Why Schema First?
While EDA promotes loose coupling, event schemas inherently form a tight contract between producers and consumers. Let’s explore why this contract matters and how a Schema Registry helps maintain compatibility.
Purpose of Event Schemas
- Define structure and format of event data.
- Enforce data consistency between producers and consumers.
- Enable validation, compatibility, and documentation.
If you’re familiar with REST APIs, this is similar to defining OpenAPI contracts between services:
Event streams function the same way—producers emit events conforming to predefined schemas; consumers process them based on those expectations.
Bad Example
{
"user_id": 123,
"user_action": 1
} // action code instead of expected string
Even with agreed-upon schemas, schema drift can occur—leading to broken consumers. Much like skipping ERD when designing databases, skipping event schemas is risky in EDA.
Common Schema Formats
| Format | Pros | Cons |
|---|---|---|
| JSON | Human-readable, widely supported | Large size, lacks strong validation |
| Protobuf | Compact, fast, schema-enforced | Hard to debug, needs precompiled schema |
| Avro | Compact binary, supports schema evolution | Less widely adopted, tooling gaps in some ecosystems |
Text formats like JSON are appealing for debugging. But size and speed matter in stream processing.
JSON Suitability Checklist
Use JSON only if:
- ✅ Messages are small.
- ✅ You can tolerate slow (de)serialization.
- ✅ Strong type validation isn’t required.
- ✅ You don’t need a schema registry.
- ✅ Volume of messages will remain low.
- ✅ Debugging via raw payload is helpful.
Advantages of Avro / Protobuf
- Strong typing and schema enforcement
- Fast (de)serialization
- Built-in backward/forward compatibility
Even small messages show over 2x performance gains in binary formats. The difference increases with message size.
Impact on Kafka
- Text-based formats like JSON consume more storage and network bandwidth.
- High volume = performance degradation at produce/consume phases.
What Is a Schema Registry?
A Schema Registry stores and version-controls data schemas.
Benefits:
- Enforces compatibility
- Enables schema evolution (backward/forward)
- Minimizes payload size by referencing schemas via ID
- Centralized schema governance
Schema Evolution in Action
- Producer publishes an event with schema v2.
- Consumer detects version mismatch and fetches v2 from registry.
- Consumer proceeds with updated schema.
No coordination required. Zero downtime schema upgrades!
Schema Registry vs. Schemaless
| Format | With Schema Registry | Without Schema Registry |
|---|---|---|
| JSON | ❌ Schemaless, can’t validate or evolve | ✅ Easy to debug, but lacks structure |
| Protobuf | ✅ Strong schema + evolution support | ❌ Needs .proto file everywhere |
| Avro | ✅ Compact, evolvable binary format | ❌ Schema must be embedded in each message |
Using a Schema Registry with schema ID avoids inflating messages with repeated schema data. This helps keep message sizes small.
Central Schema vs. Shared Code
- Registry = Central governance, live updates.
- Submodule
.proto= Tight coupling, manual versioning.
AWS Glue vs. Confluent Schema Registry
| Feature | AWS Glue Registry | Confluent Schema Registry |
|---|---|---|
| Schema versioning | ✅ Supported | ✅ Supported |
| URL persistence | ✅ ARN-based | ✅ REST endpoint-based |
| Auto upgrade for consumer | ❌ Needs explicit fetch | ✅ Auto fetch |
| Kafka support | ✅ MSK | ✅ Confluent Kafka |
Why Use a Schema Registry?
- Guarantee Compatibility: Prevent mismatched producer-consumer schemas.
- Support Evolution: Add/remove fields without breaking clients.
- Centralized Governance: No more shared
.protoheadaches. - Smaller Messages: Send schema ID, not full schema.
- Schema Validation: Prevent invalid data from entering the stream.
- Dynamic Updates: Auto-fetch new schemas at runtime.
- Compatibility Policies: Enforce forward/backward rules.
- Schema Auditing: View changes via REST API or UI.