JSON Schema Validator

To validate JSON against a schema, enter your JSON data in one input field and provide the corresponding JSON schema in another. Click the Validate JSON Schema button, and the tool will check the data against the schema rules, displaying validation results with detailed error messages if any issues are found.

A JSON schema is a JSON document that defines the structure, content, data types, required fields, and validation rules for JSON data. It acts as a contract specifying what valid JSON should look like, enabling automated validation, documentation, and ensuring data consistency across systems and APIs.

JSON schema validation is crucial for ensuring data integrity, preventing invalid data from entering systems, catching errors early in development, maintaining API contract compliance, improving data quality, enabling automated testing, and ensuring consistent data structures across distributed applications and microservices.

Yes, the JSON Schema Validator handles arbitrarily complex JSON structures including deeply nested objects, arrays of objects, conditional validation, pattern matching, type constraints, enum values, required properties, and custom validation rules. It supports the full JSON Schema specification for comprehensive validation.

If your JSON data doesn't match the schema, the validator displays detailed error messages indicating exactly what failed: which property violated constraints, expected vs actual data types, missing required fields, invalid values, or constraint violations. These errors help quickly identify and fix data issues.

JSON Schema has evolved through drafts (Draft 4, Draft 6, Draft 7, 2019-09, 2020-12). Each version adds features and improves validation capabilities. Our validator typically supports the latest drafts, ensuring compatibility with modern schema definitions and advanced validation patterns.

Define your expected JSON structure using JSON Schema format: specify required properties, data types (string, number, boolean, array, object), constraints (min/max, patterns, enum values), and nesting. Use schema generators or write manually. Our validator helps test your schema against real data.

JSON schema validation complements but doesn't replace unit tests. It validates data structure and types but not business logic, calculations, or workflows. Use schema validation for API contracts and data integrity, and unit tests for application logic and behavior.

Schema validation ensures API requests and responses match expected formats, catches malformed data before processing, documents API contracts clearly, enables automatic API documentation generation, supports API versioning, and improves integration reliability by validating data at API boundaries.

Common errors include: type mismatch (string instead of number), missing required properties, unexpected additional properties, value not in allowed enum, string pattern mismatch, array length violations, number range violations, and nested object validation failures. Each provides specific error details.

Yes, JSON schemas are excellent for form validation. Define field types, required fields, validation rules, min/max values, and patterns in a schema. Many form libraries support JSON schema for declarative validation, reducing manual validation code and ensuring consistency.

Define array schemas with type: 'array', specify item schemas with 'items', set constraints like minItems/maxItems, enforce unique values with uniqueItems, and validate nested array structures. The validator ensures all array elements meet item schema requirements.

Schema-first development defines data schemas before implementing code. Create JSON schemas documenting expected data structures, use them to validate all inputs/outputs, generate code from schemas, and ensure all components use the same data contracts. This reduces integration issues and improves reliability.