super-mario 0.0.5
Library for separating data input, output and processing in your business application.
License: MIT
Keywords: project structure

# Mario

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Library for separating data input, output and processing in your business application.

![Mario](https://raw.githubusercontent.com/best-doctor/Mario/master/docs_imgs/mario.png)

**Disclaimer**: the library is sooo pre-alpha.

## Motivation & main idea

You have tons of business logic.
You like clean architecture, but you're sane.
You like dynamic structure of Python, but you're tired of runtime errors.
You want to break things a little less and keep moving fast.
You're is the right place.

Mario is a framework for business logic.
Like Django or Flask for web-services.

It makes you put logic to pipelines: sets of pipes,
each pipe does only one thing and only non-complex types
can be transferred from pipe to pipe.

Each pipe is one of 3 types: input, output, processing.
Input and output should be non-complex (like really non-complex,
cyclomatic complexity ~3), processing pipes should be pure.

## Installation

`pip install super-mario`

## Docs

[Here they are](https://github.com/best-doctor/Mario/blob/master/docs/index.md).

## Usage example

Here is simple pipeline, that send notifications on new comments in Jira
tickets to Slack.

```python
class JiraCommentsNotificationPipeline(BasePipeline):
    pipeline = [
        'fetch_new_comments',
        'fetch_users_mapping',
        'generate_slack_message',
        'send_slack_message',
    ]

    @input_pipe
    def fetch_new_comments(jira_ticket_id: str) -> ImmutableContext:
        return {'new_comments':
            fetch_jira_comments(
                ticket_id=jira_ticket_id,
                date_from=datetime.datetime.now().replace(
                    hours=0, minutes=0, seconds=0, milliseconds=0,
                ),
            ),
        }

    @input_pipe
    def fetch_users_mapping(new_comments: List[IssueComment]) -> ImmutableContext:
        return {
            'jira_to_slack_id_mapping': dict(User.objects.filter(
                jira_id__in=[c['user_id'] for c in new_comments],
            ).values_list('jira_id', 'slack_id'))
        }

    @process_pipe
    def generate_slack_message(
        jira_ticket_id: str,
        new_comments: List[IssueComment],
        jira_to_slack_id_mapping: Mapping[str, str],
    ) -> ImmutableContext:
        message = '\n'.join([
            f'@{jira_to_slack_id_mapping[c["user_id"]]} wrote'
            f'comment for {jira_ticket_id}: "{c["text"]}"'
            for c in new_comments
        ])
        return {'message': message}

    @output_pipe
    def send_slack_message(message: str) -> None:
        send_message(
            destination='slack',
            channel=COMMENTS_SLACK_CHANNEL_ID,
            text=message,
        )

# run pipeline for specific ticket
JiraCommentsNotificationPipeline().run(jira_ticket_id='TST-12')
```

## Contributing

We would love you to contribute to our project. It's simple:

1. Create an issue with bug you found or proposal you have.
   Wait for approve from maintainer.
1. Create a pull request. Make sure all checks are green.
1. Fix review comments if any.
1. Be awesome.

Here are useful tips:

- You can run all checks and tests with `make check`.
  Please do it before TravisCI does.
- We use [BestDoctor python styleguide](https://github.com/best-doctor/guides/blob/master/guides/en/python_styleguide.md).
- We respect [Django CoC](https://www.djangoproject.com/conduct/).
  Make soft, not bullshit.