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.

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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)