In a microservices architecture, different services often require distinct data models. Should you go with a hybrid persistence approach or opt for a multi-model database? This decision can significantly impact the performance, scalability, and maintainability of your system.
Over the past decade, the rise of large-scale distributed systems has driven significant innovation in the database landscape. This period has seen an unprecedented surge in creativity, resulting in a diverse and competitive market where developers can choose from a wide range of platforms. But how do you decide which one is right for your project?
In this article, we’ll explore how to select the appropriate database schema based on your application’s needs. You’ll see that there are often multiple valid choices. We’ll also discuss data modeling patterns to help guide your decisions when selecting technologies for the data layer.
Cloud Architecture, NoSQL, and Microservices
As developers began building scalable web applications, traditional relational databases started to show their limitations under heavy loads. With the rise of social media apps and the Internet of Things (IoT), the volume of user-generated data increased dramatically, pushing the need for more flexible and scalable data storage solutions. New types of databases emerged to meet these demands.
Many of these new "NoSQL" or non-relational databases use different data models compared to traditional relational databases. They include document stores, key-value pairs, columnar databases, and even graph databases. These systems often sacrifice some of the features of relational databases, such as strong consistency, ACID transactions, and joins, in favor of scalability and flexibility.
At the same time, software architecture has evolved from Service-Oriented Architecture (SOA) to microservices. Many organizations have moved away from complex SOA infrastructures like Enterprise Service Buses (ESBs) in favor of a more decentralized approach. The microservices model allows each service to be developed, managed, and scaled independently, giving teams greater flexibility in choosing infrastructure technologies—including databases.
For example, if you're working on a microservices-based project—whether it's a new application or a refactoring of an existing one—you have the opportunity to make fresh choices about your data storage strategy.
Polyglot Persistence
One of the major advantages of microservices is the ability to encapsulate persistence logic within each service. This means you can choose different data storage technologies based on the specific needs of each service. This practice is known as polyglot persistence, a term first introduced by Martin Fowler and others. Polyglot persistence works naturally with microservices because it allows each service to use the best tool for its job.
The diagram below illustrates how different microservices might use various data models. I won’t go into specific use cases for each type of database, but I will highlight the benefits of each and explain why a mixed-persistence approach can be highly effective.
For instance, Service A might handle large-scale data management and could use a tabular database like Apache Cassandra. A retail inventory app would be a good fit for Cassandra, as it offers tunable consistency, batch operations, and lightweight transactions—alternatives to full ACID compliance.
Service B might deal with lookups using well-known keys, such as product IDs in a catalog. In this case, a key-value store would be ideal, as it allows fast access to data through simple key-based queries.
Service C could focus on semi-structured content, such as forms or web pages. Document storage, like MongoDB, would be well-suited for this type of data, offering structure and indexing capabilities for efficient retrieval.
Service D may involve complex relationships between data, such as customer information across departments. This is a more challenging scenario, as it may require navigating relationships between data owned by other services. One solution is to create a graph representation of your data and manage all changes through a central API, ensuring consistency and isolation.
Finally, some services may still rely on legacy systems or handle data that doesn’t change frequently. In such cases, a relational database might be the best choice due to its maturity and support for complex queries.
So, should a single service use polyglot persistence? It depends on the complexity of the data and the trade-offs involved. While mixing persistence can provide flexibility, it also adds complexity in terms of maintenance and integration. However, when done thoughtfully, it can lead to a more scalable and efficient system.
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