flatmap

value of the transform is a new Rdd collection, not a single value. Call a transform method, there will be no evaluation, it only gets an RDD as a parameter, and then returns a new Rdd.Transform functions include: Map,filter,flatmap,groupbykey,reducebykey,aggregatebykey,pipe and coalesce.Action: The action operation calculates and returns a new value. When an action function is called on an Rdd object, the entire data processing query is computed at

Leskov,leibniz, awkward name, what kind of work? It happened that something was found in the Scalaz source code: Scalaz/bindsyntax.scala/** Wraps a value ' self ' and provides methods related to ' Bind '*/FinalclassBindops[f[_],a]Private[Syntax] (Val Self:f[a]) (ImplicitVal f:bind[f]) extends Ops[f[a]] {////Import liskov.def Flatmap[b] (f:a= = F[b]) =F.bind (self) (F) defGT;GT;=[B] (f:a = f[b]) =F.bind (self) (F) def∗[b] (f:a= = F[b]) =F.bind (self) (

persistence of the RDD and the chunking can be specified by the programmer. For example, you can split blocks based on the primary key of the record. Many operations can be performed on the RDD. These include Count,collect and save, which can be used to count the total number of elements, return records, and save to disk or HDFs. The lineage chart stores the conversion and movement of the RDD. A series of transformations and actions are listed in Table 2.1. Table 2.1 Transforma

 Practice 2.41This problem is actually a variant of the prime example in the book, the essence of which is the same. So we're going to do it in the same order. First, let's complete the ternary group that produces 3 distinct integers. In the previous question, however, we have written a two-tuple that can produce 2 different integers. So as long as we produce a more I, so that it and the resulting two-tuple combination, it can produce ternary group. So, let's get started.(Define (unique-triple

: The map itself uses a variable parameter, so you can assign multiple values to the map: Here's a look at the option type, option represents an optional value:Option has two subcategories: some and none, and below we look at the use of option: Next, take a look at filter processing: Here's a look at the zip operation for the collection: Here's a look at the partition of the collection: We can use flatten's multi-collection for flattening operations:

to the map: Here's a look at the option type, option represents an optional value: Option has two subcategories: some and none, and below we look at the use of option: Next, take a look at filter processing: Here's a look at the zip operation for the collection: Here's a look at the partition of the collection: We can use flatten's multi-collection for flattening operations: Flatmap is a combination of map and flatten operations, first map ope

it's so ....Come back at night suddenly saw into the huge manuscript, can't help to the three ye, and then nothing did 23 o'clock ... 85th: The powerful expressive battle of the for expression in ScalaGoal:Comparison and contact of higher order functions (FLATMAP,MAP) and for loops (initial)Gains:The back of the For loop is actually called map, but it is more likely to be programmed with a for loop if the statement is concise and expressive two.MORE:R

Today "DT Big Data DreamWorks Video" 85th: The powerful expression of the for expressions in Scala51CTO Video: http://edu.51cto.com/lesson/id-71503.html(DT Big Data Dream factory Scala all videos, PPT and code in Baidu Cloud Disk Link: Http://url.cn/fSFPjS)85 Talk about the power of Scala for expressionsThe behavior of the higher-order function specifies the details of the data processing.Case class Person (name:string,ismale:boolean,children:person*)//children variable parameterObject for_expre

Intermediate: A stream can be followed by 0 or more intermediate operations. The main purpose is to open the stream, make a certain degree of data mapping/filtering, and then return a new stream to be used by the next operation. This type of operation is lazy (lazy), which means that only calls to such a method do not actually begin the traversal of the stream. Terminal: A stream can have only one Terminal operation, and when the operation is executed, the stream is used "light" and cannot b

wonder if we've got a observable, like map() this, to flatMap() change the flow of data, why introduce subject? This is because subject's work is not a conversion connection to the content of the observable data stream, but rather the dispatch of the data flow itself between observable and observer. The light may still be vague, so let's cite an example from the RxJava Essentials:We create() created a publishsubject, the Observer successfully subscri

create a new observable sequence. We will be able to merge Observable,join observables, zip observables and combine them in several situations.Say so much, or come to a practical scene.Project actual needs Download more than one picture at a time Ideas1. Create a picture URL list data stream using the from operator2. Get a single URL and downloadThe code is as follows:Note: This code does not implement the function of saving pictures, if necessary, refer to: http://blog.csdn.net/s

The conversion of the RDDSpark generates a dependency between the RDD based on the conversion and action of the RDD in the user-submitted calculation logic, and the compute chain generates a logical DAG. Next, take "Word Count" as an example to describe the implementation of this DAG build in detail.The Spark Scala version of Word count program is as follows:1:val file = Spark.textfile ("hdfs://...") 2:val counts = File.flatmap (line = Line.split ("")) 3: . Map (Word = Word, 1)) 4: . Reducebyk

(); } });RetryWithDelayPublic class retrywithdelay implementsFunc1 Observableextends throwable, Observable > {private final int maxretries; private final int retrydelaymillis; private int retrycount; Public retrywithdelay (int maxretries, int retrydelaymillis) {this.maxretries = maxretries; This.retrydelaymillis = Retrydelaymillis; } @Override Public Observable Call (Observableextends throwable> attempts) {return attempts.

complete translation of most documents, use Gitbook to publish the initial version Directory Reactivex-What is Rx,rx's philosophy and strengths Observables-A brief introduction to the Observer model of observable Single-a special observable that emits only one value The subject-observable and observer complexes are also the bridge between them. Scheduler-describes the various asynchronous task scheduling and default schedulers All Operators List-alphabetical listing of all op

; import java. lang. mathimport java. lang. mathscala> textFile. map (line => line. split (""). size ). reduce (a, B) => Math. max (a, B) res5: Int = 163. Spark can easily execute MapReaduce streams. scala> val wordCounts = textFile.flatMap(line => line.split(" ")).map(word => (word, 1)).reduceByKey((a, b) => a + b)wordCounts: spark.RDD[(java.lang.String, Int)] = spark.ShuffledAggregatedRDD@71f027b8Here we use flatMap, map, and reduceByKey in transfor

original element converted by the func function. Filter (Func) Returns a new dataset consisting of the original elements whose return value is true after the func Function FlatMap (Func) Similar to map, but each input element is mapped to 0 to multiple output elements (therefore, the return value of the func function is a Seq rather than a single element) Sample (WithReplacement,Frac,Seed) Based on the given Random

); Getactivity (). Runonuithread (NewRunnable () {@Override Public voidrun () {imagecollectorview.addimage (bitmap); } }); }}}}}.start ();Hard to see a bit cumbersome, should be able to optimize under, don't hurry first see Rx how to achieve.Rxjava:observable.from (folders). FLATMAP (NewFunc1() {@Override PublicObservableCall (file file) {returnObservable.from (File.listfiles ()); }}). filter (NewFunc1() {@Override Pu

classLinesplitterImplementsFlatmapfunction{@Override Public voidFlatMap (String value, collectorOut ) { //normalize and split the linestring[] tokens = Value.tolowercase (). Split ("\\w+"); //Emit the pairs for(String token:tokens) {if(Token.length () > 0) {Out.collect (NewTuple2)); } } } } programming steps, and spark very similar obtain an execution environment,load/ This data,specify where to put the Results of your Computations,trigger the prog

(Event.event (). GetBody (). Array ());Return Arrays.aslist (Line.split (""));}});javapairdstream@OverridePublic tuple2return new tuple2}});javapairdstream@OverridePublic integer Call (integer v1, Integer v2) throws Exception {Return v1 + v2;}});Wordscount.print ();Jsc.start ();Jsc.awaittermination ();Jsc.close ();}}Flumeutils is used in the code. Let's dissect the flumeutils used in the code.The Flumeutil method in the above code is createstream:The following CreateStream method is actually ca

StreamingContext (sparkConf, Seconds (2) // Create a socket stream on target ip: port and count the // words in input stream of \ n delimited text (eg. generated by 'nc ') // Note that no duplication in storage level only for running locally. // Replication necessary in distributed scenario for fault tolerance. // use Socke as the data source val lines = ssc. socketTextStream (args (0), args (1 ). toInt, StorageLevel. MEMORY_AND_DISK_SER) // words DStream val words = lines.