Now that you know how to write dynamic SQL queries using Spring Data JPA Specification and the Criteria API, let’s evaluate the impact they might have in the performance of your Spring Boot applications.
As a Java developer, you have the responsibility to understand what SQL statements Hibernate generates and executes. It helps you to prevent the N+1 SELECT query problem, for instance.
Another common problem Hibernate developers experience is performance and memory problems as a result of writing queries with a variable number of values in the
This blog post helps you to identify heap and garbage collection problems you might experience when using Spring Data JPA Specification with Criteria queries.
You started looking at a couple dozen JPA entities and decided to take a look at the RDBMS Entity Relationship diagram. You asked your peers and there is none.
This blog post helps you to document your relational database using SchemaSpy in different ways. Via command line, using a Maven plugin, or using Docker so that you don’t have to install SchemaSpy required software.
A dynamic SQL query refers to building a query on the fly before executing it. Not just replacing query parameters by their name or position, but also including a variable number of columns in the WHERE clause, and even joining tables based on specific conditions.
How would you implement a RESTful endpoint like
/api/films?minRentalRate=0.5&maxRentalRate=4.99 where you need to retrieve films from a relational database?
You could take advantage of Spring Data JPA support for:
- @Query-annotated method.
- Named queries.
- Query methods. Naming a method findByRentalRateBetween() in your Film repository interface.
Let’s say later on you also need to search films based on movie categories, for instance, comedy, action, horror, etc.
You might think a couple of new repository methods would do the work. One for the new category filter and another to combine both, the rental rate range filter with the category filter. You would also need to find out which repository method to call based on the presence of the request parameters.
You soon realize this approach is error-prone and it doesn’t scale as the number of request parameters to search films increases.
This blog post covers generating dynamic SQL queries using Spring Data JPA Specification and Criteria API including joining tables to filter and search data.
Your team decided to hide a requirement implementation behind a feature flag using Spring Boot and Togglz.
Now it’s time to switch the toggle state to make the new implementation available. It might also be possible your team needs to switch the flag back if anything goes wrong.
togglz-spring-boot-starter Spring Boot starter’s autoconfigures an instance of FileBasedStateRepository. This requires you to restart your application after changing a toggle value.
You could configure a different StateRepository implementation such as combining JDBCStateRepository or MongoStateRepository with CachingStateRepository to prevent restarting your application.
This blog post helps you with the configuration and implementation of Togglz feature flags to reload new toggles values using Spring Cloud Config Server and Git.
What’s the N+1 SELECT problem?
The N+1 SELECT problem happens when an ORM like Hibernate executes one SQL query to retrieve the main entity from a parent-child relationship and then one SQL query for each child object.
The more associated entities you have in your domain model, the more queries will be executed. The more results you get when retrieving a parent entity, the more queries will be executed. This will impact the performance of your application.
This blog post helps you understand what the N+1 SELECT problem is and how to fix it for Spring Boot applications using Spring Data JPA Entity Graph.
Microsoft Azure Blob Storage is a low-cost option to store your Maven or other binary artifacts. It’s an alternative to feature-rich Maven repository managers like Nexus, Artifactory when you don’t have the resources to install and maintain a server with the required software or the budget to subscribe to a hosted plan.
A choice I wouldn’t recommend is to store your artifacts in the SCM.
This tutorial covers configuring Maven and setting up the Azure Blob Storage components to deploy your Java artifacts to.
Often times API endpoint implementations involve retrieving data from some sort of storage. Retrieving data, even when applying a search criteria might result in hundreds, thousands or millions of records. Retrieving such amount of data could lead to performance issues, not meeting a contracted SLA, ultimately affecting the user experience.
One approach to overcome this problem is to implement pagination. You could retrieve a number of records from a data storage and add pagination links in the API response along with the page metadata back to the client application.
In a previous post, I showed readers how to include HAL hypermedia in Spring Boot RESTful applications using HATEOAS. Adding related links to REST responses help the client applications deciding what they might do next.
Some of the next actions a client application could help a customer do is to navigate through a list of resources. For instance to the first page of a result list.
HATEOAS, acronym for Hypermedia as the Engine of Application State, offers what your API consumers might do next when starting from a REST API entry point.
It includes hypermedia in the response, stateful links to related REST resources depending on business value or context. For instance, an upsell hypermedia link to upgrade to a Hotel suite instead of the room you might have in a shopping cart. A cancel hypermedia link to postpone a scheduled payment to a service provider.
This allows your API endpoints to reach the Level 3 of the famous Richardson Maturity Model. A more mature level than resources and verbs since it helps to provide API discoverability and self-documentation, to some degree.
This blog post covers the configuration and implementation details to include HAL representations in your API responses using Spring Boot 2.1.x.RELEASE and Spring HATEOAS 0.25.x.RELEASE.
Code coverage is a metric indicating which percentage of lines of code are executed when running automated tests. Unit and integration tests for instance.
It’s known that having automated tests as part of your build process improves the software quality and reduces the number of bugs.
Do you know if you need more unit tests? Or if your tests cover all possible branches of an if or switch statements? Or if your code coverage is decreasing over time? Especially after you join a team to work on an on-going project.
Code coverage helps to answer these questions. This post covers reporting code coverage using Maven’s jacoco-maven-plugin, a library that adds minimal overhead with normal build.
Generally, when you write Java unit tests, you stub data or mock the target class dependencies. This allows you to verify the business logic in an environment where you don’t depend on third party services.
Integration tests also play an important role in the Continuous Integration / Delivery (CI/CD) process to release software frequent and safe. As I have mentioned before, integration testing goal is to verify that interaction between different parts of the system work well. Examples of such interactions are:
- Connecting to a RDBMS to retrieve or persist data
- Consuming a message from a broker
- Sending an email
- Processing an upstream response
In large Enterprise applications development, with hundreds of unit and integration tests, the test suites take a considerable amount of time to complete, normally hours.
Wouldn’t it make sense to split the tests by their purpose and execution speed? It would be beneficial for an organization to get a quicker feedback when tests fail.
It would then be advisable to implement a fail-fast approach and break the build process when unit tests fail. This post covers configuring Maven’s maven-surefire-plugin to split running unit and integration tests.