Apache Avro is a data serialization system that provides rich data structures, a compact binary format, and schema evolution capabilities. This tool converts XML data into Avro schema definitions with automatic type detection.

Features

Automatic Type Detection: Detects int, long, double, boolean, and string types
Nullable Fields: Option to make all fields nullable with union types
Custom Schema Names: Specify custom record name and namespace
Sample Data: Optionally include sample JSON data for testing
Field Sanitization: Ensures field names are valid Avro identifiers

Supported Data Types

int: 32-bit signed integers

long: 64-bit signed integers

double: Double precision floating point

boolean: True/false values

string: Unicode text strings

null: Null values (when nullable)

Example

Input XML:

<products>
  <product>
    <id>1</id>
    <name>Laptop</name>
    <price>999.99</price>
  </product>
</products>

Output Avro Schema:

{
  "type": "record",
  "name": "Record",
  "namespace": "com.example",
  "fields": [
    {
      "name": "id",
      "type": [
        "null",
        "int"
      ],
      "default": null
    },
    {
      "name": "name",
      "type": [
        "null",
        "string"
      ],
      "default": null
    },
    {
      "name": "price",
      "type": [
        "null",
        "double"
      ],
      "default": null
    }
  ]
}

Use Cases

Apache Kafka message schemas
Hadoop data serialization
Apache Spark data processing
Data lake storage formats
Schema registry integration
Cross-language data exchange

FAQ

Q: How are Avro field types chosen from XML values?
A: The tool scans all non-empty values in each column and picks the narrowest common Avro type: int, long, double, boolean, or string. Mixed-type columns fall back to string.
Q: What does the "Make fields nullable" option do?
A: When enabled, each field type becomes a union like {"null", "int"} with a default of null. This matches common patterns used with schema registries and Kafka.
Q: How should I choose the schema name and namespace?
A: Use a meaningful record name (for example, Product) and a reverse-DNS namespace such as com.example.catalog. These identifiers appear in your Avro files and schema registry.
Q: What is the purpose of the optional sample data block?
A: The JSON array appended after the schema shows how real records would look when encoded with the generated schema. It is meant for documentation and quick testing, not as part of the .avsc file.
Q: Can I use the schema directly with Kafka and schema registries?
A: Yes. Save only the top JSON object (the Avro schema) to a .avsc file and register it with your schema registry or use it in your Kafka producer/consumer configuration.