molino 0.2.3
Presentation and transformation layer for complex data output
Requires Python: >=3.0

# Molino

Molino provides a presentation and transformation layer for complex data output, the like found in
RESTful APIs. Think of this as a view layer for your Database.

When building an API it is common for people to just grab stuff from the database and expose to the http client. This might be passable for "trivial" APIs but if they are in use by the public, or used by mobile applications then this will quickly lead to inconsistent output.

## Goals

* Create a protective "barrier" between source data and output, so schema changes do not affect users
* Systematic type-casting of data, to avoid `foreach` ing through and casting everything
* Include (a.k.a embedding, nesting or side-loading) relationships for complex data structures
* Support the pagination of data results, for small and large data sets alike
* Generally ease the subtle complexities of outputting data in a non-trivial API

## Install


``` bash
pip install molino
```

## Simple Example

For the sake of simplicity, this example has been put together as one simple
route function. In reality, you would create dedicated Transformer classes for
each model. But we will get there, let's first have a look at this:

```python
from molino import Forge

users = User.all()

data = Forge.make() \
  .collection(users, lambda user: {
    'firstname': user['first_name'],
    'lastname': user['last_name']
  }) \
  .json()
```

You may notice a few things here: First, we can import `Forge`, and then call a method `collection` on it. This method is called a
[resources](#resources) and we will cover it in the next section. We pass our
data to this method along with a [transformer](#transformers). In return, we get
the transformed data back.


## Resources

Resources are objects that represent data and have knowledge of a “Transformer”.
There are two types of resources:

- **Item** - A singular resource, probably one entry in a data store
- **Collection** - A collection of resources

The resource accepts a dictionary object or an array as the first argument, representing
the data that should be transformed. The second argument is the transformer used
for this resource.

## Transformers

The simplest transformer you can write is a callback transformer. Just return a dictionary
object that maps your data.

```python
users = User.all()

data = Forge.make() \
  .collection(users, lambda user: {
    'firstname': user['first_name'],
    'lastname': user['last_name']
  }) \
  .json()
```

But let's be honest, this is not what you want. And we would agree with you, so
let's have a look at transformer classes.


### Transformer Classes

The recommended way to use transformers is to create a transformer class. This
allows the transformer to be easily reused in multiple places.

#### Creating a Transformer

Create the class yourself, you just have to make sure that the class extends
`TransformerAbstract` and implements at least a `transform` method.

```python
from molino import TransformerAbstract

class UserTransformer(TransformerAbstract):
  def transform(self, model):
    return {
      id: model.id,
      firstname: model.first_name,
      lastname: model.last_name
    }
```

*Note:* A transformer can also return a primitive type, like a string or a
number, instead of an object. But keep in mind that including additional data,
as covered in the next section, only work when an object is returned.


#### Using the Transformer

Once the transformer class is defined, it can be passed to the resource as the
second argument.

```python
users = User.all()

data = Forge.make() \
  .collection(users, UserTransformer()) \
  .json()
```

You have to pass a reference to the transformer class
directly.

*Note:* Passing the transformer as the second argument will terminate the fluent
interface. If you want to chain more methods after the call to `collection` or
`item` you should only pass the first argument and then use the `with_transformer`
method to define the transformer. See [Fluent Interface](#fluent-interface)

#### Default Includes

Includes defined in the `default_includes` will always be included in the
returned data.

You have to specify the name of the include by returning an array of all
includes from the `default_includes`. Then you create an additional method
for each include, named like in the example: `include{Name}`.

The include method returns a new resource, that can either be an `item` or a
`collection`.  See [Resources](#resources).

```python
class BookTransformer(TransformerAbstract):
  default_includes = [
    'author'
  ]

  def transform(self, book):
    return {
      id: book.id,
      title: book.title,
      year: book.yr
    }

  def include_author(self, book)
    return self.item(book.author, AuthorTransformer())

```

*Note:* If you want to use snake_case property names, you would still name the
include function in camelCase, but list it under `default_includes` in snake_case.

#### Available Include

An `available_includes` is almost the same as a `default_includes`, except it is not
included by default.

```python
class BookTransformer(TransformerAbstract):
  available_includes = [
    'author'
  ]

  def transform(self, book):
    return {
      id: book.id,
      title: book.title,
      year: book.yr
    }

  def include_author(self, book):
    return self.item(book.relationships.author, AuthorTransformer())
```

To include this resource Forge calls the `include_author()` method before transforming.

```python
return Forge.make() \
  .item(book, BookTransformer()) \
  .include('author') \
  .json()
```

These includes can be nested with dot notation too, to include resources within
other resources.

```python
return Forge.make() \
  .item(book, BookTransformer()) \
  .include('author,publisher.something') \
  .json()
```

## Eager Loading

When you include additional models in your transformer be sure to eager load
these relations as this can quickly turn into n+1 database queries. If you have
default_includes you should load them with your initial query. Forge is framework agnostic, so it will not try to load related data.

## Metadata

Sometimes you need to add just a little bit of extra information about your
model or response. For these situations, we have the `meta` method.

```python
users = User.all()

return Forge.make() \
  .collection(users, UserTransformer) \
  .meta({
    access: 'limited'
  }) \
  .json()
```

How this data is added to the response is dependent on the
[Serializer](#serializers).


### DataSerializer

This serializer adds the `data` namespace to all of its items:

```python
# Item
{
  data: {
    foo: 'bar',
    included: {
      data: {
        name: 'test'
      }
    }
  }
}

# Collection
{
  data: [
    {
      foo: bar
    },
    {...}
  ]
}
```

The advantage over the `PlainSerializer` is that it does not conflict with meta
and pagination:

```python
# Item with meta
{
  data: {
    foo: 'bar'
  },
  meta: {
    ...
  }
}

# Collection
{
  data: [
    {...}
  ],
  meta: {...},
  pagination: {...}
}
```


### SLDataSerializer

This serializer works similarly to the DataSerializer, but it only adds the
`data` namespace on the first level.

```python
# Item
{
  data: {
    foo: 'bar',
    included: {
      name: 'test'
    }
  }
}
```


## Fluent Interface

Forge has a fluent interface for all the setter methods. This means you can
chain method calls which makes the API more readable. The following methods are
available on `Forge.make()`
(see below).

**Chainable methods:**
- `collection(data)`
- `item(data)`
- `null(data)`
- `paginate(data)`
- `meta(metadata)`
- `include(includes)`
- `transformer(transformer)`
- `variant(variant)`
- `serializer(serializer)`
- `including(includes)` (alias for `include`)
- `with_meta(metadata)` (alias for `meta`)
- `with_transformer(transformer)` (alias for `transformer`)
- `with_variant(variant)` (alias for `variant`)
- `with_serializer(serializer)` (alias for `serializer`)

**Terminating methods:**
- `json()`

## Contributing

All contibutions are welcome

## License

The MIT License (MIT).

## Credits

Special thanks to the creator(s) of [Fractal], a PHP API transformer that was
the main inspiration for this package.

[Fractal]: https://fractal.thephpleague.com