morrySnow commented on code in PR #64618:
URL: https://github.com/apache/doris/pull/64618#discussion_r3457558964


##########
fe/fe-core/src/main/java/org/apache/doris/nereids/rules/rewrite/InferSetOperatorDistinct.java:
##########
@@ -38,28 +44,137 @@
  * </pre>
  */
 public class InferSetOperatorDistinct extends OneRewriteRuleFactory {
+    private static final double LOWER_AGGREGATE_EFFECT_COEFFICIENT = 10000;
+    private static final double LOW_AGGREGATE_EFFECT_COEFFICIENT = 1000;
+    private static final double MEDIUM_AGGREGATE_EFFECT_COEFFICIENT = 100;
+
+    private final StatsDerive derive = new StatsDerive(false);
+
     @Override
     public Rule build() {
         return logicalSetOperation()
                 .when(operation -> operation.getQualifier() == 
Qualifier.DISTINCT)
                 .then(setOperation -> {
-                    if (setOperation.children().stream().anyMatch(child -> 
child instanceof LogicalAggregate)) {
-                        return null;
+                    ImmutableList.Builder<Plan> newChildren =
+                            
ImmutableList.builderWithExpectedSize(setOperation.arity());
+                    boolean hasNewChildren = false;
+                    for (Plan child : setOperation.children()) {
+                        if (shouldInferDistinct(child)) {
+                            newChildren.add(new LogicalAggregate<>(
+                                    ImmutableList.copyOf(child.getOutput()), 
true, child));
+                            hasNewChildren = true;
+                        } else {
+                            newChildren.add(child);
+                        }
                     }
-
-                    List<Plan> newChildren = setOperation.children().stream()
-                            .map(child -> isAgg(child) ? child
-                                    : new 
LogicalAggregate<>(ImmutableList.copyOf(child.getOutput()), true, child))
-                            .collect(ImmutableList.toImmutableList());
-                    if (newChildren.equals(setOperation.children())) {
+                    if (!hasNewChildren) {
                         return null;
                     }
-                    return setOperation.withChildren(newChildren);
+                    return setOperation.withChildren(newChildren.build());
                 }).toRule(RuleType.INFER_SET_OPERATOR_DISTINCT);
     }
 
+    private boolean shouldInferDistinct(Plan child) {
+        return !isAgg(child) && rejectNLJ(child)
+                && shouldGenerateAggregateByNdv(child, child.getOutput());
+    }
+
     private boolean isAgg(Plan plan) {
         return plan instanceof LogicalAggregate || (plan instanceof 
LogicalProject && plan.child(
                 0) instanceof LogicalAggregate);
     }
+
+    // if children exist NLJ, we can't infer distinct
+    // because NLJ could generate bitmap runtime filter. and it will execute 
failed when we do infer distinct.
+    private boolean rejectNLJ(Plan plan) {
+        if (plan instanceof LogicalProject) {
+            plan = plan.child(0);
+        }
+        if (plan instanceof LogicalJoin) {
+            LogicalJoin<?, ?> join = (LogicalJoin<?, ?>) plan;
+            return join.getOtherJoinConjuncts().isEmpty();
+        }
+        return true;
+    }
+
+    private boolean shouldGenerateAggregateByNdv(Plan plan, List<? extends 
NamedExpression> groupKeys) {
+        Statistics stats = plan.getStats();
+        if (stats == null) {
+            stats = plan.accept(derive, new StatsDerive.DeriveContext());
+            if (stats == null) {
+                return false;
+            }
+        }
+        if (stats.getRowCount() <= 0) {
+            return false;
+        }
+
+        List<ColumnStatistic> lower = new ArrayList<>();
+        List<ColumnStatistic> medium = new ArrayList<>();
+        List<ColumnStatistic> high = new ArrayList<>();
+
+        List<ColumnStatistic>[] cards = new List[] { lower, medium, high };
+
+        for (NamedExpression key : groupKeys) {
+            ColumnStatistic colStats = 
ExpressionEstimation.INSTANCE.estimate(key, stats);
+            if (colStats.isUnKnown) {
+                return false;
+            }
+            if (stats.getRowCount() * 0.9 <= colStats.ndv) {
+                return false;
+            }
+            cards[groupByCardinality(colStats, 
stats.getRowCount())].add(colStats);
+        }
+
+        double lowerCartesian = 1.0;
+        for (ColumnStatistic colStats : lower) {
+            lowerCartesian = lowerCartesian * colStats.ndv;
+        }
+
+        // Same NDV heuristic as EagerAggRewriter#checkStats, but kept local 
because set-op
+        // local distinct and eager aggregation have different optimization 
boundaries.

Review Comment:
   **Missing documentation for NDV heuristic thresholds**: The magic numbers 
and branching logic need inline comments explaining their rationale:
   
   - Why `rowCount / 20` for `lowerUpper`?
   - Why `* 20` for single lower column but `* 7` for two lower columns?
   - Why `pow(lowerUpper, max(lower.size()/2, 1))` as the upper bound?
   - Why `rowCount / 10000` as `lowerCartesianLowerBound`?
   
   The comment at line 134 says this follows `EagerAggRewriter#checkStats`, but 
the constants differ and the branching logic is specific to set-op inference. 
Without inline rationale, future maintainers cannot adjust these thresholds 
safely.



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