cloud-fan commented on code in PR #52334:
URL: https://github.com/apache/spark/pull/52334#discussion_r2429735625
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sql/core/src/main/scala/org/apache/spark/sql/classic/SparkSession.scala:
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@@ -448,16 +448,24 @@ class SparkSession private(
private[sql] def sql(sqlText: String, args: Array[_], tracker:
QueryPlanningTracker): DataFrame =
withActive {
val plan = tracker.measurePhase(QueryPlanningTracker.PARSING) {
- val parsedPlan = sessionState.sqlParser.parsePlan(sqlText)
- if (args.nonEmpty) {
- // Check for SQL scripting with positional parameters before
creating parameterized query
- if (parsedPlan.isInstanceOf[CompoundBody]) {
+ val parsedPlan = if (args.nonEmpty) {
+ // Use parameter context directly for parsing
+ val paramContext =
PositionalParameterContext(args.map(lit(_).expr).toSeq)
Review Comment:
To simplify the pre-parser, shall we use a fake plan to resolve and
constant-fold the parameter values first? Then we don't need
`LiteralToSqlConverter` as the parameter value is always `Literal` and we can
simply use `Literal#sql`.
We can use a fake plan `Project(parameters, OneRowRelation)`, analyze and
optimize, and get the project list. They must be all literals otherwise we
should report error for parameters.
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