compose-on-kubernetes VS Docker Compose

enjoy the title says, https://github.com/docker/compose-on-kubernetes is no longer maintained, with no explanation why and no recommendations on what to use instead. i've briefly looked at Kompose as an alternative, but i don't like how it's a converter. i'd prefer the source of truth to exist inside a single, simple‏‏‎‏‏‎‏‏‎‏‏‎­YAML file, like a docker-compose file. what would you recommend?

https://github.com/docker/compose-on-kubernetes looks dead now, maybe for misguided political reasons while they remain in denial that swarm might compete? But it's too bad, there's no reason some k8s api/operator thing can't make this frictionless.

Still, someone already mentioned docker-desktop having some tricks for deploying straight to aws and [there is definitely a way to do this with ecs](https://aws.amazon.com/blogs/containers/deploy-applications-...). There's also tools like kompose which translate configs automatically and are suitable for use in a pipeline, so the only thing you need is a compose file in version control.

So yes, you can still basically use compose in production, even on a non-swarm cluster, and it's fine. A lot of people that push back against this are perhaps just invested in their own mad kubectl'ing and endless wrangling with esoteric templates and want to push all this on their teammates. From what I've seen, that's often to the detriment of local development experience and budgeting, because if even your dev environment always requires EKS, that gets expensive. (Using external/managed databases is always a good idea, but beside the main point here I think. You can do that or not with or without docker-compose or helm packages or whatever, and you can even do that for otherwise totally local development on a shared db cluster if you design things for multi-tenant)

At this point I'll face facts that the simple myth here (docker-compose is merely a toy) is winning out over the reality (docker-compose is partly a tool but isn't a platform, and it's just a format/description language). But consider.. pure k8s, k8s-helm, ECS cloudformation, k8s-terraforming over EKS, and docker-compose all have pretty stable schemas that require almost all the same data and any one of them could pretty reasonably be considered as a lingua-franca that you could build the other specs from (even programmatically).

From this point of view there's an argument that for lots of simple yet serious projects, docker-compose should win by default because it is at the bottom of the complexity ladder. It's almost exactly the minimal usable subset of the abstract description language we're working with, and one that's easy to onboard with and requires the least dependencies to actually run. For example: even without kompose it's trivial to automate pulling data out of the canonical docker-compose yaml and injecting that into terraform as part of CD pipeline for your containers on EKS; then you keep options for local-developer experience open and you're maintaining a central source of truth for common config so that your config/platform is not diverging more than it has to.

I'm an architect who works closely with ops, and in many ways not a huge fan of docker-compose. But I like self-service and you-ship-it-you-run it kinds of things that are essential for scaling orgs. So for simple stuff I'd rather just use compose as the main single-source-of-truth than answer endless bootstrappy questions about k8s or ECS if I'm working with others who don't have my depth of knowledge. (Obviously compose has been popular for a reason, which is that kubernetes really is still too complicated for a lot of people and use-cases.) Don't like these ready-made options for compose-on-ECS, or compose-to-k8s via kompose? Ok, just give me your working docker-compose and I'll find a way to deploy it to any other new weird platform, and if I need some pull-values/place-values/render-template song and dance with one more weird DSL for one more weird target deployment platform, then so be it. I've often found the alternative here is a lot of junior devs deciding that deployment/dev-bootstrap is just too confusing, their team doesn't help them and pushes them to an external cloud-engineering team who doesn't want to explain this again because there's docs and 10 examples that went unfollowed, so then junior devs just code without testing it all until they have to when QA is broken. Sometimes the whole org is junior devs in the sense that they have zero existing familiarity with docker, much less kubernetes! Keep things as simple as possible, no simpler.

Seen this argument a million times, and no doubt platform choices are important but even pivoting on platforms is surprisingly easy these days. When you consider that compose is not itself a platform but just basically a subset of a wider description language, this all starts to seem a bit like a json vs yaml debate. If you need comments and anchors, then you want yaml. If you need serious packaging/dependencies of a bunch of related microservices, and a bunch of nontrivial JIT value lookup/rendering, then you want helm. But beyond org/situation specific considerations like this, the difference doesn't matter much. My main take-away lately is that leadership needs to actually decide/enforce where the org will stand on topics like "local development workflows"; it's crazy to have a team divided where half is saying "we develop on laptops with docker-compose" and half is saying "we expect to deploy to EKS in the dev environment". In that circumstance you just double your footprint of junk to support and because everyone wants to be perfectly pleased, everyone is annoyed.

Solutions are many, and could include Docker Compose, VS Code dev containers, Telepresence, Localstack or setting up temporary AWS accounts as a development environment for serverless applications.

Using Docker Compose to run Postgres and any third-party services locally provides a streamlined and consistent development environment. Developers can define services in a docker-compose.yml file, which enables them to configure and launch an entire stack with a single command. In this case, Postgres is encapsulated within a container with predefined configurations. Docker Compose also facilitates easy scaling, updates, and isolation of services, enhancing development efficiency and reducing the setup time for new team members or transitioning between projects. It encapsulates complex configurations, such as Postgres' performance monitoring and logging settings, in a manageable, version-controlled file, simplifying and replicating the service setup across different environments.

“Compose simplifies the control of your entire application stack, making it easy to manage services, networks, and volumes in a single, comprehensible YAML configuration file. Then, with a single command, you create and start all the services from your configuration file.” - Docker documentation

curl -fsSL https://get.docker.com -o get-docker.sh sudo sh get-docker.sh # Install docker compose sudo curl -L "https://github.com/docker/compose/releases/download/1.29.2/docker-compose-$(uname -s)-$(uname -m)" -o /usr/local/bin/docker-compose # Apply executable permissions to the binary sudo chmod +x /usr/local/bin/docker-compose # Run Project docker-compose up -d

Docker,

This foundation now opens the door to even more powerful concepts. You can explore more advanced concepts such as container networking, streamlining the management of complex applications with Docker Compose, and how to make your application data persistent using volumes.

In many projects that are containerized, especially in cases where development is also done locally with docker-compose, teams often have two Dockerfiles, 1 for Development, the other for Production. If you happen to have multiple environments like pre-prod, staging and so on, some teams could have different Dockerfiles for these environments.

sudo curl -L "https://github.com/docker/compose/releases/latest/download/docker-compose-$(uname -s)-$(uname -m)" -o /usr/local/bin/docker-compose

You can also use Docker Compose to deploy the cluster, without having to write all the previous commands.