PostgreSQL 源码解读（91）- 查询语句#76（ExecHashJoin函数#2）
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本节是ExecHashJoin函数介绍的第二部分，主要介绍了ExecHashJoin中依赖的其他函数的实现逻辑，包括ExecHashTableCreate、ExecChooseHashTableSize等。
一、数据结构
Plan
所有计划节点通过将Plan结构作为第一个字段从Plan结构“派生”。这确保了在将节点转换为计划节点时，一切都能正常工作。(在执行器中以通用方式传递时，节点指针经常被转换为Plan *)
typedef struct Plan
{
NodeTag type;//节点类型
Cost startup_cost;
Cost total_cost;
double plan_rows;
int plan_width;
bool parallel_aware;
bool parallel_safe;
int plan_node_id;
List *targetlist;
List *qual;
struct Plan *lefttree;
struct Plan *righttree;
List *initPlan;
Bitmapset *extParam;
Bitmapset *allParam;
} Plan;
JoinState
Hash/NestLoop/Merge Join的基类
typedef struct JoinState
{
PlanState ps;//基类PlanState
JoinType jointype;//连接类型
//在找到一个匹配inner tuple的时候,如需要跳转到下一个outer tuple,则该值为T
bool single_match;
//连接条件表达式(除了ps.qual)
ExprState *joinqual;
} JoinState;
HashJoinState
Hash Join运行期状态结构体
typedef struct HashJoinTupleData *HashJoinTuple;
typedef struct HashJoinTableData *HashJoinTable;
typedef struct HashJoinState
{
JoinState js;
ExprState *hashclauses;//hash连接条件
List *hj_OuterHashKeys;
List *hj_InnerHashKeys;
List *hj_HashOperators;
HashJoinTable hj_HashTable;//Hash表
uint32 hj_CurHashValue;//当前的Hash值
int hj_CurBucketNo;//当前的bucket编号
int hj_CurSkewBucketNo;//行倾斜bucket编号
HashJoinTuple hj_CurTuple;//当前元组
TupleTableSlot *hj_OuterTupleSlot;//outer relation slot
TupleTableSlot *hj_HashTupleSlot;//Hash tuple slot
TupleTableSlot *hj_NullOuterTupleSlot;//用于外连接的outer虚拟slot
TupleTableSlot *hj_NullInnerTupleSlot;//用于外连接的inner虚拟slot
TupleTableSlot *hj_FirstOuterTupleSlot;//
int hj_JoinState;//JoinState状态
bool hj_MatchedOuter;//是否匹配
bool hj_OuterNotEmpty;//outer relation是否为空
} HashJoinState;
HashJoinTable
Hash表数据结构
typedef struct HashJoinTableData
{
int nbuckets;
int log2_nbuckets;
int nbuckets_original;
int nbuckets_optimal;
int log2_nbuckets_optimal;
//bucket [i]是内存中第i个桶中的元组链表的head item
union
{
//未共享数组是按批处理存储的，所有元组均如此
struct HashJoinTupleData **unshared;
//共享数组是每个查询的DSA区域，所有元组均如此
dsa_pointer_atomic *shared;
} buckets;
bool keepNulls;
bool skewEnabled;
HashSkewBucket **skewBucket;
int skewBucketLen;
int nSkewBuckets;
int *skewBucketNums;
int nbatch;
int curbatch;
int nbatch_original;
int nbatch_outstart;
bool growEnabled;
double totalTuples;
double partialTuples;
double skewTuples;
BufFile **innerBatchFile;
BufFile **outerBatchFile;
FmgrInfo *outer_hashfunctions;
FmgrInfo *inner_hashfunctions;
bool *hashStrict;
Size spaceUsed;
Size spaceAllowed;
Size spacePeak;
Size spaceUsedSkew;
Size spaceAllowedSkew;
MemoryContext hashCxt;
MemoryContext batchCxt;
//用于密集分配元组(到链接块中)
HashMemoryChunk chunks;
//并行hash使用的共享和私有状态
HashMemoryChunk current_chunk;
dsa_area *area;
ParallelHashJoinState *parallel_state;//并行执行状态
ParallelHashJoinBatchAccessor *batches;//并行访问器
dsa_pointer current_chunk_shared;//当前chunk的开始指针
} HashJoinTableData;
typedef struct HashJoinTableData *HashJoinTable;
HashJoinTupleData
Hash连接元组数据
typedef struct HashJoinTupleData
{
//link同一个桶中的下一个元组
union
{
struct HashJoinTupleData *unshared;
dsa_pointer shared;
} next;
uint32 hashvalue;
} HashJoinTupleData;
#define HJTUPLE_OVERHEAD MAXALIGN(sizeof(HashJoinTupleData))
#define HJTUPLE_MINTUPLE(hjtup) \
((MinimalTuple) ((char *) (hjtup) + HJTUPLE_OVERHEAD))
二、源码解读
ExecHashTableCreate
ExecHashTableCreate函数初始化hashjoin需要使用的hashtable.
#define innerPlan(node) (((Plan *)(node))->righttree)
#define outerPlan(node) (((Plan *)(node))->lefttree)
HashJoinTable
ExecHashTableCreate(HashState *state, List *hashOperators, bool keepNulls)
{
Hash *node;
HashJoinTable hashtable;
Plan *outerNode;
size_t space_allowed;
int nbuckets;
int nbatch;
double rows;
int num_skew_mcvs;
int log2_nbuckets;
int nkeys;
int i;
ListCell *ho;
MemoryContext oldcxt;
node = (Hash *) state->ps.plan;//获取Hash节点
outerNode = outerPlan(node);//获取outer relation Plan节点
rows = node->plan.parallel_aware ? node->rows_total : outerNode->plan_rows;//获取总行数
ExecChooseHashTableSize(rows, outerNode->plan_width,
OidIsValid(node->skewTable),
state->parallel_state != NULL,
state->parallel_state != NULL ?
state->parallel_state->nparticipants - 1 : 0,
&space_allowed,
&nbuckets, &nbatch, &num_skew_mcvs);//计算Hash Table的大小尺寸
//nbuckets(hash桶数)必须是2的n次方
log2_nbuckets = my_log2(nbuckets);
Assert(nbuckets == (1 << log2_nbuckets));
hashtable = (HashJoinTable) palloc(sizeof(HashJoinTableData));//分配内存
hashtable->nbuckets = nbuckets;//桶数
hashtable->nbuckets_original = nbuckets;
hashtable->nbuckets_optimal = nbuckets;
hashtable->log2_nbuckets = log2_nbuckets;
hashtable->log2_nbuckets_optimal = log2_nbuckets;
hashtable->buckets.unshared = NULL;
hashtable->keepNulls = keepNulls;
hashtable->skewEnabled = false;
hashtable->skewBucket = NULL;
hashtable->skewBucketLen = 0;
hashtable->nSkewBuckets = 0;
hashtable->skewBucketNums = NULL;
hashtable->nbatch = nbatch;
hashtable->curbatch = 0;
hashtable->nbatch_original = nbatch;
hashtable->nbatch_outstart = nbatch;
hashtable->growEnabled = true;
hashtable->totalTuples = 0;
hashtable->partialTuples = 0;
hashtable->skewTuples = 0;
hashtable->innerBatchFile = NULL;
hashtable->outerBatchFile = NULL;
hashtable->spaceUsed = 0;
hashtable->spacePeak = 0;
hashtable->spaceAllowed = space_allowed;
hashtable->spaceUsedSkew = 0;
hashtable->spaceAllowedSkew =
hashtable->spaceAllowed * SKEW_WORK_MEM_PERCENT / 100;
hashtable->chunks = NULL;
hashtable->current_chunk = NULL;
hashtable->parallel_state = state->parallel_state;
hashtable->area = state->ps.state->es_query_dsa;
hashtable->batches = NULL;
#ifdef HJDEBUG
printf("Hashjoin %p: initial nbatch = %d, nbuckets = %d\n",
hashtable, nbatch, nbuckets);
#endif
hashtable->hashCxt = AllocSetContextCreate(CurrentMemoryContext,
"HashTableContext",
ALLOCSET_DEFAULT_SIZES);
hashtable->batchCxt = AllocSetContextCreate(hashtable->hashCxt,
"HashBatchContext",
ALLOCSET_DEFAULT_SIZES);
//分配内存,切换至hashCxt
oldcxt = MemoryContextSwitchTo(hashtable->hashCxt);
nkeys = list_length(hashOperators);//键值数
hashtable->outer_hashfunctions =
(FmgrInfo *) palloc(nkeys * sizeof(FmgrInfo));//outer relation所使用的hash函数
hashtable->inner_hashfunctions =
(FmgrInfo *) palloc(nkeys * sizeof(FmgrInfo));//inner relation所使用的hash函数
hashtable->hashStrict = (bool *) palloc(nkeys * sizeof(bool));//是否严格的操作符
i = 0;
foreach(ho, hashOperators)//遍历hash操作符
{
Oid hashop = lfirst_oid(ho);//hash操作符
Oid left_hashfn;//左函数
Oid right_hashfn;//右函数
//获取与给定操作符兼容的标准哈希函数的OID，并根据需要对其LHS和/或RHS数据类型进行操作。
if (!get_op_hash_functions(hashop, &left_hashfn, &right_hashfn))//获取hash函数
elog(ERROR, "could not find hash function for hash operator %u",
hashop);
fmgr_info(left_hashfn, &hashtable->outer_hashfunctions[i]);
fmgr_info(right_hashfn, &hashtable->inner_hashfunctions[i]);
hashtable->hashStrict[i] = op_strict(hashop);
i++;
}
if (nbatch > 1 && hashtable->parallel_state == NULL)//批次>1而且并行状态为NULL
{
hashtable->innerBatchFile = (BufFile **)
palloc0(nbatch * sizeof(BufFile *));//用于缓存该批次的inner relation的tuple
hashtable->outerBatchFile = (BufFile **)
palloc0(nbatch * sizeof(BufFile *));//用于缓存该批次的outerr relation的tuple
//这些文件需要时才会打开……
//…但是要确保为它们建立了临时表空间
PrepareTempTablespaces();
}
MemoryContextSwitchTo(oldcxt);//切换回原内存上下文
if (hashtable->parallel_state)//并行处理
{
ParallelHashJoinState *pstate = hashtable->parallel_state;
Barrier *build_barrier;
build_barrier = &pstate->build_barrier;
BarrierAttach(build_barrier);
if (BarrierPhase(build_barrier) == PHJ_BUILD_ELECTING &&
BarrierArriveAndWait(build_barrier, WAIT_EVENT_HASH_BUILD_ELECTING))
{
pstate->nbatch = nbatch;
pstate->space_allowed = space_allowed;
pstate->growth = PHJ_GROWTH_OK;
ExecParallelHashJoinSetUpBatches(hashtable, nbatch);
pstate->nbuckets = nbuckets;
ExecParallelHashTableAlloc(hashtable, 0);
}
}
else//非并行处理
{
MemoryContextSwitchTo(hashtable->batchCxt);//切换上下文
hashtable->buckets.unshared = (HashJoinTuple *)
palloc0(nbuckets * sizeof(HashJoinTuple));//分配内存空间
if (nbatch > 1)
ExecHashBuildSkewHash(hashtable, node, num_skew_mcvs);
MemoryContextSwitchTo(oldcxt);//切换上下文
}
return hashtable;//返回Hash表
}
void
fmgr_info(Oid functionId, FmgrInfo *finfo)
{
fmgr_info_cxt_security(functionId, finfo, CurrentMemoryContext, false);
}
ExecChooseHashTableSize
ExecChooseHashTableSize函数根据给定要散列的关系的估计大小(行数和平均行宽)，计算适当的散列表大小。
#define NTUP_PER_BUCKET 1
void
ExecChooseHashTableSize(double ntuples, int tupwidth, bool useskew,
bool try_combined_work_mem,
int parallel_workers,
size_t *space_allowed,
int *numbuckets,
int *numbatches,
int *num_skew_mcvs)
{
int tupsize;//元组大小
double inner_rel_bytes;//inner relation大小
long bucket_bytes;//桶大小
long hash_table_bytes;//hash table大小
long skew_table_bytes;//倾斜表大小
long max_pointers;//最大的指针数
long mppow2;//
int nbatch = 1;//批次
int nbuckets;//桶数
double dbuckets;//
//如relation大小没有信息,则设定为默认值1000.0
if (ntuples <= 0.0)
ntuples = 1000.0;
tupsize = HJTUPLE_OVERHEAD +
MAXALIGN(SizeofMinimalTupleHeader) +
MAXALIGN(tupwidth);//估算元组大小
inner_rel_bytes = ntuples * tupsize;//inner relation大小
hash_table_bytes = work_mem * 1024L;
if (try_combined_work_mem)//尝试融合work_mem
hash_table_bytes += hash_table_bytes * parallel_workers;
*space_allowed = hash_table_bytes;
if (useskew)
{
//倾斜优化
skew_table_bytes = hash_table_bytes * SKEW_WORK_MEM_PERCENT / 100;
*num_skew_mcvs = skew_table_bytes / (tupsize +
(8 * sizeof(HashSkewBucket *)) +
sizeof(int) +
SKEW_BUCKET_OVERHEAD);
if (*num_skew_mcvs > 0)
hash_table_bytes -= skew_table_bytes;
}
else
*num_skew_mcvs = 0;//不使用倾斜优化,默认为0
max_pointers = *space_allowed / sizeof(HashJoinTuple);//最大指针数
max_pointers = Min(max_pointers, MaxAllocSize / sizeof(HashJoinTuple));//控制上限
//如果max_pointer不是2的幂，则必须四舍五入到符合规则的某个值(如110.1 --> 128)
mppow2 = 1L << my_log2(max_pointers);
if (max_pointers != mppow2)
max_pointers = mppow2 / 2;
//还要确保在nbatch和nbucket中避免整数溢出
//(鉴于MaxAllocSize的当前值，此步骤是多余的)
max_pointers = Min(max_pointers, INT_MAX / 2);//设定上限
dbuckets = ceil(ntuples / NTUP_PER_BUCKET);//取整
dbuckets = Min(dbuckets, max_pointers);//设定上限
nbuckets = (int) dbuckets;//桶数
//但是，不要让nbucket非常小……
nbuckets = Max(nbuckets, 1024);//设定下限(1024)
//2的幂
nbuckets = 1 << my_log2(nbuckets);
bucket_bytes = sizeof(HashJoinTuple) * nbuckets;
if (inner_rel_bytes + bucket_bytes > hash_table_bytes)//inner relation大小 + 桶数大于可用空间
{
//需要多批次
long lbuckets;
double dbatch;
int minbatch;
long bucket_size;
if (try_combined_work_mem)
{
ExecChooseHashTableSize(ntuples, tupwidth, useskew,
false, parallel_workers,
space_allowed,
numbuckets,
numbatches,
num_skew_mcvs);
return;
}
bucket_size = (tupsize * NTUP_PER_BUCKET + sizeof(HashJoinTuple));//桶大小
lbuckets = 1L << my_log2(hash_table_bytes / bucket_size);
lbuckets = Min(lbuckets, max_pointers);
nbuckets = (int) lbuckets;
nbuckets = 1 << my_log2(nbuckets);
bucket_bytes = nbuckets * sizeof(HashJoinTuple);
Assert(bucket_bytes <= hash_table_bytes / 2);
//计算批次数
dbatch = ceil(inner_rel_bytes / (hash_table_bytes - bucket_bytes));
dbatch = Min(dbatch, max_pointers);
minbatch = (int) dbatch;
nbatch = 2;
while (nbatch < minbatch)
nbatch <<= 1;
}
Assert(nbuckets > 0);
Assert(nbatch > 0);
*numbuckets = nbuckets;
*numbatches = nbatch;
}
三、跟踪分析
测试脚本如下
testdb=# set enable_nestloop=false;
SET
testdb=# set enable_mergejoin=false;
SET
testdb=# explain verbose select dw.*,grjf.grbh,grjf.xm,grjf.ny,grjf.je
testdb-# from t_dwxx dw,lateral (select gr.grbh,gr.xm,jf.ny,jf.je
testdb(# from t_grxx gr inner join t_jfxx jf
testdb(# on gr.dwbh = dw.dwbh
testdb(# and gr.grbh = jf.grbh) grjf
testdb-# order by dw.dwbh;
QUERY PLAN
-----------------------------------------------------------------------------------------------
Sort (cost=14828.83..15078.46 rows=99850 width=47)
Output: dw.dwmc, dw.dwbh, dw.dwdz, gr.grbh, gr.xm, jf.ny, jf.je
Sort Key: dw.dwbh
-> Hash Join (cost=3176.00..6537.55 rows=99850 width=47)
Output: dw.dwmc, dw.dwbh, dw.dwdz, gr.grbh, gr.xm, jf.ny, jf.je
Hash Cond: ((gr.grbh)::text = (jf.grbh)::text)
-> Hash Join (cost=289.00..2277.61 rows=99850 width=32)
Output: dw.dwmc, dw.dwbh, dw.dwdz, gr.grbh, gr.xm
Inner Unique: true
Hash Cond: ((gr.dwbh)::text = (dw.dwbh)::text)
-> Seq Scan on public.t_grxx gr (cost=0.00..1726.00 rows=100000 width=16)
Output: gr.dwbh, gr.grbh, gr.xm, gr.xb, gr.nl
-> Hash (cost=164.00..164.00 rows=10000 width=20)
Output: dw.dwmc, dw.dwbh, dw.dwdz
-> Seq Scan on public.t_dwxx dw (cost=0.00..164.00 rows=10000 width=20)
Output: dw.dwmc, dw.dwbh, dw.dwdz
-> Hash (cost=1637.00..1637.00 rows=100000 width=20)
Output: jf.ny, jf.je, jf.grbh
-> Seq Scan on public.t_jfxx jf (cost=0.00..1637.00 rows=100000 width=20)
Output: jf.ny, jf.je, jf.grbh
(20 rows)
启动gdb,设置断点,进入ExecHashTableCreate
(gdb) b ExecHashTableCreate
Breakpoint 1 at 0x6fc75d: file nodeHash.c, line 449.
(gdb) c
Continuing.
Breakpoint 1, ExecHashTableCreate (state=0x1e3cbc8, hashOperators=0x1e59890, keepNulls=false) at nodeHash.c:449
449 node = (Hash *) state->ps.plan;
获取相关信息
449 node = (Hash *) state->ps.plan;
(gdb) n
450 outerNode = outerPlan(node);
(gdb)
457 rows = node->plan.parallel_aware ? node->rows_total : outerNode->plan_rows;
(gdb)
462 state->parallel_state != NULL ?
(gdb)
459 ExecChooseHashTableSize(rows, outerNode->plan_width,
(gdb)
获取Hash节点;
outer节点为顺序扫描SeqScan节点
inner(构造hash表的relation)行数为10000
(gdb) p *node
$1 = {plan = {type = T_Hash, startup_cost = 164, total_cost = 164, plan_rows = 10000, plan_width = 20,
parallel_aware = false, parallel_safe = true, plan_node_id = 4, targetlist = 0x1e4bf90, qual = 0x0,
lefttree = 0x1e493e8, righttree = 0x0, initPlan = 0x0, extParam = 0x0, allParam = 0x0}, skewTable = 16977,
skewColumn = 1, skewInherit = false, rows_total = 0}
(gdb) p *outerNode
$2 = {type = T_SeqScan, startup_cost = 0, total_cost = 164, plan_rows = 10000, plan_width = 20, parallel_aware = false,
parallel_safe = true, plan_node_id = 5, targetlist = 0x1e492b0, qual = 0x0, lefttree = 0x0, righttree = 0x0,
initPlan = 0x0, extParam = 0x0, allParam = 0x0}
(gdb) p rows
$3 = 10000
(gdb)
进入ExecChooseHashTableSize函数
(gdb) step
ExecChooseHashTableSize (ntuples=10000, tupwidth=20, useskew=true, try_combined_work_mem=false, parallel_workers=0,
space_allowed=0x7ffdcf148540, numbuckets=0x7ffdcf14853c, numbatches=0x7ffdcf148538, num_skew_mcvs=0x7ffdcf148534)
at nodeHash.c:677
677 int nbatch = 1;
ExecChooseHashTableSize->计算元组大小(56B)/inner relation大小(约560K)/hash表空间(16M)
(gdb) n
682 if (ntuples <= 0.0)
(gdb)
690 tupsize = HJTUPLE_OVERHEAD +
(gdb)
693 inner_rel_bytes = ntuples * tupsize;
(gdb)
698 hash_table_bytes = work_mem * 1024L;
(gdb)
705 if (try_combined_work_mem)
(gdb) p tupsize
$4 = 56
(gdb) p inner_rel_bytes
$5 = 560000
(gdb) p hash_table_bytes
$6 = 16777216
ExecChooseHashTableSize->使用数据倾斜优化(所需空间从Hash Table中获取)
(gdb) n
708 *space_allowed = hash_table_bytes;
(gdb)
724 if (useskew)
(gdb)
726 skew_table_bytes = hash_table_bytes * SKEW_WORK_MEM_PERCENT / 100;
(gdb) p useskew
$8 = true
(gdb) p hash_table_bytes
$9 = 16441672
(gdb) p skew_table_bytes
$10 = 335544
(gdb) p num_skew_mcvs
$11 = (int *) 0x7ffdcf148534
(gdb) p *num_skew_mcvs
$12 = 2396
(gdb)
ExecChooseHashTableSize->获取最大指针数目(2097152)
(gdb) n
756 max_pointers = Min(max_pointers, MaxAllocSize / sizeof(HashJoinTuple));
(gdb)
758 mppow2 = 1L << my_log2(max_pointers);
(gdb) n
759 if (max_pointers != mppow2)
(gdb) p max_pointers
$13 = 2097152
(gdb) p mppow2
$15 = 2097152
ExecChooseHashTableSize->计算Hash桶数
(gdb) n
764 max_pointers = Min(max_pointers, INT_MAX / 2);
(gdb)
766 dbuckets = ceil(ntuples / NTUP_PER_BUCKET);
(gdb)
767 dbuckets = Min(dbuckets, max_pointers);
(gdb)
768 nbuckets = (int) dbuckets;
(gdb)
770 nbuckets = Max(nbuckets, 1024);
(gdb)
772 nbuckets = 1 << my_log2(nbuckets);
(gdb)
778 bucket_bytes = sizeof(HashJoinTuple) * nbuckets;
(gdb) n
779 if (inner_rel_bytes + bucket_bytes > hash_table_bytes)
(gdb)
834 Assert(nbuckets > 0);
(gdb) p dbuckets
$16 = 10000
(gdb) p nbuckets
$17 = 16384
(gdb) p bucket_bytes
$18 = 131072
ExecChooseHashTableSize->只需要一个批次,赋值,返回
835 Assert(nbatch > 0);
(gdb)
837 *numbuckets = nbuckets;
(gdb)
838 *numbatches = nbatch;
(gdb)
839 }
(gdb)
(gdb)
ExecHashTableCreate (state=0x1e3cbc8, hashOperators=0x1e59890, keepNulls=false) at nodeHash.c:468
468 log2_nbuckets = my_log2(nbuckets);
初始化Hash表
468 log2_nbuckets = my_log2(nbuckets);
(gdb) p nbuckets
$19 = 16384
(gdb) n
469 Assert(nbuckets == (1 << log2_nbuckets));
(gdb)
478 hashtable = (HashJoinTable) palloc(sizeof(HashJoinTableData));
(gdb)
479 hashtable->nbuckets = nbuckets;
...
分配内存上下文
...
(gdb)
522 hashtable->hashCxt = AllocSetContextCreate(CurrentMemoryContext,
(gdb)
526 hashtable->batchCxt = AllocSetContextCreate(hashtable->hashCxt,
(gdb)
532 oldcxt = MemoryContextSwitchTo(hashtable->hashCxt);
(gdb)
切换上下文,并初始化hash函数
(gdb)
532 oldcxt = MemoryContextSwitchTo(hashtable->hashCxt);
(gdb) n
538 nkeys = list_length(hashOperators);
(gdb)
540 (FmgrInfo *) palloc(nkeys * sizeof(FmgrInfo));
(gdb) p nkeys
$20 = 1
(gdb) n
539 hashtable->outer_hashfunctions =
(gdb)
542 (FmgrInfo *) palloc(nkeys * sizeof(FmgrInfo));
(gdb)
541 hashtable->inner_hashfunctions =
(gdb)
543 hashtable->hashStrict = (bool *) palloc(nkeys * sizeof(bool));
(gdb)
544 i = 0;
初始化Hash操作符
(gdb) n
545 foreach(ho, hashOperators)
(gdb)
547 Oid hashop = lfirst_oid(ho);
(gdb)
551 if (!get_op_hash_functions(hashop, &left_hashfn, &right_hashfn))
(gdb)
554 fmgr_info(left_hashfn, &hashtable->outer_hashfunctions[i]);
(gdb)
555 fmgr_info(right_hashfn, &hashtable->inner_hashfunctions[i]);
(gdb)
556 hashtable->hashStrict[i] = op_strict(hashop);
(gdb)
557 i++;
(gdb)
545 foreach(ho, hashOperators)
(gdb) p *hashtable->hashStrict
$21 = true
(gdb) n
560 if (nbatch > 1 && hashtable->parallel_state == NULL)
分配hash桶内存空间
gdb) n
575 MemoryContextSwitchTo(oldcxt);
(gdb)
577 if (hashtable->parallel_state)
(gdb)
631 MemoryContextSwitchTo(hashtable->batchCxt);
(gdb)
634 palloc0(nbuckets * sizeof(HashJoinTuple));
(gdb)
633 hashtable->buckets.unshared = (HashJoinTuple *)
(gdb) p nbuckets
$23 = 16384
构造完成,返回hash表
(gdb) n
641 if (nbatch > 1)
(gdb)
644 MemoryContextSwitchTo(oldcxt);
(gdb)
647 return hashtable;
(gdb)
648 }
(gdb)
ExecHashJoinImpl (pstate=0x1e3c048, parallel=false) at nodeHashjoin.c:282
282 node->hj_HashTable = hashtable;
(gdb)
DONE!
四、参考资料
Hash Joins: Past, Present and Future/PGCon 2017
A Look at How Postgres Executes a Tiny Join - Part 1
A Look at How Postgres Executes a Tiny Join - Part 2
Assignment 2 Symmetric Hash Join
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