apache / apache/paimon

SparkGenericCatalog not support Iceberg

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Description

### Search before asking

- [X] I searched in the [issues](https://github.com/apache/incubator-paimon/issues) and found nothing similar.

### Paimon version

0.5.0-incubating

### Compute Engine

```
val spark = SparkSession.builder()
.master("local")
.enableHiveSupport()
.config("spark.serializer", "org.apache.spark.serializer.KryoSerializer")

.config("spark.sql.extensions", "org.apache.spark.sql.hudi.HoodieSparkSessionExtension,org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions")
.config("spark.sql.catalog.hudi_catalog", "org.apache.spark.sql.hudi.catalog.HoodieCatalog")

.config("spark.sql.catalog.iceberg_catalog", "org.apache.iceberg.spark.SparkCatalog")
.config("spark.sql.catalog.iceberg_catalog", "org.apache.iceberg.spark.SparkSessionCatalog")
.config("spark.sql.catalog.iceberg_catalog.type", "hive")
.config("spark.sql.catalog.iceberg_catalog.uri", "thrift://cdh2:9083")

.config("spark.sql.catalog.hive_metastore", "org.apache.paimon.spark.SparkGenericCatalog")
.getOrCreate()

println("hello hudi")
spark.sql("select * from hive_metastore.bigdata.hudi_sample_1").show
println("hello paimon")
spark.sql("select * from hive_metastore.bigdata.paimon_sample").show
println("hello iceberg")
spark.sql("select * from hive_metastore.bigdata.iceberg_sample_1").show
```

### Minimal reproduce step

```
hello hudi
+-------------------+--------------------+------------------+----------------------+-----------------+---+----+
|_hoodie_commit_time|_hoodie_commit_seqno|_hoodie_record_key|_hoodie_partition_path|_hoodie_file_name| id|data|
+-------------------+--------------------+------------------+----------------------+-----------------+---+----+
+-------------------+--------------------+------------------+----------------------+-----------------+---+----+

hello paimon
+---+---+
| k| v|
+---+---+
+---+---+

hello iceberg
Exception in thread "main" java.lang.reflect.UndeclaredThrowableException
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1893)
at org.example.spark.datasource.hudi.PaimonTest1$.main(PaimonTest1.scala:22)
at org.example.spark.datasource.hudi.PaimonTest1.main(PaimonTest1.scala)
Caused by: java.util.concurrent.ExecutionException: org.apache.spark.sql.AnalysisException: iceberg is not a valid Spark SQL Data Source.
at org.sparkproject.guava.util.concurrent.AbstractFuture$Sync.getValue(AbstractFuture.java:306)
at org.sparkproject.guava.util.concurrent.AbstractFuture$Sync.get(AbstractFuture.java:293)
at org.sparkproject.guava.util.concurrent.AbstractFuture.get(AbstractFuture.java:116)
at org.sparkproject.guava.util.concurrent.Uninterruptibles.getUninterruptibly(Uninterruptibles.java:135)
at org.sparkproject.guava.cache.LocalCache$Segment.getAndRecordStats(LocalCache.java:2410)
at org.sparkproject.guava.cache.LocalCache$Segment.loadSync(LocalCache.java:2380)
at org.sparkproject.guava.cache.LocalCache$Segment.lockedGetOrLoad(LocalCache.java:2342)
at org.sparkproject.guava.cache.LocalCache$Segment.get(LocalCache.java:2257)
at org.sparkproject.guava.cache.LocalCache.get(LocalCache.java:4000)
at org.sparkproject.guava.cache.LocalCache$LocalManualCache.get(LocalCache.java:4789)
at org.apache.spark.sql.catalyst.catalog.SessionCatalog.getCachedPlan(SessionCatalog.scala:209)
at org.apache.spark.sql.execution.datasources.FindDataSourceTable.org$apache$spark$sql$execution$datasources$FindDataSourceTable$$readDataSourceTable(DataSourceStrategy.scala:245)
at org.apache.spark.sql.execution.datasources.FindDataSourceTable$$anonfun$apply$2.applyOrElse(DataSourceStrategy.scala:285)
at org.apache.spark.sql.execution.datasources.FindDataSourceTable$$anonfun$apply$2.applyOrElse(DataSourceStrategy.scala:275)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsDownWithPruning$2(AnalysisHelper.scala:170)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:104)
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:31)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsDownWithPruning$4(AnalysisHelper.scala:175)
at org.apache.spark.sql.catalyst.trees.UnaryLike.mapChildren(TreeNode.scala:1249)
at org.apache.spark.sql.catalyst.trees.UnaryLike.mapChildren$(TreeNode.scala:1248)
at org.apache.spark.sql.catalyst.plans.logical.SubqueryAlias.mapChildren(basicLogicalOperators.scala:1631)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsDownWithPruning$1(AnalysisHelper.scala:175)
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:31)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsDownWithPruning$4(AnalysisHelper.scala:175)
at org.apache.spark.sql.catalyst.trees.UnaryLike.mapChildren(TreeNode.scala:1249)
at org.apache.spark.sql.catalyst.trees.UnaryLike.mapChildren$(TreeNode.scala:1248)
at org.apache.spark.sql.catalyst.plans.logical.Project.mapChildren(basicLogicalOperators.scala:69)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsDownWithPruning$1(AnalysisHelper.scala:175)
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:31)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsWithPruning(AnalysisHelper.scala:99)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsWithPruning$(AnalysisHelper.scala:96)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperatorsWithPruning(LogicalPlan.scala:31)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperators(AnalysisHelper.scala:76)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperators$(AnalysisHelper.scala:75)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:31)
at org.apache.spark.sql.execution.datasources.FindDataSourceTable.apply(DataSourceStrategy.scala:275)
at org.apache.spark.sql.execution.datasources.FindDataSourceTable.apply(DataSourceStrategy.scala:239)
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:228)
at org.apache.spark.sql.catalyst.analysis.Analyzer.$anonfun$execute$1(Analyzer.scala:224)
at org.apache.spark.sql.catalyst.analysis.AnalysisContext$.withNewAnalysisContext(Analyzer.scala:173)
at org.apache.spark.sql.catalyst.analysis.Analyzer.execute(Analyzer.scala:224)
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:88)
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$1(QueryExecution.scala:76)
at org.apache.spark.sql.catalyst.QueryPlanningTracker.measurePhase(QueryPlanningTracker.scala:111)
at org.apache.spark.sql.execution.QueryExecution.$anonfun$executePhase$2(QueryExecution.scala:202)
at org.apache.spark.sql.execution.QueryExecution$.withInternalError(QueryExecution.scala:526)
at org.apache.spark.sql.execution.QueryExecution.$anonfun$executePhase$1(QueryExecution.scala:202)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:827)
at org.apache.spark.sql.execution.QueryExecution.executePhase(QueryExecution.scala:201)
at org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:76)
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$2(Dataset.scala:97)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:827)
at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:95)
at org.apache.spark.sql.SparkSession.$anonfun$sql$1(SparkSession.scala:640)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:827)
at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:630)
at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:671)
at org.example.spark.datasource.hudi.PaimonTest1$$anon$1.run(PaimonTest1.scala:45)
at org.example.spark.datasource.hudi.PaimonTest1$$anon$1.run(PaimonTest1.scala:22)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:422)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1875)
... 2 more
Caused by: org.apache.spark.sql.AnalysisException: iceberg is not a valid Spark SQL Data Source.
at org.apache.spark.sql.errors.QueryCompilationErrors$.invalidDataSourceError(QueryCompilationErrors.scala:1457)
at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:420)
at org.apache.spark.sql.execution.datasources.FindDataSourceTable.$anonfun$readDataSourceTable$1(DataSourceStrategy.scala:257)
at org.sparkproject.guava.cache.LocalCache$LocalManualCache$1.load(LocalCache.java:4792)
at org.sparkproject.guava.cache.LocalCache$LoadingValueReference.loadFuture(LocalCache.java:3599)
at org.sparkproject.guava.cache.LocalCache$Segment.loadSync(LocalCache.java:2379)
... 84 more
```

添加spark-iceberg-bundle
![image](https://github.com/apache/incubator-paimon/assets/1145830/b5ac1b81-978a-47b6-88bc-482ec1f1d1f7)

### What doesn't meet your expectations?

Catalog hive_metastore supports access to different types of paimon, hudi, iceberg table

### Anything else?

_No response_

### Are you willing to submit a PR?

- [ ] I'm willing to submit a PR!

Contributor guide

No contributing guide indexed for this repository

Research direction

Start with org.apache.paimon.spark.SparkGenericCatalog and reproduce the failing query from PaimonTest1.scala at line 45 using the Spark catalog configuration shown. Trace why the Iceberg table is rejected, then verify that querying the Iceberg table through the configured catalog succeeds without breaking the Hudi and Paimon examples.

Written by the indexing model from the issue text.

Assessment

Tech stack
scala
Domain
data-engineering
Issue type
Bug
Difficulty
4/5
Estimated time
3-5 days
Activity status
Stale
Clarity
Mostly clear
Newbie friendliness
25/100

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