cloud-fan commented on code in PR #58077:
URL: https://github.com/apache/spark/pull/58077#discussion_r3933752087
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/subquery.scala:
##########
@@ -188,6 +267,161 @@ case class InSubqueryExec(
copy(child = newChild)
}
+/**
+ * Lightweight, serializable evaluator for multi-column IN subqueries.
+ * Holds only what executors need: the LHS expression, collected result rows,
field metadata,
+ * and the legacy empty-list behavior flag. Excludes plan (BaseSubqueryExec)
so that
+ * task-closure serialization does not carry the driver's physical plan
subtree.
+ * All @transient lazy fields are rebuilt from the serialized fieldTypes and
resultRows
+ * after deserialization, mirroring InSubqueryExec's own pattern. See
SPARK-58481.
+ */
+private[sql] class MultiColumnInSubqueryEvaluator(
+ val child: Expression,
+ val fieldTypes: Array[DataType],
+ val resultRows: Array[InternalRow],
+ val legacyNullInEmptyBehavior: Boolean) extends Serializable {
+
+ @transient private lazy val fieldOrderings: Array[Ordering[Any]] =
+ fieldTypes.map(TypeUtils.getInterpretedOrdering)
+
+ @transient private lazy val rowOrdering: 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 nonNullSet (TreeSet) for O(log
n)
+ // membership tests. Null-containing rows are deduplicated via a temporary
TreeSet and
+ // then stored as nullRows (Array) for linear scanning; each distinct
null-containing row
+ // is thus scanned at most once per outer row regardless of RHS duplicate
multiplicity.
+ @transient private lazy val (nonNullSet, nullRows) = {
+ val withNull = TreeSet.newBuilder[InternalRow](rowOrdering)
+ val nonNull = TreeSet.newBuilder[InternalRow](rowOrdering)
+ resultRows.foreach { r => if (r.anyNull) withNull += r else nonNull += r }
+ (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 set.
+ //
+ // 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.
+ def eval(inputRow: InternalRow): Any = {
+ // Current behavior (ANSI on, or legacyNullInEmptyBehavior=false): IN
(empty set) is
Review Comment:
**Nit (P3):** ANSI controls only the default here; an explicit
spark.sql.legacy.nullInEmptyListBehavior=true still evaluates the LHS when ANSI
is enabled, as the test below demonstrates. Please describe this branch as
legacyNullInEmptyBehavior=false and mention ANSI only as the defaulting rule.
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/subquery.scala:
##########
@@ -165,14 +197,61 @@ case class InSubqueryExec(
}
}
+ // Memoized lightweight evaluator instance. @transient so it is rebuilt
(from the
+ // serializable fields) when InSubqueryExec is deserialized in an executor;
constructed
+ // only for the multi-column path after prepareResult() has populated result.
+ @transient private lazy val multiColEvaluator:
MultiColumnInSubqueryEvaluator = {
+ val rows = result.map(_.asInstanceOf[InternalRow])
+ val fTypes = plan.output.map(_.dataType).toArray
+ new MultiColumnInSubqueryEvaluator(child, fTypes, rows,
legacyNullInEmptyBehavior)
+ }
+
override def eval(input: InternalRow): Any = {
prepareResult()
- if (isResultUnavailable) true else inSet.eval(input)
+ if (isResultUnavailable) {
+ true
+ } else if (plan.output.length > 1) {
+ multiColEvaluator.eval(input)
+ } else {
+ inSet.eval(input)
+ }
}
override def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode = {
prepareResult()
- if (isResultUnavailable) Literal.TrueLiteral.doGenCode(ctx, ev) else
inSet.doGenCode(ctx, ev)
+ if (isResultUnavailable) {
+ Literal.TrueLiteral.doGenCode(ctx, ev)
+ } else if (plan.output.length > 1) {
+ // Multi-column: per-candidate three-valued comparison cannot be
expressed with InSet's
+ // generated code. Fall back to the interpreted path via the lightweight
evaluator.
+ // Register the evaluator (not the full InSubqueryExec) so that task
closures do not
Review Comment:
**Non-blocking (P2):** The evaluator still captures the entire child
expression. For a multi-column LHS containing a scalar subquery, PlanSubqueries
embeds an execution ScalarSubquery whose non-transient BaseSubqueryExec remains
reachable through this reference, so driver plan state is still serialized with
generated tasks. Please remove prepared scalar-subquery plans from the captured
child and add a regression that proves no BaseSubqueryExec remains reachable.
**Recommended change:** Sanitize the evaluator child by literalizing
prepared execution ScalarSubquery nodes before it enters
CodegenContext.references.
**Why this works:** Transform the prepared child to replace each execution
ScalarSubquery with its toLiteral result, then discover and register
nondeterministic descendants from the transformed child.
**Scope:** InSubqueryExec generated evaluation and SubquerySuite
serialization coverage for multi-column left-hand expressions containing scalar
subqueries.
**Compatibility:** Preserve SQL semantics, legacy branches, and the
single-column InSet path; only remove already-evaluated scalar-subquery plan
objects from the serialized evaluator graph.
**Risks:** Nondeterministic expression identity must remain consistent with
partition initialization. Literal replacement must occur only after scalar
subquery results are available.
**Constraints:** No BaseSubqueryExec may remain reachable through
CodegenContext.references. Existing nullability and empty-result compatibility
behavior must remain unchanged.
**Success:** A reachable multi-column scalar-subquery LHS serializes and
executes with no BaseSubqueryExec reachable from task references, while the
existing evaluator behavior remains intact.
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/subquery.scala:
##########
@@ -124,8 +128,36 @@ case class InSubqueryExec(
extends ExecSubqueryExpression with UnaryLike[Expression] with Predicate {
@transient private lazy val inSet = InSet(child, result.toSet)
+ // Captured at construction time to mirror InSet's empty-result
short-circuit (SPARK-44550).
+ private val legacyNullInEmptyBehavior = SQLConf.get.legacyNullInEmptyBehavior
- override def nullable: Boolean = child.nullable
+ // Mirror the logical InSubquery.nullable: nullable when any output column
is nullable
+ // (a nullable RHS field can produce UNKNOWN on a miss) or when any LHS
field is nullable.
+ // For multi-column IN the LHS is a CreateNamedStruct whose top-level
nullable is always false
+ // even when individual field expressions are nullable (SPARK-58481). Both
PlanSubqueries and
+ // PlanAdaptiveSubqueries wrap multi-column LHS values in CreateNamedStruct,
so matching on it
+ // here is precise for the current producers. The fallback to child.nullable
is safe
+ // for the single-column case where child is the bare LHS expression.
+ // LEGACY_IN_SUBQUERY_NULLABILITY suppresses only RHS-derived nullability;
LHS field nullability
+ // is preserved in both modes so a nullable LHS field can contribute UNKNOWN
+ // when no field differs.
+ // For the multi-column case (plan.output.length > 1) the LHS is always a
generated
+ // CreateNamedStruct; a user-written single struct-valued IN also produces a
CreateNamedStruct,
Review Comment:
**Nit (P3):** A single struct-valued operand does not necessarily produce
CreateNamedStruct: both planners use values.head unchanged when values.length
== 1, so a struct attribute remains an AttributeReference. Please say that only
multi-value IN is wrapped and that the single-value case explains the
plan.output.length guard.
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/subquery.scala:
##########
@@ -165,14 +197,61 @@ case class InSubqueryExec(
}
}
+ // Memoized lightweight evaluator instance. @transient so it is rebuilt
(from the
+ // serializable fields) when InSubqueryExec is deserialized in an executor;
constructed
+ // only for the multi-column path after prepareResult() has populated result.
+ @transient private lazy val multiColEvaluator:
MultiColumnInSubqueryEvaluator = {
+ val rows = result.map(_.asInstanceOf[InternalRow])
Review Comment:
**Nit (P3):** updateResult's normal paths already retain a runtime
Array[InternalRow], so this map allocates and copies an extra n-element
reference array before the index builders traverse it again. Please reuse
result.asInstanceOf[Array[InternalRow]] on that path, with a conversion only
for any supported Array[Any] fallback caller.
##########
sql/core/src/test/scala/org/apache/spark/sql/SubquerySuite.scala:
##########
@@ -2678,4 +2682,421 @@ 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).
+ //
+ // The predicate 5 NOT IN (...) is a constant that references neither join
input, so
+ // RewritePredicateSubquery returns the original Join unchanged and
PlanSubqueries always
+ // constructs InSubqueryExec here regardless of any optimizer config.
+ 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.
+ withSQLConf(
+
"spark.sql.optimizer.optimizeUncorrelatedInSubqueriesInJoinCondition.enabled"
-> "false"
+ ) {
+ // Case A: RHS is fully non-null (stored in nonNullSet). LHS (NULL, 2)
vs RHS (99, 2):
+ // second fields equal, first field null => UNKNOWN; exercises the
nonNullSet scan with
+ // a nullable LHS. Fixture: lhs.a is nullable; rhs columns are NOT NULL
(VALUES-derived
+ // to avoid Parquet dataSchema.asNullable widening that would defeat the
RHS control).
+ 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 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)))
+ }
+ }
+
+ // Case B: RHS is fully non-null (goes into nonNullSet, not nullRows).
LHS (NULL, 1) vs
+ // RHS (99, 2): first field null, but second field 1 != 2 is a
definitive mismatch that
+ // makes the candidate FALSE. An implementation that stops at the first
UNKNOWN instead
+ // of continuing to check later fields would return UNKNOWN here instead
of FALSE, causing
+ // NOT IN to be UNKNOWN and the inner join to return no rows.
+ withTempView("lhs_nn", "rhs_nn") {
+ sql("CREATE TEMPORARY VIEW lhs_nn AS " +
+ "SELECT * FROM VALUES (CAST(NULL AS INT), 1) AS t(a, b)")
+ sql("CREATE TEMPORARY VIEW rhs_nn AS " +
+ "SELECT * FROM VALUES (99, 2) AS t(a, b)")
+ // (NULL, 1) NOT IN ((99, 2)): second field 1 != 2 => candidate FALSE
=> NOT IN TRUE.
+ // Inner join returns the single lhs row.
+ checkAnswer(
+ sql(
+ """SELECT lhs_nn.b FROM lhs_nn JOIN (SELECT 1 AS x)
+ |ON ((lhs_nn.a, lhs_nn.b) NOT IN (SELECT a, b FROM rhs_nn))
+ |""".stripMargin),
+ Seq(Row(1)))
+ }
+ }
+ }
+
+ 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 join-condition IN subquery with a nondeterministic LHS is rejected by
CheckAnalysis,
+ // so this direct-construction test provides a focused unit check: it
constructs
+ // InSubqueryExec directly (bypassing analysis) to verify that doGenCode
correctly
+ // registers each Nondeterministic descendant of the LHS child for
partition-level
+ // initialization. Without that registration, a Rand node's eval() would
throw
+ // IllegalArgumentException (via require(initialized, ...)) because
initialize() was
+ // never called.
+ //
+ // Two output columns force plan.output.length > 1, taking the
multi-column fallback
+ // branch (with the explicit foreach loop), not the single-column InSet
path where Rand
+ // would register itself through ordinary child codegen traversal.
+ //
+ // The test compiles the expression to bytecode via GeneratePredicate and
executes it,
+ // so a missing initialize() call causes an actual failure rather than
just a missing
+ // entry in partitionInitializationStatements.
+ val subplanOutput = spark
+ .range(0)
+ .select($"id".cast(IntegerType).as("a"), $"id".cast(IntegerType).as("b"))
+ .queryExecution.executedPlan.output
+ val subplan = SubqueryExec(
+ "test_subquery",
+ LocalTableScanExec(subplanOutput, Nil, None))
+ // LHS: struct of (Literal(1), Rand(42)>=0.0). Rand is always non-negative
so the struct
+ // always equals the single RHS row (1, true), making IN = true.
+ val lhs = CreateNamedStruct(Seq(
+ Literal("a"), Literal(1),
+ Literal("b"), GreaterThanOrEqual(Rand(42L), Literal(0.0))))
+ val expr = InSubqueryExec(
+ lhs, subplan, ExprId(99),
+ isDynamicPruning = false,
+ result = Array(InternalRow(1, true)))
+ // Compile to bytecode, initialize (seeds the Rand RNG), and evaluate.
+ // If initialize() were skipped the Rand eval() would throw
IllegalArgumentException.
+ val pred = GeneratePredicate.generate(expr, useSubexprElimination = false)
+ pred.initialize(0)
+ assert(pred.eval(InternalRow.empty) === true,
+ "Expected IN to be TRUE: (1, rand()>=0.0) IN ((1, true))")
+ }
+
+ test("SPARK-58481: MultiColumnInSubqueryEvaluator preserves shared
Nondeterministic " +
+ "identity after Java serialization round-trip") {
+ // In doGenCode, ctx.references receives both the
MultiColumnInSubqueryEvaluator (slot 0)
+ // and the bare Nondeterministic node (slot 1) for partition
initialization. On the driver
+ // both slots point to the same Rand instance (evaluator.child embeds it).
The risk is that
+ // Java serialization could write two independent copies, causing
initialize() called on
+ // references[1] to seed a different instance than eval() uses inside the
evaluator.
+ //
+ // Java's ObjectOutputStream tracks shared references within a single
graph (via its
+ // internal handle table), so a single serialize/deserialize round-trip of
the references
+ // array as one object produces back-references rather than copies. This
test verifies
+ // that invariant explicitly using the same JavaSerializer Spark uses for
task closures.
+ val rand = Rand(42L)
+ val lhs = CreateNamedStruct(Seq(
+ Literal("a"), Literal(1),
+ Literal("b"), GreaterThanOrEqual(rand, Literal(0.0))))
+ val evaluator = new MultiColumnInSubqueryEvaluator(
+ lhs,
+ Array(IntegerType, IntegerType),
+ Array(InternalRow(1, true)),
+ legacyNullInEmptyBehavior = false)
+ // Simulate what doGenCode does: put the evaluator and the bare
Nondeterministic into
+ // the references array as separate slots (as
WholeStageCodegenEvaluatorFactory would).
+ val references: Array[Any] = Array(evaluator, rand)
+ // Round-trip through JavaSerializer -- the same serializer Spark uses for
task closures
+ // (SparkEnv always sets closureSerializer = new JavaSerializer(...)).
+ // Serializing the array directly is equivalent to serializing it as a
field of
+ // WholeStageCodegenEvaluatorFactory: Java's ObjectOutputStream maintains
a handle table
+ // across the entire stream, so shared-reference tracking is independent
of nesting depth.
+ val ser = new JavaSerializer(new SparkConf()).newInstance()
+ val rt = ser.deserialize[Array[Any]](ser.serialize(references))
+ val rtEvaluator = rt(0).asInstanceOf[MultiColumnInSubqueryEvaluator]
+ val rtRand = rt(1).asInstanceOf[Rand]
+ // Find the Rand inside the deserialized evaluator's child expression tree.
+ var embeddedRand: Rand = null
+ rtEvaluator.child.foreach {
+ case n: Rand => embeddedRand = n
+ case _ =>
+ }
+ assert(embeddedRand ne null,
+ "Expected a Rand node inside the evaluator's child after round-trip")
+ // The critical identity check: the Rand node that would receive
initialize(partitionIndex)
+ // must be the same instance eval() uses inside the evaluator -- if Java
serialization wrote
+ // two independent copies, initialize() would seed a different instance
than eval() uses.
+ assert(embeddedRand eq rtRand,
+ "Rand inside evaluator and separately-registered Rand must be the same
instance " +
+ "after serialization round-trip")
+ }
+
+ test("SPARK-58481: multi-column IN with empty subquery short-circuits before
evaluating LHS") {
+ // Under ANSI semantics, IN (empty subquery) is always FALSE regardless of
the LHS value.
+ // evalMultiColumn must return false before calling child.eval so that a
LHS expression
Review Comment:
**Nit (P3):** evalMultiColumn no longer exists after the evaluator
extraction. Please update both occurrences in this test area to
MultiColumnInSubqueryEvaluator.eval, or simply call it the multi-column
evaluator.
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