dongjoon-hyun commented on code in PR #59992:
URL: https://github.com/apache/spark/pull/59992#discussion_r4433554380
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/constraints.scala:
##########
@@ -128,6 +132,25 @@ case class CheckConstraint(
override def inputTypes: Seq[AbstractDataType] = Seq(BooleanType)
+ /**
+ * Fails the analysis if the condition calls a user-defined function. A
write can read `condition`
+ * again and resolve the name of the function in its own session, where the
name can refer to
+ * another function or to none, so the constraint would not enforce what
this DDL declares.
+ */
+ def failIfCallsUserDefinedFunction(): Unit = {
+ child.collectFirst {
+ case f: SQLFunctionExpression => toSQLId(f.function.name.nameParts)
+ case f: UserDefinedExpression => toSQLId(Seq(f.name))
+ case i: StaticInvoke if i.scalarFunction.isDefined =>
toSQLId(Seq(i.scalarFunction.get.name))
+ case Invoke(Literal(f: ScalarFunction[_], _), _, _, _, _, _, _, _) =>
toSQLId(Seq(f.name))
+ case a: V2Aggregator[_, _] => toSQLId(Seq(a.aggrFunc.name))
+ }.foreach { functionName =>
+ child.failAnalysis(
Review Comment:
Done. `failAnalysis` is now called on the matched node, so the query context
points at the call, such as `fun_test_catalog.ns.strlen(name)`. `functionName`
still shows the bound name because the name in the condition is not available
after resolution.
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/constraints.scala:
##########
@@ -128,6 +132,25 @@ case class CheckConstraint(
override def inputTypes: Seq[AbstractDataType] = Seq(BooleanType)
+ /**
+ * Fails the analysis if the condition calls a user-defined function. A
write can read `condition`
+ * again and resolve the name of the function in its own session, where the
name can refer to
+ * another function or to none, so the constraint would not enforce what
this DDL declares.
+ */
+ def failIfCallsUserDefinedFunction(): Unit = {
+ child.collectFirst {
+ case f: SQLFunctionExpression => toSQLId(f.function.name.nameParts)
+ case f: UserDefinedExpression => toSQLId(Seq(f.name))
+ case i: StaticInvoke if i.scalarFunction.isDefined =>
toSQLId(Seq(i.scalarFunction.get.name))
+ case Invoke(Literal(f: ScalarFunction[_], _), _, _, _, _, _, _, _) =>
toSQLId(Seq(f.name))
+ case a: V2Aggregator[_, _] => toSQLId(Seq(a.aggrFunc.name))
Review Comment:
Kept. Without this case, `CREATE TABLE` accepts a V2 aggregate and every
later write fails. I'll revisit it when `CREATE TABLE` and `REPLACE TABLE`
reject all aggregates under a separate JIRA.
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala:
##########
@@ -3110,6 +3110,9 @@ class Analyzer(
// of this rule.
case f: SQLTableFunction if hasSQLFunctionExpression(f.expressions) => f
+ // CheckAnalysis rejects a SQL function in the condition of a check
constraint.
+ case a: AddCheckConstraint => a.mapChildren(rewrite)
Review Comment:
Removed. `ALTER TABLE` keeps failing with `UNSUPPORTED_SQL_UDF_USAGE` on a
SQL function, as it does with `INVALID_WHERE_CONDITION` on an aggregate.
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/ResolveTableConstraints.scala:
##########
@@ -76,7 +77,20 @@ class ResolveTableConstraints(val catalogManager:
CatalogManager) extends Rule[L
private def buildCatalystExpression(c: Check): Expression = {
Option(c.predicate())
.flatMap(V2ExpressionUtils.toCatalyst)
-
.getOrElse(catalogManager.v1SessionCatalog.parser.parseExpression(c.predicateSql()))
+ .getOrElse(qualifyBuiltinFunctions(
+
catalogManager.v1SessionCatalog.parser.parseExpression(c.predicateSql())))
+ }
+
+ /**
+ * Qualifies the built-in functions a parsed check condition calls, so that
a temporary function
+ * or the SQL path of the session of the write cannot shadow them. Any other
name is left as is:
+ * a user-defined function in a constraint that the DDL no longer accepts
still resolves here.
+ */
+ private def qualifyBuiltinFunctions(condition: Expression): Expression =
condition.transform {
+ case f @ UnresolvedFunction(Seq(name), _, _, _, _, _, _, _)
+ if FunctionRegistry.builtin.functionExists(FunctionIdentifier(name)) =>
Review Comment:
Done. The Scaladoc and the PR description say so now.
##########
sql/core/src/test/scala/org/apache/spark/sql/execution/command/v2/CheckConstraintSuite.scala:
##########
@@ -170,6 +186,152 @@ class CheckConstraintSuite extends QueryTest with
CommandSuiteBase with DDLComma
}
}
+ private def withV2Function(name: String, function: UnboundFunction)(f: =>
Unit): Unit = {
+ val funCatalog = spark.sessionState.catalogManager.catalog(s"fun_$catalog")
+ .asInstanceOf[InMemoryCatalog]
+ val ident = Identifier.of(Array("ns"), name)
+ withNamespace(s"fun_$catalog.ns") {
+ sql(s"CREATE NAMESPACE fun_$catalog.ns")
+ funCatalog.createFunction(ident, function)
+ try f finally funCatalog.dropFunction(ident)
+ }
+ }
+
+ // CREATE TABLE, REPLACE TABLE and, if `inAlter`, ALTER TABLE fail on the
condition.
+ private def checkUserDefinedFunctionRejected(
+ cond: String,
+ functionName: String,
+ inAlter: Boolean = true): Unit = {
+ withNamespaceAndTable("ns", "tbl", nonPartitionCatalog) { t =>
+ sql(s"CREATE TABLE $t (id INT, name STRING) $defaultUsing")
+ val alter = if (inAlter) Seq(s"ALTER TABLE $t ADD CONSTRAINT c1 CHECK
($cond)") else Nil
+ (alter ++ Seq(
+ s"CREATE TABLE ${t}_2 (id INT, name STRING, CONSTRAINT c1 CHECK
($cond)) $defaultUsing",
+ s"REPLACE TABLE $t (id INT, name STRING, CONSTRAINT c1 CHECK ($cond))
$defaultUsing"
+ )).foreach { query =>
+ checkError(
+ exception = intercept[AnalysisException](sql(query)),
+ condition = "USER_DEFINED_FUNCTION_IN_CHECK_CONSTRAINT",
+ sqlState = "42621",
+ parameters = Map("checkCondition" -> cond, "functionName" ->
functionName),
+ context = ExpectedContext(fragment = cond))
+ }
+ }
+ }
+
+ test("SPARK-60777: Check constraint cannot call a user-defined function") {
+ val call = s"fun_$catalog.ns.strlen"
+ Seq(
+ (StrLenDefault, s"$call(name) < 10", "`strlen_default`"),
+ // The optimizer folds a call with constant arguments, but the DDL does
not.
+ (StrLenDefault, s"id < $call('abc')", "`strlen_default`"),
+ (StrLenMagic, s"$call(name) < 10", "`strlen_magic`"),
+ (new JavaStrLenStaticMagic, s"$call(name) < 10", "`strlen`")
+ ).foreach { case (function, cond, functionName) =>
+ withV2Function("strlen", StrLen(function)) {
+ checkUserDefinedFunctionRejected(cond, functionName)
+ }
+ }
+
+ withUserDefinedFunction("tmp_len" -> true, "tmp_sql_len" -> true,
"sql_len" -> false) {
+ spark.udf.register("tmp_len", (s: String) => s.length)
+ checkUserDefinedFunctionRejected("tmp_len(name) < 10", "`tmp_len`")
+ sql("CREATE TEMPORARY FUNCTION tmp_sql_len(x STRING) RETURNS INT RETURN
length(x)")
+ checkUserDefinedFunctionRejected("tmp_sql_len(name) < 10",
"`tmp_sql_len`")
+ sql("CREATE FUNCTION sql_len(x STRING) RETURNS INT RETURN length(x)")
+ checkUserDefinedFunctionRejected("sql_len(name) < 10",
"`spark_catalog`.`default`.`sql_len`")
+ }
+ }
+
+ test("SPARK-60777: Check constraint cannot call a V2 aggregate function") {
+ withV2Function("avg", new JavaAverage) {
+ // ALTER TABLE keeps failing with the error for an aggregate in a filter.
+ checkUserDefinedFunctionRejected(
+ s"fun_$catalog.ns.avg(CAST(id AS DOUBLE)) > 0", "`davg`", inAlter =
false)
+ }
+ }
+
+ test("SPARK-60777: Existing check constraint calling a user-defined function
still works") {
+ withUserDefinedFunction("tmp_len" -> true) {
+ spark.udf.register("tmp_len", (s: String) => s.length)
+ withNamespaceAndTable("ns", "tbl", nonPartitionCatalog) { t =>
+ sql(s"CREATE TABLE $t (id INT, name STRING) $defaultUsing")
+ val check = Constraint.check("c1").predicateSql("tmp_len(name) <
10").build()
+
spark.sessionState.catalogManager.catalog(nonPartitionCatalog).asInstanceOf[TableCatalog]
+ .alterTable(Identifier.of(Array("ns"), "tbl"),
TableChange.addConstraint(check, null))
+ sql(s"INSERT INTO $t VALUES (1, 'abc')")
+ checkError(
+ exception = intercept[SparkRuntimeException] {
+ sql(s"INSERT INTO $t VALUES (2, 'abcdefghijklmnop')")
+ },
+ condition = "CHECK_CONSTRAINT_VIOLATION",
+ sqlState = "23001",
+ parameters = Map(
+ "constraintName" -> "c1",
+ "expression" -> "tmp_len(name) < 10",
+ "values" -> " - name : abcdefghijklmnop"))
+ checkAnswer(spark.table(t).select("id"), Row(1))
+ }
+ }
+ }
+
+ test("SPARK-60777: Check constraint is not shadowed in the session of a
write") {
+ def checkEnforced(t: String): Unit = {
+ // The settings of the write do shadow the built-in function outside of
the constraint.
+ checkAnswer(sql("SELECT upper('x')"), Row("OK"))
+ checkError(
+ exception = intercept[SparkRuntimeException] {
+ sql(s"INSERT INTO $t VALUES (1, 'x')")
+ },
+ condition = "CHECK_CONSTRAINT_VIOLATION",
+ sqlState = "23001",
+ parameters = Map(
+ "constraintName" -> "c1",
+ "expression" -> "UPPER(s) <> 'X'",
+ "values" -> " - s : x"))
+ checkAnswer(spark.table(t), Nil)
+ }
+
+ withV2Function("upper", StrLen(ConstantString)) {
+ withNamespaceAndTable("ns", "tbl", nonPartitionCatalog) { t =>
+ sql(s"CREATE TABLE $t (id INT, s STRING) $defaultUsing")
+ sql(s"ALTER TABLE $t ADD CONSTRAINT c1 CHECK (UPPER(s) <> 'X')")
+ withSQLConf(SQLConf.PATH_ENABLED.key -> "true") {
+ sql(s"SET PATH = fun_$catalog.ns, system.builtin")
+ try checkEnforced(t) finally sql("SET PATH = DEFAULT_PATH")
+ }
+ spark.udf.register("upper", (_: String) => "OK")
+ try {
+ withSQLConf(SQLConf.SESSION_FUNCTION_RESOLUTION_ORDER.key ->
"first") {
+ checkEnforced(t)
+ }
+ } finally {
+ sql("DROP TEMPORARY FUNCTION upper")
+ }
+ }
+ }
+ }
+
+ test("SPARK-60777: Check constraint calling built-in functions is enforced")
{
+ Seq(
+ "UPPER(s) <> 'X'",
Review Comment:
Removed, along with the `s` column that only this case used.
##########
docs/sql-migration-guide.md:
##########
@@ -67,6 +67,7 @@ license: |
- Since Spark 4.4, when a format is specified, `to_timestamp`,
`try_to_timestamp`, and `to_timestamp_ltz` cast a numeric first argument to a
string and parse it with the format, for example `to_timestamp(20201231,
'yyyyMMdd')` returns `2020-12-31 00:00:00`. This matches the existing behavior
when `spark.sql.timestampType` is `TIMESTAMP_NTZ`. Previously, with the default
`TIMESTAMP_LTZ` timestamp type, such calls failed with `INTERNAL_ERROR`. Note
that the string form of the number is parsed, so a DECIMAL `1.5` is parsed as
`"1.5"`.
- Since Spark 4.4, the Spark Thrift Server logs a warning at startup when
`hive.server2.enable.doAs` is `true` and `hive.server2.authentication` verifies
user identities (any recognized type other than `NONE`/`NOSASL`), because
executor-side data access still runs as the server's own service identity
rather than the connecting user (SPARK-5159). Set
`spark.sql.hive.thriftServer.allowIneffectiveDoAs` to `true` to acknowledge the
limitation and silence the warning. Spark 5.0 is expected to refuse to start on
this configuration unless that option is set.
- Since Spark 4.4, `UPDATE` and `MERGE` on a Data Source V2 table fail with
`DATATYPE_MISMATCH.INVALID_ROW_LEVEL_OPERATION_ASSIGNMENTS` when they assign to
a metadata column of the table. This includes `UPDATE SET *` and `INSERT *` in
`MERGE WITH SCHEMA EVOLUTION` when the source has a column with the name of a
metadata column of the target table. Previously, these assignments passed
analysis and were usually ignored. To restore the previous behavior, set
`spark.sql.legacy.ignoreMetadataColumnAssignments` to `true`.
+- Since Spark 4.4, `CREATE TABLE`, `REPLACE TABLE`, and `ALTER TABLE ... ADD
CONSTRAINT` on a Data Source V2 table fail with
`USER_DEFINED_FUNCTION_IN_CHECK_CONSTRAINT` when the condition of a CHECK
constraint calls a user-defined function, including a Data Source V2 function.
Previously, they accepted such a condition, except that `ALTER TABLE ... ADD
CONSTRAINT` failed with `UNSUPPORTED_SQL_UDF_USAGE` on a SQL function, and a
write could resolve the function name in its own session, so that it enforced
another rule or failed (SPARK-60777). To run these statements, change the
condition to call only built-in functions. Existing constraints that call a
user-defined function are not rejected. Also, a write that parses the SQL text
of a condition binds an unqualified built-in function name to `system.builtin`,
regardless of the SQL path and `spark.sql.functionResolution.sessionOrder`, so
an existing constraint no longer calls another function with the same name.
Review Comment:
Done. It is split into two bullets.
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