flatmap

Today is mainly about the list in Scala, list in Scala should be very important, next will explain the list of a series of operationsList map, FlatMap, foreach, filter operations explained1. About the list map, flatmap operation, Difference2. foreach, filter operation on List //a function expression is used in the map in list----but it is an operation on the list, which can represent a function or expres

For a, B, and C, the Akka is automatically executed sequentially by default, but for database operations we want to execute a few operations in order, and we need to use syntax to declareThere are two ways to declare a future relationship, the first is FlatMap, and the second is forImportScala.Concurrent.FutureImportScala.Concurrent.ExecutionContext.Implicits.GlobalImportScala.Concurrent.BlockingImportScala.Concurrent.Duration._DefF(Item: Int): Future

) taskScheduler.start() Taskschedend. Start is used to start the corresponding schedulerbackend and start the timer for detection. override def start() { backend.start() if (!isLocal conf.getBoolean("spark.speculation", false)) { logInfo("Starting speculative execution thread") import sc.env.actorSystem.dispatcher sc.env.actorSystem.scheduler.schedule(SPECULATION_INTERVAL milliseconds, SPECULATION_INTERVAL milliseconds) { checkSpeculatableTasks() }

1.Val lines=sc.textfile ("hdfs://") = = Loaded comes in as an rdd resilient distributed dataset elastic data setVal Errors=lines.filter (_.startswith ("ERROR")) # #transformationVal errors.persist () # #缓存RDDVal Mysql_error=errors.filter (_.contains ("MySQL")). Count # #actionVal Http_error=errors.filter (_.contains ("http")). Count # #action2.Map is an array of data returned for each row, Flatmap is all the data returned by an arrayVal rdd=sc.paralle

optionalpublic class Insurance {//insurance company must have a name, if there is an insurance company without a name is an error case private String name; Public String GetName () {return name;}}optional optional objects with flatmap linksWhen using a stream, the Flatmap method takes a function as an argument, and the return value of the function is another stream. This method is applied to each fu

(P.getage () > "D". Equalsignorecase (P.getname ())) { return true; } return false; }). Collect (Collectors.tolist ()); println (COLLECT1); } //Map Private Static voidMaptest (listlist) {ListNewArraylist(); for(person p:list) {Temp.add (P.getname ()); } System.out.println ("Temp=" +temp.tostring ()); Listlist. Stream (). Map (P-p.getname ()). Collect (Collectors.tolist ()); System.out.println ("Collect=" +collect); Listlist. Stream

of + repeatWhen flatMap + timer to achieve:Observable.just(2) .repeatWhen(objectObservable -> objectObservable .flatMap((Functiontimer(getRepeatInterval(), TimeUnit.SECONDS))) .subscribeWith(new DebugResourceObservergetRepeatInterval()You can dynamically return the time you setInterrupt CarouselUsing the above method can dynamically change the time of ea

the lines into words val words = Lines.as[string].flatmap (_.split ("")) //Generate Runni Ng Word Count val wordcounts = Words.groupby ("value"). Count () val query = Wordcounts.writestream . Outputmode ("complete") . Format ("console") . Start () query.awaittermination () Programming Model The key idea of structured flow is to treat real-time data stream as a continuous additional table basic concept The input data is treated as an input

taskScheduler = SparkContext.createTaskScheduler(this, master, appName) taskScheduler.start()Taskscheduler.start purpose is to start the corresponding schedulerbackend and start the timer to detectoverride def start() { backend.start() if (!isLocal conf.getBoolean("spark.speculation", false)) { logInfo("Starting speculative execution thread") import sc.env.actorSystem.dispatcher sc.env.actorSystem.scheduler.schedule(SPECULATION_INTERVAL milliseconds, SPECULATION_INTERVAL milliseconds) {

High-order function filter Source analysisPackage Com.dt.scala.forexpressionCase class Person (name:string, Ismale:boolean, children:person*)Object For_expressive {def main (args:array[string]) {Val Lauren = person ("Lauren", false)Val Rocky = person ("Rocky", True)Val Vivian = person ("Vivian", False, Lauren, Rocky)Val persons = List (Lauren, Rocky, Vivian)Val result = Persons filter (person =!person.ismale) FlatMap (person =(Person.children map (chi

#lang Racket (define (accumulate op initial seq) (if(NULL?seq) Initial (OP (car seq) (accumulate op initial (cdr seq)))); Accumulate (define (append Lst1 lst2)) (if(NULL?lst1) Lst2 (cons (Car lst1) (Append (Cdr lst1) lst2 ))); Append (define (enumerate-interval i j) (if(>i j)'()(Cons I (Enumerate-interval (+1i) (J ))); enumerate-interval (define (unique-pairs N) (Accumulate append'()(Map (Lambda (i) (Map (Lambda (j) (List I J)) (Enumerate-interval (+ I1) (enumerate)) (n) )-interval1N)); map); ac

good thing, don't be excited, there are many other good things waiting for us to explore.3. Transform class operation appended FlatmapSuddenly, the boss has a new demand. We just wrote a Web request. And then take out all the requested data, and now we don't need that much data, we just need the City field and WD field (that's what we've done with the last piece of code), and this time I'm not just giving you a URL. But to give you multiple URLs, write it.Bosses are always like this.A lot of pe

); }Else{ArrayListArticle>Articles= NewArrayList(); Articles.Add (article); Result.Put (article.Getauthor (), articles); } }returnResult;}Can we find a simple solution that uses flow operations to solve this problem?publicMapStringListArticle>> groupByAuthor() { return articles.stream() .collect(Collectors.groupingBy(Article::getAuthor));}Very good! Using the Groupingby operation and the Getauthor method, we get a cleaner, more readable code.Now we look at all the different tags i

Source:linkWe'll looking some opreators for combining stream in RxJS: Merge Combinelatest Withlatestfrom Concat Forkjoin Flatmap/switchmap Merge:Observable.mergeBehaves like a "logical or" to has your stream handle one interaction or another.Let btn1 = Document.queryselector ("#btn1"= Document.queryselector ("#btn2"= Rx.Observable.fromEvent (BTN1, "click"= Rx.Observable.fromEvent (btn2, "click"= btn1click$.map (ev ) =

the same type and emit the same thing.What are they being used to do?Since they do not change the flow itself, there must be other uses. I am here to state three examples of what you can accomplish with these methods.Use Doonnext () to debugUse Doonerror () as Error handling in Flatmap ().Use Doonnext () to save/cache network ResultsSo let's take a concrete look at how to use these examples.Using Doonnext () debuggingWith Rxjava, sometimes you wonder

operation, when the RDD to perform the conversion operation, The actual calculation is not executed, and only when the RDD executes the action operation will the calculation task be submitted and the corresponding calculation operation performed. The difference between conversion and action is also very simple, the conversion operation is to create a new RDD operation from an RDD, and the action is to do the actual calculation.Below is an introduction to the basic Operation API for RDD:

the IO operation. The await function seems too complex: The streamlined expression of await def Eval[f[_],a] (Fa:f[a]): process[f,a] =//Operation F[a] Await[f,a,a] (FA) {case left (err) = Halt (Err) Case right (a) = Emit (A, Halt (End)) } () def Evalio[a] (ioa:io[a]) = Eval[io,a] (IOA) // Operation Io[a] //Determining the Operation def Eval_[f[_],a,b] (Fa:f[a]): process[f,b] = Eval[f,a] (FA). Drain[b]//operation F[a] until terminatedso the arithmetic IO only needs to write this

Say Business First,In the Flatmap phase, the current indicator will be in Redis to verify whether the data is a new user, if it is a new user, then statistics, statistics will add the new user to our Redis container ...This business is actually very simple ... But the pit itself is, flatmap after the RDD, will be used by two different action ...And then... I have found that the second action is never a new

query = Arr.contains (s tr.containsstring) print (query)Read a fileLet path = Nsbundle.mainbundle (). Pathforresource ("Filter", OfType: "RTF")Let lines = try? String (contentsoffile:path!). characters.split{$0 = = "\ n"}.map (String.init)For item in lines! {Print (item)}Happy birthday to you Let name = "Forrest" (1...4). Foreach{print ("Happy Birthday" + (($ = = 3)? " Dear \ (Name) ":" To You ")}This code will "Happy Birthday to You" song lyrics output to the conso

Higher-order function Map,flatmap,reduce,filter can simplify the amount of code, can be used. 1, the map function, is the set and the String class instance method, the function is the diachronic modification Item;map does not modify the instance value, and creates a new copy. Excerpted from official documents: let-cast = ["Vivien", "Marlon", "Kim", "Karl"] let lowercasenames = cast.map {$0.lowercased ()} //' Lowercasename s ' = = ["Vivien", "Marlon"