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Last updated: August 2026

🔴 Why a Seed Turns Sample Data Into a Test Fixture

Here is a problem every developer hits eventually. You generate a hundred mock users, build a page against them, and something breaks on record 63 — a name too long for the column, an empty field your template did not expect. You regenerate to investigate, and the data is completely different. Record 63 is now someone else. The bug is gone, unreproducible, and you have learned nothing.

That happens because ordinary randomness is not repeatable. JavaScript’s built-in random number generator cannot be seeded — there is no way to ask it to produce the same sequence twice. So every generator built on it produces throwaway data by definition.

This tool uses a seeded generator instead. Give it a seed — any text or number, 42 or my-project — and the entire dataset becomes a pure function of that seed. Generate now, generate next month on a different machine, and you get byte-identical records. Suddenly your mock data has the property that matters: it is reproducible. You can note the seed in a bug report, commit the fixture to your repository, and have every teammate and every CI run see exactly the same hundred users, including the one that broke.

Untick Use the seed when you want fresh data on every click — useful for eyeballing variety. Tick it when the data needs to stay put.

🟡 Building a Schema That Matches Your Real API

Mock data is only useful if its shape matches what your application actually expects. A generator with a fixed set of fields forces you to rewrite your code around the mock, which is exactly backwards.

The Schema Builder separates two things that are usually conflated: the field name, which becomes your JSON key, CSV header and SQL column, and the field type, which decides what kind of value gets generated. They are independent. If your API returns customer_email, name the field that and give it the email type. If it returns author holding a person’s name, name it author and give it the full-name type. The generated data then drops into your code without any adaptation.

The six presets are starting points, not constraints — load User, then rename and reorder until it matches your schema. The arrows matter more than they look: field order determines column order in CSV and SQL output, which matters when you are importing into a table that already exists.

🟢 Records That Make Internal Sense

One detail worth calling out because it is the thing most generators get wrong: within a single record, the person-related fields agree with each other. If a record has both a name and an email, you get Kasun Wickramasinghe paired with kasun.wickramasinghe1@example.com, not a name from one draw and an unrelated email from another. Usernames derive from the same identity too. It sounds like a small thing, but incoherent records look obviously fake the moment anyone glances at a table of them — and they make it impossible to eyeball whether your join logic is working.

🔴 Awkward Names and Empty Values Are the Point

Clean test data is comfortable and nearly useless. Every record complete, every name a tidy seven letters of ASCII — your interface looks perfect, and then real users arrive and it falls apart.

Two options in the Realism tab exist specifically to break that comfort. Inject empty or null values sprinkles a percentage of blanks through the data, which is how you find out that your template prints the word “null” on screen, or that a sort function throws when a field is missing. Set it to 20 or 30 percent and watch what happens; most interfaces have at least one place that assumes every field is populated.

Include awkward but realistic names mixes in the ones that break naive validation: apostrophes (O'Brien), hyphens (Smith-Jones), accents (José, García), particles (van der Berg) and non-Latin characters. These are not exotic edge cases — they are ordinary names belonging to enormous numbers of real people, and they routinely get rejected by regular expressions written by someone who assumed names contain only letters A to Z. If your form rejects O'Brien, better to discover that from mock data than from an angry customer.

Note that fields set to be unique — id, email, username — are never blanked even at a high null percentage, because a null primary key is a different class of problem and would just mask the ones you are actually testing for.

🟡 Picking the Right Export Format

The same generated records can leave as seven different formats, and switching format does not regenerate the data — you are looking at one dataset rendered different ways.

• 🔵 JSON for API mocking and JavaScript fixtures. JS array is the same thing wrapped in a variable declaration for pasting straight into a file.
• 🟠 CSV and TSV for spreadsheets and bulk import. Values containing commas, quotes or line breaks are properly quoted and escaped, so they survive a round trip through Excel or a strict parser. Pick the semicolon delimiter if your Excel is set to a European locale.
• 🟣 SQL INSERT for seeding a database directly. Apostrophes are escaped correctly (O''Brien), nulls are written as unquoted NULL, numbers stay unquoted and booleans become 1 or 0. Choose one multi-row insert for speed, or one statement per record if you need to run them selectively.
• 🔵 XML for legacy and SOAP systems, with proper entity escaping. Markdown table for dropping sample output into documentation or a README.

One honest constraint: CSV, TSV and SQL are flat formats. If you have enabled nested child records, an array cannot fit in a single cell, so it is written into one column as compact JSON text. That is the only sensible option, but it means nested data really wants JSON or XML. Already have a CSV file that needs turning into SQL? That is a different job, handled by the dedicated CSV to SQL Query Converter [confirm slug].

🟢 Nested Records for Realistic API Mocking

Real APIs rarely return flat lists. A user endpoint returns a user with their orders; a post returns comments. The Nested & Relations tab builds that shape: define a child schema, name the array key, set how many children each parent gets, and every parent record arrives with its own populated array. With the foreign-key option enabled, each child also carries a parentId pointing back at its parent’s id — so you can test the join logic you would use against the real database.

Everything here runs in your browser with no network requests, which is worth knowing because mock data often mirrors the structure of a real, confidential schema. Pasting your table layout into a random online generator tells that site’s operator quite a lot about your system. For the wider argument on keeping this kind of work local, see the guide to secure offline development utilities, and if you need to tidy or validate the JSON afterwards, the JSON formatter and validator guide covers that side. The rest of the toolkit is indexed under free web tools by category.