CompleteFuture is a concurrent programming framework introduced in Java 8 to simplify asynchronous programming and the processing of concurrent tasks. It provides a set of methods that can be used to manage the execution of asynchronous tasks, processing of results, task combinations, and exception handling. The main methods are: 1. 'runAsync (Runnable runnable)': Submit an asynchronous task that does not return a result and return a CompleteFuture instance. Example code: ```java CompletableFuture<Void> future = CompletableFuture.runAsync(() -> { //Specific logic of asynchronous tasks }); ``` 2. 'supplyAsync (Supplier<U>supplier)': Submit an asynchronous task that returns a result and returns a CompleteFuture instance. Example code: ```java CompletableFuture<String> future = CompletableFuture.supplyAsync(() -> { //Specific logic of asynchronous tasks return "Hello, World!"; }); ``` 3. 'thenApply (Function<T, U>fn)': After a CompleteFuture is completed, its results can be converted. Returns a new CompleteFuture instance. Example code: ```java CompletableFuture<String> future = CompletableFuture.supplyAsync(() -> { //Specific logic of asynchronous tasks return "Hello"; }).thenApply(result -> { //Process the results return result + ", World!"; }); ``` 4. 'thenAccept (Consumer<T>action)': After a CompleteFuture is completed, it is possible to consume its results, but no results are returned. Example code: ```java CompletableFuture.supplyAsync(() -> { //Specific logic of asynchronous tasks return "Hello"; }).thenAccept(result -> { //Consume the results System.out.println(result + ", World!"); }); ``` 5. 'thenRun (Runnable Action)': When a CompleteFuture is completed, an operation can be executed without caring about the results of the previous CompleteFuture. Example code: ```java CompletableFuture.supplyAsync(() -> { //Specific logic of asynchronous tasks return "Hello"; }).thenRun(() -> { //Execute Action System.out.println("The task has completed."); }); ``` If you need to use CompleteFuture, simply add the following Maven dependencies in the 'pom. xml' file of the project: ```xml <dependency> <groupId>org.apache.commons</groupId> <artifactId>commons-lang3</artifactId> <version>3.11</version> </dependency> ``` Then, you can use CompleteFuture in the code.

Akka is an open source toolkit and runtime for building concurrent, distributed, and fault-tolerant applications. It uses the Actor model to achieve concurrency by decomposing concurrency issues into lightweight, independent Actor instances. When using Akka to write concurrent programs, it is mainly necessary to understand the following key methods: 1. Create ActorSystem: ActorSystem is the core component of Akka and serves as a container for all Actors. We need to use ActorSystem to create and start our Actor. The following is an example code for creating an ActorSystem: ```java import akka.actor.ActorSystem; ActorSystem system = ActorSystem.create("MyActorSystem"); ``` 2. Create Actor: In Akka, all concurrent logic is implemented through Actor. We can create custom Actors by inheriting the Actor class provided by Akka. The following is an example code for creating an Actor: ```java import akka.actor.AbstractActor; public class MyActor extends AbstractActor { @Override public Receive createReceive() { return receiveBuilder() .match(String.class, message -> { System.out.println("Received message: " + message); }) .build(); } } ``` 3. Create and send messages: In Akka, Actors communicate through messages. We can use the 'ActorRef' object to send messages to other Actors. The following is an example code for creating and sending messages: ```java import akka.actor.ActorRef; import akka.actor.ActorSystem; import akka.actor.Props; ActorSystem system = ActorSystem.create("MyActorSystem"); //Create an instance of MyActor ActorRef myActor = system.actorOf(Props.create(MyActor.class), "MyActor"); //Send a message to MyActor myActor.tell("Hello, Akka!", ActorRef.noSender()); ``` It should be noted that Akka's dependency management in Maven is carried out through Akka's official Maven warehouse. So, we need to add the following dependencies to the 'pom. xml' file: ```xml <dependencies> <dependency> <groupId>com.typesafe.akka</groupId> <artifactId>akka-actor_2.12</artifactId> <version>2.6.8</version> </dependency> </dependencies> ``` The above are just the basic methods for writing concurrent programs using Akka, and there are also some other methods and features that can help us better utilize Akka to build concurrent applications. If you want to delve deeper into Akka's more detailed content, you can refer to the official Akka documentation: https://doc.akka.io/docs/akka/current/

RxJava is a Java library for implementing asynchronous programming. It is based on the Observer pattern and the Iterator pattern, and provides a rich set of operators to handle the asynchronous event flow. RxJava uses the Observer pattern to process data flows, which includes two main roles: Observable and Observer. Observable generates a series of events (which can be data, errors, or completion signals), and the Observer subscribes to Observable and responds to the generated events. To facilitate processing and converting event streams, RxJava also provides a large number of operators. These operators can be used for filtering, transforming, merging, combining, and delaying, among others. By using these operators, we can handle asynchronous event flows in a declarative manner. The following is an introduction and sample code of some commonly used RxJava methods: 1. Create Observable: ```java Observable<String> observable = Observable.just("Hello", "RxJava"); observable.subscribe(new Observer<String>() { @Override public void onSubscribe(Disposable d) { //Processing logic during subscription } @Override public void onNext(String s) { //Processing logic when receiving data } @Override public void onError(Throwable e) { //Processing logic when an error occurs } @Override public void onComplete() { //Processing logic at completion } }); ``` 2. Filter operator: ```java Observable<Integer> observable = Observable.just(1, 2, 3, 4, 5); Observable. filter (number ->number% 2==0)//Filter out even numbers . subscribe (number ->System. out. println (number))// The output result is: 2, 4 ``` 3. Conversion operator (map): ```java Observable<Integer> observable = Observable.just(1, 2, 3, 4, 5); Observable. map (number ->number * 2)//Multiply each number by 2 . subscribe (number ->System. out. println (number))// The output results are: 2, 4, 6, 8, 10 ``` 4. Merge operator: ```java Observable<Integer> observable1 = Observable.just(1, 2, 3); Observable<Integer> observable2 = Observable.just(4, 5, 6); Observable<Integer>mergedObservable=Observable. merge (observable1, observable2)// Merge two Observable MergedObservable. subscribe (number ->System. out. println (number))// The output results are: 1, 2, 3, 4, 5, 6 ``` 5. Delay operator: ```java Observable<Integer> observable = Observable.just(1, 2, 3); Observable. delay (1, TimeUnit. SECONDS)//Delay transmitting data by 1 second . subscribe (number ->System. out. println (number))// The output results are: 1, 2, 3 (emitting after a delay of 1 second) ``` The Maven dependency of RxJava is: ``` <dependency> <groupId>io.reactivex.rxjava2</groupId> <artifactId>rxjava</artifactId> <version>2.2.19</version> </dependency> ```

Spring Reactor is a programming model based on the concept of responsive flow processing, used to implement asynchronous, non blocking, and scalable applications. It is an implementation based on the responsive stream specification and provides operators similar to the Stream API of Java 8+. Using Spring Reactors, asynchronous event flows can be modeled and composed in an explicit manner. Here are some commonly used key methods: 1. Flux: Represents a responsive sequence flow containing 0 to multiple elements. Flux objects can be created in various ways, such as directly assigning multiple elements, generating from collections, arrays, asynchronous streams, or other Flux objects. The following is a simple example code: ```java Flux<String> flux = Flux.just("hello", "world"); flux.subscribe(System.out::println); ``` Maven Dependency: ```xml <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-webflux</artifactId> </dependency> ``` 2. Mono: Represents a responsive sequence flow containing 0 or 1 elements. Mono objects can be created in various ways, such as directly assigning elements, generating from methods that can return elements, and converting Flux objects. The following is a simple example code: ```java Mono<String> mono = Mono.just("hello"); mono.subscribe(System.out::println); ``` Maven Dependency: ```xml <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-webflux</artifactId> </dependency> ``` 3. Map: Transforms each element in the sequence flow through a transformation function and returns a new sequence flow. The following is a simple example code: ```java Flux<Integer> flux = Flux.just(1, 2, 3); Flux<Integer> squaredFlux = flux.map(i -> i * i); squaredFlux.subscribe(System.out::println); ``` 4. FlatMap: Transforms each element in the sequence flow into a new sequence flow through a transformation function, and merges these sequence flows into a new sequence flow. The following is a simple example code: ```java Flux<String> flux = Flux.just("hello", "world"); Flux<Character> characterFlux = flux.flatMap(s -> Flux.fromArray(s.toCharArray())); characterFlux.subscribe(System.out::println); ``` These are brief introductions and sample code for some commonly used methods and operators in Spring Reactors. When using, it is necessary to introduce corresponding Maven dependencies. For example, in the Spring Boot project, the 'spring boot starter webflux' dependency can be introduced.

JDeferred is a Java asynchronous programming framework that provides a concise way to handle asynchronous tasks and callbacks. It allows developers to write code in a sequential manner without worrying about the details of asynchronous tasks, thus simplifying asynchronous programming. The commonly used key methods are as follows: 1. Promise(): Create a new Promise object that represents the results of an asynchronous task. 2. Resolve(): Pass a value or object as a successful result to Promise. 3. reject(): Pass an error object as the result of a failure to Promise. 4. done(): Add a callback function that will be called when Promise is successful. 5. fail (): Add a callback function that will be called when Promise fails. 6. always(): Add a callback function that is called when Promise succeeds or fails. The following is a Java example code for implementing asynchronous programming using JDeferred: Firstly, add the JDeferred dependency in the pom.xml file: ```xml <dependency> <groupId>org.jdeferred</groupId> <artifactId>jdeferred-core</artifactId> <version>1.2.12</version> </dependency> ``` Then, the following example code can be used to demonstrate the usage of JDeferred: ```java import org.jdeferred.Deferred; import org.jdeferred.Promise; import org.jdeferred.impl.DeferredObject; public class JDeferredExample { public static void main(String[] args) { Deferred<String, Integer, Integer> deferred = new DeferredObject<>(); Promise<String, Integer, Integer> promise = deferred.promise(); promise.done(result -> System.out.println("Success: " + result)) .fail(e -> System.out.println("Error: " + e)) .always((state, result, rejection) -> System.out.println("Always executed")); //Simulate asynchronous operations new Thread(() -> { try { Thread.sleep(2000); deferred.resolve("Hello, JDeferred!"); } catch (InterruptedException e) { deferred.reject(500); } }).start(); } } ``` In the above example code, we created a Deferred object that represents the results of an asynchronous task. Then, we created a Promise object using the promise() method to listen on the results. In the done() method, we define a callback function that is called when the asynchronous task is successful. In the fail () method, we define a callback function that is called when an asynchronous task fails. In the always () method, we define a callback function that will be called regardless of whether the asynchronous task succeeds or fails. In the simulated asynchronous operation, we pass the successful result to the Promise object by calling the resolve() method. In practical applications, asynchronous code can be replaced with specific business logic. From the above example code, we can see that the JDeferred framework simplifies the complexity of asynchronous programming, making the processing of asynchronous tasks more intuitive and sequential.

Guava is an open source Java library released by Google, providing many practical tool classes and methods. This includes support for concurrent programming, making asynchronous programming simpler and more efficient. Guava's asynchronous programming module provides a callback based approach to handling asynchronous tasks to avoid blocking the main thread and improve system performance. Guava's asynchronous programming module includes the following commonly used key methods: 1. ListenableFuture: Provides a more user-friendly interface for handling the results of asynchronous tasks. Compared to the Future interface provided by JDK, it supports registering callback functions to process results without actively blocking the retrieval of results. The following is an example code for using ListenableFuture: ```java import com.google.common.util.concurrent.*; ListenableFuture<String> future = Executors.newSingleThreadExecutor().submit(() -> "Hello World"); Futures.addCallback(future, new FutureCallback<String>() { @Override public void onSuccess(String result) { System.out.println(result); } @Override public void onFailure(Throwable t) { System.out.println("Error: " + t.getMessage()); } }); ``` 2. ListenableFutureTask: is a class that inherits from FutureTask and extends the functionality of some asynchronous operations. It provides an addListener() method that can trigger a callback function after the task is completed. The following is an example code for using ListenableFutureTask: ```java import com.google.common.util.concurrent.*; ListenableFutureTask<String> futureTask = ListenableFutureTask.create(() -> "Hello World"); futureTask.addListener(() -> { try { System.out.println(futureTask.get()); } catch (Exception e) { System.out.println("Error: " + e.getMessage()); } }, MoreExecutors.directExecutor()); ExecutorService executorService = Executors.newSingleThreadExecutor(); executorService.submit(futureTask); ``` 3. Futures: Provides some tools and methods for handling asynchronous tasks. The most commonly used methods are allAsList() and successfulAsList(), which return the results of all tasks in a ListenableFuture object list and only successful task results, respectively. The following is an example code for using Futures: ```java import com.google.common.util.concurrent.*; List<ListenableFuture<String>> futures = new ArrayList<>(); futures.add(Executors.newSingleThreadExecutor().submit(() -> "Hello")); futures.add(Executors.newSingleThreadExecutor().submit(() -> "World")); ListenableFuture<List<String>> allFutures = Futures.allAsList(futures); Futures.addCallback(allFutures, new FutureCallback<List<String>>() { @Override public void onSuccess(List<String> result) { System.out.println(result); } @Override public void onFailure(Throwable t) { System.out.println("Error: " + t.getMessage()); } }); ``` To use Guava's asynchronous programming module, you need to add the following dependencies to the pom.xml file of the project: ```xml <dependency> <groupId>com.google.guava</groupId> <artifactId>guava</artifactId> <version>30.1-jre</version> </dependency> ``` This way, you can import com. Google. common. util. concurrent. *; Let's import Guava's asynchronous programming related classes.

LMAX Interruptor is a concurrency framework used to build high-performance, low latency applications. It provides an efficient and scalable way to handle concurrent tasks through the use of lockless data structures and event driven design. The design goal of LMAX Interruptor is to maximize processor throughput and reduce latency, especially suitable for scenarios that require processing a large number of events. The core idea of LMAX Disruptor is to use Circular buffer to pass events. In the Circular buffer, the producer puts the event into the buffer and notifies the consumer to handle it. Consumers obtain events from the buffer and execute corresponding processing logic. This design avoids the use of locks and improves the execution efficiency of concurrent programs. Common key methods and classes: 1. Event: The data structure of the event. Users need to customize event objects and implement necessary get and set methods. 2. EventFactory: The factory class used to create events. By implementing the EventFactory interface, we can create event objects. 3. EventProcessor: Event processor. Responsible for obtaining events from Ring Buffer and handling them. Responsible for calling specific event processing logic. 4. EventHandler: Event processing interface. By implementing the EventHandler interface, we can define the processing logic for events. 5. Interruptor: The core class of LMAX Interruptor. Through the Interruptor class, we can create and manage Ring Buffers and register event handlers with the Interruptor. The following is a simple Java example code that demonstrates how to use LMAX Interruptor to create concurrent programs. Firstly, we need to add Maven dependencies for LMAX Interruptor: ```xml <dependency> <groupId>com.lmax</groupId> <artifactId>disruptor</artifactId> <version>3.4.2</version> </dependency> ``` Then, we define an event object: ```java public class MyEvent { private String data; public String getData() { return data; } public void setData(String data) { this.data = data; } } ``` Next, create an event factory class: ```java public class MyEventFactory implements EventFactory<MyEvent> { @Override public MyEvent newInstance() { return new MyEvent(); } } ``` Then, we define an event handler: ```java public class MyEventHandler implements EventHandler<MyEvent> { @Override public void onEvent(MyEvent event, long sequence, boolean endOfBatch) throws Exception { //Logic for handling events System.out.println("Processing event: " + event.getData()); } } ``` Finally, we create and launch the Interrupter: ```java public class MyDisruptorExample { public static void main(String[] args) { //Create Circular buffer RingBuffer<MyEvent> ringBuffer = RingBuffer.createSingleProducer(new MyEventFactory(), 1024); //Create a Interruptor instance Disruptor<MyEvent> disruptor = new Disruptor<>(new MyEventFactory(), 1024, Executors.defaultThreadFactory()); //Add Event Processor disruptor.handleEventsWith(new MyEventHandler()); //Start Interruptor disruptor.start(); //Publish Events long sequence = ringBuffer.next(); MyEvent event = ringBuffer.get(sequence); event.setData("Hello, Disruptor!"); ringBuffer.publish(sequence); //Close Interruptor disruptor.shutdown(); } } ``` The above example code demonstrates how to create a simple concurrent program using LMAX Interruptor. After the Interrupter is started, producers can publish events to the Ring Buffer, and consumers will retrieve events from the Ring Buffer and execute processing logic.