🔄 Data Sharing Between Microservices
Microservices promote loose coupling and independent data ownership, but real-world systems often require some level of data sharing across services. The challenge is to maintain service boundaries without creating tight coupling.
🧱 1. Database per Service
Each service owns and manages its own database.
Pros:
- Clear boundaries and autonomy
- Better encapsulation and scalability
Cons:
- Makes sharing data more complex
- Requires communication between services
🔁 2. Synchronous Data Sharing (via APIs)
One service queries another via HTTP/gRPC to fetch needed data.
Service A → (HTTP/REST) → Service B: GET /users/123
Use when:
- Real-time data is required
- Data ownership must remain within a service
Challenges:
- Coupling through network dependency
- Adds latency and risk of cascading failures
Tips:
- Use retries, timeouts, circuit breakers (e.g., Hystrix)
- Consider API Gateways or BFFs to simplify API access
📩 3. Asynchronous Data Sharing (via Events)
Services publish events when data changes (event-driven architecture).
User Service emits: UserCreated { user_id: 123, name: "Alice" }
Order Service subscribes and updates its local copy
Pros:
- Loose coupling
- High performance and resilience
Cons:
- Eventual consistency
- Debugging is more complex
Tools:
- Kafka, RabbitMQ, NATS, Amazon SNS/SQS
🗃️ 4. Data Replication (Read Models or Caches)
One service maintains a read-optimized copy of another service’s data, often updated via events or batch jobs.
Order Service maintains a local cache of User data for display purposes.
Use when:
- Fast read access is required
- You want to avoid cross-service calls
Best practices:
- Make it read-only
- Keep it eventually consistent
🔍 5. Shared Libraries or SDKs
For internal APIs, shared SDKs can encapsulate access logic.
user_sdk.get_user_profile(user_id)
Pros:
- Standardizes access
- Easier updates to logic
Cons:
- Versioning and deployment overhead
- Should not contain business logic
🚧 Anti-Pattern: Shared Database
Multiple services writing to the same database is a common anti-pattern.
Problems:
- Tight coupling of schema and deployment
- Violates the microservices principle of autonomy
Only acceptable:
- For legacy systems or during transitional phases
- If all services are tightly integrated and owned by a single team
✅ Best Practices
- Apply event-driven architecture for real-time async updates
- Use read replicas or materialized views for high-read access
- Respect bounded contexts from Domain-Driven Design
- Implement schema versioning and backward compatibility for events
- Use idempotent consumers and retry logic in async flows
🧭 Choosing a Strategy
| Use Case | Best Option |
|---|---|
| Real-time, latest data needed | Synchronous API (REST/gRPC) |
| Loose coupling + eventual sync | Event-driven architecture |
| High-volume reads, low writes | Local cache or read replica |
| Internal tooling | Shared SDK or internal proxy |
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