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PostgreSQL 源码解读(62)- 查询语句#47(make_one_rel函数#12-...

原创 PostgreSQL 作者:husthxd 时间:2018-10-09 12:01:53 0 删除 编辑

本节大体介绍了make_one_rel函数中的make_rel_from_joinlist函数,该函数根据连接关系链表(joinlist)构建连接路径。

一、源码解读

make_rel_from_joinlist函数根据连接关系链表(joinlist)通过外部算法(钩子函数)/遗传算法/动态规划算法构建连接路径,其中joinlist链表在主函数中已通过调用deconstruct_jointree函数生成.
动态规划算法的实现standard_join_search函数以及遗传算法在后续章节再行介绍.

 /*
  * make_rel_from_joinlist
  *    Build access paths using a "joinlist" to guide the join path search.
  *    依据deconstruct_jointree函数构造的joinlist生成连接路径.
  *    joinlist详细的数据结构参照deconstruct_jointree函数注释
  *
  * See comments for deconstruct_jointree() for definition of the joinlist
  * data structure.
  */
 static RelOptInfo *
 make_rel_from_joinlist(PlannerInfo *root, List *joinlist)
 {
     int         levels_needed;
     List       *initial_rels;
     ListCell   *jl;
 
     /*
      * Count the number of child joinlist nodes.  This is the depth of the
      * dynamic-programming algorithm we must employ to consider all ways of
      * joining the child nodes.
      * 计算joinlist链表中节点的个数。
      * 确定使用的算法(动态规划算法 vs 遗传算法),如个数<阈值,则考虑所有连接的方式。
      */
     levels_needed = list_length(joinlist);
 
     if (levels_needed <= 0)
         return NULL;            /* nothing to do? */
 
     /*
      * Construct a list of rels corresponding to the child joinlist nodes.
      * This may contain both base rels and rels constructed according to
      * sub-joinlists.
      * 构造与joinlist中元素相对应的rels链表。
      * 这可能包括base rels和通过子连接构造的base rels。
      */
     initial_rels = NIL;
     foreach(jl, joinlist)//遍历链表
     {
         Node       *jlnode = (Node *) lfirst(jl);
         RelOptInfo *thisrel;
 
         if (IsA(jlnode, RangeTblRef))//RTR
         {
             int         varno = ((RangeTblRef *) jlnode)->rtindex;
 
             thisrel = find_base_rel(root, varno);//根据编号找到相应的RelOptInfo
         }
         else if (IsA(jlnode, List))//链表
         {
             /* Recurse to handle subproblem */
             thisrel = make_rel_from_joinlist(root, (List *) jlnode);//递归调用,形成新的base rel
         }
         else//其他类型,出错
         {
             elog(ERROR, "unrecognized joinlist node type: %d",
                  (int) nodeTag(jlnode));
             thisrel = NULL;     /* keep compiler quiet */
         }
 
         initial_rels = lappend(initial_rels, thisrel);//添加到base rel链表中
     }
 
     if (levels_needed == 1)//连接链表只有1个元素
     {
         /*
          * Single joinlist node, so we're done.
          */
         return (RelOptInfo *) linitial(initial_rels);//直接返回
     }
     else//>1个元素
     {
         /*
          * Consider the different orders in which we could join the rels,
          * using a plugin, GEQO, or the regular join search code.
          * 考虑不同的连接顺序->使用外部算法/GEQO遗传算法/动态规划算法。
          *
          * We put the initial_rels list into a PlannerInfo field because
          * has_legal_joinclause() needs to look at it (ugly :-().
          *
          */
         root->initial_rels = initial_rels;
 
         if (join_search_hook)//调用钩子函数
             return (*join_search_hook) (root, levels_needed, initial_rels);
         else if (enable_geqo && levels_needed >= geqo_threshold)
             return geqo(root, levels_needed, initial_rels);//遗传算法
         else
             return standard_join_search(root, levels_needed, initial_rels);//动态规划算法
     }
 }
 
//----------------------------------------------------------------------- standard_join_search
 /*
  * standard_join_search
  *    Find possible joinpaths for a query by successively finding ways
  *    to join component relations into join relations.
  *    通过动态规划算法为查询语句构造连接路径.
  *
  * 'levels_needed' is the number of iterations needed, ie, the number of
  *      independent jointree items in the query.  This is > 1.
  * levels_needed-连接链表中的节点个数,>1
  *
  * 'initial_rels' is a list of RelOptInfo nodes for each independent
  *      jointree item.  These are the components to be joined together.
  *      Note that levels_needed == list_length(initial_rels).
  * initial_rels-与连接树每个元素相对应的RelOptInfo节点
  *
  * Returns the final level of join relations, i.e., the relation that is
  * the result of joining all the original relations together.
  * At least one implementation path must be provided for this relation and
  * all required sub-relations.
  * 返回连接的最终关系(最顶层的Relation):将所有原始关系连接在一起的最终结果。
  * 优化器为该关系及其所必需的子关系提供至少一个的实现路径。
  *
  * To support loadable plugins that modify planner behavior by changing the
  * join searching algorithm, we provide a hook variable that lets a plugin
  * replace or supplement this function.  Any such hook must return the same
  * final join relation as the standard code would, but it might have a
  * different set of implementation paths attached, and only the sub-joinrels
  * needed for these paths need have been instantiated.
  * 为了支持自定义函数,PG提供了一个钩子变量,允许外部插件替换或填充这个函数。
  * 任何这样的钩子都必须返回与PG标准函数相同的最终连接关系,
  * 但是它可能附加了一组不同的实现路径,并且只实例化了这些路径所需的子连接。
  *
  * Note to plugin authors: the functions invoked during standard_join_search()
  * modify root->join_rel_list and root->join_rel_hash.  If you want to do more
  * than one join-order search, you'll probably need to save and restore the
  * original states of those data structures.  See geqo_eval() for an example.
  */
 RelOptInfo *
 standard_join_search(PlannerInfo *root, int levels_needed, List *initial_rels)
 {
     int         lev;
     RelOptInfo *rel;
 
     /*
      * This function cannot be invoked recursively within any one planning
      * problem, so join_rel_level[] can't be in use already.
      */
     Assert(root->join_rel_level == NULL);//验证
 
     /*
      * We employ a simple "dynamic programming" algorithm: we first find all
      * ways to build joins of two jointree items, then all ways to build joins
      * of three items (from two-item joins and single items), then four-item
      * joins, and so on until we have considered all ways to join all the
      * items into one rel.
      * PG实现了一种简单的动态规划算法:首先为连接树中的两个Relation建立可能的连接路径
      * 然后为三个Relation建立所有可能的连接路径,以此类推直至已为所有的Relation建立了
      * 连接路径,从而得到最终的关系(final_rel)
      * 
      * root->join_rel_level[j] is a list of all the j-item rels.  Initially we
      * set root->join_rel_level[1] to represent all the single-jointree-item
      * relations.
      * 设置root->join_rel_level数组,[j]是所有j-item rels的链表(即1个item的放在[1]中)
      */
     root->join_rel_level = (List **) palloc0((levels_needed + 1) * sizeof(List *));
 
     root->join_rel_level[1] = initial_rels;//1个item对应的rel链表
 
     for (lev = 2; lev <= levels_needed; lev++)//构造2->N个item对应的rel链表
     {
         ListCell   *lc;
 
         /*
          * Determine all possible pairs of relations to be joined at this
          * level, and build paths for making each one from every available
          * pair of lower-level relations.
          * 确定在此级别上要连接的所有可能的关系对,并构建访问路径,
          * 以从每一对可用的较低级关系中往上创建关系。
          */
         join_search_one_level(root, lev);
 
         /*
          * Run generate_partitionwise_join_paths() and generate_gather_paths()
          * for each just-processed joinrel.  We could not do this earlier
          * because both regular and partial paths can get added to a
          * particular joinrel at multiple times within join_search_one_level.
          * 循环调用generate_partitionwise_join_paths()和generate_collect _paths()函数:
          * 参数为上一步骤生成的链表中的每个元素。
          * 由于常规路径和部分路径都可以在join_search_one_level中多次添加joinrel,因此在此处调用。
          *
          * After that, we're done creating paths for the joinrel, so run
          * set_cheapest().
          * 在此之后,PG已为joinrel(连接生成的新关系)创建了访问路径,因此可以调用函数set_cheapest
          *
          */
         foreach(lc, root->join_rel_level[lev])//遍历链表
         {
             rel = (RelOptInfo *) lfirst(lc);//新生成的关系
 
             /* Create paths for partitionwise joins. */
             generate_partitionwise_join_paths(root, rel);//创建partitionwise路径
 
             /*
              * Except for the topmost scan/join rel, consider gathering
              * partial paths.  We'll do the same for the topmost scan/join rel
              * once we know the final targetlist (see grouping_planner).
              */
             if (lev < levels_needed)
                 generate_gather_paths(root, rel, false);//并行执行需考虑gathering
 
             /* Find and save the cheapest paths for this rel */
             set_cheapest(rel);//从形成该joinrel的所有路径中找到成本最低的
 
 #ifdef OPTIMIZER_DEBUG
             debug_print_rel(root, rel);//DEBUG信息
 #endif
         }
     }
 
     /*
      * We should have a single rel at the final level.
      * 连接的最终结果,只有一个RelOptInfo
      */
     if (root->join_rel_level[levels_needed] == NIL)
         elog(ERROR, "failed to build any %d-way joins", levels_needed);
     Assert(list_length(root->join_rel_level[levels_needed]) == 1);
 
     rel = (RelOptInfo *) linitial(root->join_rel_level[levels_needed]);//获取最终结果
 
     root->join_rel_level = NULL;//重置
 
     return rel;//返回
 }

//----------------------------------------------------------------------- geqo
 /*
  * geqo
  *    solution of the query optimization problem
  *    similar to a constrained Traveling Salesman Problem (TSP)
  *    遗传算法:可参考TSP的求解算法.
  *    TSP-旅行推销员问题(最短路径问题):
  *        给定一系列城市和每对城市之间的距离,求解访问每一座城市一次并回到起始城市的最短回路。
  */
 
 RelOptInfo *
 geqo(PlannerInfo *root, int number_of_rels, List *initial_rels)
 {
     GeqoPrivateData private;
     int         generation;
     Chromosome *momma;
     Chromosome *daddy;
     Chromosome *kid;
     Pool       *pool;
     int         pool_size,
                 number_generations;
 
 #ifdef GEQO_DEBUG
     int         status_interval;
 #endif
     Gene       *best_tour;
     RelOptInfo *best_rel;
 
 #if defined(ERX)
     Edge       *edge_table;     /* list of edges */
     int         edge_failures = 0;
 #endif
 #if defined(CX) || defined(PX) || defined(OX1) || defined(OX2)
     City       *city_table;     /* list of cities */
 #endif
 #if defined(CX)
     int         cycle_diffs = 0;
     int         mutations = 0;
 #endif
 
 /* set up private information */
     root->join_search_private = (void *) &private;
     private.initial_rels = initial_rels;
 
 /* initialize private number generator */
     geqo_set_seed(root, Geqo_seed);
 
 /* set GA parameters */
     pool_size = gimme_pool_size(number_of_rels);
     number_generations = gimme_number_generations(pool_size);
 #ifdef GEQO_DEBUG
     status_interval = 10;
 #endif
 
 /* allocate genetic pool memory */
     pool = alloc_pool(root, pool_size, number_of_rels);
 
 /* random initialization of the pool */
     random_init_pool(root, pool);
 
 /* sort the pool according to cheapest path as fitness */
     sort_pool(root, pool);      /* we have to do it only one time, since all
                                  * kids replace the worst individuals in
                                  * future (-> geqo_pool.c:spread_chromo ) */
 
 #ifdef GEQO_DEBUG
     elog(DEBUG1, "GEQO selected %d pool entries, best %.2f, worst %.2f",
          pool_size,
          pool->data[0].worth,
          pool->data[pool_size - 1].worth);
 #endif
 
 /* allocate chromosome momma and daddy memory */
     momma = alloc_chromo(root, pool->string_length);
     daddy = alloc_chromo(root, pool->string_length);
 
 #if defined (ERX)
 #ifdef GEQO_DEBUG
     elog(DEBUG2, "using edge recombination crossover [ERX]");
 #endif
 /* allocate edge table memory */
     edge_table = alloc_edge_table(root, pool->string_length);
 #elif defined(PMX)
 #ifdef GEQO_DEBUG
     elog(DEBUG2, "using partially matched crossover [PMX]");
 #endif
 /* allocate chromosome kid memory */
     kid = alloc_chromo(root, pool->string_length);
 #elif defined(CX)
 #ifdef GEQO_DEBUG
     elog(DEBUG2, "using cycle crossover [CX]");
 #endif
 /* allocate city table memory */
     kid = alloc_chromo(root, pool->string_length);
     city_table = alloc_city_table(root, pool->string_length);
 #elif defined(PX)
 #ifdef GEQO_DEBUG
     elog(DEBUG2, "using position crossover [PX]");
 #endif
 /* allocate city table memory */
     kid = alloc_chromo(root, pool->string_length);
     city_table = alloc_city_table(root, pool->string_length);
 #elif defined(OX1)
 #ifdef GEQO_DEBUG
     elog(DEBUG2, "using order crossover [OX1]");
 #endif
 /* allocate city table memory */
     kid = alloc_chromo(root, pool->string_length);
     city_table = alloc_city_table(root, pool->string_length);
 #elif defined(OX2)
 #ifdef GEQO_DEBUG
     elog(DEBUG2, "using order crossover [OX2]");
 #endif
 /* allocate city table memory */
     kid = alloc_chromo(root, pool->string_length);
     city_table = alloc_city_table(root, pool->string_length);
 #endif
 
 
 /* my pain main part: */
 /* iterative optimization */
 
     for (generation = 0; generation < number_generations; generation++)
     {
         /* SELECTION: using linear bias function */
         geqo_selection(root, momma, daddy, pool, Geqo_selection_bias);
 
 #if defined (ERX)
         /* EDGE RECOMBINATION CROSSOVER */
         gimme_edge_table(root, momma->string, daddy->string, pool->string_length, edge_table);
 
         kid = momma;
 
         /* are there any edge failures ? */
         edge_failures += gimme_tour(root, edge_table, kid->string, pool->string_length);
 #elif defined(PMX)
         /* PARTIALLY MATCHED CROSSOVER */
         pmx(root, momma->string, daddy->string, kid->string, pool->string_length);
 #elif defined(CX)
         /* CYCLE CROSSOVER */
         cycle_diffs = cx(root, momma->string, daddy->string, kid->string, pool->string_length, city_table);
         /* mutate the child */
         if (cycle_diffs == 0)
         {
             mutations++;
             geqo_mutation(root, kid->string, pool->string_length);
         }
 #elif defined(PX)
         /* POSITION CROSSOVER */
         px(root, momma->string, daddy->string, kid->string, pool->string_length, city_table);
 #elif defined(OX1)
         /* ORDER CROSSOVER */
         ox1(root, momma->string, daddy->string, kid->string, pool->string_length, city_table);
 #elif defined(OX2)
         /* ORDER CROSSOVER */
         ox2(root, momma->string, daddy->string, kid->string, pool->string_length, city_table);
 #endif
 
 
         /* EVALUATE FITNESS */
         kid->worth = geqo_eval(root, kid->string, pool->string_length);
 
         /* push the kid into the wilderness of life according to its worth */
         spread_chromo(root, kid, pool);
 
 
 #ifdef GEQO_DEBUG
         if (status_interval && !(generation % status_interval))
             print_gen(stdout, pool, generation);
 #endif
 
     }
 
 
 #if defined(ERX) && defined(GEQO_DEBUG)
     if (edge_failures != 0)
         elog(LOG, "[GEQO] failures: %d, average: %d",
              edge_failures, (int) number_generations / edge_failures);
     else
         elog(LOG, "[GEQO] no edge failures detected");
 #endif
 
 #if defined(CX) && defined(GEQO_DEBUG)
     if (mutations != 0)
         elog(LOG, "[GEQO] mutations: %d, generations: %d",
              mutations, number_generations);
     else
         elog(LOG, "[GEQO] no mutations processed");
 #endif
 
 #ifdef GEQO_DEBUG
     print_pool(stdout, pool, 0, pool_size - 1);
 #endif
 
 #ifdef GEQO_DEBUG
     elog(DEBUG1, "GEQO best is %.2f after %d generations",
          pool->data[0].worth, number_generations);
 #endif
 
 
     /*
      * got the cheapest query tree processed by geqo; first element of the
      * population indicates the best query tree
      */
     best_tour = (Gene *) pool->data[0].string;
 
     best_rel = gimme_tree(root, best_tour, pool->string_length);
 
     if (best_rel == NULL)
         elog(ERROR, "geqo failed to make a valid plan");
 
     /* DBG: show the query plan */
 #ifdef NOT_USED
     print_plan(best_plan, root);
 #endif
 
     /* ... free memory stuff */
     free_chromo(root, momma);
     free_chromo(root, daddy);
 
 #if defined (ERX)
     free_edge_table(root, edge_table);
 #elif defined(PMX)
     free_chromo(root, kid);
 #elif defined(CX)
     free_chromo(root, kid);
     free_city_table(root, city_table);
 #elif defined(PX)
     free_chromo(root, kid);
     free_city_table(root, city_table);
 #elif defined(OX1)
     free_chromo(root, kid);
     free_city_table(root, city_table);
 #elif defined(OX2)
     free_chromo(root, kid);
     free_city_table(root, city_table);
 #endif
 
     free_pool(root, pool);
 
     /* ... clear root pointer to our private storage */
     root->join_search_private = NULL;
 
     return best_rel;
 }

二、跟踪分析

测试脚本以及执行计划如下:

testdb=# explain verbose select a.*,b.grbh,b.je 
testdb-# from t_dwxx a,
testdb-#     lateral (select t1.dwbh,t1.grbh,t2.je 
testdb(#      from t_grxx t1 
testdb(#           inner join t_jfxx t2 on t1.dwbh = a.dwbh and t1.grbh = t2.grbh) b
testdb-# where a.dwbh = '1001'
testdb-# order by b.dwbh;
                                              QUERY PLAN                                              
------------------------------------------------------------------------------------------------------
 Nested Loop  (cost=0.87..111.89 rows=10 width=37)
   Output: a.dwmc, a.dwbh, a.dwdz, t1.grbh, t2.je, t1.dwbh
   ->  Nested Loop  (cost=0.58..28.69 rows=10 width=29)
         Output: a.dwmc, a.dwbh, a.dwdz, t1.grbh, t1.dwbh
         ->  Index Scan using t_dwxx_pkey on public.t_dwxx a  (cost=0.29..8.30 rows=1 width=20)
               Output: a.dwmc, a.dwbh, a.dwdz
               Index Cond: ((a.dwbh)::text = '1001'::text)
         ->  Index Scan using idx_t_grxx_dwbh on public.t_grxx t1  (cost=0.29..20.29 rows=10 width=9)
               Output: t1.dwbh, t1.grbh, t1.xm, t1.xb, t1.nl
               Index Cond: ((t1.dwbh)::text = '1001'::text)
   ->  Index Scan using idx_t_jfxx_grbh on public.t_jfxx t2  (cost=0.29..8.31 rows=1 width=13)
         Output: t2.grbh, t2.ny, t2.je
         Index Cond: ((t2.grbh)::text = (t1.grbh)::text)

启动gdb跟踪

(gdb) b make_rel_from_joinlist
Breakpoint 1 at 0x73f0d3: file allpaths.c, line 2617.
(gdb) c
Continuing.

Breakpoint 1, make_rel_from_joinlist (root=0x176c750, joinlist=0x179e480) at allpaths.c:2617
2617        levels_needed = list_length(joinlist);

进入make_rel_from_joinlist函数,查看joinlist,链表中的Node为RangeTblRef,rindex分别是1/3/4

(gdb) p *joinlist
$1 = {type = T_List, length = 3, head = 0x17a0448, tail = 0x17a0408}
(gdb) p *(Node *)joinlist->head->data.ptr_value
$2 = {type = T_RangeTblRef}
(gdb) p *(RangeTblRef *)joinlist->head->data.ptr_value
$3 = {type = T_RangeTblRef, rtindex = 1}
(gdb) p *(RangeTblRef *)joinlist->head->next->data.ptr_value
$4 = {type = T_RangeTblRef, rtindex = 3}
(gdb) p *(RangeTblRef *)joinlist->head->next->next->data.ptr_value
$5 = {type = T_RangeTblRef, rtindex = 4}

链表中的Node个数为3,levels_needed=3

(gdb) n
2619        if (levels_needed <= 0)
(gdb) p levels_needed
$6 = 3

遍历链表,构造RelOptInfo,添加到initial_rels中

(gdb) 
2628        foreach(jl, joinlist)
...
(gdb) 
2637                thisrel = find_base_rel(root, varno);
(gdb) 
2651            initial_rels = lappend(initial_rels, thisrel);

完成遍历后,开始构造连接路径.
遗传算法的rels阈值为12(通过GUC参数配置)

2672            if (join_search_hook)
(gdb) 
2674            else if (enable_geqo && levels_needed >= geqo_threshold)
(gdb) 
2677                return standard_join_search(root, levels_needed, initial_rels);
(gdb) p geqo_threshold
$7 = 12

进入函数standard_join_search

(gdb) step
standard_join_search (root=0x176c750, levels_needed=3, initial_rels=0x17a6308) at allpaths.c:2733
2733        root->join_rel_level = (List **) palloc0((levels_needed + 1) * sizeof(List *));

开始构造2->N个item对应的rel链表

...
(gdb) 
2746            join_search_one_level(root, lev);
(gdb) n
2757            foreach(lc, root->join_rel_level[lev])

调用函数join_search_one_level,查看root->join_rel_level[j]

(gdb) p *root->join_rel_level[2]
$10 = {type = T_List, length = 2, head = 0x17a67a8, tail = 0x17a6ec0}

查看链表中的RelOptInfo

(gdb) p *(RelOptInfo *)root->join_rel_level[2]->head->data.ptr_value
$12 = {type = T_RelOptInfo, reloptkind = RELOPT_JOINREL, relids = 0x17a65d0, rows = 10, consider_startup = false, 
  consider_param_startup = false, consider_parallel = true, reltarget = 0x17a65e8, pathlist = 0x17a68a8, ppilist = 0x0, 
  partial_pathlist = 0x0, cheapest_startup_path = 0x0, cheapest_total_path = 0x0, cheapest_unique_path = 0x0, 
  cheapest_parameterized_paths = 0x0, direct_lateral_relids = 0x0, lateral_relids = 0x0, relid = 0, reltablespace = 0, 
  rtekind = RTE_JOIN, min_attr = 0, max_attr = 0, attr_needed = 0x0, attr_widths = 0x0, lateral_vars = 0x0, 
  lateral_referencers = 0x0, indexlist = 0x0, statlist = 0x0, pages = 0, tuples = 0, allvisfrac = 0, subroot = 0x0, 
  subplan_params = 0x0, rel_parallel_workers = -1, serverid = 0, userid = 0, useridiscurrent = false, fdwroutine = 0x0, 
  fdw_private = 0x0, unique_for_rels = 0x0, non_unique_for_rels = 0x0, baserestrictinfo = 0x0, baserestrictcost = {
    startup = 0, per_tuple = 0}, baserestrict_min_security = 4294967295, joininfo = 0x0, has_eclass_joins = true, 
  top_parent_relids = 0x0, part_scheme = 0x0, nparts = 0, boundinfo = 0x0, partition_qual = 0x0, part_rels = 0x0, 
  partexprs = 0x0, nullable_partexprs = 0x0, partitioned_child_rels = 0x0}
(gdb) p *(RelOptInfo *)root->join_rel_level[2]->head->next->data.ptr_value
$13 = {type = T_RelOptInfo, reloptkind = RELOPT_JOINREL, relids = 0x17a68d8, rows = 10, consider_startup = false, 
  consider_param_startup = false, consider_parallel = true, reltarget = 0x17a6cd0, pathlist = 0x17a7720, ppilist = 0x0, 
  partial_pathlist = 0x0, cheapest_startup_path = 0x0, cheapest_total_path = 0x0, cheapest_unique_path = 0x0, 
  cheapest_parameterized_paths = 0x0, direct_lateral_relids = 0x0, lateral_relids = 0x0, relid = 0, reltablespace = 0, 
  rtekind = RTE_JOIN, min_attr = 0, max_attr = 0, attr_needed = 0x0, attr_widths = 0x0, lateral_vars = 0x0, 
  lateral_referencers = 0x0, indexlist = 0x0, statlist = 0x0, pages = 0, tuples = 0, allvisfrac = 0, subroot = 0x0, 
  subplan_params = 0x0, rel_parallel_workers = -1, serverid = 0, userid = 0, useridiscurrent = false, fdwroutine = 0x0, 
  fdw_private = 0x0, unique_for_rels = 0x0, non_unique_for_rels = 0x0, baserestrictinfo = 0x0, baserestrictcost = {
    startup = 0, per_tuple = 0}, baserestrict_min_security = 4294967295, joininfo = 0x0, has_eclass_joins = true, 
  top_parent_relids = 0x0, part_scheme = 0x0, nparts = 0, boundinfo = 0x0, partition_qual = 0x0, part_rels = 0x0, 
  partexprs = 0x0, nullable_partexprs = 0x0, partitioned_child_rels = 0x0}

查看RelOptInfo中的relids
通过relids可知,第一个RelOptInfo是1/3号rel连接生成的Relation,第二个RelOptInfo是3/4号rel连接生成的Relation

(gdb) set $roi1=(RelOptInfo *)root->join_rel_level[2]->head->data.ptr_value
(gdb) set $roi2=(RelOptInfo *)root->join_rel_level[2]->head->next->data.ptr_value
(gdb) p *$roi1->relids
$16 = {nwords = 1, words = 0x17a65d4}
(gdb) p *$roi1->relids->words
$17 = 10  -->2 + 8 --> 1/3 号rel
(gdb) p *$roi2->relids->words
$18 = 24  -->8 + 16 --> 3/4号rel

查看第一个RelOptInfo中的pathlist,该链表有2个Node,类型均为T_NestPath(嵌套连接),总成本分别是28.69和4308.57

(gdb) p *$roi1->pathlist
$19 = {type = T_List, length = 2, head = 0x17a6888, tail = 0x17a6a10}
(gdb) p *(Node *)$roi1->pathlist->head->data.ptr_value
$20 = {type = T_NestPath}
(gdb) p *(NestPath *)$roi1->pathlist->head->data.ptr_value
$21 = {path = {type = T_NestPath, pathtype = T_NestLoop, parent = 0x17a63c0, pathtarget = 0x17a65e8, param_info = 0x0, 
    parallel_aware = false, parallel_safe = true, parallel_workers = 0, rows = 10, startup_cost = 0.57750000000000001, 
    total_cost = 28.688484322533327, pathkeys = 0x0}, jointype = JOIN_INNER, inner_unique = false, 
  outerjoinpath = 0x17a2638, innerjoinpath = 0x17a2908, joinrestrictinfo = 0x0}
(gdb) p *(Node *)$roi1->pathlist->head->next->data.ptr_value
$22 = {type = T_NestPath}
(gdb) p *(NestPath *)$roi1->pathlist->head->next->data.ptr_value
$23 = {path = {type = T_NestPath, pathtype = T_NestLoop, parent = 0x17a63c0, pathtarget = 0x17a65e8, param_info = 0x0, 
    parallel_aware = false, parallel_safe = true, parallel_workers = 0, rows = 10, startup_cost = 0.57750000000000001, 
    total_cost = 4308.5748727883229, pathkeys = 0x17a3650}, jointype = JOIN_INNER, inner_unique = false, 
  outerjoinpath = 0x17a3190, innerjoinpath = 0x17a68f0, joinrestrictinfo = 0x0}

查看第二个RelOptInfo中的pathlist,只有1个Node,类型为T_NestPath(嵌套连接),总成本为103.49

(gdb) p *$roi2->pathlist
$24 = {type = T_List, length = 1, head = 0x17a7700, tail = 0x17a7700}
(gdb) p *(Node *)$roi2->pathlist->head->data.ptr_value
$27 = {type = T_NestPath}
(gdb) p *(NestPath *)$roi2->pathlist->head->data.ptr_value
$28 = {path = {type = T_NestPath, pathtype = T_NestLoop, parent = 0x17a6ac0, pathtarget = 0x17a6cd0, param_info = 0x0, 
    parallel_aware = false, parallel_safe = true, parallel_workers = 0, rows = 10, startup_cost = 0.58499999999999996, 
    total_cost = 103.48598432253331, pathkeys = 0x0}, jointype = JOIN_INNER, inner_unique = false, 
  outerjoinpath = 0x17a2908, innerjoinpath = 0x17a5470, joinrestrictinfo = 0x0}

通过set_cheapest函数设置成本最低的访问路径,结果存储在cheapest_startup_path和cheapest_total_path中

(gdb) 
2773                set_cheapest(rel);
(gdb) 
2757            foreach(lc, root->join_rel_level[lev])
...
(gdb) p *$roi1
$35 = ..., cheapest_startup_path = 0x17a67f8, cheapest_total_path = 0x17a67f8, ...
(gdb) p *$roi2
$36 =..., cheapest_startup_path = 0x17a7750, cheapest_total_path = 0x17a7750, ...

继续循环,这时候lev=3

(gdb) n
2737        for (lev = 2; lev <= levels_needed; lev++)
(gdb) n
2746            join_search_one_level(root, lev);
(gdb) p lev
$38 = 3

得到3张表连接的final_rel

(gdb) p *root->join_rel_level[3]
$41 = {type = T_List, length = 1, head = 0x17a8090, tail = 0x17a8090}
(gdb) p *(RelOptInfo *)root->join_rel_level[3]->head->data.ptr_value
$42 = {type = T_RelOptInfo, reloptkind = RELOPT_JOINREL, relids = 0x17a74d8, rows = 10, consider_startup = false, 
  consider_param_startup = false, consider_parallel = true, reltarget = 0x17a7e40, pathlist = 0x17a8258, ppilist = 0x0, 
  partial_pathlist = 0x0, cheapest_startup_path = 0x0, cheapest_total_path = 0x0, cheapest_unique_path = 0x0, 
  cheapest_parameterized_paths = 0x0, direct_lateral_relids = 0x0, lateral_relids = 0x0, relid = 0, reltablespace = 0, 
  rtekind = RTE_JOIN, min_attr = 0, max_attr = 0, attr_needed = 0x0, attr_widths = 0x0, lateral_vars = 0x0, 
  lateral_referencers = 0x0, indexlist = 0x0, statlist = 0x0, pages = 0, tuples = 0, allvisfrac = 0, subroot = 0x0, 
  subplan_params = 0x0, rel_parallel_workers = -1, serverid = 0, userid = 0, useridiscurrent = false, fdwroutine = 0x0, 
  fdw_private = 0x0, unique_for_rels = 0x0, non_unique_for_rels = 0x0, baserestrictinfo = 0x0, baserestrictcost = {
    startup = 0, per_tuple = 0}, baserestrict_min_security = 4294967295, joininfo = 0x0, has_eclass_joins = false, 
  top_parent_relids = 0x0, part_scheme = 0x0, nparts = 0, boundinfo = 0x0, partition_qual = 0x0, part_rels = 0x0, 
  partexprs = 0x0, nullable_partexprs = 0x0, partitioned_child_rels = 0x0}

查看pathlist,只有1个元素,类型为NestPath,该访问路径成本为111.89

(gdb) set $roi=(RelOptInfo *)root->join_rel_level[3]->head->data.ptr_value
(gdb) p *$roi->pathlist
$44 = {type = T_List, length = 1, head = 0x17a8238, tail = 0x17a8238}
(gdb) p *(Node *)$roi->pathlist->head->data.ptr_value
$45 = {type = T_NestPath}
(gdb) p *(NestPath *)$roi->pathlist->head->data.ptr_value
$46 = {path = {type = T_NestPath, pathtype = T_NestLoop, parent = 0x17a7c30, pathtarget = 0x17a7e40, param_info = 0x0, 
    parallel_aware = false, parallel_safe = true, parallel_workers = 0, rows = 10, startup_cost = 0.87, 
    total_cost = 111.88848432253332, pathkeys = 0x0}, jointype = JOIN_INNER, inner_unique = false, 
  outerjoinpath = 0x17a67f8, innerjoinpath = 0x17a5470, joinrestrictinfo = 0x0}

获得最终结果

...
2792        return rel;
(gdb) p *rel
$47 = {type = T_RelOptInfo, reloptkind = RELOPT_JOINREL, relids = 0x17a74d8, rows = 10, consider_startup = false, 
  consider_param_startup = false, consider_parallel = true, reltarget = 0x17a7e40, pathlist = 0x17a8258, ppilist = 0x0, 
  partial_pathlist = 0x0, cheapest_startup_path = 0x17a8318, cheapest_total_path = 0x17a8318, cheapest_unique_path = 0x0, 
  cheapest_parameterized_paths = 0x17a89b0, direct_lateral_relids = 0x0, lateral_relids = 0x0, relid = 0, 
  reltablespace = 0, rtekind = RTE_JOIN, min_attr = 0, max_attr = 0, attr_needed = 0x0, attr_widths = 0x0, 
  lateral_vars = 0x0, lateral_referencers = 0x0, indexlist = 0x0, statlist = 0x0, pages = 0, tuples = 0, allvisfrac = 0, 
  subroot = 0x0, subplan_params = 0x0, rel_parallel_workers = -1, serverid = 0, userid = 0, useridiscurrent = false, 
  fdwroutine = 0x0, fdw_private = 0x0, unique_for_rels = 0x0, non_unique_for_rels = 0x0, baserestrictinfo = 0x0, 
  baserestrictcost = {startup = 0, per_tuple = 0}, baserestrict_min_security = 4294967295, joininfo = 0x0, 
  has_eclass_joins = false, top_parent_relids = 0x0, part_scheme = 0x0, nparts = 0, boundinfo = 0x0, partition_qual = 0x0, 
  part_rels = 0x0, partexprs = 0x0, nullable_partexprs = 0x0, partitioned_child_rels = 0x0}
(gdb) p *rel->cheapest_total_path
$48 = {type = T_NestPath, pathtype = T_NestLoop, parent = 0x17a7c30, pathtarget = 0x17a7e40, param_info = 0x0, 
  parallel_aware = false, parallel_safe = true, parallel_workers = 0, rows = 10, startup_cost = 0.87, 
  total_cost = 111.88848432253332, pathkeys = 0x0}  

DONE!

三、参考资料

allpaths.c
cost.h
costsize.c
PG Document:Query Planning

来自 “ ITPUB博客 ” ,链接:http://blog.itpub.net/6906/viewspace-2374839/,如需转载,请注明出处,否则将追究法律责任。

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