dredd
pydantic
dredd | pydantic | |
---|---|---|
15 | 167 | |
4,140 | 19,167 | |
0.6% | 4.9% | |
1.2 | 9.8 | |
21 days ago | 2 days ago | |
JavaScript | Python | |
MIT License | MIT License |
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dredd
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The Uncreative Software Engineer's Compendium to Testing
Dredd: used to test APIs based on the API blueprint or OpenAPI specification, to ensure implementation matches the specification.
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Tool for generating example API requests and responses from OpenAPI
Here are three tools that you can use to generate example API requests and responses from OpenAPI specifications. These tools should work well even if your schemas are deeply nested: Nswag (Command Line and GUI): Nswag is a Swagger/OpenAPI toolchain for .NET, TypeScript, and other platforms. It supports code generation, client generation, and API documentation. You can use NswagStudio, which is a graphical interface, or you can use the command line tool called "NSwag.exe" for generating example API requests and responses. GitHub: https://github.com/RicoSuter/NJsonSchema NswagStudio: https://github.com/RicoSuter/NSwag/wiki/NSwagStudio Dredd (Command Line): Dredd is a language-agnostic command-line tool for validating API descriptions against backend implementations. It supports OpenAPI, Swagger, and API Blueprint formats. Dredd can generate example requests and responses and validate whether your API implementation conforms to the API description. GitHub: https://github.com/apiaryio/dredd Documentation: https://dredd.org/en/latest/ Stoplight Studio (GUI): Stoplight Studio is a modern API design and documentation platform that supports OpenAPI and JSON Schema. It allows you to create, edit, and validate OpenAPI specifications and provides a powerful visual interface for generating example API requests and responses. Website: https://stoplight.io/studio/ GitHub: https://github.com/stoplightio/studio These tools should provide you with the ability to generate example API requests and responses from your OpenAPI specifications and handle deeply nested schemas.
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Integration testing best practices for API servers...
If you want to make sure the server implements a certain contract like there's an handler responding to a GET request to /API/what/ever I'd rather use something else. To be completely honest this is a topic I'm currently also searching for a really good solution but what I found so far (and looks promising) is https://dredd.org/ or https://microcks.io/ Both support OpenAPI testing so you can specify the contract as an OpenAPI spec and validate your server against it.
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Faster time-to-market with API-first
Consolidating the API specification with OpenAPI was a turning point for the project. From that moment we were able to run mock servers to build and test the UI before integrating with the backend, and we were able to validate the backend implementation against the specification. We used prism to run mock servers, and Dredd to validate the server implementation (these days I’d rather use schemathesis).
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API-first development maturity framework
In this approach, you produce an API specification first, then you build the API against the specification, and then you validate your implementation against the specification using automated API testing tools. This is the most reliable approach for building API servers, since it’s the only one that holds the server accountable and validates the implementation against the source of truth. Unfortunately, this approach isn’t as common as it should be. One of the reasons why it isn’t so common is because it requires you to produce the API specification first, which, as we saw earlier, puts off many developers who don’t know how to work with OpenAPI. However, like I said before, generating OpenAPI specifications doesn’t need to be painful since you can use tools for that. In this approach, you use automated API testing tools to validate your implementation. Tools like Dredd and schemathesis. These tools work by parsing your API specification and automatically generating tests that ensure your implementation complies with the specification. They look at every aspect of your API implementation, including use of headers, status codes, compliance with schemas, and so on. The most advanced of these tools at the moment is schemathesis, which I highly encourage you to check out.
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What advice you could give to BEGINNER?
It's missing the greatest API testing classic Dredd! Other than that the best API testing tool I've used so far is schemathesis. It works by looking at your API specification and automatically launching hundreds of tests per endpoint. It also leverages advanced OpenAPI documentation strategies such as links to test the relationship between various endpoints.
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Dealing with backend developers
One more tip for the backend developers: make sure the API implementation is tested against the API specification using contract-testing tools such as Dredd or Schemathesis. I specially recommend schemathesis as it's a lot more comprehensive. I recommend you run those tests in the CI and require them to pass before they can merge their API changes. This is the only reliable way to ensure the API works as expected.
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what are the best tools for documenting apis?
The other thing you want to make sure is that the server is implementing the API correctly. In this space, you can use tools such as Dredd and schemathesis, which look at the API specification and automatically test the server implementation against it.
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How bad models ruin an API (or why design-first is the way to go)
Schemaless schemas make testing difficult. Tools like Dredd and Schemathesis rely on your API documentation to generate tests and validate your API responses. A collection of free-form arrays like the above model will pass nearly every test, even if the length of the arrays or their contents are wrong. Schemaless schemas are also useless for API mocking, which is a fundamental part of building reliable API integrations.
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Is it possible to automate Api testing without writing any aditional code ?
Dredd: this is the classic API testing tool and it's been around for years. Dredd works by looking at your API specification and figuring out what tests need to be generated to validate your API implementation. You don't need to write any additional code, although you may want to create your own custom hooks to customise Dredd's behaviour. Dredd hooks are useful for example to test resource endpoints (the likes of /todo/{todo_id}) and to clean up your database from any resources created during the test suite. I wrote a tutorial on how to write Dredd hooks which you may find useful.
pydantic
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Advanced RAG with guided generation
First, note the method prefix_allowed_tokens_fn. This method applies a Pydantic model to constrain/guide how the LLM generates tokens. Next, see how that constrain can be applied to txtai's LLM pipeline.
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utype VS pydantic - a user suggested alternative
2 projects | 15 Feb 2024
utype is a concise alternative of pydantic with simplified parameters and usages, supporting both sync/async functions and generators parsing, and capable of using native logic operators to define logical types like AND/OR/NOT, also provides custom type parsing by register mechanism that supports libraries like pydantic, attrs and dataclasses
- Pydantic v2 ruined the elegance of Pydantic v1
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Ask HN: Pydantic has too much deprecation. Why is it popular?
I like some of the changes from v1 to v2. But then you have something like this [0] removed from the library without proper documentation or replacement, resulting in ugly workarounds in the link that wont' work properly.
[0]: https://github.com/pydantic/pydantic/discussions/6337
- OpenAI uses Pydantic for their ChatCompletions API
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🍹GinAI - Cocktails mixed with generative AI
The easiest implementation I found was to use a PyDantic class for my target schema — and use that as a parameter for the method call to “ChatCompletion.create()”. Here’s a fragment of the GinAI Python classes used.
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FastStream: Python's framework for Efficient Message Queue Handling
Also, FastStream uses Pydantic to parse input JSON-encoded data into Python objects, making it easy to work with structured data in your applications, so you can serialize your input messages just using type annotations.
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Introducing FastStream: the easiest way to write microservices for Apache Kafka and RabbitMQ in Python
Pydantic Validation: Leverage Pydantic's validation capabilities to serialize and validate incoming messages
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Cannot get Langchain to work
Not sure if it is exactly related, but there is an open issue on Github for that exact message.
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FastAPI 0.100.0:Release Notes
Well the performance increase is so huge because pydantic1 is really really slow. And for using rust, I'd have expected more tbh…
I've been benchmarking pydantic v2 against typedload (which I write) and despite the rust, it still manages to be slower than pure python in some benchmarks.
The ones on the website are still about comparing to v1 because v2 was not out yet at the time of the last release.
pydantic's author will refuse to benchmark any library that is faster (https://github.com/pydantic/pydantic/pull/3264 https://github.com/pydantic/pydantic/pull/1525 https://github.com/pydantic/pydantic/pull/1810) and keep boasting about amazing performances.
On pypy, v2 beta was really really really slow.
What are some alternatives?
Schemathesis - Supercharge your API testing, catch bugs, and ensure compliance
Cerberus - Lightweight, extensible data validation library for Python
prism - Turn any OpenAPI2/3 and Postman Collection file into an API server with mocking, transformations and validations.
nexe - 🎉 create a single executable out of your node.js apps
postman-app-support - Postman is an API platform for building and using APIs. Postman simplifies each step of the API lifecycle and streamlines collaboration so you can create better APIs—faster.
msgspec - A fast serialization and validation library, with builtin support for JSON, MessagePack, YAML, and TOML
redoc - 📘 OpenAPI/Swagger-generated API Reference Documentation
SQLAlchemy - The Database Toolkit for Python
ava - Node.js test runner that lets you develop with confidence 🚀
sqlmodel - SQL databases in Python, designed for simplicity, compatibility, and robustness.
portman - Port OpenAPI Specs to Postman Collections, inject test suite and run via Newman 👨🏽🚀
mypy - Optional static typing for Python