本节继续介绍聚合函数的实现,主要介绍了不使用Hash算法的情况下聚合函数的实现,在这种情况下会先排序后执行聚合,在ExecAgg节点执行前,已完成排序的操作.下面介绍在已完成排序的情况下聚合的实现,主要实现函数是ExecAgg->agg_retrieve_direct.
创新互联2013年至今，是专业互联网技术服务公司，拥有项目成都网站设计、做网站网站策划，项目实施与项目整合能力。我们以让每一个梦想脱颖而出为使命，1280元江宁做网站,已为上家服务,为江宁各地企业和个人服务,联系电话:18980820575
下面是不使用HashAggregate情况下GroupAggregate的计划树:
",,,,,"select bh,avg(c1),min(c1),max(c2) from t_agg_simple group by bh;",,,"psql"
2019-05-16 12:04:45.621 CST,"xdb","testdb",1545,"[local]",5cdce11a.609,5,"SELECT",2019-05-16 12:03:38 CST,3/4,0,LOG,00000,"plan:"," {PLANNEDSTMT
:commandType 1
:queryId 0
:hasReturning false
:hasModifyingCTE false
:canSetTag true
:transientPlan false
:dependsOnRole false
:parallelModeNeeded false
:jitFlags 0
:planTree
{AGG
:startup_cost 52.67
:total_cost 64.42
:plan_rows 200
:plan_width 98
:parallel_aware false
:parallel_safe true
:plan_node_id 0
:targetlist (...
)
:qual <>
:lefttree
{SORT
:startup_cost 52.67
:total_cost 54.52
:plan_rows 740
:plan_width 66
:parallel_aware false
:parallel_safe true
:plan_node_id 1
:targetlist (...
)
:qual <>
:lefttree
{SEQSCAN
:startup_cost 0.00
:total_cost 17.40
:plan_rows 740
:plan_width 66
:parallel_aware false
:parallel_safe true
:plan_node_id 2
:targetlist (...
)
:qual <>
:lefttree <>
:righttree <>
:initPlan <>
:extParam (b)
:allParam (b)
:scanrelid 1
}
:righttree <>
:initPlan <>
:extParam (b)
:allParam (b)
:numCols 1
:sortColIdx 1
:sortOperators 664
:collations 100
:nullsFirst false
}
:righttree <>
:initPlan <>
:extParam (b)
:allParam (b)
:aggstrategy 1
:aggsplit 0
:numCols 1
:grpColIdx 1
:grpOperators 98
:numGroups 200
:aggParams (b)
:groupingSets <>
:chain <>
}
:rtable (...
)
:resultRelations <>
:nonleafResultRelations <>
:rootResultRelations <>
:subplans <>
:rewindPlanIDs (b)
:rowMarks <>
:relationOids (o 270375)
:invalItems <>
:paramExecTypes <>
:utilityStmt <>
:stmt_location 0
:stmt_len 63
}
可以看到,在ExecAgg前会先执行ExecSort.
一、数据结构
AggState
聚合函数执行时状态结构体,内含AggStatePerAgg等结构体
/* ---------------------
* AggState information
*
* ss.ss_ScanTupleSlot refers to output of underlying plan.
* ss.ss_ScanTupleSlot指的是基础计划的输出.
* (ss = ScanState,ps = PlanState)
*
* Note: ss.ps.ps_ExprContext contains ecxt_aggvalues and
* ecxt_aggnulls arrays, which hold the computed agg values for the current
* input group during evaluation of an Agg node's output tuple(s). We
* create a second ExprContext, tmpcontext, in which to evaluate input
* expressions and run the aggregate transition functions.
* 注意:ss.ps.ps_ExprContext包含了ecxt_aggvalues和ecxt_aggnulls数组,
* 这两个数组保存了在计算agg节点的输出元组时当前输入组已计算的agg值.
* ---------------------
*/
/* these structs are private in nodeAgg.c: */
//在nodeAgg.c中私有的结构体
typedef struct AggStatePerAggData *AggStatePerAgg;
typedef struct AggStatePerTransData *AggStatePerTrans;
typedef struct AggStatePerGroupData *AggStatePerGroup;
typedef struct AggStatePerPhaseData *AggStatePerPhase;
typedef struct AggStatePerHashData *AggStatePerHash;
typedef struct AggState
{
//第一个字段是NodeTag(继承自ScanState)
ScanState ss; /* its first field is NodeTag */
//targetlist和quals中所有的Aggref
List *aggs; /* all Aggref nodes in targetlist & quals */
//链表的大小(可以为0)
int numaggs; /* length of list (could be zero!) */
//pertrans条目大小
int numtrans; /* number of pertrans items */
//Agg策略模式
AggStrategy aggstrategy; /* strategy mode */
//agg-splitting模式,参见nodes.h
AggSplit aggsplit; /* agg-splitting mode, see nodes.h */
//指向当前步骤数据的指针
AggStatePerPhase phase; /* pointer to current phase data */
//步骤数(包括0)
int numphases; /* number of phases (including phase 0) */
//当前步骤
int current_phase; /* current phase number */
//per-Aggref信息
AggStatePerAgg peragg; /* per-Aggref information */
//per-Trans状态信息
AggStatePerTrans pertrans; /* per-Trans state information */
//长生命周期数据的ExprContexts(hashtable)
ExprContext *hashcontext; /* econtexts for long-lived data (hashtable) */
////长生命周期数据的ExprContexts(每一个GS使用)
ExprContext **aggcontexts; /* econtexts for long-lived data (per GS) */
//输入表达式的ExprContext
ExprContext *tmpcontext; /* econtext for input expressions */
#define FIELDNO_AGGSTATE_CURAGGCONTEXT 14
//当前活跃的aggcontext
ExprContext *curaggcontext; /* currently active aggcontext */
//当前活跃的aggregate(如存在)
AggStatePerAgg curperagg; /* currently active aggregate, if any */
#define FIELDNO_AGGSTATE_CURPERTRANS 16
//当前活跃的trans state
AggStatePerTrans curpertrans; /* currently active trans state, if any */
//输入结束?
bool input_done; /* indicates end of input */
//Agg扫描结束?
bool agg_done; /* indicates completion of Agg scan */
//最后一个grouping set
int projected_set; /* The last projected grouping set */
#define FIELDNO_AGGSTATE_CURRENT_SET 20
//将要解析的当前grouping set
int current_set; /* The current grouping set being evaluated */
//当前投影操作的分组列
Bitmapset *grouped_cols; /* grouped cols in current projection */
//倒序的分组列链表
List *all_grouped_cols; /* list of all grouped cols in DESC order */
/* These fields are for grouping set phase data */
//-------- 下面的列用于grouping set步骤数据
//所有步骤中最大的sets大小
int maxsets; /* The max number of sets in any phase */
//所有步骤的数组
AggStatePerPhase phases; /* array of all phases */
//对于phases > 1,已排序的输入信息
Tuplesortstate *sort_in; /* sorted input to phases > 1 */
//对于下一个步骤,输入已拷贝
Tuplesortstate *sort_out; /* input is copied here for next phase */
//排序结果的slot
TupleTableSlot *sort_slot; /* slot for sort results */
/* these fields are used in AGG_PLAIN and AGG_SORTED modes: */
//------- 下面的列用于AGG_PLAIN和AGG_SORTED模式:
//per-group指针的grouping set编号数组
AggStatePerGroup *pergroups; /* grouping set indexed array of per-group
* pointers */
//当前组的第一个元组拷贝
HeapTuple grp_firstTuple; /* copy of first tuple of current group */
/* these fields are used in AGG_HASHED and AGG_MIXED modes: */
//--------- 下面的列用于AGG_HASHED和AGG_MIXED模式:
//是否已填充hash表?
bool table_filled; /* hash table filled yet? */
//hash桶数?
int num_hashes;
//相应的哈希表数据数组
AggStatePerHash perhash; /* array of per-hashtable data */
//per-group指针的grouping set编号数组
AggStatePerGroup *hash_pergroup; /* grouping set indexed array of
* per-group pointers */
/* support for evaluation of agg input expressions: */
//---------- agg输入表达式解析支持
#define FIELDNO_AGGSTATE_ALL_PERGROUPS 34
//首先是->pergroups,然后是hash_pergroup
AggStatePerGroup *all_pergroups; /* array of first ->pergroups, than
* ->hash_pergroup */
//投影实现机制
ProjectionInfo *combinedproj; /* projection machinery */
} AggState;
/* Primitive options supported by nodeAgg.c: */
//nodeag .c支持的基本选项
#define AGGSPLITOP_COMBINE 0x01 /* substitute combinefn for transfn */
#define AGGSPLITOP_SKIPFINAL 0x02 /* skip finalfn, return state as-is */
#define AGGSPLITOP_SERIALIZE 0x04 /* apply serializefn to output */
#define AGGSPLITOP_DESERIALIZE 0x08 /* apply deserializefn to input */
/* Supported operating modes (i.e., useful combinations of these options): */
//支持的操作模式
typedef enum AggSplit
{
/* Basic, non-split aggregation: */
//基本 : 非split聚合
AGGSPLIT_SIMPLE = 0,
/* Initial phase of partial aggregation, with serialization: */
//部分聚合的初始步骤,序列化
AGGSPLIT_INITIAL_SERIAL = AGGSPLITOP_SKIPFINAL | AGGSPLITOP_SERIALIZE,
/* Final phase of partial aggregation, with deserialization: */
//部分聚合的最终步骤,反序列化
AGGSPLIT_FINAL_DESERIAL = AGGSPLITOP_COMBINE | AGGSPLITOP_DESERIALIZE
} AggSplit;
/* Test whether an AggSplit value selects each primitive option: */
//测试AggSplit选择了哪些基本选项
#define DO_AGGSPLIT_COMBINE(as) (((as) & AGGSPLITOP_COMBINE) != 0)
#define DO_AGGSPLIT_SKIPFINAL(as) (((as) & AGGSPLITOP_SKIPFINAL) != 0)
#define DO_AGGSPLIT_SERIALIZE(as) (((as) & AGGSPLITOP_SERIALIZE) != 0)
#define DO_AGGSPLIT_DESERIALIZE(as) (((as) & AGGSPLITOP_DESERIALIZE) != 0)
二、源码解读
agg_retrieve_direct
agg_retrieve_direct计算聚合的最终结果,适用于不使用Hash算法的情况.
/*
* ExecAgg for non-hashed case
* 适用于不使用Hash算法的情况.
*/
static TupleTableSlot *
agg_retrieve_direct(AggState *aggstate)
{
Agg *node = aggstate->phase->aggnode;//aggstate Node
ExprContext *econtext;//表达式解析上下文
ExprContext *tmpcontext;//临时上下文
AggStatePerAgg peragg;//聚合
AggStatePerGroup *pergroups;//分组信息
TupleTableSlot *outerslot;//outer元组slot
TupleTableSlot *firstSlot;//第1个slot
TupleTableSlot *result;//结果元组
bool hasGroupingSets = aggstate->phase->numsets > 0;//是否有grouping set
int numGroupingSets = Max(aggstate->phase->numsets, 1);
int currentSet;
int nextSetSize;
int numReset;
int i;
/*
* get state info from node
* 获取状态信息
*
* econtext is the per-output-tuple expression context
* econtext是per-output-tuple表达式上下文
*
* tmpcontext is the per-input-tuple expression context
* tmpcontext是per-input-tuple表达式上下文
*/
econtext = aggstate->ss.ps.ps_ExprContext;
tmpcontext = aggstate->tmpcontext;
peragg = aggstate->peragg;
pergroups = aggstate->pergroups;
firstSlot = aggstate->ss.ss_ScanTupleSlot;
/*
* We loop retrieving groups until we find one matching
* aggstate->ss.ps.qual
* 循环检索分组直至找到一个匹配aggstate->ss.ps.qual表达式的分组.
*
* For grouping sets, we have the invariant that aggstate->projected_set
* is either -1 (initial call) or the index (starting from 0) in
* gset_lengths for the group we just completed (either by projecting a
* row or by discarding it in the qual).
* 对于grouping set,aggstate->projected_set是个不变量,
* 要么是-1(初始调用),要么是已完成的分组在gset_lengths中的索引编号(从0开始)
* (通过投影一行或者在表达式中丢弃一行实现)
*/
while (!aggstate->agg_done)
{
//----------- 循环处理
/*
* Clear the per-output-tuple context for each group, as well as
* aggcontext (which contains any pass-by-ref transvalues of the old
* group). Some aggregate functions store working state in child
* contexts; those now get reset automatically without us needing to
* do anything special.
* 跟aggcontext(包含原分组通过引用传递的转换值)一样,每一个分组都会重置per-output-tuple上下文.
* 某些聚合函数在子上下文中存储工作状态,这种情况下,不需要做额外的工作,会自动重置.
*
* We use ReScanExprContext not just ResetExprContext because we want
* any registered shutdown callbacks to be called. That allows
* aggregate functions to ensure they've cleaned up any non-memory
* resources.
* 使用ReScanExprContext而不是ResetExprContext是因为我们希望所有已注册的shutdown回调函数可以调用.
* 这可以允许聚合函数确保它们已清理了所有非内存类资源.
*/
ReScanExprContext(econtext);
/*
* Determine how many grouping sets need to be reset at this boundary.
* 确定有多少grouping sets在此边界下需要重置.
*/
if (aggstate->projected_set >= 0 &&
aggstate->projected_set < numGroupingSets)
numReset = aggstate->projected_set + 1;
else
numReset = numGroupingSets;
/*
* numReset can change on a phase boundary, but that's OK; we want to
* reset the contexts used in _this_ phase, and later, after possibly
* changing phase, initialize the right number of aggregates for the
* _new_ phase.
* numReset可能在每个阶段的边界处出现变化,但这样也不会出现问题.
* 我们希望重置在该阶段的上下文,并在稍后在可能变化的阶段之后,为新的阶段初始化正确的聚合编号.
*/
for (i = 0; i < numReset; i++)
{
ReScanExprContext(aggstate->aggcontexts[i]);
}
/*
* Check if input is complete and there are no more groups to project
* in this phase; move to next phase or mark as done.
* 检查输入是否完成并且没有更多的组在本阶段用于投影.
* 移到下一个阶段或者标记为已完成.
*/
if (aggstate->input_done == true &&
aggstate->projected_set >= (numGroupingSets - 1))
{
if (aggstate->current_phase < aggstate->numphases - 1)
{
//仍在处理中
initialize_phase(aggstate, aggstate->current_phase + 1);
aggstate->input_done = false;
aggstate->projected_set = -1;
numGroupingSets = Max(aggstate->phase->numsets, 1);
node = aggstate->phase->aggnode;
numReset = numGroupingSets;
}
else if (aggstate->aggstrategy == AGG_MIXED)
{
//照理,不会进入这个分支(AGG_MIXED不是Hash才有吗?)
/*
* Mixed mode; we've output all the grouped stuff and have
* full hashtables, so switch to outputting those.
*/
initialize_phase(aggstate, 0);
aggstate->table_filled = true;
ResetTupleHashIterator(aggstate->perhash[0].hashtable,
&aggstate->perhash[0].hashiter);
select_current_set(aggstate, 0, true);
return agg_retrieve_hash_table(aggstate);
}
else
{
//已完成处理
aggstate->agg_done = true;
break;
}
}
/*
* Get the number of columns in the next grouping set after the last
* projected one (if any). This is the number of columns to compare to
* see if we reached the boundary of that set too.
* 在最后一次投影操作后获得下一个grouping set的列数.
* 这是要比较的列数，看看我们是否也达到了集合的边界。
*/
if (aggstate->projected_set >= 0 &&
aggstate->projected_set < (numGroupingSets - 1))
nextSetSize = aggstate->phase->gset_lengths[aggstate->projected_set + 1];
else
nextSetSize = 0;
/*----------
* If a subgroup for the current grouping set is present, project it.
* 如果子分组已存在,则执行投影.
*
* We have a new group if:
* - we're out of input but haven't projected all grouping sets
* (checked above)
* OR
* - we already projected a row that wasn't from the last grouping
* set
* AND
* - the next grouping set has at least one grouping column (since
* empty grouping sets project only once input is exhausted)
* AND
* - the previous and pending rows differ on the grouping columns
* of the next grouping set
*
* 如果出现下面情况,则会有新的分组:
* - 已完成输入处理,但仍未投影所有的grouping set(上面会执行检查)
* - 已投影了一行,但这一行并不是从最后一个grouping set而来的
* 同时
* - 下一个grouping set至少有要一个grouping列(因为空grouping sets投影一次输入就销毁了)
* 同时
* - 上一个和接下来的行与下一个grouping set中的分组列不同
*----------
*/
tmpcontext->ecxt_innertuple = econtext->ecxt_outertuple;
if (aggstate->input_done ||
(node->aggstrategy != AGG_PLAIN &&
aggstate->projected_set != -1 &&
aggstate->projected_set < (numGroupingSets - 1) &&
nextSetSize > 0 &&
!ExecQualAndReset(aggstate->phase->eqfunctions[nextSetSize - 1],
tmpcontext)))
{
aggstate->projected_set += 1;
Assert(aggstate->projected_set < numGroupingSets);
Assert(nextSetSize > 0 || aggstate->input_done);
}
else
{
/*
* We no longer care what group we just projected, the next
* projection will always be the first (or only) grouping set
* (unless the input proves to be empty).
* 不再关心刚才已投影的分组,下一个投影通常会是第一个grouping set(除非输入已验证为空)
*/
aggstate->projected_set = 0;
/*
* If we don't already have the first tuple of the new group,
* fetch it from the outer plan.
* 如果不再有新分组的第一个元组,则从outer plan中提取一行.
*/
if (aggstate->grp_firstTuple == NULL)
{
//提取一行
outerslot = fetch_input_tuple(aggstate);
if (!TupIsNull(outerslot))
{
//成功提取一行
/*
* Make a copy of the first input tuple; we will use this
* for comparisons (in group mode) and for projection.
* 拷贝之
*/
aggstate->grp_firstTuple = ExecCopySlotTuple(outerslot);
}
else
{
/* outer plan produced no tuples at all */
//不再产生新行
if (hasGroupingSets)
{
//----------- 存在grouping set
/*
* If there was no input at all, we need to project
* rows only if there are grouping sets of size 0.
* Note that this implies that there can't be any
* references to ungrouped Vars, which would otherwise
* cause issues with the empty output slot.
* 如果根本就不存在输入,只需要在大小为0的grouping set上投影哪些行即可.
* 注意这意味着不能依赖未分组的Vars,否则的话会导致输出slot为空.
*
* XXX: This is no longer true, we currently deal with
* this in finalize_aggregates().
* XXX: 这已不复存在,已在finalize_aggregates中进行处理.
*/
aggstate->input_done = true;
while (aggstate->phase->gset_lengths[aggstate->projected_set] > 0)
{
aggstate->projected_set += 1;
if (aggstate->projected_set >= numGroupingSets)
{
/*
* We can't set agg_done here because we might
* have more phases to do, even though the
* input is empty. So we need to restart the
* whole outer loop.
* 就算输入为空,但也不能在这里还设置agg_done为T,因为可能还有后续的阶段需要处理.
* 因此需要重启整个外循环.
*/
break;
}
}
if (aggstate->projected_set >= numGroupingSets)
continue;
}
else
{
aggstate->agg_done = true;
/* If we are grouping, we should produce no tuples too */
if (node->aggstrategy != AGG_PLAIN)
return NULL;
}
}
}
/*
* Initialize working state for a new input tuple group.
* 为新输入的元组组初始化工作状态.
*/
initialize_aggregates(aggstate, pergroups, numReset);
if (aggstate->grp_firstTuple != NULL)
{
/*
* Store the copied first input tuple in the tuple table slot
* reserved for it. The tuple will be deleted when it is
* cleared from the slot.
* 在元组表slot中拷贝存储第一个输入元组.
* 该元组在清理slot时会被删除.
*/
ExecStoreTuple(aggstate->grp_firstTuple,
firstSlot,
InvalidBuffer,
true);
aggstate->grp_firstTuple = NULL; /* 不需要保留双份指针. don't keep two pointers */
/* set up for first advance_aggregates call */
//为第一次advance_aggregates调用设置参数
tmpcontext->ecxt_outertuple = firstSlot;
/*
* Process each outer-plan tuple, and then fetch the next one,
* until we exhaust the outer plan or cross a group boundary.
* 处理每一个outer-plan元组,然后提取下一个,
* 直至outer plan已消耗完毕或者已跨越分组边界.
*/
for (;;)
{
/*
* During phase 1 only of a mixed agg, we need to update
* hashtables as well in advance_aggregates.
* 只有在混合AGG的第一阶段,我们还需要在advance_aggregates中更新哈希表.
*/
if (aggstate->aggstrategy == AGG_MIXED &&
aggstate->current_phase == 1)
{
lookup_hash_entries(aggstate);
}
/* Advance the aggregates (or combine functions) */
//推动聚合(或者组合函数)
advance_aggregates(aggstate);
/* Reset per-input-tuple context after each tuple */
//在每一个元组后重置per-input-tuple上下文
ResetExprContext(tmpcontext);
outerslot = fetch_input_tuple(aggstate);
if (TupIsNull(outerslot))
{
/* no more outer-plan tuples available */
//已无更多可用的outer slot
if (hasGroupingSets)
{
aggstate->input_done = true;
break;
}
else
{
aggstate->agg_done = true;
break;
}
}
/* set up for next advance_aggregates call */
//为下一次advance_aggregates调用作准备
tmpcontext->ecxt_outertuple = outerslot;
/*
* If we are grouping, check whether we've crossed a group
* boundary.
* 如果是分组,检查是否已跨越分组边界.
*/
if (node->aggstrategy != AGG_PLAIN)
{
tmpcontext->ecxt_innertuple = firstSlot;
if (!ExecQual(aggstate->phase->eqfunctions[node->numCols - 1],
tmpcontext))
{
aggstate->grp_firstTuple = ExecCopySlotTuple(outerslot);
break;
}
}
}
}
/*
* Use the representative input tuple for any references to
* non-aggregated input columns in aggregate direct args, the node
* qual, and the tlist. (If we are not grouping, and there are no
* input rows at all, we will come here with an empty firstSlot
* ... but if not grouping, there can't be any references to
* non-aggregated input columns, so no problem.)
* 对于聚合直接参数/节点表达式和投影列tlist中的非聚合输入列的引用,使用代表性的输入元组.
* (如果不是grouping而且没有输入元组,将使用空的firstSlot,但如果是非grouping,
* 不可能存在依赖非聚合输入列,因此不会存在问题)
*/
econtext->ecxt_outertuple = firstSlot;
}
Assert(aggstate->projected_set >= 0);
currentSet = aggstate->projected_set;
//投影处理
prepare_projection_slot(aggstate, econtext->ecxt_outertuple, currentSet);
select_current_set(aggstate, currentSet, false);
finalize_aggregates(aggstate,
peragg,
pergroups[currentSet]);
/*
* If there's no row to project right now, we must continue rather
* than returning a null since there might be more groups.
* 如不需要马上进行进行投影,必须继续执行而不是返回NULL,因为还需要处理更多的groups.
*/
result = project_aggregates(aggstate);
if (result)
return result;
}
/* No more groups */
//DONE!
return NULL;
}
三、跟踪分析
测试脚本
-- 禁用并行
set max_parallel_workers_per_gather=0;
-- 禁用hashagg
set enable_hashagg = off;
select bh,avg(c1),min(c1),max(c2) from t_agg_simple group by bh;
跟踪分析
(gdb) b agg_retrieve_direct
Breakpoint 1 at 0x6ee511: file nodeAgg.c, line 1572.
(gdb) c
Continuing.
Breakpoint 1, agg_retrieve_direct (aggstate=0x268f640) at nodeAgg.c:1572
1572 Agg *node = aggstate->phase->aggnode;
输入参数
(gdb) p *aggstate
$1 = {ss = {ps = {type = T_AggState, plan = 0x25af578, state = 0x268f428, ExecProcNode = 0x6ee438 ,
ExecProcNodeReal = 0x6ee438 , instrument = 0x0, worker_instrument = 0x0, worker_jit_instrument = 0x0,
qual = 0x0, lefttree = 0x268faf0, righttree = 0x0, initPlan = 0x0, subPlan = 0x0, chgParam = 0x0,
ps_ResultTupleSlot = 0x2690d50, ps_ExprContext = 0x268fa30, ps_ProjInfo = 0x2690e90, scandesc = 0x26907a0},
ss_currentRelation = 0x0, ss_currentScanDesc = 0x0, ss_ScanTupleSlot = 0x2690a78}, aggs = 0x25d4290, numaggs = 3,
numtrans = 3, aggstrategy = AGG_SORTED, aggsplit = AGGSPLIT_SIMPLE, phase = 0x2691290, numphases = 2, current_phase = 1,
peragg = 0x2690f28, pertrans = 0x26b24d0, hashcontext = 0x0, aggcontexts = 0x268f858, tmpcontext = 0x268f878,
curaggcontext = 0x268f970, curperagg = 0x0, curpertrans = 0x0, input_done = false, agg_done = false, projected_set = -1,
current_set = 0, grouped_cols = 0x0, all_grouped_cols = 0x0, maxsets = 1, phases = 0x2691258, sort_in = 0x0,
sort_out = 0x0, sort_slot = 0x0, pergroups = 0x25d4da0, grp_firstTuple = 0x0, table_filled = false, num_hashes = 0,
perhash = 0x0, hash_pergroup = 0x0, all_pergroups = 0x25d4da0, combinedproj = 0x0}
需要2个阶段,分别是AGG_PLAIN/AGG_SORTED
(gdb) p aggstate->phases[0]
$2 = {aggstrategy = AGG_PLAIN, numsets = 0, gset_lengths = 0x0, grouped_cols = 0x0, eqfunctions = 0x0, aggnode = 0x0,
sortnode = 0x0, evaltrans = 0x0}
(gdb) p aggstate->phases[1]
$3 = {aggstrategy = AGG_SORTED, numsets = 0, gset_lengths = 0x0, grouped_cols = 0x0, eqfunctions = 0x25d4388,
aggnode = 0x25af578, sortnode = 0x0, evaltrans = 0x25d5488}
不存在grouping set.
变量numGroupingSets设置为1
(gdb) n
1580 bool hasGroupingSets = aggstate->phase->numsets > 0;
(gdb) p aggstate->phase->numsets
$5 = 0
(gdb) n
1581 int numGroupingSets = Max(aggstate->phase->numsets, 1);
(gdb) n
1594 econtext = aggstate->ss.ps.ps_ExprContext;
(gdb) p numGroupingSets
$6 = 1
设置内存上下文
(gdb) n
1595 tmpcontext = aggstate->tmpcontext;
(gdb)
1597 peragg = aggstate->peragg;
(gdb) p *econtext
$7 = {type = T_ExprContext, ecxt_scantuple = 0x0, ecxt_innertuple = 0x0, ecxt_outertuple = 0x0,
ecxt_per_query_memory = 0x268f310, ecxt_per_tuple_memory = 0x26a6370, ecxt_param_exec_vals = 0x0,
ecxt_param_list_info = 0x0, ecxt_aggvalues = 0x25d4d48, ecxt_aggnulls = 0x25d4d80, caseValue_datum = 0,
caseValue_isNull = true, domainValue_datum = 0, domainValue_isNull = true, ecxt_estate = 0x268f428, ecxt_callbacks = 0x0}
(gdb) p *tmpcontext
$8 = {type = T_ExprContext, ecxt_scantuple = 0x0, ecxt_innertuple = 0x0, ecxt_outertuple = 0x0,
ecxt_per_query_memory = 0x268f310, ecxt_per_tuple_memory = 0x2691320, ecxt_param_exec_vals = 0x0,
ecxt_param_list_info = 0x0, ecxt_aggvalues = 0x0, ecxt_aggnulls = 0x0, caseValue_datum = 0, caseValue_isNull = true,
domainValue_datum = 0, domainValue_isNull = true, ecxt_estate = 0x268f428, ecxt_callbacks = 0x0}
(gdb)
获取聚合信息,一共有3个
(gdb) n
1598 pergroups = aggstate->pergroups;
(gdb)
1599 firstSlot = aggstate->ss.ss_ScanTupleSlot;
(gdb) p *peragg
$9 = {aggref = 0x26a02d0, transno = 0, finalfn_oid = 0, finalfn = {fn_addr = 0x0, fn_oid = 0, fn_nargs = 0,
fn_strict = false, fn_retset = false, fn_stats = 0 '\000', fn_extra = 0x0, fn_mcxt = 0x0, fn_expr = 0x0},
numFinalArgs = 1, aggdirectargs = 0x0, resulttypeLen = 4, resulttypeByVal = true, shareable = true}
(gdb) p peragg[0]
$10 = {aggref = 0x26a02d0, transno = 0, finalfn_oid = 0, finalfn = {fn_addr = 0x0, fn_oid = 0, fn_nargs = 0,
fn_strict = false, fn_retset = false, fn_stats = 0 '\000', fn_extra = 0x0, fn_mcxt = 0x0, fn_expr = 0x0},
numFinalArgs = 1, aggdirectargs = 0x0, resulttypeLen = 4, resulttypeByVal = true, shareable = true}
(gdb) p peragg[1]
$11 = {aggref = 0x26a0048, transno = 1, finalfn_oid = 0, finalfn = {fn_addr = 0x0, fn_oid = 0, fn_nargs = 0,
fn_strict = false, fn_retset = false, fn_stats = 0 '\000', fn_extra = 0x0, fn_mcxt = 0x0, fn_expr = 0x0},
numFinalArgs = 1, aggdirectargs = 0x0, resulttypeLen = 4, resulttypeByVal = true, shareable = true}
(gdb) p peragg[2]
$12 = {aggref = 0x269fdc0, transno = 2, finalfn_oid = 1964, finalfn = {fn_addr = 0x978251 , fn_oid = 1964,
fn_nargs = 1, fn_strict = true, fn_retset = false, fn_stats = 2 '\002', fn_extra = 0x0, fn_mcxt = 0x268f310,
fn_expr = 0x25d5190}, numFinalArgs = 1, aggdirectargs = 0x0, resulttypeLen = -1, resulttypeByVal = false,
shareable = true}
分组只有一个
(gdb) p pergroups[0]
$14 = (AggStatePerGroup) 0x25d4dc0
(gdb) p *pergroups[0]
$15 = {transValue = 0, transValueIsNull = false, noTransValue = false}
进入循环
(gdb) n
1610 while (!aggstate->agg_done)
(gdb)
1624 ReScanExprContext(econtext);
(gdb)
重置内存上下文
(gdb)
1629 if (aggstate->projected_set >= 0 &&
(gdb)
1633 numReset = numGroupingSets;
(gdb)
1642 for (i = 0; i < numReset; i++)
(gdb)
1644 ReScanExprContext(aggstate->aggcontexts[i]);
(gdb)
1642 for (i = 0; i < numReset; i++)
(gdb)
检查输入是否已完成处理/本组已完成投影(实际不满足条件)
(gdb)
1651 if (aggstate->input_done == true &&
(gdb)
1688 if (aggstate->projected_set >= 0 &&
(gdb) p aggstate->input_done
$16 = false
(gdb) p aggstate->projected_set
$17 = -1
设置待处理的元组,为NULL
(gdb)
1711 tmpcontext->ecxt_innertuple = econtext->ecxt_outertuple;
(gdb)
1712 if (aggstate->input_done ||
(gdb)
(gdb) p *tmpcontext->ecxt_innertuple
Cannot access memory at address 0x0
如果子分组已存在,则执行投影(实际不满足条件).
(gdb) n
1713 (node->aggstrategy != AGG_PLAIN &&
(gdb) p node->aggstrategy
$18 = AGG_SORTED
(gdb) n
1712 if (aggstate->input_done ||
(gdb)
1714 aggstate->projected_set != -1 &&
(gdb)
1713 (node->aggstrategy != AGG_PLAIN &&
(gdb)
1732 aggstate->projected_set = 0;
(gdb) p aggstate->input_done
$19 = false
(gdb) p aggstate->projected_set
$20 = -1
(gdb) p node->aggstrategy
$21 = AGG_SORTED
(gdb)
从outer plan中提取一行,并拷贝为首行
(gdb) n
1738 if (aggstate->grp_firstTuple == NULL)
(gdb) p aggstate->grp_firstTuple
$22 = (HeapTuple) 0x0
(gdb) n
1740 outerslot = fetch_input_tuple(aggstate);
(gdb)
1741 if (!TupIsNull(outerslot))
(gdb) p *outerslot
$23 = {type = T_TupleTableSlot, tts_isempty = false, tts_shouldFree = false, tts_shouldFreeMin = false, tts_slow = false,
tts_tuple = 0x26909f8, tts_tupleDescriptor = 0x26907a0, tts_mcxt = 0x268f310, tts_buffer = 0, tts_nvalid = 0,
tts_values = 0x2690a18, tts_isnull = 0x2690a30, tts_mintuple = 0x26b8ad8, tts_minhdr = {t_len = 40, t_self = {ip_blkid = {
bi_hi = 0, bi_lo = 0}, ip_posid = 0}, t_tableOid = 0, t_data = 0x26b8ad0}, tts_off = 0,
tts_fixedTupleDescriptor = true}
(gdb)
(gdb) n
1747 aggstate->grp_firstTuple = ExecCopySlotTuple(outerslot);
(gdb)
为新输入的元组组初始化工作状态.
(gdb)
1797 initialize_aggregates(aggstate, pergroups, numReset);
把元组拷贝到内存上下文中,并执行聚合运算(advance_aggregates)
(gdb) n
1799 if (aggstate->grp_firstTuple != NULL)
(gdb)
1806 ExecStoreTuple(aggstate->grp_firstTuple,
(gdb)
1810 aggstate->grp_firstTuple = NULL; /* don't keep two pointers */
(gdb)
1813 tmpcontext->ecxt_outertuple = firstSlot;
(gdb)
1825 if (aggstate->aggstrategy == AGG_MIXED &&
(gdb)
1832 advance_aggregates(aggstate);
(gdb)
1835 ResetExprContext(tmpcontext);
(gdb)
继续提取行,拷贝到内存上下文中
(gdb) n
1837 outerslot = fetch_input_tuple(aggstate);
(gdb)
1838 if (TupIsNull(outerslot))
(gdb)
1853 tmpcontext->ecxt_outertuple = outerslot;
(gdb)
1859 if (node->aggstrategy != AGG_PLAIN)
(gdb)
1861 tmpcontext->ecxt_innertuple = firstSlot;
(gdb)
1862 if (!ExecQual(aggstate->phase->eqfunctions[node->numCols - 1],
(gdb)
1869 }
执行聚合运算,并继续提取下一行
825 if (aggstate->aggstrategy == AGG_MIXED &&
(gdb)
1832 advance_aggregates(aggstate);
(gdb)
1835 ResetExprContext(tmpcontext);
(gdb)
1837 outerslot = fetch_input_tuple(aggstate);
(gdb)
1838 if (TupIsNull(outerslot))
(gdb)
1853 tmpcontext->ecxt_outertuple = outerslot;
(gdb)
1859 if (node->aggstrategy != AGG_PLAIN)
(gdb)
1861 tmpcontext->ecxt_innertuple = firstSlot;
(gdb)
如果是分组,检查是否已跨越分组边界,如已越界在跳出循环.
1862 if (!ExecQual(aggstate->phase->eqfunctions[node->numCols - 1],
(gdb)
1865 aggstate->grp_firstTuple = ExecCopySlotTuple(outerslot);
(gdb)
1866 break;
已获得一行结果行,返回结果
(gdb)
1880 econtext->ecxt_outertuple = firstSlot;
(gdb) n
1883 Assert(aggstate->projected_set >= 0);
(gdb)
1885 currentSet = aggstate->projected_set;
(gdb)
1887 prepare_projection_slot(aggstate, econtext->ecxt_outertuple, currentSet);
(gdb) p aggstate->projected_set
$24 = 0
(gdb) n
1889 select_current_set(aggstate, currentSet, false);
(gdb)
1893 pergroups[currentSet]);
(gdb)
1891 finalize_aggregates(aggstate,
(gdb)
1899 result = project_aggregates(aggstate);
(gdb)
1900 if (result)
(gdb)
1901 return result;
(gdb) p *result
$25 = {type = T_TupleTableSlot, tts_isempty = false, tts_shouldFree = false, tts_shouldFreeMin = false, tts_slow = false,
tts_tuple = 0x0, tts_tupleDescriptor = 0x2690b38, tts_mcxt = 0x268f310, tts_buffer = 0, tts_nvalid = 4,
tts_values = 0x2690db0, tts_isnull = 0x2690dd0, tts_mintuple = 0x0, tts_minhdr = {t_len = 0, t_self = {ip_blkid = {
bi_hi = 0, bi_lo = 0}, ip_posid = 0}, t_tableOid = 0, t_data = 0x0}, tts_off = 0, tts_fixedTupleDescriptor = true}
(gdb)
DONE!
四、参考资料
PostgreSQL 源码解读（178）- 查询#95(聚合函数)#1相关数据结构
PostgreSQL 源码解读（186）- 查询#102(聚合函数#7-advance_aggregates)
分享标题：PostgreSQL源码解读（191）-查询#107(聚合函数#12-agg_retrieve_direct)
