flatmap

问题 Given the following piece of code public static void main(String[] args) { long start = System.currentTimeMillis(); Flux.<Long>generate(s -> s.next(System.currentTimeMillis() - start)) .flatMap(DemoApp::delayedAction) .doOnNext(l -> System.out.println(l + " -- " + (System.currentTimeMillis() - start))) .blockLast(Duration.ofSeconds(3)); } private static Publisher<? extends Long> delayedAction(Long l) { return Mono.just(l).delayElement(Duration.ofSeconds(1)); } One can see from the output that

问题 For example, Vector(Some(1), Some(2), Some(3), None).flatMap{ n => n } produces a Vector(1, 2, 3) instead of giving an error. As I have seen in other languages, flatMap is used when you have a mapper function that produces nesting so I would expect this to be a valid flatMap : Vector(1, 2, 3).flatMap{ eachNum => Vector(eachNum) } My mapper function produces a Vector which would cause nesting (i.e. Vector(Vector(1), Vector(2), Vector(3), Vector(4)) ) if I used a map due to the container

问题 I'm getting an error when running flatMap() on a list of objects of a class. It works fine for regular python data types like int, list etc. but I'm facing an error when the list contains objects of my class. Here's the entire code: from pyspark import SparkContext sc = SparkContext("local","WordCountBySparkKeyword") def func(x): if x==2: return [2, 3, 4] return [1] rdd = sc.parallelize([2]) rdd = rdd.flatMap(func) # rdd.collect() now has [2, 3, 4] rdd = rdd.flatMap(func) # rdd.collect() now

问题 Simple code that should check user by pass, user is active and after that update last login datetime. def authenticate() = Action.async { implicit request => loginForm.bindFromRequest.fold( errors => Future.successful(BadRequest(views.html.logon(errors))), usersData =>{ val cursor = this.collection.find(BSONDocument("name" -> usersData._1)).one[Account].map(_.filter(p=>p.password == hashedPass(usersData._2, usersData._1))) cursor.flatMap(p => p match { case None => Future.successful

问题 in the app I am currently working on I use retrofit to create an Observable <ArrayList<Party>> . Party has a hostId field as well as a field of type User which is null at the point of creation by Retrofits GsonConverter. I now want to use hostId to make a second request getting the user from id and adding the User to the initial Party . I have been looking into flatmap but I haven't found an example in which the first observable's results are not only kept but also modified. Currently, to get

问题 For the following codes using spark java API: JavaPairRDD<TypeOne,Long> pairs = originalRows.flatMapToPair(new PairFlatMapFunction<OriginalType,TypeOne,Long>() it takes the RDD , named OriginalType and maps it into pairs with key type of TypeOne . I am wondering that is it possible to takes OriginalType and during the map step, maps it into two types of key? Like TypeOne and TypeTwo . Or I must use two map steps to realize this... 回答1: You can create an Interface or Generic class that both

问题 Swift documentation of flatMap reads: Returns an array containing the non-nil results of calling the given transformation with each element of this sequence. In the following examples when return type of ElementOfResult is left to the compiler to infer flatMap works as documented, yet on line 5 when ElementOfResult is specified, thus inferred to an Optional<String> type it seems that flatMap stops filtering out nil 's. Why is it doing that? ~ swift Welcome to Apple Swift version 3.0.2

问题 I have a list of Objects and each object has inside it a list of other object type. I want to extract those lists and create a new list of the other object. List1:[Obj1, Obj2, Obj3] Obj1.myList = [O1, O2, O3] Obj2.myList = [O4, O5, O6] Obj3.myList = [O7, O8, O9] I need this: L = [O1, O2, O3, O4, ...., O9]; I tried extend() and reduce() but didn't work bigList = reduce(lambda acc, slice: acc.extend(slice.coresetPoints.points), self.stack, []) P.S. Looking for python flatten a list of list didn

问题 getting started with spark I would like to know how to flatmap or explode a dataframe. It was created using df.groupBy("columName").count and has the following structure if I collect it: [[Key1, count], [Key2, count2]] But I would rather like to have something like Map(bar -> 1, foo -> 1, awesome -> 1) What is the right tool to achieve something like this? Flatmap, explode or something else? Context: I want to use spark-jobserver. It only seems to provide meaningful results (e.g. a working

问题 let arr: [Int?] = [1,2,3,4,nil] let arr1 = arr.flatMap { next in next } // arr1: [1,2,3,4] let arr2: [Int?] = arr.flatMap { next -> Int? in next } // arr2: [Optional(1), Optional(2), Optional(3), Optional(4)] I'm confused by these code, why do they make a difference? update: please see these codes, I let arr: [Int?] = [1,2,3,4,nil] let arr1: [Int?] = arr.flatMap { next in next } // arr1: [Optional(1), Optional(2), Optional(3), Optional(4), nil] let arr2: [Int?] = arr.flatMap { next -> Int? in