Mapping from spark to sql types, from stack exchange http://stackoverflow.com/a/36711507/4298208

gistfile1.txt

import org.apache.spark.sql.jdbc.{JdbcDialects, JdbcType, JdbcDialect}
import org.apache.spark.sql.jdbc.JdbcType
val SQLServerDialect = new JdbcDialect {
  override def canHandle(url: String): Boolean = url.startsWith("jdbc:jtds:sqlserver") || url.contains("sqlserver")

  override def getJDBCType(dt: DataType): Option[JdbcType] = dt match {
    case StringType => Some(JdbcType("VARCHAR(5000)", java.sql.Types.VARCHAR))
    case BooleanType => Some(JdbcType("BIT(1)", java.sql.Types.BIT))
    case IntegerType => Some(JdbcType("INTEGER", java.sql.Types.INTEGER))
    case LongType => Some(JdbcType("BIGINT", java.sql.Types.BIGINT))
    case DoubleType => Some(JdbcType("DOUBLE PRECISION", java.sql.Types.DOUBLE))
    case FloatType => Some(JdbcType("REAL", java.sql.Types.REAL))
    case ShortType => Some(JdbcType("INTEGER", java.sql.Types.INTEGER))
    case ByteType => Some(JdbcType("INTEGER", java.sql.Types.INTEGER))
    case BinaryType => Some(JdbcType("BINARY", java.sql.Types.BINARY))
    case TimestampType => Some(JdbcType("DATE", java.sql.Types.DATE))
    case DateType => Some(JdbcType("DATE", java.sql.Types.DATE))
    //      case DecimalType.Fixed(precision, scale) => Some(JdbcType("NUMBER(" + precision + "," + scale + ")", java.sql.Types.NUMERIC))
    case t: DecimalType => Some(JdbcType(s"DECIMAL(${t.precision},${t.scale})", java.sql.Types.DECIMAL))
    case _ => throw new IllegalArgumentException(s"Don't know how to save ${dt.json} to JDBC")
  }
}

JdbcDialects.registerDialect(SQLServerDialect)