JDBC的ResultSet游标转spark的DataFrame,数据类型的映射以TeraData数据库为例
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1.编写给ResultSet添加spark的schema成员及DF(DataFrame)成员
/* spark、sc对象因为是全局的,没有导入,需自行定义 teradata的字段类型转换成spark的数据类型 */ import java.sql.{ResultSet, ResultSetMetaData} import org.apache.spark.sql.types._ import org.apache.spark.sql.{DataFrame, Row} object addDataframeMember { trait ResultSetMetaDataToSchema { def columnCount: Int def schema: StructType } implicit def wrapResultSetMetaData(rsmd: ResultSetMetaData) = { new ResultSetMetaDataToSchema { def columnCount = rsmd.getColumnCount def schema = { def tdCovert(tdDpeStr: String, precision: Int = 0, scale: Int = 0, className: String = ""): DataType = { tdDpeStr match { case "BYTEINT" => IntegerType case "SMALLINT" => ShortType case "INTEGER" => IntegerType case "BIGINT" => LongType case "FLOAT" => DoubleType case "CHAR" => CharType(precision) case "DECIMAL" => DecimalType(precision, scale) case "VARCHAR" => StringType case "BYTE" => ByteType case "VARBYTE" => ByteType case "DATE" => DateType case "TIME" => TimestampType case "TIMESTAMP" => TimestampType case "CLOB" => StringType case "BLOB" => BinaryType case "Structured UDT" => ObjectType(Class.forName(className)) } } def col2StructField(rsmd: ResultSetMetaData, i: Int): StructField = StructField(rsmd.getColumnName(i), tdCovert(rsmd.getColumnTypeName(i), rsmd.getPrecision(i), rsmd.getScale(i), rsmd.getColumnClassName(i)), rsmd.isNullable(i) match { case 1 => true case 0 => false }).withComment(rsmd.getColumnLabel(i)) def rsmd2Schema(rsmd: ResultSetMetaData): StructType = (1 to columnCount).map(col2StructField(rsmd, _)).foldLeft(new StructType)((s: StructType, i: StructField) => s.add(i)) rsmd2Schema(rsmd) } } } trait ResultSetToDF { def schema: StructType def DF: DataFrame } implicit def wrapResultSet(rs: ResultSet) = { def rsmd = rs.getMetaData def toList[T](retrieve: ResultSet => T): List[T] = Iterator.continually((rs.next(), rs)).takeWhile(_._1).map(r => r._2).map(retrieve).toList def rsContent2Row(rs: ResultSet): Row = Row.fromSeq(Array.tabulate[Object](rsmd.columnCount)(i => rs.getObject(i + 1)).toSeq) new ResultSetToDF { def schema = rsmd.schema def DF = spark.createDataFrame(sc.parallelize(toList(rsContent2Row)), schema) } } }
2.正常基于JDBC连接并且获得数据集游标
import java.sql.{Connection, DriverManager} /* 获取TeraData的连接 */ val (dialect, host, user, passwd, database, charset) = ("teradata", "ip", "user", "password", "database", "ASCII") val tdConf = collection.immutable.Map( "driver" -> "com.ncr.teradata.TeraDriver", "uri" -> s"jdbc:$dialect://$host/CLIENT_CHARSET=EUC_CN,TMODE=TERA,COLUMN_NAME=ON,CHARSET=ASCII,database=$database", "username" -> user, "password" -> passwd ) def getTeraConn: Connection = { Class.forName(tdConf("driver")) DriverManager.getConnection(tdConf("uri"), tdConf("username"), tdConf("password")) } val sql = "SELECT TOP 10 * FROM xxx" var conn = getTeraConn val stmt = conn.createStatement() val rs = stmt.executeQuery(sql)
3.导入隐式转换,调用成员
import addDataframeMember.wrapResultSet rs.DF.show()
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