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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. Large amounts are
11 : : * 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 : : * or radix sort 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 in memory 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 in-memory sorting, 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 "port/pg_bitutils.h"
108 : : #include "storage/shmem.h"
109 : : #include "utils/guc.h"
110 : : #include "utils/memutils.h"
111 : : #include "utils/pg_rusage.h"
112 : : #include "utils/tuplesort.h"
113 : :
114 : : /*
115 : : * Initial size of memtuples array. This must be more than
116 : : * ALLOCSET_SEPARATE_THRESHOLD; see comments in grow_memtuples(). Clamp at
117 : : * 1024 elements to avoid excessive reallocs.
118 : : */
119 : : #define INITIAL_MEMTUPSIZE Max(1024, \
120 : : ALLOCSET_SEPARATE_THRESHOLD / sizeof(SortTuple) + 1)
121 : :
122 : : /* GUC variables */
123 : : bool trace_sort = false;
124 : :
125 : : #ifdef DEBUG_BOUNDED_SORT
126 : : bool optimize_bounded_sort = true;
127 : : #endif
128 : :
129 : :
130 : : /*
131 : : * During merge, we use a pre-allocated set of fixed-size slots to hold
132 : : * tuples. To avoid palloc/pfree overhead.
133 : : *
134 : : * Merge doesn't require a lot of memory, so we can afford to waste some,
135 : : * by using gratuitously-sized slots. If a tuple is larger than 1 kB, the
136 : : * palloc() overhead is not significant anymore.
137 : : *
138 : : * 'nextfree' is valid when this chunk is in the free list. When in use, the
139 : : * slot holds a tuple.
140 : : */
141 : : #define SLAB_SLOT_SIZE 1024
142 : :
143 : : typedef union SlabSlot
144 : : {
145 : : union SlabSlot *nextfree;
146 : : char buffer[SLAB_SLOT_SIZE];
147 : : } SlabSlot;
148 : :
149 : : /*
150 : : * Possible states of a Tuplesort object. These denote the states that
151 : : * persist between calls of Tuplesort routines.
152 : : */
153 : : typedef enum
154 : : {
155 : : TSS_INITIAL, /* Loading tuples; still within memory limit */
156 : : TSS_BOUNDED, /* Loading tuples into bounded-size heap */
157 : : TSS_BUILDRUNS, /* Loading tuples; writing to tape */
158 : : TSS_SORTEDINMEM, /* Sort completed entirely in memory */
159 : : TSS_SORTEDONTAPE, /* Sort completed, final run is on tape */
160 : : TSS_FINALMERGE, /* Performing final merge on-the-fly */
161 : : } TupSortStatus;
162 : :
163 : : /*
164 : : * Parameters for calculation of number of tapes to use --- see inittapes()
165 : : * and tuplesort_merge_order().
166 : : *
167 : : * In this calculation we assume that each tape will cost us about 1 blocks
168 : : * worth of buffer space. This ignores the overhead of all the other data
169 : : * structures needed for each tape, but it's probably close enough.
170 : : *
171 : : * MERGE_BUFFER_SIZE is how much buffer space we'd like to allocate for each
172 : : * input tape, for pre-reading (see discussion at top of file). This is *in
173 : : * addition to* the 1 block already included in TAPE_BUFFER_OVERHEAD.
174 : : */
175 : : #define MINORDER 6 /* minimum merge order */
176 : : #define MAXORDER 500 /* maximum merge order */
177 : : #define TAPE_BUFFER_OVERHEAD BLCKSZ
178 : : #define MERGE_BUFFER_SIZE (BLCKSZ * 32)
179 : :
180 : :
181 : : /*
182 : : * Private state of a Tuplesort operation.
183 : : */
184 : : struct Tuplesortstate
185 : : {
186 : : TuplesortPublic base;
187 : : TupSortStatus status; /* enumerated value as shown above */
188 : : bool bounded; /* did caller specify a maximum number of
189 : : * tuples to return? */
190 : : bool boundUsed; /* true if we made use of a bounded heap */
191 : : int bound; /* if bounded, the maximum number of tuples */
192 : : int64 tupleMem; /* memory consumed by individual tuples.
193 : : * storing this separately from what we track
194 : : * in availMem allows us to subtract the
195 : : * memory consumed by all tuples when dumping
196 : : * tuples to tape */
197 : : int64 availMem; /* remaining memory available, in bytes */
198 : : int64 allowedMem; /* total memory allowed, in bytes */
199 : : int maxTapes; /* max number of input tapes to merge in each
200 : : * pass */
201 : : int64 maxSpace; /* maximum amount of space occupied among sort
202 : : * of groups, either in-memory or on-disk */
203 : : bool isMaxSpaceDisk; /* true when maxSpace tracks on-disk space,
204 : : * false means in-memory */
205 : : TupSortStatus maxSpaceStatus; /* sort status when maxSpace was reached */
206 : : LogicalTapeSet *tapeset; /* logtape.c object for tapes in a temp file */
207 : :
208 : : /*
209 : : * This array holds the tuples now in sort memory. If we are in state
210 : : * INITIAL, the tuples are in no particular order; if we are in state
211 : : * SORTEDINMEM, the tuples are in final sorted order; in states BUILDRUNS
212 : : * and FINALMERGE, the tuples are organized in "heap" order per Algorithm
213 : : * H. In state SORTEDONTAPE, the array is not used.
214 : : */
215 : : SortTuple *memtuples; /* array of SortTuple structs */
216 : : int memtupcount; /* number of tuples currently present */
217 : : int memtupsize; /* allocated length of memtuples array */
218 : : bool growmemtuples; /* memtuples' growth still underway? */
219 : :
220 : : /*
221 : : * Memory for tuples is sometimes allocated using a simple slab allocator,
222 : : * rather than with palloc(). Currently, we switch to slab allocation
223 : : * when we start merging. Merging only needs to keep a small, fixed
224 : : * number of tuples in memory at any time, so we can avoid the
225 : : * palloc/pfree overhead by recycling a fixed number of fixed-size slots
226 : : * to hold the tuples.
227 : : *
228 : : * For the slab, we use one large allocation, divided into SLAB_SLOT_SIZE
229 : : * slots. The allocation is sized to have one slot per tape, plus one
230 : : * additional slot. We need that many slots to hold all the tuples kept
231 : : * in the heap during merge, plus the one we have last returned from the
232 : : * sort, with tuplesort_gettuple.
233 : : *
234 : : * Initially, all the slots are kept in a linked list of free slots. When
235 : : * a tuple is read from a tape, it is put to the next available slot, if
236 : : * it fits. If the tuple is larger than SLAB_SLOT_SIZE, it is palloc'd
237 : : * instead.
238 : : *
239 : : * When we're done processing a tuple, we return the slot back to the free
240 : : * list, or pfree() if it was palloc'd. We know that a tuple was
241 : : * allocated from the slab, if its pointer value is between
242 : : * slabMemoryBegin and -End.
243 : : *
244 : : * When the slab allocator is used, the USEMEM/LACKMEM mechanism of
245 : : * tracking memory usage is not used.
246 : : */
247 : : bool slabAllocatorUsed;
248 : :
249 : : char *slabMemoryBegin; /* beginning of slab memory arena */
250 : : char *slabMemoryEnd; /* end of slab memory arena */
251 : : SlabSlot *slabFreeHead; /* head of free list */
252 : :
253 : : /* Memory used for input and output tape buffers. */
254 : : size_t tape_buffer_mem;
255 : :
256 : : /*
257 : : * When we return a tuple to the caller in tuplesort_gettuple_XXX, that
258 : : * came from a tape (that is, in TSS_SORTEDONTAPE or TSS_FINALMERGE
259 : : * modes), we remember the tuple in 'lastReturnedTuple', so that we can
260 : : * recycle the memory on next gettuple call.
261 : : */
262 : : void *lastReturnedTuple;
263 : :
264 : : /*
265 : : * While building initial runs, this is the current output run number.
266 : : * Afterwards, it is the number of initial runs we made.
267 : : */
268 : : int currentRun;
269 : :
270 : : /*
271 : : * Logical tapes, for merging.
272 : : *
273 : : * The initial runs are written in the output tapes. In each merge pass,
274 : : * the output tapes of the previous pass become the input tapes, and new
275 : : * output tapes are created as needed. When nInputTapes equals
276 : : * nInputRuns, there is only one merge pass left.
277 : : */
278 : : LogicalTape **inputTapes;
279 : : int nInputTapes;
280 : : int nInputRuns;
281 : :
282 : : LogicalTape **outputTapes;
283 : : int nOutputTapes;
284 : : int nOutputRuns;
285 : :
286 : : LogicalTape *destTape; /* current output tape */
287 : :
288 : : /*
289 : : * These variables are used after completion of sorting to keep track of
290 : : * the next tuple to return. (In the tape case, the tape's current read
291 : : * position is also critical state.)
292 : : */
293 : : LogicalTape *result_tape; /* actual tape of finished output */
294 : : int current; /* array index (only used if SORTEDINMEM) */
295 : : bool eof_reached; /* reached EOF (needed for cursors) */
296 : :
297 : : /* markpos_xxx holds marked position for mark and restore */
298 : : int64 markpos_block; /* tape block# (only used if SORTEDONTAPE) */
299 : : int markpos_offset; /* saved "current", or offset in tape block */
300 : : bool markpos_eof; /* saved "eof_reached" */
301 : :
302 : : /*
303 : : * These variables are used during parallel sorting.
304 : : *
305 : : * worker is our worker identifier. Follows the general convention that
306 : : * -1 value relates to a leader tuplesort, and values >= 0 worker
307 : : * tuplesorts. (-1 can also be a serial tuplesort.)
308 : : *
309 : : * shared is mutable shared memory state, which is used to coordinate
310 : : * parallel sorts.
311 : : *
312 : : * nParticipants is the number of worker Tuplesortstates known by the
313 : : * leader to have actually been launched, which implies that they must
314 : : * finish a run that the leader needs to merge. Typically includes a
315 : : * worker state held by the leader process itself. Set in the leader
316 : : * Tuplesortstate only.
317 : : */
318 : : int worker;
319 : : Sharedsort *shared;
320 : : int nParticipants;
321 : :
322 : : /*
323 : : * Additional state for managing "abbreviated key" sortsupport routines
324 : : * (which currently may be used by all cases except the hash index case).
325 : : * Tracks the intervals at which the optimization's effectiveness is
326 : : * tested.
327 : : */
328 : : int64 abbrevNext; /* Tuple # at which to next check
329 : : * applicability */
330 : :
331 : : /*
332 : : * Resource snapshot for time of sort start.
333 : : */
334 : : PGRUsage ru_start;
335 : : };
336 : :
337 : : /*
338 : : * Private mutable state of tuplesort-parallel-operation. This is allocated
339 : : * in shared memory.
340 : : */
341 : : struct Sharedsort
342 : : {
343 : : /* mutex protects all fields prior to tapes */
344 : : slock_t mutex;
345 : :
346 : : /*
347 : : * currentWorker generates ordinal identifier numbers for parallel sort
348 : : * workers. These start from 0, and are always gapless.
349 : : *
350 : : * Workers increment workersFinished to indicate having finished. If this
351 : : * is equal to state.nParticipants within the leader, leader is ready to
352 : : * merge worker runs.
353 : : */
354 : : int currentWorker;
355 : : int workersFinished;
356 : :
357 : : /* Temporary file space */
358 : : SharedFileSet fileset;
359 : :
360 : : /* Size of tapes flexible array */
361 : : int nTapes;
362 : :
363 : : /*
364 : : * Tapes array used by workers to report back information needed by the
365 : : * leader to concatenate all worker tapes into one for merging
366 : : */
367 : : TapeShare tapes[FLEXIBLE_ARRAY_MEMBER];
368 : : };
369 : :
370 : : /*
371 : : * Is the given tuple allocated from the slab memory arena?
372 : : */
373 : : #define IS_SLAB_SLOT(state, tuple) \
374 : : ((char *) (tuple) >= (state)->slabMemoryBegin && \
375 : : (char *) (tuple) < (state)->slabMemoryEnd)
376 : :
377 : : /*
378 : : * Return the given tuple to the slab memory free list, or free it
379 : : * if it was palloc'd.
380 : : */
381 : : #define RELEASE_SLAB_SLOT(state, tuple) \
382 : : do { \
383 : : SlabSlot *buf = (SlabSlot *) tuple; \
384 : : \
385 : : if (IS_SLAB_SLOT((state), buf)) \
386 : : { \
387 : : buf->nextfree = (state)->slabFreeHead; \
388 : : (state)->slabFreeHead = buf; \
389 : : } else \
390 : : pfree(buf); \
391 : : } while(0)
392 : :
393 : : #define REMOVEABBREV(state,stup,count) ((*(state)->base.removeabbrev) (state, stup, count))
394 : : #define COMPARETUP(state,a,b) ((*(state)->base.comparetup) (a, b, state))
395 : : #define WRITETUP(state,tape,stup) ((*(state)->base.writetup) (state, tape, stup))
396 : : #define READTUP(state,stup,tape,len) ((*(state)->base.readtup) (state, stup, tape, len))
397 : : #define FREESTATE(state) ((state)->base.freestate ? (*(state)->base.freestate) (state) : (void) 0)
398 : : #define LACKMEM(state) ((state)->availMem < 0 && !(state)->slabAllocatorUsed)
399 : : #define USEMEM(state,amt) ((state)->availMem -= (amt))
400 : : #define FREEMEM(state,amt) ((state)->availMem += (amt))
401 : : #define SERIAL(state) ((state)->shared == NULL)
402 : : #define WORKER(state) ((state)->shared && (state)->worker != -1)
403 : : #define LEADER(state) ((state)->shared && (state)->worker == -1)
404 : :
405 : : /*
406 : : * NOTES about on-tape representation of tuples:
407 : : *
408 : : * We require the first "unsigned int" of a stored tuple to be the total size
409 : : * on-tape of the tuple, including itself (so it is never zero; an all-zero
410 : : * unsigned int is used to delimit runs). The remainder of the stored tuple
411 : : * may or may not match the in-memory representation of the tuple ---
412 : : * any conversion needed is the job of the writetup and readtup routines.
413 : : *
414 : : * If state->sortopt contains TUPLESORT_RANDOMACCESS, then the stored
415 : : * representation of the tuple must be followed by another "unsigned int" that
416 : : * is a copy of the length --- so the total tape space used is actually
417 : : * sizeof(unsigned int) more than the stored length value. This allows
418 : : * read-backwards. When the random access flag was not specified, the
419 : : * write/read routines may omit the extra length word.
420 : : *
421 : : * writetup is expected to write both length words as well as the tuple
422 : : * data. When readtup is called, the tape is positioned just after the
423 : : * front length word; readtup must read the tuple data and advance past
424 : : * the back length word (if present).
425 : : *
426 : : * The write/read routines can make use of the tuple description data
427 : : * stored in the Tuplesortstate record, if needed. They are also expected
428 : : * to adjust state->availMem by the amount of memory space (not tape space!)
429 : : * released or consumed. There is no error return from either writetup
430 : : * or readtup; they should ereport() on failure.
431 : : *
432 : : *
433 : : * NOTES about memory consumption calculations:
434 : : *
435 : : * We count space allocated for tuples against the workMem limit, plus
436 : : * the space used by the variable-size memtuples array. Fixed-size space
437 : : * is not counted; it's small enough to not be interesting.
438 : : *
439 : : * Note that we count actual space used (as shown by GetMemoryChunkSpace)
440 : : * rather than the originally-requested size. This is important since
441 : : * palloc can add substantial overhead. It's not a complete answer since
442 : : * we won't count any wasted space in palloc allocation blocks, but it's
443 : : * a lot better than what we were doing before 7.3. As of 9.6, a
444 : : * separate memory context is used for caller passed tuples. Resetting
445 : : * it at certain key increments significantly ameliorates fragmentation.
446 : : * readtup routines use the slab allocator (they cannot use
447 : : * the reset context because it gets deleted at the point that merging
448 : : * begins).
449 : : */
450 : :
451 : :
452 : : static void tuplesort_begin_batch(Tuplesortstate *state);
453 : : static bool consider_abort_common(Tuplesortstate *state);
454 : : static void inittapes(Tuplesortstate *state, bool mergeruns);
455 : : static void inittapestate(Tuplesortstate *state, int maxTapes);
456 : : static void selectnewtape(Tuplesortstate *state);
457 : : static void init_slab_allocator(Tuplesortstate *state, int numSlots);
458 : : static void mergeruns(Tuplesortstate *state);
459 : : static void mergeonerun(Tuplesortstate *state);
460 : : static void beginmerge(Tuplesortstate *state);
461 : : static bool mergereadnext(Tuplesortstate *state, LogicalTape *srcTape, SortTuple *stup);
462 : : static void dumptuples(Tuplesortstate *state, bool alltuples);
463 : : static void make_bounded_heap(Tuplesortstate *state);
464 : : static void sort_bounded_heap(Tuplesortstate *state);
465 : : static void tuplesort_sort_memtuples(Tuplesortstate *state);
466 : : static void tuplesort_heap_insert(Tuplesortstate *state, SortTuple *tuple);
467 : : static void tuplesort_heap_replace_top(Tuplesortstate *state, SortTuple *tuple);
468 : : static void tuplesort_heap_delete_top(Tuplesortstate *state);
469 : : static void reversedirection(Tuplesortstate *state);
470 : : static unsigned int getlen(LogicalTape *tape, bool eofOK);
471 : : static void markrunend(LogicalTape *tape);
472 : : static int worker_get_identifier(Tuplesortstate *state);
473 : : static void worker_freeze_result_tape(Tuplesortstate *state);
474 : : static void worker_nomergeruns(Tuplesortstate *state);
475 : : static void leader_takeover_tapes(Tuplesortstate *state);
476 : : static void free_sort_tuple(Tuplesortstate *state, SortTuple *stup);
477 : : static void tuplesort_free(Tuplesortstate *state);
478 : : static void tuplesort_updatemax(Tuplesortstate *state);
479 : :
480 : :
481 : : /*
482 : : * Special versions of qsort just for SortTuple objects. qsort_tuple() sorts
483 : : * any variant of SortTuples, using the appropriate comparetup function.
484 : : * qsort_ssup() is specialized for the case where the comparetup function
485 : : * reduces to ApplySortComparator(), that is single-key MinimalTuple sorts
486 : : * and Datum sorts.
487 : : */
488 : :
489 : : #define ST_SORT qsort_tuple
490 : : #define ST_ELEMENT_TYPE SortTuple
491 : : #define ST_COMPARE_RUNTIME_POINTER
492 : : #define ST_COMPARE_ARG_TYPE Tuplesortstate
493 : : #define ST_CHECK_FOR_INTERRUPTS
494 : : #define ST_SCOPE static
495 : : #define ST_DECLARE
496 : : #define ST_DEFINE
497 : : #include "lib/sort_template.h"
498 : :
499 : : #define ST_SORT qsort_ssup
500 : : #define ST_ELEMENT_TYPE SortTuple
501 : : #define ST_COMPARE(a, b, ssup) \
502 : : ApplySortComparator((a)->datum1, (a)->isnull1, \
503 : : (b)->datum1, (b)->isnull1, (ssup))
504 : : #define ST_COMPARE_ARG_TYPE SortSupportData
505 : : #define ST_CHECK_FOR_INTERRUPTS
506 : : #define ST_SCOPE static
507 : : #define ST_DEFINE
508 : : #include "lib/sort_template.h"
509 : :
510 : : /* state for radix sort */
511 : : typedef struct RadixSortInfo
512 : : {
513 : : union
514 : : {
515 : : size_t count;
516 : : size_t offset;
517 : : };
518 : : size_t next_offset;
519 : : } RadixSortInfo;
520 : :
521 : : /*
522 : : * Threshold below which qsort_tuple() is generally faster than a radix sort.
523 : : */
524 : : #define QSORT_THRESHOLD 40
525 : :
526 : :
527 : : /*
528 : : * tuplesort_begin_xxx
529 : : *
530 : : * Initialize for a tuple sort operation.
531 : : *
532 : : * After calling tuplesort_begin, the caller should call tuplesort_putXXX
533 : : * zero or more times, then call tuplesort_performsort when all the tuples
534 : : * have been supplied. After performsort, retrieve the tuples in sorted
535 : : * order by calling tuplesort_getXXX until it returns false/NULL. (If random
536 : : * access was requested, rescan, markpos, and restorepos can also be called.)
537 : : * Call tuplesort_end to terminate the operation and release memory/disk space.
538 : : *
539 : : * Each variant of tuplesort_begin has a workMem parameter specifying the
540 : : * maximum number of kilobytes of RAM to use before spilling data to disk.
541 : : * (The normal value of this parameter is work_mem, but some callers use
542 : : * other values.) Each variant also has a sortopt which is a bitmask of
543 : : * sort options. See TUPLESORT_* definitions in tuplesort.h
544 : : */
545 : :
546 : : Tuplesortstate *
547 : 175491 : tuplesort_begin_common(int workMem, SortCoordinate coordinate, int sortopt)
548 : : {
549 : : Tuplesortstate *state;
550 : : MemoryContext maincontext;
551 : : MemoryContext sortcontext;
552 : : MemoryContext oldcontext;
553 : :
554 : : /* See leader_takeover_tapes() remarks on random access support */
555 [ + + - + ]: 175491 : if (coordinate && (sortopt & TUPLESORT_RANDOMACCESS))
556 [ # # ]: 0 : elog(ERROR, "random access disallowed under parallel sort");
557 : :
558 : : /*
559 : : * Memory context surviving tuplesort_reset. This memory context holds
560 : : * data which is useful to keep while sorting multiple similar batches.
561 : : */
562 : 175491 : maincontext = AllocSetContextCreate(CurrentMemoryContext,
563 : : "TupleSort main",
564 : : ALLOCSET_DEFAULT_SIZES);
565 : :
566 : : /*
567 : : * Create a working memory context for one sort operation. The content of
568 : : * this context is deleted by tuplesort_reset.
569 : : */
570 : 175491 : sortcontext = AllocSetContextCreate(maincontext,
571 : : "TupleSort sort",
572 : : ALLOCSET_DEFAULT_SIZES);
573 : :
574 : : /*
575 : : * Additionally a working memory context for tuples is setup in
576 : : * tuplesort_begin_batch.
577 : : */
578 : :
579 : : /*
580 : : * Make the Tuplesortstate within the per-sortstate context. This way, we
581 : : * don't need a separate pfree() operation for it at shutdown.
582 : : */
583 : 175491 : oldcontext = MemoryContextSwitchTo(maincontext);
584 : :
585 : 175491 : state = palloc0_object(Tuplesortstate);
586 : :
587 [ - + ]: 175491 : if (trace_sort)
588 : 0 : pg_rusage_init(&state->ru_start);
589 : :
590 : 175491 : state->base.sortopt = sortopt;
591 : 175491 : state->base.tuples = true;
592 : 175491 : state->abbrevNext = 10;
593 : :
594 : : /*
595 : : * workMem is forced to be at least 64KB, the current minimum valid value
596 : : * for the work_mem GUC. This is a defense against parallel sort callers
597 : : * that divide out memory among many workers in a way that leaves each
598 : : * with very little memory.
599 : : */
600 : 175491 : state->allowedMem = Max(workMem, 64) * (int64) 1024;
601 : 175491 : state->base.sortcontext = sortcontext;
602 : 175491 : state->base.maincontext = maincontext;
603 : :
604 : 175491 : state->memtupsize = INITIAL_MEMTUPSIZE;
605 : 175491 : state->memtuples = NULL;
606 : :
607 : : /*
608 : : * After all of the other non-parallel-related state, we setup all of the
609 : : * state needed for each batch.
610 : : */
611 : 175491 : tuplesort_begin_batch(state);
612 : :
613 : : /*
614 : : * Initialize parallel-related state based on coordination information
615 : : * from caller
616 : : */
617 [ + + ]: 175491 : if (!coordinate)
618 : : {
619 : : /* Serial sort */
620 : 174934 : state->shared = NULL;
621 : 174934 : state->worker = -1;
622 : 174934 : state->nParticipants = -1;
623 : : }
624 [ + + ]: 557 : else if (coordinate->isWorker)
625 : : {
626 : : /* Parallel worker produces exactly one final run from all input */
627 : 379 : state->shared = coordinate->sharedsort;
628 : 379 : state->worker = worker_get_identifier(state);
629 : 379 : state->nParticipants = -1;
630 : : }
631 : : else
632 : : {
633 : : /* Parallel leader state only used for final merge */
634 : 178 : state->shared = coordinate->sharedsort;
635 : 178 : state->worker = -1;
636 : 178 : state->nParticipants = coordinate->nParticipants;
637 : : Assert(state->nParticipants >= 1);
638 : : }
639 : :
640 : 175491 : MemoryContextSwitchTo(oldcontext);
641 : :
642 : 175491 : return state;
643 : : }
644 : :
645 : : /*
646 : : * tuplesort_begin_batch
647 : : *
648 : : * Setup, or reset, all state need for processing a new set of tuples with this
649 : : * sort state. Called both from tuplesort_begin_common (the first time sorting
650 : : * with this sort state) and tuplesort_reset (for subsequent usages).
651 : : */
652 : : static void
653 : 177556 : tuplesort_begin_batch(Tuplesortstate *state)
654 : : {
655 : : MemoryContext oldcontext;
656 : :
657 : 177556 : oldcontext = MemoryContextSwitchTo(state->base.maincontext);
658 : :
659 : : /*
660 : : * Caller tuple (e.g. IndexTuple) memory context.
661 : : *
662 : : * A dedicated child context used exclusively for caller passed tuples
663 : : * eases memory management. Resetting at key points reduces
664 : : * fragmentation. Note that the memtuples array of SortTuples is allocated
665 : : * in the parent context, not this context, because there is no need to
666 : : * free memtuples early. For bounded sorts, tuples may be pfreed in any
667 : : * order, so we use a regular aset.c context so that it can make use of
668 : : * free'd memory. When the sort is not bounded, we make use of a bump.c
669 : : * context as this keeps allocations more compact with less wastage.
670 : : * Allocations are also slightly more CPU efficient.
671 : : */
672 [ + + ]: 177556 : if (TupleSortUseBumpTupleCxt(state->base.sortopt))
673 : 176678 : state->base.tuplecontext = BumpContextCreate(state->base.sortcontext,
674 : : "Caller tuples",
675 : : ALLOCSET_DEFAULT_SIZES);
676 : : else
677 : 878 : state->base.tuplecontext = AllocSetContextCreate(state->base.sortcontext,
678 : : "Caller tuples",
679 : : ALLOCSET_DEFAULT_SIZES);
680 : :
681 : :
682 : 177556 : state->status = TSS_INITIAL;
683 : 177556 : state->bounded = false;
684 : 177556 : state->boundUsed = false;
685 : :
686 : 177556 : state->availMem = state->allowedMem;
687 : :
688 : 177556 : state->tapeset = NULL;
689 : :
690 : 177556 : state->memtupcount = 0;
691 : :
692 : 177556 : state->growmemtuples = true;
693 : 177556 : state->slabAllocatorUsed = false;
694 [ + + + + ]: 177556 : if (state->memtuples != NULL && state->memtupsize != INITIAL_MEMTUPSIZE)
695 : : {
696 : 48 : pfree(state->memtuples);
697 : 48 : state->memtuples = NULL;
698 : 48 : state->memtupsize = INITIAL_MEMTUPSIZE;
699 : : }
700 [ + + ]: 177556 : if (state->memtuples == NULL)
701 : : {
702 : 175539 : state->memtuples = (SortTuple *) palloc(state->memtupsize * sizeof(SortTuple));
703 : 175539 : USEMEM(state, GetMemoryChunkSpace(state->memtuples));
704 : : }
705 : :
706 : : /* workMem must be large enough for the minimal memtuples array */
707 [ - + - - ]: 177556 : if (LACKMEM(state))
708 [ # # ]: 0 : elog(ERROR, "insufficient memory allowed for sort");
709 : :
710 : 177556 : state->currentRun = 0;
711 : :
712 : : /*
713 : : * Tape variables (inputTapes, outputTapes, etc.) will be initialized by
714 : : * inittapes(), if needed.
715 : : */
716 : :
717 : 177556 : state->result_tape = NULL; /* flag that result tape has not been formed */
718 : :
719 : 177556 : MemoryContextSwitchTo(oldcontext);
720 : 177556 : }
721 : :
722 : : /*
723 : : * tuplesort_set_bound
724 : : *
725 : : * Advise tuplesort that at most the first N result tuples are required.
726 : : *
727 : : * Must be called before inserting any tuples. (Actually, we could allow it
728 : : * as long as the sort hasn't spilled to disk, but there seems no need for
729 : : * delayed calls at the moment.)
730 : : *
731 : : * This is a hint only. The tuplesort may still return more tuples than
732 : : * requested. Parallel leader tuplesorts will always ignore the hint.
733 : : */
734 : : void
735 : 789 : tuplesort_set_bound(Tuplesortstate *state, int64 bound)
736 : : {
737 : : /* Assert we're called before loading any tuples */
738 : : Assert(state->status == TSS_INITIAL && state->memtupcount == 0);
739 : : /* Assert we allow bounded sorts */
740 : : Assert(state->base.sortopt & TUPLESORT_ALLOWBOUNDED);
741 : : /* Can't set the bound twice, either */
742 : : Assert(!state->bounded);
743 : : /* Also, this shouldn't be called in a parallel worker */
744 : : Assert(!WORKER(state));
745 : :
746 : : /* Parallel leader allows but ignores hint */
747 [ - + - - ]: 789 : if (LEADER(state))
748 : 0 : return;
749 : :
750 : : #ifdef DEBUG_BOUNDED_SORT
751 : : /* Honor GUC setting that disables the feature (for easy testing) */
752 : : if (!optimize_bounded_sort)
753 : : return;
754 : : #endif
755 : :
756 : : /* We want to be able to compute bound * 2, so limit the setting */
757 [ - + ]: 789 : if (bound > (int64) (INT_MAX / 2))
758 : 0 : return;
759 : :
760 : 789 : state->bounded = true;
761 : 789 : state->bound = (int) bound;
762 : :
763 : : /*
764 : : * Bounded sorts are not an effective target for abbreviated key
765 : : * optimization. Disable by setting state to be consistent with no
766 : : * abbreviation support.
767 : : */
768 : 789 : state->base.sortKeys->abbrev_converter = NULL;
769 [ + + ]: 789 : if (state->base.sortKeys->abbrev_full_comparator)
770 : 10 : state->base.sortKeys->comparator = state->base.sortKeys->abbrev_full_comparator;
771 : :
772 : : /* Not strictly necessary, but be tidy */
773 : 789 : state->base.sortKeys->abbrev_abort = NULL;
774 : 789 : state->base.sortKeys->abbrev_full_comparator = NULL;
775 : : }
776 : :
777 : : /*
778 : : * tuplesort_used_bound
779 : : *
780 : : * Allow callers to find out if the sort state was able to use a bound.
781 : : */
782 : : bool
783 : 247 : tuplesort_used_bound(Tuplesortstate *state)
784 : : {
785 : 247 : return state->boundUsed;
786 : : }
787 : :
788 : : /*
789 : : * tuplesort_free
790 : : *
791 : : * Internal routine for freeing resources of tuplesort.
792 : : */
793 : : static void
794 : 177369 : tuplesort_free(Tuplesortstate *state)
795 : : {
796 : : /* context swap probably not needed, but let's be safe */
797 : 177369 : MemoryContext oldcontext = MemoryContextSwitchTo(state->base.sortcontext);
798 : : int64 spaceUsed;
799 : :
800 [ + + ]: 177369 : if (state->tapeset)
801 : 599 : spaceUsed = LogicalTapeSetBlocks(state->tapeset);
802 : : else
803 : 176770 : spaceUsed = (state->allowedMem - state->availMem + 1023) / 1024;
804 : :
805 : : /*
806 : : * Delete temporary "tape" files, if any.
807 : : *
808 : : * We don't bother to destroy the individual tapes here. They will go away
809 : : * with the sortcontext. (In TSS_FINALMERGE state, we have closed
810 : : * finished tapes already.)
811 : : */
812 [ + + ]: 177369 : if (state->tapeset)
813 : 599 : LogicalTapeSetClose(state->tapeset);
814 : :
815 [ - + ]: 177369 : if (trace_sort)
816 : : {
817 [ # # ]: 0 : if (state->tapeset)
818 [ # # # # ]: 0 : elog(LOG, "%s of worker %d ended, %" PRId64 " disk blocks used: %s",
819 : : SERIAL(state) ? "external sort" : "parallel external sort",
820 : : state->worker, spaceUsed, pg_rusage_show(&state->ru_start));
821 : : else
822 [ # # # # ]: 0 : elog(LOG, "%s of worker %d ended, %" PRId64 " KB used: %s",
823 : : SERIAL(state) ? "internal sort" : "unperformed parallel sort",
824 : : state->worker, spaceUsed, pg_rusage_show(&state->ru_start));
825 : : }
826 : :
827 : : TRACE_POSTGRESQL_SORT_DONE(state->tapeset != NULL, spaceUsed);
828 : :
829 [ + + ]: 177369 : FREESTATE(state);
830 : 177369 : MemoryContextSwitchTo(oldcontext);
831 : :
832 : : /*
833 : : * Free the per-sort memory context, thereby releasing all working memory.
834 : : */
835 : 177369 : MemoryContextReset(state->base.sortcontext);
836 : 177369 : }
837 : :
838 : : /*
839 : : * tuplesort_end
840 : : *
841 : : * Release resources and clean up.
842 : : *
843 : : * NOTE: after calling this, any pointers returned by tuplesort_getXXX are
844 : : * pointing to garbage. Be careful not to attempt to use or free such
845 : : * pointers afterwards!
846 : : */
847 : : void
848 : 175304 : tuplesort_end(Tuplesortstate *state)
849 : : {
850 : 175304 : tuplesort_free(state);
851 : :
852 : : /*
853 : : * Free the main memory context, including the Tuplesortstate struct
854 : : * itself.
855 : : */
856 : 175304 : MemoryContextDelete(state->base.maincontext);
857 : 175304 : }
858 : :
859 : : /*
860 : : * tuplesort_updatemax
861 : : *
862 : : * Update maximum resource usage statistics.
863 : : */
864 : : static void
865 : 2329 : tuplesort_updatemax(Tuplesortstate *state)
866 : : {
867 : : int64 spaceUsed;
868 : : bool isSpaceDisk;
869 : :
870 : : /*
871 : : * Note: it might seem we should provide both memory and disk usage for a
872 : : * disk-based sort. However, the current code doesn't track memory space
873 : : * accurately once we have begun to return tuples to the caller (since we
874 : : * don't account for pfree's the caller is expected to do), so we cannot
875 : : * rely on availMem in a disk sort. This does not seem worth the overhead
876 : : * to fix. Is it worth creating an API for the memory context code to
877 : : * tell us how much is actually used in sortcontext?
878 : : */
879 [ + + ]: 2329 : if (state->tapeset)
880 : : {
881 : 4 : isSpaceDisk = true;
882 : 4 : spaceUsed = LogicalTapeSetBlocks(state->tapeset) * BLCKSZ;
883 : : }
884 : : else
885 : : {
886 : 2325 : isSpaceDisk = false;
887 : 2325 : spaceUsed = state->allowedMem - state->availMem;
888 : : }
889 : :
890 : : /*
891 : : * Sort evicts data to the disk when it wasn't able to fit that data into
892 : : * main memory. This is why we assume space used on the disk to be more
893 : : * important for tracking resource usage than space used in memory. Note
894 : : * that the amount of space occupied by some tupleset on the disk might be
895 : : * less than amount of space occupied by the same tupleset in memory due
896 : : * to more compact representation.
897 : : */
898 [ + + - + ]: 2329 : if ((isSpaceDisk && !state->isMaxSpaceDisk) ||
899 [ + - + + ]: 2325 : (isSpaceDisk == state->isMaxSpaceDisk && spaceUsed > state->maxSpace))
900 : : {
901 : 329 : state->maxSpace = spaceUsed;
902 : 329 : state->isMaxSpaceDisk = isSpaceDisk;
903 : 329 : state->maxSpaceStatus = state->status;
904 : : }
905 : 2329 : }
906 : :
907 : : /*
908 : : * tuplesort_reset
909 : : *
910 : : * Reset the tuplesort. Reset all the data in the tuplesort, but leave the
911 : : * meta-information in. After tuplesort_reset, tuplesort is ready to start
912 : : * a new sort. This allows avoiding recreation of tuple sort states (and
913 : : * save resources) when sorting multiple small batches.
914 : : */
915 : : void
916 : 2065 : tuplesort_reset(Tuplesortstate *state)
917 : : {
918 : 2065 : tuplesort_updatemax(state);
919 : 2065 : tuplesort_free(state);
920 : :
921 : : /*
922 : : * After we've freed up per-batch memory, re-setup all of the state common
923 : : * to both the first batch and any subsequent batch.
924 : : */
925 : 2065 : tuplesort_begin_batch(state);
926 : :
927 : 2065 : state->lastReturnedTuple = NULL;
928 : 2065 : state->slabMemoryBegin = NULL;
929 : 2065 : state->slabMemoryEnd = NULL;
930 : 2065 : state->slabFreeHead = NULL;
931 : 2065 : }
932 : :
933 : : /*
934 : : * Grow the memtuples[] array, if possible within our memory constraint. We
935 : : * must not exceed INT_MAX tuples in memory or the caller-provided memory
936 : : * limit. Return true if we were able to enlarge the array, false if not.
937 : : *
938 : : * Normally, at each increment we double the size of the array. When doing
939 : : * that would exceed a limit, we attempt one last, smaller increase (and then
940 : : * clear the growmemtuples flag so we don't try any more). That allows us to
941 : : * use memory as fully as permitted; sticking to the pure doubling rule could
942 : : * result in almost half going unused. Because availMem moves around with
943 : : * tuple addition/removal, we need some rule to prevent making repeated small
944 : : * increases in memtupsize, which would just be useless thrashing. The
945 : : * growmemtuples flag accomplishes that and also prevents useless
946 : : * recalculations in this function.
947 : : */
948 : : static bool
949 : 5103 : grow_memtuples(Tuplesortstate *state)
950 : : {
951 : : int newmemtupsize;
952 : 5103 : int memtupsize = state->memtupsize;
953 : 5103 : int64 memNowUsed = state->allowedMem - state->availMem;
954 : :
955 : : /* Forget it if we've already maxed out memtuples, per comment above */
956 [ + + ]: 5103 : if (!state->growmemtuples)
957 : 90 : return false;
958 : :
959 : : /* Select new value of memtupsize */
960 [ + + ]: 5013 : if (memNowUsed <= state->availMem)
961 : : {
962 : : /*
963 : : * We've used no more than half of allowedMem; double our usage,
964 : : * clamping at INT_MAX tuples.
965 : : */
966 [ + - ]: 4919 : if (memtupsize < INT_MAX / 2)
967 : 4919 : newmemtupsize = memtupsize * 2;
968 : : else
969 : : {
970 : 0 : newmemtupsize = INT_MAX;
971 : 0 : state->growmemtuples = false;
972 : : }
973 : : }
974 : : else
975 : : {
976 : : /*
977 : : * This will be the last increment of memtupsize. Abandon doubling
978 : : * strategy and instead increase as much as we safely can.
979 : : *
980 : : * To stay within allowedMem, we can't increase memtupsize by more
981 : : * than availMem / sizeof(SortTuple) elements. In practice, we want
982 : : * to increase it by considerably less, because we need to leave some
983 : : * space for the tuples to which the new array slots will refer. We
984 : : * assume the new tuples will be about the same size as the tuples
985 : : * we've already seen, and thus we can extrapolate from the space
986 : : * consumption so far to estimate an appropriate new size for the
987 : : * memtuples array. The optimal value might be higher or lower than
988 : : * this estimate, but it's hard to know that in advance. We again
989 : : * clamp at INT_MAX tuples.
990 : : *
991 : : * This calculation is safe against enlarging the array so much that
992 : : * LACKMEM becomes true, because the memory currently used includes
993 : : * the present array; thus, there would be enough allowedMem for the
994 : : * new array elements even if no other memory were currently used.
995 : : *
996 : : * We do the arithmetic in float8, because otherwise the product of
997 : : * memtupsize and allowedMem could overflow. Any inaccuracy in the
998 : : * result should be insignificant; but even if we computed a
999 : : * completely insane result, the checks below will prevent anything
1000 : : * really bad from happening.
1001 : : */
1002 : : double grow_ratio;
1003 : :
1004 : 94 : grow_ratio = (double) state->allowedMem / (double) memNowUsed;
1005 [ + - ]: 94 : if (memtupsize * grow_ratio < INT_MAX)
1006 : 94 : newmemtupsize = (int) (memtupsize * grow_ratio);
1007 : : else
1008 : 0 : newmemtupsize = INT_MAX;
1009 : :
1010 : : /* We won't make any further enlargement attempts */
1011 : 94 : state->growmemtuples = false;
1012 : : }
1013 : :
1014 : : /* Must enlarge array by at least one element, else report failure */
1015 [ - + ]: 5013 : if (newmemtupsize <= memtupsize)
1016 : 0 : goto noalloc;
1017 : :
1018 : : /*
1019 : : * On a 32-bit machine, allowedMem could exceed MaxAllocHugeSize. Clamp
1020 : : * to ensure our request won't be rejected. Note that we can easily
1021 : : * exhaust address space before facing this outcome. (This is presently
1022 : : * impossible due to guc.c's MAX_KILOBYTES limitation on work_mem, but
1023 : : * don't rely on that at this distance.)
1024 : : */
1025 [ - + ]: 5013 : if ((Size) newmemtupsize >= MaxAllocHugeSize / sizeof(SortTuple))
1026 : : {
1027 : 0 : newmemtupsize = (int) (MaxAllocHugeSize / sizeof(SortTuple));
1028 : 0 : state->growmemtuples = false; /* can't grow any more */
1029 : : }
1030 : :
1031 : : /*
1032 : : * We need to be sure that we do not cause LACKMEM to become true, else
1033 : : * the space management algorithm will go nuts. The code above should
1034 : : * never generate a dangerous request, but to be safe, check explicitly
1035 : : * that the array growth fits within availMem. (We could still cause
1036 : : * LACKMEM if the memory chunk overhead associated with the memtuples
1037 : : * array were to increase. That shouldn't happen because we chose the
1038 : : * initial array size large enough to ensure that palloc will be treating
1039 : : * both old and new arrays as separate chunks. But we'll check LACKMEM
1040 : : * explicitly below just in case.)
1041 : : */
1042 [ - + ]: 5013 : if (state->availMem < (int64) ((newmemtupsize - memtupsize) * sizeof(SortTuple)))
1043 : 0 : goto noalloc;
1044 : :
1045 : : /* OK, do it */
1046 : 5013 : FREEMEM(state, GetMemoryChunkSpace(state->memtuples));
1047 : 5013 : state->memtupsize = newmemtupsize;
1048 : 5013 : state->memtuples = (SortTuple *)
1049 : 5013 : repalloc_huge(state->memtuples,
1050 : 5013 : state->memtupsize * sizeof(SortTuple));
1051 : 5013 : USEMEM(state, GetMemoryChunkSpace(state->memtuples));
1052 [ - + - - ]: 5013 : if (LACKMEM(state))
1053 [ # # ]: 0 : elog(ERROR, "unexpected out-of-memory situation in tuplesort");
1054 : 5013 : return true;
1055 : :
1056 : 0 : noalloc:
1057 : : /* If for any reason we didn't realloc, shut off future attempts */
1058 : 0 : state->growmemtuples = false;
1059 : 0 : return false;
1060 : : }
1061 : :
1062 : : /*
1063 : : * Shared code for tuple and datum cases.
1064 : : */
1065 : : void
1066 : 18945784 : tuplesort_puttuple_common(Tuplesortstate *state, SortTuple *tuple,
1067 : : bool useAbbrev, Size tuplen)
1068 : : {
1069 : 18945784 : MemoryContext oldcontext = MemoryContextSwitchTo(state->base.sortcontext);
1070 : :
1071 : : Assert(!LEADER(state));
1072 : :
1073 : : /* account for the memory used for this tuple */
1074 : 18945784 : USEMEM(state, tuplen);
1075 : 18945784 : state->tupleMem += tuplen;
1076 : :
1077 [ + + ]: 18945784 : if (!useAbbrev)
1078 : : {
1079 : : /*
1080 : : * Leave ordinary Datum representation, or NULL value. If there is a
1081 : : * converter it won't expect NULL values, and cost model is not
1082 : : * required to account for NULL, so in that case we avoid calling
1083 : : * converter and just set datum1 to zeroed representation (to be
1084 : : * consistent, and to support cheap inequality tests for NULL
1085 : : * abbreviated keys).
1086 : : */
1087 : : }
1088 [ + + ]: 2900601 : else if (!consider_abort_common(state))
1089 : : {
1090 : : /* Store abbreviated key representation */
1091 : 2900537 : tuple->datum1 = state->base.sortKeys->abbrev_converter(tuple->datum1,
1092 : : state->base.sortKeys);
1093 : : }
1094 : : else
1095 : : {
1096 : : /*
1097 : : * Set state to be consistent with never trying abbreviation.
1098 : : *
1099 : : * Alter datum1 representation in already-copied tuples, so as to
1100 : : * ensure a consistent representation (current tuple was just
1101 : : * handled). It does not matter if some dumped tuples are already
1102 : : * sorted on tape, since serialized tuples lack abbreviated keys
1103 : : * (TSS_BUILDRUNS state prevents control reaching here in any case).
1104 : : */
1105 : 64 : REMOVEABBREV(state, state->memtuples, state->memtupcount);
1106 : : }
1107 : :
1108 [ + + + - ]: 18945784 : switch (state->status)
1109 : : {
1110 : 16068621 : case TSS_INITIAL:
1111 : :
1112 : : /*
1113 : : * Save the tuple into the unsorted array. First, grow the array
1114 : : * as needed. Note that we try to grow the array when there is
1115 : : * still one free slot remaining --- if we fail, there'll still be
1116 : : * room to store the incoming tuple, and then we'll switch to
1117 : : * tape-based operation.
1118 : : */
1119 [ + + ]: 16068621 : if (state->memtupcount >= state->memtupsize - 1)
1120 : : {
1121 : 5103 : (void) grow_memtuples(state);
1122 : : Assert(state->memtupcount < state->memtupsize);
1123 : : }
1124 : 16068621 : state->memtuples[state->memtupcount++] = *tuple;
1125 : :
1126 : : /*
1127 : : * Check if it's time to switch over to a bounded heapsort. We do
1128 : : * so if the input tuple count exceeds twice the desired tuple
1129 : : * count (this is a heuristic for where heapsort becomes cheaper
1130 : : * than a quicksort), or if we've just filled workMem and have
1131 : : * enough tuples to meet the bound.
1132 : : *
1133 : : * Note that once we enter TSS_BOUNDED state we will always try to
1134 : : * complete the sort that way. In the worst case, if later input
1135 : : * tuples are larger than earlier ones, this might cause us to
1136 : : * exceed workMem significantly.
1137 : : */
1138 [ + + ]: 16068621 : if (state->bounded &&
1139 [ + + ]: 33322 : (state->memtupcount > state->bound * 2 ||
1140 [ + + - + : 33066 : (state->memtupcount > state->bound && LACKMEM(state))))
- - ]
1141 : : {
1142 [ - + ]: 256 : if (trace_sort)
1143 [ # # ]: 0 : elog(LOG, "switching to bounded heapsort at %d tuples: %s",
1144 : : state->memtupcount,
1145 : : pg_rusage_show(&state->ru_start));
1146 : 256 : make_bounded_heap(state);
1147 : 256 : MemoryContextSwitchTo(oldcontext);
1148 : 256 : return;
1149 : : }
1150 : :
1151 : : /*
1152 : : * Done if we still fit in available memory and have array slots.
1153 : : */
1154 [ + + - + : 16068365 : if (state->memtupcount < state->memtupsize && !LACKMEM(state))
- - ]
1155 : : {
1156 : 16068275 : MemoryContextSwitchTo(oldcontext);
1157 : 16068275 : return;
1158 : : }
1159 : :
1160 : : /*
1161 : : * Nope; time to switch to tape-based operation.
1162 : : */
1163 : 90 : inittapes(state, true);
1164 : :
1165 : : /*
1166 : : * Dump all tuples.
1167 : : */
1168 : 90 : dumptuples(state, false);
1169 : 90 : break;
1170 : :
1171 : 2143090 : case TSS_BOUNDED:
1172 : :
1173 : : /*
1174 : : * We don't want to grow the array here, so check whether the new
1175 : : * tuple can be discarded before putting it in. This should be a
1176 : : * good speed optimization, too, since when there are many more
1177 : : * input tuples than the bound, most input tuples can be discarded
1178 : : * with just this one comparison. Note that because we currently
1179 : : * have the sort direction reversed, we must check for <= not >=.
1180 : : */
1181 [ + + ]: 2143090 : if (COMPARETUP(state, tuple, &state->memtuples[0]) <= 0)
1182 : : {
1183 : : /* new tuple <= top of the heap, so we can discard it */
1184 : 1808289 : free_sort_tuple(state, tuple);
1185 [ - + ]: 1808289 : CHECK_FOR_INTERRUPTS();
1186 : : }
1187 : : else
1188 : : {
1189 : : /* discard top of heap, replacing it with the new tuple */
1190 : 334801 : free_sort_tuple(state, &state->memtuples[0]);
1191 : 334801 : tuplesort_heap_replace_top(state, tuple);
1192 : : }
1193 : 2143090 : break;
1194 : :
1195 : 734073 : case TSS_BUILDRUNS:
1196 : :
1197 : : /*
1198 : : * Save the tuple into the unsorted array (there must be space)
1199 : : */
1200 : 734073 : state->memtuples[state->memtupcount++] = *tuple;
1201 : :
1202 : : /*
1203 : : * If we are over the memory limit, dump all tuples.
1204 : : */
1205 : 734073 : dumptuples(state, false);
1206 : 734073 : break;
1207 : :
1208 : 0 : default:
1209 [ # # ]: 0 : elog(ERROR, "invalid tuplesort state");
1210 : : break;
1211 : : }
1212 : 2877253 : MemoryContextSwitchTo(oldcontext);
1213 : : }
1214 : :
1215 : : static bool
1216 : 2900601 : consider_abort_common(Tuplesortstate *state)
1217 : : {
1218 : : Assert(state->base.sortKeys[0].abbrev_converter != NULL);
1219 : : Assert(state->base.sortKeys[0].abbrev_abort != NULL);
1220 : : Assert(state->base.sortKeys[0].abbrev_full_comparator != NULL);
1221 : :
1222 : : /*
1223 : : * Check effectiveness of abbreviation optimization. Consider aborting
1224 : : * when still within memory limit.
1225 : : */
1226 [ + + ]: 2900601 : if (state->status == TSS_INITIAL &&
1227 [ + + ]: 2598568 : state->memtupcount >= state->abbrevNext)
1228 : : {
1229 : 3282 : state->abbrevNext *= 2;
1230 : :
1231 : : /*
1232 : : * Check opclass-supplied abbreviation abort routine. It may indicate
1233 : : * that abbreviation should not proceed.
1234 : : */
1235 [ + + ]: 3282 : if (!state->base.sortKeys->abbrev_abort(state->memtupcount,
1236 : : state->base.sortKeys))
1237 : 3218 : return false;
1238 : :
1239 : : /*
1240 : : * Finally, restore authoritative comparator, and indicate that
1241 : : * abbreviation is not in play by setting abbrev_converter to NULL
1242 : : */
1243 : 64 : state->base.sortKeys[0].comparator = state->base.sortKeys[0].abbrev_full_comparator;
1244 : 64 : state->base.sortKeys[0].abbrev_converter = NULL;
1245 : : /* Not strictly necessary, but be tidy */
1246 : 64 : state->base.sortKeys[0].abbrev_abort = NULL;
1247 : 64 : state->base.sortKeys[0].abbrev_full_comparator = NULL;
1248 : :
1249 : : /* Give up - expect original pass-by-value representation */
1250 : 64 : return true;
1251 : : }
1252 : :
1253 : 2897319 : return false;
1254 : : }
1255 : :
1256 : : /*
1257 : : * All tuples have been provided; finish the sort.
1258 : : */
1259 : : void
1260 : 150928 : tuplesort_performsort(Tuplesortstate *state)
1261 : : {
1262 : 150928 : MemoryContext oldcontext = MemoryContextSwitchTo(state->base.sortcontext);
1263 : :
1264 [ - + ]: 150928 : if (trace_sort)
1265 [ # # ]: 0 : elog(LOG, "performsort of worker %d starting: %s",
1266 : : state->worker, pg_rusage_show(&state->ru_start));
1267 : :
1268 [ + + + - ]: 150928 : switch (state->status)
1269 : : {
1270 : 150582 : case TSS_INITIAL:
1271 : :
1272 : : /*
1273 : : * We were able to accumulate all the tuples within the allowed
1274 : : * amount of memory, or leader to take over worker tapes
1275 : : */
1276 [ + + ]: 150582 : if (SERIAL(state))
1277 : : {
1278 : : /* Sort in memory and we're done */
1279 : 150073 : tuplesort_sort_memtuples(state);
1280 : 150013 : state->status = TSS_SORTEDINMEM;
1281 : : }
1282 [ + - + + ]: 509 : else if (WORKER(state))
1283 : : {
1284 : : /*
1285 : : * Parallel workers must still dump out tuples to tape. No
1286 : : * merge is required to produce single output run, though.
1287 : : */
1288 : 379 : inittapes(state, false);
1289 : 379 : dumptuples(state, true);
1290 : 379 : worker_nomergeruns(state);
1291 : 379 : state->status = TSS_SORTEDONTAPE;
1292 : : }
1293 : : else
1294 : : {
1295 : : /*
1296 : : * Leader will take over worker tapes and merge worker runs.
1297 : : * Note that mergeruns sets the correct state->status.
1298 : : */
1299 : 130 : leader_takeover_tapes(state);
1300 : 130 : mergeruns(state);
1301 : : }
1302 : 150522 : state->current = 0;
1303 : 150522 : state->eof_reached = false;
1304 : 150522 : state->markpos_block = 0L;
1305 : 150522 : state->markpos_offset = 0;
1306 : 150522 : state->markpos_eof = false;
1307 : 150522 : break;
1308 : :
1309 : 256 : case TSS_BOUNDED:
1310 : :
1311 : : /*
1312 : : * We were able to accumulate all the tuples required for output
1313 : : * in memory, using a heap to eliminate excess tuples. Now we
1314 : : * have to transform the heap to a properly-sorted array. Note
1315 : : * that sort_bounded_heap sets the correct state->status.
1316 : : */
1317 : 256 : sort_bounded_heap(state);
1318 : 256 : state->current = 0;
1319 : 256 : state->eof_reached = false;
1320 : 256 : state->markpos_offset = 0;
1321 : 256 : state->markpos_eof = false;
1322 : 256 : break;
1323 : :
1324 : 90 : case TSS_BUILDRUNS:
1325 : :
1326 : : /*
1327 : : * Finish tape-based sort. First, flush all tuples remaining in
1328 : : * memory out to tape; then merge until we have a single remaining
1329 : : * run (or, if !randomAccess and !WORKER(), one run per tape).
1330 : : * Note that mergeruns sets the correct state->status.
1331 : : */
1332 : 90 : dumptuples(state, true);
1333 : 90 : mergeruns(state);
1334 : 90 : state->eof_reached = false;
1335 : 90 : state->markpos_block = 0L;
1336 : 90 : state->markpos_offset = 0;
1337 : 90 : state->markpos_eof = false;
1338 : 90 : break;
1339 : :
1340 : 0 : default:
1341 [ # # ]: 0 : elog(ERROR, "invalid tuplesort state");
1342 : : break;
1343 : : }
1344 : :
1345 [ - + ]: 150868 : if (trace_sort)
1346 : : {
1347 [ # # ]: 0 : if (state->status == TSS_FINALMERGE)
1348 [ # # ]: 0 : elog(LOG, "performsort of worker %d done (except %d-way final merge): %s",
1349 : : state->worker, state->nInputTapes,
1350 : : pg_rusage_show(&state->ru_start));
1351 : : else
1352 [ # # ]: 0 : elog(LOG, "performsort of worker %d done: %s",
1353 : : state->worker, pg_rusage_show(&state->ru_start));
1354 : : }
1355 : :
1356 : 150868 : MemoryContextSwitchTo(oldcontext);
1357 : 150868 : }
1358 : :
1359 : : /*
1360 : : * Internal routine to fetch the next tuple in either forward or back
1361 : : * direction into *stup. Returns false if no more tuples.
1362 : : * Returned tuple belongs to tuplesort memory context, and must not be freed
1363 : : * by caller. Note that fetched tuple is stored in memory that may be
1364 : : * recycled by any future fetch.
1365 : : */
1366 : : bool
1367 : 17378542 : tuplesort_gettuple_common(Tuplesortstate *state, bool forward,
1368 : : SortTuple *stup)
1369 : : {
1370 : : unsigned int tuplen;
1371 : : size_t nmoved;
1372 : :
1373 : : Assert(!WORKER(state));
1374 : :
1375 [ + + + - ]: 17378542 : switch (state->status)
1376 : : {
1377 : 14436676 : case TSS_SORTEDINMEM:
1378 : : Assert(forward || state->base.sortopt & TUPLESORT_RANDOMACCESS);
1379 : : Assert(!state->slabAllocatorUsed);
1380 [ + + ]: 14436676 : if (forward)
1381 : : {
1382 [ + + ]: 14436632 : if (state->current < state->memtupcount)
1383 : : {
1384 : 14287487 : *stup = state->memtuples[state->current++];
1385 : 14287487 : return true;
1386 : : }
1387 : 149145 : state->eof_reached = true;
1388 : :
1389 : : /*
1390 : : * Complain if caller tries to retrieve more tuples than
1391 : : * originally asked for in a bounded sort. This is because
1392 : : * returning EOF here might be the wrong thing.
1393 : : */
1394 [ + + - + ]: 149145 : if (state->bounded && state->current >= state->bound)
1395 [ # # ]: 0 : elog(ERROR, "retrieved too many tuples in a bounded sort");
1396 : :
1397 : 149145 : return false;
1398 : : }
1399 : : else
1400 : : {
1401 [ - + ]: 44 : if (state->current <= 0)
1402 : 0 : return false;
1403 : :
1404 : : /*
1405 : : * if all tuples are fetched already then we return last
1406 : : * tuple, else - tuple before last returned.
1407 : : */
1408 [ + + ]: 44 : if (state->eof_reached)
1409 : 8 : state->eof_reached = false;
1410 : : else
1411 : : {
1412 : 36 : state->current--; /* last returned tuple */
1413 [ + + ]: 36 : if (state->current <= 0)
1414 : 4 : return false;
1415 : : }
1416 : 40 : *stup = state->memtuples[state->current - 1];
1417 : 40 : return true;
1418 : : }
1419 : : break;
1420 : :
1421 : 196999 : case TSS_SORTEDONTAPE:
1422 : : Assert(forward || state->base.sortopt & TUPLESORT_RANDOMACCESS);
1423 : : Assert(state->slabAllocatorUsed);
1424 : :
1425 : : /*
1426 : : * The slot that held the tuple that we returned in previous
1427 : : * gettuple call can now be reused.
1428 : : */
1429 [ + + ]: 196999 : if (state->lastReturnedTuple)
1430 : : {
1431 [ + - + - ]: 101900 : RELEASE_SLAB_SLOT(state, state->lastReturnedTuple);
1432 : 101900 : state->lastReturnedTuple = NULL;
1433 : : }
1434 : :
1435 [ + + ]: 196999 : if (forward)
1436 : : {
1437 [ - + ]: 196979 : if (state->eof_reached)
1438 : 0 : return false;
1439 : :
1440 [ + + ]: 196979 : if ((tuplen = getlen(state->result_tape, true)) != 0)
1441 : : {
1442 : 196960 : READTUP(state, stup, state->result_tape, tuplen);
1443 : :
1444 : : /*
1445 : : * Remember the tuple we return, so that we can recycle
1446 : : * its memory on next call. (This can be NULL, in the
1447 : : * !state->tuples case).
1448 : : */
1449 : 196960 : state->lastReturnedTuple = stup->tuple;
1450 : :
1451 : 196960 : return true;
1452 : : }
1453 : : else
1454 : : {
1455 : 19 : state->eof_reached = true;
1456 : 19 : return false;
1457 : : }
1458 : : }
1459 : :
1460 : : /*
1461 : : * Backward.
1462 : : *
1463 : : * if all tuples are fetched already then we return last tuple,
1464 : : * else - tuple before last returned.
1465 : : */
1466 [ + + ]: 20 : if (state->eof_reached)
1467 : : {
1468 : : /*
1469 : : * Seek position is pointing just past the zero tuplen at the
1470 : : * end of file; back up to fetch last tuple's ending length
1471 : : * word. If seek fails we must have a completely empty file.
1472 : : */
1473 : 8 : nmoved = LogicalTapeBackspace(state->result_tape,
1474 : : 2 * sizeof(unsigned int));
1475 [ - + ]: 8 : if (nmoved == 0)
1476 : 0 : return false;
1477 [ - + ]: 8 : else if (nmoved != 2 * sizeof(unsigned int))
1478 [ # # ]: 0 : elog(ERROR, "unexpected tape position");
1479 : 8 : state->eof_reached = false;
1480 : : }
1481 : : else
1482 : : {
1483 : : /*
1484 : : * Back up and fetch previously-returned tuple's ending length
1485 : : * word. If seek fails, assume we are at start of file.
1486 : : */
1487 : 12 : nmoved = LogicalTapeBackspace(state->result_tape,
1488 : : sizeof(unsigned int));
1489 [ - + ]: 12 : if (nmoved == 0)
1490 : 0 : return false;
1491 [ - + ]: 12 : else if (nmoved != sizeof(unsigned int))
1492 [ # # ]: 0 : elog(ERROR, "unexpected tape position");
1493 : 12 : tuplen = getlen(state->result_tape, false);
1494 : :
1495 : : /*
1496 : : * Back up to get ending length word of tuple before it.
1497 : : */
1498 : 12 : nmoved = LogicalTapeBackspace(state->result_tape,
1499 : : tuplen + 2 * sizeof(unsigned int));
1500 [ + + ]: 12 : if (nmoved == tuplen + sizeof(unsigned int))
1501 : : {
1502 : : /*
1503 : : * We backed up over the previous tuple, but there was no
1504 : : * ending length word before it. That means that the prev
1505 : : * tuple is the first tuple in the file. It is now the
1506 : : * next to read in forward direction (not obviously right,
1507 : : * but that is what in-memory case does).
1508 : : */
1509 : 4 : return false;
1510 : : }
1511 [ - + ]: 8 : else if (nmoved != tuplen + 2 * sizeof(unsigned int))
1512 [ # # ]: 0 : elog(ERROR, "bogus tuple length in backward scan");
1513 : : }
1514 : :
1515 : 16 : tuplen = getlen(state->result_tape, false);
1516 : :
1517 : : /*
1518 : : * Now we have the length of the prior tuple, back up and read it.
1519 : : * Note: READTUP expects we are positioned after the initial
1520 : : * length word of the tuple, so back up to that point.
1521 : : */
1522 : 16 : nmoved = LogicalTapeBackspace(state->result_tape,
1523 : : tuplen);
1524 [ - + ]: 16 : if (nmoved != tuplen)
1525 [ # # ]: 0 : elog(ERROR, "bogus tuple length in backward scan");
1526 : 16 : READTUP(state, stup, state->result_tape, tuplen);
1527 : :
1528 : : /*
1529 : : * Remember the tuple we return, so that we can recycle its memory
1530 : : * on next call. (This can be NULL, in the Datum case).
1531 : : */
1532 : 16 : state->lastReturnedTuple = stup->tuple;
1533 : :
1534 : 16 : return true;
1535 : :
1536 : 2744867 : case TSS_FINALMERGE:
1537 : : Assert(forward);
1538 : : /* We are managing memory ourselves, with the slab allocator. */
1539 : : Assert(state->slabAllocatorUsed);
1540 : :
1541 : : /*
1542 : : * The slab slot holding the tuple that we returned in previous
1543 : : * gettuple call can now be reused.
1544 : : */
1545 [ + + ]: 2744867 : if (state->lastReturnedTuple)
1546 : : {
1547 [ + - + + ]: 2674641 : RELEASE_SLAB_SLOT(state, state->lastReturnedTuple);
1548 : 2674641 : state->lastReturnedTuple = NULL;
1549 : : }
1550 : :
1551 : : /*
1552 : : * This code should match the inner loop of mergeonerun().
1553 : : */
1554 [ + + ]: 2744867 : if (state->memtupcount > 0)
1555 : : {
1556 : 2744673 : int srcTapeIndex = state->memtuples[0].srctape;
1557 : 2744673 : LogicalTape *srcTape = state->inputTapes[srcTapeIndex];
1558 : : SortTuple newtup;
1559 : :
1560 : 2744673 : *stup = state->memtuples[0];
1561 : :
1562 : : /*
1563 : : * Remember the tuple we return, so that we can recycle its
1564 : : * memory on next call. (This can be NULL, in the Datum case).
1565 : : */
1566 : 2744673 : state->lastReturnedTuple = stup->tuple;
1567 : :
1568 : : /*
1569 : : * Pull next tuple from tape, and replace the returned tuple
1570 : : * at top of the heap with it.
1571 : : */
1572 [ + + ]: 2744673 : if (!mergereadnext(state, srcTape, &newtup))
1573 : : {
1574 : : /*
1575 : : * If no more data, we've reached end of run on this tape.
1576 : : * Remove the top node from the heap.
1577 : : */
1578 : 286 : tuplesort_heap_delete_top(state);
1579 : 286 : state->nInputRuns--;
1580 : :
1581 : : /*
1582 : : * Close the tape. It'd go away at the end of the sort
1583 : : * anyway, but better to release the memory early.
1584 : : */
1585 : 286 : LogicalTapeClose(srcTape);
1586 : 286 : return true;
1587 : : }
1588 : 2744387 : newtup.srctape = srcTapeIndex;
1589 : 2744387 : tuplesort_heap_replace_top(state, &newtup);
1590 : 2744387 : return true;
1591 : : }
1592 : 194 : return false;
1593 : :
1594 : 0 : default:
1595 [ # # ]: 0 : elog(ERROR, "invalid tuplesort state");
1596 : : return false; /* keep compiler quiet */
1597 : : }
1598 : : }
1599 : :
1600 : :
1601 : : /*
1602 : : * Advance over N tuples in either forward or back direction,
1603 : : * without returning any data. N==0 is a no-op.
1604 : : * Returns true if successful, false if ran out of tuples.
1605 : : */
1606 : : bool
1607 : 258 : tuplesort_skiptuples(Tuplesortstate *state, int64 ntuples, bool forward)
1608 : : {
1609 : : MemoryContext oldcontext;
1610 : :
1611 : : /*
1612 : : * We don't actually support backwards skip yet, because no callers need
1613 : : * it. The API is designed to allow for that later, though.
1614 : : */
1615 : : Assert(forward);
1616 : : Assert(ntuples >= 0);
1617 : : Assert(!WORKER(state));
1618 : :
1619 [ + + - ]: 258 : switch (state->status)
1620 : : {
1621 : 242 : case TSS_SORTEDINMEM:
1622 [ + - ]: 242 : if (state->memtupcount - state->current >= ntuples)
1623 : : {
1624 : 242 : state->current += ntuples;
1625 : 242 : return true;
1626 : : }
1627 : 0 : state->current = state->memtupcount;
1628 : 0 : state->eof_reached = true;
1629 : :
1630 : : /*
1631 : : * Complain if caller tries to retrieve more tuples than
1632 : : * originally asked for in a bounded sort. This is because
1633 : : * returning EOF here might be the wrong thing.
1634 : : */
1635 [ # # # # ]: 0 : if (state->bounded && state->current >= state->bound)
1636 [ # # ]: 0 : elog(ERROR, "retrieved too many tuples in a bounded sort");
1637 : :
1638 : 0 : return false;
1639 : :
1640 : 16 : case TSS_SORTEDONTAPE:
1641 : : case TSS_FINALMERGE:
1642 : :
1643 : : /*
1644 : : * We could probably optimize these cases better, but for now it's
1645 : : * not worth the trouble.
1646 : : */
1647 : 16 : oldcontext = MemoryContextSwitchTo(state->base.sortcontext);
1648 [ + + ]: 160088 : while (ntuples-- > 0)
1649 : : {
1650 : : SortTuple stup;
1651 : :
1652 [ - + ]: 160072 : if (!tuplesort_gettuple_common(state, forward, &stup))
1653 : : {
1654 : 0 : MemoryContextSwitchTo(oldcontext);
1655 : 0 : return false;
1656 : : }
1657 [ - + ]: 160072 : CHECK_FOR_INTERRUPTS();
1658 : : }
1659 : 16 : MemoryContextSwitchTo(oldcontext);
1660 : 16 : return true;
1661 : :
1662 : 0 : default:
1663 [ # # ]: 0 : elog(ERROR, "invalid tuplesort state");
1664 : : return false; /* keep compiler quiet */
1665 : : }
1666 : : }
1667 : :
1668 : : /*
1669 : : * tuplesort_merge_order - report merge order we'll use for given memory
1670 : : * (note: "merge order" just means the number of input tapes in the merge).
1671 : : *
1672 : : * This is exported for use by the planner. allowedMem is in bytes.
1673 : : */
1674 : : int
1675 : 11127 : tuplesort_merge_order(int64 allowedMem)
1676 : : {
1677 : : int mOrder;
1678 : :
1679 : : /*----------
1680 : : * In the merge phase, we need buffer space for each input and output tape.
1681 : : * Each pass in the balanced merge algorithm reads from M input tapes, and
1682 : : * writes to N output tapes. Each tape consumes TAPE_BUFFER_OVERHEAD bytes
1683 : : * of memory. In addition to that, we want MERGE_BUFFER_SIZE workspace per
1684 : : * input tape.
1685 : : *
1686 : : * totalMem = M * (TAPE_BUFFER_OVERHEAD + MERGE_BUFFER_SIZE) +
1687 : : * N * TAPE_BUFFER_OVERHEAD
1688 : : *
1689 : : * Except for the last and next-to-last merge passes, where there can be
1690 : : * fewer tapes left to process, M = N. We choose M so that we have the
1691 : : * desired amount of memory available for the input buffers
1692 : : * (TAPE_BUFFER_OVERHEAD + MERGE_BUFFER_SIZE), given the total memory
1693 : : * available for the tape buffers (allowedMem).
1694 : : *
1695 : : * Note: you might be thinking we need to account for the memtuples[]
1696 : : * array in this calculation, but we effectively treat that as part of the
1697 : : * MERGE_BUFFER_SIZE workspace.
1698 : : *----------
1699 : : */
1700 : 11127 : mOrder = allowedMem /
1701 : : (2 * TAPE_BUFFER_OVERHEAD + MERGE_BUFFER_SIZE);
1702 : :
1703 : : /*
1704 : : * Even in minimum memory, use at least a MINORDER merge. On the other
1705 : : * hand, even when we have lots of memory, do not use more than a MAXORDER
1706 : : * merge. Tapes are pretty cheap, but they're not entirely free. Each
1707 : : * additional tape reduces the amount of memory available to build runs,
1708 : : * which in turn can cause the same sort to need more runs, which makes
1709 : : * merging slower even if it can still be done in a single pass. Also,
1710 : : * high order merges are quite slow due to CPU cache effects; it can be
1711 : : * faster to pay the I/O cost of a multi-pass merge than to perform a
1712 : : * single merge pass across many hundreds of tapes.
1713 : : */
1714 : 11127 : mOrder = Max(mOrder, MINORDER);
1715 : 11127 : mOrder = Min(mOrder, MAXORDER);
1716 : :
1717 : 11127 : return mOrder;
1718 : : }
1719 : :
1720 : : /*
1721 : : * Helper function to calculate how much memory to allocate for the read buffer
1722 : : * of each input tape in a merge pass.
1723 : : *
1724 : : * 'avail_mem' is the amount of memory available for the buffers of all the
1725 : : * tapes, both input and output.
1726 : : * 'nInputTapes' and 'nInputRuns' are the number of input tapes and runs.
1727 : : * 'maxOutputTapes' is the max. number of output tapes we should produce.
1728 : : */
1729 : : static int64
1730 : 240 : merge_read_buffer_size(int64 avail_mem, int nInputTapes, int nInputRuns,
1731 : : int maxOutputTapes)
1732 : : {
1733 : : int nOutputRuns;
1734 : : int nOutputTapes;
1735 : :
1736 : : /*
1737 : : * How many output tapes will we produce in this pass?
1738 : : *
1739 : : * This is nInputRuns / nInputTapes, rounded up.
1740 : : */
1741 : 240 : nOutputRuns = (nInputRuns + nInputTapes - 1) / nInputTapes;
1742 : :
1743 : 240 : nOutputTapes = Min(nOutputRuns, maxOutputTapes);
1744 : :
1745 : : /*
1746 : : * Each output tape consumes TAPE_BUFFER_OVERHEAD bytes of memory. All
1747 : : * remaining memory is divided evenly between the input tapes.
1748 : : *
1749 : : * This also follows from the formula in tuplesort_merge_order, but here
1750 : : * we derive the input buffer size from the amount of memory available,
1751 : : * and M and N.
1752 : : */
1753 : 240 : return Max((avail_mem - TAPE_BUFFER_OVERHEAD * nOutputTapes) / nInputTapes, 0);
1754 : : }
1755 : :
1756 : : /*
1757 : : * inittapes - initialize for tape sorting.
1758 : : *
1759 : : * This is called only if we have found we won't sort in memory.
1760 : : */
1761 : : static void
1762 : 469 : inittapes(Tuplesortstate *state, bool mergeruns)
1763 : : {
1764 : : Assert(!LEADER(state));
1765 : :
1766 [ + + ]: 469 : if (mergeruns)
1767 : : {
1768 : : /* Compute number of input tapes to use when merging */
1769 : 90 : state->maxTapes = tuplesort_merge_order(state->allowedMem);
1770 : : }
1771 : : else
1772 : : {
1773 : : /* Workers can sometimes produce single run, output without merge */
1774 : : Assert(WORKER(state));
1775 : 379 : state->maxTapes = MINORDER;
1776 : : }
1777 : :
1778 [ - + ]: 469 : if (trace_sort)
1779 [ # # ]: 0 : elog(LOG, "worker %d switching to external sort with %d tapes: %s",
1780 : : state->worker, state->maxTapes, pg_rusage_show(&state->ru_start));
1781 : :
1782 : : /* Create the tape set */
1783 : 469 : inittapestate(state, state->maxTapes);
1784 : 469 : state->tapeset =
1785 : 469 : LogicalTapeSetCreate(false,
1786 [ + + ]: 469 : state->shared ? &state->shared->fileset : NULL,
1787 : : state->worker);
1788 : :
1789 : 469 : state->currentRun = 0;
1790 : :
1791 : : /*
1792 : : * Initialize logical tape arrays.
1793 : : */
1794 : 469 : state->inputTapes = NULL;
1795 : 469 : state->nInputTapes = 0;
1796 : 469 : state->nInputRuns = 0;
1797 : :
1798 : 469 : state->outputTapes = palloc0(state->maxTapes * sizeof(LogicalTape *));
1799 : 469 : state->nOutputTapes = 0;
1800 : 469 : state->nOutputRuns = 0;
1801 : :
1802 : 469 : state->status = TSS_BUILDRUNS;
1803 : :
1804 : 469 : selectnewtape(state);
1805 : 469 : }
1806 : :
1807 : : /*
1808 : : * inittapestate - initialize generic tape management state
1809 : : */
1810 : : static void
1811 : 599 : inittapestate(Tuplesortstate *state, int maxTapes)
1812 : : {
1813 : : int64 tapeSpace;
1814 : :
1815 : : /*
1816 : : * Decrease availMem to reflect the space needed for tape buffers; but
1817 : : * don't decrease it to the point that we have no room for tuples. (That
1818 : : * case is only likely to occur if sorting pass-by-value Datums; in all
1819 : : * other scenarios the memtuples[] array is unlikely to occupy more than
1820 : : * half of allowedMem. In the pass-by-value case it's not important to
1821 : : * account for tuple space, so we don't care if LACKMEM becomes
1822 : : * inaccurate.)
1823 : : */
1824 : 599 : tapeSpace = (int64) maxTapes * TAPE_BUFFER_OVERHEAD;
1825 : :
1826 [ + + ]: 599 : if (tapeSpace + GetMemoryChunkSpace(state->memtuples) < state->allowedMem)
1827 : 521 : USEMEM(state, tapeSpace);
1828 : :
1829 : : /*
1830 : : * Make sure that the temp file(s) underlying the tape set are created in
1831 : : * suitable temp tablespaces. For parallel sorts, this should have been
1832 : : * called already, but it doesn't matter if it is called a second time.
1833 : : */
1834 : 599 : PrepareTempTablespaces();
1835 : 599 : }
1836 : :
1837 : : /*
1838 : : * selectnewtape -- select next tape to output to.
1839 : : *
1840 : : * This is called after finishing a run when we know another run
1841 : : * must be started. This is used both when building the initial
1842 : : * runs, and during merge passes.
1843 : : */
1844 : : static void
1845 : 1200 : selectnewtape(Tuplesortstate *state)
1846 : : {
1847 : : /*
1848 : : * At the beginning of each merge pass, nOutputTapes and nOutputRuns are
1849 : : * both zero. On each call, we create a new output tape to hold the next
1850 : : * run, until maxTapes is reached. After that, we assign new runs to the
1851 : : * existing tapes in a round robin fashion.
1852 : : */
1853 [ + + ]: 1200 : if (state->nOutputTapes < state->maxTapes)
1854 : : {
1855 : : /* Create a new tape to hold the next run */
1856 : : Assert(state->outputTapes[state->nOutputRuns] == NULL);
1857 : : Assert(state->nOutputRuns == state->nOutputTapes);
1858 : 812 : state->destTape = LogicalTapeCreate(state->tapeset);
1859 : 812 : state->outputTapes[state->nOutputTapes] = state->destTape;
1860 : 812 : state->nOutputTapes++;
1861 : 812 : state->nOutputRuns++;
1862 : : }
1863 : : else
1864 : : {
1865 : : /*
1866 : : * We have reached the max number of tapes. Append to an existing
1867 : : * tape.
1868 : : */
1869 : 388 : state->destTape = state->outputTapes[state->nOutputRuns % state->nOutputTapes];
1870 : 388 : state->nOutputRuns++;
1871 : : }
1872 : 1200 : }
1873 : :
1874 : : /*
1875 : : * Initialize the slab allocation arena, for the given number of slots.
1876 : : */
1877 : : static void
1878 : 220 : init_slab_allocator(Tuplesortstate *state, int numSlots)
1879 : : {
1880 [ + + ]: 220 : if (numSlots > 0)
1881 : : {
1882 : : char *p;
1883 : : int i;
1884 : :
1885 : 204 : state->slabMemoryBegin = palloc(numSlots * SLAB_SLOT_SIZE);
1886 : 204 : state->slabMemoryEnd = state->slabMemoryBegin +
1887 : 204 : numSlots * SLAB_SLOT_SIZE;
1888 : 204 : state->slabFreeHead = (SlabSlot *) state->slabMemoryBegin;
1889 : 204 : USEMEM(state, numSlots * SLAB_SLOT_SIZE);
1890 : :
1891 : 204 : p = state->slabMemoryBegin;
1892 [ + + ]: 778 : for (i = 0; i < numSlots - 1; i++)
1893 : : {
1894 : 574 : ((SlabSlot *) p)->nextfree = (SlabSlot *) (p + SLAB_SLOT_SIZE);
1895 : 574 : p += SLAB_SLOT_SIZE;
1896 : : }
1897 : 204 : ((SlabSlot *) p)->nextfree = NULL;
1898 : : }
1899 : : else
1900 : : {
1901 : 16 : state->slabMemoryBegin = state->slabMemoryEnd = NULL;
1902 : 16 : state->slabFreeHead = NULL;
1903 : : }
1904 : 220 : state->slabAllocatorUsed = true;
1905 : 220 : }
1906 : :
1907 : : /*
1908 : : * mergeruns -- merge all the completed initial runs.
1909 : : *
1910 : : * This implements the Balanced k-Way Merge Algorithm. All input data has
1911 : : * already been written to initial runs on tape (see dumptuples).
1912 : : */
1913 : : static void
1914 : 220 : mergeruns(Tuplesortstate *state)
1915 : : {
1916 : : int tapenum;
1917 : :
1918 : : Assert(state->status == TSS_BUILDRUNS);
1919 : : Assert(state->memtupcount == 0);
1920 : :
1921 [ + + + + ]: 220 : if (state->base.sortKeys != NULL && state->base.sortKeys->abbrev_converter != NULL)
1922 : : {
1923 : : /*
1924 : : * If there are multiple runs to be merged, when we go to read back
1925 : : * tuples from disk, abbreviated keys will not have been stored, and
1926 : : * we don't care to regenerate them. Disable abbreviation from this
1927 : : * point on.
1928 : : */
1929 : 19 : state->base.sortKeys->abbrev_converter = NULL;
1930 : 19 : state->base.sortKeys->comparator = state->base.sortKeys->abbrev_full_comparator;
1931 : :
1932 : : /* Not strictly necessary, but be tidy */
1933 : 19 : state->base.sortKeys->abbrev_abort = NULL;
1934 : 19 : state->base.sortKeys->abbrev_full_comparator = NULL;
1935 : : }
1936 : :
1937 : : /*
1938 : : * Reset tuple memory. We've freed all the tuples that we previously
1939 : : * allocated. We will use the slab allocator from now on.
1940 : : */
1941 : 220 : MemoryContextResetOnly(state->base.tuplecontext);
1942 : :
1943 : : /*
1944 : : * We no longer need a large memtuples array. (We will allocate a smaller
1945 : : * one for the heap later.)
1946 : : */
1947 : 220 : FREEMEM(state, GetMemoryChunkSpace(state->memtuples));
1948 : 220 : pfree(state->memtuples);
1949 : 220 : state->memtuples = NULL;
1950 : :
1951 : : /*
1952 : : * Initialize the slab allocator. We need one slab slot per input tape,
1953 : : * for the tuples in the heap, plus one to hold the tuple last returned
1954 : : * from tuplesort_gettuple. (If we're sorting pass-by-val Datums,
1955 : : * however, we don't need to do allocate anything.)
1956 : : *
1957 : : * In a multi-pass merge, we could shrink this allocation for the last
1958 : : * merge pass, if it has fewer tapes than previous passes, but we don't
1959 : : * bother.
1960 : : *
1961 : : * From this point on, we no longer use the USEMEM()/LACKMEM() mechanism
1962 : : * to track memory usage of individual tuples.
1963 : : */
1964 [ + + ]: 220 : if (state->base.tuples)
1965 : 204 : init_slab_allocator(state, state->nOutputTapes + 1);
1966 : : else
1967 : 16 : init_slab_allocator(state, 0);
1968 : :
1969 : : /*
1970 : : * Allocate a new 'memtuples' array, for the heap. It will hold one tuple
1971 : : * from each input tape.
1972 : : *
1973 : : * We could shrink this, too, between passes in a multi-pass merge, but we
1974 : : * don't bother. (The initial input tapes are still in outputTapes. The
1975 : : * number of input tapes will not increase between passes.)
1976 : : */
1977 : 220 : state->memtupsize = state->nOutputTapes;
1978 : 440 : state->memtuples = (SortTuple *) MemoryContextAlloc(state->base.maincontext,
1979 : 220 : state->nOutputTapes * sizeof(SortTuple));
1980 : 220 : USEMEM(state, GetMemoryChunkSpace(state->memtuples));
1981 : :
1982 : : /*
1983 : : * Use all the remaining memory we have available for tape buffers among
1984 : : * all the input tapes. At the beginning of each merge pass, we will
1985 : : * divide this memory between the input and output tapes in the pass.
1986 : : */
1987 : 220 : state->tape_buffer_mem = state->availMem;
1988 : 220 : USEMEM(state, state->tape_buffer_mem);
1989 [ - + ]: 220 : if (trace_sort)
1990 [ # # ]: 0 : elog(LOG, "worker %d using %zu KB of memory for tape buffers",
1991 : : state->worker, state->tape_buffer_mem / 1024);
1992 : :
1993 : : for (;;)
1994 : : {
1995 : : /*
1996 : : * On the first iteration, or if we have read all the runs from the
1997 : : * input tapes in a multi-pass merge, it's time to start a new pass.
1998 : : * Rewind all the output tapes, and make them inputs for the next
1999 : : * pass.
2000 : : */
2001 [ + + ]: 312 : if (state->nInputRuns == 0)
2002 : : {
2003 : : int64 input_buffer_size;
2004 : :
2005 : : /* Close the old, emptied, input tapes */
2006 [ + + ]: 240 : if (state->nInputTapes > 0)
2007 : : {
2008 [ + + ]: 140 : for (tapenum = 0; tapenum < state->nInputTapes; tapenum++)
2009 : 120 : LogicalTapeClose(state->inputTapes[tapenum]);
2010 : 20 : pfree(state->inputTapes);
2011 : : }
2012 : :
2013 : : /* Previous pass's outputs become next pass's inputs. */
2014 : 240 : state->inputTapes = state->outputTapes;
2015 : 240 : state->nInputTapes = state->nOutputTapes;
2016 : 240 : state->nInputRuns = state->nOutputRuns;
2017 : :
2018 : : /*
2019 : : * Reset output tape variables. The actual LogicalTapes will be
2020 : : * created as needed, here we only allocate the array to hold
2021 : : * them.
2022 : : */
2023 : 240 : state->outputTapes = palloc0(state->nInputTapes * sizeof(LogicalTape *));
2024 : 240 : state->nOutputTapes = 0;
2025 : 240 : state->nOutputRuns = 0;
2026 : :
2027 : : /*
2028 : : * Redistribute the memory allocated for tape buffers, among the
2029 : : * new input and output tapes.
2030 : : */
2031 : 240 : input_buffer_size = merge_read_buffer_size(state->tape_buffer_mem,
2032 : : state->nInputTapes,
2033 : : state->nInputRuns,
2034 : : state->maxTapes);
2035 : :
2036 [ - + ]: 240 : if (trace_sort)
2037 [ # # ]: 0 : elog(LOG, "starting merge pass of %d input runs on %d tapes, " INT64_FORMAT " KB of memory for each input tape: %s",
2038 : : state->nInputRuns, state->nInputTapes, input_buffer_size / 1024,
2039 : : pg_rusage_show(&state->ru_start));
2040 : :
2041 : : /* Prepare the new input tapes for merge pass. */
2042 [ + + ]: 942 : for (tapenum = 0; tapenum < state->nInputTapes; tapenum++)
2043 : 702 : LogicalTapeRewindForRead(state->inputTapes[tapenum], input_buffer_size);
2044 : :
2045 : : /*
2046 : : * If there's just one run left on each input tape, then only one
2047 : : * merge pass remains. If we don't have to produce a materialized
2048 : : * sorted tape, we can stop at this point and do the final merge
2049 : : * on-the-fly.
2050 : : */
2051 [ + + ]: 240 : if ((state->base.sortopt & TUPLESORT_RANDOMACCESS) == 0
2052 [ + + ]: 226 : && state->nInputRuns <= state->nInputTapes
2053 [ + + + - ]: 206 : && !WORKER(state))
2054 : : {
2055 : : /* Tell logtape.c we won't be writing anymore */
2056 : 206 : LogicalTapeSetForgetFreeSpace(state->tapeset);
2057 : : /* Initialize for the final merge pass */
2058 : 206 : beginmerge(state);
2059 : 206 : state->status = TSS_FINALMERGE;
2060 : 206 : return;
2061 : : }
2062 : : }
2063 : :
2064 : : /* Select an output tape */
2065 : 106 : selectnewtape(state);
2066 : :
2067 : : /* Merge one run from each input tape. */
2068 : 106 : mergeonerun(state);
2069 : :
2070 : : /*
2071 : : * If the input tapes are empty, and we output only one output run,
2072 : : * we're done. The current output tape contains the final result.
2073 : : */
2074 [ + + + + ]: 106 : if (state->nInputRuns == 0 && state->nOutputRuns <= 1)
2075 : 14 : break;
2076 : : }
2077 : :
2078 : : /*
2079 : : * Done. The result is on a single run on a single tape.
2080 : : */
2081 : 14 : state->result_tape = state->outputTapes[0];
2082 [ - + - - ]: 14 : if (!WORKER(state))
2083 : 14 : LogicalTapeFreeze(state->result_tape, NULL);
2084 : : else
2085 : 0 : worker_freeze_result_tape(state);
2086 : 14 : state->status = TSS_SORTEDONTAPE;
2087 : :
2088 : : /* Close all the now-empty input tapes, to release their read buffers. */
2089 [ + + ]: 74 : for (tapenum = 0; tapenum < state->nInputTapes; tapenum++)
2090 : 60 : LogicalTapeClose(state->inputTapes[tapenum]);
2091 : : }
2092 : :
2093 : : /*
2094 : : * Merge one run from each input tape.
2095 : : */
2096 : : static void
2097 : 106 : mergeonerun(Tuplesortstate *state)
2098 : : {
2099 : : int srcTapeIndex;
2100 : : LogicalTape *srcTape;
2101 : :
2102 : : /*
2103 : : * Start the merge by loading one tuple from each active source tape into
2104 : : * the heap.
2105 : : */
2106 : 106 : beginmerge(state);
2107 : :
2108 : : Assert(state->slabAllocatorUsed);
2109 : :
2110 : : /*
2111 : : * Execute merge by repeatedly extracting lowest tuple in heap, writing it
2112 : : * out, and replacing it with next tuple from same tape (if there is
2113 : : * another one).
2114 : : */
2115 [ + + ]: 580394 : while (state->memtupcount > 0)
2116 : : {
2117 : : SortTuple stup;
2118 : :
2119 : : /* write the tuple to destTape */
2120 : 580288 : srcTapeIndex = state->memtuples[0].srctape;
2121 : 580288 : srcTape = state->inputTapes[srcTapeIndex];
2122 : 580288 : WRITETUP(state, state->destTape, &state->memtuples[0]);
2123 : :
2124 : : /* recycle the slot of the tuple we just wrote out, for the next read */
2125 [ + + ]: 580288 : if (state->memtuples[0].tuple)
2126 [ + - + - ]: 490232 : RELEASE_SLAB_SLOT(state, state->memtuples[0].tuple);
2127 : :
2128 : : /*
2129 : : * pull next tuple from the tape, and replace the written-out tuple in
2130 : : * the heap with it.
2131 : : */
2132 [ + + ]: 580288 : if (mergereadnext(state, srcTape, &stup))
2133 : : {
2134 : 579720 : stup.srctape = srcTapeIndex;
2135 : 579720 : tuplesort_heap_replace_top(state, &stup);
2136 : : }
2137 : : else
2138 : : {
2139 : 568 : tuplesort_heap_delete_top(state);
2140 : 568 : state->nInputRuns--;
2141 : : }
2142 : : }
2143 : :
2144 : : /*
2145 : : * When the heap empties, we're done. Write an end-of-run marker on the
2146 : : * output tape.
2147 : : */
2148 : 106 : markrunend(state->destTape);
2149 : 106 : }
2150 : :
2151 : : /*
2152 : : * beginmerge - initialize for a merge pass
2153 : : *
2154 : : * Fill the merge heap with the first tuple from each input tape.
2155 : : */
2156 : : static void
2157 : 312 : beginmerge(Tuplesortstate *state)
2158 : : {
2159 : : int activeTapes;
2160 : : int srcTapeIndex;
2161 : :
2162 : : /* Heap should be empty here */
2163 : : Assert(state->memtupcount == 0);
2164 : :
2165 : 312 : activeTapes = Min(state->nInputTapes, state->nInputRuns);
2166 : :
2167 [ + + ]: 1402 : for (srcTapeIndex = 0; srcTapeIndex < activeTapes; srcTapeIndex++)
2168 : : {
2169 : : SortTuple tup;
2170 : :
2171 [ + + ]: 1090 : if (mergereadnext(state, state->inputTapes[srcTapeIndex], &tup))
2172 : : {
2173 : 886 : tup.srctape = srcTapeIndex;
2174 : 886 : tuplesort_heap_insert(state, &tup);
2175 : : }
2176 : : }
2177 : 312 : }
2178 : :
2179 : : /*
2180 : : * mergereadnext - read next tuple from one merge input tape
2181 : : *
2182 : : * Returns false on EOF.
2183 : : */
2184 : : static bool
2185 : 3326051 : mergereadnext(Tuplesortstate *state, LogicalTape *srcTape, SortTuple *stup)
2186 : : {
2187 : : unsigned int tuplen;
2188 : :
2189 : : /* read next tuple, if any */
2190 [ + + ]: 3326051 : if ((tuplen = getlen(srcTape, true)) == 0)
2191 : 1058 : return false;
2192 : 3324993 : READTUP(state, stup, srcTape, tuplen);
2193 : :
2194 : 3324993 : return true;
2195 : : }
2196 : :
2197 : : /*
2198 : : * dumptuples - remove tuples from memtuples and write initial run to tape
2199 : : *
2200 : : * When alltuples = true, dump everything currently in memory. (This case is
2201 : : * only used at end of input data.)
2202 : : */
2203 : : static void
2204 : 734632 : dumptuples(Tuplesortstate *state, bool alltuples)
2205 : : {
2206 : : int memtupwrite;
2207 : : int i;
2208 : :
2209 : : /*
2210 : : * Nothing to do if we still fit in available memory and have array slots,
2211 : : * unless this is the final call during initial run generation.
2212 : : */
2213 [ + + + + : 734632 : if (state->memtupcount < state->memtupsize && !LACKMEM(state) &&
- + ]
2214 [ + + ]: 734007 : !alltuples)
2215 : 733538 : return;
2216 : :
2217 : : /*
2218 : : * Final call might require no sorting, in rare cases where we just so
2219 : : * happen to have previously LACKMEM()'d at the point where exactly all
2220 : : * remaining tuples are loaded into memory, just before input was
2221 : : * exhausted. In general, short final runs are quite possible, but avoid
2222 : : * creating a completely empty run. In a worker, though, we must produce
2223 : : * at least one tape, even if it's empty.
2224 : : */
2225 [ + + - + ]: 1094 : if (state->memtupcount == 0 && state->currentRun > 0)
2226 : 0 : return;
2227 : :
2228 : : Assert(state->status == TSS_BUILDRUNS);
2229 : :
2230 : : /*
2231 : : * It seems unlikely that this limit will ever be exceeded, but take no
2232 : : * chances
2233 : : */
2234 [ - + ]: 1094 : if (state->currentRun == INT_MAX)
2235 [ # # ]: 0 : ereport(ERROR,
2236 : : (errcode(ERRCODE_PROGRAM_LIMIT_EXCEEDED),
2237 : : errmsg("cannot have more than %d runs for an external sort",
2238 : : INT_MAX)));
2239 : :
2240 [ + + ]: 1094 : if (state->currentRun > 0)
2241 : 625 : selectnewtape(state);
2242 : :
2243 : 1094 : state->currentRun++;
2244 : :
2245 [ - + ]: 1094 : if (trace_sort)
2246 [ # # ]: 0 : elog(LOG, "worker %d starting quicksort of run %d: %s",
2247 : : state->worker, state->currentRun,
2248 : : pg_rusage_show(&state->ru_start));
2249 : :
2250 : : /*
2251 : : * Sort all tuples accumulated within the allowed amount of memory for
2252 : : * this run.
2253 : : */
2254 : 1094 : tuplesort_sort_memtuples(state);
2255 : :
2256 [ - + ]: 1094 : if (trace_sort)
2257 [ # # ]: 0 : elog(LOG, "worker %d finished quicksort of run %d: %s",
2258 : : state->worker, state->currentRun,
2259 : : pg_rusage_show(&state->ru_start));
2260 : :
2261 : 1094 : memtupwrite = state->memtupcount;
2262 [ + + ]: 3085935 : for (i = 0; i < memtupwrite; i++)
2263 : : {
2264 : 3084841 : SortTuple *stup = &state->memtuples[i];
2265 : :
2266 : 3084841 : WRITETUP(state, state->destTape, stup);
2267 : : }
2268 : :
2269 : 1094 : state->memtupcount = 0;
2270 : :
2271 : : /*
2272 : : * Reset tuple memory. We've freed all of the tuples that we previously
2273 : : * allocated. It's important to avoid fragmentation when there is a stark
2274 : : * change in the sizes of incoming tuples. In bounded sorts,
2275 : : * fragmentation due to AllocSetFree's bucketing by size class might be
2276 : : * particularly bad if this step wasn't taken.
2277 : : */
2278 : 1094 : MemoryContextReset(state->base.tuplecontext);
2279 : :
2280 : : /*
2281 : : * Now update the memory accounting to subtract the memory used by the
2282 : : * tuple.
2283 : : */
2284 : 1094 : FREEMEM(state, state->tupleMem);
2285 : 1094 : state->tupleMem = 0;
2286 : :
2287 : 1094 : markrunend(state->destTape);
2288 : :
2289 [ - + ]: 1094 : if (trace_sort)
2290 [ # # ]: 0 : elog(LOG, "worker %d finished writing run %d to tape %d: %s",
2291 : : state->worker, state->currentRun, (state->currentRun - 1) % state->nOutputTapes + 1,
2292 : : pg_rusage_show(&state->ru_start));
2293 : : }
2294 : :
2295 : : /*
2296 : : * tuplesort_rescan - rewind and replay the scan
2297 : : */
2298 : : void
2299 : 35 : tuplesort_rescan(Tuplesortstate *state)
2300 : : {
2301 : 35 : MemoryContext oldcontext = MemoryContextSwitchTo(state->base.sortcontext);
2302 : :
2303 : : Assert(state->base.sortopt & TUPLESORT_RANDOMACCESS);
2304 : :
2305 [ + + - ]: 35 : switch (state->status)
2306 : : {
2307 : 30 : case TSS_SORTEDINMEM:
2308 : 30 : state->current = 0;
2309 : 30 : state->eof_reached = false;
2310 : 30 : state->markpos_offset = 0;
2311 : 30 : state->markpos_eof = false;
2312 : 30 : break;
2313 : 5 : case TSS_SORTEDONTAPE:
2314 : 5 : LogicalTapeRewindForRead(state->result_tape, 0);
2315 : 5 : state->eof_reached = false;
2316 : 5 : state->markpos_block = 0L;
2317 : 5 : state->markpos_offset = 0;
2318 : 5 : state->markpos_eof = false;
2319 : 5 : break;
2320 : 0 : default:
2321 [ # # ]: 0 : elog(ERROR, "invalid tuplesort state");
2322 : : break;
2323 : : }
2324 : :
2325 : 35 : MemoryContextSwitchTo(oldcontext);
2326 : 35 : }
2327 : :
2328 : : /*
2329 : : * tuplesort_markpos - saves current position in the merged sort file
2330 : : */
2331 : : void
2332 : 389424 : tuplesort_markpos(Tuplesortstate *state)
2333 : : {
2334 : 389424 : MemoryContext oldcontext = MemoryContextSwitchTo(state->base.sortcontext);
2335 : :
2336 : : Assert(state->base.sortopt & TUPLESORT_RANDOMACCESS);
2337 : :
2338 [ + + - ]: 389424 : switch (state->status)
2339 : : {
2340 : 383552 : case TSS_SORTEDINMEM:
2341 : 383552 : state->markpos_offset = state->current;
2342 : 383552 : state->markpos_eof = state->eof_reached;
2343 : 383552 : break;
2344 : 5872 : case TSS_SORTEDONTAPE:
2345 : 5872 : LogicalTapeTell(state->result_tape,
2346 : : &state->markpos_block,
2347 : : &state->markpos_offset);
2348 : 5872 : state->markpos_eof = state->eof_reached;
2349 : 5872 : break;
2350 : 0 : default:
2351 [ # # ]: 0 : elog(ERROR, "invalid tuplesort state");
2352 : : break;
2353 : : }
2354 : :
2355 : 389424 : MemoryContextSwitchTo(oldcontext);
2356 : 389424 : }
2357 : :
2358 : : /*
2359 : : * tuplesort_restorepos - restores current position in merged sort file to
2360 : : * last saved position
2361 : : */
2362 : : void
2363 : 24692 : tuplesort_restorepos(Tuplesortstate *state)
2364 : : {
2365 : 24692 : MemoryContext oldcontext = MemoryContextSwitchTo(state->base.sortcontext);
2366 : :
2367 : : Assert(state->base.sortopt & TUPLESORT_RANDOMACCESS);
2368 : :
2369 [ + + - ]: 24692 : switch (state->status)
2370 : : {
2371 : 20564 : case TSS_SORTEDINMEM:
2372 : 20564 : state->current = state->markpos_offset;
2373 : 20564 : state->eof_reached = state->markpos_eof;
2374 : 20564 : break;
2375 : 4128 : case TSS_SORTEDONTAPE:
2376 : 4128 : LogicalTapeSeek(state->result_tape,
2377 : : state->markpos_block,
2378 : : state->markpos_offset);
2379 : 4128 : state->eof_reached = state->markpos_eof;
2380 : 4128 : break;
2381 : 0 : default:
2382 [ # # ]: 0 : elog(ERROR, "invalid tuplesort state");
2383 : : break;
2384 : : }
2385 : :
2386 : 24692 : MemoryContextSwitchTo(oldcontext);
2387 : 24692 : }
2388 : :
2389 : : /*
2390 : : * tuplesort_get_stats - extract summary statistics
2391 : : *
2392 : : * This can be called after tuplesort_performsort() finishes to obtain
2393 : : * printable summary information about how the sort was performed.
2394 : : */
2395 : : void
2396 : 264 : tuplesort_get_stats(Tuplesortstate *state,
2397 : : TuplesortInstrumentation *stats)
2398 : : {
2399 : : /*
2400 : : * Note: it might seem we should provide both memory and disk usage for a
2401 : : * disk-based sort. However, the current code doesn't track memory space
2402 : : * accurately once we have begun to return tuples to the caller (since we
2403 : : * don't account for pfree's the caller is expected to do), so we cannot
2404 : : * rely on availMem in a disk sort. This does not seem worth the overhead
2405 : : * to fix. Is it worth creating an API for the memory context code to
2406 : : * tell us how much is actually used in sortcontext?
2407 : : */
2408 : 264 : tuplesort_updatemax(state);
2409 : :
2410 [ + + ]: 264 : if (state->isMaxSpaceDisk)
2411 : 4 : stats->spaceType = SORT_SPACE_TYPE_DISK;
2412 : : else
2413 : 260 : stats->spaceType = SORT_SPACE_TYPE_MEMORY;
2414 : 264 : stats->spaceUsed = (state->maxSpace + 1023) / 1024;
2415 : :
2416 [ + - + - ]: 264 : switch (state->maxSpaceStatus)
2417 : : {
2418 : 260 : case TSS_SORTEDINMEM:
2419 [ + + ]: 260 : if (state->boundUsed)
2420 : 28 : stats->sortMethod = SORT_TYPE_TOP_N_HEAPSORT;
2421 : : else
2422 : 232 : stats->sortMethod = SORT_TYPE_QUICKSORT;
2423 : 260 : break;
2424 : 0 : case TSS_SORTEDONTAPE:
2425 : 0 : stats->sortMethod = SORT_TYPE_EXTERNAL_SORT;
2426 : 0 : break;
2427 : 4 : case TSS_FINALMERGE:
2428 : 4 : stats->sortMethod = SORT_TYPE_EXTERNAL_MERGE;
2429 : 4 : break;
2430 : 0 : default:
2431 : 0 : stats->sortMethod = SORT_TYPE_STILL_IN_PROGRESS;
2432 : 0 : break;
2433 : : }
2434 : 264 : }
2435 : :
2436 : : /*
2437 : : * Convert TuplesortMethod to a string.
2438 : : */
2439 : : const char *
2440 : 196 : tuplesort_method_name(TuplesortMethod m)
2441 : : {
2442 [ - + + - : 196 : switch (m)
+ - ]
2443 : : {
2444 : 0 : case SORT_TYPE_STILL_IN_PROGRESS:
2445 : 0 : return "still in progress";
2446 : 28 : case SORT_TYPE_TOP_N_HEAPSORT:
2447 : 28 : return "top-N heapsort";
2448 : 164 : case SORT_TYPE_QUICKSORT:
2449 : 164 : return "quicksort";
2450 : 0 : case SORT_TYPE_EXTERNAL_SORT:
2451 : 0 : return "external sort";
2452 : 4 : case SORT_TYPE_EXTERNAL_MERGE:
2453 : 4 : return "external merge";
2454 : : }
2455 : :
2456 : 0 : return "unknown";
2457 : : }
2458 : :
2459 : : /*
2460 : : * Convert TuplesortSpaceType to a string.
2461 : : */
2462 : : const char *
2463 : 172 : tuplesort_space_type_name(TuplesortSpaceType t)
2464 : : {
2465 : : Assert(t == SORT_SPACE_TYPE_DISK || t == SORT_SPACE_TYPE_MEMORY);
2466 [ + + ]: 172 : return t == SORT_SPACE_TYPE_DISK ? "Disk" : "Memory";
2467 : : }
2468 : :
2469 : :
2470 : : /*
2471 : : * Heap manipulation routines, per Knuth's Algorithm 5.2.3H.
2472 : : */
2473 : :
2474 : : /*
2475 : : * Convert the existing unordered array of SortTuples to a bounded heap,
2476 : : * discarding all but the smallest "state->bound" tuples.
2477 : : *
2478 : : * When working with a bounded heap, we want to keep the largest entry
2479 : : * at the root (array entry zero), instead of the smallest as in the normal
2480 : : * sort case. This allows us to discard the largest entry cheaply.
2481 : : * Therefore, we temporarily reverse the sort direction.
2482 : : */
2483 : : static void
2484 : 256 : make_bounded_heap(Tuplesortstate *state)
2485 : : {
2486 : 256 : int tupcount = state->memtupcount;
2487 : : int i;
2488 : :
2489 : : Assert(state->status == TSS_INITIAL);
2490 : : Assert(state->bounded);
2491 : : Assert(tupcount >= state->bound);
2492 : : Assert(SERIAL(state));
2493 : :
2494 : : /* Reverse sort direction so largest entry will be at root */
2495 : 256 : reversedirection(state);
2496 : :
2497 : 256 : state->memtupcount = 0; /* make the heap empty */
2498 [ + + ]: 24274 : for (i = 0; i < tupcount; i++)
2499 : : {
2500 [ + + ]: 24018 : if (state->memtupcount < state->bound)
2501 : : {
2502 : : /* Insert next tuple into heap */
2503 : : /* Must copy source tuple to avoid possible overwrite */
2504 : 11881 : SortTuple stup = state->memtuples[i];
2505 : :
2506 : 11881 : tuplesort_heap_insert(state, &stup);
2507 : : }
2508 : : else
2509 : : {
2510 : : /*
2511 : : * The heap is full. Replace the largest entry with the new
2512 : : * tuple, or just discard it, if it's larger than anything already
2513 : : * in the heap.
2514 : : */
2515 [ + + ]: 12137 : if (COMPARETUP(state, &state->memtuples[i], &state->memtuples[0]) <= 0)
2516 : : {
2517 : 6069 : free_sort_tuple(state, &state->memtuples[i]);
2518 [ - + ]: 6069 : CHECK_FOR_INTERRUPTS();
2519 : : }
2520 : : else
2521 : 6068 : tuplesort_heap_replace_top(state, &state->memtuples[i]);
2522 : : }
2523 : : }
2524 : :
2525 : : Assert(state->memtupcount == state->bound);
2526 : 256 : state->status = TSS_BOUNDED;
2527 : 256 : }
2528 : :
2529 : : /*
2530 : : * Convert the bounded heap to a properly-sorted array
2531 : : */
2532 : : static void
2533 : 256 : sort_bounded_heap(Tuplesortstate *state)
2534 : : {
2535 : 256 : int tupcount = state->memtupcount;
2536 : :
2537 : : Assert(state->status == TSS_BOUNDED);
2538 : : Assert(state->bounded);
2539 : : Assert(tupcount == state->bound);
2540 : : Assert(SERIAL(state));
2541 : :
2542 : : /*
2543 : : * We can unheapify in place because each delete-top call will remove the
2544 : : * largest entry, which we can promptly store in the newly freed slot at
2545 : : * the end. Once we're down to a single-entry heap, we're done.
2546 : : */
2547 [ + + ]: 11881 : while (state->memtupcount > 1)
2548 : : {
2549 : 11625 : SortTuple stup = state->memtuples[0];
2550 : :
2551 : : /* this sifts-up the next-largest entry and decreases memtupcount */
2552 : 11625 : tuplesort_heap_delete_top(state);
2553 : 11625 : state->memtuples[state->memtupcount] = stup;
2554 : : }
2555 : 256 : state->memtupcount = tupcount;
2556 : :
2557 : : /*
2558 : : * Reverse sort direction back to the original state. This is not
2559 : : * actually necessary but seems like a good idea for tidiness.
2560 : : */
2561 : 256 : reversedirection(state);
2562 : :
2563 : 256 : state->status = TSS_SORTEDINMEM;
2564 : 256 : state->boundUsed = true;
2565 : 256 : }
2566 : :
2567 : :
2568 : : /* radix sort routines */
2569 : :
2570 : : /*
2571 : : * Retrieve byte from datum, indexed by 'level': 0 for MSB, 7 for LSB
2572 : : */
2573 : : static inline uint8
2574 : 26194133 : current_byte(Datum key, int level)
2575 : : {
2576 : 26194133 : int shift = (sizeof(Datum) - 1 - level) * BITS_PER_BYTE;
2577 : :
2578 : 26194133 : return (key >> shift) & 0xFF;
2579 : : }
2580 : :
2581 : : /*
2582 : : * Normalize datum such that unsigned comparison is order-preserving,
2583 : : * taking ASC/DESC into account as well.
2584 : : */
2585 : : static inline Datum
2586 : 26240689 : normalize_datum(Datum orig, SortSupport ssup)
2587 : : {
2588 : : Datum norm_datum1;
2589 : :
2590 [ + + ]: 26240689 : if (ssup->comparator == ssup_datum_signed_cmp)
2591 : : {
2592 : 1099950 : norm_datum1 = orig + (Int64GetDatum(PG_INT64_MAX)) + 1;
2593 : : }
2594 [ + + ]: 25140739 : else if (ssup->comparator == ssup_datum_int32_cmp)
2595 : : {
2596 : : /*
2597 : : * First truncate to uint32. Technically, we don't need to do this,
2598 : : * but it forces the upper half of the datum to be zero regardless of
2599 : : * sign.
2600 : : */
2601 : 7731082 : uint32 u32 = DatumGetUInt32(orig) + ((uint32) PG_INT32_MAX) + 1;
2602 : :
2603 : 7731082 : norm_datum1 = UInt32GetDatum(u32);
2604 : : }
2605 : : else
2606 : : {
2607 : : Assert(ssup->comparator == ssup_datum_unsigned_cmp);
2608 : 17409657 : norm_datum1 = orig;
2609 : : }
2610 : :
2611 [ + + ]: 26240689 : if (ssup->ssup_reverse)
2612 : 804815 : norm_datum1 = ~norm_datum1;
2613 : :
2614 : 26240689 : return norm_datum1;
2615 : : }
2616 : :
2617 : : /*
2618 : : * radix_sort_recursive
2619 : : *
2620 : : * Radix sort by (pass-by-value) datum1, diverting to qsort_tuple()
2621 : : * for tiebreaks.
2622 : : *
2623 : : * This is a modification of ska_byte_sort() from
2624 : : * https://github.com/skarupke/ska_sort
2625 : : * The original copyright notice follows:
2626 : : *
2627 : : * Copyright Malte Skarupke 2016.
2628 : : * Distributed under the Boost Software License, Version 1.0.
2629 : : *
2630 : : * Boost Software License - Version 1.0 - August 17th, 2003
2631 : : *
2632 : : * Permission is hereby granted, free of charge, to any person or organization
2633 : : * obtaining a copy of the software and accompanying documentation covered by
2634 : : * this license (the "Software") to use, reproduce, display, distribute,
2635 : : * execute, and transmit the Software, and to prepare derivative works of the
2636 : : * Software, and to permit third-parties to whom the Software is furnished to
2637 : : * do so, all subject to the following:
2638 : : *
2639 : : * The copyright notices in the Software and this entire statement, including
2640 : : * the above license grant, this restriction and the following disclaimer,
2641 : : * must be included in all copies of the Software, in whole or in part, and
2642 : : * all derivative works of the Software, unless such copies or derivative
2643 : : * works are solely in the form of machine-executable object code generated by
2644 : : * a source language processor.
2645 : : *
2646 : : * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
2647 : : * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
2648 : : * FITNESS FOR A PARTICULAR PURPOSE, TITLE AND NON-INFRINGEMENT. IN NO EVENT
2649 : : * SHALL THE COPYRIGHT HOLDERS OR ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE
2650 : : * FOR ANY DAMAGES OR OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE,
2651 : : * ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
2652 : : * DEALINGS IN THE SOFTWARE.
2653 : : */
2654 : : static void
2655 : 46556 : radix_sort_recursive(SortTuple *begin, size_t n_elems, int level, Tuplesortstate *state)
2656 : : {
2657 : 46556 : RadixSortInfo partitions[256] = {0};
2658 : : uint8 remaining_partitions[256];
2659 : 46556 : size_t total = 0;
2660 : 46556 : int num_partitions = 0;
2661 : : int num_remaining;
2662 : 46556 : SortSupport ssup = &state->base.sortKeys[0];
2663 : : Datum ref_datum;
2664 : 46556 : Datum common_upper_bits = 0;
2665 : 46556 : size_t start_offset = 0;
2666 : 46556 : SortTuple *partition_begin = begin;
2667 : : int next_level;
2668 : :
2669 : : /* count number of occurrences of each byte */
2670 : 46556 : ref_datum = normalize_datum(begin[0].datum1, ssup);
2671 [ + + ]: 26240689 : for (SortTuple *st = begin; st < begin + n_elems; st++)
2672 : : {
2673 : : Datum this_datum;
2674 : : uint8 this_partition;
2675 : :
2676 : 26194133 : this_datum = normalize_datum(st->datum1, ssup);
2677 : : /* accumulate bits different from the reference datum */
2678 : 26194133 : common_upper_bits |= ref_datum ^ this_datum;
2679 : :
2680 : : /* extract the byte for this level from the normalized datum */
2681 : 26194133 : this_partition = current_byte(this_datum, level);
2682 : :
2683 : : /* save it for the permutation step */
2684 : 26194133 : st->curbyte = this_partition;
2685 : :
2686 : 26194133 : partitions[this_partition].count++;
2687 : :
2688 [ + + ]: 26194133 : CHECK_FOR_INTERRUPTS();
2689 : : }
2690 : :
2691 : : /* compute partition offsets */
2692 [ + + ]: 11964892 : for (int i = 0; i < 256; i++)
2693 : : {
2694 : 11918336 : size_t count = partitions[i].count;
2695 : :
2696 [ + + ]: 11918336 : if (count != 0)
2697 : : {
2698 : 2619806 : partitions[i].offset = total;
2699 : 2619806 : total += count;
2700 : 2619806 : remaining_partitions[num_partitions] = i;
2701 : 2619806 : num_partitions++;
2702 : : }
2703 : 11918336 : partitions[i].next_offset = total;
2704 : : }
2705 : :
2706 : : /*
2707 : : * Swap tuples to correct partition.
2708 : : *
2709 : : * In traditional American flag sort, a swap sends the current element to
2710 : : * the correct partition, but the array pointer only advances if the
2711 : : * partner of the swap happens to be an element that belongs in the
2712 : : * current partition. That only requires one pass through the array, but
2713 : : * the disadvantage is we don't know if the pointer can advance until the
2714 : : * swap completes. Here lies the most interesting innovation from the
2715 : : * upstream ska_byte_sort: After initiating the swap, we immediately
2716 : : * proceed to the next element. This makes better use of CPU pipelining,
2717 : : * but also means that we will often need multiple iterations of this
2718 : : * loop. ska_byte_sort() maintains a separate list of which partitions
2719 : : * haven't finished, which is updated every loop iteration. Here we simply
2720 : : * check each partition during every iteration.
2721 : : *
2722 : : * If we started with a single partition, there is nothing to do. If a
2723 : : * previous loop iteration results in only one partition that hasn't been
2724 : : * counted as sorted, we know it's actually sorted and can exit the loop.
2725 : : */
2726 : 46556 : num_remaining = num_partitions;
2727 [ + + ]: 192832 : while (num_remaining > 1)
2728 : : {
2729 : : /* start the count over */
2730 : 146276 : num_remaining = num_partitions;
2731 : :
2732 [ + + ]: 11630627 : for (int i = 0; i < num_partitions; i++)
2733 : : {
2734 : 11484351 : uint8 idx = remaining_partitions[i];
2735 : :
2736 : 11484351 : for (SortTuple *st = begin + partitions[idx].offset;
2737 [ + + ]: 28433415 : st < begin + partitions[idx].next_offset;
2738 : 16949064 : st++)
2739 : : {
2740 : 16949064 : size_t offset = partitions[st->curbyte].offset++;
2741 : : SortTuple tmp;
2742 : :
2743 : : /* swap current tuple with destination position */
2744 : : Assert(offset < n_elems);
2745 : 16949064 : tmp = *st;
2746 : 16949064 : *st = begin[offset];
2747 : 16949064 : begin[offset] = tmp;
2748 : :
2749 [ + + ]: 16949064 : CHECK_FOR_INTERRUPTS();
2750 : : };
2751 : :
2752 : : /* Is this partition sorted? */
2753 [ + + ]: 11484351 : if (partitions[idx].offset == partitions[idx].next_offset)
2754 : 8856995 : num_remaining--;
2755 : : }
2756 : : }
2757 : :
2758 : : /* recurse */
2759 : :
2760 [ + + ]: 46556 : if (num_partitions == 1)
2761 : : {
2762 : : /*
2763 : : * There is only one distinct byte at the current level. It can happen
2764 : : * that some subsequent bytes are also the same for all input values,
2765 : : * such as the upper bytes of small integers. To skip unproductive
2766 : : * passes for that case, we compute the level where the input has more
2767 : : * than one distinct byte, so that the next recursion can start there.
2768 : : */
2769 [ + + ]: 9099 : if (common_upper_bits == 0)
2770 : 963 : next_level = sizeof(Datum);
2771 : : else
2772 : : {
2773 : : int diffpos;
2774 : :
2775 : : /*
2776 : : * The upper bits of common_upper_bits are zero where all datums
2777 : : * have the same bits.
2778 : : */
2779 : 8136 : diffpos = pg_leftmost_one_pos64(DatumGetUInt64(common_upper_bits));
2780 : 8136 : next_level = sizeof(Datum) - 1 - (diffpos / BITS_PER_BYTE);
2781 : : }
2782 : : }
2783 : : else
2784 : 37457 : next_level = level + 1;
2785 : :
2786 : : Assert(next_level > level);
2787 : :
2788 : 46556 : for (uint8 *rp = remaining_partitions;
2789 [ + + ]: 2666362 : rp < remaining_partitions + num_partitions;
2790 : 2619806 : rp++)
2791 : : {
2792 : 2619806 : size_t end_offset = partitions[*rp].next_offset;
2793 : 2619806 : SortTuple *partition_end = begin + end_offset;
2794 : 2619806 : size_t num_elements = end_offset - start_offset;
2795 : :
2796 [ + + ]: 2619806 : if (num_elements > 1)
2797 : : {
2798 [ + + ]: 868736 : if (next_level < sizeof(Datum))
2799 : : {
2800 [ + + ]: 681625 : if (num_elements < QSORT_THRESHOLD)
2801 : : {
2802 : 640365 : qsort_tuple(partition_begin,
2803 : : num_elements,
2804 : : state->base.comparetup,
2805 : : state);
2806 : : }
2807 : : else
2808 : : {
2809 : 41260 : radix_sort_recursive(partition_begin,
2810 : : num_elements,
2811 : : next_level,
2812 : : state);
2813 : : }
2814 : : }
2815 [ + + ]: 187111 : else if (state->base.onlyKey == NULL)
2816 : : {
2817 : : /*
2818 : : * We've finished radix sort on all bytes of the pass-by-value
2819 : : * datum (possibly abbreviated), now sort using the tiebreak
2820 : : * comparator.
2821 : : */
2822 : 93272 : qsort_tuple(partition_begin,
2823 : : num_elements,
2824 : : state->base.comparetup_tiebreak,
2825 : : state);
2826 : : }
2827 : : }
2828 : :
2829 : 2619806 : start_offset = end_offset;
2830 : 2619806 : partition_begin = partition_end;
2831 : : }
2832 : 46556 : }
2833 : :
2834 : : /*
2835 : : * Entry point for radix_sort_recursive
2836 : : *
2837 : : * Partition tuples by isnull1, then sort both partitions, using
2838 : : * radix sort on the NOT NULL partition if it's large enough.
2839 : : */
2840 : : static void
2841 : 7111 : radix_sort_tuple(SortTuple *data, size_t n, Tuplesortstate *state)
2842 : : {
2843 : 7111 : bool nulls_first = state->base.sortKeys[0].ssup_nulls_first;
2844 : : SortTuple *null_start;
2845 : : SortTuple *not_null_start;
2846 : 7111 : size_t d1 = 0,
2847 : : d2,
2848 : : null_count,
2849 : : not_null_count;
2850 : :
2851 : : /*
2852 : : * Find the first NOT NULL if NULLS FIRST, or first NULL if NULLS LAST.
2853 : : * This also serves as a quick check for the common case where all tuples
2854 : : * are NOT NULL in the first sort key with the default order ASC NULLS
2855 : : * LAST.
2856 : : */
2857 [ + + + + ]: 12916466 : while (d1 < n && data[d1].isnull1 == nulls_first)
2858 : : {
2859 : 12909355 : d1++;
2860 [ - + ]: 12909355 : CHECK_FOR_INTERRUPTS();
2861 : : }
2862 : :
2863 : : /*
2864 : : * If we have more than one tuple left after the quick check, partition
2865 : : * the remainder using branchless cyclic permutation, based on
2866 : : * https://orlp.net/blog/branchless-lomuto-partitioning/
2867 : : */
2868 : : Assert(n > 0);
2869 [ + + ]: 7111 : if (d1 < n - 1)
2870 : : {
2871 : 737 : size_t i = d1,
2872 : 737 : j = d1;
2873 : 737 : SortTuple tmp = data[d1]; /* create gap at front */
2874 : :
2875 [ + + ]: 443839 : while (j < n - 1)
2876 : : {
2877 : : /* gap is at j, move i's element to gap */
2878 : 443102 : data[j] = data[i];
2879 : : /* advance j to the first unknown element */
2880 : 443102 : j += 1;
2881 : : /* move the first unknown element back to i */
2882 : 443102 : data[i] = data[j];
2883 : : /* advance i if this element belongs in the left partition */
2884 : 443102 : i += (data[i].isnull1 == nulls_first);
2885 : :
2886 [ - + ]: 443102 : CHECK_FOR_INTERRUPTS();
2887 : : }
2888 : :
2889 : : /* place gap between left and right partitions */
2890 : 737 : data[j] = data[i];
2891 : : /* restore the saved element */
2892 : 737 : data[i] = tmp;
2893 : : /* assign it to the correct partition */
2894 : 737 : i += (data[i].isnull1 == nulls_first);
2895 : :
2896 : : /* d1 is now the number of elements in the left partition */
2897 : 737 : d1 = i;
2898 : : }
2899 : :
2900 : 7111 : d2 = n - d1;
2901 : :
2902 : : /* set pointers and counts for each partition */
2903 [ + + ]: 7111 : if (nulls_first)
2904 : : {
2905 : 632 : null_start = data;
2906 : 632 : null_count = d1;
2907 : 632 : not_null_start = data + d1;
2908 : 632 : not_null_count = d2;
2909 : : }
2910 : : else
2911 : : {
2912 : 6479 : not_null_start = data;
2913 : 6479 : not_null_count = d1;
2914 : 6479 : null_start = data + d1;
2915 : 6479 : null_count = d2;
2916 : : }
2917 : :
2918 : 7111 : for (SortTuple *st = null_start;
2919 [ + + ]: 9518 : st < null_start + null_count;
2920 : 2407 : st++)
2921 : : Assert(st->isnull1 == true);
2922 : 7111 : for (SortTuple *st = not_null_start;
2923 [ + + ]: 13357902 : st < not_null_start + not_null_count;
2924 : 13350791 : st++)
2925 : : Assert(st->isnull1 == false);
2926 : :
2927 : : /*
2928 : : * Sort the NULL partition using tiebreak comparator, if necessary.
2929 : : */
2930 [ + + + + ]: 7111 : if (state->base.onlyKey == NULL && null_count > 1)
2931 : : {
2932 : 111 : qsort_tuple(null_start,
2933 : : null_count,
2934 : : state->base.comparetup_tiebreak,
2935 : : state);
2936 : : }
2937 : :
2938 : : /*
2939 : : * Sort the NOT NULL partition, using radix sort if large enough,
2940 : : * otherwise fall back to quicksort.
2941 : : */
2942 [ + + ]: 7111 : if (not_null_count < QSORT_THRESHOLD)
2943 : : {
2944 : 14 : qsort_tuple(not_null_start,
2945 : : not_null_count,
2946 : : state->base.comparetup,
2947 : : state);
2948 : : }
2949 : : else
2950 : : {
2951 : 7097 : bool presorted = true;
2952 : :
2953 : 7097 : for (SortTuple *st = not_null_start + 1;
2954 [ + + ]: 4733100 : st < not_null_start + not_null_count;
2955 : 4726003 : st++)
2956 : : {
2957 [ + + ]: 4731299 : if (COMPARETUP(state, st - 1, st) > 0)
2958 : : {
2959 : 5296 : presorted = false;
2960 : 5296 : break;
2961 : : }
2962 : :
2963 [ - + ]: 4726003 : CHECK_FOR_INTERRUPTS();
2964 : : }
2965 : :
2966 [ + + ]: 7097 : if (presorted)
2967 : 1801 : return;
2968 : : else
2969 : : {
2970 : 5296 : radix_sort_recursive(not_null_start,
2971 : : not_null_count,
2972 : : 0,
2973 : : state);
2974 : : }
2975 : : }
2976 : : }
2977 : :
2978 : : /* Verify in-memory sort using standard comparator. */
2979 : : static void
2980 : 7111 : verify_memtuples_sorted(Tuplesortstate *state)
2981 : : {
2982 : : #ifdef USE_ASSERT_CHECKING
2983 : : for (SortTuple *st = state->memtuples + 1;
2984 : : st < state->memtuples + state->memtupcount;
2985 : : st++)
2986 : : Assert(COMPARETUP(state, st - 1, st) <= 0);
2987 : : #endif
2988 : 7111 : }
2989 : :
2990 : : /*
2991 : : * Sort all memtuples using specialized routines.
2992 : : *
2993 : : * Quicksort or radix sort is used for small in-memory sorts,
2994 : : * and external sort runs.
2995 : : */
2996 : : static void
2997 : 151167 : tuplesort_sort_memtuples(Tuplesortstate *state)
2998 : : {
2999 : : Assert(!LEADER(state));
3000 : :
3001 [ + + ]: 151167 : if (state->memtupcount > 1)
3002 : : {
3003 : : /*
3004 : : * Do we have the leading column's value or abbreviation in datum1?
3005 : : */
3006 [ + + + + ]: 44742 : if (state->base.haveDatum1 && state->base.sortKeys)
3007 : : {
3008 : 44669 : SortSupport ssup = &state->base.sortKeys[0];
3009 : :
3010 : : /* Does it compare as an integer? */
3011 [ + + ]: 44669 : if (state->memtupcount >= QSORT_THRESHOLD &&
3012 [ + + ]: 8186 : (ssup->comparator == ssup_datum_unsigned_cmp ||
3013 [ + + ]: 4187 : ssup->comparator == ssup_datum_signed_cmp ||
3014 [ + + ]: 4012 : ssup->comparator == ssup_datum_int32_cmp))
3015 : : {
3016 : 7111 : radix_sort_tuple(state->memtuples,
3017 : 7111 : state->memtupcount,
3018 : : state);
3019 : 7111 : verify_memtuples_sorted(state);
3020 : 7111 : return;
3021 : : }
3022 : : }
3023 : :
3024 : : /* Can we use the single-key sort function? */
3025 [ + + ]: 37631 : if (state->base.onlyKey != NULL)
3026 : : {
3027 : 26307 : qsort_ssup(state->memtuples, state->memtupcount,
3028 : 26307 : state->base.onlyKey);
3029 : : }
3030 : : else
3031 : : {
3032 : 11324 : qsort_tuple(state->memtuples,
3033 : 11324 : state->memtupcount,
3034 : : state->base.comparetup,
3035 : : state);
3036 : : }
3037 : : }
3038 : : }
3039 : :
3040 : : /*
3041 : : * Insert a new tuple into an empty or existing heap, maintaining the
3042 : : * heap invariant. Caller is responsible for ensuring there's room.
3043 : : *
3044 : : * Note: For some callers, tuple points to a memtuples[] entry above the
3045 : : * end of the heap. This is safe as long as it's not immediately adjacent
3046 : : * to the end of the heap (ie, in the [memtupcount] array entry) --- if it
3047 : : * is, it might get overwritten before being moved into the heap!
3048 : : */
3049 : : static void
3050 : 12767 : tuplesort_heap_insert(Tuplesortstate *state, SortTuple *tuple)
3051 : : {
3052 : : SortTuple *memtuples;
3053 : : int j;
3054 : :
3055 : 12767 : memtuples = state->memtuples;
3056 : : Assert(state->memtupcount < state->memtupsize);
3057 : :
3058 [ + + ]: 12767 : CHECK_FOR_INTERRUPTS();
3059 : :
3060 : : /*
3061 : : * Sift-up the new entry, per Knuth 5.2.3 exercise 16. Note that Knuth is
3062 : : * using 1-based array indexes, not 0-based.
3063 : : */
3064 : 12767 : j = state->memtupcount++;
3065 [ + + ]: 36124 : while (j > 0)
3066 : : {
3067 : 31995 : int i = (j - 1) >> 1;
3068 : :
3069 [ + + ]: 31995 : if (COMPARETUP(state, tuple, &memtuples[i]) >= 0)
3070 : 8638 : break;
3071 : 23357 : memtuples[j] = memtuples[i];
3072 : 23357 : j = i;
3073 : : }
3074 : 12767 : memtuples[j] = *tuple;
3075 : 12767 : }
3076 : :
3077 : : /*
3078 : : * Remove the tuple at state->memtuples[0] from the heap. Decrement
3079 : : * memtupcount, and sift up to maintain the heap invariant.
3080 : : *
3081 : : * The caller has already free'd the tuple the top node points to,
3082 : : * if necessary.
3083 : : */
3084 : : static void
3085 : 12479 : tuplesort_heap_delete_top(Tuplesortstate *state)
3086 : : {
3087 : 12479 : SortTuple *memtuples = state->memtuples;
3088 : : SortTuple *tuple;
3089 : :
3090 [ + + ]: 12479 : if (--state->memtupcount <= 0)
3091 : 217 : return;
3092 : :
3093 : : /*
3094 : : * Remove the last tuple in the heap, and re-insert it, by replacing the
3095 : : * current top node with it.
3096 : : */
3097 : 12262 : tuple = &memtuples[state->memtupcount];
3098 : 12262 : tuplesort_heap_replace_top(state, tuple);
3099 : : }
3100 : :
3101 : : /*
3102 : : * Replace the tuple at state->memtuples[0] with a new tuple. Sift up to
3103 : : * maintain the heap invariant.
3104 : : *
3105 : : * This corresponds to Knuth's "sift-up" algorithm (Algorithm 5.2.3H,
3106 : : * Heapsort, steps H3-H8).
3107 : : */
3108 : : static void
3109 : 3677238 : tuplesort_heap_replace_top(Tuplesortstate *state, SortTuple *tuple)
3110 : : {
3111 : 3677238 : SortTuple *memtuples = state->memtuples;
3112 : : unsigned int i,
3113 : : n;
3114 : :
3115 : : Assert(state->memtupcount >= 1);
3116 : :
3117 [ + + ]: 3677238 : CHECK_FOR_INTERRUPTS();
3118 : :
3119 : : /*
3120 : : * state->memtupcount is "int", but we use "unsigned int" for i, j, n.
3121 : : * This prevents overflow in the "2 * i + 1" calculation, since at the top
3122 : : * of the loop we must have i < n <= INT_MAX <= UINT_MAX/2.
3123 : : */
3124 : 3677238 : n = state->memtupcount;
3125 : 3677238 : i = 0; /* i is where the "hole" is */
3126 : : for (;;)
3127 : 1167423 : {
3128 : 4844661 : unsigned int j = 2 * i + 1;
3129 : :
3130 [ + + ]: 4844661 : if (j >= n)
3131 : 749226 : break;
3132 [ + + + + ]: 5730245 : if (j + 1 < n &&
3133 : 1634810 : COMPARETUP(state, &memtuples[j], &memtuples[j + 1]) > 0)
3134 : 646164 : j++;
3135 [ + + ]: 4095435 : if (COMPARETUP(state, tuple, &memtuples[j]) <= 0)
3136 : 2928012 : break;
3137 : 1167423 : memtuples[i] = memtuples[j];
3138 : 1167423 : i = j;
3139 : : }
3140 : 3677238 : memtuples[i] = *tuple;
3141 : 3677238 : }
3142 : :
3143 : : /*
3144 : : * Function to reverse the sort direction from its current state
3145 : : *
3146 : : * It is not safe to call this when performing hash tuplesorts
3147 : : */
3148 : : static void
3149 : 512 : reversedirection(Tuplesortstate *state)
3150 : : {
3151 : 512 : SortSupport sortKey = state->base.sortKeys;
3152 : : int nkey;
3153 : :
3154 [ + + ]: 1248 : for (nkey = 0; nkey < state->base.nKeys; nkey++, sortKey++)
3155 : : {
3156 : 736 : sortKey->ssup_reverse = !sortKey->ssup_reverse;
3157 : 736 : sortKey->ssup_nulls_first = !sortKey->ssup_nulls_first;
3158 : : }
3159 : 512 : }
3160 : :
3161 : :
3162 : : /*
3163 : : * Tape interface routines
3164 : : */
3165 : :
3166 : : static unsigned int
3167 : 3523058 : getlen(LogicalTape *tape, bool eofOK)
3168 : : {
3169 : : unsigned int len;
3170 : :
3171 [ - + ]: 3523058 : if (LogicalTapeRead(tape,
3172 : : &len, sizeof(len)) != sizeof(len))
3173 [ # # ]: 0 : elog(ERROR, "unexpected end of tape");
3174 [ + + - + ]: 3523058 : if (len == 0 && !eofOK)
3175 [ # # ]: 0 : elog(ERROR, "unexpected end of data");
3176 : 3523058 : return len;
3177 : : }
3178 : :
3179 : : static void
3180 : 1200 : markrunend(LogicalTape *tape)
3181 : : {
3182 : 1200 : unsigned int len = 0;
3183 : :
3184 : 1200 : LogicalTapeWrite(tape, &len, sizeof(len));
3185 : 1200 : }
3186 : :
3187 : : /*
3188 : : * Get memory for tuple from within READTUP() routine.
3189 : : *
3190 : : * We use next free slot from the slab allocator, or palloc() if the tuple
3191 : : * is too large for that.
3192 : : */
3193 : : void *
3194 : 3266817 : tuplesort_readtup_alloc(Tuplesortstate *state, Size tuplen)
3195 : : {
3196 : : SlabSlot *buf;
3197 : :
3198 : : /*
3199 : : * We pre-allocate enough slots in the slab arena that we should never run
3200 : : * out.
3201 : : */
3202 : : Assert(state->slabFreeHead);
3203 : :
3204 [ + + - + ]: 3266817 : if (tuplen > SLAB_SLOT_SIZE || !state->slabFreeHead)
3205 : 4 : return MemoryContextAlloc(state->base.sortcontext, tuplen);
3206 : : else
3207 : : {
3208 : 3266813 : buf = state->slabFreeHead;
3209 : : /* Reuse this slot */
3210 : 3266813 : state->slabFreeHead = buf->nextfree;
3211 : :
3212 : 3266813 : return buf;
3213 : : }
3214 : : }
3215 : :
3216 : :
3217 : : /*
3218 : : * Parallel sort routines
3219 : : */
3220 : :
3221 : : /*
3222 : : * tuplesort_estimate_shared - estimate required shared memory allocation
3223 : : *
3224 : : * nWorkers is an estimate of the number of workers (it's the number that
3225 : : * will be requested).
3226 : : */
3227 : : Size
3228 : 131 : tuplesort_estimate_shared(int nWorkers)
3229 : : {
3230 : : Size tapesSize;
3231 : :
3232 : : Assert(nWorkers > 0);
3233 : :
3234 : : /* Make sure that BufFile shared state is MAXALIGN'd */
3235 : 131 : tapesSize = mul_size(sizeof(TapeShare), nWorkers);
3236 : 131 : tapesSize = MAXALIGN(add_size(tapesSize, offsetof(Sharedsort, tapes)));
3237 : :
3238 : 131 : return tapesSize;
3239 : : }
3240 : :
3241 : : /*
3242 : : * tuplesort_initialize_shared - initialize shared tuplesort state
3243 : : *
3244 : : * Must be called from leader process before workers are launched, to
3245 : : * establish state needed up-front for worker tuplesortstates. nWorkers
3246 : : * should match the argument passed to tuplesort_estimate_shared().
3247 : : */
3248 : : void
3249 : 179 : tuplesort_initialize_shared(Sharedsort *shared, int nWorkers, dsm_segment *seg)
3250 : : {
3251 : : int i;
3252 : :
3253 : : Assert(nWorkers > 0);
3254 : :
3255 : 179 : SpinLockInit(&shared->mutex);
3256 : 179 : shared->currentWorker = 0;
3257 : 179 : shared->workersFinished = 0;
3258 : 179 : SharedFileSetInit(&shared->fileset, seg);
3259 : 179 : shared->nTapes = nWorkers;
3260 [ + + ]: 562 : for (i = 0; i < nWorkers; i++)
3261 : : {
3262 : 383 : shared->tapes[i].firstblocknumber = 0L;
3263 : : }
3264 : 179 : }
3265 : :
3266 : : /*
3267 : : * tuplesort_attach_shared - attach to shared tuplesort state
3268 : : *
3269 : : * Must be called by all worker processes.
3270 : : */
3271 : : void
3272 : 201 : tuplesort_attach_shared(Sharedsort *shared, dsm_segment *seg)
3273 : : {
3274 : : /* Attach to SharedFileSet */
3275 : 201 : SharedFileSetAttach(&shared->fileset, seg);
3276 : 201 : }
3277 : :
3278 : : /*
3279 : : * worker_get_identifier - Assign and return ordinal identifier for worker
3280 : : *
3281 : : * The order in which these are assigned is not well defined, and should not
3282 : : * matter; worker numbers across parallel sort participants need only be
3283 : : * distinct and gapless. logtape.c requires this.
3284 : : *
3285 : : * Note that the identifiers assigned from here have no relation to
3286 : : * ParallelWorkerNumber number, to avoid making any assumption about
3287 : : * caller's requirements. However, we do follow the ParallelWorkerNumber
3288 : : * convention of representing a non-worker with worker number -1. This
3289 : : * includes the leader, as well as serial Tuplesort processes.
3290 : : */
3291 : : static int
3292 : 379 : worker_get_identifier(Tuplesortstate *state)
3293 : : {
3294 : 379 : Sharedsort *shared = state->shared;
3295 : : int worker;
3296 : :
3297 : : Assert(WORKER(state));
3298 : :
3299 : 379 : SpinLockAcquire(&shared->mutex);
3300 : 379 : worker = shared->currentWorker++;
3301 : 379 : SpinLockRelease(&shared->mutex);
3302 : :
3303 : 379 : return worker;
3304 : : }
3305 : :
3306 : : /*
3307 : : * worker_freeze_result_tape - freeze worker's result tape for leader
3308 : : *
3309 : : * This is called by workers just after the result tape has been determined,
3310 : : * instead of calling LogicalTapeFreeze() directly. They do so because
3311 : : * workers require a few additional steps over similar serial
3312 : : * TSS_SORTEDONTAPE external sort cases, which also happen here. The extra
3313 : : * steps are around freeing now unneeded resources, and representing to
3314 : : * leader that worker's input run is available for its merge.
3315 : : *
3316 : : * There should only be one final output run for each worker, which consists
3317 : : * of all tuples that were originally input into worker.
3318 : : */
3319 : : static void
3320 : 379 : worker_freeze_result_tape(Tuplesortstate *state)
3321 : : {
3322 : 379 : Sharedsort *shared = state->shared;
3323 : : TapeShare output;
3324 : :
3325 : : Assert(WORKER(state));
3326 : : Assert(state->result_tape != NULL);
3327 : : Assert(state->memtupcount == 0);
3328 : :
3329 : : /*
3330 : : * Free most remaining memory, in case caller is sensitive to our holding
3331 : : * on to it. memtuples may not be a tiny merge heap at this point.
3332 : : */
3333 : 379 : pfree(state->memtuples);
3334 : : /* Be tidy */
3335 : 379 : state->memtuples = NULL;
3336 : 379 : state->memtupsize = 0;
3337 : :
3338 : : /*
3339 : : * Parallel worker requires result tape metadata, which is to be stored in
3340 : : * shared memory for leader
3341 : : */
3342 : 379 : LogicalTapeFreeze(state->result_tape, &output);
3343 : :
3344 : : /* Store properties of output tape, and update finished worker count */
3345 : 379 : SpinLockAcquire(&shared->mutex);
3346 : 379 : shared->tapes[state->worker] = output;
3347 : 379 : shared->workersFinished++;
3348 : 379 : SpinLockRelease(&shared->mutex);
3349 : 379 : }
3350 : :
3351 : : /*
3352 : : * worker_nomergeruns - dump memtuples in worker, without merging
3353 : : *
3354 : : * This called as an alternative to mergeruns() with a worker when no
3355 : : * merging is required.
3356 : : */
3357 : : static void
3358 : 379 : worker_nomergeruns(Tuplesortstate *state)
3359 : : {
3360 : : Assert(WORKER(state));
3361 : : Assert(state->result_tape == NULL);
3362 : : Assert(state->nOutputRuns == 1);
3363 : :
3364 : 379 : state->result_tape = state->destTape;
3365 : 379 : worker_freeze_result_tape(state);
3366 : 379 : }
3367 : :
3368 : : /*
3369 : : * leader_takeover_tapes - create tapeset for leader from worker tapes
3370 : : *
3371 : : * So far, leader Tuplesortstate has performed no actual sorting. By now, all
3372 : : * sorting has occurred in workers, all of which must have already returned
3373 : : * from tuplesort_performsort().
3374 : : *
3375 : : * When this returns, leader process is left in a state that is virtually
3376 : : * indistinguishable from it having generated runs as a serial external sort
3377 : : * might have.
3378 : : */
3379 : : static void
3380 : 130 : leader_takeover_tapes(Tuplesortstate *state)
3381 : : {
3382 : 130 : Sharedsort *shared = state->shared;
3383 : 130 : int nParticipants = state->nParticipants;
3384 : : int workersFinished;
3385 : : int j;
3386 : :
3387 : : Assert(LEADER(state));
3388 : : Assert(nParticipants >= 1);
3389 : :
3390 : 130 : SpinLockAcquire(&shared->mutex);
3391 : 130 : workersFinished = shared->workersFinished;
3392 : 130 : SpinLockRelease(&shared->mutex);
3393 : :
3394 [ - + ]: 130 : if (nParticipants != workersFinished)
3395 [ # # ]: 0 : elog(ERROR, "cannot take over tapes before all workers finish");
3396 : :
3397 : : /*
3398 : : * Create the tapeset from worker tapes, including a leader-owned tape at
3399 : : * the end. Parallel workers are far more expensive than logical tapes,
3400 : : * so the number of tapes allocated here should never be excessive.
3401 : : */
3402 : 130 : inittapestate(state, nParticipants);
3403 : 130 : state->tapeset = LogicalTapeSetCreate(false, &shared->fileset, -1);
3404 : :
3405 : : /*
3406 : : * Set currentRun to reflect the number of runs we will merge (it's not
3407 : : * used for anything, this is just pro forma)
3408 : : */
3409 : 130 : state->currentRun = nParticipants;
3410 : :
3411 : : /*
3412 : : * Initialize the state to look the same as after building the initial
3413 : : * runs.
3414 : : *
3415 : : * There will always be exactly 1 run per worker, and exactly one input
3416 : : * tape per run, because workers always output exactly 1 run, even when
3417 : : * there were no input tuples for workers to sort.
3418 : : */
3419 : 130 : state->inputTapes = NULL;
3420 : 130 : state->nInputTapes = 0;
3421 : 130 : state->nInputRuns = 0;
3422 : :
3423 : 130 : state->outputTapes = palloc0(nParticipants * sizeof(LogicalTape *));
3424 : 130 : state->nOutputTapes = nParticipants;
3425 : 130 : state->nOutputRuns = nParticipants;
3426 : :
3427 [ + + ]: 413 : for (j = 0; j < nParticipants; j++)
3428 : : {
3429 : 283 : state->outputTapes[j] = LogicalTapeImport(state->tapeset, j, &shared->tapes[j]);
3430 : : }
3431 : :
3432 : 130 : state->status = TSS_BUILDRUNS;
3433 : 130 : }
3434 : :
3435 : : /*
3436 : : * Convenience routine to free a tuple previously loaded into sort memory
3437 : : */
3438 : : static void
3439 : 2149159 : free_sort_tuple(Tuplesortstate *state, SortTuple *stup)
3440 : : {
3441 [ + + ]: 2149159 : if (stup->tuple)
3442 : : {
3443 : 2044152 : FREEMEM(state, GetMemoryChunkSpace(stup->tuple));
3444 : 2044152 : pfree(stup->tuple);
3445 : 2044152 : stup->tuple = NULL;
3446 : : }
3447 : 2149159 : }
3448 : :
3449 : : int
3450 : 122780141 : ssup_datum_unsigned_cmp(Datum x, Datum y, SortSupport ssup)
3451 : : {
3452 [ + + ]: 122780141 : if (x < y)
3453 : 29876537 : return -1;
3454 [ + + ]: 92903604 : else if (x > y)
3455 : 30037645 : return 1;
3456 : : else
3457 : 62865959 : return 0;
3458 : : }
3459 : :
3460 : : int
3461 : 1732113 : ssup_datum_signed_cmp(Datum x, Datum y, SortSupport ssup)
3462 : : {
3463 : 1732113 : int64 xx = DatumGetInt64(x);
3464 : 1732113 : int64 yy = DatumGetInt64(y);
3465 : :
3466 [ + + ]: 1732113 : if (xx < yy)
3467 : 1263321 : return -1;
3468 [ + + ]: 468792 : else if (xx > yy)
3469 : 231712 : return 1;
3470 : : else
3471 : 237080 : return 0;
3472 : : }
3473 : :
3474 : : int
3475 : 157058996 : ssup_datum_int32_cmp(Datum x, Datum y, SortSupport ssup)
3476 : : {
3477 : 157058996 : int32 xx = DatumGetInt32(x);
3478 : 157058996 : int32 yy = DatumGetInt32(y);
3479 : :
3480 [ + + ]: 157058996 : if (xx < yy)
3481 : 34302712 : return -1;
3482 [ + + ]: 122756284 : else if (xx > yy)
3483 : 30771166 : return 1;
3484 : : else
3485 : 91985118 : return 0;
3486 : : }
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