Generate fake names, emails, phones in bulk; export JSON / SQL / CSV.

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Output

How to use

Purpose

Mock test data generator. Define fields, batch-generate names, emails, phone numbers, addresses, companies, UUIDs, dates, IPs, URLs, Lorem Ipsum, random numbers, and dozens of other types. Supports Chinese / English, custom fields, related fields (email derived from name), output JSON / CSV / SQL INSERT. Common for frontend mock APIs, database test data fill, load test samples, UI design mockups with realistic data. All generation runs locally.

Steps

Define fields: name + type (Chinese name / email / phone / address etc.) + constraints
Set count: 10 / 100 / 1000 / 10000
Pick output format: JSON array / JSON Lines / CSV / SQL INSERT
Click Generate for instant output
Related fields: email derived from name field (tom.smith@example.com)
Custom type: random string from regex (e.g. order ID ORD\d{8})
Export / one-click copy
Save config templates: common field combinations (user table, order table)

FAQ

Is the data realistic? Could it accidentally match real people?: Not realistic. Surnames / given names / emails / phone numbers are randomly combined from public dictionaries. Theoretically a coincidence with a real person is possible, but probability is very low (fields are independent). Do NOT use for production, public release, or fraud. Suitable for internal testing, doc examples, UI mockups.
Diversity of Chinese names?: Built-in 200+ common surnames + 1000+ common characters, ~200K combinations. Ethnic minority names (Mongolian, Uyghur, Tibetan) appear at ~5%. Compound surnames (上官, 欧阳) included. If generated names repeat common ones (张伟, 王芳), it is because those characters are high-frequency — matches real distribution.
Could phone number generation leak real numbers?: Numbers follow real operator prefixes (138/139/158 etc.) with the last 8 digits random. Theoretically could match a real person (about 1 in 100 million chance). But the generated number may be in actual use — never send SMS to test numbers (may reach strangers).
How to generate related fields, e.g. orders referencing users?: Tool supports "reference previous field". Example: define users table with 100 user_id values; then define orders.user_id field as "reference users.user_id" and the tool picks random values from that list. Maintains foreign-key integrity (every orders.user_id exists in users).
Will large batches (100K rows) be slow?: 100K simple fields (numbers / UUID / short strings) complete in seconds. 1 million complex fields (Chinese / related) may take 1-2 minutes and consume lots of memory (recommend 100K per batch). Beyond that use Python Faker or specialized tools (Mockaroo).

Use cases

Frontend mock APIs: develop UI before backend is ready
Database load testing: 1M test rows to measure query performance
UI mockups: realistic-looking data for tables and cards instead of Lorem Ipsum
Demos/training: realistic datasets for internal presentations
Unit test samples: fixed-structure data for function tests

Use cases

Frontend mocks, load testing, UI mockups, demos, unit test samples. Frontend, QA, DBA, designer, tech speakers. Chinese support, related fields, multi-format output, custom types are the differentiators.