Your microservice architecture requires multiple data models. Should you go with a hybrid persistence approach or opt for a multi-model database? This is a critical decision that can significantly impact your system's performance, scalability, and maintainability.
Over the past decade, the rise of large-scale distributed systems has driven an explosion of innovation in the database landscape. This period has seen the emergence of numerous new technologies, creating a vibrant and competitive market. As a result, developers now have more choices than ever before when it comes to selecting the right data storage solution for their applications.
In this article, we'll explore how to choose the right database schema based on your application's needs. You'll see that there are often multiple valid options, and we'll also discuss data modeling patterns that can help guide your decisions at the data layer.
Cloud Architecture, NoSQL, and Microservices
As developers began building scalable web applications, traditional relational databases started to struggle under the pressure of high traffic and massive data volumes. The rise of social apps and the Internet of Things (IoT) further increased the demand for flexible, high-performance data storage solutions. In response, new types of databases emerged—often referred to as NoSQL or non-relational databases.
These databases come in various forms, such as document stores, key-value pairs, column-family databases, and graph databases. While they offer high scalability and flexibility, they often sacrifice some of the features found in relational databases, like strong consistency, ACID transactions, and complex joins.
At the same time, software architecture has evolved from Service-Oriented Architecture (SOA) to microservices. Many enterprises have moved away from monolithic and heavy SOA approaches, such as Enterprise Service Buses (ESBs), toward decentralized, lightweight microservices. This shift gives teams greater freedom in choosing infrastructure components—including databases.
For example, when building or refactoring a microservices-based application, you have the opportunity to make fresh choices about the data layer, tailored to the specific needs of each service.
Polyglot Persistence: Choosing the Right Tool for the Job
One of the main advantages of microservices is the ability to encapsulate data and use different persistence technologies for each service. This practice, known as polyglot persistence, allows you to select the best data model for each specific use case. The term was popularized by Martin Fowler and others, and it fits naturally with the microservices approach.
The diagram below illustrates how different microservices might use distinct data models. I won't go into every use case here, but I’ll highlight the strengths of each type of database and explain why a mixed persistence strategy can be highly effective.
For instance, Service A, which deals with large-scale data management, may use a tabular database like Apache Cassandra. A retail inventory app, for example, could benefit from Cassandra’s tunable consistency and support for lightweight transactions.
Service B might handle lookups using well-known keys, such as product IDs in a catalog. In this case, a key-value store would be ideal, as it supports fast reads and efficient access to data via simple keys.
Service C could focus on semi-structured content, like form data or web pages. Document databases, such as MongoDB, are well-suited for this type of data, offering structure and indexing capabilities that key-value stores lack.
Service D may involve complex relationships between data, such as customer records across departments. Here, a graph database could be useful for navigating these connections efficiently. Alternatively, you might create a read-only view of the data through other services, ensuring consistency while maintaining independence.
Finally, some services may still rely on traditional relational databases, especially if they deal with legacy systems or data that changes infrequently. Relational databases remain a solid choice in these scenarios.
So, should a single service use multiple persistence strategies? The answer depends on the complexity of the data and the requirements of the service. In many cases, a mix of technologies can lead to a more robust, scalable, and maintainable system.
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