1. 文章开始之前
先附上一句SQL，使用tpc-ds的表结构，我们围绕这句SQL讲。
- SQL：
SQL> select avg(cs_ext_discount_amt) from catalog_sales, date_dim where d_date between '1999-02-22' and cast('1999-05-22' as date) and d_date_sk = cs_sold_date_sk group by cs_sold_date_sk;
- 逻辑计划：
Aggregate [cs_sold_date_sk#24], [cast((avg(UnscaledValue(cs_ext_discount_amt#46)) / 100.0) as decimal(11,6)) AS avg(cs_ext_discount_amt)#149] +- Project [cs_sold_date_sk#24, cs_ext_discount_amt#46] +- Join Inner, (d_date_sk#58 = cs_sold_date_sk#24) :- Project [cs_sold_date_sk#24, cs_ext_discount_amt#46] : +- Filter isnotnull(cs_sold_date_sk#24) : +- Relation[cs_sold_date_sk#24,cs_sold_time_sk#25,cs_ship_date_sk#26,cs_bill_customer_sk#27,cs_bill_cdemo_sk#28,cs_bill_hdemo_sk#29,cs_bill_addr_sk#30,cs_ship_customer_sk#31,cs_ship_cdemo_sk#32,cs_ship_hdemo_sk#33,cs_ship_addr_sk#34,cs_call_center_sk#35,cs_catalog_page_sk#36,cs_ship_mode_sk#37,cs_warehouse_sk#38,cs_item_sk#39,cs_promo_sk#40,cs_order_number#41,cs_quantity#42,cs_wholesale_cost#43,cs_list_price#44,cs_sales_price#45,cs_ext_discount_amt#46,cs_ext_sales_price#47,... 10 more fields] +- Project [d_date_sk#58] +- Filter (((isnotnull(d_date#60) && (cast(d_date#60 as string) >= 1999-02-22)) && (d_date#60 <= 10733)) && isnotnull(d_date_sk#58)) +- Relation[d_date_sk#58,d_date_id#59,d_date#60,d_month_seq#61,d_week_seq#62,d_quarter_seq#63,d_year#64,d_dow#65,d_moy#66,d_dom#67,d_qoy#68,d_fy_year#69,d_fy_quarter_seq#70,d_fy_week_seq#71,d_day_name#72,d_quarter_name#73,d_holiday#74,d_weekend#75,d_following_holiday#76,d_first_dom#77,d_last_dom#78,d_same_day_ly#79,d_same_day_lq#80,d_current_day#81,... 4 more fields]
2. 物理计划源码分析
2.1 物理策略
def strategies: Seq[Strategy] = extraStrategies ++ ( FileSourceStrategy :: DataSourceStrategy :: DDLStrategy :: SpecialLimits :: Aggregation :: JoinSelection :: InMemoryScans :: BasicOperators :: Nil)
其中，extraStrategies是提供给外部人员可以自己添加的策略。调用这些strategies的代码如下：
// Collect physical plan candidates. val candidates = strategies.iterator.flatMap(_(plan))
将strategies逐个去应用在逻辑计划上，然后做flat操作，返回一个PhysicalPlan的iterator。那么每个策略什么作用？
2.1.1 FileSourceStrategy
一个针对Hadoop文件系统做的策略，当执行计划的底层Relation是HadoopFsRelation时会调用到，用来扫描文件。
2.1.2 DataSourceStrategy
Spark针对DataSource预定义了四种scan接口，TableScan、PrunedScan、PrunedFilteredScan、CatalystScan(其中CatalystScan是unstable的，也是不常用的)，如果开发者（用户）自己实现的DataSource是实现了这四种接口之一的，在scan到执行计划的底层Relation时，就会调用来扫描文件。
2.1.3 DDLStrategy（2.2中已经消失了，2.1中有）
会在create table的时候调用，因为后续版本不会存在，所以不做解释。
2.1.4 SpecialLimits
在Spark SQL中加limit n时候回调用到（如果不指定，Spark 默认也会limit 20），在源码中，会给每种case的limit节点的子节点使用PlanLater，这是个很神奇的东西下文会讲到。
2.1.5 Aggregation
顾名思义，执行聚合函数的策略。
2.1.6 JoinSelection
执行join的策略。Join的执行策略也同样分BroadcastJoin（也就是MapSideJoin），和ShuffledJoin，这个之后的文章会展开讲。
2.1.7 InMemoryScans
当数据在内存中被缓存过，就会用到该策略。
2.1.8 BasicOperators
一些基本操作的执行策略，如flatMap，sort，project等，但是实际上大都是给这些节点的子节点套上一个PlanLater。
2.2 PlanLater
Spark SQL物理计划里一个非常重要的概念。字面意思很好理解，就是之后再计划。那么经过以上策略逐个去执行以后，原来的逻辑计划会变成什么样呢？
ReturnAnswer +- GlobalLimit 21 +- LocalLimit 21 +- PlanLater Aggregate [cs_sold_date_sk#24], [cast((avg(UnscaledValue(cs_ext_discount_amt#46)) / 100.0) as decimal(11,6)) AS avg(cs_ext_discount_amt)#149] , Aggregate [cs_sold_date_sk#24], [cast((avg(UnscaledValue(cs_ext_discount_amt#46)) / 100.0) as decimal(11,6)) AS avg(cs_ext_discount_amt)#149] +- PlanLater Project [cs_sold_date_sk#24, cs_ext_discount_amt#46] , Project [cs_sold_date_sk#24, cs_ext_discount_amt#46] +- PlanLater Join Inner, (d_date_sk#58 = cs_sold_date_sk#24) :- PlanLater Project [cs_sold_date_sk#24, cs_ext_discount_amt#46] , Project [cs_sold_date_sk#24, cs_ext_discount_amt#46] : +- Filter isnotnull(cs_sold_date_sk#24) : +- Relation[cs_sold_date_sk#24,cs_sold_time_sk#25,cs_ship_date_sk#26,cs_bill_customer_sk#27,cs_bill_cdemo_sk#28,cs_bill_hdemo_sk#29,cs_bill_addr_sk#30,cs_ship_customer_sk#31,cs_ship_cdemo_sk#32,cs_ship_hdemo_sk#33,cs_ship_addr_sk#34,cs_call_center_sk#35,cs_catalog_page_sk#36,cs_ship_mode_sk#37,cs_warehouse_sk#38,cs_item_sk#39,cs_promo_sk#40,cs_order_number#41,cs_quantity#42,cs_wholesale_cost#43,cs_list_price#44,cs_sales_price#45,cs_ext_discount_amt#46,cs_ext_sales_price#47,... 10 more fields] +- PlanLater Project [d_date_sk#58] , Project [d_date_sk#58] +- Filter (((isnotnull(d_date#60) && (cast(d_date#60 as string) >= 1999-02-22)) && (d_date#60 <= 10733)) && isnotnull(d_date_sk#58)) +- Relation[d_date_sk#58,d_date_id#59,d_date#60,d_month_seq#61,d_week_seq#62,d_quarter_seq#63,d_year#64,d_dow#65,d_moy#66,d_dom#67,d_qoy#68,d_fy_year#69,d_fy_quarter_seq#70,d_fy_week_seq#71,d_day_name#72,d_quarter_name#73,d_holiday#74,d_weekend#75,d_following_holiday#76,d_first_dom#77,d_last_dom#78,d_same_day_ly#79,d_same_day_lq#80,d_current_day#81,... 4 more fields]
有什么差别呢？主要有二：
- 顶层多了个
ReturnAnswer和Limit节点 Aggregate、Project、Join节点都用了PlanLater
（其实Filter节点也是可以用PlanLater的，但是由于逻辑计划已经将Filter下推至底部，所以最底部的Project->Filter->Relation的三层节点是可以直接调用一个策略去执行的，因此只需要三层节点的最上层也就是Project节点使用PlanLater即可。）
言归正传，语法树顶部多了ReturnAnswer和Limit节点，很容易理解，Limit是Spark SQL默认限制行数，ReturnAnswer是将结果返回。那么加的PlanLater有什么作用？我的理解是，将物理计划分割成一段段，每一段物理计划会有其对应策略来执行。具体源码如下：
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 }
可以看到，经过策略迭代器和flat过后的candidates候选计划们（一般来说只有一个，是最顶层的planLater），然后收集placeholder（其实就是planlater），这个时候对placeholders进行迭代，并对每个placeholder的child plan递归调用plan方法。举例文章这句SQL，递归调用plan方法，得到每个placeholder及其child plan节点（也就是 case (candidatesWithPlaceholders, (placeholder, logicalPlan))这句话的placeholder和logicalPlan两个变量）如下：
placeholder： PlanLater Aggregate [cs_sold_date_sk#24], [cast((avg(UnscaledValue(cs_ext_discount_amt#46)) / 100.0) as decimal(11,6)) AS avg(cs_ext_discount_amt)#149] logicalPlan： Aggregate [cs_sold_date_sk#24], [cast((avg(UnscaledValue(cs_ext_discount_amt#46)) / 100.0) as decimal(11,6)) AS avg(cs_ext_discount_amt)#149] +- Project [cs_sold_date_sk#24, cs_ext_discount_amt#46] +- Join Inner, (d_date_sk#58 = cs_sold_date_sk#24) :- Project [cs_sold_date_sk#24, cs_ext_discount_amt#46] : +- Filter isnotnull(cs_sold_date_sk#24) : +- Relation[cs_sold_date_sk#24,cs_sold_time_sk#25,cs_ship_date_sk#26,cs_bill_customer_sk#27,cs_bill_cdemo_sk#28,cs_bill_hdemo_sk#29,cs_bill_addr_sk#30,cs_ship_customer_sk#31,cs_ship_cdemo_sk#32,cs_ship_hdemo_sk#33,cs_ship_addr_sk#34,cs_call_center_sk#35,cs_catalog_page_sk#36,cs_ship_mode_sk#37,cs_warehouse_sk#38,cs_item_sk#39,cs_promo_sk#40,cs_order_number#41,cs_quantity#42,cs_wholesale_cost#43,cs_list_price#44,cs_sales_price#45,cs_ext_discount_amt#46,cs_ext_sales_price#47,... 10 more fields] +- Project [d_date_sk#58] +- Filter (((isnotnull(d_date#60) && (cast(d_date#60 as string) >= 1999-02-22)) && (d_date#60 <= 10733)) && isnotnull(d_date_sk#58)) +- Relation[d_date_sk#58,d_date_id#59,d_date#60,d_month_seq#61,d_week_seq#62,d_quarter_seq#63,d_year#64,d_dow#65,d_moy#66,d_dom#67,d_qoy#68,d_fy_year#69,d_fy_quarter_seq#70,d_fy_week_seq#71,d_day_name#72,d_quarter_name#73,d_holiday#74,d_weekend#75,d_following_holiday#76,d_first_dom#77,d_last_dom#78,d_same_day_ly#79,d_same_day_lq#80,d_current_day#81,... 4 more fields]
PlanLater Project [cs_sold_date_sk#24, cs_ext_discount_amt#46] logicalPlan： Project [cs_sold_date_sk#24, cs_ext_discount_amt#46] +- Join Inner, (d_date_sk#58 = cs_sold_date_sk#24) :- Project [cs_sold_date_sk#24, cs_ext_discount_amt#46] : +- Filter isnotnull(cs_sold_date_sk#24) : +- Relation[cs_sold_date_sk#24,cs_sold_time_sk#25,cs_ship_date_sk#26,cs_bill_customer_sk#27,cs_bill_cdemo_sk#28,cs_bill_hdemo_sk#29,cs_bill_addr_sk#30,cs_ship_customer_sk#31,cs_ship_cdemo_sk#32,cs_ship_hdemo_sk#33,cs_ship_addr_sk#34,cs_call_center_sk#35,cs_catalog_page_sk#36,cs_ship_mode_sk#37,cs_warehouse_sk#38,cs_item_sk#39,cs_promo_sk#40,cs_order_number#41,cs_quantity#42,cs_wholesale_cost#43,cs_list_price#44,cs_sales_price#45,cs_ext_discount_amt#46,cs_ext_sales_price#47,... 10 more fields] +- Project [d_date_sk#58] +- Filter (((isnotnull(d_date#60) && (cast(d_date#60 as string) >= 1999-02-22)) && (d_date#60 <= 10733)) && isnotnull(d_date_sk#58)) +- Relation[d_date_sk#58,d_date_id#59,d_date#60,d_month_seq#61,d_week_seq#62,d_quarter_seq#63,d_year#64,d_dow#65,d_moy#66,d_dom#67,d_qoy#68,d_fy_year#69,d_fy_quarter_seq#70,d_fy_week_seq#71,d_day_name#72,d_quarter_name#73,d_holiday#74,d_weekend#75,d_following_holiday#76,d_first_dom#77,d_last_dom#78,d_same_day_ly#79,d_same_day_lq#80,d_current_day#81,... 4 more fields]
placeholder: PlanLater Join Inner, (d_date_sk#58 = cs_sold_date_sk#24) logicalPlan： Join Inner, (d_date_sk#58 = cs_sold_date_sk#24) :- Project [cs_sold_date_sk#24, cs_ext_discount_amt#46] : +- Filter isnotnull(cs_sold_date_sk#24) : +- Relation[cs_sold_date_sk#24,cs_sold_time_sk#25,cs_ship_date_sk#26,cs_bill_customer_sk#27,cs_bill_cdemo_sk#28,cs_bill_hdemo_sk#29,cs_bill_addr_sk#30,cs_ship_customer_sk#31,cs_ship_cdemo_sk#32,cs_ship_hdemo_sk#33,cs_ship_addr_sk#34,cs_call_center_sk#35,cs_catalog_page_sk#36,cs_ship_mode_sk#37,cs_warehouse_sk#38,cs_item_sk#39,cs_promo_sk#40,cs_order_number#41,cs_quantity#42,cs_wholesale_cost#43,cs_list_price#44,cs_sales_price#45,cs_ext_discount_amt#46,cs_ext_sales_price#47,... 10 more fields] +- Project [d_date_sk#58] +- Filter (((isnotnull(d_date#60) && (cast(d_date#60 as string) >= 1999-02-22)) && (d_date#60 <= 10733)) && isnotnull(d_date_sk#58)) +- Relation[d_date_sk#58,d_date_id#59,d_date#60,d_month_seq#61,d_week_seq#62,d_quarter_seq#63,d_year#64,d_dow#65,d_moy#66,d_dom#67,d_qoy#68,d_fy_year#69,d_fy_quarter_seq#70,d_fy_week_seq#71,d_day_name#72,d_quarter_name#73,d_holiday#74,d_weekend#75,d_following_holiday#76,d_first_dom#77,d_last_dom#78,d_same_day_ly#79,d_same_day_lq#80,d_current_day#81,... 4 more fields]
placeholder: PlanLater Project [cs_sold_date_sk#24, cs_ext_discount_amt#46] logicalPlan： Project [cs_sold_date_sk#24, cs_ext_discount_amt#46] +- Filter isnotnull(cs_sold_date_sk#24) +- Relation[cs_sold_date_sk#24,cs_sold_time_sk#25,cs_ship_date_sk#26,cs_bill_customer_sk#27,cs_bill_cdemo_sk#28,cs_bill_hdemo_sk#29,cs_bill_addr_sk#30,cs_ship_customer_sk#31,cs_ship_cdemo_sk#32,cs_ship_hdemo_sk#33,cs_ship_addr_sk#34,cs_call_center_sk#35,cs_catalog_page_sk#36,cs_ship_mode_sk#37,cs_warehouse_sk#38,cs_item_sk#39,cs_promo_sk#40,cs_order_number#41,cs_quantity#42,cs_wholesale_cost#43,cs_list_price#44,cs_sales_price#45,cs_ext_discount_amt#46,cs_ext_sales_price#47,... 10 more fields]
placeholder: PlanLater Project [d_date_sk#58] logicalPlan: Project [d_date_sk#58] +- Filter (((isnotnull(d_date#60) && (cast(d_date#60 as string) >= 1999-02-22)) && (d_date#60 <= 10733)) && isnotnull(d_date_sk#58)) +- Relation[d_date_sk#58,d_date_id#59,d_date#60,d_month_seq#61,d_week_seq#62,d_quarter_seq#63,d_year#64,d_dow#65,d_moy#66,d_dom#67,d_qoy#68,d_fy_year#69,d_fy_quarter_seq#70,d_fy_week_seq#71,d_day_name#72,d_quarter_name#73,d_holiday#74,d_weekend#75,d_following_holiday#76,d_first_dom#77,d_last_dom#78,d_same_day_ly#79,d_same_day_lq#80,d_current_day#81,... 4 more fields]
那么可以看到，递归到最底处，就是project->filter->relation的三层节点组合，由于我实际是重写过了DataSource，这个时候会调用
DataSourceStrategy，去读取获取数据，然后递归逐个返回根据每个planLater分割点会有对应的策略去对数据进行相应的操作。-----------------------------------------------
链接：https://www.jianshu.com/p/2a869e90e299
来源：简书
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