Why seed a database?
Every developer needs test data — for a local development environment, a staging server, a demo instance, or a CI pipeline. Hand-typing SQL INSERT statements is tedious and unrealistic. Real-world data has patterns: names, dates, diverse values, occasional nulls. A seeder produces data that looks and behaves like production data without the risk of using actual production records.
The Database Seeder generates SQL directly, so the output works in any SQL client, migration script, or Docker entrypoint. Combine it with your existing schema — just define the columns you need and let the tool produce the INSERT statements.
How it works
Define your table name and add columns one by one. For each column, pick a type (VARCHAR, INT, DECIMAL, BOOLEAN, DATE, etc.), set a name, and configure type-specific options like character length or decimal precision. Toggle nullability — nullable columns randomly produce NULL values to mimic real-world sparse data.
When you click Generate, the tool produces a complete SQL script: optionally a DROP TABLE IF EXISTS, optionally a CREATE TABLE statement, and then the INSERT statements. Multi-row INSERT batches all rows into a single statement for MySQL, PostgreSQL, and SQLite; individual INSERTs are used for SQL Server.
Use cases
- Filling a local development database with realistic data for frontend and API testing.
- Generating seed SQL files for Docker Compose or CI/CD pipeline setup scripts.
- Creating demo datasets for product walkthroughs, training environments, or conference booths.
- Populating test fixtures for integration tests with controlled, reproducible data.
- Generating bulk INSERT scripts for load testing and performance benchmarking.
Dialect differences
Identifier quoting follows each dialect: backticks for MySQL, double quotes for PostgreSQL, square brackets for SQL Server. SQLite accepts bare identifiers. Multi-row INSERT (VALUES (...), (...), ...) is supported by MySQL, PostgreSQL, and SQLite; SQL Server requires individual INSERT statements and the tool switches automatically.
Type syntax is adjusted per dialect where relevant. The generated SQL is intended as a starting point — review the output and adjust column types to match your exact schema.