tidb logical_plans 源码
tidb logical_plans 代码
文件路径：/planner/core/logical_plans.go
// Copyright 2016 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 (
"math"
"github.com/pingcap/tidb/expression"
"github.com/pingcap/tidb/expression/aggregation"
"github.com/pingcap/tidb/infoschema"
"github.com/pingcap/tidb/parser/ast"
"github.com/pingcap/tidb/parser/auth"
"github.com/pingcap/tidb/parser/model"
"github.com/pingcap/tidb/parser/mysql"
fd "github.com/pingcap/tidb/planner/funcdep"
"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/table"
"github.com/pingcap/tidb/types"
"github.com/pingcap/tidb/util/logutil"
"github.com/pingcap/tidb/util/ranger"
"go.uber.org/zap"
)
var (
_ LogicalPlan = &LogicalJoin{}
_ LogicalPlan = &LogicalAggregation{}
_ LogicalPlan = &LogicalProjection{}
_ LogicalPlan = &LogicalSelection{}
_ LogicalPlan = &LogicalApply{}
_ LogicalPlan = &LogicalMaxOneRow{}
_ LogicalPlan = &LogicalTableDual{}
_ LogicalPlan = &DataSource{}
_ LogicalPlan = &TiKVSingleGather{}
_ LogicalPlan = &LogicalTableScan{}
_ LogicalPlan = &LogicalIndexScan{}
_ LogicalPlan = &LogicalUnionAll{}
_ LogicalPlan = &LogicalSort{}
_ LogicalPlan = &LogicalLock{}
_ LogicalPlan = &LogicalLimit{}
_ LogicalPlan = &LogicalWindow{}
)
// JoinType contains CrossJoin, InnerJoin, LeftOuterJoin, RightOuterJoin, FullOuterJoin, SemiJoin.
type JoinType int
const (
// InnerJoin means inner join.
InnerJoin JoinType = iota
// LeftOuterJoin means left join.
LeftOuterJoin
// RightOuterJoin means right join.
RightOuterJoin
// SemiJoin means if row a in table A matches some rows in B, just output a.
SemiJoin
// AntiSemiJoin means if row a in table A does not match any row in B, then output a.
AntiSemiJoin
// LeftOuterSemiJoin means if row a in table A matches some rows in B, output (a, true), otherwise, output (a, false).
LeftOuterSemiJoin
// AntiLeftOuterSemiJoin means if row a in table A matches some rows in B, output (a, false), otherwise, output (a, true).
AntiLeftOuterSemiJoin
)
// IsOuterJoin returns if this joiner is an outer joiner
func (tp JoinType) IsOuterJoin() bool {
return tp == LeftOuterJoin || tp == RightOuterJoin ||
tp == LeftOuterSemiJoin || tp == AntiLeftOuterSemiJoin
}
// IsSemiJoin returns if this joiner is a semi/anti-semi joiner
func (tp JoinType) IsSemiJoin() bool {
return tp == SemiJoin || tp == AntiSemiJoin ||
tp == LeftOuterSemiJoin || tp == AntiLeftOuterSemiJoin
}
func (tp JoinType) String() string {
switch tp {
case InnerJoin:
return "inner join"
case LeftOuterJoin:
return "left outer join"
case RightOuterJoin:
return "right outer join"
case SemiJoin:
return "semi join"
case AntiSemiJoin:
return "anti semi join"
case LeftOuterSemiJoin:
return "left outer semi join"
case AntiLeftOuterSemiJoin:
return "anti left outer semi join"
}
return "unsupported join type"
}
const (
preferLeftAsINLJInner uint = 1 << iota
preferRightAsINLJInner
preferLeftAsINLHJInner
preferRightAsINLHJInner
preferLeftAsINLMJInner
preferRightAsINLMJInner
preferHashJoin
preferLeftAsHJBuild
preferRightAsHJBuild
preferLeftAsHJProbe
preferRightAsHJProbe
preferMergeJoin
preferBCJoin
preferRewriteSemiJoin
preferHashAgg
preferStreamAgg
)
const (
preferTiKV = 1 << iota
preferTiFlash
)
// LogicalJoin is the logical join plan.
type LogicalJoin struct {
logicalSchemaProducer
JoinType JoinType
reordered bool
cartesianJoin bool
StraightJoin bool
// hintInfo stores the join algorithm hint information specified by client.
hintInfo *tableHintInfo
preferJoinType uint
preferJoinOrder bool
EqualConditions []*expression.ScalarFunction
LeftConditions expression.CNFExprs
RightConditions expression.CNFExprs
OtherConditions expression.CNFExprs
leftProperties [][]*expression.Column
rightProperties [][]*expression.Column
// DefaultValues is only used for left/right outer join, which is values the inner row's should be when the outer table
// doesn't match any inner table's row.
// That it's nil just means the default values is a slice of NULL.
// Currently, only `aggregation push down` phase will set this.
DefaultValues []types.Datum
// fullSchema contains all the columns that the Join can output. It's ordered as [outer schema..., inner schema...].
// This is useful for natural joins and "using" joins. In these cases, the join key columns from the
// inner side (or the right side when it's an inner join) will not be in the schema of Join.
// But upper operators should be able to find those "redundant" columns, and the user also can specifically select
// those columns, so we put the "redundant" columns here to make them be able to be found.
//
// For example:
// create table t1(a int, b int); create table t2(a int, b int);
// select * from t1 join t2 using (b);
// schema of the Join will be [t1.b, t1.a, t2.a]; fullSchema will be [t1.a, t1.b, t2.a, t2.b].
//
// We record all columns and keep them ordered is for correctly handling SQLs like
// select t1.*, t2.* from t1 join t2 using (b);
// (*PlanBuilder).unfoldWildStar() handles the schema for such case.
fullSchema *expression.Schema
fullNames types.NameSlice
// equalCondOutCnt indicates the estimated count of joined rows after evaluating `EqualConditions`.
equalCondOutCnt float64
}
// Shallow shallow copies a LogicalJoin struct.
func (p *LogicalJoin) Shallow() *LogicalJoin {
join := *p
return join.Init(p.ctx, p.blockOffset)
}
// ExtractFD implements the interface LogicalPlan.
func (p *LogicalJoin) ExtractFD() *fd.FDSet {
switch p.JoinType {
case InnerJoin:
return p.extractFDForInnerJoin(nil)
case LeftOuterJoin, RightOuterJoin:
return p.extractFDForOuterJoin(nil)
case SemiJoin:
return p.extractFDForSemiJoin(nil)
default:
return &fd.FDSet{HashCodeToUniqueID: make(map[string]int)}
}
}
func (p *LogicalJoin) extractFDForSemiJoin(filtersFromApply []expression.Expression) *fd.FDSet {
// 1: since semi join will keep the part or all rows of the outer table, it's outer FD can be saved.
// 2: the un-projected column will be left for the upper layer projection or already be pruned from bottom up.
outerFD, _ := p.children[0].ExtractFD(), p.children[1].ExtractFD()
fds := outerFD
eqCondSlice := expression.ScalarFuncs2Exprs(p.EqualConditions)
allConds := append(eqCondSlice, p.OtherConditions...)
allConds = append(allConds, filtersFromApply...)
notNullColsFromFilters := extractNotNullFromConds(allConds, p)
constUniqueIDs := extractConstantCols(p.LeftConditions, p.SCtx(), fds)
fds.MakeNotNull(notNullColsFromFilters)
fds.AddConstants(constUniqueIDs)
p.fdSet = fds
return fds
}
func (p *LogicalJoin) extractFDForInnerJoin(filtersFromApply []expression.Expression) *fd.FDSet {
leftFD, rightFD := p.children[0].ExtractFD(), p.children[1].ExtractFD()
fds := leftFD
fds.MakeCartesianProduct(rightFD)
eqCondSlice := expression.ScalarFuncs2Exprs(p.EqualConditions)
// some join eq conditions are stored in the OtherConditions.
allConds := append(eqCondSlice, p.OtherConditions...)
allConds = append(allConds, filtersFromApply...)
notNullColsFromFilters := extractNotNullFromConds(allConds, p)
constUniqueIDs := extractConstantCols(allConds, p.SCtx(), fds)
equivUniqueIDs := extractEquivalenceCols(allConds, p.SCtx(), fds)
fds.MakeNotNull(notNullColsFromFilters)
fds.AddConstants(constUniqueIDs)
for _, equiv := range equivUniqueIDs {
fds.AddEquivalence(equiv[0], equiv[1])
}
// merge the not-null-cols/registered-map from both side together.
fds.NotNullCols.UnionWith(rightFD.NotNullCols)
if fds.HashCodeToUniqueID == nil {
fds.HashCodeToUniqueID = rightFD.HashCodeToUniqueID
} else {
for k, v := range rightFD.HashCodeToUniqueID {
// If there's same constant in the different subquery, we might go into this IF branch.
if _, ok := fds.HashCodeToUniqueID[k]; ok {
continue
}
fds.HashCodeToUniqueID[k] = v
}
}
for i, ok := rightFD.GroupByCols.Next(0); ok; i, ok = rightFD.GroupByCols.Next(i + 1) {
fds.GroupByCols.Insert(i)
}
fds.HasAggBuilt = fds.HasAggBuilt || rightFD.HasAggBuilt
p.fdSet = fds
return fds
}
func (p *LogicalJoin) extractFDForOuterJoin(filtersFromApply []expression.Expression) *fd.FDSet {
outerFD, innerFD := p.children[0].ExtractFD(), p.children[1].ExtractFD()
innerCondition := p.RightConditions
outerCondition := p.LeftConditions
outerCols, innerCols := fd.NewFastIntSet(), fd.NewFastIntSet()
for _, col := range p.children[0].Schema().Columns {
outerCols.Insert(int(col.UniqueID))
}
for _, col := range p.children[1].Schema().Columns {
innerCols.Insert(int(col.UniqueID))
}
if p.JoinType == RightOuterJoin {
innerFD, outerFD = outerFD, innerFD
innerCondition = p.LeftConditions
outerCondition = p.RightConditions
innerCols, outerCols = outerCols, innerCols
}
eqCondSlice := expression.ScalarFuncs2Exprs(p.EqualConditions)
allConds := append(eqCondSlice, p.OtherConditions...)
allConds = append(allConds, innerCondition...)
allConds = append(allConds, outerCondition...)
allConds = append(allConds, filtersFromApply...)
notNullColsFromFilters := extractNotNullFromConds(allConds, p)
filterFD := &fd.FDSet{HashCodeToUniqueID: make(map[string]int)}
constUniqueIDs := extractConstantCols(allConds, p.SCtx(), filterFD)
equivUniqueIDs := extractEquivalenceCols(allConds, p.SCtx(), filterFD)
filterFD.AddConstants(constUniqueIDs)
equivOuterUniqueIDs := fd.NewFastIntSet()
equivAcrossNum := 0
for _, equiv := range equivUniqueIDs {
filterFD.AddEquivalence(equiv[0], equiv[1])
if equiv[0].SubsetOf(outerCols) && equiv[1].SubsetOf(innerCols) {
equivOuterUniqueIDs.UnionWith(equiv[0])
equivAcrossNum++
continue
}
if equiv[0].SubsetOf(innerCols) && equiv[1].SubsetOf(outerCols) {
equivOuterUniqueIDs.UnionWith(equiv[1])
equivAcrossNum++
}
}
filterFD.MakeNotNull(notNullColsFromFilters)
// pre-perceive the filters for the convenience judgement of 3.3.1.
var opt fd.ArgOpts
if equivAcrossNum > 0 {
// find the equivalence FD across left and right cols.
var outConditionCols []*expression.Column
if len(outerCondition) != 0 {
outConditionCols = append(outConditionCols, expression.ExtractColumnsFromExpressions(nil, outerCondition, nil)...)
}
if len(p.OtherConditions) != 0 {
// other condition may contain right side cols, it doesn't affect the judgement of intersection of non-left-equiv cols.
outConditionCols = append(outConditionCols, expression.ExtractColumnsFromExpressions(nil, p.OtherConditions, nil)...)
}
outerConditionUniqueIDs := fd.NewFastIntSet()
for _, col := range outConditionCols {
outerConditionUniqueIDs.Insert(int(col.UniqueID))
}
// judge whether left filters is on non-left-equiv cols.
if outerConditionUniqueIDs.Intersects(outerCols.Difference(equivOuterUniqueIDs)) {
opt.SkipFDRule331 = true
}
} else {
// if there is none across equivalence condition, skip rule 3.3.1.
opt.SkipFDRule331 = true
}
opt.OnlyInnerFilter = len(eqCondSlice) == 0 && len(outerCondition) == 0 && len(p.OtherConditions) == 0
if opt.OnlyInnerFilter {
// if one of the inner condition is constant false, the inner side are all null, left make constant all of that.
for _, one := range innerCondition {
if c, ok := one.(*expression.Constant); ok && c.DeferredExpr == nil && c.ParamMarker == nil {
if isTrue, err := c.Value.ToBool(p.ctx.GetSessionVars().StmtCtx); err == nil {
if isTrue == 0 {
// c is false
opt.InnerIsFalse = true
}
}
}
}
}
fds := outerFD
fds.MakeOuterJoin(innerFD, filterFD, outerCols, innerCols, &opt)
p.fdSet = fds
return fds
}
// GetJoinKeys extracts join keys(columns) from EqualConditions. It returns left join keys, right
// join keys and an `isNullEQ` array which means the `joinKey[i]` is a `NullEQ` function. The `hasNullEQ`
// means whether there is a `NullEQ` of a join key.
func (p *LogicalJoin) GetJoinKeys() (leftKeys, rightKeys []*expression.Column, isNullEQ []bool, hasNullEQ bool) {
for _, expr := range p.EqualConditions {
leftKeys = append(leftKeys, expr.GetArgs()[0].(*expression.Column))
rightKeys = append(rightKeys, expr.GetArgs()[1].(*expression.Column))
isNullEQ = append(isNullEQ, expr.FuncName.L == ast.NullEQ)
hasNullEQ = hasNullEQ || expr.FuncName.L == ast.NullEQ
}
return
}
// GetPotentialPartitionKeys return potential partition keys for join, the potential partition keys are
// the join keys of EqualConditions
func (p *LogicalJoin) GetPotentialPartitionKeys() (leftKeys, rightKeys []*property.MPPPartitionColumn) {
for _, expr := range p.EqualConditions {
_, coll := expr.CharsetAndCollation()
collateID := property.GetCollateIDByNameForPartition(coll)
leftKeys = append(leftKeys, &property.MPPPartitionColumn{Col: expr.GetArgs()[0].(*expression.Column), CollateID: collateID})
rightKeys = append(rightKeys, &property.MPPPartitionColumn{Col: expr.GetArgs()[1].(*expression.Column), CollateID: collateID})
}
return
}
// decorrelate eliminate the correlated column with if the col is in schema.
func (p *LogicalJoin) decorrelate(schema *expression.Schema) {
for i, cond := range p.LeftConditions {
p.LeftConditions[i] = cond.Decorrelate(schema)
}
for i, cond := range p.RightConditions {
p.RightConditions[i] = cond.Decorrelate(schema)
}
for i, cond := range p.OtherConditions {
p.OtherConditions[i] = cond.Decorrelate(schema)
}
for i, cond := range p.EqualConditions {
p.EqualConditions[i] = cond.Decorrelate(schema).(*expression.ScalarFunction)
}
}
// columnSubstituteAll is used in projection elimination in apply de-correlation.
// Substitutions for all conditions should be successful, otherwise, we should keep all conditions unchanged.
func (p *LogicalJoin) columnSubstituteAll(schema *expression.Schema, exprs []expression.Expression) (hasFail bool) {
// make a copy of exprs for convenience of substitution (may change/partially change the expr tree)
cpLeftConditions := make(expression.CNFExprs, len(p.LeftConditions))
cpRightConditions := make(expression.CNFExprs, len(p.RightConditions))
cpOtherConditions := make(expression.CNFExprs, len(p.OtherConditions))
cpEqualConditions := make([]*expression.ScalarFunction, len(p.EqualConditions))
copy(cpLeftConditions, p.LeftConditions)
copy(cpRightConditions, p.RightConditions)
copy(cpOtherConditions, p.OtherConditions)
copy(cpEqualConditions, p.EqualConditions)
// try to substitute columns in these condition.
for i, cond := range cpLeftConditions {
if hasFail, cpLeftConditions[i] = expression.ColumnSubstituteAll(cond, schema, exprs); hasFail {
return
}
}
for i, cond := range cpRightConditions {
if hasFail, cpRightConditions[i] = expression.ColumnSubstituteAll(cond, schema, exprs); hasFail {
return
}
}
for i, cond := range cpOtherConditions {
if hasFail, cpOtherConditions[i] = expression.ColumnSubstituteAll(cond, schema, exprs); hasFail {
return
}
}
for i, cond := range cpEqualConditions {
var tmp expression.Expression
if hasFail, tmp = expression.ColumnSubstituteAll(cond, schema, exprs); hasFail {
return
}
cpEqualConditions[i] = tmp.(*expression.ScalarFunction)
}
// if all substituted, change them atomically here.
p.LeftConditions = cpLeftConditions
p.RightConditions = cpRightConditions
p.OtherConditions = cpOtherConditions
p.EqualConditions = cpEqualConditions
for i := len(p.EqualConditions) - 1; i >= 0; i-- {
newCond := p.EqualConditions[i]
// If the columns used in the new filter all come from the left child,
// we can push this filter to it.
if expression.ExprFromSchema(newCond, p.children[0].Schema()) {
p.LeftConditions = append(p.LeftConditions, newCond)
p.EqualConditions = append(p.EqualConditions[:i], p.EqualConditions[i+1:]...)
continue
}
// If the columns used in the new filter all come from the right
// child, we can push this filter to it.
if expression.ExprFromSchema(newCond, p.children[1].Schema()) {
p.RightConditions = append(p.RightConditions, newCond)
p.EqualConditions = append(p.EqualConditions[:i], p.EqualConditions[i+1:]...)
continue
}
_, lhsIsCol := newCond.GetArgs()[0].(*expression.Column)
_, rhsIsCol := newCond.GetArgs()[1].(*expression.Column)
// If the columns used in the new filter are not all expression.Column,
// we can not use it as join's equal condition.
if !(lhsIsCol && rhsIsCol) {
p.OtherConditions = append(p.OtherConditions, newCond)
p.EqualConditions = append(p.EqualConditions[:i], p.EqualConditions[i+1:]...)
continue
}
p.EqualConditions[i] = newCond
}
return false
}
// AttachOnConds extracts on conditions for join and set the `EqualConditions`, `LeftConditions`, `RightConditions` and
// `OtherConditions` by the result of extract.
func (p *LogicalJoin) AttachOnConds(onConds []expression.Expression) {
eq, left, right, other := p.extractOnCondition(onConds, false, false)
p.AppendJoinConds(eq, left, right, other)
}
// AppendJoinConds appends new join conditions.
func (p *LogicalJoin) AppendJoinConds(eq []*expression.ScalarFunction, left, right, other []expression.Expression) {
p.EqualConditions = append(eq, p.EqualConditions...)
p.LeftConditions = append(left, p.LeftConditions...)
p.RightConditions = append(right, p.RightConditions...)
p.OtherConditions = append(other, p.OtherConditions...)
}
// ExtractCorrelatedCols implements LogicalPlan interface.
func (p *LogicalJoin) ExtractCorrelatedCols() []*expression.CorrelatedColumn {
corCols := make([]*expression.CorrelatedColumn, 0, len(p.EqualConditions)+len(p.LeftConditions)+len(p.RightConditions)+len(p.OtherConditions))
for _, fun := range p.EqualConditions {
corCols = append(corCols, expression.ExtractCorColumns(fun)...)
}
for _, fun := range p.LeftConditions {
corCols = append(corCols, expression.ExtractCorColumns(fun)...)
}
for _, fun := range p.RightConditions {
corCols = append(corCols, expression.ExtractCorColumns(fun)...)
}
for _, fun := range p.OtherConditions {
corCols = append(corCols, expression.ExtractCorColumns(fun)...)
}
return corCols
}
// ExtractJoinKeys extract join keys as a schema for child with childIdx.
func (p *LogicalJoin) ExtractJoinKeys(childIdx int) *expression.Schema {
joinKeys := make([]*expression.Column, 0, len(p.EqualConditions))
for _, eqCond := range p.EqualConditions {
joinKeys = append(joinKeys, eqCond.GetArgs()[childIdx].(*expression.Column))
}
return expression.NewSchema(joinKeys...)
}
// LogicalProjection represents a select fields plan.
type LogicalProjection struct {
logicalSchemaProducer
Exprs []expression.Expression
// CalculateNoDelay indicates this Projection is the root Plan and should be
// calculated without delay and will not return any result to client.
// Currently it is "true" only when the current sql query is a "DO" statement.
// See "https://dev.mysql.com/doc/refman/5.7/en/do.html" for more detail.
CalculateNoDelay bool
// AvoidColumnEvaluator is a temporary variable which is ONLY used to avoid
// building columnEvaluator for the expressions of Projection which is
// built by buildProjection4Union.
// This can be removed after column pool being supported.
// Related issue: TiDB#8141(https://github.com/pingcap/tidb/issues/8141)
AvoidColumnEvaluator bool
}
// ExtractFD implements the logical plan interface, extracting the FD from bottom up.
func (p *LogicalProjection) ExtractFD() *fd.FDSet {
// basically extract the children's fdSet.
fds := p.logicalSchemaProducer.ExtractFD()
// collect the output columns' unique ID.
outputColsUniqueIDs := fd.NewFastIntSet()
notnullColsUniqueIDs := fd.NewFastIntSet()
outputColsUniqueIDsArray := make([]int, 0, len(p.Schema().Columns))
// here schema extended columns may contain expr, const and column allocated with uniqueID.
for _, one := range p.Schema().Columns {
outputColsUniqueIDs.Insert(int(one.UniqueID))
outputColsUniqueIDsArray = append(outputColsUniqueIDsArray, int(one.UniqueID))
}
// map the expr hashCode with its unique ID.
for idx, expr := range p.Exprs {
switch x := expr.(type) {
case *expression.Column:
continue
case *expression.CorrelatedColumn:
// t1(a,b,c), t2(m,n)
// select a, (select c from t2 where m=b) from t1;
// take c as constant column here.
continue
case *expression.Constant:
hashCode := string(x.HashCode(p.ctx.GetSessionVars().StmtCtx))
var (
ok bool
constantUniqueID int
)
if constantUniqueID, ok = fds.IsHashCodeRegistered(hashCode); !ok {
constantUniqueID = outputColsUniqueIDsArray[idx]
fds.RegisterUniqueID(string(x.HashCode(p.ctx.GetSessionVars().StmtCtx)), constantUniqueID)
}
fds.AddConstants(fd.NewFastIntSet(constantUniqueID))
case *expression.ScalarFunction:
// t1(a,b,c), t2(m,n)
// select a, (select c+n from t2 where m=b) from t1;
// expr(c+n) contains correlated column , but we can treat it as constant here.
hashCode := string(x.HashCode(p.ctx.GetSessionVars().StmtCtx))
var (
ok bool
scalarUniqueID int
)
// If this function is not deterministic, we skip it since it not a stable value.
if expression.CheckNonDeterministic(x) {
if scalarUniqueID, ok = fds.IsHashCodeRegistered(hashCode); !ok {
fds.RegisterUniqueID(hashCode, scalarUniqueID)
}
continue
}
if scalarUniqueID, ok = fds.IsHashCodeRegistered(hashCode); !ok {
scalarUniqueID = outputColsUniqueIDsArray[idx]
fds.RegisterUniqueID(hashCode, scalarUniqueID)
} else {
// since the scalar's hash code has been registered before, the equivalence exists between the unique ID
// allocated by phase of building-projection-for-scalar and that of previous registered unique ID.
fds.AddEquivalence(fd.NewFastIntSet(scalarUniqueID), fd.NewFastIntSet(outputColsUniqueIDsArray[idx]))
}
determinants := fd.NewFastIntSet()
extractedColumns := expression.ExtractColumns(x)
extractedCorColumns := expression.ExtractCorColumns(x)
for _, one := range extractedColumns {
determinants.Insert(int(one.UniqueID))
// the dependent columns in scalar function should be also considered as output columns as well.
outputColsUniqueIDs.Insert(int(one.UniqueID))
}
for _, one := range extractedCorColumns {
determinants.Insert(int(one.UniqueID))
// the dependent columns in scalar function should be also considered as output columns as well.
outputColsUniqueIDs.Insert(int(one.UniqueID))
}
notnull := isNullRejected(p.ctx, p.schema, x)
if notnull || determinants.SubsetOf(fds.NotNullCols) {
notnullColsUniqueIDs.Insert(scalarUniqueID)
}
fds.AddStrictFunctionalDependency(determinants, fd.NewFastIntSet(scalarUniqueID))
}
}
// apply operator's characteristic's FD setting.
// since the distinct attribute is built as firstRow agg func, we don't need to think about it here.
// let the fds itself to trace the not null, because after the outer join, some not null column can be nullable.
fds.MakeNotNull(notnullColsUniqueIDs)
// select max(a) from t group by b, we should project both `a` & `b` to maintain the FD down here, even if select-fields only contain `a`.
fds.ProjectCols(outputColsUniqueIDs.Union(fds.GroupByCols))
// just trace it down in every operator for test checking.
p.fdSet = fds
return fds
}
// ExtractCorrelatedCols implements LogicalPlan interface.
func (p *LogicalProjection) ExtractCorrelatedCols() []*expression.CorrelatedColumn {
corCols := make([]*expression.CorrelatedColumn, 0, len(p.Exprs))
for _, expr := range p.Exprs {
corCols = append(corCols, expression.ExtractCorColumns(expr)...)
}
return corCols
}
// GetUsedCols extracts all of the Columns used by proj.
func (p *LogicalProjection) GetUsedCols() (usedCols []*expression.Column) {
for _, expr := range p.Exprs {
usedCols = append(usedCols, expression.ExtractColumns(expr)...)
}
return usedCols
}
// LogicalAggregation represents an aggregate plan.
type LogicalAggregation struct {
logicalSchemaProducer
AggFuncs []*aggregation.AggFuncDesc
GroupByItems []expression.Expression
// aggHints stores aggregation hint information.
aggHints aggHintInfo
possibleProperties [][]*expression.Column
inputCount float64 // inputCount is the input count of this plan.
// noCopPushDown indicates if planner must not push this agg down to coprocessor.
// It is true when the agg is in the outer child tree of apply.
noCopPushDown bool
}
// HasDistinct shows whether LogicalAggregation has functions with distinct.
func (la *LogicalAggregation) HasDistinct() bool {
for _, aggFunc := range la.AggFuncs {
if aggFunc.HasDistinct {
return true
}
}
return false
}
// HasOrderBy shows whether LogicalAggregation has functions with order-by items.
func (la *LogicalAggregation) HasOrderBy() bool {
for _, aggFunc := range la.AggFuncs {
if len(aggFunc.OrderByItems) > 0 {
return true
}
}
return false
}
// ExtractFD implements the logical plan interface, extracting the FD from bottom up.
// 1:
// In most of the cases, using FDs to check the only_full_group_by problem should be done in the buildAggregation phase
// by extracting the bottom-up FDs graph from the `p` --- the sub plan tree that has already been built.
//
// 2:
// and this requires that some conditions push-down into the `p` like selection should be done before building aggregation,
// otherwise, 'a=1 and a can occur in the select lists of a group by' will be miss-checked because it doesn't be implied in the known FDs graph.
//
// 3:
// when a logical agg is built, it's schema columns indicates what the permitted-non-agg columns is. Therefore, we shouldn't
// depend on logicalAgg.ExtractFD() to finish the only_full_group_by checking problem rather than by 1 & 2.
func (la *LogicalAggregation) ExtractFD() *fd.FDSet {
// basically extract the children's fdSet.
fds := la.logicalSchemaProducer.ExtractFD()
// collect the output columns' unique ID.
outputColsUniqueIDs := fd.NewFastIntSet()
notnullColsUniqueIDs := fd.NewFastIntSet()
groupByColsUniqueIDs := fd.NewFastIntSet()
groupByColsOutputCols := fd.NewFastIntSet()
// Since the aggregation is build ahead of projection, the latter one will reuse the column with UniqueID allocated in aggregation
// via aggMapper, so we don't need unnecessarily maintain the <aggDes, UniqueID> mapping in the FDSet like expr did, just treating
// it as normal column.
for _, one := range la.Schema().Columns {
outputColsUniqueIDs.Insert(int(one.UniqueID))
}
// For one like sum(a), we don't need to build functional dependency from a --> sum(a), cause it's only determined by the
// group-by-item (group-by-item --> sum(a)).
for _, expr := range la.GroupByItems {
switch x := expr.(type) {
case *expression.Column:
groupByColsUniqueIDs.Insert(int(x.UniqueID))
case *expression.CorrelatedColumn:
// shouldn't be here, intercepted by plan builder as unknown column.
continue
case *expression.Constant:
// shouldn't be here, interpreted as pos param by plan builder.
continue
case *expression.ScalarFunction:
hashCode := string(x.HashCode(la.ctx.GetSessionVars().StmtCtx))
var (
ok bool
scalarUniqueID int
)
if scalarUniqueID, ok = fds.IsHashCodeRegistered(hashCode); ok {
groupByColsUniqueIDs.Insert(scalarUniqueID)
} else {
// retrieve unique plan column id. 1: completely new one, allocating new unique id. 2: registered by projection earlier, using it.
if scalarUniqueID, ok = la.ctx.GetSessionVars().MapHashCode2UniqueID4ExtendedCol[hashCode]; !ok {
scalarUniqueID = int(la.ctx.GetSessionVars().AllocPlanColumnID())
}
fds.RegisterUniqueID(hashCode, scalarUniqueID)
groupByColsUniqueIDs.Insert(scalarUniqueID)
}
determinants := fd.NewFastIntSet()
extractedColumns := expression.ExtractColumns(x)
extractedCorColumns := expression.ExtractCorColumns(x)
for _, one := range extractedColumns {
determinants.Insert(int(one.UniqueID))
groupByColsOutputCols.Insert(int(one.UniqueID))
}
for _, one := range extractedCorColumns {
determinants.Insert(int(one.UniqueID))
groupByColsOutputCols.Insert(int(one.UniqueID))
}
notnull := isNullRejected(la.ctx, la.schema, x)
if notnull || determinants.SubsetOf(fds.NotNullCols) {
notnullColsUniqueIDs.Insert(scalarUniqueID)
}
fds.AddStrictFunctionalDependency(determinants, fd.NewFastIntSet(scalarUniqueID))
}
}
// Some details:
// For now, select max(a) from t group by c, tidb will see `max(a)` as Max aggDes and `a,b,c` as firstRow aggDes,
// and keep them all in the schema columns before projection does the pruning. If we build the fake FD eg: {c} ~~> {b}
// here since we have seen b as firstRow aggDes, for the upper layer projection of `select max(a), b from t group by c`,
// it will take b as valid projection field of group by statement since it has existed in the FD with {c} ~~> {b}.
//
// and since any_value will NOT be pushed down to agg schema, which means every firstRow aggDes in the agg logical operator
// is meaningless to build the FD with. Let's only store the non-firstRow FD down: {group by items} ~~> {real aggDes}
realAggFuncUniqueID := fd.NewFastIntSet()
for i, aggDes := range la.AggFuncs {
if aggDes.Name != "firstrow" {
realAggFuncUniqueID.Insert(int(la.schema.Columns[i].UniqueID))
}
}
// apply operator's characteristic's FD setting.
if len(la.GroupByItems) == 0 {
// 1: as the details shown above, output cols (normal column seen as firstrow) of group by are not validated.
// we couldn't merge them as constant FD with origin constant FD together before projection done.
// fds.MaxOneRow(outputColsUniqueIDs.Union(groupByColsOutputCols))
//
// 2: for the convenience of later judgement, when there is no group by items, we will store a FD: {0} -> {real aggDes}
// 0 unique id is only used for here.
groupByColsUniqueIDs.Insert(0)
for i, ok := realAggFuncUniqueID.Next(0); ok; i, ok = realAggFuncUniqueID.Next(i + 1) {
fds.AddStrictFunctionalDependency(groupByColsUniqueIDs, fd.NewFastIntSet(i))
}
} else {
// eliminating input columns that are un-projected.
fds.ProjectCols(outputColsUniqueIDs.Union(groupByColsOutputCols).Union(groupByColsUniqueIDs))
// note: {a} --> {b,c} is not same with {a} --> {b} and {a} --> {c}
for i, ok := realAggFuncUniqueID.Next(0); ok; i, ok = realAggFuncUniqueID.Next(i + 1) {
// group by phrase always produce strict FD.
// 1: it can always distinguish and group the all-null/part-null group column rows.
// 2: the rows with all/part null group column are unique row after group operation.
// 3: there won't be two same group key with different agg values, so strict FD secured.
fds.AddStrictFunctionalDependency(groupByColsUniqueIDs, fd.NewFastIntSet(i))
}
// agg funcDes has been tag not null flag when building aggregation.
fds.MakeNotNull(notnullColsUniqueIDs)
}
fds.GroupByCols = groupByColsUniqueIDs
fds.HasAggBuilt = true
// just trace it down in every operator for test checking.
la.fdSet = fds
return fds
}
// CopyAggHints copies the aggHints from another LogicalAggregation.
func (la *LogicalAggregation) CopyAggHints(agg *LogicalAggregation) {
// TODO: Copy the hint may make the un-applicable hint throw the
// same warning message more than once. We'd better add a flag for
// `HaveThrownWarningMessage` to avoid this. Besides, finalAgg and
// partialAgg (in cascades planner) should share the same hint, instead
// of a copy.
la.aggHints = agg.aggHints
}
// IsPartialModeAgg returns if all of the AggFuncs are partialMode.
func (la *LogicalAggregation) IsPartialModeAgg() bool {
// Since all of the AggFunc share the same AggMode, we only need to check the first one.
return la.AggFuncs[0].Mode == aggregation.Partial1Mode
}
// IsCompleteModeAgg returns if all of the AggFuncs are CompleteMode.
func (la *LogicalAggregation) IsCompleteModeAgg() bool {
// Since all of the AggFunc share the same AggMode, we only need to check the first one.
return la.AggFuncs[0].Mode == aggregation.CompleteMode
}
// GetGroupByCols returns the columns that are group-by items.
// For example, `group by a, b, c+d` will return [a, b].
func (la *LogicalAggregation) GetGroupByCols() []*expression.Column {
groupByCols := make([]*expression.Column, 0, len(la.GroupByItems))
for _, item := range la.GroupByItems {
if col, ok := item.(*expression.Column); ok {
groupByCols = append(groupByCols, col)
}
}
return groupByCols
}
// GetPotentialPartitionKeys return potential partition keys for aggregation, the potential partition keys are the group by keys
func (la *LogicalAggregation) GetPotentialPartitionKeys() []*property.MPPPartitionColumn {
groupByCols := make([]*property.MPPPartitionColumn, 0, len(la.GroupByItems))
for _, item := range la.GroupByItems {
if col, ok := item.(*expression.Column); ok {
groupByCols = append(groupByCols, &property.MPPPartitionColumn{
Col: col,
CollateID: property.GetCollateIDByNameForPartition(col.GetType().GetCollate()),
})
}
}
return groupByCols
}
// ExtractCorrelatedCols implements LogicalPlan interface.
func (la *LogicalAggregation) ExtractCorrelatedCols() []*expression.CorrelatedColumn {
corCols := make([]*expression.CorrelatedColumn, 0, len(la.GroupByItems)+len(la.AggFuncs))
for _, expr := range la.GroupByItems {
corCols = append(corCols, expression.ExtractCorColumns(expr)...)
}
for _, fun := range la.AggFuncs {
for _, arg := range fun.Args {
corCols = append(corCols, expression.ExtractCorColumns(arg)...)
}
for _, arg := range fun.OrderByItems {
corCols = append(corCols, expression.ExtractCorColumns(arg.Expr)...)
}
}
return corCols
}
// GetUsedCols extracts all of the Columns used by agg including GroupByItems and AggFuncs.
func (la *LogicalAggregation) GetUsedCols() (usedCols []*expression.Column) {
for _, groupByItem := range la.GroupByItems {
usedCols = append(usedCols, expression.ExtractColumns(groupByItem)...)
}
for _, aggDesc := range la.AggFuncs {
for _, expr := range aggDesc.Args {
usedCols = append(usedCols, expression.ExtractColumns(expr)...)
}
for _, expr := range aggDesc.OrderByItems {
usedCols = append(usedCols, expression.ExtractColumns(expr.Expr)...)
}
}
return usedCols
}
// LogicalSelection represents a where or having predicate.
type LogicalSelection struct {
baseLogicalPlan
// Originally the WHERE or ON condition is parsed into a single expression,
// but after we converted to CNF(Conjunctive normal form), it can be
// split into a list of AND conditions.
Conditions []expression.Expression
}
func extractNotNullFromConds(Conditions []expression.Expression, p LogicalPlan) fd.FastIntSet {
// extract the column NOT NULL rejection characteristic from selection condition.
// CNF considered only, DNF doesn't have its meanings (cause that condition's eval may don't take effect)
//
// Take this case: select * from t where (a = 1) and (b is null):
//
// If we wanna where phrase eval to true, two pre-condition: {a=1} and {b is null} both need to be true.
// Hence, we assert that:
//
// 1: `a` must not be null since `NULL = 1` is evaluated as NULL.
// 2: `b` must be null since only `NULL is NULL` is evaluated as true.
//
// As a result, `a` will be extracted as not-null column to abound the FDSet.
notnullColsUniqueIDs := fd.NewFastIntSet()
for _, condition := range Conditions {
var cols []*expression.Column
cols = expression.ExtractColumnsFromExpressions(cols, []expression.Expression{condition}, nil)
if isNullRejected(p.SCtx(), p.Schema(), condition) {
for _, col := range cols {
notnullColsUniqueIDs.Insert(int(col.UniqueID))
}
}
}
return notnullColsUniqueIDs
}
func extractConstantCols(Conditions []expression.Expression, sctx sessionctx.Context, fds *fd.FDSet) fd.FastIntSet {
// extract constant cols
// eg: where a=1 and b is null and (1+c)=5.
// TODO: Some columns can only be determined to be constant from multiple constraints (e.g. x <= 1 AND x >= 1)
var (
constObjs []expression.Expression
constUniqueIDs = fd.NewFastIntSet()
)
constObjs = expression.ExtractConstantEqColumnsOrScalar(sctx, constObjs, Conditions)
for _, constObj := range constObjs {
switch x := constObj.(type) {
case *expression.Column:
constUniqueIDs.Insert(int(x.UniqueID))
case *expression.ScalarFunction:
hashCode := string(x.HashCode(sctx.GetSessionVars().StmtCtx))
if uniqueID, ok := fds.IsHashCodeRegistered(hashCode); ok {
constUniqueIDs.Insert(uniqueID)
} else {
scalarUniqueID := int(sctx.GetSessionVars().AllocPlanColumnID())
fds.RegisterUniqueID(string(x.HashCode(sctx.GetSessionVars().StmtCtx)), scalarUniqueID)
constUniqueIDs.Insert(scalarUniqueID)
}
}
}
return constUniqueIDs
}
func extractEquivalenceCols(Conditions []expression.Expression, sctx sessionctx.Context, fds *fd.FDSet) [][]fd.FastIntSet {
var equivObjsPair [][]expression.Expression
equivObjsPair = expression.ExtractEquivalenceColumns(equivObjsPair, Conditions)
equivUniqueIDs := make([][]fd.FastIntSet, 0, len(equivObjsPair))
for _, equivObjPair := range equivObjsPair {
// lhs of equivalence.
var (
lhsUniqueID int
rhsUniqueID int
)
switch x := equivObjPair[0].(type) {
case *expression.Column:
lhsUniqueID = int(x.UniqueID)
case *expression.ScalarFunction:
hashCode := string(x.HashCode(sctx.GetSessionVars().StmtCtx))
if uniqueID, ok := fds.IsHashCodeRegistered(hashCode); ok {
lhsUniqueID = uniqueID
} else {
scalarUniqueID := int(sctx.GetSessionVars().AllocPlanColumnID())
fds.RegisterUniqueID(string(x.HashCode(sctx.GetSessionVars().StmtCtx)), scalarUniqueID)
lhsUniqueID = scalarUniqueID
}
}
// rhs of equivalence.
switch x := equivObjPair[1].(type) {
case *expression.Column:
rhsUniqueID = int(x.UniqueID)
case *expression.ScalarFunction:
hashCode := string(x.HashCode(sctx.GetSessionVars().StmtCtx))
if uniqueID, ok := fds.IsHashCodeRegistered(hashCode); ok {
rhsUniqueID = uniqueID
} else {
scalarUniqueID := int(sctx.GetSessionVars().AllocPlanColumnID())
fds.RegisterUniqueID(string(x.HashCode(sctx.GetSessionVars().StmtCtx)), scalarUniqueID)
rhsUniqueID = scalarUniqueID
}
}
equivUniqueIDs = append(equivUniqueIDs, []fd.FastIntSet{fd.NewFastIntSet(lhsUniqueID), fd.NewFastIntSet(rhsUniqueID)})
}
return equivUniqueIDs
}
// ExtractFD implements the LogicalPlan interface.
func (p *LogicalSelection) ExtractFD() *fd.FDSet {
// basically extract the children's fdSet.
fds := p.baseLogicalPlan.ExtractFD()
// collect the output columns' unique ID.
outputColsUniqueIDs := fd.NewFastIntSet()
notnullColsUniqueIDs := fd.NewFastIntSet()
// eg: select t2.a, count(t2.b) from t1 join t2 using (a) where t1.a = 1
// join's schema will miss t2.a while join.full schema has. since selection
// itself doesn't contain schema, extracting schema should tell them apart.
var columns []*expression.Column
if join, ok := p.children[0].(*LogicalJoin); ok && join.fullSchema != nil {
columns = join.fullSchema.Columns
} else {
columns = p.Schema().Columns
}
for _, one := range columns {
outputColsUniqueIDs.Insert(int(one.UniqueID))
}
// extract the not null attributes from selection conditions.
notnullColsUniqueIDs.UnionWith(extractNotNullFromConds(p.Conditions, p))
// extract the constant cols from selection conditions.
constUniqueIDs := extractConstantCols(p.Conditions, p.SCtx(), fds)
// extract equivalence cols.
equivUniqueIDs := extractEquivalenceCols(p.Conditions, p.SCtx(), fds)
// apply operator's characteristic's FD setting.
fds.MakeNotNull(notnullColsUniqueIDs)
fds.AddConstants(constUniqueIDs)
for _, equiv := range equivUniqueIDs {
fds.AddEquivalence(equiv[0], equiv[1])
}
fds.ProjectCols(outputColsUniqueIDs)
// just trace it down in every operator for test checking.
p.fdSet = fds
return fds
}
// ExtractCorrelatedCols implements LogicalPlan interface.
func (p *LogicalSelection) ExtractCorrelatedCols() []*expression.CorrelatedColumn {
corCols := make([]*expression.CorrelatedColumn, 0, len(p.Conditions))
for _, cond := range p.Conditions {
corCols = append(corCols, expression.ExtractCorColumns(cond)...)
}
return corCols
}
// LogicalApply gets one row from outer executor and gets one row from inner executor according to outer row.
type LogicalApply struct {
LogicalJoin
CorCols []*expression.CorrelatedColumn
}
// ExtractCorrelatedCols implements LogicalPlan interface.
func (la *LogicalApply) ExtractCorrelatedCols() []*expression.CorrelatedColumn {
corCols := la.LogicalJoin.ExtractCorrelatedCols()
for i := len(corCols) - 1; i >= 0; i-- {
if la.children[0].Schema().Contains(&corCols[i].Column) {
corCols = append(corCols[:i], corCols[i+1:]...)
}
}
return corCols
}
// ExtractFD implements the LogicalPlan interface.
func (la *LogicalApply) ExtractFD() *fd.FDSet {
innerPlan := la.children[1]
// build the join correlated equal condition for apply join, this equal condition is used for deriving the transitive FD between outer and inner side.
correlatedCols := ExtractCorrelatedCols4LogicalPlan(innerPlan)
deduplicateCorrelatedCols := make(map[int64]*expression.CorrelatedColumn)
for _, cc := range correlatedCols {
if _, ok := deduplicateCorrelatedCols[cc.UniqueID]; !ok {
deduplicateCorrelatedCols[cc.UniqueID] = cc
}
}
eqCond := make([]expression.Expression, 0, 4)
// for case like select (select t1.a from t2) from t1. <t1.a> will be assigned with new UniqueID after sub query projection is built.
// we should distinguish them out, building the equivalence relationship from inner <t1.a> == outer <t1.a> in the apply-join for FD derivation.
for _, cc := range deduplicateCorrelatedCols {
// for every correlated column, find the connection with the inner newly built column.
for _, col := range innerPlan.Schema().Columns {
if cc.UniqueID == col.CorrelatedColUniqueID {
ccc := &cc.Column
cond := expression.NewFunctionInternal(la.ctx, ast.EQ, types.NewFieldType(mysql.TypeTiny), ccc, col)
eqCond = append(eqCond, cond.(*expression.ScalarFunction))
}
}
}
switch la.JoinType {
case InnerJoin:
return la.extractFDForInnerJoin(eqCond)
case LeftOuterJoin, RightOuterJoin:
return la.extractFDForOuterJoin(eqCond)
case SemiJoin:
return la.extractFDForSemiJoin(eqCond)
default:
return &fd.FDSet{HashCodeToUniqueID: make(map[string]int)}
}
}
// LogicalMaxOneRow checks if a query returns no more than one row.
type LogicalMaxOneRow struct {
baseLogicalPlan
}
// LogicalTableDual represents a dual table plan.
type LogicalTableDual struct {
logicalSchemaProducer
RowCount int
}
// LogicalMemTable represents a memory table or virtual table
// Some memory tables wants to take the ownership of some predications
// e.g
// SELECT * FROM cluster_log WHERE type='tikv' AND address='192.16.5.32'
// Assume that the table `cluster_log` is a memory table, which is used
// to retrieve logs from remote components. In the above situation we should
// send log search request to the target TiKV (192.16.5.32) directly instead of
// requesting all cluster components log search gRPC interface to retrieve
// log message and filtering them in TiDB node.
type LogicalMemTable struct {
logicalSchemaProducer
Extractor MemTablePredicateExtractor
DBName model.CIStr
TableInfo *model.TableInfo
Columns []*model.ColumnInfo
// QueryTimeRange is used to specify the time range for metrics summary tables and inspection tables
// e.g: select /*+ time_range('2020-02-02 12:10:00', '2020-02-02 13:00:00') */ from metrics_summary;
// select /*+ time_range('2020-02-02 12:10:00', '2020-02-02 13:00:00') */ from metrics_summary_by_label;
// select /*+ time_range('2020-02-02 12:10:00', '2020-02-02 13:00:00') */ from inspection_summary;
// select /*+ time_range('2020-02-02 12:10:00', '2020-02-02 13:00:00') */ from inspection_result;
QueryTimeRange QueryTimeRange
}
// LogicalUnionScan is used in non read-only txn or for scanning a local temporary table whose snapshot data is located in memory.
type LogicalUnionScan struct {
baseLogicalPlan
conditions []expression.Expression
handleCols HandleCols
}
// DataSource represents a tableScan without condition push down.
type DataSource struct {
logicalSchemaProducer
astIndexHints []*ast.IndexHint
IndexHints []indexHintInfo
table table.Table
tableInfo *model.TableInfo
Columns []*model.ColumnInfo
DBName model.CIStr
TableAsName *model.CIStr
// indexMergeHints are the hint for indexmerge.
indexMergeHints []indexHintInfo
// pushedDownConds are the conditions that will be pushed down to coprocessor.
pushedDownConds []expression.Expression
// allConds contains all the filters on this table. For now it's maintained
// in predicate push down and used in partition pruning/index merge.
allConds []expression.Expression
statisticTable *statistics.Table
tableStats *property.StatsInfo
// possibleAccessPaths stores all the possible access path for physical plan, including table scan.
possibleAccessPaths []*util.AccessPath
// The data source may be a partition, rather than a real table.
isPartition bool
physicalTableID int64
partitionNames []model.CIStr
// handleCol represents the handle column for the datasource, either the
// int primary key column or extra handle column.
// handleCol *expression.Column
handleCols HandleCols
// TblCols contains the original columns of table before being pruned, and it
// is used for estimating table scan cost.
TblCols []*expression.Column
// commonHandleCols and commonHandleLens save the info of primary key which is the clustered index.
commonHandleCols []*expression.Column
commonHandleLens []int
// TblColHists contains the Histogram of all original table columns,
// it is converted from statisticTable, and used for IO/network cost estimating.
TblColHists *statistics.HistColl
// preferStoreType means the DataSource is enforced to which storage.
preferStoreType int
// preferPartitions store the map, the key represents store type, the value represents the partition name list.
preferPartitions map[int][]model.CIStr
SampleInfo *TableSampleInfo
is infoschema.InfoSchema
// isForUpdateRead should be true in either of the following situations
// 1. use `inside insert`, `update`, `delete` or `select for update` statement
// 2. isolation level is RC
isForUpdateRead bool
// contain unique index and the first field is tidb_shard(),
// such as (tidb_shard(a), a ...), the fields are more than 2
containExprPrefixUk bool
}
// ExtractCorrelatedCols implements LogicalPlan interface.
func (ds *DataSource) ExtractCorrelatedCols() []*expression.CorrelatedColumn {
corCols := make([]*expression.CorrelatedColumn, 0, len(ds.pushedDownConds))
for _, expr := range ds.pushedDownConds {
corCols = append(corCols, expression.ExtractCorColumns(expr)...)
}
return corCols
}
// TiKVSingleGather is a leaf logical operator of TiDB layer to gather
// tuples from TiKV regions.
type TiKVSingleGather struct {
logicalSchemaProducer
Source *DataSource
// IsIndexGather marks if this TiKVSingleGather gathers tuples from an IndexScan.
// in implementation phase, we need this flag to determine whether to generate
// PhysicalTableReader or PhysicalIndexReader.
IsIndexGather bool
Index *model.IndexInfo
}
// LogicalTableScan is the logical table scan operator for TiKV.
type LogicalTableScan struct {
logicalSchemaProducer
Source *DataSource
HandleCols HandleCols
AccessConds expression.CNFExprs
Ranges []*ranger.Range
}
// LogicalIndexScan is the logical index scan operator for TiKV.
type LogicalIndexScan struct {
logicalSchemaProducer
// DataSource should be read-only here.
Source *DataSource
IsDoubleRead bool
EqCondCount int
AccessConds expression.CNFExprs
Ranges []*ranger.Range
Index *model.IndexInfo
Columns []*model.ColumnInfo
FullIdxCols []*expression.Column
FullIdxColLens []int
IdxCols []*expression.Column
IdxColLens []int
}
// MatchIndexProp checks if the indexScan can match the required property.
func (p *LogicalIndexScan) MatchIndexProp(prop *property.PhysicalProperty) (match bool) {
if prop.IsSortItemEmpty() {
return true
}
if all, _ := prop.AllSameOrder(); !all {
return false
}
for i, col := range p.IdxCols {
if col.Equal(nil, prop.SortItems[0].Col) {
return matchIndicesProp(p.IdxCols[i:], p.IdxColLens[i:], prop.SortItems)
} else if i >= p.EqCondCount {
break
}
}
return false
}
// getTablePath finds the TablePath from a group of accessPaths.
func getTablePath(paths []*util.AccessPath) *util.AccessPath {
for _, path := range paths {
if path.IsTablePath() {
return path
}
}
return nil
}
func (ds *DataSource) buildTableGather() LogicalPlan {
ts := LogicalTableScan{Source: ds, HandleCols: ds.handleCols}.Init(ds.ctx, ds.blockOffset)
ts.SetSchema(ds.Schema())
sg := TiKVSingleGather{Source: ds, IsIndexGather: false}.Init(ds.ctx, ds.blockOffset)
sg.SetSchema(ds.Schema())
sg.SetChildren(ts)
return sg
}
func (ds *DataSource) buildIndexGather(path *util.AccessPath) LogicalPlan {
is := LogicalIndexScan{
Source: ds,
IsDoubleRead: false,
Index: path.Index,
FullIdxCols: path.FullIdxCols,
FullIdxColLens: path.FullIdxColLens,
IdxCols: path.IdxCols,
IdxColLens: path.IdxColLens,
}.Init(ds.ctx, ds.blockOffset)
is.Columns = make([]*model.ColumnInfo, len(ds.Columns))
copy(is.Columns, ds.Columns)
is.SetSchema(ds.Schema())
is.IdxCols, is.IdxColLens = expression.IndexInfo2PrefixCols(is.Columns, is.schema.Columns, is.Index)
sg := TiKVSingleGather{
Source: ds,
IsIndexGather: true,
Index: path.Index,
}.Init(ds.ctx, ds.blockOffset)
sg.SetSchema(ds.Schema())
sg.SetChildren(is)
return sg
}
// Convert2Gathers builds logical TiKVSingleGathers from DataSource.
func (ds *DataSource) Convert2Gathers() (gathers []LogicalPlan) {
tg := ds.buildTableGather()
gathers = append(gathers, tg)
for _, path := range ds.possibleAccessPaths {
if !path.IsIntHandlePath {
path.FullIdxCols, path.FullIdxColLens = expression.IndexInfo2Cols(ds.Columns, ds.schema.Columns, path.Index)
path.IdxCols, path.IdxColLens = expression.IndexInfo2PrefixCols(ds.Columns, ds.schema.Columns, path.Index)
// If index columns can cover all of the needed columns, we can use a IndexGather + IndexScan.
if ds.isCoveringIndex(ds.schema.Columns, path.FullIdxCols, path.FullIdxColLens, ds.tableInfo) {
gathers = append(gathers, ds.buildIndexGather(path))
}
// TODO: If index columns can not cover the schema, use IndexLookUpGather.
}
}
return gathers
}
func (ds *DataSource) detachCondAndBuildRangeForPath(path *util.AccessPath, conds []expression.Expression) error {
if len(path.IdxCols) == 0 {
path.TableFilters = conds
return nil
}
res, err := ranger.DetachCondAndBuildRangeForIndex(ds.ctx, conds, path.IdxCols, path.IdxColLens)
if err != nil {
return err
}
path.Ranges = res.Ranges
path.AccessConds = res.AccessConds
path.TableFilters = res.RemainedConds
path.EqCondCount = res.EqCondCount
path.EqOrInCondCount = res.EqOrInCount
path.IsDNFCond = res.IsDNFCond
path.ConstCols = make([]bool, len(path.IdxCols))
if res.ColumnValues != nil {
for i := range path.ConstCols {
path.ConstCols[i] = res.ColumnValues[i] != nil
}
}
path.CountAfterAccess, err = ds.tableStats.HistColl.GetRowCountByIndexRanges(ds.ctx, path.Index.ID, path.Ranges)
return err
}
func (ds *DataSource) deriveCommonHandleTablePathStats(path *util.AccessPath, conds []expression.Expression, isIm bool) error {
path.CountAfterAccess = float64(ds.statisticTable.Count)
path.Ranges = ranger.FullNotNullRange()
path.IdxCols, path.IdxColLens = expression.IndexInfo2PrefixCols(ds.Columns, ds.schema.Columns, path.Index)
path.FullIdxCols, path.FullIdxColLens = expression.IndexInfo2Cols(ds.Columns, ds.schema.Columns, path.Index)
if len(conds) == 0 {
return nil
}
if err := ds.detachCondAndBuildRangeForPath(path, conds); err != nil {
return err
}
if path.EqOrInCondCount == len(path.AccessConds) {
accesses, remained := path.SplitCorColAccessCondFromFilters(ds.ctx, path.EqOrInCondCount)
path.AccessConds = append(path.AccessConds, accesses...)
path.TableFilters = remained
if len(accesses) > 0 && ds.statisticTable.Pseudo {
path.CountAfterAccess = ds.statisticTable.PseudoAvgCountPerValue()
} else {
selectivity := path.CountAfterAccess / float64(ds.statisticTable.Count)
for i := range accesses {
col := path.IdxCols[path.EqOrInCondCount+i]
ndv := ds.getColumnNDV(col.ID)
ndv *= selectivity
if ndv < 1 {
ndv = 1.0
}
path.CountAfterAccess = path.CountAfterAccess / ndv
}
}
}
// If the `CountAfterAccess` is less than `stats.RowCount`, there must be some inconsistent stats info.
// We prefer the `stats.RowCount` because it could use more stats info to calculate the selectivity.
if path.CountAfterAccess < ds.stats.RowCount && !isIm {
path.CountAfterAccess = math.Min(ds.stats.RowCount/SelectionFactor, float64(ds.statisticTable.Count))
}
return nil
}
// deriveTablePathStats will fulfill the information that the AccessPath need.
// isIm indicates whether this function is called to generate the partial path for IndexMerge.
func (ds *DataSource) deriveTablePathStats(path *util.AccessPath, conds []expression.Expression, isIm bool) error {
if path.IsCommonHandlePath {
return ds.deriveCommonHandleTablePathStats(path, conds, isIm)
}
var err error
path.CountAfterAccess = float64(ds.statisticTable.Count)
path.TableFilters = conds
var pkCol *expression.Column
columnLen := len(ds.schema.Columns)
isUnsigned := false
if ds.tableInfo.PKIsHandle {
if pkColInfo := ds.tableInfo.GetPkColInfo(); pkColInfo != nil {
isUnsigned = mysql.HasUnsignedFlag(pkColInfo.GetFlag())
pkCol = expression.ColInfo2Col(ds.schema.Columns, pkColInfo)
}
} else if columnLen > 0 && ds.schema.Columns[columnLen-1].ID == model.ExtraHandleID {
pkCol = ds.schema.Columns[columnLen-1]
}
if pkCol == nil {
path.Ranges = ranger.FullIntRange(isUnsigned)
return nil
}
path.Ranges = ranger.FullIntRange(isUnsigned)
if len(conds) == 0 {
return nil
}
path.AccessConds, path.TableFilters = ranger.DetachCondsForColumn(ds.ctx, conds, pkCol)
// If there's no access cond, we try to find that whether there's expression containing correlated column that
// can be used to access data.
corColInAccessConds := false
if len(path.AccessConds) == 0 {
for i, filter := range path.TableFilters {
eqFunc, ok := filter.(*expression.ScalarFunction)
if !ok || eqFunc.FuncName.L != ast.EQ {
continue
}
lCol, lOk := eqFunc.GetArgs()[0].(*expression.Column)
if lOk && lCol.Equal(ds.ctx, pkCol) {
_, rOk := eqFunc.GetArgs()[1].(*expression.CorrelatedColumn)
if rOk {
path.AccessConds = append(path.AccessConds, filter)
path.TableFilters = append(path.TableFilters[:i], path.TableFilters[i+1:]...)
corColInAccessConds = true
break
}
}
rCol, rOk := eqFunc.GetArgs()[1].(*expression.Column)
if rOk && rCol.Equal(ds.ctx, pkCol) {
_, lOk := eqFunc.GetArgs()[0].(*expression.CorrelatedColumn)
if lOk {
path.AccessConds = append(path.AccessConds, filter)
path.TableFilters = append(path.TableFilters[:i], path.TableFilters[i+1:]...)
corColInAccessConds = true
break
}
}
}
}
if corColInAccessConds {
path.CountAfterAccess = 1
return nil
}
var remainedConds []expression.Expression
path.Ranges, path.AccessConds, remainedConds, err = ranger.BuildTableRange(path.AccessConds, ds.ctx, pkCol.RetType, ds.ctx.GetSessionVars().RangeMaxSize)
path.TableFilters = append(path.TableFilters, remainedConds...)
if err != nil {
return err
}
path.CountAfterAccess, err = ds.statisticTable.GetRowCountByIntColumnRanges(ds.ctx, pkCol.ID, path.Ranges)
// If the `CountAfterAccess` is less than `stats.RowCount`, there must be some inconsistent stats info.
// We prefer the `stats.RowCount` because it could use more stats info to calculate the selectivity.
if path.CountAfterAccess < ds.stats.RowCount && !isIm {
path.CountAfterAccess = math.Min(ds.stats.RowCount/SelectionFactor, float64(ds.statisticTable.Count))
}
return err
}
func (ds *DataSource) fillIndexPath(path *util.AccessPath, conds []expression.Expression) error {
path.Ranges = ranger.FullRange()
path.CountAfterAccess = float64(ds.statisticTable.Count)
path.IdxCols, path.IdxColLens = expression.IndexInfo2PrefixCols(ds.Columns, ds.schema.Columns, path.Index)
path.FullIdxCols, path.FullIdxColLens = expression.IndexInfo2Cols(ds.Columns, ds.schema.Columns, path.Index)
if !path.Index.Unique && !path.Index.Primary && len(path.Index.Columns) == len(path.IdxCols) {
handleCol := ds.getPKIsHandleCol()
if handleCol != nil && !mysql.HasUnsignedFlag(handleCol.RetType.GetFlag()) {
alreadyHandle := false
for _, col := range path.IdxCols {
if col.ID == model.ExtraHandleID || col.Equal(nil, handleCol) {
alreadyHandle = true
}
}
// Don't add one column twice to the index. May cause unexpected errors.
if !alreadyHandle {
path.IdxCols = append(path.IdxCols, handleCol)
path.IdxColLens = append(path.IdxColLens, types.UnspecifiedLength)
// Also updates the map that maps the index id to its prefix column ids.
if len(ds.tableStats.HistColl.Idx2ColumnIDs[path.Index.ID]) == len(path.Index.Columns) {
ds.tableStats.HistColl.Idx2ColumnIDs[path.Index.ID] = append(ds.tableStats.HistColl.Idx2ColumnIDs[path.Index.ID], handleCol.UniqueID)
}
}
}
}
err := ds.detachCondAndBuildRangeForPath(path, conds)
return err
}
// deriveIndexPathStats will fulfill the information that the AccessPath need.
// conds is the conditions used to generate the DetachRangeResult for path.
// isIm indicates whether this function is called to generate the partial path for IndexMerge.
func (ds *DataSource) deriveIndexPathStats(path *util.AccessPath, _ []expression.Expression, isIm bool) {
if path.EqOrInCondCount == len(path.AccessConds) {
accesses, remained := path.SplitCorColAccessCondFromFilters(ds.ctx, path.EqOrInCondCount)
path.AccessConds = append(path.AccessConds, accesses...)
path.TableFilters = remained
if len(accesses) > 0 && ds.statisticTable.Pseudo {
path.CountAfterAccess = ds.statisticTable.PseudoAvgCountPerValue()
} else {
selectivity := path.CountAfterAccess / float64(ds.statisticTable.Count)
for i := range accesses {
col := path.IdxCols[path.EqOrInCondCount+i]
ndv := ds.getColumnNDV(col.ID)
ndv *= selectivity
if ndv < 1 {
ndv = 1.0
}
path.CountAfterAccess = path.CountAfterAccess / ndv
}
}
}
var indexFilters []expression.Expression
indexFilters, path.TableFilters = ds.splitIndexFilterConditions(path.TableFilters, path.FullIdxCols, path.FullIdxColLens, ds.tableInfo)
path.IndexFilters = append(path.IndexFilters, indexFilters...)
// If the `CountAfterAccess` is less than `stats.RowCount`, there must be some inconsistent stats info.
// We prefer the `stats.RowCount` because it could use more stats info to calculate the selectivity.
if path.CountAfterAccess < ds.stats.RowCount && !isIm {
path.CountAfterAccess = math.Min(ds.stats.RowCount/SelectionFactor, float64(ds.statisticTable.Count))
}
if path.IndexFilters != nil {
selectivity, _, err := ds.tableStats.HistColl.Selectivity(ds.ctx, path.IndexFilters, nil)
if err != nil {
logutil.BgLogger().Debug("calculate selectivity failed, use selection factor", zap.Error(err))
selectivity = SelectionFactor
}
if isIm {
path.CountAfterIndex = path.CountAfterAccess * selectivity
} else {
path.CountAfterIndex = math.Max(path.CountAfterAccess*selectivity, ds.stats.RowCount)
}
}
}
func getPKIsHandleColFromSchema(cols []*model.ColumnInfo, schema *expression.Schema, pkIsHandle bool) *expression.Column {
if !pkIsHandle {
// If the PKIsHandle is false, return the ExtraHandleColumn.
for i, col := range cols {
if col.ID == model.ExtraHandleID {
return schema.Columns[i]
}
}
return nil
}
for i, col := range cols {
if mysql.HasPriKeyFlag(col.GetFlag()) {
return schema.Columns[i]
}
}
return nil
}
func (ds *DataSource) getPKIsHandleCol() *expression.Column {
return getPKIsHandleColFromSchema(ds.Columns, ds.schema, ds.tableInfo.PKIsHandle)
}
func (p *LogicalIndexScan) getPKIsHandleCol(schema *expression.Schema) *expression.Column {
// We cannot use p.Source.getPKIsHandleCol() here,
// Because we may re-prune p.Columns and p.schema during the transformation.
// That will make p.Columns different from p.Source.Columns.
return getPKIsHandleColFromSchema(p.Columns, schema, p.Source.tableInfo.PKIsHandle)
}
// TableInfo returns the *TableInfo of data source.
func (ds *DataSource) TableInfo() *model.TableInfo {
return ds.tableInfo
}
// LogicalUnionAll represents LogicalUnionAll plan.
type LogicalUnionAll struct {
logicalSchemaProducer
}
// LogicalPartitionUnionAll represents the LogicalUnionAll plan is for partition table.
type LogicalPartitionUnionAll struct {
LogicalUnionAll
}
// LogicalSort stands for the order by plan.
type LogicalSort struct {
baseLogicalPlan
ByItems []*util.ByItems
}
// ExtractCorrelatedCols implements LogicalPlan interface.
func (ls *LogicalSort) ExtractCorrelatedCols() []*expression.CorrelatedColumn {
corCols := make([]*expression.CorrelatedColumn, 0, len(ls.ByItems))
for _, item := range ls.ByItems {
corCols = append(corCols, expression.ExtractCorColumns(item.Expr)...)
}
return corCols
}
// LogicalTopN represents a top-n plan.
type LogicalTopN struct {
baseLogicalPlan
ByItems []*util.ByItems
Offset uint64
Count uint64
limitHints limitHintInfo
}
// ExtractCorrelatedCols implements LogicalPlan interface.
func (lt *LogicalTopN) ExtractCorrelatedCols() []*expression.CorrelatedColumn {
corCols := make([]*expression.CorrelatedColumn, 0, len(lt.ByItems))
for _, item := range lt.ByItems {
corCols = append(corCols, expression.ExtractCorColumns(item.Expr)...)
}
return corCols
}
// isLimit checks if TopN is a limit plan.
func (lt *LogicalTopN) isLimit() bool {
return len(lt.ByItems) == 0
}
// LogicalLimit represents offset and limit plan.
type LogicalLimit struct {
logicalSchemaProducer
Offset uint64
Count uint64
limitHints limitHintInfo
}
// LogicalLock represents a select lock plan.
type LogicalLock struct {
baseLogicalPlan
Lock *ast.SelectLockInfo
tblID2Handle map[int64][]HandleCols
// tblID2phyTblIDCol is used for partitioned tables,
// the child executor need to return an extra column containing
// the Physical Table ID (i.e. from which partition the row came from)
tblID2PhysTblIDCol map[int64]*expression.Column
}
// WindowFrame represents a window function frame.
type WindowFrame struct {
Type ast.FrameType
Start *FrameBound
End *FrameBound
}
// Clone copies a window frame totally.
func (wf *WindowFrame) Clone() *WindowFrame {
cloned := new(WindowFrame)
*cloned = *wf
cloned.Start = wf.Start.Clone()
cloned.End = wf.End.Clone()
return cloned
}
// FrameBound is the boundary of a frame.
type FrameBound struct {
Type ast.BoundType
UnBounded bool
Num uint64
// CalcFuncs is used for range framed windows.
// We will build the date_add or date_sub functions for frames like `INTERVAL '2:30' MINUTE_SECOND FOLLOWING`,
// and plus or minus for frames like `1 preceding`.
CalcFuncs []expression.Expression
// CmpFuncs is used to decide whether one row is included in the current frame.
CmpFuncs []expression.CompareFunc
}
// Clone copies a frame bound totally.
func (fb *FrameBound) Clone() *FrameBound {
cloned := new(FrameBound)
*cloned = *fb
cloned.CalcFuncs = make([]expression.Expression, 0, len(fb.CalcFuncs))
for _, it := range fb.CalcFuncs {
cloned.CalcFuncs = append(cloned.CalcFuncs, it.Clone())
}
cloned.CmpFuncs = fb.CmpFuncs
return cloned
}
// LogicalWindow represents a logical window function plan.
type LogicalWindow struct {
logicalSchemaProducer
WindowFuncDescs []*aggregation.WindowFuncDesc
PartitionBy []property.SortItem
OrderBy []property.SortItem
Frame *WindowFrame
}
// EqualPartitionBy checks whether two LogicalWindow.Partitions are equal.
func (p *LogicalWindow) EqualPartitionBy(_ sessionctx.Context, newWindow *LogicalWindow) bool {
if len(p.PartitionBy) != len(newWindow.PartitionBy) {
return false
}
partitionByColsMap := make(map[int64]struct{})
for _, item := range p.PartitionBy {
partitionByColsMap[item.Col.UniqueID] = struct{}{}
}
for _, item := range newWindow.PartitionBy {
if _, ok := partitionByColsMap[item.Col.UniqueID]; !ok {
return false
}
}
return true
}
// EqualOrderBy checks whether two LogicalWindow.OrderBys are equal.
func (p *LogicalWindow) EqualOrderBy(ctx sessionctx.Context, newWindow *LogicalWindow) bool {
if len(p.OrderBy) != len(newWindow.OrderBy) {
return false
}
for i, item := range p.OrderBy {
if !item.Col.Equal(ctx, newWindow.OrderBy[i].Col) ||
item.Desc != newWindow.OrderBy[i].Desc {
return false
}
}
return true
}
// EqualFrame checks whether two LogicalWindow.Frames are equal.
func (p *LogicalWindow) EqualFrame(ctx sessionctx.Context, newWindow *LogicalWindow) bool {
if (p.Frame == nil && newWindow.Frame != nil) ||
(p.Frame != nil && newWindow.Frame == nil) {
return false
}
if p.Frame == nil && newWindow.Frame == nil {
return true
}
if p.Frame.Type != newWindow.Frame.Type ||
p.Frame.Start.Type != newWindow.Frame.Start.Type ||
p.Frame.Start.UnBounded != newWindow.Frame.Start.UnBounded ||
p.Frame.Start.Num != newWindow.Frame.Start.Num ||
p.Frame.End.Type != newWindow.Frame.End.Type ||
p.Frame.End.UnBounded != newWindow.Frame.End.UnBounded ||
p.Frame.End.Num != newWindow.Frame.End.Num {
return false
}
for i, expr := range p.Frame.Start.CalcFuncs {
if !expr.Equal(ctx, newWindow.Frame.Start.CalcFuncs[i]) {
return false
}
}
for i, expr := range p.Frame.End.CalcFuncs {
if !expr.Equal(ctx, newWindow.Frame.End.CalcFuncs[i]) {
return false
}
}
return true
}
// ExtractCorrelatedCols implements LogicalPlan interface.
func (p *LogicalWindow) ExtractCorrelatedCols() []*expression.CorrelatedColumn {
corCols := make([]*expression.CorrelatedColumn, 0, len(p.WindowFuncDescs))
for _, windowFunc := range p.WindowFuncDescs {
for _, arg := range windowFunc.Args {
corCols = append(corCols, expression.ExtractCorColumns(arg)...)
}
}
if p.Frame != nil {
if p.Frame.Start != nil {
for _, expr := range p.Frame.Start.CalcFuncs {
corCols = append(corCols, expression.ExtractCorColumns(expr)...)
}
}
if p.Frame.End != nil {
for _, expr := range p.Frame.End.CalcFuncs {
corCols = append(corCols, expression.ExtractCorColumns(expr)...)
}
}
}
return corCols
}
// GetWindowResultColumns returns the columns storing the result of the window function.
func (p *LogicalWindow) GetWindowResultColumns() []*expression.Column {
return p.schema.Columns[p.schema.Len()-len(p.WindowFuncDescs):]
}
// ExtractCorColumnsBySchema only extracts the correlated columns that match the specified schema.
// e.g. If the correlated columns from plan are [t1.a, t2.a, t3.a] and specified schema is [t2.a, t2.b, t2.c],
// only [t2.a] is returned.
func ExtractCorColumnsBySchema(corCols []*expression.CorrelatedColumn, schema *expression.Schema, resolveIndex bool) []*expression.CorrelatedColumn {
resultCorCols := make([]*expression.CorrelatedColumn, schema.Len())
for _, corCol := range corCols {
idx := schema.ColumnIndex(&corCol.Column)
if idx != -1 {
if resultCorCols[idx] == nil {
resultCorCols[idx] = &expression.CorrelatedColumn{
Column: *schema.Columns[idx],
Data: new(types.Datum),
}
}
corCol.Data = resultCorCols[idx].Data
}
}
// Shrink slice. e.g. [col1, nil, col2, nil] will be changed to [col1, col2].
length := 0
for _, col := range resultCorCols {
if col != nil {
resultCorCols[length] = col
length++
}
}
resultCorCols = resultCorCols[:length]
if resolveIndex {
for _, corCol := range resultCorCols {
corCol.Index = schema.ColumnIndex(&corCol.Column)
}
}
return resultCorCols
}
// extractCorColumnsBySchema4LogicalPlan only extracts the correlated columns that match the specified schema.
// e.g. If the correlated columns from plan are [t1.a, t2.a, t3.a] and specified schema is [t2.a, t2.b, t2.c],
// only [t2.a] is returned.
func extractCorColumnsBySchema4LogicalPlan(p LogicalPlan, schema *expression.Schema) []*expression.CorrelatedColumn {
corCols := ExtractCorrelatedCols4LogicalPlan(p)
return ExtractCorColumnsBySchema(corCols, schema, false)
}
// ExtractCorColumnsBySchema4PhysicalPlan only extracts the correlated columns that match the specified schema.
// e.g. If the correlated columns from plan are [t1.a, t2.a, t3.a] and specified schema is [t2.a, t2.b, t2.c],
// only [t2.a] is returned.
func ExtractCorColumnsBySchema4PhysicalPlan(p PhysicalPlan, schema *expression.Schema) []*expression.CorrelatedColumn {
corCols := ExtractCorrelatedCols4PhysicalPlan(p)
return ExtractCorColumnsBySchema(corCols, schema, true)
}
// ShowContents stores the contents for the `SHOW` statement.
type ShowContents struct {
Tp ast.ShowStmtType // Databases/Tables/Columns/....
DBName string
Table *ast.TableName // Used for showing columns.
Partition model.CIStr // Use for showing partition
Column *ast.ColumnName // Used for `desc table column`.
IndexName model.CIStr
Flag int // Some flag parsed from sql, such as FULL.
User *auth.UserIdentity // Used for show grants.
Roles []*auth.RoleIdentity // Used for show grants.
CountWarningsOrErrors bool // Used for showing count(*) warnings | errors
Full bool
IfNotExists bool // Used for `show create database if not exists`.
GlobalScope bool // Used by show variables.
Extended bool // Used for `show extended columns from ...`
}
// LogicalShow represents a show plan.
type LogicalShow struct {
logicalSchemaProducer
ShowContents
Extractor ShowPredicateExtractor
}
// LogicalShowDDLJobs is for showing DDL job list.
type LogicalShowDDLJobs struct {
logicalSchemaProducer
JobNumber int64
}
// CTEClass holds the information and plan for a CTE. Most of the fields in this struct are the same as cteInfo.
// But the cteInfo is used when building the plan, and CTEClass is used also for building the executor.
type CTEClass struct {
// The union between seed part and recursive part is DISTINCT or DISTINCT ALL.
IsDistinct bool
// seedPartLogicalPlan and recursivePartLogicalPlan are the logical plans for the seed part and recursive part of this CTE.
seedPartLogicalPlan LogicalPlan
recursivePartLogicalPlan LogicalPlan
// seedPartPhysicalPlan and recursivePartPhysicalPlan are the physical plans for the seed part and recursive part of this CTE.
seedPartPhysicalPlan PhysicalPlan
recursivePartPhysicalPlan PhysicalPlan
// storageID for this CTE.
IDForStorage int
// optFlag is the optFlag for the whole CTE.
optFlag uint64
HasLimit bool
LimitBeg uint64
LimitEnd uint64
IsInApply bool
// pushDownPredicates may be push-downed by different references.
pushDownPredicates []expression.Expression
ColumnMap map[string]*expression.Column
}
// LogicalCTE is for CTE.
type LogicalCTE struct {
logicalSchemaProducer
cte *CTEClass
cteAsName model.CIStr
seedStat *property.StatsInfo
isOuterMostCTE bool
}
// LogicalCTETable is for CTE table
type LogicalCTETable struct {
logicalSchemaProducer
seedStat *property.StatsInfo
name string
idForStorage int
// seedSchema is only used in columnStatsUsageCollector to get column mapping
seedSchema *expression.Schema
}
// ExtractCorrelatedCols implements LogicalPlan interface.
func (p *LogicalCTE) ExtractCorrelatedCols() []*expression.CorrelatedColumn {
corCols := ExtractCorrelatedCols4LogicalPlan(p.cte.seedPartLogicalPlan)
if p.cte.recursivePartLogicalPlan != nil {
corCols = append(corCols, ExtractCorrelatedCols4LogicalPlan(p.cte.recursivePartLogicalPlan)...)
}
return corCols
}
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