Actually, I am setting up a serverless app now. 4-5 lambdas, s3 buckets, RDS, IAM roles, and 6 weeks (easily) getting everything into CFT's and Ansible so that I can deploy this relatively small app.

You know how I would replace all that stuff? 1 single VM. (Alright, maybe 2, 1 for the database.)

A server buys you ease of deployment. It really does. Code needs an environment. Lambdas punt on dependency management and environment. You have to build the environment for the lambdas using IAM roles, RDS, s3 -- out of toothpicks. All that stuff (security, storage) is already there for you in a VM.

I was talking with my brother in law about his serverless deployment of ~23 microservices he had carved up out of a Java Spring app. A small change anywhere in the code meant he had to redeploy all of it, not because of how the code was structure but that was the only way the build tools allowed him to do it. More problematically, the dependencies had to be built into every single lambda and made the build longer. (No, lambda layers didn't work for us. Way too complicated to figure it out.)

Honestly after going through tons and tons of pain to deploy these lambdas in both cases, it's just way easier to deploy on a VM. Maybe not for developers who just can't do anything if it's not done in their IDE, but from a broader, more globally-looking DevOps perspective, there are hidden costs.

Yes, it's easy to develop for lambda. But WOW, it's awful in deployment in comparison to just putting things on VMs.

I've heard talks by experienced K8s adopters (I want to say "Kubernetes failure stories", but googling comes up with similar hits but not the specific talk I was thinking of) where they mention that when K8s goes bad, it has all the negatives or knowing the "traditional" tech stack plus all the failure modes of K8s itself. They argue that it's more stuff you have to know about, not less; and when trouble hits, it can get very complicated to understand why.

(Not picking specifically on K8s; this applies to similar orchestrators/container tech).

Besides this, the other service I like is Fly https://fly.io/ with lets me submit a container and runs that all over the world where necessary. Google Cloud Run does something similar, but its region locked. Fly is like global lambda for containers.

Between these tools and services, I’ve stopped caring about servers completely. Never need to install a package or use ssh again. I either submit just the app executable to lambda using Up (for light HTTP APIs) or send a container to Fly (for heavy servers, TCP, web sockets) and that’s it.

IMO I don't think Heroku and others fit the serverless paradigm.

To me serverless is about automatic scaling of performance and cost.

With Heroku you need to provision capacity in advance, and you pay for it whether you use it or not. Heroku doesn't scale automatically either, you do that manually.

You don't have to worry about it, you get to worry about it.

At least from a cost perspective, GKE isn't serverless. Also I always end up with 2 node pools because the default one is misconfigured. Also kube runs so much crap that the first node barely fits any app containers.

Wouldn't call GKE a win for 'not managing nodes'.

Is this true? I always thought things like Docker are massively insecure because they don't respond to the threat landscape that well, since they are kind of 'frozen in time' and kept that way for years at a time without any critical security updates.

For those who want to get some fast insight to technology which is being adopted or dropped across industries in the software space then definitely check out Thoughtworks Technology Radar. It's regularly updated.

https://www.thoughtworks.com/radar

There's also many reasons given that don't really make sense.

> Your containers are cattle, not pets. If your container crashes, a new one is automatically fired up.

If my container crashes, I want to find out why so I can prevent it from happening again. One of the issues with these serverless technologies is that they make this kind of debugging harder. I don't want code to randomly crash and just getting restarted to be the solution. It means there is something wrong. To be fair he does mention this later on in the article.

> Serverless functions force you to write your code in a stateless way

So what. If I want to have my own server I can also do it stateless if that makes sense to me. Being forced in a direction is not a positive.

I think the scaling is very interesting. But this is not something that's required for many applications. It doesn't mean everything should be serverless. It just means there's a good option to choose from for usecases that can really benefit from it.

Pushing serverless for everything is like putting blockchain into everything because it's a buzzword right now.

It depends on your work load. And your trust level in Amazon of course.

I'll take issue with this one. I'm fully aware I'm weird, but I do actually enjoy managing servers. And with that effort spent managing servers, I buy the ability to actually create the environment in which my code runs, and interact and improve it over time, rather than relying on someone else's idea of what's good for my code. Moreover, I get the ability to dictate the security environment in which my code runs, instead of hoping my cloud provider is doing it properly enough for me (and not busy snooping on what I'm doing in the process).

I think what the poster means is "I don't want to manage servers, I want to write code." Which: fine. The world needs people who focus on writing good code. But I'm sick and tired of this attitude that coding is somehow more noble or better than the important work of building and maintaining the environment in which the code runs. You don't like it? Awesome, don't do it. But stop spitting on people who do, and who legitimately enjoy doing it.

If you want to perform actions that might be throttled (cloudwatch for example) and are not critical for the response, you cannot have a singleton processing them after you return, because they might not be processed.

Ultimately I think it's just another leaky abstraction like so many other things we do. Wonderful when it works as intended and less so when it goes wrong.

I also think that the more layers of software and management you add in makes it more complicated to troubleshoot, you simply get a lot more to learn in order to understand things.

One of my life philosophies is "It's always more complicated", meaning when you think you understand something you've barely just started scratching the surface of the complexity of it.

I can understand the pull of the container and lambda style workflows, you gain feature velocity which is ultimately what gains you the business. On the flip side, I also suspect that it builds technical debt faster.

1: https://medium.com/omio-engineering/cpu-limits-and-aggressiv...