cloud-fan commented on code in PR #40734:
URL: https://github.com/apache/spark/pull/40734#discussion_r1162892352
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sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/DataSourceV2Strategy.scala:
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@@ -184,19 +175,23 @@ class DataSourceV2Strategy(session: SparkSession) extends
Strategy with Predicat
newSchema, catalog.asTableCatalog, ident, "CREATE TABLE")
CreateTableExec(catalog.asTableCatalog, ident,
structTypeToV2Columns(newSchema),
- partitioning, qualifyLocInTableSpec(tableSpec), ifNotExists) :: Nil
+ partitioning, qualifyLocInTableSpec(session, tableSpec), ifNotExists)
:: Nil
case CreateTableAsSelect(ResolvedIdentifier(catalog, ident), parts, query,
tableSpec,
- options, ifNotExists, analyzedQuery) =>
+ options, ifNotExists, analyzedQuery, table, write) =>
assert(analyzedQuery.isDefined)
val writeOptions = new CaseInsensitiveStringMap(options.asJava)
catalog match {
case staging: StagingTableCatalog =>
- AtomicCreateTableAsSelectExec(staging, ident, parts,
analyzedQuery.get, planLater(query),
- qualifyLocInTableSpec(tableSpec), writeOptions, ifNotExists) :: Nil
+ assert(table.isDefined)
+ assert(write.isDefined)
+ AtomicCreateTableAsSelectExec(
Review Comment:
It seems we can do the same thing for atomic CTAS/RTAS as well: create the
staging table, build a write plan with it, and call
`session.sessionState.executePlan`
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