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第五篇 Spark SQL Catalyst源码分析之Optimizer

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发表于 2018-1-6 21:13:02 | 显示全部楼层 |阅读模式
----------------------------------------------------------
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:
   1.png
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:
   2.png
  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] 纯文本查看 复制代码
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] 纯文本查看 复制代码
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  
}  


   3.jpeg
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] 纯文本查看 复制代码
 /** 
 * 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] 纯文本查看 复制代码
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] 纯文本查看 复制代码
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] 纯文本查看 复制代码
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] 纯文本查看 复制代码
/** 
 * 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] 纯文本查看 复制代码
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
[AppleScript] 纯文本查看 复制代码
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] 纯文本查看 复制代码
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] 纯文本查看 复制代码
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] 纯文本查看 复制代码
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] 纯文本查看 复制代码
/** 
 * 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 PushdownFilter 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] 纯文本查看 复制代码
/** 
 * 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] 纯文本查看 复制代码
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] 纯文本查看 复制代码
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] 纯文本查看 复制代码
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] 纯文本查看 复制代码
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] 纯文本查看 复制代码
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] 纯文本查看 复制代码
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] 纯文本查看 复制代码
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] 纯文本查看 复制代码
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] 纯文本查看 复制代码
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] 纯文本查看 复制代码
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] 纯文本查看 复制代码
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——
原创文章,转载请注明:


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