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stats.go
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stats.go
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// Copyright 2017 PingCAP, Inc.
//
// Licensed 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,
// See the License for the specific language governing permissions and
// limitations under the License.
package plan
import (
"fmt"
"math"
"github.com/pingcap/tidb/expression"
log "github.com/sirupsen/logrus"
)
// statsInfo stores the basic information of statistics for the plan's output. It is used for cost estimation.
type statsInfo struct {
count float64
cardinality []float64
}
func (s *statsInfo) String() string {
return fmt.Sprintf("count %v, cardinality %v", s.count, s.cardinality)
}
func (s *statsInfo) Count() int64 {
return int64(s.count)
}
// scale receives a selectivity and multiplies it with count and cardinality.
func (s *statsInfo) scale(factor float64) *statsInfo {
profile := &statsInfo{
count: s.count * factor,
cardinality: make([]float64, len(s.cardinality)),
}
for i := range profile.cardinality {
profile.cardinality[i] = s.cardinality[i] * factor
}
return profile
}
// We try to scale statsInfo to an expectCnt which must be smaller than the derived cnt.
func (s *statsInfo) scaleByExpectCnt(expectCnt float64) *statsInfo {
if expectCnt > s.count {
return s
}
if s.count > 1.0 { // if s.count is too small, it will cause overflow
return s.scale(expectCnt / s.count)
}
return s
}
func (p *basePhysicalPlan) StatsInfo() *statsInfo {
return p.stats
}
func (p *baseLogicalPlan) deriveStats() *statsInfo {
if len(p.children) == 0 {
profile := &statsInfo{
count: float64(1),
cardinality: make([]float64, p.self.Schema().Len()),
}
for i := range profile.cardinality {
profile.cardinality[i] = float64(1)
}
p.stats = profile
return profile
}
p.stats = p.children[0].deriveStats()
return p.stats
}
func (ds *DataSource) getStatsByFilter(conds expression.CNFExprs) *statsInfo {
profile := &statsInfo{
count: float64(ds.statisticTable.Count),
cardinality: make([]float64, len(ds.Columns)),
}
for i, col := range ds.Columns {
hist, ok := ds.statisticTable.Columns[col.ID]
if ok && hist.Count > 0 {
factor := float64(ds.statisticTable.Count) / float64(hist.Count)
profile.cardinality[i] = float64(hist.NDV) * factor
} else {
profile.cardinality[i] = profile.count * distinctFactor
}
}
ds.stats = profile
selectivity, err := ds.statisticTable.Selectivity(ds.ctx, conds)
if err != nil {
log.Warnf("An error happened: %v, we have to use the default selectivity", err.Error())
selectivity = selectionFactor
}
return profile.scale(selectivity)
}
func (ds *DataSource) deriveStats() *statsInfo {
// PushDownNot here can convert query 'not (a != 1)' to 'a = 1'.
for i, expr := range ds.pushedDownConds {
ds.pushedDownConds[i] = expression.PushDownNot(nil, expr, false)
}
ds.statsAfterSelect = ds.getStatsByFilter(ds.pushedDownConds)
return ds.statsAfterSelect
}
func (p *LogicalSelection) deriveStats() *statsInfo {
childProfile := p.children[0].deriveStats()
p.stats = childProfile.scale(selectionFactor)
return p.stats
}
func (p *LogicalUnionAll) deriveStats() *statsInfo {
p.stats = &statsInfo{
cardinality: make([]float64, p.Schema().Len()),
}
for _, child := range p.children {
childProfile := child.deriveStats()
p.stats.count += childProfile.count
for i := range p.stats.cardinality {
p.stats.cardinality[i] += childProfile.cardinality[i]
}
}
return p.stats
}
func (p *LogicalLimit) deriveStats() *statsInfo {
childProfile := p.children[0].deriveStats()
p.stats = &statsInfo{
count: float64(p.Count),
cardinality: make([]float64, len(childProfile.cardinality)),
}
if p.stats.count > childProfile.count {
p.stats.count = childProfile.count
}
for i := range p.stats.cardinality {
p.stats.cardinality[i] = childProfile.cardinality[i]
if p.stats.cardinality[i] > p.stats.count {
p.stats.cardinality[i] = p.stats.count
}
}
return p.stats
}
func (lt *LogicalTopN) deriveStats() *statsInfo {
childProfile := lt.children[0].deriveStats()
lt.stats = &statsInfo{
count: float64(lt.Count),
cardinality: make([]float64, len(childProfile.cardinality)),
}
if lt.stats.count > childProfile.count {
lt.stats.count = childProfile.count
}
for i := range lt.stats.cardinality {
lt.stats.cardinality[i] = childProfile.cardinality[i]
if lt.stats.cardinality[i] > lt.stats.count {
lt.stats.cardinality[i] = lt.stats.count
}
}
return lt.stats
}
// getCardinality will return the cardinality of a couple of columns. We simply return the max one, because we cannot know
// the cardinality for multi-dimension attributes properly. This is a simple and naive scheme of cardinality estimation.
func getCardinality(cols []*expression.Column, schema *expression.Schema, profile *statsInfo) float64 {
indices := schema.ColumnsIndices(cols)
if indices == nil {
log.Errorf("Cannot find column %s indices from schema %s", cols, schema)
return 0
}
var cardinality = 1.0
for _, idx := range indices {
if cardinality < profile.cardinality[idx] {
// It is a very elementary estimation.
cardinality = profile.cardinality[idx]
}
}
return cardinality
}
func (p *LogicalProjection) deriveStats() *statsInfo {
childProfile := p.children[0].deriveStats()
p.stats = &statsInfo{
count: childProfile.count,
cardinality: make([]float64, len(p.Exprs)),
}
for i, expr := range p.Exprs {
cols := expression.ExtractColumns(expr)
p.stats.cardinality[i] = getCardinality(cols, p.children[0].Schema(), childProfile)
}
return p.stats
}
func (la *LogicalAggregation) deriveStats() *statsInfo {
childProfile := la.children[0].deriveStats()
gbyCols := make([]*expression.Column, 0, len(la.GroupByItems))
for _, gbyExpr := range la.GroupByItems {
cols := expression.ExtractColumns(gbyExpr)
gbyCols = append(gbyCols, cols...)
}
cardinality := getCardinality(gbyCols, la.children[0].Schema(), childProfile)
la.stats = &statsInfo{
count: cardinality,
cardinality: make([]float64, la.schema.Len()),
}
// We cannot estimate the cardinality for every output, so we use a conservative strategy.
for i := range la.stats.cardinality {
la.stats.cardinality[i] = cardinality
}
la.inputCount = childProfile.count
return la.stats
}
// deriveStats prepares statsInfo.
// If the type of join is SemiJoin, the selectivity of it will be same as selection's.
// If the type of join is LeftOuterSemiJoin, it will not add or remove any row. The last column is a boolean value, whose cardinality should be two.
// If the type of join is inner/outer join, the output of join(s, t) should be N(s) * N(t) / (V(s.key) * V(t.key)) * Min(s.key, t.key).
// N(s) stands for the number of rows in relation s. V(s.key) means the cardinality of join key in s.
// This is a quite simple strategy: We assume every bucket of relation which will participate join has the same number of rows, and apply cross join for
// every matched bucket.
func (p *LogicalJoin) deriveStats() *statsInfo {
leftProfile := p.children[0].deriveStats()
rightProfile := p.children[1].deriveStats()
if p.JoinType == SemiJoin || p.JoinType == AntiSemiJoin {
p.stats = &statsInfo{
count: leftProfile.count * selectionFactor,
cardinality: make([]float64, len(leftProfile.cardinality)),
}
for i := range p.stats.cardinality {
p.stats.cardinality[i] = leftProfile.cardinality[i] * selectionFactor
}
return p.stats
}
if p.JoinType == LeftOuterSemiJoin || p.JoinType == AntiLeftOuterSemiJoin {
p.stats = &statsInfo{
count: leftProfile.count,
cardinality: make([]float64, p.schema.Len()),
}
copy(p.stats.cardinality, leftProfile.cardinality)
p.stats.cardinality[len(p.stats.cardinality)-1] = 2.0
return p.stats
}
if 0 == len(p.EqualConditions) {
p.stats = &statsInfo{
count: leftProfile.count * rightProfile.count,
cardinality: append(leftProfile.cardinality, rightProfile.cardinality...),
}
return p.stats
}
leftKeys := make([]*expression.Column, 0, len(p.EqualConditions))
rightKeys := make([]*expression.Column, 0, len(p.EqualConditions))
for _, eqCond := range p.EqualConditions {
leftKeys = append(leftKeys, eqCond.GetArgs()[0].(*expression.Column))
rightKeys = append(rightKeys, eqCond.GetArgs()[1].(*expression.Column))
}
leftKeyCardinality := getCardinality(leftKeys, p.children[0].Schema(), leftProfile)
rightKeyCardinality := getCardinality(rightKeys, p.children[1].Schema(), rightProfile)
count := leftProfile.count * rightProfile.count / math.Max(leftKeyCardinality, rightKeyCardinality)
if p.JoinType == LeftOuterJoin {
count = math.Max(count, leftProfile.count)
} else if p.JoinType == RightOuterJoin {
count = math.Max(count, rightProfile.count)
}
cardinality := make([]float64, 0, p.schema.Len())
cardinality = append(cardinality, leftProfile.cardinality...)
cardinality = append(cardinality, rightProfile.cardinality...)
for i := range cardinality {
cardinality[i] = math.Min(cardinality[i], count)
}
p.stats = &statsInfo{
count: count,
cardinality: cardinality,
}
return p.stats
}
func (la *LogicalApply) deriveStats() *statsInfo {
leftProfile := la.children[0].deriveStats()
_ = la.children[1].deriveStats()
la.stats = &statsInfo{
count: leftProfile.count,
cardinality: make([]float64, la.schema.Len()),
}
copy(la.stats.cardinality, leftProfile.cardinality)
if la.JoinType == LeftOuterSemiJoin || la.JoinType == AntiLeftOuterSemiJoin {
la.stats.cardinality[len(la.stats.cardinality)-1] = 2.0
} else {
for i := la.children[0].Schema().Len(); i < la.schema.Len(); i++ {
la.stats.cardinality[i] = leftProfile.count
}
}
return la.stats
}
// Exists and MaxOneRow produce at most one row, so we set the count of stats one.
func getSingletonStats(len int) *statsInfo {
ret := &statsInfo{
count: 1.0,
cardinality: make([]float64, len),
}
for i := 0; i < len; i++ {
ret.cardinality[i] = 1
}
return ret
}
func (p *LogicalExists) deriveStats() *statsInfo {
p.children[0].deriveStats()
p.stats = getSingletonStats(1)
return p.stats
}
func (p *LogicalMaxOneRow) deriveStats() *statsInfo {
p.children[0].deriveStats()
p.stats = getSingletonStats(p.Schema().Len())
return p.stats
}