本文主要介绍“PostgreSQL中make_rel_from_joinlist函数的分析”。在日常操作中，相信很多人对PostgreSQL中make_rel_from_joinlist函数的分析有所怀疑。边肖查阅了各种资料，整理出简单易用的操作方法，希望能帮你解答“PostgreSQL中make_rel_from_joinlist函数的解析”的疑惑接下来，请和边肖一起学习！
00-1010 make _ rel _ from _ joinlist函数根据主函数中调用解构_jointree函数生成的连接关系链表，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。
动态规划算法standard_join_search函数和遗传算法的实现将在后面的章节中再次介绍。
/*
*make_rel_from_joinlist
* Buildaccesspathsusinga使用一个' joinlist '来引导joinpathsearch。
*根据解构_jointree函数构造的连接列表生成连接路径。
*关于*joinlist的详细数据结构，请参考解构_jointree函数注释。
*
*请参见commenttsforecontstruct _ joint REE()了解定义
*数据结构。
*/
staticRelOptInfo*
make _ rel _ from _ join List(PlannerInfo * root，List*joinlist)
{
int levels _ required；
List * initial _ rels
ListCell * jl
/*
* countennumberofchildjoinlistnode。这是未来的发展方向
*动态编程算法我们必须使用一种方法
*加入子节点。
*计算joinlist链接列表中的节点数。
*确定使用的算法(动态规划算法vs遗传算法)，如数量阈值，则考虑所有连接模式。
*/
levels _ need=list _ length(join list)；
if(levels _ required=0)
returnNULL
nbsp; /* 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}
到此，关于“PostgreSQL中make_rel_from_joinlist函数分析”的学习就结束了，希望能够解决大家的疑惑。理论与实践的搭配能更好的帮助大家学习，快去试试吧！若想继续学习更多相关知识，请继续关注网站，小编会继续努力为大家带来更多实用的文章！
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