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JSON Schema validation | Godspeed Docs
JSON Schema validation | Godspeed Docs
The Framework provides request and response schema validation
Request schema validation​ We have the ability to define inputs and their types in our request schema, such as path parameters, query parameters, and request body. This allows the framework to validate whether the API has received the specified inputs in the expected types. Whenever an API is triggered, AJV (Another JSON Schema Validator) verifies the request schema against the provided inputs. If the defined schema matches the inputs, it allows the workflow to execute. Otherwise, it throws an error with a status code of 400 and a descriptive message indicating where the schema validation failed.
Response schema validation​ Just like request schema validation, there's also response schema validation in place. In this process, the framework checks the response type, validates the properties of the response, and ensures they align with the specified types. The process of response schema validation involves storing the response schema, enabling the workflow to execute, and checking the response body along with its properties for validation. Response schema validation includes two cases Failure in Workflow Execution Successful Workflow Execution but Fails in Response Schema Validation
If the response schema validation fails api return with 500 internal server error
In the case of failed request schema validation, the APIs respond with a status of 400 and a message indicating a "bad request." Conversely, if the response schema validation encounters an issue, the APIs return a status of 500 along with an "Internal Server Error" message.
Event with response and request schema validation​ http.post./helloworld: fn: helloworld params: - name: path_params in: path required: true schema: type: string - name: query_params in: query required: true schema: type: string body: content: application/json: schema: type: object required: [name] properties: name: type: string responses: 200: content: application/json: schema: type: object required: [name] properties: name: type: string
·godspeed.systems·
JSON Schema validation | Godspeed Docs
Generating Schemas from Examples with jtd-infer
Generating Schemas from Examples with jtd-infer
JSON Type Definition, aka RFC 8927, is an easy-to-learn, standardized way to define a schema for JSON data. You can use JSON Typedef to portably validate data across programming languages, create dummy data, generate code, and more. jtd-infer is a tool that can generate a JSON Typedef schema from example data. It lives on GitHub here. This article will go over why jtd-infer may be useful to you, how to install it, and then go through an example of using jtd-infer on a few real-world datasets.
·jsontypedef.com·
Generating Schemas from Examples with jtd-infer
JSON Hero
JSON Hero
JSON Hero makes reading and understand JSON files easy by giving you a clean and beautiful UI packed with extra features.
·jsonhero.io·
JSON Hero
How to Wrangle JSON Data in R with jsonlite, purr and dplyr - Robot Wealth
How to Wrangle JSON Data in R with jsonlite, purr and dplyr - Robot Wealth
Working with modern APIs you will often have to wrangle with data in JSON format. This article presents some tools and recipes for working with JSON data with R in the tidyverse. We’ll use purrr::map functions to extract and transform our JSON data. And we’ll provide intuitive examples of the cross-overs and differences between purrr ... Read more
·robotwealth.com·
How to Wrangle JSON Data in R with jsonlite, purr and dplyr - Robot Wealth
R - JSON Files
R - JSON Files
R - JSON Files - JSON file stores data as text in human-readable format. Json stands for JavaScript Object Notation. R can read JSON files using the rjson package.
·tutorialspoint.com·
R - JSON Files
hendrikvanb
hendrikvanb
Working with complex, hierarchically nested JSON data in R can be a bit of a pain. In this post, I illustrate how you can convert JSON data into tidy tibbles with particular emphasis on what I’ve found to be a reasonably good, general approach for converting nested JSON into nested tibbles. I use three illustrative examples of increasing complexity to help highlight some pitfalls and build up the logic underlying the approach before applying it in the context of some real-world rock climbing competition data.
·hendrikvanb.gitlab.io·
hendrikvanb
JSON files & tidy data | The Byrd Lab
JSON files & tidy data | The Byrd Lab
My lab investigates how blood pressure can be treated more effectively. Much of that work involves the painstaking development of new concepts and research methods to move forward the state of the art. For example, our work on urinary extracellular vesicles’ mRNA as an ex vivo assay of the ligand-activated transcription factor activity of mineralocorticoid receptors is challenging, fun, and rewarding. With a lot of work from Andrea Berrido and Pradeep Gunasekaran in my lab, we have been moving the ball forward on several key projects on that front.
·byrdlab.org·
JSON files & tidy data | The Byrd Lab
Converting Nested JSON to DataFrame in R? - General - Posit Community
Converting Nested JSON to DataFrame in R? - General - Posit Community
I'm currently working on a project where I need to convert a nested JSON structure into a DataFrame using R. I'm facing some issues with the current approach, and I'd appreciate any help or guidance on how to properly handle this conversion. Json file looks like this : json_data - '{ "resourceType": "QuestionnaireResponse", "id": "example-questionnaireresponse", "questionnaire": "Questionnaire/example", "status": "completed", "subject": { "reference": "Patient/example" }, "a...
·forum.posit.co·
Converting Nested JSON to DataFrame in R? - General - Posit Community