spark scala example

This course focuses onSpark, the hottest, most popular and promising technology in the big Data world today. In this course, from shallow to deep, based on a large number of case studies, in-depth analysis and explanation of Spark, and will contain completely from the enterprise real complex business needs to extract the actual case. The course will cover Scala programming,

crossvalidator is very high, however, compared with heuristic manual validation, cross-validation is still a very useful parameter selection method in existence. Scala: Import org.apache.spark.ml.Pipeline Import org.apache.spark.ml.classification.LogisticRegression Import Org.apache.spark.ml.evaluation.BinaryClassificationEvaluator import org.apache.spark.ml.feature. {HASHINGTF, tokenizer} import org.apache.spark.ml.linalg.Vector import org.apache.s

3, hands-on generics in Scalageneric generic classes and generic methods, that is, when we instantiate a class or invoke a method, you can specify its type, because Scala generics and Java generics are consistent and are not mentioned here. 4, hands on. Implicit conversions, implicit parameters, implicit classes in Scalaimplicit conversion is one of the key points that many people learn about Scala, which i

3, hands on the abstract class in ScalaThe definition of an abstract class requires the use of the abstract keyword: The above code defines and implements the abstract method, it is important to note that we put the direct running code in the trait subclass of the app, about the inside of the app helps us implement the Main method and manages the code written by the engineer;Here's a look at the use of uninitialized variables in an abstract class: 4, hands-on trait in ScalaTrait

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 operation and then flatten operation: "Spark Asia-Pacific Research ser

copied this Scala version. Sparkwordcount.scala class implements the spark Chinese word segmentation statistics core function, is in the DT Big Data dream Factory Wang Jialin Teacher's sparkwordcount code based on rewrite. First, the main functional steps are moved from the companion object's main method to the Sparkwordcount class, and split into multiple methods so that the main method of the companion o

The spark kernel is developed by the Scala language, so it is natural to develop spark applications using Scala. If you are unfamiliar with the Scala language, you can read Web tutorials A Scala Tutorial for Java programmers or re

The Java version of the spark Big Data Chinese word Segmentation Statistics program was completed, and after a week of effort, the Scala version of the sparkBig Data Chinese Word segmentation Statistics program also made out, here to share to you want to learn spark friends.The following is the final interface of the program, and the Java version is not very diff

2.10, because I am through spark-core_${scala.version} is looking for spark dependency package, Some days ago a colleague followed this to build, because the version of the last spark dependent package always fail. Please check your version yourself. Here are a few small questions to keep in mind:There's going to be Src/main/

This article first describes how to configure the Maven+scala development environment in Eclipse, and then describes how to implement the spark local run. Finally, the spark program written by Scala is successfully run. At first, my Eclipse+maven environment was well configured. System: Win7 Eclipse version: Luna rele

1. PreparationThis article focuses on how to build the Spark 2.11 stand-alone development environment in Ubuntu 16.04, which is divided into 3 parts: JDK installation, Scala installation, and spark installation. JDK 1.8:jdk-8u171-linux-x64.tar.gz Scala 11.12:scala