srielau commented on code in PR #59873:
URL: https://github.com/apache/spark/pull/59873#discussion_r4479043009
##########
sql/core/src/main/scala/org/apache/spark/sql/catalyst/parser/ParseSqlResult.scala:
##########
@@ -43,170 +85,251 @@ import
org.apache.spark.sql.execution.datasources.CreateTempViewUsing
* executors without a session, so only the stock parser is available under
* distributed eval.
*
- * Every statement object includes its 1-based Unicode code-point `start` in
the
- * original batch and its code-point `length`, excluding surrounding whitespace
- * and the terminating semicolon. On success it also includes
+ * Every statement object includes its 1-based character `start` in the
+ * original batch and its character `length`, excluding surrounding
+ * whitespace and the terminating semicolon. Characters are Unicode code
+ * points (Spark SQL `length()` / UTF-8 strings): a supplementary-plane
+ * emoji is one character. Java `String` offsets are UTF-16 (emoji = 2);
+ * ANTLR `CodePointCharStream` offsets are already code points (emoji = 1).
+ * The splitter maps ANTLR indexes to UTF-16 for `substring`; JSON converts
+ * those spans back to code points. On success it also includes
* `parse_success`, the statement
* identifier/code (ISO/IEC 9075-2:2023 Table 39), and omits unused optional
* fields (`target_table_references`, `source_table_references`,
* `function_references`, `select_list`, `parameter_markers`) when empty. On
* parse failure the statement object contains `parse_success: false` with
* source location and a nested STANDARD-format error object, and parsing
- * continues with later statements. Nested error locations are relative to the
- * individual statement, while `start` is relative to the original batch; error
- * `queryContext` indexes are code points as well. An
- * empty or closed-comment-only batch produces an empty array. Only
- * [[ParseException]] / [[SqlScriptingException]] are converted to JSON;
+ * continues with later statements. Nested `queryContext` is the STANDARD
+ * renderer output for that error. View and EXECUTE IMMEDIATE bodies keep
+ * their own text; `start` is relative to the original batch. An empty or
+ * closed-comment-only batch produces an empty array. PARSE converts
+ * [[ParseException]] / [[SqlScriptingException]] to JSON.
+ * ANALYZE also reports non-internal analysis errors as JSON;
* unexpected / internal failures propagate so the function fails.
+ * Nested error locations are statement-relative Unicode code points.
*/
object ParseSqlResult {
private val parser: ThreadLocal[SparkSqlParser] =
ThreadLocal.withInitial(() => new SparkSqlParser())
/** Parse `sql` as a batch and render the JSON result array. */
- def fromSql(sql: String): String = {
+ def fromSql(
+ sql: String,
+ mode: String = ParseSqlMode.Parse): String = {
val split = parser.get().splitStatementsWithPositions(sql)
val statements = split.completeStatements ++ split.partialStatement
- val starts = charStarts(sql, statements)
- compact(render(JArray(statements.zip(starts).map {
- case (segment, start) => parseStatement(segment, start)
- }.toList)))
+ val spans = statementSpans(sql, statements)
+ val json = if (mode == ParseSqlMode.Analyze) {
+ analyzeBatch(statements, spans)
+ } else {
+ JArray(statements.zip(spans).map { case (segment, span) =>
+ parsePlanOrError(segment, span) match {
+ case ParseFailed(errorObj) => errorObj
+ case Parsed(_, plan) =>
+ successJson(plan, analyzed = None, analyzeError = None, None, span)
+ }
+ }.toList)
+ }
+ compact(render(json))
+ }
+
+ private sealed trait ParsedStatement
+ private case class Parsed(segment: PositionedSqlStatement, plan: LogicalPlan)
+ extends ParsedStatement
+ private case class ParseFailed(errorObj: JObject) extends ParsedStatement
+
+ /**
+ * Parse every statement, then analyze each against the invoker session.
+ * Statements are not executed, so one does not see the effect of an earlier
+ * one. Analysis failures stay JSON (`analyze_success` false) per statement.
+ */
+ private def analyzeBatch(
+ statements: Seq[PositionedSqlStatement],
+ spans: Seq[StatementSpan]): JArray = {
+ val parsed = statements.zip(spans).map { case (segment, span) =>
+ parsePlanOrError(segment, span)
+ }
+ val session = ClassicSparkSession.getActiveSession
+ .getOrElse(throw QueryExecutionErrors.parseSqlAnalyzeRequiresSession())
+ // The batch is analyzed on its own: a SQL script that calls parse_sql does
+ // not lend it its locals, and CALL is not run.
+ SqlScriptingContextManager.create(null).runWith {
+ InvokeProcedures.withoutInvocation {
+ JArray(parsed.zip(spans).map {
+ case (ParseFailed(errorObj), _) => errorObj
+ case (Parsed(_, plan), span) =>
+ val (analyzed, error) = analyze(session, plan)
+ successJson(plan, analyzed, error, Some(session), span)
+ }.toList)
+ }
+ }
}
- private def parseStatement(segment: PositionedSqlStatement, charStart: Int):
JObject = {
+ /** Analyze `plan`, including nested EXPLAIN / DESCRIBE QUERY payloads. */
+ private def analyzePlan(
+ session: ClassicSparkSession,
+ plan: LogicalPlan): LogicalPlan = {
+ val toAnalyze = plan match {
+ case s: SupervisingCommand =>
+ s.withTransformedSupervisedPlan(inner => analyzePlan(session, inner))
+ case other => other
+ }
+ session.sessionState.executePlan(toAnalyze,
CommandExecutionMode.SKIP).analyzed
+ }
+
+ /** INTERNAL_ERROR / SQLSTATE XX are Spark bugs, not analysis results. */
+ private def isInternalError(e: SparkThrowable): Boolean =
+ e.isInternalError || Option(e.getSqlState).exists(_.startsWith("XX"))
+
+ /** Character span of a statement object in the original batch. */
+ private case class StatementSpan(start: Int, length: Int)
+
+ /**
+ * Code-point `start` for each statement in one forward pass over `batch`.
+ * Statements are in source order, so prefix counts are not repeated.
+ */
+ private def statementSpans(
+ batch: String,
+ statements: Seq[PositionedSqlStatement]): Seq[StatementSpan] = {
+ var lastUtf16 = 0
+ var lastCp = 0
+ statements.map { segment =>
+ val utf16 = math.max(0, math.min(segment.start, batch.length))
+ if (utf16 >= lastUtf16) {
+ lastCp += batch.codePointCount(lastUtf16, utf16)
+ lastUtf16 = utf16
+ } else {
+ lastCp = batch.codePointCount(0, utf16)
+ lastUtf16 = utf16
+ }
+ StatementSpan(lastCp + 1, charLength(segment))
+ }
+ }
+
+ /** Parsed plan, or parse-failure JSON for this statement. */
+ private def parsePlanOrError(
+ segment: PositionedSqlStatement,
+ span: StatementSpan): ParsedStatement = {
val sql = segment.statement
- val length = sql.codePointCount(0, sql.length)
try {
// Do not inherit the outer query's origin from the parse_sql expression.
- // Errors and parsed nodes refer to the individual statement. Parser
- // origins are code-point offsets, so this one is too.
+ // Errors and parsed nodes refer to the individual statement.
val origin = if (sql.nonEmpty) {
Origin(
startIndex = Some(0),
- stopIndex = Some(length - 1),
+ stopIndex = Some(sql.codePointCount(0, sql.length) - 1),
Review Comment:
Done in 6fc55256046: the stop index is `span.length - 1`, which is the same
code-point count, so the second scan is gone.
##########
sql/core/src/main/scala/org/apache/spark/sql/catalyst/expressions/ParseSql.scala:
##########
@@ -94,22 +161,84 @@ case class ParseSql(child: Expression)
"configKey" -> SQLConf.PARSE_SQL_ENABLED.key,
"configValue" -> "true"))
}
- super.checkInputDataTypes()
+ val typeCheck = super.checkInputDataTypes()
+ if (!typeCheck.isSuccess) {
+ typeCheck
+ } else {
+ if (!mode.foldable) {
+ DataTypeMismatch(
+ errorSubClass = "NON_FOLDABLE_INPUT",
+ messageParameters = Map(
+ "inputName" -> toSQLId("mode"),
+ "inputType" -> toSQLType(mode.dataType),
+ "inputExpr" -> toSQLExpr(mode)))
+ } else {
+ val parsedMode = Option(mode.eval()).map { value =>
+ val modeStr = value.asInstanceOf[UTF8String].toString
+ ParseSqlMode.parse(modeStr).getOrElse {
+ throw QueryCompilationErrors.invalidParseSqlMode(mode)
+ }
+ }
+ // ANALYZE needs the session, which only the driver has, so sqlStmt
+ // must fold. PARSE may take a column of statement text.
+ if (parsedMode.contains(ParseSqlMode.Analyze) && !sqlStmt.foldable) {
+ DataTypeMismatch(
+ errorSubClass = "NON_FOLDABLE_INPUT",
+ messageParameters = Map(
+ "inputName" -> toSQLId("sqlStmt"),
+ "inputType" -> toSQLType(sqlStmt.dataType),
+ "inputExpr" -> toSQLExpr(sqlStmt)))
+ } else {
+ TypeCheckResult.TypeCheckSuccess
+ }
+ }
+ }
}
- override def nullSafeEval(input: Any): Any = {
- val sql = input.asInstanceOf[UTF8String].toString
- UTF8String.fromString(ParseSqlResult.fromSql(sql))
+ override def eval(input: InternalRow): Any = {
+ val sqlInput = sqlStmt.eval(input)
+ if (sqlInput == null) {
+ return null
+ }
+ val modeInput = mode.eval(input)
+ if (modeInput == null) {
+ return null
+ }
+ val sqlText = sqlInput.asInstanceOf[UTF8String].toString
+ val modeStr = modeInput.asInstanceOf[UTF8String].toString
+ val parsedMode = ParseSqlMode.parse(modeStr).getOrElse {
Review Comment:
Done in 6fc55256046: the mode is a `@transient lazy val` (`constantMode`),
evaluated once. NULL `sqlStmt` and NULL `mode` still return NULL, and PARSE
over a column of statements is unchanged.
##########
sql/core/src/main/scala/org/apache/spark/sql/catalyst/parser/ParseSqlResult.scala:
##########
@@ -43,170 +85,251 @@ import
org.apache.spark.sql.execution.datasources.CreateTempViewUsing
* executors without a session, so only the stock parser is available under
* distributed eval.
*
- * Every statement object includes its 1-based Unicode code-point `start` in
the
- * original batch and its code-point `length`, excluding surrounding whitespace
- * and the terminating semicolon. On success it also includes
+ * Every statement object includes its 1-based character `start` in the
+ * original batch and its character `length`, excluding surrounding
+ * whitespace and the terminating semicolon. Characters are Unicode code
+ * points (Spark SQL `length()` / UTF-8 strings): a supplementary-plane
+ * emoji is one character. Java `String` offsets are UTF-16 (emoji = 2);
+ * ANTLR `CodePointCharStream` offsets are already code points (emoji = 1).
+ * The splitter maps ANTLR indexes to UTF-16 for `substring`; JSON converts
+ * those spans back to code points. On success it also includes
* `parse_success`, the statement
* identifier/code (ISO/IEC 9075-2:2023 Table 39), and omits unused optional
* fields (`target_table_references`, `source_table_references`,
* `function_references`, `select_list`, `parameter_markers`) when empty. On
* parse failure the statement object contains `parse_success: false` with
* source location and a nested STANDARD-format error object, and parsing
- * continues with later statements. Nested error locations are relative to the
- * individual statement, while `start` is relative to the original batch; error
- * `queryContext` indexes are code points as well. An
- * empty or closed-comment-only batch produces an empty array. Only
- * [[ParseException]] / [[SqlScriptingException]] are converted to JSON;
+ * continues with later statements. Nested `queryContext` is the STANDARD
+ * renderer output for that error. View and EXECUTE IMMEDIATE bodies keep
+ * their own text; `start` is relative to the original batch. An empty or
+ * closed-comment-only batch produces an empty array. PARSE converts
+ * [[ParseException]] / [[SqlScriptingException]] to JSON.
+ * ANALYZE also reports non-internal analysis errors as JSON;
* unexpected / internal failures propagate so the function fails.
+ * Nested error locations are statement-relative Unicode code points.
*/
object ParseSqlResult {
private val parser: ThreadLocal[SparkSqlParser] =
ThreadLocal.withInitial(() => new SparkSqlParser())
/** Parse `sql` as a batch and render the JSON result array. */
- def fromSql(sql: String): String = {
+ def fromSql(
+ sql: String,
+ mode: String = ParseSqlMode.Parse): String = {
val split = parser.get().splitStatementsWithPositions(sql)
val statements = split.completeStatements ++ split.partialStatement
- val starts = charStarts(sql, statements)
- compact(render(JArray(statements.zip(starts).map {
- case (segment, start) => parseStatement(segment, start)
- }.toList)))
+ val spans = statementSpans(sql, statements)
+ val json = if (mode == ParseSqlMode.Analyze) {
+ analyzeBatch(statements, spans)
+ } else {
+ JArray(statements.zip(spans).map { case (segment, span) =>
+ parsePlanOrError(segment, span) match {
+ case ParseFailed(errorObj) => errorObj
+ case Parsed(_, plan) =>
+ successJson(plan, analyzed = None, analyzeError = None, None, span)
+ }
+ }.toList)
+ }
+ compact(render(json))
+ }
+
+ private sealed trait ParsedStatement
+ private case class Parsed(segment: PositionedSqlStatement, plan: LogicalPlan)
+ extends ParsedStatement
+ private case class ParseFailed(errorObj: JObject) extends ParsedStatement
+
+ /**
+ * Parse every statement, then analyze each against the invoker session.
+ * Statements are not executed, so one does not see the effect of an earlier
+ * one. Analysis failures stay JSON (`analyze_success` false) per statement.
+ */
+ private def analyzeBatch(
+ statements: Seq[PositionedSqlStatement],
+ spans: Seq[StatementSpan]): JArray = {
+ val parsed = statements.zip(spans).map { case (segment, span) =>
+ parsePlanOrError(segment, span)
+ }
+ val session = ClassicSparkSession.getActiveSession
+ .getOrElse(throw QueryExecutionErrors.parseSqlAnalyzeRequiresSession())
+ // The batch is analyzed on its own: a SQL script that calls parse_sql does
+ // not lend it its locals, and CALL is not run.
+ SqlScriptingContextManager.create(null).runWith {
+ InvokeProcedures.withoutInvocation {
+ JArray(parsed.zip(spans).map {
+ case (ParseFailed(errorObj), _) => errorObj
+ case (Parsed(_, plan), span) =>
+ val (analyzed, error) = analyze(session, plan)
+ successJson(plan, analyzed, error, Some(session), span)
+ }.toList)
+ }
+ }
}
- private def parseStatement(segment: PositionedSqlStatement, charStart: Int):
JObject = {
+ /** Analyze `plan`, including nested EXPLAIN / DESCRIBE QUERY payloads. */
+ private def analyzePlan(
+ session: ClassicSparkSession,
+ plan: LogicalPlan): LogicalPlan = {
+ val toAnalyze = plan match {
+ case s: SupervisingCommand =>
+ s.withTransformedSupervisedPlan(inner => analyzePlan(session, inner))
+ case other => other
+ }
+ session.sessionState.executePlan(toAnalyze,
CommandExecutionMode.SKIP).analyzed
+ }
+
+ /** INTERNAL_ERROR / SQLSTATE XX are Spark bugs, not analysis results. */
+ private def isInternalError(e: SparkThrowable): Boolean =
+ e.isInternalError || Option(e.getSqlState).exists(_.startsWith("XX"))
+
+ /** Character span of a statement object in the original batch. */
+ private case class StatementSpan(start: Int, length: Int)
+
+ /**
+ * Code-point `start` for each statement in one forward pass over `batch`.
+ * Statements are in source order, so prefix counts are not repeated.
+ */
+ private def statementSpans(
+ batch: String,
+ statements: Seq[PositionedSqlStatement]): Seq[StatementSpan] = {
+ var lastUtf16 = 0
+ var lastCp = 0
+ statements.map { segment =>
+ val utf16 = math.max(0, math.min(segment.start, batch.length))
+ if (utf16 >= lastUtf16) {
+ lastCp += batch.codePointCount(lastUtf16, utf16)
+ lastUtf16 = utf16
+ } else {
+ lastCp = batch.codePointCount(0, utf16)
+ lastUtf16 = utf16
+ }
+ StatementSpan(lastCp + 1, charLength(segment))
+ }
+ }
+
+ /** Parsed plan, or parse-failure JSON for this statement. */
+ private def parsePlanOrError(
+ segment: PositionedSqlStatement,
+ span: StatementSpan): ParsedStatement = {
val sql = segment.statement
- val length = sql.codePointCount(0, sql.length)
try {
// Do not inherit the outer query's origin from the parse_sql expression.
- // Errors and parsed nodes refer to the individual statement. Parser
- // origins are code-point offsets, so this one is too.
+ // Errors and parsed nodes refer to the individual statement.
val origin = if (sql.nonEmpty) {
Origin(
startIndex = Some(0),
- stopIndex = Some(length - 1),
+ stopIndex = Some(sql.codePointCount(0, sql.length) - 1),
sqlText = Some(sql))
} else {
Origin(sqlText = Some(sql))
}
CurrentOrigin.withOrigin(origin) {
- val plan = parser.get().parsePlan(sql)
- successJson(plan, charStart, length)
+ Parsed(segment, parser.get().parsePlan(sql))
}
} catch {
- // User-facing parse / scripting failures become JSON; everything else
fails.
case e: ParseException =>
- errorJson(e, segment, charStart, length)
+ ParseFailed(errorJson(e, span))
case e: SqlScriptingException =>
- errorJson(e, segment, charStart, length)
+ ParseFailed(errorJson(e, span))
+ }
+ }
+
+ /** Analyze `plan` without executing it. Scripts never reach QueryExecution.
*/
+ private def analyze(
+ session: ClassicSparkSession,
+ plan: LogicalPlan): (Option[LogicalPlan], Option[SparkThrowable with
Throwable]) = {
+ plan match {
+ case _: CompoundBody =>
+ (None, Some(QueryExecutionErrors.parseSqlAnalyzeUnsupportedScript()))
+ case _ =>
+ try {
+ (Some(analyzePlan(session, plan)), None)
+ } catch {
+ case e: SparkThrowable with Throwable if !isInternalError(e) =>
(None, Some(e))
+ }
}
}
/** Build success JSON from an already-parsed unresolved plan. */
private def successJson(
plan: LogicalPlan,
- charStart: Int,
- length: Int): JObject = {
+ analyzed: Option[LogicalPlan],
+ analyzeError: Option[SparkThrowable with Throwable],
+ session: Option[ClassicSparkSession],
+ span: StatementSpan): JObject = {
val classification = SqlStatementCodes.classify(plan)
val fields = mutable.ListBuffer.empty[JField]
- fields += "start" -> JInt(charStart)
- fields += "length" -> JInt(length)
+ fields += "start" -> JInt(span.start)
+ fields += "length" -> JInt(span.length)
fields += "parse_success" -> JBool(true)
fields += "statement_identifier" ->
JString(classification.statementIdentifier)
fields += "statement_code" -> JInt(classification.statementCode)
+ if (session.nonEmpty) {
+ fields += "analyze_success" -> JBool(analyzed.isDefined)
+ }
// Omit unused collections / markers so consumers can treat absence as
empty.
- val refs = collectPlanReferences(plan)
- if (refs.targetTables.nonEmpty) {
+ // Parse names decide which references exist and in which role. ANALYZE
+ // qualifies those names from the analyzed plan when it can match them.
+ val parseRefs = collectPlanReferences(plan)
+ val analyzedRefs = analyzed.map(collectPlanReferences)
+ val (targetTables, sourceTables, functions) = try {
+ session.map { s =>
+ val analyzedTargets = analyzedRefs.map(_.targetTables).getOrElse(Nil)
+ val analyzedSources = analyzedRefs.map(_.sourceTables).getOrElse(Nil)
+ (
+ uniqueIdentities(
+ qualifyParseIdentities(
+ parseRefs.targetTables, analyzedTargets, qualifyTarget(_, plan,
s)),
+ s),
+ uniqueIdentities(
+ qualifyParseIdentities(
+ parseRefs.sourceTables, analyzedSources, qualifySource(_, s)),
+ s),
+ uniqueIdentities(parseRefs.functions.map(qualifyRoutine(_, s)), s)
+ )
+ }.getOrElse(
+ (parseRefs.targetTables, parseRefs.sourceTables, parseRefs.functions))
+ } catch {
+ case e: SparkThrowable with Throwable if !isInternalError(e) =>
+ (parseRefs.targetTables, parseRefs.sourceTables, parseRefs.functions)
+ }
+ if (targetTables.nonEmpty) {
fields += "target_table_references" ->
- JArray(refs.targetTables.map(partsToJArray).toList)
+ JArray(targetTables.map(partsToJArray).toList)
}
- if (refs.sourceTables.nonEmpty) {
+ if (sourceTables.nonEmpty) {
fields += "source_table_references" ->
- JArray(refs.sourceTables.map(partsToJArray).toList)
+ JArray(sourceTables.map(partsToJArray).toList)
}
- if (refs.functions.nonEmpty) {
- fields += "function_references" ->
JArray(refs.functions.map(partsToJArray).toList)
+ if (functions.nonEmpty) {
+ fields += "function_references" ->
JArray(functions.map(partsToJArray).toList)
}
- val selectList = collectSelectList(plan)
+ val selectList = analyzed.map(collectTypedSelectList(_, plan))
+ .getOrElse(collectSelectList(plan))
if (selectList.nonEmpty) {
fields += "select_list" -> JArray(selectList.toList)
}
- refs.parameterMarkers.foreach(markers => fields += "parameter_markers" ->
markers)
+ parseRefs.parameterMarkers.foreach(markers => fields +=
"parameter_markers" -> markers)
+ analyzeError.foreach { e =>
+ fields += "error" -> errorObject(e)
Review Comment:
Added in 6fc55256046: an ANALYZE test for `SELECT '<emoji>', nope` asserts
`queryContext` startIndex 13, stopIndex 16 and fragment `nope`, which are
statement-relative code points after a one-code-point emoji.
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/SparkOptimizer.scala:
##########
@@ -125,6 +126,8 @@ class SparkOptimizer(
override def nonExcludableRules: Seq[String] = super.nonExcludableRules ++
Seq(
+ // ANALYZE needs the driver session; do not leave it to ConstantFolding.
+ EvaluateParseSql.ruleName,
Review Comment:
Done in 6fc55256046: the driver-evaluation test now also excludes
`EvaluateParseSql`. The rule is non-excludable, so the exclusion has no effect
today, and the test pins that: ANALYZE of a constant statement still returns
typed JSON.
##########
sql/core/src/test/scala/org/apache/spark/sql/catalyst/expressions/ParseSqlAnalyzeSuite.scala:
##########
@@ -0,0 +1,603 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions
+
+import java.util.Collections
+import java.util.concurrent.atomic.AtomicInteger
+
+import org.json4s._
+import org.json4s.jackson.JsonMethods.parse
+
+import org.apache.spark.sql.{AnalysisException, QueryTest}
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.connector.catalog.{Identifier, InMemoryCatalog}
+import org.apache.spark.sql.connector.catalog.procedures.{
+ BoundProcedure,
+ ProcedureParameter,
+ UnboundProcedure
+}
+import org.apache.spark.sql.connector.read.Scan
+import org.apache.spark.sql.internal.SQLConf
+import org.apache.spark.sql.test.SharedSparkSession
+import org.apache.spark.sql.types.{DataTypes, StructType}
+
+class ParseSqlAnalyzeSuite extends QueryTest with SharedSparkSession {
+
+ private def evalJson(sql: String, mode: String = "ANALYZE"): JValue = {
+ val result = spark.sql(s"SELECT parse_sql($sql,
'$mode')").head().getString(0)
+ parse(result)
+ }
+
+ private def evalStatement(sql: String, mode: String = "ANALYZE"): JObject = {
+ evalJson("'" + sql.replace("'", "''") + "'", mode) match {
+ case JArray(List(value: JObject)) => value
+ case other => fail(s"expected one statement object, got: $other")
+ }
+ }
+
+ private def jsonType(name: String): JObject = JObject("name" ->
JString(name))
+ private val jsonInt: JObject = jsonType("int")
+ private val jsonBigint: JObject = jsonType("bigint")
+ private val jsonBoolean: JObject = jsonType("boolean")
+ private val jsonString: JObject = JObject(
+ "name" -> JString("string"),
+ "collation" -> JString("UTF8_BINARY"))
+
+ test("ANALYZE types selected columns and PARSE does not") {
+ withSQLConf(SQLConf.PARSE_SQL_ENABLED.key -> "true") {
+ val analyzed = evalStatement("SELECT 1 AS a", "ANALYZE")
+ assert(analyzed \ "parse_success" === JBool(true))
+ assert(analyzed \ "analyze_success" === JBool(true))
+ assert(analyzed \ "select_list" === JArray(List(
+ JObject("name" -> JArray(List(JString("a"))), "type" -> jsonInt))))
+
+ val parsed = evalStatement("SELECT 1 AS a", "PARSE")
+ assert(parsed \ "analyze_success" === JNothing)
+ assert(parsed \ "select_list" === JArray(List(
+ JObject("name" -> JArray(List(JString("a")))))))
+ }
+ }
+
+ test("ANALYZE fully qualifies tables and builtin routines") {
+ withSQLConf(SQLConf.PARSE_SQL_ENABLED.key -> "true") {
+ withTable("parse_sql_analyze_t") {
+ sql("CREATE TABLE parse_sql_analyze_t (a INT, b STRING) USING parquet")
+ val j = evalStatement(
+ "SELECT count(a) AS c FROM parse_sql_analyze_t", "analyze")
+ assert(j \ "analyze_success" === JBool(true))
+ assert(j \ "source_table_references" === JArray(List(JArray(List(
+ JString("spark_catalog"), JString("default"),
JString("parse_sql_analyze_t"))))))
+ assert(j \ "function_references" === JArray(List(JArray(List(
+ JString("system"), JString("builtin"), JString("count"))))))
+ assert(j \ "select_list" === JArray(List(
+ JObject("name" -> JArray(List(JString("c"))), "type" ->
jsonBigint))))
+ }
+ }
+ }
+
+ test("ANALYZE qualifies local temp views as system.session") {
+ withSQLConf(SQLConf.PARSE_SQL_ENABLED.key -> "true") {
+ withTempView("parse_sql_analyze_v") {
+ sql("CREATE OR REPLACE TEMP VIEW parse_sql_analyze_v AS SELECT 1 AS a,
'x' AS b")
+ val j = evalStatement("SELECT a, b FROM parse_sql_analyze_v")
+ assert(j \ "analyze_success" === JBool(true))
+ assert(j \ "source_table_references" === JArray(List(JArray(List(
+ JString("system"), JString("session"),
JString("parse_sql_analyze_v"))))))
+ assert(j \ "select_list" === JArray(List(
+ JObject("name" -> JArray(List(JString("a"))), "type" -> jsonInt),
+ JObject("name" -> JArray(List(JString("b"))), "type" ->
jsonString))))
+ }
+ }
+ }
+
+ test("ANALYZE keeps parse metadata when analysis fails") {
+ withSQLConf(SQLConf.PARSE_SQL_ENABLED.key -> "true") {
+ val j = evalStatement("SELECT a FROM missing_parse_sql_analyze_t")
+ assert(j \ "parse_success" === JBool(true))
+ assert(j \ "analyze_success" === JBool(false))
+ assert(j \ "statement_identifier" === JString("SELECT"))
+ assert(j \ "source_table_references" === JArray(List(JArray(List(
+ JString("spark_catalog"),
+ JString("default"),
+ JString("missing_parse_sql_analyze_t"))))))
+ val errorClass = (j \ "error" \ "errorClass").asInstanceOf[JString].s
+ assert(errorClass == "TABLE_OR_VIEW_NOT_FOUND" ||
+ errorClass.contains("TABLE_OR_VIEW_NOT_FOUND"))
+ }
+ }
+
+ test("ANALYZE inherits the invoker schema") {
+ withSQLConf(SQLConf.PARSE_SQL_ENABLED.key -> "true") {
+ sql("CREATE DATABASE parse_sql_ctx_a")
+ sql("CREATE DATABASE parse_sql_ctx_b")
+ try {
+ sql("CREATE TABLE parse_sql_ctx_a.t (a INT) USING parquet")
+ sql("USE parse_sql_ctx_b")
+ try {
+ val missing = evalStatement("SELECT a FROM t")
+ assert(missing \ "analyze_success" === JBool(false))
+ assert(missing \ "source_table_references" ===
JArray(List(JArray(List(
+ JString("spark_catalog"), JString("parse_sql_ctx_b"),
JString("t"))))))
+ } finally {
+ sql("USE parse_sql_ctx_a")
+ }
+ try {
+ val found = evalStatement("SELECT a FROM t")
+ assert(found \ "analyze_success" === JBool(true))
+ assert(found \ "source_table_references" === JArray(List(JArray(List(
+ JString("spark_catalog"), JString("parse_sql_ctx_a"),
JString("t"))))))
+ assert(spark.sql("SELECT current_schema()").head().getString(0) ===
+ "parse_sql_ctx_a")
+ } finally {
+ sql("USE default")
+ }
+ } finally {
+ sql("DROP TABLE IF EXISTS parse_sql_ctx_a.t")
+ sql("DROP DATABASE IF EXISTS parse_sql_ctx_a CASCADE")
+ sql("DROP DATABASE IF EXISTS parse_sql_ctx_b CASCADE")
+ }
+ }
+ }
+
+ test("ANALYZE named mode argument") {
+ withSQLConf(SQLConf.PARSE_SQL_ENABLED.key -> "true") {
+ val json = spark.sql(
+ """SELECT parse_sql(
+ | 'SELECT 1 AS a',
+ | mode => 'ANALYZE')""".stripMargin).head().getString(0)
+ val analyzed = parse(json) match {
+ case JArray(List(value: JObject)) => value
+ case other => fail(s"expected one statement object, got: $other")
+ }
+ assert(analyzed \ "analyze_success" === JBool(true))
+ assert(analyzed \ "select_list" === JArray(List(
+ JObject("name" -> JArray(List(JString("a"))), "type" -> jsonInt))))
+ }
+ }
+
+ test("ANALYZE without parameter marker types leaves the query unresolved") {
+ withSQLConf(SQLConf.PARSE_SQL_ENABLED.key -> "true") {
+ val json = spark.sql("SELECT parse_sql('SELECT abs(?) AS x', 'ANALYZE')")
+ .head().getString(0)
+ val analyzed = parse(json) match {
+ case JArray(List(value: JObject)) => value
+ case other => fail(s"expected one statement object, got: $other")
+ }
+ assert(analyzed \ "parse_success" === JBool(true))
+ assert(analyzed \ "analyze_success" === JBool(false))
+ }
+ }
+
+ private def evalBatch(sql: String): List[JObject] =
+ evalJson("'" + sql.replace("'", "''") + "'") match {
+ case JArray(values) => values.map(_.asInstanceOf[JObject])
+ case other => fail(s"expected a JSON array, got: $other")
+ }
+
+ private def typedColumns(columns: (String, JObject)*): JArray =
JArray(columns.map {
+ case (name, dataType) => JObject("name" -> JArray(List(JString(name))),
"type" -> dataType)
+ }.toList)
+
+ private def ref(parts: String*): JArray = JArray(parts.map(JString).toList)
+
+ /** Run a full `SELECT parse_sql(...)` and return its statement objects. */
+ private def evalCall(query: String): List[JObject] =
+ parse(spark.sql(query).head().getString(0)) match {
+ case JArray(values) => values.map(_.asInstanceOf[JObject])
+ case other => fail(s"expected a JSON array, got: $other")
+ }
+
+ private def errorClass(statement: JObject): JValue = statement \ "error" \
"errorClass"
+
+ test("ANALYZE does not run CALL, directly or through EXECUTE IMMEDIATE") {
+ withSQLConf(SQLConf.PARSE_SQL_ENABLED.key -> "true") {
+ spark.conf.set("spark.sql.catalog.parse_sql_cat",
classOf[InMemoryCatalog].getName)
+ try {
+ val catalog =
spark.sessionState.catalogManager.catalog("parse_sql_cat")
+ .asInstanceOf[InMemoryCatalog]
+ catalog.createProcedure(
+ Identifier.of(Array("ns"), "record"),
ParseSqlAnalyzeSuite.UnboundRecord)
+ ParseSqlAnalyzeSuite.calls.set(0)
+ val results = evalBatch(
+ """CALL parse_sql_cat.ns.record(1);
+ |EXECUTE IMMEDIATE 'CALL
parse_sql_cat.ns.record(2)'""".stripMargin)
+ assert(results.map(_ \ "parse_success") === List(JBool(true),
JBool(true)))
+ assert(results.map(_ \ "analyze_success") === List(JBool(true),
JBool(true)))
+ assert(ParseSqlAnalyzeSuite.calls.get === 0)
+ sql("CALL parse_sql_cat.ns.record(3)").collect()
+ assert(ParseSqlAnalyzeSuite.calls.get === 1)
+ } finally {
+ spark.sessionState.catalogManager.reset()
+ spark.sessionState.conf.unsetConf("spark.sql.catalog.parse_sql_cat")
+ }
+ }
+ }
+
+ test("ANALYZE inside a SQL script does not see the script's locals") {
+ withSQLConf(SQLConf.PARSE_SQL_ENABLED.key -> "true") {
+ val json = spark.sql(
+ """BEGIN
+ | DECLARE parse_sql_outer INT DEFAULT 5;
+ | SELECT parse_sql('SELECT parse_sql_outer AS x', 'ANALYZE');
+ |END""".stripMargin).head().getString(0)
+ val statement = parse(json) match {
+ case JArray(List(value: JObject)) => value
+ case other => fail(s"expected one statement object, got: $other")
+ }
+ assert(statement \ "analyze_success" === JBool(false))
+ val cls = (statement \ "error" \ "errorClass").asInstanceOf[JString].s
+ assert(cls === "UNRESOLVED_COLUMN.WITHOUT_SUGGESTION" ||
+ cls === "UNRESOLVED_COLUMN.WITH_SUGGESTION" ||
+ cls.startsWith("UNRESOLVED_COLUMN"))
+ }
+ }
+
+ test("ANALYZE requires constant arguments; PARSE does not") {
+ withSQLConf(SQLConf.PARSE_SQL_ENABLED.key -> "true") {
+ val query = "SELECT parse_sql(c, '%s') FROM VALUES ('SELECT 1') AS t(c)"
+ val e =
intercept[AnalysisException](spark.sql(query.format("ANALYZE")).collect())
+ assert(e.getCondition === "DATATYPE_MISMATCH.NON_FOLDABLE_INPUT")
+ assert(spark.sql(query.format("PARSE")).collect().length === 1)
+ val modeCol = intercept[AnalysisException](
+ spark.sql("SELECT parse_sql(c, m) FROM VALUES ('SELECT 1', 'PARSE') AS
t(c, m)")
+ .collect())
+ assert(modeCol.getCondition === "DATATYPE_MISMATCH.NON_FOLDABLE_INPUT")
+ }
+ }
+
+ test("ANALYZE does not execute SQL scripts") {
+ withSQLConf(SQLConf.PARSE_SQL_ENABLED.key -> "true") {
+ withTable("parse_sql_script_t", "parse_sql_script_u") {
+ sql("CREATE TABLE parse_sql_script_t (a INT) USING parquet")
+ sql("CREATE TABLE parse_sql_script_u (a INT) USING parquet")
+ val schema = spark.sql("SELECT current_schema()").head().getString(0)
+ val j = evalStatement(
+ """BEGIN
+ | INSERT INTO parse_sql_script_t VALUES (1);
+ | DROP TABLE parse_sql_script_u;
+ |END""".stripMargin)
+ assert(j \ "parse_success" === JBool(true))
+ assert(j \ "analyze_success" === JBool(false))
+ assert(errorClass(j) ===
JString("PARSE_SQL_ANALYZE_UNSUPPORTED_SCRIPT"))
+ assert(spark.table("parse_sql_script_t").count() === 0)
+ assert(spark.catalog.tableExists("parse_sql_script_u"))
+ assert(spark.sql("SELECT current_schema()").head().getString(0) ===
schema)
+ }
+ }
+ }
+
+ test("ANALYZE reports range as system.builtin and de-duplicates count") {
+ withSQLConf(SQLConf.PARSE_SQL_ENABLED.key -> "true") {
+ withTable("parse_sql_dedup_t") {
+ sql("CREATE TABLE parse_sql_dedup_t (a INT, b INT) USING parquet")
+ val rangeStmt = evalStatement("SELECT * FROM range(3)")
+ assert(rangeStmt \ "analyze_success" === JBool(true))
+ assert(rangeStmt \ "function_references" === JArray(List(
+ ref("system", "builtin", "range"))))
+ val counts = evalStatement(
+ "SELECT count(a), COUNT(b) FROM parse_sql_dedup_t")
+ assert(counts \ "analyze_success" === JBool(true))
+ assert(counts \ "function_references" === JArray(List(
+ ref("system", "builtin", "count"))))
+ }
+ }
+ }
+
+ test("ANALYZE qualifies DDL targets by statement kind") {
+ withSQLConf(SQLConf.PARSE_SQL_ENABLED.key -> "true") {
+ val tempView = evalStatement("CREATE TEMPORARY VIEW parse_sql_ddl_v AS
SELECT 1 AS a")
+ assert(tempView \ "analyze_success" === JBool(true))
+ assert(tempView \ "target_table_references" === JArray(List(
+ ref("system", "session", "parse_sql_ddl_v"))))
+ assert(!spark.catalog.tableExists("parse_sql_ddl_v"))
+ withTempView("parse_sql_ddl_t") {
+ sql("CREATE TEMP VIEW parse_sql_ddl_t AS SELECT 1 AS a")
+ val table = evalStatement("CREATE TABLE parse_sql_ddl_t (a INT) USING
parquet")
+ assert(table \ "analyze_success" === JBool(true))
+ assert(table \ "target_table_references" === JArray(List(
+ ref("spark_catalog", "default", "parse_sql_ddl_t"))))
+ }
+ }
+ }
+
+ test("ANALYZE types session-catalog CTAS query columns") {
+ withSQLConf(SQLConf.PARSE_SQL_ENABLED.key -> "true") {
+ withTable("parse_sql_ctas_t") {
+ val j = evalStatement(
+ "CREATE TABLE parse_sql_ctas_t USING parquet AS SELECT 1 AS a")
+ assert(j \ "analyze_success" === JBool(true))
+ assert(j \ "select_list" === typedColumns("a" -> jsonInt))
+ assert(!spark.catalog.tableExists("parse_sql_ctas_t"))
+ }
+ }
+ }
+
+ test("ANALYZE does not execute a mixed DDL/DML batch") {
+ withSQLConf(SQLConf.PARSE_SQL_ENABLED.key -> "true") {
+ withTable("parse_sql_batch_t", "parse_sql_batch_u") {
+ sql("CREATE TABLE parse_sql_batch_t (a INT) USING parquet")
+ val results = evalBatch(
+ """INSERT INTO parse_sql_batch_t VALUES (1);
+ |CREATE TABLE parse_sql_batch_u (a INT) USING parquet;
+ |DROP TABLE parse_sql_batch_t""".stripMargin)
+ assert(results.map(_ \ "analyze_success") ===
+ List(JBool(true), JBool(true), JBool(true)))
+ assert(spark.table("parse_sql_batch_t").count() === 0)
+ assert(!spark.catalog.tableExists("parse_sql_batch_u"))
+ assert(spark.catalog.tableExists("parse_sql_batch_t"))
+ }
+ }
+ }
+
+ test("ANALYZE qualifies global temp views and DROP of local temp views") {
+ withSQLConf(SQLConf.PARSE_SQL_ENABLED.key -> "true") {
+ withGlobalTempView("parse_sql_global_v") {
+ sql("CREATE OR REPLACE GLOBAL TEMP VIEW parse_sql_global_v AS SELECT 1
AS a")
+ val globalDb = spark.conf.get("spark.sql.globalTempDatabase")
+ val j = evalStatement(s"SELECT a FROM $globalDb.parse_sql_global_v")
+ assert(j \ "analyze_success" === JBool(true))
+ assert(j \ "source_table_references" === JArray(List(
+ ref("spark_catalog", globalDb, "parse_sql_global_v"))))
+ }
+ withTempView("parse_sql_drop_v") {
+ sql("CREATE TEMP VIEW parse_sql_drop_v AS SELECT 1 AS a")
+ val drop = evalStatement("DROP VIEW parse_sql_drop_v")
+ assert(drop \ "analyze_success" === JBool(true))
+ assert(drop \ "target_table_references" === JArray(List(
+ ref("system", "session", "parse_sql_drop_v"))))
+ assert(spark.catalog.tableExists("parse_sql_drop_v"))
+ }
+ }
+ }
+
+ test("ANALYZE types EXPLAIN and DESCRIBE QUERY inner plans") {
+ withSQLConf(SQLConf.PARSE_SQL_ENABLED.key -> "true") {
+ withTable("parse_sql_explain_t") {
+ sql("CREATE TABLE parse_sql_explain_t (a INT) USING parquet")
+ val explain = evalStatement("EXPLAIN SELECT a FROM
parse_sql_explain_t")
+ assert(explain \ "analyze_success" === JBool(true))
+ assert(explain \ "select_list" === typedColumns("a" -> jsonInt))
+ assert(explain \ "source_table_references" === JArray(List(
+ ref("spark_catalog", "default", "parse_sql_explain_t"))))
+ val missing = evalStatement("EXPLAIN SELECT nope FROM missing_t")
+ assert(missing \ "analyze_success" === JBool(false))
+ val missingClass = (missing \ "error" \
"errorClass").asInstanceOf[JString].s
+ assert(missingClass == "TABLE_OR_VIEW_NOT_FOUND" ||
+ missingClass.contains("TABLE_OR_VIEW_NOT_FOUND"))
+ val nested = evalStatement("EXPLAIN EXPLAIN SELECT nope FROM
missing_t")
+ assert(nested \ "analyze_success" === JBool(false))
+ }
+ val describe = evalStatement("DESCRIBE QUERY SELECT 1 AS a")
+ assert(describe \ "analyze_success" === JBool(true))
+ assert(describe \ "select_list" === typedColumns("a" -> jsonInt))
+ }
+ }
+
+ test("ANALYZE reports path-based datasource tables as format plus path") {
+ withSQLConf(SQLConf.PARSE_SQL_ENABLED.key -> "true") {
+ withTempDir { dir =>
+ val path = dir.getCanonicalPath
+ spark.range(1).write.mode("overwrite").parquet(path)
+ val j = evalStatement(s"SELECT id FROM parquet.`$path`")
+ assert(j \ "analyze_success" === JBool(true))
+ assert(j \ "source_table_references" === JArray(List(ref("parquet",
path))))
+ }
+ }
+ }
+
+ test("ANALYZE unknown functions stay JSON rather than INTERNAL_ERROR") {
+ withSQLConf(SQLConf.PARSE_SQL_ENABLED.key -> "true") {
+ val j = evalStatement("SELECT no_such_parse_sql_fn(1)")
+ assert(j \ "parse_success" === JBool(true))
+ assert(j \ "analyze_success" === JBool(false))
+ val cls = (j \ "error" \ "errorClass").asInstanceOf[JString].s
+ assert(cls == "UNRESOLVED_ROUTINE" || cls.contains("UNRESOLVED_ROUTINE"))
+ }
+ }
+
+ test("ANALYZE keeps distinct tables that share a name suffix") {
Review Comment:
Added in 6fc55256046: ANALYZE cases whose bound identity differs from the
fallback guess. A MERGE source under a row-level target (`staging.t`, not
`prod.t`), a SQL table function body (only the user's `default.t` is reported),
and a reference written in another case (`PARSE_SQL_CASE_T` reports the stored
`parse_sql_case_t`). The MERGE and table-function tests fail when the fix is
disabled.
##########
sql/core/src/main/scala/org/apache/spark/sql/catalyst/parser/ParseSqlResult.scala:
##########
@@ -489,19 +652,274 @@ object ParseSqlResult {
named.map(selectListItem)
}
+ /**
+ * Select-list items with SQL types from the analyzed query output, so `*`
+ * is expanded and names match analysis. INSERT reports the write projection
+ * (target columns), not the user's SELECT aliases.
+ */
+ private def collectTypedSelectList(
+ analyzed: LogicalPlan,
+ parsed: LogicalPlan): Seq[JObject] = {
+ val query = primaryQueryPlan(analyzed)
+ if (query.resolved && query.output.nonEmpty) {
+ query.output.map { attr =>
+ JObject(
+ "name" -> partsToJArray(Seq(attr.name)),
+ "type" -> DescribeRelationJsonCommand.jsonType(
+
CharVarcharUtils.getRawType(attr.metadata).getOrElse(attr.dataType)))
+ }
+ } else {
+ collectSelectList(parsed)
+ }
+ }
+
+ private def uniqueIdentities(
+ ids: Seq[Seq[String]],
+ session: ClassicSparkSession): Seq[Seq[String]] = {
+ val sensitive = session.sessionState.conf.caseSensitiveAnalysis
+ val seen = mutable.LinkedHashSet.empty[Seq[String]]
+ ids.filter { parts =>
+ val key = if (sensitive) parts else parts.map(_.toLowerCase(Locale.ROOT))
+ seen.add(key)
+ }
+ }
+
+ private def sessionIdent(name: String): Seq[String] =
+ Seq(CatalogManager.SYSTEM_CATALOG_NAME, CatalogManager.SESSION_NAMESPACE,
name)
+
+ private def catalogIdentParts(catalog: String, ident: Identifier):
Seq[String] =
+ Seq(catalog) ++ ident.namespace() :+ ident.name()
+
+ private def v2RelationParts(r: DataSourceV2Relation): Option[Seq[String]] =
+ (r.catalog, r.identifier) match {
+ case (Some(catalog), Some(ident)) =>
Some(catalogIdentParts(catalog.name(), ident))
+ case _ => None
+ }
+
+ private def logicalRelationParts(r: LogicalRelation): Option[Seq[String]] =
+ r.catalogTable.map { t =>
+ val id = t.identifier
+ if (id.catalog.isEmpty && id.database.isDefined) {
+ CatalogManager.SESSION_CATALOG_NAME +: tableIdentifierParts(id)
+ } else {
+ tableIdentifierParts(id)
+ }
+ }
+
+ private def viewParts(v: View): Seq[String] = {
+ val id = v.desc.identifier
+ if (v.isTempView) {
+ val globalTemp = SQLConf.get.getConf(StaticSQLConf.GLOBAL_TEMP_DATABASE)
+ if (id.database.exists(_.equalsIgnoreCase(globalTemp))) {
+ Seq(id.catalog.getOrElse(CatalogManager.SESSION_CATALOG_NAME)) ++
+ id.database.toSeq :+ id.table
+ } else {
+ sessionIdent(id.table)
+ }
+ } else {
+ tableIdentifierParts(id)
+ }
+ }
+
+ private def tempViewParts(ident: Identifier): Seq[String] = {
+ val parts = ident.namespace().toSeq :+ ident.name()
+ val globalTemp = SQLConf.get.getConf(StaticSQLConf.GLOBAL_TEMP_DATABASE)
+ val ns = ident.namespace()
+ if (ns.length == 1 && ns.head.equalsIgnoreCase(globalTemp)) {
+ Seq(CatalogManager.SESSION_CATALOG_NAME, ns.head, ident.name())
+ } else if (ns.isEmpty || CatalogManager.isSessionQualifiedViewName(parts))
{
+ sessionIdent(ident.name())
+ } else {
+ catalogIdentParts(CatalogManager.SESSION_CATALOG_NAME, ident)
+ }
+ }
+
+ private def globalTempIdent(name: String): Seq[String] =
+ Seq(
+ CatalogManager.SESSION_CATALOG_NAME,
+ SQLConf.get.getConf(StaticSQLConf.GLOBAL_TEMP_DATABASE),
+ name)
+
+ /**
+ * Match each parse name to an analyzed identity at most once. Longer parse
+ * names claim first so `spark_catalog.db.t` is not stolen by a later bare
+ * `t`. Equal-length matches count: a 3-part parse name is already complete.
+ */
+ private def qualifyParseIdentities(
+ parseIds: Seq[Seq[String]],
+ analyzedIds: Seq[Seq[String]],
+ fallback: Seq[String] => Seq[String]): Seq[Seq[String]] = {
+ val remaining = analyzedIds.to(mutable.ArrayBuffer)
+ val assigned = Array.fill[Option[Seq[String]]](parseIds.length)(None)
+ parseIds.zipWithIndex.sortBy { case (parts, _) => -parts.length }.foreach {
+ case (parts, i) =>
+ matchingIdentity(parts, remaining.toSeq) match {
+ case Some(matched) =>
+ val matchedKey = matched.map(_.toLowerCase(Locale.ROOT))
+ val idx = remaining.indexWhere { analyzed =>
+ analyzed.map(_.toLowerCase(Locale.ROOT)) == matchedKey
+ }
+ if (idx >= 0) {
+ remaining.remove(idx)
+ }
+ assigned(i) = Some(matched)
+ case None =>
+ val ident =
+ if (isPathReference(parts)) parts else fallback(parts)
+ assigned(i) = Some(ident)
+ }
+ }
+ assigned.map(_.get).toSeq
+ }
+
+ private def matchingIdentity(
+ parseParts: Seq[String],
+ analyzedIds: Seq[Seq[String]]): Option[Seq[String]] = {
+ val parsed = parseParts.map(_.toLowerCase(Locale.ROOT))
+ analyzedIds.find { analyzed =>
+ val ident = analyzed.map(_.toLowerCase(Locale.ROOT))
+ ident.endsWith(parsed) &&
+ (ident.length > parsed.length ||
+ ident.length == parsed.length && parsed.length >= 2)
+ }
+ }
+
+ private def isPathReference(parts: Seq[String]): Boolean = parts match {
+ case Seq(_, path) => path.contains("/") || path.contains(":")
+ case Seq(path) => path.contains("/") || path.startsWith("file:")
+ case _ => false
+ }
+
+ private def qualifyTarget(
+ parts: Seq[String],
+ plan: LogicalPlan,
+ session: ClassicSparkSession): Seq[String] = plan match {
+ case c: CreateViewCommand if c.viewType == LocalTempView =>
+ sessionIdent(parts.last)
+ case c: CreateViewCommand if c.viewType == GlobalTempView =>
+ globalTempIdent(parts.last)
+ case c: CreateTempViewUsing if c.global =>
+ globalTempIdent(parts.last)
+ case _: CreateTempViewUsing =>
+ sessionIdent(parts.last)
+ case _: CreateTableAsSelect | _: ReplaceTableAsSelect | _: CreateTable |
+ _: ReplaceTable | _: CreateView |
+ _: org.apache.spark.sql.execution.datasources.CreateTable =>
+ // Persistent DDL never names a temp view, even if one shares the name.
+ qualifyWithCurrentNamespace(parts, session)
+ case c: CreateViewCommand if c.viewType == PersistedView =>
+ qualifyWithCurrentNamespace(parts, session)
+ case _ =>
+ qualifySource(parts, session)
+ }
+
+ private def qualifySource(
+ parts: Seq[String],
+ session: ClassicSparkSession): Seq[String] = {
+ if (session.sessionState.catalog.isTempView(parts)) {
+ val globalTemp = SQLConf.get.getConf(StaticSQLConf.GLOBAL_TEMP_DATABASE)
+ val last = parts.last
+ if (parts.exists(_.equalsIgnoreCase(globalTemp))) {
+ globalTempIdent(last)
+ } else {
+ sessionIdent(last)
+ }
+ } else {
+ qualifyWithCurrentNamespace(parts, session)
+ }
+ }
+
+ private def qualifyRoutine(
+ parts: Seq[String],
+ session: ClassicSparkSession): Seq[String] = {
+ val catalog = session.sessionState.catalog
+ val tempFirst =
+
session.sessionState.conf.getConf(SQLConf.SESSION_FUNCTION_RESOLUTION_ORDER) ==
"first"
+ def isTemp(name: String): Boolean =
+ catalog.isTemporaryFunction(FunctionIdentifier(name))
+ def isBuiltin(name: String): Boolean = {
+ val id = FunctionIdentifier(name)
+ FunctionRegistry.builtin.functionExists(id) ||
+ TableFunctionRegistry.builtin.functionExists(id)
+ }
+ def isSessionBuiltin(name: String): Boolean = {
+ val id = FunctionRegistry.builtinFunctionIdentifier(name)
+ !isTemp(name) && !isBuiltin(name) &&
+ (session.sessionState.functionRegistry.functionExists(id) ||
+ session.sessionState.tableFunctionRegistry.functionExists(id))
+ }
+ def builtinIdent(name: String): Seq[String] =
+ FunctionRegistry.builtinFunctionIdentifier(name).nameParts
+ parts match {
+ case Seq(name) if tempFirst && isTemp(name) =>
Review Comment:
Partly addressed in 6fc55256046. Routine owners now follow
`spark.sql.functionResolution.sessionOrder`: with `last`, a persistent function
in the current schema is reported before a temp function of the same name (test
added). Not addressed yet: the candidate walk over the SQL path (SET PATH,
`spark.sql.defaultPath`, custom path with sessionOrder). That needs a
FunctionResolution helper that probes candidates without loading catalog
plugins. I have not done it in this PR, so I am leaving this thread open for a
decision on whether it belongs here or in a follow-up.
--
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