leave out auto col and use user providec col mapping

src/main/scala/com/microsoft/sqlserver/jdbc/spark/utils/BulkCopyUtils.scala

@@ -22,8 +22,6 @@ import org.apache.spark.sql.jdbc.JdbcDialects
 import org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils.{createConnectionFactory, getSchema, schemaString}
 import com.microsoft.sqlserver.jdbc.{SQLServerBulkCopy, SQLServerBulkCopyOptions}
 
-import scala.collection.mutable.ListBuffer
-
 /**
 * BulkCopyUtils Object implements common utility function used by both datapool and 
 */
@@ -35,7 +33,7 @@ object BulkCopyUtils extends Logging {
     * a connection, sets connection properties and does a BulkWrite. Called when writing data to 
     * master instance and data pools both. URL in options is used to create the relevant connection.
     *
-    * @param itertor - iterator for row of the partition.
+    * @param iterator - iterator for row of the partition.
     * @param dfColMetadata - array of ColumnMetadata type
     * @param options - SQLServerBulkJdbcOptions with url for the connection
     */
@@ -179,32 +177,6 @@ object BulkCopyUtils extends Logging {
         conn.createStatement.executeQuery(queryStr)
     }
 
-    /**
-    * getAutoCols
-    * utility function to get auto generated columns.
-     * Use auto generated column names to exclude them when matching schema.
-    */
-    private[spark] def getAutoCols(
-        conn: Connection, 
-        table: String): List[String] = {
-        // auto cols union computed cols, generated always cols, and node / edge table auto cols
-        val queryStr = s"""SELECT name
-        FROM sys.columns
-        WHERE object_id = OBJECT_ID('${table}')
-        AND (is_computed = 1 -- computed column
-            OR generated_always_type > 0 -- generated always / temporal table
-            OR (is_hidden = 0 AND graph_type = 2)) -- graph table
-        """
-
-        val autoColRs = conn.createStatement.executeQuery(queryStr)
-        val autoCols = ListBuffer[String]()
-        while (autoColRs.next()) {
-            val colName = autoColRs.getString("name")
-            autoCols.append(colName)
-        }
-        autoCols.toList
-    }
-
     /**
      * getColMetadataMap
      * Utility function convert result set meta data to array.
@@ -290,35 +262,30 @@ object BulkCopyUtils extends Logging {
         val dfCols = df.schema
 
         val tableCols = getSchema(rs, JdbcDialects.get(url))
-        val autoCols = getAutoCols(conn, dbtable)
-
-        val columnsToWriteSet = columnsToWrite.split(",").toSet
-        logDebug(s"columnsToWrite: $columnsToWriteSet")
 
         val prefix = "Spark Dataframe and SQL Server table have differing"
 
-        // auto columns should not exist in df
-        assertIfCheckEnabled(dfCols.length + autoCols.length == tableCols.length, strictSchemaCheck,
-            s"${prefix} numbers of columns")
-
         // if columnsToWrite provided by user, use it for metadata mapping. If not, use sql table.
-        if (columnsToWrite == "") {
-            val result = new Array[ColumnMetadata](columnsToWriteSet.size)
+        var metadataLen = tableCols.length
+        var columnsToWriteSet: Set[String] = Set()
+        if (columnsToWrite.isEmpty) {
+            assertIfCheckEnabled(dfCols.length == tableCols.length, strictSchemaCheck,
+                s"${prefix} numbers of columns")
         } else {
-            val result = new Array[ColumnMetadata](tableCols.length - autoCols.length)
+            columnsToWriteSet = columnsToWrite.split(",").map(_.trim).toSet
+            logDebug(s"columnsToWrite: $columnsToWriteSet")
+            metadataLen = columnsToWriteSet.size
         }
 
-        var nonAutoColIndex = 0
+        var colMappingIndex = 0
+        val result = new Array[ColumnMetadata](metadataLen)
 
         for (i <- 0 to tableCols.length-1) {
             val tableColName = tableCols(i).name
             var dfFieldIndex = -1
-            if (!columnsToWriteSet.isEmpty && !columnsToWriteSet.contains(tableColName)) {
-                // if columnsToWrite provided, and column name not in it, skip column mapping and ColumnMetadata
+            // if columnsToWrite provided, and column name not in it, skip column mapping and ColumnMetadata
+            if (!columnsToWrite.isEmpty && !columnsToWriteSet.contains(tableColName)) {
                 logDebug(s"skipping col index $i col name $tableColName, user not provided in columnsToWrite list")
-            } else if (autoCols.contains(tableColName)) {
-                // if auto columns, skip column mapping and ColumnMetadata
-                logDebug(s"skipping auto generated col index $i col name $tableColName dfFieldIndex $dfFieldIndex")
             }else{
                 var dfColName:String = ""
                 if (isCaseSensitive) {
@@ -361,15 +328,15 @@ object BulkCopyUtils extends Logging {
                         s" DF col ${dfColName} nullable config is ${dfCols(dfFieldIndex).nullable} " +
                         s" Table col ${tableColName} nullable config is ${tableCols(i).nullable}")
 
-                // Schema check passed for element, Create ColMetaData only for non auto generated column
-                result(nonAutoColIndex) = new ColumnMetadata(
+                // Schema check passed for element, Create ColMetaData for columns
+                result(colMappingIndex) = new ColumnMetadata(
                     rs.getMetaData().getColumnName(i+1),
                     rs.getMetaData().getColumnType(i+1),
                     rs.getMetaData().getPrecision(i+1),
                     rs.getMetaData().getScale(i+1),
                     dfFieldIndex
                 )
-                nonAutoColIndex += 1
+                colMappingIndex += 1
             }
         }
         result