You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@iceberg.apache.org by "manisin (via GitHub)" <gi...@apache.org> on 2025/06/25 23:57:17 UTC
Re: [I] Unexpected behavior when execute CREATE OR REPLACE AS SELECT with REST Catalog [iceberg]
manisin commented on issue #12738:
URL: https://github.com/apache/iceberg/issues/12738#issuecomment-3006564988
Thanks for bringing up this issue!
I have created a very simple repro with rest catalog:
`create table replaceTestTable (a int, b string) partitioned by (a) location 's3://test/replaceTestTable’;
create or replace table replaceTestTable (c int, d string) using iceberg;
select * from replaceTestTable;
`
This fails with the following stacktrace:
```
[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.
Caused by: java.lang.NullPointerException: Type cannot be null
at org.apache.iceberg.relocated.com.google.common.base.Preconditions.checkNotNull(Preconditions.java:921)
at org.apache.iceberg.types.Types$NestedField.<init>(Types.java:448)
at org.apache.iceberg.types.Types$NestedField.optional(Types.java:417)
at org.apache.iceberg.PartitionSpec.partitionType(PartitionSpec.java:132)
at org.apache.iceberg.Partitioning.buildPartitionProjectionType(Partitioning.java:274)
at org.apache.iceberg.Partitioning.partitionType(Partitioning.java:242)
at org.apache.iceberg.spark.source.SparkTable.metadataColumns(SparkTable.java:258)
at org.apache.spark.sql.execution.datasources.v2.DataSourceV2Relation.metadataOutput$lzycompute(DataSourceV2Relation.scala:59)
at org.apache.spark.sql.execution.datasources.v2.DataSourceV2Relation.metadataOutput(DataSourceV2Relation.scala:56)
at org.apache.spark.sql.catalyst.plans.logical.SubqueryAlias.metadataOutput(basicLogicalOperators.scala:1692)
at org.apache.spark.sql.catalyst.analysis.Analyzer$AddMetadataColumns$.$anonfun$hasMetadataCol$3(Analyzer.scala:1051)
at org.apache.spark.sql.catalyst.analysis.Analyzer$AddMetadataColumns$.$anonfun$hasMetadataCol$3$adapted(Analyzer.scala:1051)
at scala.collection.Iterator.exists(Iterator.scala:969)
at scala.collection.Iterator.exists$(Iterator.scala:967)
at scala.collection.AbstractIterator.exists(Iterator.scala:1431)
at scala.collection.IterableLike.exists(IterableLike.scala:79)
at scala.collection.IterableLike.exists$(IterableLike.scala:78)
at scala.collection.AbstractIterable.exists(Iterable.scala:56)
at org.apache.spark.sql.catalyst.analysis.Analyzer$AddMetadataColumns$.$anonfun$hasMetadataCol$2(Analyzer.scala:1051)
at org.apache.spark.sql.catalyst.analysis.Analyzer$AddMetadataColumns$.$anonfun$hasMetadataCol$2$adapted(Analyzer.scala:1046)
at org.apache.spark.sql.catalyst.trees.TreeNode.exists(TreeNode.scala:223)
at org.apache.spark.sql.catalyst.analysis.Analyzer$AddMetadataColumns$.$anonfun$hasMetadataCol$1(Analyzer.scala:1046)
at org.apache.spark.sql.catalyst.analysis.Analyzer$AddMetadataColumns$.$anonfun$hasMetadataCol$1$adapted(Analyzer.scala:1046)
at scala.collection.LinearSeqOptimized.exists(LinearSeqOptimized.scala:95)
at scala.collection.LinearSeqOptimized.exists$(LinearSeqOptimized.scala:92)
at scala.collection.immutable.Stream.exists(Stream.scala:204)
at org.apache.spark.sql.catalyst.analysis.Analyzer$AddMetadataColumns$.org$apache$spark$sql$catalyst$analysis$Analyzer$AddMetadataColumns$$hasMetadataCol(Analyzer.scala:1046)
at org.apache.spark.sql.catalyst.analysis.Analyzer$AddMetadataColumns$$anonfun$apply$13.applyOrElse(Analyzer.scala:1016)
at org.apache.spark.sql.catalyst.analysis.Analyzer$AddMetadataColumns$$anonfun$apply$13.applyOrElse(Analyzer.scala:1013)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsDownWithPruning$2(AnalysisHelper.scala:170)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(origin.scala:76)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsDownWithPruning$1(AnalysisHelper.scala:170)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.allowInvokingTransformsInAnalyzer(AnalysisHelper.scala:323)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsDownWithPruning(AnalysisHelper.scala:168)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsDownWithPruning$(AnalysisHelper.scala:164)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperatorsDownWithPruning(LogicalPlan.scala:32)
at org.apache.spark.sql.catalyst.analysis.Analyzer$AddMetadataColumns$.apply(Analyzer.scala:1013)
at org.apache.spark.sql.catalyst.analysis.Analyzer$AddMetadataColumns$.apply(Analyzer.scala:1009)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$2(RuleExecutor.scala:222)
at scala.collection.LinearSeqOptimized.foldLeft(LinearSeqOptimized.scala:126)
at scala.collection.LinearSeqOptimized.foldLeft$(LinearSeqOptimized.scala:122)
at scala.collection.immutable.List.foldLeft(List.scala:91)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$1(RuleExecutor.scala:219)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$1$adapted(RuleExecutor.scala:211)
at scala.collection.immutable.List.foreach(List.scala:431)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:211)
at org.apache.spark.sql.catalyst.analysis.Analyzer.org$apache$spark$sql$catalyst$analysis$Analyzer$$executeSameContext(Analyzer.scala:240)
at org.apache.spark.sql.catalyst.analysis.Analyzer.$anonfun$execute$1(Analyzer.scala:236)
at org.apache.spark.sql.catalyst.analysis.AnalysisContext$.withNewAnalysisContext(Analyzer.scala:187)
at org.apache.spark.sql.catalyst.analysis.Analyzer.execute(Analyzer.scala:236)
at org.apache.spark.sql.catalyst.analysis.Analyzer.execute(Analyzer.scala:202)
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$1(Analyzer.scala:223)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.markInAnalyzer(AnalysisHelper.scala:330)
at org.apache.spark.sql.catalyst.analysis.Analyzer.executeAndCheck(Analyzer.scala:222)
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)
```
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
To unsubscribe, e-mail: issues-unsubscribe@iceberg.apache.org
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@iceberg.apache.org
For additional commands, e-mail: issues-help@iceberg.apache.org