/** Spark SQL源码分析系列文章*/
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,本文将详细讲解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根据不同的优化策略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),也是转换成物理执行计划的前置。如下图:
一、Optimizer
Optimizer这个类是在catalyst里的optimizer包下的唯一一个类,Optimizer的工作方式其实类似Analyzer,因为它们都继承自RuleExecutor[LogicalPlan],都是执行一系列的Batch操作:
Optimizer里的batches包含了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,每个Batch里定义的优化伴随对象都定义在Optimizer里了:
[java] view plain copy
- 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,而且对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。
[java] view plain copy
- 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、优化策略详解
Optimizer的优化策略不仅有对plan进行transform的,也有对expression进行transform的,究其原理就是遍历树,然后应用优化的Rule,但是注意一点,对Logical Plantransfrom的是先序遍历(pre-order),而对Expression transfrom的时候是后序遍历(post-order):
2.1、Batch: Combine Limits
如果出现了2个Limit,则将2个Limit合并为一个,这个要求一个Limit是另一个Limit的grandChild。
[java] view plain copy
- /**
- * 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expression
- Limit(If(LessThan(ne, le), ne, le), grandChild) //expression比较,如果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 ")
[java] view plain copy
- 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
子查询里limit100,外层查询limit10,这里我们当然可以在子查询里不必查那么多,因为外层只需要10个,所以这里会合并Limit10,和Limit100 为 Limit 10。
2.2、Batch: ConstantFolding
这个Batch里包含了Rules:NullPropagation,ConstantFolding,BooleanSimplification,SimplifyFilters,SimplifyCasts,SimplifyCaseConversionExpressions。
2.2.1、Rule:NullPropagation
这里先提一下Literal字面量,它其实是一个能匹配任意基本类型的类。(为下文做铺垫)
[java] view plain copy
- 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:
[java] view plain copy
- 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
- }
现在来看一下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。
[java] view plain copy
- /**
- * 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) //如果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;">//如果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, _) => 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 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")
[java] view plain copy
- scala> query.queryExecution.analyzed
- res6: org.apache.spark.sql.catalyst.plans.logical.LogicalPlan =
- Aggregate [], [COUNT(null) AS c0#5L] //这里count的是null
- Filter IS NOT NULL key#7
- MetastoreRelation default, temp_shengli, None
调用NullPropagation
[java] view plain copy
- 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#7
- MetastoreRelation default, temp_shengli, None
2.2.2、Rule:ConstantFolding
常量合并是属于Expression优化的一种,对于可以直接计算的常量,不用放到物理执行里去生成对象来计算了,直接可以在计划里就计算出来:
[java] view plain copy
- object ConstantFolding extends Rule[LogicalPlan] {
- def apply(plan: LogicalPlan): LogicalPlan = plan transform { //先对plan进行transform
- case q: LogicalPlan => q transformExpressionsDown { //对每个plan的expression进行transform
- // Skip redundant folding of literals.
- case l: Literal => l
- case 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")
[java] view plain copy
- 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:
[java] view plain copy
- 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10
- MetastoreRelation 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判断,不过这个写的倒是很优雅。
看看布尔表达式2边能不能通过只计算1边,而省去计算另一边而提高效率,称为简化布尔表达式。
解释请看我写的注释:
[java] view plain copy
- /**
- * 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) => //如果布尔表达式是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false
- case (_, Literal(false, BooleanType)) => Literal(false)//只要有1边是false了,都是false
- case (_, _) => and
- }
- case or @ Or(left, right) =>
- (left, right) match {
- case (Literal(true, BooleanType), _) => Literal(true) //只要左边是true了,不用判断右边都是true
- case (_, Literal(true, BooleanType)) => Literal(true) //只要有一边是true,都返回true
- case (Literal(false, BooleanType), r) => r //希望右边r是true
- case (l, Literal(false, BooleanType)) => l
- case (_, _) => or
- }
- }
- }
- }
2.3 Batch: Filter Pushdown
Filter Pushdown下包含了CombineFilters、PushPredicateThroughProject、PushPredicateThroughJoin、ColumnPruning
Ps:感觉Filter Pushdown的名字起的有点不能涵盖全部比如ColumnPruning列裁剪。
2.3.1、Combine Filters
合并两个相邻的Filter,这个和上述Combine Limit差不多。合并2个节点,就可以减少树的深度从而减少重复执行过滤的代价。
[java] view plain copy
- /**
- * 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 是另一个filter的grandChild
[java] view plain copy
- scala> query.queryExecution.analyzed
- 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
优化后:其实filter也可以表达为一个复杂的boolean表达式
[java] view plain copy
- 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需要的元素来减少开销。
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:
[java] view plain copy
- 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Filter
- Project [key#31,value#32]
- MetastoreRelation default, src, None
优化后的计划为:
[java] view plain copy
- query.queryExecution.optimizedPlan
- res30: org.apache.spark.sql.catalyst.plans.logical.LogicalPlan =
- Project [key#31]
- Filter (key#31 > 100) //先filter,然后再select
- MetastoreRelation default, src, None
2.3.3、ColumnPruning
列裁剪用的比较多,就是减少不必要select的某些列。
列裁剪在3种地方可以用:
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、合并相邻的Project的列
[java] view plain copy
- 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 => ////如果project的outputSet中减去a.references的元素如果不同,那么就将Aggreagte的child替换为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 Projects
- case Project(projectList, child) if child.output == projectList => child
- }
- }
分别举三个例子来对应三种情况进行说明:
1、在聚合操作中,可以做列裁剪
给定SQL:val query = sql("SELECT 1+1 as shengli, key from (select key, value from temp_shengli)a group by key")
优化前:
[java] view plain copy
- 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:
[java] view plain copy
- 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,只select key
- MetastoreRelation default, temp_shengli, None
2、在join操作中,左右孩子可以做列裁剪
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:
[java] view plain copy
- 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value
- MetastoreRelation default, temp_shengli, None
- Project [key#43,value#44] //这里多select了一列,即value
- MetastoreRelation default, temp_shengli, None
优化后:(ColumnPruning2是我自己调试用的)
[java] view plain copy
- 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只需要select key这一个列
- MetastoreRelation default, temp_shengli, None
- Project [key#37] //经过列裁剪之后,right Child只需要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:
[java] view plain copy
- 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:
[java] view plain copy
- 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。其中主要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,本文只是基于spark1.0.0进行研究,后续如果新加入的优化策略也会在后续补充进来。
欢迎大家讨论,共同进步!
——EOF——
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