synth
A declarative tool for generating realistic, scalable test data from code or existing databases.
What is synth?
Synth is a declarative data generator that provides a robust framework for creating realistic, constraint-based test data. It solves common developer problems like populating new schemas, avoiding production data for testing, and simulating data at scale. The tool is database-agnostic and can handle millions of rows.
Developers and QA engineers who need to generate realistic, synthetic test data for applications, especially those working with SQL or NoSQL databases and requiring data privacy and scalability.
Developers choose Synth for its declarative 'data as code' approach, which allows version control and automation of data models, and its ability to automatically infer and import schemas from existing databases like Postgres, MySQL, and MongoDB.
Overview
The Declarative Data Generator
Use Cases
Best For
- Populating a fresh application schema with correct, realistic data during initial development.
- Generating synthetic test data for integration testing or QA to avoid using sensitive production data.
- Simulating database scaling scenarios by generating millions of rows of data to test system performance.
- Automating data generation workflows with version-controlled, declarative configuration files.
- Importing and replicating data models from existing Postgres, MySQL, or MongoDB databases.
- Creating semantically rich test data like names, addresses, and emails using built-in fake data libraries.
Not Ideal For
- Teams needing quick, one-off data dumps without configuration overhead
- Projects relying on databases unsupported for import (e.g., Oracle, SQLite)
- Enterprises requiring production-ready, stable tools for critical testing
- Use cases demanding highly dynamic or recursive data generation beyond basic constraints
Pros & Cons
Pros
Enables version control and automation of data models using JSON configuration files, allowing peer review and CI/CD integration as highlighted in the 'Data as Code' feature.
Infers and generates data models directly from Postgres, MySQL, or MongoDB databases, simplifying setup from existing systems with Alpha support, as shown in the import examples.
Leverages integrated libraries like fake-rs to produce realistic data such as emails and dates, enhancing test data quality without external dependencies.
Supports generating data for both SQL and NoSQL databases, making it versatile for various backends, as emphasized in the 'Database Agnostic' key feature.
Cons
The project is in Public Alpha with acknowledged 'few kinks,' making it unsuitable for production or mission-critical testing due to potential bugs and breaking changes.
Only offers Alpha support for Postgres, MySQL, and MongoDB, excluding other databases which require manual schema creation, increasing setup time for unsupported systems.
Requires writing JSON schemas for custom models, which can be verbose and time-consuming compared to GUI tools or simpler generators like Faker for basic needs.
Frequently Asked Questions
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