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How to Validate Avro Schemas - Complete Guide

Step 1

Input Your Avro Schema

Get your Apache Avro schema ready for validation. You have several convenient options:

•
Paste directly: Copy your Avro schema from your IDE or schema registry
•
Upload file: Click the Upload button to select an .avsc or .json file
•
Try sample: Click the Sample button to see a working example schema

Example Avro schema:

{
  "type": "record",
  "name": "User",
  "namespace": "com.example.avro",
  "fields": [
    { "name": "id", "type": "int" },
    { "name": "name", "type": "string" },
    { "name": "active", "type": "boolean" }
  ]
}
Step 2

Automatic Schema Validation

Watch as your Avro schema is validated in real-time using the official avsc library. The validator checks for:

•
JSON syntax: Ensures proper JSON structure with matching brackets and commas
•
Schema structure: Validates record type, name, namespace, and fields
•
Type validation: Checks all field types are valid Avro types (int, long, float, double, string, boolean, etc.)
•
Nested structures: Validates complex nested records and arrays
•
Kafka compatibility: Ensures schema works with Kafka Schema Registry
Step 3

Download or Copy Validated Schema

Once your schema is validated, you can easily save or share it:

•
Copy to clipboard: One-click copying for quick pasting into your code or schema registry
•
Download as file: Save as an .avsc file for version control
•
Real-time validation: Instant feedback as you type or edit your schema

Use your validated schema in:

•
Apache Kafka: Register with Kafka Schema Registry for data streaming
•
Hadoop ecosystem: Use with Hive, Pig, Spark for big data processing
•
Data pipelines: Ensure data integrity in ETL processes

What is an Avro Schema?

An Avro schema is a JSON document that defines the structure and data types of your data. Created by Apache, Avro schemas enable:

  • •Schema evolution: Backward and forward compatibility as your data structure changes
  • •Data validation: Type checking and structure enforcement at serialization time
  • •Efficient encoding: Compact binary serialization for fast data transfer
  • •Language interoperability: Use the same schema across Java, Python, C++, and more

Frequently Asked Questions

How do I validate an Avro schema?

Simply paste your Avro schema into the validator. The tool automatically checks JSON syntax, schema structure, field types, and compatibility with Apache Avro standards. You'll get instant feedback with detailed error messages if issues are found.

What Avro types are supported?

The validator supports all Avro primitive types (null, boolean, int, long, float, double, bytes, string) and complex types (record, enum, array, map, union, fixed). It also validates nested structures and namespace declarations.

Can I use this with Kafka Schema Registry?

Yes! This validator uses the official avsc library, ensuring full compatibility with Kafka Schema Registry and Confluent Platform. Validate your schemas before registration to avoid deployment issues.

What are common Avro schema errors?

Common errors include: missing required fields (type, name, fields), invalid type names, incorrect JSON syntax, circular references in nested records, and invalid namespace formats. The validator provides specific error messages to help you fix these quickly.

How do I create an Avro schema from JSON data?

Use our Avro Schema Generator to automatically create Avro schemas from JSON data. It infers types and generates proper schema structure for you. Then validate the generated schema here.

Is this Avro schema validator free?

Yes, completely free with no limitations on schema size or validation frequency. No registration required. Validate unlimited Avro schemas with real-time feedback and detailed error messages.

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