hanover-fiste commented on a change in pull request #31965:
URL: https://github.com/apache/spark/pull/31965#discussion_r602129852
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JDBCRelation.scala
##########
@@ -118,13 +119,28 @@ private[sql] object JDBCRelation extends Logging {
s"Upper bound: ${boundValueToString(upperBound)}.")
upperBound - lowerBound
}
- // Overflow and silliness can happen if you subtract then divide.
- // Here we get a little roundoff, but that's (hopefully) OK.
- val stride: Long = upperBound / numPartitions - lowerBound / numPartitions
+
+ // Overflow can happen if you subtract then divide. For example:
+ // (Long.MaxValue - Long.MinValue) / (numPartitions - 2).
+ // Also, using fixed-point decimals here to avoid possible inaccuracy from
floating point.
+ val strideUpperCalculation = (upperBound / BigDecimal(numPartitions))
+ .setScale(18, RoundingMode.HALF_EVEN)
+ val strideLowerCalculation = (lowerBound / BigDecimal(numPartitions))
+ .setScale(18, RoundingMode.HALF_EVEN)
Review comment:
I'm using setScale here to handle an issue that can arise with repeating
decimals. For instance, if you end up with upperStride = 1.333... and
lowerStride of 0.333..., when doing upperStride - lowerStride, you'd get
0.999.... This would then be truncated to 0 when converted to a long for
stride, which is an issue. Using setScale, the repeating decimals are rounded
and after the subtraction you end up with 1.0.
The reason I'm using `RoundingMode.HALF_EVEN` is because that's the default
for BigDecimal. As for a scale of 18, this was a number I picked from prior
defaults I've encountered (e.g. when a DecimalType is deserialized into
BigDecimal, the BigDecimal is instantiated as 38,18). However, I could lower
the scale if you think it is worthwhile. Since these calculations are at such a
small scale, I wasn't concerned about BigDecimal performance, but instead
accuracy.
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