I have used Redux-Saga. Code written with it is easy to reason so far, except JS generator function is messing up my head from time to time. From my understanding, Redux-Observable can achieve the similar job that handles side effects but without using generator function.

However, docs from Redux-Observable does not provide many opinions of why it is superior against Redux-Saga. I would want to know whether not using generator function is the only benefit of using Redux-Observable. And what could be the downsides, gotcha or compromises from using Redux-Observable instead of Redux-Saga?

Why use Redux-Observable over Redux-Saga?

I've read the Wikipedia article on reactive programming. I've also read the small article on functional reactive programming. The descriptions are quite abstract.

What does functional reactive programming (FRP) mean in practice?

What does reactive programming (as opposed to non-reactive programming?) consist of?

My background is in imperative/OO languages, so an explanation that relates to this paradigm would be appreciated.

What is (functional) reactive programming?

I have been doing some research into reactive programming recently and I'm finding it hard to find a good description of the difference between Reactive and Functional-Reactive.

Is it simply that the reactive programming is implemented using functional methods/paradigms as opposed to using declarative or OO paradigms?

The difference between Reactive and Functional-Reactive programming

Are there any libraries written for Scala enabling Functional Reactive Programming? 

Functional Reactive Programming in Scala 

I keep studying and trying Reactive Style of coding using Reactor and RxJava. I do understand that reactive coding makes better utilization of CPU compared to single threaded execution.

Is there any concrete comparison between reactive programming vs imperative programming in web based applications?

How much is the performance gain, throughput I achieve by using reactive programming over non-reactive programming?

Also what are the advantages and disadvantages of Reactive Programming?

Reactive Programming Advantages/Disadvantages

I am learning reactive programming and functional reactive programming in JavaScript. I am very confused.

Wikipedia says that there are various ways to write reactive code such as imperative, OORP and functional. I want to know if event-driven is just an another way to write reactive code?

How is reactive programming related to Promises? I think promises is an alternative to event-driven and callback hell.

How is reactive programming different than event-driven programming?

I'd like to know how Reactive Programming is tied to Functional-Programming.

Most papers refer "Reactive Programming" as "Functional Reactive Programming".

Does Reactive Programming can be implemented outside Functional Programming?

Is it easier to write Reactive Programs with Functional Language?

 It is easier to write reactive programs functionally. It is not easier to write them to perform faster by using functional programming. Reusing mutable state is often faster than creating new immutable objects all the time. So if you are using the reactive programming for performance, I wouldn't use functional programming.

Often developers feel they have to use multiple threads or cores, because they are there. This is like saying you need to use 100% of disk space or you are wasting it.

IMHO you should only add the complexity of multiple threads if it improves performance and it is the simplest way to achieve this improvement. What is often forgotten in the discussion about making concurrency easier is that the easiest solution is to use one thread, and, unless you have proven your solution is faster than that, you haven't convinced me that using multiple threads was ever helpful.

Is Reactive Programming bounded to Functional programming?

I started to use java reactor in my app due to better resource utilization thoughts (I have lot of external async integrations) but I came to the question, what is better way to write reactive applications in java? Should I use pure functional style or OOP or i can mixin both of two? I am also interested in good resources that can be helpful in learning the right reactive style. 

Java Reactive Programming: Something Old, Something New

Over time some concepts can fade away but later return as new ones as they are applied in the new environment. The same thing has happened with reactive programming. The term has been introduced to the IT world back in the 1960-70s, and nowadays, it came back to be applied in the development of mobile and web applications What is reactive programming? What are the benefits of reactive programming in Java apps? Let’s dive deep into this topic, study the examples, compare it to imperative programming, and highlight the advantages of this type of programming for apps as well as for business.What Is Reactive Programming Java?| Let’s Put It Simple

The term “Reactive Programming” is not a buzzword anymore, and over the years, it has acquired a number of meanings that might seem a little bit messy and unclear. We go with the most simple one that clearly defines the concept.Reactive Programming (RP) is a programming model that is designed to cope with asynchronous events (data streams) and the specific act of producing a change, in other words, it means that modifications are implemented to the execution environment in an effective ceratin order. Take a look at the sequences of events in real life in order to have a full understanding of the reactive programming Java paradigm. For instance, Ann wants to have a party on a Friday night. She has to invite her friends, clean the apartment, and cook dinner. What options do we have here?1. Ann finishes her work and calls her friends. Then she heads to the supermarket to buy food, waits in line to pay, and goes home. Once at home, she starts cooking. When dinner is ready, she cleans the apartment, gusts arrive, and most likely wait till Ann finishes all the work. The party starts later.This is a description of a sync approach to the problem (request), as all the tasks are completed one after another. As a result, it will take a long time for the party to start.2. Ann finishes her work and sends invitations to her friends. Then she orders food delivery online and heads home to clean the apartment. By the time the apartment is clean, food is delivered. Ann is having dinner without her friends.This is an example of the async approach.3. Ann finishes her work and sends invitations to her friends. She orders food delivery online and heads home to clean the apartment. She finishes cleaning and gets the delivery. But this time she waits for her friends to arrive and start the party.The last option is a description of the reactive approach. That is to say. The RP is when you wait for async actions to complete (make a change) to proceed with further steps.For better understanding, think of the experiment at your chemistry class. You have to combine two or more elements in order to get a certain reaction. Similar processes lay behind the reactive paradigm  

concept.Reactive Programming Java: Example Reactive programming Java paradigm concept is clear, but where, when, and how is it used?The RP is an effective solution for the applications that are high-load or multiple-user. This approach suits:Chats and social networks;

It can also be used for such components in the apps as:Real-time data streaming;

Server code that serves highly interactive UI elements. For instance, Netflix developers use Microsoft’s open-source reactive programming library Rx (Reactive Extensions) for their web application. Rx allows them to build complex and asynchronous programs much easier. Web applications like Netflix are reactive as code reacts to different events (mouse clicks, keypresses, async data arrival from the server, etc.). Thanks to the Rx library, developers can design evens as a collection of data and not as a series of callbacks. Now when events are modeled as collections, developers can make changes with events in the same way they transform in-memory collections. Events are pushed asynchronously from producer to consumer, and the consumer reacts to the available data as it comes.Java Reactive Programming Performance| How Is It  Done? The RP provides higher performance for Java apps and has lower memory requirements. It is achieved by avoiding blocking calls that always lead to process and context switches in the operating system.Now when the requirements for web apps have become much higher, the number of users has increased as well as a number of requests, it is better to use the RP approach in the application. In RP, the program flow is seen as a sequence of events that occur asynchronously.Java reactive programming consists of the four main building blocks:Observables (the data source/stream that cam emit a number of values, one or none; it can also emit errors and be infinite or finite); Subscribers (the ones who subscribe to Observables, they use the data provided, receive the errors and completion events from the Observable); Operators (used to create, transform, filter, and combine Observables); Schedulers (allows to easily add threading to our Observables and Subscribers). When using reactive programming, the spine of your application will consist of data streams that are going to convey events, calls, messages, even failures. The RP allows you to observe the streams and react when a value is emitted. In code, the developer creates streams of anything that can change or happen (click events, cache events, HTTP requests, etc.); thus, the application becomes asynchronous. To implement the RP modules into the app and compose async, event-driven programs using an observable sequence, use Rx (Reactive Extension) that gives you a great toolbox. Now you can combine, filter, merge, create, and transform data streams that are called Observables.Today you can find a number of libraries (Rx, Spring, etc.) that enable reactive programming implementation into your app with different tools and implementations.

Right way to write reactive apps in java? 

What's an elegant way of composing an Rx observable which would resemble ReplaySubject, but only emit the accumulated sequence once and for the first subscriber only (when that subscriber is connected)? After the 1st subscription, it should act just as a regular Subject.

This for a .NET project, but I'd equally appreciate JavaScript/RxJS answers.

I did google for potential solutions, and I'm about to roll out my own, similar to how I approached DistinctSubject, eventually.

An Rx observable that would act as ReplaySubject but only for the 1st subscriber?

I was wondering if using backpressure in a system generally did not provide a benefit as it just leads to higher latency / less throughput because it prevents scaling out the overwhelmed resources. Should it only be used for resources that cannot be scaled e.g. a browser client? 

No, backpressure is not solely needed when a system cannot scale. Backpressure is a mechanism used in reactive programming to handle situations where a fast-producing source is overwhelming a slow-consuming downstream.

Backpressure is essential in scenarios where the rate of data production exceeds the rate of data consumption. Without backpressure, the downstream components may become overloaded and unable to handle the incoming data. This can lead to issues such as memory exhaustion, increased latency, or even system crashes.

Backpressure allows the consumer to communicate its readiness or capacity to the producer, enabling a cooperative flow control mechanism. It allows the consumer to control the pace at which it receives data from the producer, ensuring that it can handle the workload effectively.

Even in systems that can scale, backpressure can still be important. Scaling alone may not be sufficient to handle excessive data production if the consumer cannot process the data at the same rate. Backpressure provides a mechanism for the consumer to regulate the flow and maintain stability within the system.

Reactive frameworks like RxSwift provide backpressure mechanisms, such as buffer sizes, windowing, and throttling, to handle situations where the rate of data production needs to be controlled to match the consumer's capacity.

In summary, backpressure is not limited to situations where a system cannot scale. It is a crucial mechanism for ensuring the stability and proper functioning of reactive systems, especially when the rate of data production exceeds the rate of data consumption.

Is backpressure only needed when the system cannot scale?

I need to use something like RxSwift 3.0 Variable, but in RxSwift 5.0, it's completely deprecated.

What is Variable subject alternative in Rxswift 5.1?

let overwatch = Game(price: Variable(value: 59.99))

In RxSwift 5.1 and later versions, it is recommended to use BehaviorRelay as an alternative to Variable. BehaviorRelay is a wrapper around BehaviorSubject that provides similar functionality. It is a subject that can be used both as an observable sequence and as an observer, allowing you to both read and modify its value.

Here's an example of using BehaviorRelay:

let relay = BehaviorRelay(value: "Initial value")

In the above example, the BehaviorRelay is created with an initial value of "Initial value". Then, we subscribe to its observable sequence and print any new values that are emitted. Finally, we use the accept method to update the value of the relay to "Updated value".

What is Variable subject alternative in RxSwift 5.1?

I am new to project reactor, while I am studying the project reactor I observed one thing, always project rector logs get print as the main tread. if reactor runs without caring the thread, how that happens? and how I get verified reactive codes run on different different threads? 

  as default Reactor runs base on the thread that you subscribed.

 if you subscribe from your own thread, the reactor runs base on that thread. which means that not every time the main thread. you can run this test code to verify that. here is the flux created on the main thread. and that flux subscribed from another thread that was newly created. after running the application, see the logs. the logs will prove it. the logs have been run on the new thread that we newly created.

  public void testTheThread() throws InterruptedException {
        //the flux created on the main thread.
        Flux<String> stringFlux = Flux
                .fromArray(new String[]{"a", "b"})
                .map(String::toUpperCase)
                .log();

        //the subscriber runs on another new thread. [my-new-thread]
        Thread newThread = new Thread(() -> {

            stringFlux.subscribe(s -> {
                System.out.println("String is  = " + s);
            });
        });
        newThread.setName("my-new-thread");
        newThread.start();
        //sleep the main thread until get the data from my-new-thread
        //otherwise the log will not be printed.
        Thread.sleep(1000);
    }

project reactor always runs in the main thread when the logs get prints

I want to do the pagination in Firestore using the rest API's (using springboot). Is there any way similar to spring data?

Currently I am using the Paginate data with query cursors. But here we will require the last element (last token/last count) or last snapshot to get the next batch.

Is there any other way to paginate using just page and page size in Firestore?

Unfortunately, It is not possible. Firestore's pagination was designed with a query cursor.

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