Spark SQL(7)- physicalPlan 物理计划
Spark SQL(7)- physicalPlan 物理计划
spark的物理计划是将得到的逻辑算子树进一步的处理，得到针对RDD的一系列操作的集合，之后提交作业到spark集群。
spark物理计划的生成主要经历了一下的步骤：
- 应用sparkPlanner中定义的各种策略(strategy)， 这里sparkPlanner是SparkStrategy的子类，sparkPlanner中的各种策略都是在SparkStrategy中定义的，这里面还有一个PlanLater类型的代表叶子节点的sparkplan，其主要的作用就是延迟解析，这个在后面应用规则的时候会使用到；
- 选取一个物理计划，目前直接在返回的迭代器里面获取了第一个元素
- 准备物理计划，这个过程中会针对拿到的物理计划进行一些分区、排序等方面的处理，这一步也是根据各种各种定义好的规则来进行的。
SparkPlanner简介
sparkPlanner可以理解为物理计划针对RDD操作的描述，一般分为几大类：
-
LeafExecNode 叶子节点 主要和数据源相关，用户创建RDD
-
UnaryExecNode 一元节点 主要是针对RDD的转换操作
-
BinaryExecNode 二元节点 join操作就属于这类
- 其他类型的节点
这里面有几个重要的成员变量和方法：
- metrics指标信息；
-
outputPartitioning: Partitioning以及outputOrdering: Seq[SortOrder] 描述了分区和排序的信息，子类都有各自的实现；
-
execute和doExecute方法，sparkPlanner对外提供了统一的调用触发RDD的方法execute，但是实际子类继承实现的doExecute方法。
sparkPlan物理计划的生成
sparkPlan的生成是在QueryExecution.scala中
lazy val sparkPlan: SparkPlan = {
SparkSession.setActiveSession(sparkSession)
// TODO: We use next(), i.e. take the first plan returned by the planner, here for now,
// but we will implement to choose the best plan.
planner.plan(ReturnAnswer(optimizedPlan)).next()
}
这里调用了Queryplan.plan方法：
def strategies: Seq[GenericStrategy[PhysicalPlan]]
def plan(plan: LogicalPlan): Iterator[PhysicalPlan] = {
// Obviously a lot to do here still...
// Collect physical plan candidates.
val candidates = strategies.iterator.flatMap(_(plan))
// The candidates may contain placeholders marked as [[planLater]],
// so try to replace them by their child plans.
val plans = candidates.flatMap { candidate =>
val placeholders = collectPlaceholders(candidate)
if (placeholders.isEmpty) {
// Take the candidate as is because it does not contain placeholders.
Iterator(candidate)
} else {
// Plan the logical plan marked as [[planLater]] and replace the placeholders.
placeholders.iterator.foldLeft(Iterator(candidate)) {
case (candidatesWithPlaceholders, (placeholder, logicalPlan)) =>
// Plan the logical plan for the placeholder.
val childPlans = this.plan(logicalPlan)
candidatesWithPlaceholders.flatMap { candidateWithPlaceholders =>
childPlans.map { childPlan =>
// Replace the placeholder by the child plan
candidateWithPlaceholders.transformUp {
case p if p == placeholder => childPlan
}
}
}
}
}
}
val pruned = prunePlans(plans)
assert(pruned.hasNext, s"No plan for $plan")
pruned
}
QueryPlan有点类似spark逻辑计划阶段的RulExecutor的角色，在QueryPlan同样定义了针对逻辑计划转化为物理计划的规则(strategy)，但是具体的规则策略需要子类实现（sparkPlanner）；因此类似逻辑计划阶段，我们主要关注sparkplanner中重新定义的策略规则即可，但是在此之前上述QueryPlan.plan方法的逻辑还是有必要理一理的这样也利于理解后面的规则，plan其实就是事先定义好的各种规则，但是在应用规则的时候，不一定一次性全部解析完毕，所以需要上面提到的PlanLater节点来占位，这么做之后，在一次迭代之后，会统计PlanLater节点，之后替换成其子节点，继续应用plan方法，直到所有的节点都解析完毕，此时也就相当于物理计划转换完成。下面简单看下sparkPlanner中定义的strategy有哪些：
override def strategies: Seq[Strategy] =
experimentalMethods.extraStrategies ++
extraPlanningStrategies ++ (
DataSourceV2Strategy ::
FileSourceStrategy ::
DataSourceStrategy(conf) ::
SpecialLimits ::
Aggregation ::
JoinSelection ::
InMemoryScans ::
BasicOperators :: Nil)
这里面根据名字大概能明白策略的含义，数据源相关：DataSourceV2Strategy和DataSourceStrategy(conf) 关于他俩个区别感兴趣的同学可以自己研究下，文件数据源：FileSource，聚合：Aggregation，连接: JoinSelection 等等。
以上之后相当于生成了物理计划，之后获取第一条便是后面计算需要的物理计划了，但是在真正提交之前，还有一步就是prepareForExecution，就是准备的操作，其主要的目的是为了优化物理计划，使之满足shuffle和内部行格式。
/**
* Prepares a planned [[SparkPlan]] for execution by inserting shuffle operations and internal
* row format conversions as needed.
*/
protected def prepareForExecution(plan: SparkPlan): SparkPlan = {
preparations.foldLeft(plan) { case (sp, rule) => rule.apply(sp) }
}
/** A sequence of rules that will be applied in order to the physical plan before execution. */
protected def preparations: Seq[Rule[SparkPlan]] = Seq(
python.ExtractPythonUDFs,
PlanSubqueries(sparkSession),
EnsureRequirements(sparkSession.sessionState.conf),
CollapseCodegenStages(sparkSession.sessionState.conf),
ReuseExchange(sparkSession.sessionState.conf),
ReuseSubquery(sparkSession.sessionState.conf))
上面的preparations定义了各种准备阶段的规则，有关于python-UDF函数的规则、子查询、确保分区排序准确、代码生成等规则。这里主要看下EnsureRequirements规则，这个规则主要是通过添加Exchange节点保证分区和排序的准确。
private def ensureDistributionAndOrdering(operator: SparkPlan): SparkPlan = {
val requiredChildDistributions: Seq[Distribution] = operator.requiredChildDistribution
val requiredChildOrderings: Seq[Seq[SortOrder]] = operator.requiredChildOrdering
var children: Seq[SparkPlan] = operator.children
assert(requiredChildDistributions.length == children.length)
assert(requiredChildOrderings.length == children.length)
// Ensure that the operator's children satisfy their output distribution requirements.
children = children.zip(requiredChildDistributions).map {
// 1、分区数相同、同时为UnspecifiedDistribution或者为alltuple 并且分区数为1
case (child, distribution) if child.outputPartitioning.satisfies(distribution) =>
child
case (child, BroadcastDistribution(mode)) =>
BroadcastExchangeExec(mode, child)
case (child, distribution) =>
val numPartitions = distribution.requiredNumPartitions
.getOrElse(defaultNumPreShufflePartitions)
ShuffleExchangeExec(distribution.createPartitioning(numPartitions), child)
}
// Get the indexes of children which have specified distribution requirements and need to have
// same number of partitions.
val childrenIndexes = requiredChildDistributions.zipWithIndex.filter {
case (UnspecifiedDistribution, _) => false
case (_: BroadcastDistribution, _) => false
case _ => true
}.map(_._2)
val childrenNumPartitions =
childrenIndexes.map(children(_).outputPartitioning.numPartitions).toSet
if (childrenNumPartitions.size > 1) {
// Get the number of partitions which is explicitly required by the distributions.
val requiredNumPartitions = {
val numPartitionsSet = childrenIndexes.flatMap {
index => requiredChildDistributions(index).requiredNumPartitions
}.toSet
assert(numPartitionsSet.size <= 1,
s"$operator have incompatible requirements of the number of partitions for its children")
numPartitionsSet.headOption
}
val targetNumPartitions = requiredNumPartitions.getOrElse(childrenNumPartitions.max)
children = children.zip(requiredChildDistributions).zipWithIndex.map {
case ((child, distribution), index) if childrenIndexes.contains(index) =>
if (child.outputPartitioning.numPartitions == targetNumPartitions) {
child
} else {
val defaultPartitioning = distribution.createPartitioning(targetNumPartitions)
child match {
// If child is an exchange, we replace it with a new one having defaultPartitioning.
case ShuffleExchangeExec(_, c, _) => ShuffleExchangeExec(defaultPartitioning, c)
case _ => ShuffleExchangeExec(defaultPartitioning, child)
}
}
case ((child, _), _) => child
}
}
// Now, we need to add ExchangeCoordinator if necessary.
// Actually, it is not a good idea to add ExchangeCoordinators while we are adding Exchanges.
// However, with the way that we plan the query, we do not have a place where we have a
// global picture of all shuffle dependencies of a post-shuffle stage. So, we add coordinator
// at here for now.
// Once we finish https://issues.apache.org/jira/browse/SPARK-10665,
// we can first add Exchanges and then add coordinator once we have a DAG of query fragments.
children = withExchangeCoordinator(children, requiredChildDistributions)
// Now that we've performed any necessary shuffles, add sorts to guarantee output orderings:
children = children.zip(requiredChildOrderings).map { case (child, requiredOrdering) =>
// If child.outputOrdering already satisfies the requiredOrdering, we do not need to sort.
if (SortOrder.orderingSatisfies(child.outputOrdering, requiredOrdering)) {
child
} else {
SortExec(requiredOrdering, global = false, child = child)
}
}
operator.withNewChildren(children)
}
上面的操作主要可以分成下面几步：
- 添加ExChange节点，遍历子节点，会依次判断子节点的分区方式是否满足所需的数据分布，如果不满足，则考虑是否能以广播的形式来满足，如果不行的话就添加shuffleExChange节点，之后会查看所要求的子节点输出（requiredChildDistributions），是否有特殊需求，并且要求有相同的分区数，针对这类对子节点有特殊需求的情况，则会查看每个子节点的输出分区数目，如果匹配不做改变，不然会添加ShuffleExchangeExec节点。
- 添加ExchangeCoordinator节点，主要在sql执行的时候进行自适应查询优化;
- 查看requiredChildOrderings针对排序有特殊需求的添加SortExec节点
这里简述下ExchangeCoordinator：上述2已经在最新的spark源码里面去掉，感兴趣的同学可以下载最新代码研究，这里以ShuffleExchangeExec为例，在注册了ExchangeCoordinator节点之后，会调用doPrepare注册自己到ExchangeCoordinator上，之后在调用doExecute时候，会调用exchangeCoordinator.postShuffleRDD(this)，来拿到对应的shuffleRdd。那么没拿到的时候，协调器则会要求注册的Exchange上报pre-shuffle阶段的信息，根据上报的信息来确定post-shuffle的ShuffleRDD分发。
在经历准备阶段的物理计划之后就可以提交到集群运行了。
总结：到此spark从解析-> 逻辑计划 -> 物理计划阶段，整个大概的流程已经捋清楚了，但是具体的细节，需要在工作中或者平时有机会的时候再细致研究；如果对spark sql 感兴趣的同学可以看看《spark sql 内核剖析》挺不错的，但是需要结合源码来看，不然有的地方比较难理解。后面会再参考这本书和自己的理解整理聚合和连接的脉络。
