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Synth Batch 04 (JSON)

Deterministic synthetic JSON batch 04 for bulk-import smoke tests.

Preview, first 28 linesjson

[
  {
    "id": 1,
    "value": 4,
    "label": "sample-1"
  },
  {
    "id": 2,
    "value": 8,
    "label": "sample-2"
  },
  {
    "id": 3,
    "value": 12,
    "label": "sample-3"
  },
  {
    "id": 4,
    "value": 16,
    "label": "sample-4"
  },
  {
    "id": 5,
    "value": 20,
    "label": "sample-5"
  }
]

Specifications

Records: 5
Batch: 4

Testing contract

Expected to pass

Scenario: Exercise Synth Batch 04 (JSON) in its synth workflow. Deterministic synthetic JSON batch 04 for bulk-import smoke tests.
Expected result: array length is 5; first-record keys are id, value, label. Declared feature checks: batch=4.

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

“Synth Batch 04 (JSON)” is a deterministic Novus Examples fixture for Data import, JSON parsing. Realistic faker-generated datasets with documented schemas for testing import and ETL flows.

Documented properties for this file: 5 records. 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("batch-04.json") as f:
    data = json.load(f)
print(type(data), len(data))

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