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Novus Examples
json328 B

Inventory Item JSON Schema

Draft-07 JSON Schema describing a SAMPLE inventory item object for domain-specific validator tests.

Schema / OpenAPI testing
Preview — first 21 linesjson
{
  "$schema": "http://json-schema.org/draft-07/schema#",
  "title": "inventory-item",
  "type": "object",
  "required": [
    "sku",
    "qty"
  ],
  "properties": {
    "sku": {
      "type": "string"
    },
    "qty": {
      "type": "integer"
    },
    "warehouse": {
      "type": "string"
    }
  }
}

Specifications

Spec
JSON Schema draft-07
Name
inventory-item

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

“Inventory Item JSON Schema” is a deterministic Novus Examples fixture for Schema / OpenAPI testing. Valid and intentionally invalid OpenAPI/JSON Schema documents plus request/response examples for schema validators and API tooling.

Documented properties for this file: JSON · 328 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.

Valid and intentionally invalid siblings are labelled in title and description. Assert parsers accept the valid twin and fail loudly on the invalid one; for time series, check DST gaps and duplicate keys against the spec table.

Code examples

import json

with open("inventory-item.schema.json") as f:
    data = json.load(f)
print(type(data), len(data))

Related files

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