Pyramid addon for OpenAPI3 validation of requests and responses.

Overview

Validate Pyramid views against an OpenAPI 3.0 document

CircleCI for pyramid_openapi3 (master branch) Test coverage (master branch) Test coverage (master branch) latest version of pyramid_openapi3 on PyPI Supported Python versions License: MIT Built by these great folks! Talk to us in #pyramid on Freenode IRC

Peace of Mind

The reason this package exists is to give you peace of mind when providing a RESTful API. Instead of chasing down preventable bugs and saying sorry to consumers, you can focus on more important things in life.

  • Your API documentation is never out-of-date, since it is generated out of the API document that you write.
  • The documentation comes with try-it-out examples for every endpoint in your API. You don't have to provide (and maintain) curl commands to showcase how your API works. Users can try it themselves, right in their browsers.
  • Your API document is always valid, since your Pyramid app won't even start if the document does not comply with the OpenAPI 3.0 specification.
  • Automatic request payload validation and sanitization. Your views do not require any code for validation and input sanitation. Your view code only deals with business logic. Tons of tests never need to be written since every request, and its payload, is validated against your API document before it reaches your view code.
  • Your API responses always match your API document. Every response from your view is validated against your document and a 500 Internal Server Error is returned if the response does not exactly match what your document says the output of a certain API endpoint should be. This decreases the effects of Hyrum's Law.
  • A single source of truth. Because of the checks outlined above, you can be sure that whatever your API document says is in fact what is going on in reality. You have a single source of truth to consult when asking an API related question, such as "Remind me again, which fields are returned by the endpoint /user/info?".
  • Based on Pyramid, a mature Python Web framework. Companies such as Mozilla, Yelp, RollBar and SurveyMonkey trust Pyramid, and the new pypi.org runs on Pyramid, too. Pyramid is thoroughly tested and documented, providing flexibility, performance, and a large ecosystem of high-quality add-ons.

Building Robust APIs

Features

Getting started

  1. Declare pyramid_openapi3 as a dependency in your Pyramid project.

  2. Include the following lines:

config.include("pyramid_openapi3")
config.pyramid_openapi3_spec('openapi.yaml', route='/api/v1/openapi.yaml')
config.pyramid_openapi3_add_explorer(route='/api/v1/')
  1. Use the openapi view predicate to enable request/response validation:
@view_config(route_name="foobar", openapi=True, renderer='json')
def myview(request):
    return request.openapi_validated.parameters

For requests, request.openapi_validated is available with two fields: parameters and body. For responses, if the payload does not match the API document, an exception is raised.

Advanced configuration

Relative File References in Spec

A feature introduced in OpenAPI3 is the ability to use $ref links to external files (https://github.com/OAI/OpenAPI-Specification/blob/master/versions/3.0.0.md#referenceObject).

To use this, you must ensure that you have all of your spec files in a given directory (ensure that you do not have any code in this directory as all the files in it are exposed as static files), then replace the pyramid_openapi3_spec call that you did in Getting Started with the following:

config.pyramid_openapi3_spec_directory('path/to/openapi.yaml', route='/api/v1/spec')

Some notes:

  • Do not set the route of your pyramid_openapi3_spec_directory to the same value as the route of pyramid_openapi3_add_explorer.
  • The route that you set for pyramid_openapi3_spec_directory should not contain any file extensions, as this becomes the root for all of the files in your specified filepath.
  • You cannot use pyramid_openapi3_spec_directory and pyramid_openapi3_spec in the same app.

Endpoints / Request / Response Validation

Provided with pyramid_openapi3 are a few validation features:

  • incoming request validation (i.e., what a client sends to your app)
  • outgoing response validation (i.e., what your app sends to a client)
  • endpoint validation (i.e., your app registers routes for all defined API endpoints)

These features are enabled as a default, but you can disable them if you need to:

config.registry.settings["pyramid_openapi3.enable_endpoint_validation"] = False
config.registry.settings["pyramid_openapi3.enable_request_validation"] = False
config.registry.settings["pyramid_openapi3.enable_response_validation"] = False

Warning: Disabling request validation will result in request.openapi_validated no longer being available to use.

Register Pyramid's Routes

You can register routes in your pyramid application. First, write the x-pyramid-route-name extension in the PathItem of the OpenAPI schema.

paths:
  /foo:
    x-pyramid-route-name: foo_route
    get:
      responses:
        200:
          description: GET foo

Then put the config directive pyramid_openapi3_register_routes in the app_factory of your application.

config.pyramid_openapi3_register_routes()

This means is equals to

config.add_route("foo_route", pattern="/foo")

Demo / Examples

There are three examples provided with this package:

Both examples come with tests that exhibit pyramid_openapi's error handling and validation capabilities.

A fully built-out app, with 100% test coverage, providing a RealWorld.io API is available at niteoweb/pyramid-realworld-example-app. It is a Heroku-deployable Pyramid app that provides an API for a Medium.com-like social app. You are encouraged to use it as a scaffold for your next project.

Design defense

The authors of pyramid_openapi3 believe that the approach of validating a manually-written API document is superior to the approach of generating the API document from Python code. Here are the reasons:

  1. Both generation and validation against a document are lossy processes. The underlying libraries running the generation/validation will always have something missing. Either a feature from the latest OpenAPI specification, or an implementation bug. Having to fork the underlying library in order to generate the part of your API document that might only be needed for the frontend is unfortunate.

    Validation on the other hand allows one to skip parts of validation that are not supported yet, and not block a team from shipping the document.

  2. The validation approach does sacrifice DRY-ness, and one has to write the API document and then the (view) code in Pyramid. It feels a bit redundant at first. However, this provides a clear separation between the intent and the implementation.

  3. The generation approach has the drawback of having to write Python code even for parts of the API document that the Pyramid backend does not handle, as it might be handled by a different system, or be specific only to documentation or only to the client side of the API. This bloats your Pyramid codebase with code that does not belong there.

Running tests

You need to have pipenv and Python 3.7, 3.8, or 3.9 installed on your machine. Then you can run:

$ make tests

Related packages

These packages tackle the same problem-space:

  • pyramid_oas3 seems to do things very similarly to pyramid_openapi3, but the documentation is not in English and we sadly can't fully understand what it does by just reading the code.
  • pyramid_swagger does a similar thing, but for Swagger 2.0 documents.
  • connexion takes the same "write spec first, code second" approach as pyramid_openapi3, but is based on Flask.
  • bottle-swagger takes the same "write spec first, code second" approach too, but is based on Bottle.
  • pyramid_apispec uses generation with help of apispec and the marshmallow validation library. See above why we prefer validation instead of generation.

Deprecation policy

We do our best to follow the rules below.

  • Support the latest few releases of Python, currently Python 3.7, 3.8, and 3.9.
  • Support the latest few releases of Pyramid, currently 1.10.7 through 2.0.
  • Support the latest few releases of openapi-core, currently 0.13.4 through 0.13.8.
  • See Pipfile.lock for a frozen-in-time known-good-set of all dependencies.

Use in the wild

A couple of projects that use pyramid_openapi3 in production:

Owner
Pylons Project
The Pylons Project is composed of a disparate group of project leaders with experience going back to the very start of Python web frameworks.
Pylons Project
An Unpaired Sketch-to-Photo Translation Model

Unpaired-Sketch-to-Photo-Translation We have released our code at https://github.com/rt219/Unsupervised-Sketch-to-Photo-Synthesis This project is the

38 Oct 28, 2022
Roger Labbe 13k Dec 29, 2022
All course materials for the Zero to Mastery Deep Learning with TensorFlow course.

All course materials for the Zero to Mastery Deep Learning with TensorFlow course.

Daniel Bourke 3.4k Jan 07, 2023
Official implementation of Rich Semantics Improve Few-Shot Learning (BMVC, 2021)

Rich Semantics Improve Few-Shot Learning Paper Link Abstract : Human learning benefits from multi-modal inputs that often appear as rich semantics (e.

Mohamed Afham 11 Jul 26, 2022
Research shows Google collects 20x more data from Android than Apple collects from iOS. Block this non-consensual telemetry using pihole blocklists.

pihole-antitelemetry Research shows Google collects 20x more data from Android than Apple collects from iOS. Block both using these pihole lists. Proj

Adrian Edwards 290 Jan 09, 2023
This repository contains code accompanying the paper "An End-to-End Chinese Text Normalization Model based on Rule-Guided Flat-Lattice Transformer"

FlatTN This repository contains code accompanying the paper "An End-to-End Chinese Text Normalization Model based on Rule-Guided Flat-Lattice Transfor

THUHCSI 74 Nov 28, 2022
Aggragrating Nested Transformer Official Jax Implementation

NesT is a simple method, which aggragrates nested local transformers on image blocks. The idea makes vision transformers attain better accuracy, data efficiency, and convergence on the ImageNet bench

Google Research 169 Dec 20, 2022
Sematic-Segmantation - Semantic Segmentation on MIT ADE20K dataset in PyTorch

Semantic Segmentation on MIT ADE20K dataset in PyTorch This is a PyTorch impleme

Berat Eren Terzioğlu 4 Mar 22, 2022
DecoupledNet is semantic segmentation system which using heterogeneous annotations

DecoupledNet: Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation Created by Seunghoon Hong, Hyeonwoo Noh and Bohyung Han at POSTE

Hyeonwoo Noh 74 Sep 22, 2021
Code & Experiments for "LILA: Language-Informed Latent Actions" to be presented at the Conference on Robot Learning (CoRL) 2021.

LILA LILA: Language-Informed Latent Actions Code and Experiments for Language-Informed Latent Actions (LILA), for using natural language to guide assi

Sidd Karamcheti 11 Nov 25, 2022
NAS-Bench-x11 and the Power of Learning Curves

NAS-Bench-x11 NAS-Bench-x11 and the Power of Learning Curves Shen Yan, Colin White, Yash Savani, Frank Hutter. NeurIPS 2021. Surrogate NAS benchmarks

AutoML-Freiburg-Hannover 13 Nov 18, 2022
PipeTransformer: Automated Elastic Pipelining for Distributed Training of Large-scale Models

PipeTransformer: Automated Elastic Pipelining for Distributed Training of Large-scale Models This repository is the official implementation of the fol

DistributedML 41 Dec 06, 2022
R-Drop: Regularized Dropout for Neural Networks

R-Drop: Regularized Dropout for Neural Networks R-drop is a simple yet very effective regularization method built upon dropout, by minimizing the bidi

756 Dec 27, 2022
A community run, 5-day PyTorch Deep Learning Bootcamp

Deep Learning Winter School, November 2107. Tel Aviv Deep Learning Bootcamp : http://deep-ml.com. About Tel-Aviv Deep Learning Bootcamp is an intensiv

Shlomo Kashani. 1.3k Sep 04, 2021
Udacity Suse Cloud Native Foundations Scholarship Course Walkthrough

SUSE Cloud Native Foundations Scholarship Udacity is collaborating with SUSE, a global leader in true open source solutions, to empower developers and

Shivansh Srivastava 34 Oct 18, 2022
This repository is based on Ultralytics/yolov5, with adjustments to enable rotate prediction boxes.

Rotate-Yolov5 This repository is based on Ultralytics/yolov5, with adjustments to enable rotate prediction boxes. Section I. Description The codes are

xinzelee 90 Dec 13, 2022
Official codebase for ICLR oral paper Unsupervised Vision-Language Grammar Induction with Shared Structure Modeling

CLIORA This is the official codebase for ICLR oral paper: Unsupervised Vision-Language Grammar Induction with Shared Structure Modeling. We introduce

Bo Wan 32 Dec 23, 2022
Source code and dataset for ACL2021 paper: "ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning".

ERICA Source code and dataset for ACL2021 paper: "ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive L

THUNLP 75 Nov 02, 2022
Deep and online learning with spiking neural networks in Python

Introduction The brain is the perfect place to look for inspiration to develop more efficient neural networks. One of the main differences with modern

Jason Eshraghian 447 Jan 03, 2023
The pytorch implementation of the paper "text-guided neural image inpainting" at MM'2020

TDANet: Text-Guided Neural Image Inpainting, MM'2020 (Oral) MM | ArXiv This repository implements the paper "Text-Guided Neural Image Inpainting" by L

LisaiZhang 75 Dec 22, 2022