Log Timeline Visualizer

Timestamps scattered through a raw sample don't tell you whether traffic was steady or bursty. Log Timeline Visualizer pulls every parseable timestamp, buckets events by an interval you choose (from 1 second to 1 hour), and draws a proportional ASCII bar chart of volume over time.

Each line is checked against three formats in order — ISO 8601 (2025-06-11T14:00:00Z or space-separated, with or without milliseconds and a timezone offset), a bare millisecond epoch (a 13-digit number starting with 1), and the short syslog style (Jun 11 14:00:00, assumed to be the current year since syslog omits it). The first format that matches wins, so mixed-format pastes — an app log with ISO stamps next to a syslog snippet — parse line-by-line without any manual format selection.

The stats strip — events, bucket count, peak, span — plus the raw per-bucket counts give an immediate read on “when was the load”, making spikes and quiet periods obvious at a glance.

• Auto-detected timestamps — ISO, epoch, syslog.
• Bucket intervals — 1s to 1h.
• Proportional bar chart — block characters in the report.
• Span & peak stats — scale of the burst in numbers.
• Untimed count — lines without timestamps surfaced.
• Local only — nothing uploaded.

1. Paste log lines — any sample that has timestamps in ISO, epoch millisecond, or short syslog form. Mixed formats in the same paste are fine; each line is detected independently.
2. Pick a bucket interval — 1 second for a burst that lasted seconds, 1 hour for spotting slow-building trends across a whole day.
3. Click Visualize. Every event with a parseable timestamp is sorted into its bucket, and the chart, stats strip and per-bucket rows render immediately.
4. Change the interval and re-run to zoom in on a spike or zoom out to see the shape of the whole file — no need to re-paste.

Example 1 — Peak identification. At 1-minute granularity a deploy-during-lunch shows a clear spike; switching to 1-second granularity resolves that same spike into the few hot seconds where retries actually piled up.

Example 2 — Data gaps. Hourly buckets expose dead hours in an ingestion pipeline — because only non-empty buckets are listed, a missing overnight window shows up as a visible jump in the bucket timestamps rather than a wall of zero rows.

Example 3 — Rate shaping. Per-second buckets reveal throttling behavior as a sawtooth pattern — useful for confirming a rate limiter is actually capping requests at the interval you configured.

Example 4 — Untimed lines. If a chunk of a log has no recognizable timestamp (stack traces, continuation lines), the untimed count next to the event total tells you how much of the paste was excluded from the chart, so a low bucket count is not mistaken for low traffic.

• Instant visualization — no chart library.
• Interval control — zoom through five granularities.
• Text-friendly — copy the chart anywhere.
• Quiet-gap aware — non-empty buckets only.
• Private — all processing client-side.