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Regardless of industry, if you rely on the cloud to run critical business operations, then application agility is everything. Application agility allows a continuous release of new features and functions, ensuring both external and internal users stay engaged with your solution. Developers can also address problems on the spot, reducing security threats that could expose consumer information and undermine your organization\u2019s data.<\/p>\n\n\n\n
We\u2019ve gone over the benefits of microservices<\/a> in the past, so we won\u2019t get into it too much. However, whether or not you achieve the benefits of application agility may come down to the microservices patterns you choose. In this article, we\u2019ll go over some of the most common microservice design patterns and how they fit into the overall microservices architecture<\/a>.<\/p>\n\n\n For companies hoping to achieve application agility, microservices may seem like an obvious solution \u2014 and often, it is. This approach to software development can take large, interdependent applications and transform them into tiny, independent modules that communicate with each other with ease, thanks to lightweight language-agnostic protocols like RESTful APIs.<\/p>\n\n\n\n Businesses that successfully adopt the correct microservices patterns for their use case can significantly reduce the time it takes to take an idea and transform it into a valuable solution for buyers. Microservices do not require development teams to rewrite and deploy an entire application any time they\u2019d like to add a feature. Nor do they have to consider the potential damage one tiny update might do to an old system.<\/p>\n\n\n\n Microservices apps also produce a smaller codebase, making continuous software testing and maintenance faster and easier than your legacy monolith solution. Whether your audience understands the power of patterns or not, they will notice quick resolution times when there\u2019s an issue with the product or when a company takes their feedback and puts it to work. They\u2019ll also appreciate the frequent updates, feature releases, and continuous improvements \u2014 a key factor in creating a positive customer experience. And it\u2019s worth mentioning that great experiences set the stage for lasting relationships and a nice boost to your bottom line.<\/p>\n\n\n\n Those APIs break transactions into several small modules, each responsible for one tiny piece of the transaction. While those modules offer a ton of flexibility, there\u2019s a lot that can go wrong when you\u2019re starting from scratch. To avoid the costly consequences of a poorly connected microservices architecture, many developers are looking toward solutions like OpenStack Swift and Amazon S3 along with Cloud Data Management Interface (CDMI). These solutions, designed for web-based applications, help teams execute complex automation and easily manage APIs<\/a>.<\/p>\n\n\n\n In this next section, we\u2019ll dig into some of the more popular microservices patterns and best practices.<\/p>\n\n\n Database per service is perhaps the most common microservice design pattern out there, and almost every service needs to persist data in some kind of database. The primary challenge lies in determining the database architecture across a microservices application.<\/p>\n\n\n\n Most client transactions need to access and join data that span multiple services. So to make sure each database responds correctly to each client request, you need to keep persistent data private to each microservice and accessible only via its API.<\/p>\n\n\n The saga pattern is used in microservices environments where the database per service pattern has already been put into play.<\/p>\n\n\n\n So, as mentioned, each service has a dedicated database. But in some cases, client requests span multiple services. The saga pattern is used to ensure that, no matter how many requests come through, the system controls transactions from the database side, which ensures data consistency across services.<\/p>\n\n\n\n This pattern is particularly helpful when executing transactions that need to access more than one database at the same time. A good example is an e-commerce app, where customers need access to the payment processing service and the product pages at the same time \u2014 in which case, Saga prevents one service from slowing down the other.<\/p>\n\n\n Like saga, the composite pattern can also be used after you\u2019ve applied the database per service pattern and client queries that join data from multiple services to create complex views. A high-level service makes the calls and creates the proper output while also removing complexity from the view.<\/p>\n\n\n\n You can also define an API composer to invoke the services that own the data and perform in-memory joins of the results. Using this microservices pattern offers a simple solution for your application to query data across the entire microservice architecture.<\/p>\n\n\n An API gateway pattern is typically used as a single entry point for all your services to avoid exposing the services to the front-end application and the Internet.<\/p>\n\n\n\n Microservices typically provide fine-grained APIs, which support clients that need to interact with several services at the same time.<\/p>\n\n\n\n The gateway routes the requests to the appropriate services and can be programmed to run an adapter code that exposes a different API to each client, depending on their requirements. The gateway can also be used as a security precaution, verifying a client\u2019s authorization before granting access.<\/p>\n\n\n The client load balancer ensures that payloads are equally distributed among multiple services. This ensures that the application maintains a consistently high level of performance \u2014 even when users have more than one service running at a time.<\/p>\n\n\n\n The balancer registry automatically updates its own records, so if it goes down, the registry removes that instance without any manual intervention.<\/p>\n\n\n\n Because the balancing takes place on the client-side, you can also program the load balancer based on the compute resources serving each microservice.<\/p>\n\n\n The discovery pattern is responsible for maintaining a map of names and addresses and contains information about the health of each service.<\/p>\n\n\n\n The API gateway and client load balancer rely heavily on the discovery pattern to pinpoint the exact location where clients requested a service. The discovery service queries a registry that then forwards client requests to an available service instance.<\/p>\n\n\nMicroservice Patterns Play Critical Role in Realizing Benefits<\/h2>\n<\/div>\n\n\n
Database Per Service Pattern<\/h3>\n<\/div>\n\n\n
Saga Pattern<\/h3>\n<\/div>\n\n\n
Composite Pattern<\/h3>\n<\/div>\n\n\n
API Gateway Pattern<\/h3>\n<\/div>\n\n\n
Client Load Balancer<\/h3>\n<\/div>\n\n\n
Discovery Pattern<\/h3>\n<\/div>\n\n\n