tidb stats 源码
tidb stats 代码
文件路径：/planner/core/stats.go
// 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,
// 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 core
import (
"context"
"fmt"
"math"
"strings"
"github.com/pingcap/errors"
"github.com/pingcap/tidb/expression"
"github.com/pingcap/tidb/kv"
"github.com/pingcap/tidb/parser/ast"
"github.com/pingcap/tidb/parser/model"
"github.com/pingcap/tidb/parser/mysql"
"github.com/pingcap/tidb/planner/property"
"github.com/pingcap/tidb/planner/util"
"github.com/pingcap/tidb/sessionctx"
"github.com/pingcap/tidb/statistics"
"github.com/pingcap/tidb/types"
"github.com/pingcap/tidb/util/logutil"
"github.com/pingcap/tidb/util/mathutil"
"github.com/pingcap/tidb/util/ranger"
"go.uber.org/zap"
"golang.org/x/exp/slices"
)
func (p *basePhysicalPlan) StatsCount() float64 {
return p.stats.RowCount
}
// DeriveStats implement LogicalPlan DeriveStats interface.
func (p *LogicalTableDual) DeriveStats(_ []*property.StatsInfo, selfSchema *expression.Schema, _ []*expression.Schema, _ [][]*expression.Column) (*property.StatsInfo, error) {
if p.stats != nil {
return p.stats, nil
}
profile := &property.StatsInfo{
RowCount: float64(p.RowCount),
ColNDVs: make(map[int64]float64, selfSchema.Len()),
}
for _, col := range selfSchema.Columns {
profile.ColNDVs[col.UniqueID] = float64(p.RowCount)
}
p.stats = profile
return p.stats, nil
}
// DeriveStats implement LogicalPlan DeriveStats interface.
func (p *LogicalMemTable) DeriveStats(_ []*property.StatsInfo, selfSchema *expression.Schema, _ []*expression.Schema, _ [][]*expression.Column) (*property.StatsInfo, error) {
if p.stats != nil {
return p.stats, nil
}
statsTable := statistics.PseudoTable(p.TableInfo)
stats := &property.StatsInfo{
RowCount: float64(statsTable.Count),
ColNDVs: make(map[int64]float64, len(p.TableInfo.Columns)),
HistColl: statsTable.GenerateHistCollFromColumnInfo(p.TableInfo.Columns, p.schema.Columns),
StatsVersion: statistics.PseudoVersion,
}
for _, col := range selfSchema.Columns {
stats.ColNDVs[col.UniqueID] = float64(statsTable.Count)
}
p.stats = stats
return p.stats, nil
}
// DeriveStats implement LogicalPlan DeriveStats interface.
func (p *LogicalShow) DeriveStats(_ []*property.StatsInfo, selfSchema *expression.Schema, _ []*expression.Schema, _ [][]*expression.Column) (*property.StatsInfo, error) {
if p.stats != nil {
return p.stats, nil
}
// A fake count, just to avoid panic now.
p.stats = getFakeStats(selfSchema)
return p.stats, nil
}
func getFakeStats(schema *expression.Schema) *property.StatsInfo {
profile := &property.StatsInfo{
RowCount: 1,
ColNDVs: make(map[int64]float64, schema.Len()),
}
for _, col := range schema.Columns {
profile.ColNDVs[col.UniqueID] = 1
}
return profile
}
// DeriveStats implement LogicalPlan DeriveStats interface.
func (p *LogicalShowDDLJobs) DeriveStats(_ []*property.StatsInfo, selfSchema *expression.Schema, _ []*expression.Schema, _ [][]*expression.Column) (*property.StatsInfo, error) {
if p.stats != nil {
return p.stats, nil
}
// A fake count, just to avoid panic now.
p.stats = getFakeStats(selfSchema)
return p.stats, nil
}
// RecursiveDeriveStats4Test is a exporter just for test.
func RecursiveDeriveStats4Test(p LogicalPlan) (*property.StatsInfo, error) {
return p.recursiveDeriveStats(nil)
}
// GetStats4Test is a exporter just for test.
func GetStats4Test(p LogicalPlan) *property.StatsInfo {
return p.statsInfo()
}
func (p *baseLogicalPlan) recursiveDeriveStats(colGroups [][]*expression.Column) (*property.StatsInfo, error) {
childStats := make([]*property.StatsInfo, len(p.children))
childSchema := make([]*expression.Schema, len(p.children))
cumColGroups := p.self.ExtractColGroups(colGroups)
for i, child := range p.children {
childProfile, err := child.recursiveDeriveStats(cumColGroups)
if err != nil {
return nil, err
}
childStats[i] = childProfile
childSchema[i] = child.Schema()
}
return p.self.DeriveStats(childStats, p.self.Schema(), childSchema, colGroups)
}
// ExtractColGroups implements LogicalPlan ExtractColGroups interface.
func (p *baseLogicalPlan) ExtractColGroups(_ [][]*expression.Column) [][]*expression.Column {
return nil
}
// DeriveStats implement LogicalPlan DeriveStats interface.
func (p *baseLogicalPlan) DeriveStats(childStats []*property.StatsInfo, selfSchema *expression.Schema, _ []*expression.Schema, _ [][]*expression.Column) (*property.StatsInfo, error) {
if len(childStats) == 1 {
p.stats = childStats[0]
return p.stats, nil
}
if len(childStats) > 1 {
err := ErrInternal.GenWithStack("LogicalPlans with more than one child should implement their own DeriveStats().")
return nil, err
}
if p.stats != nil {
return p.stats, nil
}
profile := &property.StatsInfo{
RowCount: float64(1),
ColNDVs: make(map[int64]float64, selfSchema.Len()),
}
for _, col := range selfSchema.Columns {
profile.ColNDVs[col.UniqueID] = 1
}
p.stats = profile
return profile, nil
}
// getColumnNDV computes estimated NDV of specified column using the original
// histogram of `DataSource` which is retrieved from storage(not the derived one).
func (ds *DataSource) getColumnNDV(colID int64) (ndv float64) {
hist, ok := ds.statisticTable.Columns[colID]
if ok && hist.Count > 0 {
factor := float64(ds.statisticTable.Count) / float64(hist.Count)
ndv = float64(hist.Histogram.NDV) * factor
} else {
ndv = float64(ds.statisticTable.Count) * distinctFactor
}
return ndv
}
func (ds *DataSource) getGroupNDVs(colGroups [][]*expression.Column) []property.GroupNDV {
if colGroups == nil {
return nil
}
tbl := ds.tableStats.HistColl
ndvs := make([]property.GroupNDV, 0, len(colGroups))
for idxID, idx := range tbl.Indices {
colsLen := len(tbl.Idx2ColumnIDs[idxID])
// tbl.Idx2ColumnIDs may only contain the prefix of index columns.
// But it may exceeds the total index since the index would contain the handle column if it's not a unique index.
// We append the handle at fillIndexPath.
if colsLen < len(idx.Info.Columns) {
continue
} else if colsLen > len(idx.Info.Columns) {
colsLen--
}
idxCols := make([]int64, colsLen)
copy(idxCols, tbl.Idx2ColumnIDs[idxID])
slices.Sort(idxCols)
for _, g := range colGroups {
// We only want those exact matches.
if len(g) != colsLen {
continue
}
match := true
for i, col := range g {
// Both slices are sorted according to UniqueID.
if col.UniqueID != idxCols[i] {
match = false
break
}
}
if match {
ndv := property.GroupNDV{
Cols: idxCols,
NDV: float64(idx.NDV),
}
ndvs = append(ndvs, ndv)
break
}
}
}
return ndvs
}
func (ds *DataSource) initStats(colGroups [][]*expression.Column) {
if ds.tableStats != nil {
// Reload GroupNDVs since colGroups may have changed.
ds.tableStats.GroupNDVs = ds.getGroupNDVs(colGroups)
return
}
if ds.statisticTable == nil {
ds.statisticTable = getStatsTable(ds.ctx, ds.tableInfo, ds.physicalTableID)
}
tableStats := &property.StatsInfo{
RowCount: float64(ds.statisticTable.Count),
ColNDVs: make(map[int64]float64, ds.schema.Len()),
HistColl: ds.statisticTable.GenerateHistCollFromColumnInfo(ds.Columns, ds.schema.Columns),
StatsVersion: ds.statisticTable.Version,
}
if ds.statisticTable.Pseudo {
tableStats.StatsVersion = statistics.PseudoVersion
}
for _, col := range ds.schema.Columns {
tableStats.ColNDVs[col.UniqueID] = ds.getColumnNDV(col.ID)
}
ds.tableStats = tableStats
ds.tableStats.GroupNDVs = ds.getGroupNDVs(colGroups)
ds.TblColHists = ds.statisticTable.ID2UniqueID(ds.TblCols)
}
func (ds *DataSource) deriveStatsByFilter(conds expression.CNFExprs, filledPaths []*util.AccessPath) *property.StatsInfo {
selectivity, nodes, err := ds.tableStats.HistColl.Selectivity(ds.ctx, conds, filledPaths)
if err != nil {
logutil.BgLogger().Debug("something wrong happened, use the default selectivity", zap.Error(err))
selectivity = SelectionFactor
}
stats := ds.tableStats.Scale(selectivity)
if ds.ctx.GetSessionVars().OptimizerSelectivityLevel >= 1 {
stats.HistColl = stats.HistColl.NewHistCollBySelectivity(ds.ctx, nodes)
}
return stats
}
// We bind logic of derivePathStats and tryHeuristics together. When some path matches the heuristic rule, we don't need
// to derive stats of subsequent paths. In this way we can save unnecessary computation of derivePathStats.
func (ds *DataSource) derivePathStatsAndTryHeuristics() error {
uniqueIdxsWithDoubleScan := make([]*util.AccessPath, 0, len(ds.possibleAccessPaths))
singleScanIdxs := make([]*util.AccessPath, 0, len(ds.possibleAccessPaths))
var (
selected, uniqueBest, refinedBest *util.AccessPath
isRefinedPath bool
)
for _, path := range ds.possibleAccessPaths {
if path.IsTablePath() {
err := ds.deriveTablePathStats(path, ds.pushedDownConds, false)
if err != nil {
return err
}
path.IsSingleScan = true
} else {
ds.deriveIndexPathStats(path, ds.pushedDownConds, false)
path.IsSingleScan = ds.isCoveringIndex(ds.schema.Columns, path.FullIdxCols, path.FullIdxColLens, ds.tableInfo)
}
// Try some heuristic rules to select access path.
if len(path.Ranges) == 0 {
selected = path
break
}
if path.OnlyPointRange(ds.SCtx()) {
if path.IsTablePath() || path.Index.Unique {
if path.IsSingleScan {
selected = path
break
}
uniqueIdxsWithDoubleScan = append(uniqueIdxsWithDoubleScan, path)
}
} else if path.IsSingleScan {
singleScanIdxs = append(singleScanIdxs, path)
}
}
if selected == nil && len(uniqueIdxsWithDoubleScan) > 0 {
uniqueIdxAccessCols := make([]util.Col2Len, 0, len(uniqueIdxsWithDoubleScan))
for _, uniqueIdx := range uniqueIdxsWithDoubleScan {
uniqueIdxAccessCols = append(uniqueIdxAccessCols, uniqueIdx.GetCol2LenFromAccessConds())
// Find the unique index with the minimal number of ranges as `uniqueBest`.
if uniqueBest == nil || len(uniqueIdx.Ranges) < len(uniqueBest.Ranges) {
uniqueBest = uniqueIdx
}
}
// `uniqueBest` may not always be the best.
// ```
// create table t(a int, b int, c int, unique index idx_b(b), index idx_b_c(b, c));
// select b, c from t where b = 5 and c > 10;
// ```
// In the case, `uniqueBest` is `idx_b`. However, `idx_b_c` is better than `idx_b`.
// Hence, for each index in `singleScanIdxs`, we check whether it is better than some index in `uniqueIdxsWithDoubleScan`.
// If yes, the index is a refined one. We find the refined index with the minimal number of ranges as `refineBest`.
for _, singleScanIdx := range singleScanIdxs {
col2Len := singleScanIdx.GetCol2LenFromAccessConds()
for _, uniqueIdxCol2Len := range uniqueIdxAccessCols {
accessResult, comparable1 := util.CompareCol2Len(col2Len, uniqueIdxCol2Len)
if comparable1 && accessResult == 1 {
if refinedBest == nil || len(singleScanIdx.Ranges) < len(refinedBest.Ranges) {
refinedBest = singleScanIdx
}
}
}
}
// `refineBest` may not always be better than `uniqueBest`.
// ```
// create table t(a int, b int, c int, d int, unique index idx_a(a), unique index idx_b_c(b, c), unique index idx_b_c_a_d(b, c, a, d));
// select a, b, c from t where a = 1 and b = 2 and c in (1, 2, 3, 4, 5);
// ```
// In the case, `refinedBest` is `idx_b_c_a_d` and `uniqueBest` is `a`. `idx_b_c_a_d` needs to access five points while `idx_a`
// only needs one point access and one table access.
// Hence we should compare `len(refinedBest.Ranges)` and `2*len(uniqueBest.Ranges)` to select the better one.
if refinedBest != nil && (uniqueBest == nil || len(refinedBest.Ranges) < 2*len(uniqueBest.Ranges)) {
selected = refinedBest
isRefinedPath = true
} else {
selected = uniqueBest
}
}
// If some path matches a heuristic rule, just remove other possible paths
if selected != nil {
ds.possibleAccessPaths[0] = selected
ds.possibleAccessPaths = ds.possibleAccessPaths[:1]
if ds.ctx.GetSessionVars().StmtCtx.InVerboseExplain {
var tableName string
if ds.TableAsName.O == "" {
tableName = ds.tableInfo.Name.O
} else {
tableName = ds.TableAsName.O
}
if selected.IsTablePath() {
// TODO: primary key / handle / real name?
ds.ctx.GetSessionVars().StmtCtx.AppendNote(fmt.Errorf("handle of %s is selected since the path only has point ranges", tableName))
} else {
var sb strings.Builder
if selected.Index.Unique {
sb.WriteString("unique ")
}
sb.WriteString(fmt.Sprintf("index %s of %s is selected since the path", selected.Index.Name.O, tableName))
if isRefinedPath {
sb.WriteString(" only fetches limited number of rows")
} else {
sb.WriteString(" only has point ranges")
}
if selected.IsSingleScan {
sb.WriteString(" with single scan")
} else {
sb.WriteString(" with double scan")
}
ds.ctx.GetSessionVars().StmtCtx.AppendNote(errors.New(sb.String()))
}
}
}
return nil
}
// DeriveStats implement LogicalPlan DeriveStats interface.
func (ds *DataSource) DeriveStats(_ []*property.StatsInfo, _ *expression.Schema, _ []*expression.Schema, colGroups [][]*expression.Column) (*property.StatsInfo, error) {
if ds.stats != nil && len(colGroups) == 0 {
return ds.stats, nil
}
ds.initStats(colGroups)
if ds.stats != nil {
// Just reload the GroupNDVs.
selectivity := ds.stats.RowCount / ds.tableStats.RowCount
ds.stats = ds.tableStats.Scale(selectivity)
return ds.stats, nil
}
// PushDownNot here can convert query 'not (a != 1)' to 'a = 1'.
for i, expr := range ds.pushedDownConds {
ds.pushedDownConds[i] = expression.PushDownNot(ds.ctx, expr)
}
for _, path := range ds.possibleAccessPaths {
if path.IsTablePath() {
continue
}
err := ds.fillIndexPath(path, ds.pushedDownConds)
if err != nil {
return nil, err
}
}
// TODO: Can we move ds.deriveStatsByFilter after pruning by heuristics? In this way some computation can be avoided
// when ds.possibleAccessPaths are pruned.
ds.stats = ds.deriveStatsByFilter(ds.pushedDownConds, ds.possibleAccessPaths)
err := ds.derivePathStatsAndTryHeuristics()
if err != nil {
return nil, err
}
// Consider the IndexMergePath. Now, we just generate `IndexMergePath` in DNF case.
// Use allConds instread of pushedDownConds,
// because we want to use IndexMerge even if some expr cannot be pushed to TiKV.
// We will create new Selection for exprs that cannot be pushed in convertToIndexMergeScan.
indexMergeConds := make([]expression.Expression, 0, len(ds.allConds))
for _, expr := range ds.allConds {
indexMergeConds = append(indexMergeConds, expression.PushDownNot(ds.ctx, expr))
}
stmtCtx := ds.ctx.GetSessionVars().StmtCtx
isPossibleIdxMerge := len(indexMergeConds) > 0 && len(ds.possibleAccessPaths) > 1
sessionAndStmtPermission := (ds.ctx.GetSessionVars().GetEnableIndexMerge() || len(ds.indexMergeHints) > 0) && !stmtCtx.NoIndexMergeHint
// We current do not consider `IndexMergePath`:
// 1. If there is an index path.
// 2. TODO: If there exists exprs that cannot be pushed down. This is to avoid wrongly estRow of Selection added by rule_predicate_push_down.
needConsiderIndexMerge := true
if len(ds.indexMergeHints) == 0 {
for i := 1; i < len(ds.possibleAccessPaths); i++ {
if len(ds.possibleAccessPaths[i].AccessConds) != 0 {
needConsiderIndexMerge = false
break
}
}
if needConsiderIndexMerge {
// PushDownExprs() will append extra warnings, which is annoying. So we reset warnings here.
warnings := stmtCtx.GetWarnings()
_, remaining := expression.PushDownExprs(stmtCtx, indexMergeConds, ds.ctx.GetClient(), kv.UnSpecified)
stmtCtx.SetWarnings(warnings)
if len(remaining) != 0 {
needConsiderIndexMerge = false
}
}
}
if isPossibleIdxMerge && sessionAndStmtPermission && needConsiderIndexMerge && ds.tableInfo.TempTableType != model.TempTableLocal {
err := ds.generateAndPruneIndexMergePath(indexMergeConds, ds.indexMergeHints != nil)
if err != nil {
return nil, err
}
} else if len(ds.indexMergeHints) > 0 {
ds.indexMergeHints = nil
var msg string
if !isPossibleIdxMerge {
msg = "No available filter or available index."
} else if !sessionAndStmtPermission {
msg = "Got no_index_merge hint or tidb_enable_index_merge is off."
} else if ds.tableInfo.TempTableType == model.TempTableLocal {
msg = "Cannot use IndexMerge on temporary table."
}
msg = fmt.Sprintf("IndexMerge is inapplicable or disabled. %s", msg)
stmtCtx.AppendWarning(errors.Errorf(msg))
logutil.BgLogger().Debug(msg)
}
return ds.stats, nil
}
func (ds *DataSource) generateAndPruneIndexMergePath(indexMergeConds []expression.Expression, needPrune bool) error {
regularPathCount := len(ds.possibleAccessPaths)
err := ds.generateIndexMergeOrPaths(indexMergeConds)
if err != nil {
return err
}
// If without hints, it means that `enableIndexMerge` is true
if len(ds.indexMergeHints) == 0 {
return nil
}
// With hints and without generated IndexMerge paths
if regularPathCount == len(ds.possibleAccessPaths) {
ds.indexMergeHints = nil
ds.ctx.GetSessionVars().StmtCtx.AppendWarning(errors.Errorf("IndexMerge is inapplicable"))
return nil
}
// Do not need to consider the regular paths in find_best_task().
// So we can use index merge's row count as DataSource's row count.
if needPrune {
ds.possibleAccessPaths = ds.possibleAccessPaths[regularPathCount:]
minRowCount := ds.possibleAccessPaths[0].CountAfterAccess
for _, path := range ds.possibleAccessPaths {
if minRowCount < path.CountAfterAccess {
minRowCount = path.CountAfterAccess
}
}
if ds.stats.RowCount > minRowCount {
ds.stats = ds.tableStats.ScaleByExpectCnt(minRowCount)
}
}
return nil
}
// DeriveStats implements LogicalPlan DeriveStats interface.
func (ts *LogicalTableScan) DeriveStats(_ []*property.StatsInfo, _ *expression.Schema, _ []*expression.Schema, _ [][]*expression.Column) (_ *property.StatsInfo, err error) {
ts.Source.initStats(nil)
// PushDownNot here can convert query 'not (a != 1)' to 'a = 1'.
for i, expr := range ts.AccessConds {
// TODO The expressions may be shared by TableScan and several IndexScans, there would be redundant
// `PushDownNot` function call in multiple `DeriveStats` then.
ts.AccessConds[i] = expression.PushDownNot(ts.ctx, expr)
}
ts.stats = ts.Source.deriveStatsByFilter(ts.AccessConds, nil)
// ts.Handle could be nil if PK is Handle, and PK column has been pruned.
// TODO: support clustered index.
if ts.HandleCols != nil {
// TODO: restrict mem usage of table ranges.
ts.Ranges, _, _, err = ranger.BuildTableRange(ts.AccessConds, ts.ctx, ts.HandleCols.GetCol(0).RetType, 0)
} else {
isUnsigned := false
if ts.Source.tableInfo.PKIsHandle {
if pkColInfo := ts.Source.tableInfo.GetPkColInfo(); pkColInfo != nil {
isUnsigned = mysql.HasUnsignedFlag(pkColInfo.GetFlag())
}
}
ts.Ranges = ranger.FullIntRange(isUnsigned)
}
if err != nil {
return nil, err
}
return ts.stats, nil
}
// DeriveStats implements LogicalPlan DeriveStats interface.
func (is *LogicalIndexScan) DeriveStats(_ []*property.StatsInfo, selfSchema *expression.Schema, _ []*expression.Schema, _ [][]*expression.Column) (*property.StatsInfo, error) {
is.Source.initStats(nil)
for i, expr := range is.AccessConds {
is.AccessConds[i] = expression.PushDownNot(is.ctx, expr)
}
is.stats = is.Source.deriveStatsByFilter(is.AccessConds, nil)
if len(is.AccessConds) == 0 {
is.Ranges = ranger.FullRange()
}
is.IdxCols, is.IdxColLens = expression.IndexInfo2PrefixCols(is.Columns, selfSchema.Columns, is.Index)
is.FullIdxCols, is.FullIdxColLens = expression.IndexInfo2Cols(is.Columns, selfSchema.Columns, is.Index)
if !is.Index.Unique && !is.Index.Primary && len(is.Index.Columns) == len(is.IdxCols) {
handleCol := is.getPKIsHandleCol(selfSchema)
if handleCol != nil && !mysql.HasUnsignedFlag(handleCol.RetType.GetFlag()) {
is.IdxCols = append(is.IdxCols, handleCol)
is.IdxColLens = append(is.IdxColLens, types.UnspecifiedLength)
}
}
return is.stats, nil
}
// getIndexMergeOrPath generates all possible IndexMergeOrPaths.
func (ds *DataSource) generateIndexMergeOrPaths(filters []expression.Expression) error {
usedIndexCount := len(ds.possibleAccessPaths)
for i, cond := range filters {
sf, ok := cond.(*expression.ScalarFunction)
if !ok || sf.FuncName.L != ast.LogicOr {
continue
}
var partialPaths = make([]*util.AccessPath, 0, usedIndexCount)
dnfItems := expression.FlattenDNFConditions(sf)
for _, item := range dnfItems {
cnfItems := expression.SplitCNFItems(item)
itemPaths := ds.accessPathsForConds(cnfItems, usedIndexCount)
if len(itemPaths) == 0 {
partialPaths = nil
break
}
partialPath, err := ds.buildIndexMergePartialPath(itemPaths)
if err != nil {
return err
}
if partialPath == nil {
partialPaths = nil
break
}
partialPaths = append(partialPaths, partialPath)
}
// If all of the partialPaths use the same index, we will not use the indexMerge.
singlePath := true
for i := len(partialPaths) - 1; i >= 1; i-- {
if partialPaths[i].Index != partialPaths[i-1].Index {
singlePath = false
break
}
}
if singlePath {
continue
}
if len(partialPaths) > 1 {
possiblePath := ds.buildIndexMergeOrPath(filters, partialPaths, i)
if possiblePath == nil {
return nil
}
accessConds := make([]expression.Expression, 0, len(partialPaths))
for _, p := range partialPaths {
indexCondsForP := p.AccessConds[:]
indexCondsForP = append(indexCondsForP, p.IndexFilters...)
if len(indexCondsForP) > 0 {
accessConds = append(accessConds, expression.ComposeCNFCondition(ds.ctx, indexCondsForP...))
}
}
accessDNF := expression.ComposeDNFCondition(ds.ctx, accessConds...)
sel, _, err := ds.tableStats.HistColl.Selectivity(ds.ctx, []expression.Expression{accessDNF}, nil)
if err != nil {
logutil.BgLogger().Debug("something wrong happened, use the default selectivity", zap.Error(err))
sel = SelectionFactor
}
possiblePath.CountAfterAccess = sel * ds.tableStats.RowCount
ds.possibleAccessPaths = append(ds.possibleAccessPaths, possiblePath)
}
}
return nil
}
// isInIndexMergeHints checks whether current index or primary key is in IndexMerge hints.
func (ds *DataSource) isInIndexMergeHints(name string) bool {
if len(ds.indexMergeHints) == 0 {
return true
}
for _, hint := range ds.indexMergeHints {
if hint.indexHint == nil || len(hint.indexHint.IndexNames) == 0 {
return true
}
for _, hintName := range hint.indexHint.IndexNames {
if strings.EqualFold(strings.ToLower(name), strings.ToLower(hintName.String())) {
return true
}
}
}
return false
}
// accessPathsForConds generates all possible index paths for conditions.
func (ds *DataSource) accessPathsForConds(conditions []expression.Expression, usedIndexCount int) []*util.AccessPath {
var results = make([]*util.AccessPath, 0, usedIndexCount)
for i := 0; i < usedIndexCount; i++ {
path := &util.AccessPath{}
if ds.possibleAccessPaths[i].IsTablePath() {
if !ds.isInIndexMergeHints("primary") {
continue
}
if ds.tableInfo.IsCommonHandle {
path.IsCommonHandlePath = true
path.Index = ds.possibleAccessPaths[i].Index
} else {
path.IsIntHandlePath = true
}
err := ds.deriveTablePathStats(path, conditions, true)
if err != nil {
logutil.BgLogger().Debug("can not derive statistics of a path", zap.Error(err))
continue
}
var unsignedIntHandle bool
if path.IsIntHandlePath && ds.tableInfo.PKIsHandle {
if pkColInfo := ds.tableInfo.GetPkColInfo(); pkColInfo != nil {
unsignedIntHandle = mysql.HasUnsignedFlag(pkColInfo.GetFlag())
}
}
// If the path contains a full range, ignore it.
if ranger.HasFullRange(path.Ranges, unsignedIntHandle) {
continue
}
// If we have point or empty range, just remove other possible paths.
if len(path.Ranges) == 0 || path.OnlyPointRange(ds.SCtx()) {
if len(results) == 0 {
results = append(results, path)
} else {
results[0] = path
results = results[:1]
}
break
}
} else {
path.Index = ds.possibleAccessPaths[i].Index
if !ds.isInIndexMergeHints(path.Index.Name.L) {
continue
}
err := ds.fillIndexPath(path, conditions)
if err != nil {
logutil.BgLogger().Debug("can not derive statistics of a path", zap.Error(err))
continue
}
ds.deriveIndexPathStats(path, conditions, true)
// If the path contains a full range, ignore it.
if ranger.HasFullRange(path.Ranges, false) {
continue
}
// If we have empty range, or point range on unique index, just remove other possible paths.
if len(path.Ranges) == 0 || (path.OnlyPointRange(ds.SCtx()) && path.Index.Unique) {
if len(results) == 0 {
results = append(results, path)
} else {
results[0] = path
results = results[:1]
}
break
}
}
results = append(results, path)
}
return results
}
// buildIndexMergePartialPath chooses the best index path from all possible paths.
// Now we choose the index with minimal estimate row count.
func (ds *DataSource) buildIndexMergePartialPath(indexAccessPaths []*util.AccessPath) (*util.AccessPath, error) {
if len(indexAccessPaths) == 1 {
return indexAccessPaths[0], nil
}
minEstRowIndex := 0
minEstRow := math.MaxFloat64
for i := 0; i < len(indexAccessPaths); i++ {
rc := indexAccessPaths[i].CountAfterAccess
if len(indexAccessPaths[i].IndexFilters) > 0 {
rc = indexAccessPaths[i].CountAfterIndex
}
if rc < minEstRow {
minEstRowIndex = i
minEstRow = rc
}
}
return indexAccessPaths[minEstRowIndex], nil
}
// buildIndexMergeOrPath generates one possible IndexMergePath.
func (ds *DataSource) buildIndexMergeOrPath(filters []expression.Expression, partialPaths []*util.AccessPath, current int) *util.AccessPath {
indexMergePath := &util.AccessPath{PartialIndexPaths: partialPaths}
indexMergePath.TableFilters = append(indexMergePath.TableFilters, filters[:current]...)
indexMergePath.TableFilters = append(indexMergePath.TableFilters, filters[current+1:]...)
var addCurrentFilter bool
for _, path := range partialPaths {
// If any partial path contains table filters, we need to keep the whole DNF filter in the Selection.
if len(path.TableFilters) > 0 {
addCurrentFilter = true
}
// If any partial path's index filter cannot be pushed to TiKV, we should keep the whole DNF filter.
if len(path.IndexFilters) != 0 && !expression.CanExprsPushDown(ds.ctx.GetSessionVars().StmtCtx, path.IndexFilters, ds.ctx.GetClient(), kv.TiKV) {
addCurrentFilter = true
// Clear IndexFilter, the whole filter will be put in indexMergePath.TableFilters.
path.IndexFilters = nil
}
if len(path.TableFilters) != 0 && !expression.CanExprsPushDown(ds.ctx.GetSessionVars().StmtCtx, path.TableFilters, ds.ctx.GetClient(), kv.TiKV) {
addCurrentFilter = true
path.TableFilters = nil
}
}
if addCurrentFilter {
indexMergePath.TableFilters = append(indexMergePath.TableFilters, filters[current])
}
return indexMergePath
}
// DeriveStats implement LogicalPlan DeriveStats interface.
func (p *LogicalSelection) DeriveStats(childStats []*property.StatsInfo, _ *expression.Schema, _ []*expression.Schema, _ [][]*expression.Column) (*property.StatsInfo, error) {
if p.stats != nil {
return p.stats, nil
}
p.stats = childStats[0].Scale(SelectionFactor)
p.stats.GroupNDVs = nil
return p.stats, nil
}
// DeriveStats implement LogicalPlan DeriveStats interface.
func (p *LogicalUnionAll) DeriveStats(childStats []*property.StatsInfo, selfSchema *expression.Schema, _ []*expression.Schema, _ [][]*expression.Column) (*property.StatsInfo, error) {
if p.stats != nil {
return p.stats, nil
}
p.stats = &property.StatsInfo{
ColNDVs: make(map[int64]float64, selfSchema.Len()),
}
for _, childProfile := range childStats {
p.stats.RowCount += childProfile.RowCount
for _, col := range selfSchema.Columns {
p.stats.ColNDVs[col.UniqueID] += childProfile.ColNDVs[col.UniqueID]
}
}
return p.stats, nil
}
func deriveLimitStats(childProfile *property.StatsInfo, limitCount float64) *property.StatsInfo {
stats := &property.StatsInfo{
RowCount: math.Min(limitCount, childProfile.RowCount),
ColNDVs: make(map[int64]float64, len(childProfile.ColNDVs)),
}
for id, c := range childProfile.ColNDVs {
stats.ColNDVs[id] = math.Min(c, stats.RowCount)
}
return stats
}
// DeriveStats implement LogicalPlan DeriveStats interface.
func (p *LogicalLimit) DeriveStats(childStats []*property.StatsInfo, _ *expression.Schema, _ []*expression.Schema, _ [][]*expression.Column) (*property.StatsInfo, error) {
if p.stats != nil {
return p.stats, nil
}
p.stats = deriveLimitStats(childStats[0], float64(p.Count))
return p.stats, nil
}
// DeriveStats implement LogicalPlan DeriveStats interface.
func (lt *LogicalTopN) DeriveStats(childStats []*property.StatsInfo, _ *expression.Schema, _ []*expression.Schema, _ [][]*expression.Column) (*property.StatsInfo, error) {
if lt.stats != nil {
return lt.stats, nil
}
lt.stats = deriveLimitStats(childStats[0], float64(lt.Count))
return lt.stats, nil
}
func getGroupNDV4Cols(cols []*expression.Column, stats *property.StatsInfo) *property.GroupNDV {
if len(cols) == 0 || len(stats.GroupNDVs) == 0 {
return nil
}
cols = expression.SortColumns(cols)
for _, groupNDV := range stats.GroupNDVs {
if len(cols) != len(groupNDV.Cols) {
continue
}
match := true
for i, col := range groupNDV.Cols {
if col != cols[i].UniqueID {
match = false
break
}
}
if match {
return &groupNDV
}
}
return nil
}
// getColsNDVWithMatchedLen returns the NDV of a couple of columns.
// If the columns match any GroupNDV maintained by child operator, we can get an accurate NDV.
// Otherwise, we simply return the max NDV among the columns, which is a lower bound.
func getColsNDVWithMatchedLen(cols []*expression.Column, schema *expression.Schema, profile *property.StatsInfo) (float64, int) {
NDV := 1.0
if groupNDV := getGroupNDV4Cols(cols, profile); groupNDV != nil {
return math.Max(groupNDV.NDV, NDV), len(groupNDV.Cols)
}
indices := schema.ColumnsIndices(cols)
if indices == nil {
logutil.BgLogger().Error("column not found in schema", zap.Any("columns", cols), zap.String("schema", schema.String()))
return NDV, 1
}
for _, idx := range indices {
// It is a very naive estimation.
col := schema.Columns[idx]
NDV = math.Max(NDV, profile.ColNDVs[col.UniqueID])
}
return NDV, 1
}
func (p *LogicalProjection) getGroupNDVs(colGroups [][]*expression.Column, childProfile *property.StatsInfo, selfSchema *expression.Schema) []property.GroupNDV {
if len(colGroups) == 0 || len(childProfile.GroupNDVs) == 0 {
return nil
}
exprCol2ProjCol := make(map[int64]int64)
for i, expr := range p.Exprs {
exprCol, ok := expr.(*expression.Column)
if !ok {
continue
}
exprCol2ProjCol[exprCol.UniqueID] = selfSchema.Columns[i].UniqueID
}
ndvs := make([]property.GroupNDV, 0, len(childProfile.GroupNDVs))
for _, childGroupNDV := range childProfile.GroupNDVs {
projCols := make([]int64, len(childGroupNDV.Cols))
for i, col := range childGroupNDV.Cols {
projCol, ok := exprCol2ProjCol[col]
if !ok {
projCols = nil
break
}
projCols[i] = projCol
}
if projCols == nil {
continue
}
slices.Sort(projCols)
groupNDV := property.GroupNDV{
Cols: projCols,
NDV: childGroupNDV.NDV,
}
ndvs = append(ndvs, groupNDV)
}
return ndvs
}
// DeriveStats implement LogicalPlan DeriveStats interface.
func (p *LogicalProjection) DeriveStats(childStats []*property.StatsInfo, selfSchema *expression.Schema, childSchema []*expression.Schema, colGroups [][]*expression.Column) (*property.StatsInfo, error) {
childProfile := childStats[0]
if p.stats != nil {
// Reload GroupNDVs since colGroups may have changed.
p.stats.GroupNDVs = p.getGroupNDVs(colGroups, childProfile, selfSchema)
return p.stats, nil
}
p.stats = &property.StatsInfo{
RowCount: childProfile.RowCount,
ColNDVs: make(map[int64]float64, len(p.Exprs)),
}
for i, expr := range p.Exprs {
cols := expression.ExtractColumns(expr)
p.stats.ColNDVs[selfSchema.Columns[i].UniqueID], _ = getColsNDVWithMatchedLen(cols, childSchema[0], childProfile)
}
p.stats.GroupNDVs = p.getGroupNDVs(colGroups, childProfile, selfSchema)
return p.stats, nil
}
// ExtractColGroups implements LogicalPlan ExtractColGroups interface.
func (p *LogicalProjection) ExtractColGroups(colGroups [][]*expression.Column) [][]*expression.Column {
if len(colGroups) == 0 {
return nil
}
extColGroups, _ := p.Schema().ExtractColGroups(colGroups)
if len(extColGroups) == 0 {
return nil
}
extracted := make([][]*expression.Column, 0, len(extColGroups))
for _, cols := range extColGroups {
exprs := make([]*expression.Column, len(cols))
allCols := true
for i, offset := range cols {
col, ok := p.Exprs[offset].(*expression.Column)
// TODO: for functional dependent projections like `col1 + 1` -> `col2`, we can maintain GroupNDVs actually.
if !ok {
allCols = false
break
}
exprs[i] = col
}
if allCols {
extracted = append(extracted, expression.SortColumns(exprs))
}
}
return extracted
}
func (la *LogicalAggregation) getGroupNDVs(colGroups [][]*expression.Column, childProfile *property.StatsInfo, gbyCols []*expression.Column) []property.GroupNDV {
if len(colGroups) == 0 {
return nil
}
// Check if the child profile provides GroupNDV for the GROUP BY columns.
// Note that gbyCols may not be the exact GROUP BY columns, e.g, GROUP BY a+b,
// but we have no other approaches for the NDV estimation of these cases
// except for using the independent assumption, unless we can use stats of expression index.
groupNDV := getGroupNDV4Cols(gbyCols, childProfile)
if groupNDV == nil {
return nil
}
return []property.GroupNDV{*groupNDV}
}
// DeriveStats implement LogicalPlan DeriveStats interface.
func (la *LogicalAggregation) DeriveStats(childStats []*property.StatsInfo, selfSchema *expression.Schema, childSchema []*expression.Schema, colGroups [][]*expression.Column) (*property.StatsInfo, error) {
childProfile := childStats[0]
gbyCols := make([]*expression.Column, 0, len(la.GroupByItems))
for _, gbyExpr := range la.GroupByItems {
cols := expression.ExtractColumns(gbyExpr)
gbyCols = append(gbyCols, cols...)
}
if la.stats != nil {
// Reload GroupNDVs since colGroups may have changed.
la.stats.GroupNDVs = la.getGroupNDVs(colGroups, childProfile, gbyCols)
return la.stats, nil
}
NDV, _ := getColsNDVWithMatchedLen(gbyCols, childSchema[0], childProfile)
la.stats = &property.StatsInfo{
RowCount: NDV,
ColNDVs: make(map[int64]float64, selfSchema.Len()),
}
// We cannot estimate the ColNDVs for every output, so we use a conservative strategy.
for _, col := range selfSchema.Columns {
la.stats.ColNDVs[col.UniqueID] = NDV
}
la.inputCount = childProfile.RowCount
la.stats.GroupNDVs = la.getGroupNDVs(colGroups, childProfile, gbyCols)
return la.stats, nil
}
// ExtractColGroups implements LogicalPlan ExtractColGroups interface.
func (la *LogicalAggregation) ExtractColGroups(_ [][]*expression.Column) [][]*expression.Column {
// Parent colGroups would be dicarded, because aggregation would make NDV of colGroups
// which does not match GroupByItems invalid.
// Note that gbyCols may not be the exact GROUP BY columns, e.g, GROUP BY a+b,
// but we have no other approaches for the NDV estimation of these cases
// except for using the independent assumption, unless we can use stats of expression index.
gbyCols := make([]*expression.Column, 0, len(la.GroupByItems))
for _, gbyExpr := range la.GroupByItems {
cols := expression.ExtractColumns(gbyExpr)
gbyCols = append(gbyCols, cols...)
}
if len(gbyCols) > 1 {
return [][]*expression.Column{expression.SortColumns(gbyCols)}
}
return nil
}
func (p *LogicalJoin) getGroupNDVs(colGroups [][]*expression.Column, childStats []*property.StatsInfo) []property.GroupNDV {
outerIdx := int(-1)
if p.JoinType == LeftOuterJoin || p.JoinType == LeftOuterSemiJoin || p.JoinType == AntiLeftOuterSemiJoin {
outerIdx = 0
} else if p.JoinType == RightOuterJoin {
outerIdx = 1
}
if outerIdx >= 0 && len(colGroups) > 0 {
return childStats[outerIdx].GroupNDVs
}
return nil
}
// DeriveStats implement LogicalPlan DeriveStats interface.
// 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 NDV 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 NDV 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(childStats []*property.StatsInfo, selfSchema *expression.Schema, childSchema []*expression.Schema, colGroups [][]*expression.Column) (*property.StatsInfo, error) {
if p.stats != nil {
// Reload GroupNDVs since colGroups may have changed.
p.stats.GroupNDVs = p.getGroupNDVs(colGroups, childStats)
return p.stats, nil
}
leftProfile, rightProfile := childStats[0], childStats[1]
leftJoinKeys, rightJoinKeys, _, _ := p.GetJoinKeys()
helper := &fullJoinRowCountHelper{
sctx: p.SCtx(),
cartesian: 0 == len(p.EqualConditions),
leftProfile: leftProfile,
rightProfile: rightProfile,
leftJoinKeys: leftJoinKeys,
rightJoinKeys: rightJoinKeys,
leftSchema: childSchema[0],
rightSchema: childSchema[1],
}
p.equalCondOutCnt = helper.estimate()
if p.JoinType == SemiJoin || p.JoinType == AntiSemiJoin {
p.stats = &property.StatsInfo{
RowCount: leftProfile.RowCount * SelectionFactor,
ColNDVs: make(map[int64]float64, len(leftProfile.ColNDVs)),
}
for id, c := range leftProfile.ColNDVs {
p.stats.ColNDVs[id] = c * SelectionFactor
}
return p.stats, nil
}
if p.JoinType == LeftOuterSemiJoin || p.JoinType == AntiLeftOuterSemiJoin {
p.stats = &property.StatsInfo{
RowCount: leftProfile.RowCount,
ColNDVs: make(map[int64]float64, selfSchema.Len()),
}
for id, c := range leftProfile.ColNDVs {
p.stats.ColNDVs[id] = c
}
p.stats.ColNDVs[selfSchema.Columns[selfSchema.Len()-1].UniqueID] = 2.0
p.stats.GroupNDVs = p.getGroupNDVs(colGroups, childStats)
return p.stats, nil
}
count := p.equalCondOutCnt
if p.JoinType == LeftOuterJoin {
count = math.Max(count, leftProfile.RowCount)
} else if p.JoinType == RightOuterJoin {
count = math.Max(count, rightProfile.RowCount)
}
colNDVs := make(map[int64]float64, selfSchema.Len())
for id, c := range leftProfile.ColNDVs {
colNDVs[id] = math.Min(c, count)
}
for id, c := range rightProfile.ColNDVs {
colNDVs[id] = math.Min(c, count)
}
p.stats = &property.StatsInfo{
RowCount: count,
ColNDVs: colNDVs,
}
p.stats.GroupNDVs = p.getGroupNDVs(colGroups, childStats)
return p.stats, nil
}
// ExtractColGroups implements LogicalPlan ExtractColGroups interface.
func (p *LogicalJoin) ExtractColGroups(colGroups [][]*expression.Column) [][]*expression.Column {
leftJoinKeys, rightJoinKeys, _, _ := p.GetJoinKeys()
extracted := make([][]*expression.Column, 0, 2+len(colGroups))
if len(leftJoinKeys) > 1 && (p.JoinType == InnerJoin || p.JoinType == LeftOuterJoin || p.JoinType == RightOuterJoin) {
extracted = append(extracted, expression.SortColumns(leftJoinKeys), expression.SortColumns(rightJoinKeys))
}
var outerSchema *expression.Schema
if p.JoinType == LeftOuterJoin || p.JoinType == LeftOuterSemiJoin || p.JoinType == AntiLeftOuterSemiJoin {
outerSchema = p.Children()[0].Schema()
} else if p.JoinType == RightOuterJoin {
outerSchema = p.Children()[1].Schema()
}
if len(colGroups) == 0 || outerSchema == nil {
return extracted
}
_, offsets := outerSchema.ExtractColGroups(colGroups)
if len(offsets) == 0 {
return extracted
}
for _, offset := range offsets {
extracted = append(extracted, colGroups[offset])
}
return extracted
}
type fullJoinRowCountHelper struct {
sctx sessionctx.Context
cartesian bool
leftProfile *property.StatsInfo
rightProfile *property.StatsInfo
leftJoinKeys []*expression.Column
rightJoinKeys []*expression.Column
leftSchema *expression.Schema
rightSchema *expression.Schema
}
func (h *fullJoinRowCountHelper) estimate() float64 {
if h.cartesian {
return h.leftProfile.RowCount * h.rightProfile.RowCount
}
leftKeyNDV, leftColCnt := getColsNDVWithMatchedLen(h.leftJoinKeys, h.leftSchema, h.leftProfile)
rightKeyNDV, rightColCnt := getColsNDVWithMatchedLen(h.rightJoinKeys, h.rightSchema, h.rightProfile)
count := h.leftProfile.RowCount * h.rightProfile.RowCount / math.Max(leftKeyNDV, rightKeyNDV)
if h.sctx.GetSessionVars().TiDBOptJoinReorderThreshold <= 0 {
return count
}
// If we enable the DP choice, we multiple the 0.9 for each remained join key supposing that 0.9 is the correlation factor between them.
// This estimation logic is referred to Presto.
return count * math.Pow(0.9, float64(len(h.leftJoinKeys)-mathutil.Max(leftColCnt, rightColCnt)))
}
func (la *LogicalApply) getGroupNDVs(colGroups [][]*expression.Column, childStats []*property.StatsInfo) []property.GroupNDV {
if len(colGroups) > 0 && (la.JoinType == LeftOuterSemiJoin || la.JoinType == AntiLeftOuterSemiJoin || la.JoinType == LeftOuterJoin) {
return childStats[0].GroupNDVs
}
return nil
}
// DeriveStats implement LogicalPlan DeriveStats interface.
func (la *LogicalApply) DeriveStats(childStats []*property.StatsInfo, selfSchema *expression.Schema, childSchema []*expression.Schema, colGroups [][]*expression.Column) (*property.StatsInfo, error) {
if la.stats != nil {
// Reload GroupNDVs since colGroups may have changed.
la.stats.GroupNDVs = la.getGroupNDVs(colGroups, childStats)
return la.stats, nil
}
leftProfile := childStats[0]
la.stats = &property.StatsInfo{
RowCount: leftProfile.RowCount,
ColNDVs: make(map[int64]float64, selfSchema.Len()),
}
for id, c := range leftProfile.ColNDVs {
la.stats.ColNDVs[id] = c
}
if la.JoinType == LeftOuterSemiJoin || la.JoinType == AntiLeftOuterSemiJoin {
la.stats.ColNDVs[selfSchema.Columns[selfSchema.Len()-1].UniqueID] = 2.0
} else {
for i := childSchema[0].Len(); i < selfSchema.Len(); i++ {
la.stats.ColNDVs[selfSchema.Columns[i].UniqueID] = leftProfile.RowCount
}
}
la.stats.GroupNDVs = la.getGroupNDVs(colGroups, childStats)
return la.stats, nil
}
// ExtractColGroups implements LogicalPlan ExtractColGroups interface.
func (la *LogicalApply) ExtractColGroups(colGroups [][]*expression.Column) [][]*expression.Column {
var outerSchema *expression.Schema
// Apply doesn't have RightOuterJoin.
if la.JoinType == LeftOuterJoin || la.JoinType == LeftOuterSemiJoin || la.JoinType == AntiLeftOuterSemiJoin {
outerSchema = la.Children()[0].Schema()
}
if len(colGroups) == 0 || outerSchema == nil {
return nil
}
_, offsets := outerSchema.ExtractColGroups(colGroups)
if len(offsets) == 0 {
return nil
}
extracted := make([][]*expression.Column, len(offsets))
for i, offset := range offsets {
extracted[i] = colGroups[offset]
}
return extracted
}
// Exists and MaxOneRow produce at most one row, so we set the RowCount of stats one.
func getSingletonStats(schema *expression.Schema) *property.StatsInfo {
ret := &property.StatsInfo{
RowCount: 1.0,
ColNDVs: make(map[int64]float64, schema.Len()),
}
for _, col := range schema.Columns {
ret.ColNDVs[col.UniqueID] = 1
}
return ret
}
// DeriveStats implement LogicalPlan DeriveStats interface.
func (p *LogicalMaxOneRow) DeriveStats(_ []*property.StatsInfo, selfSchema *expression.Schema, _ []*expression.Schema, _ [][]*expression.Column) (*property.StatsInfo, error) {
if p.stats != nil {
return p.stats, nil
}
p.stats = getSingletonStats(selfSchema)
return p.stats, nil
}
func (p *LogicalWindow) getGroupNDVs(colGroups [][]*expression.Column, childStats []*property.StatsInfo) []property.GroupNDV {
if len(colGroups) > 0 {
return childStats[0].GroupNDVs
}
return nil
}
// DeriveStats implement LogicalPlan DeriveStats interface.
func (p *LogicalWindow) DeriveStats(childStats []*property.StatsInfo, selfSchema *expression.Schema, _ []*expression.Schema, colGroups [][]*expression.Column) (*property.StatsInfo, error) {
if p.stats != nil {
// Reload GroupNDVs since colGroups may have changed.
p.stats.GroupNDVs = p.getGroupNDVs(colGroups, childStats)
return p.stats, nil
}
childProfile := childStats[0]
p.stats = &property.StatsInfo{
RowCount: childProfile.RowCount,
ColNDVs: make(map[int64]float64, selfSchema.Len()),
}
childLen := selfSchema.Len() - len(p.WindowFuncDescs)
for i := 0; i < childLen; i++ {
id := selfSchema.Columns[i].UniqueID
p.stats.ColNDVs[id] = childProfile.ColNDVs[id]
}
for i := childLen; i < selfSchema.Len(); i++ {
p.stats.ColNDVs[selfSchema.Columns[i].UniqueID] = childProfile.RowCount
}
p.stats.GroupNDVs = p.getGroupNDVs(colGroups, childStats)
return p.stats, nil
}
// ExtractColGroups implements LogicalPlan ExtractColGroups interface.
func (p *LogicalWindow) ExtractColGroups(colGroups [][]*expression.Column) [][]*expression.Column {
if len(colGroups) == 0 {
return nil
}
childSchema := p.Children()[0].Schema()
_, offsets := childSchema.ExtractColGroups(colGroups)
if len(offsets) == 0 {
return nil
}
extracted := make([][]*expression.Column, len(offsets))
for i, offset := range offsets {
extracted[i] = colGroups[offset]
}
return extracted
}
// DeriveStats implement LogicalPlan DeriveStats interface.
func (p *LogicalCTE) DeriveStats(_ []*property.StatsInfo, selfSchema *expression.Schema, _ []*expression.Schema, _ [][]*expression.Column) (*property.StatsInfo, error) {
if p.stats != nil {
return p.stats, nil
}
var err error
if p.cte.seedPartPhysicalPlan == nil {
// Build push-downed predicates.
if len(p.cte.pushDownPredicates) > 0 {
newCond := expression.ComposeDNFCondition(p.ctx, p.cte.pushDownPredicates...)
newSel := LogicalSelection{Conditions: []expression.Expression{newCond}}.Init(p.SCtx(), p.cte.seedPartLogicalPlan.SelectBlockOffset())
newSel.SetChildren(p.cte.seedPartLogicalPlan)
p.cte.seedPartLogicalPlan = newSel
}
p.cte.seedPartPhysicalPlan, _, err = DoOptimize(context.TODO(), p.ctx, p.cte.optFlag, p.cte.seedPartLogicalPlan)
if err != nil {
return nil, err
}
}
resStat := p.cte.seedPartPhysicalPlan.Stats()
// Changing the pointer so that seedStat in LogicalCTETable can get the new stat.
*p.seedStat = *resStat
p.stats = &property.StatsInfo{
RowCount: resStat.RowCount,
ColNDVs: make(map[int64]float64, selfSchema.Len()),
}
for i, col := range selfSchema.Columns {
p.stats.ColNDVs[col.UniqueID] += resStat.ColNDVs[p.cte.seedPartLogicalPlan.Schema().Columns[i].UniqueID]
}
if p.cte.recursivePartLogicalPlan != nil {
if p.cte.recursivePartPhysicalPlan == nil {
p.cte.recursivePartPhysicalPlan, _, err = DoOptimize(context.TODO(), p.ctx, p.cte.optFlag, p.cte.recursivePartLogicalPlan)
if err != nil {
return nil, err
}
}
recurStat := p.cte.recursivePartPhysicalPlan.Stats()
for i, col := range selfSchema.Columns {
p.stats.ColNDVs[col.UniqueID] += recurStat.ColNDVs[p.cte.recursivePartLogicalPlan.Schema().Columns[i].UniqueID]
}
if p.cte.IsDistinct {
p.stats.RowCount, _ = getColsNDVWithMatchedLen(p.schema.Columns, p.schema, p.stats)
} else {
p.stats.RowCount += recurStat.RowCount
}
}
return p.stats, nil
}
// DeriveStats implement LogicalPlan DeriveStats interface.
func (p *LogicalCTETable) DeriveStats(_ []*property.StatsInfo, _ *expression.Schema, _ []*expression.Schema, _ [][]*expression.Column) (*property.StatsInfo, error) {
if p.stats != nil {
return p.stats, nil
}
p.stats = p.seedStat
return p.stats, nil
}
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