Microservices Design Patterns

Microservices architecture allows us to build flexible and scalable applications as they are developed as small, manageable services. These microservices can also be difficult to manage due to the challenges associated with microservices surrounding communication, data, and security. Each design pattern in microservices allows us to implement a solution to the common problems surrounding the design of microservices, allowing for efficient and scalable application development and deployment. This article will guide you on the design patterns in microservices with examples, and also discuss the challenges with these patterns.

Table of Contents:

• What is Microservice Architecture?
• Core Design Patterns in Microservices Architecture
  1. API Gateway Pattern
  2. Database per Service Pattern
  3. Circuit Breaker Pattern
  4. Service Discovery Pattern
  5. Event Sourcing Pattern
  6. CQRS (Command Query Responsibility Segregation) Pattern
  7. Saga Pattern
  8. Strangler Fig Pattern
  9. Bulkhead Pattern
  10. Sidecar Pattern
• Deployment Patterns for Microservices
• Scaling Patterns for Microservices
• Security Patterns for Microservices
• Real-World Examples of Design Patterns in Microservices
• Challenges with Design Patterns in Microservices
• Conclusion

What is Microservice Architecture?

In a software design, an approach called microservice architecture is used, in which an application is designed as a set of small services which is loosely coupled. A microservice supports a specific business function, and it can be built, deployed, and scaled independently. Microservices communicate with each other directly by using APIs or indirectly using messaging systems. The microservice architecture provides flexibility, scalability, and fault tolerance, but there is also the additional complexity of deploying the services and communication between services. Microservices are most commonly used in cloud-native and large-scale applications.

Essential Features of Microservice Architecture 

1. Modularity: Microservices enable the system to be decomposed into manageable and smaller services. 
2. Independence: Each microservice can be developed and deployed independently. 
3. Technically Diverse: A microservice architecture can permit services to use distinct languages, databases, and frameworks. 
4. Scalability: Individual services can be independently scaled based on demand. 
5. Resilience: Service failure does not mean system failure. 
6. DevOps Friendly: CI/CD opportunities provide an intuitive way to continuously integrate and deploy the application.

Core Design Patterns in Microservices Architecture

Below are some fundamental design patterns that are used in microservices architecture:

1. API Gateway Pattern

The API Gateway pattern is a microservices design pattern that uses a single entry point to manage all client communication with a system of microservices. The API gateway is essentially a reverse proxy, which also routes requests to the proper services. The primary responsibilities of this pattern in microservices are:

• Routing requests to the proper microservices.
• Separates or authenticates the users.
• Transforms requests.
• Aggregates responses.
• Rate limiting.
• Caching or load balancing.

Example: A ride-sharing platform has an API Gateway that provides a single point of access for drivers, passengers, and admins across a set of microservices that power ride-matching, payments, notifications, and more. It reduces complexity for clients, provides security and authentication, and efficiently routes requests. 

2. Database per Service Pattern

In the database per service pattern, each microservice has its own private database that is not directly accessible by any other service. The database per service pattern provides microservice teams the flexibility to choose a database technology that meets the needs of their service. The primary characteristics of the database per service pattern are as follows:

• Each microservice has ownership of its data.
• There is no sharing of a database directly among services.
• Each service can choose the appropriate database type.
• Independent scaling and deployment.
• Encourages encapsulation of the data.

Example: In an online travel booking system that has separate services for flights, hotels, and car rentals, the service can implement its own database for each of the services, thus allowing each service the flexibility to scale, change, and manage its services without impacting other services, and avoiding data corruption and tight coupling.

3. Circuit Breaker Pattern

The circuit breaker design pattern is used to prevent a network or service failure from cascading through a system. It detects failures and stops repeated requests to a failing service, allowing it time to recover and protecting the overall system. The key responsibilities of this design pattern in microservices are:

• Monitors service calls for failures.
• Stops sending requests to a failing service temporarily.
• Automatically retries after a cooldown period.
• Prevents cascading failures in the system.
• Improves system resilience and stability.

Example: If the payment service is down in the food delivery app, the circuit breaker prevents other services, like order placement, from crashing by just cutting off the requests to the failing service until it is repaired.

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4. Service Discovery Pattern

The service discovery design pattern allows microservices to discover and communicate with each other automatically, without hardcoding network locations. It basically maintains a dynamic registry of service instances and their addresses. The main responsibilities of a service discovery design pattern in microservices:

• To maintain the registry of service instances that are available.
• Allowing services to register and deregister themselves dynamically.
• Allowing clients or gateways to find the service location at runtime.
• Enabling load balancing and failover.
• Making the communication with services easier in environments where instances are dynamic.

Example: When a new instance of the recommendation service starts in a video streaming service, it registers with a service discovery service.

5. Event Sourcing Pattern

The event-sourcing is a design pattern in microservices that stores the state of a service as a sequence of events rather than as a single snapshot in a database. The current state is rebuilt by replaying these events. The main purpose of this design pattern in microservices is:

• To store all the changes as immutable events.
• Rebuilding the state by replaying events.
• Enabling the full audit trails and time travel.
• To support the complex business logic and recovery.
• Working well with CQRS and message-driven systems.

Example: Every deposit or withdrawal is stored as an event in a banking app. The account balance is calculated by replaying these events rather than storing a single balance value.

6. CQRS (Command Query Responsibility Segregation) Pattern

CQRS is a design pattern that separates the data model for reading data (queries) from the model used for modifying data (commands), by optimizing each side independently. The main working of this design pattern in microservices is:

• To use separate models for read and write operations.
• Improving performance, scalability, and maintainability.
• To enable specialized data storage for reads and writes.
• Also, it is often used with the event sourcing design pattern in microservices.
• Supporting complex business operations.

Example: Placing an order updates one data model (write side) in an e-commerce platform, while viewing order history uses a different, optimized read model.

7. Saga Pattern

The Saga pattern manages data consistency across multiple microservices in distributed transactions using a sequence of local transactions and events. The key responsibilities of this design pattern in microservices are:

• Coordinates the long-running business processes
• Ensures eventual consistency without distributed transactions
• Uses either choreography or orchestration
• Rolls back with compensating actions
• Maintains reliability in distributed systems

Example: In a travel booking app, booking a trip involves hotel, flight, and car services. If the car booking fails, the saga compensates by canceling the flight and hotel.

8. Strangler Fig Pattern

The strangler fig pattern gradually replaces a legacy system by building new features as separate services and redirecting traffic from old to new components over time. The key responsibilities of this design pattern in microservices are:

• Enables incremental migration of legacy systems.
• Routes the specific request to the new microservices.
• Reduces risk by avoiding big-bang rewrites.
• Allows continuous deployment alongside old code.
• Simplifies modernization.

Example: A telecom company slowly migrates its billing system by replacing one function (like invoice generation) with a microservice, while keeping other parts in the legacy system until fully modernized.

9. Bulkhead Pattern

The bulkhead pattern isolates different parts of a system so that failure in one does not bring down the entire system, like compartments in a ship. The key responsibilities of this design pattern in microservices are:

• Isolates resources between services or service parts.
• Prevents cascading failures.
• Improves fault tolerance and availability.
• Enables controlled resource usage.
• Often used with thread pools, queues, or timeouts.

Example: In a hotel booking system, the booking and notification services are isolated, so if notifications fail, bookings still work smoothly.

10. Sidecar Pattern

The sidecar design pattern in microservices helps to deploy the supporting features, such as logging, monitoring, or proxying, alongside the main service in another separate container or process. Also, it is very common in service mesh architectures. The main responsibilities of this design pattern in microservices are:

• Adding the operational features without changing the core service code.
• Deploying auxiliary functions like logging, config, or proxies.
• To share the lifecycle with the main service.
• Simplifying the observability and infrastructure tasks.

Example: Each microservice pod in a Kubernetes setup has a sidecar container that is used for handling network traffic, metrics, and retries, without modifying the main app.

Deployment Patterns for Microservices

The deployment design patterns help us to understand how microservices are packaged, deployed, and managed in different environments. Their main aim is to support independence, scalability, and reliability. They are categorized into five main categories, which are discussed below:

1. Multiple Service Instances per Host Pattern

A number of microservices share the same physical or virtual host. This is simple and easy to execute, but it can create contention for resources, as well as less isolation.

Responsibilities:

• Runs multiple services on one host (shared environment).
• Is cost-conscious by taking advantage of physical resources.
• Offers little to no isolation; an issue in one service may affect other services.
• Appropriate for low-scale or low-resource environments.

2. Service Instance per Host Pattern

Basically, each microservice runs on its own dedicated host, which can be physical or virtual. This provides better isolation and resource control.

Responsibilities:

• One service instance per VM or physical server.
• Provides strong isolation and fault containment.
• Easier to monitor and allocate resources.
• Needs more resources and is expensive.

3. Service Instance per Container Pattern

Each service instance is run in its own container, which allows services to be isolated while at the same time using resources efficiently. This is most popular with Docker and Kubernetes.

Responsibilities:

• Deploys each service in a separate container.
• Lightweight compared to VMs, with better isolation than shared hosting.
• Supports fast scaling, portability, and automation.
• Works well with container orchestration platforms.

4. Serverless Deployment Pattern

Services are deployed as functions that get executed in response to events without server management. It is great for event-driven or low-traffic services.

Responsibilities:

• No need to provision or manage servers.
• Scales automatically based on demand.
• Pay-per-use pricing model.
• Ideal for lightweight, stateless operations.

5. Blue-Green Deployment Pattern

Two environments (Blue and Green) are kept up. Traffic is routed to one (active) while the other is locked down for new deployments. Once testing is complete, the traffic is switched.

Responsibilities:

• Reduced outage time when deploying.
• Enables a simple rollback by switching environments.
• Allows safer and faster releases.
• Needs duplicate infrastructure for a short time.

Scaling Patterns for Microservices

Microservices scaling patterns provide capabilities to improve the performance and availability of the overall system through environments that can dynamically scale their resources to adapt to demand. It allows an individual service to be scaled effectively and independently of the other services in the environment, which provides better utilization and fault isolation. Scaling patterns allow a system to dynamically grow and better maintain responsiveness and stability using the following categories. The four main categories of scaling patterns are:

1. Horizontal Scaling Pattern

The horizontal scaling design pattern in microservices scales a service by adding more instances rather than upgrading the hardware. It is the most common approach in microservices. It adds more service instances to handle the increased load, distributes traffic using load balancers, works well in stateless service architectures, and enables high availability and elasticity.

2. Vertical Scaling Pattern

In the vertical scaling design pattern in microservices, services are scaled by increasing resources (CPU, RAM) on the same instance. It is a simple design pattern, but it has some physical limits. This pattern adds more computing power to existing machines, used for quick fixes or stateful components, and may cause downtime during resizing. Also, it is easier to implement, but it has limited scalability.

3. Auto-Scaling Pattern

The auto-scaling pattern automatically adjusts the number of running instances based on metrics like CPU, memory, or request rates. Ideal for cloud-native environments. It is a design pattern in microservices that monitors the usage and adjusts scale in real time. It also prevents over or under-provisioning and reduces operational costs by scaling only when needed. This design pattern is commonly used in Kubernetes and cloud platforms.

4. Function-Level Scaling Pattern

In a function-level scaling design pattern, functions or modules that make up a microservice are scaled independently. This design pattern in microservices is beneficial for isolating heavy-load operations. As it does not scale the entire service, thus, it limits the wasted resources. Function-level scaling design pattern is commonly used in serverless or modular architectures.

Security Patterns for Microservices

Security patterns for microservice architecture are important for securing services from unauthorized access, data breaches, and other modes of attack. The security patterns provide solutions for many security requirements through many different levels and concerns to help allow secure and resilient communication and work to be accomplished between services. Below are some important security patterns:

1. Access Token Pattern: Use short-lived access tokens (such as JWT) to allow the client to securely pass on the identity of the user and what actions the user is allowed to do, and do it without having to go back to the authorization server for a token every time.
2. API Gateway Security Pattern: It provides a single location for authentication and authorization, and rate limiting for any traffic to the back-end services, and ultimately, any security in an application is the responsibility of the developer beyond just the API gateway.
3. Rate Limiting and Throttling Pattern: To limit how many requests a client can issue to an API. This is useful to limit the abuse of a microservice and also protects a microservice from being overloaded with excessive requests.
4. Service-to-Service Authentication Pattern: It provides a means to provide identity for an API request to a microservice from another microservice and typically over mutual TLS or tokens. Thus, only trusted services can communicate with each other as a trusted user and perform an action that is intended.
5. Least Privilege Pattern: The least privilege pattern grants the minimal allowed permission to a user or another service to help minimize security risks.
6. Encryption in Transit and at Rest Pattern: This encrypts sensitive transactional information while in-flight across the wire, as well as in-flight or in storage.
7. Audit Logging Pattern: This logs security-relevant events like login or permissions changes to a microservice to identify threats and serve as support for any investigations that may need to be conducted.

Real-World Examples of Design Patterns in Microservices

Here are a few real-world examples of design patterns in microservices:

1. Netflix: Netflix uses an API gateway design pattern in microservices to route requests from many types of clients, such as smart TVs, mobile applications, web, etc., to its microservices. The API Gateway also provides authentication, rate limiting, and aggregation of requests to improve the experience for clients.
2. Amazon: The circuit breaker design pattern in microservices is used by Amazon on specific services, such as payment services and inventory services, to protect them. So, if a downstream service is slow or failing, the circuit breaker cuts off further requests for that time, and this will help to avoid cascading failures for many services.
3. Uber: Uber uses a service discovery design pattern in microservices with tools such as Apache ZooKeeper and later with Consul, which helps thousands of microservices to discover and communicate with each other as they scale up and down.
4. Booking.com: In a booking flow for hotels, flights, and rental cars, Booking.com used the Saga design pattern in microservices to make sure they have consistency. If one nth reservation fails (a hotel, for example), then the Saga will roll back associated reservations similar to flights and cars.
5. Eventbrite: By using the event sourcing design pattern in microservices to monitor sales of tickets and user registrations, and actions taken by the system, Eventbrite tracks all actions taken by users or systems stored as an event, enabling the system to recreate state or audit behavior, with full traceability.
6. Shopify: Shopify applies the command query responsibility separation (CQRS) design pattern in microservices to distinguish read-heavy storefront queries (product browsing, product search) from write operations (inventory updates, processing orders), so each side can scale and perform as needed.
7. Spotify: Spotify gradually migrated parts of its legacy monolith, like playlists or recommendations, to microservices by routing specific features to new services while the rest remained in the old system, and this is done using the strangler fig design pattern in microservices.
8. LinkedIn: LinkedIn allows functionalities of the service, such as messaging, feed updates, and notifications, to isolate itself from the impact of change using the Bulkhead design pattern in microservices. If there is an issue with one feature, the others continue to function regardless of the impacted performance.
9. Airbnb: Airbnb uses the sidecar design pattern in microservices, in which the sidecar containers are used for functions such as service discovery, metrics collection, and logging. The sidecar runs in conjunction with the service abstraction, allowing the service to remain focused on the domain logic and not operational concerns, without the need to change the original logic.

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Challenges with Using Design Patterns in Microservices 

1. Using multiple microservices leads to increased complexity in the system.
2. Services need to communicate with each other across the network, which can fail or slow down.
3. Each service has its own database. Keeping data in sync across those services is even more difficult.
4. Testing the entire system as a whole is hard because services run separately.
5. The development, deployment, and updating of services rely on sophisticated DevOps tools.
6. Security is more difficult because more services mean more attack surfaces.
7. The teams must carefully manage the versions of services so they do not break other services in the system.
8. Debugging problems can be difficult because we don’t know if problems arise from that service or other services.

Conclusion 

Design patterns in microservices provide you with solutions to common architectural and operational challenges for developing distributed systems. The microservices design patterns, such as API Gateway, Circuit Breaker, Saga, CQRS, etc., help you to address the particular concerns, like scalability, fault tolerance, security, and data consistency. While the microservices give flexibility and scalability, they also give complexity in terms of deployment, monitoring, and communication sometimes. So, using the right design patterns in microservices with careful planning helps teams to build reliable, maintainable, and scalable systems.

Microservices Design Patterns – FAQs