{"id":93686,"date":"2026-05-26T19:57:30","date_gmt":"2026-05-26T19:57:30","guid":{"rendered":"https:\/\/www.azul.com\/?post_type=article&#038;p=93686"},"modified":"2026-05-26T19:57:35","modified_gmt":"2026-05-26T19:57:35","slug":"microservices-design-patterns","status":"publish","type":"article","link":"https:\/\/www.azul.com\/zh-hans\/articles\/microservices-design-patterns\/","title":{"rendered":"Microservices Design Patterns"},"content":{"rendered":"\n<h2 class=\"wp-block-heading\" id=\"h-what-are-microservices-design-patterns\">What Are Microservices Design Patterns?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Microservices design&nbsp;patterns<\/strong>&nbsp;are&nbsp;for developers and&nbsp;technology professionals&nbsp;to&nbsp;implement&nbsp;standardized&nbsp;solutions&nbsp;and best practices in distributed systems. Because microservices split what is traditionally a single application into multiple moving parts,&nbsp;you\u2019ll&nbsp;need to design a&nbsp;system with consistent data and user behaviors.&nbsp;You\u2019ll&nbsp;also need to ensure that&nbsp;one failing service&nbsp;doesn\u2019t&nbsp;crash&nbsp;other services or even the&nbsp;whole system.&nbsp;These patterns provide you with the design framework&nbsp;you\u2019ll&nbsp;need.&nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-decomposition-patterns\">Decomposition Patterns<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Decomposition patterns&nbsp;<\/strong>help you&nbsp;determine&nbsp;how to break up a large, monolithic application into smaller, more manageable services.&nbsp;Let\u2019s&nbsp;look at three of these patterns:&nbsp;<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Decompose\u00a0by Business Capability<\/strong>:\u00a0Organize services around the business goals and\u00a0application capabilities. For example, name and design a \u201cClaims\u201d service for an insurance-focused application. Name and design \u201cOrdering\u201d and \u201cShipping\u201d services for an e-commerce application.\u00a0Name and design a \u201cFraud Detection\u201d service to analyze financial transactions and flag suspicious activities.\u00a0\u00a0<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Decompose by Subdomain<\/strong>:\u00a0This is a practical design method, where you group services by your bounded domain contexts to ensure that each of your internal data models\u00a0don\u2019t\u00a0leak into each other.\u00a0Subdomains can be classified into\u00a0prioritization groups, including the core subdomains (the part of the system that provide a competitive advantage and require the highest performance), the supporting subdomains (necessary for the business but don\u2019t provide a business advantage, such as an internal tool), and generic subdomains (standard across all your businesses\u00a0and can be bought\u00a0as standalone software, such as email or authentication services).\u00a0<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Strangler Fig\u00a0Pattern<\/strong>:\u00a0This pattern is used\u00a0to gradually transition from\u00a0a monolithic\u00a0application\u00a0design\u00a0to microservices.\u00a0You build\u00a0your\u00a0new features as microservices and then\u00a0you \u201cstrangle\u201d the\u00a0monolith by replacing its capabilities and functions one by one until it\u2019s completely replaced (and retired)\u00a0by\u00a0your microservices solution.\u00a0This is a great method if you want to migrate to microservices, but you\u00a0don\u2019t\u00a0have the time, budget, or resources to do so. Instead, you migrate gradually over time.\u00a0<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-communication-patterns\">Communication Patterns<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Communication&nbsp;patterns<\/strong>&nbsp;define how external clients interact with your system and how your different microservices&nbsp;integrate&nbsp;with each other.&nbsp;Here are&nbsp;some&nbsp;of these patterns to consider:&nbsp;<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>API Gateway<\/strong>: A common pattern (with\u00a0PaaS cloud services built for you to implement it), you\u00a0build a\u00a0single entry\u00a0point that handles your routing, authentication, and rate limiting. The advantage of this pattern is that it prevents the client from knowing the location of the individual services.\u00a0<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Backend for\u00a0Frontend<\/strong>: A variation of the API Gateway, where you create a separate gateway for each client, to\u00a0optimize\u00a0the data that they receive. For example, you might have one\u00a0gateway for your mobile app and one for your web client.\u00a0<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Aggregator<\/strong>: This is a service that collects data from multiple other services and combines the data into a single response. It reduces the number of network calls that a client\u00a0has to\u00a0make.\u00a0\u00a0<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Service Discovery<\/strong>:\u00a0This pattern is also considered an orchestration and management pattern, but you\u00a0can\u2019t\u00a0communicate with your other services if you\u00a0can\u2019t\u00a0find them! This is a \u201clive contact list\u201d for your microservices. It\u00a0builds a service registry as a central database to track the IP address and port\u00a0of every service at any given moment.\u00a0When the service starts, it tells the registry where the service\u00a0is.\u00a0When one service needs to use another (such as the \u201cOrder\u201d service needs to\u00a0process a payment using the \u201cPayment\u201d service, then the registry sends back the IP location of the Payment service to the Order service.\u00a0<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-database-patterns\">Database Patterns<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Database patterns<\/strong>&nbsp;allow each of your&nbsp;microservices to own its own data,&nbsp;in order to&nbsp;operate&nbsp;independently. However, you need the services to interact&nbsp;as&nbsp;one cohesive unit, so that they share consistent and accessible data in transactions and queries. Here are four data management patterns to consider:&nbsp;<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Database-per-Service<\/strong>:\u00a0You include a private database for each of your services. This gives you an agile loose coupling, but it makes it harder for you to join data across your services.\u00a0<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Saga Pattern<\/strong>:\u00a0This pattern manages a sequence of local transactions. That way, if one step fails, then it triggers compensating transactions that undo the\u00a0previous\u00a0steps.\u00a0<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>CQRS (Command Query Responsibility Segregation)<\/strong>:\u00a0You separate your \u201cWrite\u201d model from your \u201cRead\u201d model to ensure you have high-performance reads (like for a product search)\u00a0that are separated from the complex logic\u00a0you\u2019ll\u00a0need to update your data.\u00a0<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Event Sourcing<\/strong>: Instead of storing just the current state of an object, you use this pattern to store every change as a sequence of events instead. You then just \u201creplay\u201d these events to reach any point in history.\u00a0It\u2019s\u00a0like\u00a0an immutable\u00a0browsing history for your data.\u00a0<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-reliability-patterns\">Reliability Patterns<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Reliability patterns<\/strong>&nbsp;help you build resilience in your&nbsp;microservices. Distributed systems&nbsp;(and most all systems)&nbsp;will fail eventually, so these three patterns help ensure that you&nbsp;contain&nbsp;each failure:&nbsp;<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Circuit Breaker<\/strong>: This pattern prevents a service from repeatedly trying to call a failing downstream service.\u00a0When that happens,\u00a0it\u00a0\u201ctrips\u201d the circuit and\u00a0immediately\u00a0returns an error, which gives your failing service time to recover.\u00a0<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Bulkhead<\/strong>:\u00a0Just like\u00a0how isolated\u00a0compartments in a ship\u00a0help prevent the ship from sinking, this pattern isolates your resources (such as thread pools or memory) so that if one service is overwhelmed, it doesn\u2019t\u00a0automatically consume all your resources and unintentionally\u00a0starve the rest of the services from your system\u2019s resources.\u00a0<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Retry<\/strong>:\u00a0This pattern automatically retries a failed request a set number of times,\u00a0which helps resolve \u201ctransient\u201d errors like a brief or temporary network flicker.\u00a0<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-observability-patterns\">Observability Patterns<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Observability patterns<\/strong>&nbsp;can be incredibly useful if you have many services. For example, if your application runs 50 services, you&nbsp;can\u2019t&nbsp;just look at one log file to see what went wrong. Here are three&nbsp;design patterns that can help:&nbsp;<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Log Aggregation<\/strong>:\u00a0This pattern pulls logs from all the services into a centralized tool (such as using ELK or Splunk).\u00a0<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Distributed Tracing<\/strong>:\u00a0This pattern assigns a unique correlation ID to a request so that you can follow how the request traveled through each of your services.\u00a0<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Health Check<\/strong>:\u00a0Each service provides an endpoint (such as \/health) that an orchestrator (such as Kubernetes) pings to see if the service is still alive or if it needs to be restarted.\u00a0\u00a0<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-scalability-patterns\">Scalability Patterns<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Scalability patterns are the blueprints that help&nbsp;make sure your system can handle a growing amount of work, including more users, more data, and higher transaction volumes.&nbsp;Your system automatically adds the necessary resources,&nbsp;so you&nbsp;don\u2019t&nbsp;have to redesign the application.&nbsp;Let\u2019s&nbsp;discuss one of these patterns in particular:&nbsp;&nbsp;<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Scale-Out<\/strong>:\u00a0Also called horizontal scaling, when a microservice faces a surge in traffic, then a load balancer or orchestrator (like Kubernetes) detects the load pressure and triggers the creation of new, temporary\u00a0\u201cclones\u201d of your service.\u00a0\u00a0<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-java-and-implementation-considerations\">Java and Implementation Considerations<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">If&nbsp;you\u2019re&nbsp;using a Java Spring Boot environment,&nbsp;common design patterns to start with include the API Gateway and Circuit Breaker patterns. They provide immediate protection and structure for your microservices&nbsp;solution.&nbsp;&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Java developers&nbsp;don\u2019t&nbsp;have to write these patterns from scratch.&nbsp;The Java ecosystem includes battle-tested libraries that help you implement many of these&nbsp;microservices&nbsp;design patterns. Here are some&nbsp;examples of&nbsp;Java tools that you can use:&nbsp;<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><th>Design pattern<\/th><th><strong>Java implementation or library<\/strong><\/th><\/tr><\/thead><tbody><tr><td>API Gateway\u00a0<\/td><td>Spring Cloud Gateway\u00a0<s>or<\/s>and\u00a0Zuul\u00a0use non-blocking APIs\u00a0to handle thousands of concurrent connections.<\/td><\/tr><tr><td>Circuit Breaker<\/td><td>Resilience4j provides annotations like @CircuitBreaker that wrap your Java methods.<\/td><\/tr><tr><td>Saga Pattern<\/td><td>Axon Framework or Eventuate can help you manage compensating transactions and undo logic across your different Java services.\u00a0<\/td><\/tr><tr><td>CQRS<\/td><td>Spring Data combines with an event store like Kafka to\u00a0use a Java entity for writing\u00a0and a lightweight DTO (data transfer object) for reading.\u00a0<\/td><\/tr><tr><td>Distributed Tracing<\/td><td>Micrometer Tracing automatically injects Trace IDs into your Java logs and headers.\u00a0<\/td><\/tr><tr><td>Services Discovery<\/td><td>Spring Cloud Netflix Eureka\u00a0builds a\u00a0registry for\u00a0locating\u00a0your services.\u00a0<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-how-nbsp-azul-prime-nbsp-enhances-these-patterns\">How&nbsp;Azul Prime&nbsp;Enhances these Patterns<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">While libraries like Resilience4j help handle the logic of the patterns, Azul Prime handles the execution environment.\u00a0Prime helps in a few\u00a0different ways:\u00a0<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>C4 GC prevents false Circuit Breaker trips<\/strong>:\u00a0A Garbage Collection (GC) pause can last for seconds. An API Gateway or Circuit Breaker might think the service is dead and not\u00a0responding, thus \u201ctripping\u201d the circuit incorrectly and cutting off a healthy service. Azul\u2019s C4 Garbage Collector\u00a0eliminates\u00a0the pauses\u00a0and keeps the response times\u00a0quick and reliable.\u00a0The Circuit Breakers\u00a0would then\u00a0trip when you\u00a0encounter\u00a0a real software or network failure, not a temporary hiccup in your JVM\u2019s performance.\u00a0<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>ReadyNow! makes decomposition and scaling instant<\/strong>: Java\u2019s warmup\u00a0times become a major problem, if you decompose a monolith into 20 microservices, and each one starts slowly while the JIT compiler optimizes your code.\u00a0Azul\u2019s\u00a0ReadyNow! technology allows your Java services to start at their peak performance, because it\u00a0\u201cremembers\u201d the optimized state of your code. The Scale-Out pattern then works in real-time.\u00a0<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Cloud Native Compiler\u00a0enhances the Sidecar and\u00a0Density\u00a0patterns<\/strong>: Microservices often use\u00a0Sidecars\u00a0(extra containers for security or logging), which can consume a lot of CPU for JIT compilation across many smaller containers. Azul\u2019s Cloud Native Compiler offloads the heavy lifting of code compilation to a separate server. This allows you to run individual microservices that are much smaller and leaner, packing even more services into a single server, which improves your Database-per-Service density.\u00a0<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>C4 GC\u00a0increases the reliability of Saga and Event processing<\/strong>: Patterns like Saga and\u00a0Event Sourcing\u00a0rely on high-throughput processing\u00a0of the message queues. A single GC\u00a0pause in your orchestrator service can delay your entire business transaction. Azul\u00a0provides a\u00a0higher sustained throughput and\u00a0a\u00a0lower\u00a0tail-end latency (the slowest 1% of the requests). This\u00a0<s>ensure<\/s>ensures\u00a0that multi-step patterns like Sagas finish faster with increased reliability.\u00a0\u00a0<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Java developers building microservices can use Azul Prime to solve major performance and consistency issues that are inherent when running a distributed Java application.\u00a0To learn more about how Prime can provide you with a high-performance JVM, see\u00a0<a href=\"https:\/\/www.azul.com\/products\/prime\/\" target=\"_blank\" rel=\"noreferrer noopener\">Azul Prime: High Performance JVM<\/a>.\u00a0<\/p>\n","protected":false},"excerpt":{"rendered":"<p>What Are Microservices Design Patterns? Microservices d [&hellip;]<\/p>\n","protected":false},"author":85,"featured_media":91820,"template":"","resource_categories":[494,524],"industry":[],"product":[280],"role":[],"resource_topic":[273],"class_list":["post-93686","article","type-article","status-publish","has-post-thumbnail","hentry","resource_categories-java-performance","resource_categories-high-performance-jvm","product-prime","resource_topic-performance"],"acf":{"article_smart_summary":"<p>Microservices design patterns are standardized solutions that help developers break up monolithic applications, manage communication between services, maintain data consistency, and build resilience into distributed systems \u2014 and this piece surveys the key pattern categories developers need to know, from decomposition strategies like the Strangler Fig pattern to communication patterns like API Gateway, database patterns like CQRS and Event Sourcing, and reliability patterns like Circuit Breaker and Bulkhead. It also covers observability and scalability patterns, maps each to their common Java implementations (Spring Cloud, Resilience4j, Axon Framework, and others), and then shows how Azul Prime strengthens the execution environment underlying all of these patterns \u2014 preventing false Circuit Breaker trips caused by GC pauses, enabling instant Scale-Out with ReadyNow!, and reducing per-service resource overhead through the Cloud Native Compiler.<\/p>\n<p>&nbsp;<\/p>\n","article_sidebar_nav_items":[{"link":{"title":"What Are Microservices Design Patterns?","url":"#h-what-are-microservices-design-patterns","target":""}},{"link":{"title":"Decomposition Patterns","url":"#h-decomposition-patterns","target":""}},{"link":{"title":"Communication Patterns","url":"#h-communication-patterns","target":""}},{"link":{"title":"Database Patterns","url":"#h-database-patterns","target":""}},{"link":{"title":"Reliability Patterns","url":"#h-reliability-patterns","target":""}},{"link":{"title":"Observability Patterns","url":"#h-observability-patterns","target":""}},{"link":{"title":"Scalability Patterns","url":"#h-scalability-patterns","target":""}},{"link":{"title":"Java and Implementation Considerations","url":"#h-java-and-implementation-considerations","target":""}},{"link":{"title":"How&nbsp;Azul Prime&nbsp;Enhances these Patterns","url":"#h-how-azul-prime-enhances-these-patterns","target":""}}],"article_cta":{"link":{"type":"url","value":"https:\/\/www.azul.com\/prime-trial\/","url":"https:\/\/www.azul.com\/prime-trial\/","name":"https:\/\/www.azul.com\/prime-trial\/","title":"Request a Trial","target":""},"appearance":"solid","color":"","show_arrow":false}},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v28.7 (Yoast SEO v28.7) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Microservices Design Patterns - Azul | Better Java Performance, Superior Java Support<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.azul.com\/articles\/microservices-design-patterns\/\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Microservices Design Patterns\" \/>\n<meta property=\"og:description\" content=\"What Are Microservices Design Patterns? 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