1. spark sql 为何要进行 analyzer ？

   • 通过 antlr 解析出来的抽象语法树 UnResolved LogicalPlan 仅仅是一种数据结构，不包含任何数据信息
   • Analyzer 阶段会使用事先定义好的 Rule 以及 SessionCatalog 等信息对 Unresolved LogicalPlan 进行 transform，绑定数据源以及数据类型

2. spark sql 的 Analyzer 主要干了什么？

   • Analyzer模块将Unresolved LogicalPlan结合元数据catalog进行绑定，最终转化为Resolved LogicalPlan

spark sql 实现 analyzer 的具体细节

1. Spark sql 中的 Rule

   • Spark sql 解析出来的抽象语法树，每个节点都是由 TreeNode 构成的，而 Rule 作为一条规则作用在 TreeNode 节点上进行 transform 操作

   • Rule 的定义如下，apple 方法由子类实现定义了每个 Rule 该如何 transform

       ```
       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
       }
       ```
     

2. spark sql 定义的 Rule

   • spark sql 在 src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala 定义了很多 Batch rule

      lazy val batches: Seq[Batch] = Seq(
         Batch("Hints", fixedPoint,
           new ResolveHints.ResolveBroadcastHints(conf),
           ResolveHints.ResolveCoalesceHints,
           ResolveHints.RemoveAllHints),
         Batch("Simple Sanity Check", Once,
           LookupFunctions),
         Batch("Substitution", fixedPoint,
           CTESubstitution,
           WindowsSubstitution,
           EliminateUnions,
           new SubstituteUnresolvedOrdinals(conf)),
         Batch("Resolution", fixedPoint,
           ResolveTableValuedFunctions ::
           ResolveRelations ::
           ResolveReferences ::
           ResolveCreateNamedStruct ::
           ResolveDeserializer ::
           ResolveNewInstance ::
           ResolveUpCast ::
           ResolveGroupingAnalytics ::
           ResolvePivot ::
           ResolveOrdinalInOrderByAndGroupBy ::
           ResolveAggAliasInGroupBy ::
           ResolveMissingReferences ::
           ExtractGenerator ::
           ResolveGenerate ::
           ResolveFunctions ::
           ResolveAliases ::
           ResolveSubquery ::
           ResolveSubqueryColumnAliases ::
           ResolveWindowOrder ::
           ResolveWindowFrame ::
           ResolveNaturalAndUsingJoin ::
           ResolveOutputRelation ::
           ExtractWindowExpressions ::
           GlobalAggregates ::
           ResolveAggregateFunctions ::
           TimeWindowing ::
           ResolveInlineTables(conf) ::
           ResolveHigherOrderFunctions(catalog) ::
           ResolveLambdaVariables(conf) ::
           ResolveTimeZone(conf) ::
           ResolveRandomSeed ::
           TypeCoercion.typeCoercionRules(conf) ++
           extendedResolutionRules : _*),
         Batch("Post-Hoc Resolution", Once, postHocResolutionRules: _*),
         Batch("View", Once,
           AliasViewChild(conf)),
         Batch("Nondeterministic", Once,
           PullOutNondeterministic),
         Batch("UDF", Once,
           HandleNullInputsForUDF),
         Batch("FixNullability", Once,
           FixNullability),
         Batch("Subquery", Once,
           UpdateOuterReferences),
         Batch("Cleanup", fixedPoint,
           CleanupAliases)
       )
     

3. Analyzer 调用父类的 execute 方法，遍历上面定义的 batches，将 Rule 作用在 Logical plan 上实现 transform

    def execute(plan: TreeType): TreeType = {
       var curPlan = plan
       val queryExecutionMetrics = RuleExecutor.queryExecutionMeter
       val planChangeLogger = new PlanChangeLogger()
   
       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 startTime = System.nanoTime()
               val result = rule(plan)
               val runTime = System.nanoTime() - startTime
   
               if (!result.fastEquals(plan)) {
                 queryExecutionMetrics.incNumEffectiveExecution(rule.ruleName)
                 queryExecutionMetrics.incTimeEffectiveExecutionBy(rule.ruleName, runTime)
                 planChangeLogger.log(rule.ruleName, plan, result)
               }
               queryExecutionMetrics.incExecutionTimeBy(rule.ruleName, runTime)
               queryExecutionMetrics.incNumExecution(rule.ruleName)
   
               // Run the structural integrity checker against the plan after each rule.
               if (!isPlanIntegral(result)) {
                 val message = s"After applying rule ${rule.ruleName} in batch ${batch.name}, " +
                   "the structural integrity of the plan is broken."
                 throw new TreeNodeException(result, message, null)
               }
   
               result
           }
           iteration += 1
           if (iteration > batch.strategy.maxIterations) {
             // Only log if this is a rule that is supposed to run more than once.
             if (iteration != 2) {
               val message = s"Max iterations (${iteration - 1}) reached for batch ${batch.name}"
               if (Utils.isTesting) {
                 throw new TreeNodeException(curPlan, message, null)
               } else {
                 logWarning(message)
               }
             }
             continue = false
           }
   
           if (curPlan.fastEquals(lastPlan)) {
             logTrace(
               s"Fixed point reached for batch ${batch.name} after ${iteration - 1} iterations.")
             continue = false
           }
           lastPlan = curPlan
         }
   
         if (!batchStartPlan.fastEquals(curPlan)) {
           logDebug(
             s"""
               |=== Result of Batch ${batch.name} ===
               |${sideBySide(batchStartPlan.treeString, curPlan.treeString).mkString("\n")}
             """.stripMargin)
         } else {
           logTrace(s"Batch ${batch.name} has no effect.")
         }
       }
   
       curPlan
     }
   

   • batch 和里面的 rules 都是连续执行的，每执行完一个 batch 都判断此 batch 执行的次数是否达到 maxIterations 和执行此 batch 前后是否有变化，达到 maxIterations 或者执行 batch 前后无变化都不再执行此batch。

4. 解析 spark sql 如何通过 Catolog 将数据信息绑定到 logical plan 上的

   • ResolveRelations 实现替换 UnresolvedRelation 为 resolveRelation
   • ResolveRelations 继承自 Rule，定义: object ResolveRelations extends Rule[LogicalPlan]
   • ResolveRelations 的 apply() 方法定义了 UnsolveRelation 到 ResolveRelation 的转换

     def apply(plan: LogicalPlan): LogicalPlan = plan.resolveOperatorsUp {
           case i @ InsertIntoTable(u: UnresolvedRelation, parts, child, _, _) if child.resolved =>
             EliminateSubqueryAliases(lookupTableFromCatalog(u)) match {
               case v: View =>
                 u.failAnalysis(s"Inserting into a view is not allowed. View: ${v.desc.identifier}.")
               case other => i.copy(table = other)
             }
           case u: UnresolvedRelation => resolveRelation(u)
         }
     

     • resolveOperatorsUp 是一个柯理化调用

       {
       case i @ InsertIntoTable(u: UnresolvedRelation, parts, child, _, _) if child.resolved =>
                       EliminateSubqueryAliases(lookupTableFromCatalog(u)) match {
                         case v: View =>
                           u.failAnalysis(s"Inserting into a view is not allowed. View: ${v.desc.identifier}.")
                         case other => i.copy(table = other)
                       }
                     case u: UnresolvedRelation => resolveRelation(u)
                   }
       

       • resolveOperatorsUp 后面的整个代码块作为参数传入 resolveOperatorsUp 方法中

       • resolveOperatorsUp 的具体实现

         def resolveOperatorsUp(rule: PartialFunction[LogicalPlan, LogicalPlan]): LogicalPlan = {
             if (!analyzed) {
               AnalysisHelper.allowInvokingTransformsInAnalyzer {
                 /* apply方法中调用LogicalPlan 的resolveOperatorsUp方法并将后面的偏函数rule作为参数。自下而上的应用于逻辑算子树上的每一个LogicalPlan节点 */
                 val afterRuleOnChildren = mapChildren(_.resolveOperatorsUp(rule))
                 if (self fastEquals afterRuleOnChildren) {
                   CurrentOrigin.withOrigin(origin) {
                     /* rule.applyOrElse(self, identity[LogicalPlan]) 将偏函数应用到LogicalPlan。偏函数会对传入的参数进行模式匹配，只有匹配成功的参数才会进行处理。在rule偏函数中可以看出，如果LogicalPlan是UnresolvedRelation类型，则调用resolveRelation(u)方法。 */
                     rule.applyOrElse(self, identity[LogicalPlan])
                   }
                 } else {
                   CurrentOrigin.withOrigin(origin) {
                     rule.applyOrElse(afterRuleOnChildren, identity[LogicalPlan])
                   }
                 }
               }
             } else {
               self
             }
           }
         

       • 首先判断此 plan 是否已经被 analyzed 过，接着调用 mapChildren，并且传入的是resolveOperators 方法，其实就是一个递归调用，它会优先处理它的子节点，然后再处理自己，如果处理后的LogicalPlan 和当前的相等就说明他没有子节点了，则处理它自己，反之处理返回的 plan。

       • 如果匹配的是 UnresolvedRelation 结点，则调用 resolveRelation 方法进行解析，
         经过resolveRelation方法之后，返回的 logical plan 是已经和实际元数据绑定好的plan，可能是从globalTempViewManager直接获取的，可能是从tempTables直接获取，也可能是从externalCatalog 获取的元数据。