'How to Build a Decoupled Microservices Using Materialize' by bobbyiliev_ microservices kubernetes
We will use the following architecture as described in the video above. The following diagram shows the architecture:We have an eCommerce website that consists of these two microservices:: This microservice is responsible for managing orders.
In order for the Backend to communicate with the Catalog service, we use a REST API. This is how the Basket service gets the products data from the Catalog service. The problem with the above architecture is that the Basket service is tightly coupled with the Catalog service. There are a few drawbacks to this architecture: If the Catalog service is down, the Basket service will not be able to get the products data. Meaning, the Basket service will not be able to process orders and would also be down. When scaling up, the Basket service will start generating load on the Catalog service meaning, the Catalog service will also need to be scaled up.To decouple the microservices, we will use a third microservice called. This microservice will be responsible for routing the requests to the other microservices. But with this approach, the API Gateway will handle all the requests and the microservices and this could turn into a bottleneck as it will have a lot of responsibilities.As each microservice is independent and owns its own data, to decouple the microservices, we can use a materialized view that will store the aggregated data of the microservices.Quick overview of the architecture above: We are adding a new materialized view to the Basket service which will store the aggregated data of the Basket service and the Catalog service.The change events could be stored in a service like Apache Kafka or RedPanda. The Basket service will now handle the change events and update the materialized view accordingly as the data in the Catalog service changes.The downsides of this approach are:The change events would add lots of complexity to the microservices. If you do not have the expertise, adding the additional components and changes means that there will be a steep learning curve for the development team.Materialize is a streaming database that takes data coming from different sources like Kafka, PostgreSQL, S3 buckets, and more and allows users to write views that aggregate/materialize that data and let you query those views using pure SQL. Unlike traditional materialized views, Materialize is designed to maintain the data in a continuous state and keeps the views incrementally updated. This means that if you have a view that is constantly updating, you can query the data in real-time. A normal materialized view would do a full table scan every time it needs to be updated which can be very expensive.
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