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I highly disagree here. Within seconds I can have all the technology I want running in a Docker container with little to no setup. Just make a docker-compose.ym

by dimgl 5y ago

I highly disagree here. Within seconds I can have all the technology I want running in a Docker container with little to no setup. Just make a docker-compose.yml file with the services I want, say Postgres and Redis, mount my local folder to a Golang Docker image, and then I just do `docker compose up` and all three are running and communicating with each other with little to no effort.

flumpcakes 5y ago
I don't think these points address the original post's points. Specifically they highlighted the issues of _finding_ images to run and having the _knowledge_ on how to use these tools in the first place for abstraction.
bonoboTP 5y ago
Thats how you get people who have no idea what the underlying tools can do and bloat the high level abstraction. Complexity has to go somewhere. If something is easier, something else is harder. These things may make the bog standard use case easy but woe to you if you ever try to go another path or change something. Then you need to figure out if the abstraction layer allows you to tweak the underlying software layer, or invent your own way of reaching into that layer which may break when any update comes in a higher layer etc. Its more learning upfront to get familiar with the basic Linux tools and services, but it is very versatile and flexible and will stay mostly the same for decades.
- axiosgunnar 5y ago
  I understand you manually manage the kernel and wrote your own TCP library? I sure hope you didn‘t just pass all of that complexity down!
  - bonoboTP 5y ago
    There are well designed abstractions that are helpful. It's very hard to come up with them. Mycrotoque aims at those who quickly jump to introduce new abstractions into their workflow before they understand what they are doing. It very often results in pain. It helps in the short term, because you are up and running faster but these shiny new frameworks are often immature and, like Potemkin villages, only look good from the correct angle, and have no meat. Once you try to use them for custom scenarios, you end up having to route around the abstraction layer because it wasn't designed so well. To take an example outside of cloud management etc, in deep learning frameworks: there were competing libraries some years ago: caffe, theano, tensorflow etc. François Chollet decided to create an abstraction layer that could unify multiple low level frameworks. Called Keras. Did it work? No. Because the different implementations were different enough that they couldn't usefully be coerced under the same abstraction layer. I mean it worked for some time, to some extent. But very soon after, they dropped support for anything but Tensorflow. Creating good abstractions is very hard especially if the underlying layer is unstable and gets big conceptual changes. With kernels it's not the case. We've had them in similar Form for like half a century.
    - axiosgunnar 5y ago
      Interesting, thanks!

by dimgl 5y ago

flumpcakes 5y ago

I don't think these points address the original post's points. Specifically they highlighted the issues of _finding_ images to run and having the _knowledge_ on how to use these tools in the first place for abstraction.

bonoboTP 5y ago

Thats how you get people who have no idea what the underlying tools can do and bloat the high level abstraction. Complexity has to go somewhere. If something is easier, something else is harder. These things may make the bog standard use case easy but woe to you if you ever try to go another path or change something. Then you need to figure out if the abstraction layer allows you to tweak the underlying software layer, or invent your own way of reaching into that layer which may break when any update comes in a higher layer etc. Its more learning upfront to get familiar with the basic Linux tools and services, but it is very versatile and flexible and will stay mostly the same for decades.

axiosgunnar 5y ago
I understand you manually manage the kernel and wrote your own TCP library? I sure hope you didn‘t just pass all of that complexity down!
- bonoboTP 5y ago
  There are well designed abstractions that are helpful. It's very hard to come up with them. Mycrotoque aims at those who quickly jump to introduce new abstractions into their workflow before they understand what they are doing. It very often results in pain. It helps in the short term, because you are up and running faster but these shiny new frameworks are often immature and, like Potemkin villages, only look good from the correct angle, and have no meat. Once you try to use them for custom scenarios, you end up having to route around the abstraction layer because it wasn't designed so well. To take an example outside of cloud management etc, in deep learning frameworks: there were competing libraries some years ago: caffe, theano, tensorflow etc. François Chollet decided to create an abstraction layer that could unify multiple low level frameworks. Called Keras. Did it work? No. Because the different implementations were different enough that they couldn't usefully be coerced under the same abstraction layer. I mean it worked for some time, to some extent. But very soon after, they dropped support for anything but Tensorflow. Creating good abstractions is very hard especially if the underlying layer is unstable and gets big conceptual changes. With kernels it's not the case. We've had them in similar Form for like half a century.
  - axiosgunnar 5y ago
    Interesting, thanks!