JSON Schema Validator
Validate JSON data against JSON Schema with real-time validation. Get detailed error messages and ensure your JSON conforms to the specified schema structure.
JSON Data & Schema
Validation Results
Key Features
Schema Validation
Validate JSON data against JSON Schema specifications with comprehensive rule checking and detailed error reporting.
Multi-Draft Support
Support for draft-07, draft-06, and draft-04 schemas ensuring compatibility with different schema versions and tools.
Real-time Validation
Live validation as you type with configurable validation timing and instant feedback for faster development.
Precise Error Location
Detailed error messages with JSON path locations making it easy to identify and fix validation issues.
Advanced Validation
Type checking, required fields, patterns, format validation (email, URI, date-time), and complex constraints.
Privacy-Focused
All validation happens locally in your browser - no data is sent to servers ensuring complete data privacy.
JSON Schema Validator Quick Guide
What is JSON Schema Validation?
JSON Schema validation ensures your JSON data conforms to predefined structural and content requirements. It acts as a contract between data producers and consumers, validating data integrity, enforcing business rules, and preventing data corruption in distributed systems.
Quick Start
- Input your JSON data in the left panel
- Enter your JSON Schema in the same panel below the data
- Choose schema version (draft-07 recommended for new projects)
- Click "Validate JSON" or enable live validation for real-time feedback
- Review validation results with detailed error messages if any issues are found
Best For
- API Development: Validate request/response payloads against documented schemas
- Data Quality: Ensure data integrity in ETL pipelines and processing workflows
- Configuration: Validate application config files and environment settings
- Testing: Verify test data and mock responses conform to expected schemas
- Documentation: Ensure API documentation examples are valid
Step-by-Step JSON Schema Validation Tutorial
Step 1: Understanding Schema Drafts
JSON Schema has evolved through different draft versions. Choose the right one for your project:
- Draft-07: Latest stable version with comprehensive features (recommended)
- Draft-06: Good balance of features and compatibility
- Draft-04: Widely supported, suitable for legacy systems
Step 2: Basic Schema Structure
A JSON Schema defines the structure and constraints for your data:
{
"$schema": "http://json-schema.org/draft-07/schema#",
"type": "object",
"properties": {
"name": { "type": "string" },
"age": { "type": "integer", "minimum": 0 }
},
"required": ["name"]
}
Step 3: Advanced Validation Rules
Implement complex validation logic:
- Pattern Matching: Use regex for string validation
- Format Validation: Built-in formats like email, uri, date-time
- Conditional Logic: if/then/else constructs for context-dependent validation
- Array Constraints: minItems, maxItems, uniqueItems
- Object Properties: additionalProperties, patternProperties
Step 4: Error Analysis and Debugging
Our validator provides detailed error information:
- JSON path to the problematic data (e.g., "$.user.email")
- Schema path that failed validation
- Clear description of the validation rule violation
- Expected vs actual values for easier debugging
Step 5: Integration Best Practices
Implement validation in your development workflow:
- Pre-commit validation for JSON configuration files
- CI/CD pipeline validation for deployment configs
- Runtime validation for API endpoints
- Test data validation in automated test suites
Real-World Use Cases
E-commerce Order Processing
Challenge: Validate complex order data including customer info, products, pricing, and payment details.
Solution: Comprehensive schema validation ensuring data integrity before payment processing.
Schema Focus:
- Required customer and product information
- Email and phone number format validation
- Positive quantities and valid pricing
- Valid shipping address formats
- Payment method and billing validation
Benefits: Prevents order processing errors, reduces customer support tickets, ensures payment compliance.
IoT Sensor Data Pipeline
Challenge: Validate high-volume streaming sensor data for anomaly detection and quality assurance.
Solution: Real-time schema validation in data ingestion pipeline with performance optimization.
Validation Requirements:
- Sensor reading numeric ranges and units
- Timestamp format and logical sequence validation
- Device identifier and location data verification
- Data completeness and required field checks
- Anomaly detection through constraint validation
Benefits: Early anomaly detection, data quality assurance, reduced processing errors, reliable analytics.
Microservices API Contract Testing
Challenge: Ensure API compatibility between microservices during development and deployment.
Solution: Automated contract testing using schema validation in CI/CD pipelines.
Contract Validation:
- Request payload structure and types
- Response format and required fields
- Error response schema consistency
- API versioning and backward compatibility
- Header and parameter validation
Benefits: Prevents breaking changes, enables confident deployments, reduces integration bugs, maintains API consistency.
Frequently Asked Questions
Which JSON Schema draft version should I use?
For new projects, use draft-07 as it's the latest stable version with comprehensive validation features. Use draft-06 for good compatibility balance, and draft-04 only for legacy system integration. Our validator supports all three versions.
Why does validation fail even though my JSON looks correct?
Common issues include: data type mismatches (strings vs numbers), missing required fields, incorrect format validation (email, date), or additional properties when they're not allowed. Check the detailed error messages with JSON paths to identify specific issues.
How can I validate large JSON files efficiently?
For large files: pre-compile schemas for better performance, use streaming validation where possible, implement selective validation for changed data only, and consider breaking large schemas into smaller, reusable components using $ref.
Can I create custom validation formats beyond built-in ones?
While our online validator supports standard formats (email, uri, date-time, etc.), for custom formats you'll need to implement them in your application code or use libraries that support custom format definitions. Consider using pattern validation with regular expressions as an alternative.
How do I handle schema evolution and versioning?
Plan for schema evolution by: using semantic versioning for schemas, maintaining backward compatibility where possible, implementing gradual migration strategies, supporting multiple schema versions during transitions, and documenting breaking changes clearly.