Microservices architecture has become a transformative approach in software development, profoundly influencing the efficiency of cloud adoption. This architectural paradigm involves breaking down applications into small, autonomous, loosely coupled services that operate independently and communicate over well-defined APIs.
Each microservice acts as a standalone feature-focused application within a larger cloud-based app, enabling simpler management and maintenance. The ability to choose a tech stack tailored to each microservice and the compatibility with containerization tools like Docker and orchestration platforms like Kubernetes make microservices well-suited for cloud environments. A decentralized application structure allows developers to build and test several microservices simultaneously, reducing time-to-market for a whole project. That gives rise to many benefits, allowing for independent scaling of services, faster development cycles, and easier maintenance through modular app construction. As an example, you may read more about the AWS microservices benefits we described in the blog earlier.
On the other hand, to develop a performant microservice is to find the solutions to data consistency, security concerns, and deployment complexity challenges. That’s why adopting microservices is contingent on following best practices in each stage of cloud application production.
Microservices Design Principles
Microservices should not be adopted just because they are trendy, but microservices architecture has to align with the specific enterprise needs. The transition from a monolithic architecture to microservices needs adequate planning through analysis of the application’s divisibility into microservices while preserving its core features and functionality.
Microservices function as independent applications that require distinct development teams dedicated to specific microservices. These teams must have skills and tools for end-to-end development, deployment, and management of their assigned services. Versatility and autonomy become critical for them to operate efficiently without unnecessary communication overhead.
When moving on to the actual design of microservices, it is vital to differentiate them from business functions and services. Striking the right balance is essential – neither building excessively large nor overly small microservices. Design principles advocate for services that are loosely coupled, exhibit high cohesion, and adhere to a single responsibility principle. The latter ensures that each service performs a distinct function effectively, contributing to a cohesive overall architecture.
Communication between microservices is usually orchestrated through APIs and events rather than direct calls. An API gateway enables this connection, handling authentication, requests, responses, and throttling. This design choice enhances the system’s flexibility and scalability, enabling reliable traffic redirection during updates.
Security considerations play a pivotal role in a microservices framework. The distributed nature of microservices makes them more susceptible to security threats, necessitating a distinct security approach compared to monolithic frameworks. Adopting the DevSecOps model becomes a best practice to fortify the microservices architecture against potential vulnerabilities.
Let’s look at the main microservice design best practices closer.
Single responsibility principle and domain-driven design
This fundamental microservices design principle defines them as software units having a single, well-defined responsibility or business capability. It aims to achieve autonomy and clarity of each unit within an app architecture. When applied, the Single Responsibility Principle (SRP) ensures that each microservice is dedicated to a specific task, making it easier to understand, develop, and maintain. This way, SRP embodies the concept of Clear Service Boundaries, reinforcing that each microservice should encapsulate a specific set of functionalities within the entire cloud application. Domain-driven design identifies bounded contexts and distinct sets of business capabilities and maps them to corresponding microservices.
On the other hand, SRP agrees with the principles of event-driven architecture (EDA). EDA advances loose coupling and responsiveness when leveraging events to trigger different service actions. Events enable microservices to communicate asynchronously. It allows them to react to changes without directly invoking each other. This asynchronous communication style aligns with SRP by promoting a more decoupled and focused interaction between services.
In practical terms, adopting SRP involves ensuring that each microservice has a clear and specific purpose. For instance, in an eCommerce portal with a microservices architecture, services like Product Listing, Order, Customer, Payment, Cart, and Wishlist should each have a limited distinct responsibility. It prevents the doubling of functions while the architecture becomes more painless, testable, and maintainable.
SRP also underlines having a separate data store for each microservice. It reduces dependencies and minimizes the risks of downtime. Finally, the security is advanced if the data used by individual instances are isolated.
Asynchronous communications based on API
Microservices can interact with each other through two main communication types: synchronous and asynchronous. While synchronous communication involves a step-by-step, dependent consequent progression of tasks, asynchronous communication allows services to operate independently and purposefully, enhancing processing speed and overall cloud application efficiency.
The stateless design principle ensures asynchronous communication within microservices. Treating the server as stateless ensures that instances don’t store session-specific data, relying on external services or databases for state maintenance. It contributes to a cloud software application’s fault tolerance and load balancing, as microservices can be easily scaled horizontally to handle increased traffic.
Used to provide asynchronous communication, API gateways serve as intermediaries between clients and microservices. They simplify interactions, aggregate responses, and manage authentication and authorization requests. Their integration simplifies complexity and enhances security, providing a centralized point for managing communication flows.
To avoid performance bottlenecks, microservices best practices often utilize asynchronous communication protocols like event-driven patterns. Asynchronous transmission keeps up for parallel execution of requests, reducing service coupling and improving general stability. Events and message buses further improve loose coupling, enabling microservices to publish events indicating state changes or failures, which other interested microservices can subscribe to and process asynchronously.
REST APIs, designed explicitly for microservices, contribute to efficient communication. Microservice API best practices can handle various calls, return different data formats, and adapt structures, as they give flexibility by not tying data to specific methods or resources.
Microservices Development Best Practices
Organize a team of devs with clear responsibilities and collaboration across roles for efficient bug resolution and deployment. Choose the right tools like Jenkins, Docker, and Kubernetes for automation. Use separate version control for each service to ease access and maintain clean logs, allowing risk-free changes.
Maintain consistency in development environments to get quick adaptation. Prioritize backward compatibility with rigorous contract tests to avoid disruptions.
For data management, employ individual databases for each microservice. Implement Micro Frontend architecture for scalable UI upgrades and quicker feedback loops.
Version microservices for breaking changes and providing new endpoints while supporting older versions. Adopt a disciplined approach to deprecate older versions to avoid code maintenance issues.
Keep code maturity consistent across microservices, simplifying overall system design and minimizing dependencies for better maintenance.
To achieve the required quality parameters, cloud engineers use specialized software for testing and monitoring automation. Implement automated tests at different levels (unit, integration, and functional) and use instruments like Jaeger and Zipkin for distributed logging and tracing. Automate infrastructure processes using tools like Terraform or Ansible for consistency.
Monitoring assures accurate functioning, detecting anomalies in microservices in time. Centralized logging and advanced monitoring microservice best practices improve error handling and security. Various tools like AWS CloudWatch, Prometheus, Datagod, or Graphite aid in monitoring microservices. They execute logging, observation, and analytics, while the visualization dashboards provide valuable data about microservices’ health state and availability in real-time.
Microservices Deployment Best Practices
Separate microservices deployment guarantees efficient coordination during maintenance or upgrades, saving time and resources. Utilizing a dedicated infrastructure for each microservice enhances fault tolerance, preventing a widespread outage if a component fails. Another practical advice is to have own release train for each microservice. This autonomy reduces coordination efforts and prevents conflicts with multiple maintenance teams.
Containers provide a lightweight, portable solution enclosing essential program configurations, libraries, and binaries. The benefits of containerization extend to the isolation of processes with minimal resource consumption, a smaller memory footprint, higher data consistency due to a shared operating system, and resilience against sudden changes in the external environment. Containerization optimizes costs and provides quicker iterations with rapid rollouts and rollbacks as needed.
Orchestrating microservices practice complements containerization. Notable orchestrating platforms are Kubernetes (K8s), Azure Kubernetes Services (AKS), Amazon Elastic Container Services (ECS), and Azure Container Apps. They manage container provisioning, deployment, load balancing, scaling range, and network communication. Kubernetes may be the most used container orchestration platform. Whether deployed on-premises or through cloud distributions like Azure Kubernetes Service, Red Hat OpenShift, or Amazon Elastic Kubernetes Service, it simplifies microservice orchestration, providing built-in scheduling, replication, and networking capabilities.
Continuous Integration and Continuous Delivery
Continuous Integration and Continuous Delivery practices automate each microservice’s development, testing, and deployment through dedicated pipelines, executing quick and secure updates to the system. Automation tools like Jenkins streamline workflows and achieve faster, more reliable software delivery, minimizing manual intervention.
Adequate microservices security begins with the implementation of robust authentication and authorization mechanisms. Each microservice should verify the identity of the entities trying to access it, ensuring that only authorized users or services can interact with the system. Authentication protocols such as OAuth, JWT, or API keys usually validate the credentials of entities seeking access.
Authorization complements authentication by defining the permissions and privileges granted to authenticated users or services. Role-based access control (RBAC) is a common approach to operating authorization, confirming that users only have access to the resources and functionalities they can use.
To protect data in transit between microservices, cloud engineers utilize API gateways and secure communication channels, such as HTTPS, to encrypt the data exchanged between microservices. Encryption protects sensitive information from unauthorized interception during transmission, mitigating the risk of data breaches and guaranteeing confidentiality. Centralizing security policies at the gateway executes consistent measures, including authentication, authorization, traffic control, and rate limiting. You can learn how this is done in practice using the example of the security of cloud healthcare applications.
Feature flags are implementing conditional statements in the code to control the visibility and activation of specific features. Developers can gradually present new features to different user segments using feature flags. Monitoring entire performance, gathering feedback, and addressing any issues before a full-scale rollout development team provides controlled and incremental release of features. Feature flags practice is valuable for A/B testing and managing the release of functionalities.
Blue-Green and Canary deployments
These strategies prioritize risk mitigation and allow for efficient rollback mechanisms.
Blue-Green deployment involves maintaining two production environments, “Blue” representing the current stable version and “Green” for the updated release. The transition is executed by switching user traffic from the Blue to the Green environment. This method ensures minimal downtime and provides a quick rollback option if issues arise.
On the other hand, Canary deployment introduces changes gradually to a subset of users before full deployment. By monitoring the performance and user experience with a small audience, developers can assess the impact before releasing the changes to the entire user base.
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Frequently Asked Questions: Microservice Best Practices
How to build microservices?
Creating microservices means coding small, separate parts of an application that perform one specific feature, using containers for easy deployment and APIs for managed communication within an app.
What are some best practices to follow when working with a microservice architecture?
Specific responsibility of each microservice, proper communication between them, and CI/CD practices are essential for perfect microservice architecture.