cognite-extractor-utils-rest 0.2.3

REST extention for the Cognite extractor-utils framework

Cognite extractor-utils REST extension

The REST extension for Cognite extractor-utils provides a way to easily write your own extractors for RESTful source systems.

The library is currently under development, and should not be used in production environments yet.

Overview

The REST extension for extractor utils templetizes how the extractor will make HTTP requests to the source, automatically serializes the response into user-defined DTO classes, and handles uploading of data to CDF.

The only part of the extractor necessary to for a user to implement are

• Describing how HTTP requests should be constructed using pre-built function decorators
• Describing the response schema using Python dataclasses
• Implementing a mapping from the source data model to the CDF data model

For example, consider CDF's Events API as a source. We could describe the response schema as an EventsList dataclass:

@dataclass
class RawEvent:
    externalId: Optional[str]
    dataSetId: Optional[int]
    startTime: Optional[int]
    endTime: Optional[int]
    type: Optional[str]
    subtype: Optional[str]
    description: Optional[str]
    metadata: Optional[Dict[str, str]]
    assetIds: Optional[List[Optional[int]]]
    source: Optional[str]
    id: Optional[int]
    lastUpdatedTime: Optional[int]
    createdTime: Optional[int]


@dataclass
class EventsList:
    items: List[RawEvent]
    nextCursor: Optional[str]

We can then write a handler that takes in one of these EventLists, and returns CDF Events, as represented by instances of the Event class from the cognite.extractorutils.rest.typing module.

extractor = RestExtractor(
    name="Event extractor",
    description="Extractor from CDF events to CDF events",
    version="1.0.0",
    base_url=f"https://api.cognitedata.com/api/v1/projects/{os.environ['COGNITE_PROJECT']}/",
    headers={"api-key": os.environ["COGNITE_API_KEY"]},
)

@extractor.get("events", response_type=EventsList)
def get_events(events: EventsList) -> Generator[Event, None, None]:
    for event in events.items:
        yield Event(
            external_id=f"testy-{event.id}",
            description=event.description,
            start_time=event.startTime,
            end_time=event.endTime,
            type=event.type,
            subtype=event.subtype,
            metadata=event.metadata,
            source=event.source,
        )

with extractor:
    extractor.run()

A full example is provided in the example.py file.

Lists at the root

Using Python dataclasses we're not able to express JSON structures where the root element is a list. To get around that responses of this nature will be automatically converted to something which can be modeled with Python dataclasses.

A JSON structure containing a list as it's root element will be converted to an object containing a single key, "items", which has the original JSON list as it's value, as in the example below.

[{"object_id": 1}, {"object_id": 2}, {"object_id": 3}]

will be converted to

{
    "items": [{"object_id": 1}, {"object_id": 2}, {"object_id": 3}]
}

Contributing

We use poetry to manage dependencies and to administrate virtual environments. To develop extractor-utils, follow the following steps to set up your local environment:

1. Install poetry: (add --user if desirable)

   $ pip install poetry
   

2. Clone repository:

   $ git clone [e-mail]:cognitedata/python-extractor-utils-rest.git
   

3. Move into the newly created local repository:

   $ cd python-extractor-utils-rest
   

4. Create virtual environment and install dependencies:

   $ poetry install
   

All code must pass typing and style checks to be merged. It is recommended to install pre-commit hooks to ensure that these checks pass before commiting code:

$ poetry run pre-commit install

This project adheres to the Contributor Covenant v2.0 as a code of conduct.