Spark SQL(4)-Unresolved Plan到Analyzed Plan

Spark Sql(4)-Unresolved Plan到Analyzed Plan

在第三篇总结了Unresolved Plan的生成过程，在此之后就是将其转换为Analyzed Plan。这这一步主要涉及到QueryExecution、Analyzer、catalog等。

spark.sql() -> Dataset.ofRows :

 def ofRows(sparkSession: SparkSession, logicalPlan: LogicalPlan): DataFrame = {
    val qe = sparkSession.sessionState.executePlan(logicalPlan)
    qe.assertAnalyzed()
    new Dataset[Row](sparkSession, qe, RowEncoder(qe.analyzed.schema))
  }

　　在上面的方法里面，qe.assertAnalyzed() 这个会触发计划树的analyzed，这么做一部分考虑是为了通过绑定catalog里面的数据信息，及时发现一些错误，例如函数名、字段名不匹配找不到。

真正的analyzed的操作是在调用QueryExecution的实例化analyzed变量的时候进行的。

 def assertAnalyzed(): Unit = analyzed
 lazy val analyzed: LogicalPlan = {
    SparkSession.setActiveSession(sparkSession)
    sparkSession.sessionState.analyzer.executeAndCheck(logical)
  }

　　那么QueryExecution以及analyzer还有catalog在什么时候实例化的呢？

这个是在sparkSession中：

 lazy val sessionState: SessionState = {
    parentSessionState
      .map(_.clone(this))
      .getOrElse {
        val state = SparkSession.instantiateSessionState(
          SparkSession.sessionStateClassName(sparkContext.conf),
          self)
        initialSessionOptions.foreach { case (k, v) => state.conf.setConfString(k, v) }
        state
      }
  }

　　这里其实是instantiateSessionState() -> BaseSessionStateBuilder.build()方法中build SessionState确定的：

  /**
   * Build the [[SessionState]].
   */
  def build(): SessionState = {
    new SessionState(
      session.sharedState,
      conf,
      experimentalMethods,
      functionRegistry,
      udfRegistration,
      () => catalog,
      sqlParser,
      () => analyzer,
      () => optimizer,
      planner,
      streamingQueryManager,
      listenerManager,
      () => resourceLoader,
      createQueryExecution,
      createClone)
  }

　　Analyzer：

protected def analyzer: Analyzer = new Analyzer(catalog, conf) {
    override val extendedResolutionRules: Seq[Rule[LogicalPlan]] =
      new FindDataSourceTable(session) +:
        new ResolveSQLOnFile(session) +:
        customResolutionRules

    override val postHocResolutionRules: Seq[Rule[LogicalPlan]] =
      PreprocessTableCreation(session) +:
        PreprocessTableInsertion(conf) +:
        DataSourceAnalysis(conf) +:
        customPostHocResolutionRules

    override val extendedCheckRules: Seq[LogicalPlan => Unit] =
      PreWriteCheck +:
        PreReadCheck +:
        HiveOnlyCheck +:
        customCheckRules
  }

　　从上面可以看到Analyzer携带了catalog和sqlconf的引用，所以analyzed中主要在这里面进行；analyzed主要是：

sparkSession.sessionState.analyzer.executeAndCheck(logical)

　　这一行代码中executeAndCheck内容在Analyzer中：

 def executeAndCheck(plan: LogicalPlan): LogicalPlan = {
    val analyzed = execute(plan)
    try {
      checkAnalysis(analyzed)
      EliminateBarriers(analyzed)
    } catch {
      case e: AnalysisException =>
        val ae = new AnalysisException(e.message, e.line, e.startPosition, Option(analyzed))
        ae.setStackTrace(e.getStackTrace)
        throw ae
    }
  }

　　val analyzed = execute(plan)这行其实调用的是RuleExecutor.execute；

def execute(plan: TreeType): TreeType = {
    var curPlan = plan
    val queryExecutionMetrics = RuleExecutor.queryExecutionMeter

    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)
              logTrace(
                s"""
                  |=== Applying Rule ${rule.ruleName} ===
                  |${sideBySide(plan.treeString, result.treeString).mkString("
")}
                """.stripMargin)
            }
            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("
")}
          """.stripMargin)
      } else {
        logTrace(s"Batch ${batch.name} has no effect.")
      }
    }

    curPlan
  }
}

　　从上面的代码可以看出，在迭代应用rule的退出条件就是大于batch现在的迭代次数，或者前一次迭代和上次一样。RuleExecutor内部提供了Seq[Batch]，子类中可以规定具体的Batch，每个Batch都包含一系列的策略和规则，这里的RuleExecutor.execute只是逻辑的依次遍历batch，然后应用具体的rule，这些具体的batch和rule都由子类去实现。

到这里其实我们主要关心的就可以放在Analyzer中重写的batch的部分，看看到底都有哪些规则和策略：

lazy val batches: Seq[Batch] = Seq(
    Batch("Hints", fixedPoint,
      new ResolveHints.ResolveBroadcastHints(conf),
      ResolveHints.RemoveAllHints),
    Batch("Simple Sanity Check", Once,
      LookupFunctions),
    Batch("Substitution", fixedPoint,
      CTESubstitution,
      WindowsSubstitution,
      EliminateUnions,
      new SubstituteUnresolvedOrdinals(conf)),
    Batch("Resolution", fixedPoint,
      ResolveTableValuedFunctions ::
      ResolveRelations :: // 解析表、列名与树中节点进行绑定
      ResolveReferences ::
      ResolveCreateNamedStruct ::
      ResolveDeserializer ::
      ResolveNewInstance ::
      ResolveUpCast ::
      ResolveGroupingAnalytics ::
      ResolvePivot ::
      ResolveOrdinalInOrderByAndGroupBy ::
      ResolveAggAliasInGroupBy ::
      ResolveMissingReferences ::
      ExtractGenerator ::
      ResolveGenerate ::
      ResolveFunctions :: // 解析函数
      ResolveAliases ::
      ResolveSubquery ::
      ResolveSubqueryColumnAliases ::
      ResolveWindowOrder ::
      ResolveWindowFrame ::
      ResolveNaturalAndUsingJoin ::
      ExtractWindowExpressions ::
      GlobalAggregates ::
      ResolveAggregateFunctions ::
      TimeWindowing ::
      ResolveInlineTables(conf) ::
      ResolveTimeZone(conf) ::
      ResolvedUuidExpressions ::
      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)
  )

　　这里主要看看ResolveRelations和ResolveFunctions看看是怎么和catalog里面的表、函数绑定的：

    def apply(plan: LogicalPlan): LogicalPlan = plan.transformUp {
      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)
    }

// Look up the table with the given name from catalog. The database we used is decided by the
    // precedence:
    // 1. Use the database part of the table identifier, if it is defined;
    // 2. Use defaultDatabase, if it is defined(In this case, no temporary objects can be used,
    //    and the default database is only used to look up a view);
    // 3. Use the currentDb of the SessionCatalog.
    private def lookupTableFromCatalog(
        u: UnresolvedRelation,
        defaultDatabase: Option[String] = None): LogicalPlan = {
      val tableIdentWithDb = u.tableIdentifier.copy(
        database = u.tableIdentifier.database.orElse(defaultDatabase))
      try {
        catalog.lookupRelation(tableIdentWithDb)
      } catch {
        case e: NoSuchTableException =>
          u.failAnalysis(s"Table or view not found: ${tableIdentWithDb.unquotedString}", e)
        // If the database is defined and that database is not found, throw an AnalysisException.
        // Note that if the database is not defined, it is possible we are looking up a temp view.
        case e: NoSuchDatabaseException =>
          u.failAnalysis(s"Table or view not found: ${tableIdentWithDb.unquotedString}, the " +
            s"database ${e.db} doesn't exist.", e)
      }
    }

　　上面是绑定表信息的过程，主要是就是调用lookupTableFromCatalog在catalog中查找匹配，其次关于函数的匹配也类似

def apply(plan: LogicalPlan): LogicalPlan = plan.transformUp {
      case q: LogicalPlan =>
        q transformExpressions {
          case u if !u.childrenResolved => u // Skip until children are resolved.
          case u: UnresolvedAttribute if resolver(u.name, VirtualColumn.hiveGroupingIdName) =>
            withPosition(u) {
              Alias(GroupingID(Nil), VirtualColumn.hiveGroupingIdName)()
            }
          case u @ UnresolvedGenerator(name, children) =>
            withPosition(u) {
              catalog.lookupFunction(name, children) match {
                case generator: Generator => generator
                case other =>
                  failAnalysis(s"$name is expected to be a generator. However, " +
                    s"its class is ${other.getClass.getCanonicalName}, which is not a generator.")
              }
            }
          case u @ UnresolvedFunction(funcId, children, isDistinct) =>
            withPosition(u) {
              catalog.lookupFunction(funcId, children) match {
                // AggregateWindowFunctions are AggregateFunctions that can only be evaluated within
                // the context of a Window clause. They do not need to be wrapped in an
                // AggregateExpression.
                case wf: AggregateWindowFunction =>
                  if (isDistinct) {
                    failAnalysis(s"${wf.prettyName} does not support the modifier DISTINCT")
                  } else {
                    wf
                  }
                // We get an aggregate function, we need to wrap it in an AggregateExpression.
                case agg: AggregateFunction => AggregateExpression(agg, Complete, isDistinct)
                // This function is not an aggregate function, just return the resolved one.
                case other =>
                  if (isDistinct) {
                    failAnalysis(s"${other.prettyName} does not support the modifier DISTINCT")
                  } else {
                    other
                  }
              }
            }
        }
    }


def lookupFunction(
      name: FunctionIdentifier,
      children: Seq[Expression]): Expression = synchronized {
    // Note: the implementation of this function is a little bit convoluted.
    // We probably shouldn't use a single FunctionRegistry to register all three kinds of functions
    // (built-in, temp, and external).
    if (name.database.isEmpty && functionRegistry.functionExists(name)) {
      // This function has been already loaded into the function registry.
      return functionRegistry.lookupFunction(name, children)
    }

    // If the name itself is not qualified, add the current database to it.
    val database = formatDatabaseName(name.database.getOrElse(getCurrentDatabase))
    val qualifiedName = name.copy(database = Some(database))

    if (functionRegistry.functionExists(qualifiedName)) {
      // This function has been already loaded into the function registry.
      // Unlike the above block, we find this function by using the qualified name.
      return functionRegistry.lookupFunction(qualifiedName, children)
    }

    // The function has not been loaded to the function registry, which means
    // that the function is a permanent function (if it actually has been registered
    // in the metastore). We need to first put the function in the FunctionRegistry.
    // TODO: why not just check whether the function exists first?
    val catalogFunction = try {
      externalCatalog.getFunction(database, name.funcName)
    } catch {
      case _: AnalysisException => failFunctionLookup(name)
      case _: NoSuchPermanentFunctionException => failFunctionLookup(name)
    }
    loadFunctionResources(catalogFunction.resources)
    // Please note that qualifiedName is provided by the user. However,
    // catalogFunction.identifier.unquotedString is returned by the underlying
    // catalog. So, it is possible that qualifiedName is not exactly the same as
    // catalogFunction.identifier.unquotedString (difference is on case-sensitivity).
    // At here, we preserve the input from the user.
    registerFunction(catalogFunction.copy(identifier = qualifiedName), overrideIfExists = false)
    // Now, we need to create the Expression.
    functionRegistry.lookupFunction(qualifiedName, children)
  }

　　函数的绑定是在functionRegistry中寻找绑定；

其他的规则和策略感兴趣的可以阅读源码来学习。这一步之后就生成了Analyzed Plan。