spark scala example

Recently, when processing data, you need to join the raw data with Redis data, in the process of reading Redis, encountered some problems, by the way to make a note, hoping for other students also helpful. During the experiment, it was not stressful to read Redis one at a time when the amount of data was 100,000 levels, but when the amount of data reached tens, the problem arose, even with the mappartitions of Spark. Therefore, consider using Redis's

1. Add a plug-in to SBT. SBT/0.13/plugins. SBT is not manually created. Addsbtplugin ("com. typesafe. sbteclipse" % "sbteclipse-plugin" % "2.5.0 ") Addsbtplugin ("com. GitHub. mpeltonen" % "SBT-idea" % "1.6.0 ")2. Create a project: mkdir-P helloworld/projectcm helloworld 3. Build File: VI build. SBT Name: = "spark" Version: = "1.0" Scalaversion: = "2.10.4" Librarydependencies + = "org. Apache. Spark"

Java+hadoop+spark+hbase+scalaUnder/etc/profile, add the following environment variablesExport java_home=/usr/java/jdk1.8.0_102Export JRE_HOME=/USR/JAVA/JDK1.8.0_102/JREExport classpath= $JAVA _home/lib/tools.jar: $JAVA _home/lib/dt.jar: $JAVA _home/lib: $JRE _home/libExport path= $JAVA _home/bin:/usr/local/nginx/sbin: $PATH: $JRE _home/binExport Scala_home=/usr/local/scalaExport path= $PATH: $SCALA _home/bi

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"). foreach (println)Val m = manifest[a[string]]pri

Today, Mr. Wang studied the function of pattern matching in Scala from the source point of view. Let's look at this pattern match in the source code:From the code we can see that case Registerworker (Id,workerhost,........) {} Here is a pattern match, and our pattern-matching class registerworker before it is defined, such as:We can see that our pattern matching class is already defined, and when our master receives a message from the worker, it makes

Jdk7Http://www.oracle.com/technetwork/java/javase/downloads/jdk7-downloads-1880260.htmlscala2.10.4Http://www.scala-lang.org/download/2.10.4.htmlScala for Eclipse plugin downloadSo I installed it through help->install new software. Add Input URL:The URL is connected from here (official website) to (:Http://scala-ide.org/download/prev-stable.htmlFor Scala 2.10.4Http://download.scala-ide.org/sdk/helium/e38/sca

Breezedensematrix, which is populated by the D1 element corresponding to the features corresponding position. -Val A2 =NewBDM (features.rows, Features.cols, D1) the ///:* means that each element is multiplied sequentially. Get Breezedensematrix. -Val features2 = Features:*A2 - ///Return (Breezedensematrix,breezedensematrix) constitutes the RDD. As a function return value, update addnoise. - (F._1, Features2) + } - ///Returns the result of the operation as a function return value. + addn

Transferred from: http://blog.csdn.net/huanbia/article/details/51318278Problem description When using SECURECRT to open Spark-shell, sometimes the following problems, when the wrong time to press the BACKSPACE (BACKSPACE) or delete (delete key), you cannot delete the previously deleted content. WorkaroundThe problem arises primarily in our securecrt, and we only need to change the emulation terminal in the session option to Linux.This can be cl

The definition of a type variable:The upper bound of the type variable, the lower bound of the type variable.The upper bound is the subclass of the type that must be defined, see the following example:Package com.dt.scala.type_parameterization//We need to define a generic class pair with a bigger method in the generic class that compares the arguments passed in//at which time the generic type must be a subclass of comparable. We can use Next: Type must be a parent of a defined typeWe use the: sy

file system, read the file from HDFs by default classification and function of spark operators value type transformation operator input partition and output partition one-to- one Map FlatMap mappartitions Glom input partition and output partition many-to-one type Union Cartesian input partition and output partition Many-to-many types GroupBy output partition as input partition subset type Filter distinct Subtract Sample

:" + events.length)var i = 1 for(Event events) {val Sensorinfo=NewString (Event.event.getBody.array ())//single-Line Records//single-line record formattingVal arrayfileds = Sensorinfo.split (",") if(Arrayfileds.length = = 6) {val shopid= Arrayfileds (0)//in-store numberVal Floorid= shopid.substring (0, 5)//Floor NumberVal mac = arrayfileds (1) Val ts= Arrayfileds (2). Tolong//time StampVal time = Sdf.format (TS * 1000) var hour= Sdfhour.format (TS * 1000) var minute= Sdfminute.format (TS

Label:Train Spark architecture Development!from basic to Advanced, one to one Training! [Technical qq:2937765541]--------------------------------------------------------------------------------------------------------------- ------------------------Course System:Get video material and training answer technical support addressCourse Presentation ( Big Data technology is very wide, has been online for you training solutions!) ):Get video material and

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CodePackage Spark_1import Org.apache.spark.SparkConfimport org.apache.spark.SparkContextclassWorkcounttest {// Custom Function def Jisuan () { //Data file path varloadfile="hdfs://Server ip:8020/tmp/datatest/z";　//Initialize Sparkcontext varconf=Newsparkconf (); Conf.setappname ("Workcountzhangxs"); Conf.setmaster ("spark://Server ip:7077") varSc=Newsparkcontext (conf); //Read files from HDFs varFilerdd=sc.textfile (LoadFile,2); //filter out cha

Random forest classifier: Introduction to the algorithm: Stochastic forest is an integrated algorithm of decision tree. Random forests contain multiple decision trees to reduce the risk of fitting. Stochastic forest has the characteristics of easy to explain, can deal with category, easy to expand to multiple classifications, and need not feature scaling. Random forests train a series of decision trees separately, so the training process is parallel. By adding stochastic processes to the algorit

, 0.5, 0.5) In fact, as with the slice parameter in Scala, the key is that the second parameter is until not To,slice (Start:int, Until:int) Val subvector:densevector[double] = X.slice (2, 5); println ("subvector:" + subvector); /** * Vectorized-set Operator: = (: = is a to the quantization set operation) * The slice operator constructs a read-through and Write-through view of the given elements in the underlying Or.

;=0). Mininfogain: Type: double-precision. Meaning: The minimum information gain required to split a node. Mininstancespernode: Type: integer type. Meaning: The minimum number of instances that are included in a node since splitting. Predictioncol: Type: String type. Meaning: The forecast result column name. Seed Type: Long integral type. Meaning: Random seeds. Subsamplingrate: Type: double-precision. Meaning: Learn a decision tree using the training data scale, range [0,1]. Stepsize: Type: doub