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Avro Schema Evolution - Baseline

The baseline Avro record schema for reader/writer compatibility tests. Reference writer schema: id and name are strings.

Schema / OpenAPI testing Schema validation API testing Conversion testing

Preview, first 16 linesjson

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

Specifications

Schema Family: Avro
Version Role: baseline
Expected Compatibility: reference
Schema Type: record
Fields: 2
Namespace: example.novus.p6

Testing contract

Reference control

Scenario: Compare the baseline Avro contract against the baseline member using a compatibility checker.
Expected result: Reference writer schema: id and name are 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

“Avro 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: 2 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-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.