viirya commented on a change in pull request #25717: [SPARK-29013][SQL]
Structurally equivalent subexpression elimination
URL: https://github.com/apache/spark/pull/25717#discussion_r324431148
##########
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
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@@ -1050,11 +1056,100 @@ class CodegenContext {
}
/**
- * Checks and sets up the state and codegen for subexpression elimination.
This finds the
- * common subexpressions, generates the functions that evaluate those
expressions and populates
- * the mapping of common subexpressions to the generated functions.
- */
- private def subexpressionElimination(expressions: Seq[Expression]): Unit = {
+ * This is for sub-expression elimination targeting structurally equivalent
expressions.
+ * This is only supported in non whole-stage codegen.
+ *
+ * Two expressions are structurally equivalent if they are the same except
for the individual
+ * input slot in current processing row (i.e., `INPUT_ROW`).
+ *
+ * For example, expression a is input[1] + input[2], expression b is
input[3] + input[4]. They
+ * are not semantically equivalent in SparkSQL, but they have the same
computation on different
+ * input data.
+ *
+ * This method generates a common function for a set of structurally
equivalent expressions.
+ * Among the set, the expressions with same semantics are replaced with a
function call to the
+ * generated function, by passing in input slots of current processing row.
+ */
+ private def structuralSubexpressionElimination(expressions:
Seq[Expression]): Seq[Expression] = {
+ // Add each expression tree and compute the structurally common
subexpressions.
+ // Those expressions are not added into structurally common
subexpressions, defer them to
+ // semantically common subexpression.
+ val exprsOut =
Review comment:
ok.
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