cloud-fan commented on code in PR #58077:
URL: https://github.com/apache/spark/pull/58077#discussion_r3920856006
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/subquery.scala:
##########
@@ -165,14 +197,188 @@ case class InSubqueryExec(
}
}
+ // Invariant schema/ordering data for the multi-column evaluator, computed
once after the result
+ // is available. @transient so that serialization (result=null) does not
trigger evaluation.
+ @transient private lazy val multiColFieldTypes: Array[DataType] =
+ plan.output.map(_.dataType).toArray
+ @transient private lazy val multiColFieldOrderings: Array[Ordering[Any]] =
+ multiColFieldTypes.map(TypeUtils.getInterpretedOrdering)
+ // Struct-level ordering used to index fully non-null result rows in a
TreeSet.
+ @transient private lazy val multiColRowOrdering: Ordering[InternalRow] =
+
TypeUtils.getInterpretedOrdering(child.dataType).asInstanceOf[Ordering[InternalRow]]
+
+ // Split collected rows using the struct-level Catalyst ordering so that
duplicates are
+ // deduplicated. Fully non-null rows go into multiColNonNullSet (TreeSet)
for O(log n)
+ // membership tests. Null-containing rows are deduplicated via a temporary
TreeSet and then
+ // stored as multiColNullRows (Array) for linear scanning; each distinct
null-containing row
+ // is thus scanned at most once per outer row regardless of RHS duplicate
multiplicity.
+ // See SPARK-58481.
+ @transient private lazy val (multiColNonNullSet, multiColNullRows) = {
+ val withNull = TreeSet.newBuilder[InternalRow](multiColRowOrdering)
+ val nonNull = TreeSet.newBuilder[InternalRow](multiColRowOrdering)
+ result.foreach { r =>
+ val row = r.asInstanceOf[InternalRow]
+ if (row.anyNull) withNull += row else nonNull += row
+ }
+ (nonNull.result(), withNull.result().toArray)
+ }
+
+ // Three-valued IN semantics for multi-column subqueries.
+ // Result rows are InternalRow objects; InSet's TreeSet uses Catalyst
ordering, but membership
+ // cannot distinguish a definitively-false candidate from an indeterminate
one.
+ //
+ // When the LHS struct has no null fields:
+ // Fast path: O(log n) TreeSet lookup against fully non-null result rows
for TRUE.
+ // Slow path: linear scan over null-containing result rows only for
potential UNKNOWN.
+ //
+ // When the LHS struct has at least one null field, the fast path cannot be
used (a null LHS
+ // field produces UNKNOWN against any non-null RHS row whose non-null fields
all match). Both
+ // sets of result rows are scanned linearly, stopping once UNKNOWN is
established.
+ //
+ // Per-candidate three-valued logic: TRUE if every field matches; UNKNOWN if
no field is
+ // definitively unequal but at least one comparison involves null; FALSE
otherwise.
+ private def evalMultiColumn(inputRow: InternalRow): Any = {
+ // Current behavior (ANSI on, or legacyNullInEmptyBehavior=false): IN
(empty set) is always
+ // FALSE without evaluating the LHS. Legacy behavior (ANSI off by default)
returns NULL when
+ // the LHS is null and FALSE otherwise, requiring the LHS to be evaluated.
Mirror InSet.eval's
+ // guard exactly: skip child.eval only when legacyNullInEmptyBehavior is
false (SPARK-44550).
+ if (result.isEmpty && !legacyNullInEmptyBehavior) return false
+ val value = child.eval(inputRow)
+ if (value == null) return null
+ val inputStruct = value.asInstanceOf[InternalRow]
+ val fieldTypes = multiColFieldTypes
+ val orderings = multiColFieldOrderings
+ val numFields = fieldTypes.length
+ // Cache lazy accessors in locals so the loop bodies do not re-enter them
on every iteration.
+ val nullRows = multiColNullRows
+ val nonNullSet = multiColNonNullSet
+
+ if (!inputStruct.anyNull) {
+ // Fast path: indexed lookup among fully non-null candidates.
+ if (nonNullSet.contains(inputStruct)) return true
+ // No null-containing candidates: no path to UNKNOWN, result is FALSE.
+ if (nullRows.isEmpty) return false
+ // Materialize LHS fields once before the candidate scans to avoid
repeated get() calls
+ // inside the per-candidate loop.
+ val inputFields = Array.tabulate(numFields)(i => inputStruct.get(i,
fieldTypes(i)))
+ // Slow path: scan null-containing candidates for potential UNKNOWN.
+ // Stop early once hasUnknown is set: the indexed lookup already ruled
out TRUE,
+ // and every row here contains NULL, so no later candidate can improve
UNKNOWN to TRUE.
+ var hasUnknown = false
+ var i = 0
+ while (i < nullRows.length && !hasUnknown) {
+ val candidate = nullRows(i)
+ var fieldIdx = 0
+ var candidateIsUnknown = false
+ var candidateIsFalse = false
+ while (fieldIdx < numFields && !candidateIsFalse) {
+ val candidateField = candidate.get(fieldIdx, fieldTypes(fieldIdx))
+ if (candidateField == null) {
+ candidateIsUnknown = true
+ } else if (orderings(fieldIdx).compare(inputFields(fieldIdx),
candidateField) != 0) {
+ candidateIsFalse = true
+ }
+ fieldIdx += 1
+ }
+ if (!candidateIsFalse && candidateIsUnknown) hasUnknown = true
+ i += 1
+ }
+ if (hasUnknown) null else false
+ } else {
+ // LHS has at least one null field: must scan both result sets, stopping
once UNKNOWN
+ // is established (a null LHS field can produce UNKNOWN against any
non-null RHS row
+ // whose other fields all match).
+ // No candidates at all: result is FALSE (no match possible).
+ if (nullRows.isEmpty && nonNullSet.isEmpty) return false
+ // Materialize LHS fields once before the scans to avoid repeated get()
calls.
+ val inputFields = Array.tabulate(numFields)(i => inputStruct.get(i,
fieldTypes(i)))
+ var hasUnknown = false
+ // Scan null-containing result rows first.
+ var i = 0
+ while (i < nullRows.length && !hasUnknown) {
+ val candidate = nullRows(i)
+ var fieldIdx = 0
+ var candidateIsUnknown = false
+ var candidateIsFalse = false
+ while (fieldIdx < numFields && !candidateIsFalse) {
+ val inputField = inputFields(fieldIdx)
+ val candidateField = candidate.get(fieldIdx, fieldTypes(fieldIdx))
+ if (candidateField == null || inputField == null) {
+ candidateIsUnknown = true
+ } else if (orderings(fieldIdx).compare(inputField, candidateField)
!= 0) {
+ candidateIsFalse = true
+ }
+ fieldIdx += 1
+ }
+ if (!candidateIsFalse && candidateIsUnknown) hasUnknown = true
+ i += 1
+ }
+ // Scan non-null rows: a null LHS comparison is UNKNOWN unless a
non-null field differs.
+ val nonNullIter = nonNullSet.iterator
+ while (nonNullIter.hasNext && !hasUnknown) {
+ val candidate = nonNullIter.next()
+ var fieldIdx = 0
+ var candidateIsUnknown = false
+ var candidateIsFalse = false
+ while (fieldIdx < numFields && !candidateIsFalse) {
+ val inputField = inputFields(fieldIdx)
+ if (inputField == null) {
+ candidateIsUnknown = true
+ } else if (orderings(fieldIdx).compare(
+ inputField, candidate.get(fieldIdx, fieldTypes(fieldIdx))) != 0)
{
+ candidateIsFalse = true
Review Comment:
**Non-blocking (P2):** This fully non-null RHS loop is distinct from the
nullRows loop, but the current late-mismatch regression exercises only
nullRows. Please extend `SPARK-58481: multi-column NOT IN with nullable LHS and
non-nullable RHS is nullable` with a fully non-null RHS such as `(99, 2)` and a
nullable LHS `(NULL, 1)`, then assert through the forced InSubqueryExec join
path that NOT IN is TRUE (the inner join returns exactly `Row(1)`). This
catches a regression that stops at the first UNKNOWN instead of allowing the
later mismatch to make the candidate FALSE.
##########
sql/core/src/test/scala/org/apache/spark/sql/SubquerySuite.scala:
##########
@@ -2678,4 +2681,361 @@ class SubquerySuite extends SharedSparkSession
assert(exposedAttribute.exprId == outerReferenceAttribute.exprId)
}
+
+ test("SPARK-58481: InSubqueryExec nullable correctly accounts for subquery
output nullability") {
+ // 5 NOT IN (99, NULL) is UNKNOWN, not TRUE or FALSE. A join condition
that is not TRUE
+ // matches no rows, so a FULL OUTER JOIN must emit null-padded rows for
every row in each
+ // side -- 3 + 3 = 6 null-padded rows -- not the full cross product (9
rows).
+ //
+ // Disable the optimizer rewrite so the query exercises
InSubqueryExec.nullable directly;
+ // with the default enabled the IN is folded away before PlanSubqueries
constructs
+ // InSubqueryExec, making the nullability fix unreachable by this test.
+ withSQLConf(
+
"spark.sql.optimizer.optimizeUncorrelatedInSubqueriesInJoinCondition.enabled"
-> "false"
+ ) {
+ withTable("t0", "t1", "t3") {
+ sql("CREATE TABLE t0(c0 INT) USING PARQUET")
+ sql("INSERT INTO t0 VALUES (1), (2), (3)")
+ sql("CREATE TABLE t1(c0 INT) USING PARQUET")
+ sql("INSERT INTO t1 VALUES (10), (20), (30)")
+ sql("CREATE TABLE t3(c0 INT) USING PARQUET")
+ sql("INSERT INTO t3 VALUES (99), (CAST(NULL AS INT))")
+
+ val query = sql(
+ "SELECT t0.c0, t1.c0 FROM t1 FULL OUTER JOIN t0" +
+ " ON (5 NOT IN (SELECT t3.c0 FROM t3))")
+ // Verify the physical plan contains InSubqueryExec so the nullability
fix is
+ // actually exercised and not silently bypassed by an optimizer path.
+ // Use AdaptiveSparkPlanHelper.find to traverse AQE wrapper nodes, and
recurse
+ // into expression subtrees via Expression.exists to find the wrapped
InSubqueryExec.
+ assert(
+ find(query.queryExecution.executedPlan) { p =>
+ p.expressions.exists(_.exists {
+ case _: InSubqueryExec => true
+ case _ => false
+ })
+ }.isDefined,
+ "Expected InSubqueryExec in the physical plan")
+ // Unmatched t1 rows null-pad t0; unmatched t0 rows null-pad t1.
+ checkAnswer(query, Seq(
+ Row(null, 10), Row(null, 20), Row(null, 30),
+ Row(1, null), Row(2, null), Row(3, null)))
+ }
+ }
+ }
+
+ test("SPARK-58481: multi-column IN subquery with nullable non-head output is
nullable") {
+ // Disable the optimizer's join-condition IN rewrite so the query
exercises InSubqueryExec.
+ // Use VALUES-derived temp views: their nullability is inferred from the
literals (no NULL
+ // literal => non-nullable), rather than declared and then widened.
Parquet file-source
+ // analysis applies dataSchema.asNullable regardless of DDL NOT NULL,
which would defeat
+ // the nullability control this test relies on.
+ withSQLConf(
+
"spark.sql.optimizer.optimizeUncorrelatedInSubqueriesInJoinCondition.enabled"
-> "false"
+ ) {
+ // Case A: NULL after a definitive match (null in non-head position
after matching head).
+ // RHS: (99,99) and (1,NULL).
+ // (1,1) vs (99,99): first field 1!=99 => FALSE.
+ // (1,1) vs (1,NULL): first fields equal, second null => UNKNOWN.
+ // Overall for (1,1): UNKNOWN => NOT IN = null-padded.
+ // (2,2) vs both: all FALSE => NOT IN = TRUE => joins with both rhs
rows.
+ withTempView("lhs", "rhs") {
+ sql("CREATE TEMPORARY VIEW lhs AS SELECT * FROM VALUES (1, 1), (2, 2)
AS t(a, b)")
+ sql(
+ """CREATE TEMPORARY VIEW rhs AS
+ |SELECT * FROM VALUES (99, 99), (1, CAST(NULL AS INT)) AS t(a,
b)""".stripMargin)
+ checkAnswer(
+ sql(
+ """SELECT lhs.a, rhs.a FROM lhs FULL OUTER JOIN rhs
+ |ON ((lhs.a, lhs.b) NOT IN (SELECT a, b FROM
rhs))""".stripMargin),
+ Seq(Row(1, null), Row(2, 99), Row(2, 1)))
+ }
+
+ // Case B: NULL in head position followed by a definitive mismatch in a
later field.
+ // RHS: (NULL, 99).
+ // (1,1) vs (NULL,99): first field null => UNKNOWN so far; second
field 1!=99 => FALSE.
+ // A later definitive mismatch must override the earlier UNKNOWN:
result is FALSE,
+ // NOT IN = TRUE. A field-order regression would leave (1,1) as
UNKNOWN instead.
+ withTempView("lhs2", "rhs2") {
+ sql("CREATE TEMPORARY VIEW lhs2 AS SELECT * FROM VALUES (1, 1) AS t(a,
b)")
+ sql(
+ """CREATE TEMPORARY VIEW rhs2 AS
+ |SELECT * FROM VALUES (CAST(NULL AS INT), 99) AS t(a,
b)""".stripMargin)
+ // (1,1) NOT IN ((NULL,99)): second field 1!=99 makes the candidate
FALSE =>
+ // NOT IN = TRUE => inner join returns the single matching row.
+ checkAnswer(
+ sql(
+ """SELECT lhs2.a FROM lhs2 JOIN (SELECT 1 AS a)
+ |ON ((lhs2.a, lhs2.b) NOT IN (SELECT a, b FROM
rhs2))""".stripMargin),
+ Seq(Row(1)))
+ }
+
+ // Case C: UNKNOWN candidate followed by an exact-match candidate => IN
= TRUE.
+ // RHS: (1,NULL) and (1,1).
+ // (1,1) vs (1,NULL): first fields equal, second null => UNKNOWN.
+ // (1,1) vs (1,1): exact match => TRUE.
+ // The exact match must dominate the UNKNOWN: IN = TRUE, NOT IN =
FALSE.
+ // A candidate-order regression would short-circuit on UNKNOWN and
miss the TRUE.
+ withTempView("lhs3", "rhs3") {
+ sql("CREATE TEMPORARY VIEW lhs3 AS SELECT * FROM VALUES (1, 1) AS t(a,
b)")
+ sql(
+ """CREATE TEMPORARY VIEW rhs3 AS
+ |SELECT * FROM VALUES (1, CAST(NULL AS INT)), (1, 1) AS t(a,
b)""".stripMargin)
+ // (1,1) IN ((1,NULL),(1,1)): exact match exists => IN = TRUE => inner
join returns row.
+ checkAnswer(
+ sql(
+ """SELECT lhs3.a FROM lhs3 JOIN (SELECT 1 AS a)
+ |ON ((lhs3.a, lhs3.b) IN (SELECT a, b FROM
rhs3))""".stripMargin),
+ Seq(Row(1)))
+ }
+ }
+ }
+
+ test("SPARK-58481: multi-column IN subquery uses Catalyst ordering for
BinaryType fields") {
+ // Object.equals on Array[Byte] compares by identity, not value; Catalyst
ordering compares
+ // by content. A multi-column IN where one field is BinaryType would
incorrectly return FALSE
+ // (no match) with JVM equality even when the bytes are equal. Use an
inner join to keep the
+ // assertion simple: the join condition is TRUE iff the IN match succeeds.
+ withSQLConf(
+
"spark.sql.optimizer.optimizeUncorrelatedInSubqueriesInJoinCondition.enabled"
-> "false"
+ ) {
+ withTable("lbin", "rbin") {
+ sql("CREATE TABLE lbin(id INT NOT NULL, b BINARY NOT NULL) USING
PARQUET")
+ sql("INSERT INTO lbin VALUES (1, X'01')")
+ sql("CREATE TABLE rbin(id INT NOT NULL, b BINARY NOT NULL) USING
PARQUET")
+ sql("INSERT INTO rbin VALUES (1, X'01')")
+ // (1, 0x01) IN ((1, 0x01)) must be TRUE; the join should return one
row.
+ checkAnswer(
+ sql(
+ """SELECT lbin.id FROM lbin JOIN rbin
+ |ON ((lbin.id, lbin.b) IN (SELECT id, b FROM
rbin))""".stripMargin),
+ Seq(Row(1)))
+ }
+ }
+ }
+
+ test("SPARK-58481: multi-column NOT IN with nullable LHS and non-nullable
RHS is nullable") {
+ // CreateNamedStruct.nullable is always false, so child.nullable would
return false for a
+ // multi-column LHS even when individual fields are nullable. The
generated NOT IN code
+ // would then suppress null handling and turn UNKNOWN into TRUE, producing
wrong results.
+ // Fixture: lhs.a is nullable; rhs columns are NOT NULL (VALUES-derived to
avoid Parquet
+ // dataSchema.asNullable widening that would defeat the RHS
non-nullability control).
+ // (NULL, 2) vs (99, 2): second fields equal (2=2), first field is null =>
UNKNOWN.
+ // (1, 1) vs (99, 2): first field 1!=99 => FALSE => NOT IN = TRUE =>
matches all rhs rows.
+ // FULL OUTER JOIN: (NULL,2) gets null-padded (UNKNOWN condition); (1,1)
joins with (99,2);
+ // since (1,1) matched rhs(99,2), rhs(99,2) is not null-padded.
+ // Pre-fix: (NULL,2) NOT IN is wrongly TRUE (null suppressed) => emits
(null,99); no
+ // null-padded rows. Post-fix: UNKNOWN propagated => emits (null,null)
for (NULL,2).
+ withSQLConf(
+
"spark.sql.optimizer.optimizeUncorrelatedInSubqueriesInJoinCondition.enabled"
-> "false"
+ ) {
+ withTable("lhs") {
+ withTempView("rhs") {
+ sql("CREATE TABLE lhs(a INT, b INT NOT NULL) USING PARQUET")
+ sql("INSERT INTO lhs VALUES (1, 1), (NULL, 2)")
+ // rhs as VALUES view: both columns inferred non-nullable from
all-literal rows.
+ sql("CREATE TEMPORARY VIEW rhs AS SELECT * FROM VALUES (99, 2) AS
t(a, b)")
+ // (NULL, 2) NOT IN ((99,2)): second fields match, first is null =>
UNKNOWN
+ // => join condition not TRUE => (NULL,2) is null-padded:
Row(null, null).
+ // (1, 1) NOT IN ((99,2)): first field 1!=99 => FALSE => NOT IN =
TRUE
+ // => (1,1) joins with rhs(99,2): Row(1, 99). rhs(99,2) is
matched; no null-padded rhs.
+ checkAnswer(
+ sql(
+ """SELECT lhs.a, rhs.a FROM lhs FULL OUTER JOIN rhs
+ |ON ((lhs.a, lhs.b) NOT IN (SELECT a, b FROM
rhs))""".stripMargin),
+ Seq(Row(1, 99), Row(null, null)))
+ }
+ }
+ }
+ }
+
+ test("SPARK-58481: LEGACY_IN_SUBQUERY_NULLABILITY suppresses RHS-only
nullability") {
+ // Legacy mode suppresses only RHS-derived nullability (plan.output
nullable).
+ // A non-nullable scalar LHS (Literal 5) has lhsNullable=false; with RHS
suppressed,
+ // nullable=false. The generated code omits null handling and NOT IN on a
subquery that
+ // returns NULL evaluates to TRUE -- the pre-fix single-column behaviour
the flag preserves.
+ // Note: intentionally codegen-specific. The interpreted path correctly
returns UNKNOWN
+ // regardless of nullable (6 rows); the assertion of 9 verifies codegen
ran.
+ withSQLConf(
+ SQLConf.LEGACY_IN_SUBQUERY_NULLABILITY.key -> "true",
+
"spark.sql.optimizer.optimizeUncorrelatedInSubqueriesInJoinCondition.enabled"
-> "false"
+ ) {
+ withTable("t0", "t1", "t3") {
+ sql("CREATE TABLE t0(c0 INT) USING PARQUET")
+ sql("INSERT INTO t0 VALUES (1), (2), (3)")
+ sql("CREATE TABLE t1(c0 INT) USING PARQUET")
+ sql("INSERT INTO t1 VALUES (10), (20), (30)")
+ sql("CREATE TABLE t3(c0 INT) USING PARQUET")
+ sql("INSERT INTO t3 VALUES (99), (CAST(NULL AS INT))")
+
+ // Legacy: lhsNullable=false (Literal 5), rhsNullable suppressed =>
nullable=false.
+ // Generated code suppresses null; 5 NOT IN (99, NULL) evaluates to
TRUE.
+ // FULL OUTER JOIN condition is TRUE => full cross product of 3 x 3 =
9 rows.
+ assert(sql(
+ "SELECT t0.c0, t1.c0 FROM t1 FULL OUTER JOIN t0 ON (5 NOT IN (SELECT
t3.c0 FROM t3))")
+ .count() === 9)
+ }
+ }
+ }
+
+ test("SPARK-58481: LEGACY_IN_SUBQUERY_NULLABILITY preserves nullable LHS
fields " +
+ "for multi-column IN subqueries") {
+ // Legacy mode suppresses RHS nullability but preserves LHS field
nullability.
+ // With a nullable LHS field, lhsNullable=true even in legacy mode, so
nullable=true.
+ // Generated NOT IN code propagates UNKNOWN correctly; result is identical
to non-legacy.
+ withSQLConf(
+ SQLConf.LEGACY_IN_SUBQUERY_NULLABILITY.key -> "true",
+
"spark.sql.optimizer.optimizeUncorrelatedInSubqueriesInJoinCondition.enabled"
-> "false"
+ ) {
+ withTable("lhs", "rhs") {
+ sql("CREATE TABLE lhs(a INT, b INT NOT NULL) USING PARQUET")
+ sql("INSERT INTO lhs VALUES (1, 1), (NULL, 2)")
+ sql("CREATE TABLE rhs(a INT NOT NULL, b INT NOT NULL) USING PARQUET")
+ sql("INSERT INTO rhs VALUES (99, 2)")
+ // (NULL, 2) NOT IN ((99,2)): first field null => UNKNOWN =>
null-padded: Row(null, null).
+ // (1, 1) NOT IN ((99,2)): 1!=99 => FALSE => NOT IN=TRUE => joins:
Row(1, 99).
+ checkAnswer(
+ sql(
+ """SELECT lhs.a, rhs.a FROM lhs FULL OUTER JOIN rhs
+ |ON ((lhs.a, lhs.b) NOT IN (SELECT a, b FROM
rhs))""".stripMargin),
+ Seq(Row(1, 99), Row(null, null)))
+ }
+ }
+ }
+
+ test("SPARK-58481: InSubqueryExec.doGenCode registers Nondeterministic
children " +
+ "for partition-level initialization") {
+ // A nondeterministic expression in the LHS of a multi-column IN subquery
is unreachable
Review Comment:
**Non-blocking (P2):** This multi-column fallback is reachable from SQL in a
projection: RewritePredicateSubquery does not rewrite Project expressions,
CheckAnalysis permits nondeterministic Project expressions, and PlanSubqueries
then creates InSubqueryExec. Please add an end-to-end projection regression
such as `SELECT (id, rand(42)) IN (SELECT id, score FROM rhs) FROM lhs` and
correct the comment. That verifies the analysis/planning/codegen path in
addition to the focused direct-construction check.
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/subquery.scala:
##########
@@ -165,14 +197,188 @@ case class InSubqueryExec(
}
}
+ // Invariant schema/ordering data for the multi-column evaluator, computed
once after the result
+ // is available. @transient so that serialization (result=null) does not
trigger evaluation.
+ @transient private lazy val multiColFieldTypes: Array[DataType] =
+ plan.output.map(_.dataType).toArray
+ @transient private lazy val multiColFieldOrderings: Array[Ordering[Any]] =
+ multiColFieldTypes.map(TypeUtils.getInterpretedOrdering)
+ // Struct-level ordering used to index fully non-null result rows in a
TreeSet.
+ @transient private lazy val multiColRowOrdering: Ordering[InternalRow] =
+
TypeUtils.getInterpretedOrdering(child.dataType).asInstanceOf[Ordering[InternalRow]]
+
+ // Split collected rows using the struct-level Catalyst ordering so that
duplicates are
+ // deduplicated. Fully non-null rows go into multiColNonNullSet (TreeSet)
for O(log n)
+ // membership tests. Null-containing rows are deduplicated via a temporary
TreeSet and then
+ // stored as multiColNullRows (Array) for linear scanning; each distinct
null-containing row
+ // is thus scanned at most once per outer row regardless of RHS duplicate
multiplicity.
+ // See SPARK-58481.
+ @transient private lazy val (multiColNonNullSet, multiColNullRows) = {
+ val withNull = TreeSet.newBuilder[InternalRow](multiColRowOrdering)
+ val nonNull = TreeSet.newBuilder[InternalRow](multiColRowOrdering)
+ result.foreach { r =>
+ val row = r.asInstanceOf[InternalRow]
+ if (row.anyNull) withNull += row else nonNull += row
+ }
+ (nonNull.result(), withNull.result().toArray)
+ }
+
+ // Three-valued IN semantics for multi-column subqueries.
+ // Result rows are InternalRow objects; InSet's TreeSet uses Catalyst
ordering, but membership
+ // cannot distinguish a definitively-false candidate from an indeterminate
one.
+ //
+ // When the LHS struct has no null fields:
+ // Fast path: O(log n) TreeSet lookup against fully non-null result rows
for TRUE.
+ // Slow path: linear scan over null-containing result rows only for
potential UNKNOWN.
+ //
+ // When the LHS struct has at least one null field, the fast path cannot be
used (a null LHS
+ // field produces UNKNOWN against any non-null RHS row whose non-null fields
all match). Both
+ // sets of result rows are scanned linearly, stopping once UNKNOWN is
established.
+ //
+ // Per-candidate three-valued logic: TRUE if every field matches; UNKNOWN if
no field is
+ // definitively unequal but at least one comparison involves null; FALSE
otherwise.
+ private def evalMultiColumn(inputRow: InternalRow): Any = {
+ // Current behavior (ANSI on, or legacyNullInEmptyBehavior=false): IN
(empty set) is always
+ // FALSE without evaluating the LHS. Legacy behavior (ANSI off by default)
returns NULL when
+ // the LHS is null and FALSE otherwise, requiring the LHS to be evaluated.
Mirror InSet.eval's
+ // guard exactly: skip child.eval only when legacyNullInEmptyBehavior is
false (SPARK-44550).
+ if (result.isEmpty && !legacyNullInEmptyBehavior) return false
+ val value = child.eval(inputRow)
+ if (value == null) return null
+ val inputStruct = value.asInstanceOf[InternalRow]
+ val fieldTypes = multiColFieldTypes
+ val orderings = multiColFieldOrderings
+ val numFields = fieldTypes.length
+ // Cache lazy accessors in locals so the loop bodies do not re-enter them
on every iteration.
+ val nullRows = multiColNullRows
+ val nonNullSet = multiColNonNullSet
+
+ if (!inputStruct.anyNull) {
+ // Fast path: indexed lookup among fully non-null candidates.
+ if (nonNullSet.contains(inputStruct)) return true
+ // No null-containing candidates: no path to UNKNOWN, result is FALSE.
+ if (nullRows.isEmpty) return false
+ // Materialize LHS fields once before the candidate scans to avoid
repeated get() calls
+ // inside the per-candidate loop.
+ val inputFields = Array.tabulate(numFields)(i => inputStruct.get(i,
fieldTypes(i)))
+ // Slow path: scan null-containing candidates for potential UNKNOWN.
+ // Stop early once hasUnknown is set: the indexed lookup already ruled
out TRUE,
+ // and every row here contains NULL, so no later candidate can improve
UNKNOWN to TRUE.
+ var hasUnknown = false
+ var i = 0
+ while (i < nullRows.length && !hasUnknown) {
+ val candidate = nullRows(i)
+ var fieldIdx = 0
+ var candidateIsUnknown = false
+ var candidateIsFalse = false
+ while (fieldIdx < numFields && !candidateIsFalse) {
+ val candidateField = candidate.get(fieldIdx, fieldTypes(fieldIdx))
+ if (candidateField == null) {
+ candidateIsUnknown = true
+ } else if (orderings(fieldIdx).compare(inputFields(fieldIdx),
candidateField) != 0) {
+ candidateIsFalse = true
+ }
+ fieldIdx += 1
+ }
+ if (!candidateIsFalse && candidateIsUnknown) hasUnknown = true
+ i += 1
+ }
+ if (hasUnknown) null else false
+ } else {
+ // LHS has at least one null field: must scan both result sets, stopping
once UNKNOWN
+ // is established (a null LHS field can produce UNKNOWN against any
non-null RHS row
+ // whose other fields all match).
+ // No candidates at all: result is FALSE (no match possible).
+ if (nullRows.isEmpty && nonNullSet.isEmpty) return false
+ // Materialize LHS fields once before the scans to avoid repeated get()
calls.
+ val inputFields = Array.tabulate(numFields)(i => inputStruct.get(i,
fieldTypes(i)))
+ var hasUnknown = false
+ // Scan null-containing result rows first.
+ var i = 0
+ while (i < nullRows.length && !hasUnknown) {
+ val candidate = nullRows(i)
+ var fieldIdx = 0
+ var candidateIsUnknown = false
+ var candidateIsFalse = false
+ while (fieldIdx < numFields && !candidateIsFalse) {
+ val inputField = inputFields(fieldIdx)
+ val candidateField = candidate.get(fieldIdx, fieldTypes(fieldIdx))
Review Comment:
**Nit (P3):** When `inputField` is null, this comparison is already UNKNOWN
regardless of the RHS value, so `candidate.get` is unnecessary and repeats for
every scanned null-containing row. Please check `inputField == null` first and
retrieve `candidateField` only in the non-null branch; this preserves the truth
table while removing cardinality-scaled row accesses.
##########
sql/core/src/test/scala/org/apache/spark/sql/SubquerySuite.scala:
##########
@@ -2678,4 +2681,361 @@ class SubquerySuite extends SharedSparkSession
assert(exposedAttribute.exprId == outerReferenceAttribute.exprId)
}
+
+ test("SPARK-58481: InSubqueryExec nullable correctly accounts for subquery
output nullability") {
+ // 5 NOT IN (99, NULL) is UNKNOWN, not TRUE or FALSE. A join condition
that is not TRUE
+ // matches no rows, so a FULL OUTER JOIN must emit null-padded rows for
every row in each
+ // side -- 3 + 3 = 6 null-padded rows -- not the full cross product (9
rows).
+ //
+ // Disable the optimizer rewrite so the query exercises
InSubqueryExec.nullable directly;
+ // with the default enabled the IN is folded away before PlanSubqueries
constructs
Review Comment:
**Nit (P3):** This rationale is not accurate for the constant predicate used
below. `5 NOT IN (...)` references neither join input, and
RewritePredicateSubquery returns the original Join when both reference groups
are empty, so PlanSubqueries still constructs InSubqueryExec with the default
setting. Please remove the unnecessary override and update the comment to
describe the actual reachability check.
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