Fix vectorized cardinality bug on certain string columns.

processing/src/main/java/org/apache/druid/query/aggregation/cardinality/vector/CardinalityVectorProcessorFactory.java

@@ -69,6 +69,7 @@ public CardinalityVectorProcessor makeLongProcessor(ColumnCapabilities capabilit
   @Override
   public CardinalityVectorProcessor makeObjectProcessor(ColumnCapabilities capabilities, VectorObjectSelector selector)
   {
-    return NilCardinalityVectorProcessor.INSTANCE;
+    // Handles string-as-object and complex types.
+    return new StringObjectCardinalityVectorProcessor(selector);
   }
 }

processing/src/main/java/org/apache/druid/query/aggregation/cardinality/vector/StringObjectCardinalityVectorProcessor.java

@@ -0,0 +1,115 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+package org.apache.druid.query.aggregation.cardinality.vector;
+
+import org.apache.druid.common.config.NullHandling;
+import org.apache.druid.hll.HyperLogLogCollector;
+import org.apache.druid.query.aggregation.cardinality.types.StringCardinalityAggregatorColumnSelectorStrategy;
+import org.apache.druid.segment.vector.VectorObjectSelector;
+
+import javax.annotation.Nullable;
+import java.nio.ByteBuffer;
+import java.util.List;
+
+public class StringObjectCardinalityVectorProcessor implements CardinalityVectorProcessor
+{
+  private final VectorObjectSelector selector;
+
+  public StringObjectCardinalityVectorProcessor(final VectorObjectSelector selector)
+  {
+    this.selector = selector;
+  }
+
+  @Override
+  public void aggregate(ByteBuffer buf, int position, int startRow, int endRow)
+  {
+    // Save position, limit and restore later instead of allocating a new ByteBuffer object
+    final int oldPosition = buf.position();
+    final int oldLimit = buf.limit();
+
+    try {
+      final Object[] vector = selector.getObjectVector();
+
+      buf.limit(position + HyperLogLogCollector.getLatestNumBytesForDenseStorage());
+      buf.position(position);
+
+      final HyperLogLogCollector collector = HyperLogLogCollector.makeCollector(buf);
+
+      for (int i = startRow; i < endRow; i++) {
+        addObjectIfString(collector, vector[i]);
+      }
+    }
+    finally {
+      buf.limit(oldLimit);
+      buf.position(oldPosition);
+    }
+  }
+
+  @Override
+  public void aggregate(ByteBuffer buf, int numRows, int[] positions, @Nullable int[] rows, int positionOffset)
+  {
+    // Save position, limit and restore later instead of allocating a new ByteBuffer object
+    final int oldPosition = buf.position();
+    final int oldLimit = buf.limit();
+
+    try {
+      final Object[] vector = selector.getObjectVector();
+
+      for (int i = 0; i < numRows; i++) {
+        final Object obj = vector[rows != null ? rows[i] : i];
+
+        if (NullHandling.replaceWithDefault() || obj != null) {
+          final int position = positions[i] + positionOffset;
+          buf.limit(position + HyperLogLogCollector.getLatestNumBytesForDenseStorage());
+          buf.position(position);
+          final HyperLogLogCollector collector = HyperLogLogCollector.makeCollector(buf);
+          addObjectIfString(collector, obj);
+        }
+      }
+    }
+    finally {
+      buf.limit(oldLimit);
+      buf.position(oldPosition);
+    }
+  }
+
+  /**
+   * Adds an Object to a HyperLogLogCollector. If the object is a {@code List<String>} or {@code String} then
+   * the individual Strings are added to the collector.
+   *
+   * If the object is any other type (including null) then behavior depends on null-handling mode:
+   *
+   * - In SQL-compatible mode, ignore non-strings and nulls.
+   * - In replace-with-default mode, treat all non-strings and nulls as empty strings.
+   */
+  private static void addObjectIfString(final HyperLogLogCollector collector, @Nullable final Object obj)
+  {
+    if (obj instanceof String) {
+      StringCardinalityAggregatorColumnSelectorStrategy.addStringToCollector(collector, (String) obj);
+    } else if (obj instanceof List) {
+      //noinspection unchecked
+      for (String s : (List<String>) obj) {
+        StringCardinalityAggregatorColumnSelectorStrategy.addStringToCollector(collector, s);
+      }
+    } else {
+      StringCardinalityAggregatorColumnSelectorStrategy.addStringToCollector(collector, null);
+    }
+  }
+}

processing/src/main/java/org/apache/druid/segment/VectorColumnProcessorFactory.java

@@ -41,17 +41,25 @@
 public interface VectorColumnProcessorFactory<T>
 {
   /**
-   * Called when {@link ColumnCapabilities#getType()} is STRING and the underlying column always has a single value
+   * Called only if {@link ColumnCapabilities#getType()} is STRING and the underlying column always has a single value
    * per row.
+   *
+   * Note that for STRING-typed columns where the dictionary does not exist or is not expected to be useful,
+   * {@link #makeObjectProcessor} may be called instead. To handle all string inputs properly, processors must implement
+   * all three methods (single-value, multi-value, object).
    */
   T makeSingleValueDimensionProcessor(
       ColumnCapabilities capabilities,
       SingleValueDimensionVectorSelector selector
   );
 
   /**
-   * Called when {@link ColumnCapabilities#getType()} is STRING and the underlying column may have multiple values
+   * Called only if {@link ColumnCapabilities#getType()} is STRING and the underlying column may have multiple values
    * per row.
+   *
+   * Note that for STRING-typed columns where the dictionary does not exist or is not expected to be useful,
+   * {@link #makeObjectProcessor} may be called instead. To handle all string inputs properly, processors must implement
+   * all three methods (single-value, multi-value, object).
    */
   T makeMultiValueDimensionProcessor(
       ColumnCapabilities capabilities,
@@ -74,7 +82,8 @@ T makeMultiValueDimensionProcessor(
   T makeLongProcessor(ColumnCapabilities capabilities, VectorValueSelector selector);
 
   /**
-   * Called when {@link ColumnCapabilities#getType()} is COMPLEX.
+   * Called when {@link ColumnCapabilities#getType()} is COMPLEX. May also be called for STRING typed columns in
+   * cases where the dictionary does not exist or is not expected to be useful.
    */
   T makeObjectProcessor(@SuppressWarnings("unused") ColumnCapabilities capabilities, VectorObjectSelector selector);
 }

processing/src/test/java/org/apache/druid/query/groupby/GroupByQueryRunnerTest.java

@@ -8855,6 +8855,78 @@ public void testGroupByCardinalityAggOnFloat()
     TestHelper.assertExpectedObjects(expectedResults, results, "cardinality-agg");
   }
 
+  @Test
+  public void testGroupByCardinalityAggOnMultiStringExpression()
+  {
+    GroupByQuery query = makeQueryBuilder()
+        .setDataSource(QueryRunnerTestHelper.DATA_SOURCE)
+        .setQuerySegmentSpec(QueryRunnerTestHelper.FIRST_TO_THIRD)
+        .setVirtualColumns(
+            new ExpressionVirtualColumn("v0", "concat(quality,market)", ValueType.STRING, TestExprMacroTable.INSTANCE)
+        )
+        .setAggregatorSpecs(
+            QueryRunnerTestHelper.ROWS_COUNT,
+            new CardinalityAggregatorFactory(
+                "numVals",
+                ImmutableList.of(DefaultDimensionSpec.of("v0")),
+                false
+            )
+        )
+        .setGranularity(QueryRunnerTestHelper.ALL_GRAN)
+        .build();
+
+    List<ResultRow> expectedResults = Collections.singletonList(
+        makeRow(
+            query,
+            "2011-04-01",
+            "rows",
+            26L,
+            "numVals",
+            13.041435202975777d
+        )
+    );
+
+    Iterable<ResultRow> results = GroupByQueryRunnerTestHelper.runQuery(factory, runner, query);
+    TestHelper.assertExpectedObjects(expectedResults, results, "cardinality-agg");
+  }
+
+  @Test
+  public void testGroupByCardinalityAggOnHyperUnique()
+  {
+    // Cardinality aggregator on complex columns (like hyperUnique) returns 0.
+
+    GroupByQuery query = makeQueryBuilder()
+        .setDataSource(QueryRunnerTestHelper.DATA_SOURCE)
+        .setQuerySegmentSpec(QueryRunnerTestHelper.FIRST_TO_THIRD)
+        .setAggregatorSpecs(
+            QueryRunnerTestHelper.ROWS_COUNT,
+            new CardinalityAggregatorFactory(
+                "cardinality",
+                ImmutableList.of(DefaultDimensionSpec.of("quality_uniques")),
+                false
+            ),
+            new HyperUniquesAggregatorFactory("hyperUnique", "quality_uniques", false, false)
+        )
+        .setGranularity(QueryRunnerTestHelper.ALL_GRAN)
+        .build();
+
+    List<ResultRow> expectedResults = Collections.singletonList(
+        makeRow(
+            query,
+            "2011-04-01",
+            "rows",
+            26L,
+            "cardinality",
+            NullHandling.replaceWithDefault() ? 1.0002442201269182 : 0.0d,
+            "hyperUnique",
+            9.019833517963864d
+        )
+    );
+
+    Iterable<ResultRow> results = GroupByQueryRunnerTestHelper.runQuery(factory, runner, query);
+    TestHelper.assertExpectedObjects(expectedResults, results, "cardinality-agg");
+  }
+
   @Test
   public void testGroupByLongColumn()
   {

processing/src/test/java/org/apache/druid/query/timeseries/TimeseriesQueryRunnerTest.java

@@ -48,9 +48,12 @@
 import org.apache.druid.query.aggregation.ExpressionLambdaAggregatorFactory;
 import org.apache.druid.query.aggregation.FilteredAggregatorFactory;
 import org.apache.druid.query.aggregation.LongSumAggregatorFactory;
+import org.apache.druid.query.aggregation.cardinality.CardinalityAggregatorFactory;
 import org.apache.druid.query.aggregation.first.DoubleFirstAggregatorFactory;
+import org.apache.druid.query.aggregation.hyperloglog.HyperUniquesAggregatorFactory;
 import org.apache.druid.query.aggregation.last.DoubleLastAggregatorFactory;
 import org.apache.druid.query.aggregation.post.FieldAccessPostAggregator;
+import org.apache.druid.query.dimension.DefaultDimensionSpec;
 import org.apache.druid.query.expression.TestExprMacroTable;
 import org.apache.druid.query.extraction.MapLookupExtractor;
 import org.apache.druid.query.filter.AndDimFilter;
@@ -3035,6 +3038,84 @@ public void testTimeseriesWithExpressionAggregator()
     assertExpectedResults(expectedResults, results);
   }
 
+  @Test
+  public void testTimeseriesCardinalityAggOnMultiStringExpression()
+  {
+    TimeseriesQuery query = Druids.newTimeseriesQueryBuilder()
+        .dataSource(QueryRunnerTestHelper.DATA_SOURCE)
+        .intervals(QueryRunnerTestHelper.FIRST_TO_THIRD)
+        .virtualColumns(
+            new ExpressionVirtualColumn("v0", "concat(quality,market)", ValueType.STRING, TestExprMacroTable.INSTANCE)
+        )
+        .aggregators(
+            QueryRunnerTestHelper.ROWS_COUNT,
+            new CardinalityAggregatorFactory(
+                "numVals",
+                ImmutableList.of(DefaultDimensionSpec.of("v0")),
+                false
+            )
+        )
+        .granularity(QueryRunnerTestHelper.ALL_GRAN)
+        .build();
+
+    List<Result<TimeseriesResultValue>> expectedResults = Collections.singletonList(
+        new Result<>(
+            DateTimes.of("2011-04-01"),
+            new TimeseriesResultValue(
+                ImmutableMap.of(
+                    "rows",
+                    26L,
+                    "numVals",
+                    13.041435202975777d
+                )
+            )
+        )
+    );
+
+    Iterable<Result<TimeseriesResultValue>> results = runner.run(QueryPlus.wrap(query)).toList();
+    assertExpectedResults(expectedResults, results);
+  }
+
+  @Test
+  public void testTimeseriesCardinalityAggOnHyperUnique()
+  {
+    // Cardinality aggregator on complex columns (like hyperUnique) returns 0.
+
+    TimeseriesQuery query = Druids.newTimeseriesQueryBuilder()
+        .dataSource(QueryRunnerTestHelper.DATA_SOURCE)
+        .intervals(QueryRunnerTestHelper.FIRST_TO_THIRD)
+        .aggregators(
+            QueryRunnerTestHelper.ROWS_COUNT,
+            new CardinalityAggregatorFactory(
+                "cardinality",
+                ImmutableList.of(DefaultDimensionSpec.of("quality_uniques")),
+                false
+            ),
+            new HyperUniquesAggregatorFactory("hyperUnique", "quality_uniques", false, false)
+        )
+        .granularity(QueryRunnerTestHelper.ALL_GRAN)
+        .build();
+
+    List<Result<TimeseriesResultValue>> expectedResults = Collections.singletonList(
+        new Result<>(
+            DateTimes.of("2011-04-01"),
+            new TimeseriesResultValue(
+                ImmutableMap.of(
+                    "rows",
+                    26L,
+                    "cardinality",
+                    NullHandling.replaceWithDefault() ? 1.0002442201269182 : 0.0d,
+                    "hyperUnique",
+                    9.019833517963864d
+                )
+            )
+        )
+    );
+
+    Iterable<Result<TimeseriesResultValue>> results = runner.run(QueryPlus.wrap(query)).toList();
+    assertExpectedResults(expectedResults, results);
+  }
+
   private Map<String, Object> makeContext()
   {
     return makeContext(ImmutableMap.of());