[Spark] Support show tblproperties and update catalog for clustered t…

…able (#3271)

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#### Which Delta project/connector is this regarding?
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- [x] Spark
- [ ] Standalone
- [ ] Flink
- [ ] Kernel
- [ ] Other (fill in here)

## Description

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- Describe what this PR changes.
- Describe why we need the change.
 
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correctly link and close the issue upon merge.
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Support show tblproperties for clustered table, and support updating the
clustering column properties in the catalog. Remove table properties
from describe detail's output since that's using the properties from
metadata.
## How was this patch tested?

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Add verification for table properties, describe detail, and catalog
table in verifyClusteringColumns.


## Does this PR introduce _any_ user-facing changes?

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No

spark/src/main/scala/org/apache/spark/sql/delta/catalog/DeltaCatalog.scala

@@ -449,7 +449,9 @@ class DeltaCatalog extends DelegatingCatalogExtension
     var validatedConfigurations =
       DeltaConfigs.validateConfigurations(tableDesc.properties)
     ClusteredTableUtils.validateExistingTableFeatureProperties(validatedConfigurations)
-    // Add needed configs for Clustered table.
+    // Add needed configs for Clustered table. Note that [[PROP_CLUSTERING_COLUMNS]] can only
+    // be added after [[DeltaConfigs.validateConfigurations]] to avoid non-user configurable check
+    // failure.
     if (maybeClusterBySpec.nonEmpty) {
       validatedConfigurations =
         validatedConfigurations ++

spark/src/main/scala/org/apache/spark/sql/delta/catalog/DeltaTableV2.scala

@@ -22,6 +22,8 @@ import java.{util => ju}
 import scala.collection.JavaConverters._
 import scala.collection.mutable
 
+import org.apache.spark.sql.delta.skipping.clustering.{ClusteredTableUtils, ClusteringColumnInfo}
+import org.apache.spark.sql.delta.skipping.clustering.temp.ClusterBySpec
 import org.apache.spark.sql.delta._
 import org.apache.spark.sql.delta.commands.WriteIntoDelta
 import org.apache.spark.sql.delta.commands.cdc.CDCReader
@@ -193,6 +195,13 @@ case class DeltaTableV2(
         base.put(TableCatalog.PROP_EXTERNAL, "true")
       }
     }
+    // Don't use [[PROP_CLUSTERING_COLUMNS]] from CatalogTable because it may be stale.
+    // Since ALTER TABLE updates it using an async post-commit hook.
+    clusterBySpec.foreach { clusterBy =>
+      ClusterBySpec.toProperties(clusterBy).foreach { case (key, value) =>
+        base.put(key, value)
+      }
+    }
     Option(initialSnapshot.metadata.description).foreach(base.put(TableCatalog.PROP_COMMENT, _))
     base.asJava
   }
@@ -322,6 +331,17 @@ case class DeltaTableV2(
   override def v1Table: CatalogTable = ttSafeCatalogTable.getOrElse {
     throw DeltaErrors.invalidV1TableCall("v1Table", "DeltaTableV2")
   }
+
+  lazy val clusterBySpec: Option[ClusterBySpec] = {
+    // Always get the clustering columns from metadata domain in delta log.
+    if (ClusteredTableUtils.isSupported(initialSnapshot.protocol)) {
+      val clusteringColumns = ClusteringColumnInfo.extractLogicalNames(
+        initialSnapshot)
+      Some(ClusterBySpec.fromColumnNames(clusteringColumns))
+    } else {
+      None
+    }
+  }
 }
 
 object DeltaTableV2 {

spark/src/main/scala/org/apache/spark/sql/delta/hooks/UpdateCatalog.scala

@@ -24,6 +24,8 @@ import scala.concurrent.{ExecutionContext, Future, TimeoutException}
 import scala.util.Try
 import scala.util.control.NonFatal
 
+import org.apache.spark.sql.delta.skipping.clustering.{ClusteredTableUtils, ClusteringColumnInfo}
+import org.apache.spark.sql.delta.skipping.clustering.temp.ClusterBySpec
 import org.apache.spark.sql.delta.{DeltaConfigs, DeltaTableIdentifier, OptimisticTransactionImpl, Snapshot}
 import org.apache.spark.sql.delta.actions.{Action, Metadata}
 import org.apache.spark.sql.delta.metering.DeltaLogging
@@ -123,6 +125,20 @@ trait UpdateCatalogBase extends PostCommitHook with DeltaLogging {
     return DeltaTableIdentifier.isDeltaPath(spark, table.identifier)
   }
 
+  /** Check if the clustering columns from snapshot doesn't match what's in the table properties. */
+  protected def clusteringColumnsChanged(snapshot: Snapshot): Boolean = {
+    if (!ClusteredTableUtils.isSupported(snapshot.protocol)) {
+      return false
+    }
+    val currentLogicalClusteringNames =
+      ClusteringColumnInfo.extractLogicalNames(snapshot).mkString(",")
+    val clusterBySpecOpt = ClusterBySpec.fromProperties(table.properties)
+
+    // Since we don't remove the clustering columns table property, this can't happen.
+    assert(!(currentLogicalClusteringNames.nonEmpty && clusterBySpecOpt.isEmpty))
+    clusterBySpecOpt.exists(_.columnNames.map(_.toString).mkString(",") !=
+      currentLogicalClusteringNames)
+  }
 
   /** Update the entry in the Catalog to reflect the latest schema and table properties. */
   protected def execute(
@@ -161,6 +177,15 @@ trait UpdateCatalogBase extends PostCommitHook with DeltaLogging {
               "delta.catalog.update.properties",
               data = loggingData
             )
+          } else if (clusteringColumnsChanged(snapshot)) {
+            // If the clustering columns changed, we'll update the catalog with the new
+            // table properties.
+            updateProperties(spark, snapshot)
+            recordDeltaEvent(
+              snapshot.deltaLog,
+              "delta.catalog.update.clusteringColumns",
+              data = loggingData
+            )
           }
         } catch {
           case NonFatal(e) =>
@@ -259,7 +284,8 @@ case class UpdateCatalog(table: CatalogTable) extends UpdateCatalogBase {
 }
 
 object UpdateCatalog {
-  private var tp: ExecutionContext = _
+  // Exposed for testing.
+  private[delta] var tp: ExecutionContext = _
 
   // This is the encoding of the database for the Hive MetaStore
   private val latin1 = Charset.forName("ISO-8859-1")
@@ -344,6 +370,14 @@ object UpdateCatalog {
       snapshot.getProperties.toMap ++ Map(
         DeltaConfigs.METASTORE_LAST_UPDATE_VERSION -> snapshot.version.toString,
         DeltaConfigs.METASTORE_LAST_COMMIT_TIMESTAMP -> snapshot.timestamp.toString)
+    if (ClusteredTableUtils.isSupported(snapshot.protocol)) {
+      val clusteringColumns = ClusteringColumnInfo.extractLogicalNames(snapshot)
+      val properties = ClusterBySpec.toProperties(
+        ClusterBySpec.fromColumnNames(clusteringColumns))
+      properties.foreach { case (key, value) =>
+        newProperties += (key -> value)
+      }
+    }
     newProperties
   }
 

spark/src/main/scala/org/apache/spark/sql/delta/schema/ImplicitMetadataOperation.scala

@@ -107,7 +107,9 @@ trait ImplicitMetadataOperation extends DeltaLogging {
         throw DeltaErrors.unexpectedDataChangeException("Create a Delta table")
       }
       val description = configuration.get("comment").orNull
-      val cleanedConfs = configuration.filterKeys(_ != "comment").toMap
+      // Filter out the property for clustering columns from Metadata action.
+      val cleanedConfs = ClusteredTableUtils.removeClusteringColumnsProperty(
+        configuration.filterKeys(_ != "comment").toMap)
       txn.updateMetadata(
         Metadata(
           description = description,

spark/src/main/scala/org/apache/spark/sql/delta/skipping/clustering/temp/ClusterBySpec.scala

@@ -67,6 +67,17 @@ object ClusterBySpec {
     ClusteredTableUtils.PROP_CLUSTERING_COLUMNS -> clusterBySpec.toJson
   }
 
+  def fromProperties(properties: Map[String, String]): Option[ClusterBySpec] = {
+    properties.get(ClusteredTableUtils.PROP_CLUSTERING_COLUMNS).map { clusteringColumns =>
+      fromProperty(clusteringColumns)
+    }
+  }
+
+  def toProperties(clusterBySpec: ClusterBySpec): Map[String, String] = {
+    val columnValue = mapper.writeValueAsString(clusterBySpec.columnNames.map(_.fieldNames))
+    Map(ClusteredTableUtils.PROP_CLUSTERING_COLUMNS -> columnValue)
+  }
+
   def fromColumnNames(names: Seq[String]): ClusterBySpec = {
     ClusterBySpec(names.map(FieldReference(_)))
   }

spark/src/test/scala/io/delta/tables/DeltaTableBuilderSuite.scala

@@ -466,15 +466,19 @@ class DeltaTableBuilderSuite
   }
 
   test("create table with clustering") {
-    withTable("test") {
-      io.delta.tables.DeltaTable.create().tableName("test")
-        .addColumn("c1", "int")
-        .clusterBy("c1")
-        .execute()
+    withSQLConf(
+      // Enable update catalog for verifyClusteringColumns.
+      DeltaSQLConf.DELTA_UPDATE_CATALOG_ENABLED.key -> "true") {
+      withTable("test") {
+        io.delta.tables.DeltaTable.create().tableName("test")
+          .addColumn("c1", "int")
+          .clusterBy("c1")
+          .execute()
 
-      val deltaLog = DeltaLog.forTable(spark, TableIdentifier("test"))
-      val metadata = deltaLog.snapshot.metadata
-      verifyClusteringColumns(TableIdentifier("test"), Seq("c1"))
+        val deltaLog = DeltaLog.forTable(spark, TableIdentifier("test"))
+        val metadata = deltaLog.snapshot.metadata
+        verifyClusteringColumns(TableIdentifier("test"), Seq("c1"))
+      }
     }
   }
 

spark/src/test/scala/org/apache/spark/sql/delta/skipping/ClusteredTableTestUtils.scala

@@ -17,10 +17,14 @@
 package org.apache.spark.sql.delta.skipping
 
 import org.apache.spark.sql.delta.skipping.clustering.{ClusteredTableUtils, ClusteringColumn, ClusteringColumnInfo}
+import org.apache.spark.sql.delta.skipping.clustering.temp.ClusterBySpec
 import org.apache.spark.sql.delta.{DeltaLog, Snapshot}
 import org.apache.spark.sql.delta.DeltaOperations.{CLUSTERING_PARAMETER_KEY, ZORDER_PARAMETER_KEY}
 import org.apache.spark.sql.delta.commands.optimize.OptimizeMetrics
+import org.apache.spark.sql.delta.hooks.UpdateCatalog
+import org.apache.spark.sql.delta.sources.DeltaSQLConf
 import org.apache.spark.sql.delta.util.JsonUtils
+import org.junit.Assert.assertEquals
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.DataFrame
@@ -210,14 +214,32 @@ trait ClusteredTableTestUtilsBase extends SparkFunSuite with SharedSparkSession
 
   def verifyClusteringColumns(
       tableIdentifier: TableIdentifier,
-      expectedLogicalClusteringColumns: Seq[String]
+      expectedLogicalClusteringColumns: Seq[String],
+      skipCatalogCheck: Boolean = false
     ): Unit = {
     val (_, snapshot) = DeltaLog.forTableWithSnapshot(spark, tableIdentifier)
     verifyClusteringColumnsInternal(
       snapshot,
       tableIdentifier.table,
       expectedLogicalClusteringColumns
     )
+
+    if (skipCatalogCheck) {
+      return
+    }
+
+    val updateCatalogEnabled = spark.conf.get(DeltaSQLConf.DELTA_UPDATE_CATALOG_ENABLED)
+    assert(updateCatalogEnabled,
+      "need to enable [[DeltaSQLConf.DELTA_UPDATE_CATALOG_ENABLED]] to verify catalog updates.")
+    UpdateCatalog.awaitCompletion(10000)
+    val catalog = spark.sessionState.catalog
+    catalog.refreshTable(tableIdentifier)
+    val table = catalog.getTableMetadata(tableIdentifier)
+
+    // Verify CatalogTable's clusterBySpec.
+    assert(ClusteredTableUtils.getClusterBySpecOptional(table).isDefined)
+    assertEquals(ClusterBySpec.fromColumnNames(expectedLogicalClusteringColumns),
+      ClusteredTableUtils.getClusterBySpecOptional(table).get)
   }
 
   def verifyClusteringColumns(
@@ -243,6 +265,24 @@ trait ClusteredTableTestUtilsBase extends SparkFunSuite with SharedSparkSession
     verifyDescribeHistoryOperationParameters(
       tableNameOrPath
     )
+
+    // Verify DESCRIBE DETAIL's properties doesn't contain the "clusteringColumns" key.
+    val describeDetailProps = sql(s"describe detail $tableNameOrPath")
+      .select("properties")
+      .first
+      .getAs[Map[String, String]](0)
+    assert(!describeDetailProps.contains(ClusteredTableUtils.PROP_CLUSTERING_COLUMNS))
+
+    // Verify SHOW TBLPROPERTIES contains the correct clustering columns.
+    val clusteringColumnsVal =
+      sql(s"show tblproperties $tableNameOrPath")
+        .filter($"key" === ClusteredTableUtils.PROP_CLUSTERING_COLUMNS)
+        .select("value")
+        .first
+        .getString(0)
+    val clusterBySpec = ClusterBySpec.fromProperties(
+      Map(ClusteredTableUtils.PROP_CLUSTERING_COLUMNS -> clusteringColumnsVal)).get
+    assert(expectedLogicalClusteringColumns === clusterBySpec.columnNames.map(_.toString))
   }
 }
 

spark/src/test/scala/org/apache/spark/sql/delta/skipping/clustering/ClusteredTableDDLSuite.scala

@@ -22,6 +22,7 @@ import com.databricks.spark.util.{Log4jUsageLogger, MetricDefinitions}
 import org.apache.spark.sql.delta.skipping.ClusteredTableTestUtils
 import org.apache.spark.sql.delta.{DeltaAnalysisException, DeltaColumnMappingEnableIdMode, DeltaColumnMappingEnableNameMode, DeltaConfigs, DeltaExcludedBySparkVersionTestMixinShims, DeltaLog, DeltaUnsupportedOperationException}
 import org.apache.spark.sql.delta.clustering.ClusteringMetadataDomain
+import org.apache.spark.sql.delta.hooks.UpdateCatalog
 import org.apache.spark.sql.delta.sources.DeltaSQLConf
 import org.apache.spark.sql.delta.stats.SkippingEligibleDataType
 import org.apache.spark.sql.delta.test.{DeltaColumnMappingSelectedTestMixin, DeltaSQLCommandTest}
@@ -37,6 +38,35 @@ trait ClusteredTableCreateOrReplaceDDLSuiteBase extends QueryTest
   with SharedSparkSession
   with ClusteredTableTestUtils {
 
+  override def beforeAll(): Unit = {
+    super.beforeAll()
+    spark.conf.set(DeltaSQLConf.DELTA_UPDATE_CATALOG_ENABLED.key, "true")
+  }
+
+  override def afterAll(): Unit = {
+    // Reset UpdateCatalog's thread pool to ensure it is re-initialized in the next test suite.
+    // This is necessary because the [[SparkThreadLocalForwardingThreadPoolExecutor]]
+    // retains a reference to the SparkContext. Without resetting, the new test suite would
+    // reuse the same SparkContext from the previous suite, despite it being stopped.
+    //
+    // This will force the UpdateCatalog's background thread to use the new SparkContext.
+    //
+    // scalastyle:off line.size.limit
+    // This is to avoid the following exception thrown from the UpdateCatalog's background thread:
+    //  java.lang.IllegalStateException: Cannot call methods on a stopped SparkContext.
+    //  This stopped SparkContext was created at:
+    //
+    //  org.apache.spark.sql.delta.skipping.clustering.ClusteredTableDDLDataSourceV2NameColumnMappingSuite.beforeAll
+    //
+    //  The currently active SparkContext was created at:
+    //
+    //  org.apache.spark.sql.delta.skipping.clustering.ClusteredTableDDLDataSourceV2Suite.beforeAll
+    // scalastyle:on line.size.limit
+    UpdateCatalog.tp = null
+
+    super.afterAll()
+  }
+
   protected val testTable: String = "test_ddl_table"
   protected val sourceTable: String = "test_ddl_source"
   protected val targetTable: String = "test_ddl_target"
@@ -627,6 +657,17 @@ trait ClusteredTableDDLSuiteBase
     }
   }
 
+  test("alter table cluster by - catalog reflects clustering columns when reordered") {
+    withClusteredTable(testTable, "id INT, a STRUCT<b INT, c STRING>, name STRING", "id, name") {
+      val tableIdentifier = TableIdentifier(testTable)
+      verifyClusteringColumns(tableIdentifier, Seq("id", "name"))
+
+      // Re-order the clustering keys and validate the catalog sees the correctly reordered keys.
+      sql(s"ALTER TABLE $testTable CLUSTER BY (name, id)")
+      verifyClusteringColumns(tableIdentifier, Seq("name", "id"))
+    }
+  }
+
   test("alter table cluster by - error scenarios") {
     withClusteredTable(testTable, "id INT, id2 INT, name STRING", "id, name") {
       // Specify non-existing columns.
@@ -862,7 +903,7 @@ trait ClusteredTableDDLSuiteBase
 
       sql(s"RESTORE TABLE $testTable TO VERSION AS OF 0")
       val (_, currentSnapshot) = DeltaLog.forTableWithSnapshot(spark, tableIdentifier)
-      verifyClusteringColumns(tableIdentifier, Seq.empty)
+      verifyClusteringColumns(tableIdentifier, Seq.empty, skipCatalogCheck = true)
     }
 
     // Scenario 2: restore clustered table to previous clustering columns.
@@ -873,7 +914,7 @@ trait ClusteredTableDDLSuiteBase
       verifyClusteringColumns(tableIdentifier, Seq("b"))
 
       sql(s"RESTORE TABLE $testTable TO VERSION AS OF 0")
-      verifyClusteringColumns(tableIdentifier, Seq("a"))
+      verifyClusteringColumns(tableIdentifier, Seq("a"), skipCatalogCheck = true)
     }
 
     // Scenario 3: restore from table with clustering columns to non-empty clustering columns
@@ -884,7 +925,7 @@ trait ClusteredTableDDLSuiteBase
       verifyClusteringColumns(tableIdentifier, Seq.empty)
 
       sql(s"RESTORE TABLE $testTable TO VERSION AS OF 0")
-      verifyClusteringColumns(tableIdentifier, Seq("a"))
+      verifyClusteringColumns(tableIdentifier, Seq("a"), skipCatalogCheck = true)
     }
 
     // Scenario 4: restore to start version.
@@ -894,7 +935,7 @@ trait ClusteredTableDDLSuiteBase
       sql(s"INSERT INTO $testTable VALUES (1)")
 
       sql(s"RESTORE TABLE $testTable TO VERSION AS OF 0")
-      verifyClusteringColumns(tableIdentifier, Seq("a"))
+      verifyClusteringColumns(tableIdentifier, Seq("a"), skipCatalogCheck = true)
     }
 
     // Scenario 5: restore unclustered table to unclustered table.
@@ -933,6 +974,7 @@ trait ClusteredTableDDLSuiteBase
 }
 
 trait ClusteredTableDDLSuite extends ClusteredTableDDLSuiteBase
+
 trait ClusteredTableDDLWithNameColumnMapping
   extends ClusteredTableCreateOrReplaceDDLSuite with DeltaColumnMappingEnableNameMode
 

spark/src/test/scala/org/apache/spark/sql/delta/skipping/clustering/IncrementalZCubeClusteringSuite.scala

@@ -191,7 +191,9 @@ class IncrementalZCubeClusteringSuite extends QueryTest
 
   test("test changing clustering columns") {
     withSQLConf(
-      SQLConf.MAX_RECORDS_PER_FILE.key -> "2") {
+      SQLConf.MAX_RECORDS_PER_FILE.key -> "2",
+      // Enable update catalog for verifyClusteringColumns.
+      DeltaSQLConf.DELTA_UPDATE_CATALOG_ENABLED.key -> "true") {
       withClusteredTable(
         table = table,
         schema = "col1 int, col2 int",