Avro Schema Validator
2025Validate Avro schema definitions (JSON) against the specification
🔧 Your Avro Schema
📋What is an Avro Schema?
Avro is a data serialization system developed by Apache that uses JSON-based schemas to define data structures. An Avro schema describes the format of your data, including field names, types, and default values.
Schemas are essential in Avro because they enable:
- Efficient binary serialization/deserialization
- Schema evolution (forward and backward compatibility)
- Data validation at read/write time
- Language-neutral data representation
Avro schemas support primitive types (string, int, boolean, etc.), complex types (records, arrays, maps, enums), and logical types (decimal, date, timestamp) that extend the primitive types.
🔍Why Validate Avro Schemas?
Validating your Avro schemas before deployment helps prevent data processing errors and ensures compatibility between systems. Here's why validation matters:
- Catch errors early: Identify schema issues before they affect production systems
- Ensure compatibility: Verify that schemas meet Avro specification requirements
- Prevent data corruption: Validate that default values match field types
- Check namespace resolution: Confirm that type references are properly resolved
- Verify logical types: Ensure logical type attributes (precision, scale) are correctly specified
🔒 Our validator runs entirely in your browser - your schemas never leave your device.
❓Frequently Asked Questions
This validator implements Avro's specification for schema validation, checking:
- All required fields are present
- Type definitions are valid
- Names follow Avro naming conventions
- Default values match field types
- Namespace resolution works correctly
- Logical types are properly specified
Note: It does not check reader/writer compatibility between schema versions.
No. All validation runs entirely in your browser—nothing is uploaded to any server. Your schemas remain private on your machine.
The validator supports Avro 1.8+ specifications, including:
- All primitive types (null, boolean, int, long, etc.)
- Complex types (records, arrays, maps, unions, enums, fixed)
- Logical types (decimal, date, time, timestamp, etc.)
- Schema references and namespaces
Common fixes for validation errors:
- Missing required fields: Ensure all record fields have name and type
- Invalid type names: Check for typos in type specifications
- Namespace issues: Verify fully-qualified names for referenced types
- Default value mismatches: Ensure defaults match the declared field type
- Enum errors: Check that symbols are unique and non-empty
Use our example schemas as references for correct syntax.