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eBook – Mockito – NPI EA (tag = Mockito)
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Mocking is an essential part of unit testing, and the Mockito library makes it easy to write clean and intuitive unit tests for your Java code.

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eBook – Java Concurrency – NPI EA (cat=Java Concurrency)
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eBook – Reactive – NPI EA (cat=Reactive)
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Spring 5 added support for reactive programming with the Spring WebFlux module, which has been improved upon ever since. Get started with the Reactor project basics and reactive programming in Spring Boot:

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eBook – Java Streams – NPI EA (cat=Java Streams)
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Since its introduction in Java 8, the Stream API has become a staple of Java development. The basic operations like iterating, filtering, mapping sequences of elements are deceptively simple to use.

But these can also be overused and fall into some common pitfalls.

To get a better understanding on how Streams work and how to combine them with other language features, check out our guide to Java Streams:

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eBook – Jackson – NPI EA (cat=Jackson)
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Do JSON right with Jackson

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eBook – Persistence – NPI EA (cat=Persistence)
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Course – LS – NPI EA (cat=Jackson)
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Course – LSS – NPI EA (cat=Spring Security)
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I built the security material as two full courses - Core and OAuth, to get practical with these more complex scenarios. We explore when and how to use each feature and code through it on the backing project.

You can explore the course here:

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Course – LSD – NPI EA (tag=Spring Data JPA)
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Partner – Moderne – NPI EA (cat=Spring Boot)
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Refactor Java code safely — and automatically — with OpenRewrite.

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Course – LJB – NPI EA (cat = Core Java)
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Distributed systems often come with complex challenges such as service-to-service communication, state management, asynchronous messaging, security, and more.

Dapr (Distributed Application Runtime) provides a set of APIs and building blocks to address these challenges, abstracting away infrastructure so we can focus on business logic.

In this tutorial, we'll focus on Dapr's pub/sub API for message brokering. Using its Spring Boot integration, we'll simplify the creation of a loosely coupled, portable, and easily testable pub/sub messaging system:

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eBook – Jackson – NPI (cat=Jackson)
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1. Overview

A typical use case when working with JSON is to perform a transformation from one model into another. For example, we might want to parse a complex, densely nested object graph into a more straightforward model for use in another domain.

In this quick tutorial, we’ll look at how to map nested values with Jackson to flatten out a complex data structure. We’ll deserialize JSON in three different ways:

  • Using@JsonProperty
  • Using JsonNode
  • Using a custom JsonDeserializer

Further reading:

Using Optional with Jackson

A quick overview of how we can use the Optional with Jackson.

Inheritance with Jackson

This tutorial will demonstrate how to handle inclusion of subtype metadata and ignoring properties inherited from superclasses with Jackson.

Using @JsonComponent in Spring Boot

Learn how to use the @JsonComponent annotation in Spring Boot.

2. Maven Dependency

Let’s first add the following dependency to pom.xml:

<dependency>
    <groupId>com.fasterxml.jackson.core</groupId>
    <artifactId>jackson-databind</artifactId>
    <version>2.17.2</version>
</dependency>

We can find the latest versions of jackson-databind on Maven Central.

3. JSON Source

Consider the following JSON as the source material for our examples.

While the structure is contrived, note that we include properties that are nested two levels deep:

{
    "id": "957c43f2-fa2e-42f9-bf75-6e3d5bb6960a",
    "name": "The Best Product",
    "brand": {
        "id": "9bcd817d-0141-42e6-8f04-e5aaab0980b6",
        "name": "ACME Products",
        "owner": {
            "id": "b21a80b1-0c09-4be3-9ebd-ea3653511c13",
            "name": "Ultimate Corp, Inc."
        }
    }  
}

4. Simplified Domain Model

In a flattened domain model described by the Product class below, we’ll extract brandName, which is nested one level deep within our source JSON.

Also, we’ll extract ownerName, which is nested two levels deep and within the nested brand object:

public class Product {

    private String id;
    private String name;
    private String brandName;
    private String ownerName;

    // standard getters and setters
}

5. Mapping With Annotations

To map the nested brandName property, we first need to unpack the nested brand object to a Map and extract the name property. To map ownerName, we unpack the nested owner object to a Map and extract its name property.

We can instruct Jackson to unpack the nested property by using a combination of @JsonProperty and some custom logic that we add to our Product class:

public class Product {
    // ...

    @SuppressWarnings("unchecked")
    @JsonProperty("brand")
    private void unpackNested(Map<String,Object> brand) {
        this.brandName = (String)brand.get("name");
        Map<String,String> owner = (Map<String,String>)brand.get("owner");
        this.ownerName = owner.get("name");
    }
}

Our client code can now use an ObjectMapper to transform our source JSON, which exists as the String constant SOURCE_JSON within the test class:

@Test
public void whenUsingAnnotations_thenOk() throws IOException {
    Product product = new ObjectMapper()
      .readerFor(Product.class)
      .readValue(SOURCE_JSON);

    assertEquals(product.getName(), "The Best Product");
    assertEquals(product.getBrandName(), "ACME Products");
    assertEquals(product.getOwnerName(), "Ultimate Corp, Inc.");
}

6. Mapping With JsonNode

Mapping a nested data structure with JsonNode requires a little more work.

Here we use ObjectMapper‘s readTree to parse out the desired fields:

@Test
public void whenUsingJsonNode_thenOk() throws IOException {
    JsonNode productNode = new ObjectMapper().readTree(SOURCE_JSON);

    Product product = new Product();
    product.setId(productNode.get("id").textValue());
    product.setName(productNode.get("name").textValue());
    product.setBrandName(productNode.get("brand")
      .get("name").textValue());
    product.setOwnerName(productNode.get("brand")
      .get("owner").get("name").textValue());

    assertEquals(product.getName(), "The Best Product");
    assertEquals(product.getBrandName(), "ACME Products");
    assertEquals(product.getOwnerName(), "Ultimate Corp, Inc.");
}

7. Mapping With Custom JsonDeserializer

Mapping a nested data structure with a custom JsonDeserializer is identical to the JsonNode approach from an implementation point of view.

We first create the JsonDeserializer:

public class ProductDeserializer extends StdDeserializer<Product> {

    public ProductDeserializer() {
        this(null);
    }

    public ProductDeserializer(Class<?> vc) {
        super(vc);
    }

    @Override
    public Product deserialize(JsonParser jp, DeserializationContext ctxt) 
      throws IOException, JsonProcessingException {
 
        JsonNode productNode = jp.getCodec().readTree(jp);
        Product product = new Product();
        product.setId(productNode.get("id").textValue());
        product.setName(productNode.get("name").textValue());
        product.setBrandName(productNode.get("brand")
          .get("name").textValue());
        product.setOwnerName(productNode.get("brand").get("owner")
          .get("name").textValue());		
        return product;
    }
}

7.1. Manual Registration of Deserializer

To manually register our custom deserializer, our client code must add the JsonDeserializer to a Module, register the Module with an ObjectMapper and call readValue:

@Test
public void whenUsingDeserializerManuallyRegistered_thenOk()
 throws IOException {
 
    ObjectMapper mapper = new ObjectMapper();
    SimpleModule module = new SimpleModule();
    module.addDeserializer(Product.class, new ProductDeserializer());
    mapper.registerModule(module);

    Product product = mapper.readValue(SOURCE_JSON, Product.class);
 
    assertEquals(product.getName(), "The Best Product");
    assertEquals(product.getBrandName(), "ACME Products");
    assertEquals(product.getOwnerName(), "Ultimate Corp, Inc.");
}

7.2. Automatic Registration of Deserializer

As an alternative to the manual registration of the JsonDeserializer, we can register the deserializer directly on the class:

@JsonDeserialize(using = ProductDeserializer.class)
public class Product {
    // ...
}

With this approach, there is no need to register manually.

Let’s take a look at our client code using automatic registration:

@Test
public void whenUsingDeserializerAutoRegistered_thenOk()
  throws IOException {
 
    ObjectMapper mapper = new ObjectMapper();
    Product product = mapper.readValue(SOURCE_JSON, Product.class);

    assertEquals(product.getName(), "The Best Product");
    assertEquals(product.getBrandName(), "ACME Products");
    assertEquals(product.getOwnerName(), "Ultimate Corp, Inc.");
}

8. Conclusion

In this article, we demonstrated several ways of using Jackson to parse JSON containing nested values. Have a look at our main Jackson Tutorial page for more examples.

The code backing this article is available on GitHub. Once you're logged in as a Baeldung Pro Member, start learning and coding on the project.
Baeldung Pro – NPI EA (cat = Baeldung)
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Baeldung Pro comes with both absolutely No-Ads as well as finally with Dark Mode, for a clean learning experience:

>> Explore a clean Baeldung

Once the early-adopter seats are all used, the price will go up and stay at $33/year.

eBook – HTTP Client – NPI EA (cat=HTTP Client-Side)
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The Apache HTTP Client is a very robust library, suitable for both simple and advanced use cases when testing HTTP endpoints. Check out our guide covering basic request and response handling, as well as security, cookies, timeouts, and more:

>> Download the eBook

eBook – Java Concurrency – NPI EA (cat=Java Concurrency)
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Handling concurrency in an application can be a tricky process with many potential pitfalls. A solid grasp of the fundamentals will go a long way to help minimize these issues.

Get started with understanding multi-threaded applications with our Java Concurrency guide:

>> Download the eBook

eBook – Java Streams – NPI EA (cat=Java Streams)
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Since its introduction in Java 8, the Stream API has become a staple of Java development. The basic operations like iterating, filtering, mapping sequences of elements are deceptively simple to use.

But these can also be overused and fall into some common pitfalls.

To get a better understanding on how Streams work and how to combine them with other language features, check out our guide to Java Streams:

>> Join Pro and download the eBook

eBook – Persistence – NPI EA (cat=Persistence)
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Working on getting your persistence layer right with Spring?

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Course – LS – NPI EA (cat=REST)

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Get started with Spring Boot and with core Spring, through the Learn Spring course:

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Partner – Moderne – NPI EA (tag=Refactoring)
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Modern Java teams move fast — but codebases don’t always keep up. Frameworks change, dependencies drift, and tech debt builds until it starts to drag on delivery. OpenRewrite was built to fix that: an open-source refactoring engine that automates repetitive code changes while keeping developer intent intact.

The monthly training series, led by the creators and maintainers of OpenRewrite at Moderne, walks through real-world migrations and modernization patterns. Whether you’re new to recipes or ready to write your own, you’ll learn practical ways to refactor safely and at scale.

If you’ve ever wished refactoring felt as natural — and as fast — as writing code, this is a good place to start.

eBook Jackson – NPI EA – 3 (cat = Jackson)
eBook Jackson – NPI (cat = Jackson)