Spark读取csv,text,Excel伪代码，学习笔记

最新推荐文章于 2023-12-15 18:00:35 发布

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最新推荐文章于 2023-12-15 18:00:35 发布

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分类专栏：大数据专栏文章标签： spark 学习 scala

本文链接：https://blog.youkuaiyun.com/weixin_44947939/article/details/125430003

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这段代码展示了如何使用 Spark 读取 CSV 和 Excel 文件，根据指定映射处理列名，并将时间戳转换为字符串。程序支持 UTF-8 和 GBK 编码，创建临时视图并创建 Hive 表，同时设置空值格式。

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伪代码，学习笔记

import jodd.io.findfile.FindFile.FileNameComparator
import org.apache.calcite.avatica.ColumnMetaData.StructType
import org.apache.spark.SparkConf
import org.apache.spark.rdd.RDD
import org.apache.spark.sql._
import org.apache.spark.sql.functions.col
import org.apache.spark.sql.types.{StringType, StructField, StructType}

import scala.collection.mutable.ListBuffer

object T {
  def main(args: Array[String]): Unit = {
    if (args(0).equals("csv")){
      if (args(1).equals("utf-8")){
        if (args(2).contains(":")){
          var dataFrame=getSpark2.read.option("inferSchema","true")
            .option("header","true")
            .csv("")
          val map2=args(3).split(",").map(data=>{
            val arr=data.split(":")
            (arr(0),arr(1))
          }).toMap
          val frame1=dataFrame.select(dataFrame.columns.map(t=> col(t).as(map2.getOrElse(t,t))):_*)
          for (elem <- frame1.dtypes) {
            if (elem._2.equals("TimestampType")){
              frame1.withColumn(elem._1+"aaa",frame1(elem._1).cast("String"))
              frame1.drop(elem._1).withColumnRenamed(elem._1+"aaa",elem._1)
            }
          }
          frame1.createOrReplaceTempView("temp")
          getSpark2.sql(" create table tablename  as select * from temp1")
          getSpark2.sql("alter table tablename set serdeproperties('serialization.null.format'='')")
      }
  }
      var dataFrame=getSpark2.read.option("inferSchema","true")
        .option("header","true")
        .option("encoding","gbk")
        .csv("")
      val map2=args(3).split(",").map(data=>{
        val arr=data.split(":")
        (arr(0),arr(1))
      }).toMap
      val frame1=dataFrame.select(dataFrame.columns.map(t=> col(t).as(map2.getOrElse(t,t))):_*)
      for (elem <- frame1.dtypes) {
        if (elem._2.equals("TimestampType")){
          frame1.withColumn(elem._1+"aaa",frame1(elem._1).cast("String"))
          frame1.drop(elem._1).withColumnRenamed(elem._1+"aaa",elem._1)
        }
      }
      frame1.createOrReplaceTempView("temp")
      getSpark2.sql(" create table tablename  as select * from temp1")
      getSpark2.sql("alter table tablename set serdeproperties('serialization.null.format'='')")
}else if (args(0).equals("Excel")){
      if (args(2).contains(":")){
        var dataFrame=getSpark2.read.option("inferSchema","true")
          .option("header","true")
          .load("")
        val map2=args(3).split(",").map(data=>{
          val arr=data.split(":")
          (arr(0),arr(1))
        }).toMap
        val frame1=dataFrame.select(dataFrame.columns.map(t=> col(t).as(map2.getOrElse(t,t))):_*)
        for (elem <- frame1.dtypes) {
          if (elem._2.equals("TimestampType")){
            frame1.withColumn(elem._1+"aaa",frame1(elem._1).cast("String"))
            frame1.drop(elem._1).withColumnRenamed(elem._1+"aaa",elem._1)
          }
        }
        frame1.createOrReplaceTempView("temp")
        getSpark2.sql(" create table tablename  as select * from temp")
        getSpark2.sql("alter table tablename set serdeproperties('serialization.null.format'='')")
      }else{
        val columns: Array[String] = args(2).split(",")
        var dataFrame=getSpark2.read.option("inferSchema","true")
          .option("header","true")
          .load("").toDF(columns: _*)
        var writeFrame=dataFrame
        for (elem <- dataFrame.dtypes) {
          if (elem._2.equals("TimestampType")){
            dataFrame.withColumn(elem._1+"aaa",dataFrame(elem._1).cast("String"))
            dataFrame.drop(elem._1).withColumnRenamed(elem._1+"aaa",elem._1)
          }
        }
        writeFrame.createGlobalTempView("temp2")
      }
}else if(args(0).equals("text")){
    val columns: Array[String] = args(3).split(",")
      val structTypes = new ListBuffer[StructField]
      for (i<-0 until columns.length){
        val col: Array[String] = columns(i).split(":")
        structTypes.+=(StructField(col(col.length-1),StringType,true))
      }
      val structType=StructType(structTypes)
      var frame=getSpark2.sparkContext.textFile("").map(data=>{
        Row.fromSeq(data.split("分隔符",-1).map(x=>{
          var r:String=null
          if ("0".equals(" ")){
            r=x
          }else if ("1".equals(" ")){
            r=x.trim()
          }
          if ("0".equals(" ")){
            r=x
          }else if ("1".equals(" ")){
            r=x.replace("\"","")
          }
          r
        }))
      })

      var rdd:RDD[Row]=null
      if ("0".equals("")){
        rdd=frame
      }else if("1".equals("")){
        val header: Row = frame.first()
        rdd= frame.filter(_!=header)
      }
      val collength=columns.length
      if ("1".equals("")){
        rdd=rdd.filter(f =>{
          if (f.length!=collength){
            false
          }else{
            true
          }
        })
      }
      getSpark2.createDataFrame(rdd,structType).createOrReplaceTempView("temp2")
    }
//  val frame = getSpark2.read.excel(header = true,inferSchema = true).load("hdfs://hadoop102:9000/ss.xlsx")
//    frame.show()
}


def getSpark2={
  val sparkSession: SparkSession = SparkSession.builder()
    .config("spark.sql.warehouse.dir","hdfs://hadoop102:9000/user/hive/warehouse")
    .master("local")
    .enableHiveSupport()
    .getOrCreate()
  sparkSession
}
}