Non-intrusive
DataFaker leverages faker.js, providing rich random data generation methods to meet data generation needs in various scenarios
DataFaker leverages faker.js, providing rich random data generation methods to meet data generation needs in various scenarios
DataFaker offers a declarative way to define data templates with TypeScript data field type hints, simplifying the user's data template definition process
DataFaker uses data models as the basic unit, encapsulates data templates, and provides a cloning and modification mechanism for model singletons, allowing models to be reused while adapting to environmental changes to the greatest extent
DataFaker adopts the data association concept from faker.js. Through a context mechanism, dependencies can be established between different data, making the generated data more reasonable
DataFaker has a powerful self-referencing data generation mechanism. Simple configuration is all that's needed to generate tree-structured data, reducing user cognitive load
DataFaker supports data post-processing based on callbacks, allowing generated data to be combined with data from other models to meet composability requirements
DataFaker supports configuring data generation rules to maximize the fulfillment of your data generation requirements, following the principle of convention over configuration. Runtime configuration takes precedence over static template configuration
DataFaker also supports decorator syntax sugar, allowing models to be defined directly on classes using annotations and enabling model inheritance mechanisms based on classes