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JSON Schema Schema Evolution - Baseline

The baseline member of a JSON Schema 2020-12 customer contract. Reference contract: id and name are required strings.

Schema / OpenAPI testingSchema validationAPI testingConversion testing
Preview, first 21 linesjson
{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "$id": "https://example.com/schemas/p6-customer-v1.json",
  "title": "P6 Customer",
  "type": "object",
  "required": [
    "id",
    "name"
  ],
  "properties": {
    "id": {
      "type": "string"
    },
    "name": {
      "type": "string",
      "minLength": 1
    }
  },
  "additionalProperties": false
}

Specifications

Schema Family
JSON Schema
Version Role
baseline
Expected Compatibility
reference
Dialect
2020-12
Properties
2
Required
2

Testing contract

Reference control
Scenario
Compare the baseline JSON Schema contract against the baseline member using a compatibility checker.
Expected result
Reference contract: id and name are required strings.

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 Schema Evolution - Baseline” is a deterministic Testaroo fixture for Schema / OpenAPI testing, Schema validation, API testing, Conversion 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 · 375 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("json-schema-baseline.json") as f:
    data = json.load(f)
print(type(data), len(data))

Generated by generation/p6_core.py. Free for any use, no attribution required, license.

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