以下代码演示的是spark读取 text，csv，json，parquet格式的file 为dataframe，

将dataframe保存为对应格式的文件

package com.jason.spark23

import org.apache.spark.sql.types.{StringType, StructField, StructType}
import org.apache.spark.sql.{Row, SaveMode, SparkSession}

object ReadTest {
  def main(args: Array[String]): Unit = {
    val spark = SparkSession.builder()
      .appName("readtest")
      .master("local")
      .getOrCreate()
    val pathjson = "C:\\notos\\code\\sparktest\\src\\main\\resources\\employees.json"
    val pathavsc = "C:\\notos\\code\\sparktest\\src\\main\\resources\\full_user.avsc"
    val pathtxt = "C:\\notos\\code\\sparktest\\src\\main\\resources\\people.txt"
    val pathcsv = "C:\\notos\\code\\sparktest\\src\\main\\resources\\people.csv"
    val pathparquet = "C:\\notos\\code\\sparktest\\src\\main\\resources\\users.parquet"
    val sc = spark.sparkContext
    println(s"-----------------read--------------------------")
    println("====txt df")
    val txtrdd = sc.textFile(pathtxt).map(_.split(",")).map(arr => Row.fromSeq(arr))
    val schemaString = "name age"
    val fields = schemaString.split(" ")
      .map(fieldName => StructField(fieldName, StringType, nullable = true))
    val schema = StructType(fields)
    val txtDf = spark.createDataFrame(txtrdd, schema)
    txtDf.show()

    println("====json df") //jsondf 会自动给schema设置类型
    val jsonDf = spark.read.json(pathjson)
    jsonDf.show()

    println("==== csvdf")
    //会根据值自动生成类型
    val csvdf = spark.read.format("csv")
      .option("sep", ";")
      .option("inferSchema", "true")
      .option("header", "true")
      .load(pathcsv)
    csvdf.show()

    println("====parquet df")
    val usersDF = spark.read.load(pathparquet)
    usersDF.show()

    println("----------------------------------write-------------------------------")
    val path = "C:\\notos\\code\\sparktest\\src\\main\\"

    println(s"====txt output")
    implicit val mapEncoder = org.apache.spark.sql.Encoders.kryo[String]
    csvdf
      .write
      .format("csv")
      .mode(SaveMode.Append)
      .options(Map("compression" -> "bzip2", "sep" -> "\t", "header" -> "false"))
      .save(path + "\\text")

    println(s"====csv output")
    csvdf.write.mode(SaveMode.Ignore)
      .format("csv")
      .option("sep", "|")
      .option("header", "true")
      .save(s"$path\\csv")

    println(s"====json output")
    csvdf.write.mode(SaveMode.Append)
      .format("json")
      .save(path + "\\json")

    println(s"====parquet output")
    csvdf.write.mode(SaveMode.Append)
      .format("parquet")
      .save(s"$path\\parquet")
    spark.stop()
  }
}

上述将dataframe保存为text为也采取了csv格式，若要保存为text，dataframe中只能有一个String类型的字段，但是一般dataframe都不止一个字段，保存为text时也要指定字段分隔符，正好与csv的要求一致，而且csv格式的文件也可以用 sc.textFile 方法来读取