这篇文章主要讲解了“PostgreSQL物理优化中的create_index_paths->choose_bitmap_and函数分析”，文中的讲解内容简单清晰，易于学习与理解，下面请大家跟着小编的思路慢慢深入，一起来研究和学习“PostgreSQL物理优化中的create_index_paths->choose_bitmap_and函数分析”吧！
该函数执行Bitmap AND操作后创建位图索引扫描访问路径(BitmapAndPath)节点。
下面是BitmapAnd访问路径的样例:
testdb=# explain verbose select t1.*
testdb-# from t_dwxx t1
testdb-# where (dwbh > '10000' and dwbh < '15000') AND (dwdz between 'DWDZ10000' and 'DWDZ15000');
QUERY PLAN
----------------------------------------------------------------------------------------------
Bitmap Heap Scan on public.t_dwxx t1 (cost=32.33..88.38 rows=33 width=20)
Output: dwmc, dwbh, dwdz
Recheck Cond: (((t1.dwbh)::text > '10000'::text) AND ((t1.dwbh)::text < '15000'::text) AND ((t1.dwdz)::text >= 'DWDZ10000'
::text) AND ((t1.dwdz)::text <= 'DWDZ15000'::text))
-> BitmapAnd (cost=32.33..32.33 rows=33 width=0) -->BitmapAnd
-> Bitmap Index Scan on t_dwxx_pkey (cost=0.00..13.86 rows=557 width=0)
Index Cond: (((t1.dwbh)::text > '10000'::text) AND ((t1.dwbh)::text < '15000'::text))
-> Bitmap Index Scan on idx_dwxx_dwdz (cost=0.00..18.21 rows=592 width=0)
Index Cond: (((t1.dwdz)::text >= 'DWDZ10000'::text) AND ((t1.dwdz)::text <= 'DWDZ15000'::text))
(8 rows)一、数据结构
Cost相关
注意:实际使用的参数值通过系统配置文件定义,而不是这里的常量定义!
typedef double Cost;
#define DEFAULT_SEQ_PAGE_COST 1.0 //顺序扫描page的成本
#define DEFAULT_RANDOM_PAGE_COST 4.0 //随机扫描page的成本
#define DEFAULT_CPU_TUPLE_COST 0.01 //处理一个元组的CPU成本
#define DEFAULT_CPU_INDEX_TUPLE_COST 0.005 //处理一个索引元组的CPU成本
#define DEFAULT_CPU_OPERATOR_COST 0.0025 //执行一次操作或函数的CPU成本
#define DEFAULT_PARALLEL_TUPLE_COST 0.1 //并行执行,从一个worker传输一个元组到另一个worker的成本
#define DEFAULT_PARALLEL_SETUP_COST 1000.0 //构建并行执行环境的成本
#define DEFAULT_EFFECTIVE_CACHE_SIZE 524288
double seq_page_cost = DEFAULT_SEQ_PAGE_COST;
double random_page_cost = DEFAULT_RANDOM_PAGE_COST;
double cpu_tuple_cost = DEFAULT_CPU_TUPLE_COST;
double cpu_index_tuple_cost = DEFAULT_CPU_INDEX_TUPLE_COST;
double cpu_operator_cost = DEFAULT_CPU_OPERATOR_COST;
double parallel_tuple_cost = DEFAULT_PARALLEL_TUPLE_COST;
double parallel_setup_cost = DEFAULT_PARALLEL_SETUP_COST;
int effective_cache_size = DEFAULT_EFFECTIVE_CACHE_SIZE;
Cost disable_cost = 1.0e10;//1后面10个0,通过设置一个巨大的成本,让优化器自动放弃此路径
int max_parallel_workers_per_gather = 2;//每次gather使用的worker数PathClauseUsage
typedef struct
{
Path *path;
List *quals;
List *preds;
Bitmapset *clauseids;
} PathClauseUsage;二、源码解读
choose_bitmap_and函数
create_index_paths->choose_bitmap_and函数,该函数给定非空的位图访问路径链表,执行AND操作后合并到一条路径中,最终得到位图索引扫描访问路径节点.
static Path *
choose_bitmap_and(PlannerInfo *root, RelOptInfo *rel, List *paths)
{
int npaths = list_length(paths);
PathClauseUsage **pathinfoarray;
PathClauseUsage *pathinfo;
List *clauselist;
List *bestpaths = NIL;
Cost bestcost = 0;
int i,
j;
ListCell *l;
Assert(npaths > 0);
if (npaths == 1)
return (Path *) linitial(paths);
pathinfoarray = (PathClauseUsage **)
palloc(npaths * sizeof(PathClauseUsage *));//数组
clauselist = NIL;
npaths = 0;
foreach(l, paths)//遍历paths
{
Path *ipath = (Path *) lfirst(l);
pathinfo = classify_index_clause_usage(ipath, &clauselist);//归类路径信息
for (i = 0; i < npaths; i++)
{
if (bms_equal(pathinfo->clauseids, pathinfoarray[i]->clauseids))
break;//只要发现子句集一样,就继续执行
}
if (i < npaths)//发现相同的
{
//相同的约束条件,只保留成本最低的
Cost ncost;
Cost ocost;
Selectivity nselec;
Selectivity oselec;
cost_bitmap_tree_node(pathinfo->path, &ncost, &nselec);//计算成本
cost_bitmap_tree_node(pathinfoarray[i]->path, &ocost, &oselec);
if (ncost < ocost)
pathinfoarray[i] = pathinfo;
}
else//没有发现条件一样的,添加到数组中
{
pathinfoarray[npaths++] = pathinfo;
}
}
if (npaths == 1)//结果只有一条,则返回之
return pathinfoarray[0]->path;
qsort(pathinfoarray, npaths, sizeof(PathClauseUsage *),
path_usage_comparator);//以索引访问成本排序现存路径
for (i = 0; i < npaths; i++)//遍历这些路径
{
Cost costsofar;
List *qualsofar;
Bitmapset *clauseidsofar;
ListCell *lastcell;
pathinfo = pathinfoarray[i];//PathClauseUsage结构体
paths = list_make1(pathinfo->path);//路径链表
costsofar = bitmap_scan_cost_est(root, rel, pathinfo->path);//当前的成本
qualsofar = list_concat(list_copy(pathinfo->quals),
list_copy(pathinfo->preds));
clauseidsofar = bms_copy(pathinfo->clauseids);
lastcell = list_head(paths);
for (j = i + 1; j < npaths; j++)//扫描后续的路径
{
Cost newcost;
pathinfo = pathinfoarray[j];
if (bms_overlap(pathinfo->clauseids, clauseidsofar))
continue;
if (pathinfo->preds)//部分索引?
{
bool redundant = false;
//单独检查每一个谓词
foreach(l, pathinfo->preds)
{
Node *np = (Node *) lfirst(l);
if (predicate_implied_by(list_make1(np), qualsofar, false))
{
redundant = true;
break;
}
}
if (redundant)
continue;
}
//尝试在路径中添加新路径，这样我们就可以估算成本
paths = lappend(paths, pathinfo->path);
newcost = bitmap_and_cost_est(root, rel, paths);//估算成本
if (newcost < costsofar)//新成本更低
{
costsofar = newcost;
qualsofar = list_concat(qualsofar,
list_copy(pathinfo->quals));//添加此条件
qualsofar = list_concat(qualsofar,
list_copy(pathinfo->preds));//添加此谓词
clauseidsofar = bms_add_members(clauseidsofar,
pathinfo->clauseids);//添加此子句ID
lastcell = lnext(lastcell);
}
else
{
paths = list_delete_cell(paths, lnext(lastcell), lastcell);//去掉新路径
}
Assert(lnext(lastcell) == NULL);
}
if (i == 0 || costsofar < bestcost)//单条路径或者取得最小的成本
{
bestpaths = paths;
bestcost = costsofar;
}
list_free(qualsofar);
}
if (list_length(bestpaths) == 1)
return (Path *) linitial(bestpaths);
return (Path *) create_bitmap_and_path(root, rel, bestpaths);//生成BitmapAndPath
}
//-------------------------------------------------------------------------- bitmap_scan_cost_est
static Cost
bitmap_scan_cost_est(PlannerInfo *root, RelOptInfo *rel, Path *ipath)
{
BitmapHeapPath bpath;
Relids required_outer;
required_outer = get_bitmap_tree_required_outer(ipath);
bpath.path.type = T_BitmapHeapPath;
bpath.path.pathtype = T_BitmapHeapScan;
bpath.path.parent = rel;
bpath.path.pathtarget = rel->reltarget;
bpath.path.param_info = get_baserel_parampathinfo(root, rel,
required_outer);
bpath.path.pathkeys = NIL;
bpath.bitmapqual = ipath;
bpath.path.parallel_workers = 0;
cost_bitmap_heap_scan(&bpath.path, root, rel,
bpath.path.param_info,
ipath,
get_loop_count(root, rel->relid, required_outer));//BitmapHeapPath计算成本
return bpath.path.total_cost;
}
//-------------------------------------------------------------------------- bitmap_and_cost_est
static Cost
bitmap_and_cost_est(PlannerInfo *root, RelOptInfo *rel, List *paths)
{
BitmapAndPath apath;
BitmapHeapPath bpath;
Relids required_outer;
apath.path.type = T_BitmapAndPath;
apath.path.pathtype = T_BitmapAnd;
apath.path.parent = rel;
apath.path.pathtarget = rel->reltarget;
apath.path.param_info = NULL;
apath.path.pathkeys = NIL;
apath.bitmapquals = paths;
cost_bitmap_and_node(&apath, root);
required_outer = get_bitmap_tree_required_outer((Path *) &apath);
bpath.path.type = T_BitmapHeapPath;
bpath.path.pathtype = T_BitmapHeapScan;
bpath.path.parent = rel;
bpath.path.pathtarget = rel->reltarget;
bpath.path.param_info = get_baserel_parampathinfo(root, rel,
required_outer);
bpath.path.pathkeys = NIL;
bpath.bitmapqual = (Path *) &apath;
bpath.path.parallel_workers = 0;
cost_bitmap_heap_scan(&bpath.path, root, rel,
bpath.path.param_info,
(Path *) &apath,
get_loop_count(root, rel->relid, required_outer));//BitmapHeapPath计算成本
return bpath.path.total_cost;
}
//-------------------------------------------------------------------------- create_bitmap_and_path
BitmapAndPath *
create_bitmap_and_path(PlannerInfo *root,
RelOptInfo *rel,
List *bitmapquals)
{
BitmapAndPath *pathnode = makeNode(BitmapAndPath);
pathnode->path.pathtype = T_BitmapAnd;
pathnode->path.parent = rel;
pathnode->path.pathtarget = rel->reltarget;
pathnode->path.param_info = NULL;
pathnode->path.parallel_aware = false;
pathnode->path.parallel_safe = rel->consider_parallel;
pathnode->path.parallel_workers = 0;
pathnode->path.pathkeys = NIL;
pathnode->bitmapquals = bitmapquals;
cost_bitmap_and_node(pathnode, root);//计算成本
return pathnode;
}
//----------------------------------------------------- cost_bitmap_and_node
void
cost_bitmap_and_node(BitmapAndPath *path, PlannerInfo *root)
{
Cost totalCost;
Selectivity selec;
ListCell *l;
totalCost = 0.0;
selec = 1.0;
foreach(l, path->bitmapquals)
{
Path *subpath = (Path *) lfirst(l);
Cost subCost;
Selectivity subselec;
cost_bitmap_tree_node(subpath, &subCost, &subselec);
selec *= subselec;
totalCost += subCost;
if (l != list_head(path->bitmapquals))
totalCost += 100.0 * cpu_operator_cost;
}
path->bitmapselectivity = selec;
path->path.rows = 0;
path->path.startup_cost = totalCost;
path->path.total_cost = totalCost;
}三、跟踪分析
测试脚本如下
select t1.*
from t_dwxx t1
where (dwbh > '10000' and dwbh < '15000') AND (dwdz between 'DWDZ10000' and 'DWDZ15000');启动gdb跟踪
(gdb) b choose_bitmap_and
Breakpoint 1 at 0x74e8c2: file indxpath.c, line 1372.
(gdb) c
Continuing.
Breakpoint 1, choose_bitmap_and (root=0x1666638, rel=0x1666a48, paths=0x166fdf0) at indxpath.c:1372
1372 int npaths = list_length(paths);输入参数
(gdb) p *paths
$1 = {type = T_List, length = 2, head = 0x166fe20, tail = 0x16706b8}
(gdb) p *(Node *)paths->head->data.ptr_value
$2 = {type = T_IndexPath}
(gdb) p *(Node *)paths->head->next->data.ptr_value
$3 = {type = T_IndexPath}
(gdb) set $p1=(IndexPath *)paths->head->data.ptr_value
(gdb) set $p2=(IndexPath *)paths->head->next->data.ptr_value
(gdb) p *$p1
$4 = {path = {type = T_IndexPath, pathtype = T_IndexScan, parent = 0x1666a48, pathtarget = 0x166d988, param_info = 0x0,
parallel_aware = false, parallel_safe = true, parallel_workers = 0, rows = 33, startup_cost = 0.28500000000000003,
total_cost = 116.20657683302848, pathkeys = 0x0}, indexinfo = 0x166e420, indexclauses = 0x166f528,
indexquals = 0x166f730, indexqualcols = 0x166f780, indexorderbys = 0x0, indexorderbycols = 0x0,
indexscandir = ForwardScanDirection, indextotalcost = 18.205000000000002, indexselectivity = 0.059246954595791879}
(gdb) p *$p2
$5 = {path = {type = T_IndexPath, pathtype = T_IndexScan, parent = 0x1666a48, pathtarget = 0x166d988, param_info = 0x0,
parallel_aware = false, parallel_safe = true, parallel_workers = 0, rows = 33, startup_cost = 0.28500000000000003,
total_cost = 111.33157683302848, pathkeys = 0x0}, indexinfo = 0x1666c58, indexclauses = 0x166fed0,
indexquals = 0x166ffc8, indexqualcols = 0x1670018, indexorderbys = 0x0, indexorderbycols = 0x0,
indexscandir = ForwardScanDirection, indextotalcost = 13.855, indexselectivity = 0.055688888888888899}paths中的第1个元素对应(dwbh > '10000' and dwbh < '15000') ,第2个元素对应(dwdz between 'DWDZ10000' and 'DWDZ15000')
(gdb) set $ri1=(RestrictInfo *)$p1->indexclauses->head->data.ptr_value
(gdb) set $tmp=(RelabelType *)((OpExpr *)$ri1->clause)->args->head->data.ptr_value
(gdb) p *(Var *)$tmp->arg
$17 = {xpr = {type = T_Var}, varno = 1, varattno = 3, vartype = 1043, vartypmod = 104, varcollid = 100, varlevelsup = 0,
varnoold = 1, varoattno = 3, location = 76}
(gdb) p *(Node *)((OpExpr *)$ri1->clause)->args->head->next->data.ptr_value
$18 = {type = T_Const}
(gdb) p *(Const *)((OpExpr *)$ri1->clause)->args->head->next->data.ptr_value
$19 = {xpr = {type = T_Const}, consttype = 25, consttypmod = -1, constcollid = 100, constlen = -1, constvalue = 23636608,
constisnull = false, constbyval = false, location = 89}开始遍历paths,提取子句条件并检测是否使用完全相同子句集的所有路径,只保留这些路径中成本最低的,这些路径被放入一个数组中进行qsort.
...
(gdb)
1444 npaths = 0;
(gdb)
1445 foreach(l, paths)
(gdb)收集信息到PathClauseUsage数组中
...
(gdb) n
1471 pathinfoarray[npaths++] = pathinfo;
(gdb)
1445 foreach(l, paths)
(gdb)
1476 if (npaths == 1)
(gdb) p npaths
$26 = 2
(gdb)按成本排序
(gdb) n
1480 qsort(pathinfoarray, npaths, sizeof(PathClauseUsage *),遍历路径,找到成本最低的AND group
1488 for (i = 0; i < npaths; i++)
(gdb) n
1495 pathinfo = pathinfoarray[i];
(gdb)
1496 paths = list_make1(pathinfo->path);
(gdb)
1497 costsofar = bitmap_scan_cost_est(root, rel, pathinfo->path);
(gdb)
1499 list_copy(pathinfo->preds));获取当前的成本,设置当前的条件子句
(gdb) p costsofar
$27 = 89.003250000000008
(gdb) n
1498 qualsofar = list_concat(list_copy(pathinfo->quals),执行AND操作(路径叠加),成本更低,调整当前成本和相关变量
(gdb) n
1531 newcost = bitmap_and_cost_est(root, rel, paths);
(gdb)
1532 if (newcost < costsofar)
(gdb) p newcost
$30 = 88.375456720095343
(gdb) n
1535 costsofar = newcost;
(gdb) n
1537 list_copy(pathinfo->quals));
(gdb)
1536 qualsofar = list_concat(qualsofar,
(gdb)
1539 list_copy(pathinfo->preds));处理下一个AND条件,单个AND条件比上一个条件成本高,保留原来的
1488 for (i = 0; i < npaths; i++)
(gdb)
1495 pathinfo = pathinfoarray[i];
(gdb)
1496 paths = list_make1(pathinfo->path);
(gdb)
1497 costsofar = bitmap_scan_cost_est(root, rel, pathinfo->path);
(gdb)
1499 list_copy(pathinfo->preds));
(gdb) p costsofar
$34 = 94.053250000000006
(gdb) n
1498 qualsofar = list_concat(list_copy(pathinfo->quals),
(gdb)
1500 clauseidsofar = bms_copy(pathinfo->clauseids);
(gdb)
1501 lastcell = list_head(paths);
(gdb)
1503 for (j = i + 1; j < npaths; j++)
(gdb)
1553 if (i == 0 || costsofar < bestcost)
(gdb) p i
$35 = 1
(gdb) p costsofar
$36 = 94.053250000000006
(gdb) p bestcost
$37 = 88.375456720095343
(gdb)构建BitmapAndPath,返回
(gdb) n
1563 if (list_length(bestpaths) == 1)
(gdb)
1565 return (Path *) create_bitmap_and_path(root, rel, bestpaths);
(gdb)
1566 }DONE!
(gdb) n
create_index_paths (root=0x1666638, rel=0x1666a48) at indxpath.c:337
337 bpath = create_bitmap_heap_path(root, rel, bitmapqual,感谢各位的阅读，以上就是“PostgreSQL物理优化中的create_index_paths->choose_bitmap_and函数分析”的内容了，经过本文的学习后，相信大家对PostgreSQL物理优化中的create_index_paths->choose_bitmap_and函数分析这一问题有了更深刻的体会，具体使用情况还需要大家实践验证。这里是亿速云，小编将为大家推送更多相关知识点的文章，欢迎关注！
