/** Spark SQL源代码分析系列*/

前几篇文章介绍了Spark SQL的Catalyst的核心运行流程、SqlParser，和Analyzer 以及核心类库TreeNode，本文将具体解说Spark SQL的Optimizer的优化思想以及Optimizer在Catalyst里的表现方式，并加上自己的实践。对Optimizer有一个直观的认识。

Optimizer的主要职责是将Analyzer给Resolved的Logical Plan依据不同的优化策略Batch。来对语法树进行优化。优化逻辑计划节点(Logical Plan)以及表达式(Expression)，也是转换成物理运行计划的前置。例如以下图：

一、Optimizer

Optimizer这个类是在catalyst里的optimizer包下的唯一一个类。Optimizer的工作方式事实上相似Analyzer，由于它们都继承自RuleExecutor[LogicalPlan]，都是运行一系列的Batch操作：

Optimizer里的batches包括了3类优化策略：1、Combine Limits 合并Limits  2、ConstantFolding 常量合并 3、Filter Pushdown 过滤器下推,每一个Batch里定义的优化伴随对象都定义在Optimizer里了：

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
}

另外提一点，Optimizer里不但对Logical Plan进行了优化，并且对Logical Plan中的Expression也进行了优化，所以有必要了解一下Expression相关类。主要是用到了references和outputSet，references主要是Logical Plan或Expression节点的所依赖的那些Expressions，而outputSet是Logical Plan所有的Attribute的输出：

如：Aggregate是一个Logical Plan， 它的references就是group by的表达式 和 aggreagate的表达式的并集去重。

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
}

二、优化策略具体解释

2.1、Batch: Combine Limits

 /*** 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为当前Limit,le为其expression。 nl是ll的grandChild，ne是nl的expressionLimit(If(LessThan(ne, le), ne, le), grandChild) //expression比較，假设ne比le小则表达式为ne，否则为le}
}

给定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 10Project [key#13,value#14]Limit 100Project [key#13,value#14]MetastoreRelation default, temp_shengli, None

子查询里limit100,外层查询limit10，这里我们当然能够在子查询里不必查那么多。由于外层仅仅须要10个，所以这里会合并Limit10。和Limit100 为 Limit 10。

2.2、Batch: ConstantFolding

这个Batch里包括了Rules：NullPropagation，ConstantFolding。BooleanSimplification，SimplifyFilters。SimplifyCasts。SimplifyCaseConversionExpressions。

2.2.1、Rule：NullPropagation

这里先提一下Literal字面量。它事实上是一个能匹配随意基本类型的类。(为下文做铺垫)

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是一个LeafExpression，核心方法是eval，给定Row。计算表达式返回值：

case class Literal(value: Any, dataType: DataType) extends LeafExpression {override def foldable = truedef nullable = value == nulldef references = Set.emptyoverride def toString = if (value != null) value.toString else "null"type EvaluatedType = Anyoverride def eval(input: Row):Any = value
}

如今来看一下NullPropagation都做了什么。

NullPropagation是一个能将Expression Expressions替换为等价的Literal值的优化。并且能够避免NULL值在SQL语法树的传播。

/*** 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] {def apply(plan: LogicalPlan): LogicalPlan = plan transform {case q: LogicalPlan => q transformExpressionsUp {case e @ Count(Literal(null, _)) => Cast(Literal(0L), e.dataType) //假设count(null)则转化为count(0)case e @ Sum(Literal(c, _)) if c == 0 => Cast(Literal(0L), e.dataType)<span style="font-family: Arial;">//假设sum(null)则转化为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, _) => falsecase _ => 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 falseValuecase e @ In(Literal(v, _), list) if (list.exists(c => c match {case Literal(candidate, _) if candidate == v => truecase _ => false})) => Literal(true, BooleanType)// Put exceptional cases above if anycase 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 Literal(null, _) :: right :: Nil => Literal(null, e.dataType)case left :: Literal(null, _) :: Nil => Literal(null, e.dataType)case _ => e}case e: StringRegexExpression => e.children match {case Literal(null, _) :: right :: Nil => Literal(null, e.dataType)case left :: Literal(null, _) :: Nil => Literal(null, e.dataType)case _ => e}}}
}

给定SQL: val query = sql("select count(null) from temp_shengli where key is not null")

scala> query.queryExecution.analyzed
res6: org.apache.spark.sql.catalyst.plans.logical.LogicalPlan =
Aggregate [], [COUNT(null) AS c0#5L] //这里count的是nullFilter IS NOT NULL key#7MetastoreRelation default, temp_shengli, None

调用NullPropagation

scala> NullPropagation(query.queryExecution.analyzed)
res7: org.apache.spark.sql.catalyst.plans.logical.LogicalPlan =
Aggregate [], [CAST(0, LongType) AS c0#5L]  //优化后为0了Filter IS NOT NULL key#7MetastoreRelation default, temp_shengli, None

2.2.2、Rule：ConstantFolding

 object ConstantFolding extends Rule[LogicalPlan] {def apply(plan: LogicalPlan): LogicalPlan = plan transform { //先对plan进行transformcase q: LogicalPlan => q transformExpressionsDown { //对每一个plan的expression进行transform// Skip redundant folding of literals.case l: Literal => lcase e if e.foldable => Literal(e.eval(null), e.dataType) //调用eval方法计算结果}}}

给定SQL： val query = sql("select 1+2+3+4 from temp_shengli")

scala> query.queryExecution.analyzed
res23: org.apache.spark.sql.catalyst.plans.logical.LogicalPlan =
Project [(((1 + 2) + 3) + 4) AS c0#21]  //这里还是常量表达式MetastoreRelation default, src, None

优化后：

scala> query.queryExecution.optimizedPlan
res24: org.apache.spark.sql.catalyst.plans.logical.LogicalPlan =
Project [10 AS c0#21] //优化后。直接合并为10MetastoreRelation default, src, None

2.2.3、BooleanSimplification

这个是对布尔表达式的优化，有点像java布尔表达式中的短路推断。只是这个写的倒是非常优雅。

看看布尔表达式2边能不能通过仅仅计算1边，而省去计算还有一边而提高效率，称为简化布尔表达式。

解释请看我写的凝视：

/*** 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] {def apply(plan: LogicalPlan): LogicalPlan = plan transform {case q: LogicalPlan => q transformExpressionsUp {case and @ And(left, right) => //假设布尔表达式是AND操作，即exp1 and exp2(left, right) match { //（左边表达式。右边表达式）case (Literal(true, BooleanType), r) => r // 左边true。返回右边的<span style="font-family: Arial;">bool</span><span style="font-family: Arial;">值</span>case (l, Literal(true, BooleanType)) => l //右边true，返回左边的bool值case (Literal(false, BooleanType), _) => Literal(false)//左边都false，右边随便。反正是返回falsecase (_, Literal(false, BooleanType)) => Literal(false)//仅仅要有1边是false了，都是falsecase (_, _) => and}case or @ Or(left, right) =>(left, right) match {case (Literal(true, BooleanType), _) => Literal(true) //仅仅要左边是true了，不用推断右边都是truecase (_, Literal(true, BooleanType)) => Literal(true) //仅仅要有一边是true，都返回truecase (Literal(false, BooleanType), r) => r //希望右边r是truecase (l, Literal(false, BooleanType)) => lcase (_, _) => or}}}
}

2.3 Batch: Filter Pushdown

2.3.1、Combine Filters

/*** Combines two adjacent [[Filter]] operators into one, merging the* conditions into one conjunctive predicate.*/
object CombineFilters extends Rule[LogicalPlan] {def apply(plan: LogicalPlan): LogicalPlan = plan transform {case ff @ Filter(fc, nf @ Filter(nc, grandChild)) => Filter(And(nc, fc), grandChild)}
}

给定SQL：val query = sql("select key from (select key from temp_shengli where key >100)a where key > 80 ")

优化前：我们看到一个filter 是还有一个filter的grandChild

scala> query.queryExecution.analyzed
res25: org.apache.spark.sql.catalyst.plans.logical.LogicalPlan =
Project [key#27]Filter (key#27 > 80) //filter>80Project [key#27]Filter (key#27 > 100) //filter>100MetastoreRelation default, src, None

优化后：事实上filter也能够表达为一个复杂的boolean表达式

scala> query.queryExecution.optimizedPlan
res26: org.apache.spark.sql.catalyst.plans.logical.LogicalPlan =
Project [key#27]Filter ((key#27 > 100) && (key#27 > 80)) //合并为1个MetastoreRelation default, src, None

2.3.2  Filter Pushdown

Filter Pushdown，过滤器下推。

原理就是更早的过滤掉不须要的元素来降低开销。

给定SQL：val query = sql("select key from (select * from temp_shengli)a where key>100")

生成的逻辑计划为：

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，然后再FilterProject [key#31,value#32]MetastoreRelation default, src, None

优化后的计划为：

query.queryExecution.optimizedPlan
res30: org.apache.spark.sql.catalyst.plans.logical.LogicalPlan =
Project [key#31]Filter (key#31 > 100) //先filter,然后再selectMetastoreRelation default, src, None

2.3.3、ColumnPruning

object ColumnPruning extends Rule[LogicalPlan] {def apply(plan: LogicalPlan): LogicalPlan = plan transform {// Eliminate attributes that are not needed to calculate the specified aggregates.case a @ Aggregate(_, _, child) if (child.outputSet -- a.references).nonEmpty => 假设project的outputSet中减去a.references的元素假设不同，那么就将Aggreagte的child替换为a.referencesa.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孩子的不必要属性，将join的左右子树的列进行裁剪// 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)} else {c}Project(projectList, Join(prunedChild(left), prunedChild(right), joinType, condition))// Combine adjacent Projects.case Project(projectList1, Project(projectList2, child)) => //合并相邻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 Projectscase Project(projectList, child) if child.output == projectList => child}
}

res57: org.apache.spark.sql.catalyst.plans.logical.LogicalPlan =
Aggregate [key#51], [(1 + 1) AS shengli#49,key#51]Project [key#51,value#52] //优化前默认select key 和 value两列MetastoreRelation default, temp_shengli, None

优化后：

scala> ColumnPruning1(query.queryExecution.analyzed)
MetastoreRelation default, temp_shengli, None
res59: org.apache.spark.sql.catalyst.plans.logical.LogicalPlan =
Aggregate [key#51], [(1 + 1) AS shengli#49,key#51]Project [key#51]  //优化后。列裁剪掉了value，仅仅select keyMetastoreRelation default, temp_shengli, None

2、在join操作中，左右孩子能够做列裁剪

给定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 ")
没有优化之前：

scala> query.queryExecution.analyzed
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]  //这里多select了一列，即valueMetastoreRelation default, temp_shengli, NoneProject [key#43,value#44]  //这里多select了一列。即valueMetastoreRelation default, temp_shengli, None

优化后：（ColumnPruning2是我自己调试用的）

scala> ColumnPruning2(query.queryExecution.analyzed)
allReferences is -> Set(key#35, key#37)
MetastoreRelation default, temp_shengli, None
MetastoreRelation default, temp_shengli, None
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]   //经过列裁剪之后，left Child仅仅须要select key这一个列MetastoreRelation default, temp_shengli, NoneProject [key#37]   //经过列裁剪之后。right Child仅仅须要select key这一个列MetastoreRelation default, temp_shengli, None

3、合并相邻的Project的列，裁剪

给定SQL：val query = sql("SELECT c + 1 FROM (SELECT 1 + 1 as c from temp_shengli ) a ")

优化前：

scala> query.queryExecution.analyzed
res61: org.apache.spark.sql.catalyst.plans.logical.LogicalPlan =
Project [(c#56 + 1) AS c0#57]Project [(1 + 1) AS c#56]MetastoreRelation default, temp_shengli, None

优化后：

scala> query.queryExecution.optimizedPlan
res62: org.apache.spark.sql.catalyst.plans.logical.LogicalPlan =
Project [(2 AS c#56 + 1) AS c0#57] //将子查询里的c 代入到 外层select里的c，直接计算结果MetastoreRelation default, temp_shengli, None

三、总结：

本文介绍了Optimizer在Catalyst里的作用即将Analyzed Logical Plan 经过对Logical Plan和Expression进行Rule的应用transfrom，从而达到树的节点进行合并和优化。当中基本的优化的策略总结起来是合并、列裁剪、过滤器下推几大类。

Catalyst应该在不断迭代中，本文仅仅是基于spark1.0.0进行研究。兴许假设新增加的优化策略也会在兴许补充进来。

欢迎大家讨论。共同进步！

——EOF——

原创文章，转载请注明：

转载自：OopsOutOfMemory盛利的Blog。作者： OopsOutOfMemory

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