Python library for using JSON API, especially with Flask.


License
Apache-2.0
Install
pip install cartographer==0.2.0-alpha5

Documentation

Cartographer

CI Status Version License Python Version

Python library for using JSON API, especially with Flask.

Table of Contents

  1. Schemas
    1. Attributes
    2. Relationships
  2. Resources
  3. Parsers
  4. Masks
  5. APIResource
  6. Installation
  7. Contributing

Schemas

The core element of the Cartographer system is the Schema. The schema is a map from our models to their API output and vice versa. It is formatted to resemble the output structure of JSON API resources:

{
    "type": "widget",
    "id": StringAttribute()
            .read_from(model_property="widget_id")
            .self_explanatory(),
    "attributes": {
        "price": IntAttribute()
            .read_from(model_property="amount_cents")
            .description('The wiget price in cents'),
    },
    "relationships": {
        "distributing-store": SchemaRelationship(
            model_type="store",
            id_attribute="store_id"
        )
    }
}

The keys in this dictionary are the keys in the JSON API output or input. The values are objects which describe what properties in our system these JSON API keys represent.

Type and ID

JSON API resources always have, at minimum, two keys: type, and id. These two keys uniquely identify the resource. Typically, in the API route structure, one can refer to resource with https://api.yourservice.com/<type>/<id>, issuing GET, PATCH, or DELETE requests to that URL.

Attributes

The attributes section defines properties of the resource in question. The values in the schema are SchemaAttribute instances, responsible for describing how to translate to and from our Python models.

SchemaAttribute is a very simple class, with two primary methods: to_json, for serializing from Python into JSON, and from_json for parsing from JSON into Python. Subclasses of SchemaAttribute can override either of these methods to customize (de)serialization behavior.

Currently, there are 5 subclasses of SchemaAttribute:

  • BoolAttribute
  • DateAttribute
  • IntAttribute
  • StringAttribute
  • JSONAttribute
    • In general for JSON API, nested attributes are discouraged, so usage of JSONAttribute is discouraged

Relationships

The relationships section defines the related resources of the resource in question. The values in the schema are SchemaRelationship instances, responsible for describing how to translate to and from our Python models.

SchemaRelationship is a more complicated class, with only one primary method, related_serializer, for creating a JSONAPISerializer instance based on its input arguments. Subclasses of SchemaResource can override this method to customize the JSONAPISerializer to be returned. Parsing of related resources is handled by the PostedDocument class in the Parsers section of this README.

Currently, there is only one subclass of SchemaRelationship, ArrayRelationship, which creates an JSONAPICollectionSerializer rather than a single JSONAPISerializer

Serializers

Serializers are the objects that are responsible for turning Python code into JSON output. Typically, this is done via subclassing SchemaSerializer and overriding the schema class method to point to a Schema class. Should you have a complete Schema (as described above), then that (plus adding your new SchemaSerializer to the resource_registry) should be all that's needed to serialize a model into JSON output in your route file / controller. For example:

class UserSerializer(SchemaSerializer):
    @classmethod
    def schema(cls):
        return UserSchema

But what's really going on behind the scenes? To understand SchemaSerializer, one must first understand its superclass JSONAPISerializer. JSONAPISerializer is a class which has four key methods, one for each of the top-level keys in a JSON API resource:

  • resource_id, to provide the id field
  • resource_type, to provide the type field
  • attributes_dictionary, to provide the attributes fields
  • linked_resources, to provide the relationships fields

Overriding and implementing these four methods in a subclass of JSONAPISerializer allows you to call as_json_api_document() on your subclass and get out a properly formatted JSON API response.

SchemaResource's core mechanic is as simple as that: it uses the provided Schema and the model it is initialized with to implement each of those four JSONAPISerializer methods.

In addition to saving you the work of overriding those four methods for each resource type in your API, SchemaResource also gives you JSON API query parameter handling for free:

  • By passing in the requested_fields named initialization parameter, SchemaResource will only serialize attributes matching those listed keys
  • By passing in the includes named initialization parameter, SchemaResource will only serialize related resources matching those listed keys.
  • But rather than either of those, you should just pass in inbound_request and inbound_session, which SchemaResource will use to parse the requested_fields and includes off of the request object, and the current_user_id off of the session object for you.
  • Also, each SchemaSerializer will initialize each related resource Serializer with the appropriate query parameter handling, via the parent_resource named initialization parameter (don't worry, you shouldn't have to worry about this unless you're manually implementing a method on a Serializer to return a custom related resource Serializer rather than relying on SchemaRelationships in the Schema).

Parsers

Parsers are the objects that are responsible for turning JSON input into Python code consumable by our model layer. Typically, this is done via subclassing SchemaParser and overriding the schema class method to point to a Schema class. Should you have a complete Schema (as described above), then that should be all that's needed to parse JSON input from your route file / controller. For example:

class UserParser(SchemaParser):
    @classmethod
    def schema(cls):
        return UserSchema

But what's really going on behind the scenes? To understand SchemaParser, one must first understand its superclass PostedDocument. PostedDocument is a class which allows easy navigation of a JSON API document which has been sent to the server. The PostedDocument provides this via three related classes:

  • PostedResource, which represents a single object in a JSON API Document
  • PostedRelationship, which represents a named relationship in a JSON API Document
  • PostedRelationshipID, which represents a the actual data of a relationship in a JSON API Document

SchemaParser uses these convenience classes and their methods to navigate the JSON input and turn it into a Python dictionary. This return value is formatted such that it can be dropped directly into a SQLAlchemy .insert() or .get(widget_id).update() call. This typically amounts to using the Schema to figure out the keys of that dictionary, and flattening the attributes and relationships sections into the top level.

SchemaParser also provides an overridable method validate that allows for validation of the JSON input. Calling validated_table_data from the route / controller is encouraged, as this will validate the input before parsing it.

Masks

Masks are the objects that are responsible for controlling user access to particular resources, and their attributes and relationships.

To control access to the resources themselves, Masks have five methods, corresponding to CRUDL:

  • can_create (true by default)
  • can_view (true by default)
  • can_list (false by default)
  • can_edit (false by default)
  • can_delete (false by default)

These can and should be used in your route file / controller to control access to the requested resource(s).

Should one be allowed access at that level, Masks can provide more fine-grained access control at the per-attribute and per-relationship level:

  • fields_cant_view (always called after can_view)
  • fields_cant_edit (always called after can_edit)
  • includes_cant_create (always called after can_edit)
  • includes_cant_view (always called after can_view)
  • includes_cant_edit (always called after can_edit)
  • includes_cant_delete (always called after can_edit)

Each of these methods returns the empty list by default, so it is strongly encouraged that you write your Mask alongside your Schema to avoid any security issues.

By using SchemaSerializer and SchemaParser, the corresponding Mask for your resource will be used at (de)serialization time to appropriately remove fields from the output, or disallow their input. SchemaSerializer and SchemaParser, however, will assume that the CRUDL checks have already been used, and will not check whether or not the resource itself should be (de)serialized.

To hook your Mask up to its corresponding Serializer and Parser, you should register your Mask in its appropriate place in the resource_registry.

Resource Registration

resource_registry

The resource_registry is a map from type strings to a dict of:

  • ResourceRegistryKeys.MODEL, the resource's corresponding model class
  • ResourceRegistryKeys.MODEL_GET, a method for fetching models by id
  • ResourceRegistryKeys.MODEL_PRIME, a method for optimizing future MODEL_GET calls
  • ResourceRegistryKeys.SCHEMA, the resource's corresponding Schema class
  • ResourceRegistryKeys.SERIALIZER, the resource's corresponding SchemaSerializer class
  • ResourceRegistryKeys.PARSER, the resource's corresponding SchemaParser class
  • ResourceRegistryKeys.MASK, the resource's corresponding Mask class

This map is used under the hood when SchemaRelationship instances need to create their related resources, and when Serializers and Parsers need to apply masking rules.

You can add your classes to the registry via cartographer.resource_registry.get_resource_registry_container().register_resource(), or (more commonly) use the APIResource convenience class and decorators outlined below.

APIResource

APIResource is a convenience class for registering the family of classes used in cartographer for a given domain object. It has a class properties corresponding to the entries in the resource_registry:

  • APIResource.SCHEMA, a subclass of Schema
  • APIResource.SERIALIZER, a subclass of SchemaSerializer
  • APIResource.PARSER, a subclass of SchemaParser
  • APIResource.MASK, a subclass of BaseMask
  • APIResource.MODEL, the object class which you are serializing and parsing
  • APIResource.MODEL_GET, a method that can be passed an id and will return an instance of APIResource.MODEL
  • APIResource.MODEL_PRIME, a method that can be passed an id which will improve the performance of future MODEL_GET calls

To use this convenience class, you subclass it and either define those class properties and then call MyAPIResourceSubclass.register_class() or use e.g.

@MyAPIResourceSubclass.register(ResourceRegistryKeys.PARSER)
class MySchemaParserSubclass(SchemaParser):
    ...

Installation

Get the egg from PyPI, typically via pip: pip install cartographer

Contributing

  1. git clone git@github.com:Patreon/cartographer.git
  2. cd cartographer
  3. git checkout -b my-meaningful-improvements
  4. Write beautiful code that improves the project, creating or modifying tests to prove correctness.
  5. Commit said code and tests in a well-organized way.
  6. Confirm tests pass with ./run_tests.sh (you may need to ./setup_environments.sh first)
  7. git push origin my-meaningful-improvements
  8. Open a pull request (hub pull-request, if you have hub)
  9. Have a chill discussion with the community about how to best integrate your improvements into mainline deployments