json266 B

Time-series — Duplicate Timestamp Keys (JSON)

JSON array with duplicate timestamp keys for ingestion pipeline tests.

Time-series data Data import CSV parsing

Preview, first 18 linesjson

[
  {
    "ts": "2026-01-01T12:00:00Z",
    "metric": "cpu",
    "value": 0.42
  },
  {
    "ts": "2026-01-01T12:00:00Z",
    "metric": "cpu",
    "value": 0.43
  },
  {
    "ts": "2026-01-01T12:01:00Z",
    "metric": "cpu",
    "value": 0.41
  }
]

Specifications

Records: 3
Edge: duplicate keys

Testing contract

Expected to pass

Scenario: Exercise Time-series — Duplicate Timestamp Keys (JSON) in its timeseries workflow. JSON array with duplicate timestamp keys for ingestion pipeline tests.
Expected result: array length is 3; first-record keys are ts, metric, value. Declared feature checks: edge=duplicate keys.

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

“Time-series — Duplicate Timestamp Keys (JSON)” is a deterministic Novus Examples fixture for Time-series data, Data import, CSV parsing. Irregular timestamps, DST gaps, and duplicate keys for time-series importers and charting libraries.

Documented properties for this file: 3 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.

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("duplicate-keys.json") as f:
    data = json.load(f)
print(type(data), len(data))

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