DSCI is simple yet super flexible pipeline engine to write CI code on regular programming languages, integrates with Forgejo using web hooks. Intended for small teams hosting Forgejo on single VM VPS and willing to create pipelines on regular programming languages

    • arran 🇦🇺@aussie.zone
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      23 minutes ago

      I mean there is only so many ways of representing a data structurally clearly. I find that yaml is good enough but like XML leads to a lot of bloat, but unlike XML isn’t nearly as complicated. I’m not sure making it a library resolves the issue better than creating a custom grammar that appropriately accommodates what people want to express in the language easily, make the possible and desired easy, the impossible and undesirable states hard if not unrepresentable. I mean having a custom language does allow for loops, and functions which isn’t something I find I need too much in CI. Perhaps templating / functions but having a full Turing complete language leads the way for bad states / nonideal uses.

      • FizzyOrange@programming.dev
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        12 hours ago

        You can (and should!) do exactly the same with “traditional” GitHub Actions style YAML.

        Avoid putting commands in the YAML - put those in separate scripts and call them from the YAML. The YAML should only be used for things that can’t be done from scripts (e.g. job matrices, uploading artifacts, reporting status etc.)

        • melezhik@programming.devOP
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          12 hours ago

          … put those in separate scripts and call them from the YAML.

          This is exactly what I try to avoid, cause:

          1. many people (in my experience ) even don’t bother refactoring YAML spaghetti code to separate scripts and we end up unmaintainable codebase

          2. and even if one has to do such a refactoring what is the point of using YAML at all ?

          All I need just a collection of tasks/jobs written on languages of choice and I don’t need YAML “programming” language at all )

          PS And btw I don’t mind having a minimal amount of YAML as configuration layer and this is what is presented in DSCI, but only minimal ))

          • FizzyOrange@programming.dev
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            12 hours ago

            and even if one has to do such a refactoring what is the point of using YAML at all ?

            It allows the CI engine to determine which jobs to start, what their steps are etc.

            To be fair I have worked on one project that had very complex CI and we almost decided to generate the CI graph procedurally with a Python script (Gitlab supports this, somewhat awkwardly). But in the end we decided it wouldn’t be worth the overhead of writing, maintaining and learning a whole new CI system on top of Gitlab’s CI.

            • melezhik@programming.devOP
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              12 hours ago

              The issues you had just proves that YAML based CI approach always leads to troubles with time. And yeah, I have been there, code generators for YAML. Hundreds of lines for YAML pipelines, etc ))

              It allows the CI engine to determine which jobs to start, what their steps are etc.

              Yep, like a said , I don’t mind to have such a configuration inside YAML, but this should NOT be pipeline code itself )

  • thesmokingman@programming.dev
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    13 hours ago

    Looking at the examples, you’ve just made a brand new GitHub Actions framework. There’s YAML to wrap everything together and a bunch of Python that’s so declarative it might as well be HCL. Do you have an example that’s a bit more than “do what YAML does only in bash?”

    • melezhik@programming.devOP
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      5 hours ago

      Ok, try to do it on GH actions, share states between tasks/jobs for example:

      tasks/task_one/task.py

      #!/usr/bin/python3
      
      update_state({
        'out1' : 'out1 value',
        'out2' : 'out2 value'
      })
      

      tasks/task_two/task.py

      #!/usr/bin/python3
      
      dict = get_state()
      print(dict["out1"])
      print(dict["out2"])
      

      Or share states between jobs:

      jobs/job1/task.py

      #!/usr/bin/python3
      
      update_state({
        'out1' : 'out1 value',
        'out2' : 'out2 value'
      })
      

      jobs/job2/task.py

      #!/usr/bin/python3
      
      dict = config()
      
      print(dict["_dsci_"]["job1"]["out1"])
      print(dict["_dsci_"]["job1"]["out2"])
      
      

      I can’t imagine how much boilerplate code (if this ever possible ) one needs to write to achieve that on YAML based pipelines (GH Actions/ etc)

      And don’t tell me about jobs artifacts ))

      UPDATE:

      Another good example is to run tasks conditionally, yes using old good if:

      #!/usr/bin/python3
      
      if some_condition(foo, bar): 
          run_task(
             'task1', {
                'foo' : 'foo value',
                'bar' : 'bar value'
             }
          )
      

      The same could be quite awkward in YAML based code

      • thesmokingman@programming.dev
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        12 hours ago

        This is a boilerplate example. I asked for something more than boilerplate. Give me some reasons why I need an incredibly stateful CI engine.

        • melezhik@programming.devOP
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          12 hours ago

          it’s just because I think in real world we have a lot of tasks where state is required or extremely beneficial, some examples on top of my head:

          • creation of virtual machines with dynamic IP addresses
          • creation of bug tracking system tickets with unique ticket IDs
          • looking up in databases where fetched records have unique IDs

          etc

          • thesmokingman@programming.dev
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            10 hours ago

            I wouldn’t do any of that in a CI-only system. If I did, I’d use tools that exist for those jobs that already allow scripting languages.

            1. Why wouldn’t you use IaC tools for this? All the majors have Python already.
            2. What build or deploy task needs to both create and reference a bug ticket?
            3. What build or deploy task needs to do database lookups? Or possibly what task needs those across multiple, independent stages?
            • melezhik@programming.devOP
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              10 hours ago

              If you use tools gluing them into YAML - you get YAML bloated with time . When you say those tools already having Python - excellent I would like to use those Python libs or SDK directly in my Python code instead of juggling those tools as cli or code blocks inside YAML

              UPDATE: and yeah , re-read again - I guess the most of automation is done today via “CI” pipelines even when those are not meant to be CI only, like you said … anyways the rest I have said stands true for me … don’t bake your code into YAML )

    • melezhik@programming.devOP
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      15 hours ago

      Here is SDK for those languages:

      Raku Perl Bash Python Ruby Powershell Php Golang

      UPDATE: so it’s not just hitting up a language from a pipeline , it’s full reach SDK, one writes a pipeline on a language, if I get your comment correctly

      • Valmond@lemmy.dbzer0.com
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        8 hours ago

        Well no I mean if you want to do something in python just call up the python interpreter, if you have some existing stuff in TCL, just run it through that interpreter and so on,no need for a specific anything.

        • melezhik@programming.devOP
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          7 hours ago

          But now when you have a YAML program as the first level abstraction how do you handle results between those multi language calls ? In DSCI this is achieved via states and normal functions , and everting is just a function on general purpose programming language, in YAML you need all these magic ( awkward ) YAML syntax to mimic all those things ( pass parameters , handle global variables , process user input , handle returned parameters , etc )