HeartSaVioR commented on code in PR #39082:
URL: https://github.com/apache/spark/pull/39082#discussion_r1054009827
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sql/core/src/main/scala/org/apache/spark/sql/execution/ExistingRDD.scala:
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@@ -183,16 +172,80 @@ object LogicalRDD {
}
}
+ val logicalPlan = originDataset.logicalPlan
val optimizedPlan = originDataset.queryExecution.optimizedPlan
val executedPlan = originDataset.queryExecution.executedPlan
+ val (stats, constraints) = rewriteStatsAndConstraints(logicalPlan,
optimizedPlan)
+
LogicalRDD(
originDataset.logicalPlan.output,
rdd,
firstLeafPartitioning(executedPlan.outputPartitioning),
executedPlan.outputOrdering,
isStreaming
- )(originDataset.sparkSession, Some(optimizedPlan.stats),
Some(optimizedPlan.constraints))
+ )(originDataset.sparkSession, stats, constraints)
+ }
+
+ private[sql] def buildOutputAssocForRewrite(
+ source: Seq[Attribute],
+ destination: Seq[Attribute]): Option[Map[Attribute, Attribute]] = {
+ // We check the name and type, allowing nullability, exprId, metadata,
qualifier be different
+ // E.g. This could happen during optimization phase.
+ val rewrite = source.zip(destination).flatMap { case (attr1, attr2) =>
+ if (attr1.name == attr2.name && attr1.dataType == attr2.dataType) {
+ Some(attr1 -> attr2)
+ } else {
+ None
+ }
+ }.toMap
+
+ if (rewrite.size == source.size) {
+ Some(rewrite)
+ } else {
+ None
+ }
+ }
+
+ private[sql] def rewriteStatsAndConstraints(
+ logicalPlan: LogicalPlan,
+ optimizedPlan: LogicalPlan): (Option[Statistics], Option[ExpressionSet])
= {
+ val rewrite = buildOutputAssocForRewrite(optimizedPlan.output,
logicalPlan.output)
+
+ rewrite.map { rw =>
+ val rewrittenStatistics = rewriteStatistics(optimizedPlan.stats, rw)
+ val rewrittenConstraints = rewriteConstraints(optimizedPlan.constraints,
rw)
+
+ (Some(rewrittenStatistics), Some(rewrittenConstraints))
+ }.getOrElse {
+ // can't rewrite stats and constraints, give up
+ logWarning("The output columns are expected to the same (for name and
type) for output " +
Review Comment:
If the query is failing, it clearly denotes the internal bug of Spark and we
expect users to report this. But I kind of agree that they shouldn't get
blocked with this if the latter part still works fine.
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