Github user hvanhovell commented on a diff in the pull request:
https://github.com/apache/spark/pull/14907#discussion_r77377079
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala
---
@@ -154,6 +163,288 @@ object JdbcUtils extends Logging {
throw new IllegalArgumentException(s"Can't get JDBC type for
${dt.simpleString}"))
}
+ /**
+ * Maps a JDBC type to a Catalyst type. This function is called only
when
+ * the JdbcDialect class corresponding to your database driver returns
null.
+ *
+ * @param sqlType - A field of java.sql.Types
+ * @return The Catalyst type corresponding to sqlType.
+ */
+ private def getCatalystType(
+ sqlType: Int,
+ precision: Int,
+ scale: Int,
+ signed: Boolean): DataType = {
+ val answer = sqlType match {
+ // scalastyle:off
+ case java.sql.Types.ARRAY => null
+ case java.sql.Types.BIGINT => if (signed) { LongType } else {
DecimalType(20,0) }
+ case java.sql.Types.BINARY => BinaryType
+ case java.sql.Types.BIT => BooleanType // @see JdbcDialect
for quirks
+ case java.sql.Types.BLOB => BinaryType
+ case java.sql.Types.BOOLEAN => BooleanType
+ case java.sql.Types.CHAR => StringType
+ case java.sql.Types.CLOB => StringType
+ case java.sql.Types.DATALINK => null
+ case java.sql.Types.DATE => DateType
+ case java.sql.Types.DECIMAL
+ if precision != 0 || scale != 0 => DecimalType.bounded(precision,
scale)
+ case java.sql.Types.DECIMAL => DecimalType.SYSTEM_DEFAULT
+ case java.sql.Types.DISTINCT => null
+ case java.sql.Types.DOUBLE => DoubleType
+ case java.sql.Types.FLOAT => FloatType
+ case java.sql.Types.INTEGER => if (signed) { IntegerType }
else { LongType }
+ case java.sql.Types.JAVA_OBJECT => null
+ case java.sql.Types.LONGNVARCHAR => StringType
+ case java.sql.Types.LONGVARBINARY => BinaryType
+ case java.sql.Types.LONGVARCHAR => StringType
+ case java.sql.Types.NCHAR => StringType
+ case java.sql.Types.NCLOB => StringType
+ case java.sql.Types.NULL => null
+ case java.sql.Types.NUMERIC
+ if precision != 0 || scale != 0 => DecimalType.bounded(precision,
scale)
+ case java.sql.Types.NUMERIC => DecimalType.SYSTEM_DEFAULT
+ case java.sql.Types.NVARCHAR => StringType
+ case java.sql.Types.OTHER => null
+ case java.sql.Types.REAL => DoubleType
+ case java.sql.Types.REF => StringType
+ case java.sql.Types.ROWID => LongType
+ case java.sql.Types.SMALLINT => IntegerType
+ case java.sql.Types.SQLXML => StringType
+ case java.sql.Types.STRUCT => StringType
+ case java.sql.Types.TIME => TimestampType
+ case java.sql.Types.TIMESTAMP => TimestampType
+ case java.sql.Types.TINYINT => IntegerType
+ case java.sql.Types.VARBINARY => BinaryType
+ case java.sql.Types.VARCHAR => StringType
+ case _ => null
+ // scalastyle:on
+ }
+
+ if (answer == null) throw new SQLException("Unsupported type " +
sqlType)
+ answer
+ }
+
+ /**
+ * Takes a [[ResultSet]] and returns its Catalyst schema.
+ *
+ * @return A [[StructType]] giving the Catalyst schema.
+ * @throws SQLException if the schema contains an unsupported type.
+ */
+ def getSchema(resultSet: ResultSet, dialect: JdbcDialect): StructType = {
+ val rsmd = resultSet.getMetaData
+ val ncols = rsmd.getColumnCount
+ val fields = new Array[StructField](ncols)
+ var i = 0
+ while (i < ncols) {
+ val columnName = rsmd.getColumnLabel(i + 1)
+ val dataType = rsmd.getColumnType(i + 1)
+ val typeName = rsmd.getColumnTypeName(i + 1)
+ val fieldSize = rsmd.getPrecision(i + 1)
+ val fieldScale = rsmd.getScale(i + 1)
+ val isSigned = rsmd.isSigned(i + 1)
+ val nullable = rsmd.isNullable(i + 1) !=
ResultSetMetaData.columnNoNulls
+ val metadata = new MetadataBuilder()
+ .putString("name", columnName)
+ .putLong("scale", fieldScale)
+ val columnType =
+ dialect.getCatalystType(dataType, typeName, fieldSize,
metadata).getOrElse(
+ getCatalystType(dataType, fieldSize, fieldScale, isSigned))
+ fields(i) = StructField(columnName, columnType, nullable,
metadata.build())
+ i = i + 1
+ }
+ new StructType(fields)
+ }
+
+ /**
+ * Convert a [[ResultSet]] into an iterator of Catalyst Rows.
+ */
+ def resultSetToRows(resultSet: ResultSet, schema: StructType):
Iterator[Row] = {
+ val inputMetrics =
+
Option(TaskContext.get()).map(_.taskMetrics().inputMetrics).getOrElse(new
InputMetrics)
+ val encoder = RowEncoder(schema).resolveAndBind()
+ val internalRows = resultSetToSparkInternalRows(resultSet, schema,
inputMetrics)
+ internalRows.map(encoder.fromRow)
+ }
+
+ private[spark] def resultSetToSparkInternalRows(
+ resultSet: ResultSet,
+ schema: StructType,
+ inputMetrics: InputMetrics): Iterator[InternalRow] = {
+ new NextIterator[InternalRow] {
+ private[this] val rs = resultSet
+ private[this] val getters: Array[JDBCValueGetter] =
makeGetters(schema)
+ private[this] val mutableRow = new
SpecificMutableRow(schema.fields.map(x => x.dataType))
+
+ override protected def close(): Unit = {
--- End diff --
That is a valid assumption.
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