JSON to Avro Schema — Free Online Avro Schema Generator

What Is Apache Avro?

Apache Avro is a data serialization system that uses JSON-based schemas to define data structures, then serializes data in a compact binary format. It is a core technology in the Apache big data ecosystem.

Key properties:

Where Apache Avro Is Used

What Is an Avro Schema?

An Avro schema is a JSON document that defines the structure of serialized data. It specifies field names, types, and optional default values.

{
 "type": "record",
 "name": "User",
 "namespace": "com.example",
 "fields": [
   {"name": "id", "type": "int"},
   {"name": "name", "type": "string"},
   {"name": "email", "type": ["null", "string"], "default": null},
   {"name": "active", "type": "boolean"}
 ]
}

Avro Schema Components

Avro Primitive Types

Avro Complex Types

Avro vs. JSON vs. Protocol Buffers

JSON-to-Avro Type Mapping

Without Smart Types (All Strings)

With Smart Types Enabled

Why Convert JSON to Avro Schema?

Core Features

1. Dual Input Modes

• File Upload: Drag and drop or select a .json file.

• Code Editor: Paste or type raw JSON with syntax highlighting and real-time validation.

2. Record Name

Sets the name field in the Avro schema. This identifies the record type.

{
 "type": "record",
 "name": "Order",
 ...
}

Use PascalCase by convention: User, Order, SensorReading, PageView.

3. Namespace

Sets the namespace field — a dot-separated identifier that uniquely qualifies the schema name.

{
 "type": "record",
 "name": "User",
 "namespace": "com.example.events",
 ...
}

Conventions:

• Java-style: com.company.project.module

• Domain-based: io.company.events

The fully qualified name becomes com.example.events.User.

4. Output Format

Use Schema Only for registering in Schema Registry. Use Schema + Data for documentation and testing.

5. Indent

Controls output JSON formatting:

6. Smart Types

When enabled, the tool infers Avro types from JSON values instead of defaulting all fields to string.

Smart Types enabled:

{"name": "id", "type": "int"}
{"name": "price", "type": "double"}
{"name": "active", "type": "boolean"}

Smart Types disabled:

{"name": "id", "type": "string"}
{"name": "price", "type": "string"}
{"name": "active", "type": "string"}

Enable Smart Types for type-safe schemas. Disable for schemas where all values should be treated as strings (e.g., CSV imports with no type information).

7. Parse JSON — Include Null

When enabled, adds null to union types for fields that may contain null values.

Enabled:

{"name": "email", "type": ["null", "string"], "default": null}

Disabled:

{"name": "email", "type": "string"}

Enable this when your JSON data may contain null values. In Avro, nullable fields must use union types ["null", "type"]. Without this, null values in your data will cause serialization errors.

8. Privacy by Design

All processing runs entirely in your browser. No data is uploaded to any server.

How to Use This JSON to Avro Schema Generator

Step 1: Provide Your JSON Data

Choose one of two input methods:

• Upload a file: Click "Choose File" and select a .json file, or drag it into the upload area.

• Paste data: Click "Enter Data" to switch to the code editor. Paste your JSON array.

Important: The tool expects a JSON array of objects.

Step 2: Configure Avro Schema

Use the Properties panel on the right:

1. Record Name: Enter a name (e.g., User, Order, Event).

2. Namespace: Enter a namespace (e.g., com.example.events).

3. Output Format: Choose Schema + Data or Schema Only.

4. Indent: Select formatting style.

5. Smart Types: Enable to infer int/boolean/double from JSON values.

6. Parse JSON — Include Null: Enable to add null to union types.

Step 3: Convert

Click Convert. The Avro schema appears in the "Output Data" panel.

Step 4: Copy or Download

• Click Copy to Clipboard to paste into your schema file.

• Premium users can click Download File to save as .avsc.

Complete Example

Example — User Event Schema

Input JSON:

[
   {"id": 1, "name": "Alice", "email": "[email protected]", "active": true, "score": 95.5},
   {"id": 2, "name": "Bob", "email": null, "active": false, "score": 88.0},
   {"id": 3, "name": "Charlie", "email": "[email protected]", "active": true, "score": 72.3}
]

Configuration:

• Record Name: UserEvent

• Namespace: com.example.events

• Output Format: Schema Only

• Indent: 2 spaces

• Smart Types: On

• Include Null: On

Output:

{
 "type": "record",
 "name": "UserEvent",
 "namespace": "com.example.events",
 "fields": [
   {"name": "id", "type": "int"},
   {"name": "name", "type": "string"},
   {"name": "email", "type": ["null", "string"], "default": null},
   {"name": "active", "type": "boolean"},
   {"name": "score", "type": "double"}
 ]
}

Example — Kafka Order Events

Input JSON:

[
{"order_id": "ORD-001", "total": 149.99, "items": 3, "status": "shipped", "timestamp": 1715011200},
{"order_id": "ORD-002", "total": 59.50, "items": 1, "status": "pending", "timestamp": 1715011300}
]

Configuration:

• Record Name: OrderEvent

• Namespace: com.company.orders

• Smart Types: On

• Include Null: Off

Output:

{
 "type": "record",
 "name": "OrderEvent",
 "namespace": "com.company.orders",
 "fields": [
   {"name": "order_id", "type": "string"},
   {"name": "total", "type": "double"},
   {"name": "items", "type": "int"},
   {"name": "status", "type": "string"},
   {"name": "timestamp", "type": "long"}
 ]
}

Register in Confluent Schema Registry:

curl -X POST \
 http://localhost:8081/subjects/order-events-value/versions \
 -H "Content-Type: application/vnd.schemaregistry.v1+json" \
 -d '{"schema": "..."}'

Example — All Strings (Smart Types Off)

Configuration:

• Smart Types: Off

Output:

{
 "type": "record",
 "name": "Data",
 "fields": [
   {"name": "id", "type": "string"},
   {"name": "name", "type": "string"},
   {"name": "active", "type": "string"}
 ]
}

Use this when all data should be treated as strings (e.g., CSV-to-Avro conversion with no type info).

Frequently Asked Questions （FAQ）

• Does the tool upload my data?

  No. All conversion happens locally in your browser using JavaScript. Your data never leaves your device.

• What is Apache Avro?

  Apache Avro is a data serialization system that uses JSON-based schemas and compact binary serialization. It is widely used in Apache Kafka, Hadoop, Spark, and big data pipelines.

• What is an Avro schema?

  An Avro schema is a JSON document that defines the structure of serialized data — field names, types, namespaces, and defaults. Data is serialized in binary using the schema.

• What is Smart Types?

  When enabled, Smart Types infers Avro types from JSON values: integers become int, floats become double, booleans become boolean. When disabled, all fields default to string.

• What does Include Null do?

  It adds null to union types for fields that may contain null values. In Avro, nullable fields must use union types like ["null", "string"]. Without this, null values cause serialization errors.

• What is a namespace?

  A namespace uniquely qualifies the schema name, similar to a Java package. Example: com.example.events. The fully qualified name becomes com.example.events.UserEvent.

• What is the difference between Schema Only and Schema + Data?

  Schema Only outputs just the Avro schema definition. Schema + Data outputs the schema followed by the JSON data formatted according to it. Use Schema Only for Schema Registry registration.

• What file extension should I use?

  Use .avsc for Avro Schema files and .avro for Avro data files.

• Can I use the output with Confluent Schema Registry?

  Yes. The generated schema is standard Avro JSON format compatible with Confluent Schema Registry, Kafka producers/consumers, and all Avro implementations.

• Is there a file size limit?

  The tool processes data entirely in your browser. Files up to 10 MB typically convert without issues on modern hardware.