reactive-programming

After learning about Observables , I find them quite similar to Node.js streams . Both have a mechanism of notifying the consumer whenever new data arrives, an error occurs or there is no more data (EOF). I would love to learn about the conceptual/functional differences between the two. Thanks! m4ktub Both Observables and node.js's Streams allow you to solve the same underlying problem: asynchronously process a sequence of values. The main difference between the two, I believe, is related to the context that motivated its appearance. That context is reflected in the terminology and API. On the

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? Thanks

The current milestone (M4) documentation shows and example about how to retrieve a Mono using WebClient : WebClient webClient = WebClient.create(new ReactorClientHttpConnector()); ClientRequest<Void> request = ClientRequest.GET("http://example.com/accounts/{id}", 1L) .accept(MediaType.APPLICATION_JSON).build(); Mono<Account> account = this.webClient .exchange(request) .then(response -> response.body(toMono(Account.class))); How can we get streamed data (from a service that returns text/event-stream ) into a Flux using WebClient? Does it support automatic Jackson conversion?. This is how I did

I have a list of URLs of pages I want to download concurrently using HttpClient . The list of URLs can be large (100 or more!) I have currently have this code: var urls = new List<string> { @"http:\\www.amazon.com", @"http:\\www.bing.com", @"http:\\www.facebook.com", @"http:\\www.twitter.com", @"http:\\www.google.com" }; var client = new HttpClient(); var contents = urls .ToObservable() .SelectMany(uri => client.GetStringAsync(new Uri(uri, UriKind.Absolute))); contents.Subscribe(Console.WriteLine); The problem: due to the usage of SelectMany , a big bunch of Tasks are created almost at the

I'm just new in ReactJS and I have a problem. I can't solve it. It seems everything is all right, but still console puts me: A valid React element (or null) must be returned. You may have returned undefined, an array or some other invalid object. Here's my code: import React from 'react'; import ReactDOM from 'react-dom'; import fetch from 'isomorphic-fetch'; import Pokemon from './Pokemon'; class PokemonList extends React.Component { constructor(props) { super(props); this.state = { species: [], fetched: false, loading: false, }; } componentWillMount(){ this.setState({ loading : true });

I need help implementing a turnstile-like operator in RxJava (RxScala). I spent quite some time thinking about it, but I seem to be stuck. The type of the function should be the following: def turnstile[T](queue: Observable[T], turnstile: Observable[Boolean]): Observable[T] The idea is that the behavior of the operator should be very similar to a real turnstile. There are people coming ( queue ), and there is a turnstile that is either ready for accepting new single person (a true element in the turnstile, you can imagine it as a token inserted into the turnstile), or closed ( false in the

I would like to use make a series of requests to a server, but the server has a hard rate limit of 10 request per second. If I try to make the requests in a loop, it will hit the rate limit since all the requests will happen at the same time. for(let i = 0; i < 20; i++) { sendRequest(); } ReactiveX has lots of tools for modifying observable streams, but I can't seem to find the tools to implement rate limiting. I tried adding a standard delay, but the requests still fire at the same time, just 100ms later than they did previously. const queueRequest$ = new Rx.Subject<number>(); queueRequest$