Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/14313#discussion_r72005471
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JDBCRDD.scala
---
@@ -407,84 +496,8 @@ private[sql] class JDBCRDD(
var i = 0
while (i < conversions.length) {
val pos = i + 1
- conversions(i) match {
- case BooleanConversion => mutableRow.setBoolean(i,
rs.getBoolean(pos))
- case DateConversion =>
- // DateTimeUtils.fromJavaDate does not handle null value, so
we need to check it.
- val dateVal = rs.getDate(pos)
- if (dateVal != null) {
- mutableRow.setInt(i, DateTimeUtils.fromJavaDate(dateVal))
- } else {
- mutableRow.update(i, 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 DecimalConversion(p, s) =>
- val decimalVal = rs.getBigDecimal(pos)
- if (decimalVal == null) {
- mutableRow.update(i, null)
- } else {
- mutableRow.update(i, Decimal(decimalVal, p, s))
- }
- case DoubleConversion => mutableRow.setDouble(i,
rs.getDouble(pos))
- case FloatConversion => mutableRow.setFloat(i,
rs.getFloat(pos))
- case IntegerConversion => mutableRow.setInt(i, rs.getInt(pos))
- case LongConversion => mutableRow.setLong(i, rs.getLong(pos))
- // TODO(davies): use getBytes for better performance, if the
encoding is UTF-8
- case StringConversion => mutableRow.update(i,
UTF8String.fromString(rs.getString(pos)))
- case TimestampConversion =>
- val t = rs.getTimestamp(pos)
- if (t != null) {
- mutableRow.setLong(i, DateTimeUtils.fromJavaTimestamp(t))
- } else {
- mutableRow.update(i, null)
- }
- case BinaryConversion => mutableRow.update(i, rs.getBytes(pos))
- case BinaryLongConversion =>
- val bytes = rs.getBytes(pos)
- var ans = 0L
- var j = 0
- while (j < bytes.size) {
- ans = 256 * ans + (255 & bytes(j))
- j = j + 1
- }
- mutableRow.setLong(i, ans)
- case ArrayConversion(elementConversion) =>
- val array = rs.getArray(pos).getArray
- if (array != null) {
- val data = elementConversion match {
- case TimestampConversion =>
- array.asInstanceOf[Array[java.sql.Timestamp]].map {
timestamp =>
- nullSafeConvert(timestamp,
DateTimeUtils.fromJavaTimestamp)
- }
- case StringConversion =>
- array.asInstanceOf[Array[java.lang.String]]
- .map(UTF8String.fromString)
- case DateConversion =>
- array.asInstanceOf[Array[java.sql.Date]].map { date =>
- nullSafeConvert(date, DateTimeUtils.fromJavaDate)
- }
- case DecimalConversion(p, s) =>
- array.asInstanceOf[Array[java.math.BigDecimal]].map {
decimal =>
- nullSafeConvert[java.math.BigDecimal](decimal, d =>
Decimal(d, p, s))
- }
- case BinaryLongConversion =>
- throw new IllegalArgumentException(s"Unsupported array
element conversion $i")
- case _: ArrayConversion =>
- throw new IllegalArgumentException("Nested arrays
unsupported")
- case _ => array.asInstanceOf[Array[Any]]
- }
- mutableRow.update(i, new GenericArrayData(data))
- } else {
- mutableRow.update(i, null)
- }
- }
+ val value = conversions(i).apply(rs, pos)
+ mutableRow.update(i, value)
--- End diff --
previously we use `mutableRow.setXXX` for primitive types, but now we
always use `update`. Is there a way we can still avoid the boxing here?
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