json328 B

Avro Schema Evolution - Backward-Compatible

The compatible Avro record schema for reader/writer compatibility tests. Backward compatible for old records: nullable email is added with a null default.

Schema / OpenAPI testing Schema validation API testing Conversion testing

Preview — first 24 linesjson

{
  "type": "record",
  "name": "Customer",
  "namespace": "example.novus.p6",
  "fields": [
    {
      "name": "id",
      "type": "string"
    },
    {
      "name": "name",
      "type": "string"
    },
    {
      "name": "email",
      "type": [
        "null",
        "string"
      ],
      "default": null
    }
  ]
}

Specifications

Schema Family: Avro
Version Role: compatible
Expected Compatibility: valid
Schema Type: record
Fields: 3
Namespace: example.novus.p6

Testing contract

Expected to pass

Scenario: Compare the compatible Avro contract against the baseline member using a compatibility checker.
Expected result: Backward compatible for old records: nullable email is added with a null default.

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

“Avro Schema Evolution - Backward-Compatible” is a deterministic Novus Examples 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: 3 fields. 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("avro-compatible.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.