本节是ExecHashJoin函数介绍的第五部分，主要介绍了ExecHashJoin中依赖的其他函数的实现逻辑，这些函数在HJ_NEED_NEW_BATCH阶段中使用，主要的函数是ExecHashJoinNewBatch。
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
ExecHashJoinNewBatch
切换到新的hashjoin批次,如成功,则返回T;已完成,返回F
/*----------------------------------------------------------------------------------------------------
HJ_FILL_OUTER_TUPLE 阶段
----------------------------------------------------------------------------------------------------*/
//参见ExecHashJoin
/*----------------------------------------------------------------------------------------------------
HJ_FILL_INNER_TUPLES 阶段
----------------------------------------------------------------------------------------------------*/
//参见ExecHashJoin
/*----------------------------------------------------------------------------------------------------
HJ_NEED_NEW_BATCH 阶段
----------------------------------------------------------------------------------------------------*/
/*
* ExecHashJoinNewBatch
* switch to a new hashjoin batch
* 切换到新的hashjoin批次
*
* Returns true if successful, false if there are no more batches.
* 如成功,则返回T;已完成,返回F
*/
static bool
ExecHashJoinNewBatch(HashJoinState *hjstate)
{
HashJoinTable hashtable = hjstate->hj_HashTable;//Hash表
int nbatch;//批次数
int curbatch;//当前批次
BufFile *innerFile;//inner relation缓存文件
TupleTableSlot *slot;//slot
uint32 hashvalue;//hash值
nbatch = hashtable->nbatch;
curbatch = hashtable->curbatch;
if (curbatch > 0)
{
/*
* We no longer need the previous outer batch file; close it right
* away to free disk space.
* 不再需要以前的外批处理文件;关闭它以释放磁盘空间。
*/
if (hashtable->outerBatchFile[curbatch])
BufFileClose(hashtable->outerBatchFile[curbatch]);
hashtable->outerBatchFile[curbatch] = NULL;
}
else /* curbatch ==0,刚刚完成了第一个批次;we just finished the first batch */
{
/*
* Reset some of the skew optimization state variables, since we no
* longer need to consider skew tuples after the first batch. The
* memory context reset we are about to do will release the skew
* hashtable itself.
* 重置一些倾斜优化状态变量，因为在第一批之后我们不再需要考虑倾斜元组。
* 我们将要进行的内存上下文重置将释放倾斜散链表本身。
*/
hashtable->skewEnabled = false;
hashtable->skewBucket = NULL;
hashtable->skewBucketNums = NULL;
hashtable->nSkewBuckets = 0;
hashtable->spaceUsedSkew = 0;
}
/*
* We can always skip over any batches that are completely empty on both
* sides. We can sometimes skip over batches that are empty on only one
* side, but there are exceptions:
* 可以跳过任何两边都是空的批次。有时我们可以跳过只在一侧为空的批处理，但也有例外:
*
* 1. In a left/full outer join, we have to process outer batches even if
* the inner batch is empty. Similarly, in a right/full outer join, we
* have to process inner batches even if the outer batch is empty.
* 1、在左/全外连接中，即使内部批是空的，我们也必须处理外批数据。
* 类似地，在右/完整外部连接中，即使外批数据为空，也必须处理内批数据。
*
* 2. If we have increased nbatch since the initial estimate, we have to
* scan inner batches since they might contain tuples that need to be
* reassigned to later inner batches.
* 2、如果在初始估算之后增加了nbatch，必须扫描内部批处理，
* 因为它们可能包含需要重新分配到后面的内部批处理的元组。
*
* 3. Similarly, if we have increased nbatch since starting the outer
* scan, we have to rescan outer batches in case they contain tuples that
* need to be reassigned.
* 3、类似地，如果在开始外部扫描之后增加了nbatch，必须重新扫描外部批处理，
* 以防它们包含需要重新分配的元组。
*/
curbatch++;
while (curbatch < nbatch &&
(hashtable->outerBatchFile[curbatch] == NULL ||
hashtable->innerBatchFile[curbatch] == NULL))
{
if (hashtable->outerBatchFile[curbatch] &&
HJ_FILL_OUTER(hjstate))
break; /* 符合规则1,需要处理;must process due to rule 1 */
if (hashtable->innerBatchFile[curbatch] &&
HJ_FILL_INNER(hjstate))
break; /* 符合规则1,需要处理;must process due to rule 1 */
if (hashtable->innerBatchFile[curbatch] &&
nbatch != hashtable->nbatch_original)
break; /* 符合规则2,需要处理;must process due to rule 2 */
if (hashtable->outerBatchFile[curbatch] &&
nbatch != hashtable->nbatch_outstart)
break; /* 符合规则3,需要处理;must process due to rule 3 */
/* We can ignore this batch. */
/* Release associated temp files right away. */
//均不符合规则1-3,则可以忽略这个批次了
//释放临时文件
if (hashtable->innerBatchFile[curbatch])
BufFileClose(hashtable->innerBatchFile[curbatch]);
hashtable->innerBatchFile[curbatch] = NULL;
if (hashtable->outerBatchFile[curbatch])
BufFileClose(hashtable->outerBatchFile[curbatch]);
hashtable->outerBatchFile[curbatch] = NULL;
curbatch++;//下一个批次
}
if (curbatch >= nbatch)
return false; /* 已完成处理所有批次;no more batches */
hashtable->curbatch = curbatch;//下一个批次
/*
* Reload the hash table with the new inner batch (which could be empty)
* 使用新的内部批处理数据(有可能是空的)重新加载哈希表
*/
ExecHashTableReset(hashtable);//重置Hash表
//inner relation文件
innerFile = hashtable->innerBatchFile[curbatch];
//批次文件不为NULL
if (innerFile != NULL)
{
if (BufFileSeek(innerFile, 0, 0L, SEEK_SET))//扫描innerFile,不成功,则报错
ereport(ERROR,
(errcode_for_file_access(),
errmsg("could not rewind hash-join temporary file: %m")));
while ((slot = ExecHashJoinGetSavedTuple(hjstate,
innerFile,
&hashvalue,
hjstate->hj_HashTupleSlot)))//
{
/*
* NOTE: some tuples may be sent to future batches. Also, it is
* possible for hashtable->nbatch to be increased here!
* 注意:一些元组可能被发送到未来的批次中。
* 另外，这里也可以增加hashtable->nbatch !
*/
ExecHashTableInsert(hashtable, slot, hashvalue);
}
/*
* after we build the hash table, the inner batch file is no longer
* needed
* 构建哈希表之后，内部批处理临时文件就不再需要了,关闭之
*/
BufFileClose(innerFile);
hashtable->innerBatchFile[curbatch] = NULL;
}
/*
* Rewind outer batch file (if present), so that we can start reading it.
*/
if (hashtable->outerBatchFile[curbatch] != NULL)
{
if (BufFileSeek(hashtable->outerBatchFile[curbatch], 0, 0L, SEEK_SET))
ereport(ERROR,
(errcode_for_file_access(),
errmsg("could not rewind hash-join temporary file: %m")));
}
return true;
}
/*
* ExecHashJoinGetSavedTuple
* read the next tuple from a batch file. Return NULL if no more.
* 从批次文件中读取下一个元组,如无则返回NULL
*
* On success, *hashvalue is set to the tuple's hash value, and the tuple
* itself is stored in the given slot.
* 如成功,*hashvalue参数设置为元组的Hash值,元组存储在给定的slot中
*/
static TupleTableSlot *
ExecHashJoinGetSavedTuple(HashJoinState *hjstate,
BufFile *file,
uint32 *hashvalue,
TupleTableSlot *tupleSlot)
{
uint32 header[2];
size_t nread;
MinimalTuple tuple;
/*
* We check for interrupts here because this is typically taken as an
* alternative code path to an ExecProcNode() call, which would include
* such a check.
* 在这里检查中断，因为这通常被作为ExecProcNode()调用的替代代码路径，通常包含这样的检查。
*/
CHECK_FOR_INTERRUPTS();
/*
* Since both the hash value and the MinimalTuple length word are uint32,
* we can read them both in one BufFileRead() call without any type
* cheating.
* 因为哈希值和最小长度字都是uint32，所以可以在一个BufFileRead()调用中读取它们，
* 而不需要任何类型的cheating。
*/
nread = BufFileRead(file, (void *) header, sizeof(header));//读取文件
if (nread == 0) /* end of file */
{
//已读取完毕,返回NULL
ExecClearTuple(tupleSlot);
return NULL;
}
if (nread != sizeof(header))//读取的大小不等于header的大小,报错
ereport(ERROR,
(errcode_for_file_access(),
errmsg("could not read from hash-join temporary file: %m")));
//hash值
*hashvalue = header[0];
//tuple,分配的内存大小为MinimalTuple结构体大小
tuple = (MinimalTuple) palloc(header[1]);
//元组大小
tuple->t_len = header[1];
//读取文件
nread = BufFileRead(file,
(void *) ((char *) tuple + sizeof(uint32)),
header[1] - sizeof(uint32));
//读取有误,报错
if (nread != header[1] - sizeof(uint32))
ereport(ERROR,
(errcode_for_file_access(),
errmsg("could not read from hash-join temporary file: %m")));
//存储到slot中
ExecForceStoreMinimalTuple(tuple, tupleSlot, true);
return tupleSlot;//返回slot
}
/*
* ExecHashTableInsert
* insert a tuple into the hash table depending on the hash value
* it may just go to a temp file for later batches
* 根据哈希值向哈希表中插入一个tuple，它可能只是转到一个临时文件中以供以后的批处理
*
* Note: the passed TupleTableSlot may contain a regular, minimal, or virtual
* tuple; the minimal case in particular is certain to happen while reloading
* tuples from batch files. We could save some cycles in the regular-tuple
* case by not forcing the slot contents into minimal form; not clear if it's
* worth the messiness required.
* 注意:传递的TupleTableSlot可能包含一个常规、最小或虚拟元组;
* 在从批处理文件中重新加载元组时，肯定会出现最小的情况。
* 如为常规元组，可以通过不强制slot内容变成最小形式来节省一些处理时间;
* 但不清楚这样的混乱是否值得。
*/
void
ExecHashTableInsert(HashJoinTable hashtable,
TupleTableSlot *slot,
uint32 hashvalue)
{
bool shouldFree;//是否释放资源
MinimalTuple tuple = ExecFetchSlotMinimalTuple(slot, &shouldFree);//获取一个MinimalTuple
int bucketno;//桶号
int batchno;//批次号
ExecHashGetBucketAndBatch(hashtable, hashvalue,
&bucketno, &batchno);//获取桶号和批次号
/*
* decide whether to put the tuple in the hash table or a temp file
* 判断是否放到hash表中还是放到临时文件中
*/
if (batchno == hashtable->curbatch)
{
//批次号==hash表的批次号,放到hash表中
/*
* put the tuple in hash table
* 把元组放到hash表中
*/
HashJoinTuple hashTuple;//hash tuple
int hashTupleSize;//大小
double ntuples = (hashtable->totalTuples - hashtable->skewTuples);//常规元组数量
/* Create the HashJoinTuple */
//创建HashJoinTuple
hashTupleSize = HJTUPLE_OVERHEAD + tuple->t_len;//大小
hashTuple = (HashJoinTuple) dense_alloc(hashtable, hashTupleSize);//分配存储空间
//hash值
hashTuple->hashvalue = hashvalue;
//拷贝数据
memcpy(HJTUPLE_MINTUPLE(hashTuple), tuple, tuple->t_len);
/*
* We always reset the tuple-matched flag on insertion. This is okay
* even when reloading a tuple from a batch file, since the tuple
* could not possibly have been matched to an outer tuple before it
* went into the batch file.
* 我们总是在插入时重置元组匹配的标志。
* 即使在从批处理文件中重新加载元组时，这也是可以的，
* 因为在元组进入批处理文件之前，它不可能与外部元组匹配。
*/
HeapTupleHeaderClearMatch(HJTUPLE_MINTUPLE(hashTuple));
/* Push it onto the front of the bucket's list */
//
hashTuple->next.unshared = hashtable->buckets.unshared[bucketno];
hashtable->buckets.unshared[bucketno] = hashTuple;
/*
* Increase the (optimal) number of buckets if we just exceeded the
* NTUP_PER_BUCKET threshold, but only when there's still a single
* batch.
* 如果刚刚超过了NTUP_PER_BUCKET阈值，但是只有在仍然有单个批处理时，
* 才增加桶的(优化后)数量。
*/
if (hashtable->nbatch == 1 &&
ntuples > (hashtable->nbuckets_optimal * NTUP_PER_BUCKET))
{
//只有1个批次而且元组数大于桶数*每桶的元组数
/* Guard against integer overflow and alloc size overflow */
//确保整数不要溢出
if (hashtable->nbuckets_optimal <= INT_MAX / 2 &&
hashtable->nbuckets_optimal * 2 <= MaxAllocSize / sizeof(HashJoinTuple))
{
hashtable->nbuckets_optimal *= 2;
hashtable->log2_nbuckets_optimal += 1;
}
}
/* Account for space used, and back off if we've used too much */
//声明使用的存储空间，如果使用太多，需要回退
hashtable->spaceUsed += hashTupleSize;
if (hashtable->spaceUsed > hashtable->spacePeak)
hashtable->spacePeak = hashtable->spaceUsed;//超出峰值,则跳转
if (hashtable->spaceUsed +
hashtable->nbuckets_optimal * sizeof(HashJoinTuple)
> hashtable->spaceAllowed)
ExecHashIncreaseNumBatches(hashtable);//超出允许的空间,则增加批次
}
else
{
//不在这个批次中
/*
* put the tuple into a temp file for later batches
* 放在临时文件中以便后续处理(减少重复扫描)
*/
Assert(batchno > hashtable->curbatch);
ExecHashJoinSaveTuple(tuple,
hashvalue,
&hashtable->innerBatchFile[batchno]);//存储tuple到临时文件中
}
if (shouldFree)//如需要释放空间,则处理之
heap_free_minimal_tuple(tuple);
}
三、跟踪分析
设置work_mem为较低的值(1MB),便于手工产生批次
testdb=# set work_mem='1MB';
SET
testdb=# show work_mem;
work_mem
----------
1MB
(1 row)
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ExecHashJoinNewBatch
(gdb) b ExecHashJoinNewBatch
Breakpoint 1 at 0x7031f5: file nodeHashjoin.c, line 943.
(gdb) c
Continuing.
Breakpoint 1, ExecHashJoinNewBatch (hjstate=0x1c40738) at nodeHashjoin.c:943
943 HashJoinTable hashtable = hjstate->hj_HashTable;
获取批次数(8个批次)和当前批次(0,第一个批次)
(gdb) n
950 nbatch = hashtable->nbatch;
(gdb)
951 curbatch = hashtable->curbatch;
(gdb)
953 if (curbatch > 0)
(gdb) p nbatch
$5 = 8
(gdb) p curbatch
$6 = 0
curbatch ==0,刚刚完成了第一个批次,重置倾斜优化的相关状态变量
(gdb) n
971 hashtable->skewEnabled = false;
(gdb)
972 hashtable->skewBucket = NULL;
(gdb)
973 hashtable->skewBucketNums = NULL;
(gdb)
974 hashtable->nSkewBuckets = 0;
(gdb)
975 hashtable->spaceUsedSkew = 0;
(gdb)
995 curbatch++;
外表为空或内表为空时的优化,本次调用均不为空
(gdb) n
996 while (curbatch < nbatch &&
(gdb)
997 (hashtable->outerBatchFile[curbatch] == NULL ||
(gdb) p hashtable->outerBatchFile[curbatch]
$7 = (BufFile *) 0x1d85290
(gdb) p hashtable->outerBatchFile[curbatch]
$8 = (BufFile *) 0x1d85290
设置当前批次,重建Hash表
(gdb)
1023 if (curbatch >= nbatch)
(gdb)
1026 hashtable->curbatch = curbatch;
(gdb)
1031 ExecHashTableReset(hashtable);
获取inner relation批次临时文件
(gdb)
1033 innerFile = hashtable->innerBatchFile[curbatch];
(gdb)
1035 if (innerFile != NULL)
(gdb) p innerFile
$9 = (BufFile *) 0x1cc0540
临时文件不为NULL,读取文件
(gdb) n
1037 if (BufFileSeek(innerFile, 0, 0L, SEEK_SET))
(gdb)
1042 while ((slot = ExecHashJoinGetSavedTuple(hjstate,
进入函数ExecHashJoinGetSavedTuple
(gdb) step
ExecHashJoinGetSavedTuple (hjstate=0x1c40fd8, file=0x1cc0540, hashvalue=0x7ffeace60824, tupleSlot=0x1c4cc20)
at nodeHashjoin.c:1259
1259 CHECK_FOR_INTERRUPTS();
(gdb)
ExecHashJoinGetSavedTuple->读取头部8个字节(header,类型为void *,在64 bit的机器上,大小8个字节)
gdb) n
1266 nread = BufFileRead(file, (void *) header, sizeof(header));
(gdb)
1267 if (nread == 0) /* end of file */
(gdb) p nread
$10 = 8
(gdb) n
1272 if (nread != sizeof(header))
(gdb)
ExecHashJoinGetSavedTuple->获取Hash值(1978434688)
(gdb)
1276 *hashvalue = header[0];
(gdb) n
1277 tuple = (MinimalTuple) palloc(header[1]);
(gdb) p *hashvalue
$11 = 1978434688
ExecHashJoinGetSavedTuple->获取tuple&元组长度
(gdb) n
1278 tuple->t_len = header[1];
(gdb)
1281 header[1] - sizeof(uint32));
(gdb) p tuple->t_len
$16 = 24
(gdb) p *tuple
$17 = {t_len = 24, mt_padding = "177177177177177177", t_infomask2 = 32639, t_infomask = 32639, t_hoff = 127 '177',
t_bits = 0x1c5202f "177177177177177177177177177~177177177177177177177"}
(gdb)
ExecHashJoinGetSavedTuple->根据大小读取文件获取元组
(gdb) n
1279 nread = BufFileRead(file,
(gdb)
1282 if (nread != header[1] - sizeof(uint32))
(gdb) p header[1]
$18 = 24
(gdb) p sizeof(uint32)
$19 = 4
(gdb) p *tuple
$20 = {t_len = 24, mt_padding = "0000000000", t_infomask2 = 3, t_infomask = 2, t_hoff = 24 '30',
t_bits = 0x1c5202f ""}
ExecHashJoinGetSavedTuple->存储到slot中,完成调用
(gdb) n
1286 return ExecStoreMinimalTuple(tuple, tupleSlot, true);
(gdb)
1287 }
(gdb)
ExecHashJoinNewBatch (hjstate=0x1c40fd8) at nodeHashjoin.c:1051
1051 ExecHashTableInsert(hashtable, slot, hashvalue);
插入到Hash表中
(gdb)
1051 ExecHashTableInsert(hashtable, slot, hashvalue);
进入ExecHashTableInsert
(gdb) step
ExecHashTableInsert (hashtable=0x1c6e1c0, slot=0x1c4cc20, hashvalue=3757101760) at nodeHash.c:1593
1593 MinimalTuple tuple = ExecFetchSlotMinimalTuple(slot);
(gdb)
ExecHashTableInsert->获取批次号和hash桶号
(gdb) n
1597 ExecHashGetBucketAndBatch(hashtable, hashvalue,
(gdb)
1603 if (batchno == hashtable->curbatch)
(gdb) p batchno
$21 = 1
(gdb) p bucketno
$22 = 21184
(gdb)
(gdb) p hashtable->curbatch
$23 = 1
ExecHashTableInsert->批次号与Hash表中的批次号一致,把元组放到Hash表中
常规元组数量=100000
(gdb) n
1610 double ntuples = (hashtable->totalTuples - hashtable->skewTuples);
(gdb) n
1613 hashTupleSize = HJTUPLE_OVERHEAD + tuple->t_len;
(gdb) p ntuples
$24 = 100000
ExecHashTableInsert->创建HashJoinTuple,重置元组匹配标记
(gdb) n
1614 hashTuple = (HashJoinTuple) dense_alloc(hashtable, hashTupleSize);
(gdb)
1616 hashTuple->hashvalue = hashvalue;
(gdb)
1617 memcpy(HJTUPLE_MINTUPLE(hashTuple), tuple, tuple->t_len);
(gdb)
1625 HeapTupleHeaderClearMatch(HJTUPLE_MINTUPLE(hashTuple));
(gdb)
ExecHashTableInsert->元组放在Hash表桶链表的前面
(gdb) n
1628 hashTuple->next.unshared = hashtable->buckets.unshared[bucketno];
(gdb)
1629 hashtable->buckets.unshared[bucketno] = hashTuple;
(gdb)
1636 if (hashtable->nbatch == 1 &&
(gdb)
ExecHashTableInsert->调整或记录Hash表内存使用的峰值并返回,回到ExecHashJoinNewBatch
(gdb)
1649 hashtable->spaceUsed += hashTupleSize;
(gdb)
...
(gdb)
1667 }
(gdb) n
ExecHashJoinNewBatch (hjstate=0x1c40fd8) at nodeHashjoin.c:1042
1042 while ((slot = ExecHashJoinGetSavedTuple(hjstate,
循环插入到Hash表中
1042 while ((slot = ExecHashJoinGetSavedTuple(hjstate,
(gdb) n
1051 ExecHashTableInsert(hashtable, slot, hashvalue);
...
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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