classtag

Package Com.leegh.parameterizationImport Scala.reflect.ClassTag/*** @author Guohui Li*/Class A[t]Object Manifest_classtag {def main (args:array[string]): Unit = {def Arraymake[t:manifest] (first:t, second:t) = {Val r = new Array[t] (2); R (0) = first; R (1) = second; R}Arraymake (1, 2). foreach (println)/** Common classtag*/def Mkarray[t:classtag] (elems:t*) = Array[t] (elems: _*)Mkarray (a). foreach (println)Mkarray ("Japan", "Brazil", "Germany"). fo

1> mainfest Context Definition 1. In Scala, arrays have to be typed, and if they are directly generic, they will get an error, which introduces the manifest context definition, requires a mainfest[t] object, and Mainifest[t] has an implicit value, 2. If Makepair is called, the compiler navigates to an implicit manifst[int] and actually calls Makepair (2,3) (intmanifest), which is called by the new Array (2) (intmanifest), Returns the base type of the array int[2] 3. An implicit manifest type is

all elements of this RDD.*/Def map [U: ClassTag] (f: T => U): RDD [U] = new MappedRDD (this, SC. clean (f ))/*** Return a new RDD by first applying a function to all elements of this* RDD, and then flattening the results.*/Def flatMap [U: ClassTag] (f: T => TraversableOnce [U]): RDD [U] =New FlatMappedRDD (this, SC. clean (f ))/*** Return a new RDD containing only the elements that satisfy a predicate.*/De

Zip def Zip[u] (Other:rdd[u]) (implicit arg0:classtag[u]): rdd[(T, U)] The ZIP function is used to synthesize two RDD groups into an rdd in the form of Key/value, where the partition number of the default two Rdd and the number of elements are the same, otherwise an exception will be thrown. scala> var rdd1 = Sc.makerdd (1 to 10,2) rdd1:org.apache.spark.rdd.rdd[int] = parallelcollectionrdd[0] at MakeRDD at:2 1 scala> var rdd1 = Sc.makerdd (1 to 5,2)

mappartitions is applied to each partition, that is, the contents of each partition are treated as a whole.Its function is defined as:def mapPartitions[U: ClassTag](f: Iterator[T] => Iterator[U], preservesPartitioning: Boolean = false): RDD[U]F is the input function, which processes the contents of each partition. The contents of each partition will be passed as iterator[t] to the input function f,f the output is iterator[u]. The final Rdd is combine

path where the image data resides# mnist-image/train/# mnist-image/test/# CLA: Category name# 0,1,2,..., 9# return: All data for a category----[Sample quantity * (image width × image height)] Matrixdef read_and_convert (imgfilelist):DataLabel = [] # Store class labelDatanum = Len (imgfilelist)Datamat = Np.zeros ((datanum, +)) # Datanum * 400 matrixFor I in Range (Datanum):IMGNAMESTR = Imgfilelist[i]Imgname = Get_img_name_str (imgnamestr) # Gets the number _ instance number. png#print ("Imgname:

= implicitly[classtag[nullwritable]]ValTextclasstag = Implicitly[classtag[text]]ValR = This. mappartitions {iter =ValText =NewText () iter.map {x = Text.set (x.tostring) (Nullwritable.get (), Text)}} RDD.RDDTOPAIRR Ddfunctions (R) (Nullwritableclasstag, Textclasstag,NULL). Saveashadoopfile[textoutputformat[nullwritable, Text]] (path)}/** * Save This RDD as a compressed text file, using string representatio

regular expression used to break down selectors, see below childre=/^\s*>/, Classtag= ' Zepto ' + (+NewDate ())functionProcess (SEL, FN) {//The decomposition selector is three parts, the first part is the selector itself, the second part is the value of the selector, the function name in the filter, and the third part is the parameter ///For example: (1)filterre.exec (": eq (2)") //Results obtained:[": Eq (2)", "", "eq", "2"] //(2)filterre.e

: Step 2:val splittedtext = rawfile.flatmap (line = Line.split (""))Flatmap converted the original mappedrdd into Flatmappedrdddef flatMap[U: ClassTag](f: T => TraversableOnce[U]): RDD[U] = new FlatMappedRDD(this, sc.clean(f))Step 3:val WordCount = splittedtext.map (Word = + (Word, 1))Using Word to generate cor

the edge is located.The first step based on the point hash to find the edge of the location of the process is similar to a query to build the index.With the official map of understanding:Efficient Data StructuresThere is a better data structure support for native type storage and reading and writing, typically the map used in edgepartition :/** * A fast hash map implementation for primitive, non-null keys. This hash map supports * insertions and updates, but not deletions. This map is about an

Mappartitionswithcontext, which can pass some state information from the process to the user-specified input function. There is also Mappartitionswithindex, which can pass the index of the partition to the user-specified input function.MapvaluesMapvalues as the name implies is that the input function is applied to the Kev-value value in the RDD, the key in the original RDD remains unchanged, and the new value is composed of the elements in the new Rdd. Therefore, the function applies only to th

=> line. Split ("")) Flatmap converts the original mappedrddFlatmappedrdd def flatMap[U: ClassTag](f: T => TraversableOnce[U]): RDD[U] = new FlatMappedRDD(this, sc.clean(f))Step 3: Val wordcount = splittedtext. Map (WORD => (word, 1 )) Use Word to generate corresponding key-value pairs. The flatmappedrdd in the previous step is converted to mappedrdd.Ste

Transfer from: https://blog.csdn.net/t1dmzks/article/details/70557249 Subtractbykey function definition def Subtractbykey[w] (other:rdd[(k, W)]) (implicit arg0:classtag[w]): rdd[(k, V)] def Subtractbykey[w] (other:rdd[( K, W)], Numpartitions:int) (implicit arg0:classtag[w]): rdd[(k, V)] def Subtractbykey[w] (other:rdd[(K, W)], P:parti Tioner) (implicit arg0:classtag