Time-series — Duplicate Timestamp Keys (JSON)
JSON array with duplicate timestamp keys for ingestion pipeline tests.
[
{
"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
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))Related files
- csvTime-series — Business HoursHourly order counts weekdays only (09–16 UTC) across three SAMPLE days.
- csvTime-series — DST Fall Back Duplicate Hour (CSV)Load readings with a repeated 01:00 local hour on fall-back DST — tests ambiguous timestamp handling.
- csvTime-series — DST Spring Forward Gap (CSV)Hourly load data skipping the 03:00 hour on DST spring-forward day — tests timezone/DST gap handling.
- jsonTime-series — Epoch BoundaryTimestamps at Unix epoch and classic 32-bit Y2038 boundary.
- csvTime-series — Gap FilledSeries with an explicit null value at a missing sample point.
- csvTime-series — Irregular Intervals (CSV)Sensor readings at irregular minute gaps — tests time-series resampling and gap detection.
Generated by generation/data_wave_f.py. Free for any use, no attribution required — license.