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Flat JSON Array

A flat JSON array of ten simple objects: the baseline case for JSON parsing and mapping.

JSON parsingData import
Preview, first 50 linesjson
[
  {
    "id": 1,
    "name": "Item 1",
    "active": false,
    "price": 1.25
  },
  {
    "id": 2,
    "name": "Item 2",
    "active": true,
    "price": 2.5
  },
  {
    "id": 3,
    "name": "Item 3",
    "active": false,
    "price": 3.75
  },
  {
    "id": 4,
    "name": "Item 4",
    "active": true,
    "price": 5.0
  },
  {
    "id": 5,
    "name": "Item 5",
    "active": false,
    "price": 6.25
  },
  {
    "id": 6,
    "name": "Item 6",
    "active": true,
    "price": 7.5
  },
  {
    "id": 7,
    "name": "Item 7",
    "active": false,
    "price": 8.75
  },
  {
    "id": 8,
    "name": "Item 8",
    "active": true,
    "price": 10.0
  },
  {
… 63 lines total: download for the full file.

Specifications

Structure
flat array of objects
Records
10
Valid
true

Testing contract

Expected to pass
Scenario
Exercise Flat JSON Array in its json workflow. A flat JSON array of ten simple objects: the baseline case for JSON parsing and mapping.
Expected result
array length is 10; first-record keys are id, name, active, price. Declared feature checks: structure=flat array of objects; valid=True.

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

“Flat JSON Array” is a deterministic Testaroo fixture for JSON parsing, Data import. Flat, deeply nested, JSON Lines, and intentionally invalid JSON for testing parsers and error handling.

Documented properties for this file: 10 records · flat array of objects. 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.

Feed the file to your parser and assert it handles the documented quirks, quoted delimiters, embedded newlines, ragged rows, or invalid syntax; the valid↔invalid distinction is labelled in the title.

Code examples

import json

with open("flat.json") as f:
    data = json.load(f)
print(type(data), len(data))

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

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