spark scala example

First, the Java Way development1, pre-development preparation: Assume that you set up the spark cluster.2, the development environment uses Eclipse MAVEN project, need to add spark streaming dependency.3. Spark streaming is calculated based on spark core and requires attention:Set local master, you must configure at le

Preface:　　　　Recently in the research Spark also has Kafka, wants to pass the data which the Kafka end obtains, uses the spark streaming to carry on some computation, but constructs the entire environment is really not easy, therefore hereby writes down this process, shares to everybody, hoped that everybody may take a little detour, can help everybody!Environment Preparation:operating system: ubuntu14.04 LT

Lesson 2nd: Scala's object-oriented mastery and spark source readingContents of this issue:1 Scala's class, object in real combat2 abstract classes and interfaces in Scala3 comprehensive case and spark source code analysisOne: Define ClassClass hiscala{private var name = "Spark"Def sayname () {println (name)}def getName = Name}In

[Introduction to Apache spark Big Data Analysis (i) (http://www.csdn.net/article/2015-11-25/2826324) Spark Note 5:sparkcontext,sparkconf Spark reads HBase Scala's powerful collection data operations example Some RDD operations and transformations in spark # Create Textfilerd

Spark example and spark example 1. Set up the Spark development environment in Java (fromHttp://www.cnblogs.com/eczhou/p/5216918.html) 1.1 jdk Installation Install jdk in oracle. I installed jdk 1.7. After installing the new system environment variable JAVA_HOME, the variabl

Java switch-case (pair value)Scala is not only for values, but also for types, collections (map,list metadata matching), Object,classScala uses a lot of pattern matching (Match case)Scala's pattern match, which differs from the Java switch case:1. Not only can match the value, can match type2. Can match the collection of arraysAn array of the same array, the same length, and an array beginning with an elementAutomatic variable assignment for arrays of

After building the Scala and Spark development environment, I couldn't wait to run the Scala program based on Spark, so I found a link to Spark's official website (http://spark.apache.org/docs/latest/ quick-start.html), describes how to run a Scala program. The detailed proc

("")))Val hamfeatures = ham.map (email + tf.transform (email.split ("")))Create labeledpoint datasets for positive (spam) and negative (ham) examples.Val positiveexamples = spamfeatures.map (features = Labeledpoint (1, features))Val negativeexamples = hamfeatures.map (features = Labeledpoint (0, features))Val Trainingdata = positiveexamples + + negativeexamplesTrainingdata.cache ()//cache data since Logistic Regression is an iterative algorithm.Create a Logistic Regression learner which uses th

Learn Spark 2.0 (new features, real projects, pure Scala language development, CDH5.7)Share--https://pan.baidu.com/s/1jhvviai Password: SirkStarting from the basics, this course focuses on Spark 2.0, which is focused, concise and easy to understand, and is designed to be fast and flexible.The course is based on practical exercises, providing a complete and detail

was successful. This is followed by some basic grammar learning in Scala. val Variable declaration: declares a Val variable to hold the expression's computed result, which is immutable.Val result = 1 + 1 Subsequent constants are available for continuation, Eg:2 * result However, the Val constant declaration does not change its value, otherwise it returns an error. var variable declaration: DECLARE var variable, can change the refere

Contents of this lesson:A thorough explanation of functional programming in 1:scalaScala functional programming in the 2:spark source code3: Cases and jobsFunctional Programming Begins:def fun1 (name:string) {println (name)}Assign a function name to a variable, then this variable is a function.Val Fun1_v = fun1_Visit Fun1_v ("Scala")Result: Scalaanonymous function: Parameter name = = point to function bodyV

://master. Hadoop:8390/gradient_data/spam.data.txt ") Val Lines=Text.map { line=Line.split (" "). Map (_.todouble)} Val points= Lines.map (Parsepoint (_))//(Parsepoint (_)) looks the samevar w = Densevector.rand (Lines.first (). size-2) Val iterations= 100 for(I to iterations) {val Gradient= Points.map (P = = (1/(1 + exp (-P.Y * (w dot p.x)))-1) * P.Y *p.x). Reduce (_+_) W-=gradient} println ("Finish data loading, W num:" + w.length + "; W: "+W)}}Then on the M42n05 machine, the first use

Listen to Liaoliang's Spark 3000 disciple series lesson four Scala pattern matching and type parameters, summarized as follows:Pattern matching:def data (array:array[string]) {Array match{Case Array (a,b,c) = println (A+b+c)Case Array ("Spark", _*) =//matches an array with spark as the first elementCase _ = ...}}After-

* @param spark*/ Private Static voidDynamictransform (Sparksession Spark) {Javardd"StuInfo.txt"). Javardd (); Javardd{string[] parts= Line.split (","); String Sid= parts[0]; String sname= parts[1]; intSage = Integer.parseint (parts[2]); returnrowfactory.create (SID, Sname, Sage ); }); ArrayListNewArraylist(); Structfield Field=NULL; Field= Datatypes.createstructfield ("Sid"

feature. Also, these features are mutually exclusive, with only one activation at a time. As a result, the data becomes sparse.The main benefits of this are: Solves the problem that the classifier does not handle the attribute data well To some extent, it also plays an important role in expanding features. Import Org.apache.spark.ml.feature._Import Org.apache.spark.ml.classification.LogisticRegressionImport Org.apache.spark.mllib.linalg. {Vector, Vectors}Import Org.apache.spar

next three numbers, the first two means that SQL parameters, must be a long type, and must have, this is the spark source code requirements, if there is no long type of condition, you can use 1=1 this parameter (the third parameter is 1) The third parameter represents a partitioned query, for example, given the first two parameters of 1 and 20, the third parameter is 2, then SQL executes two times, the fir

First, spark frame previewMainly have Core, GraphX, MLlib, spark streaming, spark SQL and so on several parts.GRAPHX is a graph calculation and graph mining, in which the mainstream diagram calculation framework now has: Pregal, HAMA, giraph (these parts are in the form of hyper-step synchronization), and Graphlab and Spark