From Monolithic to Microservices: Best Practices for Database Management During Refactoring

Introduction

As organizations scale, many find that monolithic architectures start limiting their growth. Microservices offer a solution, enabling independent scaling, faster deployments, and enhanced resilience. However, transitioning from a monolithic codebase to microservices presents unique challenges, especially around database management. This article will explore best practices for managing databases during the transition, ensuring a smooth migration while preserving data consistency and integrity.

Starting with the Old Database: Why It's Important

A common question in the refactoring process is whether to create new databases for each microservice immediately or to start by developing around the old database. Here’s why sticking with the existing database (at least initially) can benefit the transition:

• Data Integrity: Using the old database ensures a single source of truth, reducing the risk of inconsistencies.
• Incremental Refactoring: Microservices are often built incrementally, requiring access to shared data. Maintaining the monolithic database allows services to use data as they are developed.
• Lower Initial Complexity: Avoids the complexity of immediately migrating or duplicating data, which could introduce unforeseen challenges.

Establish Boundaries and Define Ownership for New Databases

A key principle in microservices is that each service should own its data. However, transitioning to this model requires thoughtful planning. Here’s how to gradually shift data ownership to individual services:

• Define Service Boundaries: Break down your monolithic application by identifying service boundaries. Group functionalities into logical services, each responsible for a specific business capability.
• Create New Databases with Ownership: As each service matures, assign it a dedicated database where it is the sole owner of that data. This promotes independent scaling and reduces inter-service dependencies.

Managing Schema Changes with Spring Boot Tools

Spring Boot offers tools to manage schema evolution, helping you apply schema changes per microservice independently:

• Flyway or Liquibase: Both are popular options for managing database schema changes in Spring Boot applications. Each service can version-control its schema, enabling smooth database evolution.
• Incremental Schema Changes: Apply schema changes gradually and version-control them to avoid disruptions. Ensure that services consuming shared data agree on schema changes to prevent compatibility issues.

Here are some great resources to help you integrate Flyway and Liquibase with Spring Boot for database migrations. For Flyway, Baeldung provides an in-depth setup guide covering the Flyway Maven plugin and managing schema changes (Baeldung). Reflectoring explains Flyway’s SQL and Java-based migrations with CI/CD tips (Reflectoring), while DEV Community offers a beginner-friendly setup guide (DEV Community). For Liquibase, Baeldung covers YAML, XML, and JSON change logs and best practices (Baeldung), and Spring Framework Guru provides setup tips and XML-based change log examples.

Communication and Consistency in a Distributed Environment

During the transition, services will need to communicate and manage data dependencies effectively. Here’s how to handle this complexity:

• API Gateways and Messaging: Use API gateways for RESTful communication and message brokers (e.g., Kafka, RabbitMQ) for event-driven data sharing. Messaging ensures that services can asynchronously share updates, promoting eventual consistency.
• Handling Shared Data: Implement patterns like CQRS (Command Query Responsibility Segregation) or Event Sourcing to manage data access and update consistency. These patterns can optimize data flow between services and reduce latency.

Testing and Monitoring: The Key to a Stable Transition

Monitoring and testing are essential to ensure that the new microservices architecture maintains data consistency and performance. Consider the following best practices:

• Automated Testing: Create automated tests for each service, covering database interactions, data consistency, and inter-service communication.
• Centralized Monitoring: Use centralized logging and monitoring tools to track the health of each service. Tools like Prometheus and Grafana allow you to visualize service interactions and data flows, helping you detect issues early.

Here are some tools for monitoring and testing in microservices. Prometheus and Grafana are popular for tracking time-series data and visualizing performance trends, especially in Kubernetes environments (Atlassian, Liquid Web). Zipkin and Jaeger offer essential distributed tracing to identify bottlenecks by tracking request flows across services (TechBeamers, UnYaml). Datadog and AWS CloudWatch provide comprehensive cloud-native monitoring, with CloudWatch tailored for AWS environments (UnYaml, Stackify). Spring Boot Actuator is a lightweight option for Spring Boot applications, exposing endpoints for monitoring health and metrics (TechBeamers). These tools support effective observability in complex microservices architectures.

Conclusion: Taking a Gradual, Controlled Approach

Migrating from a monolithic database to microservices databases is a complex but achievable process. A gradual, well-managed approach is crucial, allowing your organization to reap the benefits of microservices without compromising data integrity or system stability.

Refactoring is a journey that requires flexibility and a willingness to adapt as you discover new patterns and practices. By following these best practices, your organization can unlock the full potential of microservices while ensuring data integrity throughout the transition.

Reference Material:

Pattern: Database per service

Monoliths to microservices using domain-driven design - Azure Architecture Center | Microsoft Learn

Refactor a monolith into microservices ?|? Cloud Architecture Center ?|? Google Cloud

Database Design in a Microservices Architecture | Baeldung on Computer Science