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Spark2 加载保存文件,数据文件转换成数据框dataframe

时间:2016-10-31 00:00:00      阅读:258      评论:0      收藏:0      [点我收藏+]

标签:csv   end   build   from   rom   tostring   array   context   datafile   

hadoop fs -put /home/wangxiao/data/ml/Affairs.csv /datafile/wangxiao/

hadoop fs -ls -R /datafile
drwxr-xr-x   - wangxiao supergroup          0 2016-10-15 10:46 /datafile/wangxiao
-rw-r--r--   3 wangxiao supergroup      16755 2016-10-15 10:46 /datafile/wangxiao/Affairs.csv
-rw-r--r--   3 wangxiao supergroup      16755 2016-10-13 21:48 /datafile/wangxiao/Affairs.txt


// affairs:一年来独自外出旅游的频率 
// gender:性别 
// age:年龄 
// yearsmarried:婚龄 
// children:是否有小孩 
// religiousness:宗教信仰程度(5分制,1分表示反对,5分表示非常信仰)
// education:学历
// occupation:职业(逆向编号的戈登7种分类) 
// rating:对婚姻的自我评分(5分制,1表示非常不幸福,5表示非常幸福)

import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.DataFrame
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
import org.apache.spark.sql.Encoder

object ML1 {
  def main(args: Array[String]) {

    val spark = SparkSession.builder().appName("Spark SQL basic example").config("spark.some.config.option", "some-value").getOrCreate()

    // For implicit conversions like converting RDDs to DataFrames
    import spark.implicits._

    // 创建数据框
    // val data1:DataFrame=spark.read.csv("hdfs://ns1/datafile/wangxiao/Affairs.csv")

    val data1: DataFrame = spark.read.format("csv").load("hdfs://ns1/datafile/wangxiao/Affairs.csv")

    val df = data1.toDF("affairs", "gender", "age", "yearsmarried", "children", "religiousness", "education", "occupation", "rating")

    df.printSchema()

    //##############################################
    // 指定字段名和字段类型
    case class Affairs(affairs: Int, gender: String, age: Int,
                       yearsmarried: Double, children: String, religiousness: Int,
                       education: Double, occupation: Double, rating: Int)

    val res1 = data1.rdd.map { r =>
      Affairs(r(0).toString().toInt, r(1).toString(), r(2).toString().toInt,
        r(3).toString().toDouble, r(4).toString(), r(5).toString().toInt,
        r(6).toString().toDouble, r(7).toString().toDouble, r(8).toString().toInt)
    }.toDF()

    res1.printSchema()
    
    //################################################
    //创建RDD
    val data2: RDD[String] = spark.sparkContext.textFile("hdfs://ns1/datafile/wangxiao/Affairs.txt")

    case class Affairs1(affairs: Int, gender: String, age: Int,
                        yearsmarried: Double, children: String, religiousness: Int,
                        education: Double, occupation: Double, rating: Int)

    // RDD转换成数据框
    val res2 = data2.map { _.split(" ") }.map { line =>
      Affairs1(line(0).toInt, line(1).trim.toString(), line(2).toInt,
        line(3).toDouble, line(4).trim.toString(), line(5).toInt,
        line(6).toDouble, line(7).toDouble, line(8).toInt)
    }.toDF()

    //###############################################
    // 创建视图
    df.createOrReplaceTempView("Affairs")

    // 子查询
    //val df1 = spark.sql("SELECT * FROM Affairs WHERE age BETWEEN 20 AND 25")
    val df1 = spark.sql("select gender, age,rating from  ( SELECT * FROM Affairs WHERE age BETWEEN 20 AND 25 ) t ")

    df1.show

    // 保存数据框到文件
    df.select("gender", "age", "education").write.format("csv").save("hdfs://ns1/datafile/wangxiao/data123.csv")
  }
}


hadoop fs -ls -R /datafile
drwxr-xr-x   - wangxiao supergroup          0 2016-10-15 11:43 /datafile/wangxiao
-rw-r--r--   3 wangxiao supergroup      16755 2016-10-15 10:46 /datafile/wangxiao/Affairs.csv
-rw-r--r--   3 wangxiao supergroup      16755 2016-10-13 21:48 /datafile/wangxiao/Affairs.txt
drwxr-xr-x   - wangxiao supergroup          0 2016-10-15 11:43 /datafile/wangxiao/data123.csv

 

Spark2 加载保存文件,数据文件转换成数据框dataframe

标签:csv   end   build   from   rom   tostring   array   context   datafile   

原文地址:http://www.cnblogs.com/wwxbi/p/6014276.html

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