Age Owner Branch data TLA Line data Source code
1 : : /*-------------------------------------------------------------------------
2 : : *
3 : : * costsize.c
4 : : * Routines to compute (and set) relation sizes and path costs
5 : : *
6 : : * Path costs are measured in arbitrary units established by these basic
7 : : * parameters:
8 : : *
9 : : * seq_page_cost Cost of a sequential page fetch
10 : : * random_page_cost Cost of a non-sequential page fetch
11 : : * cpu_tuple_cost Cost of typical CPU time to process a tuple
12 : : * cpu_index_tuple_cost Cost of typical CPU time to process an index tuple
13 : : * cpu_operator_cost Cost of CPU time to execute an operator or function
14 : : * parallel_tuple_cost Cost of CPU time to pass a tuple from worker to leader backend
15 : : * parallel_setup_cost Cost of setting up shared memory for parallelism
16 : : *
17 : : * We expect that the kernel will typically do some amount of read-ahead
18 : : * optimization; this in conjunction with seek costs means that seq_page_cost
19 : : * is normally considerably less than random_page_cost. (However, if the
20 : : * database is fully cached in RAM, it is reasonable to set them equal.)
21 : : *
22 : : * We also use a rough estimate "effective_cache_size" of the number of
23 : : * disk pages in Postgres + OS-level disk cache. (We can't simply use
24 : : * NBuffers for this purpose because that would ignore the effects of
25 : : * the kernel's disk cache.)
26 : : *
27 : : * Obviously, taking constants for these values is an oversimplification,
28 : : * but it's tough enough to get any useful estimates even at this level of
29 : : * detail. Note that all of these parameters are user-settable, in case
30 : : * the default values are drastically off for a particular platform.
31 : : *
32 : : * seq_page_cost and random_page_cost can also be overridden for an individual
33 : : * tablespace, in case some data is on a fast disk and other data is on a slow
34 : : * disk. Per-tablespace overrides never apply to temporary work files such as
35 : : * an external sort or a materialize node that overflows work_mem.
36 : : *
37 : : * We compute two separate costs for each path:
38 : : * total_cost: total estimated cost to fetch all tuples
39 : : * startup_cost: cost that is expended before first tuple is fetched
40 : : * In some scenarios, such as when there is a LIMIT or we are implementing
41 : : * an EXISTS(...) sub-select, it is not necessary to fetch all tuples of the
42 : : * path's result. A caller can estimate the cost of fetching a partial
43 : : * result by interpolating between startup_cost and total_cost. In detail:
44 : : * actual_cost = startup_cost +
45 : : * (total_cost - startup_cost) * tuples_to_fetch / path->rows;
46 : : * Note that a base relation's rows count (and, by extension, plan_rows for
47 : : * plan nodes below the LIMIT node) are set without regard to any LIMIT, so
48 : : * that this equation works properly. (Note: while path->rows is never zero
49 : : * for ordinary relations, it is zero for paths for provably-empty relations,
50 : : * so beware of division-by-zero.) The LIMIT is applied as a top-level
51 : : * plan node.
52 : : *
53 : : * Each path stores the total number of disabled nodes that exist at or
54 : : * below that point in the plan tree. This is regarded as a component of
55 : : * the cost, and paths with fewer disabled nodes should be regarded as
56 : : * cheaper than those with more. Disabled nodes occur when the user sets
57 : : * a GUC like enable_seqscan=false. We can't necessarily respect such a
58 : : * setting in every part of the plan tree, but we want to respect in as many
59 : : * parts of the plan tree as possible. Simpler schemes like storing a Boolean
60 : : * here rather than a count fail to do that. We used to disable nodes by
61 : : * adding a large constant to the startup cost, but that distorted planning
62 : : * in other ways.
63 : : *
64 : : * For largely historical reasons, most of the routines in this module use
65 : : * the passed result Path only to store their results (rows, startup_cost and
66 : : * total_cost) into. All the input data they need is passed as separate
67 : : * parameters, even though much of it could be extracted from the Path.
68 : : * An exception is made for the cost_XXXjoin() routines, which expect all
69 : : * the other fields of the passed XXXPath to be filled in, and similarly
70 : : * cost_index() assumes the passed IndexPath is valid except for its output
71 : : * values.
72 : : *
73 : : *
74 : : * Portions Copyright (c) 1996-2025, PostgreSQL Global Development Group
75 : : * Portions Copyright (c) 1994, Regents of the University of California
76 : : *
77 : : * IDENTIFICATION
78 : : * src/backend/optimizer/path/costsize.c
79 : : *
80 : : *-------------------------------------------------------------------------
81 : : */
82 : :
83 : : #include "postgres.h"
84 : :
85 : : #include <limits.h>
86 : : #include <math.h>
87 : :
88 : : #include "access/amapi.h"
89 : : #include "access/htup_details.h"
90 : : #include "access/tsmapi.h"
91 : : #include "executor/executor.h"
92 : : #include "executor/nodeAgg.h"
93 : : #include "executor/nodeHash.h"
94 : : #include "executor/nodeMemoize.h"
95 : : #include "miscadmin.h"
96 : : #include "nodes/makefuncs.h"
97 : : #include "nodes/nodeFuncs.h"
98 : : #include "optimizer/clauses.h"
99 : : #include "optimizer/cost.h"
100 : : #include "optimizer/optimizer.h"
101 : : #include "optimizer/pathnode.h"
102 : : #include "optimizer/paths.h"
103 : : #include "optimizer/placeholder.h"
104 : : #include "optimizer/plancat.h"
105 : : #include "optimizer/restrictinfo.h"
106 : : #include "parser/parsetree.h"
107 : : #include "utils/lsyscache.h"
108 : : #include "utils/selfuncs.h"
109 : : #include "utils/spccache.h"
110 : : #include "utils/tuplesort.h"
111 : :
112 : :
113 : : #define LOG2(x) (log(x) / 0.693147180559945)
114 : :
115 : : /*
116 : : * Append and MergeAppend nodes are less expensive than some other operations
117 : : * which use cpu_tuple_cost; instead of adding a separate GUC, estimate the
118 : : * per-tuple cost as cpu_tuple_cost multiplied by this value.
119 : : */
120 : : #define APPEND_CPU_COST_MULTIPLIER 0.5
121 : :
122 : : /*
123 : : * Maximum value for row estimates. We cap row estimates to this to help
124 : : * ensure that costs based on these estimates remain within the range of what
125 : : * double can represent. add_path() wouldn't act sanely given infinite or NaN
126 : : * cost values.
127 : : */
128 : : #define MAXIMUM_ROWCOUNT 1e100
129 : :
130 : : double seq_page_cost = DEFAULT_SEQ_PAGE_COST;
131 : : double random_page_cost = DEFAULT_RANDOM_PAGE_COST;
132 : : double cpu_tuple_cost = DEFAULT_CPU_TUPLE_COST;
133 : : double cpu_index_tuple_cost = DEFAULT_CPU_INDEX_TUPLE_COST;
134 : : double cpu_operator_cost = DEFAULT_CPU_OPERATOR_COST;
135 : : double parallel_tuple_cost = DEFAULT_PARALLEL_TUPLE_COST;
136 : : double parallel_setup_cost = DEFAULT_PARALLEL_SETUP_COST;
137 : : double recursive_worktable_factor = DEFAULT_RECURSIVE_WORKTABLE_FACTOR;
138 : :
139 : : int effective_cache_size = DEFAULT_EFFECTIVE_CACHE_SIZE;
140 : :
141 : : Cost disable_cost = 1.0e10;
142 : :
143 : : int max_parallel_workers_per_gather = 2;
144 : :
145 : : bool enable_seqscan = true;
146 : : bool enable_indexscan = true;
147 : : bool enable_indexonlyscan = true;
148 : : bool enable_bitmapscan = true;
149 : : bool enable_tidscan = true;
150 : : bool enable_sort = true;
151 : : bool enable_incremental_sort = true;
152 : : bool enable_hashagg = true;
153 : : bool enable_nestloop = true;
154 : : bool enable_material = true;
155 : : bool enable_memoize = true;
156 : : bool enable_mergejoin = true;
157 : : bool enable_hashjoin = true;
158 : : bool enable_gathermerge = true;
159 : : bool enable_partitionwise_join = false;
160 : : bool enable_partitionwise_aggregate = false;
161 : : bool enable_parallel_append = true;
162 : : bool enable_parallel_hash = true;
163 : : bool enable_partition_pruning = true;
164 : : bool enable_presorted_aggregate = true;
165 : : bool enable_async_append = true;
166 : :
167 : : typedef struct
168 : : {
169 : : PlannerInfo *root;
170 : : QualCost total;
171 : : } cost_qual_eval_context;
172 : :
173 : : static List *extract_nonindex_conditions(List *qual_clauses, List *indexclauses);
174 : : static MergeScanSelCache *cached_scansel(PlannerInfo *root,
175 : : RestrictInfo *rinfo,
176 : : PathKey *pathkey);
177 : : static void cost_rescan(PlannerInfo *root, Path *path,
178 : : Cost *rescan_startup_cost, Cost *rescan_total_cost);
179 : : static bool cost_qual_eval_walker(Node *node, cost_qual_eval_context *context);
180 : : static void get_restriction_qual_cost(PlannerInfo *root, RelOptInfo *baserel,
181 : : ParamPathInfo *param_info,
182 : : QualCost *qpqual_cost);
183 : : static bool has_indexed_join_quals(NestPath *path);
184 : : static double approx_tuple_count(PlannerInfo *root, JoinPath *path,
185 : : List *quals);
186 : : static double calc_joinrel_size_estimate(PlannerInfo *root,
187 : : RelOptInfo *joinrel,
188 : : RelOptInfo *outer_rel,
189 : : RelOptInfo *inner_rel,
190 : : double outer_rows,
191 : : double inner_rows,
192 : : SpecialJoinInfo *sjinfo,
193 : : List *restrictlist);
194 : : static Selectivity get_foreign_key_join_selectivity(PlannerInfo *root,
195 : : Relids outer_relids,
196 : : Relids inner_relids,
197 : : SpecialJoinInfo *sjinfo,
198 : : List **restrictlist);
199 : : static Cost append_nonpartial_cost(List *subpaths, int numpaths,
200 : : int parallel_workers);
201 : : static void set_rel_width(PlannerInfo *root, RelOptInfo *rel);
202 : : static int32 get_expr_width(PlannerInfo *root, const Node *expr);
203 : : static double relation_byte_size(double tuples, int width);
204 : : static double page_size(double tuples, int width);
205 : : static double get_parallel_divisor(Path *path);
206 : :
207 : :
208 : : /*
209 : : * clamp_row_est
210 : : * Force a row-count estimate to a sane value.
211 : : */
212 : : double
7915 tgl@sss.pgh.pa.us 213 :CBC 4298481 : clamp_row_est(double nrows)
214 : : {
215 : : /*
216 : : * Avoid infinite and NaN row estimates. Costs derived from such values
217 : : * are going to be useless. Also force the estimate to be at least one
218 : : * row, to make explain output look better and to avoid possible
219 : : * divide-by-zero when interpolating costs. Make it an integer, too.
220 : : */
1783 drowley@postgresql.o 221 [ + - - + ]: 4298481 : if (nrows > MAXIMUM_ROWCOUNT || isnan(nrows))
1783 drowley@postgresql.o 222 :UBC 0 : nrows = MAXIMUM_ROWCOUNT;
1783 drowley@postgresql.o 223 [ + + ]:CBC 4298481 : else if (nrows <= 1.0)
7915 tgl@sss.pgh.pa.us 224 : 1545518 : nrows = 1.0;
225 : : else
7443 226 : 2752963 : nrows = rint(nrows);
227 : :
7915 228 : 4298481 : return nrows;
229 : : }
230 : :
231 : : /*
232 : : * clamp_width_est
233 : : * Force a tuple-width estimate to a sane value.
234 : : *
235 : : * The planner represents datatype width and tuple width estimates as int32.
236 : : * When summing column width estimates to create a tuple width estimate,
237 : : * it's possible to reach integer overflow in edge cases. To ensure sane
238 : : * behavior, we form such sums in int64 arithmetic and then apply this routine
239 : : * to clamp to int32 range.
240 : : */
241 : : int32
627 242 : 920170 : clamp_width_est(int64 tuple_width)
243 : : {
244 : : /*
245 : : * Anything more than MaxAllocSize is clearly bogus, since we could not
246 : : * create a tuple that large.
247 : : */
248 [ - + ]: 920170 : if (tuple_width > MaxAllocSize)
627 tgl@sss.pgh.pa.us 249 :UBC 0 : return (int32) MaxAllocSize;
250 : :
251 : : /*
252 : : * Unlike clamp_row_est, we just Assert that the value isn't negative,
253 : : * rather than masking such errors.
254 : : */
627 tgl@sss.pgh.pa.us 255 [ - + ]:CBC 920170 : Assert(tuple_width >= 0);
256 : :
257 : 920170 : return (int32) tuple_width;
258 : : }
259 : :
260 : : /*
261 : : * clamp_cardinality_to_long
262 : : * Cast a Cardinality value to a sane long value.
263 : : */
264 : : long
1204 265 : 21680 : clamp_cardinality_to_long(Cardinality x)
266 : : {
267 : : /*
268 : : * Just for paranoia's sake, ensure we do something sane with negative or
269 : : * NaN values.
270 : : */
271 [ - + ]: 21680 : if (isnan(x))
1204 tgl@sss.pgh.pa.us 272 :UBC 0 : return LONG_MAX;
1204 tgl@sss.pgh.pa.us 273 [ + + ]:CBC 21680 : if (x <= 0)
274 : 292 : return 0;
275 : :
276 : : /*
277 : : * If "long" is 64 bits, then LONG_MAX cannot be represented exactly as a
278 : : * double. Casting it to double and back may well result in overflow due
279 : : * to rounding, so avoid doing that. We trust that any double value that
280 : : * compares strictly less than "(double) LONG_MAX" will cast to a
281 : : * representable "long" value.
282 : : */
283 [ + - ]: 21388 : return (x < (double) LONG_MAX) ? (long) x : LONG_MAX;
284 : : }
285 : :
286 : :
287 : : /*
288 : : * cost_seqscan
289 : : * Determines and returns the cost of scanning a relation sequentially.
290 : : *
291 : : * 'baserel' is the relation to be scanned
292 : : * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
293 : : */
294 : : void
7398 295 : 209566 : cost_seqscan(Path *path, PlannerInfo *root,
296 : : RelOptInfo *baserel, ParamPathInfo *param_info)
297 : : {
9335 298 : 209566 : Cost startup_cost = 0;
299 : : Cost cpu_run_cost;
300 : : Cost disk_run_cost;
301 : : double spc_seq_page_cost;
302 : : QualCost qpqual_cost;
303 : : Cost cpu_per_tuple;
304 : :
305 : : /* Should only be applied to base relations */
8246 306 [ - + ]: 209566 : Assert(baserel->relid > 0);
8518 307 [ - + ]: 209566 : Assert(baserel->rtekind == RTE_RELATION);
308 : :
309 : : /* Mark the path with the correct row estimate */
4888 310 [ + + ]: 209566 : if (param_info)
311 : 420 : path->rows = param_info->ppi_rows;
312 : : else
313 : 209146 : path->rows = baserel->rows;
314 : :
315 : : /* fetch estimated page cost for tablespace containing table */
5723 rhaas@postgresql.org 316 : 209566 : get_tablespace_page_costs(baserel->reltablespace,
317 : : NULL,
318 : : &spc_seq_page_cost);
319 : :
320 : : /*
321 : : * disk costs
322 : : */
3517 323 : 209566 : disk_run_cost = spc_seq_page_cost * baserel->pages;
324 : :
325 : : /* CPU costs */
4888 tgl@sss.pgh.pa.us 326 : 209566 : get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
327 : :
328 : 209566 : startup_cost += qpqual_cost.startup;
329 : 209566 : cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple;
3517 rhaas@postgresql.org 330 : 209566 : cpu_run_cost = cpu_per_tuple * baserel->tuples;
331 : : /* tlist eval costs are paid per output row, not per tuple scanned */
3488 tgl@sss.pgh.pa.us 332 : 209566 : startup_cost += path->pathtarget->cost.startup;
333 : 209566 : cpu_run_cost += path->pathtarget->cost.per_tuple * path->rows;
334 : :
335 : : /* Adjust costing for parallelism, if used. */
3376 rhaas@postgresql.org 336 [ + + ]: 209566 : if (path->parallel_workers > 0)
337 : : {
3158 338 : 13116 : double parallel_divisor = get_parallel_divisor(path);
339 : :
340 : : /* The CPU cost is divided among all the workers. */
3517 341 : 13116 : cpu_run_cost /= parallel_divisor;
342 : :
343 : : /*
344 : : * It may be possible to amortize some of the I/O cost, but probably
345 : : * not very much, because most operating systems already do aggressive
346 : : * prefetching. For now, we assume that the disk run cost can't be
347 : : * amortized at all.
348 : : */
349 : :
350 : : /*
351 : : * In the case of a parallel plan, the row count needs to represent
352 : : * the number of tuples processed per worker.
353 : : */
3158 354 : 13116 : path->rows = clamp_row_est(path->rows / parallel_divisor);
355 : : }
356 : :
381 357 : 209566 : path->disabled_nodes = enable_seqscan ? 0 : 1;
3767 simon@2ndQuadrant.co 358 : 209566 : path->startup_cost = startup_cost;
3517 rhaas@postgresql.org 359 : 209566 : path->total_cost = startup_cost + cpu_run_cost + disk_run_cost;
3767 simon@2ndQuadrant.co 360 : 209566 : }
361 : :
362 : : /*
363 : : * cost_samplescan
364 : : * Determines and returns the cost of scanning a relation using sampling.
365 : : *
366 : : * 'baserel' is the relation to be scanned
367 : : * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
368 : : */
369 : : void
3696 tgl@sss.pgh.pa.us 370 : 153 : cost_samplescan(Path *path, PlannerInfo *root,
371 : : RelOptInfo *baserel, ParamPathInfo *param_info)
372 : : {
3767 simon@2ndQuadrant.co 373 : 153 : Cost startup_cost = 0;
374 : 153 : Cost run_cost = 0;
375 : : RangeTblEntry *rte;
376 : : TableSampleClause *tsc;
377 : : TsmRoutine *tsm;
378 : : double spc_seq_page_cost,
379 : : spc_random_page_cost,
380 : : spc_page_cost;
381 : : QualCost qpqual_cost;
382 : : Cost cpu_per_tuple;
383 : :
384 : : /* Should only be applied to base relations with tablesample clauses */
385 [ - + ]: 153 : Assert(baserel->relid > 0);
3696 tgl@sss.pgh.pa.us 386 [ + - ]: 153 : rte = planner_rt_fetch(baserel->relid, root);
387 [ - + ]: 153 : Assert(rte->rtekind == RTE_RELATION);
388 : 153 : tsc = rte->tablesample;
389 [ - + ]: 153 : Assert(tsc != NULL);
390 : 153 : tsm = GetTsmRoutine(tsc->tsmhandler);
391 : :
392 : : /* Mark the path with the correct row estimate */
393 [ + + ]: 153 : if (param_info)
394 : 36 : path->rows = param_info->ppi_rows;
395 : : else
3767 simon@2ndQuadrant.co 396 : 117 : path->rows = baserel->rows;
397 : :
398 : : /* fetch estimated page cost for tablespace containing table */
399 : 153 : get_tablespace_page_costs(baserel->reltablespace,
400 : : &spc_random_page_cost,
401 : : &spc_seq_page_cost);
402 : :
403 : : /* if NextSampleBlock is used, assume random access, else sequential */
3696 tgl@sss.pgh.pa.us 404 : 306 : spc_page_cost = (tsm->NextSampleBlock != NULL) ?
405 [ + + ]: 153 : spc_random_page_cost : spc_seq_page_cost;
406 : :
407 : : /*
408 : : * disk costs (recall that baserel->pages has already been set to the
409 : : * number of pages the sampling method will visit)
410 : : */
411 : 153 : run_cost += spc_page_cost * baserel->pages;
412 : :
413 : : /*
414 : : * CPU costs (recall that baserel->tuples has already been set to the
415 : : * number of tuples the sampling method will select). Note that we ignore
416 : : * execution cost of the TABLESAMPLE parameter expressions; they will be
417 : : * evaluated only once per scan, and in most usages they'll likely be
418 : : * simple constants anyway. We also don't charge anything for the
419 : : * calculations the sampling method might do internally.
420 : : */
421 : 153 : get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
422 : :
3767 simon@2ndQuadrant.co 423 : 153 : startup_cost += qpqual_cost.startup;
424 : 153 : cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple;
3696 tgl@sss.pgh.pa.us 425 : 153 : run_cost += cpu_per_tuple * baserel->tuples;
426 : : /* tlist eval costs are paid per output row, not per tuple scanned */
3488 427 : 153 : startup_cost += path->pathtarget->cost.startup;
428 : 153 : run_cost += path->pathtarget->cost.per_tuple * path->rows;
429 : :
381 rhaas@postgresql.org 430 : 153 : path->disabled_nodes = 0;
9335 tgl@sss.pgh.pa.us 431 : 153 : path->startup_cost = startup_cost;
432 : 153 : path->total_cost = startup_cost + run_cost;
10651 scrappy@hub.org 433 : 153 : }
434 : :
435 : : /*
436 : : * cost_gather
437 : : * Determines and returns the cost of gather path.
438 : : *
439 : : * 'rel' is the relation to be operated upon
440 : : * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
441 : : * 'rows' may be used to point to a row estimate; if non-NULL, it overrides
442 : : * both 'rel' and 'param_info'. This is useful when the path doesn't exactly
443 : : * correspond to any particular RelOptInfo.
444 : : */
445 : : void
3629 rhaas@postgresql.org 446 : 11422 : cost_gather(GatherPath *path, PlannerInfo *root,
447 : : RelOptInfo *rel, ParamPathInfo *param_info,
448 : : double *rows)
449 : : {
450 : 11422 : Cost startup_cost = 0;
451 : 11422 : Cost run_cost = 0;
452 : :
453 : : /* Mark the path with the correct row estimate */
3456 454 [ + + ]: 11422 : if (rows)
455 : 2700 : path->path.rows = *rows;
456 [ - + ]: 8722 : else if (param_info)
3629 rhaas@postgresql.org 457 :UBC 0 : path->path.rows = param_info->ppi_rows;
458 : : else
3629 rhaas@postgresql.org 459 :CBC 8722 : path->path.rows = rel->rows;
460 : :
461 : 11422 : startup_cost = path->subpath->startup_cost;
462 : :
463 : 11422 : run_cost = path->subpath->total_cost - path->subpath->startup_cost;
464 : :
465 : : /* Parallel setup and communication cost. */
466 : 11422 : startup_cost += parallel_setup_cost;
3587 467 : 11422 : run_cost += parallel_tuple_cost * path->path.rows;
468 : :
381 469 : 11422 : path->path.disabled_nodes = path->subpath->disabled_nodes;
3629 470 : 11422 : path->path.startup_cost = startup_cost;
471 : 11422 : path->path.total_cost = (startup_cost + run_cost);
472 : 11422 : }
473 : :
474 : : /*
475 : : * cost_gather_merge
476 : : * Determines and returns the cost of gather merge path.
477 : : *
478 : : * GatherMerge merges several pre-sorted input streams, using a heap that at
479 : : * any given instant holds the next tuple from each stream. If there are N
480 : : * streams, we need about N*log2(N) tuple comparisons to construct the heap at
481 : : * startup, and then for each output tuple, about log2(N) comparisons to
482 : : * replace the top heap entry with the next tuple from the same stream.
483 : : */
484 : : void
3103 485 : 7110 : cost_gather_merge(GatherMergePath *path, PlannerInfo *root,
486 : : RelOptInfo *rel, ParamPathInfo *param_info,
487 : : int input_disabled_nodes,
488 : : Cost input_startup_cost, Cost input_total_cost,
489 : : double *rows)
490 : : {
491 : 7110 : Cost startup_cost = 0;
492 : 7110 : Cost run_cost = 0;
493 : : Cost comparison_cost;
494 : : double N;
495 : : double logN;
496 : :
497 : : /* Mark the path with the correct row estimate */
498 [ + + ]: 7110 : if (rows)
499 : 4288 : path->path.rows = *rows;
500 [ - + ]: 2822 : else if (param_info)
3103 rhaas@postgresql.org 501 :UBC 0 : path->path.rows = param_info->ppi_rows;
502 : : else
3103 rhaas@postgresql.org 503 :CBC 2822 : path->path.rows = rel->rows;
504 : :
505 : : /*
506 : : * Add one to the number of workers to account for the leader. This might
507 : : * be overgenerous since the leader will do less work than other workers
508 : : * in typical cases, but we'll go with it for now.
509 : : */
510 [ - + ]: 7110 : Assert(path->num_workers > 0);
511 : 7110 : N = (double) path->num_workers + 1;
512 : 7110 : logN = LOG2(N);
513 : :
514 : : /* Assumed cost per tuple comparison */
515 : 7110 : comparison_cost = 2.0 * cpu_operator_cost;
516 : :
517 : : /* Heap creation cost */
518 : 7110 : startup_cost += comparison_cost * N * logN;
519 : :
520 : : /* Per-tuple heap maintenance cost */
521 : 7110 : run_cost += path->path.rows * comparison_cost * logN;
522 : :
523 : : /* small cost for heap management, like cost_merge_append */
524 : 7110 : run_cost += cpu_operator_cost * path->path.rows;
525 : :
526 : : /*
527 : : * Parallel setup and communication cost. Since Gather Merge, unlike
528 : : * Gather, requires us to block until a tuple is available from every
529 : : * worker, we bump the IPC cost up a little bit as compared with Gather.
530 : : * For lack of a better idea, charge an extra 5%.
531 : : */
532 : 7110 : startup_cost += parallel_setup_cost;
533 : 7110 : run_cost += parallel_tuple_cost * path->path.rows * 1.05;
534 : :
381 535 : 7110 : path->path.disabled_nodes = input_disabled_nodes
536 : 7110 : + (enable_gathermerge ? 0 : 1);
3103 537 : 7110 : path->path.startup_cost = startup_cost + input_startup_cost;
538 : 7110 : path->path.total_cost = (startup_cost + run_cost + input_total_cost);
539 : 7110 : }
540 : :
541 : : /*
542 : : * cost_index
543 : : * Determines and returns the cost of scanning a relation using an index.
544 : : *
545 : : * 'path' describes the indexscan under consideration, and is complete
546 : : * except for the fields to be set by this routine
547 : : * 'loop_count' is the number of repetitions of the indexscan to factor into
548 : : * estimates of caching behavior
549 : : *
550 : : * In addition to rows, startup_cost and total_cost, cost_index() sets the
551 : : * path's indextotalcost and indexselectivity fields. These values will be
552 : : * needed if the IndexPath is used in a BitmapIndexScan.
553 : : *
554 : : * NOTE: path->indexquals must contain only clauses usable as index
555 : : * restrictions. Any additional quals evaluated as qpquals may reduce the
556 : : * number of returned tuples, but they won't reduce the number of tuples
557 : : * we have to fetch from the table, so they don't reduce the scan cost.
558 : : */
559 : : void
3125 560 : 382226 : cost_index(IndexPath *path, PlannerInfo *root, double loop_count,
561 : : bool partial_path)
562 : : {
5005 tgl@sss.pgh.pa.us 563 : 382226 : IndexOptInfo *index = path->indexinfo;
7468 564 : 382226 : RelOptInfo *baserel = index->rel;
5005 565 : 382226 : bool indexonly = (path->path.pathtype == T_IndexOnlyScan);
566 : : amcostestimate_function amcostestimate;
567 : : List *qpquals;
9335 568 : 382226 : Cost startup_cost = 0;
569 : 382226 : Cost run_cost = 0;
3125 rhaas@postgresql.org 570 : 382226 : Cost cpu_run_cost = 0;
571 : : Cost indexStartupCost;
572 : : Cost indexTotalCost;
573 : : Selectivity indexSelectivity;
574 : : double indexCorrelation,
575 : : csquared;
576 : : double spc_seq_page_cost,
577 : : spc_random_page_cost;
578 : : Cost min_IO_cost,
579 : : max_IO_cost;
580 : : QualCost qpqual_cost;
581 : : Cost cpu_per_tuple;
582 : : double tuples_fetched;
583 : : double pages_fetched;
584 : : double rand_heap_pages;
585 : : double index_pages;
586 : :
587 : : /* Should only be applied to base relations */
8518 tgl@sss.pgh.pa.us 588 [ + - - + ]: 382226 : Assert(IsA(baserel, RelOptInfo) &&
589 : : IsA(index, IndexOptInfo));
8246 590 [ - + ]: 382226 : Assert(baserel->relid > 0);
8518 591 [ - + ]: 382226 : Assert(baserel->rtekind == RTE_RELATION);
592 : :
593 : : /*
594 : : * Mark the path with the correct row estimate, and identify which quals
595 : : * will need to be enforced as qpquals. We need not check any quals that
596 : : * are implied by the index's predicate, so we can use indrestrictinfo not
597 : : * baserestrictinfo as the list of relevant restriction clauses for the
598 : : * rel.
599 : : */
4888 600 [ + + ]: 382226 : if (path->path.param_info)
601 : : {
602 : 69307 : path->path.rows = path->path.param_info->ppi_rows;
603 : : /* qpquals come from the rel's restriction clauses and ppi_clauses */
2401 604 : 69307 : qpquals = list_concat(extract_nonindex_conditions(path->indexinfo->indrestrictinfo,
605 : : path->indexclauses),
2999 606 : 69307 : extract_nonindex_conditions(path->path.param_info->ppi_clauses,
607 : : path->indexclauses));
608 : : }
609 : : else
610 : : {
4888 611 : 312919 : path->path.rows = baserel->rows;
612 : : /* qpquals come from just the rel's restriction clauses */
3446 613 : 312919 : qpquals = extract_nonindex_conditions(path->indexinfo->indrestrictinfo,
614 : : path->indexclauses);
615 : : }
616 : :
617 : : /* we don't need to check enable_indexonlyscan; indxpath.c does that */
381 rhaas@postgresql.org 618 : 382226 : path->path.disabled_nodes = enable_indexscan ? 0 : 1;
619 : :
620 : : /*
621 : : * Call index-access-method-specific code to estimate the processing cost
622 : : * for scanning the index, as well as the selectivity of the index (ie,
623 : : * the fraction of main-table tuples we will have to retrieve) and its
624 : : * correlation to the main-table tuple order. We need a cast here because
625 : : * pathnodes.h uses a weak function type to avoid including amapi.h.
626 : : */
3520 tgl@sss.pgh.pa.us 627 : 382226 : amcostestimate = (amcostestimate_function) index->amcostestimate;
628 : 382226 : amcostestimate(root, path, loop_count,
629 : : &indexStartupCost, &indexTotalCost,
630 : : &indexSelectivity, &indexCorrelation,
631 : : &index_pages);
632 : :
633 : : /*
634 : : * Save amcostestimate's results for possible use in bitmap scan planning.
635 : : * We don't bother to save indexStartupCost or indexCorrelation, because a
636 : : * bitmap scan doesn't care about either.
637 : : */
7443 638 : 382226 : path->indextotalcost = indexTotalCost;
639 : 382226 : path->indexselectivity = indexSelectivity;
640 : :
641 : : /* all costs for touching index itself included here */
9335 642 : 382226 : startup_cost += indexStartupCost;
643 : 382226 : run_cost += indexTotalCost - indexStartupCost;
644 : :
645 : : /* estimate number of main-table tuples fetched */
7032 646 : 382226 : tuples_fetched = clamp_row_est(indexSelectivity * baserel->tuples);
647 : :
648 : : /* fetch estimated page costs for tablespace containing table */
5723 rhaas@postgresql.org 649 : 382226 : get_tablespace_page_costs(baserel->reltablespace,
650 : : &spc_random_page_cost,
651 : : &spc_seq_page_cost);
652 : :
653 : : /*----------
654 : : * Estimate number of main-table pages fetched, and compute I/O cost.
655 : : *
656 : : * When the index ordering is uncorrelated with the table ordering,
657 : : * we use an approximation proposed by Mackert and Lohman (see
658 : : * index_pages_fetched() for details) to compute the number of pages
659 : : * fetched, and then charge spc_random_page_cost per page fetched.
660 : : *
661 : : * When the index ordering is exactly correlated with the table ordering
662 : : * (just after a CLUSTER, for example), the number of pages fetched should
663 : : * be exactly selectivity * table_size. What's more, all but the first
664 : : * will be sequential fetches, not the random fetches that occur in the
665 : : * uncorrelated case. So if the number of pages is more than 1, we
666 : : * ought to charge
667 : : * spc_random_page_cost + (pages_fetched - 1) * spc_seq_page_cost
668 : : * For partially-correlated indexes, we ought to charge somewhere between
669 : : * these two estimates. We currently interpolate linearly between the
670 : : * estimates based on the correlation squared (XXX is that appropriate?).
671 : : *
672 : : * If it's an index-only scan, then we will not need to fetch any heap
673 : : * pages for which the visibility map shows all tuples are visible.
674 : : * Hence, reduce the estimated number of heap fetches accordingly.
675 : : * We use the measured fraction of the entire heap that is all-visible,
676 : : * which might not be particularly relevant to the subset of the heap
677 : : * that this query will fetch; but it's not clear how to do better.
678 : : *----------
679 : : */
4971 tgl@sss.pgh.pa.us 680 [ + + ]: 382226 : if (loop_count > 1)
681 : : {
682 : : /*
683 : : * For repeated indexscans, the appropriate estimate for the
684 : : * uncorrelated case is to scale up the number of tuples fetched in
685 : : * the Mackert and Lohman formula by the number of scans, so that we
686 : : * estimate the number of pages fetched by all the scans; then
687 : : * pro-rate the costs for one scan. In this case we assume all the
688 : : * fetches are random accesses.
689 : : */
690 : 39755 : pages_fetched = index_pages_fetched(tuples_fetched * loop_count,
691 : : baserel->pages,
6927 692 : 39755 : (double) index->pages,
693 : : root);
694 : :
5082 695 [ + + ]: 39755 : if (indexonly)
5076 696 : 4646 : pages_fetched = ceil(pages_fetched * (1.0 - baserel->allvisfrac));
697 : :
3125 rhaas@postgresql.org 698 : 39755 : rand_heap_pages = pages_fetched;
699 : :
4971 tgl@sss.pgh.pa.us 700 : 39755 : max_IO_cost = (pages_fetched * spc_random_page_cost) / loop_count;
701 : :
702 : : /*
703 : : * In the perfectly correlated case, the number of pages touched by
704 : : * each scan is selectivity * table_size, and we can use the Mackert
705 : : * and Lohman formula at the page level to estimate how much work is
706 : : * saved by caching across scans. We still assume all the fetches are
707 : : * random, though, which is an overestimate that's hard to correct for
708 : : * without double-counting the cache effects. (But in most cases
709 : : * where such a plan is actually interesting, only one page would get
710 : : * fetched per scan anyway, so it shouldn't matter much.)
711 : : */
6840 712 : 39755 : pages_fetched = ceil(indexSelectivity * (double) baserel->pages);
713 : :
4971 714 : 39755 : pages_fetched = index_pages_fetched(pages_fetched * loop_count,
715 : : baserel->pages,
6840 716 : 39755 : (double) index->pages,
717 : : root);
718 : :
5082 719 [ + + ]: 39755 : if (indexonly)
5076 720 : 4646 : pages_fetched = ceil(pages_fetched * (1.0 - baserel->allvisfrac));
721 : :
4971 722 : 39755 : min_IO_cost = (pages_fetched * spc_random_page_cost) / loop_count;
723 : : }
724 : : else
725 : : {
726 : : /*
727 : : * Normal case: apply the Mackert and Lohman formula, and then
728 : : * interpolate between that and the correlation-derived result.
729 : : */
7032 730 : 342471 : pages_fetched = index_pages_fetched(tuples_fetched,
731 : : baserel->pages,
6927 732 : 342471 : (double) index->pages,
733 : : root);
734 : :
5082 735 [ + + ]: 342471 : if (indexonly)
5076 736 : 30499 : pages_fetched = ceil(pages_fetched * (1.0 - baserel->allvisfrac));
737 : :
3125 rhaas@postgresql.org 738 : 342471 : rand_heap_pages = pages_fetched;
739 : :
740 : : /* max_IO_cost is for the perfectly uncorrelated case (csquared=0) */
5723 741 : 342471 : max_IO_cost = pages_fetched * spc_random_page_cost;
742 : :
743 : : /* min_IO_cost is for the perfectly correlated case (csquared=1) */
7032 tgl@sss.pgh.pa.us 744 : 342471 : pages_fetched = ceil(indexSelectivity * (double) baserel->pages);
745 : :
5082 746 [ + + ]: 342471 : if (indexonly)
5076 747 : 30499 : pages_fetched = ceil(pages_fetched * (1.0 - baserel->allvisfrac));
748 : :
5074 749 [ + + ]: 342471 : if (pages_fetched > 0)
750 : : {
751 : 313108 : min_IO_cost = spc_random_page_cost;
752 [ + + ]: 313108 : if (pages_fetched > 1)
753 : 90258 : min_IO_cost += (pages_fetched - 1) * spc_seq_page_cost;
754 : : }
755 : : else
756 : 29363 : min_IO_cost = 0;
757 : : }
758 : :
3125 rhaas@postgresql.org 759 [ + + ]: 382226 : if (partial_path)
760 : : {
761 : : /*
762 : : * For index only scans compute workers based on number of index pages
763 : : * fetched; the number of heap pages we fetch might be so small as to
764 : : * effectively rule out parallelism, which we don't want to do.
765 : : */
3098 766 [ + + ]: 133037 : if (indexonly)
767 : 10816 : rand_heap_pages = -1;
768 : :
769 : : /*
770 : : * Estimate the number of parallel workers required to scan index. Use
771 : : * the number of heap pages computed considering heap fetches won't be
772 : : * sequential as for parallel scans the pages are accessed in random
773 : : * order.
774 : : */
3125 775 : 133037 : path->path.parallel_workers = compute_parallel_worker(baserel,
776 : : rand_heap_pages,
777 : : index_pages,
778 : : max_parallel_workers_per_gather);
779 : :
780 : : /*
781 : : * Fall out if workers can't be assigned for parallel scan, because in
782 : : * such a case this path will be rejected. So there is no benefit in
783 : : * doing extra computation.
784 : : */
785 [ + + ]: 133037 : if (path->path.parallel_workers <= 0)
786 : 127981 : return;
787 : :
788 : 5056 : path->path.parallel_aware = true;
789 : : }
790 : :
791 : : /*
792 : : * Now interpolate based on estimated index order correlation to get total
793 : : * disk I/O cost for main table accesses.
794 : : */
6840 tgl@sss.pgh.pa.us 795 : 254245 : csquared = indexCorrelation * indexCorrelation;
796 : :
797 : 254245 : run_cost += max_IO_cost + csquared * (min_IO_cost - max_IO_cost);
798 : :
799 : : /*
800 : : * Estimate CPU costs per tuple.
801 : : *
802 : : * What we want here is cpu_tuple_cost plus the evaluation costs of any
803 : : * qual clauses that we have to evaluate as qpquals.
804 : : */
3840 805 : 254245 : cost_qual_eval(&qpqual_cost, qpquals, root);
806 : :
4896 807 : 254245 : startup_cost += qpqual_cost.startup;
808 : 254245 : cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple;
809 : :
3125 rhaas@postgresql.org 810 : 254245 : cpu_run_cost += cpu_per_tuple * tuples_fetched;
811 : :
812 : : /* tlist eval costs are paid per output row, not per tuple scanned */
3488 tgl@sss.pgh.pa.us 813 : 254245 : startup_cost += path->path.pathtarget->cost.startup;
3125 rhaas@postgresql.org 814 : 254245 : cpu_run_cost += path->path.pathtarget->cost.per_tuple * path->path.rows;
815 : :
816 : : /* Adjust costing for parallelism, if used. */
817 [ + + ]: 254245 : if (path->path.parallel_workers > 0)
818 : : {
819 : 5056 : double parallel_divisor = get_parallel_divisor(&path->path);
820 : :
821 : 5056 : path->path.rows = clamp_row_est(path->path.rows / parallel_divisor);
822 : :
823 : : /* The CPU cost is divided among all the workers. */
824 : 5056 : cpu_run_cost /= parallel_divisor;
825 : : }
826 : :
827 : 254245 : run_cost += cpu_run_cost;
828 : :
7443 tgl@sss.pgh.pa.us 829 : 254245 : path->path.startup_cost = startup_cost;
830 : 254245 : path->path.total_cost = startup_cost + run_cost;
831 : : }
832 : :
833 : : /*
834 : : * extract_nonindex_conditions
835 : : *
836 : : * Given a list of quals to be enforced in an indexscan, extract the ones that
837 : : * will have to be applied as qpquals (ie, the index machinery won't handle
838 : : * them). Here we detect only whether a qual clause is directly redundant
839 : : * with some indexclause. If the index path is chosen for use, createplan.c
840 : : * will try a bit harder to get rid of redundant qual conditions; specifically
841 : : * it will see if quals can be proven to be implied by the indexquals. But
842 : : * it does not seem worth the cycles to try to factor that in at this stage,
843 : : * since we're only trying to estimate qual eval costs. Otherwise this must
844 : : * match the logic in create_indexscan_plan().
845 : : *
846 : : * qual_clauses, and the result, are lists of RestrictInfos.
847 : : * indexclauses is a list of IndexClauses.
848 : : */
849 : : static List *
2401 850 : 451533 : extract_nonindex_conditions(List *qual_clauses, List *indexclauses)
851 : : {
3840 852 : 451533 : List *result = NIL;
853 : : ListCell *lc;
854 : :
855 [ + + + + : 948014 : foreach(lc, qual_clauses)
+ + ]
856 : : {
3071 857 : 496481 : RestrictInfo *rinfo = lfirst_node(RestrictInfo, lc);
858 : :
3840 859 [ + + ]: 496481 : if (rinfo->pseudoconstant)
860 : 4848 : continue; /* we may drop pseudoconstants here */
2401 861 [ + + ]: 491633 : if (is_redundant_with_indexclauses(rinfo, indexclauses))
862 : 288244 : continue; /* dup or derived from same EquivalenceClass */
863 : : /* ... skip the predicate proof attempt createplan.c will try ... */
3840 864 : 203389 : result = lappend(result, rinfo);
865 : : }
866 : 451533 : return result;
867 : : }
868 : :
869 : : /*
870 : : * index_pages_fetched
871 : : * Estimate the number of pages actually fetched after accounting for
872 : : * cache effects.
873 : : *
874 : : * We use an approximation proposed by Mackert and Lohman, "Index Scans
875 : : * Using a Finite LRU Buffer: A Validated I/O Model", ACM Transactions
876 : : * on Database Systems, Vol. 14, No. 3, September 1989, Pages 401-424.
877 : : * The Mackert and Lohman approximation is that the number of pages
878 : : * fetched is
879 : : * PF =
880 : : * min(2TNs/(2T+Ns), T) when T <= b
881 : : * 2TNs/(2T+Ns) when T > b and Ns <= 2Tb/(2T-b)
882 : : * b + (Ns - 2Tb/(2T-b))*(T-b)/T when T > b and Ns > 2Tb/(2T-b)
883 : : * where
884 : : * T = # pages in table
885 : : * N = # tuples in table
886 : : * s = selectivity = fraction of table to be scanned
887 : : * b = # buffer pages available (we include kernel space here)
888 : : *
889 : : * We assume that effective_cache_size is the total number of buffer pages
890 : : * available for the whole query, and pro-rate that space across all the
891 : : * tables in the query and the index currently under consideration. (This
892 : : * ignores space needed for other indexes used by the query, but since we
893 : : * don't know which indexes will get used, we can't estimate that very well;
894 : : * and in any case counting all the tables may well be an overestimate, since
895 : : * depending on the join plan not all the tables may be scanned concurrently.)
896 : : *
897 : : * The product Ns is the number of tuples fetched; we pass in that
898 : : * product rather than calculating it here. "pages" is the number of pages
899 : : * in the object under consideration (either an index or a table).
900 : : * "index_pages" is the amount to add to the total table space, which was
901 : : * computed for us by make_one_rel.
902 : : *
903 : : * Caller is expected to have ensured that tuples_fetched is greater than zero
904 : : * and rounded to integer (see clamp_row_est). The result will likewise be
905 : : * greater than zero and integral.
906 : : */
907 : : double
7032 908 : 531119 : index_pages_fetched(double tuples_fetched, BlockNumber pages,
909 : : double index_pages, PlannerInfo *root)
910 : : {
911 : : double pages_fetched;
912 : : double total_pages;
913 : : double T,
914 : : b;
915 : :
916 : : /* T is # pages in table, but don't allow it to be zero */
917 [ + + ]: 531119 : T = (pages > 1) ? (double) pages : 1.0;
918 : :
919 : : /* Compute number of pages assumed to be competing for cache space */
6927 920 : 531119 : total_pages = root->total_table_pages + index_pages;
921 [ + + ]: 531119 : total_pages = Max(total_pages, 1.0);
922 [ - + ]: 531119 : Assert(T <= total_pages);
923 : :
924 : : /* b is pro-rated share of effective_cache_size */
2999 925 : 531119 : b = (double) effective_cache_size * T / total_pages;
926 : :
927 : : /* force it positive and integral */
7032 928 [ - + ]: 531119 : if (b <= 1.0)
7032 tgl@sss.pgh.pa.us 929 :UBC 0 : b = 1.0;
930 : : else
7032 tgl@sss.pgh.pa.us 931 :CBC 531119 : b = ceil(b);
932 : :
933 : : /* This part is the Mackert and Lohman formula */
934 [ + - ]: 531119 : if (T <= b)
935 : : {
936 : 531119 : pages_fetched =
937 : 531119 : (2.0 * T * tuples_fetched) / (2.0 * T + tuples_fetched);
938 [ + + ]: 531119 : if (pages_fetched >= T)
939 : 306037 : pages_fetched = T;
940 : : else
941 : 225082 : pages_fetched = ceil(pages_fetched);
942 : : }
943 : : else
944 : : {
945 : : double lim;
946 : :
7032 tgl@sss.pgh.pa.us 947 :UBC 0 : lim = (2.0 * T * b) / (2.0 * T - b);
948 [ # # ]: 0 : if (tuples_fetched <= lim)
949 : : {
950 : 0 : pages_fetched =
951 : 0 : (2.0 * T * tuples_fetched) / (2.0 * T + tuples_fetched);
952 : : }
953 : : else
954 : : {
955 : 0 : pages_fetched =
956 : 0 : b + (tuples_fetched - lim) * (T - b) / T;
957 : : }
958 : 0 : pages_fetched = ceil(pages_fetched);
959 : : }
7032 tgl@sss.pgh.pa.us 960 :CBC 531119 : return pages_fetched;
961 : : }
962 : :
963 : : /*
964 : : * get_indexpath_pages
965 : : * Determine the total size of the indexes used in a bitmap index path.
966 : : *
967 : : * Note: if the same index is used more than once in a bitmap tree, we will
968 : : * count it multiple times, which perhaps is the wrong thing ... but it's
969 : : * not completely clear, and detecting duplicates is difficult, so ignore it
970 : : * for now.
971 : : */
972 : : static double
6927 973 : 89697 : get_indexpath_pages(Path *bitmapqual)
974 : : {
975 : 89697 : double result = 0;
976 : : ListCell *l;
977 : :
978 [ + + ]: 89697 : if (IsA(bitmapqual, BitmapAndPath))
979 : : {
980 : 11339 : BitmapAndPath *apath = (BitmapAndPath *) bitmapqual;
981 : :
982 [ + - + + : 34017 : foreach(l, apath->bitmapquals)
+ + ]
983 : : {
984 : 22678 : result += get_indexpath_pages((Path *) lfirst(l));
985 : : }
986 : : }
987 [ + + ]: 78358 : else if (IsA(bitmapqual, BitmapOrPath))
988 : : {
989 : 35 : BitmapOrPath *opath = (BitmapOrPath *) bitmapqual;
990 : :
991 [ + - + + : 111 : foreach(l, opath->bitmapquals)
+ + ]
992 : : {
993 : 76 : result += get_indexpath_pages((Path *) lfirst(l));
994 : : }
995 : : }
996 [ + - ]: 78323 : else if (IsA(bitmapqual, IndexPath))
997 : : {
998 : 78323 : IndexPath *ipath = (IndexPath *) bitmapqual;
999 : :
1000 : 78323 : result = (double) ipath->indexinfo->pages;
1001 : : }
1002 : : else
6927 tgl@sss.pgh.pa.us 1003 [ # # ]:UBC 0 : elog(ERROR, "unrecognized node type: %d", nodeTag(bitmapqual));
1004 : :
6927 tgl@sss.pgh.pa.us 1005 :CBC 89697 : return result;
1006 : : }
1007 : :
1008 : : /*
1009 : : * cost_bitmap_heap_scan
1010 : : * Determines and returns the cost of scanning a relation using a bitmap
1011 : : * index-then-heap plan.
1012 : : *
1013 : : * 'baserel' is the relation to be scanned
1014 : : * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
1015 : : * 'bitmapqual' is a tree of IndexPaths, BitmapAndPaths, and BitmapOrPaths
1016 : : * 'loop_count' is the number of repetitions of the indexscan to factor into
1017 : : * estimates of caching behavior
1018 : : *
1019 : : * Note: the component IndexPaths in bitmapqual should have been costed
1020 : : * using the same loop_count.
1021 : : */
1022 : : void
7398 1023 : 260738 : cost_bitmap_heap_scan(Path *path, PlannerInfo *root, RelOptInfo *baserel,
1024 : : ParamPathInfo *param_info,
1025 : : Path *bitmapqual, double loop_count)
1026 : : {
7445 1027 : 260738 : Cost startup_cost = 0;
1028 : 260738 : Cost run_cost = 0;
1029 : : Cost indexTotalCost;
1030 : : QualCost qpqual_cost;
1031 : : Cost cpu_per_tuple;
1032 : : Cost cost_per_page;
1033 : : Cost cpu_run_cost;
1034 : : double tuples_fetched;
1035 : : double pages_fetched;
1036 : : double spc_seq_page_cost,
1037 : : spc_random_page_cost;
1038 : : double T;
1039 : :
1040 : : /* Should only be applied to base relations */
1041 [ - + ]: 260738 : Assert(IsA(baserel, RelOptInfo));
1042 [ - + ]: 260738 : Assert(baserel->relid > 0);
1043 [ - + ]: 260738 : Assert(baserel->rtekind == RTE_RELATION);
1044 : :
1045 : : /* Mark the path with the correct row estimate */
4888 1046 [ + + ]: 260738 : if (param_info)
1047 : 107324 : path->rows = param_info->ppi_rows;
1048 : : else
4971 1049 : 153414 : path->rows = baserel->rows;
1050 : :
3144 rhaas@postgresql.org 1051 : 260738 : pages_fetched = compute_bitmap_pages(root, baserel, bitmapqual,
1052 : : loop_count, &indexTotalCost,
1053 : : &tuples_fetched);
1054 : :
7443 tgl@sss.pgh.pa.us 1055 : 260738 : startup_cost += indexTotalCost;
3144 rhaas@postgresql.org 1056 [ + + ]: 260738 : T = (baserel->pages > 1) ? (double) baserel->pages : 1.0;
1057 : :
1058 : : /* Fetch estimated page costs for tablespace containing table. */
5723 1059 : 260738 : get_tablespace_page_costs(baserel->reltablespace,
1060 : : &spc_random_page_cost,
1061 : : &spc_seq_page_cost);
1062 : :
1063 : : /*
1064 : : * For small numbers of pages we should charge spc_random_page_cost
1065 : : * apiece, while if nearly all the table's pages are being read, it's more
1066 : : * appropriate to charge spc_seq_page_cost apiece. The effect is
1067 : : * nonlinear, too. For lack of a better idea, interpolate like this to
1068 : : * determine the cost per page.
1069 : : */
7442 tgl@sss.pgh.pa.us 1070 [ + + ]: 260738 : if (pages_fetched >= 2.0)
5723 rhaas@postgresql.org 1071 : 53881 : cost_per_page = spc_random_page_cost -
1072 : 53881 : (spc_random_page_cost - spc_seq_page_cost)
1073 : 53881 : * sqrt(pages_fetched / T);
1074 : : else
1075 : 206857 : cost_per_page = spc_random_page_cost;
1076 : :
7443 tgl@sss.pgh.pa.us 1077 : 260738 : run_cost += pages_fetched * cost_per_page;
1078 : :
1079 : : /*
1080 : : * Estimate CPU costs per tuple.
1081 : : *
1082 : : * Often the indexquals don't need to be rechecked at each tuple ... but
1083 : : * not always, especially not if there are enough tuples involved that the
1084 : : * bitmaps become lossy. For the moment, just assume they will be
1085 : : * rechecked always. This means we charge the full freight for all the
1086 : : * scan clauses.
1087 : : */
4888 1088 : 260738 : get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
1089 : :
1090 : 260738 : startup_cost += qpqual_cost.startup;
1091 : 260738 : cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple;
3104 rhaas@postgresql.org 1092 : 260738 : cpu_run_cost = cpu_per_tuple * tuples_fetched;
1093 : :
1094 : : /* Adjust costing for parallelism, if used. */
1095 [ + + ]: 260738 : if (path->parallel_workers > 0)
1096 : : {
1097 : 2094 : double parallel_divisor = get_parallel_divisor(path);
1098 : :
1099 : : /* The CPU cost is divided among all the workers. */
1100 : 2094 : cpu_run_cost /= parallel_divisor;
1101 : :
1102 : 2094 : path->rows = clamp_row_est(path->rows / parallel_divisor);
1103 : : }
1104 : :
1105 : :
1106 : 260738 : run_cost += cpu_run_cost;
1107 : :
1108 : : /* tlist eval costs are paid per output row, not per tuple scanned */
3488 tgl@sss.pgh.pa.us 1109 : 260738 : startup_cost += path->pathtarget->cost.startup;
1110 : 260738 : run_cost += path->pathtarget->cost.per_tuple * path->rows;
1111 : :
381 rhaas@postgresql.org 1112 : 260738 : path->disabled_nodes = enable_bitmapscan ? 0 : 1;
7445 tgl@sss.pgh.pa.us 1113 : 260738 : path->startup_cost = startup_cost;
1114 : 260738 : path->total_cost = startup_cost + run_cost;
1115 : 260738 : }
1116 : :
1117 : : /*
1118 : : * cost_bitmap_tree_node
1119 : : * Extract cost and selectivity from a bitmap tree node (index/and/or)
1120 : : */
1121 : : void
7443 1122 : 488894 : cost_bitmap_tree_node(Path *path, Cost *cost, Selectivity *selec)
1123 : : {
1124 [ + + ]: 488894 : if (IsA(path, IndexPath))
1125 : : {
1126 : 461998 : *cost = ((IndexPath *) path)->indextotalcost;
1127 : 461998 : *selec = ((IndexPath *) path)->indexselectivity;
1128 : :
1129 : : /*
1130 : : * Charge a small amount per retrieved tuple to reflect the costs of
1131 : : * manipulating the bitmap. This is mostly to make sure that a bitmap
1132 : : * scan doesn't look to be the same cost as an indexscan to retrieve a
1133 : : * single tuple.
1134 : : */
4971 1135 : 461998 : *cost += 0.1 * cpu_operator_cost * path->rows;
1136 : : }
7443 1137 [ + + ]: 26896 : else if (IsA(path, BitmapAndPath))
1138 : : {
1139 : 25128 : *cost = path->total_cost;
1140 : 25128 : *selec = ((BitmapAndPath *) path)->bitmapselectivity;
1141 : : }
1142 [ + - ]: 1768 : else if (IsA(path, BitmapOrPath))
1143 : : {
1144 : 1768 : *cost = path->total_cost;
1145 : 1768 : *selec = ((BitmapOrPath *) path)->bitmapselectivity;
1146 : : }
1147 : : else
1148 : : {
7443 tgl@sss.pgh.pa.us 1149 [ # # ]:UBC 0 : elog(ERROR, "unrecognized node type: %d", nodeTag(path));
1150 : : *cost = *selec = 0; /* keep compiler quiet */
1151 : : }
7443 tgl@sss.pgh.pa.us 1152 :CBC 488894 : }
1153 : :
1154 : : /*
1155 : : * cost_bitmap_and_node
1156 : : * Estimate the cost of a BitmapAnd node
1157 : : *
1158 : : * Note that this considers only the costs of index scanning and bitmap
1159 : : * creation, not the eventual heap access. In that sense the object isn't
1160 : : * truly a Path, but it has enough path-like properties (costs in particular)
1161 : : * to warrant treating it as one. We don't bother to set the path rows field,
1162 : : * however.
1163 : : */
1164 : : void
7398 1165 : 25025 : cost_bitmap_and_node(BitmapAndPath *path, PlannerInfo *root)
1166 : : {
1167 : : Cost totalCost;
1168 : : Selectivity selec;
1169 : : ListCell *l;
1170 : :
1171 : : /*
1172 : : * We estimate AND selectivity on the assumption that the inputs are
1173 : : * independent. This is probably often wrong, but we don't have the info
1174 : : * to do better.
1175 : : *
1176 : : * The runtime cost of the BitmapAnd itself is estimated at 100x
1177 : : * cpu_operator_cost for each tbm_intersect needed. Probably too small,
1178 : : * definitely too simplistic?
1179 : : */
7443 1180 : 25025 : totalCost = 0.0;
1181 : 25025 : selec = 1.0;
1182 [ + - + + : 75075 : foreach(l, path->bitmapquals)
+ + ]
1183 : : {
7266 bruce@momjian.us 1184 : 50050 : Path *subpath = (Path *) lfirst(l);
1185 : : Cost subCost;
1186 : : Selectivity subselec;
1187 : :
7443 tgl@sss.pgh.pa.us 1188 : 50050 : cost_bitmap_tree_node(subpath, &subCost, &subselec);
1189 : :
1190 : 50050 : selec *= subselec;
1191 : :
1192 : 50050 : totalCost += subCost;
1193 [ + + ]: 50050 : if (l != list_head(path->bitmapquals))
1194 : 25025 : totalCost += 100.0 * cpu_operator_cost;
1195 : : }
1196 : 25025 : path->bitmapselectivity = selec;
4971 1197 : 25025 : path->path.rows = 0; /* per above, not used */
381 rhaas@postgresql.org 1198 : 25025 : path->path.disabled_nodes = 0;
7443 tgl@sss.pgh.pa.us 1199 : 25025 : path->path.startup_cost = totalCost;
1200 : 25025 : path->path.total_cost = totalCost;
1201 : 25025 : }
1202 : :
1203 : : /*
1204 : : * cost_bitmap_or_node
1205 : : * Estimate the cost of a BitmapOr node
1206 : : *
1207 : : * See comments for cost_bitmap_and_node.
1208 : : */
1209 : : void
7398 1210 : 508 : cost_bitmap_or_node(BitmapOrPath *path, PlannerInfo *root)
1211 : : {
1212 : : Cost totalCost;
1213 : : Selectivity selec;
1214 : : ListCell *l;
1215 : :
1216 : : /*
1217 : : * We estimate OR selectivity on the assumption that the inputs are
1218 : : * non-overlapping, since that's often the case in "x IN (list)" type
1219 : : * situations. Of course, we clamp to 1.0 at the end.
1220 : : *
1221 : : * The runtime cost of the BitmapOr itself is estimated at 100x
1222 : : * cpu_operator_cost for each tbm_union needed. Probably too small,
1223 : : * definitely too simplistic? We are aware that the tbm_unions are
1224 : : * optimized out when the inputs are BitmapIndexScans.
1225 : : */
7443 1226 : 508 : totalCost = 0.0;
1227 : 508 : selec = 0.0;
1228 [ + - + + : 1425 : foreach(l, path->bitmapquals)
+ + ]
1229 : : {
7266 bruce@momjian.us 1230 : 917 : Path *subpath = (Path *) lfirst(l);
1231 : : Cost subCost;
1232 : : Selectivity subselec;
1233 : :
7443 tgl@sss.pgh.pa.us 1234 : 917 : cost_bitmap_tree_node(subpath, &subCost, &subselec);
1235 : :
1236 : 917 : selec += subselec;
1237 : :
1238 : 917 : totalCost += subCost;
1239 [ + + ]: 917 : if (l != list_head(path->bitmapquals) &&
1240 [ - + ]: 409 : !IsA(subpath, IndexPath))
7443 tgl@sss.pgh.pa.us 1241 :LBC (3) : totalCost += 100.0 * cpu_operator_cost;
1242 : : }
7443 tgl@sss.pgh.pa.us 1243 [ + - ]:CBC 508 : path->bitmapselectivity = Min(selec, 1.0);
4971 1244 : 508 : path->path.rows = 0; /* per above, not used */
7443 1245 : 508 : path->path.startup_cost = totalCost;
1246 : 508 : path->path.total_cost = totalCost;
1247 : 508 : }
1248 : :
1249 : : /*
1250 : : * cost_tidscan
1251 : : * Determines and returns the cost of scanning a relation using TIDs.
1252 : : *
1253 : : * 'baserel' is the relation to be scanned
1254 : : * 'tidquals' is the list of TID-checkable quals
1255 : : * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
1256 : : */
1257 : : void
7398 1258 : 432 : cost_tidscan(Path *path, PlannerInfo *root,
1259 : : RelOptInfo *baserel, List *tidquals, ParamPathInfo *param_info)
1260 : : {
9335 1261 : 432 : Cost startup_cost = 0;
1262 : 432 : Cost run_cost = 0;
1263 : : QualCost qpqual_cost;
1264 : : Cost cpu_per_tuple;
1265 : : QualCost tid_qual_cost;
1266 : : double ntuples;
1267 : : ListCell *l;
1268 : : double spc_random_page_cost;
1269 : :
1270 : : /* Should only be applied to base relations */
8246 1271 [ - + ]: 432 : Assert(baserel->relid > 0);
8518 1272 [ - + ]: 432 : Assert(baserel->rtekind == RTE_RELATION);
381 rhaas@postgresql.org 1273 [ - + ]: 432 : Assert(tidquals != NIL);
1274 : :
1275 : : /* Mark the path with the correct row estimate */
4759 tgl@sss.pgh.pa.us 1276 [ + + ]: 432 : if (param_info)
1277 : 72 : path->rows = param_info->ppi_rows;
1278 : : else
1279 : 360 : path->rows = baserel->rows;
1280 : :
1281 : : /* Count how many tuples we expect to retrieve */
7224 1282 : 432 : ntuples = 0;
1283 [ + - + + : 876 : foreach(l, tidquals)
+ + ]
1284 : : {
2442 1285 : 444 : RestrictInfo *rinfo = lfirst_node(RestrictInfo, l);
1286 : 444 : Expr *qual = rinfo->clause;
1287 : :
1288 : : /*
1289 : : * We must use a TID scan for CurrentOfExpr; in any other case, we
1290 : : * should be generating a TID scan only if enable_tidscan=true. Also,
1291 : : * if CurrentOfExpr is the qual, there should be only one.
1292 : : */
381 rhaas@postgresql.org 1293 [ - + - - ]: 444 : Assert(enable_tidscan || IsA(qual, CurrentOfExpr));
1294 [ + + - + ]: 444 : Assert(list_length(tidquals) == 1 || !IsA(qual, CurrentOfExpr));
1295 : :
2442 tgl@sss.pgh.pa.us 1296 [ + + ]: 444 : if (IsA(qual, ScalarArrayOpExpr))
1297 : : {
1298 : : /* Each element of the array yields 1 tuple */
1299 : 25 : ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) qual;
6912 bruce@momjian.us 1300 : 25 : Node *arraynode = (Node *) lsecond(saop->args);
1301 : :
611 tgl@sss.pgh.pa.us 1302 : 25 : ntuples += estimate_array_length(root, arraynode);
1303 : : }
2442 1304 [ + + ]: 419 : else if (IsA(qual, CurrentOfExpr))
1305 : : {
1306 : : /* CURRENT OF yields 1 tuple */
6527 1307 : 202 : ntuples++;
1308 : : }
1309 : : else
1310 : : {
1311 : : /* It's just CTID = something, count 1 tuple */
7224 1312 : 217 : ntuples++;
1313 : : }
1314 : : }
1315 : :
1316 : : /*
1317 : : * The TID qual expressions will be computed once, any other baserestrict
1318 : : * quals once per retrieved tuple.
1319 : : */
6662 1320 : 432 : cost_qual_eval(&tid_qual_cost, tidquals, root);
1321 : :
1322 : : /* fetch estimated page cost for tablespace containing table */
5723 rhaas@postgresql.org 1323 : 432 : get_tablespace_page_costs(baserel->reltablespace,
1324 : : &spc_random_page_cost,
1325 : : NULL);
1326 : :
1327 : : /* disk costs --- assume each tuple on a different page */
1328 : 432 : run_cost += spc_random_page_cost * ntuples;
1329 : :
1330 : : /* Add scanning CPU costs */
4759 tgl@sss.pgh.pa.us 1331 : 432 : get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
1332 : :
1333 : : /* XXX currently we assume TID quals are a subset of qpquals */
1334 : 432 : startup_cost += qpqual_cost.startup + tid_qual_cost.per_tuple;
1335 : 432 : cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple -
6662 1336 : 432 : tid_qual_cost.per_tuple;
9335 1337 : 432 : run_cost += cpu_per_tuple * ntuples;
1338 : :
1339 : : /* tlist eval costs are paid per output row, not per tuple scanned */
3488 1340 : 432 : startup_cost += path->pathtarget->cost.startup;
1341 : 432 : run_cost += path->pathtarget->cost.per_tuple * path->rows;
1342 : :
1343 : : /*
1344 : : * There are assertions above verifying that we only reach this function
1345 : : * either when enable_tidscan=true or when the TID scan is the only legal
1346 : : * path, so it's safe to set disabled_nodes to zero here.
1347 : : */
381 rhaas@postgresql.org 1348 : 432 : path->disabled_nodes = 0;
9335 tgl@sss.pgh.pa.us 1349 : 432 : path->startup_cost = startup_cost;
1350 : 432 : path->total_cost = startup_cost + run_cost;
9419 bruce@momjian.us 1351 : 432 : }
1352 : :
1353 : : /*
1354 : : * cost_tidrangescan
1355 : : * Determines and sets the costs of scanning a relation using a range of
1356 : : * TIDs for 'path'
1357 : : *
1358 : : * 'baserel' is the relation to be scanned
1359 : : * 'tidrangequals' is the list of TID-checkable range quals
1360 : : * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
1361 : : */
1362 : : void
1652 drowley@postgresql.o 1363 : 968 : cost_tidrangescan(Path *path, PlannerInfo *root,
1364 : : RelOptInfo *baserel, List *tidrangequals,
1365 : : ParamPathInfo *param_info)
1366 : : {
1367 : : Selectivity selectivity;
1368 : : double pages;
1369 : 968 : Cost startup_cost = 0;
1370 : 968 : Cost run_cost = 0;
1371 : : QualCost qpqual_cost;
1372 : : Cost cpu_per_tuple;
1373 : : QualCost tid_qual_cost;
1374 : : double ntuples;
1375 : : double nseqpages;
1376 : : double spc_random_page_cost;
1377 : : double spc_seq_page_cost;
1378 : :
1379 : : /* Should only be applied to base relations */
1380 [ - + ]: 968 : Assert(baserel->relid > 0);
1381 [ - + ]: 968 : Assert(baserel->rtekind == RTE_RELATION);
1382 : :
1383 : : /* Mark the path with the correct row estimate */
1384 [ - + ]: 968 : if (param_info)
1652 drowley@postgresql.o 1385 :UBC 0 : path->rows = param_info->ppi_rows;
1386 : : else
1652 drowley@postgresql.o 1387 :CBC 968 : path->rows = baserel->rows;
1388 : :
1389 : : /* Count how many tuples and pages we expect to scan */
1390 : 968 : selectivity = clauselist_selectivity(root, tidrangequals, baserel->relid,
1391 : : JOIN_INNER, NULL);
1392 : 968 : pages = ceil(selectivity * baserel->pages);
1393 : :
1394 [ + + ]: 968 : if (pages <= 0.0)
1395 : 21 : pages = 1.0;
1396 : :
1397 : : /*
1398 : : * The first page in a range requires a random seek, but each subsequent
1399 : : * page is just a normal sequential page read. NOTE: it's desirable for
1400 : : * TID Range Scans to cost more than the equivalent Sequential Scans,
1401 : : * because Seq Scans have some performance advantages such as scan
1402 : : * synchronization and parallelizability, and we'd prefer one of them to
1403 : : * be picked unless a TID Range Scan really is better.
1404 : : */
1405 : 968 : ntuples = selectivity * baserel->tuples;
1406 : 968 : nseqpages = pages - 1.0;
1407 : :
1408 : : /*
1409 : : * The TID qual expressions will be computed once, any other baserestrict
1410 : : * quals once per retrieved tuple.
1411 : : */
1412 : 968 : cost_qual_eval(&tid_qual_cost, tidrangequals, root);
1413 : :
1414 : : /* fetch estimated page cost for tablespace containing table */
1415 : 968 : get_tablespace_page_costs(baserel->reltablespace,
1416 : : &spc_random_page_cost,
1417 : : &spc_seq_page_cost);
1418 : :
1419 : : /* disk costs; 1 random page and the remainder as seq pages */
1420 : 968 : run_cost += spc_random_page_cost + spc_seq_page_cost * nseqpages;
1421 : :
1422 : : /* Add scanning CPU costs */
1423 : 968 : get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
1424 : :
1425 : : /*
1426 : : * XXX currently we assume TID quals are a subset of qpquals at this
1427 : : * point; they will be removed (if possible) when we create the plan, so
1428 : : * we subtract their cost from the total qpqual cost. (If the TID quals
1429 : : * can't be removed, this is a mistake and we're going to underestimate
1430 : : * the CPU cost a bit.)
1431 : : */
1432 : 968 : startup_cost += qpqual_cost.startup + tid_qual_cost.per_tuple;
1433 : 968 : cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple -
1434 : 968 : tid_qual_cost.per_tuple;
1435 : 968 : run_cost += cpu_per_tuple * ntuples;
1436 : :
1437 : : /* tlist eval costs are paid per output row, not per tuple scanned */
1438 : 968 : startup_cost += path->pathtarget->cost.startup;
1439 : 968 : run_cost += path->pathtarget->cost.per_tuple * path->rows;
1440 : :
1441 : : /* we should not generate this path type when enable_tidscan=false */
381 rhaas@postgresql.org 1442 [ - + ]: 968 : Assert(enable_tidscan);
1443 : 968 : path->disabled_nodes = 0;
1652 drowley@postgresql.o 1444 : 968 : path->startup_cost = startup_cost;
1445 : 968 : path->total_cost = startup_cost + run_cost;
1446 : 968 : }
1447 : :
1448 : : /*
1449 : : * cost_subqueryscan
1450 : : * Determines and returns the cost of scanning a subquery RTE.
1451 : : *
1452 : : * 'baserel' is the relation to be scanned
1453 : : * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
1454 : : * 'trivial_pathtarget' is true if the pathtarget is believed to be trivial.
1455 : : */
1456 : : void
3470 tgl@sss.pgh.pa.us 1457 : 24276 : cost_subqueryscan(SubqueryScanPath *path, PlannerInfo *root,
1458 : : RelOptInfo *baserel, ParamPathInfo *param_info,
1459 : : bool trivial_pathtarget)
1460 : : {
1461 : : Cost startup_cost;
1462 : : Cost run_cost;
1463 : : List *qpquals;
1464 : : QualCost qpqual_cost;
1465 : : Cost cpu_per_tuple;
1466 : :
1467 : : /* Should only be applied to base relations that are subqueries */
8090 1468 [ - + ]: 24276 : Assert(baserel->relid > 0);
1469 [ - + ]: 24276 : Assert(baserel->rtekind == RTE_SUBQUERY);
1470 : :
1471 : : /*
1472 : : * We compute the rowcount estimate as the subplan's estimate times the
1473 : : * selectivity of relevant restriction clauses. In simple cases this will
1474 : : * come out the same as baserel->rows; but when dealing with parallelized
1475 : : * paths we must do it like this to get the right answer.
1476 : : */
4888 1477 [ + + ]: 24276 : if (param_info)
1221 1478 : 303 : qpquals = list_concat_copy(param_info->ppi_clauses,
1479 : 303 : baserel->baserestrictinfo);
1480 : : else
1481 : 23973 : qpquals = baserel->baserestrictinfo;
1482 : :
1483 : 24276 : path->path.rows = clamp_row_est(path->subpath->rows *
1484 : 24276 : clauselist_selectivity(root,
1485 : : qpquals,
1486 : : 0,
1487 : : JOIN_INNER,
1488 : : NULL));
1489 : :
1490 : : /*
1491 : : * Cost of path is cost of evaluating the subplan, plus cost of evaluating
1492 : : * any restriction clauses and tlist that will be attached to the
1493 : : * SubqueryScan node, plus cpu_tuple_cost to account for selection and
1494 : : * projection overhead.
1495 : : */
381 rhaas@postgresql.org 1496 : 24276 : path->path.disabled_nodes = path->subpath->disabled_nodes;
3470 tgl@sss.pgh.pa.us 1497 : 24276 : path->path.startup_cost = path->subpath->startup_cost;
1498 : 24276 : path->path.total_cost = path->subpath->total_cost;
1499 : :
1500 : : /*
1501 : : * However, if there are no relevant restriction clauses and the
1502 : : * pathtarget is trivial, then we expect that setrefs.c will optimize away
1503 : : * the SubqueryScan plan node altogether, so we should just make its cost
1504 : : * and rowcount equal to the input path's.
1505 : : *
1506 : : * Note: there are some edge cases where createplan.c will apply a
1507 : : * different targetlist to the SubqueryScan node, thus falsifying our
1508 : : * current estimate of whether the target is trivial, and making the cost
1509 : : * estimate (though not the rowcount) wrong. It does not seem worth the
1510 : : * extra complication to try to account for that exactly, especially since
1511 : : * that behavior falsifies other cost estimates as well.
1512 : : */
1145 1513 [ + + + + ]: 24276 : if (qpquals == NIL && trivial_pathtarget)
1514 : 12355 : return;
1515 : :
4888 1516 : 11921 : get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
1517 : :
1518 : 11921 : startup_cost = qpqual_cost.startup;
1519 : 11921 : cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple;
1221 1520 : 11921 : run_cost = cpu_per_tuple * path->subpath->rows;
1521 : :
1522 : : /* tlist eval costs are paid per output row, not per tuple scanned */
3470 1523 : 11921 : startup_cost += path->path.pathtarget->cost.startup;
1524 : 11921 : run_cost += path->path.pathtarget->cost.per_tuple * path->path.rows;
1525 : :
1526 : 11921 : path->path.startup_cost += startup_cost;
1527 : 11921 : path->path.total_cost += startup_cost + run_cost;
1528 : : }
1529 : :
1530 : : /*
1531 : : * cost_functionscan
1532 : : * Determines and returns the cost of scanning a function RTE.
1533 : : *
1534 : : * 'baserel' is the relation to be scanned
1535 : : * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
1536 : : */
1537 : : void
4778 1538 : 24314 : cost_functionscan(Path *path, PlannerInfo *root,
1539 : : RelOptInfo *baserel, ParamPathInfo *param_info)
1540 : : {
8518 1541 : 24314 : Cost startup_cost = 0;
1542 : 24314 : Cost run_cost = 0;
1543 : : QualCost qpqual_cost;
1544 : : Cost cpu_per_tuple;
1545 : : RangeTblEntry *rte;
1546 : : QualCost exprcost;
1547 : :
1548 : : /* Should only be applied to base relations that are functions */
8246 1549 [ - + ]: 24314 : Assert(baserel->relid > 0);
6713 1550 [ + - ]: 24314 : rte = planner_rt_fetch(baserel->relid, root);
6802 1551 [ - + ]: 24314 : Assert(rte->rtekind == RTE_FUNCTION);
1552 : :
1553 : : /* Mark the path with the correct row estimate */
4778 1554 [ + + ]: 24314 : if (param_info)
1555 : 4103 : path->rows = param_info->ppi_rows;
1556 : : else
1557 : 20211 : path->rows = baserel->rows;
1558 : :
1559 : : /*
1560 : : * Estimate costs of executing the function expression(s).
1561 : : *
1562 : : * Currently, nodeFunctionscan.c always executes the functions to
1563 : : * completion before returning any rows, and caches the results in a
1564 : : * tuplestore. So the function eval cost is all startup cost, and per-row
1565 : : * costs are minimal.
1566 : : *
1567 : : * XXX in principle we ought to charge tuplestore spill costs if the
1568 : : * number of rows is large. However, given how phony our rowcount
1569 : : * estimates for functions tend to be, there's not a lot of point in that
1570 : : * refinement right now.
1571 : : */
4307 1572 : 24314 : cost_qual_eval_node(&exprcost, (Node *) rte->functions, root);
1573 : :
5838 1574 : 24314 : startup_cost += exprcost.startup + exprcost.per_tuple;
1575 : :
1576 : : /* Add scanning CPU costs */
4778 1577 : 24314 : get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
1578 : :
1579 : 24314 : startup_cost += qpqual_cost.startup;
1580 : 24314 : cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple;
8518 1581 : 24314 : run_cost += cpu_per_tuple * baserel->tuples;
1582 : :
1583 : : /* tlist eval costs are paid per output row, not per tuple scanned */
3488 1584 : 24314 : startup_cost += path->pathtarget->cost.startup;
1585 : 24314 : run_cost += path->pathtarget->cost.per_tuple * path->rows;
1586 : :
381 rhaas@postgresql.org 1587 : 24314 : path->disabled_nodes = 0;
8518 tgl@sss.pgh.pa.us 1588 : 24314 : path->startup_cost = startup_cost;
1589 : 24314 : path->total_cost = startup_cost + run_cost;
1590 : 24314 : }
1591 : :
1592 : : /*
1593 : : * cost_tablefuncscan
1594 : : * Determines and returns the cost of scanning a table function.
1595 : : *
1596 : : * 'baserel' is the relation to be scanned
1597 : : * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
1598 : : */
1599 : : void
3104 alvherre@alvh.no-ip. 1600 : 311 : cost_tablefuncscan(Path *path, PlannerInfo *root,
1601 : : RelOptInfo *baserel, ParamPathInfo *param_info)
1602 : : {
1603 : 311 : Cost startup_cost = 0;
1604 : 311 : Cost run_cost = 0;
1605 : : QualCost qpqual_cost;
1606 : : Cost cpu_per_tuple;
1607 : : RangeTblEntry *rte;
1608 : : QualCost exprcost;
1609 : :
1610 : : /* Should only be applied to base relations that are functions */
1611 [ - + ]: 311 : Assert(baserel->relid > 0);
1612 [ + - ]: 311 : rte = planner_rt_fetch(baserel->relid, root);
1613 [ - + ]: 311 : Assert(rte->rtekind == RTE_TABLEFUNC);
1614 : :
1615 : : /* Mark the path with the correct row estimate */
1616 [ + + ]: 311 : if (param_info)
1617 : 117 : path->rows = param_info->ppi_rows;
1618 : : else
1619 : 194 : path->rows = baserel->rows;
1620 : :
1621 : : /*
1622 : : * Estimate costs of executing the table func expression(s).
1623 : : *
1624 : : * XXX in principle we ought to charge tuplestore spill costs if the
1625 : : * number of rows is large. However, given how phony our rowcount
1626 : : * estimates for tablefuncs tend to be, there's not a lot of point in that
1627 : : * refinement right now.
1628 : : */
1629 : 311 : cost_qual_eval_node(&exprcost, (Node *) rte->tablefunc, root);
1630 : :
1631 : 311 : startup_cost += exprcost.startup + exprcost.per_tuple;
1632 : :
1633 : : /* Add scanning CPU costs */
1634 : 311 : get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
1635 : :
1636 : 311 : startup_cost += qpqual_cost.startup;
1637 : 311 : cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple;
1638 : 311 : run_cost += cpu_per_tuple * baserel->tuples;
1639 : :
1640 : : /* tlist eval costs are paid per output row, not per tuple scanned */
1641 : 311 : startup_cost += path->pathtarget->cost.startup;
1642 : 311 : run_cost += path->pathtarget->cost.per_tuple * path->rows;
1643 : :
381 rhaas@postgresql.org 1644 : 311 : path->disabled_nodes = 0;
3104 alvherre@alvh.no-ip. 1645 : 311 : path->startup_cost = startup_cost;
1646 : 311 : path->total_cost = startup_cost + run_cost;
1647 : 311 : }
1648 : :
1649 : : /*
1650 : : * cost_valuesscan
1651 : : * Determines and returns the cost of scanning a VALUES RTE.
1652 : : *
1653 : : * 'baserel' is the relation to be scanned
1654 : : * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
1655 : : */
1656 : : void
4773 tgl@sss.pgh.pa.us 1657 : 4104 : cost_valuesscan(Path *path, PlannerInfo *root,
1658 : : RelOptInfo *baserel, ParamPathInfo *param_info)
1659 : : {
6975 mail@joeconway.com 1660 : 4104 : Cost startup_cost = 0;
1661 : 4104 : Cost run_cost = 0;
1662 : : QualCost qpqual_cost;
1663 : : Cost cpu_per_tuple;
1664 : :
1665 : : /* Should only be applied to base relations that are values lists */
1666 [ - + ]: 4104 : Assert(baserel->relid > 0);
1667 [ - + ]: 4104 : Assert(baserel->rtekind == RTE_VALUES);
1668 : :
1669 : : /* Mark the path with the correct row estimate */
4773 tgl@sss.pgh.pa.us 1670 [ + + ]: 4104 : if (param_info)
1671 : 33 : path->rows = param_info->ppi_rows;
1672 : : else
1673 : 4071 : path->rows = baserel->rows;
1674 : :
1675 : : /*
1676 : : * For now, estimate list evaluation cost at one operator eval per list
1677 : : * (probably pretty bogus, but is it worth being smarter?)
1678 : : */
6975 mail@joeconway.com 1679 : 4104 : cpu_per_tuple = cpu_operator_cost;
1680 : :
1681 : : /* Add scanning CPU costs */
4773 tgl@sss.pgh.pa.us 1682 : 4104 : get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
1683 : :
1684 : 4104 : startup_cost += qpqual_cost.startup;
1685 : 4104 : cpu_per_tuple += cpu_tuple_cost + qpqual_cost.per_tuple;
6975 mail@joeconway.com 1686 : 4104 : run_cost += cpu_per_tuple * baserel->tuples;
1687 : :
1688 : : /* tlist eval costs are paid per output row, not per tuple scanned */
3488 tgl@sss.pgh.pa.us 1689 : 4104 : startup_cost += path->pathtarget->cost.startup;
1690 : 4104 : run_cost += path->pathtarget->cost.per_tuple * path->rows;
1691 : :
381 rhaas@postgresql.org 1692 : 4104 : path->disabled_nodes = 0;
6975 mail@joeconway.com 1693 : 4104 : path->startup_cost = startup_cost;
1694 : 4104 : path->total_cost = startup_cost + run_cost;
1695 : 4104 : }
1696 : :
1697 : : /*
1698 : : * cost_ctescan
1699 : : * Determines and returns the cost of scanning a CTE RTE.
1700 : : *
1701 : : * Note: this is used for both self-reference and regular CTEs; the
1702 : : * possible cost differences are below the threshold of what we could
1703 : : * estimate accurately anyway. Note that the costs of evaluating the
1704 : : * referenced CTE query are added into the final plan as initplan costs,
1705 : : * and should NOT be counted here.
1706 : : */
1707 : : void
4759 tgl@sss.pgh.pa.us 1708 : 2586 : cost_ctescan(Path *path, PlannerInfo *root,
1709 : : RelOptInfo *baserel, ParamPathInfo *param_info)
1710 : : {
6181 1711 : 2586 : Cost startup_cost = 0;
1712 : 2586 : Cost run_cost = 0;
1713 : : QualCost qpqual_cost;
1714 : : Cost cpu_per_tuple;
1715 : :
1716 : : /* Should only be applied to base relations that are CTEs */
1717 [ - + ]: 2586 : Assert(baserel->relid > 0);
1718 [ - + ]: 2586 : Assert(baserel->rtekind == RTE_CTE);
1719 : :
1720 : : /* Mark the path with the correct row estimate */
4759 1721 [ - + ]: 2586 : if (param_info)
4759 tgl@sss.pgh.pa.us 1722 :UBC 0 : path->rows = param_info->ppi_rows;
1723 : : else
4759 tgl@sss.pgh.pa.us 1724 :CBC 2586 : path->rows = baserel->rows;
1725 : :
1726 : : /* Charge one CPU tuple cost per row for tuplestore manipulation */
6181 1727 : 2586 : cpu_per_tuple = cpu_tuple_cost;
1728 : :
1729 : : /* Add scanning CPU costs */
4759 1730 : 2586 : get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
1731 : :
1732 : 2586 : startup_cost += qpqual_cost.startup;
1733 : 2586 : cpu_per_tuple += cpu_tuple_cost + qpqual_cost.per_tuple;
6181 1734 : 2586 : run_cost += cpu_per_tuple * baserel->tuples;
1735 : :
1736 : : /* tlist eval costs are paid per output row, not per tuple scanned */
3488 1737 : 2586 : startup_cost += path->pathtarget->cost.startup;
1738 : 2586 : run_cost += path->pathtarget->cost.per_tuple * path->rows;
1739 : :
381 rhaas@postgresql.org 1740 : 2586 : path->disabled_nodes = 0;
6181 tgl@sss.pgh.pa.us 1741 : 2586 : path->startup_cost = startup_cost;
1742 : 2586 : path->total_cost = startup_cost + run_cost;
1743 : 2586 : }
1744 : :
1745 : : /*
1746 : : * cost_namedtuplestorescan
1747 : : * Determines and returns the cost of scanning a named tuplestore.
1748 : : */
1749 : : void
3081 kgrittn@postgresql.o 1750 : 242 : cost_namedtuplestorescan(Path *path, PlannerInfo *root,
1751 : : RelOptInfo *baserel, ParamPathInfo *param_info)
1752 : : {
1753 : 242 : Cost startup_cost = 0;
1754 : 242 : Cost run_cost = 0;
1755 : : QualCost qpqual_cost;
1756 : : Cost cpu_per_tuple;
1757 : :
1758 : : /* Should only be applied to base relations that are Tuplestores */
1759 [ - + ]: 242 : Assert(baserel->relid > 0);
1760 [ - + ]: 242 : Assert(baserel->rtekind == RTE_NAMEDTUPLESTORE);
1761 : :
1762 : : /* Mark the path with the correct row estimate */
1763 [ - + ]: 242 : if (param_info)
3081 kgrittn@postgresql.o 1764 :UBC 0 : path->rows = param_info->ppi_rows;
1765 : : else
3081 kgrittn@postgresql.o 1766 :CBC 242 : path->rows = baserel->rows;
1767 : :
1768 : : /* Charge one CPU tuple cost per row for tuplestore manipulation */
1769 : 242 : cpu_per_tuple = cpu_tuple_cost;
1770 : :
1771 : : /* Add scanning CPU costs */
1772 : 242 : get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
1773 : :
1774 : 242 : startup_cost += qpqual_cost.startup;
1775 : 242 : cpu_per_tuple += cpu_tuple_cost + qpqual_cost.per_tuple;
1776 : 242 : run_cost += cpu_per_tuple * baserel->tuples;
1777 : :
381 rhaas@postgresql.org 1778 : 242 : path->disabled_nodes = 0;
3081 kgrittn@postgresql.o 1779 : 242 : path->startup_cost = startup_cost;
1780 : 242 : path->total_cost = startup_cost + run_cost;
1781 : 242 : }
1782 : :
1783 : : /*
1784 : : * cost_resultscan
1785 : : * Determines and returns the cost of scanning an RTE_RESULT relation.
1786 : : */
1787 : : void
2413 tgl@sss.pgh.pa.us 1788 : 2134 : cost_resultscan(Path *path, PlannerInfo *root,
1789 : : RelOptInfo *baserel, ParamPathInfo *param_info)
1790 : : {
1791 : 2134 : Cost startup_cost = 0;
1792 : 2134 : Cost run_cost = 0;
1793 : : QualCost qpqual_cost;
1794 : : Cost cpu_per_tuple;
1795 : :
1796 : : /* Should only be applied to RTE_RESULT base relations */
1797 [ - + ]: 2134 : Assert(baserel->relid > 0);
1798 [ - + ]: 2134 : Assert(baserel->rtekind == RTE_RESULT);
1799 : :
1800 : : /* Mark the path with the correct row estimate */
1801 [ + + ]: 2134 : if (param_info)
1802 : 78 : path->rows = param_info->ppi_rows;
1803 : : else
1804 : 2056 : path->rows = baserel->rows;
1805 : :
1806 : : /* We charge qual cost plus cpu_tuple_cost */
1807 : 2134 : get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
1808 : :
1809 : 2134 : startup_cost += qpqual_cost.startup;
1810 : 2134 : cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple;
1811 : 2134 : run_cost += cpu_per_tuple * baserel->tuples;
1812 : :
381 rhaas@postgresql.org 1813 : 2134 : path->disabled_nodes = 0;
2413 tgl@sss.pgh.pa.us 1814 : 2134 : path->startup_cost = startup_cost;
1815 : 2134 : path->total_cost = startup_cost + run_cost;
1816 : 2134 : }
1817 : :
1818 : : /*
1819 : : * cost_recursive_union
1820 : : * Determines and returns the cost of performing a recursive union,
1821 : : * and also the estimated output size.
1822 : : *
1823 : : * We are given Paths for the nonrecursive and recursive terms.
1824 : : */
1825 : : void
3470 1826 : 463 : cost_recursive_union(Path *runion, Path *nrterm, Path *rterm)
1827 : : {
1828 : : Cost startup_cost;
1829 : : Cost total_cost;
1830 : : double total_rows;
1831 : :
1832 : : /* We probably have decent estimates for the non-recursive term */
6181 1833 : 463 : startup_cost = nrterm->startup_cost;
1834 : 463 : total_cost = nrterm->total_cost;
3470 1835 : 463 : total_rows = nrterm->rows;
1836 : :
1837 : : /*
1838 : : * We arbitrarily assume that about 10 recursive iterations will be
1839 : : * needed, and that we've managed to get a good fix on the cost and output
1840 : : * size of each one of them. These are mighty shaky assumptions but it's
1841 : : * hard to see how to do better.
1842 : : */
6181 1843 : 463 : total_cost += 10 * rterm->total_cost;
3470 1844 : 463 : total_rows += 10 * rterm->rows;
1845 : :
1846 : : /*
1847 : : * Also charge cpu_tuple_cost per row to account for the costs of
1848 : : * manipulating the tuplestores. (We don't worry about possible
1849 : : * spill-to-disk costs.)
1850 : : */
6181 1851 : 463 : total_cost += cpu_tuple_cost * total_rows;
1852 : :
381 rhaas@postgresql.org 1853 : 463 : runion->disabled_nodes = nrterm->disabled_nodes + rterm->disabled_nodes;
6181 tgl@sss.pgh.pa.us 1854 : 463 : runion->startup_cost = startup_cost;
1855 : 463 : runion->total_cost = total_cost;
3470 1856 : 463 : runion->rows = total_rows;
1857 : 463 : runion->pathtarget->width = Max(nrterm->pathtarget->width,
1858 : : rterm->pathtarget->width);
6181 1859 : 463 : }
1860 : :
1861 : : /*
1862 : : * cost_tuplesort
1863 : : * Determines and returns the cost of sorting a relation using tuplesort,
1864 : : * not including the cost of reading the input data.
1865 : : *
1866 : : * If the total volume of data to sort is less than sort_mem, we will do
1867 : : * an in-memory sort, which requires no I/O and about t*log2(t) tuple
1868 : : * comparisons for t tuples.
1869 : : *
1870 : : * If the total volume exceeds sort_mem, we switch to a tape-style merge
1871 : : * algorithm. There will still be about t*log2(t) tuple comparisons in
1872 : : * total, but we will also need to write and read each tuple once per
1873 : : * merge pass. We expect about ceil(logM(r)) merge passes where r is the
1874 : : * number of initial runs formed and M is the merge order used by tuplesort.c.
1875 : : * Since the average initial run should be about sort_mem, we have
1876 : : * disk traffic = 2 * relsize * ceil(logM(p / sort_mem))
1877 : : * cpu = comparison_cost * t * log2(t)
1878 : : *
1879 : : * If the sort is bounded (i.e., only the first k result tuples are needed)
1880 : : * and k tuples can fit into sort_mem, we use a heap method that keeps only
1881 : : * k tuples in the heap; this will require about t*log2(k) tuple comparisons.
1882 : : *
1883 : : * The disk traffic is assumed to be 3/4ths sequential and 1/4th random
1884 : : * accesses (XXX can't we refine that guess?)
1885 : : *
1886 : : * By default, we charge two operator evals per tuple comparison, which should
1887 : : * be in the right ballpark in most cases. The caller can tweak this by
1888 : : * specifying nonzero comparison_cost; typically that's used for any extra
1889 : : * work that has to be done to prepare the inputs to the comparison operators.
1890 : : *
1891 : : * 'tuples' is the number of tuples in the relation
1892 : : * 'width' is the average tuple width in bytes
1893 : : * 'comparison_cost' is the extra cost per comparison, if any
1894 : : * 'sort_mem' is the number of kilobytes of work memory allowed for the sort
1895 : : * 'limit_tuples' is the bound on the number of output tuples; -1 if no bound
1896 : : */
1897 : : static void
1069 1898 : 817552 : cost_tuplesort(Cost *startup_cost, Cost *run_cost,
1899 : : double tuples, int width,
1900 : : Cost comparison_cost, int sort_mem,
1901 : : double limit_tuples)
1902 : : {
6700 1903 : 817552 : double input_bytes = relation_byte_size(tuples, width);
1904 : : double output_bytes;
1905 : : double output_tuples;
218 1906 : 817552 : int64 sort_mem_bytes = sort_mem * (int64) 1024;
1907 : :
1908 : : /*
1909 : : * We want to be sure the cost of a sort is never estimated as zero, even
1910 : : * if passed-in tuple count is zero. Besides, mustn't do log(0)...
1911 : : */
9372 1912 [ + + ]: 817552 : if (tuples < 2.0)
1913 : 244765 : tuples = 2.0;
1914 : :
1915 : : /* Include the default cost-per-comparison */
1069 1916 : 817552 : comparison_cost += 2.0 * cpu_operator_cost;
1917 : :
1918 : : /* Do we have a useful LIMIT? */
6700 1919 [ + + + + ]: 817552 : if (limit_tuples > 0 && limit_tuples < tuples)
1920 : : {
1921 : 923 : output_tuples = limit_tuples;
1922 : 923 : output_bytes = relation_byte_size(output_tuples, width);
1923 : : }
1924 : : else
1925 : : {
1926 : 816629 : output_tuples = tuples;
1927 : 816629 : output_bytes = input_bytes;
1928 : : }
1929 : :
5448 1930 [ + + ]: 817552 : if (output_bytes > sort_mem_bytes)
1931 : : {
1932 : : /*
1933 : : * We'll have to use a disk-based sort of all the tuples
1934 : : */
6700 1935 : 8973 : double npages = ceil(input_bytes / BLCKSZ);
3438 rhaas@postgresql.org 1936 : 8973 : double nruns = input_bytes / sort_mem_bytes;
5448 tgl@sss.pgh.pa.us 1937 : 8973 : double mergeorder = tuplesort_merge_order(sort_mem_bytes);
1938 : : double log_runs;
1939 : : double npageaccesses;
1940 : :
1941 : : /*
1942 : : * CPU costs
1943 : : *
1944 : : * Assume about N log2 N comparisons
1945 : : */
1069 1946 : 8973 : *startup_cost = comparison_cost * tuples * LOG2(tuples);
1947 : :
1948 : : /* Disk costs */
1949 : :
1950 : : /* Compute logM(r) as log(r) / log(M) */
7139 1951 [ + + ]: 8973 : if (nruns > mergeorder)
1952 : 2340 : log_runs = ceil(log(nruns) / log(mergeorder));
1953 : : else
9372 1954 : 6633 : log_runs = 1.0;
9335 1955 : 8973 : npageaccesses = 2.0 * npages * log_runs;
1956 : : /* Assume 3/4ths of accesses are sequential, 1/4th are not */
1979 tomas.vondra@postgre 1957 : 8973 : *startup_cost += npageaccesses *
7033 tgl@sss.pgh.pa.us 1958 : 8973 : (seq_page_cost * 0.75 + random_page_cost * 0.25);
1959 : : }
5448 1960 [ + + - + ]: 808579 : else if (tuples > 2 * output_tuples || input_bytes > sort_mem_bytes)
1961 : : {
1962 : : /*
1963 : : * We'll use a bounded heap-sort keeping just K tuples in memory, for
1964 : : * a total number of tuple comparisons of N log2 K; but the constant
1965 : : * factor is a bit higher than for quicksort. Tweak it so that the
1966 : : * cost curve is continuous at the crossover point.
1967 : : */
1069 1968 : 679 : *startup_cost = comparison_cost * tuples * LOG2(2.0 * output_tuples);
1969 : : }
1970 : : else
1971 : : {
1972 : : /* We'll use plain quicksort on all the input tuples */
1973 : 807900 : *startup_cost = comparison_cost * tuples * LOG2(tuples);
1974 : : }
1975 : :
1976 : : /*
1977 : : * Also charge a small amount (arbitrarily set equal to operator cost) per
1978 : : * extracted tuple. We don't charge cpu_tuple_cost because a Sort node
1979 : : * doesn't do qual-checking or projection, so it has less overhead than
1980 : : * most plan nodes. Note it's correct to use tuples not output_tuples
1981 : : * here --- the upper LIMIT will pro-rate the run cost so we'd be double
1982 : : * counting the LIMIT otherwise.
1983 : : */
1979 tomas.vondra@postgre 1984 : 817552 : *run_cost = cpu_operator_cost * tuples;
1985 : 817552 : }
1986 : :
1987 : : /*
1988 : : * cost_incremental_sort
1989 : : * Determines and returns the cost of sorting a relation incrementally, when
1990 : : * the input path is presorted by a prefix of the pathkeys.
1991 : : *
1992 : : * 'presorted_keys' is the number of leading pathkeys by which the input path
1993 : : * is sorted.
1994 : : *
1995 : : * We estimate the number of groups into which the relation is divided by the
1996 : : * leading pathkeys, and then calculate the cost of sorting a single group
1997 : : * with tuplesort using cost_tuplesort().
1998 : : */
1999 : : void
2000 : 5570 : cost_incremental_sort(Path *path,
2001 : : PlannerInfo *root, List *pathkeys, int presorted_keys,
2002 : : int input_disabled_nodes,
2003 : : Cost input_startup_cost, Cost input_total_cost,
2004 : : double input_tuples, int width, Cost comparison_cost, int sort_mem,
2005 : : double limit_tuples)
2006 : : {
2007 : : Cost startup_cost,
2008 : : run_cost,
2009 : 5570 : input_run_cost = input_total_cost - input_startup_cost;
2010 : : double group_tuples,
2011 : : input_groups;
2012 : : Cost group_startup_cost,
2013 : : group_run_cost,
2014 : : group_input_run_cost;
2015 : 5570 : List *presortedExprs = NIL;
2016 : : ListCell *l;
1962 2017 : 5570 : bool unknown_varno = false;
2018 : :
995 drowley@postgresql.o 2019 [ + - - + ]: 5570 : Assert(presorted_keys > 0 && presorted_keys < list_length(pathkeys));
2020 : :
2021 : : /*
2022 : : * We want to be sure the cost of a sort is never estimated as zero, even
2023 : : * if passed-in tuple count is zero. Besides, mustn't do log(0)...
2024 : : */
1979 tomas.vondra@postgre 2025 [ + + ]: 5570 : if (input_tuples < 2.0)
2026 : 3383 : input_tuples = 2.0;
2027 : :
2028 : : /* Default estimate of number of groups, capped to one group per row. */
1962 2029 [ + + ]: 5570 : input_groups = Min(input_tuples, DEFAULT_NUM_DISTINCT);
2030 : :
2031 : : /*
2032 : : * Extract presorted keys as list of expressions.
2033 : : *
2034 : : * We need to be careful about Vars containing "varno 0" which might have
2035 : : * been introduced by generate_append_tlist, which would confuse
2036 : : * estimate_num_groups (in fact it'd fail for such expressions). See
2037 : : * recurse_set_operations which has to deal with the same issue.
2038 : : *
2039 : : * Unlike recurse_set_operations we can't access the original target list
2040 : : * here, and even if we could it's not very clear how useful would that be
2041 : : * for a set operation combining multiple tables. So we simply detect if
2042 : : * there are any expressions with "varno 0" and use the default
2043 : : * DEFAULT_NUM_DISTINCT in that case.
2044 : : *
2045 : : * We might also use either 1.0 (a single group) or input_tuples (each row
2046 : : * being a separate group), pretty much the worst and best case for
2047 : : * incremental sort. But those are extreme cases and using something in
2048 : : * between seems reasonable. Furthermore, generate_append_tlist is used
2049 : : * for set operations, which are likely to produce mostly unique output
2050 : : * anyway - from that standpoint the DEFAULT_NUM_DISTINCT is defensive
2051 : : * while maintaining lower startup cost.
2052 : : */
1979 2053 [ + - + - : 5618 : foreach(l, pathkeys)
+ - ]
2054 : : {
2055 : 5618 : PathKey *key = (PathKey *) lfirst(l);
2056 : 5618 : EquivalenceMember *member = (EquivalenceMember *)
841 tgl@sss.pgh.pa.us 2057 : 5618 : linitial(key->pk_eclass->ec_members);
2058 : :
2059 : : /*
2060 : : * Check if the expression contains Var with "varno 0" so that we
2061 : : * don't call estimate_num_groups in that case.
2062 : : */
1689 2063 [ + + ]: 5618 : if (bms_is_member(0, pull_varnos(root, (Node *) member->em_expr)))
2064 : : {
1962 tomas.vondra@postgre 2065 : 5 : unknown_varno = true;
2066 : 5 : break;
2067 : : }
2068 : :
2069 : : /* expression not containing any Vars with "varno 0" */
1979 2070 : 5613 : presortedExprs = lappend(presortedExprs, member->em_expr);
2071 : :
995 drowley@postgresql.o 2072 [ + + ]: 5613 : if (foreach_current_index(l) + 1 >= presorted_keys)
1979 tomas.vondra@postgre 2073 : 5565 : break;
2074 : : }
2075 : :
2076 : : /* Estimate the number of groups with equal presorted keys. */
1962 2077 [ + + ]: 5570 : if (!unknown_varno)
1621 drowley@postgresql.o 2078 : 5565 : input_groups = estimate_num_groups(root, presortedExprs, input_tuples,
2079 : : NULL, NULL);
2080 : :
1979 tomas.vondra@postgre 2081 : 5570 : group_tuples = input_tuples / input_groups;
2082 : 5570 : group_input_run_cost = input_run_cost / input_groups;
2083 : :
2084 : : /*
2085 : : * Estimate the average cost of sorting of one group where presorted keys
2086 : : * are equal.
2087 : : */
1069 tgl@sss.pgh.pa.us 2088 : 5570 : cost_tuplesort(&group_startup_cost, &group_run_cost,
2089 : : group_tuples, width, comparison_cost, sort_mem,
2090 : : limit_tuples);
2091 : :
2092 : : /*
2093 : : * Startup cost of incremental sort is the startup cost of its first group
2094 : : * plus the cost of its input.
2095 : : */
995 drowley@postgresql.o 2096 : 5570 : startup_cost = group_startup_cost + input_startup_cost +
2097 : : group_input_run_cost;
2098 : :
2099 : : /*
2100 : : * After we started producing tuples from the first group, the cost of
2101 : : * producing all the tuples is given by the cost to finish processing this
2102 : : * group, plus the total cost to process the remaining groups, plus the
2103 : : * remaining cost of input.
2104 : : */
2105 : 5570 : run_cost = group_run_cost + (group_run_cost + group_startup_cost) *
2106 : 5570 : (input_groups - 1) + group_input_run_cost * (input_groups - 1);
2107 : :
2108 : : /*
2109 : : * Incremental sort adds some overhead by itself. Firstly, it has to
2110 : : * detect the sort groups. This is roughly equal to one extra copy and
2111 : : * comparison per tuple.
2112 : : */
1979 tomas.vondra@postgre 2113 : 5570 : run_cost += (cpu_tuple_cost + comparison_cost) * input_tuples;
2114 : :
2115 : : /*
2116 : : * Additionally, we charge double cpu_tuple_cost for each input group to
2117 : : * account for the tuplesort_reset that's performed after each group.
2118 : : */
2119 : 5570 : run_cost += 2.0 * cpu_tuple_cost * input_groups;
2120 : :
2121 : 5570 : path->rows = input_tuples;
2122 : :
2123 : : /* should not generate these paths when enable_incremental_sort=false */
381 rhaas@postgresql.org 2124 [ - + ]: 5570 : Assert(enable_incremental_sort);
2125 : 5570 : path->disabled_nodes = input_disabled_nodes;
2126 : :
1979 tomas.vondra@postgre 2127 : 5570 : path->startup_cost = startup_cost;
2128 : 5570 : path->total_cost = startup_cost + run_cost;
2129 : 5570 : }
2130 : :
2131 : : /*
2132 : : * cost_sort
2133 : : * Determines and returns the cost of sorting a relation, including
2134 : : * the cost of reading the input data.
2135 : : *
2136 : : * NOTE: some callers currently pass NIL for pathkeys because they
2137 : : * can't conveniently supply the sort keys. Since this routine doesn't
2138 : : * currently do anything with pathkeys anyway, that doesn't matter...
2139 : : * but if it ever does, it should react gracefully to lack of key data.
2140 : : * (Actually, the thing we'd most likely be interested in is just the number
2141 : : * of sort keys, which all callers *could* supply.)
2142 : : */
2143 : : void
2144 : 811982 : cost_sort(Path *path, PlannerInfo *root,
2145 : : List *pathkeys, int input_disabled_nodes,
2146 : : Cost input_cost, double tuples, int width,
2147 : : Cost comparison_cost, int sort_mem,
2148 : : double limit_tuples)
2149 : :
2150 : : {
2151 : : Cost startup_cost;
2152 : : Cost run_cost;
2153 : :
1069 tgl@sss.pgh.pa.us 2154 : 811982 : cost_tuplesort(&startup_cost, &run_cost,
2155 : : tuples, width,
2156 : : comparison_cost, sort_mem,
2157 : : limit_tuples);
2158 : :
1979 tomas.vondra@postgre 2159 : 811982 : startup_cost += input_cost;
2160 : :
2161 : 811982 : path->rows = tuples;
381 rhaas@postgresql.org 2162 : 811982 : path->disabled_nodes = input_disabled_nodes + (enable_sort ? 0 : 1);
9335 tgl@sss.pgh.pa.us 2163 : 811982 : path->startup_cost = startup_cost;
2164 : 811982 : path->total_cost = startup_cost + run_cost;
10651 scrappy@hub.org 2165 : 811982 : }
2166 : :
2167 : : /*
2168 : : * append_nonpartial_cost
2169 : : * Estimate the cost of the non-partial paths in a Parallel Append.
2170 : : * The non-partial paths are assumed to be the first "numpaths" paths
2171 : : * from the subpaths list, and to be in order of decreasing cost.
2172 : : */
2173 : : static Cost
2832 rhaas@postgresql.org 2174 : 9191 : append_nonpartial_cost(List *subpaths, int numpaths, int parallel_workers)
2175 : : {
2176 : : Cost *costarr;
2177 : : int arrlen;
2178 : : ListCell *l;
2179 : : ListCell *cell;
2180 : : int path_index;
2181 : : int min_index;
2182 : : int max_index;
2183 : :
2184 [ + + ]: 9191 : if (numpaths == 0)
2185 : 7121 : return 0;
2186 : :
2187 : : /*
2188 : : * Array length is number of workers or number of relevant paths,
2189 : : * whichever is less.
2190 : : */
2191 : 2070 : arrlen = Min(parallel_workers, numpaths);
2192 : 2070 : costarr = (Cost *) palloc(sizeof(Cost) * arrlen);
2193 : :
2194 : : /* The first few paths will each be claimed by a different worker. */
2195 : 2070 : path_index = 0;
2196 [ + - + + : 5973 : foreach(cell, subpaths)
+ + ]
2197 : : {
2198 : 4325 : Path *subpath = (Path *) lfirst(cell);
2199 : :
2200 [ + + ]: 4325 : if (path_index == arrlen)
2201 : 422 : break;
2202 : 3903 : costarr[path_index++] = subpath->total_cost;
2203 : : }
2204 : :
2205 : : /*
2206 : : * Since subpaths are sorted by decreasing cost, the last one will have
2207 : : * the minimum cost.
2208 : : */
2209 : 2070 : min_index = arrlen - 1;
2210 : :
2211 : : /*
2212 : : * For each of the remaining subpaths, add its cost to the array element
2213 : : * with minimum cost.
2214 : : */
2245 tgl@sss.pgh.pa.us 2215 [ + - + + : 2311 : for_each_cell(l, subpaths, cell)
+ + ]
2216 : : {
2832 rhaas@postgresql.org 2217 : 514 : Path *subpath = (Path *) lfirst(l);
2218 : :
2219 : : /* Consider only the non-partial paths */
2220 [ + + ]: 514 : if (path_index++ == numpaths)
2221 : 273 : break;
2222 : :
2223 : 241 : costarr[min_index] += subpath->total_cost;
2224 : :
2225 : : /* Update the new min cost array index */
1109 drowley@postgresql.o 2226 : 241 : min_index = 0;
2227 [ + + ]: 741 : for (int i = 0; i < arrlen; i++)
2228 : : {
2832 rhaas@postgresql.org 2229 [ + + ]: 500 : if (costarr[i] < costarr[min_index])
2230 : 95 : min_index = i;
2231 : : }
2232 : : }
2233 : :
2234 : : /* Return the highest cost from the array */
1109 drowley@postgresql.o 2235 : 2070 : max_index = 0;
2236 [ + + ]: 5973 : for (int i = 0; i < arrlen; i++)
2237 : : {
2832 rhaas@postgresql.org 2238 [ + + ]: 3903 : if (costarr[i] > costarr[max_index])
2239 : 94 : max_index = i;
2240 : : }
2241 : :
2242 : 2070 : return costarr[max_index];
2243 : : }
2244 : :
2245 : : /*
2246 : : * cost_append
2247 : : * Determines and returns the cost of an Append node.
2248 : : */
2249 : : void
60 rguo@postgresql.org 2250 :GNC 27410 : cost_append(AppendPath *apath, PlannerInfo *root)
2251 : : {
2252 : : ListCell *l;
2253 : :
381 rhaas@postgresql.org 2254 :CBC 27410 : apath->path.disabled_nodes = 0;
2832 2255 : 27410 : apath->path.startup_cost = 0;
2256 : 27410 : apath->path.total_cost = 0;
2346 tgl@sss.pgh.pa.us 2257 : 27410 : apath->path.rows = 0;
2258 : :
2832 rhaas@postgresql.org 2259 [ + + ]: 27410 : if (apath->subpaths == NIL)
2260 : 885 : return;
2261 : :
2262 [ + + ]: 26525 : if (!apath->path.parallel_aware)
2263 : : {
2346 tgl@sss.pgh.pa.us 2264 : 17334 : List *pathkeys = apath->path.pathkeys;
2265 : :
2266 [ + + ]: 17334 : if (pathkeys == NIL)
2267 : : {
1067 drowley@postgresql.o 2268 : 16238 : Path *firstsubpath = (Path *) linitial(apath->subpaths);
2269 : :
2270 : : /*
2271 : : * For an unordered, non-parallel-aware Append we take the startup
2272 : : * cost as the startup cost of the first subpath.
2273 : : */
2274 : 16238 : apath->path.startup_cost = firstsubpath->startup_cost;
2275 : :
2276 : : /*
2277 : : * Compute rows, number of disabled nodes, and total cost as sums
2278 : : * of underlying subplan values.
2279 : : */
2346 tgl@sss.pgh.pa.us 2280 [ + - + + : 62707 : foreach(l, apath->subpaths)
+ + ]
2281 : : {
2282 : 46469 : Path *subpath = (Path *) lfirst(l);
2283 : :
2284 : 46469 : apath->path.rows += subpath->rows;
381 rhaas@postgresql.org 2285 : 46469 : apath->path.disabled_nodes += subpath->disabled_nodes;
2346 tgl@sss.pgh.pa.us 2286 : 46469 : apath->path.total_cost += subpath->total_cost;
2287 : : }
2288 : : }
2289 : : else
2290 : : {
2291 : : /*
2292 : : * For an ordered, non-parallel-aware Append we take the startup
2293 : : * cost as the sum of the subpath startup costs. This ensures
2294 : : * that we don't underestimate the startup cost when a query's
2295 : : * LIMIT is such that several of the children have to be run to
2296 : : * satisfy it. This might be overkill --- another plausible hack
2297 : : * would be to take the Append's startup cost as the maximum of
2298 : : * the child startup costs. But we don't want to risk believing
2299 : : * that an ORDER BY LIMIT query can be satisfied at small cost
2300 : : * when the first child has small startup cost but later ones
2301 : : * don't. (If we had the ability to deal with nonlinear cost
2302 : : * interpolation for partial retrievals, we would not need to be
2303 : : * so conservative about this.)
2304 : : *
2305 : : * This case is also different from the above in that we have to
2306 : : * account for possibly injecting sorts into subpaths that aren't
2307 : : * natively ordered.
2308 : : */
2309 [ + - + + : 4254 : foreach(l, apath->subpaths)
+ + ]
2310 : : {
2311 : 3158 : Path *subpath = (Path *) lfirst(l);
2312 : : int presorted_keys;
2313 : : Path sort_path; /* dummy for result of
2314 : : * cost_sort/cost_incremental_sort */
2315 : :
60 rguo@postgresql.org 2316 [ + + ]:GNC 3158 : if (!pathkeys_count_contained_in(pathkeys, subpath->pathkeys,
2317 : : &presorted_keys))
2318 : : {
2319 : : /*
2320 : : * We'll need to insert a Sort node, so include costs for
2321 : : * that. We choose to use incremental sort if it is
2322 : : * enabled and there are presorted keys; otherwise we use
2323 : : * full sort.
2324 : : *
2325 : : * We can use the parent's LIMIT if any, since we
2326 : : * certainly won't pull more than that many tuples from
2327 : : * any child.
2328 : : */
2329 [ + - + + ]: 28 : if (enable_incremental_sort && presorted_keys > 0)
2330 : : {
2331 : 6 : cost_incremental_sort(&sort_path,
2332 : : root,
2333 : : pathkeys,
2334 : : presorted_keys,
2335 : : subpath->disabled_nodes,
2336 : : subpath->startup_cost,
2337 : : subpath->total_cost,
2338 : : subpath->rows,
2339 : 6 : subpath->pathtarget->width,
2340 : : 0.0,
2341 : : work_mem,
2342 : : apath->limit_tuples);
2343 : : }
2344 : : else
2345 : : {
2346 : 22 : cost_sort(&sort_path,
2347 : : root,
2348 : : pathkeys,
2349 : : subpath->disabled_nodes,
2350 : : subpath->total_cost,
2351 : : subpath->rows,
2352 : 22 : subpath->pathtarget->width,
2353 : : 0.0,
2354 : : work_mem,
2355 : : apath->limit_tuples);
2356 : : }
2357 : :
2346 tgl@sss.pgh.pa.us 2358 :CBC 28 : subpath = &sort_path;
2359 : : }
2360 : :
2361 : 3158 : apath->path.rows += subpath->rows;
381 rhaas@postgresql.org 2362 : 3158 : apath->path.disabled_nodes += subpath->disabled_nodes;
2346 tgl@sss.pgh.pa.us 2363 : 3158 : apath->path.startup_cost += subpath->startup_cost;
2364 : 3158 : apath->path.total_cost += subpath->total_cost;
2365 : : }
2366 : : }
2367 : : }
2368 : : else /* parallel-aware */
2369 : : {
2832 rhaas@postgresql.org 2370 : 9191 : int i = 0;
2371 : 9191 : double parallel_divisor = get_parallel_divisor(&apath->path);
2372 : :
2373 : : /* Parallel-aware Append never produces ordered output. */
2346 tgl@sss.pgh.pa.us 2374 [ - + ]: 9191 : Assert(apath->path.pathkeys == NIL);
2375 : :
2376 : : /* Calculate startup cost. */
2832 rhaas@postgresql.org 2377 [ + - + + : 35865 : foreach(l, apath->subpaths)
+ + ]
2378 : : {
2379 : 26674 : Path *subpath = (Path *) lfirst(l);
2380 : :
2381 : : /*
2382 : : * Append will start returning tuples when the child node having
2383 : : * lowest startup cost is done setting up. We consider only the
2384 : : * first few subplans that immediately get a worker assigned.
2385 : : */
2386 [ + + ]: 26674 : if (i == 0)
2387 : 9191 : apath->path.startup_cost = subpath->startup_cost;
2388 [ + + ]: 17483 : else if (i < apath->path.parallel_workers)
2389 [ + + ]: 8912 : apath->path.startup_cost = Min(apath->path.startup_cost,
2390 : : subpath->startup_cost);
2391 : :
2392 : : /*
2393 : : * Apply parallel divisor to subpaths. Scale the number of rows
2394 : : * for each partial subpath based on the ratio of the parallel
2395 : : * divisor originally used for the subpath to the one we adopted.
2396 : : * Also add the cost of partial paths to the total cost, but
2397 : : * ignore non-partial paths for now.
2398 : : */
2399 [ + + ]: 26674 : if (i < apath->first_partial_path)
2400 : 4144 : apath->path.rows += subpath->rows / parallel_divisor;
2401 : : else
2402 : : {
2403 : : double subpath_parallel_divisor;
2404 : :
2802 2405 : 22530 : subpath_parallel_divisor = get_parallel_divisor(subpath);
2406 : 22530 : apath->path.rows += subpath->rows * (subpath_parallel_divisor /
2407 : : parallel_divisor);
2832 2408 : 22530 : apath->path.total_cost += subpath->total_cost;
2409 : : }
2410 : :
381 2411 : 26674 : apath->path.disabled_nodes += subpath->disabled_nodes;
2802 2412 : 26674 : apath->path.rows = clamp_row_est(apath->path.rows);
2413 : :
2832 2414 : 26674 : i++;
2415 : : }
2416 : :
2417 : : /* Add cost for non-partial subpaths. */
2418 : 9191 : apath->path.total_cost +=
2419 : 9191 : append_nonpartial_cost(apath->subpaths,
2420 : : apath->first_partial_path,
2421 : : apath->path.parallel_workers);
2422 : : }
2423 : :
2424 : : /*
2425 : : * Although Append does not do any selection or projection, it's not free;
2426 : : * add a small per-tuple overhead.
2427 : : */
2754 2428 : 26525 : apath->path.total_cost +=
2429 : 26525 : cpu_tuple_cost * APPEND_CPU_COST_MULTIPLIER * apath->path.rows;
2430 : : }
2431 : :
2432 : : /*
2433 : : * cost_merge_append
2434 : : * Determines and returns the cost of a MergeAppend node.
2435 : : *
2436 : : * MergeAppend merges several pre-sorted input streams, using a heap that
2437 : : * at any given instant holds the next tuple from each stream. If there
2438 : : * are N streams, we need about N*log2(N) tuple comparisons to construct
2439 : : * the heap at startup, and then for each output tuple, about log2(N)
2440 : : * comparisons to replace the top entry.
2441 : : *
2442 : : * (The effective value of N will drop once some of the input streams are
2443 : : * exhausted, but it seems unlikely to be worth trying to account for that.)
2444 : : *
2445 : : * The heap is never spilled to disk, since we assume N is not very large.
2446 : : * So this is much simpler than cost_sort.
2447 : : *
2448 : : * As in cost_sort, we charge two operator evals per tuple comparison.
2449 : : *
2450 : : * 'pathkeys' is a list of sort keys
2451 : : * 'n_streams' is the number of input streams
2452 : : * 'input_disabled_nodes' is the sum of the input streams' disabled node counts
2453 : : * 'input_startup_cost' is the sum of the input streams' startup costs
2454 : : * 'input_total_cost' is the sum of the input streams' total costs
2455 : : * 'tuples' is the number of tuples in all the streams
2456 : : */
2457 : : void
5441 tgl@sss.pgh.pa.us 2458 : 2124 : cost_merge_append(Path *path, PlannerInfo *root,
2459 : : List *pathkeys, int n_streams,
2460 : : int input_disabled_nodes,
2461 : : Cost input_startup_cost, Cost input_total_cost,
2462 : : double tuples)
2463 : : {
2464 : 2124 : Cost startup_cost = 0;
2465 : 2124 : Cost run_cost = 0;
2466 : : Cost comparison_cost;
2467 : : double N;
2468 : : double logN;
2469 : :
2470 : : /*
2471 : : * Avoid log(0)...
2472 : : */
2473 [ + - ]: 2124 : N = (n_streams < 2) ? 2.0 : (double) n_streams;
2474 : 2124 : logN = LOG2(N);
2475 : :
2476 : : /* Assumed cost per tuple comparison */
2477 : 2124 : comparison_cost = 2.0 * cpu_operator_cost;
2478 : :
2479 : : /* Heap creation cost */
2480 : 2124 : startup_cost += comparison_cost * N * logN;
2481 : :
2482 : : /* Per-tuple heap maintenance cost */
3227 2483 : 2124 : run_cost += tuples * comparison_cost * logN;
2484 : :
2485 : : /*
2486 : : * Although MergeAppend does not do any selection or projection, it's not
2487 : : * free; add a small per-tuple overhead.
2488 : : */
2754 rhaas@postgresql.org 2489 : 2124 : run_cost += cpu_tuple_cost * APPEND_CPU_COST_MULTIPLIER * tuples;
2490 : :
381 2491 : 2124 : path->disabled_nodes = input_disabled_nodes;
5441 tgl@sss.pgh.pa.us 2492 : 2124 : path->startup_cost = startup_cost + input_startup_cost;
2493 : 2124 : path->total_cost = startup_cost + run_cost + input_total_cost;
2494 : 2124 : }
2495 : :
2496 : : /*
2497 : : * cost_material
2498 : : * Determines and returns the cost of materializing a relation, including
2499 : : * the cost of reading the input data.
2500 : : *
2501 : : * If the total volume of data to materialize exceeds work_mem, we will need
2502 : : * to write it to disk, so the cost is much higher in that case.
2503 : : *
2504 : : * Note that here we are estimating the costs for the first scan of the
2505 : : * relation, so the materialization is all overhead --- any savings will
2506 : : * occur only on rescan, which is estimated in cost_rescan.
2507 : : */
2508 : : void
8316 2509 : 254289 : cost_material(Path *path,
2510 : : int input_disabled_nodes,
2511 : : Cost input_startup_cost, Cost input_total_cost,
2512 : : double tuples, int width)
2513 : : {
5838 2514 : 254289 : Cost startup_cost = input_startup_cost;
2515 : 254289 : Cost run_cost = input_total_cost - input_startup_cost;
8316 2516 : 254289 : double nbytes = relation_byte_size(tuples, width);
218 2517 : 254289 : double work_mem_bytes = work_mem * (Size) 1024;
2518 : :
4971 2519 : 254289 : path->rows = tuples;
2520 : :
2521 : : /*
2522 : : * Whether spilling or not, charge 2x cpu_operator_cost per tuple to
2523 : : * reflect bookkeeping overhead. (This rate must be more than what
2524 : : * cost_rescan charges for materialize, ie, cpu_operator_cost per tuple;
2525 : : * if it is exactly the same then there will be a cost tie between
2526 : : * nestloop with A outer, materialized B inner and nestloop with B outer,
2527 : : * materialized A inner. The extra cost ensures we'll prefer
2528 : : * materializing the smaller rel.) Note that this is normally a good deal
2529 : : * less than cpu_tuple_cost; which is OK because a Material plan node
2530 : : * doesn't do qual-checking or projection, so it's got less overhead than
2531 : : * most plan nodes.
2532 : : */
5678 2533 : 254289 : run_cost += 2 * cpu_operator_cost * tuples;
2534 : :
2535 : : /*
2536 : : * If we will spill to disk, charge at the rate of seq_page_cost per page.
2537 : : * This cost is assumed to be evenly spread through the plan run phase,
2538 : : * which isn't exactly accurate but our cost model doesn't allow for
2539 : : * nonuniform costs within the run phase.
2540 : : */
7886 2541 [ + + ]: 254289 : if (nbytes > work_mem_bytes)
2542 : : {
8316 2543 : 2483 : double npages = ceil(nbytes / BLCKSZ);
2544 : :
7033 2545 : 2483 : run_cost += seq_page_cost * npages;
2546 : : }
2547 : :
381 rhaas@postgresql.org 2548 : 254289 : path->disabled_nodes = input_disabled_nodes + (enable_material ? 0 : 1);
8316 tgl@sss.pgh.pa.us 2549 : 254289 : path->startup_cost = startup_cost;
2550 : 254289 : path->total_cost = startup_cost + run_cost;
2551 : 254289 : }
2552 : :
2553 : : /*
2554 : : * cost_memoize_rescan
2555 : : * Determines the estimated cost of rescanning a Memoize node.
2556 : : *
2557 : : * In order to estimate this, we must gain knowledge of how often we expect to
2558 : : * be called and how many distinct sets of parameters we are likely to be
2559 : : * called with. If we expect a good cache hit ratio, then we can set our
2560 : : * costs to account for that hit ratio, plus a little bit of cost for the
2561 : : * caching itself. Caching will not work out well if we expect to be called
2562 : : * with too many distinct parameter values. The worst-case here is that we
2563 : : * never see any parameter value twice, in which case we'd never get a cache
2564 : : * hit and caching would be a complete waste of effort.
2565 : : */
2566 : : static void
1515 drowley@postgresql.o 2567 : 137108 : cost_memoize_rescan(PlannerInfo *root, MemoizePath *mpath,
2568 : : Cost *rescan_startup_cost, Cost *rescan_total_cost)
2569 : : {
2570 : : EstimationInfo estinfo;
2571 : : ListCell *lc;
2572 : 137108 : Cost input_startup_cost = mpath->subpath->startup_cost;
2573 : 137108 : Cost input_total_cost = mpath->subpath->total_cost;
2574 : 137108 : double tuples = mpath->subpath->rows;
39 drowley@postgresql.o 2575 :GNC 137108 : Cardinality est_calls = mpath->est_calls;
1515 drowley@postgresql.o 2576 :CBC 137108 : int width = mpath->subpath->pathtarget->width;
2577 : :
2578 : : double hash_mem_bytes;
2579 : : double est_entry_bytes;
2580 : : Cardinality est_cache_entries;
2581 : : Cardinality ndistinct;
2582 : : double evict_ratio;
2583 : : double hit_ratio;
2584 : : Cost startup_cost;
2585 : : Cost total_cost;
2586 : :
2587 : : /* available cache space */
1504 tgl@sss.pgh.pa.us 2588 : 137108 : hash_mem_bytes = get_hash_memory_limit();
2589 : :
2590 : : /*
2591 : : * Set the number of bytes each cache entry should consume in the cache.
2592 : : * To provide us with better estimations on how many cache entries we can
2593 : : * store at once, we make a call to the executor here to ask it what
2594 : : * memory overheads there are for a single cache entry.
2595 : : */
1618 drowley@postgresql.o 2596 : 137108 : est_entry_bytes = relation_byte_size(tuples, width) +
2597 : 137108 : ExecEstimateCacheEntryOverheadBytes(tuples);
2598 : :
2599 : : /* include the estimated width for the cache keys */
901 2600 [ + - + + : 293091 : foreach(lc, mpath->param_exprs)
+ + ]
2601 : 155983 : est_entry_bytes += get_expr_width(root, (Node *) lfirst(lc));
2602 : :
2603 : : /* estimate on the upper limit of cache entries we can hold at once */
1618 2604 : 137108 : est_cache_entries = floor(hash_mem_bytes / est_entry_bytes);
2605 : :
2606 : : /* estimate on the distinct number of parameter values */
39 drowley@postgresql.o 2607 :GNC 137108 : ndistinct = estimate_num_groups(root, mpath->param_exprs, est_calls, NULL,
2608 : : &estinfo);
2609 : :
2610 : : /*
2611 : : * When the estimation fell back on using a default value, it's a bit too
2612 : : * risky to assume that it's ok to use a Memoize node. The use of a
2613 : : * default could cause us to use a Memoize node when it's really
2614 : : * inappropriate to do so. If we see that this has been done, then we'll
2615 : : * assume that every call will have unique parameters, which will almost
2616 : : * certainly mean a MemoizePath will never survive add_path().
2617 : : */
1618 drowley@postgresql.o 2618 [ + + ]:CBC 137108 : if ((estinfo.flags & SELFLAG_USED_DEFAULT) != 0)
39 drowley@postgresql.o 2619 :GNC 7795 : ndistinct = est_calls;
2620 : :
2621 : : /* Remember the ndistinct estimate for EXPLAIN */
2622 : 137108 : mpath->est_unique_keys = ndistinct;
2623 : :
2624 : : /*
2625 : : * Since we've already estimated the maximum number of entries we can
2626 : : * store at once and know the estimated number of distinct values we'll be
2627 : : * called with, we'll take this opportunity to set the path's est_entries.
2628 : : * This will ultimately determine the hash table size that the executor
2629 : : * will use. If we leave this at zero, the executor will just choose the
2630 : : * size itself. Really this is not the right place to do this, but it's
2631 : : * convenient since everything is already calculated.
2632 : : */
1515 drowley@postgresql.o 2633 [ + + + - :CBC 137108 : mpath->est_entries = Min(Min(ndistinct, est_cache_entries),
+ + ]
2634 : : PG_UINT32_MAX);
2635 : :
2636 : : /*
2637 : : * When the number of distinct parameter values is above the amount we can
2638 : : * store in the cache, then we'll have to evict some entries from the
2639 : : * cache. This is not free. Here we estimate how often we'll incur the
2640 : : * cost of that eviction.
2641 : : */
1618 2642 [ + + ]: 137108 : evict_ratio = 1.0 - Min(est_cache_entries, ndistinct) / ndistinct;
2643 : :
2644 : : /*
2645 : : * In order to estimate how costly a single scan will be, we need to
2646 : : * attempt to estimate what the cache hit ratio will be. To do that we
2647 : : * must look at how many scans are estimated in total for this node and
2648 : : * how many of those scans we expect to get a cache hit.
2649 : : */
39 drowley@postgresql.o 2650 :GNC 274216 : hit_ratio = ((est_calls - ndistinct) / est_calls) *
899 drowley@postgresql.o 2651 [ + + ]:CBC 137108 : (est_cache_entries / Max(ndistinct, est_cache_entries));
2652 : :
2653 : : /* Remember the hit ratio estimate for EXPLAIN */
39 drowley@postgresql.o 2654 :GNC 137108 : mpath->est_hit_ratio = hit_ratio;
2655 : :
899 drowley@postgresql.o 2656 [ + - - + ]:CBC 137108 : Assert(hit_ratio >= 0 && hit_ratio <= 1.0);
2657 : :
2658 : : /*
2659 : : * Set the total_cost accounting for the expected cache hit ratio. We
2660 : : * also add on a cpu_operator_cost to account for a cache lookup. This
2661 : : * will happen regardless of whether it's a cache hit or not.
2662 : : */
1618 2663 : 137108 : total_cost = input_total_cost * (1.0 - hit_ratio) + cpu_operator_cost;
2664 : :
2665 : : /* Now adjust the total cost to account for cache evictions */
2666 : :
2667 : : /* Charge a cpu_tuple_cost for evicting the actual cache entry */
2668 : 137108 : total_cost += cpu_tuple_cost * evict_ratio;
2669 : :
2670 : : /*
2671 : : * Charge a 10th of cpu_operator_cost to evict every tuple in that entry.
2672 : : * The per-tuple eviction is really just a pfree, so charging a whole
2673 : : * cpu_operator_cost seems a little excessive.
2674 : : */
2675 : 137108 : total_cost += cpu_operator_cost / 10.0 * evict_ratio * tuples;
2676 : :
2677 : : /*
2678 : : * Now adjust for storing things in the cache, since that's not free
2679 : : * either. Everything must go in the cache. We don't proportion this
2680 : : * over any ratio, just apply it once for the scan. We charge a
2681 : : * cpu_tuple_cost for the creation of the cache entry and also a
2682 : : * cpu_operator_cost for each tuple we expect to cache.
2683 : : */
2684 : 137108 : total_cost += cpu_tuple_cost + cpu_operator_cost * tuples;
2685 : :
2686 : : /*
2687 : : * Getting the first row must be also be proportioned according to the
2688 : : * expected cache hit ratio.
2689 : : */
2690 : 137108 : startup_cost = input_startup_cost * (1.0 - hit_ratio);
2691 : :
2692 : : /*
2693 : : * Additionally we charge a cpu_tuple_cost to account for cache lookups,
2694 : : * which we'll do regardless of whether it was a cache hit or not.
2695 : : */
2696 : 137108 : startup_cost += cpu_tuple_cost;
2697 : :
2698 : 137108 : *rescan_startup_cost = startup_cost;
2699 : 137108 : *rescan_total_cost = total_cost;
2700 : 137108 : }
2701 : :
2702 : : /*
2703 : : * cost_agg
2704 : : * Determines and returns the cost of performing an Agg plan node,
2705 : : * including the cost of its input.
2706 : : *
2707 : : * aggcosts can be NULL when there are no actual aggregate functions (i.e.,
2708 : : * we are using a hashed Agg node just to do grouping).
2709 : : *
2710 : : * Note: when aggstrategy == AGG_SORTED, caller must ensure that input costs
2711 : : * are for appropriately-sorted input.
2712 : : */
2713 : : void
7398 tgl@sss.pgh.pa.us 2714 : 36487 : cost_agg(Path *path, PlannerInfo *root,
2715 : : AggStrategy aggstrategy, const AggClauseCosts *aggcosts,
2716 : : int numGroupCols, double numGroups,
2717 : : List *quals,
2718 : : int disabled_nodes,
2719 : : Cost input_startup_cost, Cost input_total_cost,
2720 : : double input_tuples, double input_width)
2721 : : {
2722 : : double output_tuples;
2723 : : Cost startup_cost;
2724 : : Cost total_cost;
170 peter@eisentraut.org 2725 : 36487 : const AggClauseCosts dummy_aggcosts = {0};
2726 : :
2727 : : /* Use all-zero per-aggregate costs if NULL is passed */
5249 tgl@sss.pgh.pa.us 2728 [ + + ]: 36487 : if (aggcosts == NULL)
2729 : : {
2730 [ - + ]: 9180 : Assert(aggstrategy == AGG_HASHED);
2731 : 9180 : aggcosts = &dummy_aggcosts;
2732 : : }
2733 : :
2734 : : /*
2735 : : * The transCost.per_tuple component of aggcosts should be charged once
2736 : : * per input tuple, corresponding to the costs of evaluating the aggregate
2737 : : * transfns and their input expressions. The finalCost.per_tuple component
2738 : : * is charged once per output tuple, corresponding to the costs of
2739 : : * evaluating the finalfns. Startup costs are of course charged but once.
2740 : : *
2741 : : * If we are grouping, we charge an additional cpu_operator_cost per
2742 : : * grouping column per input tuple for grouping comparisons.
2743 : : *
2744 : : * We will produce a single output tuple if not grouping, and a tuple per
2745 : : * group otherwise. We charge cpu_tuple_cost for each output tuple.
2746 : : *
2747 : : * Note: in this cost model, AGG_SORTED and AGG_HASHED have exactly the
2748 : : * same total CPU cost, but AGG_SORTED has lower startup cost. If the
2749 : : * input path is already sorted appropriately, AGG_SORTED should be
2750 : : * preferred (since it has no risk of memory overflow). This will happen
2751 : : * as long as the computed total costs are indeed exactly equal --- but if
2752 : : * there's roundoff error we might do the wrong thing. So be sure that
2753 : : * the computations below form the same intermediate values in the same
2754 : : * order.
2755 : : */
8325 2756 [ + + ]: 36487 : if (aggstrategy == AGG_PLAIN)
2757 : : {
2758 : 17490 : startup_cost = input_total_cost;
5249 2759 : 17490 : startup_cost += aggcosts->transCost.startup;
2760 : 17490 : startup_cost += aggcosts->transCost.per_tuple * input_tuples;
2401 2761 : 17490 : startup_cost += aggcosts->finalCost.startup;
2762 : 17490 : startup_cost += aggcosts->finalCost.per_tuple;
2763 : : /* we aren't grouping */
7315 2764 : 17490 : total_cost = startup_cost + cpu_tuple_cost;
4971 2765 : 17490 : output_tuples = 1;
2766 : : }
3085 rhodiumtoad@postgres 2767 [ + + + + ]: 18997 : else if (aggstrategy == AGG_SORTED || aggstrategy == AGG_MIXED)
2768 : : {
2769 : : /* Here we are able to deliver output on-the-fly */
8325 tgl@sss.pgh.pa.us 2770 : 5627 : startup_cost = input_startup_cost;
2771 : 5627 : total_cost = input_total_cost;
3085 rhodiumtoad@postgres 2772 [ + + + + ]: 5627 : if (aggstrategy == AGG_MIXED && !enable_hashagg)
381 rhaas@postgresql.org 2773 : 228 : ++disabled_nodes;
2774 : : /* calcs phrased this way to match HASHED case, see note above */
5249 tgl@sss.pgh.pa.us 2775 : 5627 : total_cost += aggcosts->transCost.startup;
2776 : 5627 : total_cost += aggcosts->transCost.per_tuple * input_tuples;
2777 : 5627 : total_cost += (cpu_operator_cost * numGroupCols) * input_tuples;
2401 2778 : 5627 : total_cost += aggcosts->finalCost.startup;
2779 : 5627 : total_cost += aggcosts->finalCost.per_tuple * numGroups;
7315 2780 : 5627 : total_cost += cpu_tuple_cost * numGroups;
4971 2781 : 5627 : output_tuples = numGroups;
2782 : : }
2783 : : else
2784 : : {
2785 : : /* must be AGG_HASHED */
8325 2786 : 13370 : startup_cost = input_total_cost;
3456 rhaas@postgresql.org 2787 [ + + ]: 13370 : if (!enable_hashagg)
381 2788 : 789 : ++disabled_nodes;
5249 tgl@sss.pgh.pa.us 2789 : 13370 : startup_cost += aggcosts->transCost.startup;
2790 : 13370 : startup_cost += aggcosts->transCost.per_tuple * input_tuples;
2791 : : /* cost of computing hash value */
2792 : 13370 : startup_cost += (cpu_operator_cost * numGroupCols) * input_tuples;
2401 2793 : 13370 : startup_cost += aggcosts->finalCost.startup;
2794 : :
8325 2795 : 13370 : total_cost = startup_cost;
2401 2796 : 13370 : total_cost += aggcosts->finalCost.per_tuple * numGroups;
2797 : : /* cost of retrieving from hash table */
7315 2798 : 13370 : total_cost += cpu_tuple_cost * numGroups;
4971 2799 : 13370 : output_tuples = numGroups;
2800 : : }
2801 : :
2802 : : /*
2803 : : * Add the disk costs of hash aggregation that spills to disk.
2804 : : *
2805 : : * Groups that go into the hash table stay in memory until finalized, so
2806 : : * spilling and reprocessing tuples doesn't incur additional invocations
2807 : : * of transCost or finalCost. Furthermore, the computed hash value is
2808 : : * stored with the spilled tuples, so we don't incur extra invocations of
2809 : : * the hash function.
2810 : : *
2811 : : * Hash Agg begins returning tuples after the first batch is complete.
2812 : : * Accrue writes (spilled tuples) to startup_cost and to total_cost;
2813 : : * accrue reads only to total_cost.
2814 : : */
1998 jdavis@postgresql.or 2815 [ + + + + ]: 36487 : if (aggstrategy == AGG_HASHED || aggstrategy == AGG_MIXED)
2816 : : {
2817 : : double pages;
1941 tgl@sss.pgh.pa.us 2818 : 13828 : double pages_written = 0.0;
2819 : 13828 : double pages_read = 0.0;
2820 : : double spill_cost;
2821 : : double hashentrysize;
2822 : : double nbatches;
2823 : : Size mem_limit;
2824 : : uint64 ngroups_limit;
2825 : : int num_partitions;
2826 : : int depth;
2827 : :
2828 : : /*
2829 : : * Estimate number of batches based on the computed limits. If less
2830 : : * than or equal to one, all groups are expected to fit in memory;
2831 : : * otherwise we expect to spill.
2832 : : */
1747 heikki.linnakangas@i 2833 : 13828 : hashentrysize = hash_agg_entry_size(list_length(root->aggtransinfos),
2834 : : input_width,
1941 tgl@sss.pgh.pa.us 2835 : 13828 : aggcosts->transitionSpace);
1998 jdavis@postgresql.or 2836 : 13828 : hash_agg_set_limits(hashentrysize, numGroups, 0, &mem_limit,
2837 : : &ngroups_limit, &num_partitions);
2838 : :
1941 tgl@sss.pgh.pa.us 2839 [ - + ]: 13828 : nbatches = Max((numGroups * hashentrysize) / mem_limit,
2840 : : numGroups / ngroups_limit);
2841 : :
1988 jdavis@postgresql.or 2842 [ + + ]: 13828 : nbatches = Max(ceil(nbatches), 1.0);
2843 : 13828 : num_partitions = Max(num_partitions, 2);
2844 : :
2845 : : /*
2846 : : * The number of partitions can change at different levels of
2847 : : * recursion; but for the purposes of this calculation assume it stays
2848 : : * constant.
2849 : : */
1941 tgl@sss.pgh.pa.us 2850 : 13828 : depth = ceil(log(nbatches) / log(num_partitions));
2851 : :
2852 : : /*
2853 : : * Estimate number of pages read and written. For each level of
2854 : : * recursion, a tuple must be written and then later read.
2855 : : */
1988 jdavis@postgresql.or 2856 : 13828 : pages = relation_byte_size(input_tuples, input_width) / BLCKSZ;
2857 : 13828 : pages_written = pages_read = pages * depth;
2858 : :
2859 : : /*
2860 : : * HashAgg has somewhat worse IO behavior than Sort on typical
2861 : : * hardware/OS combinations. Account for this with a generic penalty.
2862 : : */
1825 2863 : 13828 : pages_read *= 2.0;
2864 : 13828 : pages_written *= 2.0;
2865 : :
1998 2866 : 13828 : startup_cost += pages_written * random_page_cost;
2867 : 13828 : total_cost += pages_written * random_page_cost;
2868 : 13828 : total_cost += pages_read * seq_page_cost;
2869 : :
2870 : : /* account for CPU cost of spilling a tuple and reading it back */
1825 2871 : 13828 : spill_cost = depth * input_tuples * 2.0 * cpu_tuple_cost;
2872 : 13828 : startup_cost += spill_cost;
2873 : 13828 : total_cost += spill_cost;
2874 : : }
2875 : :
2876 : : /*
2877 : : * If there are quals (HAVING quals), account for their cost and
2878 : : * selectivity.
2879 : : */
2865 tgl@sss.pgh.pa.us 2880 [ + + ]: 36487 : if (quals)
2881 : : {
2882 : : QualCost qual_cost;
2883 : :
2884 : 2199 : cost_qual_eval(&qual_cost, quals, root);
2885 : 2199 : startup_cost += qual_cost.startup;
2886 : 2199 : total_cost += qual_cost.startup + output_tuples * qual_cost.per_tuple;
2887 : :
2888 : 2199 : output_tuples = clamp_row_est(output_tuples *
2889 : 2199 : clauselist_selectivity(root,
2890 : : quals,
2891 : : 0,
2892 : : JOIN_INNER,
2893 : : NULL));
2894 : : }
2895 : :
4971 2896 : 36487 : path->rows = output_tuples;
381 rhaas@postgresql.org 2897 : 36487 : path->disabled_nodes = disabled_nodes;
8325 tgl@sss.pgh.pa.us 2898 : 36487 : path->startup_cost = startup_cost;
2899 : 36487 : path->total_cost = total_cost;
2900 : 36487 : }
2901 : :
2902 : : /*
2903 : : * get_windowclause_startup_tuples
2904 : : * Estimate how many tuples we'll need to fetch from a WindowAgg's
2905 : : * subnode before we can output the first WindowAgg tuple.
2906 : : *
2907 : : * How many tuples need to be read depends on the WindowClause. For example,
2908 : : * a WindowClause with no PARTITION BY and no ORDER BY requires that all
2909 : : * subnode tuples are read and aggregated before the WindowAgg can output
2910 : : * anything. If there's a PARTITION BY, then we only need to look at tuples
2911 : : * in the first partition. Here we attempt to estimate just how many
2912 : : * 'input_tuples' the WindowAgg will need to read for the given WindowClause
2913 : : * before the first tuple can be output.
2914 : : */
2915 : : static double
764 drowley@postgresql.o 2916 : 1377 : get_windowclause_startup_tuples(PlannerInfo *root, WindowClause *wc,
2917 : : double input_tuples)
2918 : : {
2919 : 1377 : int frameOptions = wc->frameOptions;
2920 : : double partition_tuples;
2921 : : double return_tuples;
2922 : : double peer_tuples;
2923 : :
2924 : : /*
2925 : : * First, figure out how many partitions there are likely to be and set
2926 : : * partition_tuples according to that estimate.
2927 : : */
2928 [ + + ]: 1377 : if (wc->partitionClause != NIL)
2929 : : {
2930 : : double num_partitions;
2931 : 358 : List *partexprs = get_sortgrouplist_exprs(wc->partitionClause,
2932 : 358 : root->parse->targetList);
2933 : :
2934 : 358 : num_partitions = estimate_num_groups(root, partexprs, input_tuples,
2935 : : NULL, NULL);
2936 : 358 : list_free(partexprs);
2937 : :
2938 : 358 : partition_tuples = input_tuples / num_partitions;
2939 : : }
2940 : : else
2941 : : {
2942 : : /* all tuples belong to the same partition */
2943 : 1019 : partition_tuples = input_tuples;
2944 : : }
2945 : :
2946 : : /* estimate the number of tuples in each peer group */
2947 [ + + ]: 1377 : if (wc->orderClause != NIL)
2948 : : {
2949 : : double num_groups;
2950 : : List *orderexprs;
2951 : :
2952 : 1137 : orderexprs = get_sortgrouplist_exprs(wc->orderClause,
2953 : 1137 : root->parse->targetList);
2954 : :
2955 : : /* estimate out how many peer groups there are in the partition */
2956 : 1137 : num_groups = estimate_num_groups(root, orderexprs,
2957 : : partition_tuples, NULL,
2958 : : NULL);
2959 : 1137 : list_free(orderexprs);
2960 : 1137 : peer_tuples = partition_tuples / num_groups;
2961 : : }
2962 : : else
2963 : : {
2964 : : /* no ORDER BY so only 1 tuple belongs in each peer group */
2965 : 240 : peer_tuples = 1.0;
2966 : : }
2967 : :
2968 [ + + ]: 1377 : if (frameOptions & FRAMEOPTION_END_UNBOUNDED_FOLLOWING)
2969 : : {
2970 : : /* include all partition rows */
2971 : 173 : return_tuples = partition_tuples;
2972 : : }
2973 [ + + ]: 1204 : else if (frameOptions & FRAMEOPTION_END_CURRENT_ROW)
2974 : : {
2975 [ + + ]: 709 : if (frameOptions & FRAMEOPTION_ROWS)
2976 : : {
2977 : : /* just count the current row */
2978 : 304 : return_tuples = 1.0;
2979 : : }
2980 [ + - ]: 405 : else if (frameOptions & (FRAMEOPTION_RANGE | FRAMEOPTION_GROUPS))
2981 : : {
2982 : : /*
2983 : : * When in RANGE/GROUPS mode, it's more complex. If there's no
2984 : : * ORDER BY, then all rows in the partition are peers, otherwise
2985 : : * we'll need to read the first group of peers.
2986 : : */
2987 [ + + ]: 405 : if (wc->orderClause == NIL)
2988 : 154 : return_tuples = partition_tuples;
2989 : : else
2990 : 251 : return_tuples = peer_tuples;
2991 : : }
2992 : : else
2993 : : {
2994 : : /*
2995 : : * Something new we don't support yet? This needs attention.
2996 : : * We'll just return 1.0 in the meantime.
2997 : : */
764 drowley@postgresql.o 2998 :UBC 0 : Assert(false);
2999 : : return_tuples = 1.0;
3000 : : }
3001 : : }
764 drowley@postgresql.o 3002 [ + + ]:CBC 495 : else if (frameOptions & FRAMEOPTION_END_OFFSET_PRECEDING)
3003 : : {
3004 : : /*
3005 : : * BETWEEN ... AND N PRECEDING will only need to read the WindowAgg's
3006 : : * subnode after N ROWS/RANGES/GROUPS. N can be 0, but not negative,
3007 : : * so we'll just assume only the current row needs to be read to fetch
3008 : : * the first WindowAgg row.
3009 : : */
3010 : 54 : return_tuples = 1.0;
3011 : : }
3012 [ + - ]: 441 : else if (frameOptions & FRAMEOPTION_END_OFFSET_FOLLOWING)
3013 : : {
3014 : 441 : Const *endOffset = (Const *) wc->endOffset;
3015 : : double end_offset_value;
3016 : :
3017 : : /* try and figure out the value specified in the endOffset. */
3018 [ + - ]: 441 : if (IsA(endOffset, Const))
3019 : : {
3020 [ - + ]: 441 : if (endOffset->constisnull)
3021 : : {
3022 : : /*
3023 : : * NULLs are not allowed, but currently, there's no code to
3024 : : * error out if there's a NULL Const. We'll only discover
3025 : : * this during execution. For now, just pretend everything is
3026 : : * fine and assume that just the first row/range/group will be
3027 : : * needed.
3028 : : */
764 drowley@postgresql.o 3029 :UBC 0 : end_offset_value = 1.0;
3030 : : }
3031 : : else
3032 : : {
764 drowley@postgresql.o 3033 [ + + + + ]:CBC 441 : switch (endOffset->consttype)
3034 : : {
3035 : 12 : case INT2OID:
3036 : 12 : end_offset_value =
3037 : 12 : (double) DatumGetInt16(endOffset->constvalue);
3038 : 12 : break;
3039 : 66 : case INT4OID:
3040 : 66 : end_offset_value =
3041 : 66 : (double) DatumGetInt32(endOffset->constvalue);
3042 : 66 : break;
3043 : 192 : case INT8OID:
3044 : 192 : end_offset_value =
3045 : 192 : (double) DatumGetInt64(endOffset->constvalue);
3046 : 192 : break;
3047 : 171 : default:
3048 : 171 : end_offset_value =
3049 : 171 : partition_tuples / peer_tuples *
3050 : : DEFAULT_INEQ_SEL;
3051 : 171 : break;
3052 : : }
3053 : : }
3054 : : }
3055 : : else
3056 : : {
3057 : : /*
3058 : : * When the end bound is not a Const, we'll just need to guess. We
3059 : : * just make use of DEFAULT_INEQ_SEL.
3060 : : */
764 drowley@postgresql.o 3061 :UBC 0 : end_offset_value =
3062 : 0 : partition_tuples / peer_tuples * DEFAULT_INEQ_SEL;
3063 : : }
3064 : :
764 drowley@postgresql.o 3065 [ + + ]:CBC 441 : if (frameOptions & FRAMEOPTION_ROWS)
3066 : : {
3067 : : /* include the N FOLLOWING and the current row */
3068 : 111 : return_tuples = end_offset_value + 1.0;
3069 : : }
3070 [ + - ]: 330 : else if (frameOptions & (FRAMEOPTION_RANGE | FRAMEOPTION_GROUPS))
3071 : : {
3072 : : /* include N FOLLOWING ranges/group and the initial range/group */
3073 : 330 : return_tuples = peer_tuples * (end_offset_value + 1.0);
3074 : : }
3075 : : else
3076 : : {
3077 : : /*
3078 : : * Something new we don't support yet? This needs attention.
3079 : : * We'll just return 1.0 in the meantime.
3080 : : */
764 drowley@postgresql.o 3081 :UBC 0 : Assert(false);
3082 : : return_tuples = 1.0;
3083 : : }
3084 : : }
3085 : : else
3086 : : {
3087 : : /*
3088 : : * Something new we don't support yet? This needs attention. We'll
3089 : : * just return 1.0 in the meantime.
3090 : : */
3091 : 0 : Assert(false);
3092 : : return_tuples = 1.0;
3093 : : }
3094 : :
764 drowley@postgresql.o 3095 [ + + + + ]:CBC 1377 : if (wc->partitionClause != NIL || wc->orderClause != NIL)
3096 : : {
3097 : : /*
3098 : : * Cap the return value to the estimated partition tuples and account
3099 : : * for the extra tuple WindowAgg will need to read to confirm the next
3100 : : * tuple does not belong to the same partition or peer group.
3101 : : */
3102 [ + + ]: 1237 : return_tuples = Min(return_tuples + 1.0, partition_tuples);
3103 : : }
3104 : : else
3105 : : {
3106 : : /*
3107 : : * Cap the return value so it's never higher than the expected tuples
3108 : : * in the partition.
3109 : : */
3110 [ + + ]: 140 : return_tuples = Min(return_tuples, partition_tuples);
3111 : : }
3112 : :
3113 : : /*
3114 : : * We needn't worry about any EXCLUDE options as those only exclude rows
3115 : : * from being aggregated, not from being read from the WindowAgg's
3116 : : * subnode.
3117 : : */
3118 : :
3119 : 1377 : return clamp_row_est(return_tuples);
3120 : : }
3121 : :
3122 : : /*
3123 : : * cost_windowagg
3124 : : * Determines and returns the cost of performing a WindowAgg plan node,
3125 : : * including the cost of its input.
3126 : : *
3127 : : * Input is assumed already properly sorted.
3128 : : */
3129 : : void
6096 tgl@sss.pgh.pa.us 3130 : 1377 : cost_windowagg(Path *path, PlannerInfo *root,
3131 : : List *windowFuncs, WindowClause *winclause,
3132 : : int input_disabled_nodes,
3133 : : Cost input_startup_cost, Cost input_total_cost,
3134 : : double input_tuples)
3135 : : {
3136 : : Cost startup_cost;
3137 : : Cost total_cost;
3138 : : double startup_tuples;
3139 : : int numPartCols;
3140 : : int numOrderCols;
3141 : : ListCell *lc;
3142 : :
764 drowley@postgresql.o 3143 : 1377 : numPartCols = list_length(winclause->partitionClause);
3144 : 1377 : numOrderCols = list_length(winclause->orderClause);
3145 : :
6096 tgl@sss.pgh.pa.us 3146 : 1377 : startup_cost = input_startup_cost;
3147 : 1377 : total_cost = input_total_cost;
3148 : :
3149 : : /*
3150 : : * Window functions are assumed to cost their stated execution cost, plus
3151 : : * the cost of evaluating their input expressions, per tuple. Since they
3152 : : * may in fact evaluate their inputs at multiple rows during each cycle,
3153 : : * this could be a drastic underestimate; but without a way to know how
3154 : : * many rows the window function will fetch, it's hard to do better. In
3155 : : * any case, it's a good estimate for all the built-in window functions,
3156 : : * so we'll just do this for now.
3157 : : */
5249 3158 [ + - + + : 3123 : foreach(lc, windowFuncs)
+ + ]
3159 : : {
3071 3160 : 1746 : WindowFunc *wfunc = lfirst_node(WindowFunc, lc);
3161 : : Cost wfunccost;
3162 : : QualCost argcosts;
3163 : :
2401 3164 : 1746 : argcosts.startup = argcosts.per_tuple = 0;
3165 : 1746 : add_function_cost(root, wfunc->winfnoid, (Node *) wfunc,
3166 : : &argcosts);
3167 : 1746 : startup_cost += argcosts.startup;
3168 : 1746 : wfunccost = argcosts.per_tuple;
3169 : :
3170 : : /* also add the input expressions' cost to per-input-row costs */
5249 3171 : 1746 : cost_qual_eval_node(&argcosts, (Node *) wfunc->args, root);
3172 : 1746 : startup_cost += argcosts.startup;
3173 : 1746 : wfunccost += argcosts.per_tuple;
3174 : :
3175 : : /*
3176 : : * Add the filter's cost to per-input-row costs. XXX We should reduce
3177 : : * input expression costs according to filter selectivity.
3178 : : */
4435 noah@leadboat.com 3179 : 1746 : cost_qual_eval_node(&argcosts, (Node *) wfunc->aggfilter, root);
3180 : 1746 : startup_cost += argcosts.startup;
3181 : 1746 : wfunccost += argcosts.per_tuple;
3182 : :
5249 tgl@sss.pgh.pa.us 3183 : 1746 : total_cost += wfunccost * input_tuples;
3184 : : }
3185 : :
3186 : : /*
3187 : : * We also charge cpu_operator_cost per grouping column per tuple for
3188 : : * grouping comparisons, plus cpu_tuple_cost per tuple for general
3189 : : * overhead.
3190 : : *
3191 : : * XXX this neglects costs of spooling the data to disk when it overflows
3192 : : * work_mem. Sooner or later that should get accounted for.
3193 : : */
3194 : 1377 : total_cost += cpu_operator_cost * (numPartCols + numOrderCols) * input_tuples;
6096 3195 : 1377 : total_cost += cpu_tuple_cost * input_tuples;
3196 : :
4971 3197 : 1377 : path->rows = input_tuples;
381 rhaas@postgresql.org 3198 : 1377 : path->disabled_nodes = input_disabled_nodes;
6096 tgl@sss.pgh.pa.us 3199 : 1377 : path->startup_cost = startup_cost;
3200 : 1377 : path->total_cost = total_cost;
3201 : :
3202 : : /*
3203 : : * Also, take into account how many tuples we need to read from the
3204 : : * subnode in order to produce the first tuple from the WindowAgg. To do
3205 : : * this we proportion the run cost (total cost not including startup cost)
3206 : : * over the estimated startup tuples. We already included the startup
3207 : : * cost of the subnode, so we only need to do this when the estimated
3208 : : * startup tuples is above 1.0.
3209 : : */
764 drowley@postgresql.o 3210 : 1377 : startup_tuples = get_windowclause_startup_tuples(root, winclause,
3211 : : input_tuples);
3212 : :
3213 [ + + ]: 1377 : if (startup_tuples > 1.0)
3214 : 1233 : path->startup_cost += (total_cost - startup_cost) / input_tuples *
3215 : 1233 : (startup_tuples - 1.0);
6096 tgl@sss.pgh.pa.us 3216 : 1377 : }
3217 : :
3218 : : /*
3219 : : * cost_group
3220 : : * Determines and returns the cost of performing a Group plan node,
3221 : : * including the cost of its input.
3222 : : *
3223 : : * Note: caller must ensure that input costs are for appropriately-sorted
3224 : : * input.
3225 : : */
3226 : : void
7398 3227 : 607 : cost_group(Path *path, PlannerInfo *root,
3228 : : int numGroupCols, double numGroups,
3229 : : List *quals,
3230 : : int input_disabled_nodes,
3231 : : Cost input_startup_cost, Cost input_total_cost,
3232 : : double input_tuples)
3233 : : {
3234 : : double output_tuples;
3235 : : Cost startup_cost;
3236 : : Cost total_cost;
3237 : :
2865 3238 : 607 : output_tuples = numGroups;
8325 3239 : 607 : startup_cost = input_startup_cost;
3240 : 607 : total_cost = input_total_cost;
3241 : :
3242 : : /*
3243 : : * Charge one cpu_operator_cost per comparison per input tuple. We assume
3244 : : * all columns get compared at most of the tuples.
3245 : : */
3246 : 607 : total_cost += cpu_operator_cost * input_tuples * numGroupCols;
3247 : :
3248 : : /*
3249 : : * If there are quals (HAVING quals), account for their cost and
3250 : : * selectivity.
3251 : : */
2865 3252 [ - + ]: 607 : if (quals)
3253 : : {
3254 : : QualCost qual_cost;
3255 : :
2865 tgl@sss.pgh.pa.us 3256 :UBC 0 : cost_qual_eval(&qual_cost, quals, root);
3257 : 0 : startup_cost += qual_cost.startup;
3258 : 0 : total_cost += qual_cost.startup + output_tuples * qual_cost.per_tuple;
3259 : :
3260 : 0 : output_tuples = clamp_row_est(output_tuples *
3261 : 0 : clauselist_selectivity(root,
3262 : : quals,
3263 : : 0,
3264 : : JOIN_INNER,
3265 : : NULL));
3266 : : }
3267 : :
2865 tgl@sss.pgh.pa.us 3268 :CBC 607 : path->rows = output_tuples;
381 rhaas@postgresql.org 3269 : 607 : path->disabled_nodes = input_disabled_nodes;
8325 tgl@sss.pgh.pa.us 3270 : 607 : path->startup_cost = startup_cost;
3271 : 607 : path->total_cost = total_cost;
3272 : 607 : }
3273 : :
3274 : : /*
3275 : : * initial_cost_nestloop
3276 : : * Preliminary estimate of the cost of a nestloop join path.
3277 : : *
3278 : : * This must quickly produce lower-bound estimates of the path's startup and
3279 : : * total costs. If we are unable to eliminate the proposed path from
3280 : : * consideration using the lower bounds, final_cost_nestloop will be called
3281 : : * to obtain the final estimates.
3282 : : *
3283 : : * The exact division of labor between this function and final_cost_nestloop
3284 : : * is private to them, and represents a tradeoff between speed of the initial
3285 : : * estimate and getting a tight lower bound. We choose to not examine the
3286 : : * join quals here, since that's by far the most expensive part of the
3287 : : * calculations. The end result is that CPU-cost considerations must be
3288 : : * left for the second phase; and for SEMI/ANTI joins, we must also postpone
3289 : : * incorporation of the inner path's run cost.
3290 : : *
3291 : : * 'workspace' is to be filled with startup_cost, total_cost, and perhaps
3292 : : * other data to be used by final_cost_nestloop
3293 : : * 'jointype' is the type of join to be performed
3294 : : * 'outer_path' is the outer input to the join
3295 : : * 'inner_path' is the inner input to the join
3296 : : * 'extra' contains miscellaneous information about the join
3297 : : */
3298 : : void
4971 3299 : 1366434 : initial_cost_nestloop(PlannerInfo *root, JoinCostWorkspace *workspace,
3300 : : JoinType jointype,
3301 : : Path *outer_path, Path *inner_path,
3302 : : JoinPathExtraData *extra)
3303 : : {
3304 : : int disabled_nodes;
9335 3305 : 1366434 : Cost startup_cost = 0;
3306 : 1366434 : Cost run_cost = 0;
4971 3307 : 1366434 : double outer_path_rows = outer_path->rows;
3308 : : Cost inner_rescan_start_cost;
3309 : : Cost inner_rescan_total_cost;
3310 : : Cost inner_run_cost;
3311 : : Cost inner_rescan_run_cost;
3312 : :
3313 : : /* Count up disabled nodes. */
381 rhaas@postgresql.org 3314 : 1366434 : disabled_nodes = enable_nestloop ? 0 : 1;
3315 : 1366434 : disabled_nodes += inner_path->disabled_nodes;
3316 : 1366434 : disabled_nodes += outer_path->disabled_nodes;
3317 : :
3318 : : /* estimate costs to rescan the inner relation */
5838 tgl@sss.pgh.pa.us 3319 : 1366434 : cost_rescan(root, inner_path,
3320 : : &inner_rescan_start_cost,
3321 : : &inner_rescan_total_cost);
3322 : :
3323 : : /* cost of source data */
3324 : :
3325 : : /*
3326 : : * NOTE: clearly, we must pay both outer and inner paths' startup_cost
3327 : : * before we can start returning tuples, so the join's startup cost is
3328 : : * their sum. We'll also pay the inner path's rescan startup cost
3329 : : * multiple times.
3330 : : */
9335 3331 : 1366434 : startup_cost += outer_path->startup_cost + inner_path->startup_cost;
3332 : 1366434 : run_cost += outer_path->total_cost - outer_path->startup_cost;
5838 3333 [ + + ]: 1366434 : if (outer_path_rows > 1)
3334 : 961267 : run_cost += (outer_path_rows - 1) * inner_rescan_start_cost;
3335 : :
5964 3336 : 1366434 : inner_run_cost = inner_path->total_cost - inner_path->startup_cost;
5838 3337 : 1366434 : inner_rescan_run_cost = inner_rescan_total_cost - inner_rescan_start_cost;
3338 : :
3074 3339 [ + + + + ]: 1366434 : if (jointype == JOIN_SEMI || jointype == JOIN_ANTI ||
3340 [ + + ]: 1335817 : extra->inner_unique)
3341 : : {
3342 : : /*
3343 : : * With a SEMI or ANTI join, or if the innerrel is known unique, the
3344 : : * executor will stop after the first match.
3345 : : *
3346 : : * Getting decent estimates requires inspection of the join quals,
3347 : : * which we choose to postpone to final_cost_nestloop.
3348 : : */
3349 : :
3350 : : /* Save private data for final_cost_nestloop */
3748 3351 : 623098 : workspace->inner_run_cost = inner_run_cost;
3352 : 623098 : workspace->inner_rescan_run_cost = inner_rescan_run_cost;
3353 : : }
3354 : : else
3355 : : {
3356 : : /* Normal case; we'll scan whole input rel for each outer row */
4971 3357 : 743336 : run_cost += inner_run_cost;
3358 [ + + ]: 743336 : if (outer_path_rows > 1)
3359 : 533646 : run_cost += (outer_path_rows - 1) * inner_rescan_run_cost;
3360 : : }
3361 : :
3362 : : /* CPU costs left for later */
3363 : :
3364 : : /* Public result fields */
381 rhaas@postgresql.org 3365 : 1366434 : workspace->disabled_nodes = disabled_nodes;
4971 tgl@sss.pgh.pa.us 3366 : 1366434 : workspace->startup_cost = startup_cost;
3367 : 1366434 : workspace->total_cost = startup_cost + run_cost;
3368 : : /* Save private data for final_cost_nestloop */
3369 : 1366434 : workspace->run_cost = run_cost;
3370 : 1366434 : }
3371 : :
3372 : : /*
3373 : : * final_cost_nestloop
3374 : : * Final estimate of the cost and result size of a nestloop join path.
3375 : : *
3376 : : * 'path' is already filled in except for the rows and cost fields
3377 : : * 'workspace' is the result from initial_cost_nestloop
3378 : : * 'extra' contains miscellaneous information about the join
3379 : : */
3380 : : void
3381 : 666725 : final_cost_nestloop(PlannerInfo *root, NestPath *path,
3382 : : JoinCostWorkspace *workspace,
3383 : : JoinPathExtraData *extra)
3384 : : {
1490 peter@eisentraut.org 3385 : 666725 : Path *outer_path = path->jpath.outerjoinpath;
3386 : 666725 : Path *inner_path = path->jpath.innerjoinpath;
4971 tgl@sss.pgh.pa.us 3387 : 666725 : double outer_path_rows = outer_path->rows;
3388 : 666725 : double inner_path_rows = inner_path->rows;
3389 : 666725 : Cost startup_cost = workspace->startup_cost;
3390 : 666725 : Cost run_cost = workspace->run_cost;
3391 : : Cost cpu_per_tuple;
3392 : : QualCost restrict_qual_cost;
3393 : : double ntuples;
3394 : :
3395 : : /* Set the number of disabled nodes. */
381 rhaas@postgresql.org 3396 : 666725 : path->jpath.path.disabled_nodes = workspace->disabled_nodes;
3397 : :
3398 : : /* Protect some assumptions below that rowcounts aren't zero */
1783 drowley@postgresql.o 3399 [ - + ]: 666725 : if (outer_path_rows <= 0)
3451 tgl@sss.pgh.pa.us 3400 :UBC 0 : outer_path_rows = 1;
1783 drowley@postgresql.o 3401 [ + + ]:CBC 666725 : if (inner_path_rows <= 0)
3451 tgl@sss.pgh.pa.us 3402 : 351 : inner_path_rows = 1;
3403 : : /* Mark the path with the correct row estimate */
1490 peter@eisentraut.org 3404 [ + + ]: 666725 : if (path->jpath.path.param_info)
3405 : 15786 : path->jpath.path.rows = path->jpath.path.param_info->ppi_rows;
3406 : : else
3407 : 650939 : path->jpath.path.rows = path->jpath.path.parent->rows;
3408 : :
3409 : : /* For partial paths, scale row estimate. */
3410 [ + + ]: 666725 : if (path->jpath.path.parallel_workers > 0)
3411 : : {
3412 : 6749 : double parallel_divisor = get_parallel_divisor(&path->jpath.path);
3413 : :
3414 : 6749 : path->jpath.path.rows =
3415 : 6749 : clamp_row_est(path->jpath.path.rows / parallel_divisor);
3416 : : }
3417 : :
3418 : : /* cost of inner-relation source data (we already dealt with outer rel) */
3419 : :
3420 [ + + + + ]: 666725 : if (path->jpath.jointype == JOIN_SEMI || path->jpath.jointype == JOIN_ANTI ||
3074 tgl@sss.pgh.pa.us 3421 [ + + ]: 645410 : extra->inner_unique)
4971 3422 : 429775 : {
3423 : : /*
3424 : : * With a SEMI or ANTI join, or if the innerrel is known unique, the
3425 : : * executor will stop after the first match.
3426 : : */
3748 3427 : 429775 : Cost inner_run_cost = workspace->inner_run_cost;
3428 : 429775 : Cost inner_rescan_run_cost = workspace->inner_rescan_run_cost;
3429 : : double outer_matched_rows;
3430 : : double outer_unmatched_rows;
3431 : : Selectivity inner_scan_frac;
3432 : :
3433 : : /*
3434 : : * For an outer-rel row that has at least one match, we can expect the
3435 : : * inner scan to stop after a fraction 1/(match_count+1) of the inner
3436 : : * rows, if the matches are evenly distributed. Since they probably
3437 : : * aren't quite evenly distributed, we apply a fuzz factor of 2.0 to
3438 : : * that fraction. (If we used a larger fuzz factor, we'd have to
3439 : : * clamp inner_scan_frac to at most 1.0; but since match_count is at
3440 : : * least 1, no such clamp is needed now.)
3441 : : */
3074 3442 : 429775 : outer_matched_rows = rint(outer_path_rows * extra->semifactors.outer_match_frac);
3017 3443 : 429775 : outer_unmatched_rows = outer_path_rows - outer_matched_rows;
3074 3444 : 429775 : inner_scan_frac = 2.0 / (extra->semifactors.match_count + 1.0);
3445 : :
3446 : : /*
3447 : : * Compute number of tuples processed (not number emitted!). First,
3448 : : * account for successfully-matched outer rows.
3449 : : */
5964 3450 : 429775 : ntuples = outer_matched_rows * inner_path_rows * inner_scan_frac;
3451 : :
3452 : : /*
3453 : : * Now we need to estimate the actual costs of scanning the inner
3454 : : * relation, which may be quite a bit less than N times inner_run_cost
3455 : : * due to early scan stops. We consider two cases. If the inner path
3456 : : * is an indexscan using all the joinquals as indexquals, then an
3457 : : * unmatched outer row results in an indexscan returning no rows,
3458 : : * which is probably quite cheap. Otherwise, the executor will have
3459 : : * to scan the whole inner rel for an unmatched row; not so cheap.
3460 : : */
4888 3461 [ + + ]: 429775 : if (has_indexed_join_quals(path))
3462 : : {
3463 : : /*
3464 : : * Successfully-matched outer rows will only require scanning
3465 : : * inner_scan_frac of the inner relation. In this case, we don't
3466 : : * need to charge the full inner_run_cost even when that's more
3467 : : * than inner_rescan_run_cost, because we can assume that none of
3468 : : * the inner scans ever scan the whole inner relation. So it's
3469 : : * okay to assume that all the inner scan executions can be
3470 : : * fractions of the full cost, even if materialization is reducing
3471 : : * the rescan cost. At this writing, it's impossible to get here
3472 : : * for a materialized inner scan, so inner_run_cost and
3473 : : * inner_rescan_run_cost will be the same anyway; but just in
3474 : : * case, use inner_run_cost for the first matched tuple and
3475 : : * inner_rescan_run_cost for additional ones.
3476 : : */
3748 3477 : 71372 : run_cost += inner_run_cost * inner_scan_frac;
3478 [ + + ]: 71372 : if (outer_matched_rows > 1)
3479 : 10009 : run_cost += (outer_matched_rows - 1) * inner_rescan_run_cost * inner_scan_frac;
3480 : :
3481 : : /*
3482 : : * Add the cost of inner-scan executions for unmatched outer rows.
3483 : : * We estimate this as the same cost as returning the first tuple
3484 : : * of a nonempty scan. We consider that these are all rescans,
3485 : : * since we used inner_run_cost once already.
3486 : : */
3017 3487 : 71372 : run_cost += outer_unmatched_rows *
5838 3488 : 71372 : inner_rescan_run_cost / inner_path_rows;
3489 : :
3490 : : /*
3491 : : * We won't be evaluating any quals at all for unmatched rows, so
3492 : : * don't add them to ntuples.
3493 : : */
3494 : : }
3495 : : else
3496 : : {
3497 : : /*
3498 : : * Here, a complicating factor is that rescans may be cheaper than
3499 : : * first scans. If we never scan all the way to the end of the
3500 : : * inner rel, it might be (depending on the plan type) that we'd
3501 : : * never pay the whole inner first-scan run cost. However it is
3502 : : * difficult to estimate whether that will happen (and it could
3503 : : * not happen if there are any unmatched outer rows!), so be
3504 : : * conservative and always charge the whole first-scan cost once.
3505 : : * We consider this charge to correspond to the first unmatched
3506 : : * outer row, unless there isn't one in our estimate, in which
3507 : : * case blame it on the first matched row.
3508 : : */
3509 : :
3510 : : /* First, count all unmatched join tuples as being processed */
3017 3511 : 358403 : ntuples += outer_unmatched_rows * inner_path_rows;
3512 : :
3513 : : /* Now add the forced full scan, and decrement appropriate count */
3748 3514 : 358403 : run_cost += inner_run_cost;
3017 3515 [ + + ]: 358403 : if (outer_unmatched_rows >= 1)
3516 : 341304 : outer_unmatched_rows -= 1;
3517 : : else
3518 : 17099 : outer_matched_rows -= 1;
3519 : :
3520 : : /* Add inner run cost for additional outer tuples having matches */
3521 [ + + ]: 358403 : if (outer_matched_rows > 0)
3522 : 130066 : run_cost += outer_matched_rows * inner_rescan_run_cost * inner_scan_frac;
3523 : :
3524 : : /* Add inner run cost for additional unmatched outer tuples */
3525 [ + + ]: 358403 : if (outer_unmatched_rows > 0)
3526 : 232590 : run_cost += outer_unmatched_rows * inner_rescan_run_cost;
3527 : : }
3528 : : }
3529 : : else
3530 : : {
3531 : : /* Normal-case source costs were included in preliminary estimate */
3532 : :
3533 : : /* Compute number of tuples processed (not number emitted!) */
5964 3534 : 236950 : ntuples = outer_path_rows * inner_path_rows;
3535 : : }
3536 : :
3537 : : /* CPU costs */
1490 peter@eisentraut.org 3538 : 666725 : cost_qual_eval(&restrict_qual_cost, path->jpath.joinrestrictinfo, root);
8273 tgl@sss.pgh.pa.us 3539 : 666725 : startup_cost += restrict_qual_cost.startup;
3540 : 666725 : cpu_per_tuple = cpu_tuple_cost + restrict_qual_cost.per_tuple;
9335 3541 : 666725 : run_cost += cpu_per_tuple * ntuples;
3542 : :
3543 : : /* tlist eval costs are paid per output row, not per tuple scanned */
1490 peter@eisentraut.org 3544 : 666725 : startup_cost += path->jpath.path.pathtarget->cost.startup;
3545 : 666725 : run_cost += path->jpath.path.pathtarget->cost.per_tuple * path->jpath.path.rows;
3546 : :
3547 : 666725 : path->jpath.path.startup_cost = startup_cost;
3548 : 666725 : path->jpath.path.total_cost = startup_cost + run_cost;
10651 scrappy@hub.org 3549 : 666725 : }
3550 : :
3551 : : /*
3552 : : * initial_cost_mergejoin
3553 : : * Preliminary estimate of the cost of a mergejoin path.
3554 : : *
3555 : : * This must quickly produce lower-bound estimates of the path's startup and
3556 : : * total costs. If we are unable to eliminate the proposed path from
3557 : : * consideration using the lower bounds, final_cost_mergejoin will be called
3558 : : * to obtain the final estimates.
3559 : : *
3560 : : * The exact division of labor between this function and final_cost_mergejoin
3561 : : * is private to them, and represents a tradeoff between speed of the initial
3562 : : * estimate and getting a tight lower bound. We choose to not examine the
3563 : : * join quals here, except for obtaining the scan selectivity estimate which
3564 : : * is really essential (but fortunately, use of caching keeps the cost of
3565 : : * getting that down to something reasonable).
3566 : : * We also assume that cost_sort/cost_incremental_sort is cheap enough to use
3567 : : * here.
3568 : : *
3569 : : * 'workspace' is to be filled with startup_cost, total_cost, and perhaps
3570 : : * other data to be used by final_cost_mergejoin
3571 : : * 'jointype' is the type of join to be performed
3572 : : * 'mergeclauses' is the list of joinclauses to be used as merge clauses
3573 : : * 'outer_path' is the outer input to the join
3574 : : * 'inner_path' is the inner input to the join
3575 : : * 'outersortkeys' is the list of sort keys for the outer path
3576 : : * 'innersortkeys' is the list of sort keys for the inner path
3577 : : * 'outer_presorted_keys' is the number of presorted keys of the outer path
3578 : : * 'extra' contains miscellaneous information about the join
3579 : : *
3580 : : * Note: outersortkeys and innersortkeys should be NIL if no explicit
3581 : : * sort is needed because the respective source path is already ordered.
3582 : : */
3583 : : void
4971 tgl@sss.pgh.pa.us 3584 : 596312 : initial_cost_mergejoin(PlannerInfo *root, JoinCostWorkspace *workspace,
3585 : : JoinType jointype,
3586 : : List *mergeclauses,
3587 : : Path *outer_path, Path *inner_path,
3588 : : List *outersortkeys, List *innersortkeys,
3589 : : int outer_presorted_keys,
3590 : : JoinPathExtraData *extra)
3591 : : {
3592 : : int disabled_nodes;
9335 3593 : 596312 : Cost startup_cost = 0;
3594 : 596312 : Cost run_cost = 0;
4971 3595 : 596312 : double outer_path_rows = outer_path->rows;
3596 : 596312 : double inner_path_rows = inner_path->rows;
3597 : : Cost inner_run_cost;
3598 : : double outer_rows,
3599 : : inner_rows,
3600 : : outer_skip_rows,
3601 : : inner_skip_rows;
3602 : : Selectivity outerstartsel,
3603 : : outerendsel,
3604 : : innerstartsel,
3605 : : innerendsel;
3606 : : Path sort_path; /* dummy for result of
3607 : : * cost_sort/cost_incremental_sort */
3608 : :
3609 : : /* Protect some assumptions below that rowcounts aren't zero */
1783 drowley@postgresql.o 3610 [ + + ]: 596312 : if (outer_path_rows <= 0)
6375 tgl@sss.pgh.pa.us 3611 : 48 : outer_path_rows = 1;
1783 drowley@postgresql.o 3612 [ + + ]: 596312 : if (inner_path_rows <= 0)
6375 tgl@sss.pgh.pa.us 3613 : 63 : inner_path_rows = 1;
3614 : :
3615 : : /*
3616 : : * A merge join will stop as soon as it exhausts either input stream
3617 : : * (unless it's an outer join, in which case the outer side has to be
3618 : : * scanned all the way anyway). Estimate fraction of the left and right
3619 : : * inputs that will actually need to be scanned. Likewise, we can
3620 : : * estimate the number of rows that will be skipped before the first join
3621 : : * pair is found, which should be factored into startup cost. We use only
3622 : : * the first (most significant) merge clause for this purpose. Since
3623 : : * mergejoinscansel() is a fairly expensive computation, we cache the
3624 : : * results in the merge clause RestrictInfo.
3625 : : */
4971 3626 [ + + + + ]: 596312 : if (mergeclauses && jointype != JOIN_FULL)
8590 3627 : 593242 : {
6804 3628 : 593242 : RestrictInfo *firstclause = (RestrictInfo *) linitial(mergeclauses);
3629 : : List *opathkeys;
3630 : : List *ipathkeys;
3631 : : PathKey *opathkey;
3632 : : PathKey *ipathkey;
3633 : : MergeScanSelCache *cache;
3634 : :
3635 : : /* Get the input pathkeys to determine the sort-order details */
3636 [ + + ]: 593242 : opathkeys = outersortkeys ? outersortkeys : outer_path->pathkeys;
3637 [ + + ]: 593242 : ipathkeys = innersortkeys ? innersortkeys : inner_path->pathkeys;
3638 [ - + ]: 593242 : Assert(opathkeys);
3639 [ - + ]: 593242 : Assert(ipathkeys);
3640 : 593242 : opathkey = (PathKey *) linitial(opathkeys);
3641 : 593242 : ipathkey = (PathKey *) linitial(ipathkeys);
3642 : : /* debugging check */
3643 [ + - ]: 593242 : if (opathkey->pk_opfamily != ipathkey->pk_opfamily ||
5285 3644 [ + - ]: 593242 : opathkey->pk_eclass->ec_collation != ipathkey->pk_eclass->ec_collation ||
155 peter@eisentraut.org 3645 [ + - ]: 593242 : opathkey->pk_cmptype != ipathkey->pk_cmptype ||
6804 tgl@sss.pgh.pa.us 3646 [ - + ]: 593242 : opathkey->pk_nulls_first != ipathkey->pk_nulls_first)
6804 tgl@sss.pgh.pa.us 3647 [ # # ]:UBC 0 : elog(ERROR, "left and right pathkeys do not match in mergejoin");
3648 : :
3649 : : /* Get the selectivity with caching */
6802 tgl@sss.pgh.pa.us 3650 :CBC 593242 : cache = cached_scansel(root, firstclause, opathkey);
3651 : :
6804 3652 [ + + ]: 593242 : if (bms_is_subset(firstclause->left_relids,
3653 : 593242 : outer_path->parent->relids))
3654 : : {
3655 : : /* left side of clause is outer */
6482 3656 : 316262 : outerstartsel = cache->leftstartsel;
3657 : 316262 : outerendsel = cache->leftendsel;
3658 : 316262 : innerstartsel = cache->rightstartsel;
3659 : 316262 : innerendsel = cache->rightendsel;
3660 : : }
3661 : : else
3662 : : {
3663 : : /* left side of clause is inner */
3664 : 276980 : outerstartsel = cache->rightstartsel;
3665 : 276980 : outerendsel = cache->rightendsel;
3666 : 276980 : innerstartsel = cache->leftstartsel;
3667 : 276980 : innerendsel = cache->leftendsel;
3668 : : }
4971 3669 [ + + + + ]: 593242 : if (jointype == JOIN_LEFT ||
3670 : : jointype == JOIN_ANTI)
3671 : : {
6482 3672 : 98293 : outerstartsel = 0.0;
3673 : 98293 : outerendsel = 1.0;
3674 : : }
885 3675 [ + + + + ]: 494949 : else if (jointype == JOIN_RIGHT ||
3676 : : jointype == JOIN_RIGHT_ANTI)
3677 : : {
6482 3678 : 98449 : innerstartsel = 0.0;
3679 : 98449 : innerendsel = 1.0;
3680 : : }
3681 : : }
3682 : : else
3683 : : {
3684 : : /* cope with clauseless or full mergejoin */
3685 : 3070 : outerstartsel = innerstartsel = 0.0;
3686 : 3070 : outerendsel = innerendsel = 1.0;
3687 : : }
3688 : :
3689 : : /*
3690 : : * Convert selectivities to row counts. We force outer_rows and
3691 : : * inner_rows to be at least 1, but the skip_rows estimates can be zero.
3692 : : */
3693 : 596312 : outer_skip_rows = rint(outer_path_rows * outerstartsel);
3694 : 596312 : inner_skip_rows = rint(inner_path_rows * innerstartsel);
3695 : 596312 : outer_rows = clamp_row_est(outer_path_rows * outerendsel);
3696 : 596312 : inner_rows = clamp_row_est(inner_path_rows * innerendsel);
3697 : :
3698 [ - + ]: 596312 : Assert(outer_skip_rows <= outer_rows);
3699 [ - + ]: 596312 : Assert(inner_skip_rows <= inner_rows);
3700 : :
3701 : : /*
3702 : : * Readjust scan selectivities to account for above rounding. This is
3703 : : * normally an insignificant effect, but when there are only a few rows in
3704 : : * the inputs, failing to do this makes for a large percentage error.
3705 : : */
3706 : 596312 : outerstartsel = outer_skip_rows / outer_path_rows;
3707 : 596312 : innerstartsel = inner_skip_rows / inner_path_rows;
3708 : 596312 : outerendsel = outer_rows / outer_path_rows;
3709 : 596312 : innerendsel = inner_rows / inner_path_rows;
3710 : :
4999 3711 [ - + ]: 596312 : Assert(outerstartsel <= outerendsel);
3712 [ - + ]: 596312 : Assert(innerstartsel <= innerendsel);
3713 : :
381 rhaas@postgresql.org 3714 : 596312 : disabled_nodes = enable_mergejoin ? 0 : 1;
3715 : :
3716 : : /* cost of source data */
3717 : :
9335 tgl@sss.pgh.pa.us 3718 [ + + ]: 596312 : if (outersortkeys) /* do we need to sort outer? */
3719 : : {
3720 : : /*
3721 : : * We can assert that the outer path is not already ordered
3722 : : * appropriately for the mergejoin; otherwise, outersortkeys would
3723 : : * have been set to NIL.
3724 : : */
121 rguo@postgresql.org 3725 [ - + ]: 285054 : Assert(!pathkeys_contained_in(outersortkeys, outer_path->pathkeys));
3726 : :
3727 : : /*
3728 : : * We choose to use incremental sort if it is enabled and there are
3729 : : * presorted keys; otherwise we use full sort.
3730 : : */
3731 [ + + + + ]: 285054 : if (enable_incremental_sort && outer_presorted_keys > 0)
3732 : : {
3733 : 846 : cost_incremental_sort(&sort_path,
3734 : : root,
3735 : : outersortkeys,
3736 : : outer_presorted_keys,
3737 : : outer_path->disabled_nodes,
3738 : : outer_path->startup_cost,
3739 : : outer_path->total_cost,
3740 : : outer_path_rows,
3741 : 846 : outer_path->pathtarget->width,
3742 : : 0.0,
3743 : : work_mem,
3744 : : -1.0);
3745 : : }
3746 : : else
3747 : : {
332 3748 : 284208 : cost_sort(&sort_path,
3749 : : root,
3750 : : outersortkeys,
3751 : : outer_path->disabled_nodes,
3752 : : outer_path->total_cost,
3753 : : outer_path_rows,
3754 : 284208 : outer_path->pathtarget->width,
3755 : : 0.0,
3756 : : work_mem,
3757 : : -1.0);
3758 : : }
3759 : :
381 rhaas@postgresql.org 3760 : 285054 : disabled_nodes += sort_path.disabled_nodes;
9335 tgl@sss.pgh.pa.us 3761 : 285054 : startup_cost += sort_path.startup_cost;
6482 3762 : 285054 : startup_cost += (sort_path.total_cost - sort_path.startup_cost)
3763 : 285054 : * outerstartsel;
8590 3764 : 285054 : run_cost += (sort_path.total_cost - sort_path.startup_cost)
6482 3765 : 285054 : * (outerendsel - outerstartsel);
3766 : : }
3767 : : else
3768 : : {
381 rhaas@postgresql.org 3769 : 311258 : disabled_nodes += outer_path->disabled_nodes;
9335 tgl@sss.pgh.pa.us 3770 : 311258 : startup_cost += outer_path->startup_cost;
6482 3771 : 311258 : startup_cost += (outer_path->total_cost - outer_path->startup_cost)
3772 : 311258 : * outerstartsel;
8590 3773 : 311258 : run_cost += (outer_path->total_cost - outer_path->startup_cost)
6482 3774 : 311258 : * (outerendsel - outerstartsel);
3775 : : }
3776 : :
9335 3777 [ + + ]: 596312 : if (innersortkeys) /* do we need to sort inner? */
3778 : : {
3779 : : /*
3780 : : * We can assert that the inner path is not already ordered
3781 : : * appropriately for the mergejoin; otherwise, innersortkeys would
3782 : : * have been set to NIL.
3783 : : */
121 rguo@postgresql.org 3784 [ - + ]: 467958 : Assert(!pathkeys_contained_in(innersortkeys, inner_path->pathkeys));
3785 : :
3786 : : /*
3787 : : * We do not consider incremental sort for inner path, because
3788 : : * incremental sort does not support mark/restore.
3789 : : */
3790 : :
9335 tgl@sss.pgh.pa.us 3791 : 467958 : cost_sort(&sort_path,
3792 : : root,
3793 : : innersortkeys,
3794 : : inner_path->disabled_nodes,
3795 : : inner_path->total_cost,
3796 : : inner_path_rows,
3488 3797 : 467958 : inner_path->pathtarget->width,
3798 : : 0.0,
3799 : : work_mem,
3800 : : -1.0);
381 rhaas@postgresql.org 3801 : 467958 : disabled_nodes += sort_path.disabled_nodes;
9335 tgl@sss.pgh.pa.us 3802 : 467958 : startup_cost += sort_path.startup_cost;
6482 3803 : 467958 : startup_cost += (sort_path.total_cost - sort_path.startup_cost)
5774 3804 : 467958 : * innerstartsel;
3805 : 467958 : inner_run_cost = (sort_path.total_cost - sort_path.startup_cost)
3806 : 467958 : * (innerendsel - innerstartsel);
3807 : : }
3808 : : else
3809 : : {
381 rhaas@postgresql.org 3810 : 128354 : disabled_nodes += inner_path->disabled_nodes;
9335 tgl@sss.pgh.pa.us 3811 : 128354 : startup_cost += inner_path->startup_cost;
6482 3812 : 128354 : startup_cost += (inner_path->total_cost - inner_path->startup_cost)
5774 3813 : 128354 : * innerstartsel;
3814 : 128354 : inner_run_cost = (inner_path->total_cost - inner_path->startup_cost)
3815 : 128354 : * (innerendsel - innerstartsel);
3816 : : }
3817 : :
3818 : : /*
3819 : : * We can't yet determine whether rescanning occurs, or whether
3820 : : * materialization of the inner input should be done. The minimum
3821 : : * possible inner input cost, regardless of rescan and materialization
3822 : : * considerations, is inner_run_cost. We include that in
3823 : : * workspace->total_cost, but not yet in run_cost.
3824 : : */
3825 : :
3826 : : /* CPU costs left for later */
3827 : :
3828 : : /* Public result fields */
381 rhaas@postgresql.org 3829 : 596312 : workspace->disabled_nodes = disabled_nodes;
4971 tgl@sss.pgh.pa.us 3830 : 596312 : workspace->startup_cost = startup_cost;
3831 : 596312 : workspace->total_cost = startup_cost + run_cost + inner_run_cost;
3832 : : /* Save private data for final_cost_mergejoin */
3833 : 596312 : workspace->run_cost = run_cost;
3834 : 596312 : workspace->inner_run_cost = inner_run_cost;
3835 : 596312 : workspace->outer_rows = outer_rows;
3836 : 596312 : workspace->inner_rows = inner_rows;
3837 : 596312 : workspace->outer_skip_rows = outer_skip_rows;
3838 : 596312 : workspace->inner_skip_rows = inner_skip_rows;
3839 : 596312 : }
3840 : :
3841 : : /*
3842 : : * final_cost_mergejoin
3843 : : * Final estimate of the cost and result size of a mergejoin path.
3844 : : *
3845 : : * Unlike other costsize functions, this routine makes two actual decisions:
3846 : : * whether the executor will need to do mark/restore, and whether we should
3847 : : * materialize the inner path. It would be logically cleaner to build
3848 : : * separate paths testing these alternatives, but that would require repeating
3849 : : * most of the cost calculations, which are not all that cheap. Since the
3850 : : * choice will not affect output pathkeys or startup cost, only total cost,
3851 : : * there is no possibility of wanting to keep more than one path. So it seems
3852 : : * best to make the decisions here and record them in the path's
3853 : : * skip_mark_restore and materialize_inner fields.
3854 : : *
3855 : : * Mark/restore overhead is usually required, but can be skipped if we know
3856 : : * that the executor need find only one match per outer tuple, and that the
3857 : : * mergeclauses are sufficient to identify a match.
3858 : : *
3859 : : * We materialize the inner path if we need mark/restore and either the inner
3860 : : * path can't support mark/restore, or it's cheaper to use an interposed
3861 : : * Material node to handle mark/restore.
3862 : : *
3863 : : * 'path' is already filled in except for the rows and cost fields and
3864 : : * skip_mark_restore and materialize_inner
3865 : : * 'workspace' is the result from initial_cost_mergejoin
3866 : : * 'extra' contains miscellaneous information about the join
3867 : : */
3868 : : void
3869 : 157961 : final_cost_mergejoin(PlannerInfo *root, MergePath *path,
3870 : : JoinCostWorkspace *workspace,
3871 : : JoinPathExtraData *extra)
3872 : : {
3873 : 157961 : Path *outer_path = path->jpath.outerjoinpath;
3874 : 157961 : Path *inner_path = path->jpath.innerjoinpath;
3875 : 157961 : double inner_path_rows = inner_path->rows;
3876 : 157961 : List *mergeclauses = path->path_mergeclauses;
3877 : 157961 : List *innersortkeys = path->innersortkeys;
3878 : 157961 : Cost startup_cost = workspace->startup_cost;
3879 : 157961 : Cost run_cost = workspace->run_cost;
3880 : 157961 : Cost inner_run_cost = workspace->inner_run_cost;
3881 : 157961 : double outer_rows = workspace->outer_rows;
3882 : 157961 : double inner_rows = workspace->inner_rows;
3883 : 157961 : double outer_skip_rows = workspace->outer_skip_rows;
3884 : 157961 : double inner_skip_rows = workspace->inner_skip_rows;
3885 : : Cost cpu_per_tuple,
3886 : : bare_inner_cost,
3887 : : mat_inner_cost;
3888 : : QualCost merge_qual_cost;
3889 : : QualCost qp_qual_cost;
3890 : : double mergejointuples,
3891 : : rescannedtuples;
3892 : : double rescanratio;
3893 : :
3894 : : /* Set the number of disabled nodes. */
381 rhaas@postgresql.org 3895 : 157961 : path->jpath.path.disabled_nodes = workspace->disabled_nodes;
3896 : :
3897 : : /* Protect some assumptions below that rowcounts aren't zero */
1783 drowley@postgresql.o 3898 [ + + ]: 157961 : if (inner_path_rows <= 0)
4971 tgl@sss.pgh.pa.us 3899 : 45 : inner_path_rows = 1;
3900 : :
3901 : : /* Mark the path with the correct row estimate */
4888 3902 [ + + ]: 157961 : if (path->jpath.path.param_info)
3903 : 449 : path->jpath.path.rows = path->jpath.path.param_info->ppi_rows;
3904 : : else
3905 : 157512 : path->jpath.path.rows = path->jpath.path.parent->rows;
3906 : :
3907 : : /* For partial paths, scale row estimate. */
3158 rhaas@postgresql.org 3908 [ + + ]: 157961 : if (path->jpath.path.parallel_workers > 0)
3909 : : {
3034 bruce@momjian.us 3910 : 5023 : double parallel_divisor = get_parallel_divisor(&path->jpath.path);
3911 : :
3097 rhaas@postgresql.org 3912 : 5023 : path->jpath.path.rows =
3913 : 5023 : clamp_row_est(path->jpath.path.rows / parallel_divisor);
3914 : : }
3915 : :
3916 : : /*
3917 : : * Compute cost of the mergequals and qpquals (other restriction clauses)
3918 : : * separately.
3919 : : */
4971 tgl@sss.pgh.pa.us 3920 : 157961 : cost_qual_eval(&merge_qual_cost, mergeclauses, root);
3921 : 157961 : cost_qual_eval(&qp_qual_cost, path->jpath.joinrestrictinfo, root);
3922 : 157961 : qp_qual_cost.startup -= merge_qual_cost.startup;
3923 : 157961 : qp_qual_cost.per_tuple -= merge_qual_cost.per_tuple;
3924 : :
3925 : : /*
3926 : : * With a SEMI or ANTI join, or if the innerrel is known unique, the
3927 : : * executor will stop scanning for matches after the first match. When
3928 : : * all the joinclauses are merge clauses, this means we don't ever need to
3929 : : * back up the merge, and so we can skip mark/restore overhead.
3930 : : */
3074 3931 [ + + ]: 157961 : if ((path->jpath.jointype == JOIN_SEMI ||
3932 [ + + ]: 154425 : path->jpath.jointype == JOIN_ANTI ||
3933 [ + + + + ]: 222616 : extra->inner_unique) &&
3934 : 71680 : (list_length(path->jpath.joinrestrictinfo) ==
3935 : 71680 : list_length(path->path_mergeclauses)))
3936 : 60307 : path->skip_mark_restore = true;
3937 : : else
3938 : 97654 : path->skip_mark_restore = false;
3939 : :
3940 : : /*
3941 : : * Get approx # tuples passing the mergequals. We use approx_tuple_count
3942 : : * here because we need an estimate done with JOIN_INNER semantics.
3943 : : */
4971 3944 : 157961 : mergejointuples = approx_tuple_count(root, &path->jpath, mergeclauses);
3945 : :
3946 : : /*
3947 : : * When there are equal merge keys in the outer relation, the mergejoin
3948 : : * must rescan any matching tuples in the inner relation. This means
3949 : : * re-fetching inner tuples; we have to estimate how often that happens.
3950 : : *
3951 : : * For regular inner and outer joins, the number of re-fetches can be
3952 : : * estimated approximately as size of merge join output minus size of
3953 : : * inner relation. Assume that the distinct key values are 1, 2, ..., and
3954 : : * denote the number of values of each key in the outer relation as m1,
3955 : : * m2, ...; in the inner relation, n1, n2, ... Then we have
3956 : : *
3957 : : * size of join = m1 * n1 + m2 * n2 + ...
3958 : : *
3959 : : * number of rescanned tuples = (m1 - 1) * n1 + (m2 - 1) * n2 + ... = m1 *
3960 : : * n1 + m2 * n2 + ... - (n1 + n2 + ...) = size of join - size of inner
3961 : : * relation
3962 : : *
3963 : : * This equation works correctly for outer tuples having no inner match
3964 : : * (nk = 0), but not for inner tuples having no outer match (mk = 0); we
3965 : : * are effectively subtracting those from the number of rescanned tuples,
3966 : : * when we should not. Can we do better without expensive selectivity
3967 : : * computations?
3968 : : *
3969 : : * The whole issue is moot if we know we don't need to mark/restore at
3970 : : * all, or if we are working from a unique-ified outer input.
3971 : : */
18 rguo@postgresql.org 3972 [ + + ]:GNC 157961 : if (path->skip_mark_restore ||
3973 [ + + + + : 97654 : RELATION_WAS_MADE_UNIQUE(outer_path->parent, extra->sjinfo,
+ + ]
3974 : : path->jpath.jointype))
4971 tgl@sss.pgh.pa.us 3975 :CBC 62288 : rescannedtuples = 0;
3976 : : else
3977 : : {
3978 : 95673 : rescannedtuples = mergejointuples - inner_path_rows;
3979 : : /* Must clamp because of possible underestimate */
3980 [ + + ]: 95673 : if (rescannedtuples < 0)
3981 : 38685 : rescannedtuples = 0;
3982 : : }
3983 : :
3984 : : /*
3985 : : * We'll inflate various costs this much to account for rescanning. Note
3986 : : * that this is to be multiplied by something involving inner_rows, or
3987 : : * another number related to the portion of the inner rel we'll scan.
3988 : : */
2454 3989 : 157961 : rescanratio = 1.0 + (rescannedtuples / inner_rows);
3990 : :
3991 : : /*
3992 : : * Decide whether we want to materialize the inner input to shield it from
3993 : : * mark/restore and performing re-fetches. Our cost model for regular
3994 : : * re-fetches is that a re-fetch costs the same as an original fetch,
3995 : : * which is probably an overestimate; but on the other hand we ignore the
3996 : : * bookkeeping costs of mark/restore. Not clear if it's worth developing
3997 : : * a more refined model. So we just need to inflate the inner run cost by
3998 : : * rescanratio.
3999 : : */
5774 4000 : 157961 : bare_inner_cost = inner_run_cost * rescanratio;
4001 : :
4002 : : /*
4003 : : * When we interpose a Material node the re-fetch cost is assumed to be
4004 : : * just cpu_operator_cost per tuple, independently of the underlying
4005 : : * plan's cost; and we charge an extra cpu_operator_cost per original
4006 : : * fetch as well. Note that we're assuming the materialize node will
4007 : : * never spill to disk, since it only has to remember tuples back to the
4008 : : * last mark. (If there are a huge number of duplicates, our other cost
4009 : : * factors will make the path so expensive that it probably won't get
4010 : : * chosen anyway.) So we don't use cost_rescan here.
4011 : : *
4012 : : * Note: keep this estimate in sync with create_mergejoin_plan's labeling
4013 : : * of the generated Material node.
4014 : : */
4015 : 157961 : mat_inner_cost = inner_run_cost +
2454 4016 : 157961 : cpu_operator_cost * inner_rows * rescanratio;
4017 : :
4018 : : /*
4019 : : * If we don't need mark/restore at all, we don't need materialization.
4020 : : */
3074 4021 [ + + ]: 157961 : if (path->skip_mark_restore)
4022 : 60307 : path->materialize_inner = false;
4023 : :
4024 : : /*
4025 : : * Prefer materializing if it looks cheaper, unless the user has asked to
4026 : : * suppress materialization.
4027 : : */
4028 [ + + + + ]: 97654 : else if (enable_material && mat_inner_cost < bare_inner_cost)
5774 4029 : 1484 : path->materialize_inner = true;
4030 : :
4031 : : /*
4032 : : * Even if materializing doesn't look cheaper, we *must* do it if the
4033 : : * inner path is to be used directly (without sorting) and it doesn't
4034 : : * support mark/restore.
4035 : : *
4036 : : * Since the inner side must be ordered, and only Sorts and IndexScans can
4037 : : * create order to begin with, and they both support mark/restore, you
4038 : : * might think there's no problem --- but you'd be wrong. Nestloop and
4039 : : * merge joins can *preserve* the order of their inputs, so they can be
4040 : : * selected as the input of a mergejoin, and they don't support
4041 : : * mark/restore at present.
4042 : : *
4043 : : * We don't test the value of enable_material here, because
4044 : : * materialization is required for correctness in this case, and turning
4045 : : * it off does not entitle us to deliver an invalid plan.
4046 : : */
4047 [ + + ]: 96170 : else if (innersortkeys == NIL &&
3956 rhaas@postgresql.org 4048 [ + + ]: 4055 : !ExecSupportsMarkRestore(inner_path))
5774 tgl@sss.pgh.pa.us 4049 : 916 : path->materialize_inner = true;
4050 : :
4051 : : /*
4052 : : * Also, force materializing if the inner path is to be sorted and the
4053 : : * sort is expected to spill to disk. This is because the final merge
4054 : : * pass can be done on-the-fly if it doesn't have to support mark/restore.
4055 : : * We don't try to adjust the cost estimates for this consideration,
4056 : : * though.
4057 : : *
4058 : : * Since materialization is a performance optimization in this case,
4059 : : * rather than necessary for correctness, we skip it if enable_material is
4060 : : * off.
4061 : : */
5619 rhaas@postgresql.org 4062 [ + + + + ]: 95254 : else if (enable_material && innersortkeys != NIL &&
3488 tgl@sss.pgh.pa.us 4063 : 92091 : relation_byte_size(inner_path_rows,
4064 : 92091 : inner_path->pathtarget->width) >
218 4065 [ + + ]: 92091 : work_mem * (Size) 1024)
5774 4066 : 134 : path->materialize_inner = true;
4067 : : else
4068 : 95120 : path->materialize_inner = false;
4069 : :
4070 : : /* Charge the right incremental cost for the chosen case */
4071 [ + + ]: 157961 : if (path->materialize_inner)
4072 : 2534 : run_cost += mat_inner_cost;
4073 : : else
4074 : 155427 : run_cost += bare_inner_cost;
4075 : :
4076 : : /* CPU costs */
4077 : :
4078 : : /*
4079 : : * The number of tuple comparisons needed is approximately number of outer
4080 : : * rows plus number of inner rows plus number of rescanned tuples (can we
4081 : : * refine this?). At each one, we need to evaluate the mergejoin quals.
4082 : : */
8258 4083 : 157961 : startup_cost += merge_qual_cost.startup;
6482 4084 : 157961 : startup_cost += merge_qual_cost.per_tuple *
4085 : 157961 : (outer_skip_rows + inner_skip_rows * rescanratio);
8258 4086 : 157961 : run_cost += merge_qual_cost.per_tuple *
6482 4087 : 157961 : ((outer_rows - outer_skip_rows) +
4088 : 157961 : (inner_rows - inner_skip_rows) * rescanratio);
4089 : :
4090 : : /*
4091 : : * For each tuple that gets through the mergejoin proper, we charge
4092 : : * cpu_tuple_cost plus the cost of evaluating additional restriction
4093 : : * clauses that are to be applied at the join. (This is pessimistic since
4094 : : * not all of the quals may get evaluated at each tuple.)
4095 : : *
4096 : : * Note: we could adjust for SEMI/ANTI joins skipping some qual
4097 : : * evaluations here, but it's probably not worth the trouble.
4098 : : */
8258 4099 : 157961 : startup_cost += qp_qual_cost.startup;
4100 : 157961 : cpu_per_tuple = cpu_tuple_cost + qp_qual_cost.per_tuple;
6230 4101 : 157961 : run_cost += cpu_per_tuple * mergejointuples;
4102 : :
4103 : : /* tlist eval costs are paid per output row, not per tuple scanned */
3488 4104 : 157961 : startup_cost += path->jpath.path.pathtarget->cost.startup;
4105 : 157961 : run_cost += path->jpath.path.pathtarget->cost.per_tuple * path->jpath.path.rows;
4106 : :
8258 4107 : 157961 : path->jpath.path.startup_cost = startup_cost;
4108 : 157961 : path->jpath.path.total_cost = startup_cost + run_cost;
10651 scrappy@hub.org 4109 : 157961 : }
4110 : :
4111 : : /*
4112 : : * run mergejoinscansel() with caching
4113 : : */
4114 : : static MergeScanSelCache *
6505 bruce@momjian.us 4115 : 593242 : cached_scansel(PlannerInfo *root, RestrictInfo *rinfo, PathKey *pathkey)
4116 : : {
4117 : : MergeScanSelCache *cache;
4118 : : ListCell *lc;
4119 : : Selectivity leftstartsel,
4120 : : leftendsel,
4121 : : rightstartsel,
4122 : : rightendsel;
4123 : : MemoryContext oldcontext;
4124 : :
4125 : : /* Do we have this result already? */
6802 tgl@sss.pgh.pa.us 4126 [ + + + + : 593281 : foreach(lc, rinfo->scansel_cache)
+ + ]
4127 : : {
4128 : 536036 : cache = (MergeScanSelCache *) lfirst(lc);
4129 [ + - ]: 536036 : if (cache->opfamily == pathkey->pk_opfamily &&
5285 4130 [ + - ]: 536036 : cache->collation == pathkey->pk_eclass->ec_collation &&
155 peter@eisentraut.org 4131 [ + + ]: 536036 : cache->cmptype == pathkey->pk_cmptype &&
6802 tgl@sss.pgh.pa.us 4132 [ + - ]: 535997 : cache->nulls_first == pathkey->pk_nulls_first)
4133 : 535997 : return cache;
4134 : : }
4135 : :
4136 : : /* Nope, do the computation */
4137 : 57245 : mergejoinscansel(root,
4138 : 57245 : (Node *) rinfo->clause,
4139 : : pathkey->pk_opfamily,
4140 : : pathkey->pk_cmptype,
4141 : 57245 : pathkey->pk_nulls_first,
4142 : : &leftstartsel,
4143 : : &leftendsel,
4144 : : &rightstartsel,
4145 : : &rightendsel);
4146 : :
4147 : : /* Cache the result in suitably long-lived workspace */
4148 : 57245 : oldcontext = MemoryContextSwitchTo(root->planner_cxt);
4149 : :
4150 : 57245 : cache = (MergeScanSelCache *) palloc(sizeof(MergeScanSelCache));
4151 : 57245 : cache->opfamily = pathkey->pk_opfamily;
5285 4152 : 57245 : cache->collation = pathkey->pk_eclass->ec_collation;
155 peter@eisentraut.org 4153 : 57245 : cache->cmptype = pathkey->pk_cmptype;
6802 tgl@sss.pgh.pa.us 4154 : 57245 : cache->nulls_first = pathkey->pk_nulls_first;
6482 4155 : 57245 : cache->leftstartsel = leftstartsel;
4156 : 57245 : cache->leftendsel = leftendsel;
4157 : 57245 : cache->rightstartsel = rightstartsel;
4158 : 57245 : cache->rightendsel = rightendsel;
4159 : :
6802 4160 : 57245 : rinfo->scansel_cache = lappend(rinfo->scansel_cache, cache);
4161 : :
4162 : 57245 : MemoryContextSwitchTo(oldcontext);
4163 : :
4164 : 57245 : return cache;
4165 : : }
4166 : :
4167 : : /*
4168 : : * initial_cost_hashjoin
4169 : : * Preliminary estimate of the cost of a hashjoin path.
4170 : : *
4171 : : * This must quickly produce lower-bound estimates of the path's startup and
4172 : : * total costs. If we are unable to eliminate the proposed path from
4173 : : * consideration using the lower bounds, final_cost_hashjoin will be called
4174 : : * to obtain the final estimates.
4175 : : *
4176 : : * The exact division of labor between this function and final_cost_hashjoin
4177 : : * is private to them, and represents a tradeoff between speed of the initial
4178 : : * estimate and getting a tight lower bound. We choose to not examine the
4179 : : * join quals here (other than by counting the number of hash clauses),
4180 : : * so we can't do much with CPU costs. We do assume that
4181 : : * ExecChooseHashTableSize is cheap enough to use here.
4182 : : *
4183 : : * 'workspace' is to be filled with startup_cost, total_cost, and perhaps
4184 : : * other data to be used by final_cost_hashjoin
4185 : : * 'jointype' is the type of join to be performed
4186 : : * 'hashclauses' is the list of joinclauses to be used as hash clauses
4187 : : * 'outer_path' is the outer input to the join
4188 : : * 'inner_path' is the inner input to the join
4189 : : * 'extra' contains miscellaneous information about the join
4190 : : * 'parallel_hash' indicates that inner_path is partial and that a shared
4191 : : * hash table will be built in parallel
4192 : : */
4193 : : void
4971 4194 : 321537 : initial_cost_hashjoin(PlannerInfo *root, JoinCostWorkspace *workspace,
4195 : : JoinType jointype,
4196 : : List *hashclauses,
4197 : : Path *outer_path, Path *inner_path,
4198 : : JoinPathExtraData *extra,
4199 : : bool parallel_hash)
4200 : : {
4201 : : int disabled_nodes;
9335 4202 : 321537 : Cost startup_cost = 0;
4203 : 321537 : Cost run_cost = 0;
4971 4204 : 321537 : double outer_path_rows = outer_path->rows;
4205 : 321537 : double inner_path_rows = inner_path->rows;
2817 andres@anarazel.de 4206 : 321537 : double inner_path_rows_total = inner_path_rows;
7769 neilc@samurai.com 4207 : 321537 : int num_hashclauses = list_length(hashclauses);
4208 : : int numbuckets;
4209 : : int numbatches;
4210 : : int num_skew_mcvs;
4211 : : size_t space_allowed; /* unused */
4212 : :
4213 : : /* Count up disabled nodes. */
381 rhaas@postgresql.org 4214 : 321537 : disabled_nodes = enable_hashjoin ? 0 : 1;
4215 : 321537 : disabled_nodes += inner_path->disabled_nodes;
4216 : 321537 : disabled_nodes += outer_path->disabled_nodes;
4217 : :
4218 : : /* cost of source data */
9335 tgl@sss.pgh.pa.us 4219 : 321537 : startup_cost += outer_path->startup_cost;
4220 : 321537 : run_cost += outer_path->total_cost - outer_path->startup_cost;
4221 : 321537 : startup_cost += inner_path->total_cost;
4222 : :
4223 : : /*
4224 : : * Cost of computing hash function: must do it once per input tuple. We
4225 : : * charge one cpu_operator_cost for each column's hash function. Also,
4226 : : * tack on one cpu_tuple_cost per inner row, to model the costs of
4227 : : * inserting the row into the hashtable.
4228 : : *
4229 : : * XXX when a hashclause is more complex than a single operator, we really
4230 : : * should charge the extra eval costs of the left or right side, as
4231 : : * appropriate, here. This seems more work than it's worth at the moment.
4232 : : */
6816 4233 : 321537 : startup_cost += (cpu_operator_cost * num_hashclauses + cpu_tuple_cost)
4234 : 321537 : * inner_path_rows;
8258 4235 : 321537 : run_cost += cpu_operator_cost * num_hashclauses * outer_path_rows;
4236 : :
4237 : : /*
4238 : : * If this is a parallel hash build, then the value we have for
4239 : : * inner_rows_total currently refers only to the rows returned by each
4240 : : * participant. For shared hash table size estimation, we need the total
4241 : : * number, so we need to undo the division.
4242 : : */
2817 andres@anarazel.de 4243 [ + + ]: 321537 : if (parallel_hash)
4244 : 6372 : inner_path_rows_total *= get_parallel_divisor(inner_path);
4245 : :
4246 : : /*
4247 : : * Get hash table size that executor would use for inner relation.
4248 : : *
4249 : : * XXX for the moment, always assume that skew optimization will be
4250 : : * performed. As long as SKEW_HASH_MEM_PERCENT is small, it's not worth
4251 : : * trying to determine that for sure.
4252 : : *
4253 : : * XXX at some point it might be interesting to try to account for skew
4254 : : * optimization in the cost estimate, but for now, we don't.
4255 : : */
4256 : 321537 : ExecChooseHashTableSize(inner_path_rows_total,
3488 tgl@sss.pgh.pa.us 4257 : 321537 : inner_path->pathtarget->width,
4258 : : true, /* useskew */
4259 : : parallel_hash, /* try_combined_hash_mem */
4260 : : outer_path->parallel_workers,
4261 : : &space_allowed,
4262 : : &numbuckets,
4263 : : &numbatches,
4264 : : &num_skew_mcvs);
4265 : :
4266 : : /*
4267 : : * If inner relation is too big then we will need to "batch" the join,
4268 : : * which implies writing and reading most of the tuples to disk an extra
4269 : : * time. Charge seq_page_cost per page, since the I/O should be nice and
4270 : : * sequential. Writing the inner rel counts as startup cost, all the rest
4271 : : * as run cost.
4272 : : */
4971 4273 [ + + ]: 321537 : if (numbatches > 1)
4274 : : {
4275 : 2317 : double outerpages = page_size(outer_path_rows,
3488 4276 : 2317 : outer_path->pathtarget->width);
4971 4277 : 2317 : double innerpages = page_size(inner_path_rows,
3488 4278 : 2317 : inner_path->pathtarget->width);
4279 : :
4971 4280 : 2317 : startup_cost += seq_page_cost * innerpages;
4281 : 2317 : run_cost += seq_page_cost * (innerpages + 2 * outerpages);
4282 : : }
4283 : :
4284 : : /* CPU costs left for later */
4285 : :
4286 : : /* Public result fields */
381 rhaas@postgresql.org 4287 : 321537 : workspace->disabled_nodes = disabled_nodes;
4971 tgl@sss.pgh.pa.us 4288 : 321537 : workspace->startup_cost = startup_cost;
4289 : 321537 : workspace->total_cost = startup_cost + run_cost;
4290 : : /* Save private data for final_cost_hashjoin */
4291 : 321537 : workspace->run_cost = run_cost;
4292 : 321537 : workspace->numbuckets = numbuckets;
4293 : 321537 : workspace->numbatches = numbatches;
2817 andres@anarazel.de 4294 : 321537 : workspace->inner_rows_total = inner_path_rows_total;
4971 tgl@sss.pgh.pa.us 4295 : 321537 : }
4296 : :
4297 : : /*
4298 : : * final_cost_hashjoin
4299 : : * Final estimate of the cost and result size of a hashjoin path.
4300 : : *
4301 : : * Note: the numbatches estimate is also saved into 'path' for use later
4302 : : *
4303 : : * 'path' is already filled in except for the rows and cost fields and
4304 : : * num_batches
4305 : : * 'workspace' is the result from initial_cost_hashjoin
4306 : : * 'extra' contains miscellaneous information about the join
4307 : : */
4308 : : void
4309 : 141494 : final_cost_hashjoin(PlannerInfo *root, HashPath *path,
4310 : : JoinCostWorkspace *workspace,
4311 : : JoinPathExtraData *extra)
4312 : : {
4313 : 141494 : Path *outer_path = path->jpath.outerjoinpath;
4314 : 141494 : Path *inner_path = path->jpath.innerjoinpath;
4315 : 141494 : double outer_path_rows = outer_path->rows;
4316 : 141494 : double inner_path_rows = inner_path->rows;
2817 andres@anarazel.de 4317 : 141494 : double inner_path_rows_total = workspace->inner_rows_total;
4971 tgl@sss.pgh.pa.us 4318 : 141494 : List *hashclauses = path->path_hashclauses;
4319 : 141494 : Cost startup_cost = workspace->startup_cost;
4320 : 141494 : Cost run_cost = workspace->run_cost;
4321 : 141494 : int numbuckets = workspace->numbuckets;
4322 : 141494 : int numbatches = workspace->numbatches;
4323 : : Cost cpu_per_tuple;
4324 : : QualCost hash_qual_cost;
4325 : : QualCost qp_qual_cost;
4326 : : double hashjointuples;
4327 : : double virtualbuckets;
4328 : : Selectivity innerbucketsize;
4329 : : Selectivity innermcvfreq;
4330 : : ListCell *hcl;
4331 : :
4332 : : /* Set the number of disabled nodes. */
381 rhaas@postgresql.org 4333 : 141494 : path->jpath.path.disabled_nodes = workspace->disabled_nodes;
4334 : :
4335 : : /* Mark the path with the correct row estimate */
4888 tgl@sss.pgh.pa.us 4336 [ + + ]: 141494 : if (path->jpath.path.param_info)
4337 : 1037 : path->jpath.path.rows = path->jpath.path.param_info->ppi_rows;
4338 : : else
4339 : 140457 : path->jpath.path.rows = path->jpath.path.parent->rows;
4340 : :
4341 : : /* For partial paths, scale row estimate. */
3158 rhaas@postgresql.org 4342 [ + + ]: 141494 : if (path->jpath.path.parallel_workers > 0)
4343 : : {
3034 bruce@momjian.us 4344 : 5884 : double parallel_divisor = get_parallel_divisor(&path->jpath.path);
4345 : :
3097 rhaas@postgresql.org 4346 : 5884 : path->jpath.path.rows =
4347 : 5884 : clamp_row_est(path->jpath.path.rows / parallel_divisor);
4348 : : }
4349 : :
4350 : : /* mark the path with estimated # of batches */
6008 tgl@sss.pgh.pa.us 4351 : 141494 : path->num_batches = numbatches;
4352 : :
4353 : : /* store the total number of tuples (sum of partial row estimates) */
2817 andres@anarazel.de 4354 : 141494 : path->inner_rows_total = inner_path_rows_total;
4355 : :
4356 : : /* and compute the number of "virtual" buckets in the whole join */
2999 tgl@sss.pgh.pa.us 4357 : 141494 : virtualbuckets = (double) numbuckets * (double) numbatches;
4358 : :
4359 : : /*
4360 : : * Determine bucketsize fraction and MCV frequency for the inner relation.
4361 : : * We use the smallest bucketsize or MCV frequency estimated for any
4362 : : * individual hashclause; this is undoubtedly conservative.
4363 : : *
4364 : : * BUT: if inner relation has been unique-ified, we can assume it's good
4365 : : * for hashing. This is important both because it's the right answer, and
4366 : : * because we avoid contaminating the cache with a value that's wrong for
4367 : : * non-unique-ified paths.
4368 : : */
18 rguo@postgresql.org 4369 [ + + + + :GNC 141494 : if (RELATION_WAS_MADE_UNIQUE(inner_path->parent, extra->sjinfo,
+ + ]
4370 : : path->jpath.jointype))
4371 : : {
8257 tgl@sss.pgh.pa.us 4372 :CBC 2090 : innerbucketsize = 1.0 / virtualbuckets;
2944 4373 : 2090 : innermcvfreq = 0.0;
4374 : : }
4375 : : else
4376 : : {
4377 : : List *otherclauses;
4378 : :
8257 4379 : 139404 : innerbucketsize = 1.0;
2944 4380 : 139404 : innermcvfreq = 1.0;
4381 : :
4382 : : /* At first, try to estimate bucket size using extended statistics. */
180 akorotkov@postgresql 4383 : 139404 : otherclauses = estimate_multivariate_bucketsize(root,
4384 : : inner_path->parent,
4385 : : hashclauses,
4386 : : &innerbucketsize);
4387 : :
4388 : : /* Pass through the remaining clauses */
4389 [ + + + + : 295455 : foreach(hcl, otherclauses)
+ + ]
4390 : : {
3071 tgl@sss.pgh.pa.us 4391 : 156051 : RestrictInfo *restrictinfo = lfirst_node(RestrictInfo, hcl);
4392 : : Selectivity thisbucketsize;
4393 : : Selectivity thismcvfreq;
4394 : :
4395 : : /*
4396 : : * First we have to figure out which side of the hashjoin clause
4397 : : * is the inner side.
4398 : : *
4399 : : * Since we tend to visit the same clauses over and over when
4400 : : * planning a large query, we cache the bucket stats estimates in
4401 : : * the RestrictInfo node to avoid repeated lookups of statistics.
4402 : : */
8246 4403 [ + + ]: 156051 : if (bms_is_subset(restrictinfo->right_relids,
4404 : 156051 : inner_path->parent->relids))
4405 : : {
4406 : : /* righthand side is inner */
8257 4407 : 81471 : thisbucketsize = restrictinfo->right_bucketsize;
4408 [ + + ]: 81471 : if (thisbucketsize < 0)
4409 : : {
4410 : : /* not cached yet */
2944 4411 : 43890 : estimate_hash_bucket_stats(root,
4412 : 43890 : get_rightop(restrictinfo->clause),
4413 : : virtualbuckets,
4414 : : &restrictinfo->right_mcvfreq,
4415 : : &restrictinfo->right_bucketsize);
4416 : 43890 : thisbucketsize = restrictinfo->right_bucketsize;
4417 : : }
4418 : 81471 : thismcvfreq = restrictinfo->right_mcvfreq;
4419 : : }
4420 : : else
4421 : : {
8246 4422 [ - + ]: 74580 : Assert(bms_is_subset(restrictinfo->left_relids,
4423 : : inner_path->parent->relids));
4424 : : /* lefthand side is inner */
8257 4425 : 74580 : thisbucketsize = restrictinfo->left_bucketsize;
4426 [ + + ]: 74580 : if (thisbucketsize < 0)
4427 : : {
4428 : : /* not cached yet */
2944 4429 : 38328 : estimate_hash_bucket_stats(root,
4430 : 38328 : get_leftop(restrictinfo->clause),
4431 : : virtualbuckets,
4432 : : &restrictinfo->left_mcvfreq,
4433 : : &restrictinfo->left_bucketsize);
4434 : 38328 : thisbucketsize = restrictinfo->left_bucketsize;
4435 : : }
4436 : 74580 : thismcvfreq = restrictinfo->left_mcvfreq;
4437 : : }
4438 : :
8257 4439 [ + + ]: 156051 : if (innerbucketsize > thisbucketsize)
4440 : 98537 : innerbucketsize = thisbucketsize;
2944 4441 [ + + ]: 156051 : if (innermcvfreq > thismcvfreq)
4442 : 141537 : innermcvfreq = thismcvfreq;
4443 : : }
4444 : : }
4445 : :
4446 : : /*
4447 : : * If the bucket holding the inner MCV would exceed hash_mem, we don't
4448 : : * want to hash unless there is really no other alternative, so apply
4449 : : * disable_cost. (The executor normally copes with excessive memory usage
4450 : : * by splitting batches, but obviously it cannot separate equal values
4451 : : * that way, so it will be unable to drive the batch size below hash_mem
4452 : : * when this is true.)
4453 : : */
4454 : 141494 : if (relation_byte_size(clamp_row_est(inner_path_rows * innermcvfreq),
1504 4455 [ + + ]: 282988 : inner_path->pathtarget->width) > get_hash_memory_limit())
2944 4456 : 3 : startup_cost += disable_cost;
4457 : :
4458 : : /*
4459 : : * Compute cost of the hashquals and qpquals (other restriction clauses)
4460 : : * separately.
4461 : : */
4971 4462 : 141494 : cost_qual_eval(&hash_qual_cost, hashclauses, root);
4463 : 141494 : cost_qual_eval(&qp_qual_cost, path->jpath.joinrestrictinfo, root);
4464 : 141494 : qp_qual_cost.startup -= hash_qual_cost.startup;
4465 : 141494 : qp_qual_cost.per_tuple -= hash_qual_cost.per_tuple;
4466 : :
4467 : : /* CPU costs */
4468 : :
3074 4469 [ + + ]: 141494 : if (path->jpath.jointype == JOIN_SEMI ||
4470 [ + + ]: 138396 : path->jpath.jointype == JOIN_ANTI ||
4471 [ + + ]: 136128 : extra->inner_unique)
5964 4472 : 59392 : {
4473 : : double outer_matched_rows;
4474 : : Selectivity inner_scan_frac;
4475 : :
4476 : : /*
4477 : : * With a SEMI or ANTI join, or if the innerrel is known unique, the
4478 : : * executor will stop after the first match.
4479 : : *
4480 : : * For an outer-rel row that has at least one match, we can expect the
4481 : : * bucket scan to stop after a fraction 1/(match_count+1) of the
4482 : : * bucket's rows, if the matches are evenly distributed. Since they
4483 : : * probably aren't quite evenly distributed, we apply a fuzz factor of
4484 : : * 2.0 to that fraction. (If we used a larger fuzz factor, we'd have
4485 : : * to clamp inner_scan_frac to at most 1.0; but since match_count is
4486 : : * at least 1, no such clamp is needed now.)
4487 : : */
3074 4488 : 59392 : outer_matched_rows = rint(outer_path_rows * extra->semifactors.outer_match_frac);
4489 : 59392 : inner_scan_frac = 2.0 / (extra->semifactors.match_count + 1.0);
4490 : :
5964 4491 : 59392 : startup_cost += hash_qual_cost.startup;
4492 : 118784 : run_cost += hash_qual_cost.per_tuple * outer_matched_rows *
4493 : 59392 : clamp_row_est(inner_path_rows * innerbucketsize * inner_scan_frac) * 0.5;
4494 : :
4495 : : /*
4496 : : * For unmatched outer-rel rows, the picture is quite a lot different.
4497 : : * In the first place, there is no reason to assume that these rows
4498 : : * preferentially hit heavily-populated buckets; instead assume they
4499 : : * are uncorrelated with the inner distribution and so they see an
4500 : : * average bucket size of inner_path_rows / virtualbuckets. In the
4501 : : * second place, it seems likely that they will have few if any exact
4502 : : * hash-code matches and so very few of the tuples in the bucket will
4503 : : * actually require eval of the hash quals. We don't have any good
4504 : : * way to estimate how many will, but for the moment assume that the
4505 : : * effective cost per bucket entry is one-tenth what it is for
4506 : : * matchable tuples.
4507 : : */
4508 : 118784 : run_cost += hash_qual_cost.per_tuple *
4509 : 118784 : (outer_path_rows - outer_matched_rows) *
4510 : 59392 : clamp_row_est(inner_path_rows / virtualbuckets) * 0.05;
4511 : :
4512 : : /* Get # of tuples that will pass the basic join */
2611 4513 [ + + ]: 59392 : if (path->jpath.jointype == JOIN_ANTI)
5964 4514 : 2268 : hashjointuples = outer_path_rows - outer_matched_rows;
4515 : : else
2611 4516 : 57124 : hashjointuples = outer_matched_rows;
4517 : : }
4518 : : else
4519 : : {
4520 : : /*
4521 : : * The number of tuple comparisons needed is the number of outer
4522 : : * tuples times the typical number of tuples in a hash bucket, which
4523 : : * is the inner relation size times its bucketsize fraction. At each
4524 : : * one, we need to evaluate the hashjoin quals. But actually,
4525 : : * charging the full qual eval cost at each tuple is pessimistic,
4526 : : * since we don't evaluate the quals unless the hash values match
4527 : : * exactly. For lack of a better idea, halve the cost estimate to
4528 : : * allow for that.
4529 : : */
5964 4530 : 82102 : startup_cost += hash_qual_cost.startup;
4531 : 164204 : run_cost += hash_qual_cost.per_tuple * outer_path_rows *
4532 : 82102 : clamp_row_est(inner_path_rows * innerbucketsize) * 0.5;
4533 : :
4534 : : /*
4535 : : * Get approx # tuples passing the hashquals. We use
4536 : : * approx_tuple_count here because we need an estimate done with
4537 : : * JOIN_INNER semantics.
4538 : : */
4539 : 82102 : hashjointuples = approx_tuple_count(root, &path->jpath, hashclauses);
4540 : : }
4541 : :
4542 : : /*
4543 : : * For each tuple that gets through the hashjoin proper, we charge
4544 : : * cpu_tuple_cost plus the cost of evaluating additional restriction
4545 : : * clauses that are to be applied at the join. (This is pessimistic since
4546 : : * not all of the quals may get evaluated at each tuple.)
4547 : : */
8258 4548 : 141494 : startup_cost += qp_qual_cost.startup;
4549 : 141494 : cpu_per_tuple = cpu_tuple_cost + qp_qual_cost.per_tuple;
6230 4550 : 141494 : run_cost += cpu_per_tuple * hashjointuples;
4551 : :
4552 : : /* tlist eval costs are paid per output row, not per tuple scanned */
3488 4553 : 141494 : startup_cost += path->jpath.path.pathtarget->cost.startup;
4554 : 141494 : run_cost += path->jpath.path.pathtarget->cost.per_tuple * path->jpath.path.rows;
4555 : :
8258 4556 : 141494 : path->jpath.path.startup_cost = startup_cost;
4557 : 141494 : path->jpath.path.total_cost = startup_cost + run_cost;
9335 4558 : 141494 : }
4559 : :
4560 : :
4561 : : /*
4562 : : * cost_subplan
4563 : : * Figure the costs for a SubPlan (or initplan).
4564 : : *
4565 : : * Note: we could dig the subplan's Plan out of the root list, but in practice
4566 : : * all callers have it handy already, so we make them pass it.
4567 : : */
4568 : : void
6224 4569 : 21913 : cost_subplan(PlannerInfo *root, SubPlan *subplan, Plan *plan)
4570 : : {
4571 : : QualCost sp_cost;
4572 : :
4573 : : /*
4574 : : * Figure any cost for evaluating the testexpr.
4575 : : *
4576 : : * Usually, SubPlan nodes are built very early, before we have constructed
4577 : : * any RelOptInfos for the parent query level, which means the parent root
4578 : : * does not yet contain enough information to safely consult statistics.
4579 : : * Therefore, we pass root as NULL here. cost_qual_eval() is already
4580 : : * well-equipped to handle a NULL root.
4581 : : *
4582 : : * One exception is SubPlan nodes built for the initplans of MIN/MAX
4583 : : * aggregates from indexes (cf. SS_make_initplan_from_plan). In this
4584 : : * case, having a NULL root is safe because testexpr will be NULL.
4585 : : * Besides, an initplan will by definition not consult anything from the
4586 : : * parent plan.
4587 : : */
4588 : 21913 : cost_qual_eval(&sp_cost,
4589 : 21913 : make_ands_implicit((Expr *) subplan->testexpr),
4590 : : NULL);
4591 : :
4592 [ + + ]: 21913 : if (subplan->useHashTable)
4593 : : {
4594 : : /*
4595 : : * If we are using a hash table for the subquery outputs, then the
4596 : : * cost of evaluating the query is a one-time cost. We charge one
4597 : : * cpu_operator_cost per tuple for the work of loading the hashtable,
4598 : : * too.
4599 : : */
4600 : 1054 : sp_cost.startup += plan->total_cost +
4601 : 1054 : cpu_operator_cost * plan->plan_rows;
4602 : :
4603 : : /*
4604 : : * The per-tuple costs include the cost of evaluating the lefthand
4605 : : * expressions, plus the cost of probing the hashtable. We already
4606 : : * accounted for the lefthand expressions as part of the testexpr, and
4607 : : * will also have counted one cpu_operator_cost for each comparison
4608 : : * operator. That is probably too low for the probing cost, but it's
4609 : : * hard to make a better estimate, so live with it for now.
4610 : : */
4611 : : }
4612 : : else
4613 : : {
4614 : : /*
4615 : : * Otherwise we will be rescanning the subplan output on each
4616 : : * evaluation. We need to estimate how much of the output we will
4617 : : * actually need to scan. NOTE: this logic should agree with the
4618 : : * tuple_fraction estimates used by make_subplan() in
4619 : : * plan/subselect.c.
4620 : : */
4621 : 20859 : Cost plan_run_cost = plan->total_cost - plan->startup_cost;
4622 : :
4623 [ + + ]: 20859 : if (subplan->subLinkType == EXISTS_SUBLINK)
4624 : : {
4625 : : /* we only need to fetch 1 tuple; clamp to avoid zero divide */
3451 4626 : 1227 : sp_cost.per_tuple += plan_run_cost / clamp_row_est(plan->plan_rows);
4627 : : }
6224 4628 [ + + ]: 19632 : else if (subplan->subLinkType == ALL_SUBLINK ||
4629 [ + + ]: 19623 : subplan->subLinkType == ANY_SUBLINK)
4630 : : {
4631 : : /* assume we need 50% of the tuples */
4632 : 72 : sp_cost.per_tuple += 0.50 * plan_run_cost;
4633 : : /* also charge a cpu_operator_cost per row examined */
4634 : 72 : sp_cost.per_tuple += 0.50 * plan->plan_rows * cpu_operator_cost;
4635 : : }
4636 : : else
4637 : : {
4638 : : /* assume we need all tuples */
4639 : 19560 : sp_cost.per_tuple += plan_run_cost;
4640 : : }
4641 : :
4642 : : /*
4643 : : * Also account for subplan's startup cost. If the subplan is
4644 : : * uncorrelated or undirect correlated, AND its topmost node is one
4645 : : * that materializes its output, assume that we'll only need to pay
4646 : : * its startup cost once; otherwise assume we pay the startup cost
4647 : : * every time.
4648 : : */
4649 [ + + + + ]: 27534 : if (subplan->parParam == NIL &&
5838 4650 : 6675 : ExecMaterializesOutput(nodeTag(plan)))
6224 4651 : 522 : sp_cost.startup += plan->startup_cost;
4652 : : else
4653 : 20337 : sp_cost.per_tuple += plan->startup_cost;
4654 : : }
4655 : :
4656 : 21913 : subplan->startup_cost = sp_cost.startup;
4657 : 21913 : subplan->per_call_cost = sp_cost.per_tuple;
4658 : 21913 : }
4659 : :
4660 : :
4661 : : /*
4662 : : * cost_rescan
4663 : : * Given a finished Path, estimate the costs of rescanning it after
4664 : : * having done so the first time. For some Path types a rescan is
4665 : : * cheaper than an original scan (if no parameters change), and this
4666 : : * function embodies knowledge about that. The default is to return
4667 : : * the same costs stored in the Path. (Note that the cost estimates
4668 : : * actually stored in Paths are always for first scans.)
4669 : : *
4670 : : * This function is not currently intended to model effects such as rescans
4671 : : * being cheaper due to disk block caching; what we are concerned with is
4672 : : * plan types wherein the executor caches results explicitly, or doesn't
4673 : : * redo startup calculations, etc.
4674 : : */
4675 : : static void
5838 4676 : 1366434 : cost_rescan(PlannerInfo *root, Path *path,
4677 : : Cost *rescan_startup_cost, /* output parameters */
4678 : : Cost *rescan_total_cost)
4679 : : {
4680 [ + + + + : 1366434 : switch (path->pathtype)
+ + ]
4681 : : {
4682 : 24380 : case T_FunctionScan:
4683 : :
4684 : : /*
4685 : : * Currently, nodeFunctionscan.c always executes the function to
4686 : : * completion before returning any rows, and caches the results in
4687 : : * a tuplestore. So the function eval cost is all startup cost
4688 : : * and isn't paid over again on rescans. However, all run costs
4689 : : * will be paid over again.
4690 : : */
4691 : 24380 : *rescan_startup_cost = 0;
4692 : 24380 : *rescan_total_cost = path->total_cost - path->startup_cost;
4693 : 24380 : break;
4694 : 60634 : case T_HashJoin:
4695 : :
4696 : : /*
4697 : : * If it's a single-batch join, we don't need to rebuild the hash
4698 : : * table during a rescan.
4699 : : */
3328 4700 [ + - ]: 60634 : if (((HashPath *) path)->num_batches == 1)
4701 : : {
4702 : : /* Startup cost is exactly the cost of hash table building */
4703 : 60634 : *rescan_startup_cost = 0;
4704 : 60634 : *rescan_total_cost = path->total_cost - path->startup_cost;
4705 : : }
4706 : : else
4707 : : {
4708 : : /* Otherwise, no special treatment */
3328 tgl@sss.pgh.pa.us 4709 :UBC 0 : *rescan_startup_cost = path->startup_cost;
4710 : 0 : *rescan_total_cost = path->total_cost;
4711 : : }
5838 tgl@sss.pgh.pa.us 4712 :CBC 60634 : break;
4713 : 3966 : case T_CteScan:
4714 : : case T_WorkTableScan:
4715 : : {
4716 : : /*
4717 : : * These plan types materialize their final result in a
4718 : : * tuplestore or tuplesort object. So the rescan cost is only
4719 : : * cpu_tuple_cost per tuple, unless the result is large enough
4720 : : * to spill to disk.
4721 : : */
4971 4722 : 3966 : Cost run_cost = cpu_tuple_cost * path->rows;
4723 : 3966 : double nbytes = relation_byte_size(path->rows,
2999 4724 : 3966 : path->pathtarget->width);
218 4725 : 3966 : double work_mem_bytes = work_mem * (Size) 1024;
4726 : :
5838 4727 [ + + ]: 3966 : if (nbytes > work_mem_bytes)
4728 : : {
4729 : : /* It will spill, so account for re-read cost */
4730 : 176 : double npages = ceil(nbytes / BLCKSZ);
4731 : :
4732 : 176 : run_cost += seq_page_cost * npages;
4733 : : }
4734 : 3966 : *rescan_startup_cost = 0;
4735 : 3966 : *rescan_total_cost = run_cost;
4736 : : }
4737 : 3966 : break;
5678 4738 : 469954 : case T_Material:
4739 : : case T_Sort:
4740 : : {
4741 : : /*
4742 : : * These plan types not only materialize their results, but do
4743 : : * not implement qual filtering or projection. So they are
4744 : : * even cheaper to rescan than the ones above. We charge only
4745 : : * cpu_operator_cost per tuple. (Note: keep that in sync with
4746 : : * the run_cost charge in cost_sort, and also see comments in
4747 : : * cost_material before you change it.)
4748 : : */
4971 4749 : 469954 : Cost run_cost = cpu_operator_cost * path->rows;
4750 : 469954 : double nbytes = relation_byte_size(path->rows,
2999 4751 : 469954 : path->pathtarget->width);
218 4752 : 469954 : double work_mem_bytes = work_mem * (Size) 1024;
4753 : :
5678 4754 [ + + ]: 469954 : if (nbytes > work_mem_bytes)
4755 : : {
4756 : : /* It will spill, so account for re-read cost */
4757 : 4946 : double npages = ceil(nbytes / BLCKSZ);
4758 : :
4759 : 4946 : run_cost += seq_page_cost * npages;
4760 : : }
4761 : 469954 : *rescan_startup_cost = 0;
4762 : 469954 : *rescan_total_cost = run_cost;
4763 : : }
4764 : 469954 : break;
1515 drowley@postgresql.o 4765 : 137108 : case T_Memoize:
4766 : : /* All the hard work is done by cost_memoize_rescan */
4767 : 137108 : cost_memoize_rescan(root, (MemoizePath *) path,
4768 : : rescan_startup_cost, rescan_total_cost);
1618 4769 : 137108 : break;
5838 tgl@sss.pgh.pa.us 4770 : 670392 : default:
4771 : 670392 : *rescan_startup_cost = path->startup_cost;
4772 : 670392 : *rescan_total_cost = path->total_cost;
4773 : 670392 : break;
4774 : : }
4775 : 1366434 : }
4776 : :
4777 : :
4778 : : /*
4779 : : * cost_qual_eval
4780 : : * Estimate the CPU costs of evaluating a WHERE clause.
4781 : : * The input can be either an implicitly-ANDed list of boolean
4782 : : * expressions, or a list of RestrictInfo nodes. (The latter is
4783 : : * preferred since it allows caching of the results.)
4784 : : * The result includes both a one-time (startup) component,
4785 : : * and a per-evaluation component.
4786 : : *
4787 : : * Note: in some code paths root can be passed as NULL, resulting in
4788 : : * slightly worse estimates.
4789 : : */
4790 : : void
6771 4791 : 1907721 : cost_qual_eval(QualCost *cost, List *quals, PlannerInfo *root)
4792 : : {
4793 : : cost_qual_eval_context context;
4794 : : ListCell *l;
4795 : :
4796 : 1907721 : context.root = root;
4797 : 1907721 : context.total.startup = 0;
4798 : 1907721 : context.total.per_tuple = 0;
4799 : :
4800 : : /* We don't charge any cost for the implicit ANDing at top level ... */
4801 : :
9034 4802 [ + + + + : 3600642 : foreach(l, quals)
+ + ]
4803 : : {
8934 bruce@momjian.us 4804 : 1692921 : Node *qual = (Node *) lfirst(l);
4805 : :
6771 tgl@sss.pgh.pa.us 4806 : 1692921 : cost_qual_eval_walker(qual, &context);
4807 : : }
4808 : :
4809 : 1907721 : *cost = context.total;
9335 4810 : 1907721 : }
4811 : :
4812 : : /*
4813 : : * cost_qual_eval_node
4814 : : * As above, for a single RestrictInfo or expression.
4815 : : */
4816 : : void
6771 4817 : 871437 : cost_qual_eval_node(QualCost *cost, Node *qual, PlannerInfo *root)
4818 : : {
4819 : : cost_qual_eval_context context;
4820 : :
4821 : 871437 : context.root = root;
4822 : 871437 : context.total.startup = 0;
4823 : 871437 : context.total.per_tuple = 0;
4824 : :
4825 : 871437 : cost_qual_eval_walker(qual, &context);
4826 : :
4827 : 871437 : *cost = context.total;
6802 4828 : 871437 : }
4829 : :
4830 : : static bool
6505 bruce@momjian.us 4831 : 4229398 : cost_qual_eval_walker(Node *node, cost_qual_eval_context *context)
4832 : : {
9335 tgl@sss.pgh.pa.us 4833 [ + + ]: 4229398 : if (node == NULL)
4834 : 42674 : return false;
4835 : :
4836 : : /*
4837 : : * RestrictInfo nodes contain an eval_cost field reserved for this
4838 : : * routine's use, so that it's not necessary to evaluate the qual clause's
4839 : : * cost more than once. If the clause's cost hasn't been computed yet,
4840 : : * the field's startup value will contain -1.
4841 : : */
6802 4842 [ + + ]: 4186724 : if (IsA(node, RestrictInfo))
4843 : : {
4844 : 1783327 : RestrictInfo *rinfo = (RestrictInfo *) node;
4845 : :
4846 [ + + ]: 1783327 : if (rinfo->eval_cost.startup < 0)
4847 : : {
4848 : : cost_qual_eval_context locContext;
4849 : :
6771 4850 : 283514 : locContext.root = context->root;
4851 : 283514 : locContext.total.startup = 0;
4852 : 283514 : locContext.total.per_tuple = 0;
4853 : :
4854 : : /*
4855 : : * For an OR clause, recurse into the marked-up tree so that we
4856 : : * set the eval_cost for contained RestrictInfos too.
4857 : : */
6802 4858 [ + + ]: 283514 : if (rinfo->orclause)
6771 4859 : 4764 : cost_qual_eval_walker((Node *) rinfo->orclause, &locContext);
4860 : : else
4861 : 278750 : cost_qual_eval_walker((Node *) rinfo->clause, &locContext);
4862 : :
4863 : : /*
4864 : : * If the RestrictInfo is marked pseudoconstant, it will be tested
4865 : : * only once, so treat its cost as all startup cost.
4866 : : */
6802 4867 [ + + ]: 283514 : if (rinfo->pseudoconstant)
4868 : : {
4869 : : /* count one execution during startup */
6771 4870 : 4947 : locContext.total.startup += locContext.total.per_tuple;
4871 : 4947 : locContext.total.per_tuple = 0;
4872 : : }
4873 : 283514 : rinfo->eval_cost = locContext.total;
4874 : : }
4875 : 1783327 : context->total.startup += rinfo->eval_cost.startup;
4876 : 1783327 : context->total.per_tuple += rinfo->eval_cost.per_tuple;
4877 : : /* do NOT recurse into children */
6802 4878 : 1783327 : return false;
4879 : : }
4880 : :
4881 : : /*
4882 : : * For each operator or function node in the given tree, we charge the
4883 : : * estimated execution cost given by pg_proc.procost (remember to multiply
4884 : : * this by cpu_operator_cost).
4885 : : *
4886 : : * Vars and Consts are charged zero, and so are boolean operators (AND,
4887 : : * OR, NOT). Simplistic, but a lot better than no model at all.
4888 : : *
4889 : : * Should we try to account for the possibility of short-circuit
4890 : : * evaluation of AND/OR? Probably *not*, because that would make the
4891 : : * results depend on the clause ordering, and we are not in any position
4892 : : * to expect that the current ordering of the clauses is the one that's
4893 : : * going to end up being used. The above per-RestrictInfo caching would
4894 : : * not mix well with trying to re-order clauses anyway.
4895 : : *
4896 : : * Another issue that is entirely ignored here is that if a set-returning
4897 : : * function is below top level in the tree, the functions/operators above
4898 : : * it will need to be evaluated multiple times. In practical use, such
4899 : : * cases arise so seldom as to not be worth the added complexity needed;
4900 : : * moreover, since our rowcount estimates for functions tend to be pretty
4901 : : * phony, the results would also be pretty phony.
4902 : : */
4903 [ + + ]: 2403397 : if (IsA(node, FuncExpr))
4904 : : {
2401 4905 : 167083 : add_function_cost(context->root, ((FuncExpr *) node)->funcid, node,
4906 : : &context->total);
4907 : : }
6802 4908 [ + + ]: 2236314 : else if (IsA(node, OpExpr) ||
4909 [ + + ]: 1925153 : IsA(node, DistinctExpr) ||
4910 [ + + ]: 1924618 : IsA(node, NullIfExpr))
4911 : : {
4912 : : /* rely on struct equivalence to treat these all alike */
4913 : 311757 : set_opfuncid((OpExpr *) node);
2401 4914 : 311757 : add_function_cost(context->root, ((OpExpr *) node)->opfuncid, node,
4915 : : &context->total);
4916 : : }
8105 4917 [ + + ]: 1924557 : else if (IsA(node, ScalarArrayOpExpr))
4918 : : {
7224 4919 : 21690 : ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) node;
6912 bruce@momjian.us 4920 : 21690 : Node *arraynode = (Node *) lsecond(saop->args);
4921 : : QualCost sacosts;
4922 : : QualCost hcosts;
611 tgl@sss.pgh.pa.us 4923 : 21690 : double estarraylen = estimate_array_length(context->root, arraynode);
4924 : :
6802 4925 : 21690 : set_sa_opfuncid(saop);
2401 4926 : 21690 : sacosts.startup = sacosts.per_tuple = 0;
4927 : 21690 : add_function_cost(context->root, saop->opfuncid, NULL,
4928 : : &sacosts);
4929 : :
1612 drowley@postgresql.o 4930 [ + + ]: 21690 : if (OidIsValid(saop->hashfuncid))
4931 : : {
4932 : : /* Handle costs for hashed ScalarArrayOpExpr */
4933 : 221 : hcosts.startup = hcosts.per_tuple = 0;
4934 : :
4935 : 221 : add_function_cost(context->root, saop->hashfuncid, NULL, &hcosts);
4936 : 221 : context->total.startup += sacosts.startup + hcosts.startup;
4937 : :
4938 : : /* Estimate the cost of building the hashtable. */
4939 : 221 : context->total.startup += estarraylen * hcosts.per_tuple;
4940 : :
4941 : : /*
4942 : : * XXX should we charge a little bit for sacosts.per_tuple when
4943 : : * building the table, or is it ok to assume there will be zero
4944 : : * hash collision?
4945 : : */
4946 : :
4947 : : /*
4948 : : * Charge for hashtable lookups. Charge a single hash and a
4949 : : * single comparison.
4950 : : */
4951 : 221 : context->total.per_tuple += hcosts.per_tuple + sacosts.per_tuple;
4952 : : }
4953 : : else
4954 : : {
4955 : : /*
4956 : : * Estimate that the operator will be applied to about half of the
4957 : : * array elements before the answer is determined.
4958 : : */
4959 : 21469 : context->total.startup += sacosts.startup;
4960 : 42938 : context->total.per_tuple += sacosts.per_tuple *
611 tgl@sss.pgh.pa.us 4961 : 21469 : estimate_array_length(context->root, arraynode) * 0.5;
4962 : : }
4963 : : }
5249 4964 [ + + ]: 1902867 : else if (IsA(node, Aggref) ||
4965 [ + + ]: 1876330 : IsA(node, WindowFunc))
4966 : : {
4967 : : /*
4968 : : * Aggref and WindowFunc nodes are (and should be) treated like Vars,
4969 : : * ie, zero execution cost in the current model, because they behave
4970 : : * essentially like Vars at execution. We disregard the costs of
4971 : : * their input expressions for the same reason. The actual execution
4972 : : * costs of the aggregate/window functions and their arguments have to
4973 : : * be factored into plan-node-specific costing of the Agg or WindowAgg
4974 : : * plan node.
4975 : : */
4976 : 28299 : return false; /* don't recurse into children */
4977 : : }
1265 4978 [ + + ]: 1874568 : else if (IsA(node, GroupingFunc))
4979 : : {
4980 : : /* Treat this as having cost 1 */
4981 : 211 : context->total.per_tuple += cpu_operator_cost;
4982 : 211 : return false; /* don't recurse into children */
4983 : : }
6668 4984 [ + + ]: 1874357 : else if (IsA(node, CoerceViaIO))
4985 : : {
4986 : 10622 : CoerceViaIO *iocoerce = (CoerceViaIO *) node;
4987 : : Oid iofunc;
4988 : : Oid typioparam;
4989 : : bool typisvarlena;
4990 : :
4991 : : /* check the result type's input function */
4992 : 10622 : getTypeInputInfo(iocoerce->resulttype,
4993 : : &iofunc, &typioparam);
2401 4994 : 10622 : add_function_cost(context->root, iofunc, NULL,
4995 : : &context->total);
4996 : : /* check the input type's output function */
6668 4997 : 10622 : getTypeOutputInfo(exprType((Node *) iocoerce->arg),
4998 : : &iofunc, &typisvarlena);
2401 4999 : 10622 : add_function_cost(context->root, iofunc, NULL,
5000 : : &context->total);
5001 : : }
6738 5002 [ + + ]: 1863735 : else if (IsA(node, ArrayCoerceExpr))
5003 : : {
5004 : 2548 : ArrayCoerceExpr *acoerce = (ArrayCoerceExpr *) node;
5005 : : QualCost perelemcost;
5006 : :
2898 5007 : 2548 : cost_qual_eval_node(&perelemcost, (Node *) acoerce->elemexpr,
5008 : : context->root);
5009 : 2548 : context->total.startup += perelemcost.startup;
5010 [ + + ]: 2548 : if (perelemcost.per_tuple > 0)
5011 : 33 : context->total.per_tuple += perelemcost.per_tuple *
611 5012 : 33 : estimate_array_length(context->root, (Node *) acoerce->arg);
5013 : : }
7192 5014 [ + + ]: 1861187 : else if (IsA(node, RowCompareExpr))
5015 : : {
5016 : : /* Conservatively assume we will check all the columns */
5017 : 126 : RowCompareExpr *rcexpr = (RowCompareExpr *) node;
5018 : : ListCell *lc;
5019 : :
6802 5020 [ + - + + : 405 : foreach(lc, rcexpr->opnos)
+ + ]
5021 : : {
6505 bruce@momjian.us 5022 : 279 : Oid opid = lfirst_oid(lc);
5023 : :
2401 tgl@sss.pgh.pa.us 5024 : 279 : add_function_cost(context->root, get_opcode(opid), NULL,
5025 : : &context->total);
5026 : : }
5027 : : }
2976 5028 [ + + ]: 1861061 : else if (IsA(node, MinMaxExpr) ||
843 michael@paquier.xyz 5029 [ + + ]: 1860931 : IsA(node, SQLValueFunction) ||
2976 tgl@sss.pgh.pa.us 5030 [ + + ]: 1858547 : IsA(node, XmlExpr) ||
5031 [ + + ]: 1858196 : IsA(node, CoerceToDomain) ||
534 amitlan@postgresql.o 5032 [ + + ]: 1853337 : IsA(node, NextValueExpr) ||
5033 [ + + ]: 1853156 : IsA(node, JsonExpr))
5034 : : {
5035 : : /* Treat all these as having cost 1 */
2976 tgl@sss.pgh.pa.us 5036 : 9175 : context->total.per_tuple += cpu_operator_cost;
5037 : : }
8273 5038 [ - + ]: 1851886 : else if (IsA(node, SubLink))
5039 : : {
5040 : : /* This routine should not be applied to un-planned expressions */
8079 tgl@sss.pgh.pa.us 5041 [ # # ]:UBC 0 : elog(ERROR, "cannot handle unplanned sub-select");
5042 : : }
8302 tgl@sss.pgh.pa.us 5043 [ + + ]:CBC 1851886 : else if (IsA(node, SubPlan))
5044 : : {
5045 : : /*
5046 : : * A subplan node in an expression typically indicates that the
5047 : : * subplan will be executed on each evaluation, so charge accordingly.
5048 : : * (Sub-selects that can be executed as InitPlans have already been
5049 : : * removed from the expression.)
5050 : : */
8069 bruce@momjian.us 5051 : 21552 : SubPlan *subplan = (SubPlan *) node;
5052 : :
6224 tgl@sss.pgh.pa.us 5053 : 21552 : context->total.startup += subplan->startup_cost;
5054 : 21552 : context->total.per_tuple += subplan->per_call_cost;
5055 : :
5056 : : /*
5057 : : * We don't want to recurse into the testexpr, because it was already
5058 : : * counted in the SubPlan node's costs. So we're done.
5059 : : */
5060 : 21552 : return false;
5061 : : }
5062 [ + + ]: 1830334 : else if (IsA(node, AlternativeSubPlan))
5063 : : {
5064 : : /*
5065 : : * Arbitrarily use the first alternative plan for costing. (We should
5066 : : * certainly only include one alternative, and we don't yet have
5067 : : * enough information to know which one the executor is most likely to
5068 : : * use.)
5069 : : */
5070 : 917 : AlternativeSubPlan *asplan = (AlternativeSubPlan *) node;
5071 : :
5072 : 917 : return cost_qual_eval_walker((Node *) linitial(asplan->subplans),
5073 : : context);
5074 : : }
3488 5075 [ + + ]: 1829417 : else if (IsA(node, PlaceHolderVar))
5076 : : {
5077 : : /*
5078 : : * A PlaceHolderVar should be given cost zero when considering general
5079 : : * expression evaluation costs. The expense of doing the contained
5080 : : * expression is charged as part of the tlist eval costs of the scan
5081 : : * or join where the PHV is first computed (see set_rel_width and
5082 : : * add_placeholders_to_joinrel). If we charged it again here, we'd be
5083 : : * double-counting the cost for each level of plan that the PHV
5084 : : * bubbles up through. Hence, return without recursing into the
5085 : : * phexpr.
5086 : : */
5087 : 2580 : return false;
5088 : : }
5089 : :
5090 : : /* recurse into children */
282 peter@eisentraut.org 5091 : 2349838 : return expression_tree_walker(node, cost_qual_eval_walker, context);
5092 : : }
5093 : :
5094 : : /*
5095 : : * get_restriction_qual_cost
5096 : : * Compute evaluation costs of a baserel's restriction quals, plus any
5097 : : * movable join quals that have been pushed down to the scan.
5098 : : * Results are returned into *qpqual_cost.
5099 : : *
5100 : : * This is a convenience subroutine that works for seqscans and other cases
5101 : : * where all the given quals will be evaluated the hard way. It's not useful
5102 : : * for cost_index(), for example, where the index machinery takes care of
5103 : : * some of the quals. We assume baserestrictcost was previously set by
5104 : : * set_baserel_size_estimates().
5105 : : */
5106 : : static void
4888 tgl@sss.pgh.pa.us 5107 : 517469 : get_restriction_qual_cost(PlannerInfo *root, RelOptInfo *baserel,
5108 : : ParamPathInfo *param_info,
5109 : : QualCost *qpqual_cost)
5110 : : {
5111 [ + + ]: 517469 : if (param_info)
5112 : : {
5113 : : /* Include costs of pushed-down clauses */
5114 : 112299 : cost_qual_eval(qpqual_cost, param_info->ppi_clauses, root);
5115 : :
5116 : 112299 : qpqual_cost->startup += baserel->baserestrictcost.startup;
5117 : 112299 : qpqual_cost->per_tuple += baserel->baserestrictcost.per_tuple;
5118 : : }
5119 : : else
5120 : 405170 : *qpqual_cost = baserel->baserestrictcost;
5121 : 517469 : }
5122 : :
5123 : :
5124 : : /*
5125 : : * compute_semi_anti_join_factors
5126 : : * Estimate how much of the inner input a SEMI, ANTI, or inner_unique join
5127 : : * can be expected to scan.
5128 : : *
5129 : : * In a hash or nestloop SEMI/ANTI join, the executor will stop scanning
5130 : : * inner rows as soon as it finds a match to the current outer row.
5131 : : * The same happens if we have detected the inner rel is unique.
5132 : : * We should therefore adjust some of the cost components for this effect.
5133 : : * This function computes some estimates needed for these adjustments.
5134 : : * These estimates will be the same regardless of the particular paths used
5135 : : * for the outer and inner relation, so we compute these once and then pass
5136 : : * them to all the join cost estimation functions.
5137 : : *
5138 : : * Input parameters:
5139 : : * joinrel: join relation under consideration
5140 : : * outerrel: outer relation under consideration
5141 : : * innerrel: inner relation under consideration
5142 : : * jointype: if not JOIN_SEMI or JOIN_ANTI, we assume it's inner_unique
5143 : : * sjinfo: SpecialJoinInfo relevant to this join
5144 : : * restrictlist: join quals
5145 : : * Output parameters:
5146 : : * *semifactors is filled in (see pathnodes.h for field definitions)
5147 : : */
5148 : : void
4971 5149 : 100271 : compute_semi_anti_join_factors(PlannerInfo *root,
5150 : : RelOptInfo *joinrel,
5151 : : RelOptInfo *outerrel,
5152 : : RelOptInfo *innerrel,
5153 : : JoinType jointype,
5154 : : SpecialJoinInfo *sjinfo,
5155 : : List *restrictlist,
5156 : : SemiAntiJoinFactors *semifactors)
5157 : : {
5158 : : Selectivity jselec;
5159 : : Selectivity nselec;
5160 : : Selectivity avgmatch;
5161 : : SpecialJoinInfo norm_sjinfo;
5162 : : List *joinquals;
5163 : : ListCell *l;
5164 : :
5165 : : /*
5166 : : * In an ANTI join, we must ignore clauses that are "pushed down", since
5167 : : * those won't affect the match logic. In a SEMI join, we do not
5168 : : * distinguish joinquals from "pushed down" quals, so just use the whole
5169 : : * restrictinfo list. For other outer join types, we should consider only
5170 : : * non-pushed-down quals, so that this devolves to an IS_OUTER_JOIN check.
5171 : : */
3074 5172 [ + + ]: 100271 : if (IS_OUTER_JOIN(jointype))
5173 : : {
5964 5174 : 37306 : joinquals = NIL;
4971 5175 [ + + + + : 81720 : foreach(l, restrictlist)
+ + ]
5176 : : {
3071 5177 : 44414 : RestrictInfo *rinfo = lfirst_node(RestrictInfo, l);
5178 : :
2696 5179 [ + + + - ]: 44414 : if (!RINFO_IS_PUSHED_DOWN(rinfo, joinrel->relids))
5964 5180 : 41976 : joinquals = lappend(joinquals, rinfo);
5181 : : }
5182 : : }
5183 : : else
4971 5184 : 62965 : joinquals = restrictlist;
5185 : :
5186 : : /*
5187 : : * Get the JOIN_SEMI or JOIN_ANTI selectivity of the join clauses.
5188 : : */
5964 5189 [ + + ]: 100271 : jselec = clauselist_selectivity(root,
5190 : : joinquals,
5191 : : 0,
5192 : : (jointype == JOIN_ANTI) ? JOIN_ANTI : JOIN_SEMI,
5193 : : sjinfo);
5194 : :
5195 : : /*
5196 : : * Also get the normal inner-join selectivity of the join clauses.
5197 : : */
530 amitlan@postgresql.o 5198 : 100271 : init_dummy_sjinfo(&norm_sjinfo, outerrel->relids, innerrel->relids);
5199 : :
5964 tgl@sss.pgh.pa.us 5200 : 100271 : nselec = clauselist_selectivity(root,
5201 : : joinquals,
5202 : : 0,
5203 : : JOIN_INNER,
5204 : : &norm_sjinfo);
5205 : :
5206 : : /* Avoid leaking a lot of ListCells */
3074 5207 [ + + ]: 100271 : if (IS_OUTER_JOIN(jointype))
5964 5208 : 37306 : list_free(joinquals);
5209 : :
5210 : : /*
5211 : : * jselec can be interpreted as the fraction of outer-rel rows that have
5212 : : * any matches (this is true for both SEMI and ANTI cases). And nselec is
5213 : : * the fraction of the Cartesian product that matches. So, the average
5214 : : * number of matches for each outer-rel row that has at least one match is
5215 : : * nselec * inner_rows / jselec.
5216 : : *
5217 : : * Note: it is correct to use the inner rel's "rows" count here, even
5218 : : * though we might later be considering a parameterized inner path with
5219 : : * fewer rows. This is because we have included all the join clauses in
5220 : : * the selectivity estimate.
5221 : : */
5222 [ + + ]: 100271 : if (jselec > 0) /* protect against zero divide */
5223 : : {
4971 5224 : 100061 : avgmatch = nselec * innerrel->rows / jselec;
5225 : : /* Clamp to sane range */
5964 5226 [ + + ]: 100061 : avgmatch = Max(1.0, avgmatch);
5227 : : }
5228 : : else
5229 : 210 : avgmatch = 1.0;
5230 : :
4971 5231 : 100271 : semifactors->outer_match_frac = jselec;
5232 : 100271 : semifactors->match_count = avgmatch;
5233 : 100271 : }
5234 : :
5235 : : /*
5236 : : * has_indexed_join_quals
5237 : : * Check whether all the joinquals of a nestloop join are used as
5238 : : * inner index quals.
5239 : : *
5240 : : * If the inner path of a SEMI/ANTI join is an indexscan (including bitmap
5241 : : * indexscan) that uses all the joinquals as indexquals, we can assume that an
5242 : : * unmatched outer tuple is cheap to process, whereas otherwise it's probably
5243 : : * expensive.
5244 : : */
5245 : : static bool
1490 peter@eisentraut.org 5246 : 429775 : has_indexed_join_quals(NestPath *path)
5247 : : {
5248 : 429775 : JoinPath *joinpath = &path->jpath;
4888 tgl@sss.pgh.pa.us 5249 : 429775 : Relids joinrelids = joinpath->path.parent->relids;
5250 : 429775 : Path *innerpath = joinpath->innerjoinpath;
5251 : : List *indexclauses;
5252 : : bool found_one;
5253 : : ListCell *lc;
5254 : :
5255 : : /* If join still has quals to evaluate, it's not fast */
5256 [ + + ]: 429775 : if (joinpath->joinrestrictinfo != NIL)
5257 : 306218 : return false;
5258 : : /* Nor if the inner path isn't parameterized at all */
5259 [ + + ]: 123557 : if (innerpath->param_info == NULL)
5260 : 1650 : return false;
5261 : :
5262 : : /* Find the indexclauses list for the inner scan */
5263 [ + + + ]: 121907 : switch (innerpath->pathtype)
5264 : : {
5265 : 73647 : case T_IndexScan:
5266 : : case T_IndexOnlyScan:
5267 : 73647 : indexclauses = ((IndexPath *) innerpath)->indexclauses;
5268 : 73647 : break;
5269 : 135 : case T_BitmapHeapScan:
5270 : : {
5271 : : /* Accept only a simple bitmap scan, not AND/OR cases */
4836 bruce@momjian.us 5272 : 135 : Path *bmqual = ((BitmapHeapPath *) innerpath)->bitmapqual;
5273 : :
5274 [ + + ]: 135 : if (IsA(bmqual, IndexPath))
5275 : 111 : indexclauses = ((IndexPath *) bmqual)->indexclauses;
5276 : : else
5277 : 24 : return false;
5278 : 111 : break;
5279 : : }
4888 tgl@sss.pgh.pa.us 5280 : 48125 : default:
5281 : :
5282 : : /*
5283 : : * If it's not a simple indexscan, it probably doesn't run quickly
5284 : : * for zero rows out, even if it's a parameterized path using all
5285 : : * the joinquals.
5286 : : */
4971 5287 : 48125 : return false;
5288 : : }
5289 : :
5290 : : /*
5291 : : * Examine the inner path's param clauses. Any that are from the outer
5292 : : * path must be found in the indexclauses list, either exactly or in an
5293 : : * equivalent form generated by equivclass.c. Also, we must find at least
5294 : : * one such clause, else it's a clauseless join which isn't fast.
5295 : : */
4888 5296 : 73758 : found_one = false;
5297 [ + - + + : 147027 : foreach(lc, innerpath->param_info->ppi_clauses)
+ + ]
5298 : : {
5299 : 75379 : RestrictInfo *rinfo = (RestrictInfo *) lfirst(lc);
5300 : :
5301 [ + + ]: 75379 : if (join_clause_is_movable_into(rinfo,
5302 : 75379 : innerpath->parent->relids,
5303 : : joinrelids))
5304 : : {
2401 5305 [ + + ]: 75103 : if (!is_redundant_with_indexclauses(rinfo, indexclauses))
4888 5306 : 2110 : return false;
5307 : 72993 : found_one = true;
5308 : : }
5309 : : }
5310 : 71648 : return found_one;
5311 : : }
5312 : :
5313 : :
5314 : : /*
5315 : : * approx_tuple_count
5316 : : * Quick-and-dirty estimation of the number of join rows passing
5317 : : * a set of qual conditions.
5318 : : *
5319 : : * The quals can be either an implicitly-ANDed list of boolean expressions,
5320 : : * or a list of RestrictInfo nodes (typically the latter).
5321 : : *
5322 : : * We intentionally compute the selectivity under JOIN_INNER rules, even
5323 : : * if it's some type of outer join. This is appropriate because we are
5324 : : * trying to figure out how many tuples pass the initial merge or hash
5325 : : * join step.
5326 : : *
5327 : : * This is quick-and-dirty because we bypass clauselist_selectivity, and
5328 : : * simply multiply the independent clause selectivities together. Now
5329 : : * clauselist_selectivity often can't do any better than that anyhow, but
5330 : : * for some situations (such as range constraints) it is smarter. However,
5331 : : * we can't effectively cache the results of clauselist_selectivity, whereas
5332 : : * the individual clause selectivities can be and are cached.
5333 : : *
5334 : : * Since we are only using the results to estimate how many potential
5335 : : * output tuples are generated and passed through qpqual checking, it
5336 : : * seems OK to live with the approximation.
5337 : : */
5338 : : static double
6056 5339 : 240063 : approx_tuple_count(PlannerInfo *root, JoinPath *path, List *quals)
5340 : : {
5341 : : double tuples;
4971 5342 : 240063 : double outer_tuples = path->outerjoinpath->rows;
5343 : 240063 : double inner_tuples = path->innerjoinpath->rows;
5344 : : SpecialJoinInfo sjinfo;
6230 5345 : 240063 : Selectivity selec = 1.0;
5346 : : ListCell *l;
5347 : :
5348 : : /*
5349 : : * Make up a SpecialJoinInfo for JOIN_INNER semantics.
5350 : : */
530 amitlan@postgresql.o 5351 : 240063 : init_dummy_sjinfo(&sjinfo, path->outerjoinpath->parent->relids,
5352 : 240063 : path->innerjoinpath->parent->relids);
5353 : :
5354 : : /* Get the approximate selectivity */
8859 tgl@sss.pgh.pa.us 5355 [ + + + + : 518467 : foreach(l, quals)
+ + ]
5356 : : {
5357 : 278404 : Node *qual = (Node *) lfirst(l);
5358 : :
5359 : : /* Note that clause_selectivity will be able to cache its result */
3075 simon@2ndQuadrant.co 5360 : 278404 : selec *= clause_selectivity(root, qual, 0, JOIN_INNER, &sjinfo);
5361 : : }
5362 : :
5363 : : /* Apply it to the input relation sizes */
6056 tgl@sss.pgh.pa.us 5364 : 240063 : tuples = selec * outer_tuples * inner_tuples;
5365 : :
6230 5366 : 240063 : return clamp_row_est(tuples);
5367 : : }
5368 : :
5369 : :
5370 : : /*
5371 : : * set_baserel_size_estimates
5372 : : * Set the size estimates for the given base relation.
5373 : : *
5374 : : * The rel's targetlist and restrictinfo list must have been constructed
5375 : : * already, and rel->tuples must be set.
5376 : : *
5377 : : * We set the following fields of the rel node:
5378 : : * rows: the estimated number of output tuples (after applying
5379 : : * restriction clauses).
5380 : : * width: the estimated average output tuple width in bytes.
5381 : : * baserestrictcost: estimated cost of evaluating baserestrictinfo clauses.
5382 : : */
5383 : : void
7398 5384 : 244977 : set_baserel_size_estimates(PlannerInfo *root, RelOptInfo *rel)
5385 : : {
5386 : : double nrows;
5387 : :
5388 : : /* Should only be applied to base relations */
8246 5389 [ - + ]: 244977 : Assert(rel->relid > 0);
5390 : :
7915 5391 : 489939 : nrows = rel->tuples *
7916 5392 : 244977 : clauselist_selectivity(root,
5393 : : rel->baserestrictinfo,
5394 : : 0,
5395 : : JOIN_INNER,
5396 : : NULL);
5397 : :
7915 5398 : 244962 : rel->rows = clamp_row_est(nrows);
5399 : :
6771 5400 : 244962 : cost_qual_eval(&rel->baserestrictcost, rel->baserestrictinfo, root);
5401 : :
9372 5402 : 244962 : set_rel_width(root, rel);
10651 scrappy@hub.org 5403 : 244962 : }
5404 : :
5405 : : /*
5406 : : * get_parameterized_baserel_size
5407 : : * Make a size estimate for a parameterized scan of a base relation.
5408 : : *
5409 : : * 'param_clauses' lists the additional join clauses to be used.
5410 : : *
5411 : : * set_baserel_size_estimates must have been applied already.
5412 : : */
5413 : : double
4888 tgl@sss.pgh.pa.us 5414 : 72087 : get_parameterized_baserel_size(PlannerInfo *root, RelOptInfo *rel,
5415 : : List *param_clauses)
5416 : : {
5417 : : List *allclauses;
5418 : : double nrows;
5419 : :
5420 : : /*
5421 : : * Estimate the number of rows returned by the parameterized scan, knowing
5422 : : * that it will apply all the extra join clauses as well as the rel's own
5423 : : * restriction clauses. Note that we force the clauses to be treated as
5424 : : * non-join clauses during selectivity estimation.
5425 : : */
2217 5426 : 72087 : allclauses = list_concat_copy(param_clauses, rel->baserestrictinfo);
4888 5427 : 144174 : nrows = rel->tuples *
5428 : 72087 : clauselist_selectivity(root,
5429 : : allclauses,
2999 5430 : 72087 : rel->relid, /* do not use 0! */
5431 : : JOIN_INNER,
5432 : : NULL);
4888 5433 : 72087 : nrows = clamp_row_est(nrows);
5434 : : /* For safety, make sure result is not more than the base estimate */
5435 [ - + ]: 72087 : if (nrows > rel->rows)
4888 tgl@sss.pgh.pa.us 5436 :UBC 0 : nrows = rel->rows;
4888 tgl@sss.pgh.pa.us 5437 :CBC 72087 : return nrows;
5438 : : }
5439 : :
5440 : : /*
5441 : : * set_joinrel_size_estimates
5442 : : * Set the size estimates for the given join relation.
5443 : : *
5444 : : * The rel's targetlist must have been constructed already, and a
5445 : : * restriction clause list that matches the given component rels must
5446 : : * be provided.
5447 : : *
5448 : : * Since there is more than one way to make a joinrel for more than two
5449 : : * base relations, the results we get here could depend on which component
5450 : : * rel pair is provided. In theory we should get the same answers no matter
5451 : : * which pair is provided; in practice, since the selectivity estimation
5452 : : * routines don't handle all cases equally well, we might not. But there's
5453 : : * not much to be done about it. (Would it make sense to repeat the
5454 : : * calculations for each pair of input rels that's encountered, and somehow
5455 : : * average the results? Probably way more trouble than it's worth, and
5456 : : * anyway we must keep the rowcount estimate the same for all paths for the
5457 : : * joinrel.)
5458 : : *
5459 : : * We set only the rows field here. The reltarget field was already set by
5460 : : * build_joinrel_tlist, and baserestrictcost is not used for join rels.
5461 : : */
5462 : : void
7398 5463 : 103826 : set_joinrel_size_estimates(PlannerInfo *root, RelOptInfo *rel,
5464 : : RelOptInfo *outer_rel,
5465 : : RelOptInfo *inner_rel,
5466 : : SpecialJoinInfo *sjinfo,
5467 : : List *restrictlist)
5468 : : {
4971 5469 : 103826 : rel->rows = calc_joinrel_size_estimate(root,
5470 : : rel,
5471 : : outer_rel,
5472 : : inner_rel,
5473 : : outer_rel->rows,
5474 : : inner_rel->rows,
5475 : : sjinfo,
5476 : : restrictlist);
5477 : 103826 : }
5478 : :
5479 : : /*
5480 : : * get_parameterized_joinrel_size
5481 : : * Make a size estimate for a parameterized scan of a join relation.
5482 : : *
5483 : : * 'rel' is the joinrel under consideration.
5484 : : * 'outer_path', 'inner_path' are (probably also parameterized) Paths that
5485 : : * produce the relations being joined.
5486 : : * 'sjinfo' is any SpecialJoinInfo relevant to this join.
5487 : : * 'restrict_clauses' lists the join clauses that need to be applied at the
5488 : : * join node (including any movable clauses that were moved down to this join,
5489 : : * and not including any movable clauses that were pushed down into the
5490 : : * child paths).
5491 : : *
5492 : : * set_joinrel_size_estimates must have been applied already.
5493 : : */
5494 : : double
4888 5495 : 4577 : get_parameterized_joinrel_size(PlannerInfo *root, RelOptInfo *rel,
5496 : : Path *outer_path,
5497 : : Path *inner_path,
5498 : : SpecialJoinInfo *sjinfo,
5499 : : List *restrict_clauses)
5500 : : {
5501 : : double nrows;
5502 : :
5503 : : /*
5504 : : * Estimate the number of rows returned by the parameterized join as the
5505 : : * sizes of the input paths times the selectivity of the clauses that have
5506 : : * ended up at this join node.
5507 : : *
5508 : : * As with set_joinrel_size_estimates, the rowcount estimate could depend
5509 : : * on the pair of input paths provided, though ideally we'd get the same
5510 : : * estimate for any pair with the same parameterization.
5511 : : */
5512 : 4577 : nrows = calc_joinrel_size_estimate(root,
5513 : : rel,
5514 : : outer_path->parent,
5515 : : inner_path->parent,
5516 : : outer_path->rows,
5517 : : inner_path->rows,
5518 : : sjinfo,
5519 : : restrict_clauses);
5520 : : /* For safety, make sure result is not more than the base estimate */
5521 [ + + ]: 4577 : if (nrows > rel->rows)
5522 : 6 : nrows = rel->rows;
5523 : 4577 : return nrows;
5524 : : }
5525 : :
5526 : : /*
5527 : : * calc_joinrel_size_estimate
5528 : : * Workhorse for set_joinrel_size_estimates and
5529 : : * get_parameterized_joinrel_size.
5530 : : *
5531 : : * outer_rel/inner_rel are the relations being joined, but they should be
5532 : : * assumed to have sizes outer_rows/inner_rows; those numbers might be less
5533 : : * than what rel->rows says, when we are considering parameterized paths.
5534 : : */
5535 : : static double
4971 5536 : 108403 : calc_joinrel_size_estimate(PlannerInfo *root,
5537 : : RelOptInfo *joinrel,
5538 : : RelOptInfo *outer_rel,
5539 : : RelOptInfo *inner_rel,
5540 : : double outer_rows,
5541 : : double inner_rows,
5542 : : SpecialJoinInfo *sjinfo,
5543 : : List *restrictlist)
5544 : : {
6232 5545 : 108403 : JoinType jointype = sjinfo->jointype;
5546 : : Selectivity fkselec;
5547 : : Selectivity jselec;
5548 : : Selectivity pselec;
5549 : : double nrows;
5550 : :
5551 : : /*
5552 : : * Compute joinclause selectivity. Note that we are only considering
5553 : : * clauses that become restriction clauses at this join level; we are not
5554 : : * double-counting them because they were not considered in estimating the
5555 : : * sizes of the component rels.
5556 : : *
5557 : : * First, see whether any of the joinclauses can be matched to known FK
5558 : : * constraints. If so, drop those clauses from the restrictlist, and
5559 : : * instead estimate their selectivity using FK semantics. (We do this
5560 : : * without regard to whether said clauses are local or "pushed down".
5561 : : * Probably, an FK-matching clause could never be seen as pushed down at
5562 : : * an outer join, since it would be strict and hence would be grounds for
5563 : : * join strength reduction.) fkselec gets the net selectivity for
5564 : : * FK-matching clauses, or 1.0 if there are none.
5565 : : */
3367 5566 : 108403 : fkselec = get_foreign_key_join_selectivity(root,
5567 : : outer_rel->relids,
5568 : : inner_rel->relids,
5569 : : sjinfo,
5570 : : &restrictlist);
5571 : :
5572 : : /*
5573 : : * For an outer join, we have to distinguish the selectivity of the join's
5574 : : * own clauses (JOIN/ON conditions) from any clauses that were "pushed
5575 : : * down". For inner joins we just count them all as joinclauses.
5576 : : */
6875 5577 [ + + ]: 108403 : if (IS_OUTER_JOIN(jointype))
5578 : : {
5579 : 40020 : List *joinquals = NIL;
5580 : 40020 : List *pushedquals = NIL;
5581 : : ListCell *l;
5582 : :
5583 : : /* Grovel through the clauses to separate into two lists */
5584 [ + + + + : 90169 : foreach(l, restrictlist)
+ + ]
5585 : : {
3071 5586 : 50149 : RestrictInfo *rinfo = lfirst_node(RestrictInfo, l);
5587 : :
2696 5588 [ + + + + ]: 50149 : if (RINFO_IS_PUSHED_DOWN(rinfo, joinrel->relids))
6875 5589 : 2141 : pushedquals = lappend(pushedquals, rinfo);
5590 : : else
5591 : 48008 : joinquals = lappend(joinquals, rinfo);
5592 : : }
5593 : :
5594 : : /* Get the separate selectivities */
3378 5595 : 40020 : jselec = clauselist_selectivity(root,
5596 : : joinquals,
5597 : : 0,
5598 : : jointype,
5599 : : sjinfo);
6875 5600 : 40020 : pselec = clauselist_selectivity(root,
5601 : : pushedquals,
5602 : : 0,
5603 : : jointype,
5604 : : sjinfo);
5605 : :
5606 : : /* Avoid leaking a lot of ListCells */
5607 : 40020 : list_free(joinquals);
5608 : 40020 : list_free(pushedquals);
5609 : : }
5610 : : else
5611 : : {
3378 5612 : 68383 : jselec = clauselist_selectivity(root,
5613 : : restrictlist,
5614 : : 0,
5615 : : jointype,
5616 : : sjinfo);
6875 5617 : 68383 : pselec = 0.0; /* not used, keep compiler quiet */
5618 : : }
5619 : :
5620 : : /*
5621 : : * Basically, we multiply size of Cartesian product by selectivity.
5622 : : *
5623 : : * If we are doing an outer join, take that into account: the joinqual
5624 : : * selectivity has to be clamped using the knowledge that the output must
5625 : : * be at least as large as the non-nullable input. However, any
5626 : : * pushed-down quals are applied after the outer join, so their
5627 : : * selectivity applies fully.
5628 : : *
5629 : : * For JOIN_SEMI and JOIN_ANTI, the selectivity is defined as the fraction
5630 : : * of LHS rows that have matches, and we apply that straightforwardly.
5631 : : */
8968 5632 [ + + + + : 108403 : switch (jointype)
+ - ]
5633 : : {
5634 : 64334 : case JOIN_INNER:
3367 5635 : 64334 : nrows = outer_rows * inner_rows * fkselec * jselec;
5636 : : /* pselec not used */
8968 5637 : 64334 : break;
5638 : 36667 : case JOIN_LEFT:
3367 5639 : 36667 : nrows = outer_rows * inner_rows * fkselec * jselec;
4971 5640 [ + + ]: 36667 : if (nrows < outer_rows)
5641 : 14543 : nrows = outer_rows;
6875 5642 : 36667 : nrows *= pselec;
8968 5643 : 36667 : break;
5644 : 857 : case JOIN_FULL:
3367 5645 : 857 : nrows = outer_rows * inner_rows * fkselec * jselec;
4971 5646 [ + + ]: 857 : if (nrows < outer_rows)
5647 : 568 : nrows = outer_rows;
5648 [ + + ]: 857 : if (nrows < inner_rows)
5649 : 60 : nrows = inner_rows;
6875 5650 : 857 : nrows *= pselec;
8968 5651 : 857 : break;
6232 5652 : 4049 : case JOIN_SEMI:
3367 5653 : 4049 : nrows = outer_rows * fkselec * jselec;
5654 : : /* pselec not used */
8265 5655 : 4049 : break;
6232 5656 : 2496 : case JOIN_ANTI:
3367 5657 : 2496 : nrows = outer_rows * (1.0 - fkselec * jselec);
6232 5658 : 2496 : nrows *= pselec;
8265 5659 : 2496 : break;
8968 tgl@sss.pgh.pa.us 5660 :UBC 0 : default:
5661 : : /* other values not expected here */
8079 5662 [ # # ]: 0 : elog(ERROR, "unrecognized join type: %d", (int) jointype);
5663 : : nrows = 0; /* keep compiler quiet */
5664 : : break;
5665 : : }
5666 : :
4971 tgl@sss.pgh.pa.us 5667 :CBC 108403 : return clamp_row_est(nrows);
5668 : : }
5669 : :
5670 : : /*
5671 : : * get_foreign_key_join_selectivity
5672 : : * Estimate join selectivity for foreign-key-related clauses.
5673 : : *
5674 : : * Remove any clauses that can be matched to FK constraints from *restrictlist,
5675 : : * and return a substitute estimate of their selectivity. 1.0 is returned
5676 : : * when there are no such clauses.
5677 : : *
5678 : : * The reason for treating such clauses specially is that we can get better
5679 : : * estimates this way than by relying on clauselist_selectivity(), especially
5680 : : * for multi-column FKs where that function's assumption that the clauses are
5681 : : * independent falls down badly. But even with single-column FKs, we may be
5682 : : * able to get a better answer when the pg_statistic stats are missing or out
5683 : : * of date.
5684 : : */
5685 : : static Selectivity
3367 5686 : 108403 : get_foreign_key_join_selectivity(PlannerInfo *root,
5687 : : Relids outer_relids,
5688 : : Relids inner_relids,
5689 : : SpecialJoinInfo *sjinfo,
5690 : : List **restrictlist)
5691 : : {
5692 : 108403 : Selectivity fkselec = 1.0;
5693 : 108403 : JoinType jointype = sjinfo->jointype;
5694 : 108403 : List *worklist = *restrictlist;
5695 : : ListCell *lc;
5696 : :
5697 : : /* Consider each FK constraint that is known to match the query */
5698 [ + + + + : 109386 : foreach(lc, root->fkey_list)
+ + ]
5699 : : {
5700 : 983 : ForeignKeyOptInfo *fkinfo = (ForeignKeyOptInfo *) lfirst(lc);
5701 : : bool ref_is_outer;
5702 : : List *removedlist;
5703 : : ListCell *cell;
5704 : :
5705 : : /*
5706 : : * This FK is not relevant unless it connects a baserel on one side of
5707 : : * this join to a baserel on the other side.
5708 : : */
5709 [ + + + + ]: 1790 : if (bms_is_member(fkinfo->con_relid, outer_relids) &&
5710 : 807 : bms_is_member(fkinfo->ref_relid, inner_relids))
5711 : 720 : ref_is_outer = false;
5712 [ + + + + ]: 433 : else if (bms_is_member(fkinfo->ref_relid, outer_relids) &&
5713 : 170 : bms_is_member(fkinfo->con_relid, inner_relids))
5714 : 65 : ref_is_outer = true;
5715 : : else
5716 : 198 : continue;
5717 : :
5718 : : /*
5719 : : * If we're dealing with a semi/anti join, and the FK's referenced
5720 : : * relation is on the outside, then knowledge of the FK doesn't help
5721 : : * us figure out what we need to know (which is the fraction of outer
5722 : : * rows that have matches). On the other hand, if the referenced rel
5723 : : * is on the inside, then all outer rows must have matches in the
5724 : : * referenced table (ignoring nulls). But any restriction or join
5725 : : * clauses that filter that table will reduce the fraction of matches.
5726 : : * We can account for restriction clauses, but it's too hard to guess
5727 : : * how many table rows would get through a join that's inside the RHS.
5728 : : * Hence, if either case applies, punt and ignore the FK.
5729 : : */
3001 5730 [ + - + + : 785 : if ((jointype == JOIN_SEMI || jointype == JOIN_ANTI) &&
+ + ]
5731 [ - + ]: 524 : (ref_is_outer || bms_membership(inner_relids) != BMS_SINGLETON))
5732 : 6 : continue;
5733 : :
5734 : : /*
5735 : : * Modify the restrictlist by removing clauses that match the FK (and
5736 : : * putting them into removedlist instead). It seems unsafe to modify
5737 : : * the originally-passed List structure, so we make a shallow copy the
5738 : : * first time through.
5739 : : */
3367 5740 [ + + ]: 779 : if (worklist == *restrictlist)
5741 : 667 : worklist = list_copy(worklist);
5742 : :
5743 : 779 : removedlist = NIL;
2245 5744 [ + + + + : 1626 : foreach(cell, worklist)
+ + ]
5745 : : {
3367 5746 : 847 : RestrictInfo *rinfo = (RestrictInfo *) lfirst(cell);
5747 : 847 : bool remove_it = false;
5748 : : int i;
5749 : :
5750 : : /* Drop this clause if it matches any column of the FK */
5751 [ + + ]: 1070 : for (i = 0; i < fkinfo->nkeys; i++)
5752 : : {
5753 [ + + ]: 1055 : if (rinfo->parent_ec)
5754 : : {
5755 : : /*
5756 : : * EC-derived clauses can only match by EC. It is okay to
5757 : : * consider any clause derived from the same EC as
5758 : : * matching the FK: even if equivclass.c chose to generate
5759 : : * a clause equating some other pair of Vars, it could
5760 : : * have generated one equating the FK's Vars. So for
5761 : : * purposes of estimation, we can act as though it did so.
5762 : : *
5763 : : * Note: checking parent_ec is a bit of a cheat because
5764 : : * there are EC-derived clauses that don't have parent_ec
5765 : : * set; but such clauses must compare expressions that
5766 : : * aren't just Vars, so they cannot match the FK anyway.
5767 : : */
5768 [ + + ]: 152 : if (fkinfo->eclass[i] == rinfo->parent_ec)
5769 : : {
5770 : 149 : remove_it = true;
5771 : 149 : break;
5772 : : }
5773 : : }
5774 : : else
5775 : : {
5776 : : /*
5777 : : * Otherwise, see if rinfo was previously matched to FK as
5778 : : * a "loose" clause.
5779 : : */
5780 [ + + ]: 903 : if (list_member_ptr(fkinfo->rinfos[i], rinfo))
5781 : : {
5782 : 683 : remove_it = true;
5783 : 683 : break;
5784 : : }
5785 : : }
5786 : : }
5787 [ + + ]: 847 : if (remove_it)
5788 : : {
2245 5789 : 832 : worklist = foreach_delete_current(worklist, cell);
3367 5790 : 832 : removedlist = lappend(removedlist, rinfo);
5791 : : }
5792 : : }
5793 : :
5794 : : /*
5795 : : * If we failed to remove all the matching clauses we expected to
5796 : : * find, chicken out and ignore this FK; applying its selectivity
5797 : : * might result in double-counting. Put any clauses we did manage to
5798 : : * remove back into the worklist.
5799 : : *
5800 : : * Since the matching clauses are known not outerjoin-delayed, they
5801 : : * would normally have appeared in the initial joinclause list. If we
5802 : : * didn't find them, there are two possibilities:
5803 : : *
5804 : : * 1. If the FK match is based on an EC that is ec_has_const, it won't
5805 : : * have generated any join clauses at all. We discount such ECs while
5806 : : * checking to see if we have "all" the clauses. (Below, we'll adjust
5807 : : * the selectivity estimate for this case.)
5808 : : *
5809 : : * 2. The clauses were matched to some other FK in a previous
5810 : : * iteration of this loop, and thus removed from worklist. (A likely
5811 : : * case is that two FKs are matched to the same EC; there will be only
5812 : : * one EC-derived clause in the initial list, so the first FK will
5813 : : * consume it.) Applying both FKs' selectivity independently risks
5814 : : * underestimating the join size; in particular, this would undo one
5815 : : * of the main things that ECs were invented for, namely to avoid
5816 : : * double-counting the selectivity of redundant equality conditions.
5817 : : * Later we might think of a reasonable way to combine the estimates,
5818 : : * but for now, just punt, since this is a fairly uncommon situation.
5819 : : */
1774 5820 [ + + ]: 779 : if (removedlist == NIL ||
5821 : 636 : list_length(removedlist) !=
5822 [ - + ]: 636 : (fkinfo->nmatched_ec - fkinfo->nconst_ec + fkinfo->nmatched_ri))
5823 : : {
3367 5824 : 143 : worklist = list_concat(worklist, removedlist);
5825 : 143 : continue;
5826 : : }
5827 : :
5828 : : /*
5829 : : * Finally we get to the payoff: estimate selectivity using the
5830 : : * knowledge that each referencing row will match exactly one row in
5831 : : * the referenced table.
5832 : : *
5833 : : * XXX that's not true in the presence of nulls in the referencing
5834 : : * column(s), so in principle we should derate the estimate for those.
5835 : : * However (1) if there are any strict restriction clauses for the
5836 : : * referencing column(s) elsewhere in the query, derating here would
5837 : : * be double-counting the null fraction, and (2) it's not very clear
5838 : : * how to combine null fractions for multiple referencing columns. So
5839 : : * we do nothing for now about correcting for nulls.
5840 : : *
5841 : : * XXX another point here is that if either side of an FK constraint
5842 : : * is an inheritance parent, we estimate as though the constraint
5843 : : * covers all its children as well. This is not an unreasonable
5844 : : * assumption for a referencing table, ie the user probably applied
5845 : : * identical constraints to all child tables (though perhaps we ought
5846 : : * to check that). But it's not possible to have done that for a
5847 : : * referenced table. Fortunately, precisely because that doesn't
5848 : : * work, it is uncommon in practice to have an FK referencing a parent
5849 : : * table. So, at least for now, disregard inheritance here.
5850 : : */
3001 5851 [ + - + + ]: 636 : if (jointype == JOIN_SEMI || jointype == JOIN_ANTI)
3367 5852 : 412 : {
5853 : : /*
5854 : : * For JOIN_SEMI and JOIN_ANTI, we only get here when the FK's
5855 : : * referenced table is exactly the inside of the join. The join
5856 : : * selectivity is defined as the fraction of LHS rows that have
5857 : : * matches. The FK implies that every LHS row has a match *in the
5858 : : * referenced table*; but any restriction clauses on it will
5859 : : * reduce the number of matches. Hence we take the join
5860 : : * selectivity as equal to the selectivity of the table's
5861 : : * restriction clauses, which is rows / tuples; but we must guard
5862 : : * against tuples == 0.
5863 : : */
3001 5864 : 412 : RelOptInfo *ref_rel = find_base_rel(root, fkinfo->ref_relid);
5865 [ + + ]: 412 : double ref_tuples = Max(ref_rel->tuples, 1.0);
5866 : :
5867 : 412 : fkselec *= ref_rel->rows / ref_tuples;
5868 : : }
5869 : : else
5870 : : {
5871 : : /*
5872 : : * Otherwise, selectivity is exactly 1/referenced-table-size; but
5873 : : * guard against tuples == 0. Note we should use the raw table
5874 : : * tuple count, not any estimate of its filtered or joined size.
5875 : : */
3367 5876 : 224 : RelOptInfo *ref_rel = find_base_rel(root, fkinfo->ref_relid);
5877 [ + - ]: 224 : double ref_tuples = Max(ref_rel->tuples, 1.0);
5878 : :
5879 : 224 : fkselec *= 1.0 / ref_tuples;
5880 : : }
5881 : :
5882 : : /*
5883 : : * If any of the FK columns participated in ec_has_const ECs, then
5884 : : * equivclass.c will have generated "var = const" restrictions for
5885 : : * each side of the join, thus reducing the sizes of both input
5886 : : * relations. Taking the fkselec at face value would amount to
5887 : : * double-counting the selectivity of the constant restriction for the
5888 : : * referencing Var. Hence, look for the restriction clause(s) that
5889 : : * were applied to the referencing Var(s), and divide out their
5890 : : * selectivity to correct for this.
5891 : : */
1774 5892 [ + + ]: 636 : if (fkinfo->nconst_ec > 0)
5893 : : {
5894 [ + + ]: 12 : for (int i = 0; i < fkinfo->nkeys; i++)
5895 : : {
5896 : 9 : EquivalenceClass *ec = fkinfo->eclass[i];
5897 : :
5898 [ + - + + ]: 9 : if (ec && ec->ec_has_const)
5899 : : {
5900 : 3 : EquivalenceMember *em = fkinfo->fk_eclass_member[i];
155 amitlan@postgresql.o 5901 : 3 : RestrictInfo *rinfo = find_derived_clause_for_ec_member(root,
5902 : : ec,
5903 : : em);
5904 : :
1774 tgl@sss.pgh.pa.us 5905 [ + - ]: 3 : if (rinfo)
5906 : : {
5907 : : Selectivity s0;
5908 : :
5909 : 3 : s0 = clause_selectivity(root,
5910 : : (Node *) rinfo,
5911 : : 0,
5912 : : jointype,
5913 : : sjinfo);
5914 [ + - ]: 3 : if (s0 > 0)
5915 : 3 : fkselec /= s0;
5916 : : }
5917 : : }
5918 : : }
5919 : : }
5920 : : }
5921 : :
3367 5922 : 108403 : *restrictlist = worklist;
1774 5923 [ - + - + ]: 108403 : CLAMP_PROBABILITY(fkselec);
3367 5924 : 108403 : return fkselec;
5925 : : }
5926 : :
5927 : : /*
5928 : : * set_subquery_size_estimates
5929 : : * Set the size estimates for a base relation that is a subquery.
5930 : : *
5931 : : * The rel's targetlist and restrictinfo list must have been constructed
5932 : : * already, and the Paths for the subquery must have been completed.
5933 : : * We look at the subquery's PlannerInfo to extract data.
5934 : : *
5935 : : * We set the same fields as set_baserel_size_estimates.
5936 : : */
5937 : : void
5117 5938 : 13828 : set_subquery_size_estimates(PlannerInfo *root, RelOptInfo *rel)
5939 : : {
5940 : 13828 : PlannerInfo *subroot = rel->subroot;
5941 : : RelOptInfo *sub_final_rel;
5942 : : ListCell *lc;
5943 : :
5944 : : /* Should only be applied to base relations that are subqueries */
5405 5945 [ - + ]: 13828 : Assert(rel->relid > 0);
2907 andrew@dunslane.net 5946 [ + - - + ]: 13828 : Assert(planner_rt_fetch(rel->relid, root)->rtekind == RTE_SUBQUERY);
5947 : :
5948 : : /*
5949 : : * Copy raw number of output rows from subquery. All of its paths should
5950 : : * have the same output rowcount, so just look at cheapest-total.
5951 : : */
3470 tgl@sss.pgh.pa.us 5952 : 13828 : sub_final_rel = fetch_upper_rel(subroot, UPPERREL_FINAL, NULL);
5953 : 13828 : rel->tuples = sub_final_rel->cheapest_total_path->rows;
5954 : :
5955 : : /*
5956 : : * Compute per-output-column width estimates by examining the subquery's
5957 : : * targetlist. For any output that is a plain Var, get the width estimate
5958 : : * that was made while planning the subquery. Otherwise, we leave it to
5959 : : * set_rel_width to fill in a datatype-based default estimate.
5960 : : */
5405 5961 [ + + + + : 56034 : foreach(lc, subroot->parse->targetList)
+ + ]
5962 : : {
3071 5963 : 42206 : TargetEntry *te = lfirst_node(TargetEntry, lc);
5405 5964 : 42206 : Node *texpr = (Node *) te->expr;
5128 5965 : 42206 : int32 item_width = 0;
5966 : :
5967 : : /* junk columns aren't visible to upper query */
5405 5968 [ + + ]: 42206 : if (te->resjunk)
5969 : 643 : continue;
5970 : :
5971 : : /*
5972 : : * The subquery could be an expansion of a view that's had columns
5973 : : * added to it since the current query was parsed, so that there are
5974 : : * non-junk tlist columns in it that don't correspond to any column
5975 : : * visible at our query level. Ignore such columns.
5976 : : */
4542 5977 [ + - - + ]: 41563 : if (te->resno < rel->min_attr || te->resno > rel->max_attr)
4542 tgl@sss.pgh.pa.us 5978 :UBC 0 : continue;
5979 : :
5980 : : /*
5981 : : * XXX This currently doesn't work for subqueries containing set
5982 : : * operations, because the Vars in their tlists are bogus references
5983 : : * to the first leaf subquery, which wouldn't give the right answer
5984 : : * even if we could still get to its PlannerInfo.
5985 : : *
5986 : : * Also, the subquery could be an appendrel for which all branches are
5987 : : * known empty due to constraint exclusion, in which case
5988 : : * set_append_rel_pathlist will have left the attr_widths set to zero.
5989 : : *
5990 : : * In either case, we just leave the width estimate zero until
5991 : : * set_rel_width fixes it.
5992 : : */
5405 tgl@sss.pgh.pa.us 5993 [ + + ]:CBC 41563 : if (IsA(texpr, Var) &&
5994 [ + + ]: 18518 : subroot->parse->setOperations == NULL)
5995 : : {
5263 bruce@momjian.us 5996 : 17605 : Var *var = (Var *) texpr;
5405 tgl@sss.pgh.pa.us 5997 : 17605 : RelOptInfo *subrel = find_base_rel(subroot, var->varno);
5998 : :
5999 : 17605 : item_width = subrel->attr_widths[var->varattno - subrel->min_attr];
6000 : : }
6001 : 41563 : rel->attr_widths[te->resno - rel->min_attr] = item_width;
6002 : : }
6003 : :
6004 : : /* Now estimate number of output rows, etc */
6005 : 13828 : set_baserel_size_estimates(root, rel);
6006 : 13828 : }
6007 : :
6008 : : /*
6009 : : * set_function_size_estimates
6010 : : * Set the size estimates for a base relation that is a function call.
6011 : : *
6012 : : * The rel's targetlist and restrictinfo list must have been constructed
6013 : : * already.
6014 : : *
6015 : : * We set the same fields as set_baserel_size_estimates.
6016 : : */
6017 : : void
7398 6018 : 24314 : set_function_size_estimates(PlannerInfo *root, RelOptInfo *rel)
6019 : : {
6020 : : RangeTblEntry *rte;
6021 : : ListCell *lc;
6022 : :
6023 : : /* Should only be applied to base relations that are functions */
8246 6024 [ - + ]: 24314 : Assert(rel->relid > 0);
6713 6025 [ + - ]: 24314 : rte = planner_rt_fetch(rel->relid, root);
7276 6026 [ - + ]: 24314 : Assert(rte->rtekind == RTE_FUNCTION);
6027 : :
6028 : : /*
6029 : : * Estimate number of rows the functions will return. The rowcount of the
6030 : : * node is that of the largest function result.
6031 : : */
4307 6032 : 24314 : rel->tuples = 0;
6033 [ + - + + : 48883 : foreach(lc, rte->functions)
+ + ]
6034 : : {
6035 : 24569 : RangeTblFunction *rtfunc = (RangeTblFunction *) lfirst(lc);
2401 6036 : 24569 : double ntup = expression_returns_set_rows(root, rtfunc->funcexpr);
6037 : :
4307 6038 [ + + ]: 24569 : if (ntup > rel->tuples)
6039 : 24326 : rel->tuples = ntup;
6040 : : }
6041 : :
6042 : : /* Now estimate number of output rows, etc */
7915 6043 : 24314 : set_baserel_size_estimates(root, rel);
8518 6044 : 24314 : }
6045 : :
6046 : : /*
6047 : : * set_function_size_estimates
6048 : : * Set the size estimates for a base relation that is a function call.
6049 : : *
6050 : : * The rel's targetlist and restrictinfo list must have been constructed
6051 : : * already.
6052 : : *
6053 : : * We set the same fields as set_tablefunc_size_estimates.
6054 : : */
6055 : : void
3104 alvherre@alvh.no-ip. 6056 : 311 : set_tablefunc_size_estimates(PlannerInfo *root, RelOptInfo *rel)
6057 : : {
6058 : : /* Should only be applied to base relations that are functions */
6059 [ - + ]: 311 : Assert(rel->relid > 0);
2907 andrew@dunslane.net 6060 [ + - - + ]: 311 : Assert(planner_rt_fetch(rel->relid, root)->rtekind == RTE_TABLEFUNC);
6061 : :
3104 alvherre@alvh.no-ip. 6062 : 311 : rel->tuples = 100;
6063 : :
6064 : : /* Now estimate number of output rows, etc */
6065 : 311 : set_baserel_size_estimates(root, rel);
6066 : 311 : }
6067 : :
6068 : : /*
6069 : : * set_values_size_estimates
6070 : : * Set the size estimates for a base relation that is a values list.
6071 : : *
6072 : : * The rel's targetlist and restrictinfo list must have been constructed
6073 : : * already.
6074 : : *
6075 : : * We set the same fields as set_baserel_size_estimates.
6076 : : */
6077 : : void
6975 mail@joeconway.com 6078 : 4104 : set_values_size_estimates(PlannerInfo *root, RelOptInfo *rel)
6079 : : {
6080 : : RangeTblEntry *rte;
6081 : :
6082 : : /* Should only be applied to base relations that are values lists */
6083 [ - + ]: 4104 : Assert(rel->relid > 0);
6713 tgl@sss.pgh.pa.us 6084 [ + - ]: 4104 : rte = planner_rt_fetch(rel->relid, root);
6975 mail@joeconway.com 6085 [ - + ]: 4104 : Assert(rte->rtekind == RTE_VALUES);
6086 : :
6087 : : /*
6088 : : * Estimate number of rows the values list will return. We know this
6089 : : * precisely based on the list length (well, barring set-returning
6090 : : * functions in list items, but that's a refinement not catered for
6091 : : * anywhere else either).
6092 : : */
6093 : 4104 : rel->tuples = list_length(rte->values_lists);
6094 : :
6095 : : /* Now estimate number of output rows, etc */
6096 : 4104 : set_baserel_size_estimates(root, rel);
6097 : 4104 : }
6098 : :
6099 : : /*
6100 : : * set_cte_size_estimates
6101 : : * Set the size estimates for a base relation that is a CTE reference.
6102 : : *
6103 : : * The rel's targetlist and restrictinfo list must have been constructed
6104 : : * already, and we need an estimate of the number of rows returned by the CTE
6105 : : * (if a regular CTE) or the non-recursive term (if a self-reference).
6106 : : *
6107 : : * We set the same fields as set_baserel_size_estimates.
6108 : : */
6109 : : void
3470 tgl@sss.pgh.pa.us 6110 : 2586 : set_cte_size_estimates(PlannerInfo *root, RelOptInfo *rel, double cte_rows)
6111 : : {
6112 : : RangeTblEntry *rte;
6113 : :
6114 : : /* Should only be applied to base relations that are CTE references */
6181 6115 [ - + ]: 2586 : Assert(rel->relid > 0);
6116 [ + - ]: 2586 : rte = planner_rt_fetch(rel->relid, root);
6117 [ - + ]: 2586 : Assert(rte->rtekind == RTE_CTE);
6118 : :
6119 [ + + ]: 2586 : if (rte->self_reference)
6120 : : {
6121 : : /*
6122 : : * In a self-reference, we assume the average worktable size is a
6123 : : * multiple of the nonrecursive term's size. The best multiplier will
6124 : : * vary depending on query "fan-out", so make its value adjustable.
6125 : : */
1262 6126 : 466 : rel->tuples = clamp_row_est(recursive_worktable_factor * cte_rows);
6127 : : }
6128 : : else
6129 : : {
6130 : : /* Otherwise just believe the CTE's rowcount estimate */
3470 6131 : 2120 : rel->tuples = cte_rows;
6132 : : }
6133 : :
6134 : : /* Now estimate number of output rows, etc */
6181 6135 : 2586 : set_baserel_size_estimates(root, rel);
6136 : 2586 : }
6137 : :
6138 : : /*
6139 : : * set_namedtuplestore_size_estimates
6140 : : * Set the size estimates for a base relation that is a tuplestore reference.
6141 : : *
6142 : : * The rel's targetlist and restrictinfo list must have been constructed
6143 : : * already.
6144 : : *
6145 : : * We set the same fields as set_baserel_size_estimates.
6146 : : */
6147 : : void
3081 kgrittn@postgresql.o 6148 : 242 : set_namedtuplestore_size_estimates(PlannerInfo *root, RelOptInfo *rel)
6149 : : {
6150 : : RangeTblEntry *rte;
6151 : :
6152 : : /* Should only be applied to base relations that are tuplestore references */
6153 [ - + ]: 242 : Assert(rel->relid > 0);
6154 [ + - ]: 242 : rte = planner_rt_fetch(rel->relid, root);
6155 [ - + ]: 242 : Assert(rte->rtekind == RTE_NAMEDTUPLESTORE);
6156 : :
6157 : : /*
6158 : : * Use the estimate provided by the code which is generating the named
6159 : : * tuplestore. In some cases, the actual number might be available; in
6160 : : * others the same plan will be re-used, so a "typical" value might be
6161 : : * estimated and used.
6162 : : */
6163 : 242 : rel->tuples = rte->enrtuples;
6164 [ - + ]: 242 : if (rel->tuples < 0)
3081 kgrittn@postgresql.o 6165 :UBC 0 : rel->tuples = 1000;
6166 : :
6167 : : /* Now estimate number of output rows, etc */
3081 kgrittn@postgresql.o 6168 :CBC 242 : set_baserel_size_estimates(root, rel);
6169 : 242 : }
6170 : :
6171 : : /*
6172 : : * set_result_size_estimates
6173 : : * Set the size estimates for an RTE_RESULT base relation
6174 : : *
6175 : : * The rel's targetlist and restrictinfo list must have been constructed
6176 : : * already.
6177 : : *
6178 : : * We set the same fields as set_baserel_size_estimates.
6179 : : */
6180 : : void
2413 tgl@sss.pgh.pa.us 6181 : 2104 : set_result_size_estimates(PlannerInfo *root, RelOptInfo *rel)
6182 : : {
6183 : : /* Should only be applied to RTE_RESULT base relations */
6184 [ - + ]: 2104 : Assert(rel->relid > 0);
6185 [ + - - + ]: 2104 : Assert(planner_rt_fetch(rel->relid, root)->rtekind == RTE_RESULT);
6186 : :
6187 : : /* RTE_RESULT always generates a single row, natively */
6188 : 2104 : rel->tuples = 1;
6189 : :
6190 : : /* Now estimate number of output rows, etc */
6191 : 2104 : set_baserel_size_estimates(root, rel);
6192 : 2104 : }
6193 : :
6194 : : /*
6195 : : * set_foreign_size_estimates
6196 : : * Set the size estimates for a base relation that is a foreign table.
6197 : : *
6198 : : * There is not a whole lot that we can do here; the foreign-data wrapper
6199 : : * is responsible for producing useful estimates. We can do a decent job
6200 : : * of estimating baserestrictcost, so we set that, and we also set up width
6201 : : * using what will be purely datatype-driven estimates from the targetlist.
6202 : : * There is no way to do anything sane with the rows value, so we just put
6203 : : * a default estimate and hope that the wrapper can improve on it. The
6204 : : * wrapper's GetForeignRelSize function will be called momentarily.
6205 : : *
6206 : : * The rel's targetlist and restrictinfo list must have been constructed
6207 : : * already.
6208 : : */
6209 : : void
5312 6210 : 1225 : set_foreign_size_estimates(PlannerInfo *root, RelOptInfo *rel)
6211 : : {
6212 : : /* Should only be applied to base relations */
6213 [ - + ]: 1225 : Assert(rel->relid > 0);
6214 : :
6215 : 1225 : rel->rows = 1000; /* entirely bogus default estimate */
6216 : :
6217 : 1225 : cost_qual_eval(&rel->baserestrictcost, rel->baserestrictinfo, root);
6218 : :
6219 : 1225 : set_rel_width(root, rel);
6220 : 1225 : }
6221 : :
6222 : :
6223 : : /*
6224 : : * set_rel_width
6225 : : * Set the estimated output width of a base relation.
6226 : : *
6227 : : * The estimated output width is the sum of the per-attribute width estimates
6228 : : * for the actually-referenced columns, plus any PHVs or other expressions
6229 : : * that have to be calculated at this relation. This is the amount of data
6230 : : * we'd need to pass upwards in case of a sort, hash, etc.
6231 : : *
6232 : : * This function also sets reltarget->cost, so it's a bit misnamed now.
6233 : : *
6234 : : * NB: this works best on plain relations because it prefers to look at
6235 : : * real Vars. For subqueries, set_subquery_size_estimates will already have
6236 : : * copied up whatever per-column estimates were made within the subquery,
6237 : : * and for other types of rels there isn't much we can do anyway. We fall
6238 : : * back on (fairly stupid) datatype-based width estimates if we can't get
6239 : : * any better number.
6240 : : *
6241 : : * The per-attribute width estimates are cached for possible re-use while
6242 : : * building join relations or post-scan/join pathtargets.
6243 : : */
6244 : : static void
7398 6245 : 246187 : set_rel_width(PlannerInfo *root, RelOptInfo *rel)
6246 : : {
6168 6247 [ + - ]: 246187 : Oid reloid = planner_rt_fetch(rel->relid, root)->relid;
627 6248 : 246187 : int64 tuple_width = 0;
5405 6249 : 246187 : bool have_wholerow_var = false;
6250 : : ListCell *lc;
6251 : :
6252 : : /* Vars are assumed to have cost zero, but other exprs do not */
3463 6253 : 246187 : rel->reltarget->cost.startup = 0;
6254 : 246187 : rel->reltarget->cost.per_tuple = 0;
6255 : :
6256 [ + + + + : 892927 : foreach(lc, rel->reltarget->exprs)
+ + ]
6257 : : {
6164 6258 : 646740 : Node *node = (Node *) lfirst(lc);
6259 : :
6260 : : /*
6261 : : * Ordinarily, a Var in a rel's targetlist must belong to that rel;
6262 : : * but there are corner cases involving LATERAL references where that
6263 : : * isn't so. If the Var has the wrong varno, fall through to the
6264 : : * generic case (it doesn't seem worth the trouble to be any smarter).
6265 : : */
4759 6266 [ + + ]: 646740 : if (IsA(node, Var) &&
6267 [ + + ]: 634741 : ((Var *) node)->varno == rel->relid)
7763 6268 : 160616 : {
6164 6269 : 634708 : Var *var = (Var *) node;
6270 : : int ndx;
6271 : : int32 item_width;
6272 : :
6273 [ - + ]: 634708 : Assert(var->varattno >= rel->min_attr);
6274 [ - + ]: 634708 : Assert(var->varattno <= rel->max_attr);
6275 : :
6276 : 634708 : ndx = var->varattno - rel->min_attr;
6277 : :
6278 : : /*
6279 : : * If it's a whole-row Var, we'll deal with it below after we have
6280 : : * already cached as many attr widths as possible.
6281 : : */
5405 6282 [ + + ]: 634708 : if (var->varattno == 0)
6283 : : {
6284 : 1512 : have_wholerow_var = true;
6285 : 1512 : continue;
6286 : : }
6287 : :
6288 : : /*
6289 : : * The width may have been cached already (especially if it's a
6290 : : * subquery), so don't duplicate effort.
6291 : : */
6164 6292 [ + + ]: 633196 : if (rel->attr_widths[ndx] > 0)
6293 : : {
6294 : 111550 : tuple_width += rel->attr_widths[ndx];
8105 6295 : 111550 : continue;
6296 : : }
6297 : :
6298 : : /* Try to get column width from statistics */
5405 6299 [ + + + + ]: 521646 : if (reloid != InvalidOid && var->varattno > 0)
6300 : : {
6164 6301 : 417953 : item_width = get_attavgwidth(reloid, var->varattno);
6302 [ + + ]: 417953 : if (item_width > 0)
6303 : : {
6304 : 361030 : rel->attr_widths[ndx] = item_width;
6305 : 361030 : tuple_width += item_width;
6306 : 361030 : continue;
6307 : : }
6308 : : }
6309 : :
6310 : : /*
6311 : : * Not a plain relation, or can't find statistics for it. Estimate
6312 : : * using just the type info.
6313 : : */
6314 : 160616 : item_width = get_typavgwidth(var->vartype, var->vartypmod);
6315 [ - + ]: 160616 : Assert(item_width > 0);
6316 : 160616 : rel->attr_widths[ndx] = item_width;
6317 : 160616 : tuple_width += item_width;
6318 : : }
6319 [ + + ]: 12032 : else if (IsA(node, PlaceHolderVar))
6320 : : {
6321 : : /*
6322 : : * We will need to evaluate the PHV's contained expression while
6323 : : * scanning this rel, so be sure to include it in reltarget->cost.
6324 : : */
6325 : 989 : PlaceHolderVar *phv = (PlaceHolderVar *) node;
1116 6326 : 989 : PlaceHolderInfo *phinfo = find_placeholder_info(root, phv);
6327 : : QualCost cost;
6328 : :
6164 6329 : 989 : tuple_width += phinfo->ph_width;
3488 6330 : 989 : cost_qual_eval_node(&cost, (Node *) phv->phexpr, root);
3463 6331 : 989 : rel->reltarget->cost.startup += cost.startup;
6332 : 989 : rel->reltarget->cost.per_tuple += cost.per_tuple;
6333 : : }
6334 : : else
6335 : : {
6336 : : /*
6337 : : * We could be looking at an expression pulled up from a subquery,
6338 : : * or a ROW() representing a whole-row child Var, etc. Do what we
6339 : : * can using the expression type information.
6340 : : */
6341 : : int32 item_width;
6342 : : QualCost cost;
6343 : :
5901 6344 : 11043 : item_width = get_typavgwidth(exprType(node), exprTypmod(node));
6345 [ - + ]: 11043 : Assert(item_width > 0);
6346 : 11043 : tuple_width += item_width;
6347 : : /* Not entirely clear if we need to account for cost, but do so */
3488 6348 : 11043 : cost_qual_eval_node(&cost, node, root);
3463 6349 : 11043 : rel->reltarget->cost.startup += cost.startup;
6350 : 11043 : rel->reltarget->cost.per_tuple += cost.per_tuple;
6351 : : }
6352 : : }
6353 : :
6354 : : /*
6355 : : * If we have a whole-row reference, estimate its width as the sum of
6356 : : * per-column widths plus heap tuple header overhead.
6357 : : */
5405 6358 [ + + ]: 246187 : if (have_wholerow_var)
6359 : : {
627 6360 : 1512 : int64 wholerow_width = MAXALIGN(SizeofHeapTupleHeader);
6361 : :
5405 6362 [ + + ]: 1512 : if (reloid != InvalidOid)
6363 : : {
6364 : : /* Real relation, so estimate true tuple width */
6365 : 1183 : wholerow_width += get_relation_data_width(reloid,
2999 6366 : 1183 : rel->attr_widths - rel->min_attr);
6367 : : }
6368 : : else
6369 : : {
6370 : : /* Do what we can with info for a phony rel */
6371 : : AttrNumber i;
6372 : :
5405 6373 [ + + ]: 897 : for (i = 1; i <= rel->max_attr; i++)
6374 : 568 : wholerow_width += rel->attr_widths[i - rel->min_attr];
6375 : : }
6376 : :
627 6377 : 1512 : rel->attr_widths[0 - rel->min_attr] = clamp_width_est(wholerow_width);
6378 : :
6379 : : /*
6380 : : * Include the whole-row Var as part of the output tuple. Yes, that
6381 : : * really is what happens at runtime.
6382 : : */
5405 6383 : 1512 : tuple_width += wholerow_width;
6384 : : }
6385 : :
627 6386 : 246187 : rel->reltarget->width = clamp_width_est(tuple_width);
10651 scrappy@hub.org 6387 : 246187 : }
6388 : :
6389 : : /*
6390 : : * set_pathtarget_cost_width
6391 : : * Set the estimated eval cost and output width of a PathTarget tlist.
6392 : : *
6393 : : * As a notational convenience, returns the same PathTarget pointer passed in.
6394 : : *
6395 : : * Most, though not quite all, uses of this function occur after we've run
6396 : : * set_rel_width() for base relations; so we can usually obtain cached width
6397 : : * estimates for Vars. If we can't, fall back on datatype-based width
6398 : : * estimates. Present early-planning uses of PathTargets don't need accurate
6399 : : * widths badly enough to justify going to the catalogs for better data.
6400 : : */
6401 : : PathTarget *
3470 tgl@sss.pgh.pa.us 6402 : 294981 : set_pathtarget_cost_width(PlannerInfo *root, PathTarget *target)
6403 : : {
627 6404 : 294981 : int64 tuple_width = 0;
6405 : : ListCell *lc;
6406 : :
6407 : : /* Vars are assumed to have cost zero, but other exprs do not */
3470 6408 : 294981 : target->cost.startup = 0;
6409 : 294981 : target->cost.per_tuple = 0;
6410 : :
6411 [ + + + + : 1030528 : foreach(lc, target->exprs)
+ + ]
6412 : : {
6413 : 735547 : Node *node = (Node *) lfirst(lc);
6414 : :
901 drowley@postgresql.o 6415 : 735547 : tuple_width += get_expr_width(root, node);
6416 : :
6417 : : /* For non-Vars, account for evaluation cost */
6418 [ + + ]: 735547 : if (!IsA(node, Var))
6419 : : {
6420 : : QualCost cost;
6421 : :
3470 tgl@sss.pgh.pa.us 6422 : 304201 : cost_qual_eval_node(&cost, node, root);
6423 : 304201 : target->cost.startup += cost.startup;
6424 : 304201 : target->cost.per_tuple += cost.per_tuple;
6425 : : }
6426 : : }
6427 : :
627 6428 : 294981 : target->width = clamp_width_est(tuple_width);
6429 : :
3470 6430 : 294981 : return target;
6431 : : }
6432 : :
6433 : : /*
6434 : : * get_expr_width
6435 : : * Estimate the width of the given expr attempting to use the width
6436 : : * cached in a Var's owning RelOptInfo, else fallback on the type's
6437 : : * average width when unable to or when the given Node is not a Var.
6438 : : */
6439 : : static int32
901 drowley@postgresql.o 6440 : 891530 : get_expr_width(PlannerInfo *root, const Node *expr)
6441 : : {
6442 : : int32 width;
6443 : :
6444 [ + + ]: 891530 : if (IsA(expr, Var))
6445 : : {
6446 : 581007 : const Var *var = (const Var *) expr;
6447 : :
6448 : : /* We should not see any upper-level Vars here */
6449 [ - + ]: 581007 : Assert(var->varlevelsup == 0);
6450 : :
6451 : : /* Try to get data from RelOptInfo cache */
6452 [ + + ]: 581007 : if (!IS_SPECIAL_VARNO(var->varno) &&
6453 [ + - ]: 578106 : var->varno < root->simple_rel_array_size)
6454 : : {
6455 : 578106 : RelOptInfo *rel = root->simple_rel_array[var->varno];
6456 : :
6457 [ + + ]: 578106 : if (rel != NULL &&
6458 [ + - ]: 564020 : var->varattno >= rel->min_attr &&
6459 [ + - ]: 564020 : var->varattno <= rel->max_attr)
6460 : : {
6461 : 564020 : int ndx = var->varattno - rel->min_attr;
6462 : :
6463 [ + + ]: 564020 : if (rel->attr_widths[ndx] > 0)
6464 : 548175 : return rel->attr_widths[ndx];
6465 : : }
6466 : : }
6467 : :
6468 : : /*
6469 : : * No cached data available, so estimate using just the type info.
6470 : : */
6471 : 32832 : width = get_typavgwidth(var->vartype, var->vartypmod);
6472 [ - + ]: 32832 : Assert(width > 0);
6473 : :
6474 : 32832 : return width;
6475 : : }
6476 : :
6477 : 310523 : width = get_typavgwidth(exprType(expr), exprTypmod(expr));
6478 [ - + ]: 310523 : Assert(width > 0);
6479 : 310523 : return width;
6480 : : }
6481 : :
6482 : : /*
6483 : : * relation_byte_size
6484 : : * Estimate the storage space in bytes for a given number of tuples
6485 : : * of a given width (size in bytes).
6486 : : */
6487 : : static double
9372 tgl@sss.pgh.pa.us 6488 : 1935839 : relation_byte_size(double tuples, int width)
6489 : : {
3850 6490 : 1935839 : return tuples * (MAXALIGN(width) + MAXALIGN(SizeofHeapTupleHeader));
6491 : : }
6492 : :
6493 : : /*
6494 : : * page_size
6495 : : * Returns an estimate of the number of pages covered by a given
6496 : : * number of tuples of a given width (size in bytes).
6497 : : */
6498 : : static double
9372 6499 : 4634 : page_size(double tuples, int width)
6500 : : {
6501 : 4634 : return ceil(relation_byte_size(tuples, width) / BLCKSZ);
6502 : : }
6503 : :
6504 : : /*
6505 : : * Estimate the fraction of the work that each worker will do given the
6506 : : * number of workers budgeted for the path.
6507 : : */
6508 : : static double
3158 rhaas@postgresql.org 6509 : 94544 : get_parallel_divisor(Path *path)
6510 : : {
6511 : 94544 : double parallel_divisor = path->parallel_workers;
6512 : :
6513 : : /*
6514 : : * Early experience with parallel query suggests that when there is only
6515 : : * one worker, the leader often makes a very substantial contribution to
6516 : : * executing the parallel portion of the plan, but as more workers are
6517 : : * added, it does less and less, because it's busy reading tuples from the
6518 : : * workers and doing whatever non-parallel post-processing is needed. By
6519 : : * the time we reach 4 workers, the leader no longer makes a meaningful
6520 : : * contribution. Thus, for now, estimate that the leader spends 30% of
6521 : : * its time servicing each worker, and the remainder executing the
6522 : : * parallel plan.
6523 : : */
2852 6524 [ + + ]: 94544 : if (parallel_leader_participation)
6525 : : {
6526 : : double leader_contribution;
6527 : :
6528 : 93893 : leader_contribution = 1.0 - (0.3 * path->parallel_workers);
6529 [ + + ]: 93893 : if (leader_contribution > 0)
6530 : 92735 : parallel_divisor += leader_contribution;
6531 : : }
6532 : :
3158 6533 : 94544 : return parallel_divisor;
6534 : : }
6535 : :
6536 : : /*
6537 : : * compute_bitmap_pages
6538 : : * Estimate number of pages fetched from heap in a bitmap heap scan.
6539 : : *
6540 : : * 'baserel' is the relation to be scanned
6541 : : * 'bitmapqual' is a tree of IndexPaths, BitmapAndPaths, and BitmapOrPaths
6542 : : * 'loop_count' is the number of repetitions of the indexscan to factor into
6543 : : * estimates of caching behavior
6544 : : *
6545 : : * If cost_p isn't NULL, the indexTotalCost estimate is returned in *cost_p.
6546 : : * If tuples_p isn't NULL, the tuples_fetched estimate is returned in *tuples_p.
6547 : : */
6548 : : double
628 tgl@sss.pgh.pa.us 6549 : 332487 : compute_bitmap_pages(PlannerInfo *root, RelOptInfo *baserel,
6550 : : Path *bitmapqual, double loop_count,
6551 : : Cost *cost_p, double *tuples_p)
6552 : : {
6553 : : Cost indexTotalCost;
6554 : : Selectivity indexSelectivity;
6555 : : double T;
6556 : : double pages_fetched;
6557 : : double tuples_fetched;
6558 : : double heap_pages;
6559 : : double maxentries;
6560 : :
6561 : : /*
6562 : : * Fetch total cost of obtaining the bitmap, as well as its total
6563 : : * selectivity.
6564 : : */
3144 rhaas@postgresql.org 6565 : 332487 : cost_bitmap_tree_node(bitmapqual, &indexTotalCost, &indexSelectivity);
6566 : :
6567 : : /*
6568 : : * Estimate number of main-table pages fetched.
6569 : : */
6570 : 332487 : tuples_fetched = clamp_row_est(indexSelectivity * baserel->tuples);
6571 : :
6572 [ + + ]: 332487 : T = (baserel->pages > 1) ? (double) baserel->pages : 1.0;
6573 : :
6574 : : /*
6575 : : * For a single scan, the number of heap pages that need to be fetched is
6576 : : * the same as the Mackert and Lohman formula for the case T <= b (ie, no
6577 : : * re-reads needed).
6578 : : */
2857 6579 : 332487 : pages_fetched = (2.0 * T * tuples_fetched) / (2.0 * T + tuples_fetched);
6580 : :
6581 : : /*
6582 : : * Calculate the number of pages fetched from the heap. Then based on
6583 : : * current work_mem estimate get the estimated maxentries in the bitmap.
6584 : : * (Note that we always do this calculation based on the number of pages
6585 : : * that would be fetched in a single iteration, even if loop_count > 1.
6586 : : * That's correct, because only that number of entries will be stored in
6587 : : * the bitmap at one time.)
6588 : : */
6589 [ + + ]: 332487 : heap_pages = Min(pages_fetched, baserel->pages);
218 tgl@sss.pgh.pa.us 6590 : 332487 : maxentries = tbm_calculate_entries(work_mem * (Size) 1024);
6591 : :
3144 rhaas@postgresql.org 6592 [ + + ]: 332487 : if (loop_count > 1)
6593 : : {
6594 : : /*
6595 : : * For repeated bitmap scans, scale up the number of tuples fetched in
6596 : : * the Mackert and Lohman formula by the number of scans, so that we
6597 : : * estimate the number of pages fetched by all the scans. Then
6598 : : * pro-rate for one scan.
6599 : : */
6600 : 66943 : pages_fetched = index_pages_fetched(tuples_fetched * loop_count,
6601 : : baserel->pages,
6602 : : get_indexpath_pages(bitmapqual),
6603 : : root);
6604 : 66943 : pages_fetched /= loop_count;
6605 : : }
6606 : :
6607 [ + + ]: 332487 : if (pages_fetched >= T)
6608 : 33054 : pages_fetched = T;
6609 : : else
6610 : 299433 : pages_fetched = ceil(pages_fetched);
6611 : :
2857 6612 [ + + ]: 332487 : if (maxentries < heap_pages)
6613 : : {
6614 : : double exact_pages;
6615 : : double lossy_pages;
6616 : :
6617 : : /*
6618 : : * Crude approximation of the number of lossy pages. Because of the
6619 : : * way tbm_lossify() is coded, the number of lossy pages increases
6620 : : * very sharply as soon as we run short of memory; this formula has
6621 : : * that property and seems to perform adequately in testing, but it's
6622 : : * possible we could do better somehow.
6623 : : */
6624 [ - + ]: 9 : lossy_pages = Max(0, heap_pages - maxentries / 2);
6625 : 9 : exact_pages = heap_pages - lossy_pages;
6626 : :
6627 : : /*
6628 : : * If there are lossy pages then recompute the number of tuples
6629 : : * processed by the bitmap heap node. We assume here that the chance
6630 : : * of a given tuple coming from an exact page is the same as the
6631 : : * chance that a given page is exact. This might not be true, but
6632 : : * it's not clear how we can do any better.
6633 : : */
6634 [ + - ]: 9 : if (lossy_pages > 0)
6635 : : tuples_fetched =
6636 : 9 : clamp_row_est(indexSelectivity *
6637 : 9 : (exact_pages / heap_pages) * baserel->tuples +
6638 : 9 : (lossy_pages / heap_pages) * baserel->tuples);
6639 : : }
6640 : :
628 tgl@sss.pgh.pa.us 6641 [ + + ]: 332487 : if (cost_p)
6642 : 260738 : *cost_p = indexTotalCost;
6643 [ + + ]: 332487 : if (tuples_p)
6644 : 260738 : *tuples_p = tuples_fetched;
6645 : :
3144 rhaas@postgresql.org 6646 : 332487 : return pages_fetched;
6647 : : }
6648 : :
6649 : : /*
6650 : : * compute_gather_rows
6651 : : * Estimate number of rows for gather (merge) nodes.
6652 : : *
6653 : : * In a parallel plan, each worker's row estimate is determined by dividing the
6654 : : * total number of rows by parallel_divisor, which accounts for the leader's
6655 : : * contribution in addition to the number of workers. Accordingly, when
6656 : : * estimating the number of rows for gather (merge) nodes, we multiply the rows
6657 : : * per worker by the same parallel_divisor to undo the division.
6658 : : */
6659 : : double
410 rguo@postgresql.org 6660 : 18529 : compute_gather_rows(Path *path)
6661 : : {
6662 [ - + ]: 18529 : Assert(path->parallel_workers > 0);
6663 : :
6664 : 18529 : return clamp_row_est(path->rows * get_parallel_divisor(path));
6665 : : }
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