PostgreSQL源码解读（58）-查询语句#43（make_one_rel函数#8-B...
这一小节继续介绍查询物理优化中的create_index_paths->create_bitmap_heap_path函数,该函数创建位图堆扫描访问路径节点。
关于BitmapHeapScan的相关知识,请参照PostgreSQL DBA(6) - SeqScan vs IndexScan vs BitmapHeapScan这篇文章.
本节没有描述具体的Cost成本计算方法(公式)，后续再行详述。
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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; /* execution cost (in page-access units) */
/* defaults for costsize.c's Cost parameters */
/* NB: cost-estimation code should use the variables, not these constants! */
/* 注意:实际值通过系统配置文件定义,而不是这里的常量定义! */
/* If you change these, update backend/utils/misc/postgresql.sample.conf */
#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 /*先前已有介绍, measured in pages */
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
二、源码解读
create_bitmap_heap_path函数
create_index_paths->create_bitmap_heap_path函数,创建位图堆扫描访问路径节点.
/*
* create_bitmap_heap_path
* Creates a path node for a bitmap scan.
* 创建位图堆扫描访问路径节点
*
* 'bitmapqual' is a tree of IndexPath, BitmapAndPath, and BitmapOrPath nodes.
* bitmapqual-IndexPath, BitmapAndPath, and BitmapOrPath节点组成的树
* 'required_outer' is the set of outer relids for a parameterized path.
* required_outer-参数化路径中依赖的外部relids
* 'loop_count' is the number of repetitions of the indexscan to factor into
* estimates of caching behavior.
* loop_count-上一节已介绍
*
* loop_count should match the value used when creating the component
* IndexPaths.
*/
BitmapHeapPath *
create_bitmap_heap_path(PlannerInfo *root,
RelOptInfo *rel,
Path *bitmapqual,
Relids required_outer,
double loop_count,
int parallel_degree)
{
BitmapHeapPath *pathnode = makeNode(BitmapHeapPath);//创建节点
pathnode->path.pathtype = T_BitmapHeapScan;
pathnode->path.parent = rel;
pathnode->path.pathtarget = rel->reltarget;
pathnode->path.param_info = get_baserel_parampathinfo(root, rel,
required_outer);
pathnode->path.parallel_aware = parallel_degree > 0 ? true : false;
pathnode->path.parallel_safe = rel->consider_parallel;
pathnode->path.parallel_workers = parallel_degree;
pathnode->path.pathkeys = NIL; /* always unordered */
pathnode->bitmapqual = bitmapqual;
cost_bitmap_heap_scan(&pathnode->path, root, rel,
pathnode->path.param_info,
bitmapqual, loop_count);//成本估算
return pathnode;//返回结果
}
//-------------------------------------------------------- cost_bitmap_heap_scan
/*
* cost_bitmap_heap_scan
* Determines and returns the cost of scanning a relation using a bitmap
* index-then-heap plan.
* 确定并返回使用BitmapIndexScan和BitmapHeapScan的成本.
*
* 'baserel' is the relation to be scanned
* baserel-需扫描的Relation
* 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
* param_info-参数化信息
* 'bitmapqual' is a tree of IndexPaths, BitmapAndPaths, and BitmapOrPaths
* bitmapqual-位图条件表达式,IndexPath, BitmapAndPath, and BitmapOrPath节点组成的树
* 'loop_count' is the number of repetitions of the indexscan to factor into
* estimates of caching behavior
*
* Note: the component IndexPaths in bitmapqual should have been costed
* using the same loop_count.
*/
void
cost_bitmap_heap_scan(Path *path, PlannerInfo *root, RelOptInfo *baserel,
ParamPathInfo *param_info,
Path *bitmapqual, double loop_count)
{
Cost startup_cost = 0;//启动成本
Cost run_cost = 0;//执行成本
Cost indexTotalCost;//索引扫描总成本
QualCost qpqual_cost;//表达式成本
Cost cpu_per_tuple;
Cost cost_per_page;
Cost cpu_run_cost;
double tuples_fetched;
double pages_fetched;
double spc_seq_page_cost,
spc_random_page_cost;
double T;
/* Should only be applied to base relations */
Assert(IsA(baserel, RelOptInfo));
Assert(baserel->relid > 0);
Assert(baserel->rtekind == RTE_RELATION);
/* Mark the path with the correct row estimate */
if (param_info)
path->rows = param_info->ppi_rows;
else
path->rows = baserel->rows;
if (!enable_bitmapscan)//不允许位图扫描
startup_cost += disable_cost;//禁用之
pages_fetched = compute_bitmap_pages(root, baserel, bitmapqual,
loop_count, &indexTotalCost,
&tuples_fetched);//计算页面数
startup_cost += indexTotalCost;//启动成本为BitmapIndexScan的总成本
T = (baserel->pages > 1) ? (double) baserel->pages : 1.0;//页面数
/* Fetch estimated page costs for tablespace containing table. */
get_tablespace_page_costs(baserel->reltablespace,
&spc_random_page_cost,
&spc_seq_page_cost);//访问表空间页面成本
/*
* For small numbers of pages we should charge spc_random_page_cost
* apiece, while if nearly all the table's pages are being read, it's more
* appropriate to charge spc_seq_page_cost apiece. The effect is
* nonlinear, too. For lack of a better idea, interpolate like this to
* determine the cost per page.
* 对于少量的页面，每个页面的成本为spc_random_page_cost，
* 而如果几乎所有的页面都被读取，则每个页面的成本为spc_seq_page_cost。
* 这种影响也是非线性的。由于缺乏更好的方法，通过插值法确定每页的成本。
*/
if (pages_fetched >= 2.0)
cost_per_page = spc_random_page_cost -
(spc_random_page_cost - spc_seq_page_cost)
* sqrt(pages_fetched / T);
else
cost_per_page = spc_random_page_cost;
run_cost += pages_fetched * cost_per_page;//执行成本
/*
* Estimate CPU costs per tuple.
* 为每个元组估算CPU成本(Rechck步骤的成本)
*
* Often the indexquals don't need to be rechecked at each tuple ... but
* not always, especially not if there are enough tuples involved that the
* bitmaps become lossy. For the moment, just assume they will be
* rechecked always. This means we charge the full freight for all the
* scan clauses.
* 通常情况下，索引约束条件不需要在每个元组上重新检查,但现实并非如此理想，
* 尤其是当涉及到较多的元组时。就目前而言，
* 优化器会假设它们总是会被重新检查。这意味着我们需要为所有扫描条件计算成本。
*/
get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);//获取条件表达式
startup_cost += qpqual_cost.startup;//增加启动成本
cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple;//增加处理每个元组的CPU成本
cpu_run_cost = cpu_per_tuple * tuples_fetched;//CPU运行成本
/* Adjust costing for parallelism, if used. */
if (path->parallel_workers > 0)//是否并行?
{
double parallel_divisor = get_parallel_divisor(path);
/* The CPU cost is divided among all the workers. */
cpu_run_cost /= parallel_divisor;
path->rows = clamp_row_est(path->rows / parallel_divisor);
}
//计算最终成本
run_cost += cpu_run_cost;
/* tlist eval costs are paid per output row, not per tuple scanned */
startup_cost += path->pathtarget->cost.startup;
run_cost += path->pathtarget->cost.per_tuple * path->rows;
path->startup_cost = startup_cost;
path->total_cost = startup_cost + run_cost;
}
//--------------------------------------- compute_bitmap_pages
/*
* compute_bitmap_pages
*
* compute number of pages fetched from heap in bitmap heap scan.
* 计算页面数
*/
double
compute_bitmap_pages(PlannerInfo *root, RelOptInfo *baserel, Path *bitmapqual,
int loop_count, Cost *cost, double *tuple)
{
Cost indexTotalCost;
Selectivity indexSelectivity;
double T;
double pages_fetched;
double tuples_fetched;
double heap_pages;
long maxentries;
/*
* Fetch total cost of obtaining the bitmap, as well as its total
* selectivity.
* 获取位图的总成本，以及它的总选择性。
*/
cost_bitmap_tree_node(bitmapqual, &indexTotalCost, &indexSelectivity);
/*
* Estimate number of main-table pages fetched.
* 估算主表的页面数
*/
tuples_fetched = clamp_row_est(indexSelectivity * baserel->tuples);//计算总元组数
T = (baserel->pages > 1) ? (double) baserel->pages : 1.0;
/*
* For a single scan, the number of heap pages that need to be fetched is
* the same as the Mackert and Lohman formula for the case T <= b (ie, no
* re-reads needed).
* 对于单个扫描，需要获取的堆页面数量与T <= b(即不需要重新读取)的Mackert和Lohman公式相同。
*/
pages_fetched = (2.0 * T * tuples_fetched) / (2.0 * T + tuples_fetched);
/*
* Calculate the number of pages fetched from the heap. Then based on
* current work_mem estimate get the estimated maxentries in the bitmap.
* (Note that we always do this calculation based on the number of pages
* that would be fetched in a single iteration, even if loop_count > 1.
* That's correct, because only that number of entries will be stored in
* the bitmap at one time.)
* 计算从堆中读取的页面数.
* 根据当前的work_mem估算得到位图中粗略的最大访问入口(entries)。
* (请注意，我们总是根据单个迭代中获取的页面数来进行计算，
* 即使loop_count > 1也是如此。因为只有该数量的条目在位图中只存储一次。
*/
heap_pages = Min(pages_fetched, baserel->pages);//堆页面数
maxentries = tbm_calculate_entries(work_mem * 1024L);//位图最大入口数
if (loop_count > 1)
{
/*
* For repeated bitmap scans, scale up the number of tuples fetched in
* the Mackert and Lohman formula by the number of scans, so that we
* estimate the number of pages fetched by all the scans. Then
* pro-rate for one scan.
*/
pages_fetched = index_pages_fetched(tuples_fetched * loop_count,
baserel->pages,
get_indexpath_pages(bitmapqual),
root);
pages_fetched /= loop_count;
}
if (pages_fetched >= T)
pages_fetched = T;//数据字典中的页面数
else
pages_fetched = ceil(pages_fetched);
if (maxentries < heap_pages)//最大入口数小于堆页面数
{
double exact_pages;
double lossy_pages;
/*
* Crude approximation of the number of lossy pages. Because of the
* way tbm_lossify() is coded, the number of lossy pages increases
* very sharply as soon as we run short of memory; this formula has
* that property and seems to perform adequately in testing, but it's
* possible we could do better somehow.
* 粗略估计缺页的数目。由于tbm_lossify()的编码方式，
* 一旦内存不足，缺页的数量就会急剧增加;
* 这个公式有这个性质，在测试中表现得很好，但有可能做得更好。
*/
lossy_pages = Max(0, heap_pages - maxentries / 2);
exact_pages = heap_pages - lossy_pages;
/*
* If there are lossy pages then recompute the number of tuples
* processed by the bitmap heap node. We assume here that the chance
* of a given tuple coming from an exact page is the same as the
* chance that a given page is exact. This might not be true, but
* it's not clear how we can do any better.
* 如果存在缺页面，则重新计算位图堆节点处理的元组数量。
* 这里假设给定元组来自精确页面的概率与给定页面的概率相同。
* 但这可能不符合实际情况，但我们不清楚如何才能做得更好:(
*/
if (lossy_pages > 0)
tuples_fetched =
clamp_row_est(indexSelectivity *
(exact_pages / heap_pages) * baserel->tuples +
(lossy_pages / heap_pages) * baserel->tuples);
}
if (cost)
*cost = indexTotalCost;
if (tuple)
*tuple = tuples_fetched;
return pages_fetched;
}
//--------------------------- tbm_calculate_entries
/*
* tbm_calculate_entries
*
* Estimate number of hashtable entries we can have within maxbytes.
*/
long
tbm_calculate_entries(double maxbytes)
{
long nbuckets;
/*
* Estimate number of hashtable entries we can have within maxbytes. This
* estimates the hash cost as sizeof(PagetableEntry), which is good enough
* for our purpose. Also count an extra Pointer per entry for the arrays
* created during iteration readout.
* 估计maxbytes中可以包含的哈希表条目的数量。
* 这将散列成本估计为sizeof(PagetableEntry)，这已经足够好了。
* 还要为迭代读出期间创建的数组中每个条目计算额外的指针。
*/
nbuckets = maxbytes /
(sizeof(PagetableEntry) + sizeof(Pointer) + sizeof(Pointer));//桶数
nbuckets = Min(nbuckets, INT_MAX - 1); /* safety limit */
nbuckets = Max(nbuckets, 16); /* sanity limit */
return nbuckets;
}
//--------------------------- cost_bitmap_tree_node
/*
* cost_bitmap_tree_node
* Extract cost and selectivity from a bitmap tree node (index/and/or)
*/
void
cost_bitmap_tree_node(Path *path, Cost *cost, Selectivity *selec)
{
if (IsA(path, IndexPath))//索引访问路径
{
*cost = ((IndexPath *) path)->indextotalcost;
*selec = ((IndexPath *) path)->indexselectivity;
/*
* Charge a small amount per retrieved tuple to reflect the costs of
* manipulating the bitmap. This is mostly to make sure that a bitmap
* scan doesn't look to be the same cost as an indexscan to retrieve a
* single tuple.
* 对每个检索到的元组计算少量成本，以反映操作位图的成本。
* 这主要是为了确保位图扫描与索引扫描检索单个元组的成本不一样。
*/
*cost += 0.1 * cpu_operator_cost * path->rows;
}
else if (IsA(path, BitmapAndPath))//BitmapAndPath
{
*cost = path->total_cost;
*selec = ((BitmapAndPath *) path)->bitmapselectivity;
}
else if (IsA(path, BitmapOrPath))//BitmapOrPath
{
*cost = path->total_cost;
*selec = ((BitmapOrPath *) path)->bitmapselectivity;
}
else
{
elog(ERROR, "unrecognized node type: %d", nodeTag(path));
*cost = *selec = 0; /* keep compiler quiet */
}
}
三、跟踪分析
测试脚本如下
select t1.*
from t_dwxx t1
where dwbh > '10000' and dwbh < '30000';
启动gdb跟踪
(gdb) b create_bitmap_heap_path
Breakpoint 1 at 0x78f1c1: file pathnode.c, line 1090.
(gdb) c
Continuing.
Breakpoint 1, create_bitmap_heap_path (root=0x23d93d8, rel=0x248a788, bitmapqual=0x2473a08, required_outer=0x0,
loop_count=1, parallel_degree=0) at pathnode.c:1090
1090 BitmapHeapPath *pathnode = makeNode(BitmapHeapPath);
创建节点,并赋值
1090 BitmapHeapPath *pathnode = makeNode(BitmapHeapPath);
(gdb) n
1092 pathnode->path.pathtype = T_BitmapHeapScan;
(gdb) n
1093 pathnode->path.parent = rel;
(gdb) n
1094 pathnode->path.pathtarget = rel->reltarget;
(gdb) n
1095 pathnode->path.param_info = get_baserel_parampathinfo(root, rel,
(gdb)
1097 pathnode->path.parallel_aware = parallel_degree > 0 ? true : false;
(gdb)
1098 pathnode->path.parallel_safe = rel->consider_parallel;
(gdb)
1099 pathnode->path.parallel_workers = parallel_degree;
(gdb)
1100 pathnode->path.pathkeys = NIL; /* always unordered */
(gdb)
1102 pathnode->bitmapqual = bitmapqual;
进入cost_bitmap_heap_scan函数
(gdb)
1104 cost_bitmap_heap_scan(&pathnode->path, root, rel,
(gdb) step
cost_bitmap_heap_scan (path=0x24737d8, root=0x23d93d8, baserel=0x248a788, param_info=0x0, bitmapqual=0x2473a08,
loop_count=1) at costsize.c:949
949 Cost startup_cost = 0;
输入参数,其中bitmapqual为T_IndexPath节点
路径的其他关键信息:rows = 2223, startup_cost = 0.28500000000000003, total_cost = 169.23871600907944
(gdb) p *(IndexPath *)bitmapqual
$2 = {path = {type = T_IndexPath, pathtype = T_IndexScan, parent = 0x248a788, pathtarget = 0x248a998, param_info = 0x0,
parallel_aware = false, parallel_safe = true, parallel_workers = 0, rows = 2223, startup_cost = 0.28500000000000003,
total_cost = 169.23871600907944, pathkeys = 0x0}, indexinfo = 0x23b63b8, indexclauses = 0x2473948,
indexquals = 0x2473b38, indexqualcols = 0x2473b88, indexorderbys = 0x0, indexorderbycols = 0x0,
indexscandir = ForwardScanDirection, indextotalcost = 50.515000000000001, indexselectivity = 0.22227191011235958}
开始计算成本
...
980 startup_cost += indexTotalCost;
(gdb) p indexTotalCost
$16 = 51.070750000000004
(gdb) p startup_cost
$17 = 0
(gdb) p pages_fetched
$18 = 64
(gdb) p baserel->pages
$19 = 64
...
(gdb) p qpqual_cost
$20 = {startup = 0, per_tuple = 0.0050000000000000001}
最终的访问路径信息
(gdb) p *(BitmapHeapPath *)path
$22 = {path = {type = T_BitmapHeapPath, pathtype = T_BitmapHeapScan, parent = 0x248a788, pathtarget = 0x248a998,
param_info = 0x0, parallel_aware = false, parallel_safe = true, parallel_workers = 0, rows = 2223,
startup_cost = 51.070750000000004, total_cost = 148.41575, pathkeys = 0x0}, bitmapqual = 0x2473a08}
除了BitmapHeapPath,还有BitmapOr和BitmapAnd,这两种Path的解析后续再详述.
四、参考资料
allpaths.c
cost.h
costsize.c
PG Document:Query Planning
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