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1102 points codesmash | 8 comments | | HN request time: 1.044s | source | bottom
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ttul ◴[] No.45142410[source]
Back in 2001/2002, I was charged with building a WiFi hotspot box. I was a fan of OpenBSD and wanted to slim down our deployment, which was running on Python, to avoid having to copy a ton of unnecessary files to the destination systems. I also wanted to avoid dependency-hell. Naturally, I turned to `chroot` and the jails concept.

My deployment code worked by running the software outside of the jail environment and monitoring the running processes using `ptrace` to see what files it was trying to open. The `ptrace` output generated a list of dependencies, which could then be copied to create a deployment package.

This worked brilliantly and kept our deployments small and immutable and somewhat immune to attack -- not that being attacked was a huge concern in 2001 as it is today. When Docker came along, I couldn't help but recall that early work and wonder whether anyone has done a similar thing to monitor file usage within Docker containers and trim them down to size after observing actual use.

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sroerick ◴[] No.45142674[source]
The best CI/CD pipeline I ever used was my first freelance deployment using Django. I didn't have a clue what I was doing and had to phone a friend.

We set up a git post receive hook which built static files and restarted httpd on a git receive. Deployment was just 'git push live master'.

While I've used Docker a lot since then, that remains the single easiest deployment I've ever had.

I genuinely don't understand what docker brings to the table. I mean, I get the value prop. But it's really not that hard to set up http on vanilla Ubuntu (or God forbid, OpenBSD) and not really have issues.

Is the reproducibility of docker really worth the added overhead of managing containers, docker compose, and running daemons on your devbox 24/7?

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rcv ◴[] No.45143733[source]
> I genuinely don't understand what docker brings to the table. I mean, I get the value prop. But it's really not that hard to set up http on vanilla Ubuntu (or God forbid, OpenBSD) and not really have issues.

Sounds great if you're only running a single web server or whatever. My team builds a fairly complex system that's comprised of ~45 unique services. Those services are managed by different teams with slightly different language/library/etc needs and preferences. Before we containerized everything it was a nightmare keeping everything in sync and making sure different teams didn't step on each others dependencies. Some languages have good tooling to help here (e.g. Python virtual environments) but it's not so great if two services require a different version of Boost.

With Docker, each team is just responsible for making sure their own containers build and run. Use whatever you need to get your job done. Our containers get built in CI, so there is basically a zero percent chance I'll come in in the morning and not be able to run the latest head of develop because someone else's dev machine is slightly different from mine. And if it runs on my machine, I have very good confidence it will run on production.

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1. Bnjoroge ◴[] No.45144293[source]
what they described is a fairly common set up in damn near most enterprises
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2. nazgul17 ◴[] No.45145037[source]
Both can be true
3. latentsea ◴[] No.45145663[source]
I like how you didn't even ask for any context that would help you evaluate whether or not their chosen architecture is actually suitable for their environment before just blurting out advice that may or may not be applicable (though you would have no idea, not having enquired).
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4. sroerick ◴[] No.45151236[source]
Wherefore art thou IBM
5. fulafel ◴[] No.45151484[source]
Enterprises are frequently antipattern zoos. If you have many teams you can use the modular monolith pattern instead of microservices, that way you have the separation but not the distributed system.
6. const_cast ◴[] No.45172505[source]
Yeah most enterprise software barely works and is an absolute maintenance nightmare because they're sprawling distrivuted systems.

Ask yourself: how does an enterprise with 1000 engineers manage to push a feature out 1000x slower than two dudes in a garage? Well, processes, but also architecture.

Distributed systems slow down your development velocity by many orders of magnitude, because they create extremely fragile systems and maintenance becomes extremely high risk.

We're all just so used to the fragility and risk we might think it's normal. But no, it's really not, it's just bad. Don't do that.

7. const_cast ◴[] No.45172519[source]
Much like parallel programming, distributed systems have a very small window of requirement.

Really less than 1% of systems need to be distributed. Are you Google? No? Then you probably don't need it.

The rest is just for fun. Or, well, pain. Usually pain.

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8. latentsea ◴[] No.45207624{3}[source]
I like how you didn't even enquire as to what size organisation they worked in order to determine if it might actually be applicable in their case.