![\"Azure](\"https:\/\/devblogs.microsoft.com\/cosmosdb\/wp-content\/uploads\/sites\/52\/2024\/02\/azure-samples-cosmsos-db-design-patterns.jpeg\")
<\/p>\nWe’re excited to present the sixth edition of our blog series, dedicated to exploring design patterns in Azure Cosmos DB for NoSQL applications. Drawing from real-world customer experiences, our aim is to help you navigate the intricacies of JSON-based NoSQL databases. In this chapter, we delve deeper into prevalent NoSQL patterns, perfect for those new to this database type. We also focus on patterns specific to Azure Cosmos DB, demonstrating how to harness its distinct capabilities to tackle complex architectural issues.<\/p>\n
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These patterns, previously shared individually with customers, are now being made more widely accessible. We believe it’s the right moment to broadly publish these insights, enhancing their discoverability among users. Thus, we’ve created Azure Cosmos DB Design Patterns<\/a> \u2014 a GitHub repository filled with an array of examples. These samples are meticulously curated to demonstrate the implementation of specific patterns, assisting you in addressing design challenges when utilizing Azure Cosmos DB in your projects.<\/p>\n To help share these, we have created a blog post series on each of them. Each post will focus on a specific design pattern with a corresponding sample application that is featured in this repository. We hope you enjoy it and find this series useful.<\/p>\n Here is a list of the previous posts in this series:<\/p>\n Event sourcing is a powerful architectural pattern that has its roots in Domain-Driven Design. It represents a fundamental shift in how entities maintain their internal state. Unlike traditional methods that rely on direct serialization or object-relational mapping, event sourcing achieves this by recording a series of events in an event store.<\/p>\n At its core, event sourcing involves a container-like structure that records events in an append-only manner. This means that events are added to the log, but not updated or deleted. The result is a comprehensive historical record of all events, providing a valuable resource for various system functions.<\/p>\n This approach offers several significant advantages in application design. A key benefit is its natural pairing with the Command Query Responsibility Segregation (CQRS) architecture pattern. In such combinations, especially with Azure Cosmos DB, the Change Feed feature plays a crucial role. It facilitates the read layer of the architecture, supporting patterns like Materialized Views, which are also often used with CQRS. Additionally, event sourcing can establish a communication layer for loosely coupled services, enhancing system flexibility and scalability.<\/p>\n While event sourcing can be implemented with different database types, it finds resonance with developers using Azure Cosmos DB for several reasons:<\/p>\n A common scenario for using the NoSQL event sourcing data pattern is in building event-driven microservices architectures. Event sourcing can be particularly valuable in such scenarios due to its ability to capture and store all changes to an application’s state as a sequence of events.<\/p>\n Overall, the NoSQL event sourcing data pattern is well-suited for event-driven microservices architectures, enabling scalability, resilience, and loose coupling among microservices. It’s important to design the events carefully and choose the appropriate NoSQL database that fits the application’s requirements and expected event throughput. Additionally, this pattern requires careful consideration of event schema evolution to ensure backward and forward compatibility as services evolve over time.<\/p>\n Case Study: eCommerce Shopping Cart Application<\/strong><\/p>\n Design and Implementation<\/strong><\/p>\n Consider the scenario of an eCommerce company needing to track all changes to a shopping cart. Event sourcing is an ideal choice for this scenario, as it ensures the retention of all history and allows the calculation of the state at any point in time.<\/p>\n In our case study, whenever a change is made to the cart, an event is appended to the `shopping_cart_event`<\/strong> <\/span>event store collection. This approach avoids the need for updating multiple containers for each change. The partition key \u2018\/cartId\u2019<\/strong><\/span> is used to support the most common queries by the shopping cart service. Other services can consume data from the change feed and leverage solutions like materialized views for different query patterns.<\/p>\n Code Examples<\/strong><\/p>\n Consider the state of products in the cart maintained as \u2018productsInCart\u2019<\/strong>.<\/span> This could also be dynamically derived by each query or consumer. Here are sample events in the event store:<\/p>\n Cost Considerations<\/strong><\/p>\n When designing and implementing this pattern, it’s essential to consider the cost implications, especially regarding the frequency and volume of events generated.<\/p>\n Querying the Event Source Data<\/strong><\/p>\n After populating the event store with data, you can query it directly using Data Explorer in the Azure Portal. Here\u2019s how:<\/p>\n The most common query for this appends-only store is to retrieve events for a specific `CartId`<\/strong><\/span>, ordered by `EventTimestamp`<\/strong><\/span>. For example, to retrieve the latest event for a cart to know its last status and contents, use a query like:<\/p>\n Advanced Queries<\/strong><\/p>\n For more complex scenarios, you can run sophisticated queries on the event container, ideally using the partition key for cost optimization. For instance, if your application does not track `productsInCart`<\/strong><\/span>, you can derive this information with a query like:<\/p>\n In our sample implementation, we explored the event sourcing pattern through the lens of a shopping cart application for an eCommerce company using Azure Cosmos DB. The design emphasized tracking all cart changes as events within a single event store collection named `shopping_cart_event`<\/strong><\/span>, with `\/cartId`<\/strong><\/span> as the partition key to <\/a>optimize query performance. This setup ensures comprehensive historical data tracking and helps efficient data retrieval and processing by various consuming services. We gave concrete examples of event entries and detailed how to query this data effectively using Azure Portal’s Data Explorer, highlighting the practicality and efficiency of event sourcing in handling dynamic, event-driven data requirements in a NoSQL database environment.<\/p>\n Event Sourcing with NoSQL is particularly important for developers due to:<\/strong><\/p>\n In conclusion, the Event Sourcing NoSQL design pattern offers developers a robust framework for building scalable, resilient, and flexible applications, particularly in complex, distributed environments. Its advantages in terms of system reliability, data integrity, and architectural flexibility make it a valuable pattern in the toolkit of modern software development.<\/p>\n You can review the sample code by visiting the\u00a0Global Distributed Event Sourcing on GitHub<\/a>. You can also try this out for yourself by visiting the\u00a0Azure Cosmos DB Design Patterns<\/a>\u00a0GitHub repo and cloning or forking it. Then run locally or from Code Spaces in GitHub. If you are new to Azure Cosmos DB, we have you covered with a\u00a0free Azure Cosmos DB account\u00a0<\/a>for <\/a>30 days, no credit card needed. If you want more time, you can extend the free period. You can even upgrade too.<\/p>\n Sign up for your free Azure Cosmos DB account at\u00a0aka.ms\/trycosmosdb<\/a>.<\/p>\n Explore this and the other design patterns and see how Azure Cosmos DB can enhance your application development and data modeling efforts. Whether you\u2019re an experienced developer or just getting started, the free trial allows you to discover the benefits firsthand.<\/p>\n To get started with Azure Cosmos DB Design Patterns, follow these steps:<\/p>\n Azure Cosmos DB is a fully managed and serverless distributed database for modern app development, with SLA-backed speed and availability, automatic and instant scalability, and support for open-source PostgreSQL, MongoDB, and Apache Cassandra.\u00a0Try Azure Cosmos DB for free here.<\/a>\u00a0To stay in the loop on Azure Cosmos DB updates, follow us on\u00a0Twitter<\/a>,\u00a0YouTube<\/a>, and\u00a0LinkedIn<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":" We’re excited to present the sixth edition of our blog series, dedicated to exploring design patterns in Azure Cosmos DB for NoSQL applications. Drawing from real-world customer experiences, our aim is to help you navigate the intricacies of JSON-based NoSQL databases. In this chapter, we delve deeper into prevalent NoSQL patterns, perfect for those new […]<\/p>\n","protected":false},"author":39313,"featured_media":7547,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[14],"tags":[1882],"class_list":["post-7546","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-core-sql-api","tag-design-patterns"],"acf":[],"blog_post_summary":" We’re excited to present the sixth edition of our blog series, dedicated to exploring design patterns in Azure Cosmos DB for NoSQL applications. Drawing from real-world customer experiences, our aim is to help you navigate the intricacies of JSON-based NoSQL databases. In this chapter, we delve deeper into prevalent NoSQL patterns, perfect for those new […]<\/p>\n","_links":{"self":[{"href":"https:\/\/devblogs.microsoft.com\/cosmosdb\/wp-json\/wp\/v2\/posts\/7546","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/devblogs.microsoft.com\/cosmosdb\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/devblogs.microsoft.com\/cosmosdb\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/devblogs.microsoft.com\/cosmosdb\/wp-json\/wp\/v2\/users\/39313"}],"replies":[{"embeddable":true,"href":"https:\/\/devblogs.microsoft.com\/cosmosdb\/wp-json\/wp\/v2\/comments?post=7546"}],"version-history":[{"count":0,"href":"https:\/\/devblogs.microsoft.com\/cosmosdb\/wp-json\/wp\/v2\/posts\/7546\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/devblogs.microsoft.com\/cosmosdb\/wp-json\/wp\/v2\/media\/7547"}],"wp:attachment":[{"href":"https:\/\/devblogs.microsoft.com\/cosmosdb\/wp-json\/wp\/v2\/media?parent=7546"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/devblogs.microsoft.com\/cosmosdb\/wp-json\/wp\/v2\/categories?post=7546"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/devblogs.microsoft.com\/cosmosdb\/wp-json\/wp\/v2\/tags?post=7546"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}\n
Azure Cosmos DB design pattern: Event Sourcing<\/h2>\n
![\"Image](\"https:\/\/devblogs.microsoft.com\/cosmosdb\/wp-content\/uploads\/sites\/52\/2024\/02\/event-sourcing.jpg\")
<\/a><\/p>\n\n
The Scenario:<\/h2>\n
Scenario: Event-Driven Microservices Architecture<\/strong><\/h3>\n
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<\/h3>\n
Sample Implementation:<\/h3>\n
{\r\n \"cartId\": guid,\r\n \"sessionId\": guid,\r\n \"userId\": guid,\r\n \"eventType\": \"cart_created\",\r\n \"eventTimestamp\": \"2022-11-28 01:22:04\"\r\n},\r\n{\r\n \"cartId\": guid,\r\n \"sessionId\": guid,\r\n \"userId\": guid,\r\n \"eventType\": \"product_added\",\r\n \"product\": \"Product 1\",\r\n \"quantityChange\": 1,\r\n \"productsInCart\": [{\"productName\": \"Product 1\", \"quantity\": 1}],\r\n \"eventTimestamp\": \"2022-11-28 01:22:34\"\r\n},\r\n{\r\n \"cartId\": guid,\r\n \"sessionId\": guid,\r\n \"userId\": guid,\r\n \"eventType\": \"product_added\",\r\n \"product\": \"Product 2\",\r\n \"quantityChange\": 3,\r\n \"productsInCart\": [{\"productName\": \"Product 1\", \"quantity\": 1},\r\n {\"productName\": \"Product 2\", \"quantity\": 3}],\r\n \"eventTimestamp\": \"2022-11-28 01:22:58\"\r\n},\r\n{\r\n \"cartId\": guid,\r\n \"sessionId\": guid,\r\n \"userId\": guid,\r\n \"eventType\": \"product_deleted\",\r\n \"product\": \"Product 2\",\r\n \"quantityChange\": -1,\r\n \"productsInCart\": [{\"productName\": \"Product 1\", \"quantity\": 1},\r\n {\"productName\": \"Product 2\", \"quantity\": 2}],\r\n \"eventTimestamp\": \"2022-11-28 01:23:12\"\r\n},\r\n{\r\n \"cartId\": guid,\r\n \"sessionId\": guid,\r\n \"userId\": guid,\r\n \"eventType\": \"cart_purchased\",\r\n \"productsInCart\": [{\"productName\": \"Product 1\", \"quantity\": 1},\r\n {\"productName\": \"Product 2\", \"quantity\": 2}],\r\n \"eventTimestamp\": \"2022-11-28 01:24:45\"\r\n}<\/code><\/pre>\n![](\"https:\/\/devblogs.microsoft.com\/cosmosdb\/wp-content\/uploads\/sites\/52\/2024\/02\/word-image-7546-2.png\")
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SELECT *\r\n\r\nFROM CartEvents c\r\n\r\nWHERE c.CartId = \"38f4687d-35f2-4933-aadd-8776f4134589\"\r\n\r\nORDER BY c.EventTimestamp DESC<\/code><\/pre>\nSELECT c.CartId, c.UserId, c.Product,\r\n\r\nSUM(c.QuantityChange) as Quantity\r\n\r\nFROM CartEvents c\r\n\r\nWHERE c.CartId = \"38f4687d-35f2-4933-aadd-8776f4134589\"\r\n\r\nand IS_NULL(c.Product) = false\r\n\r\nGROUP BY c.CartId, c.UserId, c.Product<\/code><\/pre>\nWhy it Matters.<\/h2>\n
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Getting Started with Azure Cosmos DB Design Patterns<\/h2>\n
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About Azure Cosmos DB<\/h3>\n