flatmap

elements, and the return value is the new RddBayiJavarddLines the. Map (NewFunction() { the PublicInteger Call (String s) { - returns.length (); - } the }); the //reduce aggregates, but the incoming function is two parameter inputs that return a value that must satisfy the Exchange law and the binding law the intTotallength =linelengths the. Reduce (NewFunction2() { - Publicinte

Result simply by extracting the inner obj ECT inside Value The, or the Error .A flatten function can be found and other contexts. For example, one can an flatten array of arrays into a contiguous, one-dimensional array.With this, we can implement our Result transformation by combining map and flatten :Let Stringresult = ResultThis was so common, which you'll find this defined in many places flatMap as or flattenMap , which we could implement for Resu

; name.toUpperCase()) .orElse(null); FlatMap The Flatmap method is similar to the map method, except that the return value of the mapping function is different. The mapping function return value of the map method can be any type T, and the mapping function of the Flatmap method must be optional.upperName = name.flatMap((value) -> Optional.

stream, and the elements in the newly generated stream are of type int. There are three variants that can eliminate the extra cost of automatic boxing/unboxing; Map method Diagram:4. Flatmap: And map Similar, the difference is that each element of its conversion to get the stream object, will be the target stream elements compressed into the parent set; Flatmap method Diagram:5. Peek: Generates a new strea

=> (x._2,x._1)). Sortbykey (FALSE). Map (x=> (x._2,x._1)). Saveastextfile ("hdfs://h201:9000/output1" )The difference between FLATMAP () and map ()Both FlatMap () and map () do the same thing for each line of input but produce different results;Example Sample:Hello,worldHello,hadoopHello,oracleimport the file into RDD = "var file=sc.textfile ("hdfs://xxx:9000/xx.txt")the same is separated by a comma using t

over-current:. Skip (2) 4. Map Each element in the stream executes a function to convert the element to another type of output. The stream delivers each element to the map function, executes the lambda expression in the map, and finally stores the execution result in a new stream. For example, convert each integer type element in the list to the string type after auto-increment. List List . Map (X-> string. valueof (++ X )). Collect (collectors. tolist ()); 5. Merge multiple st

Step:Call start and awaittermination of the portal object to begin reading the data streamWordCount word counts are completed using different spark streaming processing methods, respectivelyHDFs file test Object hdfswordcount { defMain (args:array[string]) {//Parameter Settings if(Args.length 2) {System.err.println ("Usgae: ) System.exit (1) }//First step: Create StreamingContext entry Valsparkconf =NewSparkconf (). Setmaster (Args (0). Setappname ("Hdfswordcount")Valstreaming =NewStr

(6) Transformation operation, through the external different RDD expression form to achieve internal data processing process . This type of operation does not trigger the execution of the job, and is often referred to as a lazy operation. Most operations generate and return a new RDD, in which case Sortbykey will not produce a new rdd. 1) Map function, a row of data after the Map function processing or a row of data Functions the map function on all elements of the RDD and returns

frame design: Trait Either[+e,+a] Case class Left[+e] (value:e) extends either[e,nothing] case class Right[+a] (VALUE:A) Extends Either[nothing,a]The above visible either need to handle two types of E and a:e representing the exception type, and a for the calculation type. As with option, either also has two states: the left represents the inability to complete the calculation, the return value E is a description of the exception condition, and right represents the normal completion of the

[option[i]],//resource content read function Trans:process[i,o]// output mode) extends Source[o] {def | Gt [O2] (P:process[o,o2]): source[o2] = //Implement abstract function resourcer (Acquire,release,step,trans |> p)// Each input produces a resourcer. Its trans and P pipe docking}This is a read-only data source. We see that all of the actions are embedded in the IO type, which can delay the generation of side effects to some source interpreter. Here we can just use the simplest IO to illust

Pit 1Observable.just ("Hello world!"). Compose ( This.binduntilevent (Activityevent.pause)). FlatMap (NewFunc1() {@Override PublicObservableCall (String s) {returnObservable.interval (1, Timeunit.seconds); }}). Subscribe (NewAction1() {@Override Public voidCall (Long along) {log.i (TAG,"..... oh,oh,no!! ..........." +along); } });When the activity life cycle pausedWill it be executed?? Will be ...If you want to not do it all:Observable.just

The Spark operator can be broadly divided into the following two categories:1)Transformation Transform/conversion operator : This transformation does not trigger the submission of the job, the completion of the job intermediate process processing.The transformation operation is deferred, meaning that the conversion from one RDD conversion to another is not performed immediately, and the operation is not actually triggered until there is an action action. 1. Map operator2,

/tmp directory spark+ a random number directory for driver itself, driver created TMP directory, Httpfileserver created directory spark-local directory to hold shuffle output and cache content generated during executor executionTemporary files in the runWhen the executor is running, it generates shuffle Output, and if the RDD is cache, it is possible to spit the contents of the RDD onto the disk. All of this means that you need to have a folder to hold these things. The spark-local-*-like direct

Observable .interval(3, TimeUnit.SECONDS) .observeOn(AndroidSchedulers.mainThread()) .subscribe(new Action1 () { @Override public void call(Long s) { Log.i("info",s); } } );

For if: ElseConst showpage () { if(current_user) { return renderpage (current_user); Else { return showlogin ();} } Const showpage () { fromnullable (current_user) . Fold (Showlogin, renderpage)}ConstGetprefs = user + = { if(user.premium) {returnloadprefs (user.preferences)}Else { returndefaultprefs; }}ConstGetprefs = user + =(User.premium? Right (user): Left ('Not Premium'). Map (P=user.preferences). Fold (x=defaultprefs, x=loadprefs (x))ConstStreetName = user + = { ConstA

collection type, with the following example:let collections = [[5,2,7],[4,8],[9,1,3]] let flat = collections.flatMap { $0 } // [5, 2, 7, 4, 8, 9, 1, 3]In addition, for the collection type of the optional type, the function can also remove the null value:let codes: [String?] = ["Big",nil,"nerd",nil,"coding"] let values = codes.flatMap {$0} // ["Big","nerd","coding"]The place that really embodies the power of FlatMap is combined with one of the above f

): RDD [T]) RDD [T]FlatMap (f: T) Seq [U]): RDD [T]) RDD [U]Sample (fraction: Float): RDD [T]) RDD [T] (Deterministic sampling)GroupByKey (): RDD [(K, V)]) RDD [(K, Seq [V])]ReduceByKey (f: (V; V): RDD [(K, V)]) RDD [(K, V)]Union (): (RDD [T]; RDD [T]) RDD [T]Join (): (RDD [(K, V)]; RDD [(K, W)]) RDD [(K, (V, W)]Cogroup (): (RDD [(K, V)]; RDD [(K, W)]) RDD [(K, (Seq [V], seq [W])]CrossProduct (): (RDD [T]; RDD [U]) RDD [(T, U)]MapValues (f: V) W): RDD

large. It is best to put the locally loaded cache image in one thread for execution, the code is getting messy. Let's see how RxJava is used? Protected void onCreate (Bundle savedInstanceState) {super. onCreate (savedInstanceState); setContentView (R. layout. activity_welcome); ImageView view = (ImageView) findViewById (R. id. iv_welcome); view. setImageResource (R. mipmap. welcome); Observable. mergeDelayError (// load the local cached image loadBitmapFromLocal () in the new thread (). subscri

requirement to generate the following character sequences or more: 1 A .. Z, AA, AB, .. ZZ, AAA, AAB, .. ZZZ Therefore, we will use rangeClosed () again (): 1 2 3 4 // 1 = A .. Z, 2 = AA .. ZZ, 3 = AAA .. ZZZ Seq.rangeClosed(1, 2) .flatMap(length -> ...) .forEach(System.out::println); This method generates a separate stream for each length in the range [1 .. 2], and then combines the streams into a

OptionalOptional Brief introductionIn the literal sense, the meaning should be optional. At first I thought it was a default parameter usage like Python, and the result semantics was that a value might or might not be (null).The sensation of name acquisition is not very intuitive. I don't think it's better to call nullable? Python: DEF fn (a= ' default_value '): print (a) pass Optional methodOptional there is no public constructor method, only the static factory method: optional The differ