually I need an environment for every MR ), you've probably incrementially built something more complex and bespoke.
This will explain why there are dozens of us who are quite happy with the product. The only real question is, do you already know it and do you find it much harder to ship a deployment to a managed k8s cluster vs systemd unit files?
If not, it might be an abstraction worth having. If you don't already know how though, then you might have better things to be doing with your time.
kubectl, kustomize, helm, istio, graphana, various flavors of ingress controllers, overlay networks, service meshes, storage controllers, etcd, etc.
from tfa: https://landscape.cncf.io/
you still have to learn 15 tools, just now they are hidden behind the scenes, and you still have to understand the underlying systems to reason about your containers.
this isn't for or against k8s - i'm a right tool for the job guy - but as a tool kubernetes doesn't solve problems, it encapsulates them and shifts them around.
So after an enjoyable time crafting a 30 level deep json file you get a failed helm deployment with a error message like "timed out waiting for the condition".
Any serious Kubernetes environment is not 5 lines per pod, its the hell of rbac and pod security policies and all sorts of overly cryptic cruft.
Or… you could not.
There is value in creating a vertically integrated solution in a space, similar to what Dropbox did, so if you find yourself building many of the pieces of Kubernetes internally, it's worth considering if adopting Kubernetes wouldn't be a more efficient use of resources.
Thanks for that!
Yes, you could. Some people do. Others don't, because even if you need a small portion of the features a turnkey solution is likely a better choice in the long run than hand-rolling your own mix of 15+ different technologies to achieve the same thing.
I love k8s but I do want to say that I hate the 'standard' way that people write general purpose Helm charts. They all try to be super configurable and template everything, but most make assumptions that undermine that idea, and I end up having to dig through them to make changes anyway.
I have found much more success by writing my own helm charts for everything I deploy, and putting in exactly the amount of templating that makes sense for me. Much more simple that way. Doing things this way has avoided a Rube Goldberg scenario.
just wait till you have a predecessor
Not to mention operations. K8s give you for free things that are a pain to setup otherwise. Want to autoscale your VMs based on load? Trivial in most cloud managed k8s.
Yep, I remember when I deployed insecure apps to prod and copied secrets into running instances, too.
Read the intended way, it's borderline wrong.
Read as "remember when people assumed security without knowing" is basically most of computing the further back in time you go.
A place to store secrets and provide those secrets to your services.
"A problem that is unique to kubernetes and serverless. Remember the days of assuming that your box was secure without having to do 10123 layers of abstraction?"
I remember 10 years ago things were not secur, you know when people baked their credentials in svn for example.
FROM python:3.8
RUN apt install libsomething
ENV RELEASE=production
COPY . .
RUN poetry install
CMD ["poetry", "run", "server"]
what is python:3.8? is this reproducible?
what is apt? where is the install coming from?
What about poetry?
Yeah it’s cool for shits and giggles but when this thing breaks you’re going to be in a world of pain
for the rpm equivalent:
python setup.py bdist_rpm
wat?
I take it from the lack of an answer to the question that the equivalent non-docker packaging would be much more complex.
Setting up all the infra for publishing APT packages (one place to start: https://jenkins-debian-glue.org ) is far easier than trying to understand all the rest of the things you mention.
I’ve been through the rigmarole for various Linux package managers over the years and I’m sure you could automate a great deal of it, but even if it were as easy as running a bash script (and it’s not,) setting up Kubernetes covers like half this list whereas setting up an Apt repo covers one item in it.
Its complexity and fragility are features. It's working as intended.
I am not the biggest fan of the complexity Kubernetes is, but it solves a problems there is no way I want to solve individually and on my own.
The security one is the big one. Things were just not as secure (and did not need to be as secure) “back then”. K8s has a lot of complexity, and security should definitely be simpler so it’s harder to misconfigure, but not doing anything is not viable.
Saying “Package Managers” is fine until you realise they solve only part of the problem. The mainstream ones are good tools to update package (and dependencies) from version X to Y. When you’re running a distributed system, it’s often not that simple if you want to be reliable. Coordinating a slow global update of your application from version X to Y (safely) is pretty tricky and I’m not aware of good self-contained solutions to this.
Unit tests and type systems are useful across the whole stack. Systems developers, application developers, embedded developers, mobile developers, even sysadmins and IT people - they all have a use for these basic principles of how to design a piece of software.
GraphQL and Kubernetes, on the other hand, are solutions designed exclusively for web services deployed into the cloud, and they're primarily useful in situations where there are many different teams each working on different services, with differing release schedules and engineering priorities. These situations might seem very common in large companies, but I don't think they represent common aspects of software development in general.
I’m far from an expert, but ISTM that Kubernetes is complex both conceptually and in implementation. This has implications well beyond just operational reliability.
It's not a question of k8s or bespoke. That's a false dichotomy.
I see way too many young/inexperienced tech teams using k8s to build things that could probably be hosted on a couple of AWS instances (if that). The parasitic costs are high.
Before k8s every org I worked for had an absolute mess of tangled infrastructure
"We can do it ourselves!" attitude by people who are unskilled is the source of many legacy hell-webs sitting in companies all over the world that are desperately trying to be maintained by their inheritors.
Kubernetes was made by Google. Google is not your startup, it has millions of servers serving billions of users, of course it needs complex systems, and it has thousands of people to maintain them.
In a small company, you probably don't need much of what's in that "need to" list. Rent a server, maybe a second one for redundancy, install your packages, run your app, and if you did things well, you can do quite a lot with a single machine.
But a lot of people think they are Google, and get ready to scale to a level they will never reach, and do it badly.
I think that where most of the pooh-poohing comes from, the use of overly complicated solutions for your scale.
Whereas on kubernetes... things break all the time. There is no well-defined semantic model for how the thing works. This is a far cry from something like the calculus of inductive constructions (basis of COQ) for which there is a well-understood 'spec'. Anyone can implement COIC in their language if they understand the spec. You cannot say the same for kubernetes.
Kubernetes is a nice bit of engineering. But it does not provide the same guarantees as type systems. In fact, of the four 'complicated' things you mentioned, only one thing has a well-defined semantic model and mathematically provable guarantees behind it. GraphQL is a particular language (and not one based on any great algebra either, like SQL), Kubernetes is just a program, and unit tests are just a technique. None of them are abstract entities with proven, unbreakable guarantees.
Really, comparing Kubernetes to something like system FC or COIC is like comparing Microsoft Word to Stoke's theorem.
The last thing I'll say is that type systems are incredibly easy. There are a few rules to memorize, but they are applied systematically. The same is not true of Kubernetes. Kubernetes breaks constantly. Its abstractions are incredibly leaky. It provides no guarantees other than an 'eventually'. And it is very complicated. There are myriad entities. Myriad operations. Myriad specs, working groups, etc. Type systems are relatively easy. There is a standard format for rules, and some proofs you don't really need to read through if you trust the experts.
I don’t understand teenagers any more, and I’m barely 30. I don’t think this analogy really works.
I agree with your point though.
We probably replaced tens of thousands line of Ansible code with a few thousands line of K8S code. We found the new code easier to maintain: because K8S is much stricter than Ansible, it's harder to deviate from the norm. Granted, we might be biased because K8S is all new and shiny, but so far we haven't regretted moving to K8S.
Those are not even in the same ballpark in terms of how complicated they are. Unit tests and type systems are not complicated at all. GraphQL not really either. But Kubernetes is very, very much.
Perhaps the reason people question these complicated things is because they are, whether intentionally or not, being marketed to an audience on HN that includes small scale non-enterprise users.
I shall paraphrase others here: A problem does not exist for you simply because it exists for LARGE SCALE ENTERPRISE users.
What I would add to that is there is nothing particularly noteworthy about a large organisation's IT work simply because it is a large organisation or making billions in ad revenue, unless one is also working in a similar organisation. If some organisations are writing the next "Multics", it really should not be interesting to everyone. A single person who can do all the individual tasks you listed is likely to think critically when presented with "news" of organisations where no single individual can do those things. Its like how many Initech Corporation employees does it take to screw in a lightbulb.
I find some of the most interesting work is found in projects started by individual programmers working alone. luajit for example.
I'd be very curious to find your proposed cost savings after accounting for those teams of engineers tasked with maintaining a company's K8s clusters. There is no free lunch.
If your dependencies are not too many / well supported you may get good results with nix.
Another problem with kubernetes is the flexibility it gives you. Look at five engineering teams using kubernetes, and you'll see five wildly different setups. Within that maneuverability, in a project that ostensibly makes things "simple", hides the devils that will bite you when you least expect it.
Resume driven development is very real if people can't write their own load balancer.
I used GCP autopilot k8 cluster… and it was a slam dunk. I got it done in 30 minutes. I would highly recommend to others! And the cost is totally reasonable!
Running a k8 cluster from scratch is def a bigco thing, but if you’re in the cloud then the solutions are awesome. Plus you can always move your workload elsewhere later if necessary.
It doesn’t configure itself.
I focused on fixing Kubernetes problems at my last job (usually networking). How is that supporting the business (hint: it didn’t so management forced us off Kubernetes)
No piece of software is a panacea and shilling for project that’s intended to remind people Google exists, is not really putting time on anything useful either
According to what I put behind the concept, implicating Devs is at the core. You built it, you run it!
I think that's what sold me on DevOps.
> You built it, you run it!
This adds too much responsibilities for devs but also makes it hard to find good enough developers who can also manage deployments and infrastructure. I have never seen happy and competent Dev+DevOps person. There is just too much cognitive load for a same person to do these two things right at the same time. The Hello World of Kubernetes deployment is easy on Cloud but anytime you need to do something a bit more complex, learning curve increases tremendously.
What seems to work is that each team having one or more dedicated DevOps person. Or I have seen a dedicated DevOps team in large orgs managing infrastructure for many other teams.
Perhaps the reason people question these complicated things is because they are, whether intentionally or not, being marketed to an audience on HN that includes small scale non-enterprise users.
I shall quote thyself here: A problem does not exist for you simply because it exists for LARGE SCALE ENTERPRISE users.
At least then you’ll have a shot at actually understanding it. I can’t trust kubernetes when anything goes wrong because the system just isn’t very transparent.
F5, autosys, spunk, etc are all much better products to do the tasks you mentioned but cost $$$ vs kubernetes.
So, just like mainframes?
/s
Seriously, though, I think that was the point. The future needs to be a much less complicated tool.
Configuring these services to work together is not all trivial either (considering proper security, such as TLS everywhere) and there aren't many solutions available from the community (or managed) that package this in an easy way.
So Kubernetes can become invaluable once you need to scale, but when you are getting started it will probably only slow you down.
I press right click - publish and for prod, i have to enter the password.
Collecting logs uses the same mechanism as backups. They go to a cloud provider and are then easy to view by a web app.
Never needed more for after hours than this, perhaps upgrading a server instance from running too many apps on 1 server.
The problem to me is that Kubernetes is not solving a specific problem, but a whole slew of problems. And some of them it's solving really poorly. For example, you can't really have downtime-free deploys in kubernetes (set a longish timer from SIGTERM to increase the chance that there's no downtime).
Instead I'd rather solve each problem in a good way. It's not that hard. I'm not implementing it from scratch, but with good tools that exists outside of kubernetes and actually solve a specific problem.
If your server is incapable of gracefully exiting, that's not a K8s problem.
> If your server is incapable of gracefully exiting, that's not a K8s problem.
Also whatever load balancer/service mesh you have can be configured for 503 rerouting within DC as necessary too.
The problem is that k8s will send requests to your application after SIGTERM. So you have to wait some amount of time before shutting down to allow for that.
This was at least the case last time I used k8s, and it seemed like it was due to the distributed architecture, so something that was more than a mere bugfix away.
https://cloud.google.com/blog/products/containers-kubernetes...
I will quote myself here: A problem does not cease to exist just because you decided to ignore it.
Without Kubernetes, you still need to:
- Install software onto your machines
- Start services
- Configure your virtual machines to listen on specific ports
- have a load balancer directing traffic to and watching the health of those ports
- a system to re-start processes when they exit
- something to take the logs of your systems and ship them to a centralized place so you can analyze them.
- A place to store secrets and provide those secrets to your services.
- A system to replace outdated services with newer versions ( for either security updates, or feature updates ).
- A system to direct traffic to allow your services to communicate with one another. ( Service discovery )
- A way to add additional instances to a running service and tell the load balancer about them
- A way to remove instances when they are no longer needed due to decreased load.
So sure, you don't need Kubernetes at an enterprise organization! Just write all of that yourself! Great use of your time, instead of concentrating on writing features that will make your organization more money.