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TupleTableSlot
执行器在”tuple table”中存储元组,这个表是各自独立的TupleTableSlots链表.
/*----------
* The executor stores tuples in a "tuple table" which is a List of
* independent TupleTableSlots. There are several cases we need to handle:
* 1. physical tuple in a disk buffer page
* 2. physical tuple constructed in palloc'ed memory
* 3. "minimal" physical tuple constructed in palloc'ed memory
* 4. "virtual" tuple consisting of Datum/isnull arrays
* 执行器在"tuple table"中存储元组,这个表是各自独立的TupleTableSlots链表.
* 有以下情况需要处理:
* 1. 磁盘缓存页中的物理元组
* 2. 在已分配内存中构造的物理元组
* 3. 在已分配内存中构造的"minimal"物理元组
* 4. 含有Datum/isnull数组的"virtual"虚拟元组
*
* The first two cases are similar in that they both deal with "materialized"
* tuples, but resource management is different. For a tuple in a disk page
* we need to hold a pin on the buffer until the TupleTableSlot's reference
* to the tuple is dropped; while for a palloc'd tuple we usually want the
* tuple pfree'd when the TupleTableSlot's reference is dropped.
* 最上面2种情况跟"物化"元组的处理方式类似,但资源管理是不同的.
* 对于在磁盘页中的元组,需要pin在缓存中直至TupleTableSlot依赖的元组被清除,
* 而对于通过palloc分配的元组在TupleTableSlot依赖被清除后通常希望使用pfree释放
*
* A "minimal" tuple is handled similarly to a palloc'd regular tuple.
* At present, minimal tuples never are stored in buffers, so there is no
* parallel to case 1. Note that a minimal tuple has no "system columns".
* (Actually, it could have an OID, but we have no need to access the OID.)
* "minimal"元组与通常的palloc分配的元组处理类似.
* 截止目前为止,"minimal"元组不会存储在缓存中,因此对于第一种情况不会存在并行的问题.
* 注意"minimal"没有"system columns"系统列
* (实际上,可以有OID,但不需要访问OID列)
*
* A "virtual" tuple is an optimization used to minimize physical data
* copying in a nest of plan nodes. Any pass-by-reference Datums in the
* tuple point to storage that is not directly associated with the
* TupleTableSlot; generally they will point to part of a tuple stored in
* a lower plan node's output TupleTableSlot, or to a function result
* constructed in a plan node's per-tuple econtext. It is the responsibility
* of the generating plan node to be sure these resources are not released
* for as long as the virtual tuple needs to be valid. We only use virtual
* tuples in the result slots of plan nodes --- tuples to be copied anywhere
* else need to be "materialized" into physical tuples. Note also that a
* virtual tuple does not have any "system columns".
* "virtual"元组是用于在嵌套计划节点中拷贝时最小化物理数据的优化.
* 所有通过引用传递指向与TupleTableSlot非直接相关的存储的元组的Datums使用,
* 通常它们会指向存储在低层节点输出的TupleTableSlot中的元组的一部分,
* 或者指向在计划节点的per-tuple内存上下文econtext中构造的函数结果.
* 产生计划节点的时候有责任确保这些资源未被释放,确保virtual元组是有效的.
* 我们使用计划节点中的结果slots中的虚拟元组 --- 元组会拷贝到其他地方需要"物化"到物理元组中.
* 注意virtual元组不需要有"system columns"
*
* It is also possible for a TupleTableSlot to hold both physical and minimal
* copies of a tuple. This is done when the slot is requested to provide
* the format other than the one it currently holds. (Originally we attempted
* to handle such requests by replacing one format with the other, but that
* had the fatal defect of invalidating any pass-by-reference Datums pointing
* into the existing slot contents.) Both copies must contain identical data
* payloads when this is the case.
* TupleTableSlot包含物理和minimal元组拷贝是可能的.
* 在slot需要提供格式化而不是当前持有的格式时会出现这种情况.
* (原始的情况是我们准备通过另外一种格式进行替换来处理这种请求,但在校验引用传递Datums时会出现致命错误)
* 同时在这种情况下,拷贝必须含有唯一的数据payloads.
*
* The Datum/isnull arrays of a TupleTableSlot serve double duty. When the
* slot contains a virtual tuple, they are the authoritative data. When the
* slot contains a physical tuple, the arrays contain data extracted from
* the tuple. (In this state, any pass-by-reference Datums point into
* the physical tuple.) The extracted information is built "lazily",
* ie, only as needed. This serves to avoid repeated extraction of data
* from the physical tuple.
* TupleTableSlot中的Datum/isnull数组有双重职责.
* 在slot包含虚拟元组时,它们是authoritative(权威)数据.
* 在slot包含物理元组时,时包含从元组中提取的数据的数组.
* (在这种情况下,所有通过引用传递的Datums指向物理元组)
* 提取的信息通过'lazily'在需要的时候才构建.
* 这样可以避免从物理元组的重复数据提取.
*
* A TupleTableSlot can also be "empty", holding no valid data. This is
* the only valid state for a freshly-created slot that has not yet had a
* tuple descriptor assigned to it. In this state, tts_isempty must be
* true, tts_shouldFree false, tts_tuple NULL, tts_buffer InvalidBuffer,
* and tts_nvalid zero.
* TupleTableSlot可能为"empty",没有有效数据.
* 对于新鲜创建仍未分配描述的的slot来说这是唯一有效的状态.
* 在这种状态下,tts_isempty必须为T,tts_shouldFree为F, tts_tuple为NULL,
* tts_buffer为InvalidBuffer,tts_nvalid为0.
*
* The tupleDescriptor is simply referenced, not copied, by the TupleTableSlot
* code. The caller of ExecSetSlotDescriptor() is responsible for providing
* a descriptor that will live as long as the slot does. (Typically, both
* slots and descriptors are in per-query memory and are freed by memory
* context deallocation at query end; so it's not worth providing any extra
* mechanism to do more. However, the slot will increment the tupdesc
* reference count if a reference-counted tupdesc is supplied.)
* tupleDescriptor只是简单的引用并没有通过TupleTableSlot中的代码进行拷贝.
* ExecSetSlotDescriptor()的调用者有责任提供与slot生命周期一样的描述符.
* (典型的,不管是slots还是描述符会在per-query内存中,
* 并且会在查询结束时通过内存上下文的析构器释放,因此不需要提供额外的机制来处理.
* 但是,如果使用了引用计数型tupdesc,slot会增加tupdesc引用计数)
*
* When tts_shouldFree is true, the physical tuple is "owned" by the slot
* and should be freed when the slot's reference to the tuple is dropped.
* 在tts_shouldFree为T的情况下,物理元组由slot持有,并且在slot引用元组被清除时释放内存.
*
* If tts_buffer is not InvalidBuffer, then the slot is holding a pin
* on the indicated buffer page; drop the pin when we release the
* slot's reference to that buffer. (tts_shouldFree should always be
* false in such a case, since presumably tts_tuple is pointing at the
* buffer page.)
* 如tts_buffer不是InvalidBuffer,那么slot持有缓存页中的pin,在释放引用该buffer的slot时会清除该pin.
* (tts_shouldFree通常来说应为F,因为tts_tuple会指向缓存页)
*
* tts_nvalid indicates the number of valid columns in the tts_values/isnull
* arrays. When the slot is holding a "virtual" tuple this must be equal
* to the descriptor's natts. When the slot is holding a physical tuple
* this is equal to the number of columns we have extracted (we always
* extract columns from left to right, so there are no holes).
* tts_nvalid指示了tts_values/isnull数组中的有效列数.
* 如果slot含有虚拟元组,该字段必须跟描述符的natts一样.
* 在slot含有物理元组时,该字段等于我们提取的列数.
* (我们通常从左到右提取列,因此不会有空洞存在)
*
* tts_values/tts_isnull are allocated when a descriptor is assigned to the
* slot; they are of length equal to the descriptor's natts.
* 在描述符分配给slot时tts_values/tts_isnull会被分配内存,长度与描述符natts长度一样.
*
* tts_mintuple must always be NULL if the slot does not hold a "minimal"
* tuple. When it does, tts_mintuple points to the actual MinimalTupleData
* object (the thing to be pfree'd if tts_shouldFreeMin is true). If the slot
* has only a minimal and not also a regular physical tuple, then tts_tuple
* points at tts_minhdr and the fields of that struct are set correctly
* for access to the minimal tuple; in particular, tts_minhdr.t_data points
* MINIMAL_TUPLE_OFFSET bytes before tts_mintuple. This allows column
* extraction to treat the case identically to regular physical tuples.
* 如果slot没有包含minimal元组,tts_mintuple通常必须为NULL.
* 如含有,则tts_mintuple执行实际的MinimalTupleData对象(如tts_shouldFreeMin为T,则需要通过pfree释放内存).
* 如果slot只有一个minimal而没有通常的物理元组,那么tts_tuple指向tts_minhdr,
* 结构体的其他字段会被正确的设置为用于访问minimal元组.
* 特别的, tts_minhdr.t_data指向tts_mintuple前的MINIMAL_TUPLE_OFFSET字节.
* 这可以让列提取可以独立处理通常的物理元组.
*
* tts_slow/tts_off are saved state for slot_deform_tuple, and should not
* be touched by any other code.
* tts_slow/tts_off用于存储slot_deform_tuple状态,不应通过其他代码修改.
*----------
*/
typedef struct TupleTableSlot
{
NodeTag type;//Node标记
//如slot为空,则为T
bool tts_isempty; /* true = slot is empty */
//是否需要pfree tts_tuple?
bool tts_shouldFree; /* should pfree tts_tuple? */
//是否需要pfree tts_mintuple?
bool tts_shouldFreeMin; /* should pfree tts_mintuple? */
#define FIELDNO_TUPLETABLESLOT_SLOW 4
//为slot_deform_tuple存储状态?
bool tts_slow; /* saved state for slot_deform_tuple */
#define FIELDNO_TUPLETABLESLOT_TUPLE 5
//物理元组,如为虚拟元组则为NULL
HeapTuple tts_tuple; /* physical tuple, or NULL if virtual */
#define FIELDNO_TUPLETABLESLOT_TUPLEDESCRIPTOR 6
//slot中的元组描述符
TupleDesc tts_tupleDescriptor; /* slot's tuple descriptor */
//slot所在的上下文
MemoryContext tts_mcxt; /* slot itself is in this context */
//元组缓存,如无则为InvalidBuffer
Buffer tts_buffer; /* tuple's buffer, or InvalidBuffer */
#define FIELDNO_TUPLETABLESLOT_NVALID 9
//tts_values中的有效值
int tts_nvalid; /* # of valid values in tts_values */
#define FIELDNO_TUPLETABLESLOT_VALUES 10
//当前每个属性的值
Datum *tts_values; /* current per-attribute values */
#define FIELDNO_TUPLETABLESLOT_ISNULL 11
//isnull数组
bool *tts_isnull; /* current per-attribute isnull flags */
//minimal元组,如无则为NULL
MinimalTuple tts_mintuple; /* minimal tuple, or NULL if none */
//在minimal情况下的工作空间
HeapTupleData tts_minhdr; /* workspace for minimal-tuple-only case */
#define FIELDNO_TUPLETABLESLOT_OFF 14
//slot_deform_tuple的存储状态
uint32 tts_off; /* saved state for slot_deform_tuple */
//不能被变更的描述符(固定描述符)
bool tts_fixedTupleDescriptor; /* descriptor can't be changed */
} TupleTableSlot;
/* base tuple table slot type */
typedef struct TupleTableSlot
{
NodeTag type;//Node标记
#define FIELDNO_TUPLETABLESLOT_FLAGS 1
uint16 tts_flags; /* 布尔状态;Boolean states */
#define FIELDNO_TUPLETABLESLOT_NVALID 2
AttrNumber tts_nvalid; /* 在tts_values中有多少有效的values;# of valid values in tts_values */
const TupleTableSlotOps *const tts_ops; /* slot的实际实现;implementation of slot */
#define FIELDNO_TUPLETABLESLOT_TUPLEDESCRIPTOR 4
TupleDesc tts_tupleDescriptor; /* slot的元组描述符;slot's tuple descriptor */
#define FIELDNO_TUPLETABLESLOT_VALUES 5
Datum *tts_values; /* 当前属性值;current per-attribute values */
#define FIELDNO_TUPLETABLESLOT_ISNULL 6
bool *tts_isnull; /* 当前属性isnull标记;current per-attribute isnull flags */
MemoryContext tts_mcxt; /*内存上下文; slot itself is in this context */
} TupleTableSlot;
/* routines for a TupleTableSlot implementation */
//TupleTableSlot的"小程序"
struct TupleTableSlotOps
{
/* Minimum size of the slot */
//slot的最小化大小
size_t base_slot_size;
/* Initialization. */
//初始化方法
void (*init)(TupleTableSlot *slot);
/* Destruction. */
//析构方法
void (*release)(TupleTableSlot *slot);
/*
* Clear the contents of the slot. Only the contents are expected to be
* cleared and not the tuple descriptor. Typically an implementation of
* this callback should free the memory allocated for the tuple contained
* in the slot.
* 清除slot中的内容。
* 只希望清除内容，而不希望清除元组描述符。
* 通常，这个回调的实现应该释放为slot中包含的元组分配的内存。
*/
void (*clear)(TupleTableSlot *slot);
/*
* Fill up first natts entries of tts_values and tts_isnull arrays with
* values from the tuple contained in the slot. The function may be called
* with natts more than the number of attributes available in the tuple,
* in which case it should set tts_nvalid to the number of returned
* columns.
* 用slot中包含的元组的值填充tts_values和tts_isnull数组的第一个natts条目。
* 在调用该函数时，natts可能多于元组中可用属性的数量，在这种情况下，
* 应该将tts_nvalid设置为返回列的数量。
*/
void (*getsomeattrs)(TupleTableSlot *slot, int natts);
/*
* Returns value of the given system attribute as a datum and sets isnull
* to false, if it's not NULL. Throws an error if the slot type does not
* support system attributes.
* 将给定系统属性的值作为基准返回，如果不为NULL，
* 则将isnull设置为false。如果slot类型不支持系统属性，则引发错误。
*/
Datum (*getsysattr)(TupleTableSlot *slot, int attnum, bool *isnull);
/*
* Make the contents of the slot solely depend on the slot, and not on
* underlying resources (like another memory context, buffers, etc).
* 使slot的内容完全依赖于slot，而不是底层资源(如另一个内存上下文、缓冲区等)。
*/
void (*materialize)(TupleTableSlot *slot);
/*
* Copy the contents of the source slot into the destination slot's own
* context. Invoked using callback of the destination slot.
* 将源slot的内容复制到目标slot自己的上下文中。
* 使用目标slot的回调函数调用。
*/
void (*copyslot) (TupleTableSlot *dstslot, TupleTableSlot *srcslot);
/*
* Return a heap tuple "owned" by the slot. It is slot's responsibility to
* free the memory consumed by the heap tuple. If the slot can not "own" a
* heap tuple, it should not implement this callback and should set it as
* NULL.
* 返回slot“拥有”的堆元组。
* slot负责释放堆元组分配的内存。
* 如果slot不能“拥有”堆元组，它不应该实现这个回调函数，应该将它设置为NULL。
*/
HeapTuple (*get_heap_tuple)(TupleTableSlot *slot);
/*
* Return a minimal tuple "owned" by the slot. It is slot's responsibility
* to free the memory consumed by the minimal tuple. If the slot can not
* "own" a minimal tuple, it should not implement this callback and should
* set it as NULL.
* 返回slot“拥有”的最小元组。
* slot负责释放最小元组分配的内存。
* 如果slot不能“拥有”最小元组，它不应该实现这个回调函数，应该将它设置为NULL。
*/
MinimalTuple (*get_minimal_tuple)(TupleTableSlot *slot);
/*
* Return a copy of heap tuple representing the contents of the slot. The
* copy needs to be palloc'd in the current memory context. The slot
* itself is expected to remain unaffected. It is *not* expected to have
* meaningful "system columns" in the copy. The copy is not be "owned" by
* the slot i.e. the caller has to take responsibilty to free memory
* consumed by the slot.
* 返回表示slot内容的堆元组副本。
* 需要在当前内存上下文中对副本进行内存分配palloc。
* 预计slot本身不会受到影响。
* 它不希望在副本中有有意义的“系统列”。副本不是slot“拥有”的，即调用方必须负责释放slot消耗的内存。
*/
HeapTuple (*copy_heap_tuple)(TupleTableSlot *slot);
/*
* Return a copy of minimal tuple representing the contents of the slot. The
* copy needs to be palloc'd in the current memory context. The slot
* itself is expected to remain unaffected. It is *not* expected to have
* meaningful "system columns" in the copy. The copy is not be "owned" by
* the slot i.e. the caller has to take responsibilty to free memory
* consumed by the slot.
* 返回表示slot内容的最小元组的副本。
* 需要在当前内存上下文中对副本进行palloc。
* 预计slot本身不会受到影响。
* 它不希望在副本中有有意义的“系统列”。副本不是slot“拥有”的，即调用方必须负责释放slot消耗的内存。
*/
MinimalTuple (*copy_minimal_tuple)(TupleTableSlot *slot);
};
typedef struct tupleDesc
{
int natts; /* tuple中的属性数量;number of attributes in the tuple */
Oid tdtypeid; /* tuple类型的组合类型ID;composite type ID for tuple type */
int32 tdtypmod; /* tuple类型的typmode;typmod for tuple type */
int tdrefcount; /* 依赖计数,如为-1,则没有依赖;reference count, or -1 if not counting */
TupleConstr *constr; /* 约束,如无则为NULL;constraints, or NULL if none */
/* attrs[N] is the description of Attribute Number N+1 */
//attrs[N]是第N+1个属性的描述符
FormData_pg_attribute attrs[FLEXIBLE_ARRAY_MEMBER];
} *TupleDesc;SortState
排序运行期状态信息
/* ----------------
* SortState information
* 排序运行期状态信息
* ----------------
*/
typedef struct SortState
{
//基类
ScanState ss; /* its first field is NodeTag */
//是否需要随机访问排序输出?
bool randomAccess; /* need random access to sort output? */
//结果集是否存在边界?
bool bounded; /* is the result set bounded? */
//如存在边界,需要多少个元组?
int64 bound; /* if bounded, how many tuples are needed */
//是否已完成排序?
bool sort_Done; /* sort completed yet? */
//是否使用有界值?
bool bounded_Done; /* value of bounded we did the sort with */
//使用的有界值?
int64 bound_Done; /* value of bound we did the sort with */
//tuplesort.c的私有状态
void *tuplesortstate; /* private state of tuplesort.c */
//是否worker?
bool am_worker; /* are we a worker? */
//每个worker对应一个条目
SharedSortInfo *shared_info; /* one entry per worker */
} SortState;
/* ----------------
* Shared memory container for per-worker sort information
* per-worker排序信息的共享内存容器
* ----------------
*/
typedef struct SharedSortInfo
{
//worker个数?
int num_workers;
//排序机制
TuplesortInstrumentation sinstrument[FLEXIBLE_ARRAY_MEMBER];
} SharedSortInfo;TuplesortInstrumentation
报告排序统计的数据结构.
/*
* Data structures for reporting sort statistics. Note that
* TuplesortInstrumentation can't contain any pointers because we
* sometimes put it in shared memory.
* 报告排序统计的数据结构.
* 注意TuplesortInstrumentation不能包含指针因为有时候会把该结构体放在共享内存中.
*/
typedef enum
{
SORT_TYPE_STILL_IN_PROGRESS = 0,//仍然在排序中
SORT_TYPE_TOP_N_HEAPSORT,//TOP N 堆排序
SORT_TYPE_QUICKSORT,//快速排序
SORT_TYPE_EXTERNAL_SORT,//外排序
SORT_TYPE_EXTERNAL_MERGE//外排序后的合并
} TuplesortMethod;//排序方法
typedef enum
{
SORT_SPACE_TYPE_DISK,//需要用上磁盘
SORT_SPACE_TYPE_MEMORY//使用内存
} TuplesortSpaceType;
typedef struct TuplesortInstrumentation
{
//使用的排序算法
TuplesortMethod sortMethod; /* sort algorithm used */
//排序使用空间类型
TuplesortSpaceType spaceType; /* type of space spaceUsed represents */
//空间消耗(以K为单位)
long spaceUsed; /* space consumption, in kB */
} TuplesortInstrumentation;mergeruns归并所有已完成初始轮的数据.
/*
* mergeruns -- merge all the completed initial runs.
* mergeruns -- 归并所有已完成的数据.
*
* This implements steps D5, D6 of Algorithm D. All input data has
* already been written to initial runs on tape (see dumptuples).
* 实现了算法D中的D5和D6.
* 所有输入数据已写入到磁盘上(dumptuples函数负责完成).
*/
static void
mergeruns(Tuplesortstate *state)
{
int tapenum,
svTape,
svRuns,
svDummy;
int numTapes;
int numInputTapes;
Assert(state->status == TSS_BUILDRUNS);
Assert(state->memtupcount == 0);
if (state->sortKeys != NULL && state->sortKeys->abbrev_converter != NULL)
{
/*
* If there are multiple runs to be merged, when we go to read back
* tuples from disk, abbreviated keys will not have been stored, and
* we don't care to regenerate them. Disable abbreviation from this
* point on.
* 如果从磁盘上读回元组时存在多个运行需要被归并,
* 缩写键不会被存储,并不关系是否需要重新生成它们.
* 在这一刻起,禁用缩写.
*/
state->sortKeys->abbrev_converter = NULL;
state->sortKeys->comparator = state->sortKeys->abbrev_full_comparator;
/* Not strictly necessary, but be tidy */
//非严格性需要,但需要tidy
state->sortKeys->abbrev_abort = NULL;
state->sortKeys->abbrev_full_comparator = NULL;
}
/*
* Reset tuple memory. We've freed all the tuples that we previously
* allocated. We will use the slab allocator from now on.
* 重置元组内存.
* 已释放了先前分配的内存.从现在起使用slab分配器.
*/
MemoryContextDelete(state->tuplecontext);
state->tuplecontext = NULL;
/*
* We no longer need a large memtuples array. (We will allocate a smaller
* one for the heap later.)
* 不再需要大块的memtuples数组.(将为后面的堆分配更小块的内存)
*/
FREEMEM(state, GetMemoryChunkSpace(state->memtuples));
pfree(state->memtuples);
state->memtuples = NULL;
/*
* If we had fewer runs than tapes, refund the memory that we imagined we
* would need for the tape buffers of the unused tapes.
* 比起tapes,如果runs要少, 退还我们认为需要用于tape缓存但其实用不上的内存.
*
* numTapes and numInputTapes reflect the actual number of tapes we will
* use. Note that the output tape's tape number is maxTapes - 1, so the
* tape numbers of the used tapes are not consecutive, and you cannot just
* loop from 0 to numTapes to visit all used tapes!
* numTapes和numInputTapes反映了实际的使用tapes数.
* 注意输出的tape编号是maxTapes - 1,因此已使用的tape编号不是连续的,
* 不能简单的从0 - numTapes循环访问所有已使用的tapes.
*/
if (state->Level == 1)
{
numInputTapes = state->currentRun;
numTapes = numInputTapes + 1;
FREEMEM(state, (state->maxTapes - numTapes) * TAPE_BUFFER_OVERHEAD);
}
else
{
numInputTapes = state->tapeRange;
numTapes = state->maxTapes;
}
/*
* Initialize the slab allocator. We need one slab slot per input tape,
* for the tuples in the heap, plus one to hold the tuple last returned
* from tuplesort_gettuple. (If we're sorting pass-by-val Datums,
* however, we don't need to do allocate anything.)
* 初始化slab分配器.每一个输入的tape都有一个slab slot,对于堆中的元组,
* 外加1用于保存最后从tuplesort_gettuple返回的元组.
* (但是,如果通过传值的方式传递Datums,不需要执行内存分配)
*
* From this point on, we no longer use the USEMEM()/LACKMEM() mechanism
* to track memory usage of individual tuples.
* 从这点起,不再使用USEMEM()/LACKMEM()这种机制来跟踪独立元组的内存使用.
*/
if (state->tuples)
init_slab_allocator(state, numInputTapes + 1);
else
init_slab_allocator(state, 0);
/*
* Allocate a new 'memtuples' array, for the heap. It will hold one tuple
* from each input tape.
* 为堆分配新的'memtuples'数组
* 对于每一个输入的tape,都会保存有一个元组.
*/
state->memtupsize = numInputTapes;
state->memtuples = (SortTuple *) palloc(numInputTapes * sizeof(SortTuple));
USEMEM(state, GetMemoryChunkSpace(state->memtuples));
/*
* Use all the remaining memory we have available for read buffers among
* the input tapes.
* 使用所有可使用的剩余内存读取输入tapes之间的缓存.
*
* We don't try to "rebalance" the memory among tapes, when we start a new
* merge phase, even if some tapes are inactive in the new phase. That
* would be hard, because logtape.c doesn't know where one run ends and
* another begins. When a new merge phase begins, and a tape doesn't
* participate in it, its buffer nevertheless already contains tuples from
* the next run on same tape, so we cannot release the buffer. That's OK
* in practice, merge performance isn't that sensitive to the amount of
* buffers used, and most merge phases use all or almost all tapes,
* anyway.
* 在新的阶段就算存在某些tapes不再活动,在开始新的归并阶段时,不再尝试在tapes之间重平衡内存.
* 这是比较难以实现的,因为logtape.c不知道某个运行在哪里结束了,那个运行在哪里开始.
* 在新的归并阶段开始时,tape不需要分享,尽管如此,它的缓冲区已包含来自同一tape上下一次运行需要的元组,
* 因此不需要释放缓冲区.
* 实践中,这是没有问题的,归并的性能对于缓存的使用不是性能敏感的,大多数归并阶段使用所有或大多数的tapes.
*/
#ifdef TRACE_SORT
if (trace_sort)
elog(LOG, "worker %d using " INT64_FORMAT " KB of memory for read buffers among %d input tapes",
state->worker, state->availMem / 1024, numInputTapes);
#endif
state->read_buffer_size = Max(state->availMem / numInputTapes, 0);
USEMEM(state, state->read_buffer_size * numInputTapes);
/* End of step D2: rewind all output tapes to prepare for merging */
//D2完成,倒回所有输出tapes准备归并
for (tapenum = 0; tapenum < state->tapeRange; tapenum++)
LogicalTapeRewindForRead(state->tapeset, tapenum, state->read_buffer_size);
for (;;)
{
//------------- 循环
/*
* At this point we know that tape[T] is empty. If there's just one
* (real or dummy) run left on each input tape, then only one merge
* pass remains. If we don't have to produce a materialized sorted
* tape, we can stop at this point and do the final merge on-the-fly.
* 在这时候,我们已知tape[T]是空的.
* 如果正好在每一个输入tape上只剩下某个run(实际或者虚拟的),那么只剩下一次归并.
* 如果不需要产生物化排序后的tape,这时候可以停止并执行内存中的最终归并.
*/
if (!state->randomAccess && !WORKER(state))
{
bool allOneRun = true;
Assert(state->tp_runs[state->tapeRange] == 0);
for (tapenum = 0; tapenum < state->tapeRange; tapenum++)
{
if (state->tp_runs[tapenum] + state->tp_dummy[tapenum] != 1)
{
allOneRun = false;
break;
}
}
if (allOneRun)
{
/* Tell logtape.c we won't be writing anymore */
//通知logtape.c,不再写入.
LogicalTapeSetForgetFreeSpace(state->tapeset);
/* Initialize for the final merge pass */
//为最终的归并做准备
beginmerge(state);
state->status = TSS_FINALMERGE;
return;
}
}
/* Step D5: merge runs onto tape[T] until tape[P] is empty */
//步骤D5:归并runs到tape[T]中直至tape[P]为空
while (state->tp_runs[state->tapeRange - 1] ||
state->tp_dummy[state->tapeRange - 1])
{
bool allDummy = true;
for (tapenum = 0; tapenum < state->tapeRange; tapenum++)
{
if (state->tp_dummy[tapenum] == 0)
{
allDummy = false;
break;
}
}
if (allDummy)
{
state->tp_dummy[state->tapeRange]++;
for (tapenum = 0; tapenum < state->tapeRange; tapenum++)
state->tp_dummy[tapenum]--;
}
else
mergeonerun(state);
}
/* Step D6: decrease level */
//步骤D6:往上层汇总
if (--state->Level == 0)
break;
/* rewind output tape T to use as new input */
//倒回输入的Tape T作为新的输入
LogicalTapeRewindForRead(state->tapeset, state->tp_tapenum[state->tapeRange],
state->read_buffer_size);
/* rewind used-up input tape P, and prepare it for write pass */
//倒回使用上的输入tape P,并为写入轮准备
LogicalTapeRewindForWrite(state->tapeset, state->tp_tapenum[state->tapeRange - 1]);
state->tp_runs[state->tapeRange - 1] = 0;
/*
* reassign tape units per step D6; note we no longer care about A[]
* 每一个步骤D6,重分配tape单元.
* 注意我们不再关心A[]了.
*/
svTape = state->tp_tapenum[state->tapeRange];
svDummy = state->tp_dummy[state->tapeRange];
svRuns = state->tp_runs[state->tapeRange];
for (tapenum = state->tapeRange; tapenum > 0; tapenum--)
{
state->tp_tapenum[tapenum] = state->tp_tapenum[tapenum - 1];
state->tp_dummy[tapenum] = state->tp_dummy[tapenum - 1];
state->tp_runs[tapenum] = state->tp_runs[tapenum - 1];
}
state->tp_tapenum[0] = svTape;
state->tp_dummy[0] = svDummy;
state->tp_runs[0] = svRuns;
}
/*
* Done. Knuth says that the result is on TAPE[1], but since we exited
* the loop without performing the last iteration of step D6, we have not
* rearranged the tape unit assignment, and therefore the result is on
* TAPE[T]. We need to do it this way so that we can freeze the final
* output tape while rewinding it. The last iteration of step D6 would be
* a waste of cycles anyway...
* 大功告成!结果位于TAPE[1]中,但因为没有执行步骤D6中最后一个迭代就退出了循环,
* 因此不需要重新整理tape单元分配,因此结果在TAPE[T]中.
* 通过这种方法来处理一遍可以在倒回时冻结结果输出TAPE.
* 步骤D6的最后一轮迭代会是浪费.
*/
state->result_tape = state->tp_tapenum[state->tapeRange];
if (!WORKER(state))
LogicalTapeFreeze(state->tapeset, state->result_tape, NULL);
else
worker_freeze_result_tape(state);
state->status = TSS_SORTEDONTAPE;
/* Release the read buffers of all the other tapes, by rewinding them. */
//通过倒回tapes,释放所有其他tapes的读缓存
for (tapenum = 0; tapenum < state->maxTapes; tapenum++)
{
if (tapenum != state->result_tape)
LogicalTapeRewindForWrite(state->tapeset, tapenum);
}
}测试脚本
select * from t_sort order by c1,c2;
跟踪分析
(gdb) b mergeruns Breakpoint 1 at 0xa73508: file tuplesort.c, line 2570. (gdb) Note: breakpoint 1 also set at pc 0xa73508. Breakpoint 2 at 0xa73508: file tuplesort.c, line 2570.
输入参数
(gdb) c
Continuing.
Breakpoint 1, mergeruns (state=0x2b808a8) at tuplesort.c:2570
2570 Assert(state->status == TSS_BUILDRUNS);
(gdb) p *state
$1 = {status = TSS_BUILDRUNS, nKeys = 2, randomAccess = false, bounded = false, boundUsed = false, bound = 0,
tuples = true, availMem = 3164456, allowedMem = 4194304, maxTapes = 16, tapeRange = 15, sortcontext = 0x2b80790,
tuplecontext = 0x2b827a0, tapeset = 0x2b81480, comparetup = 0xa7525b <comparetup_heap>,
copytup = 0xa76247 <copytup_heap>, writetup = 0xa76de1 <writetup_heap>, readtup = 0xa76ec6 <readtup_heap>,
memtuples = 0x7f0cfeb14050, memtupcount = 0, memtupsize = 37448, growmemtuples = false, slabAllocatorUsed = false,
slabMemoryBegin = 0x0, slabMemoryEnd = 0x0, slabFreeHead = 0x0, read_buffer_size = 0, lastReturnedTuple = 0x0,
currentRun = 3, mergeactive = 0x2b81350, Level = 1, destTape = 2, tp_fib = 0x2b80d58, tp_runs = 0x2b81378,
tp_dummy = 0x2b813d0, tp_tapenum = 0x2b81428, activeTapes = 0, result_tape = -1, current = 0, eof_reached = false,
markpos_block = 0, markpos_offset = 0, markpos_eof = false, worker = -1, shared = 0x0, nParticipants = -1,
tupDesc = 0x2b67ae0, sortKeys = 0x2b80cc0, onlyKey = 0x0, abbrevNext = 10, indexInfo = 0x0, estate = 0x0, heapRel = 0x0,
indexRel = 0x0, enforceUnique = false, high_mask = 0, low_mask = 0, max_buckets = 0, datumType = 0, datumTypeLen = 0,
ru_start = {tv = {tv_sec = 0, tv_usec = 0}, ru = {ru_utime = {tv_sec = 0, tv_usec = 0}, ru_stime = {tv_sec = 0,
tv_usec = 0}, {ru_maxrss = 0, __ru_maxrss_word = 0}, {ru_ixrss = 0, __ru_ixrss_word = 0}, {ru_idrss = 0,
__ru_idrss_word = 0}, {ru_isrss = 0, __ru_isrss_word = 0}, {ru_minflt = 0, __ru_minflt_word = 0}, {ru_majflt = 0,
__ru_majflt_word = 0}, {ru_nswap = 0, __ru_nswap_word = 0}, {ru_inblock = 0, __ru_inblock_word = 0}, {
ru_oublock = 0, __ru_oublock_word = 0}, {ru_msgsnd = 0, __ru_msgsnd_word = 0}, {ru_msgrcv = 0,
__ru_msgrcv_word = 0}, {ru_nsignals = 0, __ru_nsignals_word = 0}, {ru_nvcsw = 0, __ru_nvcsw_word = 0}, {
ru_nivcsw = 0, __ru_nivcsw_word = 0}}}}
(gdb)排序键等信息
(gdb) n
2571 Assert(state->memtupcount == 0);
(gdb)
2573 if (state->sortKeys != NULL && state->sortKeys->abbrev_converter != NULL)
(gdb) p *state->sortKeys
$2 = {ssup_cxt = 0x2b80790, ssup_collation = 0, ssup_reverse = false, ssup_nulls_first = false, ssup_attno = 2,
ssup_extra = 0x0, comparator = 0x4fd4af <btint4fastcmp>, abbreviate = true, abbrev_converter = 0x0, abbrev_abort = 0x0,
abbrev_full_comparator = 0x0}
(gdb) p *state->sortKeys->abbrev_converter
Cannot access memory at address 0x0重置元组内存,不再需要大块的memtuples数组.
(gdb) n 2593 MemoryContextDelete(state->tuplecontext); (gdb) 2594 state->tuplecontext = NULL; (gdb) (gdb) n 2600 FREEMEM(state, GetMemoryChunkSpace(state->memtuples)); (gdb) 2601 pfree(state->memtuples); (gdb) 2602 state->memtuples = NULL; (gdb) 2613 if (state->Level == 1) (gdb)
计算Tapes数
(gdb) n 2615 numInputTapes = state->currentRun; (gdb) p state->currentRun $3 = 3 (gdb) p state->Level $4 = 1 (gdb) p state->tapeRange $5 = 15 (gdb) p state->maxTapes $6 = 16 (gdb) n 2616 numTapes = numInputTapes + 1; (gdb) 2617 FREEMEM(state, (state->maxTapes - numTapes) * TAPE_BUFFER_OVERHEAD); (gdb) 2634 if (state->tuples) (gdb) p numInputTapes $7 = 3 (gdb) p numTapes $8 = 4 (gdb)
初始化slab分配器/为堆分配新的’memtuples’数组/倒回所有输出tapes准备归并
(gdb) n 2635 init_slab_allocator(state, numInputTapes + 1); (gdb) n 2643 state->memtupsize = numInputTapes; (gdb) 2644 state->memtuples = (SortTuple *) palloc(numInputTapes * sizeof(SortTuple)); (gdb) 2645 USEMEM(state, GetMemoryChunkSpace(state->memtuples)); (gdb) p state->memtupsize $9 = 3 (gdb) n 2662 if (trace_sort) (gdb) 2667 state->read_buffer_size = Max(state->availMem / numInputTapes, 0); (gdb) 2668 USEMEM(state, state->read_buffer_size * numInputTapes); (gdb) p state->read_buffer_size $10 = 1385762 (gdb) n 2671 for (tapenum = 0; tapenum < state->tapeRange; tapenum++) (gdb) 2672 LogicalTapeRewindForRead(state->tapeset, tapenum, state->read_buffer_size); (gdb) p state->tapeRange $11 = 15 (gdb) p state->status $12 = TSS_BUILDRUNS (gdb)
进入循环
2671 for (tapenum = 0; tapenum < state->tapeRange; tapenum++) (gdb) 2682 if (!state->randomAccess && !WORKER(state)) (gdb) 2684 bool allOneRun = true; (gdb) p state->randomAccess $15 = false (gdb) p WORKER(state) $16 = 0 (gdb)
循环判断allOneRun是否为F
2687 for (tapenum = 0; tapenum < state->tapeRange; tapenum++) (gdb) 2695 if (allOneRun) (gdb) p allOneRun $19 = true (gdb)
开始归并,并设置状态,返回
(gdb) n 2698 LogicalTapeSetForgetFreeSpace(state->tapeset); (gdb) 2700 beginmerge(state); (gdb) 2701 state->status = TSS_FINALMERGE; (gdb) 2702 return; (gdb) 2779 } (gdb) tuplesort_performsort (state=0x2b808a8) at tuplesort.c:1866 1866 state->eof_reached = false; (gdb)
完成排序
(gdb) n 1867 state->markpos_block = 0L; (gdb) 1868 state->markpos_offset = 0; (gdb) 1869 state->markpos_eof = false; (gdb) 1870 break; (gdb) 1878 if (trace_sort) (gdb) 1890 MemoryContextSwitchTo(oldcontext); (gdb) 1891 } (gdb) ExecSort (pstate=0x2b67640) at nodeSort.c:123 123 estate->es_direction = dir; (gdb) c Continuing.
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