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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.

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eBook – Jackson – NPI EA (cat=Jackson)
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eBook – HTTP Client – NPI EA (cat=Http Client-Side)
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eBook – Persistence – NPI EA (cat=Persistence)
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eBook – RwS – NPI EA (cat=Spring MVC)
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Course – LS – NPI EA (cat=Jackson)
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Course – RWSB – NPI EA (cat=REST)
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Course – LSS – NPI EA (cat=Spring Security)
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Yes, Spring Security can be complex, from the more advanced functionality within the Core to the deep OAuth support in the framework.

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.

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Course – LSD – NPI EA (tag=Spring Data JPA)
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Spring Data JPA is a great way to handle the complexity of JPA with the powerful simplicity of Spring Boot.

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

Refactoring big codebases by hand is slow, risky, and easy to put off. That’s where OpenRewrite comes in. The open-source framework for large-scale, automated code transformations helps teams modernize safely and consistently.

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Course – LJB – NPI EA (cat = Core Java)
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Partner – LambdaTest – NPI EA (cat= Testing)
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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 – Java Concurrency – NPI (cat=Java Concurrency)
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1. Overview

In this tutorial, we’ll learn how to convert a List<CompletableFuture<T>> object to a CompletableFuture<List<T>>.

This conversion can be very useful in many cases. A prime example would be when we have to make multiple calls to a remote service, typically an asynchronous operation, and aggregate the results into a single List. Additionally, we end up waiting on a single CompletableFuture object which provides us the results list when all operations are finished or throws an exception if one or more end in failure.

We’ll first see a naïve way of doing the conversion and then look at a simpler and safer approach.

2. Chaining CompletableFutures

One way of doing this is to chain the CompletableFutures using their thenCompose() method. This way, we can create a single object that resolves once all the previous futures resolve, one by one, akin to a domino construct.

2.1. Implementation

First, let’s create a mock asynchronous operation:

public class Application {
    ScheduledExecutorService asyncOperationEmulation;
    Application initialize() {
        asyncOperationEmulation = Executors.newScheduledThreadPool(10);
        return this;
    }

    CompletableFuture<String> asyncOperation(String operationId) {
        CompletableFuture<String> cf = new CompletableFuture<>();
        asyncOperationEmulation.submit(() -> {
            Thread.sleep(100);
            cf.complete(operationId);
        });
        return cf;
    }

We’ve created an Application class to host our test code and the asyncOperation() method which simply sleeps for 100 ms. We employ an Executor with 10 threads to dispatch everything asynchronously.

To gather all of our operation results, in this case, simple operationId strings, we’ll chain the CompletableFutures generated from the asyncOperation() method:

void startNaive() {
    List<CompletableFuture<String>> futures = new ArrayList<>();
    for (int i = 1; i <= 1000; i++) {
        String operationId = "Naive-Operation-" + i;
        futures.add(asyncOperation(operationId));
    }
    CompletableFuture<List<String>> aggregate = CompletableFuture.completedFuture(new ArrayList<>());
    for (CompletableFuture<String> future : futures) {
        aggregate = aggregate.thenCompose(list -> {
            list.add(future.get());
            return CompletableFuture.completedFuture(list);
        });
    }
    final List<String> results = aggregate.join();
    for (int i = 0; i < 10; i++) {
        System.out.println("Finished " + results.get(i));
    }

    close();
}

We start by creating a completed CompleteableFuture using the static completedFuture() method and provide an empty List as the completion result. Using thenCompose() we create a Runnable that executes once the previous future has finished, in this case immediately. The thenCompose() method returns a new CompletableFuture which resolves once both the first and second future finish. We replace the aggregate reference with this new future object. This allows us to keep chaining these calls inside the iteration loop over the futures list.

Inside the Runnable we’ve created, we wait for the future to finish and add the result to the list. We then return a completed future with that list and the result. This will pass the list further down the thenCompose() chain, letting us add the future results one by one.

Once all futures are chained, we call join() on the aggregate CompletableFuture. This is done specifically for the example, so that we can retrieve the results and block the main Java thread from exiting before aggregate is finished. In a real asynchronous scenario we’d probably add our callback logic inside a thenAccept() or whenComplete() call.

One thing to notice is we add a close() call at the end with the following implementation:

void close() {
    asyncOperationEmulation.shutdownNow();
}

Closing all Executors is mandatory when an application exits, otherwise, the Java process will hang.

2.2. Implementation Problems

The naïve implementation has a few problems. Not only the future chaining introduces unwanted complexity, but it also creates a large number of unneeded objects, such as all the new CompletableFutures generated by thenCompose().

Another potential issue appears when we execute a large number of operations. If an operation fails, and depending on how the Java implementation resolves the CompletableFuture chain, we might get a StackOverflowError if the resolutions are done recursively.

To test the exception scenario we can introduce an error on one of the operations by changing the asyncOperation() method:

CompletableFuture<String> asyncOperation(String operationId) {
    CompletableFuture<String> cf = new CompletableFuture<>();
    asyncOperationEmulation.submit(() -> {
        if (operationId.endsWith("567")) {
            cf.completeExceptionally(new Exception("Error on operation " + operationId));
            return;
        }
        Thread.sleep(100);
        cf.complete(operationId);
    });
    return cf;
}

The future for the 567th operation will complete exceptionally in this case, making the aggregate.join() call also throw a runtime exception.

3. Using CompletableFuture.allOf()

A different and better approach is to use the allOf() method of the CompletableFuture API. This method takes an array of CompletableFuture objects and creates a new one that resolves when all the provided futures themselves resolve.

Additionally, if one of the futures fails then the aggregate future also fails. The new future doesn’t contain the list of results. To obtain them we have to inspect the respective CompletableFuture object.

3.1. Implementation

Let’s create a new start() method using allOf():

void start() {
    List<CompletableFuture<String>> futures = new ArrayList<>();
    for (int i = 1; i <= 1000; i++) {
        String operationId = "Operation-" + i;
        futures.add(asyncOperation(operationId));
    }
    CompletableFuture<?>[] futuresArray = futures.toArray(new CompletableFuture<?>[0]);
    CompletableFuture<List<String>> listFuture = CompletableFuture.allOf(futuresArray)
      .thenApply(v -> futures.stream().map(CompletableFuture::join).collect(Collectors.toList()));
    final List<String> results = listFuture.join();
    System.out.println("Printing first 10 results");
    for (int i = 0; i < 10; i++) {
        System.out.println("Finished " + results.get(i));
    }

    close();
}

The setup and result printing are the same, however we now have a futuresArray and provide it to allOf(). We use thenApply() to add logic after allOf() is resolved. In this callback, we gather all futures results using the CompletableFuture.join() method and collect them into a List. This list is the result contained inside the CompletableFuture generated by thenApply(), namely the listFuture.

To showcase the aggregate results we use the join() method which blocks the main thread until listFuture is complete. We shouldn’t forget the close() call at the end.

3.2. Pros of allOf()

The allOf() based implementation is a simpler and cleaner way of handling multiple asynchronous operations than future chaining. The aggregate CompletableFuture provides atomicity to the whole operation and completes when all futures succeed or fail when even one fails. This protects us from potential partial processing of results.

Additionally, it lets us wait for all futures to complete in a non-blocking manner. Notice that in the example code, we call join() for the listFuture object but in a realistic scenario we’d rely on just the callback.

4. Conclusion

In this article, we learned how to convert a List<CompletableFuture<T>> into a CompletableFuture<List<T>>. We understood why this conversion is useful and saw two ways of doing it, one naïve implementation and one using the proper Java APIs. We discussed the potential issues with the former and how the latter avoids them.

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.
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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:

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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:

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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 – Persistence – NPI EA (cat=Persistence)
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Course – LS – NPI EA (cat=REST)

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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 – Java Concurrency – NPI (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.

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