Spark SQL Catalyst源码分析之Optimizer

  /** 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：

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。

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。

 /** * 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 ") 

scala> query.queryExecution.analyzedres12: 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)

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 = 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。

/** * 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")

scala> query.queryExecution.analyzedres6: 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

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：

 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")

scala> query.queryExecution.analyzedres23: 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.optimizedPlanres24: 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：

/** * 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。

/** * 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

scala> query.queryExecution.analyzedres25: 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

scala> query.queryExecution.optimizedPlanres26: 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：

scala> scala> query.queryExecution.analyzedres29: 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：

query.queryExecution.optimizedPlanres30: 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

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：

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, Noneres59: 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：

scala> query.queryExecution.analyzedres51: 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）

scala> ColumnPruning2(query.queryExecution.analyzed)allReferences is -> Set(key#35, key#37)MetastoreRelation default, temp_shengli, NoneMetastoreRelation default, temp_shengli, Noneres47: 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：

scala> query.queryExecution.analyzedres61: 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.optimizedPlanres62: 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——

原创文章，转载请注明：

转载自：OopsOutOfMemory盛利的Blog，作者： OopsOutOfMemory

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