Many-to-One GraphQL Queries with React, Spring Boot, and SQL
In a previous blog “GraphQL Scaling, Querying & Mutations for Java Backends” I described the connectivity of dynamic parameters from properties available on the client or browser side. Once retrieved data like ids, titles, and slugs can be used in caching to express variables and continue stateful transactions through the software stack.
This philosophy can be iterated upon to unite the client with the server and exchange these interactions to enable a further scope of information from relational parameters. Exploring this concept means creating a custom component, to fetch and return additional information, based on the initial variable, from React (client-side) to Spring Boot (server-side) and into SQL (database).
Enabling insights from cached queries with React, Apollo GraphQL, and Spring Boot
- Passing props from Higher-Order Components with React
- Rendering data to Functional Components from user actions
- Querying GraphQL services with Apollo GraphQL and cached queries
- Getting environment variables from Java GraphQL server
- Unique GraphQL queries and Data Fetchers
- Custom JPA Repository Queries by Example (QBE)
Planning Queries From SQL & GraphQL Schema
A sturdy entry point for this process is, to begin with, the actual data, from the ground up this will build reference to the translative nature of the steps from the database to the client-side, as its requirements from the layers in between.
Creating a schema and a table in SQL dictates the data structure, the points of its conditions whether nullable or auto-generated and how the data relates to its surrounding data. Hibernate will implement these tables, as we explored previously, though setting up a schema, for testing implementation, and then updating Spring & Hibernate configuration is a better development workflow.
Unique constraints in hibernate are implemented similarly to the existing columns, though make sure to align them with your schema, find more on Baeldung.
The important element of this schema is that the titles are unique and the length is flexible, this will make future queries more precise, as there is only one possible relation of the many relations where the title matches the blog.
Once the schema and table are set in SQL you can manually load a few test examples for querying once the rest of the solution is implemented. From here we move to GraphQL and establish the query that follows the schema.
This GraphQL schema references the initial Blog type, which if you have followed to this point you already have, if not, it is provided here for reference. The query of this type accepts the string of titles to find the blog of that title, hence the many-to-one relation.
Implementing Spring Data JPA Repository Queries & Java GraphQL Data Fetchers
Many-to-one relations mean specifying unique datatypes within a database to query and evaluate relative data. The importance of the previously mentioned unique keys outside of the ordinary id primary key is identifying relations that are structured from abstraction or usage and less system generated.
Executing queries through the Java GraphQL service means extending Spring Boot JPA repositories with Query by Example (QBE) which will, in turn, allow for GraphQL to pass the query parameters to Spring Boot JPA to make specified queries.
Once an example exists within the correlated repository, the wired repository can now execute queries on its behalf, with data exchanged in the environment via the Java GraphQL service and the query’s data fetcher.
The getArgument method implements the type which is instantiated by the GraphQL query, which is then passed to the custom Spring Boot JPA query, in turn, returning the related data to the example query from SQL.
Once these steps are implemented the last condition to be met, is introducing the custom query within the GraphQL service and auto-wiring the query to proceed the result to the Java GraphQL server.
Querying and Rendering Data With React Higher-Order and Functional Components
Structural components such as higher-order components organize all of the interactive components, functions, hooks, and methods of a given component and export as a class-based function. These are not usually required in React, especially with the introduction of React Hooks, though can provide a concise utility for implementation of how to combine components as a pattern.
Inheriting the parameters from this higher-order components invocation, the properties are passed down to dynamically render value within this view. As to how this above blog component has its origin, the origin for its functional components relies on the data passed downstream.
Statistical value implementations here are driven by the useParams Hook at the top-level component, passed down to the Apollo GraphQL server for querying and extracting that views unique data. After the initial view is set, the result informs the next query in the same pattern to retrieve the relative values with Apollo GraphQL from another query or source.
Continue Exploring This Stack, on Github and Previous Blogs
The blog in reference to initializing this software solution, preempting the statistical step, can shed more light on React, Spring Boot, GraphQL, and SQL. Primer code for the stack mentioned in this article can be found on GitHub.