event handling in Java

import javax.swing.*;

import java.awt.event.*;

public class SimpleGui implements ActionListener {
    JButton button;
    
    public static void main(String[] args) {
        SimpleGui gui = new SimpleGui();
        gui.go();
    }
    
    public void go() {
        JFrame frame = new JFrame();
        button = new JButton("click me");
        
        button.addActionListener(this);
        
        frame.getContentPane().add(button);
        frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
        frame.setSize(300, 300);
        frame.setVisible(true);
    }

    @Override
    public void actionPerformed(ActionEvent event) {
        // TODO Auto-generated method stub
        button.setText("I've been clicked!");
    }
    
}


### Java Event Stream Implementation and Usage In Java, event streams can be implemented using various libraries and frameworks designed specifically for handling asynchronous data processing efficiently. One popular approach involves leveraging **Reactive Streams** principles through libraries like Project Reactor or RxJava. #### Using Project Reactor for Event Streams Project Reactor provides a comprehensive set of tools for building non-blocking applications on top of the JVM. It supports backpressure, which ensures efficient resource management when dealing with high-throughput systems where producers might outpace consumers[^1]. ```java import reactor.core.publisher.Flux; import java.time.Duration; public class EventStreamExample { public static void main(String[] args) throws InterruptedException { Flux<String> events = Flux.interval(Duration.ofSeconds(1)) .map(sequence -> "Event-" + sequence); events.subscribe(System.out::println); Thread.sleep(10_000); // Keep application running to observe output. } } ``` This code snippet demonstrates how to create an infinite stream of timed events using `Flux`, one of the core abstractions provided by Reactor. Each element emitted represents an individual event within this continuous flow. For more complex scenarios involving stateful operations such as filtering, transforming, or aggregating over these events before they reach their final destination (e.g., triggering specific actions), additional operators offered by Reactor may come into play: - Filtering unwanted elements via `.filter()`. - Transforming each item individually with `.map()` or applying bulk transformations across multiple items simultaneously utilizing `.flatMap()`. - Accumulating results incrementally throughout the entire lifecycle of the pipeline employing methods like `.reduce()` or `.collectList()`. These capabilities allow developers to construct sophisticated pipelines capable of reacting intelligently to different types of occurrences while maintaining performance efficiency even under heavy load conditions. Additionally, integrating message brokers facilitates decoupling between components responsible for producing versus consuming messages, thereby enhancing modularity and scalability properties inherent in distributed architectures[^2]. For instance, Apache Kafka serves well here due to its robustness concerning fault tolerance alongside support for real-time analytics features directly built-in. Lastly, specialized protocols stacks tailored towards particular domains—such as Bluetooth audio streaming handled by btstack protocol stack mentioned earlier—can incorporate custom logic inside dedicated handlers responding appropriately according to varying states encountered during operation[^3].
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