安装配置IntelliJ IDEA

最新推荐文章于 2025-09-23 09:00:00 发布

转载最新推荐文章于 2025-09-23 09:00:00 发布 · 96 阅读

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原文链接：https://my.oschina.net/kyo4321/blog/3064355

该博客主要涉及IntelliJ IDEA的配置内容，虽未给出具体配置信息，但明确指向了IntelliJ IDEA这一开发工具的配置相关，属于信息技术领域开发工具范畴。

##参考资料：
https://blog.youkuaiyun.com/Haidaiya/article/details/81230636
https://www.cnblogs.com/yjmyzz/p/4694219.html
https://my.oschina.net/u/3825598/blog/1789861
https://www.jianshu.com/p/160cefd354a2

具体步骤如下：
1、安装IntelliJ IDEA 
2、安装Java SKD
3、安装Scala
4、添加配置文件,如hadoop-common-2.6.0-bin-master.zip等
5、测试HelloWorld代码demo

//demo one

package com.xx.HelloWorld


object Test {
  def sum(args: Int*) = {
    var result = 0
    for (arg <- args) result += arg
    result
       }

  def main(args: Array[String]) {
    val mylist = List(1,2,3,4,5)

    println("Final Results: " + sum(mylist: _*))

  }
}

//demo two
package com.xx.feature_engineer

import org.apache.spark.SparkConf
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types._

object demo {
  def main(args: Array[String]) {
    val sparkConf = new SparkConf().setAppName("ReadData").setMaster("local").set("spark.sql.warehouse.dir", "file:///C:/Users/username/IdeaProjects/spark_demo/spark-warehouse")
    val sparkSession = SparkSession.builder().config(sparkConf).getOrCreate()
    val input_path = "D:/spark_data/datatypes.csv"
    val colTypes = sparkSession.sqlContext.read.format("csv")
                                           .option("sep", ",")
                                           .option("inferSchema", "false")
                                           .option("header", "true")
                                           .load(input_path)

    //println(colTypes.printSchema)
    println("*******************************")
    //println(colTypes.show)

    val df = sparkSession.sqlContext.read.format("csv")
      .option("sep", ",")
      .option("inferSchema", "false")
      .option("header", "true")
      .load("D:/spark_data/train.csv")

    val typeMap = colTypes.rdd.collect().map(line => (line.getString(0) -> line.getString(1))).toMap
    val cols = df.columns.map{f=> if(typeMap.getOrElse(f,"")=="continuous") col(f).cast(IntegerType) else col(f)}
    val results = df.select(cols: _*)
    results.dtypes.map{line=>println(line)}

    sparkSession.stop()

  }
}

转载于:https://my.oschina.net/kyo4321/blog/3064355