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Structured Log — Ten Timestamp Formats, One Instant (jsonl)

The same instant written ten ways — RFC 3339 at three precisions and with a non-UTC offset, epoch seconds as integer and float, epoch milliseconds and nanoseconds, Common Log Format and the log4j comma-decimal form. What a timestamp autodetector must resolve to one moment.

Preview — first 11 linesjsonl
{"ts":"2026-03-17T09:14:32.850Z","fmt":"rfc3339_millis","level":"info","msg":"canonical form"}
{"ts":"2026-03-17T09:14:32.850000000Z","fmt":"rfc3339_nanos","level":"info","msg":"nanosecond precision"}
{"ts":"2026-03-17T09:14:32Z","fmt":"rfc3339_seconds","level":"info","msg":"no fractional part"}
{"ts":"2026-03-17T09:14:32.850+02:00","fmt":"rfc3339_offset","level":"info","msg":"non-UTC offset, same instant"}
{"ts":1773738872,"fmt":"epoch_seconds","level":"info","msg":"integer seconds"}
{"ts":1773738872.85,"fmt":"epoch_seconds_float","level":"info","msg":"float seconds"}
{"ts":1773738872850,"fmt":"epoch_millis","level":"info","msg":"integer milliseconds"}
{"ts":1773738872850000000,"fmt":"epoch_nanos","level":"info","msg":"integer nanoseconds, beyond 2^53"}
{"ts":"17/Mar/2026:09:14:32 +0000","fmt":"clf","level":"info","msg":"common log format stamp"}
{"ts":"2026-03-17 09:14:32,850","fmt":"log4j","level":"info","msg":"comma as the decimal separator"}

Specifications

Lines
10
Distinct Formats
10
Same Instant
true
Instant Utc
2026-03-17T09:14:32.850Z
Includes Non Utc Offset
true
Epoch Nanos Exceeds Float53
true

Testing contract

Expected to pass
Scenario
Auto-detect each ts field and normalise it to UTC.
Expected result
All ten records resolve to 2026-03-17T09:14:32.850Z (the seconds-precision record to the same second), including the +02:00 offset record and the nanosecond integer that overflows a double.

What is a .jsonl file?

JSONL (JSON Lines) is a text format where each line is a complete, independent JSON value, allowing records to be streamed and appended without parsing the whole file. It is not itself a JSON array and each line must stand alone. It is common in logging, machine learning datasets, and data pipelines.

How to use this file

Use an example JSONL to test line-by-line streaming parsers, append-and-resume ingestion, and batch pipelines that process one record per line.

How to use this file for testing

“Structured Log — Ten Timestamp Formats, One Instant (jsonl)” is a deterministic Novus Examples fixture for Observability, Log parsing, Encoding detection. Structured and plain-text telemetry with known timestamps, levels, request identifiers, and error states for testing log ingestion, correlation, dashboards, and alert pipelines.

Documented properties for this file: JSONL · 961 bytes. 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.

Telemetry fixtures use fixed trace IDs, span IDs, and timestamps so ingestion is reproducible run to run. Point your collector, parser, or query layer at the file and assert the documented span tree, metric families, or severity mix; service and host names are invented.

Code examples

import json

with open("app-mixed-timestamp-formats.jsonl") as f:
    rows = [json.loads(line) for line in f]
print(len(rows), rows[0])

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