Postgresql源码（135）生成执行计划——Var的调整set_plan

Postgresql源码（135）生成执行计划——Var的调整set_plan_references

1 总结

set_plan_references主要有两个功能：
- 拉平：生成拉平后的RTE列表（add_rtes_to_flat_rtable）。
- 调整：调整前每一层计划中varno的引用都是相对于本层RTE的偏移量。放在一个整体计划后，需要指向一个统一的RTE列表，所以需要把varno调整下指向拉平后的RTE表。
- 例如下面计划中，RTE记录了6张表：
  - 1 → `{rtekind = RTE_RELATION, relid = 16656, inh = false, relkind = 114 ‘r’} -> student
  - 2 → `{rtekind = RTE_RELATION, relid = 16671, inh = false, relkind = 114 ‘r’} -> score
  - 3 → `{rtekind = RTE_JOIN, relid = 0, inh = false, relkind = 0 } -> {score join student}
  - 4 → `{rtekind = RTE_RELATION, relid = 16661, inh = false, relkind = 114 ‘r’} -> course
  - 5 → `{rtekind = RTE_JOIN, relid = 0, inh = false, relkind = 0 } -> {被优化掉的join course}
- Result节点的第一列是STUDENT.sname，他的varno一开始是1，varattno是2，显然他不应该直接引用RTE中的某一张表，因为Result节点的数据应该使用下面SORT节点中取出来的，所以：
  - varno被调整为-2（表示引用OUTTER节点也就是LEFT树返回的结果）
  - varattno被调整1，表示从结果中拿第一列。

explain
SELECT STUDENT.sname, random(), SCORE.degree
FROM STUDENT
LEFT JOIN SCORE ON STUDENT.sno = SCORE.sno
LEFT JOIN COURSE ON SCORE.cno = COURSE.cno
ORDER BY STUDENT.sno;QUERY PLAN
------------------------------------------------------------------------------------Result  (cost=182.67..213.27 rows=2040 width=54)->  Sort  (cost=182.67..187.77 rows=2040 width=46)Sort Key: student.sno->  Hash Right Join  (cost=34.75..70.53 rows=2040 width=46)Hash Cond: (score.sno = student.sno)->  Seq Scan on score  (cost=0.00..30.40 rows=2040 width=12)->  Hash  (cost=21.00..21.00 rows=1100 width=42)->  Seq Scan on student  (cost=0.00..21.00 rows=1100 width=42)

上面用例经过set_plan_references调整前后的完整例子：
在这里插入图片描述

2 数据结构

PlannerInfo

当前查询优化的状态，包含了当前查询的所有信息：

当前查询的目标列表（target list）
子句（例如，WHERE、GROUP BY、ORDER BY 等）
范围表（range table）
可用的索引信息
统计信息
子查询和参数信息
优化器的各种临时数据和结果

PlannerGlobal

全局结构，包含了跨多个查询级别的信息。例如一个包含子查询或CTE的查询中，每个子查询都会有自己的 PlannerInfo结构，会共享同一个PlannerGlobal。包含了：

全局范围表（finalrtable）
全局子计划列表
全局初始化计划列表
全局参数表达式列表
重写规则和其他全局状态信息

varno宏

#define    INNER_VAR		(-1)	/* reference to inner subplan */
#define    OUTER_VAR		(-2)	/* reference to outer subplan */
#define    INDEX_VAR		(-3)	/* reference to index column */
#define    ROWID_VAR		(-4)	/* row identity column during planning */

3 set_plan_references

1 计算全局flat_rtable

set_plan_references → add_rtes_to_flat_rtable

首先把引用的rtable全部拉平到一个级别，重新排列RTE。

具体在PlannerGlobal中构造全局范围表finalrtable，所有子PlannerInfo共享的一套RTE。

	p *root->glob->finalrtable
$7 = {type = T_List, length = 5, max_length = 5, elements = 0x3085520, initial_elements = 0x3085520}

add_rtes_to_flat_rtable后生成五个RTE：

RangeTblEntry {rtekind = RTE_RELATION, relid = 16656, inh = false, relkind = 114 'r'}
RangeTblEntry {rtekind = RTE_RELATION, relid = 16671, inh = false, relkind = 114 'r'}
RangeTblEntry {rtekind = RTE_JOIN, relid = 0, inh = false, relkind = 0}
RangeTblEntry {rtekind = RTE_RELATION, relid = 16661, inh = false, relkind = 114 'r'}
RangeTblEntry {rtekind = RTE_JOIN, relid = 0, inh = false, relkind = 0}

PlannerInfo→PlannerGlobal：

2 开始修正RTE的引用

set_plan_references → set_plan_refs

2.1 处理Result

set_plan_refs
- → case T_Result: … 处理result子树
- → plan->lefttree = set_plan_refs(root, plan->lefttree, rtoffset); 递归处理左树
- → plan->righttree = set_plan_refs(root, plan->righttree, rtoffset); 递归处理右树
根据内层的sort节点，重新排列result节点的三个var的varno和varattno，result已经是最外层节点了，当前使用到的var还是从sort节点继承的，需要修复下。

处理前 vs 处理后
在这里插入图片描述

set_plan_refs处理T_Result节点：

set_plan_refs......case T_Result:Result     *splan = (Result *) plan;if (splan->plan.lefttree != NULL)set_upper_references(root, plan, rtoffset);......// subplan 是 SORT节点// subplan->targetlist 中返回三列：STUDENT.sname, SCORE.degree,  STUDENT.sno// 注意缺了一列random函数subplan_itlist = build_tlist_index(subplan->targetlist);

subplan->targetlist
- varno = 1, varattno = 2, vartype = 1043
- varno = 2, varattno = 3, vartype = 23
- varno = 1, varattno = 1, vartype = 23
subplan_itlist
- subplan_itlist->tlist = subplan->targetlist
- subplan_itlist->vars[0] = {varno = 1, varattno = 2, resno = 1, varnullingrels = 0x0}
- subplan_itlist->vars[1] = {varno = 2, varattno = 3, resno = 2, varnullingrels = ...}
- subplan_itlist->vars[2] = {varno = 1, varattno = 1, resno = 3, varnullingrels = 0x0}

				foreach(l, plan->targetlist)...newexpr = fix_upper_expr(...)...// 计算完成plan->targetlist = output_targetlist;

output_targetlist
- expr = 0x308f0c8, resno = 1, resname = 0x2f4d670 "sname"
  - varno = OUTER_VAR = -2, varattno = 1, vartype = 1043
- expr = 0x308f1b8, resno = 2, resname = 0x2f4d7e8 "random"
  - funcid = 1598, funcresulttype = 701, funcretset = false
- expr = 0x308f258, resno = 3, resname = 0x2f4d928 "degree"
  - varno = OUTER_VAR = -2, varattno = 2, vartype = 23
- expr = 0x308f2f8, resno = 4, resname = 0x0, ressortgroupref = 1
  - varno = OUTER_VAR = -2, varattno = 3, vartype = 23

2.2 处理SORT

set_plan_refs
- → case T_Sort: … 处理sort子树set_dummy_tlist_references
- → plan->lefttree = set_plan_refs(root, plan->lefttree, rtoffset); 递归处理左树
- → plan->righttree = set_plan_refs(root, plan->righttree, rtoffset); 递归处理右树

排序只需要引用下面一层的结果即可。

// These plan types don't actually bother to evaluate their
// targetlists, because they just return their unmodified input
// tuples.  Even though the targetlist won't be used by the
// executor, we fix it up for possible use by EXPLAIN (not to
// mention ease of debugging --- wrong varnos are very confusing).set_dummy_tlist_references

2.3 处理Hash Right Join

set_plan_refs
- → case T_HashJoin: … 处理join子树set_join_references
- → plan->lefttree = set_plan_refs(root, plan->lefttree, rtoffset); 递归处理左树
- → plan->righttree = set_plan_refs(root, plan->righttree, rtoffset); 递归处理右树

在这里插入图片描述

4 用例

explain
SELECT STUDENT.sname, random(), SCORE.degree
FROM STUDENT
LEFT JOIN SCORE ON STUDENT.sno = SCORE.sno
LEFT JOIN COURSE ON SCORE.cno = COURSE.cno
ORDER BY STUDENT.sno;QUERY PLAN
------------------------------------------------------------------------------------Result  (cost=182.67..213.27 rows=2040 width=54)->  Sort  (cost=182.67..187.77 rows=2040 width=46)Sort Key: student.sno->  Hash Right Join  (cost=34.75..70.53 rows=2040 width=46)Hash Cond: (score.sno = student.sno)->  Seq Scan on score  (cost=0.00..30.40 rows=2040 width=12)->  Hash  (cost=21.00..21.00 rows=1100 width=42)->  Seq Scan on student  (cost=0.00..21.00 rows=1100 width=42)