Product JSON Schema (draft-07)
A JSON Schema (draft-07) describing a product object, with required fields, types, and constraints — paired with a conforming and a deliberately non-conforming instance for testing validators.
{
"$schema": "http://json-schema.org/draft-07/schema#",
"title": "Product",
"type": "object",
"required": [
"product_id",
"name",
"price",
"in_stock"
],
"properties": {
"product_id": {
"type": "integer",
"minimum": 1
},
"name": {
"type": "string",
"minLength": 1
},
"price": {
"type": "number",
"exclusiveMinimum": 0
},
"in_stock": {
"type": "boolean"
},
"tags": {
"type": "array",
"items": {
"type": "string"
}
}
},
"additionalProperties": false
}
Specifications
- Spec
- JSON Schema draft-07
- Required
- product_id, name, price, in_stock
- Additional Properties
- 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
“Product JSON Schema (draft-07)” is a deterministic Novus Examples fixture for Schema validation, Data import. JSON Schema documents describing a data shape — for testing validators and schema-aware tooling.
Documented properties for this file: JSON · 589 bytes. 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("product.schema.json") as f:
data = json.load(f)
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