Branch data Line data Source code
1 : : /*-------------------------------------------------------------------------
2 : : *
3 : : * tuplesort.c
4 : : * Generalized tuple sorting routines.
5 : : *
6 : : * This module provides a generalized facility for tuple sorting, which can be
7 : : * applied to different kinds of sortable objects. Implementation of
8 : : * the particular sorting variants is given in tuplesortvariants.c.
9 : : * This module works efficiently for both small and large amounts
10 : : * of data. Small amounts are sorted in-memory using qsort(). Large
11 : : * amounts are sorted using temporary files and a standard external sort
12 : : * algorithm.
13 : : *
14 : : * See Knuth, volume 3, for more than you want to know about external
15 : : * sorting algorithms. The algorithm we use is a balanced k-way merge.
16 : : * Before PostgreSQL 15, we used the polyphase merge algorithm (Knuth's
17 : : * Algorithm 5.4.2D), but with modern hardware, a straightforward balanced
18 : : * merge is better. Knuth is assuming that tape drives are expensive
19 : : * beasts, and in particular that there will always be many more runs than
20 : : * tape drives. The polyphase merge algorithm was good at keeping all the
21 : : * tape drives busy, but in our implementation a "tape drive" doesn't cost
22 : : * much more than a few Kb of memory buffers, so we can afford to have
23 : : * lots of them. In particular, if we can have as many tape drives as
24 : : * sorted runs, we can eliminate any repeated I/O at all.
25 : : *
26 : : * Historically, we divided the input into sorted runs using replacement
27 : : * selection, in the form of a priority tree implemented as a heap
28 : : * (essentially Knuth's Algorithm 5.2.3H), but now we always use quicksort
29 : : * for run generation.
30 : : *
31 : : * The approximate amount of memory allowed for any one sort operation
32 : : * is specified in kilobytes by the caller (most pass work_mem). Initially,
33 : : * we absorb tuples and simply store them in an unsorted array as long as
34 : : * we haven't exceeded workMem. If we reach the end of the input without
35 : : * exceeding workMem, we sort the array using qsort() and subsequently return
36 : : * tuples just by scanning the tuple array sequentially. If we do exceed
37 : : * workMem, we begin to emit tuples into sorted runs in temporary tapes.
38 : : * When tuples are dumped in batch after quicksorting, we begin a new run
39 : : * with a new output tape. If we reach the max number of tapes, we write
40 : : * subsequent runs on the existing tapes in a round-robin fashion. We will
41 : : * need multiple merge passes to finish the merge in that case. After the
42 : : * end of the input is reached, we dump out remaining tuples in memory into
43 : : * a final run, then merge the runs.
44 : : *
45 : : * When merging runs, we use a heap containing just the frontmost tuple from
46 : : * each source run; we repeatedly output the smallest tuple and replace it
47 : : * with the next tuple from its source tape (if any). When the heap empties,
48 : : * the merge is complete. The basic merge algorithm thus needs very little
49 : : * memory --- only M tuples for an M-way merge, and M is constrained to a
50 : : * small number. However, we can still make good use of our full workMem
51 : : * allocation by pre-reading additional blocks from each source tape. Without
52 : : * prereading, our access pattern to the temporary file would be very erratic;
53 : : * on average we'd read one block from each of M source tapes during the same
54 : : * time that we're writing M blocks to the output tape, so there is no
55 : : * sequentiality of access at all, defeating the read-ahead methods used by
56 : : * most Unix kernels. Worse, the output tape gets written into a very random
57 : : * sequence of blocks of the temp file, ensuring that things will be even
58 : : * worse when it comes time to read that tape. A straightforward merge pass
59 : : * thus ends up doing a lot of waiting for disk seeks. We can improve matters
60 : : * by prereading from each source tape sequentially, loading about workMem/M
61 : : * bytes from each tape in turn, and making the sequential blocks immediately
62 : : * available for reuse. This approach helps to localize both read and write
63 : : * accesses. The pre-reading is handled by logtape.c, we just tell it how
64 : : * much memory to use for the buffers.
65 : : *
66 : : * In the current code we determine the number of input tapes M on the basis
67 : : * of workMem: we want workMem/M to be large enough that we read a fair
68 : : * amount of data each time we read from a tape, so as to maintain the
69 : : * locality of access described above. Nonetheless, with large workMem we
70 : : * can have many tapes. The logical "tapes" are implemented by logtape.c,
71 : : * which avoids space wastage by recycling disk space as soon as each block
72 : : * is read from its "tape".
73 : : *
74 : : * When the caller requests random access to the sort result, we form
75 : : * the final sorted run on a logical tape which is then "frozen", so
76 : : * that we can access it randomly. When the caller does not need random
77 : : * access, we return from tuplesort_performsort() as soon as we are down
78 : : * to one run per logical tape. The final merge is then performed
79 : : * on-the-fly as the caller repeatedly calls tuplesort_getXXX; this
80 : : * saves one cycle of writing all the data out to disk and reading it in.
81 : : *
82 : : * This module supports parallel sorting. Parallel sorts involve coordination
83 : : * among one or more worker processes, and a leader process, each with its own
84 : : * tuplesort state. The leader process (or, more accurately, the
85 : : * Tuplesortstate associated with a leader process) creates a full tapeset
86 : : * consisting of worker tapes with one run to merge; a run for every
87 : : * worker process. This is then merged. Worker processes are guaranteed to
88 : : * produce exactly one output run from their partial input.
89 : : *
90 : : *
91 : : * Portions Copyright (c) 1996-2026, PostgreSQL Global Development Group
92 : : * Portions Copyright (c) 1994, Regents of the University of California
93 : : *
94 : : * IDENTIFICATION
95 : : * src/backend/utils/sort/tuplesort.c
96 : : *
97 : : *-------------------------------------------------------------------------
98 : : */
99 : :
100 : : #include "postgres.h"
101 : :
102 : : #include <limits.h>
103 : :
104 : : #include "commands/tablespace.h"
105 : : #include "miscadmin.h"
106 : : #include "pg_trace.h"
107 : : #include "storage/shmem.h"
108 : : #include "utils/guc.h"
109 : : #include "utils/memutils.h"
110 : : #include "utils/pg_rusage.h"
111 : : #include "utils/tuplesort.h"
112 : :
113 : : /*
114 : : * Initial size of memtuples array. This must be more than
115 : : * ALLOCSET_SEPARATE_THRESHOLD; see comments in grow_memtuples(). Clamp at
116 : : * 1024 elements to avoid excessive reallocs.
117 : : */
118 : : #define INITIAL_MEMTUPSIZE Max(1024, \
119 : : ALLOCSET_SEPARATE_THRESHOLD / sizeof(SortTuple) + 1)
120 : :
121 : : /* GUC variables */
122 : : bool trace_sort = false;
123 : :
124 : : #ifdef DEBUG_BOUNDED_SORT
125 : : bool optimize_bounded_sort = true;
126 : : #endif
127 : :
128 : :
129 : : /*
130 : : * During merge, we use a pre-allocated set of fixed-size slots to hold
131 : : * tuples. To avoid palloc/pfree overhead.
132 : : *
133 : : * Merge doesn't require a lot of memory, so we can afford to waste some,
134 : : * by using gratuitously-sized slots. If a tuple is larger than 1 kB, the
135 : : * palloc() overhead is not significant anymore.
136 : : *
137 : : * 'nextfree' is valid when this chunk is in the free list. When in use, the
138 : : * slot holds a tuple.
139 : : */
140 : : #define SLAB_SLOT_SIZE 1024
141 : :
142 : : typedef union SlabSlot
143 : : {
144 : : union SlabSlot *nextfree;
145 : : char buffer[SLAB_SLOT_SIZE];
146 : : } SlabSlot;
147 : :
148 : : /*
149 : : * Possible states of a Tuplesort object. These denote the states that
150 : : * persist between calls of Tuplesort routines.
151 : : */
152 : : typedef enum
153 : : {
154 : : TSS_INITIAL, /* Loading tuples; still within memory limit */
155 : : TSS_BOUNDED, /* Loading tuples into bounded-size heap */
156 : : TSS_BUILDRUNS, /* Loading tuples; writing to tape */
157 : : TSS_SORTEDINMEM, /* Sort completed entirely in memory */
158 : : TSS_SORTEDONTAPE, /* Sort completed, final run is on tape */
159 : : TSS_FINALMERGE, /* Performing final merge on-the-fly */
160 : : } TupSortStatus;
161 : :
162 : : /*
163 : : * Parameters for calculation of number of tapes to use --- see inittapes()
164 : : * and tuplesort_merge_order().
165 : : *
166 : : * In this calculation we assume that each tape will cost us about 1 blocks
167 : : * worth of buffer space. This ignores the overhead of all the other data
168 : : * structures needed for each tape, but it's probably close enough.
169 : : *
170 : : * MERGE_BUFFER_SIZE is how much buffer space we'd like to allocate for each
171 : : * input tape, for pre-reading (see discussion at top of file). This is *in
172 : : * addition to* the 1 block already included in TAPE_BUFFER_OVERHEAD.
173 : : */
174 : : #define MINORDER 6 /* minimum merge order */
175 : : #define MAXORDER 500 /* maximum merge order */
176 : : #define TAPE_BUFFER_OVERHEAD BLCKSZ
177 : : #define MERGE_BUFFER_SIZE (BLCKSZ * 32)
178 : :
179 : :
180 : : /*
181 : : * Private state of a Tuplesort operation.
182 : : */
183 : : struct Tuplesortstate
184 : : {
185 : : TuplesortPublic base;
186 : : TupSortStatus status; /* enumerated value as shown above */
187 : : bool bounded; /* did caller specify a maximum number of
188 : : * tuples to return? */
189 : : bool boundUsed; /* true if we made use of a bounded heap */
190 : : int bound; /* if bounded, the maximum number of tuples */
191 : : int64 tupleMem; /* memory consumed by individual tuples.
192 : : * storing this separately from what we track
193 : : * in availMem allows us to subtract the
194 : : * memory consumed by all tuples when dumping
195 : : * tuples to tape */
196 : : int64 availMem; /* remaining memory available, in bytes */
197 : : int64 allowedMem; /* total memory allowed, in bytes */
198 : : int maxTapes; /* max number of input tapes to merge in each
199 : : * pass */
200 : : int64 maxSpace; /* maximum amount of space occupied among sort
201 : : * of groups, either in-memory or on-disk */
202 : : bool isMaxSpaceDisk; /* true when maxSpace tracks on-disk space,
203 : : * false means in-memory */
204 : : TupSortStatus maxSpaceStatus; /* sort status when maxSpace was reached */
205 : : LogicalTapeSet *tapeset; /* logtape.c object for tapes in a temp file */
206 : :
207 : : /*
208 : : * This array holds the tuples now in sort memory. If we are in state
209 : : * INITIAL, the tuples are in no particular order; if we are in state
210 : : * SORTEDINMEM, the tuples are in final sorted order; in states BUILDRUNS
211 : : * and FINALMERGE, the tuples are organized in "heap" order per Algorithm
212 : : * H. In state SORTEDONTAPE, the array is not used.
213 : : */
214 : : SortTuple *memtuples; /* array of SortTuple structs */
215 : : int memtupcount; /* number of tuples currently present */
216 : : int memtupsize; /* allocated length of memtuples array */
217 : : bool growmemtuples; /* memtuples' growth still underway? */
218 : :
219 : : /*
220 : : * Memory for tuples is sometimes allocated using a simple slab allocator,
221 : : * rather than with palloc(). Currently, we switch to slab allocation
222 : : * when we start merging. Merging only needs to keep a small, fixed
223 : : * number of tuples in memory at any time, so we can avoid the
224 : : * palloc/pfree overhead by recycling a fixed number of fixed-size slots
225 : : * to hold the tuples.
226 : : *
227 : : * For the slab, we use one large allocation, divided into SLAB_SLOT_SIZE
228 : : * slots. The allocation is sized to have one slot per tape, plus one
229 : : * additional slot. We need that many slots to hold all the tuples kept
230 : : * in the heap during merge, plus the one we have last returned from the
231 : : * sort, with tuplesort_gettuple.
232 : : *
233 : : * Initially, all the slots are kept in a linked list of free slots. When
234 : : * a tuple is read from a tape, it is put to the next available slot, if
235 : : * it fits. If the tuple is larger than SLAB_SLOT_SIZE, it is palloc'd
236 : : * instead.
237 : : *
238 : : * When we're done processing a tuple, we return the slot back to the free
239 : : * list, or pfree() if it was palloc'd. We know that a tuple was
240 : : * allocated from the slab, if its pointer value is between
241 : : * slabMemoryBegin and -End.
242 : : *
243 : : * When the slab allocator is used, the USEMEM/LACKMEM mechanism of
244 : : * tracking memory usage is not used.
245 : : */
246 : : bool slabAllocatorUsed;
247 : :
248 : : char *slabMemoryBegin; /* beginning of slab memory arena */
249 : : char *slabMemoryEnd; /* end of slab memory arena */
250 : : SlabSlot *slabFreeHead; /* head of free list */
251 : :
252 : : /* Memory used for input and output tape buffers. */
253 : : size_t tape_buffer_mem;
254 : :
255 : : /*
256 : : * When we return a tuple to the caller in tuplesort_gettuple_XXX, that
257 : : * came from a tape (that is, in TSS_SORTEDONTAPE or TSS_FINALMERGE
258 : : * modes), we remember the tuple in 'lastReturnedTuple', so that we can
259 : : * recycle the memory on next gettuple call.
260 : : */
261 : : void *lastReturnedTuple;
262 : :
263 : : /*
264 : : * While building initial runs, this is the current output run number.
265 : : * Afterwards, it is the number of initial runs we made.
266 : : */
267 : : int currentRun;
268 : :
269 : : /*
270 : : * Logical tapes, for merging.
271 : : *
272 : : * The initial runs are written in the output tapes. In each merge pass,
273 : : * the output tapes of the previous pass become the input tapes, and new
274 : : * output tapes are created as needed. When nInputTapes equals
275 : : * nInputRuns, there is only one merge pass left.
276 : : */
277 : : LogicalTape **inputTapes;
278 : : int nInputTapes;
279 : : int nInputRuns;
280 : :
281 : : LogicalTape **outputTapes;
282 : : int nOutputTapes;
283 : : int nOutputRuns;
284 : :
285 : : LogicalTape *destTape; /* current output tape */
286 : :
287 : : /*
288 : : * These variables are used after completion of sorting to keep track of
289 : : * the next tuple to return. (In the tape case, the tape's current read
290 : : * position is also critical state.)
291 : : */
292 : : LogicalTape *result_tape; /* actual tape of finished output */
293 : : int current; /* array index (only used if SORTEDINMEM) */
294 : : bool eof_reached; /* reached EOF (needed for cursors) */
295 : :
296 : : /* markpos_xxx holds marked position for mark and restore */
297 : : int64 markpos_block; /* tape block# (only used if SORTEDONTAPE) */
298 : : int markpos_offset; /* saved "current", or offset in tape block */
299 : : bool markpos_eof; /* saved "eof_reached" */
300 : :
301 : : /*
302 : : * These variables are used during parallel sorting.
303 : : *
304 : : * worker is our worker identifier. Follows the general convention that
305 : : * -1 value relates to a leader tuplesort, and values >= 0 worker
306 : : * tuplesorts. (-1 can also be a serial tuplesort.)
307 : : *
308 : : * shared is mutable shared memory state, which is used to coordinate
309 : : * parallel sorts.
310 : : *
311 : : * nParticipants is the number of worker Tuplesortstates known by the
312 : : * leader to have actually been launched, which implies that they must
313 : : * finish a run that the leader needs to merge. Typically includes a
314 : : * worker state held by the leader process itself. Set in the leader
315 : : * Tuplesortstate only.
316 : : */
317 : : int worker;
318 : : Sharedsort *shared;
319 : : int nParticipants;
320 : :
321 : : /*
322 : : * Additional state for managing "abbreviated key" sortsupport routines
323 : : * (which currently may be used by all cases except the hash index case).
324 : : * Tracks the intervals at which the optimization's effectiveness is
325 : : * tested.
326 : : */
327 : : int64 abbrevNext; /* Tuple # at which to next check
328 : : * applicability */
329 : :
330 : : /*
331 : : * Resource snapshot for time of sort start.
332 : : */
333 : : PGRUsage ru_start;
334 : : };
335 : :
336 : : /*
337 : : * Private mutable state of tuplesort-parallel-operation. This is allocated
338 : : * in shared memory.
339 : : */
340 : : struct Sharedsort
341 : : {
342 : : /* mutex protects all fields prior to tapes */
343 : : slock_t mutex;
344 : :
345 : : /*
346 : : * currentWorker generates ordinal identifier numbers for parallel sort
347 : : * workers. These start from 0, and are always gapless.
348 : : *
349 : : * Workers increment workersFinished to indicate having finished. If this
350 : : * is equal to state.nParticipants within the leader, leader is ready to
351 : : * merge worker runs.
352 : : */
353 : : int currentWorker;
354 : : int workersFinished;
355 : :
356 : : /* Temporary file space */
357 : : SharedFileSet fileset;
358 : :
359 : : /* Size of tapes flexible array */
360 : : int nTapes;
361 : :
362 : : /*
363 : : * Tapes array used by workers to report back information needed by the
364 : : * leader to concatenate all worker tapes into one for merging
365 : : */
366 : : TapeShare tapes[FLEXIBLE_ARRAY_MEMBER];
367 : : };
368 : :
369 : : /*
370 : : * Is the given tuple allocated from the slab memory arena?
371 : : */
372 : : #define IS_SLAB_SLOT(state, tuple) \
373 : : ((char *) (tuple) >= (state)->slabMemoryBegin && \
374 : : (char *) (tuple) < (state)->slabMemoryEnd)
375 : :
376 : : /*
377 : : * Return the given tuple to the slab memory free list, or free it
378 : : * if it was palloc'd.
379 : : */
380 : : #define RELEASE_SLAB_SLOT(state, tuple) \
381 : : do { \
382 : : SlabSlot *buf = (SlabSlot *) tuple; \
383 : : \
384 : : if (IS_SLAB_SLOT((state), buf)) \
385 : : { \
386 : : buf->nextfree = (state)->slabFreeHead; \
387 : : (state)->slabFreeHead = buf; \
388 : : } else \
389 : : pfree(buf); \
390 : : } while(0)
391 : :
392 : : #define REMOVEABBREV(state,stup,count) ((*(state)->base.removeabbrev) (state, stup, count))
393 : : #define COMPARETUP(state,a,b) ((*(state)->base.comparetup) (a, b, state))
394 : : #define WRITETUP(state,tape,stup) ((*(state)->base.writetup) (state, tape, stup))
395 : : #define READTUP(state,stup,tape,len) ((*(state)->base.readtup) (state, stup, tape, len))
396 : : #define FREESTATE(state) ((state)->base.freestate ? (*(state)->base.freestate) (state) : (void) 0)
397 : : #define LACKMEM(state) ((state)->availMem < 0 && !(state)->slabAllocatorUsed)
398 : : #define USEMEM(state,amt) ((state)->availMem -= (amt))
399 : : #define FREEMEM(state,amt) ((state)->availMem += (amt))
400 : : #define SERIAL(state) ((state)->shared == NULL)
401 : : #define WORKER(state) ((state)->shared && (state)->worker != -1)
402 : : #define LEADER(state) ((state)->shared && (state)->worker == -1)
403 : :
404 : : /*
405 : : * NOTES about on-tape representation of tuples:
406 : : *
407 : : * We require the first "unsigned int" of a stored tuple to be the total size
408 : : * on-tape of the tuple, including itself (so it is never zero; an all-zero
409 : : * unsigned int is used to delimit runs). The remainder of the stored tuple
410 : : * may or may not match the in-memory representation of the tuple ---
411 : : * any conversion needed is the job of the writetup and readtup routines.
412 : : *
413 : : * If state->sortopt contains TUPLESORT_RANDOMACCESS, then the stored
414 : : * representation of the tuple must be followed by another "unsigned int" that
415 : : * is a copy of the length --- so the total tape space used is actually
416 : : * sizeof(unsigned int) more than the stored length value. This allows
417 : : * read-backwards. When the random access flag was not specified, the
418 : : * write/read routines may omit the extra length word.
419 : : *
420 : : * writetup is expected to write both length words as well as the tuple
421 : : * data. When readtup is called, the tape is positioned just after the
422 : : * front length word; readtup must read the tuple data and advance past
423 : : * the back length word (if present).
424 : : *
425 : : * The write/read routines can make use of the tuple description data
426 : : * stored in the Tuplesortstate record, if needed. They are also expected
427 : : * to adjust state->availMem by the amount of memory space (not tape space!)
428 : : * released or consumed. There is no error return from either writetup
429 : : * or readtup; they should ereport() on failure.
430 : : *
431 : : *
432 : : * NOTES about memory consumption calculations:
433 : : *
434 : : * We count space allocated for tuples against the workMem limit, plus
435 : : * the space used by the variable-size memtuples array. Fixed-size space
436 : : * is not counted; it's small enough to not be interesting.
437 : : *
438 : : * Note that we count actual space used (as shown by GetMemoryChunkSpace)
439 : : * rather than the originally-requested size. This is important since
440 : : * palloc can add substantial overhead. It's not a complete answer since
441 : : * we won't count any wasted space in palloc allocation blocks, but it's
442 : : * a lot better than what we were doing before 7.3. As of 9.6, a
443 : : * separate memory context is used for caller passed tuples. Resetting
444 : : * it at certain key increments significantly ameliorates fragmentation.
445 : : * readtup routines use the slab allocator (they cannot use
446 : : * the reset context because it gets deleted at the point that merging
447 : : * begins).
448 : : */
449 : :
450 : :
451 : : static void tuplesort_begin_batch(Tuplesortstate *state);
452 : : static bool consider_abort_common(Tuplesortstate *state);
453 : : static void inittapes(Tuplesortstate *state, bool mergeruns);
454 : : static void inittapestate(Tuplesortstate *state, int maxTapes);
455 : : static void selectnewtape(Tuplesortstate *state);
456 : : static void init_slab_allocator(Tuplesortstate *state, int numSlots);
457 : : static void mergeruns(Tuplesortstate *state);
458 : : static void mergeonerun(Tuplesortstate *state);
459 : : static void beginmerge(Tuplesortstate *state);
460 : : static bool mergereadnext(Tuplesortstate *state, LogicalTape *srcTape, SortTuple *stup);
461 : : static void dumptuples(Tuplesortstate *state, bool alltuples);
462 : : static void make_bounded_heap(Tuplesortstate *state);
463 : : static void sort_bounded_heap(Tuplesortstate *state);
464 : : static void tuplesort_sort_memtuples(Tuplesortstate *state);
465 : : static void tuplesort_heap_insert(Tuplesortstate *state, SortTuple *tuple);
466 : : static void tuplesort_heap_replace_top(Tuplesortstate *state, SortTuple *tuple);
467 : : static void tuplesort_heap_delete_top(Tuplesortstate *state);
468 : : static void reversedirection(Tuplesortstate *state);
469 : : static unsigned int getlen(LogicalTape *tape, bool eofOK);
470 : : static void markrunend(LogicalTape *tape);
471 : : static int worker_get_identifier(Tuplesortstate *state);
472 : : static void worker_freeze_result_tape(Tuplesortstate *state);
473 : : static void worker_nomergeruns(Tuplesortstate *state);
474 : : static void leader_takeover_tapes(Tuplesortstate *state);
475 : : static void free_sort_tuple(Tuplesortstate *state, SortTuple *stup);
476 : : static void tuplesort_free(Tuplesortstate *state);
477 : : static void tuplesort_updatemax(Tuplesortstate *state);
478 : :
479 : : /*
480 : : * Specialized comparators that we can inline into specialized sorts. The goal
481 : : * is to try to sort two tuples without having to follow the pointers to the
482 : : * comparator or the tuple.
483 : : *
484 : : * XXX: For now, there is no specialization for cases where datum1 is
485 : : * authoritative and we don't even need to fall back to a callback at all (that
486 : : * would be true for types like int4/int8/timestamp/date, but not true for
487 : : * abbreviations of text or multi-key sorts. There could be! Is it worth it?
488 : : */
489 : :
490 : : /* Used if first key's comparator is ssup_datum_unsigned_cmp */
491 : : static pg_attribute_always_inline int
492 : 7199828 : qsort_tuple_unsigned_compare(SortTuple *a, SortTuple *b, Tuplesortstate *state)
493 : : {
494 : 7199828 : int compare;
495 : :
496 : 14399656 : compare = ApplyUnsignedSortComparator(a->datum1, a->isnull1,
497 : 7199828 : b->datum1, b->isnull1,
498 : 7199828 : &state->base.sortKeys[0]);
499 [ + + ]: 7199828 : if (compare != 0)
500 : 6478955 : return compare;
501 : :
502 : : /*
503 : : * No need to waste effort calling the tiebreak function when there are no
504 : : * other keys to sort on.
505 : : */
506 [ - + ]: 720873 : if (state->base.onlyKey != NULL)
507 : 0 : return 0;
508 : :
509 : 720873 : return state->base.comparetup_tiebreak(a, b, state);
510 : 7199828 : }
511 : :
512 : : /* Used if first key's comparator is ssup_datum_signed_cmp */
513 : : static pg_attribute_always_inline int
514 : 182602 : qsort_tuple_signed_compare(SortTuple *a, SortTuple *b, Tuplesortstate *state)
515 : : {
516 : 182602 : int compare;
517 : :
518 : 365204 : compare = ApplySignedSortComparator(a->datum1, a->isnull1,
519 : 182602 : b->datum1, b->isnull1,
520 : 182602 : &state->base.sortKeys[0]);
521 : :
522 [ + + ]: 182602 : if (compare != 0)
523 : 180473 : return compare;
524 : :
525 : : /*
526 : : * No need to waste effort calling the tiebreak function when there are no
527 : : * other keys to sort on.
528 : : */
529 [ + + ]: 2129 : if (state->base.onlyKey != NULL)
530 : 121 : return 0;
531 : :
532 : 2008 : return state->base.comparetup_tiebreak(a, b, state);
533 : 182602 : }
534 : :
535 : : /* Used if first key's comparator is ssup_datum_int32_cmp */
536 : : static pg_attribute_always_inline int
537 : 9777453 : qsort_tuple_int32_compare(SortTuple *a, SortTuple *b, Tuplesortstate *state)
538 : : {
539 : 9777453 : int compare;
540 : :
541 : 19554906 : compare = ApplyInt32SortComparator(a->datum1, a->isnull1,
542 : 9777453 : b->datum1, b->isnull1,
543 : 9777453 : &state->base.sortKeys[0]);
544 : :
545 [ + + ]: 9777453 : if (compare != 0)
546 : 5537976 : return compare;
547 : :
548 : : /*
549 : : * No need to waste effort calling the tiebreak function when there are no
550 : : * other keys to sort on.
551 : : */
552 [ + + ]: 4239477 : if (state->base.onlyKey != NULL)
553 : 381478 : return 0;
554 : :
555 : 3857999 : return state->base.comparetup_tiebreak(a, b, state);
556 : 9777453 : }
557 : :
558 : : /*
559 : : * Special versions of qsort just for SortTuple objects. qsort_tuple() sorts
560 : : * any variant of SortTuples, using the appropriate comparetup function.
561 : : * qsort_ssup() is specialized for the case where the comparetup function
562 : : * reduces to ApplySortComparator(), that is single-key MinimalTuple sorts
563 : : * and Datum sorts. qsort_tuple_{unsigned,signed,int32} are specialized for
564 : : * common comparison functions on pass-by-value leading datums.
565 : : */
566 : :
567 : : #define ST_SORT qsort_tuple_unsigned
568 : : #define ST_ELEMENT_TYPE SortTuple
569 : : #define ST_COMPARE(a, b, state) qsort_tuple_unsigned_compare(a, b, state)
570 : : #define ST_COMPARE_ARG_TYPE Tuplesortstate
571 : : #define ST_CHECK_FOR_INTERRUPTS
572 : : #define ST_SCOPE static
573 : : #define ST_DEFINE
574 : : #include "lib/sort_template.h"
575 : :
576 : : #define ST_SORT qsort_tuple_signed
577 : : #define ST_ELEMENT_TYPE SortTuple
578 : : #define ST_COMPARE(a, b, state) qsort_tuple_signed_compare(a, b, state)
579 : : #define ST_COMPARE_ARG_TYPE Tuplesortstate
580 : : #define ST_CHECK_FOR_INTERRUPTS
581 : : #define ST_SCOPE static
582 : : #define ST_DEFINE
583 : : #include "lib/sort_template.h"
584 : :
585 : : #define ST_SORT qsort_tuple_int32
586 : : #define ST_ELEMENT_TYPE SortTuple
587 : : #define ST_COMPARE(a, b, state) qsort_tuple_int32_compare(a, b, state)
588 : : #define ST_COMPARE_ARG_TYPE Tuplesortstate
589 : : #define ST_CHECK_FOR_INTERRUPTS
590 : : #define ST_SCOPE static
591 : : #define ST_DEFINE
592 : : #include "lib/sort_template.h"
593 : :
594 : : #define ST_SORT qsort_tuple
595 : : #define ST_ELEMENT_TYPE SortTuple
596 : : #define ST_COMPARE_RUNTIME_POINTER
597 : : #define ST_COMPARE_ARG_TYPE Tuplesortstate
598 : : #define ST_CHECK_FOR_INTERRUPTS
599 : : #define ST_SCOPE static
600 : : #define ST_DECLARE
601 : : #define ST_DEFINE
602 : : #include "lib/sort_template.h"
603 : :
604 : : #define ST_SORT qsort_ssup
605 : : #define ST_ELEMENT_TYPE SortTuple
606 : : #define ST_COMPARE(a, b, ssup) \
607 : : ApplySortComparator((a)->datum1, (a)->isnull1, \
608 : : (b)->datum1, (b)->isnull1, (ssup))
609 : : #define ST_COMPARE_ARG_TYPE SortSupportData
610 : : #define ST_CHECK_FOR_INTERRUPTS
611 : : #define ST_SCOPE static
612 : : #define ST_DEFINE
613 : : #include "lib/sort_template.h"
614 : :
615 : : /*
616 : : * tuplesort_begin_xxx
617 : : *
618 : : * Initialize for a tuple sort operation.
619 : : *
620 : : * After calling tuplesort_begin, the caller should call tuplesort_putXXX
621 : : * zero or more times, then call tuplesort_performsort when all the tuples
622 : : * have been supplied. After performsort, retrieve the tuples in sorted
623 : : * order by calling tuplesort_getXXX until it returns false/NULL. (If random
624 : : * access was requested, rescan, markpos, and restorepos can also be called.)
625 : : * Call tuplesort_end to terminate the operation and release memory/disk space.
626 : : *
627 : : * Each variant of tuplesort_begin has a workMem parameter specifying the
628 : : * maximum number of kilobytes of RAM to use before spilling data to disk.
629 : : * (The normal value of this parameter is work_mem, but some callers use
630 : : * other values.) Each variant also has a sortopt which is a bitmask of
631 : : * sort options. See TUPLESORT_* definitions in tuplesort.h
632 : : */
633 : :
634 : : Tuplesortstate *
635 : 24386 : tuplesort_begin_common(int workMem, SortCoordinate coordinate, int sortopt)
636 : : {
637 : 24386 : Tuplesortstate *state;
638 : 24386 : MemoryContext maincontext;
639 : 24386 : MemoryContext sortcontext;
640 : 24386 : MemoryContext oldcontext;
641 : :
642 : : /* See leader_takeover_tapes() remarks on random access support */
643 [ + + + - ]: 24386 : if (coordinate && (sortopt & TUPLESORT_RANDOMACCESS))
644 [ # # # # ]: 0 : elog(ERROR, "random access disallowed under parallel sort");
645 : :
646 : : /*
647 : : * Memory context surviving tuplesort_reset. This memory context holds
648 : : * data which is useful to keep while sorting multiple similar batches.
649 : : */
650 : 24386 : maincontext = AllocSetContextCreate(CurrentMemoryContext,
651 : : "TupleSort main",
652 : : ALLOCSET_DEFAULT_SIZES);
653 : :
654 : : /*
655 : : * Create a working memory context for one sort operation. The content of
656 : : * this context is deleted by tuplesort_reset.
657 : : */
658 : 24386 : sortcontext = AllocSetContextCreate(maincontext,
659 : : "TupleSort sort",
660 : : ALLOCSET_DEFAULT_SIZES);
661 : :
662 : : /*
663 : : * Additionally a working memory context for tuples is setup in
664 : : * tuplesort_begin_batch.
665 : : */
666 : :
667 : : /*
668 : : * Make the Tuplesortstate within the per-sortstate context. This way, we
669 : : * don't need a separate pfree() operation for it at shutdown.
670 : : */
671 : 24386 : oldcontext = MemoryContextSwitchTo(maincontext);
672 : :
673 : 24386 : state = palloc0_object(Tuplesortstate);
674 : :
675 [ + - ]: 24386 : if (trace_sort)
676 : 0 : pg_rusage_init(&state->ru_start);
677 : :
678 : 24386 : state->base.sortopt = sortopt;
679 : 24386 : state->base.tuples = true;
680 : 24386 : state->abbrevNext = 10;
681 : :
682 : : /*
683 : : * workMem is forced to be at least 64KB, the current minimum valid value
684 : : * for the work_mem GUC. This is a defense against parallel sort callers
685 : : * that divide out memory among many workers in a way that leaves each
686 : : * with very little memory.
687 : : */
688 [ + + ]: 24386 : state->allowedMem = Max(workMem, 64) * (int64) 1024;
689 : 24386 : state->base.sortcontext = sortcontext;
690 : 24386 : state->base.maincontext = maincontext;
691 : :
692 : 24386 : state->memtupsize = INITIAL_MEMTUPSIZE;
693 : 24386 : state->memtuples = NULL;
694 : :
695 : : /*
696 : : * After all of the other non-parallel-related state, we setup all of the
697 : : * state needed for each batch.
698 : : */
699 : 24386 : tuplesort_begin_batch(state);
700 : :
701 : : /*
702 : : * Initialize parallel-related state based on coordination information
703 : : * from caller
704 : : */
705 [ + + ]: 24386 : if (!coordinate)
706 : : {
707 : : /* Serial sort */
708 : 24263 : state->shared = NULL;
709 : 24263 : state->worker = -1;
710 : 24263 : state->nParticipants = -1;
711 : 24263 : }
712 [ + + ]: 123 : else if (coordinate->isWorker)
713 : : {
714 : : /* Parallel worker produces exactly one final run from all input */
715 : 82 : state->shared = coordinate->sharedsort;
716 : 82 : state->worker = worker_get_identifier(state);
717 : 82 : state->nParticipants = -1;
718 : 82 : }
719 : : else
720 : : {
721 : : /* Parallel leader state only used for final merge */
722 : 41 : state->shared = coordinate->sharedsort;
723 : 41 : state->worker = -1;
724 : 41 : state->nParticipants = coordinate->nParticipants;
725 [ + - ]: 41 : Assert(state->nParticipants >= 1);
726 : : }
727 : :
728 : 24386 : MemoryContextSwitchTo(oldcontext);
729 : :
730 : 48772 : return state;
731 : 24386 : }
732 : :
733 : : /*
734 : : * tuplesort_begin_batch
735 : : *
736 : : * Setup, or reset, all state need for processing a new set of tuples with this
737 : : * sort state. Called both from tuplesort_begin_common (the first time sorting
738 : : * with this sort state) and tuplesort_reset (for subsequent usages).
739 : : */
740 : : static void
741 : 24805 : tuplesort_begin_batch(Tuplesortstate *state)
742 : : {
743 : 24805 : MemoryContext oldcontext;
744 : :
745 : 24805 : oldcontext = MemoryContextSwitchTo(state->base.maincontext);
746 : :
747 : : /*
748 : : * Caller tuple (e.g. IndexTuple) memory context.
749 : : *
750 : : * A dedicated child context used exclusively for caller passed tuples
751 : : * eases memory management. Resetting at key points reduces
752 : : * fragmentation. Note that the memtuples array of SortTuples is allocated
753 : : * in the parent context, not this context, because there is no need to
754 : : * free memtuples early. For bounded sorts, tuples may be pfreed in any
755 : : * order, so we use a regular aset.c context so that it can make use of
756 : : * free'd memory. When the sort is not bounded, we make use of a bump.c
757 : : * context as this keeps allocations more compact with less wastage.
758 : : * Allocations are also slightly more CPU efficient.
759 : : */
760 [ + + ]: 24805 : if (TupleSortUseBumpTupleCxt(state->base.sortopt))
761 : 24620 : state->base.tuplecontext = BumpContextCreate(state->base.sortcontext,
762 : : "Caller tuples",
763 : : ALLOCSET_DEFAULT_SIZES);
764 : : else
765 : 185 : state->base.tuplecontext = AllocSetContextCreate(state->base.sortcontext,
766 : : "Caller tuples",
767 : : ALLOCSET_DEFAULT_SIZES);
768 : :
769 : :
770 : 24805 : state->status = TSS_INITIAL;
771 : 24805 : state->bounded = false;
772 : 24805 : state->boundUsed = false;
773 : :
774 : 24805 : state->availMem = state->allowedMem;
775 : :
776 : 24805 : state->tapeset = NULL;
777 : :
778 : 24805 : state->memtupcount = 0;
779 : :
780 : 24805 : state->growmemtuples = true;
781 : 24805 : state->slabAllocatorUsed = false;
782 [ + + + + ]: 24805 : if (state->memtuples != NULL && state->memtupsize != INITIAL_MEMTUPSIZE)
783 : : {
784 : 12 : pfree(state->memtuples);
785 : 12 : state->memtuples = NULL;
786 : 12 : state->memtupsize = INITIAL_MEMTUPSIZE;
787 : 12 : }
788 [ + + ]: 24805 : if (state->memtuples == NULL)
789 : : {
790 : 24398 : state->memtuples = (SortTuple *) palloc(state->memtupsize * sizeof(SortTuple));
791 : 24398 : USEMEM(state, GetMemoryChunkSpace(state->memtuples));
792 : 24398 : }
793 : :
794 : : /* workMem must be large enough for the minimal memtuples array */
795 [ - + # # ]: 24805 : if (LACKMEM(state))
796 [ # # # # ]: 0 : elog(ERROR, "insufficient memory allowed for sort");
797 : :
798 : 24805 : state->currentRun = 0;
799 : :
800 : : /*
801 : : * Tape variables (inputTapes, outputTapes, etc.) will be initialized by
802 : : * inittapes(), if needed.
803 : : */
804 : :
805 : 24805 : state->result_tape = NULL; /* flag that result tape has not been formed */
806 : :
807 : 24805 : MemoryContextSwitchTo(oldcontext);
808 : 24805 : }
809 : :
810 : : /*
811 : : * tuplesort_set_bound
812 : : *
813 : : * Advise tuplesort that at most the first N result tuples are required.
814 : : *
815 : : * Must be called before inserting any tuples. (Actually, we could allow it
816 : : * as long as the sort hasn't spilled to disk, but there seems no need for
817 : : * delayed calls at the moment.)
818 : : *
819 : : * This is a hint only. The tuplesort may still return more tuples than
820 : : * requested. Parallel leader tuplesorts will always ignore the hint.
821 : : */
822 : : void
823 : 163 : tuplesort_set_bound(Tuplesortstate *state, int64 bound)
824 : : {
825 : : /* Assert we're called before loading any tuples */
826 [ + - ]: 163 : Assert(state->status == TSS_INITIAL && state->memtupcount == 0);
827 : : /* Assert we allow bounded sorts */
828 [ + - ]: 163 : Assert(state->base.sortopt & TUPLESORT_ALLOWBOUNDED);
829 : : /* Can't set the bound twice, either */
830 [ + - ]: 163 : Assert(!state->bounded);
831 : : /* Also, this shouldn't be called in a parallel worker */
832 [ - + # # ]: 163 : Assert(!WORKER(state));
833 : :
834 : : /* Parallel leader allows but ignores hint */
835 [ - + # # ]: 163 : if (LEADER(state))
836 : 0 : return;
837 : :
838 : : #ifdef DEBUG_BOUNDED_SORT
839 : : /* Honor GUC setting that disables the feature (for easy testing) */
840 : : if (!optimize_bounded_sort)
841 : : return;
842 : : #endif
843 : :
844 : : /* We want to be able to compute bound * 2, so limit the setting */
845 [ - + ]: 163 : if (bound > (int64) (INT_MAX / 2))
846 : 0 : return;
847 : :
848 : 163 : state->bounded = true;
849 : 163 : state->bound = (int) bound;
850 : :
851 : : /*
852 : : * Bounded sorts are not an effective target for abbreviated key
853 : : * optimization. Disable by setting state to be consistent with no
854 : : * abbreviation support.
855 : : */
856 : 163 : state->base.sortKeys->abbrev_converter = NULL;
857 [ + + ]: 163 : if (state->base.sortKeys->abbrev_full_comparator)
858 : 2 : state->base.sortKeys->comparator = state->base.sortKeys->abbrev_full_comparator;
859 : :
860 : : /* Not strictly necessary, but be tidy */
861 : 163 : state->base.sortKeys->abbrev_abort = NULL;
862 : 163 : state->base.sortKeys->abbrev_full_comparator = NULL;
863 : 163 : }
864 : :
865 : : /*
866 : : * tuplesort_used_bound
867 : : *
868 : : * Allow callers to find out if the sort state was able to use a bound.
869 : : */
870 : : bool
871 : 57 : tuplesort_used_bound(Tuplesortstate *state)
872 : : {
873 : 57 : return state->boundUsed;
874 : : }
875 : :
876 : : /*
877 : : * tuplesort_free
878 : : *
879 : : * Internal routine for freeing resources of tuplesort.
880 : : */
881 : : static void
882 : 24762 : tuplesort_free(Tuplesortstate *state)
883 : : {
884 : : /* context swap probably not needed, but let's be safe */
885 : 24762 : MemoryContext oldcontext = MemoryContextSwitchTo(state->base.sortcontext);
886 : 24762 : int64 spaceUsed;
887 : :
888 [ + + ]: 24762 : if (state->tapeset)
889 : 130 : spaceUsed = LogicalTapeSetBlocks(state->tapeset);
890 : : else
891 : 24632 : spaceUsed = (state->allowedMem - state->availMem + 1023) / 1024;
892 : :
893 : : /*
894 : : * Delete temporary "tape" files, if any.
895 : : *
896 : : * We don't bother to destroy the individual tapes here. They will go away
897 : : * with the sortcontext. (In TSS_FINALMERGE state, we have closed
898 : : * finished tapes already.)
899 : : */
900 [ + + ]: 24762 : if (state->tapeset)
901 : 130 : LogicalTapeSetClose(state->tapeset);
902 : :
903 [ + - ]: 24762 : if (trace_sort)
904 : : {
905 [ # # ]: 0 : if (state->tapeset)
906 [ # # # # ]: 0 : elog(LOG, "%s of worker %d ended, %" PRId64 " disk blocks used: %s",
907 : : SERIAL(state) ? "external sort" : "parallel external sort",
908 : : state->worker, spaceUsed, pg_rusage_show(&state->ru_start));
909 : : else
910 [ # # # # ]: 0 : elog(LOG, "%s of worker %d ended, %" PRId64 " KB used: %s",
911 : : SERIAL(state) ? "internal sort" : "unperformed parallel sort",
912 : : state->worker, spaceUsed, pg_rusage_show(&state->ru_start));
913 : 0 : }
914 : :
915 : 24762 : TRACE_POSTGRESQL_SORT_DONE(state->tapeset != NULL, spaceUsed);
916 : :
917 [ + + ]: 24762 : FREESTATE(state);
918 : 24762 : MemoryContextSwitchTo(oldcontext);
919 : :
920 : : /*
921 : : * Free the per-sort memory context, thereby releasing all working memory.
922 : : */
923 : 24762 : MemoryContextReset(state->base.sortcontext);
924 : 24762 : }
925 : :
926 : : /*
927 : : * tuplesort_end
928 : : *
929 : : * Release resources and clean up.
930 : : *
931 : : * NOTE: after calling this, any pointers returned by tuplesort_getXXX are
932 : : * pointing to garbage. Be careful not to attempt to use or free such
933 : : * pointers afterwards!
934 : : */
935 : : void
936 : 24343 : tuplesort_end(Tuplesortstate *state)
937 : : {
938 : 24343 : tuplesort_free(state);
939 : :
940 : : /*
941 : : * Free the main memory context, including the Tuplesortstate struct
942 : : * itself.
943 : : */
944 : 24343 : MemoryContextDelete(state->base.maincontext);
945 : 24343 : }
946 : :
947 : : /*
948 : : * tuplesort_updatemax
949 : : *
950 : : * Update maximum resource usage statistics.
951 : : */
952 : : static void
953 : 485 : tuplesort_updatemax(Tuplesortstate *state)
954 : : {
955 : 485 : int64 spaceUsed;
956 : 485 : bool isSpaceDisk;
957 : :
958 : : /*
959 : : * Note: it might seem we should provide both memory and disk usage for a
960 : : * disk-based sort. However, the current code doesn't track memory space
961 : : * accurately once we have begun to return tuples to the caller (since we
962 : : * don't account for pfree's the caller is expected to do), so we cannot
963 : : * rely on availMem in a disk sort. This does not seem worth the overhead
964 : : * to fix. Is it worth creating an API for the memory context code to
965 : : * tell us how much is actually used in sortcontext?
966 : : */
967 [ + + ]: 485 : if (state->tapeset)
968 : : {
969 : 1 : isSpaceDisk = true;
970 : 1 : spaceUsed = LogicalTapeSetBlocks(state->tapeset) * BLCKSZ;
971 : 1 : }
972 : : else
973 : : {
974 : 484 : isSpaceDisk = false;
975 : 484 : spaceUsed = state->allowedMem - state->availMem;
976 : : }
977 : :
978 : : /*
979 : : * Sort evicts data to the disk when it wasn't able to fit that data into
980 : : * main memory. This is why we assume space used on the disk to be more
981 : : * important for tracking resource usage than space used in memory. Note
982 : : * that the amount of space occupied by some tupleset on the disk might be
983 : : * less than amount of space occupied by the same tupleset in memory due
984 : : * to more compact representation.
985 : : */
986 [ + + + + ]: 969 : if ((isSpaceDisk && !state->isMaxSpaceDisk) ||
987 [ + + ]: 891 : (isSpaceDisk == state->isMaxSpaceDisk && spaceUsed > state->maxSpace))
988 : : {
989 : 892 : state->maxSpace = spaceUsed;
990 : 892 : state->isMaxSpaceDisk = isSpaceDisk;
991 : 892 : state->maxSpaceStatus = state->status;
992 : 892 : }
993 : 483 : }
994 : :
995 : : /*
996 : : * tuplesort_reset
997 : : *
998 : : * Reset the tuplesort. Reset all the data in the tuplesort, but leave the
999 : : * meta-information in. After tuplesort_reset, tuplesort is ready to start
1000 : : * a new sort. This allows avoiding recreation of tuple sort states (and
1001 : : * save resources) when sorting multiple small batches.
1002 : : */
1003 : : void
1004 : 419 : tuplesort_reset(Tuplesortstate *state)
1005 : : {
1006 : 419 : tuplesort_updatemax(state);
1007 : 419 : tuplesort_free(state);
1008 : :
1009 : : /*
1010 : : * After we've freed up per-batch memory, re-setup all of the state common
1011 : : * to both the first batch and any subsequent batch.
1012 : : */
1013 : 419 : tuplesort_begin_batch(state);
1014 : :
1015 : 419 : state->lastReturnedTuple = NULL;
1016 : 419 : state->slabMemoryBegin = NULL;
1017 : 419 : state->slabMemoryEnd = NULL;
1018 : 419 : state->slabFreeHead = NULL;
1019 : 419 : }
1020 : :
1021 : : /*
1022 : : * Grow the memtuples[] array, if possible within our memory constraint. We
1023 : : * must not exceed INT_MAX tuples in memory or the caller-provided memory
1024 : : * limit. Return true if we were able to enlarge the array, false if not.
1025 : : *
1026 : : * Normally, at each increment we double the size of the array. When doing
1027 : : * that would exceed a limit, we attempt one last, smaller increase (and then
1028 : : * clear the growmemtuples flag so we don't try any more). That allows us to
1029 : : * use memory as fully as permitted; sticking to the pure doubling rule could
1030 : : * result in almost half going unused. Because availMem moves around with
1031 : : * tuple addition/removal, we need some rule to prevent making repeated small
1032 : : * increases in memtupsize, which would just be useless thrashing. The
1033 : : * growmemtuples flag accomplishes that and also prevents useless
1034 : : * recalculations in this function.
1035 : : */
1036 : : static bool
1037 : 746 : grow_memtuples(Tuplesortstate *state)
1038 : : {
1039 : 746 : int newmemtupsize;
1040 : 746 : int memtupsize = state->memtupsize;
1041 : 746 : int64 memNowUsed = state->allowedMem - state->availMem;
1042 : :
1043 : : /* Forget it if we've already maxed out memtuples, per comment above */
1044 [ + + ]: 746 : if (!state->growmemtuples)
1045 : 20 : return false;
1046 : :
1047 : : /* Select new value of memtupsize */
1048 [ + + ]: 726 : if (memNowUsed <= state->availMem)
1049 : : {
1050 : : /*
1051 : : * We've used no more than half of allowedMem; double our usage,
1052 : : * clamping at INT_MAX tuples.
1053 : : */
1054 [ + - ]: 705 : if (memtupsize < INT_MAX / 2)
1055 : 705 : newmemtupsize = memtupsize * 2;
1056 : : else
1057 : : {
1058 : 0 : newmemtupsize = INT_MAX;
1059 : 0 : state->growmemtuples = false;
1060 : : }
1061 : 705 : }
1062 : : else
1063 : : {
1064 : : /*
1065 : : * This will be the last increment of memtupsize. Abandon doubling
1066 : : * strategy and instead increase as much as we safely can.
1067 : : *
1068 : : * To stay within allowedMem, we can't increase memtupsize by more
1069 : : * than availMem / sizeof(SortTuple) elements. In practice, we want
1070 : : * to increase it by considerably less, because we need to leave some
1071 : : * space for the tuples to which the new array slots will refer. We
1072 : : * assume the new tuples will be about the same size as the tuples
1073 : : * we've already seen, and thus we can extrapolate from the space
1074 : : * consumption so far to estimate an appropriate new size for the
1075 : : * memtuples array. The optimal value might be higher or lower than
1076 : : * this estimate, but it's hard to know that in advance. We again
1077 : : * clamp at INT_MAX tuples.
1078 : : *
1079 : : * This calculation is safe against enlarging the array so much that
1080 : : * LACKMEM becomes true, because the memory currently used includes
1081 : : * the present array; thus, there would be enough allowedMem for the
1082 : : * new array elements even if no other memory were currently used.
1083 : : *
1084 : : * We do the arithmetic in float8, because otherwise the product of
1085 : : * memtupsize and allowedMem could overflow. Any inaccuracy in the
1086 : : * result should be insignificant; but even if we computed a
1087 : : * completely insane result, the checks below will prevent anything
1088 : : * really bad from happening.
1089 : : */
1090 : 21 : double grow_ratio;
1091 : :
1092 : 21 : grow_ratio = (double) state->allowedMem / (double) memNowUsed;
1093 [ + - ]: 21 : if (memtupsize * grow_ratio < INT_MAX)
1094 : 21 : newmemtupsize = (int) (memtupsize * grow_ratio);
1095 : : else
1096 : 0 : newmemtupsize = INT_MAX;
1097 : :
1098 : : /* We won't make any further enlargement attempts */
1099 : 21 : state->growmemtuples = false;
1100 : 21 : }
1101 : :
1102 : : /* Must enlarge array by at least one element, else report failure */
1103 [ - + ]: 726 : if (newmemtupsize <= memtupsize)
1104 : 0 : goto noalloc;
1105 : :
1106 : : /*
1107 : : * On a 32-bit machine, allowedMem could exceed MaxAllocHugeSize. Clamp
1108 : : * to ensure our request won't be rejected. Note that we can easily
1109 : : * exhaust address space before facing this outcome. (This is presently
1110 : : * impossible due to guc.c's MAX_KILOBYTES limitation on work_mem, but
1111 : : * don't rely on that at this distance.)
1112 : : */
1113 [ + - ]: 726 : if ((Size) newmemtupsize >= MaxAllocHugeSize / sizeof(SortTuple))
1114 : : {
1115 : 0 : newmemtupsize = (int) (MaxAllocHugeSize / sizeof(SortTuple));
1116 : 0 : state->growmemtuples = false; /* can't grow any more */
1117 : 0 : }
1118 : :
1119 : : /*
1120 : : * We need to be sure that we do not cause LACKMEM to become true, else
1121 : : * the space management algorithm will go nuts. The code above should
1122 : : * never generate a dangerous request, but to be safe, check explicitly
1123 : : * that the array growth fits within availMem. (We could still cause
1124 : : * LACKMEM if the memory chunk overhead associated with the memtuples
1125 : : * array were to increase. That shouldn't happen because we chose the
1126 : : * initial array size large enough to ensure that palloc will be treating
1127 : : * both old and new arrays as separate chunks. But we'll check LACKMEM
1128 : : * explicitly below just in case.)
1129 : : */
1130 [ - + ]: 726 : if (state->availMem < (int64) ((newmemtupsize - memtupsize) * sizeof(SortTuple)))
1131 : 0 : goto noalloc;
1132 : :
1133 : : /* OK, do it */
1134 : 726 : FREEMEM(state, GetMemoryChunkSpace(state->memtuples));
1135 : 726 : state->memtupsize = newmemtupsize;
1136 : 726 : state->memtuples = (SortTuple *)
1137 : 1452 : repalloc_huge(state->memtuples,
1138 : 726 : state->memtupsize * sizeof(SortTuple));
1139 : 726 : USEMEM(state, GetMemoryChunkSpace(state->memtuples));
1140 [ - + # # ]: 726 : if (LACKMEM(state))
1141 [ # # # # ]: 0 : elog(ERROR, "unexpected out-of-memory situation in tuplesort");
1142 : 726 : return true;
1143 : :
1144 : : noalloc:
1145 : : /* If for any reason we didn't realloc, shut off future attempts */
1146 : 0 : state->growmemtuples = false;
1147 : 0 : return false;
1148 : 746 : }
1149 : :
1150 : : /*
1151 : : * Shared code for tuple and datum cases.
1152 : : */
1153 : : void
1154 : 2957008 : tuplesort_puttuple_common(Tuplesortstate *state, SortTuple *tuple,
1155 : : bool useAbbrev, Size tuplen)
1156 : : {
1157 : 2957008 : MemoryContext oldcontext = MemoryContextSwitchTo(state->base.sortcontext);
1158 : :
1159 [ + + + - ]: 2957008 : Assert(!LEADER(state));
1160 : :
1161 : : /* account for the memory used for this tuple */
1162 : 2957008 : USEMEM(state, tuplen);
1163 : 2957008 : state->tupleMem += tuplen;
1164 : :
1165 [ + + ]: 2957008 : if (!useAbbrev)
1166 : : {
1167 : : /*
1168 : : * Leave ordinary Datum representation, or NULL value. If there is a
1169 : : * converter it won't expect NULL values, and cost model is not
1170 : : * required to account for NULL, so in that case we avoid calling
1171 : : * converter and just set datum1 to zeroed representation (to be
1172 : : * consistent, and to support cheap inequality tests for NULL
1173 : : * abbreviated keys).
1174 : : */
1175 : 2281865 : }
1176 [ + + ]: 675143 : else if (!consider_abort_common(state))
1177 : : {
1178 : : /* Store abbreviated key representation */
1179 : 1350254 : tuple->datum1 = state->base.sortKeys->abbrev_converter(tuple->datum1,
1180 : 675127 : state->base.sortKeys);
1181 : 675127 : }
1182 : : else
1183 : : {
1184 : : /*
1185 : : * Set state to be consistent with never trying abbreviation.
1186 : : *
1187 : : * Alter datum1 representation in already-copied tuples, so as to
1188 : : * ensure a consistent representation (current tuple was just
1189 : : * handled). It does not matter if some dumped tuples are already
1190 : : * sorted on tape, since serialized tuples lack abbreviated keys
1191 : : * (TSS_BUILDRUNS state prevents control reaching here in any case).
1192 : : */
1193 : 16 : REMOVEABBREV(state, state->memtuples, state->memtupcount);
1194 : : }
1195 : :
1196 [ + + + - ]: 2957008 : switch (state->status)
1197 : : {
1198 : : case TSS_INITIAL:
1199 : :
1200 : : /*
1201 : : * Save the tuple into the unsorted array. First, grow the array
1202 : : * as needed. Note that we try to grow the array when there is
1203 : : * still one free slot remaining --- if we fail, there'll still be
1204 : : * room to store the incoming tuple, and then we'll switch to
1205 : : * tape-based operation.
1206 : : */
1207 [ + + ]: 2509226 : if (state->memtupcount >= state->memtupsize - 1)
1208 : : {
1209 : 746 : (void) grow_memtuples(state);
1210 [ + - ]: 746 : Assert(state->memtupcount < state->memtupsize);
1211 : 746 : }
1212 : 2509226 : state->memtuples[state->memtupcount++] = *tuple;
1213 : :
1214 : : /*
1215 : : * Check if it's time to switch over to a bounded heapsort. We do
1216 : : * so if the input tuple count exceeds twice the desired tuple
1217 : : * count (this is a heuristic for where heapsort becomes cheaper
1218 : : * than a quicksort), or if we've just filled workMem and have
1219 : : * enough tuples to meet the bound.
1220 : : *
1221 : : * Note that once we enter TSS_BOUNDED state we will always try to
1222 : : * complete the sort that way. In the worst case, if later input
1223 : : * tuples are larger than earlier ones, this might cause us to
1224 : : * exceed workMem significantly.
1225 : : */
1226 [ + + # # ]: 2509226 : if (state->bounded &&
1227 [ + + ]: 5823 : (state->memtupcount > state->bound * 2 ||
1228 [ + + - + ]: 5777 : (state->memtupcount > state->bound && LACKMEM(state))))
1229 : : {
1230 [ + - ]: 46 : if (trace_sort)
1231 [ # # # # ]: 0 : elog(LOG, "switching to bounded heapsort at %d tuples: %s",
1232 : : state->memtupcount,
1233 : : pg_rusage_show(&state->ru_start));
1234 : 46 : make_bounded_heap(state);
1235 : 46 : MemoryContextSwitchTo(oldcontext);
1236 : 46 : return;
1237 : : }
1238 : :
1239 : : /*
1240 : : * Done if we still fit in available memory and have array slots.
1241 : : */
1242 [ + + - + : 2509180 : if (state->memtupcount < state->memtupsize && !LACKMEM(state))
# # ]
1243 : : {
1244 : 2509160 : MemoryContextSwitchTo(oldcontext);
1245 : 2509160 : return;
1246 : : }
1247 : :
1248 : : /*
1249 : : * Nope; time to switch to tape-based operation.
1250 : : */
1251 : 20 : inittapes(state, true);
1252 : :
1253 : : /*
1254 : : * Dump all tuples.
1255 : : */
1256 : 20 : dumptuples(state, false);
1257 : 20 : break;
1258 : :
1259 : : case TSS_BOUNDED:
1260 : :
1261 : : /*
1262 : : * We don't want to grow the array here, so check whether the new
1263 : : * tuple can be discarded before putting it in. This should be a
1264 : : * good speed optimization, too, since when there are many more
1265 : : * input tuples than the bound, most input tuples can be discarded
1266 : : * with just this one comparison. Note that because we currently
1267 : : * have the sort direction reversed, we must check for <= not >=.
1268 : : */
1269 [ + + ]: 269834 : if (COMPARETUP(state, tuple, &state->memtuples[0]) <= 0)
1270 : : {
1271 : : /* new tuple <= top of the heap, so we can discard it */
1272 : 186371 : free_sort_tuple(state, tuple);
1273 [ + - ]: 186371 : CHECK_FOR_INTERRUPTS();
1274 : 186371 : }
1275 : : else
1276 : : {
1277 : : /* discard top of heap, replacing it with the new tuple */
1278 : 83463 : free_sort_tuple(state, &state->memtuples[0]);
1279 : 83463 : tuplesort_heap_replace_top(state, tuple);
1280 : : }
1281 : 269834 : break;
1282 : :
1283 : : case TSS_BUILDRUNS:
1284 : :
1285 : : /*
1286 : : * Save the tuple into the unsorted array (there must be space)
1287 : : */
1288 : 177948 : state->memtuples[state->memtupcount++] = *tuple;
1289 : :
1290 : : /*
1291 : : * If we are over the memory limit, dump all tuples.
1292 : : */
1293 : 177948 : dumptuples(state, false);
1294 : 177948 : break;
1295 : :
1296 : : default:
1297 [ # # # # ]: 0 : elog(ERROR, "invalid tuplesort state");
1298 : 0 : break;
1299 : : }
1300 : 447802 : MemoryContextSwitchTo(oldcontext);
1301 [ - + ]: 2957008 : }
1302 : :
1303 : : static bool
1304 : 675143 : consider_abort_common(Tuplesortstate *state)
1305 : : {
1306 [ + - ]: 675143 : Assert(state->base.sortKeys[0].abbrev_converter != NULL);
1307 [ + - ]: 675143 : Assert(state->base.sortKeys[0].abbrev_abort != NULL);
1308 [ + - ]: 675143 : Assert(state->base.sortKeys[0].abbrev_full_comparator != NULL);
1309 : :
1310 : : /*
1311 : : * Check effectiveness of abbreviation optimization. Consider aborting
1312 : : * when still within memory limit.
1313 : : */
1314 [ + + + + ]: 675143 : if (state->status == TSS_INITIAL &&
1315 : 609321 : state->memtupcount >= state->abbrevNext)
1316 : : {
1317 : 680 : state->abbrevNext *= 2;
1318 : :
1319 : : /*
1320 : : * Check opclass-supplied abbreviation abort routine. It may indicate
1321 : : * that abbreviation should not proceed.
1322 : : */
1323 [ + + + + ]: 1360 : if (!state->base.sortKeys->abbrev_abort(state->memtupcount,
1324 : 680 : state->base.sortKeys))
1325 : 664 : return false;
1326 : :
1327 : : /*
1328 : : * Finally, restore authoritative comparator, and indicate that
1329 : : * abbreviation is not in play by setting abbrev_converter to NULL
1330 : : */
1331 : 16 : state->base.sortKeys[0].comparator = state->base.sortKeys[0].abbrev_full_comparator;
1332 : 16 : state->base.sortKeys[0].abbrev_converter = NULL;
1333 : : /* Not strictly necessary, but be tidy */
1334 : 16 : state->base.sortKeys[0].abbrev_abort = NULL;
1335 : 16 : state->base.sortKeys[0].abbrev_full_comparator = NULL;
1336 : :
1337 : : /* Give up - expect original pass-by-value representation */
1338 : 16 : return true;
1339 : : }
1340 : :
1341 : 674463 : return false;
1342 : 675143 : }
1343 : :
1344 : : /*
1345 : : * All tuples have been provided; finish the sort.
1346 : : */
1347 : : void
1348 : 22172 : tuplesort_performsort(Tuplesortstate *state)
1349 : : {
1350 : 22172 : MemoryContext oldcontext = MemoryContextSwitchTo(state->base.sortcontext);
1351 : :
1352 [ + - ]: 22172 : if (trace_sort)
1353 [ # # # # ]: 0 : elog(LOG, "performsort of worker %d starting: %s",
1354 : : state->worker, pg_rusage_show(&state->ru_start));
1355 : :
1356 [ + + + - ]: 22172 : switch (state->status)
1357 : : {
1358 : : case TSS_INITIAL:
1359 : :
1360 : : /*
1361 : : * We were able to accumulate all the tuples within the allowed
1362 : : * amount of memory, or leader to take over worker tapes
1363 : : */
1364 [ + + ]: 22106 : if (SERIAL(state))
1365 : : {
1366 : : /* Just qsort 'em and we're done */
1367 : 21996 : tuplesort_sort_memtuples(state);
1368 : 21996 : state->status = TSS_SORTEDINMEM;
1369 : 21996 : }
1370 [ + - + + ]: 110 : else if (WORKER(state))
1371 : : {
1372 : : /*
1373 : : * Parallel workers must still dump out tuples to tape. No
1374 : : * merge is required to produce single output run, though.
1375 : : */
1376 : 82 : inittapes(state, false);
1377 : 82 : dumptuples(state, true);
1378 : 82 : worker_nomergeruns(state);
1379 : 82 : state->status = TSS_SORTEDONTAPE;
1380 : 82 : }
1381 : : else
1382 : : {
1383 : : /*
1384 : : * Leader will take over worker tapes and merge worker runs.
1385 : : * Note that mergeruns sets the correct state->status.
1386 : : */
1387 : 28 : leader_takeover_tapes(state);
1388 : 28 : mergeruns(state);
1389 : : }
1390 : 22106 : state->current = 0;
1391 : 22106 : state->eof_reached = false;
1392 : 22106 : state->markpos_block = 0L;
1393 : 22106 : state->markpos_offset = 0;
1394 : 22106 : state->markpos_eof = false;
1395 : 22106 : break;
1396 : :
1397 : : case TSS_BOUNDED:
1398 : :
1399 : : /*
1400 : : * We were able to accumulate all the tuples required for output
1401 : : * in memory, using a heap to eliminate excess tuples. Now we
1402 : : * have to transform the heap to a properly-sorted array. Note
1403 : : * that sort_bounded_heap sets the correct state->status.
1404 : : */
1405 : 46 : sort_bounded_heap(state);
1406 : 46 : state->current = 0;
1407 : 46 : state->eof_reached = false;
1408 : 46 : state->markpos_offset = 0;
1409 : 46 : state->markpos_eof = false;
1410 : 46 : break;
1411 : :
1412 : : case TSS_BUILDRUNS:
1413 : :
1414 : : /*
1415 : : * Finish tape-based sort. First, flush all tuples remaining in
1416 : : * memory out to tape; then merge until we have a single remaining
1417 : : * run (or, if !randomAccess and !WORKER(), one run per tape).
1418 : : * Note that mergeruns sets the correct state->status.
1419 : : */
1420 : 20 : dumptuples(state, true);
1421 : 20 : mergeruns(state);
1422 : 20 : state->eof_reached = false;
1423 : 20 : state->markpos_block = 0L;
1424 : 20 : state->markpos_offset = 0;
1425 : 20 : state->markpos_eof = false;
1426 : 20 : break;
1427 : :
1428 : : default:
1429 [ # # # # ]: 0 : elog(ERROR, "invalid tuplesort state");
1430 : 0 : break;
1431 : : }
1432 : :
1433 [ + - ]: 22172 : if (trace_sort)
1434 : : {
1435 [ # # ]: 0 : if (state->status == TSS_FINALMERGE)
1436 [ # # # # ]: 0 : elog(LOG, "performsort of worker %d done (except %d-way final merge): %s",
1437 : : state->worker, state->nInputTapes,
1438 : : pg_rusage_show(&state->ru_start));
1439 : : else
1440 [ # # # # ]: 0 : elog(LOG, "performsort of worker %d done: %s",
1441 : : state->worker, pg_rusage_show(&state->ru_start));
1442 : 0 : }
1443 : :
1444 : 22172 : MemoryContextSwitchTo(oldcontext);
1445 : 22172 : }
1446 : :
1447 : : /*
1448 : : * Internal routine to fetch the next tuple in either forward or back
1449 : : * direction into *stup. Returns false if no more tuples.
1450 : : * Returned tuple belongs to tuplesort memory context, and must not be freed
1451 : : * by caller. Note that fetched tuple is stored in memory that may be
1452 : : * recycled by any future fetch.
1453 : : */
1454 : : bool
1455 : 2571192 : tuplesort_gettuple_common(Tuplesortstate *state, bool forward,
1456 : : SortTuple *stup)
1457 : : {
1458 : 2571192 : unsigned int tuplen;
1459 : 2571192 : size_t nmoved;
1460 : :
1461 [ + + + - ]: 2571192 : Assert(!WORKER(state));
1462 : :
1463 [ + + + - ]: 2571192 : switch (state->status)
1464 : : {
1465 : : case TSS_SORTEDINMEM:
1466 [ + + + - ]: 2130601 : Assert(forward || state->base.sortopt & TUPLESORT_RANDOMACCESS);
1467 [ + - ]: 2130601 : Assert(!state->slabAllocatorUsed);
1468 [ + + ]: 2130601 : if (forward)
1469 : : {
1470 [ + + ]: 2130590 : if (state->current < state->memtupcount)
1471 : : {
1472 : 2108830 : *stup = state->memtuples[state->current++];
1473 : 2108830 : return true;
1474 : : }
1475 : 21760 : state->eof_reached = true;
1476 : :
1477 : : /*
1478 : : * Complain if caller tries to retrieve more tuples than
1479 : : * originally asked for in a bounded sort. This is because
1480 : : * returning EOF here might be the wrong thing.
1481 : : */
1482 [ + + + - ]: 21760 : if (state->bounded && state->current >= state->bound)
1483 [ # # # # ]: 0 : elog(ERROR, "retrieved too many tuples in a bounded sort");
1484 : :
1485 : 21760 : return false;
1486 : : }
1487 : : else
1488 : : {
1489 [ - + ]: 11 : if (state->current <= 0)
1490 : 0 : return false;
1491 : :
1492 : : /*
1493 : : * if all tuples are fetched already then we return last
1494 : : * tuple, else - tuple before last returned.
1495 : : */
1496 [ + + ]: 11 : if (state->eof_reached)
1497 : 2 : state->eof_reached = false;
1498 : : else
1499 : : {
1500 : 9 : state->current--; /* last returned tuple */
1501 [ + + ]: 9 : if (state->current <= 0)
1502 : 1 : return false;
1503 : : }
1504 : 10 : *stup = state->memtuples[state->current - 1];
1505 : 10 : return true;
1506 : : }
1507 : : break;
1508 : :
1509 : : case TSS_SORTEDONTAPE:
1510 [ + + + - ]: 45499 : Assert(forward || state->base.sortopt & TUPLESORT_RANDOMACCESS);
1511 [ + - ]: 45499 : Assert(state->slabAllocatorUsed);
1512 : :
1513 : : /*
1514 : : * The slot that held the tuple that we returned in previous
1515 : : * gettuple call can now be reused.
1516 : : */
1517 [ + + ]: 45499 : if (state->lastReturnedTuple)
1518 : : {
1519 [ + - - + ]: 25475 : RELEASE_SLAB_SLOT(state, state->lastReturnedTuple);
1520 : 25475 : state->lastReturnedTuple = NULL;
1521 : 25475 : }
1522 : :
1523 [ + + ]: 45499 : if (forward)
1524 : : {
1525 [ - + ]: 45494 : if (state->eof_reached)
1526 : 0 : return false;
1527 : :
1528 [ + + ]: 45494 : if ((tuplen = getlen(state->result_tape, true)) != 0)
1529 : : {
1530 : 45490 : READTUP(state, stup, state->result_tape, tuplen);
1531 : :
1532 : : /*
1533 : : * Remember the tuple we return, so that we can recycle
1534 : : * its memory on next call. (This can be NULL, in the
1535 : : * !state->tuples case).
1536 : : */
1537 : 45490 : state->lastReturnedTuple = stup->tuple;
1538 : :
1539 : 45490 : return true;
1540 : : }
1541 : : else
1542 : : {
1543 : 4 : state->eof_reached = true;
1544 : 4 : return false;
1545 : : }
1546 : : }
1547 : :
1548 : : /*
1549 : : * Backward.
1550 : : *
1551 : : * if all tuples are fetched already then we return last tuple,
1552 : : * else - tuple before last returned.
1553 : : */
1554 [ + + ]: 5 : if (state->eof_reached)
1555 : : {
1556 : : /*
1557 : : * Seek position is pointing just past the zero tuplen at the
1558 : : * end of file; back up to fetch last tuple's ending length
1559 : : * word. If seek fails we must have a completely empty file.
1560 : : */
1561 : 2 : nmoved = LogicalTapeBackspace(state->result_tape,
1562 : : 2 * sizeof(unsigned int));
1563 [ + - ]: 2 : if (nmoved == 0)
1564 : 0 : return false;
1565 [ + - ]: 2 : else if (nmoved != 2 * sizeof(unsigned int))
1566 [ # # # # ]: 0 : elog(ERROR, "unexpected tape position");
1567 : 2 : state->eof_reached = false;
1568 : 2 : }
1569 : : else
1570 : : {
1571 : : /*
1572 : : * Back up and fetch previously-returned tuple's ending length
1573 : : * word. If seek fails, assume we are at start of file.
1574 : : */
1575 : 3 : nmoved = LogicalTapeBackspace(state->result_tape,
1576 : : sizeof(unsigned int));
1577 [ + - ]: 3 : if (nmoved == 0)
1578 : 0 : return false;
1579 [ + - ]: 3 : else if (nmoved != sizeof(unsigned int))
1580 [ # # # # ]: 0 : elog(ERROR, "unexpected tape position");
1581 : 3 : tuplen = getlen(state->result_tape, false);
1582 : :
1583 : : /*
1584 : : * Back up to get ending length word of tuple before it.
1585 : : */
1586 : 6 : nmoved = LogicalTapeBackspace(state->result_tape,
1587 : 3 : tuplen + 2 * sizeof(unsigned int));
1588 [ + + ]: 3 : if (nmoved == tuplen + sizeof(unsigned int))
1589 : : {
1590 : : /*
1591 : : * We backed up over the previous tuple, but there was no
1592 : : * ending length word before it. That means that the prev
1593 : : * tuple is the first tuple in the file. It is now the
1594 : : * next to read in forward direction (not obviously right,
1595 : : * but that is what in-memory case does).
1596 : : */
1597 : 1 : return false;
1598 : : }
1599 [ + - ]: 2 : else if (nmoved != tuplen + 2 * sizeof(unsigned int))
1600 [ # # # # ]: 0 : elog(ERROR, "bogus tuple length in backward scan");
1601 : : }
1602 : :
1603 : 4 : tuplen = getlen(state->result_tape, false);
1604 : :
1605 : : /*
1606 : : * Now we have the length of the prior tuple, back up and read it.
1607 : : * Note: READTUP expects we are positioned after the initial
1608 : : * length word of the tuple, so back up to that point.
1609 : : */
1610 : 8 : nmoved = LogicalTapeBackspace(state->result_tape,
1611 : 4 : tuplen);
1612 [ + - ]: 4 : if (nmoved != tuplen)
1613 [ # # # # ]: 0 : elog(ERROR, "bogus tuple length in backward scan");
1614 : 4 : READTUP(state, stup, state->result_tape, tuplen);
1615 : :
1616 : : /*
1617 : : * Remember the tuple we return, so that we can recycle its memory
1618 : : * on next call. (This can be NULL, in the Datum case).
1619 : : */
1620 : 4 : state->lastReturnedTuple = stup->tuple;
1621 : :
1622 : 4 : return true;
1623 : :
1624 : : case TSS_FINALMERGE:
1625 [ + - ]: 395092 : Assert(forward);
1626 : : /* We are managing memory ourselves, with the slab allocator. */
1627 [ + - ]: 395092 : Assert(state->slabAllocatorUsed);
1628 : :
1629 : : /*
1630 : : * The slab slot holding the tuple that we returned in previous
1631 : : * gettuple call can now be reused.
1632 : : */
1633 [ + + ]: 395092 : if (state->lastReturnedTuple)
1634 : : {
1635 [ + - - + ]: 385042 : RELEASE_SLAB_SLOT(state, state->lastReturnedTuple);
1636 : 385042 : state->lastReturnedTuple = NULL;
1637 : 385042 : }
1638 : :
1639 : : /*
1640 : : * This code should match the inner loop of mergeonerun().
1641 : : */
1642 [ + + ]: 395092 : if (state->memtupcount > 0)
1643 : : {
1644 : 395050 : int srcTapeIndex = state->memtuples[0].srctape;
1645 : 395050 : LogicalTape *srcTape = state->inputTapes[srcTapeIndex];
1646 : 395050 : SortTuple newtup;
1647 : :
1648 : 395050 : *stup = state->memtuples[0];
1649 : :
1650 : : /*
1651 : : * Remember the tuple we return, so that we can recycle its
1652 : : * memory on next call. (This can be NULL, in the Datum case).
1653 : : */
1654 : 395050 : state->lastReturnedTuple = stup->tuple;
1655 : :
1656 : : /*
1657 : : * Pull next tuple from tape, and replace the returned tuple
1658 : : * at top of the heap with it.
1659 : : */
1660 [ + + ]: 395050 : if (!mergereadnext(state, srcTape, &newtup))
1661 : : {
1662 : : /*
1663 : : * If no more data, we've reached end of run on this tape.
1664 : : * Remove the top node from the heap.
1665 : : */
1666 : 56 : tuplesort_heap_delete_top(state);
1667 : 56 : state->nInputRuns--;
1668 : :
1669 : : /*
1670 : : * Close the tape. It'd go away at the end of the sort
1671 : : * anyway, but better to release the memory early.
1672 : : */
1673 : 56 : LogicalTapeClose(srcTape);
1674 : 56 : return true;
1675 : : }
1676 : 394994 : newtup.srctape = srcTapeIndex;
1677 : 394994 : tuplesort_heap_replace_top(state, &newtup);
1678 : 394994 : return true;
1679 : 395050 : }
1680 : 42 : return false;
1681 : :
1682 : : default:
1683 [ # # # # ]: 0 : elog(ERROR, "invalid tuplesort state");
1684 : 0 : return false; /* keep compiler quiet */
1685 : : }
1686 : 2571192 : }
1687 : :
1688 : :
1689 : : /*
1690 : : * Advance over N tuples in either forward or back direction,
1691 : : * without returning any data. N==0 is a no-op.
1692 : : * Returns true if successful, false if ran out of tuples.
1693 : : */
1694 : : bool
1695 : 62 : tuplesort_skiptuples(Tuplesortstate *state, int64 ntuples, bool forward)
1696 : : {
1697 : 62 : MemoryContext oldcontext;
1698 : :
1699 : : /*
1700 : : * We don't actually support backwards skip yet, because no callers need
1701 : : * it. The API is designed to allow for that later, though.
1702 : : */
1703 [ + - ]: 62 : Assert(forward);
1704 [ + - ]: 62 : Assert(ntuples >= 0);
1705 [ - + # # ]: 62 : Assert(!WORKER(state));
1706 : :
1707 [ + + - ]: 62 : switch (state->status)
1708 : : {
1709 : : case TSS_SORTEDINMEM:
1710 [ + - ]: 58 : if (state->memtupcount - state->current >= ntuples)
1711 : : {
1712 : 58 : state->current += ntuples;
1713 : 58 : return true;
1714 : : }
1715 : 0 : state->current = state->memtupcount;
1716 : 0 : state->eof_reached = true;
1717 : :
1718 : : /*
1719 : : * Complain if caller tries to retrieve more tuples than
1720 : : * originally asked for in a bounded sort. This is because
1721 : : * returning EOF here might be the wrong thing.
1722 : : */
1723 [ # # # # ]: 0 : if (state->bounded && state->current >= state->bound)
1724 [ # # # # ]: 0 : elog(ERROR, "retrieved too many tuples in a bounded sort");
1725 : :
1726 : 0 : return false;
1727 : :
1728 : : case TSS_SORTEDONTAPE:
1729 : : case TSS_FINALMERGE:
1730 : :
1731 : : /*
1732 : : * We could probably optimize these cases better, but for now it's
1733 : : * not worth the trouble.
1734 : : */
1735 : 4 : oldcontext = MemoryContextSwitchTo(state->base.sortcontext);
1736 [ + + ]: 40022 : while (ntuples-- > 0)
1737 : : {
1738 : 40018 : SortTuple stup;
1739 : :
1740 [ + - ]: 40018 : if (!tuplesort_gettuple_common(state, forward, &stup))
1741 : : {
1742 : 0 : MemoryContextSwitchTo(oldcontext);
1743 : 0 : return false;
1744 : : }
1745 [ + - ]: 40018 : CHECK_FOR_INTERRUPTS();
1746 [ - + ]: 40018 : }
1747 : 4 : MemoryContextSwitchTo(oldcontext);
1748 : 4 : return true;
1749 : :
1750 : : default:
1751 [ # # # # ]: 0 : elog(ERROR, "invalid tuplesort state");
1752 : 0 : return false; /* keep compiler quiet */
1753 : : }
1754 : 62 : }
1755 : :
1756 : : /*
1757 : : * tuplesort_merge_order - report merge order we'll use for given memory
1758 : : * (note: "merge order" just means the number of input tapes in the merge).
1759 : : *
1760 : : * This is exported for use by the planner. allowedMem is in bytes.
1761 : : */
1762 : : int
1763 : 1277 : tuplesort_merge_order(int64 allowedMem)
1764 : : {
1765 : 1277 : int mOrder;
1766 : :
1767 : : /*----------
1768 : : * In the merge phase, we need buffer space for each input and output tape.
1769 : : * Each pass in the balanced merge algorithm reads from M input tapes, and
1770 : : * writes to N output tapes. Each tape consumes TAPE_BUFFER_OVERHEAD bytes
1771 : : * of memory. In addition to that, we want MERGE_BUFFER_SIZE workspace per
1772 : : * input tape.
1773 : : *
1774 : : * totalMem = M * (TAPE_BUFFER_OVERHEAD + MERGE_BUFFER_SIZE) +
1775 : : * N * TAPE_BUFFER_OVERHEAD
1776 : : *
1777 : : * Except for the last and next-to-last merge passes, where there can be
1778 : : * fewer tapes left to process, M = N. We choose M so that we have the
1779 : : * desired amount of memory available for the input buffers
1780 : : * (TAPE_BUFFER_OVERHEAD + MERGE_BUFFER_SIZE), given the total memory
1781 : : * available for the tape buffers (allowedMem).
1782 : : *
1783 : : * Note: you might be thinking we need to account for the memtuples[]
1784 : : * array in this calculation, but we effectively treat that as part of the
1785 : : * MERGE_BUFFER_SIZE workspace.
1786 : : *----------
1787 : : */
1788 : 1277 : mOrder = allowedMem /
1789 : : (2 * TAPE_BUFFER_OVERHEAD + MERGE_BUFFER_SIZE);
1790 : :
1791 : : /*
1792 : : * Even in minimum memory, use at least a MINORDER merge. On the other
1793 : : * hand, even when we have lots of memory, do not use more than a MAXORDER
1794 : : * merge. Tapes are pretty cheap, but they're not entirely free. Each
1795 : : * additional tape reduces the amount of memory available to build runs,
1796 : : * which in turn can cause the same sort to need more runs, which makes
1797 : : * merging slower even if it can still be done in a single pass. Also,
1798 : : * high order merges are quite slow due to CPU cache effects; it can be
1799 : : * faster to pay the I/O cost of a multi-pass merge than to perform a
1800 : : * single merge pass across many hundreds of tapes.
1801 : : */
1802 [ + + ]: 1277 : mOrder = Max(mOrder, MINORDER);
1803 [ + - ]: 1277 : mOrder = Min(mOrder, MAXORDER);
1804 : :
1805 : 2554 : return mOrder;
1806 : 1277 : }
1807 : :
1808 : : /*
1809 : : * Helper function to calculate how much memory to allocate for the read buffer
1810 : : * of each input tape in a merge pass.
1811 : : *
1812 : : * 'avail_mem' is the amount of memory available for the buffers of all the
1813 : : * tapes, both input and output.
1814 : : * 'nInputTapes' and 'nInputRuns' are the number of input tapes and runs.
1815 : : * 'maxOutputTapes' is the max. number of output tapes we should produce.
1816 : : */
1817 : : static int64
1818 : 53 : merge_read_buffer_size(int64 avail_mem, int nInputTapes, int nInputRuns,
1819 : : int maxOutputTapes)
1820 : : {
1821 : 53 : int nOutputRuns;
1822 : 53 : int nOutputTapes;
1823 : :
1824 : : /*
1825 : : * How many output tapes will we produce in this pass?
1826 : : *
1827 : : * This is nInputRuns / nInputTapes, rounded up.
1828 : : */
1829 : 53 : nOutputRuns = (nInputRuns + nInputTapes - 1) / nInputTapes;
1830 : :
1831 [ + + ]: 53 : nOutputTapes = Min(nOutputRuns, maxOutputTapes);
1832 : :
1833 : : /*
1834 : : * Each output tape consumes TAPE_BUFFER_OVERHEAD bytes of memory. All
1835 : : * remaining memory is divided evenly between the input tapes.
1836 : : *
1837 : : * This also follows from the formula in tuplesort_merge_order, but here
1838 : : * we derive the input buffer size from the amount of memory available,
1839 : : * and M and N.
1840 : : */
1841 [ + + ]: 53 : return Max((avail_mem - TAPE_BUFFER_OVERHEAD * nOutputTapes) / nInputTapes, 0);
1842 : 53 : }
1843 : :
1844 : : /*
1845 : : * inittapes - initialize for tape sorting.
1846 : : *
1847 : : * This is called only if we have found we won't sort in memory.
1848 : : */
1849 : : static void
1850 : 102 : inittapes(Tuplesortstate *state, bool mergeruns)
1851 : : {
1852 [ + + + - ]: 102 : Assert(!LEADER(state));
1853 : :
1854 [ + + ]: 102 : if (mergeruns)
1855 : : {
1856 : : /* Compute number of input tapes to use when merging */
1857 : 20 : state->maxTapes = tuplesort_merge_order(state->allowedMem);
1858 : 20 : }
1859 : : else
1860 : : {
1861 : : /* Workers can sometimes produce single run, output without merge */
1862 [ + - ]: 82 : Assert(WORKER(state));
1863 : 82 : state->maxTapes = MINORDER;
1864 : : }
1865 : :
1866 [ + - ]: 102 : if (trace_sort)
1867 [ # # # # ]: 0 : elog(LOG, "worker %d switching to external sort with %d tapes: %s",
1868 : : state->worker, state->maxTapes, pg_rusage_show(&state->ru_start));
1869 : :
1870 : : /* Create the tape set */
1871 : 102 : inittapestate(state, state->maxTapes);
1872 : 102 : state->tapeset =
1873 : 102 : LogicalTapeSetCreate(false,
1874 [ + + ]: 102 : state->shared ? &state->shared->fileset : NULL,
1875 : 102 : state->worker);
1876 : :
1877 : 102 : state->currentRun = 0;
1878 : :
1879 : : /*
1880 : : * Initialize logical tape arrays.
1881 : : */
1882 : 102 : state->inputTapes = NULL;
1883 : 102 : state->nInputTapes = 0;
1884 : 102 : state->nInputRuns = 0;
1885 : :
1886 : 102 : state->outputTapes = palloc0(state->maxTapes * sizeof(LogicalTape *));
1887 : 102 : state->nOutputTapes = 0;
1888 : 102 : state->nOutputRuns = 0;
1889 : :
1890 : 102 : state->status = TSS_BUILDRUNS;
1891 : :
1892 : 102 : selectnewtape(state);
1893 : 102 : }
1894 : :
1895 : : /*
1896 : : * inittapestate - initialize generic tape management state
1897 : : */
1898 : : static void
1899 : 130 : inittapestate(Tuplesortstate *state, int maxTapes)
1900 : : {
1901 : 130 : int64 tapeSpace;
1902 : :
1903 : : /*
1904 : : * Decrease availMem to reflect the space needed for tape buffers; but
1905 : : * don't decrease it to the point that we have no room for tuples. (That
1906 : : * case is only likely to occur if sorting pass-by-value Datums; in all
1907 : : * other scenarios the memtuples[] array is unlikely to occupy more than
1908 : : * half of allowedMem. In the pass-by-value case it's not important to
1909 : : * account for tuple space, so we don't care if LACKMEM becomes
1910 : : * inaccurate.)
1911 : : */
1912 : 130 : tapeSpace = (int64) maxTapes * TAPE_BUFFER_OVERHEAD;
1913 : :
1914 [ + + ]: 130 : if (tapeSpace + GetMemoryChunkSpace(state->memtuples) < state->allowedMem)
1915 : 113 : USEMEM(state, tapeSpace);
1916 : :
1917 : : /*
1918 : : * Make sure that the temp file(s) underlying the tape set are created in
1919 : : * suitable temp tablespaces. For parallel sorts, this should have been
1920 : : * called already, but it doesn't matter if it is called a second time.
1921 : : */
1922 : 130 : PrepareTempTablespaces();
1923 : 130 : }
1924 : :
1925 : : /*
1926 : : * selectnewtape -- select next tape to output to.
1927 : : *
1928 : : * This is called after finishing a run when we know another run
1929 : : * must be started. This is used both when building the initial
1930 : : * runs, and during merge passes.
1931 : : */
1932 : : static void
1933 : 281 : selectnewtape(Tuplesortstate *state)
1934 : : {
1935 : : /*
1936 : : * At the beginning of each merge pass, nOutputTapes and nOutputRuns are
1937 : : * both zero. On each call, we create a new output tape to hold the next
1938 : : * run, until maxTapes is reached. After that, we assign new runs to the
1939 : : * existing tapes in a round robin fashion.
1940 : : */
1941 [ + + ]: 281 : if (state->nOutputTapes < state->maxTapes)
1942 : : {
1943 : : /* Create a new tape to hold the next run */
1944 [ + - ]: 184 : Assert(state->outputTapes[state->nOutputRuns] == NULL);
1945 [ + - ]: 184 : Assert(state->nOutputRuns == state->nOutputTapes);
1946 : 184 : state->destTape = LogicalTapeCreate(state->tapeset);
1947 : 184 : state->outputTapes[state->nOutputTapes] = state->destTape;
1948 : 184 : state->nOutputTapes++;
1949 : 184 : state->nOutputRuns++;
1950 : 184 : }
1951 : : else
1952 : : {
1953 : : /*
1954 : : * We have reached the max number of tapes. Append to an existing
1955 : : * tape.
1956 : : */
1957 : 97 : state->destTape = state->outputTapes[state->nOutputRuns % state->nOutputTapes];
1958 : 97 : state->nOutputRuns++;
1959 : : }
1960 : 281 : }
1961 : :
1962 : : /*
1963 : : * Initialize the slab allocation arena, for the given number of slots.
1964 : : */
1965 : : static void
1966 : 48 : init_slab_allocator(Tuplesortstate *state, int numSlots)
1967 : : {
1968 [ + + ]: 48 : if (numSlots > 0)
1969 : : {
1970 : 46 : char *p;
1971 : 46 : int i;
1972 : :
1973 : 46 : state->slabMemoryBegin = palloc(numSlots * SLAB_SLOT_SIZE);
1974 : 92 : state->slabMemoryEnd = state->slabMemoryBegin +
1975 : 46 : numSlots * SLAB_SLOT_SIZE;
1976 : 46 : state->slabFreeHead = (SlabSlot *) state->slabMemoryBegin;
1977 : 46 : USEMEM(state, numSlots * SLAB_SLOT_SIZE);
1978 : :
1979 : 46 : p = state->slabMemoryBegin;
1980 [ + + ]: 173 : for (i = 0; i < numSlots - 1; i++)
1981 : : {
1982 : 127 : ((SlabSlot *) p)->nextfree = (SlabSlot *) (p + SLAB_SLOT_SIZE);
1983 : 127 : p += SLAB_SLOT_SIZE;
1984 : 127 : }
1985 : 46 : ((SlabSlot *) p)->nextfree = NULL;
1986 : 46 : }
1987 : : else
1988 : : {
1989 : 2 : state->slabMemoryBegin = state->slabMemoryEnd = NULL;
1990 : 2 : state->slabFreeHead = NULL;
1991 : : }
1992 : 48 : state->slabAllocatorUsed = true;
1993 : 48 : }
1994 : :
1995 : : /*
1996 : : * mergeruns -- merge all the completed initial runs.
1997 : : *
1998 : : * This implements the Balanced k-Way Merge Algorithm. All input data has
1999 : : * already been written to initial runs on tape (see dumptuples).
2000 : : */
2001 : : static void
2002 : 48 : mergeruns(Tuplesortstate *state)
2003 : : {
2004 : 48 : int tapenum;
2005 : :
2006 [ + - ]: 48 : Assert(state->status == TSS_BUILDRUNS);
2007 [ + - ]: 48 : Assert(state->memtupcount == 0);
2008 : :
2009 [ + + + + ]: 48 : if (state->base.sortKeys != NULL && state->base.sortKeys->abbrev_converter != NULL)
2010 : : {
2011 : : /*
2012 : : * If there are multiple runs to be merged, when we go to read back
2013 : : * tuples from disk, abbreviated keys will not have been stored, and
2014 : : * we don't care to regenerate them. Disable abbreviation from this
2015 : : * point on.
2016 : : */
2017 : 4 : state->base.sortKeys->abbrev_converter = NULL;
2018 : 4 : state->base.sortKeys->comparator = state->base.sortKeys->abbrev_full_comparator;
2019 : :
2020 : : /* Not strictly necessary, but be tidy */
2021 : 4 : state->base.sortKeys->abbrev_abort = NULL;
2022 : 4 : state->base.sortKeys->abbrev_full_comparator = NULL;
2023 : 4 : }
2024 : :
2025 : : /*
2026 : : * Reset tuple memory. We've freed all the tuples that we previously
2027 : : * allocated. We will use the slab allocator from now on.
2028 : : */
2029 : 48 : MemoryContextResetOnly(state->base.tuplecontext);
2030 : :
2031 : : /*
2032 : : * We no longer need a large memtuples array. (We will allocate a smaller
2033 : : * one for the heap later.)
2034 : : */
2035 : 48 : FREEMEM(state, GetMemoryChunkSpace(state->memtuples));
2036 : 48 : pfree(state->memtuples);
2037 : 48 : state->memtuples = NULL;
2038 : :
2039 : : /*
2040 : : * Initialize the slab allocator. We need one slab slot per input tape,
2041 : : * for the tuples in the heap, plus one to hold the tuple last returned
2042 : : * from tuplesort_gettuple. (If we're sorting pass-by-val Datums,
2043 : : * however, we don't need to do allocate anything.)
2044 : : *
2045 : : * In a multi-pass merge, we could shrink this allocation for the last
2046 : : * merge pass, if it has fewer tapes than previous passes, but we don't
2047 : : * bother.
2048 : : *
2049 : : * From this point on, we no longer use the USEMEM()/LACKMEM() mechanism
2050 : : * to track memory usage of individual tuples.
2051 : : */
2052 [ + + ]: 48 : if (state->base.tuples)
2053 : 46 : init_slab_allocator(state, state->nOutputTapes + 1);
2054 : : else
2055 : 2 : init_slab_allocator(state, 0);
2056 : :
2057 : : /*
2058 : : * Allocate a new 'memtuples' array, for the heap. It will hold one tuple
2059 : : * from each input tape.
2060 : : *
2061 : : * We could shrink this, too, between passes in a multi-pass merge, but we
2062 : : * don't bother. (The initial input tapes are still in outputTapes. The
2063 : : * number of input tapes will not increase between passes.)
2064 : : */
2065 : 48 : state->memtupsize = state->nOutputTapes;
2066 : 96 : state->memtuples = (SortTuple *) MemoryContextAlloc(state->base.maincontext,
2067 : 48 : state->nOutputTapes * sizeof(SortTuple));
2068 : 48 : USEMEM(state, GetMemoryChunkSpace(state->memtuples));
2069 : :
2070 : : /*
2071 : : * Use all the remaining memory we have available for tape buffers among
2072 : : * all the input tapes. At the beginning of each merge pass, we will
2073 : : * divide this memory between the input and output tapes in the pass.
2074 : : */
2075 : 48 : state->tape_buffer_mem = state->availMem;
2076 : 48 : USEMEM(state, state->tape_buffer_mem);
2077 [ + - ]: 48 : if (trace_sort)
2078 [ # # # # ]: 0 : elog(LOG, "worker %d using %zu KB of memory for tape buffers",
2079 : : state->worker, state->tape_buffer_mem / 1024);
2080 : :
2081 : 71 : for (;;)
2082 : : {
2083 : : /*
2084 : : * On the first iteration, or if we have read all the runs from the
2085 : : * input tapes in a multi-pass merge, it's time to start a new pass.
2086 : : * Rewind all the output tapes, and make them inputs for the next
2087 : : * pass.
2088 : : */
2089 [ + + ]: 71 : if (state->nInputRuns == 0)
2090 : : {
2091 : 53 : int64 input_buffer_size;
2092 : :
2093 : : /* Close the old, emptied, input tapes */
2094 [ + + ]: 53 : if (state->nInputTapes > 0)
2095 : : {
2096 [ + + ]: 35 : for (tapenum = 0; tapenum < state->nInputTapes; tapenum++)
2097 : 30 : LogicalTapeClose(state->inputTapes[tapenum]);
2098 : 5 : pfree(state->inputTapes);
2099 : 5 : }
2100 : :
2101 : : /* Previous pass's outputs become next pass's inputs. */
2102 : 53 : state->inputTapes = state->outputTapes;
2103 : 53 : state->nInputTapes = state->nOutputTapes;
2104 : 53 : state->nInputRuns = state->nOutputRuns;
2105 : :
2106 : : /*
2107 : : * Reset output tape variables. The actual LogicalTapes will be
2108 : : * created as needed, here we only allocate the array to hold
2109 : : * them.
2110 : : */
2111 : 53 : state->outputTapes = palloc0(state->nInputTapes * sizeof(LogicalTape *));
2112 : 53 : state->nOutputTapes = 0;
2113 : 53 : state->nOutputRuns = 0;
2114 : :
2115 : : /*
2116 : : * Redistribute the memory allocated for tape buffers, among the
2117 : : * new input and output tapes.
2118 : : */
2119 : 106 : input_buffer_size = merge_read_buffer_size(state->tape_buffer_mem,
2120 : 53 : state->nInputTapes,
2121 : 53 : state->nInputRuns,
2122 : 53 : state->maxTapes);
2123 : :
2124 [ + - ]: 53 : if (trace_sort)
2125 [ # # # # ]: 0 : elog(LOG, "starting merge pass of %d input runs on %d tapes, " INT64_FORMAT " KB of memory for each input tape: %s",
2126 : : state->nInputRuns, state->nInputTapes, input_buffer_size / 1024,
2127 : : pg_rusage_show(&state->ru_start));
2128 : :
2129 : : /* Prepare the new input tapes for merge pass. */
2130 [ + + ]: 208 : for (tapenum = 0; tapenum < state->nInputTapes; tapenum++)
2131 : 155 : LogicalTapeRewindForRead(state->inputTapes[tapenum], input_buffer_size);
2132 : :
2133 : : /*
2134 : : * If there's just one run left on each input tape, then only one
2135 : : * merge pass remains. If we don't have to produce a materialized
2136 : : * sorted tape, we can stop at this point and do the final merge
2137 : : * on-the-fly.
2138 : : */
2139 : 53 : if ((state->base.sortopt & TUPLESORT_RANDOMACCESS) == 0
2140 [ + + ]: 53 : && state->nInputRuns <= state->nInputTapes
2141 [ + + + + : 50 : && !WORKER(state))
- + ]
2142 : : {
2143 : : /* Tell logtape.c we won't be writing anymore */
2144 : 45 : LogicalTapeSetForgetFreeSpace(state->tapeset);
2145 : : /* Initialize for the final merge pass */
2146 : 45 : beginmerge(state);
2147 : 45 : state->status = TSS_FINALMERGE;
2148 : 45 : return;
2149 : : }
2150 [ + + ]: 53 : }
2151 : :
2152 : : /* Select an output tape */
2153 : 26 : selectnewtape(state);
2154 : :
2155 : : /* Merge one run from each input tape. */
2156 : 26 : mergeonerun(state);
2157 : :
2158 : : /*
2159 : : * If the input tapes are empty, and we output only one output run,
2160 : : * we're done. The current output tape contains the final result.
2161 : : */
2162 [ + + + + ]: 26 : if (state->nInputRuns == 0 && state->nOutputRuns <= 1)
2163 : 3 : break;
2164 : : }
2165 : :
2166 : : /*
2167 : : * Done. The result is on a single run on a single tape.
2168 : : */
2169 : 3 : state->result_tape = state->outputTapes[0];
2170 [ - + # # ]: 3 : if (!WORKER(state))
2171 : 3 : LogicalTapeFreeze(state->result_tape, NULL);
2172 : : else
2173 : 0 : worker_freeze_result_tape(state);
2174 : 3 : state->status = TSS_SORTEDONTAPE;
2175 : :
2176 : : /* Close all the now-empty input tapes, to release their read buffers. */
2177 [ + + ]: 17 : for (tapenum = 0; tapenum < state->nInputTapes; tapenum++)
2178 : 14 : LogicalTapeClose(state->inputTapes[tapenum]);
2179 [ - + ]: 48 : }
2180 : :
2181 : : /*
2182 : : * Merge one run from each input tape.
2183 : : */
2184 : : static void
2185 : 26 : mergeonerun(Tuplesortstate *state)
2186 : : {
2187 : 26 : int srcTapeIndex;
2188 : 26 : LogicalTape *srcTape;
2189 : :
2190 : : /*
2191 : : * Start the merge by loading one tuple from each active source tape into
2192 : : * the heap.
2193 : : */
2194 : 26 : beginmerge(state);
2195 : :
2196 [ + - ]: 26 : Assert(state->slabAllocatorUsed);
2197 : :
2198 : : /*
2199 : : * Execute merge by repeatedly extracting lowest tuple in heap, writing it
2200 : : * out, and replacing it with next tuple from same tape (if there is
2201 : : * another one).
2202 : : */
2203 [ + + ]: 142598 : while (state->memtupcount > 0)
2204 : : {
2205 : 142572 : SortTuple stup;
2206 : :
2207 : : /* write the tuple to destTape */
2208 : 142572 : srcTapeIndex = state->memtuples[0].srctape;
2209 : 142572 : srcTape = state->inputTapes[srcTapeIndex];
2210 : 142572 : WRITETUP(state, state->destTape, &state->memtuples[0]);
2211 : :
2212 : : /* recycle the slot of the tuple we just wrote out, for the next read */
2213 [ + + ]: 142572 : if (state->memtuples[0].tuple)
2214 [ + - - + ]: 122558 : RELEASE_SLAB_SLOT(state, state->memtuples[0].tuple);
2215 : :
2216 : : /*
2217 : : * pull next tuple from the tape, and replace the written-out tuple in
2218 : : * the heap with it.
2219 : : */
2220 [ + + ]: 142572 : if (mergereadnext(state, srcTape, &stup))
2221 : : {
2222 : 142431 : stup.srctape = srcTapeIndex;
2223 : 142431 : tuplesort_heap_replace_top(state, &stup);
2224 : 142431 : }
2225 : : else
2226 : : {
2227 : 141 : tuplesort_heap_delete_top(state);
2228 : 141 : state->nInputRuns--;
2229 : : }
2230 : 142572 : }
2231 : :
2232 : : /*
2233 : : * When the heap empties, we're done. Write an end-of-run marker on the
2234 : : * output tape.
2235 : : */
2236 : 26 : markrunend(state->destTape);
2237 : 26 : }
2238 : :
2239 : : /*
2240 : : * beginmerge - initialize for a merge pass
2241 : : *
2242 : : * Fill the merge heap with the first tuple from each input tape.
2243 : : */
2244 : : static void
2245 : 71 : beginmerge(Tuplesortstate *state)
2246 : : {
2247 : 71 : int activeTapes;
2248 : 71 : int srcTapeIndex;
2249 : :
2250 : : /* Heap should be empty here */
2251 [ + - ]: 71 : Assert(state->memtupcount == 0);
2252 : :
2253 [ + + ]: 71 : activeTapes = Min(state->nInputTapes, state->nInputRuns);
2254 : :
2255 [ + + ]: 323 : for (srcTapeIndex = 0; srcTapeIndex < activeTapes; srcTapeIndex++)
2256 : : {
2257 : 252 : SortTuple tup;
2258 : :
2259 [ + + ]: 252 : if (mergereadnext(state, state->inputTapes[srcTapeIndex], &tup))
2260 : : {
2261 : 205 : tup.srctape = srcTapeIndex;
2262 : 205 : tuplesort_heap_insert(state, &tup);
2263 : 205 : }
2264 : 252 : }
2265 : 71 : }
2266 : :
2267 : : /*
2268 : : * mergereadnext - read next tuple from one merge input tape
2269 : : *
2270 : : * Returns false on EOF.
2271 : : */
2272 : : static bool
2273 : 537874 : mergereadnext(Tuplesortstate *state, LogicalTape *srcTape, SortTuple *stup)
2274 : : {
2275 : 537874 : unsigned int tuplen;
2276 : :
2277 : : /* read next tuple, if any */
2278 [ + + ]: 537874 : if ((tuplen = getlen(srcTape, true)) == 0)
2279 : 244 : return false;
2280 : 537630 : READTUP(state, stup, srcTape, tuplen);
2281 : :
2282 : 537630 : return true;
2283 : 537874 : }
2284 : :
2285 : : /*
2286 : : * dumptuples - remove tuples from memtuples and write initial run to tape
2287 : : *
2288 : : * When alltuples = true, dump everything currently in memory. (This case is
2289 : : * only used at end of input data.)
2290 : : */
2291 : : static void
2292 : 178070 : dumptuples(Tuplesortstate *state, bool alltuples)
2293 : : {
2294 : 178070 : int memtupwrite;
2295 : 178070 : int i;
2296 : :
2297 : : /*
2298 : : * Nothing to do if we still fit in available memory and have array slots,
2299 : : * unless this is the final call during initial run generation.
2300 : : */
2301 [ + + + + : 178070 : if (state->memtupcount < state->memtupsize && !LACKMEM(state) &&
+ + ]
2302 : 178015 : !alltuples)
2303 : 177815 : return;
2304 : :
2305 : : /*
2306 : : * Final call might require no sorting, in rare cases where we just so
2307 : : * happen to have previously LACKMEM()'d at the point where exactly all
2308 : : * remaining tuples are loaded into memory, just before input was
2309 : : * exhausted. In general, short final runs are quite possible, but avoid
2310 : : * creating a completely empty run. In a worker, though, we must produce
2311 : : * at least one tape, even if it's empty.
2312 : : */
2313 [ + + - + ]: 255 : if (state->memtupcount == 0 && state->currentRun > 0)
2314 : 0 : return;
2315 : :
2316 [ + - ]: 255 : Assert(state->status == TSS_BUILDRUNS);
2317 : :
2318 : : /*
2319 : : * It seems unlikely that this limit will ever be exceeded, but take no
2320 : : * chances
2321 : : */
2322 [ + - ]: 255 : if (state->currentRun == INT_MAX)
2323 [ # # # # ]: 0 : ereport(ERROR,
2324 : : (errcode(ERRCODE_PROGRAM_LIMIT_EXCEEDED),
2325 : : errmsg("cannot have more than %d runs for an external sort",
2326 : : INT_MAX)));
2327 : :
2328 [ + + ]: 255 : if (state->currentRun > 0)
2329 : 153 : selectnewtape(state);
2330 : :
2331 : 255 : state->currentRun++;
2332 : :
2333 [ + - ]: 255 : if (trace_sort)
2334 [ # # # # ]: 0 : elog(LOG, "worker %d starting quicksort of run %d: %s",
2335 : : state->worker, state->currentRun,
2336 : : pg_rusage_show(&state->ru_start));
2337 : :
2338 : : /*
2339 : : * Sort all tuples accumulated within the allowed amount of memory for
2340 : : * this run using quicksort
2341 : : */
2342 : 255 : tuplesort_sort_memtuples(state);
2343 : :
2344 [ + - ]: 255 : if (trace_sort)
2345 [ # # # # ]: 0 : elog(LOG, "worker %d finished quicksort of run %d: %s",
2346 : : state->worker, state->currentRun,
2347 : : pg_rusage_show(&state->ru_start));
2348 : :
2349 : 255 : memtupwrite = state->memtupcount;
2350 [ + + ]: 477847 : for (i = 0; i < memtupwrite; i++)
2351 : : {
2352 : 477592 : SortTuple *stup = &state->memtuples[i];
2353 : :
2354 : 477592 : WRITETUP(state, state->destTape, stup);
2355 : 477592 : }
2356 : :
2357 : 255 : state->memtupcount = 0;
2358 : :
2359 : : /*
2360 : : * Reset tuple memory. We've freed all of the tuples that we previously
2361 : : * allocated. It's important to avoid fragmentation when there is a stark
2362 : : * change in the sizes of incoming tuples. In bounded sorts,
2363 : : * fragmentation due to AllocSetFree's bucketing by size class might be
2364 : : * particularly bad if this step wasn't taken.
2365 : : */
2366 : 255 : MemoryContextReset(state->base.tuplecontext);
2367 : :
2368 : : /*
2369 : : * Now update the memory accounting to subtract the memory used by the
2370 : : * tuple.
2371 : : */
2372 : 255 : FREEMEM(state, state->tupleMem);
2373 : 255 : state->tupleMem = 0;
2374 : :
2375 : 255 : markrunend(state->destTape);
2376 : :
2377 [ + - ]: 255 : if (trace_sort)
2378 [ # # # # ]: 0 : elog(LOG, "worker %d finished writing run %d to tape %d: %s",
2379 : : state->worker, state->currentRun, (state->currentRun - 1) % state->nOutputTapes + 1,
2380 : : pg_rusage_show(&state->ru_start));
2381 [ - + ]: 178070 : }
2382 : :
2383 : : /*
2384 : : * tuplesort_rescan - rewind and replay the scan
2385 : : */
2386 : : void
2387 : 5 : tuplesort_rescan(Tuplesortstate *state)
2388 : : {
2389 : 5 : MemoryContext oldcontext = MemoryContextSwitchTo(state->base.sortcontext);
2390 : :
2391 [ + - ]: 5 : Assert(state->base.sortopt & TUPLESORT_RANDOMACCESS);
2392 : :
2393 [ + + - ]: 5 : switch (state->status)
2394 : : {
2395 : : case TSS_SORTEDINMEM:
2396 : 4 : state->current = 0;
2397 : 4 : state->eof_reached = false;
2398 : 4 : state->markpos_offset = 0;
2399 : 4 : state->markpos_eof = false;
2400 : 4 : break;
2401 : : case TSS_SORTEDONTAPE:
2402 : 1 : LogicalTapeRewindForRead(state->result_tape, 0);
2403 : 1 : state->eof_reached = false;
2404 : 1 : state->markpos_block = 0L;
2405 : 1 : state->markpos_offset = 0;
2406 : 1 : state->markpos_eof = false;
2407 : 1 : break;
2408 : : default:
2409 [ # # # # ]: 0 : elog(ERROR, "invalid tuplesort state");
2410 : 0 : break;
2411 : : }
2412 : :
2413 : 5 : MemoryContextSwitchTo(oldcontext);
2414 : 5 : }
2415 : :
2416 : : /*
2417 : : * tuplesort_markpos - saves current position in the merged sort file
2418 : : */
2419 : : void
2420 : 93465 : tuplesort_markpos(Tuplesortstate *state)
2421 : : {
2422 : 93465 : MemoryContext oldcontext = MemoryContextSwitchTo(state->base.sortcontext);
2423 : :
2424 [ + - ]: 93465 : Assert(state->base.sortopt & TUPLESORT_RANDOMACCESS);
2425 : :
2426 [ + + - ]: 93465 : switch (state->status)
2427 : : {
2428 : : case TSS_SORTEDINMEM:
2429 : 91997 : state->markpos_offset = state->current;
2430 : 91997 : state->markpos_eof = state->eof_reached;
2431 : 91997 : break;
2432 : : case TSS_SORTEDONTAPE:
2433 : 2936 : LogicalTapeTell(state->result_tape,
2434 : 1468 : &state->markpos_block,
2435 : 1468 : &state->markpos_offset);
2436 : 1468 : state->markpos_eof = state->eof_reached;
2437 : 1468 : break;
2438 : : default:
2439 [ # # # # ]: 0 : elog(ERROR, "invalid tuplesort state");
2440 : 0 : break;
2441 : : }
2442 : :
2443 : 93465 : MemoryContextSwitchTo(oldcontext);
2444 : 93465 : }
2445 : :
2446 : : /*
2447 : : * tuplesort_restorepos - restores current position in merged sort file to
2448 : : * last saved position
2449 : : */
2450 : : void
2451 : 4886 : tuplesort_restorepos(Tuplesortstate *state)
2452 : : {
2453 : 4886 : MemoryContext oldcontext = MemoryContextSwitchTo(state->base.sortcontext);
2454 : :
2455 [ + - ]: 4886 : Assert(state->base.sortopt & TUPLESORT_RANDOMACCESS);
2456 : :
2457 [ + + - ]: 4886 : switch (state->status)
2458 : : {
2459 : : case TSS_SORTEDINMEM:
2460 : 3854 : state->current = state->markpos_offset;
2461 : 3854 : state->eof_reached = state->markpos_eof;
2462 : 3854 : break;
2463 : : case TSS_SORTEDONTAPE:
2464 : 2064 : LogicalTapeSeek(state->result_tape,
2465 : 1032 : state->markpos_block,
2466 : 1032 : state->markpos_offset);
2467 : 1032 : state->eof_reached = state->markpos_eof;
2468 : 1032 : break;
2469 : : default:
2470 [ # # # # ]: 0 : elog(ERROR, "invalid tuplesort state");
2471 : 0 : break;
2472 : : }
2473 : :
2474 : 4886 : MemoryContextSwitchTo(oldcontext);
2475 : 4886 : }
2476 : :
2477 : : /*
2478 : : * tuplesort_get_stats - extract summary statistics
2479 : : *
2480 : : * This can be called after tuplesort_performsort() finishes to obtain
2481 : : * printable summary information about how the sort was performed.
2482 : : */
2483 : : void
2484 : 66 : tuplesort_get_stats(Tuplesortstate *state,
2485 : : TuplesortInstrumentation *stats)
2486 : : {
2487 : : /*
2488 : : * Note: it might seem we should provide both memory and disk usage for a
2489 : : * disk-based sort. However, the current code doesn't track memory space
2490 : : * accurately once we have begun to return tuples to the caller (since we
2491 : : * don't account for pfree's the caller is expected to do), so we cannot
2492 : : * rely on availMem in a disk sort. This does not seem worth the overhead
2493 : : * to fix. Is it worth creating an API for the memory context code to
2494 : : * tell us how much is actually used in sortcontext?
2495 : : */
2496 : 66 : tuplesort_updatemax(state);
2497 : :
2498 [ + + ]: 66 : if (state->isMaxSpaceDisk)
2499 : 1 : stats->spaceType = SORT_SPACE_TYPE_DISK;
2500 : : else
2501 : 65 : stats->spaceType = SORT_SPACE_TYPE_MEMORY;
2502 : 66 : stats->spaceUsed = (state->maxSpace + 1023) / 1024;
2503 : :
2504 [ - - + + ]: 66 : switch (state->maxSpaceStatus)
2505 : : {
2506 : : case TSS_SORTEDINMEM:
2507 [ + + ]: 65 : if (state->boundUsed)
2508 : 7 : stats->sortMethod = SORT_TYPE_TOP_N_HEAPSORT;
2509 : : else
2510 : 58 : stats->sortMethod = SORT_TYPE_QUICKSORT;
2511 : 65 : break;
2512 : : case TSS_SORTEDONTAPE:
2513 : 0 : stats->sortMethod = SORT_TYPE_EXTERNAL_SORT;
2514 : 0 : break;
2515 : : case TSS_FINALMERGE:
2516 : 1 : stats->sortMethod = SORT_TYPE_EXTERNAL_MERGE;
2517 : 1 : break;
2518 : : default:
2519 : 0 : stats->sortMethod = SORT_TYPE_STILL_IN_PROGRESS;
2520 : 0 : break;
2521 : : }
2522 : 66 : }
2523 : :
2524 : : /*
2525 : : * Convert TuplesortMethod to a string.
2526 : : */
2527 : : const char *
2528 : 49 : tuplesort_method_name(TuplesortMethod m)
2529 : : {
2530 [ - + + - : 49 : switch (m)
- + ]
2531 : : {
2532 : : case SORT_TYPE_STILL_IN_PROGRESS:
2533 : 0 : return "still in progress";
2534 : : case SORT_TYPE_TOP_N_HEAPSORT:
2535 : 7 : return "top-N heapsort";
2536 : : case SORT_TYPE_QUICKSORT:
2537 : 41 : return "quicksort";
2538 : : case SORT_TYPE_EXTERNAL_SORT:
2539 : 0 : return "external sort";
2540 : : case SORT_TYPE_EXTERNAL_MERGE:
2541 : 1 : return "external merge";
2542 : : }
2543 : :
2544 : 0 : return "unknown";
2545 : 49 : }
2546 : :
2547 : : /*
2548 : : * Convert TuplesortSpaceType to a string.
2549 : : */
2550 : : const char *
2551 : 43 : tuplesort_space_type_name(TuplesortSpaceType t)
2552 : : {
2553 [ + + + - ]: 43 : Assert(t == SORT_SPACE_TYPE_DISK || t == SORT_SPACE_TYPE_MEMORY);
2554 : 43 : return t == SORT_SPACE_TYPE_DISK ? "Disk" : "Memory";
2555 : : }
2556 : :
2557 : :
2558 : : /*
2559 : : * Heap manipulation routines, per Knuth's Algorithm 5.2.3H.
2560 : : */
2561 : :
2562 : : /*
2563 : : * Convert the existing unordered array of SortTuples to a bounded heap,
2564 : : * discarding all but the smallest "state->bound" tuples.
2565 : : *
2566 : : * When working with a bounded heap, we want to keep the largest entry
2567 : : * at the root (array entry zero), instead of the smallest as in the normal
2568 : : * sort case. This allows us to discard the largest entry cheaply.
2569 : : * Therefore, we temporarily reverse the sort direction.
2570 : : */
2571 : : static void
2572 : 46 : make_bounded_heap(Tuplesortstate *state)
2573 : : {
2574 : 46 : int tupcount = state->memtupcount;
2575 : 46 : int i;
2576 : :
2577 [ + - ]: 46 : Assert(state->status == TSS_INITIAL);
2578 [ + - ]: 46 : Assert(state->bounded);
2579 [ + - ]: 46 : Assert(tupcount >= state->bound);
2580 [ + - ]: 46 : Assert(SERIAL(state));
2581 : :
2582 : : /* Reverse sort direction so largest entry will be at root */
2583 : 46 : reversedirection(state);
2584 : :
2585 : 46 : state->memtupcount = 0; /* make the heap empty */
2586 [ + + ]: 5236 : for (i = 0; i < tupcount; i++)
2587 : : {
2588 [ + + ]: 5190 : if (state->memtupcount < state->bound)
2589 : : {
2590 : : /* Insert next tuple into heap */
2591 : : /* Must copy source tuple to avoid possible overwrite */
2592 : 2572 : SortTuple stup = state->memtuples[i];
2593 : :
2594 : 2572 : tuplesort_heap_insert(state, &stup);
2595 : 2572 : }
2596 : : else
2597 : : {
2598 : : /*
2599 : : * The heap is full. Replace the largest entry with the new
2600 : : * tuple, or just discard it, if it's larger than anything already
2601 : : * in the heap.
2602 : : */
2603 [ + + ]: 2618 : if (COMPARETUP(state, &state->memtuples[i], &state->memtuples[0]) <= 0)
2604 : : {
2605 : 1161 : free_sort_tuple(state, &state->memtuples[i]);
2606 [ + - ]: 1161 : CHECK_FOR_INTERRUPTS();
2607 : 1161 : }
2608 : : else
2609 : 1457 : tuplesort_heap_replace_top(state, &state->memtuples[i]);
2610 : : }
2611 : 5190 : }
2612 : :
2613 [ + - ]: 46 : Assert(state->memtupcount == state->bound);
2614 : 46 : state->status = TSS_BOUNDED;
2615 : 46 : }
2616 : :
2617 : : /*
2618 : : * Convert the bounded heap to a properly-sorted array
2619 : : */
2620 : : static void
2621 : 46 : sort_bounded_heap(Tuplesortstate *state)
2622 : : {
2623 : 46 : int tupcount = state->memtupcount;
2624 : :
2625 [ + - ]: 46 : Assert(state->status == TSS_BOUNDED);
2626 [ + - ]: 46 : Assert(state->bounded);
2627 [ + - ]: 46 : Assert(tupcount == state->bound);
2628 [ + - ]: 46 : Assert(SERIAL(state));
2629 : :
2630 : : /*
2631 : : * We can unheapify in place because each delete-top call will remove the
2632 : : * largest entry, which we can promptly store in the newly freed slot at
2633 : : * the end. Once we're down to a single-entry heap, we're done.
2634 : : */
2635 [ + + ]: 2572 : while (state->memtupcount > 1)
2636 : : {
2637 : 2526 : SortTuple stup = state->memtuples[0];
2638 : :
2639 : : /* this sifts-up the next-largest entry and decreases memtupcount */
2640 : 2526 : tuplesort_heap_delete_top(state);
2641 : 2526 : state->memtuples[state->memtupcount] = stup;
2642 : 2526 : }
2643 : 46 : state->memtupcount = tupcount;
2644 : :
2645 : : /*
2646 : : * Reverse sort direction back to the original state. This is not
2647 : : * actually necessary but seems like a good idea for tidiness.
2648 : : */
2649 : 46 : reversedirection(state);
2650 : :
2651 : 46 : state->status = TSS_SORTEDINMEM;
2652 : 46 : state->boundUsed = true;
2653 : 46 : }
2654 : :
2655 : : /*
2656 : : * Sort all memtuples using specialized qsort() routines.
2657 : : *
2658 : : * Quicksort is used for small in-memory sorts, and external sort runs.
2659 : : */
2660 : : static void
2661 : 22251 : tuplesort_sort_memtuples(Tuplesortstate *state)
2662 : : {
2663 [ + + + - ]: 22251 : Assert(!LEADER(state));
2664 : :
2665 [ + + ]: 22251 : if (state->memtupcount > 1)
2666 : : {
2667 : : /*
2668 : : * Do we have the leading column's value or abbreviation in datum1,
2669 : : * and is there a specialization for its comparator?
2670 : : */
2671 [ + + + + ]: 8291 : if (state->base.haveDatum1 && state->base.sortKeys)
2672 : : {
2673 [ + + ]: 8286 : if (state->base.sortKeys[0].comparator == ssup_datum_unsigned_cmp)
2674 : : {
2675 : 750 : qsort_tuple_unsigned(state->memtuples,
2676 : 375 : state->memtupcount,
2677 : 375 : state);
2678 : 375 : return;
2679 : : }
2680 [ + + ]: 7911 : else if (state->base.sortKeys[0].comparator == ssup_datum_signed_cmp)
2681 : : {
2682 : 286 : qsort_tuple_signed(state->memtuples,
2683 : 143 : state->memtupcount,
2684 : 143 : state);
2685 : 143 : return;
2686 : : }
2687 [ + + ]: 7768 : else if (state->base.sortKeys[0].comparator == ssup_datum_int32_cmp)
2688 : : {
2689 : 12614 : qsort_tuple_int32(state->memtuples,
2690 : 6307 : state->memtupcount,
2691 : 6307 : state);
2692 : 6307 : return;
2693 : : }
2694 : 1461 : }
2695 : :
2696 : : /* Can we use the single-key sort function? */
2697 [ + + ]: 1466 : if (state->base.onlyKey != NULL)
2698 : : {
2699 : 1690 : qsort_ssup(state->memtuples, state->memtupcount,
2700 : 845 : state->base.onlyKey);
2701 : 845 : }
2702 : : else
2703 : : {
2704 : 1242 : qsort_tuple(state->memtuples,
2705 : 621 : state->memtupcount,
2706 : 621 : state->base.comparetup,
2707 : 621 : state);
2708 : : }
2709 : 1466 : }
2710 : 22251 : }
2711 : :
2712 : : /*
2713 : : * Insert a new tuple into an empty or existing heap, maintaining the
2714 : : * heap invariant. Caller is responsible for ensuring there's room.
2715 : : *
2716 : : * Note: For some callers, tuple points to a memtuples[] entry above the
2717 : : * end of the heap. This is safe as long as it's not immediately adjacent
2718 : : * to the end of the heap (ie, in the [memtupcount] array entry) --- if it
2719 : : * is, it might get overwritten before being moved into the heap!
2720 : : */
2721 : : static void
2722 : 2777 : tuplesort_heap_insert(Tuplesortstate *state, SortTuple *tuple)
2723 : : {
2724 : 2777 : SortTuple *memtuples;
2725 : 2777 : int j;
2726 : :
2727 : 2777 : memtuples = state->memtuples;
2728 [ + - ]: 2777 : Assert(state->memtupcount < state->memtupsize);
2729 : :
2730 [ + + ]: 2777 : CHECK_FOR_INTERRUPTS();
2731 : :
2732 : : /*
2733 : : * Sift-up the new entry, per Knuth 5.2.3 exercise 16. Note that Knuth is
2734 : : * using 1-based array indexes, not 0-based.
2735 : : */
2736 : 2777 : j = state->memtupcount++;
2737 [ + + ]: 7184 : while (j > 0)
2738 : : {
2739 : 6492 : int i = (j - 1) >> 1;
2740 : :
2741 [ + + ]: 6492 : if (COMPARETUP(state, tuple, &memtuples[i]) >= 0)
2742 : 2085 : break;
2743 : 4407 : memtuples[j] = memtuples[i];
2744 : 4407 : j = i;
2745 [ - + + ]: 6492 : }
2746 : 2777 : memtuples[j] = *tuple;
2747 : 2777 : }
2748 : :
2749 : : /*
2750 : : * Remove the tuple at state->memtuples[0] from the heap. Decrement
2751 : : * memtupcount, and sift up to maintain the heap invariant.
2752 : : *
2753 : : * The caller has already free'd the tuple the top node points to,
2754 : : * if necessary.
2755 : : */
2756 : : static void
2757 : 2723 : tuplesort_heap_delete_top(Tuplesortstate *state)
2758 : : {
2759 : 2723 : SortTuple *memtuples = state->memtuples;
2760 : 2723 : SortTuple *tuple;
2761 : :
2762 [ + + ]: 2723 : if (--state->memtupcount <= 0)
2763 : 46 : return;
2764 : :
2765 : : /*
2766 : : * Remove the last tuple in the heap, and re-insert it, by replacing the
2767 : : * current top node with it.
2768 : : */
2769 : 2677 : tuple = &memtuples[state->memtupcount];
2770 : 2677 : tuplesort_heap_replace_top(state, tuple);
2771 [ - + ]: 2723 : }
2772 : :
2773 : : /*
2774 : : * Replace the tuple at state->memtuples[0] with a new tuple. Sift up to
2775 : : * maintain the heap invariant.
2776 : : *
2777 : : * This corresponds to Knuth's "sift-up" algorithm (Algorithm 5.2.3H,
2778 : : * Heapsort, steps H3-H8).
2779 : : */
2780 : : static void
2781 : 625022 : tuplesort_heap_replace_top(Tuplesortstate *state, SortTuple *tuple)
2782 : : {
2783 : 625022 : SortTuple *memtuples = state->memtuples;
2784 : 625022 : unsigned int i,
2785 : : n;
2786 : :
2787 [ + - ]: 625022 : Assert(state->memtupcount >= 1);
2788 : :
2789 [ + - ]: 625022 : CHECK_FOR_INTERRUPTS();
2790 : :
2791 : : /*
2792 : : * state->memtupcount is "int", but we use "unsigned int" for i, j, n.
2793 : : * This prevents overflow in the "2 * i + 1" calculation, since at the top
2794 : : * of the loop we must have i < n <= INT_MAX <= UINT_MAX/2.
2795 : : */
2796 : 625022 : n = state->memtupcount;
2797 : 625022 : i = 0; /* i is where the "hole" is */
2798 : 930774 : for (;;)
2799 : : {
2800 : 930774 : unsigned int j = 2 * i + 1;
2801 : :
2802 [ + + ]: 930774 : if (j >= n)
2803 : 203165 : break;
2804 [ + + + + ]: 727609 : if (j + 1 < n &&
2805 : 402579 : COMPARETUP(state, &memtuples[j], &memtuples[j + 1]) > 0)
2806 : 160373 : j++;
2807 [ + + ]: 727609 : if (COMPARETUP(state, tuple, &memtuples[j]) <= 0)
2808 : 421857 : break;
2809 : 305752 : memtuples[i] = memtuples[j];
2810 : 305752 : i = j;
2811 [ - + + ]: 930774 : }
2812 : 625022 : memtuples[i] = *tuple;
2813 : 625022 : }
2814 : :
2815 : : /*
2816 : : * Function to reverse the sort direction from its current state
2817 : : *
2818 : : * It is not safe to call this when performing hash tuplesorts
2819 : : */
2820 : : static void
2821 : 92 : reversedirection(Tuplesortstate *state)
2822 : : {
2823 : 92 : SortSupport sortKey = state->base.sortKeys;
2824 : 92 : int nkey;
2825 : :
2826 [ + + ]: 228 : for (nkey = 0; nkey < state->base.nKeys; nkey++, sortKey++)
2827 : : {
2828 : 136 : sortKey->ssup_reverse = !sortKey->ssup_reverse;
2829 : 136 : sortKey->ssup_nulls_first = !sortKey->ssup_nulls_first;
2830 : 136 : }
2831 : 92 : }
2832 : :
2833 : :
2834 : : /*
2835 : : * Tape interface routines
2836 : : */
2837 : :
2838 : : static unsigned int
2839 : 583375 : getlen(LogicalTape *tape, bool eofOK)
2840 : : {
2841 : 583375 : unsigned int len;
2842 : :
2843 : 583375 : if (LogicalTapeRead(tape,
2844 [ + - ]: 583375 : &len, sizeof(len)) != sizeof(len))
2845 [ # # # # ]: 0 : elog(ERROR, "unexpected end of tape");
2846 [ + + + - ]: 583375 : if (len == 0 && !eofOK)
2847 [ # # # # ]: 0 : elog(ERROR, "unexpected end of data");
2848 : 1166750 : return len;
2849 : 583375 : }
2850 : :
2851 : : static void
2852 : 281 : markrunend(LogicalTape *tape)
2853 : : {
2854 : 281 : unsigned int len = 0;
2855 : :
2856 : 281 : LogicalTapeWrite(tape, &len, sizeof(len));
2857 : 281 : }
2858 : :
2859 : : /*
2860 : : * Get memory for tuple from within READTUP() routine.
2861 : : *
2862 : : * We use next free slot from the slab allocator, or palloc() if the tuple
2863 : : * is too large for that.
2864 : : */
2865 : : void *
2866 : 533086 : tuplesort_readtup_alloc(Tuplesortstate *state, Size tuplen)
2867 : : {
2868 : 533086 : SlabSlot *buf;
2869 : :
2870 : : /*
2871 : : * We pre-allocate enough slots in the slab arena that we should never run
2872 : : * out.
2873 : : */
2874 [ + - ]: 533086 : Assert(state->slabFreeHead);
2875 : :
2876 [ + - - + ]: 533086 : if (tuplen > SLAB_SLOT_SIZE || !state->slabFreeHead)
2877 : 0 : return MemoryContextAlloc(state->base.sortcontext, tuplen);
2878 : : else
2879 : : {
2880 : 533086 : buf = state->slabFreeHead;
2881 : : /* Reuse this slot */
2882 : 533086 : state->slabFreeHead = buf->nextfree;
2883 : :
2884 : 533086 : return buf;
2885 : : }
2886 : 533086 : }
2887 : :
2888 : :
2889 : : /*
2890 : : * Parallel sort routines
2891 : : */
2892 : :
2893 : : /*
2894 : : * tuplesort_estimate_shared - estimate required shared memory allocation
2895 : : *
2896 : : * nWorkers is an estimate of the number of workers (it's the number that
2897 : : * will be requested).
2898 : : */
2899 : : Size
2900 : 28 : tuplesort_estimate_shared(int nWorkers)
2901 : : {
2902 : 28 : Size tapesSize;
2903 : :
2904 [ + - ]: 28 : Assert(nWorkers > 0);
2905 : :
2906 : : /* Make sure that BufFile shared state is MAXALIGN'd */
2907 : 28 : tapesSize = mul_size(sizeof(TapeShare), nWorkers);
2908 : 28 : tapesSize = MAXALIGN(add_size(tapesSize, offsetof(Sharedsort, tapes)));
2909 : :
2910 : 56 : return tapesSize;
2911 : 28 : }
2912 : :
2913 : : /*
2914 : : * tuplesort_initialize_shared - initialize shared tuplesort state
2915 : : *
2916 : : * Must be called from leader process before workers are launched, to
2917 : : * establish state needed up-front for worker tuplesortstates. nWorkers
2918 : : * should match the argument passed to tuplesort_estimate_shared().
2919 : : */
2920 : : void
2921 : 41 : tuplesort_initialize_shared(Sharedsort *shared, int nWorkers, dsm_segment *seg)
2922 : : {
2923 : 41 : int i;
2924 : :
2925 [ + - ]: 41 : Assert(nWorkers > 0);
2926 : :
2927 : 41 : SpinLockInit(&shared->mutex);
2928 : 41 : shared->currentWorker = 0;
2929 : 41 : shared->workersFinished = 0;
2930 : 41 : SharedFileSetInit(&shared->fileset, seg);
2931 : 41 : shared->nTapes = nWorkers;
2932 [ + + ]: 123 : for (i = 0; i < nWorkers; i++)
2933 : : {
2934 : 82 : shared->tapes[i].firstblocknumber = 0L;
2935 : 82 : }
2936 : 41 : }
2937 : :
2938 : : /*
2939 : : * tuplesort_attach_shared - attach to shared tuplesort state
2940 : : *
2941 : : * Must be called by all worker processes.
2942 : : */
2943 : : void
2944 : 41 : tuplesort_attach_shared(Sharedsort *shared, dsm_segment *seg)
2945 : : {
2946 : : /* Attach to SharedFileSet */
2947 : 41 : SharedFileSetAttach(&shared->fileset, seg);
2948 : 41 : }
2949 : :
2950 : : /*
2951 : : * worker_get_identifier - Assign and return ordinal identifier for worker
2952 : : *
2953 : : * The order in which these are assigned is not well defined, and should not
2954 : : * matter; worker numbers across parallel sort participants need only be
2955 : : * distinct and gapless. logtape.c requires this.
2956 : : *
2957 : : * Note that the identifiers assigned from here have no relation to
2958 : : * ParallelWorkerNumber number, to avoid making any assumption about
2959 : : * caller's requirements. However, we do follow the ParallelWorkerNumber
2960 : : * convention of representing a non-worker with worker number -1. This
2961 : : * includes the leader, as well as serial Tuplesort processes.
2962 : : */
2963 : : static int
2964 : 82 : worker_get_identifier(Tuplesortstate *state)
2965 : : {
2966 : 82 : Sharedsort *shared = state->shared;
2967 : 82 : int worker;
2968 : :
2969 [ + - ]: 82 : Assert(WORKER(state));
2970 : :
2971 [ - + ]: 82 : SpinLockAcquire(&shared->mutex);
2972 : 82 : worker = shared->currentWorker++;
2973 : 82 : SpinLockRelease(&shared->mutex);
2974 : :
2975 : 164 : return worker;
2976 : 82 : }
2977 : :
2978 : : /*
2979 : : * worker_freeze_result_tape - freeze worker's result tape for leader
2980 : : *
2981 : : * This is called by workers just after the result tape has been determined,
2982 : : * instead of calling LogicalTapeFreeze() directly. They do so because
2983 : : * workers require a few additional steps over similar serial
2984 : : * TSS_SORTEDONTAPE external sort cases, which also happen here. The extra
2985 : : * steps are around freeing now unneeded resources, and representing to
2986 : : * leader that worker's input run is available for its merge.
2987 : : *
2988 : : * There should only be one final output run for each worker, which consists
2989 : : * of all tuples that were originally input into worker.
2990 : : */
2991 : : static void
2992 : 82 : worker_freeze_result_tape(Tuplesortstate *state)
2993 : : {
2994 : 82 : Sharedsort *shared = state->shared;
2995 : 82 : TapeShare output;
2996 : :
2997 [ + - ]: 82 : Assert(WORKER(state));
2998 [ + - ]: 82 : Assert(state->result_tape != NULL);
2999 [ + - ]: 82 : Assert(state->memtupcount == 0);
3000 : :
3001 : : /*
3002 : : * Free most remaining memory, in case caller is sensitive to our holding
3003 : : * on to it. memtuples may not be a tiny merge heap at this point.
3004 : : */
3005 : 82 : pfree(state->memtuples);
3006 : : /* Be tidy */
3007 : 82 : state->memtuples = NULL;
3008 : 82 : state->memtupsize = 0;
3009 : :
3010 : : /*
3011 : : * Parallel worker requires result tape metadata, which is to be stored in
3012 : : * shared memory for leader
3013 : : */
3014 : 82 : LogicalTapeFreeze(state->result_tape, &output);
3015 : :
3016 : : /* Store properties of output tape, and update finished worker count */
3017 [ - + ]: 82 : SpinLockAcquire(&shared->mutex);
3018 : 82 : shared->tapes[state->worker] = output;
3019 : 82 : shared->workersFinished++;
3020 : 82 : SpinLockRelease(&shared->mutex);
3021 : 82 : }
3022 : :
3023 : : /*
3024 : : * worker_nomergeruns - dump memtuples in worker, without merging
3025 : : *
3026 : : * This called as an alternative to mergeruns() with a worker when no
3027 : : * merging is required.
3028 : : */
3029 : : static void
3030 : 82 : worker_nomergeruns(Tuplesortstate *state)
3031 : : {
3032 [ + - ]: 82 : Assert(WORKER(state));
3033 [ + - ]: 82 : Assert(state->result_tape == NULL);
3034 [ + - ]: 82 : Assert(state->nOutputRuns == 1);
3035 : :
3036 : 82 : state->result_tape = state->destTape;
3037 : 82 : worker_freeze_result_tape(state);
3038 : 82 : }
3039 : :
3040 : : /*
3041 : : * leader_takeover_tapes - create tapeset for leader from worker tapes
3042 : : *
3043 : : * So far, leader Tuplesortstate has performed no actual sorting. By now, all
3044 : : * sorting has occurred in workers, all of which must have already returned
3045 : : * from tuplesort_performsort().
3046 : : *
3047 : : * When this returns, leader process is left in a state that is virtually
3048 : : * indistinguishable from it having generated runs as a serial external sort
3049 : : * might have.
3050 : : */
3051 : : static void
3052 : 28 : leader_takeover_tapes(Tuplesortstate *state)
3053 : : {
3054 : 28 : Sharedsort *shared = state->shared;
3055 : 28 : int nParticipants = state->nParticipants;
3056 : 28 : int workersFinished;
3057 : 28 : int j;
3058 : :
3059 [ + - ]: 28 : Assert(LEADER(state));
3060 [ + - ]: 28 : Assert(nParticipants >= 1);
3061 : :
3062 [ - + ]: 28 : SpinLockAcquire(&shared->mutex);
3063 : 28 : workersFinished = shared->workersFinished;
3064 : 28 : SpinLockRelease(&shared->mutex);
3065 : :
3066 [ + - ]: 28 : if (nParticipants != workersFinished)
3067 [ # # # # ]: 0 : elog(ERROR, "cannot take over tapes before all workers finish");
3068 : :
3069 : : /*
3070 : : * Create the tapeset from worker tapes, including a leader-owned tape at
3071 : : * the end. Parallel workers are far more expensive than logical tapes,
3072 : : * so the number of tapes allocated here should never be excessive.
3073 : : */
3074 : 28 : inittapestate(state, nParticipants);
3075 : 28 : state->tapeset = LogicalTapeSetCreate(false, &shared->fileset, -1);
3076 : :
3077 : : /*
3078 : : * Set currentRun to reflect the number of runs we will merge (it's not
3079 : : * used for anything, this is just pro forma)
3080 : : */
3081 : 28 : state->currentRun = nParticipants;
3082 : :
3083 : : /*
3084 : : * Initialize the state to look the same as after building the initial
3085 : : * runs.
3086 : : *
3087 : : * There will always be exactly 1 run per worker, and exactly one input
3088 : : * tape per run, because workers always output exactly 1 run, even when
3089 : : * there were no input tuples for workers to sort.
3090 : : */
3091 : 28 : state->inputTapes = NULL;
3092 : 28 : state->nInputTapes = 0;
3093 : 28 : state->nInputRuns = 0;
3094 : :
3095 : 28 : state->outputTapes = palloc0(nParticipants * sizeof(LogicalTape *));
3096 : 28 : state->nOutputTapes = nParticipants;
3097 : 28 : state->nOutputRuns = nParticipants;
3098 : :
3099 [ + + ]: 84 : for (j = 0; j < nParticipants; j++)
3100 : : {
3101 : 56 : state->outputTapes[j] = LogicalTapeImport(state->tapeset, j, &shared->tapes[j]);
3102 : 56 : }
3103 : :
3104 : 28 : state->status = TSS_BUILDRUNS;
3105 : 28 : }
3106 : :
3107 : : /*
3108 : : * Convenience routine to free a tuple previously loaded into sort memory
3109 : : */
3110 : : static void
3111 : 270995 : free_sort_tuple(Tuplesortstate *state, SortTuple *stup)
3112 : : {
3113 [ + + ]: 270995 : if (stup->tuple)
3114 : : {
3115 : 244305 : FREEMEM(state, GetMemoryChunkSpace(stup->tuple));
3116 : 244305 : pfree(stup->tuple);
3117 : 244305 : stup->tuple = NULL;
3118 : 244305 : }
3119 : 270995 : }
3120 : :
3121 : : int
3122 : 0 : ssup_datum_unsigned_cmp(Datum x, Datum y, SortSupport ssup)
3123 : : {
3124 [ # # ]: 0 : if (x < y)
3125 : 0 : return -1;
3126 [ # # ]: 0 : else if (x > y)
3127 : 0 : return 1;
3128 : : else
3129 : 0 : return 0;
3130 : 0 : }
3131 : :
3132 : : int
3133 : 147140 : ssup_datum_signed_cmp(Datum x, Datum y, SortSupport ssup)
3134 : : {
3135 : 147140 : int64 xx = DatumGetInt64(x);
3136 : 147140 : int64 yy = DatumGetInt64(y);
3137 : :
3138 [ + + ]: 147140 : if (xx < yy)
3139 : 52394 : return -1;
3140 [ + + ]: 94746 : else if (xx > yy)
3141 : 47257 : return 1;
3142 : : else
3143 : 47489 : return 0;
3144 : 147140 : }
3145 : :
3146 : : int
3147 : 26768967 : ssup_datum_int32_cmp(Datum x, Datum y, SortSupport ssup)
3148 : : {
3149 : 26768967 : int32 xx = DatumGetInt32(x);
3150 : 26768967 : int32 yy = DatumGetInt32(y);
3151 : :
3152 [ + + ]: 26768967 : if (xx < yy)
3153 : 6154522 : return -1;
3154 [ + + ]: 20614445 : else if (xx > yy)
3155 : 5850804 : return 1;
3156 : : else
3157 : 14763641 : return 0;
3158 : 26768967 : }
|
