Is JSON Structure the Future of Schema Languages for Messaging and Data Interchange

In a world where systems constantly exchange structured data, having robust and versatile schema definitions is more crucial than ever. As organizations rely on queues, event streams, and messaging topics, the limitations of existing schema languages become apparent, especially when aiming for seamless code generation or clear database mapping. The new JSON Structure language emerges to bridge these gaps, offering a strictly typed, modern solution designed for smooth data interchange, not just validation.

Limitations of Existing Schema Options

Developers often start with JSON Schema due to its simplicity, but soon encounter challenges in complex scenarios. While JSON Schema is the go-to for document validation, it lacks vital features needed for code generation and precise data mapping. Tooling can be inconsistent or inadequate for intricate use cases. 

Alternatives such as Avro and Protobuf support code generation but are tightly coupled to specific serialization frameworks, making them less ideal for polyglot, distributed teams. To address these widespread pain points, Microsoft and collaborators have introduced JSON Structure as a neutral, standards-driven alternative.

What Sets JSON Structure Apart?

JSON Structure was purpose-built for describing JSON-encoded data in a way that aligns naturally with programming languages and databases. Its familiar appearance for those used to JSON Schema masks its focus on type definition and code generation, with validation treated as an additional layer. Noteworthy features include:

Copy

• Rich date/time types like date Copy, time Copy, datetime Copy, and duration Copy
• Extended compound types: set Copy, map Copy, tuple Copy, and choice Copy for discriminated unions
• Namespaces and modular imports to organize schemas effectively
• Annotations that add metadata for currency and measurement units
• Precise numeric types such as int32 Copy, int64 Copy, decimal Copy (with precision/scale), float Copy, and double

Here's a simple example: a postal address schema on the left, and the SQL Server table definition generated by running structurize struct2sql postaladdress.json --dialect sqlserver on the right:

## JSON Structure Schema
{
  "$schema": "https://json-structure.org/meta/extended/v0/#",
  "$id": "https://example.com/schemas/PostalAddress.json",
  "name": "PostalAddress",
  "description": "A postal address for shipping or billing",
  "type": "object",
  "properties": {
    "id": { 
      "type": "uuid",
      "description": "Unique identifier for the address"
    },
    "street": { 
      "type": "string", 
      "description": "Street address with house number"
    },
    "city": { 
      "type": "string", 
      "description": "City or municipality"
    },
    "state": { 
      "type": "string", 
      "description": "State, province, or region"
    },
    "postalCode": { 
      "type": "string", 
      "description": "ZIP or postal code"
    },
    "country": { 
      "type": "string", 
      "description": "ISO 3166-1 alpha-2 country code"
    },
    "createdAt": {
      "type": "datetime",
      "description": "When the address was created"
    }
  },
  "required": ["id", "street", "city", "postalCode", "country"]
}

## Generated SQL Server DDL

CREATE TABLE [PostalAddress] (
    [id] UNIQUEIDENTIFIER,
    [street] NVARCHAR(200),
    [city] NVARCHAR(100),
    [state] NVARCHAR(50),
    [postalCode] NVARCHAR(20),
    [country] NVARCHAR(2),
    [createdAt] DATETIME2,
    PRIMARY KEY ([id], [street], [city], 
                 [postalCode], [country])
);

EXEC sp_addextendedproperty 
  'MS_Description', 
  'A postal address for shipping or billing', 
  'SCHEMA', 'dbo', 
  'TABLE', 'PostalAddress';

EXEC sp_addextendedproperty 
  'MS_Description', 
  'Unique identifier for the address', 
  'SCHEMA', 'dbo', 
  'TABLE', 'PostalAddress', 
  'COLUMN', 'id';

EXEC sp_addextendedproperty 
  'MS_Description', 
  'Street address with house number', 
  'SCHEMA', 'dbo', 
  'TABLE', 'PostalAddress', 
  'COLUMN', 'street';

-- ... additional column descriptions

Real-World Schema Example

Consider an OrderEvent Copy schema: it defines required fields with specific types (like uuid Copy for IDs, decimal Copy for monetary values), ensuring clear mapping to various programming languages. Its status Copy property uses discriminated unions to represent states such as pending Copy, shipped Copy, and delivered Copy, each with their own structure. Serialization is consistent, choices appear as objects with a single key, decimals are strings to avoid floating-point errors, ensuring unambiguous data exchange across systems.

Why Schema Clarity Matters in Messaging

Robust, consistent schema definitions are essential when integrating through platforms like Azure Service Bus, Event Hubs, or Event Grid. Producers and consumers often use different languages, making a schema language that supports clean code generation across C#, Python, TypeScript, and others a necessity. JSON Structure’s comprehensive type system ensures that data types align perfectly with mainstream languages, reducing integration friction and costly errors.

SDKs and Tooling Ecosystem

JSON Structure isn’t just a specification,it comes with SDKs for major languages, including TypeScript, Python, .NET, Java, Go, Rust, Ruby, Perl, PHP, Swift, and C. These SDKs provide both schema and instance validation. Additionally, a VS Code extension offers IntelliSense and real-time diagnostics, streamlining developer workflows.

The Structurize tool further accelerates development by generating database schemas (SQL DDL) and self-serializing classes from schema definitions. It also converts between JSON Structure and other formats like Avro, Protobuf, and JSON Schema, making integration with existing data pipelines straightforward.

Industry Standardization and Community Involvement

Submitted as Internet Drafts to the IETF, JSON Structure is on track to become a formal RFC and industry standard. The open, community-driven approach encourages feedback and contributions, ensuring the language evolves to meet practical demands. Developers are invited to follow progress, review specifications, and participate via GitHub.

A Schema Language for the Future

JSON Structure fills a vital gap in the data landscape, offering a strictly typed, extensible schema language for code generation, validation, and interoperable data interchange. Its thoughtful design directly addresses the shortcomings of existing tools, empowering organizations to build reliable, maintainable distributed systems. As adoption grows and tooling advances, JSON Structure is poised to become the new gold standard for JSON data definition.

Source: Microsoft Community Hub , JSON Structure: A JSON schema language you'll love