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Novus Examples
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SUPPORT — Tickets (JSON)

JSON twin of the support/tickets mini-dataset.

Data importJSON parsing
Preview — first 39 linesjson
[
  {
    "ticket_id": "T0001",
    "priority": "high",
    "status": "open",
    "channel": "email"
  },
  {
    "ticket_id": "T0002",
    "priority": "low",
    "status": "closed",
    "channel": "chat"
  },
  {
    "ticket_id": "T0003",
    "priority": "med",
    "status": "pending",
    "channel": "email"
  },
  {
    "ticket_id": "T0004",
    "priority": "high",
    "status": "open",
    "channel": "phone"
  },
  {
    "ticket_id": "T0005",
    "priority": "low",
    "status": "closed",
    "channel": "email"
  },
  {
    "ticket_id": "T0006",
    "priority": "med",
    "status": "open",
    "channel": "chat"
  }
]

Specifications

Records
6
Schema
ticket_id, priority, status, channel

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

“SUPPORT — Tickets (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: 6 records · schema: ticket_id, priority, status, channel. 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("tickets.json") as f:
    data = json.load(f)
print(type(data), len(data))

Related files

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