[Spark] OPTIMIZE on clustered table with no clustering columns should…

… run compaction (delta-io#2777)

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#### Which Delta project/connector is this regarding?
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request title
For example: [Spark] Title of my pull request
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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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Currently, when running OPTIMIZE on a clustered table without any
clustering columns(after ALTER TABLE CLUSTER BY NONE), it would fail
with a long stack trace:
```
[info]   org.apache.spark.SparkException: Exception thrown in awaitResult:
[info]   at org.apache.spark.util.SparkThreadUtils$.awaitResult(SparkThreadUtils.scala:56)
[info]   at org.apache.spark.util.ThreadUtils$.awaitResult(ThreadUtils.scala:310)
[info]   at org.apache.spark.util.ThreadUtils$.parmap(ThreadUtils.scala:383)
[info]   at org.apache.spark.sql.delta.commands.OptimizeExecutor.$anonfun$optimize$1(OptimizeTableCommand.scala:276)
...
[info]   Cause: java.util.concurrent.ExecutionException: Boxed Error
[info]   at scala.concurrent.impl.Promise$.resolver(Promise.scala:87)
...
[info]   Cause: java.lang.AssertionError: assertion failed: Cannot cluster by zero columns!
[info]   at scala.Predef$.assert(Predef.scala:223)
[info]   at org.apache.spark.sql.delta.skipping.MultiDimClustering$.cluster(MultiDimClustering.scala:51)
[info]   at org.apache.spark.sql.delta.commands.OptimizeExecutor.runOptimizeBinJob(OptimizeTableCommand.scala:427)
[info]   at org.apache.spark.sql.delta.commands.OptimizeExecutor.$anonfun$optimize$6(OptimizeTableCommand.scala:277)
...
```

This change makes OPTIMIZE on a clustered table without any clustering
columns run regular compaction, which is the desired behavior.
## How was this patch tested?

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This change adds a new test to verify the correct behavior. The test
would fail without the fix.
## Does this PR introduce _any_ user-facing changes?

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No.

spark/src/main/scala/org/apache/spark/sql/delta/commands/OptimizeTableCommand.scala

@@ -239,7 +239,9 @@ class OptimizeExecutor(
 
   private val isClusteredTable = ClusteredTableUtils.isSupported(txn.snapshot.protocol)
 
-  private val isMultiDimClustering = isClusteredTable || zOrderByColumns.nonEmpty
+  private val isMultiDimClustering =
+    optimizeStrategy.isInstanceOf[ClusteringStrategy] ||
+    optimizeStrategy.isInstanceOf[ZOrderStrategy]
 
   private val clusteringColumns: Seq[String] = {
     if (zOrderByColumns.nonEmpty) {

spark/src/main/scala/org/apache/spark/sql/delta/commands/OptimizeTableStrategy.scala

@@ -118,8 +118,9 @@ object OptimizeTableStrategy {
 
   private def getMode(snapshot: Snapshot, zOrderBy: Seq[String]): OptimizeTableMode.Value = {
     val isClusteredTable = ClusteredTableUtils.isSupported(snapshot.protocol)
+    val hasClusteringColumns = ClusteringColumnInfo.extractLogicalNames(snapshot).nonEmpty
     val isZOrderBy = zOrderBy.nonEmpty
-    if (isClusteredTable) {
+    if (isClusteredTable && hasClusteringColumns) {
       assert(!isZOrderBy)
       OptimizeTableMode.CLUSTERING
     } else if (isZOrderBy) {

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

@@ -504,6 +504,8 @@ trait ClusteredTableDDLSuiteBase
   extends ClusteredTableCreateOrReplaceDDLSuite
     with DeltaSQLCommandTest {
 
+  import testImplicits._
+
   test("cluster by with more than 4 columns - alter table") {
     val testTable = "test_table"
     withClusteredTable(testTable, "a INT, b INT, c INT, d INT, e INT", "a") {
@@ -564,6 +566,49 @@ trait ClusteredTableDDLSuiteBase
     }
   }
 
+  test("optimize clustered table and trigger regular compaction") {
+    withClusteredTable(testTable, "a INT, b STRING", "a, b") {
+      val tableIdentifier = TableIdentifier(testTable)
+      verifyClusteringColumns(tableIdentifier, "a, b")
+
+      (1 to 1000).map(i => (i, i.toString)).toDF("a", "b")
+        .write.mode("append").format("delta").saveAsTable(testTable)
+
+      val (deltaLog, snapshot) = DeltaLog.forTableWithSnapshot(spark, TableIdentifier(testTable))
+      val targetFileSize = (snapshot.sizeInBytes / 10).toString
+      withSQLConf(
+        DeltaSQLConf.DELTA_OPTIMIZE_MAX_FILE_SIZE.key -> targetFileSize,
+        DeltaSQLConf.DELTA_OPTIMIZE_MIN_FILE_SIZE.key -> targetFileSize) {
+        runOptimize(testTable) { metrics =>
+          assert(metrics.numFilesAdded > 0)
+          assert(metrics.numFilesRemoved > 0)
+          assert(metrics.clusteringStats.nonEmpty)
+          assert(metrics.clusteringStats.get.numOutputZCubes == 1)
+        }
+      }
+
+      // ALTER TABLE CLUSTER BY NONE and then OPTIMIZE to trigger regular compaction.
+      sql(s"ALTER TABLE $testTable CLUSTER BY NONE")
+      verifyClusteringColumns(tableIdentifier, "")
+
+      (1001 to 2000).map(i => (i, i.toString)).toDF("a", "b")
+        .repartition(10).write.mode("append").format("delta").saveAsTable(testTable)
+      val newSnapshot = deltaLog.update()
+      val newTargetFileSize = (newSnapshot.sizeInBytes / 10).toString
+      withSQLConf(
+        DeltaSQLConf.DELTA_OPTIMIZE_MAX_FILE_SIZE.key -> newTargetFileSize,
+        DeltaSQLConf.DELTA_OPTIMIZE_MIN_FILE_SIZE.key -> newTargetFileSize) {
+        runOptimize(testTable) { metrics =>
+          assert(metrics.numFilesAdded > 0)
+          assert(metrics.numFilesRemoved > 0)
+          // No clustering or zorder stats indicates regular compaction.
+          assert(metrics.clusteringStats.isEmpty)
+          assert(metrics.zOrderStats.isEmpty)
+        }
+      }
+    }
+  }
+
   test("optimize clustered table - error scenarios") {
     withClusteredTable(testTable, "a INT, b STRING", "a") {
       // Specify partition predicate.