Looking to get started with microservices design patterns? First things first: microservice architecture is an organizational approach to software development where the programmer has to use a collection of small autonomous services. These services are modelled around a business domain. As autonomous services, they are able to complete certain functions independently from other services with which they interact.
Microservice architecture has several benefits over monolithic structures. These include increased visibility, improved resilience, and reduced production time. It also reduces the cost of designing, implementing, and maintaining IT services. Since each service is autonomous, it can be deployed independently if needed.
Microservices design patterns follow several principles, as you can see in this list.
- Independent and autonomous services– This refers to the fact that each service is able to work independently of other services. They can also be deployed independently.
- Scalability– Microservices are able to scale their specs on demand. This architecture is able to administer resources where and when they are required.
- Decentralization– Microservices adopt decentralization, and this enables the team of developers to build and deploy the code in a smoother manner. Decentralized architecture also means that there is no central point of failure.
- Resilience– This architecture also ensures that the app is resilient to failure and able to restart on another machine. The interconnectivity that would normally cause issues in monolithic architecture can easily be avoided in a microservice architecture.
- Real-time load balancing– Load balancing refers to the way a system efficiently distributes incoming traffic across a group of backend servers. Microservices use two types of load-balancing architecture, and these are server-side load balancing and client-side load balancing.
- Availability– High availability systems are able to provide continuous service to the client. All hosts in the system must point to the same storage. The microservice has to be available for function even if there is a failure.
- Continuous delivery through DevOps Integration– This is the automation of the building, testing, configuring, and deploying of code. The developers regularly merge their code changes to a central repository, and then they run the build and test processes.
- Seamless API Integration and Continuous Monitoring– This helps to bring visibility into the performance, availability, and functional correctness of the system. It involves collecting and analyzing data about the performance of the API.
- Isolation from Failures– With this architecture, the application will be able to keep running, even if one service crashes.
- Auto-provisioning– Every service in the microservice application is self-sufficient and runs within its own container.
Some examples of Design Patterns
Design patterns solve many issues in programming. Before thinking about fully customized solutions, make sure to explore the different pattern solutions, as they will most likely help you tackle any common issues or situations more effectively.
There are different kinds of patterns and they can also be divided mainly into these groups:
1. Decomposition Patterns:
- Service per Business Capability: Divide services based on business functionalities.
- Service per Team: Align services with team structures to enhance ownership and agility.
2. Integration Patterns:
- API Gateway Pattern: Acts as a single entry point for clients, routing requests to appropriate microservices.
- Backend for Frontend (BFF): Customizes APIs for different client applications to optimize performance and user experience.
3. Data Management Patterns:
- Database per Service: Each microservice manages its database, ensuring data autonomy and scalability.
- Event Sourcing: Captures changes in the form of events, allowing for a flexible data retrieval mechanism.
4. Observability Patterns:
- Log Aggregation: Centralizes logs from various services for streamlined monitoring and troubleshooting.
- Distributed Tracing: Tracks requests across services to diagnose performance issues and understand service dependencies.
Now lets take a closer look at each of them:
Decomposition Patterns
Decomposition patterns are used to break down large or small applications into smaller services. You can break down the program based on business capabilities, transactions, or sub-domains. If you want to break it down using business capabilities, you will first have to evaluate the nature of the enterprise. As an example, a tech company could have capabilities like sales and accounting, and each of these capabilities can be considered a service.
Decomposing an application by business capabilities may be challenging because of God classes. To solve this problem, you can break down the app using sub-domains. This involves using the domain-driven design. You will have to start by breaking the domain model into subdomains. Each of these subdomains has a model whose scope is referred to as the bounded context. The microservices will be developed based on these models.
You can also break down the app using transactions. These design patterns involve a preparation phase and a commitment or rollback phase. In the preparation phase, the participants have to commit and notify the transaction coordinator that they can process the transaction. The commitment or rollback command is given to all participants by the coordinator.
Other design patterns include the sidecar pattern, vine pattern, and bulkhead pattern.
Recommended: Also, check more about the strangler fig pattern for microservices here!
Integration Patterns
Integration patterns are used to connect data, applications, and systems. This can be achieved using API gateway patterns, aggregator patterns, and client-side UI composition patterns. The API gateway pattern helps to solve several issues that arise when an application is broken down into smaller microservices. Firstly, it provides a single point of entry for microservice calls. It also works as a proxy service and can route requests to the required microservice.
Integration patterns can solve the aggregating data problem in the API gateway pattern. The aggregator pattern is meant to collaborate the data returned by every service in the system.
The client-side UI composition pattern also solves a specific problem in a microservice architecture. In monolithic systems, the data could be called from the UI to a backend service in order to retrieve the data and refresh the UI page. However, this is different in systems where the services are broken down by decomposing business capabilities or subdomains. The services responsible for user experience have to pull data from multiple components. To solve this, developers can use the client-side UI composition pattern. It enables the program to refresh specific parts of the screen instead of the entire page.
Database Patterns
This pattern can also be referred to as the shared data design pattern. With the large amount of data available for each application, the system could either have one database for each service or a shared database for each service. A shared database helps to solve several issues. These include data duplication, data inconsistency, and data denormalization. You should also note that different services will need different forms of storage. With this pattern, each microservice will have its own database ID that will prevent other services from using that database.
Shared data designs can be contrasted to event-based systems. With event-based systems, the components communicate through procedure calls or message passing. On the other hand, database systems use the data source as the main medium of interaction.
Observability Patterns
One observability pattern to discuss is log aggregation. This pattern enables clients to use a centralized logging service to aggregate logs from every service instance. Users can also set alerts for specific texts that appear in the logs. This system is essential since requests often spam several service instances.
Performance metric services are used to collect statistics on certain operations. You can either use the push or pull model to aggregate metrics.
The third aspect of observability patterns is distributed tracing. This is essential since microservice architecture requests cut across different services. This makes it hard to trace end-to-end requests when finding the root causes of certain issues. With distributed tracing, each external request will get its own request ID, and the service has to pass the external request ID to all services. The ID must be included in the log messages. Finally, the service has to record information on the requests and operations performed.
Cross-Cutting concern Patterns
Changes in the configuration properties of the services and databases may require the developer to build or deploy the service again. With this design pattern, you can avoid code modification for configuration changes. You can externalize all the configurations to make the app load at startup. This design pattern also includes the service discovery pattern, which involves creating a service registry for the metadata of each service.
Benefits of Microservices Architecture
Microservices architecture has many benefits over monolithic systems. These benefits include:
- Increased scalability
- Better fault isolation systems
- Easy deployment
- Quick time-to-market
- Enhanced data security
- Increased ease of experimentation
- Better resilience
Businesses will generally enjoy higher productivity with this architecture as they will be able to quickly build and maintain their systems.
Recommended: Best Languages for Microservices
Conclusions on Microservices Design Patterns
Microservices architecture involves the use of small autonomous components or services. This architecture comes with many benefits, including an increase in scalability and faster time-to-market. Fault isolation is also extremely easy with microservices. These design patterns come in many forms, including the decomposition pattern, integration pattern, database pattern, observability pattern, and cross-cutting concern pattern.
