Table Generator

Pick columns that mirror real UI: short strings, long strings, numbers, booleans, and dates. Stress auto‑layout with extremes to catch overflow early.

A production‑ready Table Generator should let you dial distribution, min/max lengths, and probability of nulls. Use column templates to simulate realistic datasets—IDs, names, emails, currency, and ISO dates—so downstream components like sorters and validators get exercised under conditions your product actually faces.

Push beyond uniform randomness. Model skew—Zipfian name frequencies, long‑tail product codes, and clustered timestamps—to reveal pagination and indexing pitfalls. Provide nullable fields and foreign‑key like relationships so tables can mimic list‑detail patterns found in real apps.

Finally, encode constraints directly in the generator: unique keys, allowed sets, and validation rules. When the data generator mirrors business rules, UI errors show up during design, not in production.

Quote fields with commas and newlines, output UTF‑8 with BOM when targeting Excel, and normalize line breaks for cross‑platform imports.

Also escape quotes by doubling them and consider TSV when consumers mishandle commas. The Table Generator should preview how files open in common tools and provide toggles for headers, delimiters, and BOM so handoffs never fail at the last minute.

Beware locale formatting. Decimal commas, date orders (MDY vs DMY vs ISO), and thousand separators break naive imports. Provide a “safe export” mode that serializes numbers as raw digits and dates as ISO‑8601, and let consumers format later.

When files exceed email limits, stream to object storage and share signed links. Add checksums and file size to logs so recipients can verify integrity.

Use a seed for reproducible datasets in tests. Document seed + parameters so QA can regenerate bugs precisely.

Expose the seed in the UI and include it in exports. Deterministic randomness turns a one‑off repro into a permanent test fixture, reducing flaky bugs and speeding up incident response.

Offer multiple PRNG backends (e.g., xorshift, mulberry32) for balance between speed and distribution quality, and warn when browser Math.random() is used. Persist the seed in the URL so scenarios can be shared with a link.

For CI, lock seeds and snapshot outputs; diffs should be intentional, not incidental.

Generate in chunks, stream to CSV, and virtualize rendering. Keep memory bounded and avoid blocking the main thread.

Chunked generation with requestIdleCallback or web workers keeps the UI responsive. The Table Generator can surface estimated time and size so users pick parameters that won’t freeze the page.

Use transferable streams where available and fall back gracefully. For preview, render only the viewport plus buffer rows; measure row height instead of guessing. Expose a guardrail when the requested dataset exceeds typical browser memory.

Profile with performance marks and ship sane defaults. Engineering defaults save users from accidental denial‑of‑service situations.

Never ship real data in fixtures. Keep names generic, dates current, and ensure no accidental PII enters test exports.

Mark datasets as synthetic and avoid values that look real (e.g., live emails, phone numbers). Provide redaction and obfuscation helpers so teams sanitize samples before sharing outside the organization.

Respect regulatory zones. If samples resemble production tables, strip columns that could be sensitive, and label datasets clearly in filenames and headers. Provide a “compliance mode” that blocks risky fields by default.

Finally, log generation parameters with exports so auditors can reconstruct provenance without the original author.