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Posted to reviews@spark.apache.org by "yaooqinn (via GitHub)" <gi...@apache.org> on 2024/02/02 10:46:37 UTC
[PR] [SPARK-46958][SQL] Fix bug when canUpcast and result non-foldable expression [spark]
yaooqinn opened a new pull request, #45000:
URL: https://github.com/apache/spark/pull/45000
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### What changes were proposed in this pull request?
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If the default value can be upcast to a column definition, we shall create lit instead of cast expression; otherwise, it varies and fails the queries afterward.
### Why are the changes needed?
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1. If you propose a new API, clarify the use case for a new API.
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bugfix
```
spark-sql (default)> create table src(key int, c string DEFAULT date'2018-11-17') using parquet;
Time taken: 0.133 seconds
spark-sql (default)> desc src;
[INTERNAL_ERROR] The Spark SQL phase analysis failed with an internal error. You hit a bug in Spark or the Spark plugins you use. Please, report this bug to the corresponding communities or vendors, and provide the full stack trace.
org.apache.spark.SparkException: [INTERNAL_ERROR] The Spark SQL phase analysis failed with an internal error. You hit a bug in Spark or the Spark plugins you use. Please, report this bug to the corresponding communities or vendors, and provide the full stack trace.
at org.apache.spark.SparkException$.internalError(SparkException.scala:107)
at org.apache.spark.sql.execution.QueryExecution$.toInternalError(QueryExecution.scala:536)
at org.apache.spark.sql.execution.QueryExecution$.withInternalError(QueryExecution.scala:548)
at org.apache.spark.sql.execution.QueryExecution.$anonfun$executePhase$1(QueryExecution.scala:219)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:900)
at org.apache.spark.sql.execution.QueryExecution.executePhase(QueryExecution.scala:218)
at org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:77)
at org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:74)
at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:66)
at org.apache.spark.sql.Dataset$.$anonfun$ofRows$2(Dataset.scala:99)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:900)
at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:97)
at org.apache.spark.sql.SparkSession.$anonfun$sql$4(SparkSession.scala:691)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:900)
at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:682)
at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:713)
at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:744)
at org.apache.spark.sql.SQLContext.sql(SQLContext.scala:651)
at org.apache.spark.sql.hive.thriftserver.SparkSQLDriver.run(SparkSQLDriver.scala:68)
at org.apache.spark.sql.hive.thriftserver.SparkSQLCLIDriver.processCmd(SparkSQLCLIDriver.scala:501)
at org.apache.spark.sql.hive.thriftserver.SparkSQLCLIDriver.$anonfun$processLine$1(SparkSQLCLIDriver.scala:619)
at org.apache.spark.sql.hive.thriftserver.SparkSQLCLIDriver.$anonfun$processLine$1$adapted(SparkSQLCLIDriver.scala:613)
at scala.collection.Iterator.foreach(Iterator.scala:943)
at scala.collection.Iterator.foreach$(Iterator.scala:943)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1431)
at scala.collection.IterableLike.foreach(IterableLike.scala:74)
at scala.collection.IterableLike.foreach$(IterableLike.scala:73)
at scala.collection.AbstractIterable.foreach(Iterable.scala:56)
at org.apache.spark.sql.hive.thriftserver.SparkSQLCLIDriver.processLine(SparkSQLCLIDriver.scala:613)
at org.apache.spark.sql.hive.thriftserver.SparkSQLCLIDriver$.main(SparkSQLCLIDriver.scala:310)
at org.apache.spark.sql.hive.thriftserver.SparkSQLCLIDriver.main(SparkSQLCLIDriver.scala)
at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:77)
at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.base/java.lang.reflect.Method.invoke(Method.java:568)
at org.apache.spark.deploy.JavaMainApplication.start(SparkApplication.scala:52)
at org.apache.spark.deploy.SparkSubmit.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:1029)
at org.apache.spark.deploy.SparkSubmit.doRunMain$1(SparkSubmit.scala:194)
at org.apache.spark.deploy.SparkSubmit.submit(SparkSubmit.scala:217)
at org.apache.spark.deploy.SparkSubmit.doSubmit(SparkSubmit.scala:91)
at org.apache.spark.deploy.SparkSubmit$$anon$2.doSubmit(SparkSubmit.scala:1120)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:1129)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
Caused by: java.lang.AssertionError: assertion failed: The existence default value must be a simple SQL string that is resolved and foldable, but got: CAST(DATE '2018-11-17' AS STRING)
at scala.Predef$.assert(Predef.scala:223)
at org.apache.spark.sql.connector.catalog.CatalogV2Util$.structFieldToV2Column(CatalogV2Util.scala:518)
at org.apache.spark.sql.connector.catalog.CatalogV2Util$.$anonfun$structTypeToV2Columns$1(CatalogV2Util.scala:483)
at scala.collection.TraversableLike.$anonfun$map$1(TraversableLike.scala:286)
at scala.collection.IndexedSeqOptimized.foreach(IndexedSeqOptimized.scala:36)
at scala.collection.IndexedSeqOptimized.foreach$(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:198)
at scala.collection.TraversableLike.map(TraversableLike.scala:286)
at scala.collection.TraversableLike.map$(TraversableLike.scala:279)
at scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:198)
at org.apache.spark.sql.connector.catalog.CatalogV2Util$.structTypeToV2Columns(CatalogV2Util.scala:483)
at org.apache.spark.sql.connector.catalog.CatalogV2Util.structTypeToV2Columns(CatalogV2Util.scala)
at org.apache.spark.sql.connector.catalog.Table.columns(Table.java:65)
at org.apache.spark.sql.connector.catalog.V1Table.columns(V1Table.scala:34)
at org.apache.spark.sql.catalyst.analysis.ResolvedTable$.create(v2ResolutionPlans.scala:165)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.$anonfun$lookupTableOrView$3(Analyzer.scala:1205)
at scala.Option.map(Option.scala:230)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.$anonfun$lookupTableOrView$2(Analyzer.scala:1198)
at scala.Option.orElse(Option.scala:447)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.org$apache$spark$sql$catalyst$analysis$Analyzer$ResolveRelations$$lookupTableOrView(Analyzer.scala:1193)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$$anonfun$apply$14.applyOrElse(Analyzer.scala:1152)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$$anonfun$apply$14.applyOrElse(Analyzer.scala:1087)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsUpWithPruning$3(AnalysisHelper.scala:138)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(origin.scala:76)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsUpWithPruning$1(AnalysisHelper.scala:138)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.allowInvokingTransformsInAnalyzer(AnalysisHelper.scala:323)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsUpWithPruning(AnalysisHelper.scala:134)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsUpWithPruning$(AnalysisHelper.scala:130)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperatorsUpWithPruning(LogicalPlan.scala:32)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsUpWithPruning$2(AnalysisHelper.scala:135)
at org.apache.spark.sql.catalyst.trees.UnaryLike.mapChildren(TreeNode.scala:1215)
at org.apache.spark.sql.catalyst.trees.UnaryLike.mapChildren$(TreeNode.scala:1214)
at org.apache.spark.sql.catalyst.plans.logical.DescribeRelation.mapChildren(v2Commands.scala:633)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsUpWithPruning$1(AnalysisHelper.scala:135)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.allowInvokingTransformsInAnalyzer(AnalysisHelper.scala:323)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsUpWithPruning(AnalysisHelper.scala:134)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsUpWithPruning$(AnalysisHelper.scala:130)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperatorsUpWithPruning(LogicalPlan.scala:32)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.apply(Analyzer.scala:1087)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.apply(Analyzer.scala:1046)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$2(RuleExecutor.scala:222)
at scala.collection.LinearSeqOptimized.foldLeft(LinearSeqOptimized.scala:126)
at scala.collection.LinearSeqOptimized.foldLeft$(LinearSeqOptimized.scala:122)
at scala.collection.immutable.List.foldLeft(List.scala:91)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$1(RuleExecutor.scala:219)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$1$adapted(RuleExecutor.scala:211)
at scala.collection.immutable.List.foreach(List.scala:431)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:211)
at org.apache.spark.sql.catalyst.analysis.Analyzer.org$apache$spark$sql$catalyst$analysis$Analyzer$$executeSameContext(Analyzer.scala:226)
at org.apache.spark.sql.catalyst.analysis.Analyzer.$anonfun$execute$1(Analyzer.scala:222)
at org.apache.spark.sql.catalyst.analysis.AnalysisContext$.withNewAnalysisContext(Analyzer.scala:173)
at org.apache.spark.sql.catalyst.analysis.Analyzer.execute(Analyzer.scala:222)
at org.apache.spark.sql.catalyst.analysis.Analyzer.execute(Analyzer.scala:188)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$executeAndTrack$1(RuleExecutor.scala:182)
at org.apache.spark.sql.catalyst.QueryPlanningTracker$.withTracker(QueryPlanningTracker.scala:89)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.executeAndTrack(RuleExecutor.scala:182)
at org.apache.spark.sql.catalyst.analysis.Analyzer.$anonfun$executeAndCheck$1(Analyzer.scala:209)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.markInAnalyzer(AnalysisHelper.scala:330)
at org.apache.spark.sql.catalyst.analysis.Analyzer.executeAndCheck(Analyzer.scala:208)
at org.apache.spark.sql.execution.QueryExecution.$anonfun$analyzed$1(QueryExecution.scala:77)
at org.apache.spark.sql.catalyst.QueryPlanningTracker.measurePhase(QueryPlanningTracker.scala:138)
at org.apache.spark.sql.execution.QueryExecution.$anonfun$executePhase$2(QueryExecution.scala:219)
at org.apache.spark.sql.execution.QueryExecution$.withInternalError(QueryExecution.scala:546)
... 40 more
```
### Does this PR introduce _any_ user-facing change?
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no
### How was this patch tested?
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new test
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no
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Re: [PR] [SPARK-46958][SQL] Add missing timezone to coerce default values [spark]
Posted by "cloud-fan (via GitHub)" <gi...@apache.org>.
cloud-fan commented on code in PR #45000:
URL: https://github.com/apache/spark/pull/45000#discussion_r1478541834
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/ResolveDefaultColumnsUtil.scala:
##########
@@ -311,36 +313,35 @@ object ResolveDefaultColumns extends QueryErrorsBase
defaultSQL: String): Expression = {
val supplanted = CharVarcharUtils.replaceCharVarcharWithString(dataType)
// Perform implicit coercion from the provided expression type to the required column type.
- val ret = if (supplanted == analyzed.dataType) {
- analyzed
- } else if (Cast.canUpCast(analyzed.dataType, supplanted)) {
- Cast(analyzed, supplanted)
- } else {
- // If the provided default value is a literal of a wider type than the target column, but the
- // literal value fits within the narrower type, just coerce it for convenience. Exclude
- // boolean/array/struct/map types from consideration for this type coercion to avoid
- // surprising behavior like interpreting "false" as integer zero.
- val result = if (analyzed.isInstanceOf[Literal] &&
- !Seq(supplanted, analyzed.dataType).exists(_ match {
+ val ret = analyzed match {
+ case equivalent if equivalent.dataType == supplanted =>
+ equivalent
+ case canUpCast if Cast.canUpCast(canUpCast.dataType, supplanted) =>
+ Cast(analyzed, supplanted, Some(conf.sessionLocalTimeZone))
+ case l: Literal
+ if !Seq(supplanted, l.dataType).exists(_ match {
case _: BooleanType | _: ArrayType | _: StructType | _: MapType => true
case _ => false
- })) {
- try {
- val casted = Cast(analyzed, supplanted, evalMode = EvalMode.TRY).eval()
Review Comment:
shall we make the change smaller and only add the missing timezone here?
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Re: [PR] [SPARK-46958][SQL] Add missing timezone to coerce default values [spark]
Posted by "yaooqinn (via GitHub)" <gi...@apache.org>.
yaooqinn commented on code in PR #45000:
URL: https://github.com/apache/spark/pull/45000#discussion_r1477172195
##########
sql/core/src/test/scala/org/apache/spark/sql/ResolveDefaultColumnsSuite.scala:
##########
@@ -182,14 +182,14 @@ class ResolveDefaultColumnsSuite extends QueryTest with SharedSparkSession {
"actualType" -> "\"STRING\""))
checkError(
exception = intercept[AnalysisException] {
- sql("create table demos.test_ts_other (a timestamp default '2022-01-02') using parquet")
Review Comment:
The original test seems incorrect as the expression `a timestamp default '2022-01-02'` is actually valid, which meets the requirement of fitting within the narrower typ
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Re: [PR] [SPARK-46958][SQL] Fix bug when the canUpcast branch result non-foldable default value expression [spark]
Posted by "cloud-fan (via GitHub)" <gi...@apache.org>.
cloud-fan commented on code in PR #45000:
URL: https://github.com/apache/spark/pull/45000#discussion_r1475971219
##########
sql/core/src/test/scala/org/apache/spark/sql/ResolveDefaultColumnsSuite.scala:
##########
@@ -256,4 +256,11 @@ class ResolveDefaultColumnsSuite extends QueryTest with SharedSparkSession {
checkAnswer(sql("select * from t"), Row(1, "apache", "spark "))
}
}
+
+ test("SPARK-46958: Fix bug when canUpcast and result non-foldable expression") {
+ sql(s"CREATE TABLE t(key int, c STRING DEFAULT date '2018-11-17') " +
Review Comment:
does it work if the default expression is `CAST(date '2018-11-17' AS STRING)`? I think we should find the root cause and understand why the casted expression was considered as non-foldable.
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Re: [PR] [SPARK-46958][SQL] Add missing timezone to coerce default values [spark]
Posted by "yaooqinn (via GitHub)" <gi...@apache.org>.
yaooqinn commented on code in PR #45000:
URL: https://github.com/apache/spark/pull/45000#discussion_r1479174067
##########
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/ResolveDefaultColumnsUtil.scala:
##########
@@ -311,36 +313,35 @@ object ResolveDefaultColumns extends QueryErrorsBase
defaultSQL: String): Expression = {
val supplanted = CharVarcharUtils.replaceCharVarcharWithString(dataType)
// Perform implicit coercion from the provided expression type to the required column type.
- val ret = if (supplanted == analyzed.dataType) {
- analyzed
- } else if (Cast.canUpCast(analyzed.dataType, supplanted)) {
- Cast(analyzed, supplanted)
- } else {
- // If the provided default value is a literal of a wider type than the target column, but the
- // literal value fits within the narrower type, just coerce it for convenience. Exclude
- // boolean/array/struct/map types from consideration for this type coercion to avoid
- // surprising behavior like interpreting "false" as integer zero.
- val result = if (analyzed.isInstanceOf[Literal] &&
- !Seq(supplanted, analyzed.dataType).exists(_ match {
+ val ret = analyzed match {
+ case equivalent if equivalent.dataType == supplanted =>
+ equivalent
+ case canUpCast if Cast.canUpCast(canUpCast.dataType, supplanted) =>
+ Cast(analyzed, supplanted, Some(conf.sessionLocalTimeZone))
+ case l: Literal
+ if !Seq(supplanted, l.dataType).exists(_ match {
case _: BooleanType | _: ArrayType | _: StructType | _: MapType => true
case _ => false
- })) {
- try {
- val casted = Cast(analyzed, supplanted, evalMode = EvalMode.TRY).eval()
Review Comment:
The syntax using match-case here is more elegant than if-else. Although it increases the LOC, it's limited to a single method, so it's safe from the perspective of `git diff`. However, if you insist, I can change it.
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Re: [PR] [SPARK-46958][SQL] Add missing timezone to coerce default values [spark]
Posted by "yaooqinn (via GitHub)" <gi...@apache.org>.
yaooqinn closed pull request #45000: [SPARK-46958][SQL] Add missing timezone to coerce default values
URL: https://github.com/apache/spark/pull/45000
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Re: [PR] [SPARK-46958][SQL] Fix bug when canUpcast and result non-foldable expression [spark]
Posted by "yaooqinn (via GitHub)" <gi...@apache.org>.
yaooqinn commented on PR #45000:
URL: https://github.com/apache/spark/pull/45000#issuecomment-1923548597
cc @cloud-fan
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Re: [PR] [SPARK-46958][SQL] Fix bug when the canUpcast branch result non-foldable default value expression [spark]
Posted by "yaooqinn (via GitHub)" <gi...@apache.org>.
yaooqinn commented on code in PR #45000:
URL: https://github.com/apache/spark/pull/45000#discussion_r1477163489
##########
sql/core/src/test/scala/org/apache/spark/sql/ResolveDefaultColumnsSuite.scala:
##########
@@ -256,4 +256,11 @@ class ResolveDefaultColumnsSuite extends QueryTest with SharedSparkSession {
checkAnswer(sql("select * from t"), Row(1, "apache", "spark "))
}
}
+
+ test("SPARK-46958: Fix bug when canUpcast and result non-foldable expression") {
+ sql(s"CREATE TABLE t(key int, c STRING DEFAULT date '2018-11-17') " +
Review Comment:
The case for the date type seems to be restored on the master branch, but ts values still have an issue caused by missing timeZone, thus, unresolved.
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Re: [PR] [SPARK-46958][SQL] Add missing timezone to coerce default values [spark]
Posted by "yaooqinn (via GitHub)" <gi...@apache.org>.
yaooqinn commented on PR #45000:
URL: https://github.com/apache/spark/pull/45000#issuecomment-1928779135
Thanks @cloud-fan, merged to master
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