SQL的Optimizer的優化思想以及Optimizer在Catalyst裡的表現方式，并加上自己的實踐，對Optimizer有一個直覺的認識。
Optimizer的主要職責是将Analyzer給Resolved的Logical Plan根據不同的優化政策Batch，來對文法樹進行優化，優化邏輯計劃節點(Logical Plan)以及表達式(Expression)，也是轉換成實體執行計劃的前置。如下圖：
Optimizer裡的batches包含了3類優化政策：1、Combine Limits 合并Limits 2、ConstantFolding 常量合并 3、Filter Pushdown 過濾器下推,每個Batch裡定義的優化伴随對象都定義在Optimizer裡了：
object Optimizer extends RuleExecutor[LogicalPlan] {
val batches =
Batch("Combine Limits", FixedPoint(100),
CombineLimits) ::
Batch("ConstantFolding", FixedPoint(100),
NullPropagation,
ConstantFolding,
BooleanSimplification,
SimplifyFilters,
SimplifyCasts,
SimplifyCaseConversionExpressions) ::
Batch("Filter Pushdown", FixedPoint(100),
CombineFilters,
PushPredicateThroughProject,
PushPredicateThroughJoin,
ColumnPruning) :: Nil
}
另外提一點，Optimizer裡不但對Logical Plan進行了優化，而且對Logical Plan中的Expression也進行了優化，是以有必要了解一下Expression相關類，主要是用到了references和outputSet，references主要是Logical Plan或Expression節點的所依賴的那些Expressions，而outputSet是Logical Plan所有的Attribute的輸出：
如：Aggregate是一個Logical Plan， 它的references就是group by的表達式 和 aggreagate的表達式的并集去重。
case class Aggregate(
groupingExpressions: Seq[Expression],
aggregateExpressions: Seq[NamedExpression],
child: LogicalPlan)
extends UnaryNode {
override def output = aggregateExpressions.map(_.toAttribute)
override def references =
(groupingExpressions ++ aggregateExpressions).flatMap(_.references).toSet
Optimizer的優化政策不僅有對plan進行transform的，也有對expression進行transform的，究其原理就是周遊樹，然後應用優化的Rule，但是注意一點，對Logical Plantransfrom的是先序周遊(pre-order)，而對Expression transfrom的時候是後序周遊(post-order)：
如果出現了2個Limit，則将2個Limit合并為一個，這個要求一個Limit是另一個Limit的grandChild。
/**
* Combines two adjacent [[Limit]] operators into one, merging the
* expressions into one single expression.
*/
object CombineLimits extends Rule[LogicalPlan] {
def apply(plan: LogicalPlan): LogicalPlan = plan transform {
case ll @ Limit(le, nl @ Limit(ne, grandChild)) => //ll為目前Limit,le為其expression， nl是ll的grandChild，ne是nl的expression
Limit(If(LessThan(ne, le), ne, le), grandChild) //expression比較，如果ne比le小則表達式為ne，否則為le
}
給定SQL：val query = sql("select * from (select * from temp_shengli limit 100)a limit 10 ")
scala> query.queryExecution.analyzed
res12: org.apache.spark.sql.catalyst.plans.logical.LogicalPlan =
Limit 10
Project [key#13,value#14]
Limit 100
Project [key#13,value#14]
MetastoreRelation default, temp_shengli, None
子查詢裡limit100,外層查詢limit10，這裡我們當然可以在子查詢裡不必查那麼多，因為外層隻需要10個，是以這裡會合并Limit10，和Limit100 為 Limit 10。
這個Batch裡包含了Rules：NullPropagation，ConstantFolding，BooleanSimplification，SimplifyFilters，SimplifyCasts，SimplifyCaseConversionExpressions。
這裡先提一下Literal字面量，它其實是一個能比對任意基本類型的類。(為下文做鋪墊)
object Literal {
def apply(v: Any): Literal = v match {
case i: Int => Literal(i, IntegerType)
case l: Long => Literal(l, LongType)
case d: Double => Literal(d, DoubleType)
case f: Float => Literal(f, FloatType)
case b: Byte => Literal(b, ByteType)
case s: Short => Literal(s, ShortType)
case s: String => Literal(s, StringType)
case b: Boolean => Literal(b, BooleanType)
case d: BigDecimal => Literal(d, DecimalType)
case t: Timestamp => Literal(t, TimestampType)
case a: Array[Byte] => Literal(a, BinaryType)
case null => Literal(null, NullType)
注意Literal是一個LeafExpression，核心方法是eval，給定Row，計算表達式傳回值：
case class Literal(value: Any, dataType: DataType) extends LeafExpression {
override def foldable = true
def nullable = value == null
def references = Set.empty
override def toString = if (value != null) value.toString else "null"
type EvaluatedType = Any
override def eval(input: Row):Any = value
現在來看一下NullPropagation都做了什麼。
NullPropagation是一個能将Expression Expressions替換為等價的Literal值的優化，并且能夠避免NULL值在SQL文法樹的傳播。
/**
* Replaces [[Expression Expressions]] that can be statically evaluated with
* equivalent [[Literal]] values. This rule is more specific with
* Null value propagation from bottom to top of the expression tree.
object NullPropagation extends Rule[LogicalPlan] {
case q: LogicalPlan => q transformExpressionsUp {
case e @ Count(Literal(null, _)) => Cast(Literal(0L), e.dataType) //如果count(null)則轉化為count(0)
case e @ Sum(Literal(c, _)) if c == 0 => Cast(Literal(0L), e.dataType)<span style="font-family: Arial;">//如果sum(null)則轉化為sum(0)</span>
case e @ Average(Literal(c, _)) if c == 0 => Literal(0.0, e.dataType)
case e @ IsNull(c) if !c.nullable => Literal(false, BooleanType)
case e @ IsNotNull(c) if !c.nullable => Literal(true, BooleanType)
case e @ GetItem(Literal(null, _), _) => Literal(null, e.dataType)
case e @ GetItem(_, Literal(null, _)) => Literal(null, e.dataType)
case e @ GetField(Literal(null, _), _) => Literal(null, e.dataType)
case e @ Coalesce(children) => {
val newChildren = children.filter(c => c match {
case Literal(null, _) => false
case _ => true
})
if (newChildren.length == 0) {
Literal(null, e.dataType)
} else if (newChildren.length == 1) {
newChildren(0)
} else {
Coalesce(newChildren)
}
}
case e @ If(Literal(v, _), trueValue, falseValue) => if (v == true) trueValue else falseValue
case e @ In(Literal(v, _), list) if (list.exists(c => c match {
case Literal(candidate, _) if candidate == v => true
case _ => false
})) => Literal(true, BooleanType)
// Put exceptional cases above if any
case e: BinaryArithmetic => e.children match {
case Literal(null, _) :: right :: Nil => Literal(null, e.dataType)
case left :: Literal(null, _) :: Nil => Literal(null, e.dataType)
case _ => e
case e: BinaryComparison => e.children match {
case e: StringRegexExpression => e.children match {
}
給定SQL: val query = sql("select count(null) from temp_shengli where key is not null")
res6: org.apache.spark.sql.catalyst.plans.logical.LogicalPlan =
Aggregate [], [COUNT(null) AS c0#5L] //這裡count的是null
Filter IS NOT NULL key#7
MetastoreRelation default, temp_shengli, None
調用NullPropagation
scala> NullPropagation(query.queryExecution.analyzed)
res7: org.apache.spark.sql.catalyst.plans.logical.LogicalPlan =
Aggregate [], [CAST(0, LongType) AS c0#5L] //優化後為0了
常量合并是屬于Expression優化的一種，對于可以直接計算的常量，不用放到實體執行裡去生成對象來計算了，直接可以在計劃裡就計算出來：
object ConstantFolding extends Rule[LogicalPlan] {
def apply(plan: LogicalPlan): LogicalPlan = plan transform { //先對plan進行transform
case q: LogicalPlan => q transformExpressionsDown { //對每個plan的expression進行transform
// Skip redundant folding of literals.
case l: Literal => l
case e if e.foldable => Literal(e.eval(null), e.dataType) //調用eval方法計算結果
}
}
}
給定SQL： val query = sql("select 1+2+3+4 from temp_shengli")
res23: org.apache.spark.sql.catalyst.plans.logical.LogicalPlan =
Project [(((1 + 2) + 3) + 4) AS c0#21] //這裡還是常量表達式
MetastoreRelation default, src, None
優化後：
scala> query.queryExecution.optimizedPlan
res24: org.apache.spark.sql.catalyst.plans.logical.LogicalPlan =
Project [10 AS c0#21] //優化後，直接合并為10
看看布爾表達式2邊能不能通過隻計算1邊，而省去計算另一邊而提高效率，稱為簡化布爾表達式。
解釋請看我寫的注釋：
* Simplifies boolean expressions where the answer can be determined without evaluating both sides.
* Note that this rule can eliminate expressions that might otherwise have been evaluated and thus
* is only safe when evaluations of expressions does not result in side effects.
object BooleanSimplification extends Rule[LogicalPlan] {
case and @ And(left, right) => //如果布爾表達式是AND操作，即exp1 and exp2
(left, right) match { //（左邊表達式，右邊表達式）
case (Literal(true, BooleanType), r) => r // 左邊true，傳回右邊的<span style="font-family: Arial;">bool</span><span style="font-family: Arial;">值</span>
case (l, Literal(true, BooleanType)) => l //右邊true，傳回左邊的bool值
case (Literal(false, BooleanType), _) => Literal(false)//左邊都false，右邊随便，反正是傳回false
case (_, Literal(false, BooleanType)) => Literal(false)//隻要有1邊是false了，都是false
case (_, _) => and
case or @ Or(left, right) =>
(left, right) match {
case (Literal(true, BooleanType), _) => Literal(true) //隻要左邊是true了，不用判斷右邊都是true
case (_, Literal(true, BooleanType)) => Literal(true) //隻要有一邊是true，都傳回true
case (Literal(false, BooleanType), r) => r //希望右邊r是true
case (l, Literal(false, BooleanType)) => l
case (_, _) => or
Filter Pushdown下包含了CombineFilters、PushPredicateThroughProject、PushPredicateThroughJoin、ColumnPruning
Ps：感覺Filter Pushdown的名字起的有點不能涵蓋全部比如ColumnPruning列裁剪。
合并兩個相鄰的Filter,這個和上述Combine Limit差不多。合并2個節點，就可以減少樹的深度進而減少重複執行過濾的代價。
* Combines two adjacent [[Filter]] operators into one, merging the
* conditions into one conjunctive predicate.
object CombineFilters extends Rule[LogicalPlan] {
case ff @ Filter(fc, nf @ Filter(nc, grandChild)) => Filter(And(nc, fc), grandChild)
給定SQL：val query = sql("select key from (select key from temp_shengli where key >100)a where key > 80 ")
優化前：我們看到一個filter 是另一個filter的grandChild
res25: org.apache.spark.sql.catalyst.plans.logical.LogicalPlan =
Project [key#27]
Filter (key#27 > 80) //filter>80
Project [key#27]
Filter (key#27 > 100) //filter>100
MetastoreRelation default, src, None
優化後：其實filter也可以表達為一個複雜的boolean表達式
res26: org.apache.spark.sql.catalyst.plans.logical.LogicalPlan =
Filter ((key#27 > 100) && (key#27 > 80)) //合并為1個
MetastoreRelation default, src, None
Filter Pushdown，過濾器下推。
原理就是更早的過濾掉不需要的元素來減少開銷。
給定SQL：val query = sql("select key from (select * from temp_shengli)a where key>100")
生成的邏輯計劃為：
scala> scala> query.queryExecution.analyzed
res29: org.apache.spark.sql.catalyst.plans.logical.LogicalPlan =
Project [key#31]
Filter (key#31 > 100) //先select key, value，然後再Filter
Project [key#31,value#32]
MetastoreRelation default, src, None
優化後的計劃為：
query.queryExecution.optimizedPlan
res30: org.apache.spark.sql.catalyst.plans.logical.LogicalPlan =
Filter (key#31 > 100) //先filter,然後再select
列裁剪用的比較多，就是減少不必要select的某些列。
列裁剪在3種地方可以用：
1、在聚合操作中，可以做列裁剪
2、在join操作中，左右孩子可以做列裁剪
3、合并相鄰的Project的列
object ColumnPruning extends Rule[LogicalPlan] {
// Eliminate attributes that are not needed to calculate the specified aggregates.
case a @ Aggregate(_, _, child) if (child.outputSet -- a.references).nonEmpty => ////如果project的outputSet中減去a.references的元素如果不同，那麼就将Aggreagte的child替換為a.references
a.copy(child = Project(a.references.toSeq, child))
// Eliminate unneeded attributes from either side of a Join.
case Project(projectList, Join(left, right, joinType, condition)) =>// 消除join的left 和 right孩子的不必要屬性，将join的左右子樹的列進行裁剪
// Collect the list of off references required either above or to evaluate the condition.
val allReferences: Set[Attribute] =
projectList.flatMap(_.references).toSet ++ condition.map(_.references).getOrElse(Set.empty)
/** Applies a projection only when the child is producing unnecessary attributes */
def prunedChild(c: LogicalPlan) =
if ((c.outputSet -- allReferences.filter(c.outputSet.contains)).nonEmpty) {
Project(allReferences.filter(c.outputSet.contains).toSeq, c)
c
Project(projectList, Join(prunedChild(left), prunedChild(right), joinType, condition))
// Combine adjacent Projects.
case Project(projectList1, Project(projectList2, child)) => //合并相鄰Project的列
// Create a map of Aliases to their values from the child projection.
// e.g., 'SELECT ... FROM (SELECT a + b AS c, d ...)' produces Map(c -> Alias(a + b, c)).
val aliasMap = projectList2.collect {
case a @ Alias(e, _) => (a.toAttribute: Expression, a)
}.toMap
// Substitute any attributes that are produced by the child projection, so that we safely
// eliminate it.
// e.g., 'SELECT c + 1 FROM (SELECT a + b AS C ...' produces 'SELECT a + b + 1 ...'
// TODO: Fix TransformBase to avoid the cast below.
val substitutedProjection = projectList1.map(_.transform {
case a if aliasMap.contains(a) => aliasMap(a)
}).asInstanceOf[Seq[NamedExpression]]
Project(substitutedProjection, child)
// Eliminate no-op Projects
case Project(projectList, child) if child.output == projectList => child
分别舉三個例子來對應三種情況進行說明：
1、在聚合操作中，可以做列裁剪
給定SQL：val query = sql("SELECT 1+1 as shengli, key from (select key, value from temp_shengli)a group by key")
優化前：
res57: org.apache.spark.sql.catalyst.plans.logical.LogicalPlan =
Aggregate [key#51], [(1 + 1) AS shengli#49,key#51]
Project [key#51,value#52] //優化前預設select key 和 value兩列
scala> ColumnPruning1(query.queryExecution.analyzed)
MetastoreRelation default, temp_shengli, None
res59: org.apache.spark.sql.catalyst.plans.logical.LogicalPlan =
Project [key#51] //優化後，列裁剪掉了value，隻select key
2、在join操作中，左右孩子可以做列裁剪
給定SQL：val query = sql("select a.value qween from (select * from temp_shengli) a join (select * from temp_shengli)b on a.key =b.key ")
沒有優化之前：
res51: org.apache.spark.sql.catalyst.plans.logical.LogicalPlan =
Project [value#42 AS qween#39]
Join Inner, Some((key#41 = key#43))
Project [key#41,value#42] //這裡多select了一列，即value
MetastoreRelation default, temp_shengli, None
Project [key#43,value#44] //這裡多select了一列，即value
優化後：（ColumnPruning2是我自己調試用的）
scala> ColumnPruning2(query.queryExecution.analyzed)
allReferences is -> Set(key#35, key#37)
res47: org.apache.spark.sql.catalyst.plans.logical.LogicalPlan =
Project [key#35 AS qween#33]
Join Inner, Some((key#35 = key#37))
Project [key#35] //經過列裁剪之後，left Child隻需要select key這一個列
Project [key#37] //經過列裁剪之後，right Child隻需要select key這一個列
3、合并相鄰的Project的列，裁剪
給定SQL：val query = sql("SELECT c + 1 FROM (SELECT 1 + 1 as c from temp_shengli ) a ")
res61: org.apache.spark.sql.catalyst.plans.logical.LogicalPlan =
Project [(c#56 + 1) AS c0#57]
Project [(1 + 1) AS c#56]
res62: org.apache.spark.sql.catalyst.plans.logical.LogicalPlan =
Project [(2 AS c#56 + 1) AS c0#57] //将子查詢裡的c 代入到 外層select裡的c，直接計算結果
MetastoreRelation default, temp_shengli, None
本文介紹了Optimizer在Catalyst裡的作用即将Analyzed Logical Plan 經過對Logical Plan和Expression進行Rule的應用transfrom，進而達到樹的節點進行合并和優化。其中主要的優化的政策總結起來是合并、列裁剪、過濾器下推幾大類。
Catalyst應該在不斷疊代中，本文隻是基于spark1.0.0進行研究，後續如果新加入的優化政策也會在後續補充進來。
歡迎大家讨論，共同進步！
——EOF——
原創文章，轉載請注明：