JSON Schema (User)
A draft-07 JSON Schema describing a user object, for testing schema validators and schema-aware tooling.
{
"$schema": "http://json-schema.org/draft-07/schema#",
"title": "User",
"type": "object",
"required": ["id", "name", "email"],
"properties": {
"id": { "type": "integer", "minimum": 1 },
"name": { "type": "string", "minLength": 1 },
"email": { "type": "string", "format": "email" },
"active": { "type": "boolean", "default": true }
},
"additionalProperties": false
}
Specifications
- Format
- JSON Schema draft-07
- Properties
- 4
- Required
- 3
- Additional Properties
- false
- Encoding
- UTF-8
Testing contract
Expected to pass- Scenario
- Exercise JSON Schema (User) in its json workflow. A draft-07 JSON Schema describing a user object, for testing schema validators and schema-aware tooling.
- Expected result
- top-level keys are $schema, title, type, required, properties, additionalProperties; array lengths: required=3; selected values: {"type": "object", "additionalProperties": false}. Declared feature checks: properties=4; required=3; additionalProperties=False.
What is a .json file?
JSON (JavaScript Object Notation) is a lightweight, text-based data-interchange format representing objects, arrays, strings, numbers, booleans, and null. It is language-independent, human-readable, and the dominant format for web APIs and configuration. It requires a single well-formed root value.
How to use this file
Use an example JSON file to test parsers and serializers, schema validation, Unicode and number-precision handling, and API request or response processing.
How to use this file for testing
“JSON Schema (User)” is a deterministic Testaroo fixture for Schema validation, JSON parsing. JSON Schema documents describing a data shape, for testing validators and schema-aware tooling.
Documented properties for this file: UTF-8 · JSON Schema draft-07. Compare results against paired or grouped companions on this page when present (clean↔damaged, searchable↔scanned, or format twins) so scores stay reproducible across runs.
Download the file once, keep the path stable in CI or local scripts, and treat the spec table as the contract: dimensions, seeds, field lists, and roles are intentional. Corrupt or invalid samples are labelled as such, expect parsers to fail loudly rather than silently accept them.
Data fixtures document their exact quirks (delimiters, encodings, null handling, schema, and row counts) in the spec table. Point your parser or importer at the file and assert it handles the documented edge cases; clean and deliberately-messy siblings make before/after diffs straightforward.
Code examples
import json
with open("user-schema.json") as f:
data = json.load(f)
print(type(data), len(data))Generated by generation/data_formats.py. Free for any use, no attribution required, license.
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