Log Analyzer

Logs

JSON logs, Java/Node prefixes, nginx access logs or plain text — capped at 50,000 lines.
7lines3errors1unique errors0unknown level

Analysis

7 lines
ERROR3WARN1INFO2DEBUG1

Log Analyzer

Analyze log files in seconds — paste your logs and get level counts, deduplicated error groups and an hourly timeline. Runs locally, so server logs never leave your machine.

About this tool

What is it?
DataFormatter Log Analyzer is a free online tool that counts log levels, deduplicates error groups and builds an hourly timeline from up to 50,000 pasted log lines.
Who is it for?
Developers triaging server logs to find which errors repeat and when spikes happened without shipping logs anywhere.
What makes DataFormatter's tool different?
It is entirely browser-based, so server logs with real data never leave your machine, and it handles large inputs locally.

Quick start

  1. Copy the log lines you care about (any size up to 50,000 lines).
  2. Paste them into the editor — analysis updates as you type.
  3. Read the summary chips: total lines, errors, unique error groups.
  4. Flip between Levels, Error groups and Timeline to dig in.

What log analysis delivers

  • Per-level counts — FATAL, ERROR, WARN, INFO, DEBUG, TRACE, plus an unknown bucket for unprefixed lines.
  • Deduplicated error groups with occurrence counts, not 400 copies of the same stack line.
  • An hourly timeline (UTC) separating all lines, errors and warnings to surface spikes.
  • Message extraction from JSON logs, level-prefixed lines, nginx access logs and plain text.

How to analyze logs online

  • Paste a representative window of your log, not a trimmed stub — grouping is more useful at volume.
  • Check the Levels tab for the overall mix, then Errors to see what actually repeats.
  • Use Timeline when you suspect a time-correlated problem (deploys, cron, traffic peaks).
  • Copy Report into your incident doc or GitHub issue for a shareable summary.
Raw error lines
2026-08-30T08:02:45.000Z ERROR Rejected order 4815: stock unavailable
2026-08-30T08:03:11.900Z ERROR Rejected order 1022: stock unavailable
Grouped summary
Rejected order *: stock unavailable  (2x)

Who analyzes logs — and when

Incident triage

You have a 5,000 line tail and ten minutes. Paste it, find the one error that repeats 400 times, and chase it instead of the noise.

Verifying a deploy

Compare error groups before and after a rollout to confirm the change introduced (or fixed) specific failures.

Privacy-safe log sharing

Because analysis is local, you can inspect logs that contain customer data instead of redacting them for an online tool.

When analysis looks off

Everything lands in one bucket

Why: Level-less log formats (like pure access-log lines with only an IP and status) have no recognizable level label.

Fix: Paste into Levels and accept the UNKNOWN bucket, or switch to the Timeline view which works off timestamps alone.

Two errors that look different grouped together

Why: Normalized grouping strips IDs/numbers on purpose so repeats collapse.

Fix: Check the raw log line for the true distinguishing field — grouping only needs to flag 'likely the same problem'.

Empty timeline

Why: No timestamps were detected in RFC-3339 or nginx access-log form.

Fix: Use the Epoch/millisecond columns of your log, or convert timestamps with the Timestamp tool first.

Pro tips

  • Parse a tail -f window rather than the whole file when hunting recent incidents — the analyzer is fast, but focused slices make the summary clearer.
  • Pair with the Stack Trace analyzer: copy an error group's stack out of the Raw log and clean it there.
  • The summary report downloads as plain text — pipe it straight into Slack or a ticket.

Frequently asked questions

What kinds of logs can it analyze?

Line-based text: JSON logs, Java/Node style lines with a level label (DEBUG, INFO, WARN, ERROR, FATAL, TRACE), console-prefixed output, nginx access logs and generic timestamped lines. Each line is scored for level, timestamp and message separately.

How are error groups built?

ERROR/FATAL lines are grouped by a normalized message — parameter values, numbers and IDs are stripped, so 'Rejected order 4815: stock unavailable' and 'Rejected order 9999: stock unavailable' collapse into one group with a count.

What does the timeline show?

When timestamps are detected, lines are bucketed into UTC hours with separate counts for all lines, errors and warnings. It's a quick way to spot error spikes around a deploy or a batch run.

Does size matter?

Analysis covers up to 50,000 lines so the page stays fast. For bigger files, slice the relevant window or filter in your log tool first.

Is my log uploaded?

No. Logs are analyzed entirely in your browser. This is the safe way to inspect a log that contains live user data or internal endpoint names.

Can I share the summary?

Yes — Copy Report produces a plain-text summary (totals, level counts, error groups) and Download saves it. Raw log lines are never sent anywhere.

Related tools

Last reviewed September 2026 · DataFormatter team — this tool processes data locally in your browser.