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planner: change predicateColumnCollector to columnStatsUsageCollector and collect histogram-needed columns #30671

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merged 14 commits into from
Dec 25, 2021
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planner: change predicateColumnCollector to columnStatsUsageCollector and collect histogram-needed columns #30671

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b8aacf0
collect hist-needed columns
xuyifangreeneyes Dec 13, 2021
58a46fe
remove log
xuyifangreeneyes Dec 13, 2021
14fc7b8
Merge branch 'master' into hist-needed-columns
xuyifangreeneyes Dec 15, 2021
f716767
Merge branch 'master' of https://github.com/pingcap/tidb into hist-ne…
xuyifangreeneyes Dec 16, 2021
cf91ae5
use ds.physicalTableID rather than ds.TableInfo().ID since we need to…
xuyifangreeneyes Dec 16, 2021
bf0ff94
Merge branch 'master' into hist-needed-columns
xuyifangreeneyes Dec 20, 2021
7e2cb16
rename functions
xuyifangreeneyes Dec 20, 2021
3e0da0e
update exported function
xuyifangreeneyes Dec 20, 2021
9de2fe1
move test from core_test to core
xuyifangreeneyes Dec 21, 2021
608046c
upd
xuyifangreeneyes Dec 21, 2021
0a1245e
address comment
xuyifangreeneyes Dec 24, 2021
92aab0b
upd
xuyifangreeneyes Dec 24, 2021
a9c6432
remove debug
xuyifangreeneyes Dec 24, 2021
e4cdf25
Merge branch 'master' into hist-needed-columns
ti-chi-bot Dec 25, 2021
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286 changes: 164 additions & 122 deletions planner/core/collect_column_stats_usage.go
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Original file line number Diff line number Diff line change
Expand Up @@ -19,27 +19,46 @@ import (
"github.com/pingcap/tidb/parser/model"
)

// predicateColumnCollector collects predicate columns from logical plan. Predicate columns are the columns whose statistics
// are utilized when making query plans, which usually occur in where conditions, join conditions and so on.
type predicateColumnCollector struct {
// colMap maps expression.Column.UniqueID to the table columns whose statistics are utilized to calculate statistics of the column.
colMap map[int64]map[model.TableColumnID]struct{}
const (
collectPredicateColumns uint64 = 1 << iota
collectHistNeededColumns
)

// columnStatsUsageCollector collects predicate columns and/or histogram-needed columns from logical plan.
// Predicate columns are the columns whose statistics are utilized when making query plans, which usually occur in where conditions, join conditions and so on.
// Histogram-needed columns are the columns whose histograms are utilized when making query plans, which usually occur in the conditions pushed down to DataSource.
// The set of histogram-needed columns is the subset of that of predicate columns.
type columnStatsUsageCollector struct {
// collectMode indicates whether to collect predicate columns and/or histogram-needed columns
collectMode uint64
// predicateCols records predicate columns.
predicateCols map[model.TableColumnID]struct{}
// colMap maps expression.Column.UniqueID to the table columns whose statistics are utilized to calculate statistics of the column.
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Can you add more comments about the special cases to explain this? It's not easy to understand why an expression.Column.UniqueID needs more table columns to calculate.

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Done.

// It is used for collecting predicate columns.
colMap map[int64]map[model.TableColumnID]struct{}
// histNeededCols records histogram-needed columns
histNeededCols map[model.TableColumnID]struct{}
// cols is used to store columns collected from expressions and saves some allocation.
cols []*expression.Column
}

func newPredicateColumnCollector() *predicateColumnCollector {
return &predicateColumnCollector{
colMap: make(map[int64]map[model.TableColumnID]struct{}),
predicateCols: make(map[model.TableColumnID]struct{}),
func newColumnStatsUsageCollector(collectMode uint64) *columnStatsUsageCollector {
collector := &columnStatsUsageCollector{
collectMode: collectMode,
// Pre-allocate a slice to reduce allocation, 8 doesn't have special meaning.
cols: make([]*expression.Column, 0, 8),
}
if collectMode&collectPredicateColumns != 0 {
collector.predicateCols = make(map[model.TableColumnID]struct{})
collector.colMap = make(map[int64]map[model.TableColumnID]struct{})
}
if collectMode&collectHistNeededColumns != 0 {
collector.histNeededCols = make(map[model.TableColumnID]struct{})
}
return collector
}

func (c *predicateColumnCollector) addPredicateColumn(col *expression.Column) {
func (c *columnStatsUsageCollector) addPredicateColumn(col *expression.Column) {
tblColIDs, ok := c.colMap[col.UniqueID]
if !ok {
// It may happen if some leaf of logical plan is LogicalMemTable/LogicalShow/LogicalShowDDLJobs.
Expand All @@ -50,21 +69,21 @@ func (c *predicateColumnCollector) addPredicateColumn(col *expression.Column) {
}
}

func (c *predicateColumnCollector) addPredicateColumnsFromExpression(expr expression.Expression) {
func (c *columnStatsUsageCollector) addPredicateColumnsFromExpression(expr expression.Expression) {
cols := expression.ExtractColumnsAndCorColumns(c.cols[:0], expr)
for _, col := range cols {
c.addPredicateColumn(col)
}
}

func (c *predicateColumnCollector) addPredicateColumnsFromExpressions(list []expression.Expression) {
func (c *columnStatsUsageCollector) addPredicateColumnsFromExpressions(list []expression.Expression) {
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Can we merge this part? It seems the only difference is whether the expression is a slice or not.
Besides, we only need to export expression.ExtractColumnsAndCorColumnsFromExpressions, not all of them.

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Done.

cols := expression.ExtractColumnsAndCorColumnsFromExpressions(c.cols[:0], list)
for _, col := range cols {
c.addPredicateColumn(col)
}
}

func (c *predicateColumnCollector) updateColMap(col *expression.Column, relatedCols []*expression.Column) {
func (c *columnStatsUsageCollector) updateColMap(col *expression.Column, relatedCols []*expression.Column) {
if _, ok := c.colMap[col.UniqueID]; !ok {
c.colMap[col.UniqueID] = map[model.TableColumnID]struct{}{}
}
Expand All @@ -80,15 +99,15 @@ func (c *predicateColumnCollector) updateColMap(col *expression.Column, relatedC
}
}

func (c *predicateColumnCollector) updateColMapFromExpression(col *expression.Column, expr expression.Expression) {
func (c *columnStatsUsageCollector) updateColMapFromExpression(col *expression.Column, expr expression.Expression) {
c.updateColMap(col, expression.ExtractColumnsAndCorColumns(c.cols[:0], expr))
}

func (c *predicateColumnCollector) updateColMapFromExpressions(col *expression.Column, list []expression.Expression) {
func (c *columnStatsUsageCollector) updateColMapFromExpressions(col *expression.Column, list []expression.Expression) {
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Ditto.

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Done.

c.updateColMap(col, expression.ExtractColumnsAndCorColumnsFromExpressions(c.cols[:0], list))
}

func (ds *DataSource) updateColMapAndAddPredicateColumns(c *predicateColumnCollector) {
func (c *columnStatsUsageCollector) collectPredicateColumnsForDataSource(ds *DataSource) {
tblID := ds.TableInfo().ID
for _, col := range ds.Schema().Columns {
tblColID := model.TableColumnID{TableID: tblID, ColumnID: col.ID}
Expand All @@ -98,7 +117,7 @@ func (ds *DataSource) updateColMapAndAddPredicateColumns(c *predicateColumnColle
c.addPredicateColumnsFromExpressions(ds.pushedDownConds)
}

func (p *LogicalJoin) updateColMapAndAddPredicateColumns(c *predicateColumnCollector) {
func (c *columnStatsUsageCollector) collectPredicateColumnsForJoin(p *LogicalJoin) {
// The only schema change is merging two schemas so there is no new column.
// Assume statistics of all the columns in EqualConditions/LeftConditions/RightConditions/OtherConditions are needed.
exprs := make([]expression.Expression, 0, len(p.EqualConditions)+len(p.LeftConditions)+len(p.RightConditions)+len(p.OtherConditions))
Expand All @@ -117,7 +136,7 @@ func (p *LogicalJoin) updateColMapAndAddPredicateColumns(c *predicateColumnColle
c.addPredicateColumnsFromExpressions(exprs)
}

func (p *LogicalUnionAll) updateColMapAndAddPredicateColumns(c *predicateColumnCollector) {
func (c *columnStatsUsageCollector) collectPredicateColumnsForUnionAll(p *LogicalUnionAll) {
// statistics of the ith column of UnionAll come from statistics of the ith column of each child.
schemas := make([]*expression.Schema, 0, len(p.Children()))
relatedCols := make([]*expression.Column, 0, len(p.Children()))
Expand All @@ -133,120 +152,143 @@ func (p *LogicalUnionAll) updateColMapAndAddPredicateColumns(c *predicateColumnC
}
}

func (c *predicateColumnCollector) collectFromPlan(lp LogicalPlan) {
func (c *columnStatsUsageCollector) addHistNeededColumns(ds *DataSource) {
columns := expression.ExtractColumnsFromExpressions(c.cols[:0], ds.pushedDownConds, nil)
for _, col := range columns {
tblColID := model.TableColumnID{TableID: ds.physicalTableID, ColumnID: col.ID}
c.histNeededCols[tblColID] = struct{}{}
}
}

func (c *columnStatsUsageCollector) collectFromPlan(lp LogicalPlan) {
for _, child := range lp.Children() {
c.collectFromPlan(child)
}
switch x := lp.(type) {
case *DataSource:
x.updateColMapAndAddPredicateColumns(c)
case *LogicalIndexScan:
x.Source.updateColMapAndAddPredicateColumns(c)
// TODO: Is it redundant to add predicate columns from LogicalIndexScan.AccessConds? Is LogicalIndexScan.AccessConds a subset of LogicalIndexScan.Source.pushedDownConds.
c.addPredicateColumnsFromExpressions(x.AccessConds)
case *LogicalTableScan:
x.Source.updateColMapAndAddPredicateColumns(c)
// TODO: Is it redundant to add predicate columns from LogicalTableScan.AccessConds? Is LogicalTableScan.AccessConds a subset of LogicalTableScan.Source.pushedDownConds.
c.addPredicateColumnsFromExpressions(x.AccessConds)
case *TiKVSingleGather:
// TODO: Is it redundant?
x.Source.updateColMapAndAddPredicateColumns(c)
case *LogicalProjection:
// Schema change from children to self.
schema := x.Schema()
for i, expr := range x.Exprs {
c.updateColMapFromExpression(schema.Columns[i], expr)
}
case *LogicalSelection:
// Though the conditions in LogicalSelection are complex conditions which cannot be pushed down to DataSource, we still
// regard statistics of the columns in the conditions as needed.
c.addPredicateColumnsFromExpressions(x.Conditions)
case *LogicalAggregation:
// Just assume statistics of all the columns in GroupByItems are needed.
c.addPredicateColumnsFromExpressions(x.GroupByItems)
// Schema change from children to self.
schema := x.Schema()
for i, aggFunc := range x.AggFuncs {
c.updateColMapFromExpressions(schema.Columns[i], aggFunc.Args)
}
case *LogicalWindow:
// Statistics of the columns in LogicalWindow.PartitionBy are used in optimizeByShuffle4Window.
// It seems that we don't use statistics of the columns in LogicalWindow.OrderBy currently?
for _, item := range x.PartitionBy {
c.addPredicateColumn(item.Col)
}
// Schema change from children to self.
windowColumns := x.GetWindowResultColumns()
for i, col := range windowColumns {
c.updateColMapFromExpressions(col, x.WindowFuncDescs[i].Args)
}
case *LogicalJoin:
x.updateColMapAndAddPredicateColumns(c)
case *LogicalApply:
x.updateColMapAndAddPredicateColumns(c)
// Assume statistics of correlated columns are needed.
// Correlated columns can be found in LogicalApply.Children()[0].Schema(). Since we already visit LogicalApply.Children()[0],
// correlated columns must have existed in predicateColumnCollector.colMap.
for _, corCols := range x.CorCols {
c.addPredicateColumn(&corCols.Column)
}
case *LogicalSort:
// Assume statistics of all the columns in ByItems are needed.
for _, item := range x.ByItems {
c.addPredicateColumnsFromExpression(item.Expr)
}
case *LogicalTopN:
// Assume statistics of all the columns in ByItems are needed.
for _, item := range x.ByItems {
c.addPredicateColumnsFromExpression(item.Expr)
}
case *LogicalUnionAll:
x.updateColMapAndAddPredicateColumns(c)
case *LogicalPartitionUnionAll:
x.updateColMapAndAddPredicateColumns(c)
case *LogicalCTE:
// Visit seedPartLogicalPlan and recursivePartLogicalPlan first.
c.collectFromPlan(x.cte.seedPartLogicalPlan)
if x.cte.recursivePartLogicalPlan != nil {
c.collectFromPlan(x.cte.recursivePartLogicalPlan)
}
// Schema change from seedPlan/recursivePlan to self.
columns := x.Schema().Columns
seedColumns := x.cte.seedPartLogicalPlan.Schema().Columns
var recursiveColumns []*expression.Column
if x.cte.recursivePartLogicalPlan != nil {
recursiveColumns = x.cte.recursivePartLogicalPlan.Schema().Columns
}
relatedCols := make([]*expression.Column, 0, 2)
for i, col := range columns {
relatedCols = append(relatedCols[:0], seedColumns[i])
if recursiveColumns != nil {
relatedCols = append(relatedCols, recursiveColumns[i])
if c.collectMode&collectPredicateColumns != 0 {
switch x := lp.(type) {
case *DataSource:
c.collectPredicateColumnsForDataSource(x)
case *LogicalIndexScan:
c.collectPredicateColumnsForDataSource(x.Source)
c.addPredicateColumnsFromExpressions(x.AccessConds)
case *LogicalTableScan:
c.collectPredicateColumnsForDataSource(x.Source)
c.addPredicateColumnsFromExpressions(x.AccessConds)
case *LogicalProjection:
// Schema change from children to self.
schema := x.Schema()
for i, expr := range x.Exprs {
c.updateColMapFromExpression(schema.Columns[i], expr)
}
c.updateColMap(col, relatedCols)
}
// If IsDistinct is true, then we use getColsNDV to calculate row count(see (*LogicalCTE).DeriveStat). In this case
// statistics of all the columns are needed.
if x.cte.IsDistinct {
for _, col := range columns {
c.addPredicateColumn(col)
case *LogicalSelection:
// Though the conditions in LogicalSelection are complex conditions which cannot be pushed down to DataSource, we still
// regard statistics of the columns in the conditions as needed.
c.addPredicateColumnsFromExpressions(x.Conditions)
case *LogicalAggregation:
// Just assume statistics of all the columns in GroupByItems are needed.
c.addPredicateColumnsFromExpressions(x.GroupByItems)
// Schema change from children to self.
schema := x.Schema()
for i, aggFunc := range x.AggFuncs {
c.updateColMapFromExpressions(schema.Columns[i], aggFunc.Args)
}
case *LogicalWindow:
// Statistics of the columns in LogicalWindow.PartitionBy are used in optimizeByShuffle4Window.
// We don't use statistics of the columns in LogicalWindow.OrderBy currently.
for _, item := range x.PartitionBy {
c.addPredicateColumn(item.Col)
}
// Schema change from children to self.
windowColumns := x.GetWindowResultColumns()
for i, col := range windowColumns {
c.updateColMapFromExpressions(col, x.WindowFuncDescs[i].Args)
}
case *LogicalJoin:
c.collectPredicateColumnsForJoin(x)
case *LogicalApply:
c.collectPredicateColumnsForJoin(&x.LogicalJoin)
// Assume statistics of correlated columns are needed.
// Correlated columns can be found in LogicalApply.Children()[0].Schema(). Since we already visit LogicalApply.Children()[0],
// correlated columns must have existed in columnStatsUsageCollector.colMap.
for _, corCols := range x.CorCols {
c.addPredicateColumn(&corCols.Column)
}
case *LogicalSort:
// Assume statistics of all the columns in ByItems are needed.
for _, item := range x.ByItems {
c.addPredicateColumnsFromExpression(item.Expr)
}
case *LogicalTopN:
// Assume statistics of all the columns in ByItems are needed.
for _, item := range x.ByItems {
c.addPredicateColumnsFromExpression(item.Expr)
}
case *LogicalUnionAll:
c.collectPredicateColumnsForUnionAll(x)
case *LogicalPartitionUnionAll:
c.collectPredicateColumnsForUnionAll(&x.LogicalUnionAll)
case *LogicalCTE:
// Visit seedPartLogicalPlan and recursivePartLogicalPlan first.
c.collectFromPlan(x.cte.seedPartLogicalPlan)
if x.cte.recursivePartLogicalPlan != nil {
c.collectFromPlan(x.cte.recursivePartLogicalPlan)
}
// Schema change from seedPlan/recursivePlan to self.
columns := x.Schema().Columns
seedColumns := x.cte.seedPartLogicalPlan.Schema().Columns
var recursiveColumns []*expression.Column
if x.cte.recursivePartLogicalPlan != nil {
recursiveColumns = x.cte.recursivePartLogicalPlan.Schema().Columns
}
relatedCols := make([]*expression.Column, 0, 2)
for i, col := range columns {
relatedCols = append(relatedCols[:0], seedColumns[i])
if recursiveColumns != nil {
relatedCols = append(relatedCols, recursiveColumns[i])
}
c.updateColMap(col, relatedCols)
}
// If IsDistinct is true, then we use getColsNDV to calculate row count(see (*LogicalCTE).DeriveStat). In this case
// statistics of all the columns are needed.
if x.cte.IsDistinct {
for _, col := range columns {
c.addPredicateColumn(col)
}
}
case *LogicalCTETable:
// Schema change from seedPlan to self.
for i, col := range x.Schema().Columns {
c.updateColMap(col, []*expression.Column{x.seedSchema.Columns[i]})
}
}
case *LogicalCTETable:
// Schema change from seedPlan to self.
for i, col := range x.Schema().Columns {
c.updateColMap(col, []*expression.Column{x.seedSchema.Columns[i]})
}
if c.collectMode&collectHistNeededColumns != 0 {
// Histogram-needed columns are the columns which occur in the conditions pushed down to DataSource.
// We don't consider LogicalCTE because seedLogicalPlan and recursiveLogicalPlan haven't got logical optimization
// yet(seedLogicalPlan and recursiveLogicalPlan are optimized in DeriveStats phase). Without logical optimization,
// there is no condition pushed down to DataSource so no histogram-needed column can be collected.
switch x := lp.(type) {
case *DataSource:
c.addHistNeededColumns(x)
case *LogicalIndexScan:
c.addHistNeededColumns(x.Source)
case *LogicalTableScan:
c.addHistNeededColumns(x.Source)
}
}
}

// CollectPredicateColumnsForTest collects predicate columns from logical plan. It is only for test.
func CollectPredicateColumnsForTest(lp LogicalPlan) []model.TableColumnID {
collector := newPredicateColumnCollector()
// CollectColumnStatsUsage collects column stats usage from logical plan.
// The first return value is predicate columns and the second return value is histogram-needed columns.
func CollectColumnStatsUsage(lp LogicalPlan) ([]model.TableColumnID, []model.TableColumnID) {
collector := newColumnStatsUsageCollector(collectPredicateColumns | collectHistNeededColumns)
collector.collectFromPlan(lp)
tblColIDs := make([]model.TableColumnID, 0, len(collector.predicateCols))
for tblColID := range collector.predicateCols {
tblColIDs = append(tblColIDs, tblColID)
set2slice := func(set map[model.TableColumnID]struct{}) []model.TableColumnID {
ret := make([]model.TableColumnID, 0, len(set))
for tblColID := range set {
ret = append(ret, tblColID)
}
return ret
}
return tblColIDs
return set2slice(collector.predicateCols), set2slice(collector.histNeededCols)
}
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