Spark SQL(6) OptimizedPlan

Spark SQL(6) OptimizedPlan

在这一步spark sql主要应用一些规则，优化生成的Resolved Plan，这一步涉及到的有Optimizer。

之前介绍在sparksession实例化的是会实例化sessionState，进而确定QueryExecution、Analyzer，Optimizer也是在这一步确定的：

  protected def optimizer: Optimizer = {
    new SparkOptimizer(catalog, experimentalMethods) {
      override def extendedOperatorOptimizationRules: Seq[Rule[LogicalPlan]] =
        super.extendedOperatorOptimizationRules ++ customOperatorOptimizationRules
    }
  }

　 Optimizer也是RuleExecutor的子类，而SparkOptimizer是Optimizer子类，在analyzed步骤知道，其实主要规则就是RuleExecutor子类定义的batchs的规则

sparkOptimizer：

 override def batches: Seq[Batch] = (preOptimizationBatches ++ super.batches :+
    Batch("Optimize Metadata Only Query", Once, OptimizeMetadataOnlyQuery(catalog)) :+
    Batch("Extract Python UDF from Aggregate", Once, ExtractPythonUDFFromAggregate) :+
    Batch("Prune File Source Table Partitions", Once, PruneFileSourcePartitions) :+
    Batch("Push down operators to data source scan", Once, PushDownOperatorsToDataSource)) ++
    postHocOptimizationBatches :+
    Batch("User Provided Optimizers", fixedPoint, experimentalMethods.extraOptimizations: _*)

Optimizer：

def batches: Seq[Batch] = {
    val operatorOptimizationRuleSet =
      Seq(
        // Operator push down
        PushProjectionThroughUnion,
        ReorderJoin,
        EliminateOuterJoin,
        PushPredicateThroughJoin,
        PushDownPredicate,
        LimitPushDown,
        ColumnPruning,
        InferFiltersFromConstraints,
        // Operator combine
        CollapseRepartition,
        CollapseProject,
        CollapseWindow,
        CombineFilters,
        CombineLimits,
        CombineUnions,
        // Constant folding and strength reduction
        NullPropagation,
        ConstantPropagation,
        FoldablePropagation,
        OptimizeIn,
        ConstantFolding,
        ReorderAssociativeOperator,
        LikeSimplification,
        BooleanSimplification,
        SimplifyConditionals,
        RemoveDispensableExpressions,
        SimplifyBinaryComparison,
        PruneFilters,
        EliminateSorts,
        SimplifyCasts,
        SimplifyCaseConversionExpressions,
        RewriteCorrelatedScalarSubquery,
        EliminateSerialization,
        RemoveRedundantAliases,
        RemoveRedundantProject,
        SimplifyCreateStructOps,
        SimplifyCreateArrayOps,
        SimplifyCreateMapOps,
        CombineConcats) ++
        extendedOperatorOptimizationRules

    val operatorOptimizationBatch: Seq[Batch] = {
      val rulesWithoutInferFiltersFromConstraints =
        operatorOptimizationRuleSet.filterNot(_ == InferFiltersFromConstraints)
      Batch("Operator Optimization before Inferring Filters", fixedPoint,
        rulesWithoutInferFiltersFromConstraints: _*) ::
      Batch("Infer Filters", Once,
        InferFiltersFromConstraints) ::
      Batch("Operator Optimization after Inferring Filters", fixedPoint,
        rulesWithoutInferFiltersFromConstraints: _*) :: Nil
    }

    (Batch("Eliminate Distinct", Once, EliminateDistinct) ::
    // Technically some of the rules in Finish Analysis are not optimizer rules and belong more
    // in the analyzer, because they are needed for correctness (e.g. ComputeCurrentTime).
    // However, because we also use the analyzer to canonicalized queries (for view definition),
    // we do not eliminate subqueries or compute current time in the analyzer.
    Batch("Finish Analysis", Once,
      EliminateSubqueryAliases,
      EliminateView,
      ReplaceExpressions,
      ComputeCurrentTime,
      GetCurrentDatabase(sessionCatalog),
      RewriteDistinctAggregates,
      ReplaceDeduplicateWithAggregate) ::
    //////////////////////////////////////////////////////////////////////////////////////////
    // Optimizer rules start here
    //////////////////////////////////////////////////////////////////////////////////////////
    // - Do the first call of CombineUnions before starting the major Optimizer rules,
    //   since it can reduce the number of iteration and the other rules could add/move
    //   extra operators between two adjacent Union operators.
    // - Call CombineUnions again in Batch("Operator Optimizations"),
    //   since the other rules might make two separate Unions operators adjacent.
    Batch("Union", Once,
      CombineUnions) ::
    Batch("Pullup Correlated Expressions", Once,
      PullupCorrelatedPredicates) ::
    Batch("Subquery", Once,
      OptimizeSubqueries) ::
    Batch("Replace Operators", fixedPoint,
      ReplaceIntersectWithSemiJoin,
      ReplaceExceptWithFilter,
      ReplaceExceptWithAntiJoin,
      ReplaceDistinctWithAggregate) ::
    Batch("Aggregate", fixedPoint,
      RemoveLiteralFromGroupExpressions,
      RemoveRepetitionFromGroupExpressions) :: Nil ++
    operatorOptimizationBatch) :+
    Batch("Join Reorder", Once,
      CostBasedJoinReorder) :+
    Batch("Decimal Optimizations", fixedPoint,
      DecimalAggregates) :+
    Batch("Object Expressions Optimization", fixedPoint,
      EliminateMapObjects,
      CombineTypedFilters) :+
    Batch("LocalRelation", fixedPoint,
      ConvertToLocalRelation,
      PropagateEmptyRelation) :+
    // The following batch should be executed after batch "Join Reorder" and "LocalRelation".
    Batch("Check Cartesian Products", Once,
      CheckCartesianProducts) :+
    Batch("RewriteSubquery", Once,
      RewritePredicateSubquery,
      ColumnPruning,
      CollapseProject,
      RemoveRedundantProject)
  }

　　如上这便是在优化这步的所有的规则和策略例如消除子查询别名，表达式替换、算子下推、常量折叠等优化规则，经过这一步之后，就进入物理计划阶段了。

举个栗子：

在这里面标出来的子查询别名是在解析的时候加上的，但是在优化之后的logicalPlan中已经去掉，对应上述规则的（消除子查询别名），还有里面的算子下推，在analyzed的算子树中，Join Inner中对age字段做了条件限制，但是在优化后的LogicPlan中已经下推到离Project最近的位置；之后还有InferFiltersFromConstraints规则，对age、name增加了不为null的限定条件。