Microservices data management used to be so complex in enterprise database administration. In this article, we are trying to discuss some of the popular ways to handle data more effectively in microservice apps. In standard monolithic systems, data management can be very complex, but it can be a different ballgame in microservices architectures. As of late, every software applications and web applications largely rely on data. The success or failure of any business relied on proper data management. Successful businesses tend to make the data always available to the stakeholder as and when needed. Failing to do so can result in catastrophes.
Data management practices are continuously evolving, and so you can find various patterns in terms of microservices database management. It is a great way for brands to integrate their operations seamlessly. Further, let us look into some of the most reliable data management patterns to manage your enterprise databases effectively.
Database per service pattern
In this model, each of the microservice manages its unique data. This means that no other microservices running simultaneously can access the unique data of other microservices directly. Exchange of data and inter-services communication is only possible through well-defined APIs. This is said much easier than it can be implemented. These microservices applications are usually not very well demarcated. So, these may require data from other related services to implement the logic. This will further need spaghetti-model interactions between different services under the same application.
The success of the per-service approach relies on how effectively you define the contexts in the application. For some specific systems and applications, it is much easier to accomplish this. However, for the larger monolithic systems, this may be troublesome. Some other major challenges of the per-service approach include implementing business transactions that may span across various microservices. Another big challenge is maybe implementing the most appropriate queries, which may want to expose the date from many bounded contexts.
If done properly, the major advantage of database per service is a loose coupling among the microservices. You can individually scale up the microservices and can also stay off the impact-analysis chaos while scaling up. This will offer the developers optimum freedom to choose among a specific set of solutions for the given microservices.
The shared database may be a highly viable choice if you find the Database Per Service risks become difficult to handle. The shared DB approach is trying to solve similar problems as Database Per Service. Still, it achieves the same through a more flexible and lenient approach using a shared database to be accessed by various distinct microservices.
This is identified as a much safer pattern for the developers as they can continue to work in their existing approach. In this model, too, the most familiar ACID-compliant transactions are followed to ensure consistency. But there is a chance that this approach may wade off some of the benefits offered by microservices. Developers across various teams have to coordinate for the schema changes to the database tables. There may also be some run-time conflicts, while many microservices try to access the same database simultaneously. So overall, this approach may do more harm than benefits over the longer-term. For adopting the most appropriate data management practices, you can take the expert assistance of RemoteDBA.com.
Saga pattern is an alternate solution that helps implement the business transactions that span across many simultaneous microservices. Saga is primarily a sequence of local transactions, and for each transaction performed with the Saga, the services which perform a transaction will publish an event. Further to this, subsequent transactions are getting triggered based on the output of preceding transactions. However, if any one of these transactions fails, Saga will execute a set of compensating transactions, which will further undo the impact of previous transactions.
We may consider an example to understand this approach better. Take the functions of a food delivery app. While someone orders food through it, a sequence of steps is executed as below:
- ‘Order’ service may create a new order, which will be PENDING. There, the Saga will manage the sequence of events.
- Saga will contact the concerned restaurant through the ‘Restaurant’ service, which then attempts order placement. It sends a reply on order being confirmed.
- On receipt of the reply, the Saga will support or reject the order.
- Next, the ‘Order’ service will change the order’s status, i.e., if it is approved, then the customer will be given further details. The rejection of the order delivers a preset apology message to the customer and presents alternate options.
As evident, this is a significantly different approach compared to the point-to-point call. Even though it seems to be more complex, Saga is proved out to be very powerful in resolving even the trickiest challenges in multi-services environments.
This is a pattern that puts forth a direct solution for implementing complex queries in the microservices architectures. In this approach, there is an API composer that will invoke the microservices in the desired order. After fetching the results, it performs the in-memory data joins before offering it to the users. The downside of these patterns is the inefficiency of the in-memory joins while dealing with larger datasets.
CQRS or Command Query Responsibility Segregation is an option to handle the troubles related to the API composition patterns effectively. Here, the application listens to the domain events from other microservices and further updates the query database views. You can even serve complex aggregation queries on this DB. However, this has increased complexity.
Another solution is event sourcing, which resolves the issue of updating the database automatically and publishing events. In the case of event sourcing, you can store the entity’s state or aggregate it as a sequence of events changing states. New events are created when there is an update or insert. Event sourcing can be used in association with CQRS, with which you can effectively handle many challenges in handling query data.