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Posted to commits@doris.apache.org by GitBox <gi...@apache.org> on 2022/10/08 04:05:08 UTC
[GitHub] [doris] englefly commented on a diff in pull request #12987: [feature](nereids) refactor statistics framework and introduce StatsCalculatorV2
englefly commented on code in PR #12987:
URL: https://github.com/apache/doris/pull/12987#discussion_r990582926
##########
fe/fe-core/src/main/java/org/apache/doris/nereids/stats/ExpressionEstimation.java:
##########
@@ -0,0 +1,210 @@
+// 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.doris.nereids.stats;
+
+import org.apache.doris.nereids.trees.expressions.Add;
+import org.apache.doris.nereids.trees.expressions.BinaryArithmetic;
+import org.apache.doris.nereids.trees.expressions.Divide;
+import org.apache.doris.nereids.trees.expressions.Expression;
+import org.apache.doris.nereids.trees.expressions.Multiply;
+import org.apache.doris.nereids.trees.expressions.SlotReference;
+import org.apache.doris.nereids.trees.expressions.Subtract;
+import org.apache.doris.nereids.trees.expressions.functions.agg.Avg;
+import org.apache.doris.nereids.trees.expressions.functions.agg.Count;
+import org.apache.doris.nereids.trees.expressions.functions.agg.Max;
+import org.apache.doris.nereids.trees.expressions.functions.agg.Min;
+import org.apache.doris.nereids.trees.expressions.functions.agg.Sum;
+import org.apache.doris.nereids.trees.expressions.functions.scalar.Substring;
+import org.apache.doris.nereids.trees.expressions.functions.scalar.WeekOfYear;
+import org.apache.doris.nereids.trees.expressions.functions.scalar.Year;
+import org.apache.doris.nereids.trees.expressions.literal.Literal;
+import org.apache.doris.nereids.trees.expressions.visitor.ExpressionVisitor;
+import org.apache.doris.nereids.util.Utils;
+import org.apache.doris.statistics.ColumnStat;
+import org.apache.doris.statistics.StatsDeriveResult;
+
+import com.google.common.base.Preconditions;
+
+/**
+ * Used to estimate for expressions that not producing boolean value.
+ */
+public class ExpressionEstimation extends ExpressionVisitor<ColumnStat, StatsDeriveResult> {
+
+ private static ExpressionEstimation INSTANCE = new ExpressionEstimation();
+
+ public static ColumnStat estimate(Expression expression, StatsDeriveResult stats) {
+ return INSTANCE.visit(expression, stats);
+ }
+
+ @Override
+ public ColumnStat visit(Expression expr, StatsDeriveResult context) {
+ return expr.accept(this, context);
+ }
+
+ @Override
+ public ColumnStat visitLiteral(Literal literal, StatsDeriveResult context) {
+ if (literal.isStringLiteral()) {
+ return ColumnStat.UNKNOWN;
+ }
+ double literalVal = Double.parseDouble(literal.getValue().toString());
+ ColumnStat columnStat = new ColumnStat();
+ columnStat.setMaxValue(literalVal);
+ columnStat.setMinValue(literalVal);
+ columnStat.setNdv(1);
+ columnStat.setNumNulls(1);
+ columnStat.setAvgSizeByte(1);
+ return columnStat;
+ }
+
+ @Override
+ public ColumnStat visitSlotReference(SlotReference slotReference, StatsDeriveResult context) {
+ ColumnStat columnStat = context.getColumnStatsBySlot(slotReference);
+ Preconditions.checkState(columnStat != null);
+ return columnStat;
+ }
+
+ @Override
+ public ColumnStat visitBinaryArithmetic(BinaryArithmetic binaryArithmetic, StatsDeriveResult context) {
+ ColumnStat leftColStats = binaryArithmetic.left().accept(this, context);
+ ColumnStat rightColStats = binaryArithmetic.right().accept(this, context);
+ double leftNdv = leftColStats.getNdv();
+ double rightNdv = rightColStats.getNdv();
+ double ndv = Math.max(leftNdv, rightNdv);
+ double leftNullCount = leftColStats.getNumNulls();
+ double rightNullCount = rightColStats.getNumNulls();
+ double rowCount = context.getRowCount();
Review Comment:
this rowCount is for left, right or binary result?
in L92, `leftNullCount / rowCount ` makes sense only if rowCount is the for left.
##########
fe/fe-core/src/main/java/org/apache/doris/analysis/SlotDescriptor.java:
##########
@@ -219,7 +219,7 @@ public ColumnStats getStats() {
}
}
// FIXME(dhc): mock ndv
- stats.setNumDistinctValues(parent.getCardinality());
+ stats.setNumDistinctValues((long) parent.getCardinality());
Review Comment:
getCardinality() return type is already 'long'
##########
fe/fe-core/src/main/java/org/apache/doris/nereids/stats/ExpressionEstimation.java:
##########
@@ -0,0 +1,210 @@
+// 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.doris.nereids.stats;
+
+import org.apache.doris.nereids.trees.expressions.Add;
+import org.apache.doris.nereids.trees.expressions.BinaryArithmetic;
+import org.apache.doris.nereids.trees.expressions.Divide;
+import org.apache.doris.nereids.trees.expressions.Expression;
+import org.apache.doris.nereids.trees.expressions.Multiply;
+import org.apache.doris.nereids.trees.expressions.SlotReference;
+import org.apache.doris.nereids.trees.expressions.Subtract;
+import org.apache.doris.nereids.trees.expressions.functions.agg.Avg;
+import org.apache.doris.nereids.trees.expressions.functions.agg.Count;
+import org.apache.doris.nereids.trees.expressions.functions.agg.Max;
+import org.apache.doris.nereids.trees.expressions.functions.agg.Min;
+import org.apache.doris.nereids.trees.expressions.functions.agg.Sum;
+import org.apache.doris.nereids.trees.expressions.functions.scalar.Substring;
+import org.apache.doris.nereids.trees.expressions.functions.scalar.WeekOfYear;
+import org.apache.doris.nereids.trees.expressions.functions.scalar.Year;
+import org.apache.doris.nereids.trees.expressions.literal.Literal;
+import org.apache.doris.nereids.trees.expressions.visitor.ExpressionVisitor;
+import org.apache.doris.nereids.util.Utils;
+import org.apache.doris.statistics.ColumnStat;
+import org.apache.doris.statistics.StatsDeriveResult;
+
+import com.google.common.base.Preconditions;
+
+/**
+ * Used to estimate for expressions that not producing boolean value.
+ */
+public class ExpressionEstimation extends ExpressionVisitor<ColumnStat, StatsDeriveResult> {
+
+ private static ExpressionEstimation INSTANCE = new ExpressionEstimation();
+
+ public static ColumnStat estimate(Expression expression, StatsDeriveResult stats) {
+ return INSTANCE.visit(expression, stats);
+ }
+
+ @Override
+ public ColumnStat visit(Expression expr, StatsDeriveResult context) {
+ return expr.accept(this, context);
+ }
+
+ @Override
+ public ColumnStat visitLiteral(Literal literal, StatsDeriveResult context) {
+ if (literal.isStringLiteral()) {
+ return ColumnStat.UNKNOWN;
+ }
+ double literalVal = Double.parseDouble(literal.getValue().toString());
+ ColumnStat columnStat = new ColumnStat();
+ columnStat.setMaxValue(literalVal);
+ columnStat.setMinValue(literalVal);
+ columnStat.setNdv(1);
+ columnStat.setNumNulls(1);
+ columnStat.setAvgSizeByte(1);
+ return columnStat;
+ }
+
+ @Override
+ public ColumnStat visitSlotReference(SlotReference slotReference, StatsDeriveResult context) {
+ ColumnStat columnStat = context.getColumnStatsBySlot(slotReference);
+ Preconditions.checkState(columnStat != null);
+ return columnStat;
+ }
+
+ @Override
+ public ColumnStat visitBinaryArithmetic(BinaryArithmetic binaryArithmetic, StatsDeriveResult context) {
+ ColumnStat leftColStats = binaryArithmetic.left().accept(this, context);
+ ColumnStat rightColStats = binaryArithmetic.right().accept(this, context);
+ double leftNdv = leftColStats.getNdv();
+ double rightNdv = rightColStats.getNdv();
+ double ndv = Math.max(leftNdv, rightNdv);
+ double leftNullCount = leftColStats.getNumNulls();
+ double rightNullCount = rightColStats.getNumNulls();
+ double rowCount = context.getRowCount();
+ double numNulls = context.getRowCount()
+ * (1 - (1 - (leftNullCount / rowCount) * (1 - rightNullCount / rowCount)));
+ double leftMax = leftColStats.getMaxValue();
+ double rightMax = rightColStats.getMaxValue();
+ double leftMin = leftColStats.getMinValue();
+ double rightMin = rightColStats.getMinValue();
+
+ if (binaryArithmetic instanceof Add) {
+ return new ColumnStat(ndv, leftColStats.getAvgSizeByte(), leftColStats.getMaxSizeByte(),
+ numNulls, leftMin + rightMin, leftMax + rightMax);
+ }
+ if (binaryArithmetic instanceof Subtract) {
+ return new ColumnStat(ndv, leftColStats.getAvgSizeByte(), leftColStats.getMaxSizeByte(),
+ numNulls, leftMin - rightMax, leftMax - rightMin);
+ }
+ // TODO: stat for multiply and divide produced by below algorithm may have huge deviation with reality.
+ if (binaryArithmetic instanceof Multiply) {
+ double min = Math.min(
+ Math.min(
+ Math.min(leftMin * rightMin, leftMin * rightMax),
+ leftMax * rightMin),
+ leftMax * rightMax);
+ double max = Math.max(
+ Math.max(
+ Math.max(leftMin * rightMin, leftMin * rightMax),
+ leftMax * rightMin),
+ leftMax * rightMax);
+ return new ColumnStat(ndv, leftColStats.getAvgSizeByte(), leftColStats.getMaxSizeByte(),
+ numNulls, min, max);
+ }
+ if (binaryArithmetic instanceof Divide) {
+ double min = Math.min(
+ Math.min(
+ Math.min(leftMin / noneZeroDivisor(rightMin), leftMin / noneZeroDivisor(rightMax)),
+ leftMax / noneZeroDivisor(rightMin)),
+ leftMax / noneZeroDivisor(rightMax));
+ double max = Math.max(
+ Math.max(
+ Math.max(leftMin / noneZeroDivisor(rightMin), leftMin / noneZeroDivisor(rightMax)),
+ leftMax / noneZeroDivisor(rightMin)),
+ leftMax / noneZeroDivisor(rightMax));
+ return new ColumnStat(ndv, leftColStats.getAvgSizeByte(), leftColStats.getMaxSizeByte(),
+ numNulls, min, max);
+ }
+ return ColumnStat.UNKNOWN;
+ }
+
+ private double noneZeroDivisor(double d) {
+ return d == 0.0 ? 1.0 : d;
+ }
+
+ @Override
+ public ColumnStat visitMin(Min min, StatsDeriveResult context) {
+ Expression child = min.child();
+ ColumnStat columnStat = child.accept(this, context);
+ if (columnStat == ColumnStat.UNKNOWN) {
+ return ColumnStat.UNKNOWN;
+ }
+ return new ColumnStat(1, min.child().getDataType().width(),
+ min.child().getDataType().width(), 1, columnStat.getMinValue(), columnStat.getMinValue());
+ }
+
+ @Override
+ public ColumnStat visitMax(Max max, StatsDeriveResult context) {
+ Expression child = max.child();
+ ColumnStat columnStat = child.accept(this, context);
+ if (columnStat == ColumnStat.UNKNOWN) {
+ return ColumnStat.UNKNOWN;
+ }
+ return new ColumnStat(1, max.child().getDataType().width(),
+ max.child().getDataType().width(), 0, columnStat.getMaxValue(), columnStat.getMaxValue());
+ }
+
+ @Override
+ public ColumnStat visitCount(Count count, StatsDeriveResult context) {
+ Expression child = count.child(0);
+ ColumnStat columnStat = child.accept(this, context);
+ if (columnStat == ColumnStat.UNKNOWN) {
+ return ColumnStat.UNKNOWN;
+ }
+ double expectedValue = context.getRowCount() - columnStat.getNumNulls();
+ return new ColumnStat(1,
+ count.getDataType().width(), count.getDataType().width(), 0, expectedValue, expectedValue);
+ }
+
+ // TODO: return a proper estimated stat after supports histogram
+ @Override
+ public ColumnStat visitSum(Sum sum, StatsDeriveResult context) {
+ return sum.child().accept(this, context);
Review Comment:
stats of `sum` is different from that of its child. Sum(A) has one row, or the group number of rows, but A has many rows.
##########
fe/fe-core/src/main/java/org/apache/doris/nereids/stats/FilterEstimation.java:
##########
@@ -0,0 +1,300 @@
+// 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.doris.nereids.stats;
+
+import org.apache.doris.nereids.stats.FilterEstimation.EstimationContext;
+import org.apache.doris.nereids.trees.expressions.And;
+import org.apache.doris.nereids.trees.expressions.ComparisonPredicate;
+import org.apache.doris.nereids.trees.expressions.CompoundPredicate;
+import org.apache.doris.nereids.trees.expressions.EqualTo;
+import org.apache.doris.nereids.trees.expressions.Expression;
+import org.apache.doris.nereids.trees.expressions.GreaterThan;
+import org.apache.doris.nereids.trees.expressions.GreaterThanEqual;
+import org.apache.doris.nereids.trees.expressions.InPredicate;
+import org.apache.doris.nereids.trees.expressions.LessThan;
+import org.apache.doris.nereids.trees.expressions.LessThanEqual;
+import org.apache.doris.nereids.trees.expressions.Not;
+import org.apache.doris.nereids.trees.expressions.Or;
+import org.apache.doris.nereids.trees.expressions.Slot;
+import org.apache.doris.nereids.trees.expressions.SlotReference;
+import org.apache.doris.nereids.trees.expressions.literal.Literal;
+import org.apache.doris.nereids.trees.expressions.visitor.ExpressionVisitor;
+import org.apache.doris.statistics.ColumnStat;
+import org.apache.doris.statistics.StatsDeriveResult;
+
+import com.google.common.base.Preconditions;
+
+import java.util.List;
+import java.util.Map;
+
+/**
+ * Calculate selectivity of expression that produces boolean value.
+ * TODO: Should consider the distribution of data.
+ */
+public class FilterEstimation extends ExpressionVisitor<StatsDeriveResult, EstimationContext> {
+
+ private static final double DEFAULT_SELECTIVITY = 0.1;
+
+ private static final double DEFAULT_INEQUALITY_COMPARISON_SELECTIVITY = 1.0 / 3.0;
+
+ private static final double DEFAULT_EQUALITY_COMPARISON_SELECTIVITY = 0.1;
+
+ private final StatsDeriveResult stats;
+
+ public FilterEstimation(StatsDeriveResult stats) {
+ Preconditions.checkNotNull(stats);
+ this.stats = stats;
+ }
+
+ /**
+ * This method will update the stats according to the selectivity.
+ */
+ public StatsDeriveResult estimate(Expression expression) {
+ // For a comparison predicate, only when it's left side is a slot and right side is a literal, we would
+ // consider is a valid predicate.
+ StatsDeriveResult stats = calculate(expression);
+ double expectedRowCount = stats.getRowCount();
+ for (ColumnStat columnStat : stats.getSlotToColumnStats().values()) {
+ if (columnStat.getNdv() > expectedRowCount) {
Review Comment:
Here we missed a lot of useful information. For example, expression is `A=1`, A.stat.ndv should be 1.
##########
fe/fe-core/src/main/java/org/apache/doris/nereids/stats/StatsCalculator.java:
##########
@@ -320,17 +323,17 @@ private StatsDeriveResult computeAggregate(Aggregate aggregate) {
// .map(childSlotToColumnStats::get)
// .map(ColumnStats::getNdv)
// .reduce(1L, (a, b) -> a * b);
- long resultSetCount = childStats.getRowCount() / DEFAULT_AGGREGATE_RATIO;
+ long resultSetCount = (long) childStats.getRowCount() / DEFAULT_AGGREGATE_RATIO;
Review Comment:
how about use define rowCount as double?
##########
fe/fe-core/src/main/java/org/apache/doris/nereids/stats/ExpressionEstimation.java:
##########
@@ -0,0 +1,210 @@
+// 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.doris.nereids.stats;
+
+import org.apache.doris.nereids.trees.expressions.Add;
+import org.apache.doris.nereids.trees.expressions.BinaryArithmetic;
+import org.apache.doris.nereids.trees.expressions.Divide;
+import org.apache.doris.nereids.trees.expressions.Expression;
+import org.apache.doris.nereids.trees.expressions.Multiply;
+import org.apache.doris.nereids.trees.expressions.SlotReference;
+import org.apache.doris.nereids.trees.expressions.Subtract;
+import org.apache.doris.nereids.trees.expressions.functions.agg.Avg;
+import org.apache.doris.nereids.trees.expressions.functions.agg.Count;
+import org.apache.doris.nereids.trees.expressions.functions.agg.Max;
+import org.apache.doris.nereids.trees.expressions.functions.agg.Min;
+import org.apache.doris.nereids.trees.expressions.functions.agg.Sum;
+import org.apache.doris.nereids.trees.expressions.functions.scalar.Substring;
+import org.apache.doris.nereids.trees.expressions.functions.scalar.WeekOfYear;
+import org.apache.doris.nereids.trees.expressions.functions.scalar.Year;
+import org.apache.doris.nereids.trees.expressions.literal.Literal;
+import org.apache.doris.nereids.trees.expressions.visitor.ExpressionVisitor;
+import org.apache.doris.nereids.util.Utils;
+import org.apache.doris.statistics.ColumnStat;
+import org.apache.doris.statistics.StatsDeriveResult;
+
+import com.google.common.base.Preconditions;
+
+/**
+ * Used to estimate for expressions that not producing boolean value.
+ */
+public class ExpressionEstimation extends ExpressionVisitor<ColumnStat, StatsDeriveResult> {
+
+ private static ExpressionEstimation INSTANCE = new ExpressionEstimation();
+
+ public static ColumnStat estimate(Expression expression, StatsDeriveResult stats) {
+ return INSTANCE.visit(expression, stats);
+ }
+
+ @Override
+ public ColumnStat visit(Expression expr, StatsDeriveResult context) {
+ return expr.accept(this, context);
+ }
+
+ @Override
+ public ColumnStat visitLiteral(Literal literal, StatsDeriveResult context) {
+ if (literal.isStringLiteral()) {
+ return ColumnStat.UNKNOWN;
Review Comment:
for string literal, we have its avgSize, ndv, numNulls...
why return UNKNOWN?
##########
fe/fe-core/src/main/java/org/apache/doris/nereids/stats/ExpressionEstimation.java:
##########
@@ -0,0 +1,210 @@
+// 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.doris.nereids.stats;
+
+import org.apache.doris.nereids.trees.expressions.Add;
+import org.apache.doris.nereids.trees.expressions.BinaryArithmetic;
+import org.apache.doris.nereids.trees.expressions.Divide;
+import org.apache.doris.nereids.trees.expressions.Expression;
+import org.apache.doris.nereids.trees.expressions.Multiply;
+import org.apache.doris.nereids.trees.expressions.SlotReference;
+import org.apache.doris.nereids.trees.expressions.Subtract;
+import org.apache.doris.nereids.trees.expressions.functions.agg.Avg;
+import org.apache.doris.nereids.trees.expressions.functions.agg.Count;
+import org.apache.doris.nereids.trees.expressions.functions.agg.Max;
+import org.apache.doris.nereids.trees.expressions.functions.agg.Min;
+import org.apache.doris.nereids.trees.expressions.functions.agg.Sum;
+import org.apache.doris.nereids.trees.expressions.functions.scalar.Substring;
+import org.apache.doris.nereids.trees.expressions.functions.scalar.WeekOfYear;
+import org.apache.doris.nereids.trees.expressions.functions.scalar.Year;
+import org.apache.doris.nereids.trees.expressions.literal.Literal;
+import org.apache.doris.nereids.trees.expressions.visitor.ExpressionVisitor;
+import org.apache.doris.nereids.util.Utils;
+import org.apache.doris.statistics.ColumnStat;
+import org.apache.doris.statistics.StatsDeriveResult;
+
+import com.google.common.base.Preconditions;
+
+/**
+ * Used to estimate for expressions that not producing boolean value.
+ */
+public class ExpressionEstimation extends ExpressionVisitor<ColumnStat, StatsDeriveResult> {
+
+ private static ExpressionEstimation INSTANCE = new ExpressionEstimation();
+
+ public static ColumnStat estimate(Expression expression, StatsDeriveResult stats) {
+ return INSTANCE.visit(expression, stats);
+ }
+
+ @Override
+ public ColumnStat visit(Expression expr, StatsDeriveResult context) {
+ return expr.accept(this, context);
+ }
+
+ @Override
+ public ColumnStat visitLiteral(Literal literal, StatsDeriveResult context) {
+ if (literal.isStringLiteral()) {
+ return ColumnStat.UNKNOWN;
+ }
+ double literalVal = Double.parseDouble(literal.getValue().toString());
+ ColumnStat columnStat = new ColumnStat();
+ columnStat.setMaxValue(literalVal);
+ columnStat.setMinValue(literalVal);
+ columnStat.setNdv(1);
+ columnStat.setNumNulls(1);
+ columnStat.setAvgSizeByte(1);
+ return columnStat;
+ }
+
+ @Override
+ public ColumnStat visitSlotReference(SlotReference slotReference, StatsDeriveResult context) {
+ ColumnStat columnStat = context.getColumnStatsBySlot(slotReference);
+ Preconditions.checkState(columnStat != null);
+ return columnStat;
+ }
+
+ @Override
+ public ColumnStat visitBinaryArithmetic(BinaryArithmetic binaryArithmetic, StatsDeriveResult context) {
+ ColumnStat leftColStats = binaryArithmetic.left().accept(this, context);
+ ColumnStat rightColStats = binaryArithmetic.right().accept(this, context);
+ double leftNdv = leftColStats.getNdv();
+ double rightNdv = rightColStats.getNdv();
+ double ndv = Math.max(leftNdv, rightNdv);
+ double leftNullCount = leftColStats.getNumNulls();
+ double rightNullCount = rightColStats.getNumNulls();
+ double rowCount = context.getRowCount();
+ double numNulls = context.getRowCount()
+ * (1 - (1 - (leftNullCount / rowCount) * (1 - rightNullCount / rowCount)));
+ double leftMax = leftColStats.getMaxValue();
+ double rightMax = rightColStats.getMaxValue();
+ double leftMin = leftColStats.getMinValue();
+ double rightMin = rightColStats.getMinValue();
+
+ if (binaryArithmetic instanceof Add) {
+ return new ColumnStat(ndv, leftColStats.getAvgSizeByte(), leftColStats.getMaxSizeByte(),
+ numNulls, leftMin + rightMin, leftMax + rightMax);
+ }
+ if (binaryArithmetic instanceof Subtract) {
+ return new ColumnStat(ndv, leftColStats.getAvgSizeByte(), leftColStats.getMaxSizeByte(),
+ numNulls, leftMin - rightMax, leftMax - rightMin);
+ }
+ // TODO: stat for multiply and divide produced by below algorithm may have huge deviation with reality.
+ if (binaryArithmetic instanceof Multiply) {
+ double min = Math.min(
+ Math.min(
+ Math.min(leftMin * rightMin, leftMin * rightMax),
+ leftMax * rightMin),
+ leftMax * rightMax);
+ double max = Math.max(
+ Math.max(
+ Math.max(leftMin * rightMin, leftMin * rightMax),
+ leftMax * rightMin),
+ leftMax * rightMax);
+ return new ColumnStat(ndv, leftColStats.getAvgSizeByte(), leftColStats.getMaxSizeByte(),
+ numNulls, min, max);
+ }
+ if (binaryArithmetic instanceof Divide) {
+ double min = Math.min(
+ Math.min(
+ Math.min(leftMin / noneZeroDivisor(rightMin), leftMin / noneZeroDivisor(rightMax)),
+ leftMax / noneZeroDivisor(rightMin)),
+ leftMax / noneZeroDivisor(rightMax));
+ double max = Math.max(
+ Math.max(
+ Math.max(leftMin / noneZeroDivisor(rightMin), leftMin / noneZeroDivisor(rightMax)),
+ leftMax / noneZeroDivisor(rightMin)),
+ leftMax / noneZeroDivisor(rightMax));
+ return new ColumnStat(ndv, leftColStats.getAvgSizeByte(), leftColStats.getMaxSizeByte(),
+ numNulls, min, max);
+ }
+ return ColumnStat.UNKNOWN;
+ }
+
+ private double noneZeroDivisor(double d) {
+ return d == 0.0 ? 1.0 : d;
+ }
+
+ @Override
+ public ColumnStat visitMin(Min min, StatsDeriveResult context) {
+ Expression child = min.child();
+ ColumnStat columnStat = child.accept(this, context);
+ if (columnStat == ColumnStat.UNKNOWN) {
+ return ColumnStat.UNKNOWN;
Review Comment:
I think UNKNOWN is not reasonable. We get UNKNOWN, iff the stats job has never invoked.
##########
fe/fe-core/src/main/java/org/apache/doris/nereids/stats/ExpressionEstimation.java:
##########
@@ -0,0 +1,210 @@
+// 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.doris.nereids.stats;
+
+import org.apache.doris.nereids.trees.expressions.Add;
+import org.apache.doris.nereids.trees.expressions.BinaryArithmetic;
+import org.apache.doris.nereids.trees.expressions.Divide;
+import org.apache.doris.nereids.trees.expressions.Expression;
+import org.apache.doris.nereids.trees.expressions.Multiply;
+import org.apache.doris.nereids.trees.expressions.SlotReference;
+import org.apache.doris.nereids.trees.expressions.Subtract;
+import org.apache.doris.nereids.trees.expressions.functions.agg.Avg;
+import org.apache.doris.nereids.trees.expressions.functions.agg.Count;
+import org.apache.doris.nereids.trees.expressions.functions.agg.Max;
+import org.apache.doris.nereids.trees.expressions.functions.agg.Min;
+import org.apache.doris.nereids.trees.expressions.functions.agg.Sum;
+import org.apache.doris.nereids.trees.expressions.functions.scalar.Substring;
+import org.apache.doris.nereids.trees.expressions.functions.scalar.WeekOfYear;
+import org.apache.doris.nereids.trees.expressions.functions.scalar.Year;
+import org.apache.doris.nereids.trees.expressions.literal.Literal;
+import org.apache.doris.nereids.trees.expressions.visitor.ExpressionVisitor;
+import org.apache.doris.nereids.util.Utils;
+import org.apache.doris.statistics.ColumnStat;
+import org.apache.doris.statistics.StatsDeriveResult;
+
+import com.google.common.base.Preconditions;
+
+/**
+ * Used to estimate for expressions that not producing boolean value.
+ */
+public class ExpressionEstimation extends ExpressionVisitor<ColumnStat, StatsDeriveResult> {
+
+ private static ExpressionEstimation INSTANCE = new ExpressionEstimation();
+
+ public static ColumnStat estimate(Expression expression, StatsDeriveResult stats) {
+ return INSTANCE.visit(expression, stats);
+ }
+
+ @Override
+ public ColumnStat visit(Expression expr, StatsDeriveResult context) {
+ return expr.accept(this, context);
+ }
+
+ @Override
+ public ColumnStat visitLiteral(Literal literal, StatsDeriveResult context) {
+ if (literal.isStringLiteral()) {
+ return ColumnStat.UNKNOWN;
+ }
+ double literalVal = Double.parseDouble(literal.getValue().toString());
Review Comment:
why we regard all literals except string as Double?
##########
fe/fe-core/src/main/java/org/apache/doris/nereids/stats/ExpressionEstimation.java:
##########
@@ -0,0 +1,210 @@
+// 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.doris.nereids.stats;
+
+import org.apache.doris.nereids.trees.expressions.Add;
+import org.apache.doris.nereids.trees.expressions.BinaryArithmetic;
+import org.apache.doris.nereids.trees.expressions.Divide;
+import org.apache.doris.nereids.trees.expressions.Expression;
+import org.apache.doris.nereids.trees.expressions.Multiply;
+import org.apache.doris.nereids.trees.expressions.SlotReference;
+import org.apache.doris.nereids.trees.expressions.Subtract;
+import org.apache.doris.nereids.trees.expressions.functions.agg.Avg;
+import org.apache.doris.nereids.trees.expressions.functions.agg.Count;
+import org.apache.doris.nereids.trees.expressions.functions.agg.Max;
+import org.apache.doris.nereids.trees.expressions.functions.agg.Min;
+import org.apache.doris.nereids.trees.expressions.functions.agg.Sum;
+import org.apache.doris.nereids.trees.expressions.functions.scalar.Substring;
+import org.apache.doris.nereids.trees.expressions.functions.scalar.WeekOfYear;
+import org.apache.doris.nereids.trees.expressions.functions.scalar.Year;
+import org.apache.doris.nereids.trees.expressions.literal.Literal;
+import org.apache.doris.nereids.trees.expressions.visitor.ExpressionVisitor;
+import org.apache.doris.nereids.util.Utils;
+import org.apache.doris.statistics.ColumnStat;
+import org.apache.doris.statistics.StatsDeriveResult;
+
+import com.google.common.base.Preconditions;
+
+/**
+ * Used to estimate for expressions that not producing boolean value.
+ */
+public class ExpressionEstimation extends ExpressionVisitor<ColumnStat, StatsDeriveResult> {
+
+ private static ExpressionEstimation INSTANCE = new ExpressionEstimation();
+
+ public static ColumnStat estimate(Expression expression, StatsDeriveResult stats) {
+ return INSTANCE.visit(expression, stats);
+ }
+
+ @Override
+ public ColumnStat visit(Expression expr, StatsDeriveResult context) {
+ return expr.accept(this, context);
+ }
+
+ @Override
+ public ColumnStat visitLiteral(Literal literal, StatsDeriveResult context) {
+ if (literal.isStringLiteral()) {
+ return ColumnStat.UNKNOWN;
+ }
+ double literalVal = Double.parseDouble(literal.getValue().toString());
+ ColumnStat columnStat = new ColumnStat();
+ columnStat.setMaxValue(literalVal);
+ columnStat.setMinValue(literalVal);
+ columnStat.setNdv(1);
+ columnStat.setNumNulls(1);
+ columnStat.setAvgSizeByte(1);
+ return columnStat;
+ }
+
+ @Override
+ public ColumnStat visitSlotReference(SlotReference slotReference, StatsDeriveResult context) {
+ ColumnStat columnStat = context.getColumnStatsBySlot(slotReference);
+ Preconditions.checkState(columnStat != null);
+ return columnStat;
+ }
+
+ @Override
+ public ColumnStat visitBinaryArithmetic(BinaryArithmetic binaryArithmetic, StatsDeriveResult context) {
+ ColumnStat leftColStats = binaryArithmetic.left().accept(this, context);
+ ColumnStat rightColStats = binaryArithmetic.right().accept(this, context);
+ double leftNdv = leftColStats.getNdv();
+ double rightNdv = rightColStats.getNdv();
+ double ndv = Math.max(leftNdv, rightNdv);
+ double leftNullCount = leftColStats.getNumNulls();
+ double rightNullCount = rightColStats.getNumNulls();
+ double rowCount = context.getRowCount();
+ double numNulls = context.getRowCount()
+ * (1 - (1 - (leftNullCount / rowCount) * (1 - rightNullCount / rowCount)));
+ double leftMax = leftColStats.getMaxValue();
+ double rightMax = rightColStats.getMaxValue();
+ double leftMin = leftColStats.getMinValue();
+ double rightMin = rightColStats.getMinValue();
+
+ if (binaryArithmetic instanceof Add) {
+ return new ColumnStat(ndv, leftColStats.getAvgSizeByte(), leftColStats.getMaxSizeByte(),
+ numNulls, leftMin + rightMin, leftMax + rightMax);
+ }
+ if (binaryArithmetic instanceof Subtract) {
+ return new ColumnStat(ndv, leftColStats.getAvgSizeByte(), leftColStats.getMaxSizeByte(),
+ numNulls, leftMin - rightMax, leftMax - rightMin);
+ }
+ // TODO: stat for multiply and divide produced by below algorithm may have huge deviation with reality.
+ if (binaryArithmetic instanceof Multiply) {
+ double min = Math.min(
+ Math.min(
+ Math.min(leftMin * rightMin, leftMin * rightMax),
+ leftMax * rightMin),
+ leftMax * rightMax);
+ double max = Math.max(
+ Math.max(
+ Math.max(leftMin * rightMin, leftMin * rightMax),
+ leftMax * rightMin),
+ leftMax * rightMax);
+ return new ColumnStat(ndv, leftColStats.getAvgSizeByte(), leftColStats.getMaxSizeByte(),
+ numNulls, min, max);
+ }
+ if (binaryArithmetic instanceof Divide) {
+ double min = Math.min(
+ Math.min(
+ Math.min(leftMin / noneZeroDivisor(rightMin), leftMin / noneZeroDivisor(rightMax)),
+ leftMax / noneZeroDivisor(rightMin)),
+ leftMax / noneZeroDivisor(rightMax));
+ double max = Math.max(
+ Math.max(
+ Math.max(leftMin / noneZeroDivisor(rightMin), leftMin / noneZeroDivisor(rightMax)),
+ leftMax / noneZeroDivisor(rightMin)),
+ leftMax / noneZeroDivisor(rightMax));
+ return new ColumnStat(ndv, leftColStats.getAvgSizeByte(), leftColStats.getMaxSizeByte(),
+ numNulls, min, max);
+ }
+ return ColumnStat.UNKNOWN;
+ }
+
+ private double noneZeroDivisor(double d) {
+ return d == 0.0 ? 1.0 : d;
+ }
+
+ @Override
+ public ColumnStat visitMin(Min min, StatsDeriveResult context) {
+ Expression child = min.child();
+ ColumnStat columnStat = child.accept(this, context);
+ if (columnStat == ColumnStat.UNKNOWN) {
+ return ColumnStat.UNKNOWN;
+ }
+ return new ColumnStat(1, min.child().getDataType().width(),
+ min.child().getDataType().width(), 1, columnStat.getMinValue(), columnStat.getMinValue());
+ }
+
+ @Override
+ public ColumnStat visitMax(Max max, StatsDeriveResult context) {
+ Expression child = max.child();
+ ColumnStat columnStat = child.accept(this, context);
+ if (columnStat == ColumnStat.UNKNOWN) {
+ return ColumnStat.UNKNOWN;
+ }
+ return new ColumnStat(1, max.child().getDataType().width(),
+ max.child().getDataType().width(), 0, columnStat.getMaxValue(), columnStat.getMaxValue());
+ }
+
+ @Override
+ public ColumnStat visitCount(Count count, StatsDeriveResult context) {
+ Expression child = count.child(0);
+ ColumnStat columnStat = child.accept(this, context);
+ if (columnStat == ColumnStat.UNKNOWN) {
+ return ColumnStat.UNKNOWN;
+ }
+ double expectedValue = context.getRowCount() - columnStat.getNumNulls();
+ return new ColumnStat(1,
+ count.getDataType().width(), count.getDataType().width(), 0, expectedValue, expectedValue);
+ }
+
+ // TODO: return a proper estimated stat after supports histogram
+ @Override
+ public ColumnStat visitSum(Sum sum, StatsDeriveResult context) {
+ return sum.child().accept(this, context);
+ }
+
+ // TODO: return a proper estimated stat after supports histogram
+ @Override
+ public ColumnStat visitAvg(Avg avg, StatsDeriveResult context) {
+ return avg.child().accept(this, context);
+ }
+
+ @Override
+ public ColumnStat visitYear(Year year, StatsDeriveResult context) {
+ ColumnStat childStat = year.child().accept(this, context);
+ double maxVal = childStat.getMaxValue();
+ double minVal = childStat.getMinValue();
+ long minYear = Utils.getLocalDatetimeFromLong((long) minVal).getYear();
+ long maxYear = Utils.getLocalDatetimeFromLong((long) maxVal).getYear();
+ return new ColumnStat(childStat.getNdv(), 4, 4,
+ maxYear - minYear + 1, minYear, maxYear);
+ }
+
+ @Override
+ public ColumnStat visitWeekOfYear(WeekOfYear weekOfYear, StatsDeriveResult context) {
+ ColumnStat childStat = weekOfYear.child().accept(this, context);
+ return new ColumnStat(52, 2, 2, childStat.getNumNulls(), 1, 52);
+ }
+
+ // TODO: find a proper way to predicate stat of substring
+ @Override
+ public ColumnStat visitSubstring(Substring substring, StatsDeriveResult context) {
+ return substring.child(0).accept(this, context);
Review Comment:
maxSize and avgSerializedSize are changed
##########
fe/fe-core/src/main/java/org/apache/doris/nereids/stats/ExpressionEstimation.java:
##########
@@ -0,0 +1,210 @@
+// 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.doris.nereids.stats;
+
+import org.apache.doris.nereids.trees.expressions.Add;
+import org.apache.doris.nereids.trees.expressions.BinaryArithmetic;
+import org.apache.doris.nereids.trees.expressions.Divide;
+import org.apache.doris.nereids.trees.expressions.Expression;
+import org.apache.doris.nereids.trees.expressions.Multiply;
+import org.apache.doris.nereids.trees.expressions.SlotReference;
+import org.apache.doris.nereids.trees.expressions.Subtract;
+import org.apache.doris.nereids.trees.expressions.functions.agg.Avg;
+import org.apache.doris.nereids.trees.expressions.functions.agg.Count;
+import org.apache.doris.nereids.trees.expressions.functions.agg.Max;
+import org.apache.doris.nereids.trees.expressions.functions.agg.Min;
+import org.apache.doris.nereids.trees.expressions.functions.agg.Sum;
+import org.apache.doris.nereids.trees.expressions.functions.scalar.Substring;
+import org.apache.doris.nereids.trees.expressions.functions.scalar.WeekOfYear;
+import org.apache.doris.nereids.trees.expressions.functions.scalar.Year;
+import org.apache.doris.nereids.trees.expressions.literal.Literal;
+import org.apache.doris.nereids.trees.expressions.visitor.ExpressionVisitor;
+import org.apache.doris.nereids.util.Utils;
+import org.apache.doris.statistics.ColumnStat;
+import org.apache.doris.statistics.StatsDeriveResult;
+
+import com.google.common.base.Preconditions;
+
+/**
+ * Used to estimate for expressions that not producing boolean value.
+ */
+public class ExpressionEstimation extends ExpressionVisitor<ColumnStat, StatsDeriveResult> {
+
+ private static ExpressionEstimation INSTANCE = new ExpressionEstimation();
+
+ public static ColumnStat estimate(Expression expression, StatsDeriveResult stats) {
+ return INSTANCE.visit(expression, stats);
+ }
+
+ @Override
+ public ColumnStat visit(Expression expr, StatsDeriveResult context) {
+ return expr.accept(this, context);
+ }
+
+ @Override
+ public ColumnStat visitLiteral(Literal literal, StatsDeriveResult context) {
+ if (literal.isStringLiteral()) {
+ return ColumnStat.UNKNOWN;
+ }
+ double literalVal = Double.parseDouble(literal.getValue().toString());
+ ColumnStat columnStat = new ColumnStat();
+ columnStat.setMaxValue(literalVal);
+ columnStat.setMinValue(literalVal);
+ columnStat.setNdv(1);
+ columnStat.setNumNulls(1);
+ columnStat.setAvgSizeByte(1);
+ return columnStat;
+ }
+
+ @Override
+ public ColumnStat visitSlotReference(SlotReference slotReference, StatsDeriveResult context) {
+ ColumnStat columnStat = context.getColumnStatsBySlot(slotReference);
+ Preconditions.checkState(columnStat != null);
+ return columnStat;
+ }
+
+ @Override
+ public ColumnStat visitBinaryArithmetic(BinaryArithmetic binaryArithmetic, StatsDeriveResult context) {
+ ColumnStat leftColStats = binaryArithmetic.left().accept(this, context);
+ ColumnStat rightColStats = binaryArithmetic.right().accept(this, context);
+ double leftNdv = leftColStats.getNdv();
+ double rightNdv = rightColStats.getNdv();
+ double ndv = Math.max(leftNdv, rightNdv);
+ double leftNullCount = leftColStats.getNumNulls();
+ double rightNullCount = rightColStats.getNumNulls();
+ double rowCount = context.getRowCount();
+ double numNulls = context.getRowCount()
+ * (1 - (1 - (leftNullCount / rowCount) * (1 - rightNullCount / rowCount)));
+ double leftMax = leftColStats.getMaxValue();
+ double rightMax = rightColStats.getMaxValue();
+ double leftMin = leftColStats.getMinValue();
+ double rightMin = rightColStats.getMinValue();
+
+ if (binaryArithmetic instanceof Add) {
+ return new ColumnStat(ndv, leftColStats.getAvgSizeByte(), leftColStats.getMaxSizeByte(),
+ numNulls, leftMin + rightMin, leftMax + rightMax);
+ }
+ if (binaryArithmetic instanceof Subtract) {
+ return new ColumnStat(ndv, leftColStats.getAvgSizeByte(), leftColStats.getMaxSizeByte(),
+ numNulls, leftMin - rightMax, leftMax - rightMin);
+ }
+ // TODO: stat for multiply and divide produced by below algorithm may have huge deviation with reality.
+ if (binaryArithmetic instanceof Multiply) {
+ double min = Math.min(
+ Math.min(
+ Math.min(leftMin * rightMin, leftMin * rightMax),
+ leftMax * rightMin),
+ leftMax * rightMax);
+ double max = Math.max(
+ Math.max(
+ Math.max(leftMin * rightMin, leftMin * rightMax),
+ leftMax * rightMin),
+ leftMax * rightMax);
+ return new ColumnStat(ndv, leftColStats.getAvgSizeByte(), leftColStats.getMaxSizeByte(),
+ numNulls, min, max);
+ }
+ if (binaryArithmetic instanceof Divide) {
+ double min = Math.min(
+ Math.min(
+ Math.min(leftMin / noneZeroDivisor(rightMin), leftMin / noneZeroDivisor(rightMax)),
+ leftMax / noneZeroDivisor(rightMin)),
+ leftMax / noneZeroDivisor(rightMax));
+ double max = Math.max(
+ Math.max(
+ Math.max(leftMin / noneZeroDivisor(rightMin), leftMin / noneZeroDivisor(rightMax)),
+ leftMax / noneZeroDivisor(rightMin)),
+ leftMax / noneZeroDivisor(rightMax));
+ return new ColumnStat(ndv, leftColStats.getAvgSizeByte(), leftColStats.getMaxSizeByte(),
+ numNulls, min, max);
+ }
+ return ColumnStat.UNKNOWN;
+ }
+
+ private double noneZeroDivisor(double d) {
+ return d == 0.0 ? 1.0 : d;
+ }
+
+ @Override
+ public ColumnStat visitMin(Min min, StatsDeriveResult context) {
+ Expression child = min.child();
+ ColumnStat columnStat = child.accept(this, context);
+ if (columnStat == ColumnStat.UNKNOWN) {
+ return ColumnStat.UNKNOWN;
+ }
+ return new ColumnStat(1, min.child().getDataType().width(),
+ min.child().getDataType().width(), 1, columnStat.getMinValue(), columnStat.getMinValue());
+ }
+
+ @Override
+ public ColumnStat visitMax(Max max, StatsDeriveResult context) {
+ Expression child = max.child();
+ ColumnStat columnStat = child.accept(this, context);
+ if (columnStat == ColumnStat.UNKNOWN) {
+ return ColumnStat.UNKNOWN;
+ }
+ return new ColumnStat(1, max.child().getDataType().width(),
+ max.child().getDataType().width(), 0, columnStat.getMaxValue(), columnStat.getMaxValue());
+ }
+
+ @Override
+ public ColumnStat visitCount(Count count, StatsDeriveResult context) {
+ Expression child = count.child(0);
+ ColumnStat columnStat = child.accept(this, context);
+ if (columnStat == ColumnStat.UNKNOWN) {
+ return ColumnStat.UNKNOWN;
+ }
+ double expectedValue = context.getRowCount() - columnStat.getNumNulls();
+ return new ColumnStat(1,
+ count.getDataType().width(), count.getDataType().width(), 0, expectedValue, expectedValue);
+ }
+
+ // TODO: return a proper estimated stat after supports histogram
+ @Override
+ public ColumnStat visitSum(Sum sum, StatsDeriveResult context) {
+ return sum.child().accept(this, context);
+ }
+
+ // TODO: return a proper estimated stat after supports histogram
+ @Override
+ public ColumnStat visitAvg(Avg avg, StatsDeriveResult context) {
+ return avg.child().accept(this, context);
Review Comment:
`avg` stats is different from stats of its child.
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