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Reactive Programming vs Thread Based Programming

I am new to this concept and want to have a great understanding of this topic.
To make my point clear I want to take an analogy.
Let's take a scenario of Node JS which is single-threaded and provide fast IO operation using an event loop. Now that makes sense since It is single-threaded and is not blocked for any task.
While studying reactive programming in Java using reactor. I came to a situation where the main thread is blocked when an object subscribes and some delay event took place.
Then I came to know the concept of subscribeOn.boundedElastic and many more pipelines like this.
I got it that they are trying to make it asynchronous by moving those subscribers to other threads.
But if it occurs like this then why is the asynchronous. Is it not thread-based programming?
If we are trying to achieve the async behaviour of Node JS then according to my view it should be in a single thread.
Summary of my question is:
So I don't get the fact of using or calling reactive programming as asynchronous or functional programming because of two reason
Main thread is blocked
We can manage the thread and can run it in another pool. Runnable service/ callable we can also define.

First of all you can't compare asynchronous with functional programming. Its like comparing a rock with a banana. Its two separate things.
Functional programming is compared to other types of programming, like object oriented programming or procedural programming etc. etc.
Reactor is a java library, and java is an object oriented programming language with functional features.
Asynchronous i will explain with what wikipedia says
Asynchrony, in computer programming, refers to the occurrence of events independent of the main program flow and ways to deal with such events.
So basically how to handle stuff "around" your application, that is not a part of the main flow of your program.
In comparison to Blocking, wikipedia again:
A process that is blocked is one that is waiting for some event, such as a resource becoming available or the completion of an I/O operation.
A traditional servlet application works by assigning one thread per request.
So every time a request comes in, a thread is spawned, and this thread follows along the request until the request returns. If there is something blocking during this request, for instance reading a file from the operating system, or making a request to another service. The assigned thread will block and wait until the reading of the file is completed, or the request has returned etc.
Reactive works with subscribers and producers and makes heavy use of the observer pattern. Which means that as soon as some thing blocks, reactor can take that thread and use it for something else. And then it is un-blocked any thread can pick up where it left off. This makes sure that every thread is always in use, and utilized at 100%.
All things processed in reactor is done by the event loop the event loop is a single threaded loop that just processes events as quick as possible. Schedulers schedule things to be processed on the event loop, and after they are processed a scheduler picks up the result and carries on.
If you just run reactor you get a default scheduler that will schedule things for you completely automatically.
But lets say you have something blocking. Well then you will stop the event loop. And everything needs to wait for that thing to finish.
When you run a fully reactive application you usually get one event loop per core during startup. Which means lets say you have 4 cores, you get 4 event loops and you block one, then during that period of blockages your application runs 25% slower.
25% slower is a lot!
Well sometimes you have something that is blocking that you can't avoid. For instance an old database that doesn't have a non-blocking driver. Or you need to read files from the operating system in a blocking manor. How do you do then?
Well the reactor team built in a fallback, so that if you use onSubscribe in combination with its own elastic thread pool, then you will get the old servlet behaviour back for that single subscriber to a specific say endpoint etc.
This makes sure that you can run fully reactive stuff side by side with old legacy blocking things. So that maybe some reaquests usese the old servlet behaviour, while other requests are fully non-blocking.
You question is not very clear so i am giving you a very unclear answer. I suggest you read the reactor documentation and try out all their examples, as most of this information comes from there.

Project loom: what makes the performance better when using virtual threads?

To give some context here, I have been following Project Loom for some time now. I have read The state of Loom. I have done asynchronous programming.
Asynchronous programming (provided by Java NIO) returns the thread to the thread pool when the task waits and it goes to great lengths to not block threads. And this gives a large performance gain, we can now handle many more request as they are not directly bound by the number of OS threads. But what we lose here, is the context. The same task is now NOT associated with just one thread. All the context is lost once we dissociate tasks from threads. Exception traces do not provide very useful information and debugging is difficult.
In comes Project Loom with virtual threads that become the single unit of concurrency. And now you can perform a single task on a single virtual thread.
It's all fine until now, but the article goes on to state, with Project Loom:
A simple, synchronous web server will be able to handle many more requests without requiring more hardware.
I don't understand how we get performance benefits with Project Loom over asynchronous APIs? The asynchrounous API:s make sure to not keep any thread idle. So, what does Project Loom do to make it more efficient and performant that asynchronous API:s?
EDIT
Let me re-phrase the question. Let's say we have an http server that takes in requests and does some crud operations with a backing persistent database. Say, this http server handles a lot of requests - 100K RPM. Two ways of implementing this:
The HTTP server has a dedicated pool of threads. When a request comes in, a thread carries the task up until it reaches the DB, wherein the task has to wait for the response from DB. At this point, the thread is returned to the thread pool and goes on to do the other tasks. When DB responds, it is again handled by some thread from the thread pool and it returns an HTTP response.
The HTTP server just spawns virtual threads for every request. If there is an IO, the virtual thread just waits for the task to complete. And then returns the HTTP Response. Basically, there is no pooling business going on for the virtual threads.
Given that the hardware and the throughput remain the same, would any one solution fare better than the other in terms of response times or handling more throughput?
My guess is that there would not be any difference w.r.t performance.

We don't get benefit over asynchronous API. What we potentially will get is performance similar to asynchronous, but with synchronous code.

The answer by #talex puts it crisply. Adding further to it.
Loom is more about a native concurrency abstraction, which additionally helps one write asynchronous code. Given its a VM level abstraction, rather than just code level (like what we have been doing till now with CompletableFuture etc), It lets one implement asynchronous behavior but with reduce boiler plate.
With Loom, a more powerful abstraction is the savior. We have seen this repeatedly on how abstraction with syntactic sugar, makes one effectively write programs. Whether it was FunctionalInterfaces in JDK8, for-comprehensions in Scala.
With loom, there isn't a need to chain multiple CompletableFuture's (to save on resources). But one can write the code synchronously. And with each blocking operation encountered (ReentrantLock, i/o, JDBC calls), the virtual-thread gets parked. And because these are light-weight threads, the context switch is way-cheaper, distinguishing itself from kernel-threads.
When blocked, the actual carrier-thread (that was running the run-body of the virtual thread), gets engaged for executing some other virtual-thread's run. So effectively, the carrier-thread is not sitting idle but executing some other work. And comes back to continue the execution of the original virtual-thread whenever unparked. Just like how a thread-pool would work. But here, you have a single carrier-thread in a way executing the body of multiple virtual-threads, switching from one to another when blocked.
We get the same behavior (and hence performance) as manually written asynchronous code, but instead avoiding the boiler-plate to do the same thing.
Consider the case of a web-framework, where there is a separate thread-pool to handle i/o and the other for execution of http requests. For simple HTTP requests, one might serve the request from the http-pool thread itself. But if there are any blocking (or) high CPU operations, we let this activity happen on a separate thread asynchronously.
This thread would collect the information from an incoming request, spawn a CompletableFuture, and chain it with a pipeline (read from database as one stage, followed by computation from it, followed by another stage to write back to database case, web service calls etc). Each one is a stage, and the resultant CompletablFuture is returned back to the web-framework.
When the resultant future is complete, the web-framework uses the results to be relayed back to the client. This is how Play-Framework and others, have been dealing with it. Providing an isolation between the http thread handling pool, and the execution of each request. But if we dive deeper in this, why is it that we do this?
One core reason is to use the resources effectively. Particularly blocking calls. And hence we chain with thenApply etc so that no thread is blocked on any activity, and we do more with less number of threads.
This works great, but quite verbose. And debugging is indeed painful, and if one of the intermediary stages results with an exception, the control-flow goes hay-wire, resulting in further code to handle it.
With Loom, we write synchronous code, and let someone else decide what to do when blocked. Rather than sleep and do nothing.

The http server has a dedicated pool of threads ....
How big of a pool? (Number of CPUs)*N + C? N>1 one can fall back to anti-scaling, as lock contention extends latency; where as N=1 can under-utilize available bandwidth. There is a good analysis here.
The http server just spawns...
That would be a very naive implementation of this concept. A more realistic one would strive for collecting from a dynamic pool which kept one real thread for every blocked system call + one for every real CPU. At least that is what the folks behind Go came up with.
The crux is to keep the {handlers, callbacks, completions, virtual threads, goroutines : all PEAs in a pod} from fighting over internal resources; thus they do not lean on system based blocking mechanisms until absolutely necessary This falls under the banner of lock avoidance, and might be accomplished with various queuing strategies (see libdispatch), etc.. Note that this leaves the PEA divorced from the underlying system thread, because they are internally multiplexed between them. This is your concern about divorcing the concepts. In practice, you pass around your favourite languages abstraction of a context pointer.
As 1 indicates, there are tangible results that can be directly linked to this approach; and a few intangibles. Locking is easy -- you just make one big lock around your transactions and you are good to go. That doesn't scale; but fine-grained locking is hard. Hard to get working, hard to choose the fineness of the grain. When to use { locks, CVs, semaphores, barriers, ... } are obvious in textbook examples; a little less so in deeply nested logic. Lock avoidance makes that, for the most part, go away, and be limited to contended leaf components like malloc().
I maintain some skepticism, as the research typically shows a poorly scaled system, which is transformed into a lock avoidance model, then shown to be better. I have yet to see one which unleashes some experienced developers to analyze the synchronization behavior of the system, transform it for scalability, then measure the result. But, even if that were a win experienced developers are a rare(ish) and expensive commodity; the heart of scalability is really financial.

Java support for three different concurrency models

I am going through different concurrency model in multi-threading environment (http://tutorials.jenkov.com/java-concurrency/concurrency-models.html)
The article highlights about three concurrency models.
Parallel Workers
The first concurrency model is what I call the parallel worker model. Incoming jobs are assigned to different workers.
Assembly Line
The workers are organized like workers at an assembly line in a factory. Each worker only performs a part of the full job. When that part is finished the worker forwards the job to the next worker.
Each worker is running in its own thread, and shares no state with other workers. This is also sometimes referred to as a shared nothing concurrency model.
Functional Parallelism
The basic idea of functional parallelism is that you implement your program using function calls. Functions can be seen as "agents" or "actors" that send messages to each other, just like in the assembly line concurrency model (AKA reactive or event driven systems). When one function calls another, that is similar to sending a message.
Now I want to map java API support for these three concepts
Parallel Workers : Is it ExecutorService,ThreadPoolExecutor, CountDownLatch API?
Assembly Line : Sending an event to messaging system like JMS & using messaging concepts of Queues & Topics.
Functional Parallelism: ForkJoinPool to some extent & java 8 streams. ForkJoin pool is easy to understand compared to streams.
Am I correct in mapping these concurrency models? If not please correct me.

Each of those models says how the work is done/splitted from a general point of view, but when it comes to implementation, it really depends on your exact problem. Generally I see it like this:
Parallel Workers: a producer creates new jobs somewhere (e.g in a BlockingQueue) and many threads (via an ExecutorService) process those jobs in parallel. Of course, you could also use a CountDownLatch, but that means you want to trigger an action after exactly N subproblems have been processed (e.g you know your big problem may be split in N smaller problems, check the second example here).
Assembly Line: for every intermediate step, you have a BlockingQueue and one Thread or an ExecutorService. On each step the jobs are taken from one BlickingQueue and put in the next one, to be processed further. To your idea with JMS: JMS is there to connect distributed components and is part of the Java EE and was not thought to be used in a high concurrent context (messages are kept usually on the hard disk, before being processed).
Functional Parallelism: ForkJoinPool is a good example on how you could implement this.

An excellent question to which the answer might not be quite as satisfying. The concurrency models listed show some of the ways you might want to go about implementing an concurrent system. The API provides tools used to implementing any of these models.
Lets start with ExecutorService. It allows you to submit tasks to be executed in a non-blocking way. The ThreadPoolExecutor implementation then limits the maximum number of threads available. The ExecutorService does not require the task to perform the complete process as you might expect of a parallel worker. The task may be limited to specific part of the process and send a message upon completion that starts the next step in an assembly line.
The CountDownLatch and the ExecutorService provide a means to block until all workers have completed that may come in handy if a certain process has been divided to different concurrent sub-tasks.
The point of JMS is to provide a means for messaging between components. It does not enforce a specific model for concurrency. Queues and topics denote how a message is sent from a publisher to a subscriber. When you use queues the message is sent to exactly one subscriber. Topics on the other hand broadcast the message to all subscribers of the topic.
Similar behavior could be achieved within a single component by for example using the observer pattern.
ForkJoinPool is actually one implementation of ExecutorService (which might highlight the difficulty of matching a model and an implementation detail). It just happens to be optimized for working with large amount of small tasks.
Summary: There are multiple ways to implement a certain concurrency model in the Java environment. The interfaces, classes and frameworks used in implementing a program may vary regardless of the concurrency model chosen.

Actor model is another example for an Assembly line. Ex: akka

How to implement Asynchronous application in Java

So let me start with making the requirement pretty clear
I am creating a Java Server side application which has a couple of REST APIs. Now I want to collect stats and do some logging when the API is invoked via HTTP
Now logging and collecting/persisting stats can slow down the API call so I want this to happen asynchronously so API execution can go ahead.
So what solution would be best for this? I want to make sure that no data is lost and also the solution can scale with load. I thought of Akka framework but not sure it will meet my requirement. Creating simple threads to do the job might not scale. Is there any standard Java solution/pattern for this?

Implement a Thread pool with fixed number of threads(whatever is optimum)
create custom objects that has all the details required w.
have a Queue that can hold these custom objects
Whenever API is called, create these objects and place them in Queue
Threads should wait on the queue, if empty, dequeue these Tasks(custom objects), do the logging asyncronously.
After the job is done, dont kill these threads. have a while() loop inside the thread, so that it can wait again on the queue to dequeue another task and perform it.

Sending objects back and forth between threads in java?

I have multiple client handler threads, these threads need to pass received object to a server queue and the sever queue will pass another type of object back to the sending thread. The server queue is started and keeps running when the server starts.I am not sure which thread mechanism to use for the client handler threads notified an object is sent back. I don't intend to use socket or writing to a file.

If you wanted to do actual message passing take a look at SynchronusQueue. Each thread will have reference to the queue and would wait until one thread passed the reference through the queue.
This would be thread safe and address your requirements.
Though if you are simply looking to have threads read and write a shared variable you can use normalocity's suggestion though it's thread-safety depends on how you access it (via sychronized or volatile)

As far as making objects accessible in Java, there's no difference between multi-thread and single-thread. You just follow the scope rules (public, private, protected), and that's it. Multiple threads all run within the same process, so there isn't any special thread-only scope rules to know about.
For example, define a method where you pass the object in, and make that method accessible from the other thread. The object you want to pass around simply needs to be accessible from the other thread's scope.
As far as thread-safety, you can synchronize your writes, and for the most part, that will take care of things. Thread safety can get a bit hairy the more complicated your code, but I think this will get you started.
One method for processing objects, and producing result objects is to have a shared array or LinkedList that acts as a queue of objects, containing the objects to be processed, and the resulting objects from that processing. It's hard to go into much more detail than that without more specifics on what exactly you're trying to do, but most shared access to objects between threads comes down to either inter-thread method calls, or some shared collection/queue of objects.

Unless you are absolutely certain that it will always be only a single object at a time, use some sort of Queue.
If you are certain that it will always be only a single object at a time, use some sort of Queue anyway. :-)

Use a concurrent queue from the java.util.concurrent.*.
why? Almost guaranteed to provide better general performance than any thing hand rolled.
recommendation: use a bound queue and you will get back-pressure for free.
note: the depth of queue determines your general latency characteristics: shallower queues will have lower latencies at the cost of reduced bandwidth.
Use Future semantics
why? Futures provide a proven and standard means of getting asynchronous result.
recommendation: create a simple Request class and expose a method #getFutureResponse(). The implementation of this method can use a variety of signaling strategies, such as Lock, flag (using Atomic/CAS), etc.
note: use of timeout semantics in Future will allow you to link server behavior to your server SLA e.g. #getFutureResponse(sla_timeout_ms).

A book tip for if you want to dive a bit more into communication between threads (or processes, or systems): Pattern-Oriented Software Architecture Volume 2: Patterns for Concurrent and Networked Objects

Just use simple dependency injection.
MyFirstThread extends Thread{
public void setData(Object o){...}
}
MySecondThread extends Thread{
MyFirstThread callback;
MySecondThread(MyFirstThread callback){this.callback=callback)
}
MyFirstThread t1 = new MyFirstThread();
MySecondThread t2 = new MySecondThread(t1);
t1.start();
t2.start();
You can now do callback.setData(...) in your second thread.
I find this to be the safest way. Other solutions involve using volatile or some kind of shared object which I think is an overkill.
You may also want to use BlockingQueue and pass both of those to each thread. If you plan to have more than one thread then it is probably a better solution.