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R2DBC is a project that enables us to develop reactive programs API with relational databases, as we can do with databases that natively offer reactive drivers, like for example, Mongo or Cassandra.
Spring Data R2DBC is an abstraction of Spring to use repositories that support R2DBC and allows us a functional approach to interact with a relational database.
For several years, Spring Data JPA has established itself as one of the most commonly used persistence frameworks in the Java world. It gets most of its features from the very popular Hibernate object-relational mapping (ORM) implementation. The ORM features provide great developer productivity, and the basic functionality is very easy to learn.
But as so often, you need to know a lot more than just the basic parts if you want to build enterprise applications. Without a good understanding of its internals and some advanced features, you will struggle with severe performance issues. Spring Data’s and Hibernate’s ease of use sometimes makes it way too easy to build a slow application.
Our previous set-up works as expected. So what we shall do now is to get one step further and configure two separate entity managers without affecting the functionality we achieved previously.
Check out previous posts: Read Replicas and Spring Data, Part 1: Configuring the Databases & Read Replicas and Spring Data, Part 2: Configuring the Base Project
The first step would be to set the default entity manager configuration to a primary one.
This is the first step:
As you may already be aware, Spring Data JPA is part of the larger Spring Data family. In this article, we are going to use Spring Data JPA along with Spring Boot to communicate with the MariaDB database.
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-data-jpa</artifactId>
</dependency>Not too long ago, a reactive variant of the JDBC driver was released, known as R2DBC. It allows data to be streamed asynchronously to any endpoints that have subscribed to it. Using a reactive driver like R2DBC together with Spring WebFlux allows you to write a full application that handles the receiving and sending of data asynchronously. In this post, we will focus on the database and on connecting to the database and then finally saving and retrieving data. To do this, we will be using Spring Data. As with all Spring Data modules, it provides us with out-of-the-box configuration, decreasing the amount of boilerplate code that we need to write to get our application setup. On top of that, it provides a layer upon the database driver that makes doing the simple tasks easier and the more difficult tasks a little less painful.
For the content of this post, I am making use of a Postgres database. At the time of writing only Postgres, H2 and Microsoft SQL Server have their own implementations of R2DBC drivers.
Welcome to the Spring Boot with Spring Data JPA tutorial! In this tutorial, we are going to see how Spring Data JPA provides complete abstraction over the DAO layer. We don’t need to write the implementation for the DAO layer anymore; Spring Data auto-generates the implementation DAO implementations.
We already had an introduction to Spring Boot, and for this tutorial, we will use Spring Boot along with Spring Data. You will also see how Spring Boot auto-configuration helps to get data source configurations done, hassle-free.