Microservice design patterns are a collection of best practices used in the design and development of software applications. These patterns provide a standard blueprint and rules for developing software systems. They apply to a vast number of programming languages and technologies.
Further, these patterns help in the manageability and scalability of your products. This enables you to provide services to customers more efficiently.
In this article, we will explore the most useful microservices design patterns along with their illustrations.
What are Microservices?
They are the ideal approach for dividing an application into smaller, more logically connected components. The concept was created so that various applications can exist within a single framework rather than existing as wholly separate entities.
Microservices are an extension of an existing framework or service, even though they are often referred to as an architectural solution for developing complicated web-based systems.
Due to their rising popularity, they have become an essential part of software development. They enable businesses to develop and maintain their own solutions, hence increasing processing power.
As microservices-based applications enable a higher level of security for manual procedures, developers focus more on their dynamic and agile capabilities.
Each microservice is responsible for a specific business capability and can be developed, deployed, and scaled independently. This architectural style aims to improve the overall system's scalability, maintainability, and flexibility.
Design patterns are solutions to common software design problems. They are a collection of confirmed good practices and solutions for various design issues. They provide a common terminology and set of standards for planning and building software systems. You can apply them to a large number of programming languages and technologies.
Here are a few essential points that explain how design patterns help you:
Modularity: Microservices are designed to be small and decoupled, making them simple to modify, test, and manage. Design patterns ensure that each microservice has its own responsibility and is loosely connected to others. Changing one will not affect others.
Scalability: Design patterns such as API Gateway, Service Discovery, and Load Balancing ensure that the system can handle many requests and scale up or down according to the requirement.
For instance, if 1 million users are using the application, you may need to scale up your resources to manage this large number of requests.
Resilience: Design patterns such as Circuit Breaker and Bulkhead ensure that the system can withstand failures and continue to work even when one or more services are unavailable. For instance, if one of the database nodes fails, other database nodes can continue to deliver services to the users without affecting performance.
Reusability: Microservices are designed to be reusable across various parts of the system. Design patterns like Command and Query Responsibility Segregation (CQRS) and Event Sourcing can be used in implementing the reusing feature. For instance, if you want to call a microservice from multiple different microservices, you can do it easily by following suitable design patterns.
Overall, design patterns enhance the system's adaptability, scalability, and resilience, as well as its maintainability and extensibility over time. This ultimately increases your product performance. You can deliver the services to your users efficiently without any failure.
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The aggregate pattern is useful to group all similar data entities together and ensure the consistency of the application. It ensures that all related data entities are updated atomically and consistently and that data consistency is maintained despite concurrent updates.
Further, it defines a boundary around a collection of related entities and ensures that all updates to those entities happen within that boundary. This can be accomplished by defining an aggregate root, the entry point to the group of related entities, and ensuring that all entity updates are performed via the aggregate root.
Below is the illustration of the aggregate pattern where two services, “A” and “B,” interact with different databases in separate groups.
API Gateway Pattern
The API Gateway pattern provides external customers with a microservice architecture with a single point of entry to the application. The API Gateway is in charge of routing requests from external clients to the appropriate microservice, and applying the required security, rate-limiting, and other regulations. An API gateway can contribute to several advantages, including:
- Routing: The API Gateway routes requests from external clients to the relevant microservice. This provides architectural flexibility, as the microservices can expand and alter without impacting external customers.
- Security: It can enforce authentication and authorization for incoming requests, acting as a security layer.
- Rate-limiting: It is helpful to apply rate-limiting constraints to prevent the microservices from becoming overloaded.
- Caching: It can cache responses from the microservices to reduce load and enhance the system's performance.
Below is the illustration of how the API gateway layer resides between the client and the server layer for routing the requests appropriately.
Chain of Responsibility Design Pattern
It is helpful in executing a request via a sequence of handlers or processing pieces. Each handler can either process the request or forward it to the following handler in the chain. The approach is beneficial when there are various request handlers, and the appropriate handler is unknown at the design time.
The Chain of Responsibility pattern can route a request to the proper microservice in an architecture. For instance, a request for user data can be routed through a chain of microservices, each of which has the option to either handle the request or forward it to the next microservice in the chain.
This pattern enables a flexible and dynamic method of handling requests, as new microservices can be added or withdrawn without impacting the existing ones.
Also, it facilitates better system maintenance and scalability, as each microservice is accountable for a distinct function and can evolve and alter independently.
The figure below shows how the data is passed through 3 different services in a “chain”.
Asynchronous Messaging Pattern
This pattern decouples the communication between microservices using message queues. It enables them to send and receive messages asynchronously without waiting for a response from another.
Each microservice communicates with the other via a message queue. It sends a message to a queue, after which another microservice consumes and processes the message. This permits loosely coupled communication between microservices and enables them to function independently.
The asynchronous messaging pattern offers several advantages:
- Decoupling: Message queues decouple the communication between microservices, allowing them to function independently and decreasing interdependencies.
- Scaling: Message queues enable horizontal scaling of microservices, as additional instances of a microservice can be deployed to consume messages from the queue.
- Flexibility: They enable different microservices to be written in various languages and executed on various platforms.
- Asynchronous: Using message queues enables asynchronous communication.
The below figure shows how a queue resides between services “B” and “C” for communication.
Circuit Breaker Pattern
Circuit breaker protects a service from being overloaded by errors or failures in another service. The design is based on the concept of an electrical circuit breaker, which is used to prevent overload damage to an electrical circuit.
The pattern operates by putting a proxy or "circuit breaker" between the communicating service and the other services. The circuit breaker checks the communication between the services. If it detects excessive failures or faults, it will stop transmitting requests to the other services.
Here are the benefits of the Circuit Breaker pattern:
- Reliability: It increases the system's reliability by avoiding one service from being overloaded by mistakes or failures in another service.
- Fault-tolerance: The pattern enables the system to continue operating even if a service is unavailable.
- Performance: It improves the system's performance by decreasing the number of requests made to a broken service.
- Monitoring: It enables the system to monitor the health of other services and take necessary action.
The figure below shows how a monitoring circuit breaker consistently identifies if something is wrong in a service and acts accordingly.
Database or Shared Pattern
A shared database pattern helps reduce development time. Sharing the database reduces development efforts by a massive margin, even though this is not the best practice.
For instance, while developing a multi-user blogging application, we might have a blog service that manages all blog-related information. Also, we have an analytics service that handles different analytics, such as Likes, Shares, and Views, among others.
The analytics service updates the information directly in the blog's database asynchronously; for example, total views for the blog. This can be easily accomplished by allowing Blogs and Analytics to share a database, known as the Shared Database pattern.
- Convenient: There is no middleman involved in the process.
- Open To All: By sharing the database across services, an external party (Analytics) would gain access to the service's internal information.
Load Balancing Pattern
This pattern distributes incoming requests across multiple microservice instances evenly. This helps increase performance and reduce the likelihood of failures.
The load balancer sits in front of the microservices and routes incoming requests based on predefined rules to the appropriate instance. This may include the current load on each instance, the number of available instances, and the overall health of each instance.
Depending on the requirements of the system, various types of load-balancing algorithms may be implemented, such as
- Round Robin: This algorithm assigns incoming requests to the next available instance in a circular pattern.
- Least Connections: This mechanism forwards incoming requests to the instance with the least active connections.
- IP Hash: This algorithm assigns incoming requests to the same instance based on the IP address of the client.
- Random: This approach assigns incoming requests to a random instance at random.
The figure below shows a load balancer between the internet layer and the servers to redirect the traffic to appropriate nodes.
Service Discovery Pattern
This pattern aims to make it simple for microservices to discover and communicate with other microservices regardless of where they are running.
Each microservice operates independently in an architecture, and their locations can change over time due to scalability, updates, or failures. The service discovery pattern allows them to dynamically learn the location of other microservices without having to hard-code the locations.
There are various approaches for service discovery, but one of the most common is using a service registry. A service registry is a centralized database that tracks the locations of all microservices.
Each microservice registers and de-registers itself with the service registry upon startup and shutdown. When it needs to communicate with the other one, it queries the service registry for its location.
Event Source Pattern
Event sourcing is a design pattern for storing the history of data changes within a microservice. This pattern provides a way for quickly rolling back and repeating events, enabling better data flexibility and control within a microservice.
In event sourcing, rather than keeping the present state of the data, the system stores a series of events that explain the data's evolution with time. Each event indicates a change to the data, such as an update or deletion, and contains all the information necessary to recreate the data's current state.
Event sourcing is a powerful pattern providing various benefits:
- Consistency: Event sourcing ensures that all data changes within a microservice are logged as a sequence of events, preserving data consistency.
- Flexibility: It facilitates simple rollbacks and replays of events, allowing for greater data management flexibility.
- Debugging: The history of data changes is available for debugging, making identifying and resolving errors easier.
It is a design approach to break an application into smaller, more logically connected components, making it easy to maintain the system.
Multiple granularity levels may be used for decomposition depending on the system's needs. It may be applied to the entire system, a particular module or feature, or a particular piece of functionality.
There are lots of techniques for splitting an application into microservices, including:
- Business Capabilities: Decomposing the application into various business capabilities, including customer management, inventory management, and order management.
- Domain-Driven Design (DDD): Using DDD to identify the fundamental business entities and their relationships and break down the system based on these elements.
- Functional Decomposition: The process of dividing a system into smaller, independent functions that can be independently developed, implemented, and scaled.
The Command Query Responsibility Segregation
The Command Query Responsibility Segregation (CQRS) pattern is useful for separating the responsibilities of commands and queries of a microservice.
- The command side of the service manages requests to modify the system's state, which can be generating, updating, or deleting data.
- The query side of the service manages requests to retrieve data from the system, including reading and searching.
Splitting these roles makes the system more scalable and efficient, as the command and query sides can be separately tuned and scaled.
This method is commonly used to improve performance and scalability in microservices architectures.
Here is the list of the design patterns commonly used on microservices. Microservices are software architecture where different services are de-coupled to enhance scalability. To make them work efficiently, we have different design patterns that allow developers to utilize the services efficiently. These design patterns help to handle failures, enhance modularity, and provide complete control of the configuration of applications to the developers.