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json143 B

Product Instance: Valid (JSON)

A product object that conforms to the product JSON Schema: the positive case for testing a JSON-Schema validator.

Schema validation
Preview, first 11 linesjson
{
  "product_id": 101,
  "name": "Wireless Headphones",
  "price": 79.99,
  "in_stock": true,
  "tags": [
    "audio",
    "electronics"
  ]
}

Specifications

Conforms To
product.schema.json
Valid
true

Testing contract

Expected to pass
Scenario
Exercise Product Instance: Valid (JSON) in its schema workflow. A product object that conforms to the product JSON Schema: the positive case for testing a JSON-Schema validator.
Expected result
top-level keys are product_id, name, price, in_stock, tags; array lengths: tags=2; selected values: {"product_id": 101, "price": 79.99, "in_stock": true}. Declared feature checks: conformsTo=product.schema.json; valid=True. The declared comparison counterpart is data-rw-schema-invalid; preserve the stated difference instead of expecting the container bytes to match.

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 Instance: Valid (JSON)” is a deterministic Testaroo fixture for Schema validation. JSON Schema documents describing a data shape, for testing validators and schema-aware tooling.

Documented properties for this file: JSON · 143 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-valid.json") as f:
    data = json.load(f)
print(type(data), len(data))

Generated by generation/data_realworld.py. Free for any use, no attribution required, license.

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