Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/14313#discussion_r72013658
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JDBCRDD.scala
---
@@ -322,43 +322,130 @@ private[sql] class JDBCRDD(
}
}
- // Each JDBC-to-Catalyst conversion corresponds to a tag defined here so
that
- // we don't have to potentially poke around in the Metadata once for
every
- // row.
- // Is there a better way to do this? I'd rather be using a type that
- // contains only the tags I define.
- abstract class JDBCConversion
- case object BooleanConversion extends JDBCConversion
- case object DateConversion extends JDBCConversion
- case class DecimalConversion(precision: Int, scale: Int) extends
JDBCConversion
- case object DoubleConversion extends JDBCConversion
- case object FloatConversion extends JDBCConversion
- case object IntegerConversion extends JDBCConversion
- case object LongConversion extends JDBCConversion
- case object BinaryLongConversion extends JDBCConversion
- case object StringConversion extends JDBCConversion
- case object TimestampConversion extends JDBCConversion
- case object BinaryConversion extends JDBCConversion
- case class ArrayConversion(elementConversion: JDBCConversion) extends
JDBCConversion
+ // A `JDBCValueSetter` is responsible for converting and setting a value
from `ResultSet`
+ // into a field for `MutableRow`. The last argument `Int` means the
index for the
+ // value to be set in the row and also used for the value to retrieve
from `ResultSet`.
+ private type ValueSetter = (ResultSet, MutableRow, Int) => Unit
/**
- * Maps a StructType to a type tag list.
+ * Creates a StructType to setters for each type.
*/
- def getConversions(schema: StructType): Array[JDBCConversion] =
- schema.fields.map(sf => getConversions(sf.dataType, sf.metadata))
-
- private def getConversions(dt: DataType, metadata: Metadata):
JDBCConversion = dt match {
- case BooleanType => BooleanConversion
- case DateType => DateConversion
- case DecimalType.Fixed(p, s) => DecimalConversion(p, s)
- case DoubleType => DoubleConversion
- case FloatType => FloatConversion
- case IntegerType => IntegerConversion
- case LongType => if (metadata.contains("binarylong"))
BinaryLongConversion else LongConversion
- case StringType => StringConversion
- case TimestampType => TimestampConversion
- case BinaryType => BinaryConversion
- case ArrayType(et, _) => ArrayConversion(getConversions(et, metadata))
+ def makeSetters(schema: StructType): Array[ValueSetter] =
+ schema.fields.map(sf => makeSetters(sf.dataType, sf.metadata))
+
+ private def makeSetters(dt: DataType, metadata: Metadata): ValueSetter =
dt match {
+ case BooleanType =>
+ (rs: ResultSet, row: MutableRow, pos: Int) =>
+ row.setBoolean(pos, rs.getBoolean(pos + 1))
+
+ case DateType =>
+ (rs: ResultSet, row: MutableRow, pos: Int) =>
+ // DateTimeUtils.fromJavaDate does not handle null value, so we
need to check it.
+ val dateVal = rs.getDate(pos + 1)
+ if (dateVal != null) {
+ row.setInt(pos, DateTimeUtils.fromJavaDate(dateVal))
+ } else {
+ row.update(pos, null)
+ }
+
+ // When connecting with Oracle DB through JDBC, the precision and
scale of BigDecimal
+ // object returned by ResultSet.getBigDecimal is not correctly matched
to the table
+ // schema reported by ResultSetMetaData.getPrecision and
ResultSetMetaData.getScale.
+ // If inserting values like 19999 into a column with NUMBER(12, 2)
type, you get through
+ // a BigDecimal object with scale as 0. But the dataframe schema has
correct type as
+ // DecimalType(12, 2). Thus, after saving the dataframe into parquet
file and then
+ // retrieve it, you will get wrong result 199.99.
+ // So it is needed to set precision and scale for Decimal based on
JDBC metadata.
+ case DecimalType.Fixed(p, s) =>
+ (rs: ResultSet, row: MutableRow, pos: Int) =>
+ val decimal =
+ nullSafeConvert[java.math.BigDecimal](rs.getBigDecimal(pos + 1),
d => Decimal(d, p, s))
+ row.update(pos, decimal)
+
+ case DoubleType =>
+ (rs: ResultSet, row: MutableRow, pos: Int) =>
+ row.setDouble(pos, rs.getDouble(pos + 1))
+
+ case FloatType =>
+ (rs: ResultSet, row: MutableRow, pos: Int) =>
+ row.setFloat(pos, rs.getFloat(pos + 1))
+
+ case IntegerType =>
+ (rs: ResultSet, row: MutableRow, pos: Int) =>
+ row.setInt(pos, rs.getInt(pos + 1))
+
+ case LongType if metadata.contains("binarylong") =>
+ (rs: ResultSet, row: MutableRow, pos: Int) =>
+ val bytes = rs.getBytes(pos + 1)
+ var ans = 0L
+ var j = 0
+ while (j < bytes.size) {
+ ans = 256 * ans + (255 & bytes(j))
+ j = j + 1
+ }
+ row.setLong(pos, ans)
+
+ case LongType =>
+ (rs: ResultSet, row: MutableRow, pos: Int) =>
+ row.setLong(pos, rs.getLong(pos + 1))
+
+ case StringType =>
+ (rs: ResultSet, row: MutableRow, pos: Int) =>
+ // TODO(davies): use getBytes for better performance, if the
encoding is UTF-8
+ row.update(pos, UTF8String.fromString(rs.getString(pos + 1)))
+
+ case TimestampType =>
+ (rs: ResultSet, row: MutableRow, pos: Int) =>
+ val t = rs.getTimestamp(pos + 1)
+ if (t != null) {
+ row.setLong(pos, DateTimeUtils.fromJavaTimestamp(t))
+ } else {
+ row.update(pos, null)
+ }
+
+ case BinaryType =>
+ (rs: ResultSet, row: MutableRow, pos: Int) =>
+ row.update(pos, rs.getBytes(pos + 1))
+
+ case ArrayType(et, _) =>
+ val elementConversion = et match {
+ case TimestampType =>
+ (array: Object) =>
+ array.asInstanceOf[Array[java.sql.Timestamp]].map { timestamp
=>
+ nullSafeConvert(timestamp, DateTimeUtils.fromJavaTimestamp)
+ }
+
+ case StringType =>
+ (array: Object) =>
+ array.asInstanceOf[Array[java.lang.String]]
+ .map(UTF8String.fromString)
+
+ case DateType =>
+ (array: Object) =>
+ array.asInstanceOf[Array[java.sql.Date]].map { date =>
+ nullSafeConvert(date, DateTimeUtils.fromJavaDate)
+ }
+
+ case dt: DecimalType =>
+ (array: Object) =>
+ array.asInstanceOf[Array[java.math.BigDecimal]].map { decimal
=>
+ nullSafeConvert[java.math.BigDecimal](
+ decimal, d => Decimal(d, dt.precision, dt.scale))
+ }
+
+ case LongType if metadata.contains("binarylong") =>
+ throw new IllegalArgumentException(s"Unsupported array element "
+
+ s"type ${dt.simpleString} based on binary")
+
+ case ArrayType(_, _) =>
+ throw new IllegalArgumentException("Nested arrays unsupported")
+
+ case _ => (array: Object) => array.asInstanceOf[Array[Any]]
+ }
+
+ (rs: ResultSet, row: MutableRow, pos: Int) =>
+ row.update(pos, nullSafeConvert(rs.getArray(pos + 1).getArray,
elementConversion))
--- End diff --
`new GenericArrayData`
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