Github user gatorsmile commented on a diff in the pull request:
https://github.com/apache/spark/pull/13773#discussion_r67783068
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JDBCRelation.scala
---
@@ -52,29 +52,44 @@ private[sql] object JDBCRelation {
* @return an array of partitions with where clause for each partition
*/
def columnPartition(partitioning: JDBCPartitioningInfo):
Array[Partition] = {
- if (partitioning == null) return Array[Partition](JDBCPartition(null,
0))
+ if (partitioning == null || partitioning.numPartitions <= 1 ||
+ partitioning.lowerBound == partitioning.upperBound) {
+ return Array[Partition](JDBCPartition(null, 0))
+ }
- val numPartitions = partitioning.numPartitions
- val column = partitioning.column
- if (numPartitions == 1) return Array[Partition](JDBCPartition(null, 0))
+ val lowerBound = partitioning.lowerBound
+ val upperBound = partitioning.upperBound
+ require (lowerBound <= upperBound,
+ "Operation not allowed: the lower bound of partitioning column is
larger than the upper " +
+ s"bound. Lower bound: $lowerBound; Upper bound: $upperBound")
+
+ val numPartitions =
+ if ((upperBound - lowerBound) >= partitioning.numPartitions) {
+ partitioning.numPartitions
+ } else {
+ 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 = (partitioning.upperBound / numPartitions
- - partitioning.lowerBound / numPartitions)
+ val stride: Long = upperBound / numPartitions - lowerBound /
numPartitions
+ // The automatic adjustment of numPartitions can ensure the following
checking condition.
+ assert(stride >= 1, "The specified number of partitions should be
greater than " +
--- End diff --
BTW, a few years ago, I wrote codes for multi-column `column partitioning`.
It is pretty complicated, but I am not sure if this is a good fit here. When
the num of columns is large, the where clause could be very long. The major
issues are the JDBC data sources might not always perform very fastly when the
where clause is very complicated.
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