When designing application architectures in the cloud, it’s important to choose the correct architecture design patterns to ensure scalability, reliability, and performance. There is a very large list of architecture design patterns to choose from. This article, however, covers 5 of the most common and useful cloud architecture design patterns to use when designing application architectures in the cloud, along with some recommendations on which Microsoft Azure services can help with each design pattern.
Let’s dive in to the cloud design patterns!
The Retry pattern is a design pattern that allows an application to automatically retry failed operations, improving the overall reliability of the application. This pattern is useful in any application architecture, including cloud applications, since connectivity and other errors do happen, and the Retry patter will improve the reliability of the application.
The Retry pattern can be implemented in any application by adding a loop or other logic that will retry a database, API, or other service connection multiple times if certain error codes are received. This will help catch and handle transient errors that occur so the application doesn’t immediately fail, but instead retries the operation a specified number of times. There are helper libraries available for some development frameworks to help with implementation, but it’s generally pretty straight forward to code in as needed.
With Azure Functions, you can set up automatic retries for any failed operations, ensuring that your application can handle any errors that may occur. Additionally, Azure Functions integrates with other Azure services, such as Azure Event Grid and Azure Service Bus, allowing you to create powerful message-based or event-driven architectures that lend themselves to Retry pattern nicely.
Circuit Breaker Pattern
The Circuit Breaker pattern is a design pattern that allows an application to detect failures and encapsulate logic of preventing failures from constantly recurring. This can be useful during times of service maintenance, temporary system failure, or unexpected service outages. This pattern allows the application to detect failures and prevent the application from attempting an operation that is known to fail so it can gracefully degrade functionality instead of full application failure.
This can be implemented using Azure Application Gateway, which allows you to set up a load balancer that can automatically detect and handle failed requests. With App Gateway, a circuit breaker policy can be configured that will automatically break the circuit when a certain threshold of failed requests is reached. Then, the application can handle any failures that may occur without affecting the overall performance.
The Throttling pattern is a design pattern that allows an application or API to limit the number of requests it receives, preventing overloading and helping to improve the overall performance. This can help to slow down service requests to a rate that is easily handled, rather than having the service fail due to being overloaded. When providing an API or other service to customers, it can be useful to implement the throttling pattern so each customer is limited in how much their usage may impact the performance of other customers of the same service.
This can be implemented on API services using Azure API Management, which allows you to create an API gateway that can manage and throttle incoming requests. You can set up policies that will automatically throttle incoming requests based on a variety of factors, such as the number of requests per second or the type of request being made. Then, you the service can ensure that it can handle any amount of traffic without overloading and affecting its overall performance.
Queue-based Load Leveling Pattern
The Queue-based Load Leveling pattern is a design pattern that allows an application to balance the workload across multiple instances of the application, improving overall performance and scalability. Load leveling is often implemented by using a message queue service to pass service requests from one part of the application to another. Using a message queue helps to implement a form of “eventually consistent” type of system, where the sender knows the request will be handled eventually and can continue with its work once the message is submitted. Then the receiver can pull request messages off the queue to handle them at a managable scalability rate.
Within Microsoft Azure, Queue-based Load Leveling pattern can be implemented with the use of the Azure Service Bus Queues or Topics, which allows you to set up a message queue that can handle incoming requests. For higher scalability, the Azure Event Hub or Event Grid services can also be used for this.
The Bulkhead pattern is a design pattern that’s implemented by isolating parts of an application into separate pools so that if one fails, the others will continue to function. This pattern is named after the sectioned partitions (aka bulkheads) of a ship’s hull. This pattern is useful for application architectures that require high availability while also needing to handle large volumes of traffic. This is a design pattern that can be very useful in large scale applications using a microservices architecture.
Within Microsoft Azure, the Azure Kubernetes Service (AKS) could be used to implement the Bulkhead pattern. Using a microservices architecture, the various application components could be isolated into separate pools. AKS allows you to automatically scale each of the application component pools individually to meet the scalability requirements of the overall system. This will allow each pool to be scaled independently to meet virtually any scalability requirements.
Choosing the right cloud architecture design patterns is essential for ensuring the scalability, reliability, and performance of any application architecture. By leveraging the power of various Microsoft Azure services offered, you can implement these patterns to create highly available, scalable, and performant applications that can meet the demands of any size enterprise workload. The Microsoft Azure services given as examples for each design pattern are just single possible services to use when implementing the design pattern. There are obviously many options to choose from when implementing each design pattern so that it fits the unique requirements of the system architecture being built.