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Deeply Nested JSON

A deeply nested JSON document with objects inside arrays inside objects — for testing recursive parsing and path access.

JSON parsing
Preview — first 50 linesjson
{
  "company": "Novus Examples",
  "config": {
    "server": {
      "host": "localhost",
      "port": 8080,
      "tls": {
        "enabled": true,
        "minVersion": "1.2"
      }
    },
    "features": {
      "search": {
        "engine": "fuse",
        "options": {
          "threshold": 0.3,
          "keys": [
            "title",
            "purposes"
          ]
        }
      }
    }
  },
  "teams": [
    {
      "name": "core",
      "members": [
        {
          "name": "Ada",
          "roles": [
            "admin",
            "editor"
          ]
        },
        {
          "name": "Grace",
          "roles": [
            "editor"
          ]
        }
      ]
    },
    {
      "name": "ops",
      "members": [
        {
          "name": "Alan",
          "roles": [
            "viewer"
… 57 lines total — download for the full file.

Specifications

Structure
nested objects and arrays
Max Depth
5
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

“Deeply Nested JSON” is a deterministic Novus Examples fixture for JSON parsing. Flat, deeply nested, JSON Lines, and intentionally invalid JSON for testing parsers and error handling.

Documented properties for this file: nested objects and arrays. 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("nested.json") as f:
    data = json.load(f)
print(type(data), len(data))

Related files

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