这篇文章主要介绍“PostgreSQL中ExecHashJoin依赖其他函数的实现逻辑分析”，在日常操作中，相信很多人在PostgreSQL中ExecHashJoin依赖其他函数的实现逻辑分析问题上存在疑惑，小编查阅了各式资料，整理出简单好用的操作方法，希望对大家解答”PostgreSQL中ExecHashJoin依赖其他函数的实现逻辑分析”的疑惑有所帮助！接下来，请跟着小编一起来学习吧！
这些函数在HJ_NEED_NEW_OUTER阶段中使用，包括ExecHashJoinOuterGetTuple、ExecPrepHashTableForUnmatched、ExecHashGetBucketAndBatch、ExecHashGetSkewBucket、ExecHashJoinSaveTuple和ExecFetchSlotMinimalTuple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 *)
/* ----------------
* Plan node
*
* All plan nodes "derive" from the Plan structure by having the
* Plan structure as the first field. This ensures that everything works
* when nodes are cast to Plan's. (node pointers are frequently cast to Plan*
* when passed around generically in the executor)
* 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 *)
*
* We never actually instantiate any Plan nodes; this is just the common
* abstract superclass for all Plan-type nodes.
* 从未实例化任何Plan节点;这只是所有Plan-type节点的通用抽象超类。
* ----------------
*/
typedef struct Plan
{
NodeTag type;//节点类型
/*
* 成本估算信息;estimated execution costs for plan (see costsize.c for more info)
*/
Cost startup_cost; /* 启动成本;cost expended before fetching any tuples */
Cost total_cost; /* 总成本;total cost (assuming all tuples fetched) */
/*
* 优化器估算信息;planner's estimate of result size of this plan step
*/
double plan_rows; /* 行数;number of rows plan is expected to emit */
int plan_width; /* 平均行大小(Byte为单位);average row width in bytes */
/*
* 并行执行相关的信息;information needed for parallel query
*/
bool parallel_aware; /* 是否参与并行执行逻辑?engage parallel-aware logic? */
bool parallel_safe; /* 是否并行安全;OK to use as part of parallel plan? */
/*
* Plan类型节点通用的信息.Common structural data for all Plan types.
*/
int plan_node_id; /* unique across entire final plan tree */
List *targetlist; /* target list to be computed at this node */
List *qual; /* implicitly-ANDed qual conditions */
struct Plan *lefttree; /* input plan tree(s) */
struct Plan *righttree;
List *initPlan; /* Init Plan nodes (un-correlated expr
* subselects) */
/*
* Information for management of parameter-change-driven rescanning
* parameter-change-driven重扫描的管理信息.
*
* extParam includes the paramIDs of all external PARAM_EXEC params
* affecting this plan node or its children. setParam params from the
* node's initPlans are not included, but their extParams are.
*
* allParam includes all the extParam paramIDs, plus the IDs of local
* params that affect the node (i.e., the setParams of its initplans).
* These are _all_ the PARAM_EXEC params that affect this node.
*/
Bitmapset *extParam;
Bitmapset *allParam;
} Plan;
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
ExecHashJoinOuterGetTuple
获取非并行模式下hashjoin的下一个外部元组:要么在第一次执行外部plan节点，要么从hashjoin批处理的临时文件中获取。
/*----------------------------------------------------------------------------------------------------
HJ_NEED_NEW_OUTER 阶段
----------------------------------------------------------------------------------------------------*/
/*
* ExecHashJoinOuterGetTuple
*
* get the next outer tuple for a parallel oblivious hashjoin: either by
* executing the outer plan node in the first pass, or from the temp
* files for the hashjoin batches.
* 获取非并行模式下hashjoin的下一个外部元组:要么在第一次执行外部plan节点，要么从hashjoin批处理的临时文件中获取。
*
* Returns a null slot if no more outer tuples (within the current batch).
* 如果没有更多外部元组(在当前批处理中)，则返回空slot。
*
* On success, the tuple's hash value is stored at *hashvalue --- this is
* either originally computed, or re-read from the temp file.
* 如果成功，tuple的散列值存储在输入参数*hashvalue中——这是最初计算的，或者是从临时文件中重新读取的。
*/
static TupleTableSlot *
ExecHashJoinOuterGetTuple(PlanState *outerNode,//outer 节点
HashJoinState *hjstate,//Hash Join执行状态
uint32 *hashvalue)//Hash值
{
HashJoinTable hashtable = hjstate->hj_HashTable;//hash表
int curbatch = hashtable->curbatch;//当前批次
TupleTableSlot *slot;//返回的slot
if (curbatch == 0) /* 第一个批次;if it is the first pass */
{
/*
* Check to see if first outer tuple was already fetched by
* ExecHashJoin() and not used yet.
* 检查第一个外部元组是否已经由ExecHashJoin()函数获取且尚未使用。
*/
slot = hjstate->hj_FirstOuterTupleSlot;
if (!TupIsNull(slot))
hjstate->hj_FirstOuterTupleSlot = NULL;//重置slot
else
slot = ExecProcNode(outerNode);//如为NULL,则获取slot
while (!TupIsNull(slot))//slot不为NULL
{
/*
* We have to compute the tuple's hash value.
* 计算hash值
*/
ExprContext *econtext = hjstate->js.ps.ps_ExprContext;//表达式计算上下文
econtext->ecxt_outertuple = slot;//存储获取的slot
if (ExecHashGetHashValue(hashtable, econtext,
hjstate->hj_OuterHashKeys,
true, /* outer tuple */
HJ_FILL_OUTER(hjstate),
hashvalue))//计算Hash值
{
/* remember outer relation is not empty for possible rescan */
hjstate->hj_OuterNotEmpty = true;//设置标记(outer不为空)
return slot;//返回匹配的slot
}
/*
* That tuple couldn't match because of a NULL, so discard it and
* continue with the next one.
* 该元组无法匹配，丢弃它，继续下一个元组。
*/
slot = ExecProcNode(outerNode);//继续获取下一个
}
}
else if (curbatch < hashtable->nbatch)//不是第一个批次
{
BufFile *file = hashtable->outerBatchFile[curbatch];//获取缓冲的文件
/*
* In outer-join cases, we could get here even though the batch file
* is empty.
* 在外连接的情况下，即使批处理文件是空的，也可以在这里进行处理。
*/
if (file == NULL)
return NULL;//如文件为NULL,则返回
slot = ExecHashJoinGetSavedTuple(hjstate,
file,
hashvalue,
hjstate->hj_OuterTupleSlot);//从文件中获取slot
if (!TupIsNull(slot))
return slot;//非NULL,则返回
}
/* End of this batch */
//已完成,则返回NULL
return NULL;
}
/*
* ExecHashGetHashValue
* Compute the hash value for a tuple
* ExecHashGetHashValue - 计算元组的Hash值
*
* The tuple to be tested must be in either econtext->ecxt_outertuple or
* econtext->ecxt_innertuple. Vars in the hashkeys expressions should have
* varno either OUTER_VAR or INNER_VAR.
* 要测试的元组必须位于econtext->ecxt_outertuple或econtext->ecxt_innertuple中。
* hashkeys表达式中的Vars应该具有varno，即OUTER_VAR或INNER_VAR。
*
* A true result means the tuple's hash value has been successfully computed
* and stored at *hashvalue. A false result means the tuple cannot match
* because it contains a null attribute, and hence it should be discarded
* immediately. (If keep_nulls is true then false is never returned.)
* T意味着tuple的散列值已经成功计算并存储在*hashvalue参数中。
* F意味着元组不能匹配，因为它包含null属性，因此应该立即丢弃它。
* (如果keep_nulls为真，则永远不会返回F。)
*/
bool
ExecHashGetHashValue(HashJoinTable hashtable,//Hash表
ExprContext *econtext,//上下文
List *hashkeys,//Hash键值链表
bool outer_tuple,//是否外表元组
bool keep_nulls,//是否保存NULL
uint32 *hashvalue)//返回的Hash值
{
uint32 hashkey = 0;//hash键
FmgrInfo *hashfunctions;//hash函数
ListCell *hk;//临时变量
int i = 0;
MemoryContext oldContext;
/*
* We reset the eval context each time to reclaim any memory leaked in the
* hashkey expressions.
* 我们每次重置eval上下文来回收hashkey表达式中分配的内存。
*/
ResetExprContext(econtext);
//切换上下文
oldContext = MemoryContextSwitchTo(econtext->ecxt_per_tuple_memory);
if (outer_tuple)
hashfunctions = hashtable->outer_hashfunctions;//外表元组
else
hashfunctions = hashtable->inner_hashfunctions;//内表元组
foreach(hk, hashkeys)//遍历Hash键值
{
ExprState *keyexpr = (ExprState *) lfirst(hk);//键值表达式
Datum keyval;
bool isNull;
/* rotate hashkey left 1 bit at each step */
//哈希键左移1位
hashkey = (hashkey << 1) | ((hashkey & 0x80000000) ? 1 : 0);
/*
* Get the join attribute value of the tuple
* 获取元组的连接属性值
*/
keyval = ExecEvalExpr(keyexpr, econtext, &isNull);
/*
* If the attribute is NULL, and the join operator is strict, then
* this tuple cannot pass the join qual so we can reject it
* immediately (unless we're scanning the outside of an outer join, in
* which case we must not reject it). Otherwise we act like the
* hashcode of NULL is zero (this will support operators that act like
* IS NOT DISTINCT, though not any more-random behavior). We treat
* the hash support function as strict even if the operator is not.
* 如果属性为NULL，并且join操作符是严格的，那么这个元组不能传递连接条件join qual，
* 因此可以立即拒绝它(除非正在扫描外连接的外表，在这种情况下不能拒绝它)。
* 否则，我们的行为就好像NULL的哈希码是零一样(这将支持IS NOT DISTINCT操作符，但不会有任何随机的情况出现)。
* 即使操作符不是严格的，也将哈希函数视为严格的。
*
* Note: currently, all hashjoinable operators must be strict since
* the hash index AM assumes that. However, it takes so little extra
* code here to allow non-strict that we may as well do it.
* 注意:目前，所有哈希可连接操作符都必须严格，因为哈希索引AM假定如此。
* 但是，这里只需要很少的额外代码就可以实现非严格性，我们也可以这样做。
*/
if (isNull)
{
//NULL值
if (hashtable->hashStrict[i] && !keep_nulls)
{
MemoryContextSwitchTo(oldContext);
//不保持NULL值,不匹配
return false; /* cannot match */
}
/* else, leave hashkey unmodified, equivalent to hashcode 0 */
//否则的话,不修改hashkey,仍为0
}
else
{
//不为NULL
/* Compute the hash function */
//计算hash值
uint32 hkey;
hkey = DatumGetUInt32(FunctionCall1(&hashfunctions[i], keyval));
hashkey ^= hkey;
}
i++;//下一个键
}
//切换上下文
MemoryContextSwitchTo(oldContext);
//返回Hash键值
*hashvalue = hashkey;
return true;//成功获取
}
ExecPrepHashTableForUnmatched
为ExecScanHashTableForUnmatched函数调用作准备
/*
* ExecPrepHashTableForUnmatched
* set up for a series of ExecScanHashTableForUnmatched calls
* 为ExecScanHashTableForUnmatched函数调用作准备
*/
void
ExecPrepHashTableForUnmatched(HashJoinState *hjstate)
{
/*----------
* During this scan we use the HashJoinState fields as follows:
*
* hj_CurBucketNo: next regular bucket to scan
* hj_CurSkewBucketNo: next skew bucket (an index into skewBucketNums)
* hj_CurTuple: last tuple returned, or NULL to start next bucket
* 在这次扫描期间,我们使用HashJoinState结构体中的字段如下:
* hj_CurBucketNo: 下一个常规的bucket
* hj_CurSkewBucketNo: 下一个个倾斜的bucket
* hj_CurTuple: 最后返回的元组,或者为NULL(下一个bucket开始)
*----------
*/
hjstate->hj_CurBucketNo = 0;
hjstate->hj_CurSkewBucketNo = 0;
hjstate->hj_CurTuple = NULL;
}
ExecHashGetBucketAndBatch
确定哈希值的bucket号和批处理号
/*
* ExecHashGetBucketAndBatch
* Determine the bucket number and batch number for a hash value
* ExecHashGetBucketAndBatch
* 确定哈希值的bucket号和批处理号
*
* Note: on-the-fly increases of nbatch must not change the bucket number
* for a given hash code (since we don't move tuples to different hash
* chains), and must only cause the batch number to remain the same or
* increase. Our algorithm is
* bucketno = hashvalue MOD nbuckets
* batchno = (hashvalue DIV nbuckets) MOD nbatch
* where nbuckets and nbatch are both expected to be powers of 2, so we can
* do the computations by shifting and masking. (This assumes that all hash
* functions are good about randomizing all their output bits, else we are
* likely to have very skewed bucket or batch occupancy.)
* 注意:nbatch的动态增加不能更改给定哈希码的桶号(因为我们不将元组移动到不同的哈希链)，
* 并且只能使批号保持不变或增加。我们的算法是:
* bucketno = hashvalue MOD nbuckets
* batchno = (hashvalue DIV nbuckets) MOD nbatch
* 这里nbucket和nbatch都是2的幂，所以我们可以通过移动和屏蔽来进行计算。
* (这假定所有哈希函数都能很好地随机化它们的所有输出位，否则很可能会出现非常倾斜的桶或批处理占用。)
*
* nbuckets and log2_nbuckets may change while nbatch == 1 because of dynamic
* bucket count growth. Once we start batching, the value is fixed and does
* not change over the course of the join (making it possible to compute batch
* number the way we do here).
* 当nbatch == 1时，由于动态bucket计数的增长，nbucket和log2_nbucket可能会发生变化。
* 一旦开始批处理，这个值就固定了，并且在连接过程中不会改变(这使得我们可以像这里那样计算批号)。
*
* nbatch is always a power of 2; we increase it only by doubling it. This
* effectively adds one more bit to the top of the batchno.
* nbatch总是2的幂;我们只是通过x2来调整。这相当于为批号的头部增加了一位。
*/
void
ExecHashGetBucketAndBatch(HashJoinTable hashtable,
uint32 hashvalue,
int *bucketno,
int *batchno)
{
uint32 nbuckets = (uint32) hashtable->nbuckets;//桶数
uint32 nbatch = (uint32) hashtable->nbatch;//批次号
if (nbatch > 1)//批次>1
{
/* we can do MOD by masking, DIV by shifting */
//我们可以通过屏蔽来实现MOD，通过移动来实现DIV
*bucketno = hashvalue & (nbuckets - 1);//nbuckets - 1后相当于N个1
*batchno = (hashvalue >> hashtable->log2_nbuckets) & (nbatch - 1);
}
else
{
*bucketno = hashvalue & (nbuckets - 1);//只有一个批次,简单处理即可
*batchno = 0;
}
}
ExecHashGetSkewBucket
返回这个哈希值的倾斜桶的索引，如果哈希值与任何活动的倾斜桶没有关联，则返回INVALID_SKEW_BUCKET_NO。
/*
* ExecHashGetSkewBucket
*
* Returns the index of the skew bucket for this hashvalue,
* or INVALID_SKEW_BUCKET_NO if the hashvalue is not
* associated with any active skew bucket.
* 返回这个哈希值的倾斜桶的索引，如果哈希值与任何活动的倾斜桶没有关联，则返回INVALID_SKEW_BUCKET_NO。
*/
int
ExecHashGetSkewBucket(HashJoinTable hashtable, uint32 hashvalue)
{
int bucket;
/*
* Always return INVALID_SKEW_BUCKET_NO if not doing skew optimization (in
* particular, this happens after the initial batch is done).
* 如果不进行倾斜优化(特别是在初始批处理完成之后)，则返回INVALID_SKEW_BUCKET_NO。
*/
if (!hashtable->skewEnabled)
return INVALID_SKEW_BUCKET_NO;
/*
* Since skewBucketLen is a power of 2, we can do a modulo by ANDing.'
* 由于skewBucketLen是2的幂，可以通过AND操作来做一个模。
*/
bucket = hashvalue & (hashtable->skewBucketLen - 1);
/*
* While we have not hit a hole in the hashtable and have not hit the
* desired bucket, we have collided with some other hash value, so try the
* next bucket location.
* 虽然我们没有在哈希表中找到一个hole，也没有找到所需的bucket，
* 但是与其他一些哈希值发生了冲突，所以尝试下一个bucket位置。
*/
while (hashtable->skewBucket[bucket] != NULL &&
hashtable->skewBucket[bucket]->hashvalue != hashvalue)
bucket = (bucket + 1) & (hashtable->skewBucketLen - 1);
/*
* Found the desired bucket?
* 找到了bucket,返回
*/
if (hashtable->skewBucket[bucket] != NULL)
return bucket;
/*
* There must not be any hashtable entry for this hash value.
*/
//否则返回INVALID_SKEW_BUCKET_NO
return INVALID_SKEW_BUCKET_NO;
}
ExecHashJoinSaveTuple
在批处理文件中保存元组.每个元组在文件中记录的是它的散列值，然后是最小化格式的元组。
/*
* ExecHashJoinSaveTuple
* save a tuple to a batch file.
* 在批处理文件中保存元组
*
* The data recorded in the file for each tuple is its hash value,
* then the tuple in MinimalTuple format.
* 每个元组在文件中记录的是它的散列值，然后是最小化格式的元组。
*
* Note: it is important always to call this in the regular executor
* context, not in a shorter-lived context; else the temp file buffers
* will get messed up.
* 注意:在常规执行程序上下文中调用它总是很重要的，而不是在较短的生命周期中调用它;
* 否则临时文件缓冲区就会出现混乱。
*/
void
ExecHashJoinSaveTuple(MinimalTuple tuple, uint32 hashvalue,
BufFile **fileptr)
{
BufFile *file = *fileptr;//文件指针
size_t written;//写入大小
if (file == NULL)
{
/* First write to this batch file, so open it. */
//文件指针为NULL,首次写入,则打开批处理文件
file = BufFileCreateTemp(false);
*fileptr = file;
}
//首先写入hash值,返回写入的大小
written = BufFileWrite(file, (void *) &hashvalue, sizeof(uint32));
if (written != sizeof(uint32))//写入有误,报错
ereport(ERROR,
(errcode_for_file_access(),
errmsg("could not write to hash-join temporary file: %m")));
//写入tuple
written = BufFileWrite(file, (void *) tuple, tuple->t_len);
if (written != tuple->t_len)//写入有误,报错
ereport(ERROR,
(errcode_for_file_access(),
errmsg("could not write to hash-join temporary file: %m")));
}
ExecFetchSlotMinimalTuple
以最小化物理元组的格式提取slot的数据
/* --------------------------------
* ExecFetchSlotMinimalTuple
* Fetch the slot's minimal physical tuple.
* 以最小化物理元组的格式提取slot的数据.
*
* If the given tuple table slot can hold a minimal tuple, indicated by a
* non-NULL get_minimal_tuple callback, the function returns the minimal
* tuple returned by that callback. It assumes that the minimal tuple
* returned by the callback is "owned" by the slot i.e. the slot is
* responsible for freeing the memory consumed by the tuple. Hence it sets
* *shouldFree to false, indicating that the caller should not free the
* memory consumed by the minimal tuple. In this case the returned minimal
* tuple should be considered as read-only.
* 如果给定的元组table slot可以保存由non-NULL get_minimal_tuple回调函数指示的最小元组，
* 则函数将返回该回调函数返回的最小元组。
* 它假定回调函数返回的最小元组由slot“拥有”，即slot负责释放元组所消耗的内存。
* 因此，它将*shouldFree设置为false，表示调用方不应该释放内存。
* 在这种情况下，返回的最小元组应该被认为是只读的。
*
* If that callback is not supported, it calls copy_minimal_tuple callback
* which is expected to return a copy of minimal tuple represnting the
* contents of the slot. In this case *shouldFree is set to true,
* indicating the caller that it should free the memory consumed by the
* minimal tuple. In this case the returned minimal tuple may be written
* up.
* 如果不支持该回调函数，则调用copy_minimal_tuple回调函数，
* 该回调将返回一个表示slot内容的最小元组副本。
* *shouldFree被设置为true，这表示调用者应该释放内存。
* 在这种情况下，可以写入返回的最小元组。
* --------------------------------
*/
MinimalTuple
ExecFetchSlotMinimalTuple(TupleTableSlot *slot,
bool *shouldFree)
{
/*
* sanity checks
* 安全检查
*/
Assert(slot != NULL);
Assert(!TTS_EMPTY(slot));
if (slot->tts_ops->get_minimal_tuple)//调用slot->tts_ops->get_minimal_tuple
{
//调用成功,则该元组为只读,由slot负责释放
if (shouldFree)
*shouldFree = false;
return slot->tts_ops->get_minimal_tuple(slot);
}
else
{
//调用不成功,设置为true,由调用方释放
if (shouldFree)
*shouldFree = true;
return slot->tts_ops->copy_minimal_tuple(slot);//调用copy_minimal_tuple函数
}
}
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 ExecHashJoinOuterGetTuple Breakpoint 1 at 0x702edc: file nodeHashjoin.c, line 807. (gdb) b ExecHashGetHashValue Breakpoint 2 at 0x6ff060: file nodeHash.c, line 1778. (gdb) b ExecHashGetBucketAndBatch Breakpoint 3 at 0x6ff1df: file nodeHash.c, line 1880. (gdb) b ExecHashJoinSaveTuple Breakpoint 4 at 0x703973: file nodeHashjoin.c, line 1214. (gdb)
ExecHashGetHashValue
ExecHashGetHashValue->进入函数ExecHashGetHashValue
(gdb) c Continuing. Breakpoint 2, ExecHashGetHashValue (hashtable=0x14acde8, econtext=0x149c3d0, hashkeys=0x14a8e40, outer_tuple=false, keep_nulls=false, hashvalue=0x7ffc7eba5c20) at nodeHash.c:1778 1778 uint32 hashkey = 0;
ExecHashGetHashValue->初始化,切换内存上下文
1778 uint32 hashkey = 0; (gdb) n 1781 int i = 0; (gdb) 1788 ResetExprContext(econtext); (gdb) 1790 oldContext = MemoryContextSwitchTo(econtext->ecxt_per_tuple_memory); (gdb) 1792 if (outer_tuple)
ExecHashGetHashValue->inner hash函数
1792 if (outer_tuple) (gdb) 1795 hashfunctions = hashtable->inner_hashfunctions;
ExecHashGetHashValue->获取hahs键信息
1号RTE(varnoold = 1,即t_dwxx)的dwbh字段(varattno = 2)
(gdb)
1797 foreach(hk, hashkeys)
(gdb)
1799 ExprState *keyexpr = (ExprState *) lfirst(hk);
(gdb)
1804 hashkey = (hashkey << 1) | ((hashkey & 0x80000000) ? 1 : 0);
(gdb) p *keyexpr
$1 = {tag = {type = T_ExprState}, flags = 2 '/002', resnull = false, resvalue = 0, resultslot = 0x0, steps = 0x14a8a00,
evalfunc = 0x6d1a6e <ExecInterpExprStillValid>, expr = 0x1498fc0, evalfunc_private = 0x6d1e97 <ExecJustInnerVar>,
steps_len = 3, steps_alloc = 16, parent = 0x149b738, ext_params = 0x0, innermost_caseval = 0x0, innermost_casenull = 0x0,
innermost_domainval = 0x0, innermost_domainnull = 0x0}
(gdb) p *(RelabelType *)keyexpr->expr
$3 = {xpr = {type = T_RelabelType}, arg = 0x1499018, resulttype = 25, resulttypmod = -1, resultcollid = 100,
relabelformat = COERCE_IMPLICIT_CAST, location = -1}
(gdb) p *((RelabelType *)keyexpr->expr)->arg
$4 = {type = T_Var}
(gdb) p *(Var *)((RelabelType *)keyexpr->expr)->arg
$5 = {xpr = {type = T_Var}, varno = 65000, varattno = 2, vartype = 1043, vartypmod = 24, varcollid = 100, varlevelsup = 0,
varnoold = 1, varoattno = 2, location = 218}
(gdb)
ExecHashGetHashValue->获取hash值,解析表达式
(gdb) n 1809 keyval = ExecEvalExpr(keyexpr, econtext, &isNull); (gdb) 1824 if (isNull) (gdb) p hashkey $6 = 0 (gdb) p keyval $7 = 140460362257270 (gdb)
ExecHashGetHashValue->返回值不为NULL
(gdb) p isNull $8 = false (gdb) n 1838 hkey = DatumGetUInt32(FunctionCall1(&hashfunctions[i], keyval));
ExecHashGetHashValue->计算Hash值
(gdb) n 1839 hashkey ^= hkey; (gdb) p hkey $9 = 3663833849 (gdb) p hashkey $10 = 0 (gdb) n 1842 i++; (gdb) p hashkey $11 = 3663833849 (gdb)
ExecHashGetHashValue->返回计算结果
(gdb) n 1797 foreach(hk, hashkeys) (gdb) 1845 MemoryContextSwitchTo(oldContext); (gdb) 1847 *hashvalue = hashkey; (gdb) 1848 return true; (gdb) 1849 }
ExecHashGetBucketAndBatch
ExecHashGetBucketAndBatch->进入ExecHashGetBucketAndBatch
(gdb) c Continuing. Breakpoint 3, ExecHashGetBucketAndBatch (hashtable=0x14acde8, hashvalue=3663833849, bucketno=0x7ffc7eba5bdc, batchno=0x7ffc7eba5bd8) at nodeHash.c:1880 1880 uint32 nbuckets = (uint32) hashtable->nbuckets;
ExecHashGetBucketAndBatch->获取bucket数和批次数
1880 uint32 nbuckets = (uint32) hashtable->nbuckets; (gdb) n 1881 uint32 nbatch = (uint32) hashtable->nbatch; (gdb) 1883 if (nbatch > 1) (gdb) p nbuckets $12 = 16384 (gdb) p nbatch $13 = 1 (gdb)
ExecHashGetBucketAndBatch->计算桶号和批次号(只有一个批次,设置为0)
(gdb) n 1891 *bucketno = hashvalue & (nbuckets - 1); (gdb) 1892 *batchno = 0; (gdb) 1894 } (gdb) p bucketno $14 = (int *) 0x7ffc7eba5bdc (gdb) p *bucketno $15 = 11001 (gdb)
ExecHashJoinOuterGetTuple
ExecHashJoinOuterGetTuple->进入ExecHashJoinOuterGetTuple函数
(gdb) info break Num Type Disp Enb Address What 1 breakpoint keep y 0x0000000000702edc in ExecHashJoinOuterGetTuple at nodeHashjoin.c:807 2 breakpoint keep y 0x00000000006ff060 in ExecHashGetHashValue at nodeHash.c:1778 breakpoint already hit 4 times 3 breakpoint keep y 0x00000000006ff1df in ExecHashGetBucketAndBatch at nodeHash.c:1880 breakpoint already hit 4 times 4 breakpoint keep y 0x0000000000703973 in ExecHashJoinSaveTuple at nodeHashjoin.c:1214 (gdb) del 2 (gdb) del 3 (gdb) c Continuing. Breakpoint 1, ExecHashJoinOuterGetTuple (outerNode=0x149ba10, hjstate=0x149b738, hashvalue=0x7ffc7eba5ccc) at nodeHashjoin.c:807 807 HashJoinTable hashtable = hjstate->hj_HashTable; (gdb)
ExecHashJoinOuterGetTuple->查看输入参数
outerNode:outer relation为顺序扫描得到的relation(对t_jfxx进行顺序扫描)
hjstate:Hash Join执行状态
hashvalue:Hash值
(gdb) p *outerNode
$16 = {type = T_SeqScanState, plan = 0x1494d10, state = 0x149b0f8, ExecProcNode = 0x71578d <ExecSeqScan>,
ExecProcNodeReal = 0x71578d <ExecSeqScan>, instrument = 0x0, worker_instrument = 0x0, worker_jit_instrument = 0x0,
qual = 0x0, lefttree = 0x0, righttree = 0x0, initPlan = 0x0, subPlan = 0x0, chgParam = 0x0,
ps_ResultTupleSlot = 0x149c178, ps_ExprContext = 0x149bb28, ps_ProjInfo = 0x0, scandesc = 0x7fbfa69a8308}
(gdb) p *hjstate
$17 = {js = {ps = {type = T_HashJoinState, plan = 0x1496d18, state = 0x149b0f8, ExecProcNode = 0x70291d <ExecHashJoin>,
ExecProcNodeReal = 0x70291d <ExecHashJoin>, instrument = 0x0, worker_instrument = 0x0, worker_jit_instrument = 0x0,
qual = 0x0, lefttree = 0x149ba10, righttree = 0x149c2b8, initPlan = 0x0, subPlan = 0x0, chgParam = 0x0,
ps_ResultTupleSlot = 0x14a7498, ps_ExprContext = 0x149b950, ps_ProjInfo = 0x149cef0, scandesc = 0x0},
jointype = JOIN_INNER, single_match = true, joinqual = 0x0}, hashclauses = 0x14a7b30, hj_OuterHashKeys = 0x14a8930,
hj_InnerHashKeys = 0x14a8e40, hj_HashOperators = 0x14a8ea0, hj_HashTable = 0x14acde8, hj_CurHashValue = 0,
hj_CurBucketNo = 0, hj_CurSkewBucketNo = -1, hj_CurTuple = 0x0, hj_OuterTupleSlot = 0x14a79f0,
hj_HashTupleSlot = 0x149cc18, hj_NullOuterTupleSlot = 0x0, hj_NullInnerTupleSlot = 0x0,
hj_FirstOuterTupleSlot = 0x149bbe8, hj_JoinState = 2, hj_MatchedOuter = false, hj_OuterNotEmpty = false}
(gdb) p *hashvalue
$18 = 32703
(gdb)
ExecHashJoinOuterGetTuple->只有一个批次,批次号为0
(gdb) n 808 int curbatch = hashtable->curbatch; (gdb) 811 if (curbatch == 0) /* if it is the first pass */ (gdb) p curbatch $20 = 0
ExecHashJoinOuterGetTuple->获取首个outer tuple slot(不为NULL),重置hjstate->hj_FirstOuterTupleSlot为NULL
(gdb) n
817 slot = hjstate->hj_FirstOuterTupleSlot;
(gdb)
818 if (!TupIsNull(slot))
(gdb) p *slot
$21 = {type = T_TupleTableSlot, tts_isempty = false, tts_shouldFree = false, tts_shouldFreeMin = false, tts_slow = false,
tts_tuple = 0x14ac200, tts_tupleDescriptor = 0x7fbfa69a8308, tts_mcxt = 0x149afe0, tts_buffer = 345, tts_nvalid = 0,
tts_values = 0x149bc48, tts_isnull = 0x149bc70, 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)
(gdb) n
819 hjstate->hj_FirstOuterTupleSlot = NULL;
(gdb)
ExecHashJoinOuterGetTuple->循环获取,找到匹配的slot
(gdb) 823 while (!TupIsNull(slot)) (gdb) n 828 ExprContext *econtext = hjstate->js.ps.ps_ExprContext; (gdb)
ExecHashJoinOuterGetTuple->成功匹配,返回slot
(gdb) n
830 econtext->ecxt_outertuple = slot;
(gdb)
834 HJ_FILL_OUTER(hjstate),
(gdb)
831 if (ExecHashGetHashValue(hashtable, econtext,
(gdb)
838 hjstate->hj_OuterNotEmpty = true;
(gdb)
840 return slot;
(gdb) p *slot
$22 = {type = T_TupleTableSlot, tts_isempty = false, tts_shouldFree = false, tts_shouldFreeMin = false, tts_slow = true,
tts_tuple = 0x14ac200, tts_tupleDescriptor = 0x7fbfa69a8308, tts_mcxt = 0x149afe0, tts_buffer = 345, tts_nvalid = 1,
tts_values = 0x149bc48, tts_isnull = 0x149bc70, 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 = 2, tts_fixedTupleDescriptor = true}
(gdb)
到此，关于“PostgreSQL中ExecHashJoin依赖其他函数的实现逻辑分析”的学习就结束了，希望能够解决大家的疑惑。理论与实践的搭配能更好的帮助大家学习，快去试试吧！若想继续学习更多相关知识，请继续关注亿速云网站，小编会继续努力为大家带来更多实用的文章！
原创文章，作者：kepupublish，如若转载，请注明出处：https://blog.ytso.com/205019.html