flatmap

(!Hasnext ()) { + Thrownosuchelementexception () - } +Val answer =NextValue ANextValue =NULL at returnanswer!! - } - } -}Retrieving view recursive listsIt is useful to get a list of views and function on them. Therefore, we can first create a top-level view list and then use it recursively to retrieve ViewGroup The In-view in a hierarchical manner. let us for ViewGroup Create a new Extended Properties . Extended properties are very similar to e

,map (PID-2, PName-Digital)), (3,map (PID---3, PName-Mobile)) , (4,map (PID, 4, PName, Huawei Mate7))) Next, combine the two rdd and reduce by key (key is the parent class ID):valmerged=(left++right).reduceByKey(_++_)The contents of the merged are: Array (4,map (PID-4, PName, Huawei Mate7)), (0,map (ID-1, name-to-Life)), (1,map (ID, 2, name-and digital goods, PID--1, pname---2,map (ID-3, name---Phone, PID---2, pname-and digital Goods)), (3,map (ID-4, NA Me--Huawei Mate7, PID---3, P

)//No printDivideby (1,0)Match{ CaseSuccess (i) = println (s"Success, value is: $i") CaseFailure (s) = println (s"Failed, message is: $s")}//failed, message is:java.lang.ArithmeticException:/by ZeroReadTextFile ExampleIf the method returns successfully, the contents of the file will be printed, and /etc/passwd if an exception occurs, the error message will be printed.java.io.FileNotFoundException: Foo.bar (No such file or directory)def readTextFile(filename: String): Try[List[String]] = { Try(S

",//"Fpid2_sessionid2_a_c_d_d_b_a_d_a_f_b",//"Fpid1_sessionid1_a_f_a_c_d_a_b_a_v_a_n"))var Root:node =NULL /*** Recursive the calculated node into the tree structure * *@parampageName page name*/def Compute (pagename:string): Unit={val Currenregex= PAGENAME.R//Regular Expressions for pagesVal Containsrdd = Log.filter (_.contains (PageName)). Persist ()//contains the page name of the RDD, the next steps to use theVal CURRENTPV = Containsrdd.map (s = = {//Calculate PVCurrenregex Findallin (s)})

data and improve memory utilization. At the same time, when users are using it, they find that the API of the general Rdd is very similar and provides many of the same functional transformations. The following code, which splits the words in the text.RDDs:Val lines = Sc.textfile ("/wikipedia"= lines . FlatMap (_.split ("")) ! = "")DataSets:Val lines = SqlContext.read.text ("/wikipedia"= lines . FlatMap

isLazy operation, while the collect operation is eager operation. Basic Operations1.collect (ToList ())This action is eager operation, which produces some type of numeric list from the stream. list2.mapwe often need to convert data from one form to another, and map operations can apply this function to data in the stream, resulting in a stream of data containing the new value. list3.filterrefer to the previous code example. The function of filtering data in the data stream is the same as before

need not be ordered. Step: ① Create a Stream;② in one or more steps, you specify an intermediate operation that converts the initial stream to another stream; ③ uses a terminating operation to produce a result. The stream will not be available after that. The new method in the collection interface, you can turn any object into a stream. If it is an array, you can use Stream.of () to turn to stream. and:stream If you want to perform an action on each object in the stream, use Str

() { @Override public Bitmap call(String s) { return BitmapFactory.decodeFile(s); } }) .subscribe(new Action1 () { @Override public void call(Bitmap bitmap) {// showBitmap(bitmap); } }); Funcx function to implement type conversion. Here is a one-to-one conversion5.2

Apache project that is advertised as "lightning-fast Cluster Computing". It has a thriving open source community and is currently the most active Apache project.Spark provides a faster, more general-purpose data processing platform. Compared to Hadoop, Spark can make your program run 100 times times faster in-memory or 10 times times faster on disk. Last year, in the Daytona Graysort game, Spark beat Hadoop, which used only one-tenth of the machines, but ran 3 times times faster. Spark has also

, etc.)Next, let's start writing Java code!First step: Create a Sparkconf objectStep Two: Create SparkstreamingcontextWe create Sparkstreamingcontext objects in a configuration-based manner:The third step is to create the spark streaming input data source:We configure the data source as local port 9999 (note that port requirements are not being used):Fourth step: As with the RDD programming, we program based on Dstream, because Dstream is the template that the RDD generates, and before spark str

("Spark.testing.memory","2147480000");Javastreamingcontext JSSC = new Javastreamingcontext (conf, durations. Seconds(1));System. out. println(JSSC);Create a DStream that would connect to hostname:port, like//localhost:9999javareceiverinputdstream. Sockettextstream("Master",9999);javadstream. Textfilestream("Hdfs://master:9000/stream");Split each line into words javadstream. FlatMap(New flatmapfunctionPager(Stringx) {System. out. println(Arrays. Aslist

)->Map(pid->x(0),pname->x(1)))) The content of right is: Array (1, Map (pid-> 1, pname-> supplies), (2, Map (pid-> 2, pname-> digital products )), (3, Map (pid-> 3, pname-> mobile phone), (4, Map (pid-> 4, pname-> Huawei Mate7 ))) Next, merge the two RDDs andParent class ID) For reduce: val merged = (left++right).reduceByKey(_++_) Merged content: Array (4, Map (pid-> 4, pname-> Huawei Mate7), (0, Map (id-> 1, name-> supplies )), (1, Map (id-> 2, name-> digital products, pid-> 1, pname-> sup

多包含一个元素 asStream 1234567891011 @Test public void maptest() {String province = "Guangdong"; String City = "Zhongshan"; optionalnew Address (province, city)); //mapper:address, String //optional optional FlatMap Stream.flatMapDefinition of: Optional.flatMapDefinition of: 123456789101112 @Testpublic void flatmaptest() { String province ="Guangdong";String City ="Zhongshan";option

, the time complexity should also be O (n^2). Lexrus provides the following Swift version of the code:/// without FlatMap extension UIView { --UIView? { if let S = superview { if view.isdescendant (of:s) { Return s else { return S.commonsuperview (of:view }}} " return nil}}" In particular, if we use the Optina

ObjectiveThis article will learn about RxSwift the four conversion operators: map flatMap flatMapLatest scan MapConverts the original Observable sequence to a new sequence by using a closure function Observable .let disposeBag = DisposeBag() Observable.of(1,2,3).map({return 10 * $0}).subscribe({print($0)}).disposed(by: disposeBag)Printing results:next(10)next(20)next(30)completedFlatMapConverts a Observable sequence to

Import Akka.actor. {Props, Actorsystem}import akka.event. {loggingadapter, Logging}import akka.http.scaladsl.Httpimport akka.http.scaladsl.model._import Akka.http.scaladsl.server. {Exceptionhandler, Routeresult}import akka.http.scaladsl.server.RouteResult.Completeimport Akka.http.scaladsl.server.directives. {logentry, loggingmagnet, Debuggingdirectives}import akka.stream.ActorMaterializerimport Akka.http.scaladsl.server.directives._import Utils. Commonutilimport scala.concurrent.Futureimport sc

!")); //Orelseget is similar to OrElse, except that the default value is passed in. //Orelseget accepts a lambda expression to generate a default value. System.out.println (Empty.orelseget (), "Default Value")); System.out.println (Name.orelseget ()"Default Value")); Try { //Orelsethrow is similar to the OrElse method, except that the return value is the difference. //Orelsethrow thrown by an incoming lambda expression/method generates an exception. Empty.orelsethrow (valueabsentexception::

need to use it favoriteService to getFavoriteObject. Because you want only 5, use stream. Callback again. This time for each ID, the Get Favorite object is pushed to the front-end display in the UI thread. How do you write with a responsive stream? Use the Reactor3 library to express:userService.getFavorites(userId) // We get the stream to the favorite ID. We convert them asynchronously (ID) to an Favorite object (using flatMap

reason we use RxJava? Of course not.Is the chain programming very bad?Chained programming is also a high-frequency word that comes up every time you mention RxJava, and many people describe chained programming as the "killer" of RxJava to solve asynchronous tasks:Observable.from (folders). FlatMap (FUNC1) (folder), {Observable.from (File.listfiles ())}). Filter ((FUNC1) (file), {file.getname (). EndsWith (". png")}). Map (Func1) (file), {getbitmapfro

One, the Stream API can express complex data processing queries. Common operations are as follows Operation Type return type Function-Type interface Function descriptor Filter Middle Stream Predicate T->boolean Distinct Middle-stateful Stream Skip Middle-stateful Stream Long Limit Middle-stateful Stream Long Map Middle Stream Func