Decomposing the Monolith
When building software, especially as a system grows, it often starts as a monolith—a single codebase that handles everything. While this might be easy to manage at first, it becomes hard to maintain, scale, and modify as complexity increases. To solve these issues, many teams choose to decompose the monolith into smaller, more manageable components.
Decomposing a monolith involves breaking down an application into smaller, independent services or modules. These smaller parts can be easier to understand, develop, and deploy. It also makes it easier to scale specific parts of the system instead of the whole monolith.
Let’s explore how this process works and the key steps you need to take.
Why Decompose a Monolith?
A monolith can quickly become a maintenance nightmare. As the codebase grows, adding new features without introducing bugs becomes harder. Deploying the monolith also means updating the entire system, even if you’ve only changed a small part. Furthermore, scaling is inefficient because you need to scale everything, not just the part of the system that needs it.
Benefits of Decomposition
- Scalability: You can scale individual components based on demand.
- Maintainability: Smaller codebases are easier to understand and maintain.
- Flexibility: You can use different technologies for different services.
- Independent Deployments: Changes to one service don’t require redeploying the entire system.
Approaches to Decomposition
There are several strategies for breaking a monolith into smaller parts, each with different levels of complexity and use cases. Let's discuss the most common approaches.
1. Horizontal Decomposition
In horizontal decomposition, you split the system into layers. Each layer represents a certain part of the system, such as the presentation layer, business logic layer, and data access layer. Each of these layers can be extracted as separate services.
- Advantages: It’s straightforward and logical, as it aligns with how many applications are already organized.
- Disadvantages: Often, the layers are still tightly coupled, so changing one layer may still require changes in others.
2. Vertical Decomposition
In vertical decomposition, you split the monolith by business functionality or domain. For example, in an e-commerce system, you could separate the order management, inventory, and user management services.
- Advantages: Each service can evolve independently and is easier to scale based on domain-specific requirements.
- Disadvantages: Requires careful handling of cross-service communication and consistency.
Decomposition Techniques
After choosing an approach, you need to select specific techniques to perform the decomposition.
1. Strangler Fig Pattern
Gradually Replacing - The strangler fig pattern involves gradually replacing parts of the monolith with services. You build new functionality as separate services, while legacy functionality remains in the monolith. Over time, the monolith "shrinks" as new services take over its functionality.
This pattern is helpful because it allows you to incrementally migrate to a new architecture without disrupting the entire system.
Let’s revise the explanation and make the diagram more creative and intuitive to demonstrate decomposition by subdomain in domain-driven design.
2. Decompose by Subdomain
In domain-driven design, breaking a system into smaller subdomains helps manage complexity. Each subdomain focuses on a specific part of the business, allowing for clearer boundaries and responsibilities. For example, in an order management system, subdomains could include Order Processing, Payments, and Shipping. Each of these subdomains would be responsible for handling its own data and logic, without relying on other parts of the system.
3. Database Decomposition
When decomposing a monolith, handling the database becomes a crucial challenge. Monolithic systems typically use a single database where all data is stored in shared tables. This tight coupling makes it hard to scale and maintain. With database decomposition, you split the monolithic database into smaller, service-specific databases. Each service manages its own data and is no longer dependent on other services' database schemas.
This is often done using bounded contexts, where each microservice has its own data and domain-specific logic, ensuring data isolation. Let’s visualize this:
- Monolithic Database: Represents the traditional structure where all tables (like Orders, Payments, and Shipping) are tightly coupled within a single database. This setup can lead to performance bottlenecks and challenges in managing data when the system scales.
- Decomposed Databases: Each service now has its own isolated database. The Order Service has a dedicated Order Database, the Payment Service has its own Payment Database, and the Shipping Service maintains a separate Shipping Database. This isolation allows each service to operate independently and be scaled individually, without affecting others.
- Data Ownership: Each microservice is responsible for its own data (bounded context). The Order Service manages everything related to orders, Payment Service handles transactions, and Shipping Service manages logistics. This removes the dependency on a shared database schema, ensuring scalability and flexibility.
Why Decompose the Database?
- Scalability: Each service’s database can be optimized and scaled based on the specific needs of the service.
- Data Ownership: Each service has full control over its own data, avoiding the risk of breaking other services due to schema changes.
- Performance: Decomposing databases enables better load distribution and database optimization tailored to each service’s needs.
With this approach, the monolithic database structure is broken down into service-specific databases, enabling easier scaling, maintenance, and development agility.
Challenges and Considerations
- Data Consistency: In a monolith, data consistency is easy because everything is in one database. In a microservices architecture, you might have to deal with eventual consistency and distributed transactions.
- Service Boundaries: Setting the right boundaries between services is critical. If you get it wrong, services might become too dependent on each other, causing unnecessary complexity.
- Latency: Decomposing into services introduces network latency because services communicate over a network instead of in-memory function calls.
- Monitoring and Debugging: With many services, it can be harder to trace issues. You need proper monitoring, logging, and tracing tools in place.
FAQs
How do you decide when to decompose a monolith?
Decomposing a monolith becomes necessary when the system grows too large to maintain easily. If it’s hard to add new features, difficult to scale, or if deployments are becoming too risky, it’s a good sign that you should consider breaking it down into smaller services.
What is the Strangler Fig pattern?
The Strangler Fig pattern is a technique where new features are built as separate services, while legacy features remain in the monolith. Over time, the monolith is "strangled" as its functionality is gradually replaced by services.
How do you handle data consistency across services?
One approach is to use eventual consistency, where each service manages its own data and communicates through events. In critical cases, you can implement distributed transactions, but these can be complex and should be used sparingly.
What communication protocols are used between services?
The most common protocols are HTTP/REST, gRPC (for synchronous communication), and message queues like Kafka or RabbitMQ (for asynchronous communication).
How do you monitor a microservices architecture?
Monitoring a microservices architecture involves using tools for distributed tracing (like Jaeger), centralized logging (like Elasticsearch), and monitoring systems (like Prometheus and Grafana) to track the health and performance of each service.
Conclusion
Decomposing a monolith is an important step in scaling and improving your application’s flexibility and maintainability. Whether you choose to decompose vertically by domain or horizontally by layers, the key is to ensure proper boundaries, communication, and monitoring. The result is a system that’s more scalable, maintainable, and adaptable to future changes.