Refactoring Microservices to Eliminate Shared Database

Refactoring Microservices A and B to Eliminate Shared Database Dependency

Problem Statement

Microservices A and B share a database for certain entities due to a legacy design. When Microservice A updates an entity, Microservice B does not immediately reflect the change, leading to inconsistent behavior. This blog post outlines a step-by-step refactoring plan to eliminate the shared database dependency while ensuring zero downtime. 

Refactoring Strategy

1. Introduce Data Ownership

• Objective: Establish a single source of truth for shared entities.
• Steps:
  • Identify which microservice (A or B) is the primary owner of each entity based on business logic.
  • Assign ownership (e.g., Microservice A owns the "User" entity).

2. Create a New Database for Microservice B

• Objective: Provide Microservice B with its own database to store entity data.
• Steps:
  • Set up a new database for B, matching the schema requirements for the relevant entities.
  • Plan for initial data population and ongoing synchronization during the transition.

3. Implement Event-Driven Synchronization

• Objective: Keep B’s database in sync with A’s changes using an event-driven approach.
• Steps:
  • Configure Microservice A to publish events (e.g., UserUpdated) to a message broker (Kafka, RabbitMQ, or AWS SNS/SQS) when an entity is updated.
    
  • Configure Microservice B to subscribe to these events and update its local database.
  • Ensure events contain sufficient data (full entity state or deltas) for B to process updates.
  • Use a reliable message broker with at-least-once delivery to prevent data loss.

4. Gradual Migration with Dual-Write Strategy

• Objective: Transition B to its own database without downtime.
• Phases:
  • Phase 1: Dual Reads and Writes
    • Update B to read from both the shared database and its new database, prioritizing the new database when data is available.
    • A updates the shared database and publishes events to update B’s database.
    • Run a sync job to copy existing data from the shared database to B’s database.
  • Phase 2: Write to B’s Database Only
    • Once B’s database is fully populated and verified, configure B to write only to its new database.
    • Continue syncing via events from A.
  • Phase 3: Remove Shared Database Dependency
    • Update B to read exclusively from its own database.
    • Remove B’s access to the shared database.

5. Handle Consistency

• Objective: Manage eventual consistency introduced by event-driven updates.
• Steps:
  • Acknowledge minor delays in B reflecting A’s updates due to event processing.
  • For critical operations, allow B to query A’s API (if available) for the latest state.
  • Use correlation IDs in events to track and deduplicate updates.
  • Implement monitoring to detect and alert on event processing failures or delays.

6. Ensure Zero Downtime

• Objective: Deploy changes without service interruptions.
• Steps:
  • Use blue-green deployments or canary releases for incremental updates.
  • Maintain backward compatibility (e.g., B falls back to the shared database if needed).
  • Test the event pipeline in a staging environment.
  • Use feature flags to toggle between shared database and event-driven behavior in B.

7. Clean Up

• Objective: Remove legacy dependencies post-migration.
• Steps:
  • Remove B’s legacy code for accessing the shared database.
  • Optimize A’s database schema if B’s departure allows simplification.

8. Long-Term Improvement

• Objective: Prevent similar issues and enhance decoupling.
• Steps:
  • Introduce APIs for synchronous communication if eventual consistency is problematic.
  • Apply domain-driven design to align entity boundaries with microservice responsibilities.

Tools and Technologies

• Message Broker: Kafka, RabbitMQ, or AWS SNS/SQS.
• Database: PostgreSQL, MongoDB, or other based on existing stack.
• Monitoring: Prometheus, Grafana, or ELK stack for event pipeline health.
• Schema Management: Flyway or Liquibase for B’s database migrations.

Challenges and Mitigations

• Data Sync Errors:
  • Implement idempotent event handling in B.
  • Use a reconciliation job to fix inconsistencies.
• Performance:
  • Optimize event processing for low latency.
  • Batch initial data migration to avoid system overload.
• Testing:
  • Use contract testing (e.g., Pact) to validate events.
  • Simulate message broker failures to ensure robustness.

Conclusion

This refactoring approach leverages an event-driven architecture and phased migration to eliminate the shared database dependency between Microservices A and B. By implementing dual reads/writes, event synchronization, and careful deployment strategies, the system achieves consistency and zero downtime, paving the way for a more scalable and maintainable architecture.