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第三篇 Spark SQL Catalyst源码分析之Analyzer

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发表于 2018-1-3 23:56:04 | 显示全部楼层 |阅读模式
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Spark SQL源码分析系列文章
    前面几篇文章讲解了Spark SQL的核心执行流程和Spark SQL的Catalyst框架的Sql Parser是怎样接受用户输入sql,经过解析生成Unresolved Logical Plan的。我们记得Spark SQL的执行流程中另一个核心的组件式Analyzer,本文将会介绍Analyzer在Spark SQL里起到了什么作用。
    Analyzer位于Catalyst的analysis package下,主要职责是将Sql Parser 未能Resolved的Logical Plan 给Resolved掉。

一、Analyzer构造
    Analyzer会使用Catalog和FunctionRegistry将UnresolvedAttribute和UnresolvedRelation转换为catalyst里全类型的对象。
    Analyzer里面有fixedPoint对象,一个Seq[Batch].
[Java] 纯文本查看 复制代码
class Analyzer(catalog: Catalog, registry: FunctionRegistry, caseSensitive: Boolean)  
  extends RuleExecutor[LogicalPlan] with HiveTypeCoercion {  
  
  // TODO: pass this in as a parameter.  
  val fixedPoint = FixedPoint(100)  
  
  val batches: Seq[Batch] = Seq(  
    Batch("MultiInstanceRelations", Once,  
      NewRelationInstances),  
    Batch("CaseInsensitiveAttributeReferences", Once,  
      (if (caseSensitive) Nil else LowercaseAttributeReferences :: Nil) : _*),  
    Batch("Resolution", fixedPoint,  
      ResolveReferences ::  
      ResolveRelations ::  
      NewRelationInstances ::  
      ImplicitGenerate ::  
      StarExpansion ::  
      ResolveFunctions ::  
      GlobalAggregates ::  
      typeCoercionRules :_*),  
    Batch("AnalysisOperators", fixedPoint,  
      EliminateAnalysisOperators)  
  )  

    Analyzer里的一些对象解释:
    FixedPoint:相当于迭代次数的上限。
[Java] 纯文本查看 复制代码
/** A strategy that runs until fix point or maxIterations times, whichever comes first. */  
case class FixedPoint(maxIterations: Int) extends Strategy  


    Batch: 批次,这个对象是由一系列Rule组成的,采用一个策略(策略其实是迭代几次的别名吧,eg:Once)
[Java] 纯文本查看 复制代码
/** A batch of rules. */,  
protected case class Batch(name: String, strategy: Strategy, rules: Rule[TreeType]*)  

   Rule:理解为一种规则,这种规则会应用到Logical Plan 从而将UnResolved 转变为Resolved
[Java] 纯文本查看 复制代码
abstract class Rule[TreeType <: TreeNode[_]] extends Logging {  
  
  /** Name for this rule, automatically inferred based on class name. */  
  val ruleName: String = {  
    val className = getClass.getName  
    if (className endsWith "$") className.dropRight(1) else className  
  }  
  
  def apply(plan: TreeType): TreeType  
}  



   Strategy:最大的执行次数,如果执行次数在最大迭代次数之前就达到了fix point,策略就会停止,不再应用了。
[Java] 纯文本查看 复制代码
/** 
 * An execution strategy for rules that indicates the maximum number of executions. If the 
 * execution reaches fix point (i.e. converge) before maxIterations, it will stop. 
 */  
abstract class Strategy { def maxIterations: Int }  


   Analyzer解析主要是根据这些Batch里面定义的策略和Rule来对Unresolved的逻辑计划进行解析的。
   这里Analyzer类本身并没有定义执行的方法,而是要从它的父类RuleExecutor[LogicalPlan]寻找,Analyzer也实现了HiveTypeCosercion,这个类是参考Hive的类型自动兼容转换的原理。如图:

    RuleExecutor:执行Rule的执行环境,它会将包含了一系列的Rule的Batch进行执行,这个过程都是串行的。
    具体的执行方法定义在apply里:
    可以看到这里是一个while循环,每个batch下的rules都对当前的plan进行作用,这个过程是迭代的,直到达到Fix Point或者最大迭代次数。
[Java] 纯文本查看 复制代码
def apply(plan: TreeType): TreeType = {  
   var curPlan = plan  
  
   batches.foreach { batch =>  
     val batchStartPlan = curPlan  
     var iteration = 1  
     var lastPlan = curPlan  
     var continue = true  
  
     // Run until fix point (or the max number of iterations as specified in the strategy.  
     while (continue) {  
       curPlan = batch.rules.foldLeft(curPlan) {  
         case (plan, rule) =>  
           val result = rule(plan) //这里将调用各个不同Rule的apply方法,将UnResolved Relations,Attrubute和Function进行Resolve  
           if (!result.fastEquals(plan)) {  
             logger.trace(  
               s"""  
                 |=== Applying Rule ${rule.ruleName} ===  
                 |${sideBySide(plan.treeString, result.treeString).mkString("\n")}  
               """.stripMargin)  
           }  
  
           result //返回作用后的result plan  
       }  
       iteration += 1  
       if (iteration > batch.strategy.maxIterations) { //如果迭代次数已经大于该策略的最大迭代次数,就停止循环  
         logger.info(s"Max iterations ($iteration) reached for batch ${batch.name}")  
         continue = false  
       }  
  
       if (curPlan.fastEquals(lastPlan)) { //如果在多次迭代中不再变化,因为plan有个unique id,就停止循环。  
         logger.trace(s"Fixed point reached for batch ${batch.name} after $iteration iterations.")  
         continue = false  
       }  
       lastPlan = curPlan  
     }  
  
     if (!batchStartPlan.fastEquals(curPlan)) {  
       logger.debug(  
         s"""  
         |=== Result of Batch ${batch.name} ===  
         |${sideBySide(plan.treeString, curPlan.treeString).mkString("\n")}  
       """.stripMargin)  
     } else {  
       logger.trace(s"Batch ${batch.name} has no effect.")  
     }  
   }  
  
   curPlan //返回Resolved的Logical Plan  
 }  

二、Rules介绍    目前Spark SQL 1.0.0的Rule都定义在了Analyzer.scala的内部类。
    在batches里面定义了4个Batch。
    MultiInstanceRelations、CaseInsensitiveAttributeReferences、Resolution、AnalysisOperators 四个。
    这4个Batch是将不同的Rule进行归类,每种类别采用不同的策略来进行Resolve。
   
2.1、MultiInstanceRelation 如果一个实例在Logical Plan里出现了多次,则会应用NewRelationInstances这儿Rule
[Java] 纯文本查看 复制代码
Batch("MultiInstanceRelations", Once,  
     NewRelationInstances)  

[Java] 纯文本查看 复制代码
trait MultiInstanceRelation {  
  def newInstance: this.type  
}  


[Java] 纯文本查看 复制代码
object NewRelationInstances extends Rule[LogicalPlan] {   
  def apply(plan: LogicalPlan): LogicalPlan = {  
    val localRelations = plan collect { case l: MultiInstanceRelation => l} //将logical plan应用partial function得到所有MultiInstanceRelation的plan的集合   
    val multiAppearance = localRelations  
      .groupBy(identity[MultiInstanceRelation]) //group by操作  
      .filter { case (_, ls) => ls.size > 1 } //如果只取size大于1的进行后续操作  
      .map(_._1)  
      .toSet  
  
    //更新plan,使得每个实例的expId是唯一的。  
    plan transform {  
      case l: MultiInstanceRelation if multiAppearance contains l => l.newInstance  
    }  
  }  
}  


2.2、LowercaseAttributeReferences同样是partital function,对当前plan应用,将所有匹配的如UnresolvedRelation的别名alise转换为小写,将Subquery的别名也转换为小写。
总结:这是一个使属性名大小写不敏感的Rule,因为它将所有属性都to lower case了。
[Java] 纯文本查看 复制代码
object LowercaseAttributeReferences extends Rule[LogicalPlan] {  
  def apply(plan: LogicalPlan): LogicalPlan = plan transform {  
    case UnresolvedRelation(databaseName, name, alias) =>  
      UnresolvedRelation(databaseName, name, alias.map(_.toLowerCase))  
    case Subquery(alias, child) => Subquery(alias.toLowerCase, child)  
    case q: LogicalPlan => q transformExpressions {  
      case s: Star => s.copy(table = s.table.map(_.toLowerCase))  
      case UnresolvedAttribute(name) => UnresolvedAttribute(name.toLowerCase)  
      case Alias(c, name) => Alias(c, name.toLowerCase)()  
      case GetField(c, name) => GetField(c, name.toLowerCase)  
    }  
  }  
}  


2.3、ResolveReferences
将Sql parser解析出来的UnresolvedAttribute全部都转为对应的实际的catalyst.expressions.AttributeReference AttributeReferences
这里调用了logical plan 的resolve方法,将属性转为NamedExepression。
[Java] 纯文本查看 复制代码
object ResolveReferences extends Rule[LogicalPlan] {  
  def apply(plan: LogicalPlan): LogicalPlan = plan transformUp {  
    case q: LogicalPlan if q.childrenResolved =>  
      logger.trace(s"Attempting to resolve ${q.simpleString}")  
      q transformExpressions {  
        case u @ UnresolvedAttribute(name) =>  
          // Leave unchanged if resolution fails.  Hopefully will be resolved next round.  
          val result = q.resolve(name).getOrElse(u)//转化为NamedExpression  
          logger.debug(s"Resolving $u to $result")  
          result  
      }  
  }  
}  


2.4、 ResolveRelations
这个比较好理解,还记得前面Sql parser吗,比如select * from src,这个src表parse后就是一个UnresolvedRelation节点。
这一步ResolveRelations调用了catalog这个对象。Catalog对象里面维护了一个tableName,Logical Plan的HashMap结果。
通过这个Catalog目录来寻找当前表的结构,从而从中解析出这个表的字段,如UnResolvedRelations 会得到一个tableWithQualifiers。(即表和字段)
这也解释了为什么流程图那,我会画一个catalog在上面,因为它是Analyzer工作时需要的meta data。
[Java] 纯文本查看 复制代码
object ResolveRelations extends Rule[LogicalPlan] {  
    def apply(plan: LogicalPlan): LogicalPlan = plan transform {  
      case UnresolvedRelation(databaseName, name, alias) =>  
        catalog.lookupRelation(databaseName, name, alias)  
    }  
  }  


2.5、ImplicitGenerate
如果在select语句里只有一个表达式,而且这个表达式是一个Generator(Generator是一个1条记录生成到N条记录的映射)
当在解析逻辑计划时,遇到Project节点的时候,就可以将它转换为Generate类(Generate类是将输入流应用一个函数,从而生成一个新的流)。
[Java] 纯文本查看 复制代码
object ImplicitGenerate extends Rule[LogicalPlan] {  
  def apply(plan: LogicalPlan): LogicalPlan = plan transform {  
    case Project(Seq(Alias(g: Generator, _)), child) =>  
      Generate(g, join = false, outer = false, None, child)  
  }  
}  


2.6 StarExpansion

在Project操作符里,如果是*符号,即select * 语句,可以将所有的references都展开,即将select * 中的*展开成实际的字段。
[Java] 纯文本查看 复制代码
object StarExpansion extends Rule[LogicalPlan] {  
    def apply(plan: LogicalPlan): LogicalPlan = plan transform {  
      // Wait until children are resolved  
      case p: LogicalPlan if !p.childrenResolved => p  
      // If the projection list contains Stars, expand it.  
      case p @ Project(projectList, child) if containsStar(projectList) =>   
        Project(  
          projectList.flatMap {  
            case s: Star => s.expand(child.output) //展开,将输入的Attributeexpand(input: Seq[Attribute]) 转化为Seq[NamedExpression]  
            case o => o :: Nil  
          },  
          child)  
      case t: ScriptTransformation if containsStar(t.input) =>  
        t.copy(  
          input = t.input.flatMap {  
            case s: Star => s.expand(t.child.output)  
            case o => o :: Nil  
          }  
        )  
      // If the aggregate function argument contains Stars, expand it.  
      case a: Aggregate if containsStar(a.aggregateExpressions) =>  
        a.copy(  
          aggregateExpressions = a.aggregateExpressions.flatMap {  
            case s: Star => s.expand(a.child.output)  
            case o => o :: Nil  
          }  
        )  
    }  
    /** 
     * Returns true if `exprs` contains a [[Star]]. 
     */  
    protected def containsStar(exprs: Seq[Expression]): Boolean =  
      exprs.collect { case _: Star => true }.nonEmpty  
  }  
}  


2.7 ResolveFunctions
这个和ResolveReferences差不多,这里主要是对udf进行resolve。
将这些UDF都在FunctionRegistry里进行查找。
[Java] 纯文本查看 复制代码
object ResolveFunctions extends Rule[LogicalPlan] {  
  def apply(plan: LogicalPlan): LogicalPlan = plan transform {  
    case q: LogicalPlan =>  
      q transformExpressions {  
        case u @ UnresolvedFunction(name, children) if u.childrenResolved =>  
          registry.lookupFunction(name, children) //看是否注册了当前udf  
      }  
  }  
}  

2.8 GlobalAggregates
全局的聚合,如果遇到了Project就返回一个Aggregate.
[Java] 纯文本查看 复制代码
object GlobalAggregates extends Rule[LogicalPlan] {  
  def apply(plan: LogicalPlan): LogicalPlan = plan transform {  
    case Project(projectList, child) if containsAggregates(projectList) =>  
      Aggregate(Nil, projectList, child)  
  }  
  
  def containsAggregates(exprs: Seq[Expression]): Boolean = {  
    exprs.foreach(_.foreach {  
      case agg: AggregateExpression => return true  
      case _ =>  
    })  
    false  
  }  
}  


2.9 typeCoercionRules这个是Hive里的兼容SQL语法,比如将String和Int互相转换,不需要显示的调用cast xxx  as yyy了。如StringToIntegerCasts。
[Java] 纯文本查看 复制代码
val typeCoercionRules =  
  PropagateTypes ::  
  ConvertNaNs ::  
  WidenTypes ::  
  PromoteStrings ::  
  BooleanComparisons ::  
  BooleanCasts ::  
  StringToIntegralCasts ::  
  FunctionArgumentConversion ::  
  CastNulls ::  
  Nil  

2.10 EliminateAnalysisOperators将分析的操作符移除,这里仅支持2种,一种是Subquery需要移除,一种是LowerCaseSchema。这些节点都会从Logical Plan里移除。
[Java] 纯文本查看 复制代码
object EliminateAnalysisOperators extends Rule[LogicalPlan] {  
  def apply(plan: LogicalPlan): LogicalPlan = plan transform {  
    case Subquery(_, child) => child //遇到Subquery,不反悔本身,返回它的Child,即删除了该元素  
    case LowerCaseSchema(child) => child  
  }  
}  

三、实践  补充昨天DEBUG的一个例子,这个例子证实了如何将上面的规则应用到Unresolved Logical Plan:
  当传递sql语句的时候,的确调用了ResolveReferences将mobile解析成NamedExpression。
  可以对照这看执行流程,左边是Unresolved Logical Plan,右边是Resoveld Logical Plan。
  先是执行了Batch Resolution,eg: 调用ResovelRalation这个RUle来使 Unresovled Relation 转化为 SparkLogicalPlan并通过Catalog找到了其对于的字段属性。
  然后执行了Batch Analysis Operator。eg:调用EliminateAnalysisOperators来将SubQuery给remove掉了。
  可能格式显示的不太好,可以向右边拖动下滚动轴看下结果。 :)
[Java] 纯文本查看 复制代码
val exec = sqlContext.sql("select mobile as mb, sid as id, mobile*2 multi2mobile, count(1) times from (select * from temp_shengli_mobile)a where pfrom_id=0.0 group by mobile, sid,  mobile*2")  
14/07/21 18:23:32 DEBUG SparkILoop$SparkILoopInterpreter: Invoking: public static java.lang.String $line47.$eval.$print()  
14/07/21 18:23:33 INFO Analyzer: Max iterations (2) reached for batch MultiInstanceRelations  
14/07/21 18:23:33 INFO Analyzer: Max iterations (2) reached for batch CaseInsensitiveAttributeReferences  
14/07/21 18:23:33 DEBUG Analyzer$ResolveReferences$: Resolving 'pfrom_id to pfrom_id#5  
14/07/21 18:23:33 DEBUG Analyzer$ResolveReferences$: Resolving 'mobile to mobile#2  
14/07/21 18:23:33 DEBUG Analyzer$ResolveReferences$: Resolving 'sid to sid#1  
14/07/21 18:23:33 DEBUG Analyzer$ResolveReferences$: Resolving 'mobile to mobile#2  
14/07/21 18:23:33 DEBUG Analyzer$ResolveReferences$: Resolving 'mobile to mobile#2  
14/07/21 18:23:33 DEBUG Analyzer$ResolveReferences$: Resolving 'sid to sid#1  
14/07/21 18:23:33 DEBUG Analyzer$ResolveReferences$: Resolving 'mobile to mobile#2  
14/07/21 18:23:33 DEBUG Analyzer:   
=== Result of Batch Resolution ===  
!Aggregate ['mobile,'sid,('mobile * 2) AS c2#27], ['mobile AS mb#23,'sid AS id#24,('mobile * 2) AS multi2mobile#25,COUNT(1) AS times#26L]   Aggregate [mobile#2,sid#1,(CAST(mobile#2, DoubleType) * CAST(2, DoubleType)) AS c2#27], [mobile#2 AS mb#23,sid#1 AS id#24,(CAST(mobile#2, DoubleType) * CAST(2, DoubleType)) AS multi2mobile#25,COUNT(1) AS times#26L]  
! Filter ('pfrom_id = 0.0)                                                                                                                   Filter (CAST(pfrom_id#5, DoubleType) = 0.0)  
   Subquery a                                                                                                                                 Subquery a  
!   Project                                                                                                                                Project [data_date#0,sid#1,mobile#2,pverify_type#3,create_time#4,pfrom_id#5,p_status#6,pvalidate_time#7,feffect_time#8,plastupdate_ip#9,update_time#10,status#11,preserve_int#12]  
!    UnresolvedRelation None, temp_shengli_mobile, None                                                                                         Subquery temp_shengli_mobile  
!                                                                                                                                                SparkLogicalPlan (ExistingRdd [data_date#0,sid#1,mobile#2,pverify_type#3,create_time#4,pfrom_id#5,p_status#6,pvalidate_time#7,feffect_time#8,plastupdate_ip#9,update_time#10,status#11,preserve_int#12], MapPartitionsRDD[4] at mapPartitions at basicOperators.scala:174)  
          
14/07/21 18:23:33 DEBUG Analyzer:   
=== Result of Batch AnalysisOperators ===  
!Aggregate ['mobile,'sid,('mobile * 2) AS c2#27], ['mobile AS mb#23,'sid AS id#24,('mobile * 2) AS multi2mobile#25,COUNT(1) AS times#26L]   Aggregate [mobile#2,sid#1,(CAST(mobile#2, DoubleType) * CAST(2, DoubleType)) AS c2#27], [mobile#2 AS mb#23,sid#1 AS id#24,(CAST(mobile#2, DoubleType) * CAST(2, DoubleType)) AS multi2mobile#25,COUNT(1) AS times#26L]  
! Filter ('pfrom_id = 0.0)                                                                                                                   Filter (CAST(pfrom_id#5, DoubleType) = 0.0)  
!  Subquery a                                                                                                                                 Project [data_date#0,sid#1,mobile#2,pverify_type#3,create_time#4,pfrom_id#5,p_status#6,pvalidate_time#7,feffect_time#8,plastupdate_ip#9,update_time#10,status#11,preserve_int#12]  
!   Project                                                                                                                                SparkLogicalPlan (ExistingRdd [data_date#0,sid#1,mobile#2,pverify_type#3,create_time#4,pfrom_id#5,p_status#6,pvalidate_time#7,feffect_time#8,plastupdate_ip#9,update_time#10,status#11,preserve_int#12], MapPartitionsRDD[4] at mapPartitions at basicOperators.scala:174)  
!    UnresolvedRelation None, temp_shengli_mobile, None   
   

四、总结
    本文从源代码角度分析了Analyzer在对Sql Parser解析出的UnResolve Logical Plan 进行analyze的过程中,所执行的流程。
    流程是实例化一个SimpleAnalyzer,定义一些Batch,然后遍历这些Batch在RuleExecutor的环境下,执行Batch里面的Rules,每个Rule会对Unresolved Logical Plan进行Resolve,有些可能不能一次解析出,需要多次迭代,直到达到max迭代次数或者达到fix point。这里Rule里比较常用的就是ResolveReferences、ResolveRelations、StarExpansion、GlobalAggregates、typeCoercionRules和EliminateAnalysisOperators。


——EOF——
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