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The Microservices Principles: Building Scalable and Resilient Systems

Updated: Jul 8, 2023

Microservice architecture has gained significant popularity due to its ability to create scalable and modular systems. To effectively design and implement microservices, it's important to follow key principles that ensure loose coupling, domain-driven design, deployability, interface segregation, event-driven architecture, and prioritizing availability over consistency. In this article, we explore the microservice principles and their significance in building robust and flexible applications.

The Microservices Principles: Building Scalable and Resilient Systems

Microservices Principles

Below are the principles and patterns commonly associated with microservice architecture. These principles will help you to design and implement microservices effectively to achieve desired goals. And also these principles will shape the architecture and design decisions when building microservice-based solutions.


1. Domain-driven design (Single Responsibility)

In domain-driven design, the focus is on understanding the problem space and organizing it into distinct domains. Each domain represents a specific area of the application's functionality. These domains are further divided into bounded contexts, which are isolated segments that correspond to individual microservices in a microservice architecture.


The idea behind bounded contexts is to create clear boundaries between different parts of the system, allowing teams to work independently and develop services that align with specific business capabilities. By defining these boundaries, domain-driven design helps establish a common language and understanding among team members when discussing the intricacies of each domain.


Within each bounded context, domain-driven design suggests using various technical concepts and patterns to model the domain effectively. This includes the use of domain entities, which represent the key elements within a specific domain and have rich, detailed models that capture their behavior and attributes.


Additionally, value objects are used to represent small, immutable components within a domain that hold specific values and provide encapsulated behavior. Aggregates are clusters of domain entities and value objects that are treated as a single unit, ensuring consistency and enforcing business rules. The aggregate root, or root entity, acts as the primary access point for interacting with the aggregate, maintaining the integrity of the domain's entity hierarchy.


By following these technical concepts and patterns, domain-driven design helps developers create a well-structured and cohesive model of the problem space. This, in turn, facilitates easier communication, better collaboration among teams, and a more maintainable and scalable microservice architecture.


2. Loose-coupling

In microservice architecture, we use APIs (Application Programming Interfaces) to establish communication between different microservices. An API acts as a contract between the microservice and the outside world, which can include other microservices or end-users. It's important that this contract is not tightly tied to specific implementation details or technologies.


To achieve loose coupling in microservice-based solutions, developers should consider the following strategies and technologies:

  1. Contract-first design: By designing the API contract independently of existing code, we avoid creating APIs that are tightly coupled to a specific technology or implementation. This approach allows multiple teams to work concurrently, improving delivery time and flexibility.

  2. Service ownership: Following the principle that a team should own a microservice throughout its entire lifetime, developers take responsibility for the software in production. This ensures that developers continue to maintain and improve the microservice even after the initial release.

  3. Database per microservice: Assigning a dedicated database to each microservice provides autonomy and avoids direct coupling to shared databases. Each microservice can manage its own data storage independently, reducing dependencies and promoting scalability.

  4. Avoid shared libraries between microservices: Microservices are designed to have independent development and release cycles. Sharing libraries among microservices goes against this concept. Shared libraries should be treated as third-party dependencies, managed separately to avoid coupling and maintain autonomy.


3. Deployability

The ability to deploy microservices is vital for their success, as they offer scalability and rapid growth in deployment units. To achieve this, a good CI/CD (Continuous Integration/Continuous Deployment) system is crucial.


In order to improve the deployability of microservice-based solutions, developers should consider the following strategies and technologies:

  1. Docker: Using Docker, microservices can be containerized and easily replicated and deployed across various platforms and cloud providers. Container orchestration platforms like Docker and Kubernetes provide shared resources and mechanisms for routing, scaling, replication, and load balancing.

  2. Service discovery: Service discovery helps locate the correct instance of a microservice for client APIs. Multiple instances of a microservice can coexist, each having different IP addresses or ports. To track these instances and their addresses, service discovery is essential.

  3. Logging tools: Developers need the ability to search, analyze, and generate alerts from the logs of each microservice. Popular logging tools such as Fluentd, Graylog, Splunk, and ELK (Elasticsearch, Logstash, Kibana) facilitate effective log management and analysis.

  4. Monitoring tools: Given that microservices are deployed across various on-premises and cloud infrastructure, it's crucial to predict, detect, and respond to system health issues. Monitoring tools like New Relic, CloudWatch, Datadog, Prometheus, and Grafana enable proactive monitoring and timely notifications.

  5. Serverless architecture: Deploying services to serverless platforms, following the Function-as-a-Service (FaaS) paradigm, reduces complexity and operational costs compared to container orchestration. Platforms such as AWS Lambda, Azure Functions, and Google Cloud Functions offer serverless capabilities.

  6. Infrastructure as Code (IaC): Implementing Infrastructure as Code allows for automating the build and deployment process, minimizing human involvement. This practice results in faster, less error-prone deployments and improved auditability.


4. Interface segregation

it's important to separate interfaces to cater to different types of front ends, ensuring that each front end receives the most suitable service contract. Instead of using a single service contract that applies to all clients (canonical models), we separate the interface so that each type of client gets the specific service interface it requires. To achieve this, we can utilize the "Backends For Frontends" pattern.


One effective way to implement this pattern is by exposing GraphQL APIs for each microservice. GraphQL allows clients to specify their specific data requirements, avoiding over-fetching or under-fetching of data. With GraphQL, clients can request only the data they need, resulting in efficient and optimized communication between microservices and clients. By utilizing this approach, we ensure that each front end receives a tailored service interface, enhancing flexibility and improving the overall performance of the microservices architecture.


5. Event-driven architecture

In modern microservices-based applications, event-driven architecture is used to trigger and facilitate communication between loosely coupled services. An event represents something that happens, like adding an item to a shopping cart on a website. An event can contain information about a specific state (e.g., purchased item, price, delivery address) or act as an identifier (e.g., order shipping confirmation).


The event-driven architecture consists of three main components: the event producer, event router, and event consumer. The producer generates and publishes events to the router, which then filters and distributes them to the relevant consumers. This decoupling of producers and consumers enables independent scaling, updating, and deployment of services.


Apache Kafka is a widely adopted distributed data streaming platform used for event processing. It excels in handling real-time event streams by supporting publishing, subscribing, storing, and processing of events. With its high throughput and scalability, Apache Kafka is ideal for use cases where fast and efficient data sharing is crucial. By minimizing the need for direct point-to-point integrations, Kafka reduces latency to milliseconds, contributing to efficient event-driven architectures.


6. Availability over Consistency

In most cases, the availability of data is more important than its consistency for a company. Imagine encountering an error on the screen, which is worse than seeing slightly outdated information. In microservices, data replication plays a crucial role in achieving high availability. This pattern is employed when microservices need to access data owned by other applications, and traditional API calls may not be sufficient.


To ensure availability, we create replicas of the data and make them readily accessible to the microservices. This approach is sometimes referred to as failover or master-slave replication. It allows for seamless transitions during failures or disruptions. For instance, if a component fails, it can be restarted, and a new instance takes over its functionality, similar to passing the baton from one runner to the next in a relay race.


By prioritizing data availability over perfect consistency, microservices can maintain uninterrupted operations even in the face of failures. This enables a more resilient and reliable system, minimizing the impact of disruptions and ensuring a smooth user experience.


Conclusion

By adhering to these microservice principles, developers can create scalable, flexible, and resilient systems. Each principle plays a crucial role in designing microservices that are loosely coupled, domain-focused, easily deployable, interface-segregated, event-driven, and prioritize availability. Embracing these principles empowers teams to build robust microservice architectures that can adapt and evolve with changing business needs, paving the way for scalable and highly maintainable applications.

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