dry-rb/dry-validation
Dry Validation
Dry-validation is a Ruby library designed for defining type-safe schemas and complex validation rules to verify and sanitize input data structures. It provides a formal framework for constructing modular validation logic, ensuring that incoming information meets specific business requirements and data formats before it is processed by an application.
The library utilizes a domain-specific language to declare validation rules, which are then parsed into executable objects. It distinguishes itself through a macro-driven system that bundles common validation logic into reusable shortcuts, alongside a recursive composition model that allows developers to nest validation objects to build complex, hierarchical structures. This approach enables the creation of consistent, modular logic that prevents code duplication across large applications.
Beyond core schema definition, the library includes a type-coercion pipeline that transforms raw input into expected formats and a structured error aggregation system that maps failures to specific input paths. It supports configuration hooks for external dependency integration and provides tools for sanitizing data, making it suitable for enforcing standards on incoming API requests and complex data objects.
Features
- Data Validation Libraries - Provides a library for defining type-safe schemas and complex validation rules to verify and sanitize input data structures in Ruby.
- Schema Definitions - Provides tools to define formal rules and schemas to verify that incoming information matches expected formats.
- Data Schema Validation - Defines formal rules and schemas to verify that incoming information matches expected formats and business requirements.
- Schema Definition Frameworks - Builds modular and composable validation logic that ensures incoming information meets specific business requirements.
- Coercion Pipelines - Transforms raw input data into expected types during the validation process to ensure consistent downstream information.
- Schema Validation Libraries - Allows developers to combine and nest multiple validation rules to create modular logic.
- Reusable Rule Logic - Bundles common validation logic into reusable shortcuts to simplify the definition of repetitive business rules.
- Complex Structure Validation - Supports defining detailed rules for nested or complicated data objects to ensure structural correctness.
- Data Sanitization Utilities - Converts raw input into consistent types and formats while reporting clear error messages for invalid entries.
- Data Validation and Sanitization - Cleans and converts raw input into consistent types and formats during the validation process.
- Boolean Predicates - Executes atomic logic checks against input data by mapping field keys to a library of reusable boolean verification methods.
- DSL-Driven Validations - Provides a domain-specific language to declare validation rules and data structures that are parsed into executable objects.
- Validation Error Reporters - Reports validation errors with clear messages and custom details to help users understand input failures.
- Validation Error Aggregators - Collects validation failures into a structured object that maps specific input paths to descriptive error messages.
- Request Validation - Ensures that incoming data from external clients meets specific standards before it is accepted by backend services.
- Schema Composition Libraries - Supports nesting validation objects within one another to build complex hierarchical structures from modular components.
- Validation Logic Decoupling - Composes and nests reusable validation rules to keep code clean and prevent duplication.
- Reusable Validators - Provides shared validation shortcuts that bundle common rules together for consistent application across the codebase.
Star history
How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
