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Welcome to DataGeneration

DataGeneration is a Java library for generating complex, realistic test data using a declarative JSON DSL. Create interconnected datasets with relationships, filtering, and advanced constraints for testing, development, and data seeding scenarios.

Why DataGeneration?​

  • Declarative DSL: Define complex data structures in simple JSON
  • Built-in Generators: 18 generators for common data types (names, emails, addresses, UUIDs, dates, etc.)
  • Relationships: Cross-collection references and foreign key relationships
  • Memory Efficient: Lazy generation mode for streaming large datasets
  • Reproducible: Seed-based generation for consistent test data
  • Multiple Outputs: JSON, SQL inserts, and custom formats

Quick Example​

{
  "users": {
    "count": 5,
    "item": {
      "id": {"gen": "uuid"},
      "name": {"gen": "name.fullName"},
      "email": {"gen": "internet.emailAddress"}
    }
  },
  "orders": {
    "count": 20,
    "item": {
      "id": {"gen": "uuid"},
      "userId": {"ref": "users[*].id"},
      "total": {"gen": "float", "min": 10, "max": 1000, "decimals": 2}
    }
  }
}
Generation generation = DslDataGenerator.create()
    .withSeed(123L)
    .fromJsonString(dsl)
    .generate();

// Stream as JSON
generation.streamJsonNodes("users").forEach(user -> {
    System.out.println(user.get("name").asText());
});

// Or export as SQL
generation.streamSqlInserts("users").forEach(System.out::println);

Get Started​

Get Started:

Features at a Glance​

Declarative DSL​

Define your data structure in JSON - no code required for basic scenarios.

Relationships​

Create realistic relationships between collections with references, sequential access, and filtering.

18 Built-in Generators​

UUID, Name, Internet, Address, Company, Country, Book, Finance, Phone, Number, Float, Boolean, String, Date, Lorem, Sequence, Choice, CSV.

Performance​

Lazy generation mode streams data without loading everything into memory.

Reproducible​

Use seeds to generate the same data every time - perfect for testing.

Multiple Outputs​

Export as JSON, SQL INSERT statements, or implement custom serializers.

Use Cases​

  • Testing: Generate realistic test data for unit and integration tests
  • Development: Populate development databases with meaningful data
  • Demos: Create convincing demo data for presentations
  • Data Seeding: Initialize databases with structured, related data
  • Load Testing: Generate large datasets for performance testing

Next Steps​

  1. Install DataGeneration
  2. Follow the Quick Start Guide
  3. Learn Core Concepts
  4. Explore Generators
  5. Browse Common Patterns