Github user viirya commented on a diff in the pull request:
https://github.com/apache/spark/pull/18931#discussion_r136742282
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/WholeStageCodegenExec.scala
---
@@ -149,14 +149,146 @@ trait CodegenSupport extends SparkPlan {
ctx.freshNamePrefix = parent.variablePrefix
val evaluated = evaluateRequiredVariables(output, inputVars,
parent.usedInputs)
+
+ // Under certain conditions, we can put the logic to consume the rows
of this operator into
+ // another function. So we can prevent a generated function too long
to be optimized by JIT.
+ // The conditions:
+ // 1. The parent uses all variables in output. we can't defer variable
evaluation when consume
+ // in another function.
+ // 2. The output variables are not empty. If it's empty, we don't
bother to do that.
+ // 3. We don't use row variable. The construction of row uses deferred
variable evaluation. We
+ // can't do it.
+ // 4. The number of output variables must less than maximum number of
parameters in Java method
+ // declaration.
+ val requireAllOutput = output.forall(parent.usedInputs.contains(_))
+ val consumeFunc =
+ if (row == null && outputVars.nonEmpty && requireAllOutput &&
outputVars.length < 255) {
+ constructDoConsumeFunction(ctx, inputVars)
--- End diff --
I was thinking to check it. But the whole-stage codegen is a non-breaking
processing which produce/consume calls are embeded together. You don't have a
break to check the function length here.
Actually I think it should have no negative effect to split consume
functions always. From the benchmarking numbers, looks it shows no harm to
normal queries.
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