Introduction
In modern distributed systems, services rarely share the same technology stack. Finance may use .NET, analytics may run on Python, inventory services may rely on Java, and real-time notifications may be built in Node.js.
When these services exchange domain events, the biggest challenge is ensuring that the event contracts remain stable, versioned, backward compatible, and language-independent.
This article explains how to design Distributed Domain Event Contracts that support:
The focus is practical: how to design, store, validate, publish, and evolve event structures across large enterprise systems.
Why Domain Event Contracts Matter
Distributed systems fail not because of infrastructure, but because of contract mismatches.
A producer publishes a new field.
A consumer breaks.
Compatibility errors propagate.
CI/CD becomes risky.
Teams begin to fear deployments.
A well-defined event contract layer prevents this.
Challenges in Polyglot Event Architectures
Technology Heterogeneity
Each service uses different languages, frameworks, and libraries.
Serialization Differences
.NET → JSON
Python → Avro
Java → Protobuf
Node → raw JSON
Event Evolution
How do you add/remove fields without breaking existing consumers?
Governance
Who owns the contract?
How do teams propose changes?
How are changes reviewed?
Tooling Gaps
Type generation must work across all stacks.
Architecture Overview
Below is the high-level architecture for distributed event contracts.
┌───────────────────────────────┐
│ Event Contract Registry │
│ (Schema Store + Versioning) │
└───────────┬────────────────────┘
│ Publish/Fetch
▼
┌─────────────────────────────────────────────┐
│ Code Generators (C#, Java, TS, Python, Go) │
└───────────┬──────────────────────────────────┘
│ Generate DTOs/Models
▼
┌──────────────────────────────────────────────────────────┐
│ Polyglot Services (.NET, Java, Node, Python, Go, Rust) │
└─────────────────┬──────────────┬───────────────┬────────┘
│ Produce/Consume Events
▼
┌─────────────────────────────┐
│ Message Broker (Kafka/ESB) │
└─────────────────────────────┘
The key rule:
Events are not owned by services. Events are owned by the domain. Contracts are owned by the organization.
Core Principles for Event Contract Design
1. Producers Must Never Break Consumers
Only additive changes are allowed.
Removals require deprecation periods and contract versioning.
2. Contract First, Code Second
Contracts are defined in the schema registry.
Code is generated from contracts.
3. Language Agnostic Contracts
Choose one:
4. Contracts Are Immutable Per Version
If you change a field:
New version → OrderCreatedEvent.v2
5. Consumers Decide When to Upgrade
Never force sudden consumer changes.
Contract Format Example (Avro)
{"type": "record","name": "OrderCreated","namespace": "com.company.order","version": "1.0","fields": [
{ "name": "orderId", "type": "string" },
{ "name": "customerId", "type": "string" },
{ "name": "amount", "type": "double" },
{ "name": "createdAt", "type": "string" }]}
Why Avro?
Example Contract Evolution (Safe Changes)
Version 1.0
customerId: stringamount: double
Additive Change (Allowed): Version 1.1
customerType: string (nullable)
discountApplied: double (nullable)
Consumers using 1.0 will continue functioning.
Unsafe Changes Requiring New Version
| Change | Must Bump Version? |
|---|
| Removing a field | Yes |
| Changing field type | Yes |
| Changing field meaning | Yes |
| Renaming a field | Yes |
| Breaking default values | Yes |
Example:
If you rename customerId → clientId,
then 2.0 version is required.
Sequence Diagram: Contract-Centered Event Flow
Producer Service Schema Registry Consumer Service
| | |
| --- Fetch Contract v1.1 ----------> | |
| | |
| --- Generate DTOs ----------------> | |
| | |
| --- Publish Event (OrderCreated) ---> |
| | |
| | --- Validate Against Schema -> |
| | |
| | <------ Subscribe ------------ |
| | |
| | ------> Deliver Event --------|
| | |
Ensuring Backward Compatibility
Use Nullable Fields for New Additions
"fields": [{ "name": "discountApplied", "type": ["null", "double"], "default": null }]
Do Not Change Field Semantics
If amount originally meant total after tax,
never change it to total before tax.
Deprecate Before Remove
Users need migration time.
Example
"deprecated": true
Contract Registry Design
A proper system must manage:
Versioning
Ownership
Deprecation
Documentation
CI validation
Change proposals
Recommended Tech
| Component | Options |
|---|
| Schema Registry | Confluent, Redpanda, Custom SQL-based |
| Contract Storage | Git, SQL Server, Mongo |
| Approval Workflow | Git PR, ADR, API portal |
Example SQL Server Table for Contract Storage
CREATE TABLE EventContracts (
ContractId INT IDENTITY PRIMARY KEY,
Domain NVARCHAR(200),
Name NVARCHAR(200),
Version NVARCHAR(20),
SchemaJson NVARCHAR(MAX),
Status NVARCHAR(20),
CreatedAt DATETIME DEFAULT GETDATE()
);
Code Generation Layer
Why Generate Code?
Because manually maintaining 5 versions in 6 languages leads to drift.
Sample .NET Generator Stub
public class AvroToCSharpGenerator
{
public string GenerateCSharpClass(string avroSchemaJson)
{
var schema = Avro.Schema.Parse(avroSchemaJson);
// Parse fields and auto-generate DTO
return csharpClass;
}
}
Sample TypeScript Generator Stub
function generateTs(schema: any): string {
return schema.fields
.map(f => ` ${f.name}?: ${mapTsType(f.type)};`)
.join('\n');
}
Domain Event Contract Governance
Ownership Model
| Area | Responsibility |
|---|
| Domain Teams | Define event semantics |
| Architecture | Ensure backward compatibility |
| Platform Team | Operate registry / generator |
| Service Teams | Consume contracts |
Contract Approval Workflow
Developer proposes a contract change
Pull Request opened
Event Contract Review Board checks for breaking changes
Schema published after approval
Code generators update models for all languages
Services pull new version when ready
This removes chaos and tribal knowledge.
Cross-Language Serialization Guidelines
Recommended Formats
| Format | Notes |
|---|
| JSON | Universal but heavy and slow |
| Avro | Best for polyglot + Kafka |
| Protobuf | Fastest, widely used |
| FlatBuffers | Good for embedded systems |
Avoid Service-Specific Serialization
Do not let .NET JSON be your global contract definition.
JSON.NET settings vary across services, leading to breaking changes.
Hybrid Strategy: Canonical Event Format + Local Envelope
Example:
Canonical (Shared)
OrderCreatedEvent
{
orderId: stringamount: doublecreatedAt: datetime
}
Local Envelope (Service-Specific)
{
tenantId: 2201,
correlationId: "a7f7...",
event: { ... canonical event ... }
}
Avoids mixing domain data and operational metadata.
Flowchart: Event Evolution Logic
┌─────────────────────────┐
│ Propose Contract Change │
└─────────────┬──────────┘
▼
┌───────────────────────────────┐
│ Is Change Backward Compatible? │
└───────────┬───────────────────┘
│Yes
▼
┌────────────────────────┐
│ Publish New Minor Ver │
└────────────────────────┘
│No
▼
┌──────────────────────────┐
│ Publish Major Version │
│ (New Contract) │
└──────────────────────────┘
Applying It to a Real System
.NET + Python + Node + Java**
.NET Producer Example
var avroWriter = new SpecificDatumWriter<OrderCreated>(schema);
using var ms = new MemoryStream();
avroWriter.Write(orderCreated, new BinaryEncoder(ms));
_kafkaProducer.Produce("orders", ms.ToArray());
Python Consumer Example
schema = avro.schema.parse(schema_json)
reader = DatumReader(schema)
event = reader.read(BinaryDecoder(payload))
Node.js Consumer Example
const message = avro.Type.forSchema(schema).fromBuffer(buffer);
Java Consumer (Kafka Streams)
OrderCreated event = orderSerde.deserializer().deserialize("orders", data);
All use the same contract.
Testing Strategy
Contract Tests
Does event conform to schema?
Are default values set?
Are optional fields nullable?
Consumer Driven Contracts (CDC)
Each consumer publishes a small test suite defining its expectations.
Producers must meet them before deploying.
Event Replay Compatibility
To support replay of older events:
Never modify old contract versions
Consumers should always understand older versions
Use version number inside header
Example
headers["event-version"] = "1.0"
Zero-Downtime Rollout Strategy
Publish new version: v2
Producers continue sending v1 for now
Consumers upgrade at their own pace
Producers begin dual-writing: v1 + v2
After 90 days, deprecate v1
Conclusion
Distributed domain event contracts form the backbone of scalable microservice ecosystems.
They ensure:
A proper contract registry and evolution process enables teams to build reliable event-driven systems without breaking downstream services.