Why this random log generator?
- Generate Nginx/Apache combined or JSONL logs.
- Control status/path/UA distributions for realistic data.
- Preview 50 lines and export instantly.
- Share settings safely without log output.
Use cases
- Parser and pipeline development.
- Dashboard or alert testing.
- Demo data for workshops.
Quick presets
Pick a preset scenario for common log shapes.
Generate
Random log generator
Choose a format, tune distributions, then generate logs.
Examples
203.0.113.45 - - [19/Jan/2026:09:12:33 +0000] "GET /api/v1/users HTTP/1.1" 200 512 "-" "Mozilla/5.0 ..."
How to use this tool effectively
This guide helps you use Random Log Generator (Nginx/Apache/JSON) in a repeatable way: define a baseline, change one variable at a time, and interpret outputs with explicit assumptions before you share or act on results.
How it works
The page applies deterministic logic to your inputs and shows rounded output for readability. Treat it as a comparison workflow: run one baseline case, adjust a single parameter, and measure both absolute and percentage deltas. If a result seems off, verify units, time basis, and sign conventions before drawing conclusions. This approach keeps your analysis reproducible across teammates and sessions.
When to use
Use this page when you need a fast estimate, a classroom check, or a practical what-if comparison. It works best for planning and prioritization steps where you need direction and magnitude quickly before investing in deeper modeling, manual spreadsheets, or formal external review.
Common mistakes to avoid
- Changing multiple parameters at once, which hides the true cause of output movement.
- Mixing units (percent vs decimal, monthly vs yearly, gross vs net) across scenarios.
- Comparing with another tool without aligning defaults, constants, and rounding rules.
- Using rounded display values as exact downstream inputs without re-checking precision.
Interpretation and worked example
Run a baseline scenario and keep that result visible. Next, modify one assumption to reflect your realistic alternative and compare direction plus size of change. If the direction matches your domain expectation and the size is plausible, your setup is usually coherent. If not, check hidden defaults, boundary conditions, and interpretation notes before deciding which scenario to adopt.
See also
How to use this tool effectively
This tool is designed to make scenario checks fast. Use a repeatable workflow: baseline first, one variable change at a time, then compare output direction and magnitude.
How it works
Run your first scenario with defaults. Then, change exactly one assumption and observe which result changes most. That is the fastest way to identify sensitivity and explain what drives the outcome.
When to use
Use this page when you need practical planning support, side-by-side alternatives, or a clean baseline for further discussion.
Common mistakes to avoid
- Changing multiple assumptions simultaneously.
- Confusing percent and decimal inputs.
- Mixing unit systems across scenarios.
- Relying only on rounded display output for final conclusions.
Worked example
Prepare a base case and one alternative case, then compare outputs and validate the direction, scale, and interpretation with the same assumptions across both cases.
See also
FAQ
What is JSONL?
JSONL is newline-delimited JSON: one JSON object per line.
What is a time burst?
Bursty mode concentrates log lines into weighted time windows.
Is seeded mode secure?
No. Seeded mode is for reproducibility only.
What should I do first on this page?
Start with the minimum required inputs or the first action shown near the primary button. Keep optional settings at defaults for a baseline run, then change one setting at a time so you can explain what caused each output change.
Why does this page differ from another tool?
Different pages often use different defaults, units, rounding rules, or assumptions. Align those settings before comparing outputs. If differences remain, compare each intermediate step rather than only the final number.