Java 如何基于DataFrame从SparkSQL更改表，而不删除配置单元表，如删除/添加列？

我正在尝试使用Spark修改配置单元表，比如根据Spark数据帧输出从配置单元表添加列或删除列。下面是我试过的，有点大的代码

    def main(args: Array[String]): Unit = {
    implicit val spark = SparkSession.builder
      .appName("SchemaHandle")
      .enableHiveSupport
      .getOrCreate

    //Assume below is my generated DataFrame
    import spark.implicits._
    val dfSample = Seq(
      (12, "Dallas", "Texas", 55, "BOOK S","hello","Hellotwo"),
      (12, "SF", "CA", 25, "RULER","hello","Hellotwo"),
      (13, "NYC", "NY", 53, "PENCIL S","hello","Hellotwo"),
      (14, "Miami", "Fl", 45, "RULER","hello","Hellotwo"),
      (12, "Houston", "Texas", 75, "MARKER","hello","Hellotwo"),
      (11, "jersey", "NJ", 53, "WHITE NE R","hello","Hellotwo"),
      (19, "new orleans", "LO", 45, "HIGHLIGHTNER","hello","Hellotwo")
    ).toDF("id", "city", "state", "qty", "item","columnone","columntwo")

    try {
      spark.sql("truncate table database.schematest")
      println("Successfully truncated database.schematest")
    } catch {
      case _: Throwable => println("This Job is running for the very first time, so no table to truncate - We'll create the table below")
        dfSample.write.format("parquet").mode(SaveMode.Overwrite).saveAsTable(s"database.schematest")
        println("Output Table Saved to database.schematest")
    }

    //Assume this is Spark DF Schema.
    val seqone: Seq[StructField] = dfSample.schema
    //Assume this is Existing Table Schema.
    val seqtwo: Seq[StructField] = spark.table("database.schematest").schema

    //Get Cols- with Schema to be Added
    val diffedSeq = seqone diff seqtwo
    //Get Cols- with Schema to be Dropped
    val diffedSeqTwo = seqtwo diff seqone

    //Get Cols- names to just make the diff
    val seqonecolumns = dfSample.columns

    //Get Cols- names to just make the diff
    val seqtwocolumns = spark.table("dscoewrk_ing_qa.schematest").columns

    val diffedSeqArrayOne = seqonecolumns diff seqtwocolumns

    val diffedSeqArrayTwo = seqtwocolumns diff seqonecolumns

    var fixedAlterColumns: String = ""
    for (i <- diffedSeqArrayOne) {
      for (j <- diffedSeq) {
        if (i.equals(j.name)) {
          fixedAlterColumns +=""+j.name +" "+ datatypeCheckFunction(j.dataType.toString)+","
        }
      }
    }
    if(fixedAlterColumns.length>0) {
      println(s"Result---> ${fixedAlterColumns.substring(0, fixedAlterColumns.length - 1)}")
      //Lets add new columns to table database.schematest.
      spark.sql(s"ALTER TABLE database.schematest ADD COLUMNS (${fixedAlterColumns.substring(0, fixedAlterColumns.length - 1)})")
      println("Alter Table Success")
    }else{
      println("No Columns to Add")
    }

    println("------------------------------BREAK---------------------------")

    //Now lets think about dropping the columns
    val dfSampleCurrentTable:Seq[StructField] = spark.table("dscoewrk_ing_qa.schematest").schema
    //Since we cannot drop columns from Hive Table, lets do REPLACE COLUMNS.
    val dfSampleFinalDiff = dfSampleCurrentTable diff diffedSeqTwo
    dfSampleFinalDiff.foreach(println)
    val dfSampleFinalDiffColArray = (spark.table("database.schematest").columns) diff diffedSeqArrayTwo
    dfSampleFinalDiffColArray.foreach(println)
    var fixedDropColumns:String = ""

    for(i <- dfSampleFinalDiffColArray){
      println("The i is"+i)
      for(j <-dfSampleCurrentTable){
        println("This is j"+j)
        if(i.equals(j.name)){
          fixedDropColumns+=""+j.name +" "+ datatypeCheckFunction(j.dataType.toString)+","
        }
      }
    }
    //Let's drop the columns that aren't required.

    if(fixedDropColumns.length>0) {
      println(s"Result---> ${fixedDropColumns.substring(0, fixedDropColumns.length - 1)}")
      spark.sql(s"ALTER TABLE database.schematest REPLACE COLUMNS(${fixedDropColumns.substring(0,fixedDropColumns.length-1)})")
      println("Alter Drop Table Success")
    }else{
      println("No Columns to Drop")
    }

    //Now let's save the DF to Output in the Table. By using Append as below.
    dfSample.withColumn("mybool",functions.lit(null)).coalesce(50).write.format("parquet").mode(SaveMode.Append).insertInto("database.schematest")

    println("Saving output Table Successful.")

  }

   def datatypeCheckFunction(datatypePassed: String): String = {
    datatypePassed match {
      case "BinaryType" | "ByteType" | "DateType" | "NullType" | "StringType" | "TimestampType" => "String"
      case "BooleanType" => "boolean"
      case "DoubleType" | "FloatType" => "Double"
      case "IntegerType" | "ShortType" => "Int"
      case "LongType" => "BigInt"
      case _ => "String"
    }
  }
}

假设REPLACE Columns已经工作，它还会删除该删除列的数据吗

以下是我使用的配置单元创建表语句：

create table schematest(`id` int, `city` string, `state` string, `qty` int, `mybool` boolean) stored as parquet

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非常感谢您的帮助，提前谢谢大家。

我刚刚在：

替换列删除所有现有列并添加新的列集。这只能对具有本机SerDe（DynamicSerDe、MetadataTypedColumnsetSerDe、LazySimpleSerDe和ColumnarSerDe）的表执行

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据我所知，似乎不支持使用拼花地板。

这是否意味着我可以使用ORC文件格式？

create table schematest(`id` int, `city` string, `state` string, `qty` int, `mybool` boolean) stored as parquet