PostgreSQL的ExecHashJoin依赖其他函数的实现逻辑是什么
本篇内容介绍了“PostgreSQL的ExecHashJoin依赖其他函数的实现逻辑是什么”的有关知识，在实际案例的操作过程中，不少人都会遇到这样的困境，接下来就让小编带领大家学习一下如何处理这些情况吧！希望大家仔细阅读，能够学有所成！
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一、数据结构
JoinState
Hash/NestLoop/Merge Join的基类
/* ----------------
* JoinState information
*
* Superclass for state nodes of join plans.
* 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; /* True if we should skip to next outer tuple
* after finding one inner match */
//连接条件表达式(除了ps.qual)
ExprState *joinqual; /* JOIN quals (in addition to ps.qual) */
} JoinState;HashJoinState
Hash Join运行期状态结构体
/* these structs are defined in executor/hashjoin.h: */
typedef struct HashJoinTupleData *HashJoinTuple;
typedef struct HashJoinTableData *HashJoinTable;
typedef struct HashJoinState
{
JoinState js; /* 基类;its first field is NodeTag */
ExprState *hashclauses;//hash连接条件
List *hj_OuterHashKeys; /* 外表条件链表;list of ExprState nodes */
List *hj_InnerHashKeys; /* 内表连接条件;list of ExprState nodes */
List *hj_HashOperators; /* 操作符OIDs链表;list of operator OIDs */
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; /* 内存中的hash桶数;# buckets in the in-memory hash table */
int log2_nbuckets; /* 2的对数(nbuckets必须是2的幂);its log2 (nbuckets must be a power of 2) */
int nbuckets_original; /* 首次hash时的桶数;# buckets when starting the first hash */
int nbuckets_optimal; /* 优化后的桶数(每个批次);optimal # buckets (per batch) */
int log2_nbuckets_optimal; /* 2的对数;log2(nbuckets_optimal) */
/* buckets[i] is head of list of tuples in i'th in-memory bucket */
//bucket [i]是内存中第i个桶中的元组链表的head item
union
{
/* unshared array is per-batch storage, as are all the tuples */
//未共享数组是按批处理存储的，所有元组均如此
struct HashJoinTupleData **unshared;
/* shared array is per-query DSA area, as are all the tuples */
//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; /*如不匹配则存储NULL元组,该值为T;true to store unmatchable NULL tuples */
bool skewEnabled; /*是否使用倾斜优化?;are we using skew optimization? */
HashSkewBucket **skewBucket; /* 倾斜的hash表桶数;hashtable of skew buckets */
int skewBucketLen; /* skewBucket数组大小;size of skewBucket array (a power of 2!) */
int nSkewBuckets; /* 活动的倾斜桶数;number of active skew buckets */
int *skewBucketNums; /* 活动倾斜桶数组索引;array indexes of active skew buckets */
int nbatch; /* 批次数;number of batches */
int curbatch; /* 当前批次,第一轮为0;current batch #; 0 during 1st pass */
int nbatch_original; /* 在开始inner扫描时的批次;nbatch when we started inner scan */
int nbatch_outstart; /* 在开始outer扫描时的批次;nbatch when we started outer scan */
bool growEnabled; /* 关闭nbatch增加的标记;flag to shut off nbatch increases */
double totalTuples; /* 从inner plan获得的元组数;# tuples obtained from inner plan */
double partialTuples; /* 通过hashjoin获得的inner元组数;# tuples obtained from inner plan by me */
double skewTuples; /* 倾斜元组数;# tuples inserted into skew tuples */
/*
* These arrays are allocated for the life of the hash join, but only if
* nbatch > 1. A file is opened only when we first write a tuple into it
* (otherwise its pointer remains NULL). Note that the zero'th array
* elements never get used, since we will process rather than dump out any
* tuples of batch zero.
* 这些数组在散列连接的生命周期内分配，但仅当nbatch > 1时分配。
* 只有当第一次将元组写入文件时，文件才会打开(否则它的指针将保持NULL)。
* 注意，第0个数组元素永远不会被使用，因为批次0的元组永远不会转储.
*/
BufFile **innerBatchFile; /* 每个批次的inner虚拟临时文件缓存;buffered virtual temp file per batch */
BufFile **outerBatchFile; /* 每个批次的outer虚拟临时文件缓存;buffered virtual temp file per batch */
/*
* Info about the datatype-specific hash functions for the datatypes being
* hashed. These are arrays of the same length as the number of hash join
* clauses (hash keys).
* 有关正在散列的数据类型的特定于数据类型的散列函数的信息。
* 这些数组的长度与散列连接子句(散列键)的数量相同。
*/
FmgrInfo *outer_hashfunctions; /* outer hash函数FmgrInfo结构体;lookup data for hash functions */
FmgrInfo *inner_hashfunctions; /* inner hash函数FmgrInfo结构体;lookup data for hash functions */
bool *hashStrict; /* 每个hash操作符是严格?is each hash join operator strict? */
Size spaceUsed; /* 元组使用的当前内存空间大小;memory space currently used by tuples */
Size spaceAllowed; /* 空间使用上限;upper limit for space used */
Size spacePeak; /* 峰值的空间使用;peak space used */
Size spaceUsedSkew; /* 倾斜哈希表的当前空间使用情况;skew hash table's current space usage */
Size spaceAllowedSkew; /* 倾斜哈希表的使用上限;upper limit for skew hashtable */
MemoryContext hashCxt; /* 整个散列连接存储的上下文;context for whole-hash-join storage */
MemoryContext batchCxt; /* 该批次存储的上下文;context for this-batch-only storage */
/* used for dense allocation of tuples (into linked chunks) */
//用于密集分配元组(到链接块中)
HashMemoryChunk chunks; /* 整个批次使用一个链表;one list for the whole batch */
/* Shared and private state for Parallel Hash. */
//并行hash使用的共享和私有状态
HashMemoryChunk current_chunk; /* 后台进程的当前chunk;this backend's current chunk */
dsa_area *area; /* 用于分配内存的DSA区域;DSA area to allocate memory from */
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
/* ----------------------------------------------------------------
* hash-join hash table structures
*
* Each active hashjoin has a HashJoinTable control block, which is
* palloc'd in the executor's per-query context. All other storage needed
* for the hashjoin is kept in private memory contexts, two for each hashjoin.
* This makes it easy and fast to release the storage when we don't need it
* anymore. (Exception: data associated with the temp files lives in the
* per-query context too, since we always call buffile.c in that context.)
* 每个活动的hashjoin都有一个可散列的控制块，它在执行程序的每个查询上下文中都是通过palloc分配的。
* hashjoin所需的所有其他存储都保存在私有内存上下文中，每个hashjoin有两个。
* 当不再需要它的时候，这使得释放它变得简单和快速。
* (例外:与临时文件相关的数据也存在于每个查询上下文中，因为在这种情况下总是调用buffile.c。)
*
* The hashtable contexts are made children of the per-query context, ensuring
* that they will be discarded at end of statement even if the join is
* aborted early by an error. (Likewise, any temporary files we make will
* be cleaned up by the virtual file manager in event of an error.)
* hashtable上下文是每个查询上下文的子上下文，确保在语句结束时丢弃它们，即使连接因错误而提前中止。
* (同样，如果出现错误，虚拟文件管理器将清理创建的任何临时文件。)
*
* Storage that should live through the entire join is allocated from the
* "hashCxt", while storage that is only wanted for the current batch is
* allocated in the "batchCxt". By resetting the batchCxt at the end of
* each batch, we free all the per-batch storage reliably and without tedium.
* 通过整个连接的存储空间应从“hashCxt”分配，而只需要当前批处理的存储空间在“batchCxt”中分配。
* 通过在每个批处理结束时重置batchCxt，可以可靠地释放每个批处理的所有存储，而不会感到单调乏味。
*
* During first scan of inner relation, we get its tuples from executor.
* If nbatch > 1 then tuples that don't belong in first batch get saved
* into inner-batch temp files. The same statements apply for the
* first scan of the outer relation, except we write tuples to outer-batch
* temp files. After finishing the first scan, we do the following for
* each remaining batch:
* 1. Read tuples from inner batch file, load into hash buckets.
* 2. Read tuples from outer batch file, match to hash buckets and output.
* 在内部关系的第一次扫描中，从执行者那里得到了它的元组。
* 如果nbatch > 1，那么不属于第一批的元组将保存到批内临时文件中。
* 相同的语句适用于外关系的第一次扫描，但是我们将元组写入外部批处理临时文件。
* 完成第一次扫描后，我们对每批剩余的元组做如下处理:
* 1.从内部批处理文件读取元组，加载到散列桶中。
* 2.从外部批处理文件读取元组，匹配哈希桶和输出。
*
* It is possible to increase nbatch on the fly if the in-memory hash table
* gets too big. The hash-value-to-batch computation is arranged so that this
* can only cause a tuple to go into a later batch than previously thought,
* never into an earlier batch. When we increase nbatch, we rescan the hash
* table and dump out any tuples that are now of a later batch to the correct
* inner batch file. Subsequently, while reading either inner or outer batch
* files, we might find tuples that no longer belong to the current batch;
* if so, we just dump them out to the correct batch file.
* 如果内存中的哈希表太大，可以动态增加nbatch。
* 散列值到批处理的计算是这样安排的:
* 这只会导致元组进入比以前认为的更晚的批处理，而不会进入更早的批处理。
* 当增加nbatch时，重新扫描哈希表，并将现在属于后面批处理的任何元组转储到正确的内部批处理文件。
* 随后，在读取内部或外部批处理文件时，可能会发现不再属于当前批处理的元组;
* 如果是这样，只需将它们转储到正确的批处理文件即可。
* ----------------------------------------------------------------
*/
/* these are in nodes/execnodes.h: */
/* typedef struct HashJoinTupleData *HashJoinTuple; */
/* typedef struct HashJoinTableData *HashJoinTable; */
typedef struct HashJoinTupleData
{
/* link to next tuple in same bucket */
//link同一个桶中的下一个元组
union
{
struct HashJoinTupleData *unshared;
dsa_pointer shared;
} next;
uint32 hashvalue; /* 元组的hash值;tuple's hash code */
/* Tuple data, in MinimalTuple format, follows on a MAXALIGN boundary */
} HashJoinTupleData;
#define HJTUPLE_OVERHEAD MAXALIGN(sizeof(HashJoinTupleData))
#define HJTUPLE_MINTUPLE(hjtup) \
((MinimalTuple) ((char *) (hjtup) + HJTUPLE_OVERHEAD))二、源码解读
ExecScanHashBucket
搜索匹配当前outer relation tuple的hash桶,寻找匹配的inner relation元组。
/*----------------------------------------------------------------------------------------------------
HJ_SCAN_BUCKET 阶段
----------------------------------------------------------------------------------------------------*/
/*
* ExecScanHashBucket
* scan a hash bucket for matches to the current outer tuple
* 搜索匹配当前outer relation tuple的hash桶
*
* The current outer tuple must be stored in econtext->ecxt_outertuple.
* 当前的outer relation tuple必须存储在econtext->ecxt_outertuple中
*
* On success, the inner tuple is stored into hjstate->hj_CurTuple and
* econtext->ecxt_innertuple, using hjstate->hj_HashTupleSlot as the slot
* for the latter.
* 成功后，内部元组存储到hjstate->hj_CurTuple和econtext->ecxt_innertuple中，
* 使用hjstate->hj_HashTupleSlot作为后者的slot。
*/
bool
ExecScanHashBucket(HashJoinState *hjstate,
ExprContext *econtext)
{
ExprState *hjclauses = hjstate->hashclauses;//hash连接条件表达式
HashJoinTable hashtable = hjstate->hj_HashTable;//Hash表
HashJoinTuple hashTuple = hjstate->hj_CurTuple;//当前的Tuple
uint32 hashvalue = hjstate->hj_CurHashValue;//hash值
/*
* hj_CurTuple is the address of the tuple last returned from the current
* bucket, or NULL if it's time to start scanning a new bucket.
* hj_CurTuple是最近从当前桶返回的元组的地址，如果需要开始扫描新桶，则为NULL。
*
* If the tuple hashed to a skew bucket then scan the skew bucket
* otherwise scan the standard hashtable bucket.
* 如果元组散列到倾斜桶，则扫描倾斜桶，否则扫描标准哈希表桶。
*/
if (hashTuple != NULL)
hashTuple = hashTuple->next.unshared;//hashTuple,通过指针获取下一个
else if (hjstate->hj_CurSkewBucketNo != INVALID_SKEW_BUCKET_NO)
//如为NULL,而且使用倾斜优化,则从倾斜桶中获取
hashTuple = hashtable->skewBucket[hjstate->hj_CurSkewBucketNo]->tuples;
else
////如为NULL,不使用倾斜优化,从常规的bucket中获取
hashTuple = hashtable->buckets.unshared[hjstate->hj_CurBucketNo];
while (hashTuple != NULL)//循环
{
if (hashTuple->hashvalue == hashvalue)//hash值一致
{
TupleTableSlot *inntuple;//inner tuple
/* insert hashtable's tuple into exec slot so ExecQual sees it */
//把Hash表中的tuple插入到执行器的slot中,作为函数ExecQual的输入使用
inntuple = ExecStoreMinimalTuple(HJTUPLE_MINTUPLE(hashTuple),
hjstate->hj_HashTupleSlot,
false); /* do not pfree */
econtext->ecxt_innertuple = inntuple;//赋值
if (ExecQualAndReset(hjclauses, econtext))//判断连接条件是否满足
{
hjstate->hj_CurTuple = hashTuple;//满足,则赋值&返回T
return true;
}
}
hashTuple = hashTuple->next.unshared;//从Hash表中获取下一个tuple
}
/*
* no match
* 不匹配,返回F
*/
return false;
}
/*
* Store a minimal tuple into TTSOpsMinimalTuple type slot.
* 存储最小化的tuple到TTSOpsMinimalTuple类型的slot中
*
* If the target slot is not guaranteed to be TTSOpsMinimalTuple type slot,
* use the, more expensive, ExecForceStoreMinimalTuple().
* 如果目标slot不能确保是TTSOpsMinimalTuple类型,使用代价更高的ExecForceStoreMinimalTuple()函数
*/
TupleTableSlot *
ExecStoreMinimalTuple(MinimalTuple mtup,
TupleTableSlot *slot,
bool shouldFree)
{
/*
* sanity checks
* 安全检查
*/
Assert(mtup != NULL);
Assert(slot != NULL);
Assert(slot->tts_tupleDescriptor != NULL);
if (unlikely(!TTS_IS_MINIMALTUPLE(slot)))//类型检查
elog(ERROR, "trying to store a minimal tuple into wrong type of slot");
tts_minimal_store_tuple(slot, mtup, shouldFree);//存储
return slot;//返回slot
}
static void
tts_minimal_store_tuple(TupleTableSlot *slot, MinimalTuple mtup, bool shouldFree)
{
MinimalTupleTableSlot *mslot = (MinimalTupleTableSlot *) slot;//获取slot
tts_minimal_clear(slot);//清除原来的slot
//安全检查
Assert(!TTS_SHOULDFREE(slot));
Assert(TTS_EMPTY(slot));
//设置slot信息
slot->tts_flags &= ~TTS_FLAG_EMPTY;
slot->tts_nvalid = 0;
mslot->off = 0;
//存储到mslot中
mslot->mintuple = mtup;
Assert(mslot->tuple == &mslot->minhdr);
mslot->minhdr.t_len = mtup->t_len + MINIMAL_TUPLE_OFFSET;
mslot->minhdr.t_data = (HeapTupleHeader) ((char *) mtup - MINIMAL_TUPLE_OFFSET);
/* no need to set t_self or t_tableOid since we won't allow access */
//不需要设置t_sefl或者t_tableOid,因为不允许访问
if (shouldFree)
slot->tts_flags |= TTS_FLAG_SHOULDFREE;
else
Assert(!TTS_SHOULDFREE(slot));
}
/*
* ExecQualAndReset() - evaluate qual with ExecQual() and reset expression
* context.
* ExecQualAndReset() - 使用ExecQual()解析并重置表达式
*/
#ifndef FRONTEND
static inline bool
ExecQualAndReset(ExprState *state, ExprContext *econtext)
{
bool ret = ExecQual(state, econtext);//调用ExecQual
/* inline ResetExprContext, to avoid ordering issue in this file */
//内联ResetExprContext,避免在这个文件中的ordering
MemoryContextReset(econtext->ecxt_per_tuple_memory);
return ret;
}
#endif
#define HeapTupleHeaderSetMatch(tup) \
( \
(tup)->t_infomask2 |= HEAP_TUPLE_HAS_MATCH \
)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
(gdb) b ExecScanHashBucket Breakpoint 1 at 0x6ff25b: file nodeHash.c, line 1910. (gdb) c Continuing. Breakpoint 1, ExecScanHashBucket (hjstate=0x2bb8738, econtext=0x2bb8950) at nodeHash.c:1910 1910 ExprState *hjclauses = hjstate->hashclauses;
设置相关变量
1910 ExprState *hjclauses = hjstate->hashclauses; (gdb) n 1911 HashJoinTable hashtable = hjstate->hj_HashTable; (gdb) 1912 HashJoinTuple hashTuple = hjstate->hj_CurTuple; (gdb) 1913 uint32 hashvalue = hjstate->hj_CurHashValue; (gdb) 1922 if (hashTuple != NULL)
hash join连接条件
(gdb) p *hjclauses
$1 = {tag = {type = T_ExprState}, flags = 7 '\a', resnull = false, resvalue = 0, resultslot = 0x0, steps = 0x2bc4bc8,
evalfunc = 0x6d1a6e , expr = 0x2bb60c0, evalfunc_private = 0x6cf625 ,
steps_len = 7, steps_alloc = 16, parent = 0x2bb8738, ext_params = 0x0, innermost_caseval = 0x0, innermost_casenull = 0x0,
innermost_domainval = 0x0, innermost_domainnull = 0x0} hash表
(gdb) p hashtable
$2 = (HashJoinTable) 0x2bc9de8
(gdb) p *hashtable
$3 = {nbuckets = 16384, log2_nbuckets = 14, nbuckets_original = 16384, nbuckets_optimal = 16384,
log2_nbuckets_optimal = 14, buckets = {unshared = 0x7f0fc1345050, shared = 0x7f0fc1345050}, keepNulls = false,
skewEnabled = false, skewBucket = 0x0, skewBucketLen = 0, nSkewBuckets = 0, skewBucketNums = 0x0, nbatch = 1,
curbatch = 0, nbatch_original = 1, nbatch_outstart = 1, growEnabled = true, totalTuples = 10000, partialTuples = 10000,
skewTuples = 0, innerBatchFile = 0x0, outerBatchFile = 0x0, outer_hashfunctions = 0x2bdc228,
inner_hashfunctions = 0x2bdc280, hashStrict = 0x2bdc2d8, spaceUsed = 677754, spaceAllowed = 16777216, spacePeak = 677754,
spaceUsedSkew = 0, spaceAllowedSkew = 335544, hashCxt = 0x2bdc110, batchCxt = 0x2bde120, chunks = 0x2c708f0,
current_chunk = 0x0, area = 0x0, parallel_state = 0x0, batches = 0x0, current_chunk_shared = 0}hash桶中的元组&hash值
(gdb) p *hashTuple Cannot access memory at address 0x0 (gdb) p hashvalue $4 = 2324234220 (gdb)
从常规hash桶中获取hash元组
(gdb) n
1924 else if (hjstate->hj_CurSkewBucketNo != INVALID_SKEW_BUCKET_NO)
(gdb) p hjstate->hj_CurSkewBucketNo
$5 = -1
(gdb) n
1927 hashTuple = hashtable->buckets.unshared[hjstate->hj_CurBucketNo];
(gdb)
1929 while (hashTuple != NULL)
(gdb) p hjstate->hj_CurBucketNo
$7 = 16364
(gdb) p *hashTuple
$6 = {next = {unshared = 0x0, shared = 0}, hashvalue = 1822113772}判断hash值是否一致
(gdb) n 1931 if (hashTuple->hashvalue == hashvalue) (gdb) p hashTuple->hashvalue $8 = 1822113772 (gdb) p hashvalue $9 = 2324234220 (gdb)
不一致,继续下一个元组
(gdb) n 1948 hashTuple = hashTuple->next.unshared; (gdb) 1929 while (hashTuple != NULL)
下一个元组为NULL,返回F,说明没有匹配的元组
(gdb) p *hashTuple Cannot access memory at address 0x0 (gdb) n 1954 return false;
在ExecStoreMinimalTuple上设置断点(这时候Hash值是一致的)
(gdb) b ExecStoreMinimalTuple Breakpoint 2 at 0x6e8cbf: file execTuples.c, line 427. (gdb) c Continuing. Breakpoint 1, ExecScanHashBucket (hjstate=0x2bb8738, econtext=0x2bb8950) at nodeHash.c:1910 1910 ExprState *hjclauses = hjstate->hashclauses; (gdb) del 1 (gdb) c Continuing. Breakpoint 2, ExecStoreMinimalTuple (mtup=0x2be81b0, slot=0x2bb9c18, shouldFree=false) at execTuples.c:427 427 Assert(mtup != NULL); (gdb) finish Run till exit from #0 ExecStoreMinimalTuple (mtup=0x2be81b0, slot=0x2bb9c18, shouldFree=false) at execTuples.c:427 0x00000000006ff335 in ExecScanHashBucket (hjstate=0x2bb8738, econtext=0x2bb8950) at nodeHash.c:1936 1936 inntuple = ExecStoreMinimalTuple(HJTUPLE_MINTUPLE(hashTuple), Value returned is $10 = (TupleTableSlot *) 0x2bb9c18 (gdb) n 1939 econtext->ecxt_innertuple = inntuple;
匹配成功,返回T
(gdb) n 1941 if (ExecQualAndReset(hjclauses, econtext)) (gdb) 1943 hjstate->hj_CurTuple = hashTuple; (gdb) 1944 return true; (gdb) 1955 } (gdb)
HJ_SCAN_BUCKET阶段,实现的逻辑是扫描Hash桶,寻找inner relation中与outer relation元组匹配的元组,如匹配,则把匹配的Tuple存储在hjstate->hj_CurTuple中.
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文章标题：PostgreSQL的ExecHashJoin依赖其他函数的实现逻辑是什么
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