Spark-Sql源码解析之八 Codegen

Codegen,动态字节码技术,那么什么是动态字节码技术呢?先看来一段代码,假设SparkPlanSort
case class Sort(
    sortOrder: Seq[SortOrder],
    global: Boolean,
    child: SparkPlan)
  extends UnaryNode {
  override def requiredChildDistribution: Seq[Distribution] =
    if (global) OrderedDistribution(sortOrder) :: Nil else UnspecifiedDistribution :: Nil

  protected override def doExecute(): RDD[Row] = attachTree(this, "sort") {
    child.execute().mapPartitions( { iterator =>
      val ordering = newOrdering(sortOrder, child.output)
      iterator.map(_.copy()).toArray.sorted(ordering).iterator
    }, preservesPartitioning = true)
  }

  override def output: Seq[Attribute] = child.output

  override def outputOrdering: Seq[SortOrder] = sortOrder
}

abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializable {
protected def newOrdering(order: Seq[SortOrder], inputSchema: Seq[Attribute]): Ordering[Row] = {
  if (codegenEnabled) {//开启动态字节码技术
    GenerateOrdering.generate(order, inputSchema)
  } else {//否则关闭
    new RowOrdering(order, inputSchema)
  }
}
}

可见针对Sort的SparkPlan,针对是否开启动态字节码技术的情况下会发生两种情况:当关闭的时候,其Compare函数如下:

class RowOrdering(ordering: Seq[SortOrder]) extends Ordering[Row] {
  def this(ordering: Seq[SortOrder], inputSchema: Seq[Attribute]) =
    this(ordering.map(BindReferences.bindReference(_, inputSchema)))

  def compare(a: Row, b: Row): Int = {
    var i = 0
    while (i < ordering.size) {
      val order = ordering(i)
      val left = order.child.eval(a)//虚函数调用,然后装箱
      val right = order.child.eval(b)//虚函数调用,然后装箱

      if (left == null && right == null) {
        // Both null, continue looking.
      } else if (left == null) {
        return if (order.direction == Ascending) -1 else 1
      } else if (right == null) {
        return if (order.direction == Ascending) 1 else -1
      } else {
        val comparison = order.dataType match {
          case n: AtomicType if order.direction == Ascending =>
            n.ordering.asInstanceOf[Ordering[Any]].compare(left, right)//调用具体对象的compare函数
          case n: AtomicType if order.direction == Descending =>
            n.ordering.asInstanceOf[Ordering[Any]].reverse.compare(left, right)//调用具体对象的compare函数
          case other => sys.error(s"Type $other does not support ordered operations")
        }
        if (comparison != 0) return comparison
      }
      i += 1
    }
    return 0
  }
}

其涉及到虚函数调用及装箱,虚函数的调用相对普通函数而言比较耗时。

当开启动态字节码技术的时候,其Compare函数如下:

object GenerateOrdering extends CodeGenerator[Seq[SortOrder], Ordering[Row]] with Logging {
  import scala.reflect.runtime.{universe => ru}
  import scala.reflect.runtime.universe._

 protected def canonicalize(in: Seq[SortOrder]): Seq[SortOrder] =
    in.map(ExpressionCanonicalizer.execute(_).asInstanceOf[SortOrder])

  protected def bind(in: Seq[SortOrder], inputSchema: Seq[Attribute]): Seq[SortOrder] =
    in.map(BindReferences.bindReference(_, inputSchema))

  protected def create(ordering: Seq[SortOrder]): Ordering[Row] = {
    val a = newTermName("a")
    val b = newTermName("b")
    val comparisons = ordering.zipWithIndex.map { case (order, i) =>
      val evalA = expressionEvaluator(order.child)
      val evalB = expressionEvaluator(order.child)

      val compare = order.child.dataType match {
        case BinaryType =>
          q"""
          val x = ${if (order.direction == Ascending) evalA.primitiveTerm else evalB.primitiveTerm}//直接指定类型,不涉及虚函数调用
          val y = ${if (order.direction != Ascending) evalB.primitiveTerm else evalA.primitiveTerm}//直接指定类型,不涉及虚函数调用
          var i = 0
          while (i < x.length && i < y.length) {
            val res = x(i).compareTo(y(i))
            if (res != 0) return res
            i = i+1
          }
          return x.length - y.length
          """
        case _: NumericType =>
          q"""
          val comp = ${evalA.primitiveTerm} - ${evalB.primitiveTerm}//直接指定类型
          if(comp != 0) {
            return ${if (order.direction == Ascending) q"comp.toInt" else q"-comp.toInt"}
          }
          """
        case StringType =>
          if (order.direction == Ascending) {
            q"""return ${evalA.primitiveTerm}.compare(${evalB.primitiveTerm})"""//直接指定类型,不涉及虚函数调用
          } else {
            q"""return ${evalB.primitiveTerm}.compare(${evalA.primitiveTerm})"""
          }
      }

      q"""
        i = $a
        ..${evalA.code}
        i = $b
        ..${evalB.code}
        if (${evalA.nullTerm} && ${evalB.nullTerm}) {
          // Nothing
        } else if (${evalA.nullTerm}) {
          return ${if (order.direction == Ascending) q"-1" else q"1"}
        } else if (${evalB.nullTerm}) {
          return ${if (order.direction == Ascending) q"1" else q"-1"}
        } else {
          $compare
        }
      """
    }

    val q"class $orderingName extends $orderingType { ..$body }" = reify {
      class SpecificOrdering extends Ordering[Row] {
        val o = ordering
      }
    }.tree.children.head

    val code = q"""
      class $orderingName extends $orderingType {
        ..$body
        def compare(a: $rowType, b: $rowType): Int = {
          var i: $rowType = null // Holds current row being evaluated.
          ..$comparisons
          return 0
        }
      }
      new $orderingName()
      """
    logDebug(s"Generated Ordering: $code")
    toolBox.eval(code).asInstanceOf[Ordering[Row]]
  }
}

可见动态字节码技术中不涉及虚函数的调用,其本质就是scala的反射机制。关于虚调用为什么耗时的原因如下:

以具体的SQL语句 select a+b fromtable 为例进行说明,下面是它的解析过程: 
    1.调用虚函数Add.eval(),需确认Add两边数据类型 
    2.调用虚函数a.eval(),需要确认a的数据类型 
    3.确认a的数据类型是int,装箱 
    4.调用虚函数b.eval(),需确认b的数据类型 
    5.确认b的数据类型是int,装箱 
    6.调用int类型的add 
    7.返回装箱后的计算结果 
    从上面的步骤可以看出,一条SQL语句的解析需要进行多次虚函数的调用。我们知道,虚函数的调用会极大的降低效率。那么,虚函数的调用为什么会影响效率呢? 
    有人答案是:虚函数调用会进行一次间接寻址过程。事实上这一步间接寻址真的会显著降低运行效率?显然不是。 
    流水线的打断才是真正降低效率的原因。 
    我们知道,虚函数的调用时是运行时多态,意思就是在编译期你是无法知道虚函数的具体调用。设想一下,如果说不是虚函数,那么在编译时期,其相对地址是确定的,编译器可以直接生成jmp/invoke指令; 如果是虚函数,多出来的一次查找vtable所带来的开销,倒是次要的,关键在于,这个函数地址是动态的,譬如 取到的地址在eax里,则在call eax之后的那些已经被预取进入流水线的所有指令都将失效。流水线越长,一次分支预测失败的代价也就越大,如下所示: 
   pf->test 
    001E146D mov eax,dword ptr[pf] 
    011E1470 mov edx,dword,ptr[eax] 
    011E1472 mov esi,esp 
    011E1474 mov ecx,dword ptr[pf] 
    011E1477 mov eax,dword ptr[edx] 
    011E1479 eax <-----------------------分支预测失败 
    011E147B cmp esi esp 
    011E147D @ILT+355(__RTC_CheckEsp)(11E1168h) 

你可能感兴趣的:(源码解析,Spark,Spark,SQL,1.4.1,详解)