Hello Worlds in 22 Langs on Kraken CI

There is a new, quite funny workflow example for Kraken CI. It shows how to compile and run Hello World programs in various programming languages, 22 languages.

Hello World Programs

Every programmer knows what does Hello World program looks like.

Here is an example in C language:

#include<stdio.h>

int main(){
  printf("Hello World\n");
  return 0;
}

But 22? These 22 can be seen in Kraken's repo on GitHub: https://github.com/kraken-CI/hello-worlds

Still, there are more languages with Hello Worlds. The biggest collection can be found here: http://helloworldcollection.de/.

These are just examples. In our case, they can be compiled and run. After downloading the repo you may run ./run.sh script providing the language folder, e.g.:

$ ./run.sh C

This will display, compile and run a C example.

In Kraken's case, there is defined a workflow that does it for us for each language. But first, we need to have systems with a compiler or a runtime for these languages. Docker to the rescue.

Kraken Base Language Images

For each language, there is prepared a Docker image. They are based on official images for these languages. For Kraken, they got some extras like Kraken Agent that is running using kraken user (not root).

The list of all lang images is available in the docs.

The Workflow

The mentioned workflow can be found in kraken-workflow.py file.

In short, it looks as follows:

def stage(ctx):
    langs = [
        ('C', 'gcc:11.3'),
        ('C++', 'gcc:11.3'),
        ('C#', 'mono:6.12'),
        # and more...
    ]

    jobs = []

    for lang_name, lang_image in langs:
        jobs.append({
            "name": "hello world %s" % lang_name,
            "steps": [{
            "tool": "git",
                "checkout": "https://github.com/Kraken-CI/hello-worlds.git",
                "branch": "main"
            }, {
                "tool": "shell",
                "cmd": "./run.sh %s" % lang_name,
                "cwd": "hello-worlds"
            }],
            "environments": [{
                "executor": "docker",
                "system": "krakenci/%s" % lang_image,
                "agents_group": "all",
                "config": "default"
            }]
        })

    return {
        "parent": "root",
        "triggers": {
            "parent": True,
        },
        "parameters": [],
        "configs": [],
        "jobs": jobs
    }

As you may notice, the list of jobs is built dynamically. For each entry of the list of tuples (lang-name, docker-image), a job is defined with the same steps but various values for language and Docker image name. The lang name is passed to ./run.sh script in shell step. And Docker image name is used in the environments section, in the system field. Such a list of jobs is then returned in the whole workflow definition. Each job will be run in parallel in separate Docker containers.

The example of workflow execution is present in Kraken lab: https://lab.kraken.ci/runs/5009/jobs. Check the workflow definitions in branch management page.

This screenshot shows the execution of this workflow:

Summary

This article shows how Kraken CI can build programs written in various languages. Kraken provides prepared Docker images for many languages. It is easy to define sophisticated workflows that dynamically define jobs that can be run in parallel. In the end, it is nice to observe the results of workflow execution in Kraken UI.