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
8017 tgl@sss.pgh.pa.us 213 :CBC 5308336 : 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 : : */
1885 drowley@postgresql.o 221 [ + - - + ]: 5308336 : if (nrows > MAXIMUM_ROWCOUNT || isnan(nrows))
1885 drowley@postgresql.o 222 :UBC 0 : nrows = MAXIMUM_ROWCOUNT;
1885 drowley@postgresql.o 223 [ + + ]:CBC 5308336 : else if (nrows <= 1.0)
8017 tgl@sss.pgh.pa.us 224 : 1679723 : nrows = 1.0;
225 : : else
7545 226 : 3628613 : nrows = rint(nrows);
227 : :
8017 228 : 5308336 : 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
729 242 : 998862 : 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 [ - + ]: 998862 : if (tuple_width > MaxAllocSize)
729 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 : : */
729 tgl@sss.pgh.pa.us 255 [ - + ]:CBC 998862 : Assert(tuple_width >= 0);
256 : :
257 : 998862 : return (int32) tuple_width;
258 : : }
259 : :
260 : :
261 : : /*
262 : : * cost_seqscan
263 : : * Determines and returns the cost of scanning a relation sequentially.
264 : : *
265 : : * 'baserel' is the relation to be scanned
266 : : * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
267 : : */
268 : : void
7500 269 : 224080 : cost_seqscan(Path *path, PlannerInfo *root,
270 : : RelOptInfo *baserel, ParamPathInfo *param_info)
271 : : {
9437 272 : 224080 : Cost startup_cost = 0;
273 : : Cost cpu_run_cost;
274 : : Cost disk_run_cost;
275 : : double spc_seq_page_cost;
276 : : QualCost qpqual_cost;
277 : : Cost cpu_per_tuple;
278 : :
279 : : /* Should only be applied to base relations */
8348 280 [ - + ]: 224080 : Assert(baserel->relid > 0);
8620 281 [ - + ]: 224080 : Assert(baserel->rtekind == RTE_RELATION);
282 : :
283 : : /* Mark the path with the correct row estimate */
4990 284 [ + + ]: 224080 : if (param_info)
285 : 420 : path->rows = param_info->ppi_rows;
286 : : else
287 : 223660 : path->rows = baserel->rows;
288 : :
289 : : /* fetch estimated page cost for tablespace containing table */
5825 rhaas@postgresql.org 290 : 224080 : get_tablespace_page_costs(baserel->reltablespace,
291 : : NULL,
292 : : &spc_seq_page_cost);
293 : :
294 : : /*
295 : : * disk costs
296 : : */
3619 297 : 224080 : disk_run_cost = spc_seq_page_cost * baserel->pages;
298 : :
299 : : /* CPU costs */
4990 tgl@sss.pgh.pa.us 300 : 224080 : get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
301 : :
302 : 224080 : startup_cost += qpqual_cost.startup;
303 : 224080 : cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple;
3619 rhaas@postgresql.org 304 : 224080 : cpu_run_cost = cpu_per_tuple * baserel->tuples;
305 : : /* tlist eval costs are paid per output row, not per tuple scanned */
3590 tgl@sss.pgh.pa.us 306 : 224080 : startup_cost += path->pathtarget->cost.startup;
307 : 224080 : cpu_run_cost += path->pathtarget->cost.per_tuple * path->rows;
308 : :
309 : : /* Adjust costing for parallelism, if used. */
3478 rhaas@postgresql.org 310 [ + + ]: 224080 : if (path->parallel_workers > 0)
311 : : {
3260 312 : 14206 : double parallel_divisor = get_parallel_divisor(path);
313 : :
314 : : /* The CPU cost is divided among all the workers. */
3619 315 : 14206 : cpu_run_cost /= parallel_divisor;
316 : :
317 : : /*
318 : : * It may be possible to amortize some of the I/O cost, but probably
319 : : * not very much, because most operating systems already do aggressive
320 : : * prefetching. For now, we assume that the disk run cost can't be
321 : : * amortized at all.
322 : : */
323 : :
324 : : /*
325 : : * In the case of a parallel plan, the row count needs to represent
326 : : * the number of tuples processed per worker.
327 : : */
3260 328 : 14206 : path->rows = clamp_row_est(path->rows / parallel_divisor);
329 : : }
330 : :
483 331 : 224080 : path->disabled_nodes = enable_seqscan ? 0 : 1;
3869 simon@2ndQuadrant.co 332 : 224080 : path->startup_cost = startup_cost;
3619 rhaas@postgresql.org 333 : 224080 : path->total_cost = startup_cost + cpu_run_cost + disk_run_cost;
3869 simon@2ndQuadrant.co 334 : 224080 : }
335 : :
336 : : /*
337 : : * cost_samplescan
338 : : * Determines and returns the cost of scanning a relation using sampling.
339 : : *
340 : : * 'baserel' is the relation to be scanned
341 : : * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
342 : : */
343 : : void
3798 tgl@sss.pgh.pa.us 344 : 153 : cost_samplescan(Path *path, PlannerInfo *root,
345 : : RelOptInfo *baserel, ParamPathInfo *param_info)
346 : : {
3869 simon@2ndQuadrant.co 347 : 153 : Cost startup_cost = 0;
348 : 153 : Cost run_cost = 0;
349 : : RangeTblEntry *rte;
350 : : TableSampleClause *tsc;
351 : : TsmRoutine *tsm;
352 : : double spc_seq_page_cost,
353 : : spc_random_page_cost,
354 : : spc_page_cost;
355 : : QualCost qpqual_cost;
356 : : Cost cpu_per_tuple;
357 : :
358 : : /* Should only be applied to base relations with tablesample clauses */
359 [ - + ]: 153 : Assert(baserel->relid > 0);
3798 tgl@sss.pgh.pa.us 360 [ + - ]: 153 : rte = planner_rt_fetch(baserel->relid, root);
361 [ - + ]: 153 : Assert(rte->rtekind == RTE_RELATION);
362 : 153 : tsc = rte->tablesample;
363 [ - + ]: 153 : Assert(tsc != NULL);
364 : 153 : tsm = GetTsmRoutine(tsc->tsmhandler);
365 : :
366 : : /* Mark the path with the correct row estimate */
367 [ + + ]: 153 : if (param_info)
368 : 36 : path->rows = param_info->ppi_rows;
369 : : else
3869 simon@2ndQuadrant.co 370 : 117 : path->rows = baserel->rows;
371 : :
372 : : /* fetch estimated page cost for tablespace containing table */
373 : 153 : get_tablespace_page_costs(baserel->reltablespace,
374 : : &spc_random_page_cost,
375 : : &spc_seq_page_cost);
376 : :
377 : : /* if NextSampleBlock is used, assume random access, else sequential */
3798 tgl@sss.pgh.pa.us 378 : 306 : spc_page_cost = (tsm->NextSampleBlock != NULL) ?
379 [ + + ]: 153 : spc_random_page_cost : spc_seq_page_cost;
380 : :
381 : : /*
382 : : * disk costs (recall that baserel->pages has already been set to the
383 : : * number of pages the sampling method will visit)
384 : : */
385 : 153 : run_cost += spc_page_cost * baserel->pages;
386 : :
387 : : /*
388 : : * CPU costs (recall that baserel->tuples has already been set to the
389 : : * number of tuples the sampling method will select). Note that we ignore
390 : : * execution cost of the TABLESAMPLE parameter expressions; they will be
391 : : * evaluated only once per scan, and in most usages they'll likely be
392 : : * simple constants anyway. We also don't charge anything for the
393 : : * calculations the sampling method might do internally.
394 : : */
395 : 153 : get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
396 : :
3869 simon@2ndQuadrant.co 397 : 153 : startup_cost += qpqual_cost.startup;
398 : 153 : cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple;
3798 tgl@sss.pgh.pa.us 399 : 153 : run_cost += cpu_per_tuple * baserel->tuples;
400 : : /* tlist eval costs are paid per output row, not per tuple scanned */
3590 401 : 153 : startup_cost += path->pathtarget->cost.startup;
402 : 153 : run_cost += path->pathtarget->cost.per_tuple * path->rows;
403 : :
483 rhaas@postgresql.org 404 : 153 : path->disabled_nodes = 0;
9437 tgl@sss.pgh.pa.us 405 : 153 : path->startup_cost = startup_cost;
406 : 153 : path->total_cost = startup_cost + run_cost;
10753 scrappy@hub.org 407 : 153 : }
408 : :
409 : : /*
410 : : * cost_gather
411 : : * Determines and returns the cost of gather path.
412 : : *
413 : : * 'rel' is the relation to be operated upon
414 : : * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
415 : : * 'rows' may be used to point to a row estimate; if non-NULL, it overrides
416 : : * both 'rel' and 'param_info'. This is useful when the path doesn't exactly
417 : : * correspond to any particular RelOptInfo.
418 : : */
419 : : void
3731 rhaas@postgresql.org 420 : 12717 : cost_gather(GatherPath *path, PlannerInfo *root,
421 : : RelOptInfo *rel, ParamPathInfo *param_info,
422 : : double *rows)
423 : : {
424 : 12717 : Cost startup_cost = 0;
425 : 12717 : Cost run_cost = 0;
426 : :
427 : : /* Mark the path with the correct row estimate */
3558 428 [ + + ]: 12717 : if (rows)
429 : 3133 : path->path.rows = *rows;
430 [ - + ]: 9584 : else if (param_info)
3731 rhaas@postgresql.org 431 :UBC 0 : path->path.rows = param_info->ppi_rows;
432 : : else
3731 rhaas@postgresql.org 433 :CBC 9584 : path->path.rows = rel->rows;
434 : :
435 : 12717 : startup_cost = path->subpath->startup_cost;
436 : :
437 : 12717 : run_cost = path->subpath->total_cost - path->subpath->startup_cost;
438 : :
439 : : /* Parallel setup and communication cost. */
440 : 12717 : startup_cost += parallel_setup_cost;
3689 441 : 12717 : run_cost += parallel_tuple_cost * path->path.rows;
442 : :
483 443 : 12717 : path->path.disabled_nodes = path->subpath->disabled_nodes;
3731 444 : 12717 : path->path.startup_cost = startup_cost;
445 : 12717 : path->path.total_cost = (startup_cost + run_cost);
446 : 12717 : }
447 : :
448 : : /*
449 : : * cost_gather_merge
450 : : * Determines and returns the cost of gather merge path.
451 : : *
452 : : * GatherMerge merges several pre-sorted input streams, using a heap that at
453 : : * any given instant holds the next tuple from each stream. If there are N
454 : : * streams, we need about N*log2(N) tuple comparisons to construct the heap at
455 : : * startup, and then for each output tuple, about log2(N) comparisons to
456 : : * replace the top heap entry with the next tuple from the same stream.
457 : : */
458 : : void
3205 459 : 9464 : cost_gather_merge(GatherMergePath *path, PlannerInfo *root,
460 : : RelOptInfo *rel, ParamPathInfo *param_info,
461 : : int input_disabled_nodes,
462 : : Cost input_startup_cost, Cost input_total_cost,
463 : : double *rows)
464 : : {
465 : 9464 : Cost startup_cost = 0;
466 : 9464 : Cost run_cost = 0;
467 : : Cost comparison_cost;
468 : : double N;
469 : : double logN;
470 : :
471 : : /* Mark the path with the correct row estimate */
472 [ + + ]: 9464 : if (rows)
473 : 5643 : path->path.rows = *rows;
474 [ - + ]: 3821 : else if (param_info)
3205 rhaas@postgresql.org 475 :UBC 0 : path->path.rows = param_info->ppi_rows;
476 : : else
3205 rhaas@postgresql.org 477 :CBC 3821 : path->path.rows = rel->rows;
478 : :
479 : : /*
480 : : * Add one to the number of workers to account for the leader. This might
481 : : * be overgenerous since the leader will do less work than other workers
482 : : * in typical cases, but we'll go with it for now.
483 : : */
484 [ - + ]: 9464 : Assert(path->num_workers > 0);
485 : 9464 : N = (double) path->num_workers + 1;
486 : 9464 : logN = LOG2(N);
487 : :
488 : : /* Assumed cost per tuple comparison */
489 : 9464 : comparison_cost = 2.0 * cpu_operator_cost;
490 : :
491 : : /* Heap creation cost */
492 : 9464 : startup_cost += comparison_cost * N * logN;
493 : :
494 : : /* Per-tuple heap maintenance cost */
495 : 9464 : run_cost += path->path.rows * comparison_cost * logN;
496 : :
497 : : /* small cost for heap management, like cost_merge_append */
498 : 9464 : run_cost += cpu_operator_cost * path->path.rows;
499 : :
500 : : /*
501 : : * Parallel setup and communication cost. Since Gather Merge, unlike
502 : : * Gather, requires us to block until a tuple is available from every
503 : : * worker, we bump the IPC cost up a little bit as compared with Gather.
504 : : * For lack of a better idea, charge an extra 5%.
505 : : */
506 : 9464 : startup_cost += parallel_setup_cost;
507 : 9464 : run_cost += parallel_tuple_cost * path->path.rows * 1.05;
508 : :
483 509 : 9464 : path->path.disabled_nodes = input_disabled_nodes
510 : 9464 : + (enable_gathermerge ? 0 : 1);
3205 511 : 9464 : path->path.startup_cost = startup_cost + input_startup_cost;
512 : 9464 : path->path.total_cost = (startup_cost + run_cost + input_total_cost);
513 : 9464 : }
514 : :
515 : : /*
516 : : * cost_index
517 : : * Determines and returns the cost of scanning a relation using an index.
518 : : *
519 : : * 'path' describes the indexscan under consideration, and is complete
520 : : * except for the fields to be set by this routine
521 : : * 'loop_count' is the number of repetitions of the indexscan to factor into
522 : : * estimates of caching behavior
523 : : *
524 : : * In addition to rows, startup_cost and total_cost, cost_index() sets the
525 : : * path's indextotalcost and indexselectivity fields. These values will be
526 : : * needed if the IndexPath is used in a BitmapIndexScan.
527 : : *
528 : : * NOTE: path->indexquals must contain only clauses usable as index
529 : : * restrictions. Any additional quals evaluated as qpquals may reduce the
530 : : * number of returned tuples, but they won't reduce the number of tuples
531 : : * we have to fetch from the table, so they don't reduce the scan cost.
532 : : */
533 : : void
3227 534 : 413600 : cost_index(IndexPath *path, PlannerInfo *root, double loop_count,
535 : : bool partial_path)
536 : : {
5107 tgl@sss.pgh.pa.us 537 : 413600 : IndexOptInfo *index = path->indexinfo;
7570 538 : 413600 : RelOptInfo *baserel = index->rel;
5107 539 : 413600 : bool indexonly = (path->path.pathtype == T_IndexOnlyScan);
540 : : amcostestimate_function amcostestimate;
541 : : List *qpquals;
9437 542 : 413600 : Cost startup_cost = 0;
543 : 413600 : Cost run_cost = 0;
3227 rhaas@postgresql.org 544 : 413600 : Cost cpu_run_cost = 0;
545 : : Cost indexStartupCost;
546 : : Cost indexTotalCost;
547 : : Selectivity indexSelectivity;
548 : : double indexCorrelation,
549 : : csquared;
550 : : double spc_seq_page_cost,
551 : : spc_random_page_cost;
552 : : Cost min_IO_cost,
553 : : max_IO_cost;
554 : : QualCost qpqual_cost;
555 : : Cost cpu_per_tuple;
556 : : double tuples_fetched;
557 : : double pages_fetched;
558 : : double rand_heap_pages;
559 : : double index_pages;
560 : :
561 : : /* Should only be applied to base relations */
8620 tgl@sss.pgh.pa.us 562 [ + - - + ]: 413600 : Assert(IsA(baserel, RelOptInfo) &&
563 : : IsA(index, IndexOptInfo));
8348 564 [ - + ]: 413600 : Assert(baserel->relid > 0);
8620 565 [ - + ]: 413600 : Assert(baserel->rtekind == RTE_RELATION);
566 : :
567 : : /*
568 : : * Mark the path with the correct row estimate, and identify which quals
569 : : * will need to be enforced as qpquals. We need not check any quals that
570 : : * are implied by the index's predicate, so we can use indrestrictinfo not
571 : : * baserestrictinfo as the list of relevant restriction clauses for the
572 : : * rel.
573 : : */
4990 574 [ + + ]: 413600 : if (path->path.param_info)
575 : : {
576 : 78730 : path->path.rows = path->path.param_info->ppi_rows;
577 : : /* qpquals come from the rel's restriction clauses and ppi_clauses */
2503 578 : 78730 : qpquals = list_concat(extract_nonindex_conditions(path->indexinfo->indrestrictinfo,
579 : : path->indexclauses),
3101 580 : 78730 : extract_nonindex_conditions(path->path.param_info->ppi_clauses,
581 : : path->indexclauses));
582 : : }
583 : : else
584 : : {
4990 585 : 334870 : path->path.rows = baserel->rows;
586 : : /* qpquals come from just the rel's restriction clauses */
3548 587 : 334870 : qpquals = extract_nonindex_conditions(path->indexinfo->indrestrictinfo,
588 : : path->indexclauses);
589 : : }
590 : :
591 : : /* we don't need to check enable_indexonlyscan; indxpath.c does that */
483 rhaas@postgresql.org 592 : 413600 : path->path.disabled_nodes = enable_indexscan ? 0 : 1;
593 : :
594 : : /*
595 : : * Call index-access-method-specific code to estimate the processing cost
596 : : * for scanning the index, as well as the selectivity of the index (ie,
597 : : * the fraction of main-table tuples we will have to retrieve) and its
598 : : * correlation to the main-table tuple order. We need a cast here because
599 : : * pathnodes.h uses a weak function type to avoid including amapi.h.
600 : : */
3622 tgl@sss.pgh.pa.us 601 : 413600 : amcostestimate = (amcostestimate_function) index->amcostestimate;
602 : 413600 : amcostestimate(root, path, loop_count,
603 : : &indexStartupCost, &indexTotalCost,
604 : : &indexSelectivity, &indexCorrelation,
605 : : &index_pages);
606 : :
607 : : /*
608 : : * Save amcostestimate's results for possible use in bitmap scan planning.
609 : : * We don't bother to save indexStartupCost or indexCorrelation, because a
610 : : * bitmap scan doesn't care about either.
611 : : */
7545 612 : 413600 : path->indextotalcost = indexTotalCost;
613 : 413600 : path->indexselectivity = indexSelectivity;
614 : :
615 : : /* all costs for touching index itself included here */
9437 616 : 413600 : startup_cost += indexStartupCost;
617 : 413600 : run_cost += indexTotalCost - indexStartupCost;
618 : :
619 : : /* estimate number of main-table tuples fetched */
7134 620 : 413600 : tuples_fetched = clamp_row_est(indexSelectivity * baserel->tuples);
621 : :
622 : : /* fetch estimated page costs for tablespace containing table */
5825 rhaas@postgresql.org 623 : 413600 : get_tablespace_page_costs(baserel->reltablespace,
624 : : &spc_random_page_cost,
625 : : &spc_seq_page_cost);
626 : :
627 : : /*----------
628 : : * Estimate number of main-table pages fetched, and compute I/O cost.
629 : : *
630 : : * When the index ordering is uncorrelated with the table ordering,
631 : : * we use an approximation proposed by Mackert and Lohman (see
632 : : * index_pages_fetched() for details) to compute the number of pages
633 : : * fetched, and then charge spc_random_page_cost per page fetched.
634 : : *
635 : : * When the index ordering is exactly correlated with the table ordering
636 : : * (just after a CLUSTER, for example), the number of pages fetched should
637 : : * be exactly selectivity * table_size. What's more, all but the first
638 : : * will be sequential fetches, not the random fetches that occur in the
639 : : * uncorrelated case. So if the number of pages is more than 1, we
640 : : * ought to charge
641 : : * spc_random_page_cost + (pages_fetched - 1) * spc_seq_page_cost
642 : : * For partially-correlated indexes, we ought to charge somewhere between
643 : : * these two estimates. We currently interpolate linearly between the
644 : : * estimates based on the correlation squared (XXX is that appropriate?).
645 : : *
646 : : * If it's an index-only scan, then we will not need to fetch any heap
647 : : * pages for which the visibility map shows all tuples are visible.
648 : : * Hence, reduce the estimated number of heap fetches accordingly.
649 : : * We use the measured fraction of the entire heap that is all-visible,
650 : : * which might not be particularly relevant to the subset of the heap
651 : : * that this query will fetch; but it's not clear how to do better.
652 : : *----------
653 : : */
5073 tgl@sss.pgh.pa.us 654 [ + + ]: 413600 : if (loop_count > 1)
655 : : {
656 : : /*
657 : : * For repeated indexscans, the appropriate estimate for the
658 : : * uncorrelated case is to scale up the number of tuples fetched in
659 : : * the Mackert and Lohman formula by the number of scans, so that we
660 : : * estimate the number of pages fetched by all the scans; then
661 : : * pro-rate the costs for one scan. In this case we assume all the
662 : : * fetches are random accesses.
663 : : */
664 : 45363 : pages_fetched = index_pages_fetched(tuples_fetched * loop_count,
665 : : baserel->pages,
7029 666 : 45363 : (double) index->pages,
667 : : root);
668 : :
5184 669 [ + + ]: 45363 : if (indexonly)
5178 670 : 5694 : pages_fetched = ceil(pages_fetched * (1.0 - baserel->allvisfrac));
671 : :
3227 rhaas@postgresql.org 672 : 45363 : rand_heap_pages = pages_fetched;
673 : :
5073 tgl@sss.pgh.pa.us 674 : 45363 : max_IO_cost = (pages_fetched * spc_random_page_cost) / loop_count;
675 : :
676 : : /*
677 : : * In the perfectly correlated case, the number of pages touched by
678 : : * each scan is selectivity * table_size, and we can use the Mackert
679 : : * and Lohman formula at the page level to estimate how much work is
680 : : * saved by caching across scans. We still assume all the fetches are
681 : : * random, though, which is an overestimate that's hard to correct for
682 : : * without double-counting the cache effects. (But in most cases
683 : : * where such a plan is actually interesting, only one page would get
684 : : * fetched per scan anyway, so it shouldn't matter much.)
685 : : */
6942 686 : 45363 : pages_fetched = ceil(indexSelectivity * (double) baserel->pages);
687 : :
5073 688 : 45363 : pages_fetched = index_pages_fetched(pages_fetched * loop_count,
689 : : baserel->pages,
6942 690 : 45363 : (double) index->pages,
691 : : root);
692 : :
5184 693 [ + + ]: 45363 : if (indexonly)
5178 694 : 5694 : pages_fetched = ceil(pages_fetched * (1.0 - baserel->allvisfrac));
695 : :
5073 696 : 45363 : min_IO_cost = (pages_fetched * spc_random_page_cost) / loop_count;
697 : : }
698 : : else
699 : : {
700 : : /*
701 : : * Normal case: apply the Mackert and Lohman formula, and then
702 : : * interpolate between that and the correlation-derived result.
703 : : */
7134 704 : 368237 : pages_fetched = index_pages_fetched(tuples_fetched,
705 : : baserel->pages,
7029 706 : 368237 : (double) index->pages,
707 : : root);
708 : :
5184 709 [ + + ]: 368237 : if (indexonly)
5178 710 : 34199 : pages_fetched = ceil(pages_fetched * (1.0 - baserel->allvisfrac));
711 : :
3227 rhaas@postgresql.org 712 : 368237 : rand_heap_pages = pages_fetched;
713 : :
714 : : /* max_IO_cost is for the perfectly uncorrelated case (csquared=0) */
5825 715 : 368237 : max_IO_cost = pages_fetched * spc_random_page_cost;
716 : :
717 : : /* min_IO_cost is for the perfectly correlated case (csquared=1) */
7134 tgl@sss.pgh.pa.us 718 : 368237 : pages_fetched = ceil(indexSelectivity * (double) baserel->pages);
719 : :
5184 720 [ + + ]: 368237 : if (indexonly)
5178 721 : 34199 : pages_fetched = ceil(pages_fetched * (1.0 - baserel->allvisfrac));
722 : :
5176 723 [ + + ]: 368237 : if (pages_fetched > 0)
724 : : {
725 : 329183 : min_IO_cost = spc_random_page_cost;
726 [ + + ]: 329183 : if (pages_fetched > 1)
727 : 100345 : min_IO_cost += (pages_fetched - 1) * spc_seq_page_cost;
728 : : }
729 : : else
730 : 39054 : min_IO_cost = 0;
731 : : }
732 : :
3227 rhaas@postgresql.org 733 [ + + ]: 413600 : if (partial_path)
734 : : {
735 : : /*
736 : : * For index only scans compute workers based on number of index pages
737 : : * fetched; the number of heap pages we fetch might be so small as to
738 : : * effectively rule out parallelism, which we don't want to do.
739 : : */
3200 740 [ + + ]: 142166 : if (indexonly)
741 : 12404 : rand_heap_pages = -1;
742 : :
743 : : /*
744 : : * Estimate the number of parallel workers required to scan index. Use
745 : : * the number of heap pages computed considering heap fetches won't be
746 : : * sequential as for parallel scans the pages are accessed in random
747 : : * order.
748 : : */
3227 749 : 142166 : path->path.parallel_workers = compute_parallel_worker(baserel,
750 : : rand_heap_pages,
751 : : index_pages,
752 : : max_parallel_workers_per_gather);
753 : :
754 : : /*
755 : : * Fall out if workers can't be assigned for parallel scan, because in
756 : : * such a case this path will be rejected. So there is no benefit in
757 : : * doing extra computation.
758 : : */
759 [ + + ]: 142166 : if (path->path.parallel_workers <= 0)
760 : 137054 : return;
761 : :
762 : 5112 : path->path.parallel_aware = true;
763 : : }
764 : :
765 : : /*
766 : : * Now interpolate based on estimated index order correlation to get total
767 : : * disk I/O cost for main table accesses.
768 : : */
6942 tgl@sss.pgh.pa.us 769 : 276546 : csquared = indexCorrelation * indexCorrelation;
770 : :
771 : 276546 : run_cost += max_IO_cost + csquared * (min_IO_cost - max_IO_cost);
772 : :
773 : : /*
774 : : * Estimate CPU costs per tuple.
775 : : *
776 : : * What we want here is cpu_tuple_cost plus the evaluation costs of any
777 : : * qual clauses that we have to evaluate as qpquals.
778 : : */
3942 779 : 276546 : cost_qual_eval(&qpqual_cost, qpquals, root);
780 : :
4998 781 : 276546 : startup_cost += qpqual_cost.startup;
782 : 276546 : cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple;
783 : :
3227 rhaas@postgresql.org 784 : 276546 : cpu_run_cost += cpu_per_tuple * tuples_fetched;
785 : :
786 : : /* tlist eval costs are paid per output row, not per tuple scanned */
3590 tgl@sss.pgh.pa.us 787 : 276546 : startup_cost += path->path.pathtarget->cost.startup;
3227 rhaas@postgresql.org 788 : 276546 : cpu_run_cost += path->path.pathtarget->cost.per_tuple * path->path.rows;
789 : :
790 : : /* Adjust costing for parallelism, if used. */
791 [ + + ]: 276546 : if (path->path.parallel_workers > 0)
792 : : {
793 : 5112 : double parallel_divisor = get_parallel_divisor(&path->path);
794 : :
795 : 5112 : path->path.rows = clamp_row_est(path->path.rows / parallel_divisor);
796 : :
797 : : /* The CPU cost is divided among all the workers. */
798 : 5112 : cpu_run_cost /= parallel_divisor;
799 : : }
800 : :
801 : 276546 : run_cost += cpu_run_cost;
802 : :
7545 tgl@sss.pgh.pa.us 803 : 276546 : path->path.startup_cost = startup_cost;
804 : 276546 : path->path.total_cost = startup_cost + run_cost;
805 : : }
806 : :
807 : : /*
808 : : * extract_nonindex_conditions
809 : : *
810 : : * Given a list of quals to be enforced in an indexscan, extract the ones that
811 : : * will have to be applied as qpquals (ie, the index machinery won't handle
812 : : * them). Here we detect only whether a qual clause is directly redundant
813 : : * with some indexclause. If the index path is chosen for use, createplan.c
814 : : * will try a bit harder to get rid of redundant qual conditions; specifically
815 : : * it will see if quals can be proven to be implied by the indexquals. But
816 : : * it does not seem worth the cycles to try to factor that in at this stage,
817 : : * since we're only trying to estimate qual eval costs. Otherwise this must
818 : : * match the logic in create_indexscan_plan().
819 : : *
820 : : * qual_clauses, and the result, are lists of RestrictInfos.
821 : : * indexclauses is a list of IndexClauses.
822 : : */
823 : : static List *
2503 824 : 492330 : extract_nonindex_conditions(List *qual_clauses, List *indexclauses)
825 : : {
3942 826 : 492330 : List *result = NIL;
827 : : ListCell *lc;
828 : :
829 [ + + + + : 1018927 : foreach(lc, qual_clauses)
+ + ]
830 : : {
3173 831 : 526597 : RestrictInfo *rinfo = lfirst_node(RestrictInfo, lc);
832 : :
3942 833 [ + + ]: 526597 : if (rinfo->pseudoconstant)
834 : 5152 : continue; /* we may drop pseudoconstants here */
2503 835 [ + + ]: 521445 : if (is_redundant_with_indexclauses(rinfo, indexclauses))
836 : 311706 : continue; /* dup or derived from same EquivalenceClass */
837 : : /* ... skip the predicate proof attempt createplan.c will try ... */
3942 838 : 209739 : result = lappend(result, rinfo);
839 : : }
840 : 492330 : return result;
841 : : }
842 : :
843 : : /*
844 : : * index_pages_fetched
845 : : * Estimate the number of pages actually fetched after accounting for
846 : : * cache effects.
847 : : *
848 : : * We use an approximation proposed by Mackert and Lohman, "Index Scans
849 : : * Using a Finite LRU Buffer: A Validated I/O Model", ACM Transactions
850 : : * on Database Systems, Vol. 14, No. 3, September 1989, Pages 401-424.
851 : : * The Mackert and Lohman approximation is that the number of pages
852 : : * fetched is
853 : : * PF =
854 : : * min(2TNs/(2T+Ns), T) when T <= b
855 : : * 2TNs/(2T+Ns) when T > b and Ns <= 2Tb/(2T-b)
856 : : * b + (Ns - 2Tb/(2T-b))*(T-b)/T when T > b and Ns > 2Tb/(2T-b)
857 : : * where
858 : : * T = # pages in table
859 : : * N = # tuples in table
860 : : * s = selectivity = fraction of table to be scanned
861 : : * b = # buffer pages available (we include kernel space here)
862 : : *
863 : : * We assume that effective_cache_size is the total number of buffer pages
864 : : * available for the whole query, and pro-rate that space across all the
865 : : * tables in the query and the index currently under consideration. (This
866 : : * ignores space needed for other indexes used by the query, but since we
867 : : * don't know which indexes will get used, we can't estimate that very well;
868 : : * and in any case counting all the tables may well be an overestimate, since
869 : : * depending on the join plan not all the tables may be scanned concurrently.)
870 : : *
871 : : * The product Ns is the number of tuples fetched; we pass in that
872 : : * product rather than calculating it here. "pages" is the number of pages
873 : : * in the object under consideration (either an index or a table).
874 : : * "index_pages" is the amount to add to the total table space, which was
875 : : * computed for us by make_one_rel.
876 : : *
877 : : * Caller is expected to have ensured that tuples_fetched is greater than zero
878 : : * and rounded to integer (see clamp_row_est). The result will likewise be
879 : : * greater than zero and integral.
880 : : */
881 : : double
7134 882 : 581179 : index_pages_fetched(double tuples_fetched, BlockNumber pages,
883 : : double index_pages, PlannerInfo *root)
884 : : {
885 : : double pages_fetched;
886 : : double total_pages;
887 : : double T,
888 : : b;
889 : :
890 : : /* T is # pages in table, but don't allow it to be zero */
891 [ + + ]: 581179 : T = (pages > 1) ? (double) pages : 1.0;
892 : :
893 : : /* Compute number of pages assumed to be competing for cache space */
7029 894 : 581179 : total_pages = root->total_table_pages + index_pages;
895 [ + + ]: 581179 : total_pages = Max(total_pages, 1.0);
896 [ - + ]: 581179 : Assert(T <= total_pages);
897 : :
898 : : /* b is pro-rated share of effective_cache_size */
3101 899 : 581179 : b = (double) effective_cache_size * T / total_pages;
900 : :
901 : : /* force it positive and integral */
7134 902 [ - + ]: 581179 : if (b <= 1.0)
7134 tgl@sss.pgh.pa.us 903 :UBC 0 : b = 1.0;
904 : : else
7134 tgl@sss.pgh.pa.us 905 :CBC 581179 : b = ceil(b);
906 : :
907 : : /* This part is the Mackert and Lohman formula */
908 [ + - ]: 581179 : if (T <= b)
909 : : {
910 : 581179 : pages_fetched =
911 : 581179 : (2.0 * T * tuples_fetched) / (2.0 * T + tuples_fetched);
912 [ + + ]: 581179 : if (pages_fetched >= T)
913 : 339462 : pages_fetched = T;
914 : : else
915 : 241717 : pages_fetched = ceil(pages_fetched);
916 : : }
917 : : else
918 : : {
919 : : double lim;
920 : :
7134 tgl@sss.pgh.pa.us 921 :UBC 0 : lim = (2.0 * T * b) / (2.0 * T - b);
922 [ # # ]: 0 : if (tuples_fetched <= lim)
923 : : {
924 : 0 : pages_fetched =
925 : 0 : (2.0 * T * tuples_fetched) / (2.0 * T + tuples_fetched);
926 : : }
927 : : else
928 : : {
929 : 0 : pages_fetched =
930 : 0 : b + (tuples_fetched - lim) * (T - b) / T;
931 : : }
932 : 0 : pages_fetched = ceil(pages_fetched);
933 : : }
7134 tgl@sss.pgh.pa.us 934 :CBC 581179 : return pages_fetched;
935 : : }
936 : :
937 : : /*
938 : : * get_indexpath_pages
939 : : * Determine the total size of the indexes used in a bitmap index path.
940 : : *
941 : : * Note: if the same index is used more than once in a bitmap tree, we will
942 : : * count it multiple times, which perhaps is the wrong thing ... but it's
943 : : * not completely clear, and detecting duplicates is difficult, so ignore it
944 : : * for now.
945 : : */
946 : : static double
7029 947 : 97364 : get_indexpath_pages(Path *bitmapqual)
948 : : {
949 : 97364 : double result = 0;
950 : : ListCell *l;
951 : :
952 [ + + ]: 97364 : if (IsA(bitmapqual, BitmapAndPath))
953 : : {
954 : 11590 : BitmapAndPath *apath = (BitmapAndPath *) bitmapqual;
955 : :
956 [ + - + + : 34770 : foreach(l, apath->bitmapquals)
+ + ]
957 : : {
958 : 23180 : result += get_indexpath_pages((Path *) lfirst(l));
959 : : }
960 : : }
961 [ + + ]: 85774 : else if (IsA(bitmapqual, BitmapOrPath))
962 : : {
963 : 35 : BitmapOrPath *opath = (BitmapOrPath *) bitmapqual;
964 : :
965 [ + - + + : 111 : foreach(l, opath->bitmapquals)
+ + ]
966 : : {
967 : 76 : result += get_indexpath_pages((Path *) lfirst(l));
968 : : }
969 : : }
970 [ + - ]: 85739 : else if (IsA(bitmapqual, IndexPath))
971 : : {
972 : 85739 : IndexPath *ipath = (IndexPath *) bitmapqual;
973 : :
974 : 85739 : result = (double) ipath->indexinfo->pages;
975 : : }
976 : : else
7029 tgl@sss.pgh.pa.us 977 [ # # ]:UBC 0 : elog(ERROR, "unrecognized node type: %d", nodeTag(bitmapqual));
978 : :
7029 tgl@sss.pgh.pa.us 979 :CBC 97364 : return result;
980 : : }
981 : :
982 : : /*
983 : : * cost_bitmap_heap_scan
984 : : * Determines and returns the cost of scanning a relation using a bitmap
985 : : * index-then-heap plan.
986 : : *
987 : : * 'baserel' is the relation to be scanned
988 : : * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
989 : : * 'bitmapqual' is a tree of IndexPaths, BitmapAndPaths, and BitmapOrPaths
990 : : * 'loop_count' is the number of repetitions of the indexscan to factor into
991 : : * estimates of caching behavior
992 : : *
993 : : * Note: the component IndexPaths in bitmapqual should have been costed
994 : : * using the same loop_count.
995 : : */
996 : : void
7500 997 : 280403 : cost_bitmap_heap_scan(Path *path, PlannerInfo *root, RelOptInfo *baserel,
998 : : ParamPathInfo *param_info,
999 : : Path *bitmapqual, double loop_count)
1000 : : {
7547 1001 : 280403 : Cost startup_cost = 0;
1002 : 280403 : Cost run_cost = 0;
1003 : : Cost indexTotalCost;
1004 : : QualCost qpqual_cost;
1005 : : Cost cpu_per_tuple;
1006 : : Cost cost_per_page;
1007 : : Cost cpu_run_cost;
1008 : : double tuples_fetched;
1009 : : double pages_fetched;
1010 : : double spc_seq_page_cost,
1011 : : spc_random_page_cost;
1012 : : double T;
1013 : :
1014 : : /* Should only be applied to base relations */
1015 [ - + ]: 280403 : Assert(IsA(baserel, RelOptInfo));
1016 [ - + ]: 280403 : Assert(baserel->relid > 0);
1017 [ - + ]: 280403 : Assert(baserel->rtekind == RTE_RELATION);
1018 : :
1019 : : /* Mark the path with the correct row estimate */
4990 1020 [ + + ]: 280403 : if (param_info)
1021 : 118615 : path->rows = param_info->ppi_rows;
1022 : : else
5073 1023 : 161788 : path->rows = baserel->rows;
1024 : :
3246 rhaas@postgresql.org 1025 : 280403 : pages_fetched = compute_bitmap_pages(root, baserel, bitmapqual,
1026 : : loop_count, &indexTotalCost,
1027 : : &tuples_fetched);
1028 : :
7545 tgl@sss.pgh.pa.us 1029 : 280403 : startup_cost += indexTotalCost;
3246 rhaas@postgresql.org 1030 [ + + ]: 280403 : T = (baserel->pages > 1) ? (double) baserel->pages : 1.0;
1031 : :
1032 : : /* Fetch estimated page costs for tablespace containing table. */
5825 1033 : 280403 : get_tablespace_page_costs(baserel->reltablespace,
1034 : : &spc_random_page_cost,
1035 : : &spc_seq_page_cost);
1036 : :
1037 : : /*
1038 : : * For small numbers of pages we should charge spc_random_page_cost
1039 : : * apiece, while if nearly all the table's pages are being read, it's more
1040 : : * appropriate to charge spc_seq_page_cost apiece. The effect is
1041 : : * nonlinear, too. For lack of a better idea, interpolate like this to
1042 : : * determine the cost per page.
1043 : : */
7544 tgl@sss.pgh.pa.us 1044 [ + + ]: 280403 : if (pages_fetched >= 2.0)
5825 rhaas@postgresql.org 1045 : 58645 : cost_per_page = spc_random_page_cost -
1046 : 58645 : (spc_random_page_cost - spc_seq_page_cost)
1047 : 58645 : * sqrt(pages_fetched / T);
1048 : : else
1049 : 221758 : cost_per_page = spc_random_page_cost;
1050 : :
7545 tgl@sss.pgh.pa.us 1051 : 280403 : run_cost += pages_fetched * cost_per_page;
1052 : :
1053 : : /*
1054 : : * Estimate CPU costs per tuple.
1055 : : *
1056 : : * Often the indexquals don't need to be rechecked at each tuple ... but
1057 : : * not always, especially not if there are enough tuples involved that the
1058 : : * bitmaps become lossy. For the moment, just assume they will be
1059 : : * rechecked always. This means we charge the full freight for all the
1060 : : * scan clauses.
1061 : : */
4990 1062 : 280403 : get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
1063 : :
1064 : 280403 : startup_cost += qpqual_cost.startup;
1065 : 280403 : cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple;
3206 rhaas@postgresql.org 1066 : 280403 : cpu_run_cost = cpu_per_tuple * tuples_fetched;
1067 : :
1068 : : /* Adjust costing for parallelism, if used. */
1069 [ + + ]: 280403 : if (path->parallel_workers > 0)
1070 : : {
1071 : 2144 : double parallel_divisor = get_parallel_divisor(path);
1072 : :
1073 : : /* The CPU cost is divided among all the workers. */
1074 : 2144 : cpu_run_cost /= parallel_divisor;
1075 : :
1076 : 2144 : path->rows = clamp_row_est(path->rows / parallel_divisor);
1077 : : }
1078 : :
1079 : :
1080 : 280403 : run_cost += cpu_run_cost;
1081 : :
1082 : : /* tlist eval costs are paid per output row, not per tuple scanned */
3590 tgl@sss.pgh.pa.us 1083 : 280403 : startup_cost += path->pathtarget->cost.startup;
1084 : 280403 : run_cost += path->pathtarget->cost.per_tuple * path->rows;
1085 : :
483 rhaas@postgresql.org 1086 : 280403 : path->disabled_nodes = enable_bitmapscan ? 0 : 1;
7547 tgl@sss.pgh.pa.us 1087 : 280403 : path->startup_cost = startup_cost;
1088 : 280403 : path->total_cost = startup_cost + run_cost;
1089 : 280403 : }
1090 : :
1091 : : /*
1092 : : * cost_bitmap_tree_node
1093 : : * Extract cost and selectivity from a bitmap tree node (index/and/or)
1094 : : */
1095 : : void
7545 1096 : 516591 : cost_bitmap_tree_node(Path *path, Cost *cost, Selectivity *selec)
1097 : : {
1098 [ + + ]: 516591 : if (IsA(path, IndexPath))
1099 : : {
1100 : 489433 : *cost = ((IndexPath *) path)->indextotalcost;
1101 : 489433 : *selec = ((IndexPath *) path)->indexselectivity;
1102 : :
1103 : : /*
1104 : : * Charge a small amount per retrieved tuple to reflect the costs of
1105 : : * manipulating the bitmap. This is mostly to make sure that a bitmap
1106 : : * scan doesn't look to be the same cost as an indexscan to retrieve a
1107 : : * single tuple.
1108 : : */
5073 1109 : 489433 : *cost += 0.1 * cpu_operator_cost * path->rows;
1110 : : }
7545 1111 [ + + ]: 27158 : else if (IsA(path, BitmapAndPath))
1112 : : {
1113 : 25324 : *cost = path->total_cost;
1114 : 25324 : *selec = ((BitmapAndPath *) path)->bitmapselectivity;
1115 : : }
1116 [ + - ]: 1834 : else if (IsA(path, BitmapOrPath))
1117 : : {
1118 : 1834 : *cost = path->total_cost;
1119 : 1834 : *selec = ((BitmapOrPath *) path)->bitmapselectivity;
1120 : : }
1121 : : else
1122 : : {
7545 tgl@sss.pgh.pa.us 1123 [ # # ]:UBC 0 : elog(ERROR, "unrecognized node type: %d", nodeTag(path));
1124 : : *cost = *selec = 0; /* keep compiler quiet */
1125 : : }
7545 tgl@sss.pgh.pa.us 1126 :CBC 516591 : }
1127 : :
1128 : : /*
1129 : : * cost_bitmap_and_node
1130 : : * Estimate the cost of a BitmapAnd node
1131 : : *
1132 : : * Note that this considers only the costs of index scanning and bitmap
1133 : : * creation, not the eventual heap access. In that sense the object isn't
1134 : : * truly a Path, but it has enough path-like properties (costs in particular)
1135 : : * to warrant treating it as one. We don't bother to set the path rows field,
1136 : : * however.
1137 : : */
1138 : : void
7500 1139 : 25217 : cost_bitmap_and_node(BitmapAndPath *path, PlannerInfo *root)
1140 : : {
1141 : : Cost totalCost;
1142 : : Selectivity selec;
1143 : : ListCell *l;
1144 : :
1145 : : /*
1146 : : * We estimate AND selectivity on the assumption that the inputs are
1147 : : * independent. This is probably often wrong, but we don't have the info
1148 : : * to do better.
1149 : : *
1150 : : * The runtime cost of the BitmapAnd itself is estimated at 100x
1151 : : * cpu_operator_cost for each tbm_intersect needed. Probably too small,
1152 : : * definitely too simplistic?
1153 : : */
7545 1154 : 25217 : totalCost = 0.0;
1155 : 25217 : selec = 1.0;
1156 [ + - + + : 75651 : foreach(l, path->bitmapquals)
+ + ]
1157 : : {
7368 bruce@momjian.us 1158 : 50434 : Path *subpath = (Path *) lfirst(l);
1159 : : Cost subCost;
1160 : : Selectivity subselec;
1161 : :
7545 tgl@sss.pgh.pa.us 1162 : 50434 : cost_bitmap_tree_node(subpath, &subCost, &subselec);
1163 : :
1164 : 50434 : selec *= subselec;
1165 : :
1166 : 50434 : totalCost += subCost;
1167 [ + + ]: 50434 : if (l != list_head(path->bitmapquals))
1168 : 25217 : totalCost += 100.0 * cpu_operator_cost;
1169 : : }
1170 : 25217 : path->bitmapselectivity = selec;
5073 1171 : 25217 : path->path.rows = 0; /* per above, not used */
483 rhaas@postgresql.org 1172 : 25217 : path->path.disabled_nodes = 0;
7545 tgl@sss.pgh.pa.us 1173 : 25217 : path->path.startup_cost = totalCost;
1174 : 25217 : path->path.total_cost = totalCost;
1175 : 25217 : }
1176 : :
1177 : : /*
1178 : : * cost_bitmap_or_node
1179 : : * Estimate the cost of a BitmapOr node
1180 : : *
1181 : : * See comments for cost_bitmap_and_node.
1182 : : */
1183 : : void
7500 1184 : 520 : cost_bitmap_or_node(BitmapOrPath *path, PlannerInfo *root)
1185 : : {
1186 : : Cost totalCost;
1187 : : Selectivity selec;
1188 : : ListCell *l;
1189 : :
1190 : : /*
1191 : : * We estimate OR selectivity on the assumption that the inputs are
1192 : : * non-overlapping, since that's often the case in "x IN (list)" type
1193 : : * situations. Of course, we clamp to 1.0 at the end.
1194 : : *
1195 : : * The runtime cost of the BitmapOr itself is estimated at 100x
1196 : : * cpu_operator_cost for each tbm_union needed. Probably too small,
1197 : : * definitely too simplistic? We are aware that the tbm_unions are
1198 : : * optimized out when the inputs are BitmapIndexScans.
1199 : : */
7545 1200 : 520 : totalCost = 0.0;
1201 : 520 : selec = 0.0;
1202 [ + - + + : 1461 : foreach(l, path->bitmapquals)
+ + ]
1203 : : {
7368 bruce@momjian.us 1204 : 941 : Path *subpath = (Path *) lfirst(l);
1205 : : Cost subCost;
1206 : : Selectivity subselec;
1207 : :
7545 tgl@sss.pgh.pa.us 1208 : 941 : cost_bitmap_tree_node(subpath, &subCost, &subselec);
1209 : :
1210 : 941 : selec += subselec;
1211 : :
1212 : 941 : totalCost += subCost;
1213 [ + + ]: 941 : if (l != list_head(path->bitmapquals) &&
1214 [ - + ]: 421 : !IsA(subpath, IndexPath))
7545 tgl@sss.pgh.pa.us 1215 :LBC (3) : totalCost += 100.0 * cpu_operator_cost;
1216 : : }
7545 tgl@sss.pgh.pa.us 1217 [ + - ]:CBC 520 : path->bitmapselectivity = Min(selec, 1.0);
5073 1218 : 520 : path->path.rows = 0; /* per above, not used */
7545 1219 : 520 : path->path.startup_cost = totalCost;
1220 : 520 : path->path.total_cost = totalCost;
1221 : 520 : }
1222 : :
1223 : : /*
1224 : : * cost_tidscan
1225 : : * Determines and returns the cost of scanning a relation using TIDs.
1226 : : *
1227 : : * 'baserel' is the relation to be scanned
1228 : : * 'tidquals' is the list of TID-checkable quals
1229 : : * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
1230 : : */
1231 : : void
7500 1232 : 438 : cost_tidscan(Path *path, PlannerInfo *root,
1233 : : RelOptInfo *baserel, List *tidquals, ParamPathInfo *param_info)
1234 : : {
9437 1235 : 438 : Cost startup_cost = 0;
1236 : 438 : Cost run_cost = 0;
1237 : : QualCost qpqual_cost;
1238 : : Cost cpu_per_tuple;
1239 : : QualCost tid_qual_cost;
1240 : : double ntuples;
1241 : : ListCell *l;
1242 : : double spc_random_page_cost;
1243 : :
1244 : : /* Should only be applied to base relations */
8348 1245 [ - + ]: 438 : Assert(baserel->relid > 0);
8620 1246 [ - + ]: 438 : Assert(baserel->rtekind == RTE_RELATION);
483 rhaas@postgresql.org 1247 [ - + ]: 438 : Assert(tidquals != NIL);
1248 : :
1249 : : /* Mark the path with the correct row estimate */
4861 tgl@sss.pgh.pa.us 1250 [ + + ]: 438 : if (param_info)
1251 : 72 : path->rows = param_info->ppi_rows;
1252 : : else
1253 : 366 : path->rows = baserel->rows;
1254 : :
1255 : : /* Count how many tuples we expect to retrieve */
7326 1256 : 438 : ntuples = 0;
1257 [ + - + + : 889 : foreach(l, tidquals)
+ + ]
1258 : : {
2544 1259 : 451 : RestrictInfo *rinfo = lfirst_node(RestrictInfo, l);
1260 : 451 : Expr *qual = rinfo->clause;
1261 : :
1262 : : /*
1263 : : * We must use a TID scan for CurrentOfExpr; in any other case, we
1264 : : * should be generating a TID scan only if enable_tidscan=true. Also,
1265 : : * if CurrentOfExpr is the qual, there should be only one.
1266 : : */
483 rhaas@postgresql.org 1267 [ - + - - ]: 451 : Assert(enable_tidscan || IsA(qual, CurrentOfExpr));
1268 [ + + - + ]: 451 : Assert(list_length(tidquals) == 1 || !IsA(qual, CurrentOfExpr));
1269 : :
2544 tgl@sss.pgh.pa.us 1270 [ + + ]: 451 : if (IsA(qual, ScalarArrayOpExpr))
1271 : : {
1272 : : /* Each element of the array yields 1 tuple */
1273 : 25 : ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) qual;
7014 bruce@momjian.us 1274 : 25 : Node *arraynode = (Node *) lsecond(saop->args);
1275 : :
713 tgl@sss.pgh.pa.us 1276 : 25 : ntuples += estimate_array_length(root, arraynode);
1277 : : }
2544 1278 [ + + ]: 426 : else if (IsA(qual, CurrentOfExpr))
1279 : : {
1280 : : /* CURRENT OF yields 1 tuple */
6629 1281 : 202 : ntuples++;
1282 : : }
1283 : : else
1284 : : {
1285 : : /* It's just CTID = something, count 1 tuple */
7326 1286 : 224 : ntuples++;
1287 : : }
1288 : : }
1289 : :
1290 : : /*
1291 : : * The TID qual expressions will be computed once, any other baserestrict
1292 : : * quals once per retrieved tuple.
1293 : : */
6764 1294 : 438 : cost_qual_eval(&tid_qual_cost, tidquals, root);
1295 : :
1296 : : /* fetch estimated page cost for tablespace containing table */
5825 rhaas@postgresql.org 1297 : 438 : get_tablespace_page_costs(baserel->reltablespace,
1298 : : &spc_random_page_cost,
1299 : : NULL);
1300 : :
1301 : : /* disk costs --- assume each tuple on a different page */
1302 : 438 : run_cost += spc_random_page_cost * ntuples;
1303 : :
1304 : : /* Add scanning CPU costs */
4861 tgl@sss.pgh.pa.us 1305 : 438 : get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
1306 : :
1307 : : /* XXX currently we assume TID quals are a subset of qpquals */
1308 : 438 : startup_cost += qpqual_cost.startup + tid_qual_cost.per_tuple;
1309 : 438 : cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple -
6764 1310 : 438 : tid_qual_cost.per_tuple;
9437 1311 : 438 : run_cost += cpu_per_tuple * ntuples;
1312 : :
1313 : : /* tlist eval costs are paid per output row, not per tuple scanned */
3590 1314 : 438 : startup_cost += path->pathtarget->cost.startup;
1315 : 438 : run_cost += path->pathtarget->cost.per_tuple * path->rows;
1316 : :
1317 : : /*
1318 : : * There are assertions above verifying that we only reach this function
1319 : : * either when enable_tidscan=true or when the TID scan is the only legal
1320 : : * path, so it's safe to set disabled_nodes to zero here.
1321 : : */
483 rhaas@postgresql.org 1322 : 438 : path->disabled_nodes = 0;
9437 tgl@sss.pgh.pa.us 1323 : 438 : path->startup_cost = startup_cost;
1324 : 438 : path->total_cost = startup_cost + run_cost;
9521 bruce@momjian.us 1325 : 438 : }
1326 : :
1327 : : /*
1328 : : * cost_tidrangescan
1329 : : * Determines and sets the costs of scanning a relation using a range of
1330 : : * TIDs for 'path'
1331 : : *
1332 : : * 'baserel' is the relation to be scanned
1333 : : * 'tidrangequals' is the list of TID-checkable range quals
1334 : : * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
1335 : : */
1336 : : void
1754 drowley@postgresql.o 1337 : 1024 : cost_tidrangescan(Path *path, PlannerInfo *root,
1338 : : RelOptInfo *baserel, List *tidrangequals,
1339 : : ParamPathInfo *param_info)
1340 : : {
1341 : : Selectivity selectivity;
1342 : : double pages;
1343 : : Cost startup_cost;
1344 : : Cost cpu_run_cost;
1345 : : Cost disk_run_cost;
1346 : : QualCost qpqual_cost;
1347 : : Cost cpu_per_tuple;
1348 : : QualCost tid_qual_cost;
1349 : : double ntuples;
1350 : : double nseqpages;
1351 : : double spc_random_page_cost;
1352 : : double spc_seq_page_cost;
1353 : :
1354 : : /* Should only be applied to base relations */
1355 [ - + ]: 1024 : Assert(baserel->relid > 0);
1356 [ - + ]: 1024 : Assert(baserel->rtekind == RTE_RELATION);
1357 : :
1358 : : /* Mark the path with the correct row estimate */
1359 [ - + ]: 1024 : if (param_info)
1754 drowley@postgresql.o 1360 :UBC 0 : path->rows = param_info->ppi_rows;
1361 : : else
1754 drowley@postgresql.o 1362 :CBC 1024 : path->rows = baserel->rows;
1363 : :
1364 : : /* Count how many tuples and pages we expect to scan */
1365 : 1024 : selectivity = clauselist_selectivity(root, tidrangequals, baserel->relid,
1366 : : JOIN_INNER, NULL);
1367 : 1024 : pages = ceil(selectivity * baserel->pages);
1368 : :
1369 [ + + ]: 1024 : if (pages <= 0.0)
1370 : 21 : pages = 1.0;
1371 : :
1372 : : /*
1373 : : * The first page in a range requires a random seek, but each subsequent
1374 : : * page is just a normal sequential page read. NOTE: it's desirable for
1375 : : * TID Range Scans to cost more than the equivalent Sequential Scans,
1376 : : * because Seq Scans have some performance advantages such as scan
1377 : : * synchronization, and we'd prefer one of them to be picked unless a TID
1378 : : * Range Scan really is better.
1379 : : */
1380 : 1024 : ntuples = selectivity * baserel->tuples;
1381 : 1024 : nseqpages = pages - 1.0;
1382 : :
1383 : : /*
1384 : : * The TID qual expressions will be computed once, any other baserestrict
1385 : : * quals once per retrieved tuple.
1386 : : */
1387 : 1024 : cost_qual_eval(&tid_qual_cost, tidrangequals, root);
1388 : :
1389 : : /* fetch estimated page cost for tablespace containing table */
1390 : 1024 : get_tablespace_page_costs(baserel->reltablespace,
1391 : : &spc_random_page_cost,
1392 : : &spc_seq_page_cost);
1393 : :
1394 : : /* disk costs; 1 random page and the remainder as seq pages */
20 drowley@postgresql.o 1395 :GNC 1024 : disk_run_cost = spc_random_page_cost + spc_seq_page_cost * nseqpages;
1396 : :
1397 : : /* Add scanning CPU costs */
1754 drowley@postgresql.o 1398 :CBC 1024 : get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
1399 : :
1400 : : /*
1401 : : * XXX currently we assume TID quals are a subset of qpquals at this
1402 : : * point; they will be removed (if possible) when we create the plan, so
1403 : : * we subtract their cost from the total qpqual cost. (If the TID quals
1404 : : * can't be removed, this is a mistake and we're going to underestimate
1405 : : * the CPU cost a bit.)
1406 : : */
20 drowley@postgresql.o 1407 :GNC 1024 : startup_cost = qpqual_cost.startup + tid_qual_cost.per_tuple;
1754 drowley@postgresql.o 1408 :CBC 1024 : cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple -
1409 : 1024 : tid_qual_cost.per_tuple;
20 drowley@postgresql.o 1410 :GNC 1024 : cpu_run_cost = cpu_per_tuple * ntuples;
1411 : :
1412 : : /* tlist eval costs are paid per output row, not per tuple scanned */
1754 drowley@postgresql.o 1413 :CBC 1024 : startup_cost += path->pathtarget->cost.startup;
20 drowley@postgresql.o 1414 :GNC 1024 : cpu_run_cost += path->pathtarget->cost.per_tuple * path->rows;
1415 : :
1416 : : /* Adjust costing for parallelism, if used. */
1417 [ + + ]: 1024 : if (path->parallel_workers > 0)
1418 : : {
1419 : 24 : double parallel_divisor = get_parallel_divisor(path);
1420 : :
1421 : : /* The CPU cost is divided among all the workers. */
1422 : 24 : cpu_run_cost /= parallel_divisor;
1423 : :
1424 : : /*
1425 : : * In the case of a parallel plan, the row count needs to represent
1426 : : * the number of tuples processed per worker.
1427 : : */
1428 : 24 : path->rows = clamp_row_est(path->rows / parallel_divisor);
1429 : : }
1430 : :
1431 : : /* we should not generate this path type when enable_tidscan=false */
483 rhaas@postgresql.org 1432 [ - + ]:CBC 1024 : Assert(enable_tidscan);
1433 : 1024 : path->disabled_nodes = 0;
1754 drowley@postgresql.o 1434 : 1024 : path->startup_cost = startup_cost;
20 drowley@postgresql.o 1435 :GNC 1024 : path->total_cost = startup_cost + cpu_run_cost + disk_run_cost;
1754 drowley@postgresql.o 1436 :CBC 1024 : }
1437 : :
1438 : : /*
1439 : : * cost_subqueryscan
1440 : : * Determines and returns the cost of scanning a subquery RTE.
1441 : : *
1442 : : * 'baserel' is the relation to be scanned
1443 : : * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
1444 : : * 'trivial_pathtarget' is true if the pathtarget is believed to be trivial.
1445 : : */
1446 : : void
3572 tgl@sss.pgh.pa.us 1447 : 31284 : cost_subqueryscan(SubqueryScanPath *path, PlannerInfo *root,
1448 : : RelOptInfo *baserel, ParamPathInfo *param_info,
1449 : : bool trivial_pathtarget)
1450 : : {
1451 : : Cost startup_cost;
1452 : : Cost run_cost;
1453 : : List *qpquals;
1454 : : QualCost qpqual_cost;
1455 : : Cost cpu_per_tuple;
1456 : :
1457 : : /* Should only be applied to base relations that are subqueries */
8192 1458 [ - + ]: 31284 : Assert(baserel->relid > 0);
1459 [ - + ]: 31284 : Assert(baserel->rtekind == RTE_SUBQUERY);
1460 : :
1461 : : /*
1462 : : * We compute the rowcount estimate as the subplan's estimate times the
1463 : : * selectivity of relevant restriction clauses. In simple cases this will
1464 : : * come out the same as baserel->rows; but when dealing with parallelized
1465 : : * paths we must do it like this to get the right answer.
1466 : : */
4990 1467 [ + + ]: 31284 : if (param_info)
1323 1468 : 309 : qpquals = list_concat_copy(param_info->ppi_clauses,
1469 : 309 : baserel->baserestrictinfo);
1470 : : else
1471 : 30975 : qpquals = baserel->baserestrictinfo;
1472 : :
1473 : 31284 : path->path.rows = clamp_row_est(path->subpath->rows *
1474 : 31284 : clauselist_selectivity(root,
1475 : : qpquals,
1476 : : 0,
1477 : : JOIN_INNER,
1478 : : NULL));
1479 : :
1480 : : /*
1481 : : * Cost of path is cost of evaluating the subplan, plus cost of evaluating
1482 : : * any restriction clauses and tlist that will be attached to the
1483 : : * SubqueryScan node, plus cpu_tuple_cost to account for selection and
1484 : : * projection overhead.
1485 : : */
483 rhaas@postgresql.org 1486 : 31284 : path->path.disabled_nodes = path->subpath->disabled_nodes;
3572 tgl@sss.pgh.pa.us 1487 : 31284 : path->path.startup_cost = path->subpath->startup_cost;
1488 : 31284 : path->path.total_cost = path->subpath->total_cost;
1489 : :
1490 : : /*
1491 : : * However, if there are no relevant restriction clauses and the
1492 : : * pathtarget is trivial, then we expect that setrefs.c will optimize away
1493 : : * the SubqueryScan plan node altogether, so we should just make its cost
1494 : : * and rowcount equal to the input path's.
1495 : : *
1496 : : * Note: there are some edge cases where createplan.c will apply a
1497 : : * different targetlist to the SubqueryScan node, thus falsifying our
1498 : : * current estimate of whether the target is trivial, and making the cost
1499 : : * estimate (though not the rowcount) wrong. It does not seem worth the
1500 : : * extra complication to try to account for that exactly, especially since
1501 : : * that behavior falsifies other cost estimates as well.
1502 : : */
1247 1503 [ + + + + ]: 31284 : if (qpquals == NIL && trivial_pathtarget)
1504 : 13810 : return;
1505 : :
4990 1506 : 17474 : get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
1507 : :
1508 : 17474 : startup_cost = qpqual_cost.startup;
1509 : 17474 : cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple;
1323 1510 : 17474 : run_cost = cpu_per_tuple * path->subpath->rows;
1511 : :
1512 : : /* tlist eval costs are paid per output row, not per tuple scanned */
3572 1513 : 17474 : startup_cost += path->path.pathtarget->cost.startup;
1514 : 17474 : run_cost += path->path.pathtarget->cost.per_tuple * path->path.rows;
1515 : :
1516 : 17474 : path->path.startup_cost += startup_cost;
1517 : 17474 : path->path.total_cost += startup_cost + run_cost;
1518 : : }
1519 : :
1520 : : /*
1521 : : * cost_functionscan
1522 : : * Determines and returns the cost of scanning a function RTE.
1523 : : *
1524 : : * 'baserel' is the relation to be scanned
1525 : : * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
1526 : : */
1527 : : void
4880 1528 : 25133 : cost_functionscan(Path *path, PlannerInfo *root,
1529 : : RelOptInfo *baserel, ParamPathInfo *param_info)
1530 : : {
8620 1531 : 25133 : Cost startup_cost = 0;
1532 : 25133 : Cost run_cost = 0;
1533 : : QualCost qpqual_cost;
1534 : : Cost cpu_per_tuple;
1535 : : RangeTblEntry *rte;
1536 : : QualCost exprcost;
1537 : :
1538 : : /* Should only be applied to base relations that are functions */
8348 1539 [ - + ]: 25133 : Assert(baserel->relid > 0);
6815 1540 [ + - ]: 25133 : rte = planner_rt_fetch(baserel->relid, root);
6904 1541 [ - + ]: 25133 : Assert(rte->rtekind == RTE_FUNCTION);
1542 : :
1543 : : /* Mark the path with the correct row estimate */
4880 1544 [ + + ]: 25133 : if (param_info)
1545 : 4294 : path->rows = param_info->ppi_rows;
1546 : : else
1547 : 20839 : path->rows = baserel->rows;
1548 : :
1549 : : /*
1550 : : * Estimate costs of executing the function expression(s).
1551 : : *
1552 : : * Currently, nodeFunctionscan.c always executes the functions to
1553 : : * completion before returning any rows, and caches the results in a
1554 : : * tuplestore. So the function eval cost is all startup cost, and per-row
1555 : : * costs are minimal.
1556 : : *
1557 : : * XXX in principle we ought to charge tuplestore spill costs if the
1558 : : * number of rows is large. However, given how phony our rowcount
1559 : : * estimates for functions tend to be, there's not a lot of point in that
1560 : : * refinement right now.
1561 : : */
4409 1562 : 25133 : cost_qual_eval_node(&exprcost, (Node *) rte->functions, root);
1563 : :
5940 1564 : 25133 : startup_cost += exprcost.startup + exprcost.per_tuple;
1565 : :
1566 : : /* Add scanning CPU costs */
4880 1567 : 25133 : get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
1568 : :
1569 : 25133 : startup_cost += qpqual_cost.startup;
1570 : 25133 : cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple;
8620 1571 : 25133 : run_cost += cpu_per_tuple * baserel->tuples;
1572 : :
1573 : : /* tlist eval costs are paid per output row, not per tuple scanned */
3590 1574 : 25133 : startup_cost += path->pathtarget->cost.startup;
1575 : 25133 : run_cost += path->pathtarget->cost.per_tuple * path->rows;
1576 : :
483 rhaas@postgresql.org 1577 : 25133 : path->disabled_nodes = 0;
8620 tgl@sss.pgh.pa.us 1578 : 25133 : path->startup_cost = startup_cost;
1579 : 25133 : path->total_cost = startup_cost + run_cost;
1580 : 25133 : }
1581 : :
1582 : : /*
1583 : : * cost_tablefuncscan
1584 : : * Determines and returns the cost of scanning a table function.
1585 : : *
1586 : : * 'baserel' is the relation to be scanned
1587 : : * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
1588 : : */
1589 : : void
3206 alvherre@alvh.no-ip. 1590 : 311 : cost_tablefuncscan(Path *path, PlannerInfo *root,
1591 : : RelOptInfo *baserel, ParamPathInfo *param_info)
1592 : : {
1593 : 311 : Cost startup_cost = 0;
1594 : 311 : Cost run_cost = 0;
1595 : : QualCost qpqual_cost;
1596 : : Cost cpu_per_tuple;
1597 : : RangeTblEntry *rte;
1598 : : QualCost exprcost;
1599 : :
1600 : : /* Should only be applied to base relations that are functions */
1601 [ - + ]: 311 : Assert(baserel->relid > 0);
1602 [ + - ]: 311 : rte = planner_rt_fetch(baserel->relid, root);
1603 [ - + ]: 311 : Assert(rte->rtekind == RTE_TABLEFUNC);
1604 : :
1605 : : /* Mark the path with the correct row estimate */
1606 [ + + ]: 311 : if (param_info)
1607 : 117 : path->rows = param_info->ppi_rows;
1608 : : else
1609 : 194 : path->rows = baserel->rows;
1610 : :
1611 : : /*
1612 : : * Estimate costs of executing the table func expression(s).
1613 : : *
1614 : : * XXX in principle we ought to charge tuplestore spill costs if the
1615 : : * number of rows is large. However, given how phony our rowcount
1616 : : * estimates for tablefuncs tend to be, there's not a lot of point in that
1617 : : * refinement right now.
1618 : : */
1619 : 311 : cost_qual_eval_node(&exprcost, (Node *) rte->tablefunc, root);
1620 : :
1621 : 311 : startup_cost += exprcost.startup + exprcost.per_tuple;
1622 : :
1623 : : /* Add scanning CPU costs */
1624 : 311 : get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
1625 : :
1626 : 311 : startup_cost += qpqual_cost.startup;
1627 : 311 : cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple;
1628 : 311 : run_cost += cpu_per_tuple * baserel->tuples;
1629 : :
1630 : : /* tlist eval costs are paid per output row, not per tuple scanned */
1631 : 311 : startup_cost += path->pathtarget->cost.startup;
1632 : 311 : run_cost += path->pathtarget->cost.per_tuple * path->rows;
1633 : :
483 rhaas@postgresql.org 1634 : 311 : path->disabled_nodes = 0;
3206 alvherre@alvh.no-ip. 1635 : 311 : path->startup_cost = startup_cost;
1636 : 311 : path->total_cost = startup_cost + run_cost;
1637 : 311 : }
1638 : :
1639 : : /*
1640 : : * cost_valuesscan
1641 : : * Determines and returns the cost of scanning a VALUES RTE.
1642 : : *
1643 : : * 'baserel' is the relation to be scanned
1644 : : * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
1645 : : */
1646 : : void
4875 tgl@sss.pgh.pa.us 1647 : 4236 : cost_valuesscan(Path *path, PlannerInfo *root,
1648 : : RelOptInfo *baserel, ParamPathInfo *param_info)
1649 : : {
7077 mail@joeconway.com 1650 : 4236 : Cost startup_cost = 0;
1651 : 4236 : Cost run_cost = 0;
1652 : : QualCost qpqual_cost;
1653 : : Cost cpu_per_tuple;
1654 : :
1655 : : /* Should only be applied to base relations that are values lists */
1656 [ - + ]: 4236 : Assert(baserel->relid > 0);
1657 [ - + ]: 4236 : Assert(baserel->rtekind == RTE_VALUES);
1658 : :
1659 : : /* Mark the path with the correct row estimate */
4875 tgl@sss.pgh.pa.us 1660 [ + + ]: 4236 : if (param_info)
1661 : 33 : path->rows = param_info->ppi_rows;
1662 : : else
1663 : 4203 : path->rows = baserel->rows;
1664 : :
1665 : : /*
1666 : : * For now, estimate list evaluation cost at one operator eval per list
1667 : : * (probably pretty bogus, but is it worth being smarter?)
1668 : : */
7077 mail@joeconway.com 1669 : 4236 : cpu_per_tuple = cpu_operator_cost;
1670 : :
1671 : : /* Add scanning CPU costs */
4875 tgl@sss.pgh.pa.us 1672 : 4236 : get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
1673 : :
1674 : 4236 : startup_cost += qpqual_cost.startup;
1675 : 4236 : cpu_per_tuple += cpu_tuple_cost + qpqual_cost.per_tuple;
7077 mail@joeconway.com 1676 : 4236 : run_cost += cpu_per_tuple * baserel->tuples;
1677 : :
1678 : : /* tlist eval costs are paid per output row, not per tuple scanned */
3590 tgl@sss.pgh.pa.us 1679 : 4236 : startup_cost += path->pathtarget->cost.startup;
1680 : 4236 : run_cost += path->pathtarget->cost.per_tuple * path->rows;
1681 : :
483 rhaas@postgresql.org 1682 : 4236 : path->disabled_nodes = 0;
7077 mail@joeconway.com 1683 : 4236 : path->startup_cost = startup_cost;
1684 : 4236 : path->total_cost = startup_cost + run_cost;
1685 : 4236 : }
1686 : :
1687 : : /*
1688 : : * cost_ctescan
1689 : : * Determines and returns the cost of scanning a CTE RTE.
1690 : : *
1691 : : * Note: this is used for both self-reference and regular CTEs; the
1692 : : * possible cost differences are below the threshold of what we could
1693 : : * estimate accurately anyway. Note that the costs of evaluating the
1694 : : * referenced CTE query are added into the final plan as initplan costs,
1695 : : * and should NOT be counted here.
1696 : : */
1697 : : void
4861 tgl@sss.pgh.pa.us 1698 : 2657 : cost_ctescan(Path *path, PlannerInfo *root,
1699 : : RelOptInfo *baserel, ParamPathInfo *param_info)
1700 : : {
6283 1701 : 2657 : Cost startup_cost = 0;
1702 : 2657 : Cost run_cost = 0;
1703 : : QualCost qpqual_cost;
1704 : : Cost cpu_per_tuple;
1705 : :
1706 : : /* Should only be applied to base relations that are CTEs */
1707 [ - + ]: 2657 : Assert(baserel->relid > 0);
1708 [ - + ]: 2657 : Assert(baserel->rtekind == RTE_CTE);
1709 : :
1710 : : /* Mark the path with the correct row estimate */
4861 1711 [ - + ]: 2657 : if (param_info)
4861 tgl@sss.pgh.pa.us 1712 :UBC 0 : path->rows = param_info->ppi_rows;
1713 : : else
4861 tgl@sss.pgh.pa.us 1714 :CBC 2657 : path->rows = baserel->rows;
1715 : :
1716 : : /* Charge one CPU tuple cost per row for tuplestore manipulation */
6283 1717 : 2657 : cpu_per_tuple = cpu_tuple_cost;
1718 : :
1719 : : /* Add scanning CPU costs */
4861 1720 : 2657 : get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
1721 : :
1722 : 2657 : startup_cost += qpqual_cost.startup;
1723 : 2657 : cpu_per_tuple += cpu_tuple_cost + qpqual_cost.per_tuple;
6283 1724 : 2657 : run_cost += cpu_per_tuple * baserel->tuples;
1725 : :
1726 : : /* tlist eval costs are paid per output row, not per tuple scanned */
3590 1727 : 2657 : startup_cost += path->pathtarget->cost.startup;
1728 : 2657 : run_cost += path->pathtarget->cost.per_tuple * path->rows;
1729 : :
483 rhaas@postgresql.org 1730 : 2657 : path->disabled_nodes = 0;
6283 tgl@sss.pgh.pa.us 1731 : 2657 : path->startup_cost = startup_cost;
1732 : 2657 : path->total_cost = startup_cost + run_cost;
1733 : 2657 : }
1734 : :
1735 : : /*
1736 : : * cost_namedtuplestorescan
1737 : : * Determines and returns the cost of scanning a named tuplestore.
1738 : : */
1739 : : void
3183 kgrittn@postgresql.o 1740 : 241 : cost_namedtuplestorescan(Path *path, PlannerInfo *root,
1741 : : RelOptInfo *baserel, ParamPathInfo *param_info)
1742 : : {
1743 : 241 : Cost startup_cost = 0;
1744 : 241 : Cost run_cost = 0;
1745 : : QualCost qpqual_cost;
1746 : : Cost cpu_per_tuple;
1747 : :
1748 : : /* Should only be applied to base relations that are Tuplestores */
1749 [ - + ]: 241 : Assert(baserel->relid > 0);
1750 [ - + ]: 241 : Assert(baserel->rtekind == RTE_NAMEDTUPLESTORE);
1751 : :
1752 : : /* Mark the path with the correct row estimate */
1753 [ - + ]: 241 : if (param_info)
3183 kgrittn@postgresql.o 1754 :UBC 0 : path->rows = param_info->ppi_rows;
1755 : : else
3183 kgrittn@postgresql.o 1756 :CBC 241 : path->rows = baserel->rows;
1757 : :
1758 : : /* Charge one CPU tuple cost per row for tuplestore manipulation */
1759 : 241 : cpu_per_tuple = cpu_tuple_cost;
1760 : :
1761 : : /* Add scanning CPU costs */
1762 : 241 : get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
1763 : :
1764 : 241 : startup_cost += qpqual_cost.startup;
1765 : 241 : cpu_per_tuple += cpu_tuple_cost + qpqual_cost.per_tuple;
1766 : 241 : run_cost += cpu_per_tuple * baserel->tuples;
1767 : :
483 rhaas@postgresql.org 1768 : 241 : path->disabled_nodes = 0;
3183 kgrittn@postgresql.o 1769 : 241 : path->startup_cost = startup_cost;
1770 : 241 : path->total_cost = startup_cost + run_cost;
1771 : 241 : }
1772 : :
1773 : : /*
1774 : : * cost_resultscan
1775 : : * Determines and returns the cost of scanning an RTE_RESULT relation.
1776 : : */
1777 : : void
2515 tgl@sss.pgh.pa.us 1778 : 2173 : cost_resultscan(Path *path, PlannerInfo *root,
1779 : : RelOptInfo *baserel, ParamPathInfo *param_info)
1780 : : {
1781 : 2173 : Cost startup_cost = 0;
1782 : 2173 : Cost run_cost = 0;
1783 : : QualCost qpqual_cost;
1784 : : Cost cpu_per_tuple;
1785 : :
1786 : : /* Should only be applied to RTE_RESULT base relations */
1787 [ - + ]: 2173 : Assert(baserel->relid > 0);
1788 [ - + ]: 2173 : Assert(baserel->rtekind == RTE_RESULT);
1789 : :
1790 : : /* Mark the path with the correct row estimate */
1791 [ + + ]: 2173 : if (param_info)
1792 : 78 : path->rows = param_info->ppi_rows;
1793 : : else
1794 : 2095 : path->rows = baserel->rows;
1795 : :
1796 : : /* We charge qual cost plus cpu_tuple_cost */
1797 : 2173 : get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
1798 : :
1799 : 2173 : startup_cost += qpqual_cost.startup;
1800 : 2173 : cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple;
1801 : 2173 : run_cost += cpu_per_tuple * baserel->tuples;
1802 : :
483 rhaas@postgresql.org 1803 : 2173 : path->disabled_nodes = 0;
2515 tgl@sss.pgh.pa.us 1804 : 2173 : path->startup_cost = startup_cost;
1805 : 2173 : path->total_cost = startup_cost + run_cost;
1806 : 2173 : }
1807 : :
1808 : : /*
1809 : : * cost_recursive_union
1810 : : * Determines and returns the cost of performing a recursive union,
1811 : : * and also the estimated output size.
1812 : : *
1813 : : * We are given Paths for the nonrecursive and recursive terms.
1814 : : */
1815 : : void
3572 1816 : 471 : cost_recursive_union(Path *runion, Path *nrterm, Path *rterm)
1817 : : {
1818 : : Cost startup_cost;
1819 : : Cost total_cost;
1820 : : double total_rows;
1821 : :
1822 : : /* We probably have decent estimates for the non-recursive term */
6283 1823 : 471 : startup_cost = nrterm->startup_cost;
1824 : 471 : total_cost = nrterm->total_cost;
3572 1825 : 471 : total_rows = nrterm->rows;
1826 : :
1827 : : /*
1828 : : * We arbitrarily assume that about 10 recursive iterations will be
1829 : : * needed, and that we've managed to get a good fix on the cost and output
1830 : : * size of each one of them. These are mighty shaky assumptions but it's
1831 : : * hard to see how to do better.
1832 : : */
6283 1833 : 471 : total_cost += 10 * rterm->total_cost;
3572 1834 : 471 : total_rows += 10 * rterm->rows;
1835 : :
1836 : : /*
1837 : : * Also charge cpu_tuple_cost per row to account for the costs of
1838 : : * manipulating the tuplestores. (We don't worry about possible
1839 : : * spill-to-disk costs.)
1840 : : */
6283 1841 : 471 : total_cost += cpu_tuple_cost * total_rows;
1842 : :
483 rhaas@postgresql.org 1843 : 471 : runion->disabled_nodes = nrterm->disabled_nodes + rterm->disabled_nodes;
6283 tgl@sss.pgh.pa.us 1844 : 471 : runion->startup_cost = startup_cost;
1845 : 471 : runion->total_cost = total_cost;
3572 1846 : 471 : runion->rows = total_rows;
1847 : 471 : runion->pathtarget->width = Max(nrterm->pathtarget->width,
1848 : : rterm->pathtarget->width);
6283 1849 : 471 : }
1850 : :
1851 : : /*
1852 : : * cost_tuplesort
1853 : : * Determines and returns the cost of sorting a relation using tuplesort,
1854 : : * not including the cost of reading the input data.
1855 : : *
1856 : : * If the total volume of data to sort is less than sort_mem, we will do
1857 : : * an in-memory sort, which requires no I/O and about t*log2(t) tuple
1858 : : * comparisons for t tuples.
1859 : : *
1860 : : * If the total volume exceeds sort_mem, we switch to a tape-style merge
1861 : : * algorithm. There will still be about t*log2(t) tuple comparisons in
1862 : : * total, but we will also need to write and read each tuple once per
1863 : : * merge pass. We expect about ceil(logM(r)) merge passes where r is the
1864 : : * number of initial runs formed and M is the merge order used by tuplesort.c.
1865 : : * Since the average initial run should be about sort_mem, we have
1866 : : * disk traffic = 2 * relsize * ceil(logM(p / sort_mem))
1867 : : * cpu = comparison_cost * t * log2(t)
1868 : : *
1869 : : * If the sort is bounded (i.e., only the first k result tuples are needed)
1870 : : * and k tuples can fit into sort_mem, we use a heap method that keeps only
1871 : : * k tuples in the heap; this will require about t*log2(k) tuple comparisons.
1872 : : *
1873 : : * The disk traffic is assumed to be 3/4ths sequential and 1/4th random
1874 : : * accesses (XXX can't we refine that guess?)
1875 : : *
1876 : : * By default, we charge two operator evals per tuple comparison, which should
1877 : : * be in the right ballpark in most cases. The caller can tweak this by
1878 : : * specifying nonzero comparison_cost; typically that's used for any extra
1879 : : * work that has to be done to prepare the inputs to the comparison operators.
1880 : : *
1881 : : * 'tuples' is the number of tuples in the relation
1882 : : * 'width' is the average tuple width in bytes
1883 : : * 'comparison_cost' is the extra cost per comparison, if any
1884 : : * 'sort_mem' is the number of kilobytes of work memory allowed for the sort
1885 : : * 'limit_tuples' is the bound on the number of output tuples; -1 if no bound
1886 : : */
1887 : : static void
1171 1888 : 1055213 : cost_tuplesort(Cost *startup_cost, Cost *run_cost,
1889 : : double tuples, int width,
1890 : : Cost comparison_cost, int sort_mem,
1891 : : double limit_tuples)
1892 : : {
6802 1893 : 1055213 : double input_bytes = relation_byte_size(tuples, width);
1894 : : double output_bytes;
1895 : : double output_tuples;
320 1896 : 1055213 : int64 sort_mem_bytes = sort_mem * (int64) 1024;
1897 : :
1898 : : /*
1899 : : * We want to be sure the cost of a sort is never estimated as zero, even
1900 : : * if passed-in tuple count is zero. Besides, mustn't do log(0)...
1901 : : */
9474 1902 [ + + ]: 1055213 : if (tuples < 2.0)
1903 : 278591 : tuples = 2.0;
1904 : :
1905 : : /* Include the default cost-per-comparison */
1171 1906 : 1055213 : comparison_cost += 2.0 * cpu_operator_cost;
1907 : :
1908 : : /* Do we have a useful LIMIT? */
6802 1909 [ + + + + ]: 1055213 : if (limit_tuples > 0 && limit_tuples < tuples)
1910 : : {
1911 : 915 : output_tuples = limit_tuples;
1912 : 915 : output_bytes = relation_byte_size(output_tuples, width);
1913 : : }
1914 : : else
1915 : : {
1916 : 1054298 : output_tuples = tuples;
1917 : 1054298 : output_bytes = input_bytes;
1918 : : }
1919 : :
5550 1920 [ + + ]: 1055213 : if (output_bytes > sort_mem_bytes)
1921 : : {
1922 : : /*
1923 : : * We'll have to use a disk-based sort of all the tuples
1924 : : */
6802 1925 : 9295 : double npages = ceil(input_bytes / BLCKSZ);
3540 rhaas@postgresql.org 1926 : 9295 : double nruns = input_bytes / sort_mem_bytes;
5550 tgl@sss.pgh.pa.us 1927 : 9295 : double mergeorder = tuplesort_merge_order(sort_mem_bytes);
1928 : : double log_runs;
1929 : : double npageaccesses;
1930 : :
1931 : : /*
1932 : : * CPU costs
1933 : : *
1934 : : * Assume about N log2 N comparisons
1935 : : */
1171 1936 : 9295 : *startup_cost = comparison_cost * tuples * LOG2(tuples);
1937 : :
1938 : : /* Disk costs */
1939 : :
1940 : : /* Compute logM(r) as log(r) / log(M) */
7241 1941 [ + + ]: 9295 : if (nruns > mergeorder)
1942 : 2467 : log_runs = ceil(log(nruns) / log(mergeorder));
1943 : : else
9474 1944 : 6828 : log_runs = 1.0;
9437 1945 : 9295 : npageaccesses = 2.0 * npages * log_runs;
1946 : : /* Assume 3/4ths of accesses are sequential, 1/4th are not */
2081 tomas.vondra@postgre 1947 : 9295 : *startup_cost += npageaccesses *
7135 tgl@sss.pgh.pa.us 1948 : 9295 : (seq_page_cost * 0.75 + random_page_cost * 0.25);
1949 : : }
5550 1950 [ + + - + ]: 1045918 : else if (tuples > 2 * output_tuples || input_bytes > sort_mem_bytes)
1951 : : {
1952 : : /*
1953 : : * We'll use a bounded heap-sort keeping just K tuples in memory, for
1954 : : * a total number of tuple comparisons of N log2 K; but the constant
1955 : : * factor is a bit higher than for quicksort. Tweak it so that the
1956 : : * cost curve is continuous at the crossover point.
1957 : : */
1171 1958 : 670 : *startup_cost = comparison_cost * tuples * LOG2(2.0 * output_tuples);
1959 : : }
1960 : : else
1961 : : {
1962 : : /* We'll use plain quicksort on all the input tuples */
1963 : 1045248 : *startup_cost = comparison_cost * tuples * LOG2(tuples);
1964 : : }
1965 : :
1966 : : /*
1967 : : * Also charge a small amount (arbitrarily set equal to operator cost) per
1968 : : * extracted tuple. We don't charge cpu_tuple_cost because a Sort node
1969 : : * doesn't do qual-checking or projection, so it has less overhead than
1970 : : * most plan nodes. Note it's correct to use tuples not output_tuples
1971 : : * here --- the upper LIMIT will pro-rate the run cost so we'd be double
1972 : : * counting the LIMIT otherwise.
1973 : : */
2081 tomas.vondra@postgre 1974 : 1055213 : *run_cost = cpu_operator_cost * tuples;
1975 : 1055213 : }
1976 : :
1977 : : /*
1978 : : * cost_incremental_sort
1979 : : * Determines and returns the cost of sorting a relation incrementally, when
1980 : : * the input path is presorted by a prefix of the pathkeys.
1981 : : *
1982 : : * 'presorted_keys' is the number of leading pathkeys by which the input path
1983 : : * is sorted.
1984 : : *
1985 : : * We estimate the number of groups into which the relation is divided by the
1986 : : * leading pathkeys, and then calculate the cost of sorting a single group
1987 : : * with tuplesort using cost_tuplesort().
1988 : : */
1989 : : void
1990 : 6325 : cost_incremental_sort(Path *path,
1991 : : PlannerInfo *root, List *pathkeys, int presorted_keys,
1992 : : int input_disabled_nodes,
1993 : : Cost input_startup_cost, Cost input_total_cost,
1994 : : double input_tuples, int width, Cost comparison_cost, int sort_mem,
1995 : : double limit_tuples)
1996 : : {
1997 : : Cost startup_cost,
1998 : : run_cost,
1999 : 6325 : input_run_cost = input_total_cost - input_startup_cost;
2000 : : double group_tuples,
2001 : : input_groups;
2002 : : Cost group_startup_cost,
2003 : : group_run_cost,
2004 : : group_input_run_cost;
2005 : 6325 : List *presortedExprs = NIL;
2006 : : ListCell *l;
2064 2007 : 6325 : bool unknown_varno = false;
2008 : :
1097 drowley@postgresql.o 2009 [ + - - + ]: 6325 : Assert(presorted_keys > 0 && presorted_keys < list_length(pathkeys));
2010 : :
2011 : : /*
2012 : : * We want to be sure the cost of a sort is never estimated as zero, even
2013 : : * if passed-in tuple count is zero. Besides, mustn't do log(0)...
2014 : : */
2081 tomas.vondra@postgre 2015 [ + + ]: 6325 : if (input_tuples < 2.0)
2016 : 3495 : input_tuples = 2.0;
2017 : :
2018 : : /* Default estimate of number of groups, capped to one group per row. */
2064 2019 [ + + ]: 6325 : input_groups = Min(input_tuples, DEFAULT_NUM_DISTINCT);
2020 : :
2021 : : /*
2022 : : * Extract presorted keys as list of expressions.
2023 : : *
2024 : : * We need to be careful about Vars containing "varno 0" which might have
2025 : : * been introduced by generate_append_tlist, which would confuse
2026 : : * estimate_num_groups (in fact it'd fail for such expressions). See
2027 : : * recurse_set_operations which has to deal with the same issue.
2028 : : *
2029 : : * Unlike recurse_set_operations we can't access the original target list
2030 : : * here, and even if we could it's not very clear how useful would that be
2031 : : * for a set operation combining multiple tables. So we simply detect if
2032 : : * there are any expressions with "varno 0" and use the default
2033 : : * DEFAULT_NUM_DISTINCT in that case.
2034 : : *
2035 : : * We might also use either 1.0 (a single group) or input_tuples (each row
2036 : : * being a separate group), pretty much the worst and best case for
2037 : : * incremental sort. But those are extreme cases and using something in
2038 : : * between seems reasonable. Furthermore, generate_append_tlist is used
2039 : : * for set operations, which are likely to produce mostly unique output
2040 : : * anyway - from that standpoint the DEFAULT_NUM_DISTINCT is defensive
2041 : : * while maintaining lower startup cost.
2042 : : */
2081 2043 [ + - + - : 6373 : foreach(l, pathkeys)
+ - ]
2044 : : {
2045 : 6373 : PathKey *key = (PathKey *) lfirst(l);
2046 : 6373 : EquivalenceMember *member = (EquivalenceMember *)
943 tgl@sss.pgh.pa.us 2047 : 6373 : linitial(key->pk_eclass->ec_members);
2048 : :
2049 : : /*
2050 : : * Check if the expression contains Var with "varno 0" so that we
2051 : : * don't call estimate_num_groups in that case.
2052 : : */
1791 2053 [ + + ]: 6373 : if (bms_is_member(0, pull_varnos(root, (Node *) member->em_expr)))
2054 : : {
2064 tomas.vondra@postgre 2055 : 5 : unknown_varno = true;
2056 : 5 : break;
2057 : : }
2058 : :
2059 : : /* expression not containing any Vars with "varno 0" */
2081 2060 : 6368 : presortedExprs = lappend(presortedExprs, member->em_expr);
2061 : :
1097 drowley@postgresql.o 2062 [ + + ]: 6368 : if (foreach_current_index(l) + 1 >= presorted_keys)
2081 tomas.vondra@postgre 2063 : 6320 : break;
2064 : : }
2065 : :
2066 : : /* Estimate the number of groups with equal presorted keys. */
2064 2067 [ + + ]: 6325 : if (!unknown_varno)
1723 drowley@postgresql.o 2068 : 6320 : input_groups = estimate_num_groups(root, presortedExprs, input_tuples,
2069 : : NULL, NULL);
2070 : :
2081 tomas.vondra@postgre 2071 : 6325 : group_tuples = input_tuples / input_groups;
2072 : 6325 : group_input_run_cost = input_run_cost / input_groups;
2073 : :
2074 : : /*
2075 : : * Estimate the average cost of sorting of one group where presorted keys
2076 : : * are equal.
2077 : : */
1171 tgl@sss.pgh.pa.us 2078 : 6325 : cost_tuplesort(&group_startup_cost, &group_run_cost,
2079 : : group_tuples, width, comparison_cost, sort_mem,
2080 : : limit_tuples);
2081 : :
2082 : : /*
2083 : : * Startup cost of incremental sort is the startup cost of its first group
2084 : : * plus the cost of its input.
2085 : : */
1097 drowley@postgresql.o 2086 : 6325 : startup_cost = group_startup_cost + input_startup_cost +
2087 : : group_input_run_cost;
2088 : :
2089 : : /*
2090 : : * After we started producing tuples from the first group, the cost of
2091 : : * producing all the tuples is given by the cost to finish processing this
2092 : : * group, plus the total cost to process the remaining groups, plus the
2093 : : * remaining cost of input.
2094 : : */
2095 : 6325 : run_cost = group_run_cost + (group_run_cost + group_startup_cost) *
2096 : 6325 : (input_groups - 1) + group_input_run_cost * (input_groups - 1);
2097 : :
2098 : : /*
2099 : : * Incremental sort adds some overhead by itself. Firstly, it has to
2100 : : * detect the sort groups. This is roughly equal to one extra copy and
2101 : : * comparison per tuple.
2102 : : */
2081 tomas.vondra@postgre 2103 : 6325 : run_cost += (cpu_tuple_cost + comparison_cost) * input_tuples;
2104 : :
2105 : : /*
2106 : : * Additionally, we charge double cpu_tuple_cost for each input group to
2107 : : * account for the tuplesort_reset that's performed after each group.
2108 : : */
2109 : 6325 : run_cost += 2.0 * cpu_tuple_cost * input_groups;
2110 : :
2111 : 6325 : path->rows = input_tuples;
2112 : :
2113 : : /* should not generate these paths when enable_incremental_sort=false */
483 rhaas@postgresql.org 2114 [ - + ]: 6325 : Assert(enable_incremental_sort);
2115 : 6325 : path->disabled_nodes = input_disabled_nodes;
2116 : :
2081 tomas.vondra@postgre 2117 : 6325 : path->startup_cost = startup_cost;
2118 : 6325 : path->total_cost = startup_cost + run_cost;
2119 : 6325 : }
2120 : :
2121 : : /*
2122 : : * cost_sort
2123 : : * Determines and returns the cost of sorting a relation, including
2124 : : * the cost of reading the input data.
2125 : : *
2126 : : * NOTE: some callers currently pass NIL for pathkeys because they
2127 : : * can't conveniently supply the sort keys. Since this routine doesn't
2128 : : * currently do anything with pathkeys anyway, that doesn't matter...
2129 : : * but if it ever does, it should react gracefully to lack of key data.
2130 : : * (Actually, the thing we'd most likely be interested in is just the number
2131 : : * of sort keys, which all callers *could* supply.)
2132 : : */
2133 : : void
2134 : 1048888 : cost_sort(Path *path, PlannerInfo *root,
2135 : : List *pathkeys, int input_disabled_nodes,
2136 : : Cost input_cost, double tuples, int width,
2137 : : Cost comparison_cost, int sort_mem,
2138 : : double limit_tuples)
2139 : :
2140 : : {
2141 : : Cost startup_cost;
2142 : : Cost run_cost;
2143 : :
1171 tgl@sss.pgh.pa.us 2144 : 1048888 : cost_tuplesort(&startup_cost, &run_cost,
2145 : : tuples, width,
2146 : : comparison_cost, sort_mem,
2147 : : limit_tuples);
2148 : :
2081 tomas.vondra@postgre 2149 : 1048888 : startup_cost += input_cost;
2150 : :
2151 : 1048888 : path->rows = tuples;
483 rhaas@postgresql.org 2152 : 1048888 : path->disabled_nodes = input_disabled_nodes + (enable_sort ? 0 : 1);
9437 tgl@sss.pgh.pa.us 2153 : 1048888 : path->startup_cost = startup_cost;
2154 : 1048888 : path->total_cost = startup_cost + run_cost;
10753 scrappy@hub.org 2155 : 1048888 : }
2156 : :
2157 : : /*
2158 : : * append_nonpartial_cost
2159 : : * Estimate the cost of the non-partial paths in a Parallel Append.
2160 : : * The non-partial paths are assumed to be the first "numpaths" paths
2161 : : * from the subpaths list, and to be in order of decreasing cost.
2162 : : */
2163 : : static Cost
2934 rhaas@postgresql.org 2164 : 13065 : append_nonpartial_cost(List *subpaths, int numpaths, int parallel_workers)
2165 : : {
2166 : : Cost *costarr;
2167 : : int arrlen;
2168 : : ListCell *l;
2169 : : ListCell *cell;
2170 : : int path_index;
2171 : : int min_index;
2172 : : int max_index;
2173 : :
2174 [ + + ]: 13065 : if (numpaths == 0)
2175 : 10538 : return 0;
2176 : :
2177 : : /*
2178 : : * Array length is number of workers or number of relevant paths,
2179 : : * whichever is less.
2180 : : */
2181 : 2527 : arrlen = Min(parallel_workers, numpaths);
7 michael@paquier.xyz 2182 :GNC 2527 : costarr = palloc_array(Cost, arrlen);
2183 : :
2184 : : /* The first few paths will each be claimed by a different worker. */
2934 rhaas@postgresql.org 2185 :CBC 2527 : path_index = 0;
2186 [ + - + + : 7344 : foreach(cell, subpaths)
+ + ]
2187 : : {
2188 : 5471 : Path *subpath = (Path *) lfirst(cell);
2189 : :
2190 [ + + ]: 5471 : if (path_index == arrlen)
2191 : 654 : break;
2192 : 4817 : costarr[path_index++] = subpath->total_cost;
2193 : : }
2194 : :
2195 : : /*
2196 : : * Since subpaths are sorted by decreasing cost, the last one will have
2197 : : * the minimum cost.
2198 : : */
2199 : 2527 : min_index = arrlen - 1;
2200 : :
2201 : : /*
2202 : : * For each of the remaining subpaths, add its cost to the array element
2203 : : * with minimum cost.
2204 : : */
2347 tgl@sss.pgh.pa.us 2205 [ + - + + : 4936 : for_each_cell(l, subpaths, cell)
+ + ]
2206 : : {
2934 rhaas@postgresql.org 2207 : 2682 : Path *subpath = (Path *) lfirst(l);
2208 : :
2209 : : /* Consider only the non-partial paths */
2210 [ + + ]: 2682 : if (path_index++ == numpaths)
2211 : 273 : break;
2212 : :
2213 : 2409 : costarr[min_index] += subpath->total_cost;
2214 : :
2215 : : /* Update the new min cost array index */
1211 drowley@postgresql.o 2216 : 2409 : min_index = 0;
2217 [ + + ]: 7245 : for (int i = 0; i < arrlen; i++)
2218 : : {
2934 rhaas@postgresql.org 2219 [ + + ]: 4836 : if (costarr[i] < costarr[min_index])
2220 : 796 : min_index = i;
2221 : : }
2222 : : }
2223 : :
2224 : : /* Return the highest cost from the array */
1211 drowley@postgresql.o 2225 : 2527 : max_index = 0;
2226 [ + + ]: 7344 : for (int i = 0; i < arrlen; i++)
2227 : : {
2934 rhaas@postgresql.org 2228 [ + + ]: 4817 : if (costarr[i] > costarr[max_index])
2229 : 201 : max_index = i;
2230 : : }
2231 : :
2232 : 2527 : return costarr[max_index];
2233 : : }
2234 : :
2235 : : /*
2236 : : * cost_append
2237 : : * Determines and returns the cost of an Append node.
2238 : : */
2239 : : void
162 rguo@postgresql.org 2240 :GNC 35448 : cost_append(AppendPath *apath, PlannerInfo *root)
2241 : : {
2242 : : ListCell *l;
2243 : :
483 rhaas@postgresql.org 2244 :CBC 35448 : apath->path.disabled_nodes = 0;
2934 2245 : 35448 : apath->path.startup_cost = 0;
2246 : 35448 : apath->path.total_cost = 0;
2448 tgl@sss.pgh.pa.us 2247 : 35448 : apath->path.rows = 0;
2248 : :
2934 rhaas@postgresql.org 2249 [ + + ]: 35448 : if (apath->subpaths == NIL)
2250 : 1017 : return;
2251 : :
2252 [ + + ]: 34431 : if (!apath->path.parallel_aware)
2253 : : {
2448 tgl@sss.pgh.pa.us 2254 : 21366 : List *pathkeys = apath->path.pathkeys;
2255 : :
2256 [ + + ]: 21366 : if (pathkeys == NIL)
2257 : : {
1169 drowley@postgresql.o 2258 : 20294 : Path *firstsubpath = (Path *) linitial(apath->subpaths);
2259 : :
2260 : : /*
2261 : : * For an unordered, non-parallel-aware Append we take the startup
2262 : : * cost as the startup cost of the first subpath.
2263 : : */
2264 : 20294 : apath->path.startup_cost = firstsubpath->startup_cost;
2265 : :
2266 : : /*
2267 : : * Compute rows, number of disabled nodes, and total cost as sums
2268 : : * of underlying subplan values.
2269 : : */
2448 tgl@sss.pgh.pa.us 2270 [ + - + + : 79452 : foreach(l, apath->subpaths)
+ + ]
2271 : : {
2272 : 59158 : Path *subpath = (Path *) lfirst(l);
2273 : :
2274 : 59158 : apath->path.rows += subpath->rows;
483 rhaas@postgresql.org 2275 : 59158 : apath->path.disabled_nodes += subpath->disabled_nodes;
2448 tgl@sss.pgh.pa.us 2276 : 59158 : apath->path.total_cost += subpath->total_cost;
2277 : : }
2278 : : }
2279 : : else
2280 : : {
2281 : : /*
2282 : : * For an ordered, non-parallel-aware Append we take the startup
2283 : : * cost as the sum of the subpath startup costs. This ensures
2284 : : * that we don't underestimate the startup cost when a query's
2285 : : * LIMIT is such that several of the children have to be run to
2286 : : * satisfy it. This might be overkill --- another plausible hack
2287 : : * would be to take the Append's startup cost as the maximum of
2288 : : * the child startup costs. But we don't want to risk believing
2289 : : * that an ORDER BY LIMIT query can be satisfied at small cost
2290 : : * when the first child has small startup cost but later ones
2291 : : * don't. (If we had the ability to deal with nonlinear cost
2292 : : * interpolation for partial retrievals, we would not need to be
2293 : : * so conservative about this.)
2294 : : *
2295 : : * This case is also different from the above in that we have to
2296 : : * account for possibly injecting sorts into subpaths that aren't
2297 : : * natively ordered.
2298 : : */
2299 [ + - + + : 4170 : foreach(l, apath->subpaths)
+ + ]
2300 : : {
2301 : 3098 : Path *subpath = (Path *) lfirst(l);
2302 : : int presorted_keys;
2303 : : Path sort_path; /* dummy for result of
2304 : : * cost_sort/cost_incremental_sort */
2305 : :
162 rguo@postgresql.org 2306 [ + + ]:GNC 3098 : if (!pathkeys_count_contained_in(pathkeys, subpath->pathkeys,
2307 : : &presorted_keys))
2308 : : {
2309 : : /*
2310 : : * We'll need to insert a Sort node, so include costs for
2311 : : * that. We choose to use incremental sort if it is
2312 : : * enabled and there are presorted keys; otherwise we use
2313 : : * full sort.
2314 : : *
2315 : : * We can use the parent's LIMIT if any, since we
2316 : : * certainly won't pull more than that many tuples from
2317 : : * any child.
2318 : : */
2319 [ + - + + ]: 22 : if (enable_incremental_sort && presorted_keys > 0)
2320 : : {
2321 : 6 : cost_incremental_sort(&sort_path,
2322 : : root,
2323 : : pathkeys,
2324 : : presorted_keys,
2325 : : subpath->disabled_nodes,
2326 : : subpath->startup_cost,
2327 : : subpath->total_cost,
2328 : : subpath->rows,
2329 : 6 : subpath->pathtarget->width,
2330 : : 0.0,
2331 : : work_mem,
2332 : : apath->limit_tuples);
2333 : : }
2334 : : else
2335 : : {
2336 : 16 : cost_sort(&sort_path,
2337 : : root,
2338 : : pathkeys,
2339 : : subpath->disabled_nodes,
2340 : : subpath->total_cost,
2341 : : subpath->rows,
2342 : 16 : subpath->pathtarget->width,
2343 : : 0.0,
2344 : : work_mem,
2345 : : apath->limit_tuples);
2346 : : }
2347 : :
2448 tgl@sss.pgh.pa.us 2348 :CBC 22 : subpath = &sort_path;
2349 : : }
2350 : :
2351 : 3098 : apath->path.rows += subpath->rows;
483 rhaas@postgresql.org 2352 : 3098 : apath->path.disabled_nodes += subpath->disabled_nodes;
2448 tgl@sss.pgh.pa.us 2353 : 3098 : apath->path.startup_cost += subpath->startup_cost;
2354 : 3098 : apath->path.total_cost += subpath->total_cost;
2355 : : }
2356 : : }
2357 : : }
2358 : : else /* parallel-aware */
2359 : : {
2934 rhaas@postgresql.org 2360 : 13065 : int i = 0;
2361 : 13065 : double parallel_divisor = get_parallel_divisor(&apath->path);
2362 : :
2363 : : /* Parallel-aware Append never produces ordered output. */
2448 tgl@sss.pgh.pa.us 2364 [ - + ]: 13065 : Assert(apath->path.pathkeys == NIL);
2365 : :
2366 : : /* Calculate startup cost. */
2934 rhaas@postgresql.org 2367 [ + - + + : 51836 : foreach(l, apath->subpaths)
+ + ]
2368 : : {
2369 : 38771 : Path *subpath = (Path *) lfirst(l);
2370 : :
2371 : : /*
2372 : : * Append will start returning tuples when the child node having
2373 : : * lowest startup cost is done setting up. We consider only the
2374 : : * first few subplans that immediately get a worker assigned.
2375 : : */
2376 [ + + ]: 38771 : if (i == 0)
2377 : 13065 : apath->path.startup_cost = subpath->startup_cost;
2378 [ + + ]: 25706 : else if (i < apath->path.parallel_workers)
2379 [ + + ]: 12777 : apath->path.startup_cost = Min(apath->path.startup_cost,
2380 : : subpath->startup_cost);
2381 : :
2382 : : /*
2383 : : * Apply parallel divisor to subpaths. Scale the number of rows
2384 : : * for each partial subpath based on the ratio of the parallel
2385 : : * divisor originally used for the subpath to the one we adopted.
2386 : : * Also add the cost of partial paths to the total cost, but
2387 : : * ignore non-partial paths for now.
2388 : : */
2389 [ + + ]: 38771 : if (i < apath->first_partial_path)
2390 : 7226 : apath->path.rows += subpath->rows / parallel_divisor;
2391 : : else
2392 : : {
2393 : : double subpath_parallel_divisor;
2394 : :
2904 2395 : 31545 : subpath_parallel_divisor = get_parallel_divisor(subpath);
2396 : 31545 : apath->path.rows += subpath->rows * (subpath_parallel_divisor /
2397 : : parallel_divisor);
2934 2398 : 31545 : apath->path.total_cost += subpath->total_cost;
2399 : : }
2400 : :
483 2401 : 38771 : apath->path.disabled_nodes += subpath->disabled_nodes;
2904 2402 : 38771 : apath->path.rows = clamp_row_est(apath->path.rows);
2403 : :
2934 2404 : 38771 : i++;
2405 : : }
2406 : :
2407 : : /* Add cost for non-partial subpaths. */
2408 : 13065 : apath->path.total_cost +=
2409 : 13065 : append_nonpartial_cost(apath->subpaths,
2410 : : apath->first_partial_path,
2411 : : apath->path.parallel_workers);
2412 : : }
2413 : :
2414 : : /*
2415 : : * Although Append does not do any selection or projection, it's not free;
2416 : : * add a small per-tuple overhead.
2417 : : */
2856 2418 : 34431 : apath->path.total_cost +=
2419 : 34431 : cpu_tuple_cost * APPEND_CPU_COST_MULTIPLIER * apath->path.rows;
2420 : : }
2421 : :
2422 : : /*
2423 : : * cost_merge_append
2424 : : * Determines and returns the cost of a MergeAppend node.
2425 : : *
2426 : : * MergeAppend merges several pre-sorted input streams, using a heap that
2427 : : * at any given instant holds the next tuple from each stream. If there
2428 : : * are N streams, we need about N*log2(N) tuple comparisons to construct
2429 : : * the heap at startup, and then for each output tuple, about log2(N)
2430 : : * comparisons to replace the top entry.
2431 : : *
2432 : : * (The effective value of N will drop once some of the input streams are
2433 : : * exhausted, but it seems unlikely to be worth trying to account for that.)
2434 : : *
2435 : : * The heap is never spilled to disk, since we assume N is not very large.
2436 : : * So this is much simpler than cost_sort.
2437 : : *
2438 : : * As in cost_sort, we charge two operator evals per tuple comparison.
2439 : : *
2440 : : * 'pathkeys' is a list of sort keys
2441 : : * 'n_streams' is the number of input streams
2442 : : * 'input_disabled_nodes' is the sum of the input streams' disabled node counts
2443 : : * 'input_startup_cost' is the sum of the input streams' startup costs
2444 : : * 'input_total_cost' is the sum of the input streams' total costs
2445 : : * 'tuples' is the number of tuples in all the streams
2446 : : */
2447 : : void
5543 tgl@sss.pgh.pa.us 2448 : 4950 : cost_merge_append(Path *path, PlannerInfo *root,
2449 : : List *pathkeys, int n_streams,
2450 : : int input_disabled_nodes,
2451 : : Cost input_startup_cost, Cost input_total_cost,
2452 : : double tuples)
2453 : : {
2454 : 4950 : Cost startup_cost = 0;
2455 : 4950 : Cost run_cost = 0;
2456 : : Cost comparison_cost;
2457 : : double N;
2458 : : double logN;
2459 : :
2460 : : /*
2461 : : * Avoid log(0)...
2462 : : */
2463 [ + - ]: 4950 : N = (n_streams < 2) ? 2.0 : (double) n_streams;
2464 : 4950 : logN = LOG2(N);
2465 : :
2466 : : /* Assumed cost per tuple comparison */
2467 : 4950 : comparison_cost = 2.0 * cpu_operator_cost;
2468 : :
2469 : : /* Heap creation cost */
2470 : 4950 : startup_cost += comparison_cost * N * logN;
2471 : :
2472 : : /* Per-tuple heap maintenance cost */
3329 2473 : 4950 : run_cost += tuples * comparison_cost * logN;
2474 : :
2475 : : /*
2476 : : * Although MergeAppend does not do any selection or projection, it's not
2477 : : * free; add a small per-tuple overhead.
2478 : : */
2856 rhaas@postgresql.org 2479 : 4950 : run_cost += cpu_tuple_cost * APPEND_CPU_COST_MULTIPLIER * tuples;
2480 : :
483 2481 : 4950 : path->disabled_nodes = input_disabled_nodes;
5543 tgl@sss.pgh.pa.us 2482 : 4950 : path->startup_cost = startup_cost + input_startup_cost;
2483 : 4950 : path->total_cost = startup_cost + run_cost + input_total_cost;
2484 : 4950 : }
2485 : :
2486 : : /*
2487 : : * cost_material
2488 : : * Determines and returns the cost of materializing a relation, including
2489 : : * the cost of reading the input data.
2490 : : *
2491 : : * If the total volume of data to materialize exceeds work_mem, we will need
2492 : : * to write it to disk, so the cost is much higher in that case.
2493 : : *
2494 : : * Note that here we are estimating the costs for the first scan of the
2495 : : * relation, so the materialization is all overhead --- any savings will
2496 : : * occur only on rescan, which is estimated in cost_rescan.
2497 : : */
2498 : : void
8418 2499 : 341573 : cost_material(Path *path,
2500 : : int input_disabled_nodes,
2501 : : Cost input_startup_cost, Cost input_total_cost,
2502 : : double tuples, int width)
2503 : : {
5940 2504 : 341573 : Cost startup_cost = input_startup_cost;
2505 : 341573 : Cost run_cost = input_total_cost - input_startup_cost;
8418 2506 : 341573 : double nbytes = relation_byte_size(tuples, width);
320 2507 : 341573 : double work_mem_bytes = work_mem * (Size) 1024;
2508 : :
5073 2509 : 341573 : path->rows = tuples;
2510 : :
2511 : : /*
2512 : : * Whether spilling or not, charge 2x cpu_operator_cost per tuple to
2513 : : * reflect bookkeeping overhead. (This rate must be more than what
2514 : : * cost_rescan charges for materialize, ie, cpu_operator_cost per tuple;
2515 : : * if it is exactly the same then there will be a cost tie between
2516 : : * nestloop with A outer, materialized B inner and nestloop with B outer,
2517 : : * materialized A inner. The extra cost ensures we'll prefer
2518 : : * materializing the smaller rel.) Note that this is normally a good deal
2519 : : * less than cpu_tuple_cost; which is OK because a Material plan node
2520 : : * doesn't do qual-checking or projection, so it's got less overhead than
2521 : : * most plan nodes.
2522 : : */
5780 2523 : 341573 : run_cost += 2 * cpu_operator_cost * tuples;
2524 : :
2525 : : /*
2526 : : * If we will spill to disk, charge at the rate of seq_page_cost per page.
2527 : : * This cost is assumed to be evenly spread through the plan run phase,
2528 : : * which isn't exactly accurate but our cost model doesn't allow for
2529 : : * nonuniform costs within the run phase.
2530 : : */
7988 2531 [ + + ]: 341573 : if (nbytes > work_mem_bytes)
2532 : : {
8418 2533 : 2515 : double npages = ceil(nbytes / BLCKSZ);
2534 : :
7135 2535 : 2515 : run_cost += seq_page_cost * npages;
2536 : : }
2537 : :
483 rhaas@postgresql.org 2538 : 341573 : path->disabled_nodes = input_disabled_nodes + (enable_material ? 0 : 1);
8418 tgl@sss.pgh.pa.us 2539 : 341573 : path->startup_cost = startup_cost;
2540 : 341573 : path->total_cost = startup_cost + run_cost;
2541 : 341573 : }
2542 : :
2543 : : /*
2544 : : * cost_memoize_rescan
2545 : : * Determines the estimated cost of rescanning a Memoize node.
2546 : : *
2547 : : * In order to estimate this, we must gain knowledge of how often we expect to
2548 : : * be called and how many distinct sets of parameters we are likely to be
2549 : : * called with. If we expect a good cache hit ratio, then we can set our
2550 : : * costs to account for that hit ratio, plus a little bit of cost for the
2551 : : * caching itself. Caching will not work out well if we expect to be called
2552 : : * with too many distinct parameter values. The worst-case here is that we
2553 : : * never see any parameter value twice, in which case we'd never get a cache
2554 : : * hit and caching would be a complete waste of effort.
2555 : : */
2556 : : static void
1617 drowley@postgresql.o 2557 : 148681 : cost_memoize_rescan(PlannerInfo *root, MemoizePath *mpath,
2558 : : Cost *rescan_startup_cost, Cost *rescan_total_cost)
2559 : : {
2560 : : EstimationInfo estinfo;
2561 : : ListCell *lc;
2562 : 148681 : Cost input_startup_cost = mpath->subpath->startup_cost;
2563 : 148681 : Cost input_total_cost = mpath->subpath->total_cost;
2564 : 148681 : double tuples = mpath->subpath->rows;
141 drowley@postgresql.o 2565 :GNC 148681 : Cardinality est_calls = mpath->est_calls;
1617 drowley@postgresql.o 2566 :CBC 148681 : int width = mpath->subpath->pathtarget->width;
2567 : :
2568 : : double hash_mem_bytes;
2569 : : double est_entry_bytes;
2570 : : Cardinality est_cache_entries;
2571 : : Cardinality ndistinct;
2572 : : double evict_ratio;
2573 : : double hit_ratio;
2574 : : Cost startup_cost;
2575 : : Cost total_cost;
2576 : :
2577 : : /* available cache space */
1606 tgl@sss.pgh.pa.us 2578 : 148681 : hash_mem_bytes = get_hash_memory_limit();
2579 : :
2580 : : /*
2581 : : * Set the number of bytes each cache entry should consume in the cache.
2582 : : * To provide us with better estimations on how many cache entries we can
2583 : : * store at once, we make a call to the executor here to ask it what
2584 : : * memory overheads there are for a single cache entry.
2585 : : */
1720 drowley@postgresql.o 2586 : 148681 : est_entry_bytes = relation_byte_size(tuples, width) +
2587 : 148681 : ExecEstimateCacheEntryOverheadBytes(tuples);
2588 : :
2589 : : /* include the estimated width for the cache keys */
1003 2590 [ + - + + : 316869 : foreach(lc, mpath->param_exprs)
+ + ]
2591 : 168188 : est_entry_bytes += get_expr_width(root, (Node *) lfirst(lc));
2592 : :
2593 : : /* estimate on the upper limit of cache entries we can hold at once */
1720 2594 : 148681 : est_cache_entries = floor(hash_mem_bytes / est_entry_bytes);
2595 : :
2596 : : /* estimate on the distinct number of parameter values */
141 drowley@postgresql.o 2597 :GNC 148681 : ndistinct = estimate_num_groups(root, mpath->param_exprs, est_calls, NULL,
2598 : : &estinfo);
2599 : :
2600 : : /*
2601 : : * When the estimation fell back on using a default value, it's a bit too
2602 : : * risky to assume that it's ok to use a Memoize node. The use of a
2603 : : * default could cause us to use a Memoize node when it's really
2604 : : * inappropriate to do so. If we see that this has been done, then we'll
2605 : : * assume that every call will have unique parameters, which will almost
2606 : : * certainly mean a MemoizePath will never survive add_path().
2607 : : */
1720 drowley@postgresql.o 2608 [ + + ]:CBC 148681 : if ((estinfo.flags & SELFLAG_USED_DEFAULT) != 0)
141 drowley@postgresql.o 2609 :GNC 8909 : ndistinct = est_calls;
2610 : :
2611 : : /* Remember the ndistinct estimate for EXPLAIN */
2612 : 148681 : mpath->est_unique_keys = ndistinct;
2613 : :
2614 : : /*
2615 : : * Since we've already estimated the maximum number of entries we can
2616 : : * store at once and know the estimated number of distinct values we'll be
2617 : : * called with, we'll take this opportunity to set the path's est_entries.
2618 : : * This will ultimately determine the hash table size that the executor
2619 : : * will use. If we leave this at zero, the executor will just choose the
2620 : : * size itself. Really this is not the right place to do this, but it's
2621 : : * convenient since everything is already calculated.
2622 : : */
1617 drowley@postgresql.o 2623 [ + + + - :CBC 148681 : mpath->est_entries = Min(Min(ndistinct, est_cache_entries),
+ + ]
2624 : : PG_UINT32_MAX);
2625 : :
2626 : : /*
2627 : : * When the number of distinct parameter values is above the amount we can
2628 : : * store in the cache, then we'll have to evict some entries from the
2629 : : * cache. This is not free. Here we estimate how often we'll incur the
2630 : : * cost of that eviction.
2631 : : */
1720 2632 [ + + ]: 148681 : evict_ratio = 1.0 - Min(est_cache_entries, ndistinct) / ndistinct;
2633 : :
2634 : : /*
2635 : : * In order to estimate how costly a single scan will be, we need to
2636 : : * attempt to estimate what the cache hit ratio will be. To do that we
2637 : : * must look at how many scans are estimated in total for this node and
2638 : : * how many of those scans we expect to get a cache hit.
2639 : : */
141 drowley@postgresql.o 2640 :GNC 297362 : hit_ratio = ((est_calls - ndistinct) / est_calls) *
1001 drowley@postgresql.o 2641 [ + + ]:CBC 148681 : (est_cache_entries / Max(ndistinct, est_cache_entries));
2642 : :
2643 : : /* Remember the hit ratio estimate for EXPLAIN */
141 drowley@postgresql.o 2644 :GNC 148681 : mpath->est_hit_ratio = hit_ratio;
2645 : :
1001 drowley@postgresql.o 2646 [ + - - + ]:CBC 148681 : Assert(hit_ratio >= 0 && hit_ratio <= 1.0);
2647 : :
2648 : : /*
2649 : : * Set the total_cost accounting for the expected cache hit ratio. We
2650 : : * also add on a cpu_operator_cost to account for a cache lookup. This
2651 : : * will happen regardless of whether it's a cache hit or not.
2652 : : */
1720 2653 : 148681 : total_cost = input_total_cost * (1.0 - hit_ratio) + cpu_operator_cost;
2654 : :
2655 : : /* Now adjust the total cost to account for cache evictions */
2656 : :
2657 : : /* Charge a cpu_tuple_cost for evicting the actual cache entry */
2658 : 148681 : total_cost += cpu_tuple_cost * evict_ratio;
2659 : :
2660 : : /*
2661 : : * Charge a 10th of cpu_operator_cost to evict every tuple in that entry.
2662 : : * The per-tuple eviction is really just a pfree, so charging a whole
2663 : : * cpu_operator_cost seems a little excessive.
2664 : : */
2665 : 148681 : total_cost += cpu_operator_cost / 10.0 * evict_ratio * tuples;
2666 : :
2667 : : /*
2668 : : * Now adjust for storing things in the cache, since that's not free
2669 : : * either. Everything must go in the cache. We don't proportion this
2670 : : * over any ratio, just apply it once for the scan. We charge a
2671 : : * cpu_tuple_cost for the creation of the cache entry and also a
2672 : : * cpu_operator_cost for each tuple we expect to cache.
2673 : : */
2674 : 148681 : total_cost += cpu_tuple_cost + cpu_operator_cost * tuples;
2675 : :
2676 : : /*
2677 : : * Getting the first row must be also be proportioned according to the
2678 : : * expected cache hit ratio.
2679 : : */
2680 : 148681 : startup_cost = input_startup_cost * (1.0 - hit_ratio);
2681 : :
2682 : : /*
2683 : : * Additionally we charge a cpu_tuple_cost to account for cache lookups,
2684 : : * which we'll do regardless of whether it was a cache hit or not.
2685 : : */
2686 : 148681 : startup_cost += cpu_tuple_cost;
2687 : :
2688 : 148681 : *rescan_startup_cost = startup_cost;
2689 : 148681 : *rescan_total_cost = total_cost;
2690 : 148681 : }
2691 : :
2692 : : /*
2693 : : * cost_agg
2694 : : * Determines and returns the cost of performing an Agg plan node,
2695 : : * including the cost of its input.
2696 : : *
2697 : : * aggcosts can be NULL when there are no actual aggregate functions (i.e.,
2698 : : * we are using a hashed Agg node just to do grouping).
2699 : : *
2700 : : * Note: when aggstrategy == AGG_SORTED, caller must ensure that input costs
2701 : : * are for appropriately-sorted input.
2702 : : */
2703 : : void
7500 tgl@sss.pgh.pa.us 2704 : 45489 : cost_agg(Path *path, PlannerInfo *root,
2705 : : AggStrategy aggstrategy, const AggClauseCosts *aggcosts,
2706 : : int numGroupCols, double numGroups,
2707 : : List *quals,
2708 : : int disabled_nodes,
2709 : : Cost input_startup_cost, Cost input_total_cost,
2710 : : double input_tuples, double input_width)
2711 : : {
2712 : : double output_tuples;
2713 : : Cost startup_cost;
2714 : : Cost total_cost;
272 peter@eisentraut.org 2715 : 45489 : const AggClauseCosts dummy_aggcosts = {0};
2716 : :
2717 : : /* Use all-zero per-aggregate costs if NULL is passed */
5351 tgl@sss.pgh.pa.us 2718 [ + + ]: 45489 : if (aggcosts == NULL)
2719 : : {
2720 [ - + ]: 9614 : Assert(aggstrategy == AGG_HASHED);
2721 : 9614 : aggcosts = &dummy_aggcosts;
2722 : : }
2723 : :
2724 : : /*
2725 : : * The transCost.per_tuple component of aggcosts should be charged once
2726 : : * per input tuple, corresponding to the costs of evaluating the aggregate
2727 : : * transfns and their input expressions. The finalCost.per_tuple component
2728 : : * is charged once per output tuple, corresponding to the costs of
2729 : : * evaluating the finalfns. Startup costs are of course charged but once.
2730 : : *
2731 : : * If we are grouping, we charge an additional cpu_operator_cost per
2732 : : * grouping column per input tuple for grouping comparisons.
2733 : : *
2734 : : * We will produce a single output tuple if not grouping, and a tuple per
2735 : : * group otherwise. We charge cpu_tuple_cost for each output tuple.
2736 : : *
2737 : : * Note: in this cost model, AGG_SORTED and AGG_HASHED have exactly the
2738 : : * same total CPU cost, but AGG_SORTED has lower startup cost. If the
2739 : : * input path is already sorted appropriately, AGG_SORTED should be
2740 : : * preferred (since it has no risk of memory overflow). This will happen
2741 : : * as long as the computed total costs are indeed exactly equal --- but if
2742 : : * there's roundoff error we might do the wrong thing. So be sure that
2743 : : * the computations below form the same intermediate values in the same
2744 : : * order.
2745 : : */
8427 2746 [ + + ]: 45489 : if (aggstrategy == AGG_PLAIN)
2747 : : {
2748 : 20633 : startup_cost = input_total_cost;
5351 2749 : 20633 : startup_cost += aggcosts->transCost.startup;
2750 : 20633 : startup_cost += aggcosts->transCost.per_tuple * input_tuples;
2503 2751 : 20633 : startup_cost += aggcosts->finalCost.startup;
2752 : 20633 : startup_cost += aggcosts->finalCost.per_tuple;
2753 : : /* we aren't grouping */
7417 2754 : 20633 : total_cost = startup_cost + cpu_tuple_cost;
5073 2755 : 20633 : output_tuples = 1;
2756 : : }
3187 rhodiumtoad@postgres 2757 [ + + + + ]: 24856 : else if (aggstrategy == AGG_SORTED || aggstrategy == AGG_MIXED)
2758 : : {
2759 : : /* Here we are able to deliver output on-the-fly */
8427 tgl@sss.pgh.pa.us 2760 : 8980 : startup_cost = input_startup_cost;
2761 : 8980 : total_cost = input_total_cost;
3187 rhodiumtoad@postgres 2762 [ + + + + ]: 8980 : if (aggstrategy == AGG_MIXED && !enable_hashagg)
483 rhaas@postgresql.org 2763 : 240 : ++disabled_nodes;
2764 : : /* calcs phrased this way to match HASHED case, see note above */
5351 tgl@sss.pgh.pa.us 2765 : 8980 : total_cost += aggcosts->transCost.startup;
2766 : 8980 : total_cost += aggcosts->transCost.per_tuple * input_tuples;
2767 : 8980 : total_cost += (cpu_operator_cost * numGroupCols) * input_tuples;
2503 2768 : 8980 : total_cost += aggcosts->finalCost.startup;
2769 : 8980 : total_cost += aggcosts->finalCost.per_tuple * numGroups;
7417 2770 : 8980 : total_cost += cpu_tuple_cost * numGroups;
5073 2771 : 8980 : output_tuples = numGroups;
2772 : : }
2773 : : else
2774 : : {
2775 : : /* must be AGG_HASHED */
8427 2776 : 15876 : startup_cost = input_total_cost;
3558 rhaas@postgresql.org 2777 [ + + ]: 15876 : if (!enable_hashagg)
483 2778 : 933 : ++disabled_nodes;
5351 tgl@sss.pgh.pa.us 2779 : 15876 : startup_cost += aggcosts->transCost.startup;
2780 : 15876 : startup_cost += aggcosts->transCost.per_tuple * input_tuples;
2781 : : /* cost of computing hash value */
2782 : 15876 : startup_cost += (cpu_operator_cost * numGroupCols) * input_tuples;
2503 2783 : 15876 : startup_cost += aggcosts->finalCost.startup;
2784 : :
8427 2785 : 15876 : total_cost = startup_cost;
2503 2786 : 15876 : total_cost += aggcosts->finalCost.per_tuple * numGroups;
2787 : : /* cost of retrieving from hash table */
7417 2788 : 15876 : total_cost += cpu_tuple_cost * numGroups;
5073 2789 : 15876 : output_tuples = numGroups;
2790 : : }
2791 : :
2792 : : /*
2793 : : * Add the disk costs of hash aggregation that spills to disk.
2794 : : *
2795 : : * Groups that go into the hash table stay in memory until finalized, so
2796 : : * spilling and reprocessing tuples doesn't incur additional invocations
2797 : : * of transCost or finalCost. Furthermore, the computed hash value is
2798 : : * stored with the spilled tuples, so we don't incur extra invocations of
2799 : : * the hash function.
2800 : : *
2801 : : * Hash Agg begins returning tuples after the first batch is complete.
2802 : : * Accrue writes (spilled tuples) to startup_cost and to total_cost;
2803 : : * accrue reads only to total_cost.
2804 : : */
2100 jdavis@postgresql.or 2805 [ + + + + ]: 45489 : if (aggstrategy == AGG_HASHED || aggstrategy == AGG_MIXED)
2806 : : {
2807 : : double pages;
2043 tgl@sss.pgh.pa.us 2808 : 16349 : double pages_written = 0.0;
2809 : 16349 : double pages_read = 0.0;
2810 : : double spill_cost;
2811 : : double hashentrysize;
2812 : : double nbatches;
2813 : : Size mem_limit;
2814 : : uint64 ngroups_limit;
2815 : : int num_partitions;
2816 : : int depth;
2817 : :
2818 : : /*
2819 : : * Estimate number of batches based on the computed limits. If less
2820 : : * than or equal to one, all groups are expected to fit in memory;
2821 : : * otherwise we expect to spill.
2822 : : */
1849 heikki.linnakangas@i 2823 : 16349 : hashentrysize = hash_agg_entry_size(list_length(root->aggtransinfos),
2824 : : input_width,
2043 tgl@sss.pgh.pa.us 2825 : 16349 : aggcosts->transitionSpace);
2100 jdavis@postgresql.or 2826 : 16349 : hash_agg_set_limits(hashentrysize, numGroups, 0, &mem_limit,
2827 : : &ngroups_limit, &num_partitions);
2828 : :
2043 tgl@sss.pgh.pa.us 2829 [ - + ]: 16349 : nbatches = Max((numGroups * hashentrysize) / mem_limit,
2830 : : numGroups / ngroups_limit);
2831 : :
2090 jdavis@postgresql.or 2832 [ + + ]: 16349 : nbatches = Max(ceil(nbatches), 1.0);
2833 : 16349 : num_partitions = Max(num_partitions, 2);
2834 : :
2835 : : /*
2836 : : * The number of partitions can change at different levels of
2837 : : * recursion; but for the purposes of this calculation assume it stays
2838 : : * constant.
2839 : : */
2043 tgl@sss.pgh.pa.us 2840 : 16349 : depth = ceil(log(nbatches) / log(num_partitions));
2841 : :
2842 : : /*
2843 : : * Estimate number of pages read and written. For each level of
2844 : : * recursion, a tuple must be written and then later read.
2845 : : */
2090 jdavis@postgresql.or 2846 : 16349 : pages = relation_byte_size(input_tuples, input_width) / BLCKSZ;
2847 : 16349 : pages_written = pages_read = pages * depth;
2848 : :
2849 : : /*
2850 : : * HashAgg has somewhat worse IO behavior than Sort on typical
2851 : : * hardware/OS combinations. Account for this with a generic penalty.
2852 : : */
1927 2853 : 16349 : pages_read *= 2.0;
2854 : 16349 : pages_written *= 2.0;
2855 : :
2100 2856 : 16349 : startup_cost += pages_written * random_page_cost;
2857 : 16349 : total_cost += pages_written * random_page_cost;
2858 : 16349 : total_cost += pages_read * seq_page_cost;
2859 : :
2860 : : /* account for CPU cost of spilling a tuple and reading it back */
1927 2861 : 16349 : spill_cost = depth * input_tuples * 2.0 * cpu_tuple_cost;
2862 : 16349 : startup_cost += spill_cost;
2863 : 16349 : total_cost += spill_cost;
2864 : : }
2865 : :
2866 : : /*
2867 : : * If there are quals (HAVING quals), account for their cost and
2868 : : * selectivity.
2869 : : */
2967 tgl@sss.pgh.pa.us 2870 [ + + ]: 45489 : if (quals)
2871 : : {
2872 : : QualCost qual_cost;
2873 : :
2874 : 2310 : cost_qual_eval(&qual_cost, quals, root);
2875 : 2310 : startup_cost += qual_cost.startup;
2876 : 2310 : total_cost += qual_cost.startup + output_tuples * qual_cost.per_tuple;
2877 : :
2878 : 2310 : output_tuples = clamp_row_est(output_tuples *
2879 : 2310 : clauselist_selectivity(root,
2880 : : quals,
2881 : : 0,
2882 : : JOIN_INNER,
2883 : : NULL));
2884 : : }
2885 : :
5073 2886 : 45489 : path->rows = output_tuples;
483 rhaas@postgresql.org 2887 : 45489 : path->disabled_nodes = disabled_nodes;
8427 tgl@sss.pgh.pa.us 2888 : 45489 : path->startup_cost = startup_cost;
2889 : 45489 : path->total_cost = total_cost;
2890 : 45489 : }
2891 : :
2892 : : /*
2893 : : * get_windowclause_startup_tuples
2894 : : * Estimate how many tuples we'll need to fetch from a WindowAgg's
2895 : : * subnode before we can output the first WindowAgg tuple.
2896 : : *
2897 : : * How many tuples need to be read depends on the WindowClause. For example,
2898 : : * a WindowClause with no PARTITION BY and no ORDER BY requires that all
2899 : : * subnode tuples are read and aggregated before the WindowAgg can output
2900 : : * anything. If there's a PARTITION BY, then we only need to look at tuples
2901 : : * in the first partition. Here we attempt to estimate just how many
2902 : : * 'input_tuples' the WindowAgg will need to read for the given WindowClause
2903 : : * before the first tuple can be output.
2904 : : */
2905 : : static double
866 drowley@postgresql.o 2906 : 1488 : get_windowclause_startup_tuples(PlannerInfo *root, WindowClause *wc,
2907 : : double input_tuples)
2908 : : {
2909 : 1488 : int frameOptions = wc->frameOptions;
2910 : : double partition_tuples;
2911 : : double return_tuples;
2912 : : double peer_tuples;
2913 : :
2914 : : /*
2915 : : * First, figure out how many partitions there are likely to be and set
2916 : : * partition_tuples according to that estimate.
2917 : : */
2918 [ + + ]: 1488 : if (wc->partitionClause != NIL)
2919 : : {
2920 : : double num_partitions;
2921 : 367 : List *partexprs = get_sortgrouplist_exprs(wc->partitionClause,
2922 : 367 : root->parse->targetList);
2923 : :
2924 : 367 : num_partitions = estimate_num_groups(root, partexprs, input_tuples,
2925 : : NULL, NULL);
2926 : 367 : list_free(partexprs);
2927 : :
2928 : 367 : partition_tuples = input_tuples / num_partitions;
2929 : : }
2930 : : else
2931 : : {
2932 : : /* all tuples belong to the same partition */
2933 : 1121 : partition_tuples = input_tuples;
2934 : : }
2935 : :
2936 : : /* estimate the number of tuples in each peer group */
2937 [ + + ]: 1488 : if (wc->orderClause != NIL)
2938 : : {
2939 : : double num_groups;
2940 : : List *orderexprs;
2941 : :
2942 : 1185 : orderexprs = get_sortgrouplist_exprs(wc->orderClause,
2943 : 1185 : root->parse->targetList);
2944 : :
2945 : : /* estimate out how many peer groups there are in the partition */
2946 : 1185 : num_groups = estimate_num_groups(root, orderexprs,
2947 : : partition_tuples, NULL,
2948 : : NULL);
2949 : 1185 : list_free(orderexprs);
2950 : 1185 : peer_tuples = partition_tuples / num_groups;
2951 : : }
2952 : : else
2953 : : {
2954 : : /* no ORDER BY so only 1 tuple belongs in each peer group */
2955 : 303 : peer_tuples = 1.0;
2956 : : }
2957 : :
2958 [ + + ]: 1488 : if (frameOptions & FRAMEOPTION_END_UNBOUNDED_FOLLOWING)
2959 : : {
2960 : : /* include all partition rows */
2961 : 182 : return_tuples = partition_tuples;
2962 : : }
2963 [ + + ]: 1306 : else if (frameOptions & FRAMEOPTION_END_CURRENT_ROW)
2964 : : {
2965 [ + + ]: 787 : if (frameOptions & FRAMEOPTION_ROWS)
2966 : : {
2967 : : /* just count the current row */
2968 : 361 : return_tuples = 1.0;
2969 : : }
2970 [ + - ]: 426 : else if (frameOptions & (FRAMEOPTION_RANGE | FRAMEOPTION_GROUPS))
2971 : : {
2972 : : /*
2973 : : * When in RANGE/GROUPS mode, it's more complex. If there's no
2974 : : * ORDER BY, then all rows in the partition are peers, otherwise
2975 : : * we'll need to read the first group of peers.
2976 : : */
2977 [ + + ]: 426 : if (wc->orderClause == NIL)
2978 : 163 : return_tuples = partition_tuples;
2979 : : else
2980 : 263 : return_tuples = peer_tuples;
2981 : : }
2982 : : else
2983 : : {
2984 : : /*
2985 : : * Something new we don't support yet? This needs attention.
2986 : : * We'll just return 1.0 in the meantime.
2987 : : */
866 drowley@postgresql.o 2988 :UBC 0 : Assert(false);
2989 : : return_tuples = 1.0;
2990 : : }
2991 : : }
866 drowley@postgresql.o 2992 [ + + ]:CBC 519 : else if (frameOptions & FRAMEOPTION_END_OFFSET_PRECEDING)
2993 : : {
2994 : : /*
2995 : : * BETWEEN ... AND N PRECEDING will only need to read the WindowAgg's
2996 : : * subnode after N ROWS/RANGES/GROUPS. N can be 0, but not negative,
2997 : : * so we'll just assume only the current row needs to be read to fetch
2998 : : * the first WindowAgg row.
2999 : : */
3000 : 54 : return_tuples = 1.0;
3001 : : }
3002 [ + - ]: 465 : else if (frameOptions & FRAMEOPTION_END_OFFSET_FOLLOWING)
3003 : : {
3004 : 465 : Const *endOffset = (Const *) wc->endOffset;
3005 : : double end_offset_value;
3006 : :
3007 : : /* try and figure out the value specified in the endOffset. */
3008 [ + - ]: 465 : if (IsA(endOffset, Const))
3009 : : {
3010 [ - + ]: 465 : if (endOffset->constisnull)
3011 : : {
3012 : : /*
3013 : : * NULLs are not allowed, but currently, there's no code to
3014 : : * error out if there's a NULL Const. We'll only discover
3015 : : * this during execution. For now, just pretend everything is
3016 : : * fine and assume that just the first row/range/group will be
3017 : : * needed.
3018 : : */
866 drowley@postgresql.o 3019 :UBC 0 : end_offset_value = 1.0;
3020 : : }
3021 : : else
3022 : : {
866 drowley@postgresql.o 3023 [ + + + + ]:CBC 465 : switch (endOffset->consttype)
3024 : : {
3025 : 12 : case INT2OID:
3026 : 12 : end_offset_value =
3027 : 12 : (double) DatumGetInt16(endOffset->constvalue);
3028 : 12 : break;
3029 : 66 : case INT4OID:
3030 : 66 : end_offset_value =
3031 : 66 : (double) DatumGetInt32(endOffset->constvalue);
3032 : 66 : break;
3033 : 216 : case INT8OID:
3034 : 216 : end_offset_value =
3035 : 216 : (double) DatumGetInt64(endOffset->constvalue);
3036 : 216 : break;
3037 : 171 : default:
3038 : 171 : end_offset_value =
3039 : 171 : partition_tuples / peer_tuples *
3040 : : DEFAULT_INEQ_SEL;
3041 : 171 : break;
3042 : : }
3043 : : }
3044 : : }
3045 : : else
3046 : : {
3047 : : /*
3048 : : * When the end bound is not a Const, we'll just need to guess. We
3049 : : * just make use of DEFAULT_INEQ_SEL.
3050 : : */
866 drowley@postgresql.o 3051 :UBC 0 : end_offset_value =
3052 : 0 : partition_tuples / peer_tuples * DEFAULT_INEQ_SEL;
3053 : : }
3054 : :
866 drowley@postgresql.o 3055 [ + + ]:CBC 465 : if (frameOptions & FRAMEOPTION_ROWS)
3056 : : {
3057 : : /* include the N FOLLOWING and the current row */
3058 : 135 : return_tuples = end_offset_value + 1.0;
3059 : : }
3060 [ + - ]: 330 : else if (frameOptions & (FRAMEOPTION_RANGE | FRAMEOPTION_GROUPS))
3061 : : {
3062 : : /* include N FOLLOWING ranges/group and the initial range/group */
3063 : 330 : return_tuples = peer_tuples * (end_offset_value + 1.0);
3064 : : }
3065 : : else
3066 : : {
3067 : : /*
3068 : : * Something new we don't support yet? This needs attention.
3069 : : * We'll just return 1.0 in the meantime.
3070 : : */
866 drowley@postgresql.o 3071 :UBC 0 : Assert(false);
3072 : : return_tuples = 1.0;
3073 : : }
3074 : : }
3075 : : else
3076 : : {
3077 : : /*
3078 : : * Something new we don't support yet? This needs attention. We'll
3079 : : * just return 1.0 in the meantime.
3080 : : */
3081 : 0 : Assert(false);
3082 : : return_tuples = 1.0;
3083 : : }
3084 : :
866 drowley@postgresql.o 3085 [ + + + + ]:CBC 1488 : if (wc->partitionClause != NIL || wc->orderClause != NIL)
3086 : : {
3087 : : /*
3088 : : * Cap the return value to the estimated partition tuples and account
3089 : : * for the extra tuple WindowAgg will need to read to confirm the next
3090 : : * tuple does not belong to the same partition or peer group.
3091 : : */
3092 [ + + ]: 1291 : return_tuples = Min(return_tuples + 1.0, partition_tuples);
3093 : : }
3094 : : else
3095 : : {
3096 : : /*
3097 : : * Cap the return value so it's never higher than the expected tuples
3098 : : * in the partition.
3099 : : */
3100 [ + + ]: 197 : return_tuples = Min(return_tuples, partition_tuples);
3101 : : }
3102 : :
3103 : : /*
3104 : : * We needn't worry about any EXCLUDE options as those only exclude rows
3105 : : * from being aggregated, not from being read from the WindowAgg's
3106 : : * subnode.
3107 : : */
3108 : :
3109 : 1488 : return clamp_row_est(return_tuples);
3110 : : }
3111 : :
3112 : : /*
3113 : : * cost_windowagg
3114 : : * Determines and returns the cost of performing a WindowAgg plan node,
3115 : : * including the cost of its input.
3116 : : *
3117 : : * Input is assumed already properly sorted.
3118 : : */
3119 : : void
6198 tgl@sss.pgh.pa.us 3120 : 1488 : cost_windowagg(Path *path, PlannerInfo *root,
3121 : : List *windowFuncs, WindowClause *winclause,
3122 : : int input_disabled_nodes,
3123 : : Cost input_startup_cost, Cost input_total_cost,
3124 : : double input_tuples)
3125 : : {
3126 : : Cost startup_cost;
3127 : : Cost total_cost;
3128 : : double startup_tuples;
3129 : : int numPartCols;
3130 : : int numOrderCols;
3131 : : ListCell *lc;
3132 : :
866 drowley@postgresql.o 3133 : 1488 : numPartCols = list_length(winclause->partitionClause);
3134 : 1488 : numOrderCols = list_length(winclause->orderClause);
3135 : :
6198 tgl@sss.pgh.pa.us 3136 : 1488 : startup_cost = input_startup_cost;
3137 : 1488 : total_cost = input_total_cost;
3138 : :
3139 : : /*
3140 : : * Window functions are assumed to cost their stated execution cost, plus
3141 : : * the cost of evaluating their input expressions, per tuple. Since they
3142 : : * may in fact evaluate their inputs at multiple rows during each cycle,
3143 : : * this could be a drastic underestimate; but without a way to know how
3144 : : * many rows the window function will fetch, it's hard to do better. In
3145 : : * any case, it's a good estimate for all the built-in window functions,
3146 : : * so we'll just do this for now.
3147 : : */
5351 3148 [ + - + + : 3411 : foreach(lc, windowFuncs)
+ + ]
3149 : : {
3173 3150 : 1923 : WindowFunc *wfunc = lfirst_node(WindowFunc, lc);
3151 : : Cost wfunccost;
3152 : : QualCost argcosts;
3153 : :
2503 3154 : 1923 : argcosts.startup = argcosts.per_tuple = 0;
3155 : 1923 : add_function_cost(root, wfunc->winfnoid, (Node *) wfunc,
3156 : : &argcosts);
3157 : 1923 : startup_cost += argcosts.startup;
3158 : 1923 : wfunccost = argcosts.per_tuple;
3159 : :
3160 : : /* also add the input expressions' cost to per-input-row costs */
5351 3161 : 1923 : cost_qual_eval_node(&argcosts, (Node *) wfunc->args, root);
3162 : 1923 : startup_cost += argcosts.startup;
3163 : 1923 : wfunccost += argcosts.per_tuple;
3164 : :
3165 : : /*
3166 : : * Add the filter's cost to per-input-row costs. XXX We should reduce
3167 : : * input expression costs according to filter selectivity.
3168 : : */
4537 noah@leadboat.com 3169 : 1923 : cost_qual_eval_node(&argcosts, (Node *) wfunc->aggfilter, root);
3170 : 1923 : startup_cost += argcosts.startup;
3171 : 1923 : wfunccost += argcosts.per_tuple;
3172 : :
5351 tgl@sss.pgh.pa.us 3173 : 1923 : total_cost += wfunccost * input_tuples;
3174 : : }
3175 : :
3176 : : /*
3177 : : * We also charge cpu_operator_cost per grouping column per tuple for
3178 : : * grouping comparisons, plus cpu_tuple_cost per tuple for general
3179 : : * overhead.
3180 : : *
3181 : : * XXX this neglects costs of spooling the data to disk when it overflows
3182 : : * work_mem. Sooner or later that should get accounted for.
3183 : : */
3184 : 1488 : total_cost += cpu_operator_cost * (numPartCols + numOrderCols) * input_tuples;
6198 3185 : 1488 : total_cost += cpu_tuple_cost * input_tuples;
3186 : :
5073 3187 : 1488 : path->rows = input_tuples;
483 rhaas@postgresql.org 3188 : 1488 : path->disabled_nodes = input_disabled_nodes;
6198 tgl@sss.pgh.pa.us 3189 : 1488 : path->startup_cost = startup_cost;
3190 : 1488 : path->total_cost = total_cost;
3191 : :
3192 : : /*
3193 : : * Also, take into account how many tuples we need to read from the
3194 : : * subnode in order to produce the first tuple from the WindowAgg. To do
3195 : : * this we proportion the run cost (total cost not including startup cost)
3196 : : * over the estimated startup tuples. We already included the startup
3197 : : * cost of the subnode, so we only need to do this when the estimated
3198 : : * startup tuples is above 1.0.
3199 : : */
866 drowley@postgresql.o 3200 : 1488 : startup_tuples = get_windowclause_startup_tuples(root, winclause,
3201 : : input_tuples);
3202 : :
3203 [ + + ]: 1488 : if (startup_tuples > 1.0)
3204 : 1284 : path->startup_cost += (total_cost - startup_cost) / input_tuples *
3205 : 1284 : (startup_tuples - 1.0);
6198 tgl@sss.pgh.pa.us 3206 : 1488 : }
3207 : :
3208 : : /*
3209 : : * cost_group
3210 : : * Determines and returns the cost of performing a Group plan node,
3211 : : * including the cost of its input.
3212 : : *
3213 : : * Note: caller must ensure that input costs are for appropriately-sorted
3214 : : * input.
3215 : : */
3216 : : void
7500 3217 : 613 : cost_group(Path *path, PlannerInfo *root,
3218 : : int numGroupCols, double numGroups,
3219 : : List *quals,
3220 : : int input_disabled_nodes,
3221 : : Cost input_startup_cost, Cost input_total_cost,
3222 : : double input_tuples)
3223 : : {
3224 : : double output_tuples;
3225 : : Cost startup_cost;
3226 : : Cost total_cost;
3227 : :
2967 3228 : 613 : output_tuples = numGroups;
8427 3229 : 613 : startup_cost = input_startup_cost;
3230 : 613 : total_cost = input_total_cost;
3231 : :
3232 : : /*
3233 : : * Charge one cpu_operator_cost per comparison per input tuple. We assume
3234 : : * all columns get compared at most of the tuples.
3235 : : */
3236 : 613 : total_cost += cpu_operator_cost * input_tuples * numGroupCols;
3237 : :
3238 : : /*
3239 : : * If there are quals (HAVING quals), account for their cost and
3240 : : * selectivity.
3241 : : */
2967 3242 [ - + ]: 613 : if (quals)
3243 : : {
3244 : : QualCost qual_cost;
3245 : :
2967 tgl@sss.pgh.pa.us 3246 :UBC 0 : cost_qual_eval(&qual_cost, quals, root);
3247 : 0 : startup_cost += qual_cost.startup;
3248 : 0 : total_cost += qual_cost.startup + output_tuples * qual_cost.per_tuple;
3249 : :
3250 : 0 : output_tuples = clamp_row_est(output_tuples *
3251 : 0 : clauselist_selectivity(root,
3252 : : quals,
3253 : : 0,
3254 : : JOIN_INNER,
3255 : : NULL));
3256 : : }
3257 : :
2967 tgl@sss.pgh.pa.us 3258 :CBC 613 : path->rows = output_tuples;
483 rhaas@postgresql.org 3259 : 613 : path->disabled_nodes = input_disabled_nodes;
8427 tgl@sss.pgh.pa.us 3260 : 613 : path->startup_cost = startup_cost;
3261 : 613 : path->total_cost = total_cost;
3262 : 613 : }
3263 : :
3264 : : /*
3265 : : * initial_cost_nestloop
3266 : : * Preliminary estimate of the cost of a nestloop join path.
3267 : : *
3268 : : * This must quickly produce lower-bound estimates of the path's startup and
3269 : : * total costs. If we are unable to eliminate the proposed path from
3270 : : * consideration using the lower bounds, final_cost_nestloop will be called
3271 : : * to obtain the final estimates.
3272 : : *
3273 : : * The exact division of labor between this function and final_cost_nestloop
3274 : : * is private to them, and represents a tradeoff between speed of the initial
3275 : : * estimate and getting a tight lower bound. We choose to not examine the
3276 : : * join quals here, since that's by far the most expensive part of the
3277 : : * calculations. The end result is that CPU-cost considerations must be
3278 : : * left for the second phase; and for SEMI/ANTI joins, we must also postpone
3279 : : * incorporation of the inner path's run cost.
3280 : : *
3281 : : * 'workspace' is to be filled with startup_cost, total_cost, and perhaps
3282 : : * other data to be used by final_cost_nestloop
3283 : : * 'jointype' is the type of join to be performed
3284 : : * 'outer_path' is the outer input to the join
3285 : : * 'inner_path' is the inner input to the join
3286 : : * 'extra' contains miscellaneous information about the join
3287 : : */
3288 : : void
5073 3289 : 1656569 : initial_cost_nestloop(PlannerInfo *root, JoinCostWorkspace *workspace,
3290 : : JoinType jointype,
3291 : : Path *outer_path, Path *inner_path,
3292 : : JoinPathExtraData *extra)
3293 : : {
3294 : : int disabled_nodes;
9437 3295 : 1656569 : Cost startup_cost = 0;
3296 : 1656569 : Cost run_cost = 0;
5073 3297 : 1656569 : double outer_path_rows = outer_path->rows;
3298 : : Cost inner_rescan_start_cost;
3299 : : Cost inner_rescan_total_cost;
3300 : : Cost inner_run_cost;
3301 : : Cost inner_rescan_run_cost;
3302 : :
3303 : : /* Count up disabled nodes. */
483 rhaas@postgresql.org 3304 : 1656569 : disabled_nodes = enable_nestloop ? 0 : 1;
3305 : 1656569 : disabled_nodes += inner_path->disabled_nodes;
3306 : 1656569 : disabled_nodes += outer_path->disabled_nodes;
3307 : :
3308 : : /* estimate costs to rescan the inner relation */
5940 tgl@sss.pgh.pa.us 3309 : 1656569 : cost_rescan(root, inner_path,
3310 : : &inner_rescan_start_cost,
3311 : : &inner_rescan_total_cost);
3312 : :
3313 : : /* cost of source data */
3314 : :
3315 : : /*
3316 : : * NOTE: clearly, we must pay both outer and inner paths' startup_cost
3317 : : * before we can start returning tuples, so the join's startup cost is
3318 : : * their sum. We'll also pay the inner path's rescan startup cost
3319 : : * multiple times.
3320 : : */
9437 3321 : 1656569 : startup_cost += outer_path->startup_cost + inner_path->startup_cost;
3322 : 1656569 : run_cost += outer_path->total_cost - outer_path->startup_cost;
5940 3323 [ + + ]: 1656569 : if (outer_path_rows > 1)
3324 : 1212065 : run_cost += (outer_path_rows - 1) * inner_rescan_start_cost;
3325 : :
6066 3326 : 1656569 : inner_run_cost = inner_path->total_cost - inner_path->startup_cost;
5940 3327 : 1656569 : inner_rescan_run_cost = inner_rescan_total_cost - inner_rescan_start_cost;
3328 : :
3176 3329 [ + + + + ]: 1656569 : if (jointype == JOIN_SEMI || jointype == JOIN_ANTI ||
3330 [ + + ]: 1625458 : extra->inner_unique)
3331 : : {
3332 : : /*
3333 : : * With a SEMI or ANTI join, or if the innerrel is known unique, the
3334 : : * executor will stop after the first match.
3335 : : *
3336 : : * Getting decent estimates requires inspection of the join quals,
3337 : : * which we choose to postpone to final_cost_nestloop.
3338 : : */
3339 : :
3340 : : /* Save private data for final_cost_nestloop */
3850 3341 : 675778 : workspace->inner_run_cost = inner_run_cost;
3342 : 675778 : workspace->inner_rescan_run_cost = inner_rescan_run_cost;
3343 : : }
3344 : : else
3345 : : {
3346 : : /* Normal case; we'll scan whole input rel for each outer row */
5073 3347 : 980791 : run_cost += inner_run_cost;
3348 [ + + ]: 980791 : if (outer_path_rows > 1)
3349 : 758777 : run_cost += (outer_path_rows - 1) * inner_rescan_run_cost;
3350 : : }
3351 : :
3352 : : /* CPU costs left for later */
3353 : :
3354 : : /* Public result fields */
483 rhaas@postgresql.org 3355 : 1656569 : workspace->disabled_nodes = disabled_nodes;
5073 tgl@sss.pgh.pa.us 3356 : 1656569 : workspace->startup_cost = startup_cost;
3357 : 1656569 : workspace->total_cost = startup_cost + run_cost;
3358 : : /* Save private data for final_cost_nestloop */
3359 : 1656569 : workspace->run_cost = run_cost;
3360 : 1656569 : }
3361 : :
3362 : : /*
3363 : : * final_cost_nestloop
3364 : : * Final estimate of the cost and result size of a nestloop join path.
3365 : : *
3366 : : * 'path' is already filled in except for the rows and cost fields
3367 : : * 'workspace' is the result from initial_cost_nestloop
3368 : : * 'extra' contains miscellaneous information about the join
3369 : : */
3370 : : void
3371 : 741688 : final_cost_nestloop(PlannerInfo *root, NestPath *path,
3372 : : JoinCostWorkspace *workspace,
3373 : : JoinPathExtraData *extra)
3374 : : {
1592 peter@eisentraut.org 3375 : 741688 : Path *outer_path = path->jpath.outerjoinpath;
3376 : 741688 : Path *inner_path = path->jpath.innerjoinpath;
5073 tgl@sss.pgh.pa.us 3377 : 741688 : double outer_path_rows = outer_path->rows;
3378 : 741688 : double inner_path_rows = inner_path->rows;
3379 : 741688 : Cost startup_cost = workspace->startup_cost;
3380 : 741688 : Cost run_cost = workspace->run_cost;
3381 : : Cost cpu_per_tuple;
3382 : : QualCost restrict_qual_cost;
3383 : : double ntuples;
3384 : :
3385 : : /* Set the number of disabled nodes. */
483 rhaas@postgresql.org 3386 : 741688 : path->jpath.path.disabled_nodes = workspace->disabled_nodes;
3387 : :
3388 : : /* Protect some assumptions below that rowcounts aren't zero */
1885 drowley@postgresql.o 3389 [ - + ]: 741688 : if (outer_path_rows <= 0)
3553 tgl@sss.pgh.pa.us 3390 :UBC 0 : outer_path_rows = 1;
1885 drowley@postgresql.o 3391 [ + + ]:CBC 741688 : if (inner_path_rows <= 0)
3553 tgl@sss.pgh.pa.us 3392 : 363 : inner_path_rows = 1;
3393 : : /* Mark the path with the correct row estimate */
1592 peter@eisentraut.org 3394 [ + + ]: 741688 : if (path->jpath.path.param_info)
3395 : 16097 : path->jpath.path.rows = path->jpath.path.param_info->ppi_rows;
3396 : : else
3397 : 725591 : path->jpath.path.rows = path->jpath.path.parent->rows;
3398 : :
3399 : : /* For partial paths, scale row estimate. */
3400 [ + + ]: 741688 : if (path->jpath.path.parallel_workers > 0)
3401 : : {
3402 : 22087 : double parallel_divisor = get_parallel_divisor(&path->jpath.path);
3403 : :
3404 : 22087 : path->jpath.path.rows =
3405 : 22087 : clamp_row_est(path->jpath.path.rows / parallel_divisor);
3406 : : }
3407 : :
3408 : : /* cost of inner-relation source data (we already dealt with outer rel) */
3409 : :
3410 [ + + + + ]: 741688 : if (path->jpath.jointype == JOIN_SEMI || path->jpath.jointype == JOIN_ANTI ||
3176 tgl@sss.pgh.pa.us 3411 [ + + ]: 720048 : extra->inner_unique)
5073 3412 : 465106 : {
3413 : : /*
3414 : : * With a SEMI or ANTI join, or if the innerrel is known unique, the
3415 : : * executor will stop after the first match.
3416 : : */
3850 3417 : 465106 : Cost inner_run_cost = workspace->inner_run_cost;
3418 : 465106 : Cost inner_rescan_run_cost = workspace->inner_rescan_run_cost;
3419 : : double outer_matched_rows;
3420 : : double outer_unmatched_rows;
3421 : : Selectivity inner_scan_frac;
3422 : :
3423 : : /*
3424 : : * For an outer-rel row that has at least one match, we can expect the
3425 : : * inner scan to stop after a fraction 1/(match_count+1) of the inner
3426 : : * rows, if the matches are evenly distributed. Since they probably
3427 : : * aren't quite evenly distributed, we apply a fuzz factor of 2.0 to
3428 : : * that fraction. (If we used a larger fuzz factor, we'd have to
3429 : : * clamp inner_scan_frac to at most 1.0; but since match_count is at
3430 : : * least 1, no such clamp is needed now.)
3431 : : */
3176 3432 : 465106 : outer_matched_rows = rint(outer_path_rows * extra->semifactors.outer_match_frac);
3119 3433 : 465106 : outer_unmatched_rows = outer_path_rows - outer_matched_rows;
3176 3434 : 465106 : inner_scan_frac = 2.0 / (extra->semifactors.match_count + 1.0);
3435 : :
3436 : : /*
3437 : : * Compute number of tuples processed (not number emitted!). First,
3438 : : * account for successfully-matched outer rows.
3439 : : */
6066 3440 : 465106 : ntuples = outer_matched_rows * inner_path_rows * inner_scan_frac;
3441 : :
3442 : : /*
3443 : : * Now we need to estimate the actual costs of scanning the inner
3444 : : * relation, which may be quite a bit less than N times inner_run_cost
3445 : : * due to early scan stops. We consider two cases. If the inner path
3446 : : * is an indexscan using all the joinquals as indexquals, then an
3447 : : * unmatched outer row results in an indexscan returning no rows,
3448 : : * which is probably quite cheap. Otherwise, the executor will have
3449 : : * to scan the whole inner rel for an unmatched row; not so cheap.
3450 : : */
4990 3451 [ + + ]: 465106 : if (has_indexed_join_quals(path))
3452 : : {
3453 : : /*
3454 : : * Successfully-matched outer rows will only require scanning
3455 : : * inner_scan_frac of the inner relation. In this case, we don't
3456 : : * need to charge the full inner_run_cost even when that's more
3457 : : * than inner_rescan_run_cost, because we can assume that none of
3458 : : * the inner scans ever scan the whole inner relation. So it's
3459 : : * okay to assume that all the inner scan executions can be
3460 : : * fractions of the full cost, even if materialization is reducing
3461 : : * the rescan cost. At this writing, it's impossible to get here
3462 : : * for a materialized inner scan, so inner_run_cost and
3463 : : * inner_rescan_run_cost will be the same anyway; but just in
3464 : : * case, use inner_run_cost for the first matched tuple and
3465 : : * inner_rescan_run_cost for additional ones.
3466 : : */
3850 3467 : 76400 : run_cost += inner_run_cost * inner_scan_frac;
3468 [ + + ]: 76400 : if (outer_matched_rows > 1)
3469 : 10578 : run_cost += (outer_matched_rows - 1) * inner_rescan_run_cost * inner_scan_frac;
3470 : :
3471 : : /*
3472 : : * Add the cost of inner-scan executions for unmatched outer rows.
3473 : : * We estimate this as the same cost as returning the first tuple
3474 : : * of a nonempty scan. We consider that these are all rescans,
3475 : : * since we used inner_run_cost once already.
3476 : : */
3119 3477 : 76400 : run_cost += outer_unmatched_rows *
5940 3478 : 76400 : inner_rescan_run_cost / inner_path_rows;
3479 : :
3480 : : /*
3481 : : * We won't be evaluating any quals at all for unmatched rows, so
3482 : : * don't add them to ntuples.
3483 : : */
3484 : : }
3485 : : else
3486 : : {
3487 : : /*
3488 : : * Here, a complicating factor is that rescans may be cheaper than
3489 : : * first scans. If we never scan all the way to the end of the
3490 : : * inner rel, it might be (depending on the plan type) that we'd
3491 : : * never pay the whole inner first-scan run cost. However it is
3492 : : * difficult to estimate whether that will happen (and it could
3493 : : * not happen if there are any unmatched outer rows!), so be
3494 : : * conservative and always charge the whole first-scan cost once.
3495 : : * We consider this charge to correspond to the first unmatched
3496 : : * outer row, unless there isn't one in our estimate, in which
3497 : : * case blame it on the first matched row.
3498 : : */
3499 : :
3500 : : /* First, count all unmatched join tuples as being processed */
3119 3501 : 388706 : ntuples += outer_unmatched_rows * inner_path_rows;
3502 : :
3503 : : /* Now add the forced full scan, and decrement appropriate count */
3850 3504 : 388706 : run_cost += inner_run_cost;
3119 3505 [ + + ]: 388706 : if (outer_unmatched_rows >= 1)
3506 : 369498 : outer_unmatched_rows -= 1;
3507 : : else
3508 : 19208 : outer_matched_rows -= 1;
3509 : :
3510 : : /* Add inner run cost for additional outer tuples having matches */
3511 [ + + ]: 388706 : if (outer_matched_rows > 0)
3512 : 141568 : run_cost += outer_matched_rows * inner_rescan_run_cost * inner_scan_frac;
3513 : :
3514 : : /* Add inner run cost for additional unmatched outer tuples */
3515 [ + + ]: 388706 : if (outer_unmatched_rows > 0)
3516 : 245765 : run_cost += outer_unmatched_rows * inner_rescan_run_cost;
3517 : : }
3518 : : }
3519 : : else
3520 : : {
3521 : : /* Normal-case source costs were included in preliminary estimate */
3522 : :
3523 : : /* Compute number of tuples processed (not number emitted!) */
6066 3524 : 276582 : ntuples = outer_path_rows * inner_path_rows;
3525 : : }
3526 : :
3527 : : /* CPU costs */
1592 peter@eisentraut.org 3528 : 741688 : cost_qual_eval(&restrict_qual_cost, path->jpath.joinrestrictinfo, root);
8375 tgl@sss.pgh.pa.us 3529 : 741688 : startup_cost += restrict_qual_cost.startup;
3530 : 741688 : cpu_per_tuple = cpu_tuple_cost + restrict_qual_cost.per_tuple;
9437 3531 : 741688 : run_cost += cpu_per_tuple * ntuples;
3532 : :
3533 : : /* tlist eval costs are paid per output row, not per tuple scanned */
1592 peter@eisentraut.org 3534 : 741688 : startup_cost += path->jpath.path.pathtarget->cost.startup;
3535 : 741688 : run_cost += path->jpath.path.pathtarget->cost.per_tuple * path->jpath.path.rows;
3536 : :
3537 : 741688 : path->jpath.path.startup_cost = startup_cost;
3538 : 741688 : path->jpath.path.total_cost = startup_cost + run_cost;
10753 scrappy@hub.org 3539 : 741688 : }
3540 : :
3541 : : /*
3542 : : * initial_cost_mergejoin
3543 : : * Preliminary estimate of the cost of a mergejoin path.
3544 : : *
3545 : : * This must quickly produce lower-bound estimates of the path's startup and
3546 : : * total costs. If we are unable to eliminate the proposed path from
3547 : : * consideration using the lower bounds, final_cost_mergejoin will be called
3548 : : * to obtain the final estimates.
3549 : : *
3550 : : * The exact division of labor between this function and final_cost_mergejoin
3551 : : * is private to them, and represents a tradeoff between speed of the initial
3552 : : * estimate and getting a tight lower bound. We choose to not examine the
3553 : : * join quals here, except for obtaining the scan selectivity estimate which
3554 : : * is really essential (but fortunately, use of caching keeps the cost of
3555 : : * getting that down to something reasonable).
3556 : : * We also assume that cost_sort/cost_incremental_sort is cheap enough to use
3557 : : * here.
3558 : : *
3559 : : * 'workspace' is to be filled with startup_cost, total_cost, and perhaps
3560 : : * other data to be used by final_cost_mergejoin
3561 : : * 'jointype' is the type of join to be performed
3562 : : * 'mergeclauses' is the list of joinclauses to be used as merge clauses
3563 : : * 'outer_path' is the outer input to the join
3564 : : * 'inner_path' is the inner input to the join
3565 : : * 'outersortkeys' is the list of sort keys for the outer path
3566 : : * 'innersortkeys' is the list of sort keys for the inner path
3567 : : * 'outer_presorted_keys' is the number of presorted keys of the outer path
3568 : : * 'extra' contains miscellaneous information about the join
3569 : : *
3570 : : * Note: outersortkeys and innersortkeys should be NIL if no explicit
3571 : : * sort is needed because the respective source path is already ordered.
3572 : : */
3573 : : void
5073 tgl@sss.pgh.pa.us 3574 : 749873 : initial_cost_mergejoin(PlannerInfo *root, JoinCostWorkspace *workspace,
3575 : : JoinType jointype,
3576 : : List *mergeclauses,
3577 : : Path *outer_path, Path *inner_path,
3578 : : List *outersortkeys, List *innersortkeys,
3579 : : int outer_presorted_keys,
3580 : : JoinPathExtraData *extra)
3581 : : {
3582 : : int disabled_nodes;
9437 3583 : 749873 : Cost startup_cost = 0;
3584 : 749873 : Cost run_cost = 0;
5073 3585 : 749873 : double outer_path_rows = outer_path->rows;
3586 : 749873 : double inner_path_rows = inner_path->rows;
3587 : : Cost inner_run_cost;
3588 : : double outer_rows,
3589 : : inner_rows,
3590 : : outer_skip_rows,
3591 : : inner_skip_rows;
3592 : : Selectivity outerstartsel,
3593 : : outerendsel,
3594 : : innerstartsel,
3595 : : innerendsel;
3596 : : Path sort_path; /* dummy for result of
3597 : : * cost_sort/cost_incremental_sort */
3598 : :
3599 : : /* Protect some assumptions below that rowcounts aren't zero */
1885 drowley@postgresql.o 3600 [ + + ]: 749873 : if (outer_path_rows <= 0)
6477 tgl@sss.pgh.pa.us 3601 : 48 : outer_path_rows = 1;
1885 drowley@postgresql.o 3602 [ + + ]: 749873 : if (inner_path_rows <= 0)
6477 tgl@sss.pgh.pa.us 3603 : 63 : inner_path_rows = 1;
3604 : :
3605 : : /*
3606 : : * A merge join will stop as soon as it exhausts either input stream
3607 : : * (unless it's an outer join, in which case the outer side has to be
3608 : : * scanned all the way anyway). Estimate fraction of the left and right
3609 : : * inputs that will actually need to be scanned. Likewise, we can
3610 : : * estimate the number of rows that will be skipped before the first join
3611 : : * pair is found, which should be factored into startup cost. We use only
3612 : : * the first (most significant) merge clause for this purpose. Since
3613 : : * mergejoinscansel() is a fairly expensive computation, we cache the
3614 : : * results in the merge clause RestrictInfo.
3615 : : */
5073 3616 [ + + + + ]: 749873 : if (mergeclauses && jointype != JOIN_FULL)
8692 3617 : 746741 : {
6906 3618 : 746741 : RestrictInfo *firstclause = (RestrictInfo *) linitial(mergeclauses);
3619 : : List *opathkeys;
3620 : : List *ipathkeys;
3621 : : PathKey *opathkey;
3622 : : PathKey *ipathkey;
3623 : : MergeScanSelCache *cache;
3624 : :
3625 : : /* Get the input pathkeys to determine the sort-order details */
3626 [ + + ]: 746741 : opathkeys = outersortkeys ? outersortkeys : outer_path->pathkeys;
3627 [ + + ]: 746741 : ipathkeys = innersortkeys ? innersortkeys : inner_path->pathkeys;
3628 [ - + ]: 746741 : Assert(opathkeys);
3629 [ - + ]: 746741 : Assert(ipathkeys);
3630 : 746741 : opathkey = (PathKey *) linitial(opathkeys);
3631 : 746741 : ipathkey = (PathKey *) linitial(ipathkeys);
3632 : : /* debugging check */
3633 [ + - ]: 746741 : if (opathkey->pk_opfamily != ipathkey->pk_opfamily ||
5387 3634 [ + - ]: 746741 : opathkey->pk_eclass->ec_collation != ipathkey->pk_eclass->ec_collation ||
257 peter@eisentraut.org 3635 [ + - ]: 746741 : opathkey->pk_cmptype != ipathkey->pk_cmptype ||
6906 tgl@sss.pgh.pa.us 3636 [ - + ]: 746741 : opathkey->pk_nulls_first != ipathkey->pk_nulls_first)
6906 tgl@sss.pgh.pa.us 3637 [ # # ]:UBC 0 : elog(ERROR, "left and right pathkeys do not match in mergejoin");
3638 : :
3639 : : /* Get the selectivity with caching */
6904 tgl@sss.pgh.pa.us 3640 :CBC 746741 : cache = cached_scansel(root, firstclause, opathkey);
3641 : :
6906 3642 [ + + ]: 746741 : if (bms_is_subset(firstclause->left_relids,
3643 : 746741 : outer_path->parent->relids))
3644 : : {
3645 : : /* left side of clause is outer */
6584 3646 : 389454 : outerstartsel = cache->leftstartsel;
3647 : 389454 : outerendsel = cache->leftendsel;
3648 : 389454 : innerstartsel = cache->rightstartsel;
3649 : 389454 : innerendsel = cache->rightendsel;
3650 : : }
3651 : : else
3652 : : {
3653 : : /* left side of clause is inner */
3654 : 357287 : outerstartsel = cache->rightstartsel;
3655 : 357287 : outerendsel = cache->rightendsel;
3656 : 357287 : innerstartsel = cache->leftstartsel;
3657 : 357287 : innerendsel = cache->leftendsel;
3658 : : }
5073 3659 [ + + + + ]: 746741 : if (jointype == JOIN_LEFT ||
3660 : : jointype == JOIN_ANTI)
3661 : : {
6584 3662 : 100864 : outerstartsel = 0.0;
3663 : 100864 : outerendsel = 1.0;
3664 : : }
987 3665 [ + + + + ]: 645877 : else if (jointype == JOIN_RIGHT ||
3666 : : jointype == JOIN_RIGHT_ANTI)
3667 : : {
6584 3668 : 100389 : innerstartsel = 0.0;
3669 : 100389 : innerendsel = 1.0;
3670 : : }
3671 : : }
3672 : : else
3673 : : {
3674 : : /* cope with clauseless or full mergejoin */
3675 : 3132 : outerstartsel = innerstartsel = 0.0;
3676 : 3132 : outerendsel = innerendsel = 1.0;
3677 : : }
3678 : :
3679 : : /*
3680 : : * Convert selectivities to row counts. We force outer_rows and
3681 : : * inner_rows to be at least 1, but the skip_rows estimates can be zero.
3682 : : */
3683 : 749873 : outer_skip_rows = rint(outer_path_rows * outerstartsel);
3684 : 749873 : inner_skip_rows = rint(inner_path_rows * innerstartsel);
3685 : 749873 : outer_rows = clamp_row_est(outer_path_rows * outerendsel);
3686 : 749873 : inner_rows = clamp_row_est(inner_path_rows * innerendsel);
3687 : :
3688 [ - + ]: 749873 : Assert(outer_skip_rows <= outer_rows);
3689 [ - + ]: 749873 : Assert(inner_skip_rows <= inner_rows);
3690 : :
3691 : : /*
3692 : : * Readjust scan selectivities to account for above rounding. This is
3693 : : * normally an insignificant effect, but when there are only a few rows in
3694 : : * the inputs, failing to do this makes for a large percentage error.
3695 : : */
3696 : 749873 : outerstartsel = outer_skip_rows / outer_path_rows;
3697 : 749873 : innerstartsel = inner_skip_rows / inner_path_rows;
3698 : 749873 : outerendsel = outer_rows / outer_path_rows;
3699 : 749873 : innerendsel = inner_rows / inner_path_rows;
3700 : :
5101 3701 [ - + ]: 749873 : Assert(outerstartsel <= outerendsel);
3702 [ - + ]: 749873 : Assert(innerstartsel <= innerendsel);
3703 : :
483 rhaas@postgresql.org 3704 : 749873 : disabled_nodes = enable_mergejoin ? 0 : 1;
3705 : :
3706 : : /* cost of source data */
3707 : :
9437 tgl@sss.pgh.pa.us 3708 [ + + ]: 749873 : if (outersortkeys) /* do we need to sort outer? */
3709 : : {
3710 : : /*
3711 : : * We can assert that the outer path is not already ordered
3712 : : * appropriately for the mergejoin; otherwise, outersortkeys would
3713 : : * have been set to NIL.
3714 : : */
223 rguo@postgresql.org 3715 [ - + ]: 382372 : Assert(!pathkeys_contained_in(outersortkeys, outer_path->pathkeys));
3716 : :
3717 : : /*
3718 : : * We choose to use incremental sort if it is enabled and there are
3719 : : * presorted keys; otherwise we use full sort.
3720 : : */
3721 [ + + + + ]: 382372 : if (enable_incremental_sort && outer_presorted_keys > 0)
3722 : : {
3723 : 887 : cost_incremental_sort(&sort_path,
3724 : : root,
3725 : : outersortkeys,
3726 : : outer_presorted_keys,
3727 : : outer_path->disabled_nodes,
3728 : : outer_path->startup_cost,
3729 : : outer_path->total_cost,
3730 : : outer_path_rows,
3731 : 887 : outer_path->pathtarget->width,
3732 : : 0.0,
3733 : : work_mem,
3734 : : -1.0);
3735 : : }
3736 : : else
3737 : : {
434 3738 : 381485 : cost_sort(&sort_path,
3739 : : root,
3740 : : outersortkeys,
3741 : : outer_path->disabled_nodes,
3742 : : outer_path->total_cost,
3743 : : outer_path_rows,
3744 : 381485 : outer_path->pathtarget->width,
3745 : : 0.0,
3746 : : work_mem,
3747 : : -1.0);
3748 : : }
3749 : :
483 rhaas@postgresql.org 3750 : 382372 : disabled_nodes += sort_path.disabled_nodes;
9437 tgl@sss.pgh.pa.us 3751 : 382372 : startup_cost += sort_path.startup_cost;
6584 3752 : 382372 : startup_cost += (sort_path.total_cost - sort_path.startup_cost)
3753 : 382372 : * outerstartsel;
8692 3754 : 382372 : run_cost += (sort_path.total_cost - sort_path.startup_cost)
6584 3755 : 382372 : * (outerendsel - outerstartsel);
3756 : : }
3757 : : else
3758 : : {
483 rhaas@postgresql.org 3759 : 367501 : disabled_nodes += outer_path->disabled_nodes;
9437 tgl@sss.pgh.pa.us 3760 : 367501 : startup_cost += outer_path->startup_cost;
6584 3761 : 367501 : startup_cost += (outer_path->total_cost - outer_path->startup_cost)
3762 : 367501 : * outerstartsel;
8692 3763 : 367501 : run_cost += (outer_path->total_cost - outer_path->startup_cost)
6584 3764 : 367501 : * (outerendsel - outerstartsel);
3765 : : }
3766 : :
9437 3767 [ + + ]: 749873 : if (innersortkeys) /* do we need to sort inner? */
3768 : : {
3769 : : /*
3770 : : * We can assert that the inner path is not already ordered
3771 : : * appropriately for the mergejoin; otherwise, innersortkeys would
3772 : : * have been set to NIL.
3773 : : */
223 rguo@postgresql.org 3774 [ - + ]: 602911 : Assert(!pathkeys_contained_in(innersortkeys, inner_path->pathkeys));
3775 : :
3776 : : /*
3777 : : * We do not consider incremental sort for inner path, because
3778 : : * incremental sort does not support mark/restore.
3779 : : */
3780 : :
9437 tgl@sss.pgh.pa.us 3781 : 602911 : cost_sort(&sort_path,
3782 : : root,
3783 : : innersortkeys,
3784 : : inner_path->disabled_nodes,
3785 : : inner_path->total_cost,
3786 : : inner_path_rows,
3590 3787 : 602911 : inner_path->pathtarget->width,
3788 : : 0.0,
3789 : : work_mem,
3790 : : -1.0);
483 rhaas@postgresql.org 3791 : 602911 : disabled_nodes += sort_path.disabled_nodes;
9437 tgl@sss.pgh.pa.us 3792 : 602911 : startup_cost += sort_path.startup_cost;
6584 3793 : 602911 : startup_cost += (sort_path.total_cost - sort_path.startup_cost)
5876 3794 : 602911 : * innerstartsel;
3795 : 602911 : inner_run_cost = (sort_path.total_cost - sort_path.startup_cost)
3796 : 602911 : * (innerendsel - innerstartsel);
3797 : : }
3798 : : else
3799 : : {
483 rhaas@postgresql.org 3800 : 146962 : disabled_nodes += inner_path->disabled_nodes;
9437 tgl@sss.pgh.pa.us 3801 : 146962 : startup_cost += inner_path->startup_cost;
6584 3802 : 146962 : startup_cost += (inner_path->total_cost - inner_path->startup_cost)
5876 3803 : 146962 : * innerstartsel;
3804 : 146962 : inner_run_cost = (inner_path->total_cost - inner_path->startup_cost)
3805 : 146962 : * (innerendsel - innerstartsel);
3806 : : }
3807 : :
3808 : : /*
3809 : : * We can't yet determine whether rescanning occurs, or whether
3810 : : * materialization of the inner input should be done. The minimum
3811 : : * possible inner input cost, regardless of rescan and materialization
3812 : : * considerations, is inner_run_cost. We include that in
3813 : : * workspace->total_cost, but not yet in run_cost.
3814 : : */
3815 : :
3816 : : /* CPU costs left for later */
3817 : :
3818 : : /* Public result fields */
483 rhaas@postgresql.org 3819 : 749873 : workspace->disabled_nodes = disabled_nodes;
5073 tgl@sss.pgh.pa.us 3820 : 749873 : workspace->startup_cost = startup_cost;
3821 : 749873 : workspace->total_cost = startup_cost + run_cost + inner_run_cost;
3822 : : /* Save private data for final_cost_mergejoin */
3823 : 749873 : workspace->run_cost = run_cost;
3824 : 749873 : workspace->inner_run_cost = inner_run_cost;
3825 : 749873 : workspace->outer_rows = outer_rows;
3826 : 749873 : workspace->inner_rows = inner_rows;
3827 : 749873 : workspace->outer_skip_rows = outer_skip_rows;
3828 : 749873 : workspace->inner_skip_rows = inner_skip_rows;
3829 : 749873 : }
3830 : :
3831 : : /*
3832 : : * final_cost_mergejoin
3833 : : * Final estimate of the cost and result size of a mergejoin path.
3834 : : *
3835 : : * Unlike other costsize functions, this routine makes two actual decisions:
3836 : : * whether the executor will need to do mark/restore, and whether we should
3837 : : * materialize the inner path. It would be logically cleaner to build
3838 : : * separate paths testing these alternatives, but that would require repeating
3839 : : * most of the cost calculations, which are not all that cheap. Since the
3840 : : * choice will not affect output pathkeys or startup cost, only total cost,
3841 : : * there is no possibility of wanting to keep more than one path. So it seems
3842 : : * best to make the decisions here and record them in the path's
3843 : : * skip_mark_restore and materialize_inner fields.
3844 : : *
3845 : : * Mark/restore overhead is usually required, but can be skipped if we know
3846 : : * that the executor need find only one match per outer tuple, and that the
3847 : : * mergeclauses are sufficient to identify a match.
3848 : : *
3849 : : * We materialize the inner path if we need mark/restore and either the inner
3850 : : * path can't support mark/restore, or it's cheaper to use an interposed
3851 : : * Material node to handle mark/restore.
3852 : : *
3853 : : * 'path' is already filled in except for the rows and cost fields and
3854 : : * skip_mark_restore and materialize_inner
3855 : : * 'workspace' is the result from initial_cost_mergejoin
3856 : : * 'extra' contains miscellaneous information about the join
3857 : : */
3858 : : void
3859 : 232080 : final_cost_mergejoin(PlannerInfo *root, MergePath *path,
3860 : : JoinCostWorkspace *workspace,
3861 : : JoinPathExtraData *extra)
3862 : : {
3863 : 232080 : Path *outer_path = path->jpath.outerjoinpath;
3864 : 232080 : Path *inner_path = path->jpath.innerjoinpath;
3865 : 232080 : double inner_path_rows = inner_path->rows;
3866 : 232080 : List *mergeclauses = path->path_mergeclauses;
3867 : 232080 : List *innersortkeys = path->innersortkeys;
3868 : 232080 : Cost startup_cost = workspace->startup_cost;
3869 : 232080 : Cost run_cost = workspace->run_cost;
3870 : 232080 : Cost inner_run_cost = workspace->inner_run_cost;
3871 : 232080 : double outer_rows = workspace->outer_rows;
3872 : 232080 : double inner_rows = workspace->inner_rows;
3873 : 232080 : double outer_skip_rows = workspace->outer_skip_rows;
3874 : 232080 : double inner_skip_rows = workspace->inner_skip_rows;
3875 : : Cost cpu_per_tuple,
3876 : : bare_inner_cost,
3877 : : mat_inner_cost;
3878 : : QualCost merge_qual_cost;
3879 : : QualCost qp_qual_cost;
3880 : : double mergejointuples,
3881 : : rescannedtuples;
3882 : : double rescanratio;
3883 : :
3884 : : /* Set the number of disabled nodes. */
483 rhaas@postgresql.org 3885 : 232080 : path->jpath.path.disabled_nodes = workspace->disabled_nodes;
3886 : :
3887 : : /* Protect some assumptions below that rowcounts aren't zero */
1885 drowley@postgresql.o 3888 [ + + ]: 232080 : if (inner_path_rows <= 0)
5073 tgl@sss.pgh.pa.us 3889 : 45 : inner_path_rows = 1;
3890 : :
3891 : : /* Mark the path with the correct row estimate */
4990 3892 [ + + ]: 232080 : if (path->jpath.path.param_info)
3893 : 810 : path->jpath.path.rows = path->jpath.path.param_info->ppi_rows;
3894 : : else
3895 : 231270 : path->jpath.path.rows = path->jpath.path.parent->rows;
3896 : :
3897 : : /* For partial paths, scale row estimate. */
3260 rhaas@postgresql.org 3898 [ + + ]: 232080 : if (path->jpath.path.parallel_workers > 0)
3899 : : {
3136 bruce@momjian.us 3900 : 32920 : double parallel_divisor = get_parallel_divisor(&path->jpath.path);
3901 : :
3199 rhaas@postgresql.org 3902 : 32920 : path->jpath.path.rows =
3903 : 32920 : clamp_row_est(path->jpath.path.rows / parallel_divisor);
3904 : : }
3905 : :
3906 : : /*
3907 : : * Compute cost of the mergequals and qpquals (other restriction clauses)
3908 : : * separately.
3909 : : */
5073 tgl@sss.pgh.pa.us 3910 : 232080 : cost_qual_eval(&merge_qual_cost, mergeclauses, root);
3911 : 232080 : cost_qual_eval(&qp_qual_cost, path->jpath.joinrestrictinfo, root);
3912 : 232080 : qp_qual_cost.startup -= merge_qual_cost.startup;
3913 : 232080 : qp_qual_cost.per_tuple -= merge_qual_cost.per_tuple;
3914 : :
3915 : : /*
3916 : : * With a SEMI or ANTI join, or if the innerrel is known unique, the
3917 : : * executor will stop scanning for matches after the first match. When
3918 : : * all the joinclauses are merge clauses, this means we don't ever need to
3919 : : * back up the merge, and so we can skip mark/restore overhead.
3920 : : */
3176 3921 [ + + ]: 232080 : if ((path->jpath.jointype == JOIN_SEMI ||
3922 [ + + ]: 228457 : path->jpath.jointype == JOIN_ANTI ||
3923 [ + + + + ]: 305380 : extra->inner_unique) &&
3924 : 80463 : (list_length(path->jpath.joinrestrictinfo) ==
3925 : 80463 : list_length(path->path_mergeclauses)))
3926 : 69636 : path->skip_mark_restore = true;
3927 : : else
3928 : 162444 : path->skip_mark_restore = false;
3929 : :
3930 : : /*
3931 : : * Get approx # tuples passing the mergequals. We use approx_tuple_count
3932 : : * here because we need an estimate done with JOIN_INNER semantics.
3933 : : */
5073 3934 : 232080 : mergejointuples = approx_tuple_count(root, &path->jpath, mergeclauses);
3935 : :
3936 : : /*
3937 : : * When there are equal merge keys in the outer relation, the mergejoin
3938 : : * must rescan any matching tuples in the inner relation. This means
3939 : : * re-fetching inner tuples; we have to estimate how often that happens.
3940 : : *
3941 : : * For regular inner and outer joins, the number of re-fetches can be
3942 : : * estimated approximately as size of merge join output minus size of
3943 : : * inner relation. Assume that the distinct key values are 1, 2, ..., and
3944 : : * denote the number of values of each key in the outer relation as m1,
3945 : : * m2, ...; in the inner relation, n1, n2, ... Then we have
3946 : : *
3947 : : * size of join = m1 * n1 + m2 * n2 + ...
3948 : : *
3949 : : * number of rescanned tuples = (m1 - 1) * n1 + (m2 - 1) * n2 + ... = m1 *
3950 : : * n1 + m2 * n2 + ... - (n1 + n2 + ...) = size of join - size of inner
3951 : : * relation
3952 : : *
3953 : : * This equation works correctly for outer tuples having no inner match
3954 : : * (nk = 0), but not for inner tuples having no outer match (mk = 0); we
3955 : : * are effectively subtracting those from the number of rescanned tuples,
3956 : : * when we should not. Can we do better without expensive selectivity
3957 : : * computations?
3958 : : *
3959 : : * The whole issue is moot if we know we don't need to mark/restore at
3960 : : * all, or if we are working from a unique-ified outer input.
3961 : : */
120 rguo@postgresql.org 3962 [ + + ]:GNC 232080 : if (path->skip_mark_restore ||
3963 [ + + + + : 162444 : RELATION_WAS_MADE_UNIQUE(outer_path->parent, extra->sjinfo,
+ + ]
3964 : : path->jpath.jointype))
5073 tgl@sss.pgh.pa.us 3965 :CBC 72219 : rescannedtuples = 0;
3966 : : else
3967 : : {
3968 : 159861 : rescannedtuples = mergejointuples - inner_path_rows;
3969 : : /* Must clamp because of possible underestimate */
3970 [ + + ]: 159861 : if (rescannedtuples < 0)
3971 : 38946 : rescannedtuples = 0;
3972 : : }
3973 : :
3974 : : /*
3975 : : * We'll inflate various costs this much to account for rescanning. Note
3976 : : * that this is to be multiplied by something involving inner_rows, or
3977 : : * another number related to the portion of the inner rel we'll scan.
3978 : : */
2556 3979 : 232080 : rescanratio = 1.0 + (rescannedtuples / inner_rows);
3980 : :
3981 : : /*
3982 : : * Decide whether we want to materialize the inner input to shield it from
3983 : : * mark/restore and performing re-fetches. Our cost model for regular
3984 : : * re-fetches is that a re-fetch costs the same as an original fetch,
3985 : : * which is probably an overestimate; but on the other hand we ignore the
3986 : : * bookkeeping costs of mark/restore. Not clear if it's worth developing
3987 : : * a more refined model. So we just need to inflate the inner run cost by
3988 : : * rescanratio.
3989 : : */
5876 3990 : 232080 : bare_inner_cost = inner_run_cost * rescanratio;
3991 : :
3992 : : /*
3993 : : * When we interpose a Material node the re-fetch cost is assumed to be
3994 : : * just cpu_operator_cost per tuple, independently of the underlying
3995 : : * plan's cost; and we charge an extra cpu_operator_cost per original
3996 : : * fetch as well. Note that we're assuming the materialize node will
3997 : : * never spill to disk, since it only has to remember tuples back to the
3998 : : * last mark. (If there are a huge number of duplicates, our other cost
3999 : : * factors will make the path so expensive that it probably won't get
4000 : : * chosen anyway.) So we don't use cost_rescan here.
4001 : : *
4002 : : * Note: keep this estimate in sync with create_mergejoin_plan's labeling
4003 : : * of the generated Material node.
4004 : : */
4005 : 232080 : mat_inner_cost = inner_run_cost +
2556 4006 : 232080 : cpu_operator_cost * inner_rows * rescanratio;
4007 : :
4008 : : /*
4009 : : * If we don't need mark/restore at all, we don't need materialization.
4010 : : */
3176 4011 [ + + ]: 232080 : if (path->skip_mark_restore)
4012 : 69636 : path->materialize_inner = false;
4013 : :
4014 : : /*
4015 : : * Prefer materializing if it looks cheaper, unless the user has asked to
4016 : : * suppress materialization.
4017 : : */
4018 [ + + + + ]: 162444 : else if (enable_material && mat_inner_cost < bare_inner_cost)
5876 4019 : 1727 : path->materialize_inner = true;
4020 : :
4021 : : /*
4022 : : * Even if materializing doesn't look cheaper, we *must* do it if the
4023 : : * inner path is to be used directly (without sorting) and it doesn't
4024 : : * support mark/restore.
4025 : : *
4026 : : * Since the inner side must be ordered, and only Sorts and IndexScans can
4027 : : * create order to begin with, and they both support mark/restore, you
4028 : : * might think there's no problem --- but you'd be wrong. Nestloop and
4029 : : * merge joins can *preserve* the order of their inputs, so they can be
4030 : : * selected as the input of a mergejoin, and they don't support
4031 : : * mark/restore at present.
4032 : : *
4033 : : * We don't test the value of enable_material here, because
4034 : : * materialization is required for correctness in this case, and turning
4035 : : * it off does not entitle us to deliver an invalid plan.
4036 : : */
4037 [ + + ]: 160717 : else if (innersortkeys == NIL &&
4058 rhaas@postgresql.org 4038 [ + + ]: 4418 : !ExecSupportsMarkRestore(inner_path))
5876 tgl@sss.pgh.pa.us 4039 : 950 : path->materialize_inner = true;
4040 : :
4041 : : /*
4042 : : * Also, force materializing if the inner path is to be sorted and the
4043 : : * sort is expected to spill to disk. This is because the final merge
4044 : : * pass can be done on-the-fly if it doesn't have to support mark/restore.
4045 : : * We don't try to adjust the cost estimates for this consideration,
4046 : : * though.
4047 : : *
4048 : : * Since materialization is a performance optimization in this case,
4049 : : * rather than necessary for correctness, we skip it if enable_material is
4050 : : * off.
4051 : : */
5721 rhaas@postgresql.org 4052 [ + + + + ]: 159767 : else if (enable_material && innersortkeys != NIL &&
3590 tgl@sss.pgh.pa.us 4053 : 156275 : relation_byte_size(inner_path_rows,
4054 : 156275 : inner_path->pathtarget->width) >
320 4055 [ + + ]: 156275 : work_mem * (Size) 1024)
5876 4056 : 142 : path->materialize_inner = true;
4057 : : else
4058 : 159625 : path->materialize_inner = false;
4059 : :
4060 : : /* Charge the right incremental cost for the chosen case */
4061 [ + + ]: 232080 : if (path->materialize_inner)
4062 : 2819 : run_cost += mat_inner_cost;
4063 : : else
4064 : 229261 : run_cost += bare_inner_cost;
4065 : :
4066 : : /* CPU costs */
4067 : :
4068 : : /*
4069 : : * The number of tuple comparisons needed is approximately number of outer
4070 : : * rows plus number of inner rows plus number of rescanned tuples (can we
4071 : : * refine this?). At each one, we need to evaluate the mergejoin quals.
4072 : : */
8360 4073 : 232080 : startup_cost += merge_qual_cost.startup;
6584 4074 : 232080 : startup_cost += merge_qual_cost.per_tuple *
4075 : 232080 : (outer_skip_rows + inner_skip_rows * rescanratio);
8360 4076 : 232080 : run_cost += merge_qual_cost.per_tuple *
6584 4077 : 232080 : ((outer_rows - outer_skip_rows) +
4078 : 232080 : (inner_rows - inner_skip_rows) * rescanratio);
4079 : :
4080 : : /*
4081 : : * For each tuple that gets through the mergejoin proper, we charge
4082 : : * cpu_tuple_cost plus the cost of evaluating additional restriction
4083 : : * clauses that are to be applied at the join. (This is pessimistic since
4084 : : * not all of the quals may get evaluated at each tuple.)
4085 : : *
4086 : : * Note: we could adjust for SEMI/ANTI joins skipping some qual
4087 : : * evaluations here, but it's probably not worth the trouble.
4088 : : */
8360 4089 : 232080 : startup_cost += qp_qual_cost.startup;
4090 : 232080 : cpu_per_tuple = cpu_tuple_cost + qp_qual_cost.per_tuple;
6332 4091 : 232080 : run_cost += cpu_per_tuple * mergejointuples;
4092 : :
4093 : : /* tlist eval costs are paid per output row, not per tuple scanned */
3590 4094 : 232080 : startup_cost += path->jpath.path.pathtarget->cost.startup;
4095 : 232080 : run_cost += path->jpath.path.pathtarget->cost.per_tuple * path->jpath.path.rows;
4096 : :
8360 4097 : 232080 : path->jpath.path.startup_cost = startup_cost;
4098 : 232080 : path->jpath.path.total_cost = startup_cost + run_cost;
10753 scrappy@hub.org 4099 : 232080 : }
4100 : :
4101 : : /*
4102 : : * run mergejoinscansel() with caching
4103 : : */
4104 : : static MergeScanSelCache *
6607 bruce@momjian.us 4105 : 746741 : cached_scansel(PlannerInfo *root, RestrictInfo *rinfo, PathKey *pathkey)
4106 : : {
4107 : : MergeScanSelCache *cache;
4108 : : ListCell *lc;
4109 : : Selectivity leftstartsel,
4110 : : leftendsel,
4111 : : rightstartsel,
4112 : : rightendsel;
4113 : : MemoryContext oldcontext;
4114 : :
4115 : : /* Do we have this result already? */
6904 tgl@sss.pgh.pa.us 4116 [ + + + + : 746744 : foreach(lc, rinfo->scansel_cache)
+ + ]
4117 : : {
4118 : 675110 : cache = (MergeScanSelCache *) lfirst(lc);
4119 [ + - ]: 675110 : if (cache->opfamily == pathkey->pk_opfamily &&
5387 4120 [ + - ]: 675110 : cache->collation == pathkey->pk_eclass->ec_collation &&
257 peter@eisentraut.org 4121 [ + + ]: 675110 : cache->cmptype == pathkey->pk_cmptype &&
6904 tgl@sss.pgh.pa.us 4122 [ + - ]: 675107 : cache->nulls_first == pathkey->pk_nulls_first)
4123 : 675107 : return cache;
4124 : : }
4125 : :
4126 : : /* Nope, do the computation */
4127 : 71634 : mergejoinscansel(root,
4128 : 71634 : (Node *) rinfo->clause,
4129 : : pathkey->pk_opfamily,
4130 : : pathkey->pk_cmptype,
4131 : 71634 : pathkey->pk_nulls_first,
4132 : : &leftstartsel,
4133 : : &leftendsel,
4134 : : &rightstartsel,
4135 : : &rightendsel);
4136 : :
4137 : : /* Cache the result in suitably long-lived workspace */
4138 : 71634 : oldcontext = MemoryContextSwitchTo(root->planner_cxt);
4139 : :
7 michael@paquier.xyz 4140 :GNC 71634 : cache = palloc_object(MergeScanSelCache);
6904 tgl@sss.pgh.pa.us 4141 :CBC 71634 : cache->opfamily = pathkey->pk_opfamily;
5387 4142 : 71634 : cache->collation = pathkey->pk_eclass->ec_collation;
257 peter@eisentraut.org 4143 : 71634 : cache->cmptype = pathkey->pk_cmptype;
6904 tgl@sss.pgh.pa.us 4144 : 71634 : cache->nulls_first = pathkey->pk_nulls_first;
6584 4145 : 71634 : cache->leftstartsel = leftstartsel;
4146 : 71634 : cache->leftendsel = leftendsel;
4147 : 71634 : cache->rightstartsel = rightstartsel;
4148 : 71634 : cache->rightendsel = rightendsel;
4149 : :
6904 4150 : 71634 : rinfo->scansel_cache = lappend(rinfo->scansel_cache, cache);
4151 : :
4152 : 71634 : MemoryContextSwitchTo(oldcontext);
4153 : :
4154 : 71634 : return cache;
4155 : : }
4156 : :
4157 : : /*
4158 : : * initial_cost_hashjoin
4159 : : * Preliminary estimate of the cost of a hashjoin path.
4160 : : *
4161 : : * This must quickly produce lower-bound estimates of the path's startup and
4162 : : * total costs. If we are unable to eliminate the proposed path from
4163 : : * consideration using the lower bounds, final_cost_hashjoin will be called
4164 : : * to obtain the final estimates.
4165 : : *
4166 : : * The exact division of labor between this function and final_cost_hashjoin
4167 : : * is private to them, and represents a tradeoff between speed of the initial
4168 : : * estimate and getting a tight lower bound. We choose to not examine the
4169 : : * join quals here (other than by counting the number of hash clauses),
4170 : : * so we can't do much with CPU costs. We do assume that
4171 : : * ExecChooseHashTableSize is cheap enough to use here.
4172 : : *
4173 : : * 'workspace' is to be filled with startup_cost, total_cost, and perhaps
4174 : : * other data to be used by final_cost_hashjoin
4175 : : * 'jointype' is the type of join to be performed
4176 : : * 'hashclauses' is the list of joinclauses to be used as hash clauses
4177 : : * 'outer_path' is the outer input to the join
4178 : : * 'inner_path' is the inner input to the join
4179 : : * 'extra' contains miscellaneous information about the join
4180 : : * 'parallel_hash' indicates that inner_path is partial and that a shared
4181 : : * hash table will be built in parallel
4182 : : */
4183 : : void
5073 4184 : 441818 : initial_cost_hashjoin(PlannerInfo *root, JoinCostWorkspace *workspace,
4185 : : JoinType jointype,
4186 : : List *hashclauses,
4187 : : Path *outer_path, Path *inner_path,
4188 : : JoinPathExtraData *extra,
4189 : : bool parallel_hash)
4190 : : {
4191 : : int disabled_nodes;
9437 4192 : 441818 : Cost startup_cost = 0;
4193 : 441818 : Cost run_cost = 0;
5073 4194 : 441818 : double outer_path_rows = outer_path->rows;
4195 : 441818 : double inner_path_rows = inner_path->rows;
2919 andres@anarazel.de 4196 : 441818 : double inner_path_rows_total = inner_path_rows;
7871 neilc@samurai.com 4197 : 441818 : int num_hashclauses = list_length(hashclauses);
4198 : : int numbuckets;
4199 : : int numbatches;
4200 : : int num_skew_mcvs;
4201 : : size_t space_allowed; /* unused */
4202 : :
4203 : : /* Count up disabled nodes. */
483 rhaas@postgresql.org 4204 : 441818 : disabled_nodes = enable_hashjoin ? 0 : 1;
4205 : 441818 : disabled_nodes += inner_path->disabled_nodes;
4206 : 441818 : disabled_nodes += outer_path->disabled_nodes;
4207 : :
4208 : : /* cost of source data */
9437 tgl@sss.pgh.pa.us 4209 : 441818 : startup_cost += outer_path->startup_cost;
4210 : 441818 : run_cost += outer_path->total_cost - outer_path->startup_cost;
4211 : 441818 : startup_cost += inner_path->total_cost;
4212 : :
4213 : : /*
4214 : : * Cost of computing hash function: must do it once per input tuple. We
4215 : : * charge one cpu_operator_cost for each column's hash function. Also,
4216 : : * tack on one cpu_tuple_cost per inner row, to model the costs of
4217 : : * inserting the row into the hashtable.
4218 : : *
4219 : : * XXX when a hashclause is more complex than a single operator, we really
4220 : : * should charge the extra eval costs of the left or right side, as
4221 : : * appropriate, here. This seems more work than it's worth at the moment.
4222 : : */
6918 4223 : 441818 : startup_cost += (cpu_operator_cost * num_hashclauses + cpu_tuple_cost)
4224 : 441818 : * inner_path_rows;
8360 4225 : 441818 : run_cost += cpu_operator_cost * num_hashclauses * outer_path_rows;
4226 : :
4227 : : /*
4228 : : * If this is a parallel hash build, then the value we have for
4229 : : * inner_rows_total currently refers only to the rows returned by each
4230 : : * participant. For shared hash table size estimation, we need the total
4231 : : * number, so we need to undo the division.
4232 : : */
2919 andres@anarazel.de 4233 [ + + ]: 441818 : if (parallel_hash)
4234 : 37593 : inner_path_rows_total *= get_parallel_divisor(inner_path);
4235 : :
4236 : : /*
4237 : : * Get hash table size that executor would use for inner relation.
4238 : : *
4239 : : * XXX for the moment, always assume that skew optimization will be
4240 : : * performed. As long as SKEW_HASH_MEM_PERCENT is small, it's not worth
4241 : : * trying to determine that for sure.
4242 : : *
4243 : : * XXX at some point it might be interesting to try to account for skew
4244 : : * optimization in the cost estimate, but for now, we don't.
4245 : : */
4246 : 441818 : ExecChooseHashTableSize(inner_path_rows_total,
3590 tgl@sss.pgh.pa.us 4247 : 441818 : inner_path->pathtarget->width,
4248 : : true, /* useskew */
4249 : : parallel_hash, /* try_combined_hash_mem */
4250 : : outer_path->parallel_workers,
4251 : : &space_allowed,
4252 : : &numbuckets,
4253 : : &numbatches,
4254 : : &num_skew_mcvs);
4255 : :
4256 : : /*
4257 : : * If inner relation is too big then we will need to "batch" the join,
4258 : : * which implies writing and reading most of the tuples to disk an extra
4259 : : * time. Charge seq_page_cost per page, since the I/O should be nice and
4260 : : * sequential. Writing the inner rel counts as startup cost, all the rest
4261 : : * as run cost.
4262 : : */
5073 4263 [ + + ]: 441818 : if (numbatches > 1)
4264 : : {
4265 : 2351 : double outerpages = page_size(outer_path_rows,
3590 4266 : 2351 : outer_path->pathtarget->width);
5073 4267 : 2351 : double innerpages = page_size(inner_path_rows,
3590 4268 : 2351 : inner_path->pathtarget->width);
4269 : :
5073 4270 : 2351 : startup_cost += seq_page_cost * innerpages;
4271 : 2351 : run_cost += seq_page_cost * (innerpages + 2 * outerpages);
4272 : : }
4273 : :
4274 : : /* CPU costs left for later */
4275 : :
4276 : : /* Public result fields */
483 rhaas@postgresql.org 4277 : 441818 : workspace->disabled_nodes = disabled_nodes;
5073 tgl@sss.pgh.pa.us 4278 : 441818 : workspace->startup_cost = startup_cost;
4279 : 441818 : workspace->total_cost = startup_cost + run_cost;
4280 : : /* Save private data for final_cost_hashjoin */
4281 : 441818 : workspace->run_cost = run_cost;
4282 : 441818 : workspace->numbuckets = numbuckets;
4283 : 441818 : workspace->numbatches = numbatches;
2919 andres@anarazel.de 4284 : 441818 : workspace->inner_rows_total = inner_path_rows_total;
5073 tgl@sss.pgh.pa.us 4285 : 441818 : }
4286 : :
4287 : : /*
4288 : : * final_cost_hashjoin
4289 : : * Final estimate of the cost and result size of a hashjoin path.
4290 : : *
4291 : : * Note: the numbatches estimate is also saved into 'path' for use later
4292 : : *
4293 : : * 'path' is already filled in except for the rows and cost fields and
4294 : : * num_batches
4295 : : * 'workspace' is the result from initial_cost_hashjoin
4296 : : * 'extra' contains miscellaneous information about the join
4297 : : */
4298 : : void
4299 : 228127 : final_cost_hashjoin(PlannerInfo *root, HashPath *path,
4300 : : JoinCostWorkspace *workspace,
4301 : : JoinPathExtraData *extra)
4302 : : {
4303 : 228127 : Path *outer_path = path->jpath.outerjoinpath;
4304 : 228127 : Path *inner_path = path->jpath.innerjoinpath;
4305 : 228127 : double outer_path_rows = outer_path->rows;
4306 : 228127 : double inner_path_rows = inner_path->rows;
2919 andres@anarazel.de 4307 : 228127 : double inner_path_rows_total = workspace->inner_rows_total;
5073 tgl@sss.pgh.pa.us 4308 : 228127 : List *hashclauses = path->path_hashclauses;
4309 : 228127 : Cost startup_cost = workspace->startup_cost;
4310 : 228127 : Cost run_cost = workspace->run_cost;
4311 : 228127 : int numbuckets = workspace->numbuckets;
4312 : 228127 : int numbatches = workspace->numbatches;
4313 : : Cost cpu_per_tuple;
4314 : : QualCost hash_qual_cost;
4315 : : QualCost qp_qual_cost;
4316 : : double hashjointuples;
4317 : : double virtualbuckets;
4318 : : Selectivity innerbucketsize;
4319 : : Selectivity innermcvfreq;
4320 : : ListCell *hcl;
4321 : :
4322 : : /* Set the number of disabled nodes. */
483 rhaas@postgresql.org 4323 : 228127 : path->jpath.path.disabled_nodes = workspace->disabled_nodes;
4324 : :
4325 : : /* Mark the path with the correct row estimate */
4990 tgl@sss.pgh.pa.us 4326 [ + + ]: 228127 : if (path->jpath.path.param_info)
4327 : 1838 : path->jpath.path.rows = path->jpath.path.param_info->ppi_rows;
4328 : : else
4329 : 226289 : path->jpath.path.rows = path->jpath.path.parent->rows;
4330 : :
4331 : : /* For partial paths, scale row estimate. */
3260 rhaas@postgresql.org 4332 [ + + ]: 228127 : if (path->jpath.path.parallel_workers > 0)
4333 : : {
3136 bruce@momjian.us 4334 : 53568 : double parallel_divisor = get_parallel_divisor(&path->jpath.path);
4335 : :
3199 rhaas@postgresql.org 4336 : 53568 : path->jpath.path.rows =
4337 : 53568 : clamp_row_est(path->jpath.path.rows / parallel_divisor);
4338 : : }
4339 : :
4340 : : /* mark the path with estimated # of batches */
6110 tgl@sss.pgh.pa.us 4341 : 228127 : path->num_batches = numbatches;
4342 : :
4343 : : /* store the total number of tuples (sum of partial row estimates) */
2919 andres@anarazel.de 4344 : 228127 : path->inner_rows_total = inner_path_rows_total;
4345 : :
4346 : : /* and compute the number of "virtual" buckets in the whole join */
3101 tgl@sss.pgh.pa.us 4347 : 228127 : virtualbuckets = (double) numbuckets * (double) numbatches;
4348 : :
4349 : : /*
4350 : : * Determine bucketsize fraction and MCV frequency for the inner relation.
4351 : : * We use the smallest bucketsize or MCV frequency estimated for any
4352 : : * individual hashclause; this is undoubtedly conservative.
4353 : : *
4354 : : * BUT: if inner relation has been unique-ified, we can assume it's good
4355 : : * for hashing. This is important both because it's the right answer, and
4356 : : * because we avoid contaminating the cache with a value that's wrong for
4357 : : * non-unique-ified paths.
4358 : : */
120 rguo@postgresql.org 4359 [ + + + + :GNC 228127 : if (RELATION_WAS_MADE_UNIQUE(inner_path->parent, extra->sjinfo,
+ + ]
4360 : : path->jpath.jointype))
4361 : : {
8359 tgl@sss.pgh.pa.us 4362 :CBC 2118 : innerbucketsize = 1.0 / virtualbuckets;
3046 4363 : 2118 : innermcvfreq = 0.0;
4364 : : }
4365 : : else
4366 : : {
4367 : : List *otherclauses;
4368 : :
8359 4369 : 226009 : innerbucketsize = 1.0;
3046 4370 : 226009 : innermcvfreq = 1.0;
4371 : :
4372 : : /* At first, try to estimate bucket size using extended statistics. */
282 akorotkov@postgresql 4373 : 226009 : otherclauses = estimate_multivariate_bucketsize(root,
4374 : : inner_path->parent,
4375 : : hashclauses,
4376 : : &innerbucketsize);
4377 : :
4378 : : /* Pass through the remaining clauses */
4379 [ + + + + : 470581 : foreach(hcl, otherclauses)
+ + ]
4380 : : {
3173 tgl@sss.pgh.pa.us 4381 : 244572 : RestrictInfo *restrictinfo = lfirst_node(RestrictInfo, hcl);
4382 : : Selectivity thisbucketsize;
4383 : : Selectivity thismcvfreq;
4384 : :
4385 : : /*
4386 : : * First we have to figure out which side of the hashjoin clause
4387 : : * is the inner side.
4388 : : *
4389 : : * Since we tend to visit the same clauses over and over when
4390 : : * planning a large query, we cache the bucket stats estimates in
4391 : : * the RestrictInfo node to avoid repeated lookups of statistics.
4392 : : */
8348 4393 [ + + ]: 244572 : if (bms_is_subset(restrictinfo->right_relids,
4394 : 244572 : inner_path->parent->relids))
4395 : : {
4396 : : /* righthand side is inner */
8359 4397 : 127361 : thisbucketsize = restrictinfo->right_bucketsize;
4398 [ + + ]: 127361 : if (thisbucketsize < 0)
4399 : : {
4400 : : /* not cached yet */
3046 4401 : 55321 : estimate_hash_bucket_stats(root,
4402 : 55321 : get_rightop(restrictinfo->clause),
4403 : : virtualbuckets,
4404 : : &restrictinfo->right_mcvfreq,
4405 : : &restrictinfo->right_bucketsize);
4406 : 55321 : thisbucketsize = restrictinfo->right_bucketsize;
4407 : : }
4408 : 127361 : thismcvfreq = restrictinfo->right_mcvfreq;
4409 : : }
4410 : : else
4411 : : {
8348 4412 [ - + ]: 117211 : Assert(bms_is_subset(restrictinfo->left_relids,
4413 : : inner_path->parent->relids));
4414 : : /* lefthand side is inner */
8359 4415 : 117211 : thisbucketsize = restrictinfo->left_bucketsize;
4416 [ + + ]: 117211 : if (thisbucketsize < 0)
4417 : : {
4418 : : /* not cached yet */
3046 4419 : 46880 : estimate_hash_bucket_stats(root,
4420 : 46880 : get_leftop(restrictinfo->clause),
4421 : : virtualbuckets,
4422 : : &restrictinfo->left_mcvfreq,
4423 : : &restrictinfo->left_bucketsize);
4424 : 46880 : thisbucketsize = restrictinfo->left_bucketsize;
4425 : : }
4426 : 117211 : thismcvfreq = restrictinfo->left_mcvfreq;
4427 : : }
4428 : :
8359 4429 [ + + ]: 244572 : if (innerbucketsize > thisbucketsize)
4430 : 184245 : innerbucketsize = thisbucketsize;
3046 4431 [ + + ]: 244572 : if (innermcvfreq > thismcvfreq)
4432 : 228339 : innermcvfreq = thismcvfreq;
4433 : : }
4434 : : }
4435 : :
4436 : : /*
4437 : : * If the bucket holding the inner MCV would exceed hash_mem, we don't
4438 : : * want to hash unless there is really no other alternative, so apply
4439 : : * disable_cost. (The executor normally copes with excessive memory usage
4440 : : * by splitting batches, but obviously it cannot separate equal values
4441 : : * that way, so it will be unable to drive the batch size below hash_mem
4442 : : * when this is true.)
4443 : : */
4444 : 228127 : if (relation_byte_size(clamp_row_est(inner_path_rows * innermcvfreq),
1606 4445 [ + + ]: 456254 : inner_path->pathtarget->width) > get_hash_memory_limit())
3046 4446 : 4 : startup_cost += disable_cost;
4447 : :
4448 : : /*
4449 : : * Compute cost of the hashquals and qpquals (other restriction clauses)
4450 : : * separately.
4451 : : */
5073 4452 : 228127 : cost_qual_eval(&hash_qual_cost, hashclauses, root);
4453 : 228127 : cost_qual_eval(&qp_qual_cost, path->jpath.joinrestrictinfo, root);
4454 : 228127 : qp_qual_cost.startup -= hash_qual_cost.startup;
4455 : 228127 : qp_qual_cost.per_tuple -= hash_qual_cost.per_tuple;
4456 : :
4457 : : /* CPU costs */
4458 : :
3176 4459 [ + + ]: 228127 : if (path->jpath.jointype == JOIN_SEMI ||
4460 [ + + ]: 224945 : path->jpath.jointype == JOIN_ANTI ||
4461 [ + + ]: 222656 : extra->inner_unique)
6066 4462 : 61875 : {
4463 : : double outer_matched_rows;
4464 : : Selectivity inner_scan_frac;
4465 : :
4466 : : /*
4467 : : * With a SEMI or ANTI join, or if the innerrel is known unique, the
4468 : : * executor will stop after the first match.
4469 : : *
4470 : : * For an outer-rel row that has at least one match, we can expect the
4471 : : * bucket scan to stop after a fraction 1/(match_count+1) of the
4472 : : * bucket's rows, if the matches are evenly distributed. Since they
4473 : : * probably aren't quite evenly distributed, we apply a fuzz factor of
4474 : : * 2.0 to that fraction. (If we used a larger fuzz factor, we'd have
4475 : : * to clamp inner_scan_frac to at most 1.0; but since match_count is
4476 : : * at least 1, no such clamp is needed now.)
4477 : : */
3176 4478 : 61875 : outer_matched_rows = rint(outer_path_rows * extra->semifactors.outer_match_frac);
4479 : 61875 : inner_scan_frac = 2.0 / (extra->semifactors.match_count + 1.0);
4480 : :
6066 4481 : 61875 : startup_cost += hash_qual_cost.startup;
4482 : 123750 : run_cost += hash_qual_cost.per_tuple * outer_matched_rows *
4483 : 61875 : clamp_row_est(inner_path_rows * innerbucketsize * inner_scan_frac) * 0.5;
4484 : :
4485 : : /*
4486 : : * For unmatched outer-rel rows, the picture is quite a lot different.
4487 : : * In the first place, there is no reason to assume that these rows
4488 : : * preferentially hit heavily-populated buckets; instead assume they
4489 : : * are uncorrelated with the inner distribution and so they see an
4490 : : * average bucket size of inner_path_rows / virtualbuckets. In the
4491 : : * second place, it seems likely that they will have few if any exact
4492 : : * hash-code matches and so very few of the tuples in the bucket will
4493 : : * actually require eval of the hash quals. We don't have any good
4494 : : * way to estimate how many will, but for the moment assume that the
4495 : : * effective cost per bucket entry is one-tenth what it is for
4496 : : * matchable tuples.
4497 : : */
4498 : 123750 : run_cost += hash_qual_cost.per_tuple *
4499 : 123750 : (outer_path_rows - outer_matched_rows) *
4500 : 61875 : clamp_row_est(inner_path_rows / virtualbuckets) * 0.05;
4501 : :
4502 : : /* Get # of tuples that will pass the basic join */
2713 4503 [ + + ]: 61875 : if (path->jpath.jointype == JOIN_ANTI)
6066 4504 : 2289 : hashjointuples = outer_path_rows - outer_matched_rows;
4505 : : else
2713 4506 : 59586 : hashjointuples = outer_matched_rows;
4507 : : }
4508 : : else
4509 : : {
4510 : : /*
4511 : : * The number of tuple comparisons needed is the number of outer
4512 : : * tuples times the typical number of tuples in a hash bucket, which
4513 : : * is the inner relation size times its bucketsize fraction. At each
4514 : : * one, we need to evaluate the hashjoin quals. But actually,
4515 : : * charging the full qual eval cost at each tuple is pessimistic,
4516 : : * since we don't evaluate the quals unless the hash values match
4517 : : * exactly. For lack of a better idea, halve the cost estimate to
4518 : : * allow for that.
4519 : : */
6066 4520 : 166252 : startup_cost += hash_qual_cost.startup;
4521 : 332504 : run_cost += hash_qual_cost.per_tuple * outer_path_rows *
4522 : 166252 : clamp_row_est(inner_path_rows * innerbucketsize) * 0.5;
4523 : :
4524 : : /*
4525 : : * Get approx # tuples passing the hashquals. We use
4526 : : * approx_tuple_count here because we need an estimate done with
4527 : : * JOIN_INNER semantics.
4528 : : */
4529 : 166252 : hashjointuples = approx_tuple_count(root, &path->jpath, hashclauses);
4530 : : }
4531 : :
4532 : : /*
4533 : : * For each tuple that gets through the hashjoin proper, we charge
4534 : : * cpu_tuple_cost plus the cost of evaluating additional restriction
4535 : : * clauses that are to be applied at the join. (This is pessimistic since
4536 : : * not all of the quals may get evaluated at each tuple.)
4537 : : */
8360 4538 : 228127 : startup_cost += qp_qual_cost.startup;
4539 : 228127 : cpu_per_tuple = cpu_tuple_cost + qp_qual_cost.per_tuple;
6332 4540 : 228127 : run_cost += cpu_per_tuple * hashjointuples;
4541 : :
4542 : : /* tlist eval costs are paid per output row, not per tuple scanned */
3590 4543 : 228127 : startup_cost += path->jpath.path.pathtarget->cost.startup;
4544 : 228127 : run_cost += path->jpath.path.pathtarget->cost.per_tuple * path->jpath.path.rows;
4545 : :
8360 4546 : 228127 : path->jpath.path.startup_cost = startup_cost;
4547 : 228127 : path->jpath.path.total_cost = startup_cost + run_cost;
9437 4548 : 228127 : }
4549 : :
4550 : :
4551 : : /*
4552 : : * cost_subplan
4553 : : * Figure the costs for a SubPlan (or initplan).
4554 : : *
4555 : : * Note: we could dig the subplan's Plan out of the root list, but in practice
4556 : : * all callers have it handy already, so we make them pass it.
4557 : : */
4558 : : void
6326 4559 : 22332 : cost_subplan(PlannerInfo *root, SubPlan *subplan, Plan *plan)
4560 : : {
4561 : : QualCost sp_cost;
4562 : :
4563 : : /*
4564 : : * Figure any cost for evaluating the testexpr.
4565 : : *
4566 : : * Usually, SubPlan nodes are built very early, before we have constructed
4567 : : * any RelOptInfos for the parent query level, which means the parent root
4568 : : * does not yet contain enough information to safely consult statistics.
4569 : : * Therefore, we pass root as NULL here. cost_qual_eval() is already
4570 : : * well-equipped to handle a NULL root.
4571 : : *
4572 : : * One exception is SubPlan nodes built for the initplans of MIN/MAX
4573 : : * aggregates from indexes (cf. SS_make_initplan_from_plan). In this
4574 : : * case, having a NULL root is safe because testexpr will be NULL.
4575 : : * Besides, an initplan will by definition not consult anything from the
4576 : : * parent plan.
4577 : : */
4578 : 22332 : cost_qual_eval(&sp_cost,
4579 : 22332 : make_ands_implicit((Expr *) subplan->testexpr),
4580 : : NULL);
4581 : :
4582 [ + + ]: 22332 : if (subplan->useHashTable)
4583 : : {
4584 : : /*
4585 : : * If we are using a hash table for the subquery outputs, then the
4586 : : * cost of evaluating the query is a one-time cost. We charge one
4587 : : * cpu_operator_cost per tuple for the work of loading the hashtable,
4588 : : * too.
4589 : : */
4590 : 1067 : sp_cost.startup += plan->total_cost +
4591 : 1067 : cpu_operator_cost * plan->plan_rows;
4592 : :
4593 : : /*
4594 : : * The per-tuple costs include the cost of evaluating the lefthand
4595 : : * expressions, plus the cost of probing the hashtable. We already
4596 : : * accounted for the lefthand expressions as part of the testexpr, and
4597 : : * will also have counted one cpu_operator_cost for each comparison
4598 : : * operator. That is probably too low for the probing cost, but it's
4599 : : * hard to make a better estimate, so live with it for now.
4600 : : */
4601 : : }
4602 : : else
4603 : : {
4604 : : /*
4605 : : * Otherwise we will be rescanning the subplan output on each
4606 : : * evaluation. We need to estimate how much of the output we will
4607 : : * actually need to scan. NOTE: this logic should agree with the
4608 : : * tuple_fraction estimates used by make_subplan() in
4609 : : * plan/subselect.c.
4610 : : */
4611 : 21265 : Cost plan_run_cost = plan->total_cost - plan->startup_cost;
4612 : :
4613 [ + + ]: 21265 : if (subplan->subLinkType == EXISTS_SUBLINK)
4614 : : {
4615 : : /* we only need to fetch 1 tuple; clamp to avoid zero divide */
3553 4616 : 1254 : sp_cost.per_tuple += plan_run_cost / clamp_row_est(plan->plan_rows);
4617 : : }
6326 4618 [ + + ]: 20011 : else if (subplan->subLinkType == ALL_SUBLINK ||
4619 [ + + ]: 20002 : subplan->subLinkType == ANY_SUBLINK)
4620 : : {
4621 : : /* assume we need 50% of the tuples */
4622 : 73 : sp_cost.per_tuple += 0.50 * plan_run_cost;
4623 : : /* also charge a cpu_operator_cost per row examined */
4624 : 73 : sp_cost.per_tuple += 0.50 * plan->plan_rows * cpu_operator_cost;
4625 : : }
4626 : : else
4627 : : {
4628 : : /* assume we need all tuples */
4629 : 19938 : sp_cost.per_tuple += plan_run_cost;
4630 : : }
4631 : :
4632 : : /*
4633 : : * Also account for subplan's startup cost. If the subplan is
4634 : : * uncorrelated or undirect correlated, AND its topmost node is one
4635 : : * that materializes its output, assume that we'll only need to pay
4636 : : * its startup cost once; otherwise assume we pay the startup cost
4637 : : * every time.
4638 : : */
4639 [ + + + + ]: 28101 : if (subplan->parParam == NIL &&
5940 4640 : 6836 : ExecMaterializesOutput(nodeTag(plan)))
6326 4641 : 528 : sp_cost.startup += plan->startup_cost;
4642 : : else
4643 : 20737 : sp_cost.per_tuple += plan->startup_cost;
4644 : : }
4645 : :
4646 : 22332 : subplan->startup_cost = sp_cost.startup;
4647 : 22332 : subplan->per_call_cost = sp_cost.per_tuple;
4648 : 22332 : }
4649 : :
4650 : :
4651 : : /*
4652 : : * cost_rescan
4653 : : * Given a finished Path, estimate the costs of rescanning it after
4654 : : * having done so the first time. For some Path types a rescan is
4655 : : * cheaper than an original scan (if no parameters change), and this
4656 : : * function embodies knowledge about that. The default is to return
4657 : : * the same costs stored in the Path. (Note that the cost estimates
4658 : : * actually stored in Paths are always for first scans.)
4659 : : *
4660 : : * This function is not currently intended to model effects such as rescans
4661 : : * being cheaper due to disk block caching; what we are concerned with is
4662 : : * plan types wherein the executor caches results explicitly, or doesn't
4663 : : * redo startup calculations, etc.
4664 : : */
4665 : : static void
5940 4666 : 1656569 : cost_rescan(PlannerInfo *root, Path *path,
4667 : : Cost *rescan_startup_cost, /* output parameters */
4668 : : Cost *rescan_total_cost)
4669 : : {
4670 [ + + + + : 1656569 : switch (path->pathtype)
+ + ]
4671 : : {
4672 : 26596 : case T_FunctionScan:
4673 : :
4674 : : /*
4675 : : * Currently, nodeFunctionscan.c always executes the function to
4676 : : * completion before returning any rows, and caches the results in
4677 : : * a tuplestore. So the function eval cost is all startup cost
4678 : : * and isn't paid over again on rescans. However, all run costs
4679 : : * will be paid over again.
4680 : : */
4681 : 26596 : *rescan_startup_cost = 0;
4682 : 26596 : *rescan_total_cost = path->total_cost - path->startup_cost;
4683 : 26596 : break;
4684 : 66372 : case T_HashJoin:
4685 : :
4686 : : /*
4687 : : * If it's a single-batch join, we don't need to rebuild the hash
4688 : : * table during a rescan.
4689 : : */
3430 4690 [ + - ]: 66372 : if (((HashPath *) path)->num_batches == 1)
4691 : : {
4692 : : /* Startup cost is exactly the cost of hash table building */
4693 : 66372 : *rescan_startup_cost = 0;
4694 : 66372 : *rescan_total_cost = path->total_cost - path->startup_cost;
4695 : : }
4696 : : else
4697 : : {
4698 : : /* Otherwise, no special treatment */
3430 tgl@sss.pgh.pa.us 4699 :UBC 0 : *rescan_startup_cost = path->startup_cost;
4700 : 0 : *rescan_total_cost = path->total_cost;
4701 : : }
5940 tgl@sss.pgh.pa.us 4702 :CBC 66372 : break;
4703 : 4104 : case T_CteScan:
4704 : : case T_WorkTableScan:
4705 : : {
4706 : : /*
4707 : : * These plan types materialize their final result in a
4708 : : * tuplestore or tuplesort object. So the rescan cost is only
4709 : : * cpu_tuple_cost per tuple, unless the result is large enough
4710 : : * to spill to disk.
4711 : : */
5073 4712 : 4104 : Cost run_cost = cpu_tuple_cost * path->rows;
4713 : 4104 : double nbytes = relation_byte_size(path->rows,
3101 4714 : 4104 : path->pathtarget->width);
320 4715 : 4104 : double work_mem_bytes = work_mem * (Size) 1024;
4716 : :
5940 4717 [ + + ]: 4104 : if (nbytes > work_mem_bytes)
4718 : : {
4719 : : /* It will spill, so account for re-read cost */
4720 : 184 : double npages = ceil(nbytes / BLCKSZ);
4721 : :
4722 : 184 : run_cost += seq_page_cost * npages;
4723 : : }
4724 : 4104 : *rescan_startup_cost = 0;
4725 : 4104 : *rescan_total_cost = run_cost;
4726 : : }
4727 : 4104 : break;
5780 4728 : 597603 : case T_Material:
4729 : : case T_Sort:
4730 : : {
4731 : : /*
4732 : : * These plan types not only materialize their results, but do
4733 : : * not implement qual filtering or projection. So they are
4734 : : * even cheaper to rescan than the ones above. We charge only
4735 : : * cpu_operator_cost per tuple. (Note: keep that in sync with
4736 : : * the run_cost charge in cost_sort, and also see comments in
4737 : : * cost_material before you change it.)
4738 : : */
5073 4739 : 597603 : Cost run_cost = cpu_operator_cost * path->rows;
4740 : 597603 : double nbytes = relation_byte_size(path->rows,
3101 4741 : 597603 : path->pathtarget->width);
320 4742 : 597603 : double work_mem_bytes = work_mem * (Size) 1024;
4743 : :
5780 4744 [ + + ]: 597603 : if (nbytes > work_mem_bytes)
4745 : : {
4746 : : /* It will spill, so account for re-read cost */
4747 : 5027 : double npages = ceil(nbytes / BLCKSZ);
4748 : :
4749 : 5027 : run_cost += seq_page_cost * npages;
4750 : : }
4751 : 597603 : *rescan_startup_cost = 0;
4752 : 597603 : *rescan_total_cost = run_cost;
4753 : : }
4754 : 597603 : break;
1617 drowley@postgresql.o 4755 : 148681 : case T_Memoize:
4756 : : /* All the hard work is done by cost_memoize_rescan */
4757 : 148681 : cost_memoize_rescan(root, (MemoizePath *) path,
4758 : : rescan_startup_cost, rescan_total_cost);
1720 4759 : 148681 : break;
5940 tgl@sss.pgh.pa.us 4760 : 813213 : default:
4761 : 813213 : *rescan_startup_cost = path->startup_cost;
4762 : 813213 : *rescan_total_cost = path->total_cost;
4763 : 813213 : break;
4764 : : }
4765 : 1656569 : }
4766 : :
4767 : :
4768 : : /*
4769 : : * cost_qual_eval
4770 : : * Estimate the CPU costs of evaluating a WHERE clause.
4771 : : * The input can be either an implicitly-ANDed list of boolean
4772 : : * expressions, or a list of RestrictInfo nodes. (The latter is
4773 : : * preferred since it allows caching of the results.)
4774 : : * The result includes both a one-time (startup) component,
4775 : : * and a per-evaluation component.
4776 : : *
4777 : : * Note: in some code paths root can be passed as NULL, resulting in
4778 : : * slightly worse estimates.
4779 : : */
4780 : : void
6873 4781 : 2359167 : cost_qual_eval(QualCost *cost, List *quals, PlannerInfo *root)
4782 : : {
4783 : : cost_qual_eval_context context;
4784 : : ListCell *l;
4785 : :
4786 : 2359167 : context.root = root;
4787 : 2359167 : context.total.startup = 0;
4788 : 2359167 : context.total.per_tuple = 0;
4789 : :
4790 : : /* We don't charge any cost for the implicit ANDing at top level ... */
4791 : :
9136 4792 [ + + + + : 4490623 : foreach(l, quals)
+ + ]
4793 : : {
9036 bruce@momjian.us 4794 : 2131456 : Node *qual = (Node *) lfirst(l);
4795 : :
6873 tgl@sss.pgh.pa.us 4796 : 2131456 : cost_qual_eval_walker(qual, &context);
4797 : : }
4798 : :
4799 : 2359167 : *cost = context.total;
9437 4800 : 2359167 : }
4801 : :
4802 : : /*
4803 : : * cost_qual_eval_node
4804 : : * As above, for a single RestrictInfo or expression.
4805 : : */
4806 : : void
6873 4807 : 939155 : cost_qual_eval_node(QualCost *cost, Node *qual, PlannerInfo *root)
4808 : : {
4809 : : cost_qual_eval_context context;
4810 : :
4811 : 939155 : context.root = root;
4812 : 939155 : context.total.startup = 0;
4813 : 939155 : context.total.per_tuple = 0;
4814 : :
4815 : 939155 : cost_qual_eval_walker(qual, &context);
4816 : :
4817 : 939155 : *cost = context.total;
6904 4818 : 939155 : }
4819 : :
4820 : : static bool
6607 bruce@momjian.us 4821 : 4822879 : cost_qual_eval_walker(Node *node, cost_qual_eval_context *context)
4822 : : {
9437 tgl@sss.pgh.pa.us 4823 [ + + ]: 4822879 : if (node == NULL)
4824 : 47361 : return false;
4825 : :
4826 : : /*
4827 : : * RestrictInfo nodes contain an eval_cost field reserved for this
4828 : : * routine's use, so that it's not necessary to evaluate the qual clause's
4829 : : * cost more than once. If the clause's cost hasn't been computed yet,
4830 : : * the field's startup value will contain -1.
4831 : : */
6904 4832 [ + + ]: 4775518 : if (IsA(node, RestrictInfo))
4833 : : {
4834 : 2226325 : RestrictInfo *rinfo = (RestrictInfo *) node;
4835 : :
4836 [ + + ]: 2226325 : if (rinfo->eval_cost.startup < 0)
4837 : : {
4838 : : cost_qual_eval_context locContext;
4839 : :
6873 4840 : 306639 : locContext.root = context->root;
4841 : 306639 : locContext.total.startup = 0;
4842 : 306639 : locContext.total.per_tuple = 0;
4843 : :
4844 : : /*
4845 : : * For an OR clause, recurse into the marked-up tree so that we
4846 : : * set the eval_cost for contained RestrictInfos too.
4847 : : */
6904 4848 [ + + ]: 306639 : if (rinfo->orclause)
6873 4849 : 4848 : cost_qual_eval_walker((Node *) rinfo->orclause, &locContext);
4850 : : else
4851 : 301791 : cost_qual_eval_walker((Node *) rinfo->clause, &locContext);
4852 : :
4853 : : /*
4854 : : * If the RestrictInfo is marked pseudoconstant, it will be tested
4855 : : * only once, so treat its cost as all startup cost.
4856 : : */
6904 4857 [ + + ]: 306639 : if (rinfo->pseudoconstant)
4858 : : {
4859 : : /* count one execution during startup */
6873 4860 : 5155 : locContext.total.startup += locContext.total.per_tuple;
4861 : 5155 : locContext.total.per_tuple = 0;
4862 : : }
4863 : 306639 : rinfo->eval_cost = locContext.total;
4864 : : }
4865 : 2226325 : context->total.startup += rinfo->eval_cost.startup;
4866 : 2226325 : context->total.per_tuple += rinfo->eval_cost.per_tuple;
4867 : : /* do NOT recurse into children */
6904 4868 : 2226325 : return false;
4869 : : }
4870 : :
4871 : : /*
4872 : : * For each operator or function node in the given tree, we charge the
4873 : : * estimated execution cost given by pg_proc.procost (remember to multiply
4874 : : * this by cpu_operator_cost).
4875 : : *
4876 : : * Vars and Consts are charged zero, and so are boolean operators (AND,
4877 : : * OR, NOT). Simplistic, but a lot better than no model at all.
4878 : : *
4879 : : * Should we try to account for the possibility of short-circuit
4880 : : * evaluation of AND/OR? Probably *not*, because that would make the
4881 : : * results depend on the clause ordering, and we are not in any position
4882 : : * to expect that the current ordering of the clauses is the one that's
4883 : : * going to end up being used. The above per-RestrictInfo caching would
4884 : : * not mix well with trying to re-order clauses anyway.
4885 : : *
4886 : : * Another issue that is entirely ignored here is that if a set-returning
4887 : : * function is below top level in the tree, the functions/operators above
4888 : : * it will need to be evaluated multiple times. In practical use, such
4889 : : * cases arise so seldom as to not be worth the added complexity needed;
4890 : : * moreover, since our rowcount estimates for functions tend to be pretty
4891 : : * phony, the results would also be pretty phony.
4892 : : */
4893 [ + + ]: 2549193 : if (IsA(node, FuncExpr))
4894 : : {
2503 4895 : 171010 : add_function_cost(context->root, ((FuncExpr *) node)->funcid, node,
4896 : : &context->total);
4897 : : }
6904 4898 [ + + ]: 2378183 : else if (IsA(node, OpExpr) ||
4899 [ + + ]: 2043293 : IsA(node, DistinctExpr) ||
4900 [ + + ]: 2042745 : IsA(node, NullIfExpr))
4901 : : {
4902 : : /* rely on struct equivalence to treat these all alike */
4903 : 335499 : set_opfuncid((OpExpr *) node);
2503 4904 : 335499 : add_function_cost(context->root, ((OpExpr *) node)->opfuncid, node,
4905 : : &context->total);
4906 : : }
8207 4907 [ + + ]: 2042684 : else if (IsA(node, ScalarArrayOpExpr))
4908 : : {
7326 4909 : 22541 : ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) node;
7014 bruce@momjian.us 4910 : 22541 : Node *arraynode = (Node *) lsecond(saop->args);
4911 : : QualCost sacosts;
4912 : : QualCost hcosts;
713 tgl@sss.pgh.pa.us 4913 : 22541 : double estarraylen = estimate_array_length(context->root, arraynode);
4914 : :
6904 4915 : 22541 : set_sa_opfuncid(saop);
2503 4916 : 22541 : sacosts.startup = sacosts.per_tuple = 0;
4917 : 22541 : add_function_cost(context->root, saop->opfuncid, NULL,
4918 : : &sacosts);
4919 : :
1714 drowley@postgresql.o 4920 [ + + ]: 22541 : if (OidIsValid(saop->hashfuncid))
4921 : : {
4922 : : /* Handle costs for hashed ScalarArrayOpExpr */
4923 : 218 : hcosts.startup = hcosts.per_tuple = 0;
4924 : :
4925 : 218 : add_function_cost(context->root, saop->hashfuncid, NULL, &hcosts);
4926 : 218 : context->total.startup += sacosts.startup + hcosts.startup;
4927 : :
4928 : : /* Estimate the cost of building the hashtable. */
4929 : 218 : context->total.startup += estarraylen * hcosts.per_tuple;
4930 : :
4931 : : /*
4932 : : * XXX should we charge a little bit for sacosts.per_tuple when
4933 : : * building the table, or is it ok to assume there will be zero
4934 : : * hash collision?
4935 : : */
4936 : :
4937 : : /*
4938 : : * Charge for hashtable lookups. Charge a single hash and a
4939 : : * single comparison.
4940 : : */
4941 : 218 : context->total.per_tuple += hcosts.per_tuple + sacosts.per_tuple;
4942 : : }
4943 : : else
4944 : : {
4945 : : /*
4946 : : * Estimate that the operator will be applied to about half of the
4947 : : * array elements before the answer is determined.
4948 : : */
4949 : 22323 : context->total.startup += sacosts.startup;
4950 : 44646 : context->total.per_tuple += sacosts.per_tuple *
713 tgl@sss.pgh.pa.us 4951 : 22323 : estimate_array_length(context->root, arraynode) * 0.5;
4952 : : }
4953 : : }
5351 4954 [ + + ]: 2020143 : else if (IsA(node, Aggref) ||
4955 [ + + ]: 1985542 : IsA(node, WindowFunc))
4956 : : {
4957 : : /*
4958 : : * Aggref and WindowFunc nodes are (and should be) treated like Vars,
4959 : : * ie, zero execution cost in the current model, because they behave
4960 : : * essentially like Vars at execution. We disregard the costs of
4961 : : * their input expressions for the same reason. The actual execution
4962 : : * costs of the aggregate/window functions and their arguments have to
4963 : : * be factored into plan-node-specific costing of the Agg or WindowAgg
4964 : : * plan node.
4965 : : */
4966 : 36540 : return false; /* don't recurse into children */
4967 : : }
1367 4968 [ + + ]: 1983603 : else if (IsA(node, GroupingFunc))
4969 : : {
4970 : : /* Treat this as having cost 1 */
4971 : 211 : context->total.per_tuple += cpu_operator_cost;
4972 : 211 : return false; /* don't recurse into children */
4973 : : }
6770 4974 [ + + ]: 1983392 : else if (IsA(node, CoerceViaIO))
4975 : : {
4976 : 10891 : CoerceViaIO *iocoerce = (CoerceViaIO *) node;
4977 : : Oid iofunc;
4978 : : Oid typioparam;
4979 : : bool typisvarlena;
4980 : :
4981 : : /* check the result type's input function */
4982 : 10891 : getTypeInputInfo(iocoerce->resulttype,
4983 : : &iofunc, &typioparam);
2503 4984 : 10891 : add_function_cost(context->root, iofunc, NULL,
4985 : : &context->total);
4986 : : /* check the input type's output function */
6770 4987 : 10891 : getTypeOutputInfo(exprType((Node *) iocoerce->arg),
4988 : : &iofunc, &typisvarlena);
2503 4989 : 10891 : add_function_cost(context->root, iofunc, NULL,
4990 : : &context->total);
4991 : : }
6840 4992 [ + + ]: 1972501 : else if (IsA(node, ArrayCoerceExpr))
4993 : : {
4994 : 2609 : ArrayCoerceExpr *acoerce = (ArrayCoerceExpr *) node;
4995 : : QualCost perelemcost;
4996 : :
3000 4997 : 2609 : cost_qual_eval_node(&perelemcost, (Node *) acoerce->elemexpr,
4998 : : context->root);
4999 : 2609 : context->total.startup += perelemcost.startup;
5000 [ + + ]: 2609 : if (perelemcost.per_tuple > 0)
5001 : 33 : context->total.per_tuple += perelemcost.per_tuple *
713 5002 : 33 : estimate_array_length(context->root, (Node *) acoerce->arg);
5003 : : }
7294 5004 [ + + ]: 1969892 : else if (IsA(node, RowCompareExpr))
5005 : : {
5006 : : /* Conservatively assume we will check all the columns */
5007 : 126 : RowCompareExpr *rcexpr = (RowCompareExpr *) node;
5008 : : ListCell *lc;
5009 : :
6904 5010 [ + - + + : 405 : foreach(lc, rcexpr->opnos)
+ + ]
5011 : : {
6607 bruce@momjian.us 5012 : 279 : Oid opid = lfirst_oid(lc);
5013 : :
2503 tgl@sss.pgh.pa.us 5014 : 279 : add_function_cost(context->root, get_opcode(opid), NULL,
5015 : : &context->total);
5016 : : }
5017 : : }
3078 5018 [ + + ]: 1969766 : else if (IsA(node, MinMaxExpr) ||
945 michael@paquier.xyz 5019 [ + + ]: 1969630 : IsA(node, SQLValueFunction) ||
3078 tgl@sss.pgh.pa.us 5020 [ + + ]: 1967228 : IsA(node, XmlExpr) ||
5021 [ + + ]: 1966877 : IsA(node, CoerceToDomain) ||
636 amitlan@postgresql.o 5022 [ + + ]: 1961948 : IsA(node, NextValueExpr) ||
5023 [ + + ]: 1961750 : IsA(node, JsonExpr))
5024 : : {
5025 : : /* Treat all these as having cost 1 */
3078 tgl@sss.pgh.pa.us 5026 : 9298 : context->total.per_tuple += cpu_operator_cost;
5027 : : }
8375 5028 [ - + ]: 1960468 : else if (IsA(node, SubLink))
5029 : : {
5030 : : /* This routine should not be applied to un-planned expressions */
8181 tgl@sss.pgh.pa.us 5031 [ # # ]:UBC 0 : elog(ERROR, "cannot handle unplanned sub-select");
5032 : : }
8404 tgl@sss.pgh.pa.us 5033 [ + + ]:CBC 1960468 : else if (IsA(node, SubPlan))
5034 : : {
5035 : : /*
5036 : : * A subplan node in an expression typically indicates that the
5037 : : * subplan will be executed on each evaluation, so charge accordingly.
5038 : : * (Sub-selects that can be executed as InitPlans have already been
5039 : : * removed from the expression.)
5040 : : */
8171 bruce@momjian.us 5041 : 21949 : SubPlan *subplan = (SubPlan *) node;
5042 : :
6326 tgl@sss.pgh.pa.us 5043 : 21949 : context->total.startup += subplan->startup_cost;
5044 : 21949 : context->total.per_tuple += subplan->per_call_cost;
5045 : :
5046 : : /*
5047 : : * We don't want to recurse into the testexpr, because it was already
5048 : : * counted in the SubPlan node's costs. So we're done.
5049 : : */
5050 : 21949 : return false;
5051 : : }
5052 [ + + ]: 1938519 : else if (IsA(node, AlternativeSubPlan))
5053 : : {
5054 : : /*
5055 : : * Arbitrarily use the first alternative plan for costing. (We should
5056 : : * certainly only include one alternative, and we don't yet have
5057 : : * enough information to know which one the executor is most likely to
5058 : : * use.)
5059 : : */
5060 : 933 : AlternativeSubPlan *asplan = (AlternativeSubPlan *) node;
5061 : :
5062 : 933 : return cost_qual_eval_walker((Node *) linitial(asplan->subplans),
5063 : : context);
5064 : : }
3590 5065 [ + + ]: 1937586 : else if (IsA(node, PlaceHolderVar))
5066 : : {
5067 : : /*
5068 : : * A PlaceHolderVar should be given cost zero when considering general
5069 : : * expression evaluation costs. The expense of doing the contained
5070 : : * expression is charged as part of the tlist eval costs of the scan
5071 : : * or join where the PHV is first computed (see set_rel_width and
5072 : : * add_placeholders_to_joinrel). If we charged it again here, we'd be
5073 : : * double-counting the cost for each level of plan that the PHV
5074 : : * bubbles up through. Hence, return without recursing into the
5075 : : * phexpr.
5076 : : */
5077 : 2610 : return false;
5078 : : }
5079 : :
5080 : : /* recurse into children */
384 peter@eisentraut.org 5081 : 2486950 : return expression_tree_walker(node, cost_qual_eval_walker, context);
5082 : : }
5083 : :
5084 : : /*
5085 : : * get_restriction_qual_cost
5086 : : * Compute evaluation costs of a baserel's restriction quals, plus any
5087 : : * movable join quals that have been pushed down to the scan.
5088 : : * Results are returned into *qpqual_cost.
5089 : : *
5090 : : * This is a convenience subroutine that works for seqscans and other cases
5091 : : * where all the given quals will be evaluated the hard way. It's not useful
5092 : : * for cost_index(), for example, where the index machinery takes care of
5093 : : * some of the quals. We assume baserestrictcost was previously set by
5094 : : * set_baserel_size_estimates().
5095 : : */
5096 : : static void
4990 tgl@sss.pgh.pa.us 5097 : 558323 : get_restriction_qual_cost(PlannerInfo *root, RelOptInfo *baserel,
5098 : : ParamPathInfo *param_info,
5099 : : QualCost *qpqual_cost)
5100 : : {
5101 [ + + ]: 558323 : if (param_info)
5102 : : {
5103 : : /* Include costs of pushed-down clauses */
5104 : 123781 : cost_qual_eval(qpqual_cost, param_info->ppi_clauses, root);
5105 : :
5106 : 123781 : qpqual_cost->startup += baserel->baserestrictcost.startup;
5107 : 123781 : qpqual_cost->per_tuple += baserel->baserestrictcost.per_tuple;
5108 : : }
5109 : : else
5110 : 434542 : *qpqual_cost = baserel->baserestrictcost;
5111 : 558323 : }
5112 : :
5113 : :
5114 : : /*
5115 : : * compute_semi_anti_join_factors
5116 : : * Estimate how much of the inner input a SEMI, ANTI, or inner_unique join
5117 : : * can be expected to scan.
5118 : : *
5119 : : * In a hash or nestloop SEMI/ANTI join, the executor will stop scanning
5120 : : * inner rows as soon as it finds a match to the current outer row.
5121 : : * The same happens if we have detected the inner rel is unique.
5122 : : * We should therefore adjust some of the cost components for this effect.
5123 : : * This function computes some estimates needed for these adjustments.
5124 : : * These estimates will be the same regardless of the particular paths used
5125 : : * for the outer and inner relation, so we compute these once and then pass
5126 : : * them to all the join cost estimation functions.
5127 : : *
5128 : : * Input parameters:
5129 : : * joinrel: join relation under consideration
5130 : : * outerrel: outer relation under consideration
5131 : : * innerrel: inner relation under consideration
5132 : : * jointype: if not JOIN_SEMI or JOIN_ANTI, we assume it's inner_unique
5133 : : * sjinfo: SpecialJoinInfo relevant to this join
5134 : : * restrictlist: join quals
5135 : : * Output parameters:
5136 : : * *semifactors is filled in (see pathnodes.h for field definitions)
5137 : : */
5138 : : void
5073 5139 : 108570 : compute_semi_anti_join_factors(PlannerInfo *root,
5140 : : RelOptInfo *joinrel,
5141 : : RelOptInfo *outerrel,
5142 : : RelOptInfo *innerrel,
5143 : : JoinType jointype,
5144 : : SpecialJoinInfo *sjinfo,
5145 : : List *restrictlist,
5146 : : SemiAntiJoinFactors *semifactors)
5147 : : {
5148 : : Selectivity jselec;
5149 : : Selectivity nselec;
5150 : : Selectivity avgmatch;
5151 : : SpecialJoinInfo norm_sjinfo;
5152 : : List *joinquals;
5153 : : ListCell *l;
5154 : :
5155 : : /*
5156 : : * In an ANTI join, we must ignore clauses that are "pushed down", since
5157 : : * those won't affect the match logic. In a SEMI join, we do not
5158 : : * distinguish joinquals from "pushed down" quals, so just use the whole
5159 : : * restrictinfo list. For other outer join types, we should consider only
5160 : : * non-pushed-down quals, so that this devolves to an IS_OUTER_JOIN check.
5161 : : */
3176 5162 [ + + ]: 108570 : if (IS_OUTER_JOIN(jointype))
5163 : : {
6066 5164 : 37988 : joinquals = NIL;
5073 5165 [ + + + + : 83191 : foreach(l, restrictlist)
+ + ]
5166 : : {
3173 5167 : 45203 : RestrictInfo *rinfo = lfirst_node(RestrictInfo, l);
5168 : :
2798 5169 [ + + + - ]: 45203 : if (!RINFO_IS_PUSHED_DOWN(rinfo, joinrel->relids))
6066 5170 : 42729 : joinquals = lappend(joinquals, rinfo);
5171 : : }
5172 : : }
5173 : : else
5073 5174 : 70582 : joinquals = restrictlist;
5175 : :
5176 : : /*
5177 : : * Get the JOIN_SEMI or JOIN_ANTI selectivity of the join clauses.
5178 : : */
6066 5179 [ + + ]: 108570 : jselec = clauselist_selectivity(root,
5180 : : joinquals,
5181 : : 0,
5182 : : (jointype == JOIN_ANTI) ? JOIN_ANTI : JOIN_SEMI,
5183 : : sjinfo);
5184 : :
5185 : : /*
5186 : : * Also get the normal inner-join selectivity of the join clauses.
5187 : : */
632 amitlan@postgresql.o 5188 : 108570 : init_dummy_sjinfo(&norm_sjinfo, outerrel->relids, innerrel->relids);
5189 : :
6066 tgl@sss.pgh.pa.us 5190 : 108570 : nselec = clauselist_selectivity(root,
5191 : : joinquals,
5192 : : 0,
5193 : : JOIN_INNER,
5194 : : &norm_sjinfo);
5195 : :
5196 : : /* Avoid leaking a lot of ListCells */
3176 5197 [ + + ]: 108570 : if (IS_OUTER_JOIN(jointype))
6066 5198 : 37988 : list_free(joinquals);
5199 : :
5200 : : /*
5201 : : * jselec can be interpreted as the fraction of outer-rel rows that have
5202 : : * any matches (this is true for both SEMI and ANTI cases). And nselec is
5203 : : * the fraction of the Cartesian product that matches. So, the average
5204 : : * number of matches for each outer-rel row that has at least one match is
5205 : : * nselec * inner_rows / jselec.
5206 : : *
5207 : : * Note: it is correct to use the inner rel's "rows" count here, even
5208 : : * though we might later be considering a parameterized inner path with
5209 : : * fewer rows. This is because we have included all the join clauses in
5210 : : * the selectivity estimate.
5211 : : */
5212 [ + + ]: 108570 : if (jselec > 0) /* protect against zero divide */
5213 : : {
5073 5214 : 108369 : avgmatch = nselec * innerrel->rows / jselec;
5215 : : /* Clamp to sane range */
6066 5216 [ + + ]: 108369 : avgmatch = Max(1.0, avgmatch);
5217 : : }
5218 : : else
5219 : 201 : avgmatch = 1.0;
5220 : :
5073 5221 : 108570 : semifactors->outer_match_frac = jselec;
5222 : 108570 : semifactors->match_count = avgmatch;
5223 : 108570 : }
5224 : :
5225 : : /*
5226 : : * has_indexed_join_quals
5227 : : * Check whether all the joinquals of a nestloop join are used as
5228 : : * inner index quals.
5229 : : *
5230 : : * If the inner path of a SEMI/ANTI join is an indexscan (including bitmap
5231 : : * indexscan) that uses all the joinquals as indexquals, we can assume that an
5232 : : * unmatched outer tuple is cheap to process, whereas otherwise it's probably
5233 : : * expensive.
5234 : : */
5235 : : static bool
1592 peter@eisentraut.org 5236 : 465106 : has_indexed_join_quals(NestPath *path)
5237 : : {
5238 : 465106 : JoinPath *joinpath = &path->jpath;
4990 tgl@sss.pgh.pa.us 5239 : 465106 : Relids joinrelids = joinpath->path.parent->relids;
5240 : 465106 : Path *innerpath = joinpath->innerjoinpath;
5241 : : List *indexclauses;
5242 : : bool found_one;
5243 : : ListCell *lc;
5244 : :
5245 : : /* If join still has quals to evaluate, it's not fast */
5246 [ + + ]: 465106 : if (joinpath->joinrestrictinfo != NIL)
5247 : 330174 : return false;
5248 : : /* Nor if the inner path isn't parameterized at all */
5249 [ + + ]: 134932 : if (innerpath->param_info == NULL)
5250 : 1650 : return false;
5251 : :
5252 : : /* Find the indexclauses list for the inner scan */
5253 [ + + + ]: 133282 : switch (innerpath->pathtype)
5254 : : {
5255 : 80979 : case T_IndexScan:
5256 : : case T_IndexOnlyScan:
5257 : 80979 : indexclauses = ((IndexPath *) innerpath)->indexclauses;
5258 : 80979 : break;
5259 : 135 : case T_BitmapHeapScan:
5260 : : {
5261 : : /* Accept only a simple bitmap scan, not AND/OR cases */
4938 bruce@momjian.us 5262 : 135 : Path *bmqual = ((BitmapHeapPath *) innerpath)->bitmapqual;
5263 : :
5264 [ + + ]: 135 : if (IsA(bmqual, IndexPath))
5265 : 111 : indexclauses = ((IndexPath *) bmqual)->indexclauses;
5266 : : else
5267 : 24 : return false;
5268 : 111 : break;
5269 : : }
4990 tgl@sss.pgh.pa.us 5270 : 52168 : default:
5271 : :
5272 : : /*
5273 : : * If it's not a simple indexscan, it probably doesn't run quickly
5274 : : * for zero rows out, even if it's a parameterized path using all
5275 : : * the joinquals.
5276 : : */
5073 5277 : 52168 : return false;
5278 : : }
5279 : :
5280 : : /*
5281 : : * Examine the inner path's param clauses. Any that are from the outer
5282 : : * path must be found in the indexclauses list, either exactly or in an
5283 : : * equivalent form generated by equivclass.c. Also, we must find at least
5284 : : * one such clause, else it's a clauseless join which isn't fast.
5285 : : */
4990 5286 : 81090 : found_one = false;
5287 [ + - + + : 160089 : foreach(lc, innerpath->param_info->ppi_clauses)
+ + ]
5288 : : {
5289 : 83413 : RestrictInfo *rinfo = (RestrictInfo *) lfirst(lc);
5290 : :
5291 [ + + ]: 83413 : if (join_clause_is_movable_into(rinfo,
5292 : 83413 : innerpath->parent->relids,
5293 : : joinrelids))
5294 : : {
2503 5295 [ + + ]: 83137 : if (!is_redundant_with_indexclauses(rinfo, indexclauses))
4990 5296 : 4414 : return false;
5297 : 78723 : found_one = true;
5298 : : }
5299 : : }
5300 : 76676 : return found_one;
5301 : : }
5302 : :
5303 : :
5304 : : /*
5305 : : * approx_tuple_count
5306 : : * Quick-and-dirty estimation of the number of join rows passing
5307 : : * a set of qual conditions.
5308 : : *
5309 : : * The quals can be either an implicitly-ANDed list of boolean expressions,
5310 : : * or a list of RestrictInfo nodes (typically the latter).
5311 : : *
5312 : : * We intentionally compute the selectivity under JOIN_INNER rules, even
5313 : : * if it's some type of outer join. This is appropriate because we are
5314 : : * trying to figure out how many tuples pass the initial merge or hash
5315 : : * join step.
5316 : : *
5317 : : * This is quick-and-dirty because we bypass clauselist_selectivity, and
5318 : : * simply multiply the independent clause selectivities together. Now
5319 : : * clauselist_selectivity often can't do any better than that anyhow, but
5320 : : * for some situations (such as range constraints) it is smarter. However,
5321 : : * we can't effectively cache the results of clauselist_selectivity, whereas
5322 : : * the individual clause selectivities can be and are cached.
5323 : : *
5324 : : * Since we are only using the results to estimate how many potential
5325 : : * output tuples are generated and passed through qpqual checking, it
5326 : : * seems OK to live with the approximation.
5327 : : */
5328 : : static double
6158 5329 : 398332 : approx_tuple_count(PlannerInfo *root, JoinPath *path, List *quals)
5330 : : {
5331 : : double tuples;
5073 5332 : 398332 : double outer_tuples = path->outerjoinpath->rows;
5333 : 398332 : double inner_tuples = path->innerjoinpath->rows;
5334 : : SpecialJoinInfo sjinfo;
6332 5335 : 398332 : Selectivity selec = 1.0;
5336 : : ListCell *l;
5337 : :
5338 : : /*
5339 : : * Make up a SpecialJoinInfo for JOIN_INNER semantics.
5340 : : */
632 amitlan@postgresql.o 5341 : 398332 : init_dummy_sjinfo(&sjinfo, path->outerjoinpath->parent->relids,
5342 : 398332 : path->innerjoinpath->parent->relids);
5343 : :
5344 : : /* Get the approximate selectivity */
8961 tgl@sss.pgh.pa.us 5345 [ + + + + : 842357 : foreach(l, quals)
+ + ]
5346 : : {
5347 : 444025 : Node *qual = (Node *) lfirst(l);
5348 : :
5349 : : /* Note that clause_selectivity will be able to cache its result */
3177 simon@2ndQuadrant.co 5350 : 444025 : selec *= clause_selectivity(root, qual, 0, JOIN_INNER, &sjinfo);
5351 : : }
5352 : :
5353 : : /* Apply it to the input relation sizes */
6158 tgl@sss.pgh.pa.us 5354 : 398332 : tuples = selec * outer_tuples * inner_tuples;
5355 : :
6332 5356 : 398332 : return clamp_row_est(tuples);
5357 : : }
5358 : :
5359 : :
5360 : : /*
5361 : : * set_baserel_size_estimates
5362 : : * Set the size estimates for the given base relation.
5363 : : *
5364 : : * The rel's targetlist and restrictinfo list must have been constructed
5365 : : * already, and rel->tuples must be set.
5366 : : *
5367 : : * We set the following fields of the rel node:
5368 : : * rows: the estimated number of output tuples (after applying
5369 : : * restriction clauses).
5370 : : * width: the estimated average output tuple width in bytes.
5371 : : * baserestrictcost: estimated cost of evaluating baserestrictinfo clauses.
5372 : : */
5373 : : void
7500 5374 : 265514 : set_baserel_size_estimates(PlannerInfo *root, RelOptInfo *rel)
5375 : : {
5376 : : double nrows;
5377 : :
5378 : : /* Should only be applied to base relations */
8348 5379 [ - + ]: 265514 : Assert(rel->relid > 0);
5380 : :
8017 5381 : 531013 : nrows = rel->tuples *
8018 5382 : 265514 : clauselist_selectivity(root,
5383 : : rel->baserestrictinfo,
5384 : : 0,
5385 : : JOIN_INNER,
5386 : : NULL);
5387 : :
8017 5388 : 265499 : rel->rows = clamp_row_est(nrows);
5389 : :
6873 5390 : 265499 : cost_qual_eval(&rel->baserestrictcost, rel->baserestrictinfo, root);
5391 : :
9474 5392 : 265499 : set_rel_width(root, rel);
10753 scrappy@hub.org 5393 : 265499 : }
5394 : :
5395 : : /*
5396 : : * get_parameterized_baserel_size
5397 : : * Make a size estimate for a parameterized scan of a base relation.
5398 : : *
5399 : : * 'param_clauses' lists the additional join clauses to be used.
5400 : : *
5401 : : * set_baserel_size_estimates must have been applied already.
5402 : : */
5403 : : double
4990 tgl@sss.pgh.pa.us 5404 : 81287 : get_parameterized_baserel_size(PlannerInfo *root, RelOptInfo *rel,
5405 : : List *param_clauses)
5406 : : {
5407 : : List *allclauses;
5408 : : double nrows;
5409 : :
5410 : : /*
5411 : : * Estimate the number of rows returned by the parameterized scan, knowing
5412 : : * that it will apply all the extra join clauses as well as the rel's own
5413 : : * restriction clauses. Note that we force the clauses to be treated as
5414 : : * non-join clauses during selectivity estimation.
5415 : : */
2319 5416 : 81287 : allclauses = list_concat_copy(param_clauses, rel->baserestrictinfo);
4990 5417 : 162574 : nrows = rel->tuples *
5418 : 81287 : clauselist_selectivity(root,
5419 : : allclauses,
3101 5420 : 81287 : rel->relid, /* do not use 0! */
5421 : : JOIN_INNER,
5422 : : NULL);
4990 5423 : 81287 : nrows = clamp_row_est(nrows);
5424 : : /* For safety, make sure result is not more than the base estimate */
5425 [ - + ]: 81287 : if (nrows > rel->rows)
4990 tgl@sss.pgh.pa.us 5426 :UBC 0 : nrows = rel->rows;
4990 tgl@sss.pgh.pa.us 5427 :CBC 81287 : return nrows;
5428 : : }
5429 : :
5430 : : /*
5431 : : * set_joinrel_size_estimates
5432 : : * Set the size estimates for the given join relation.
5433 : : *
5434 : : * The rel's targetlist must have been constructed already, and a
5435 : : * restriction clause list that matches the given component rels must
5436 : : * be provided.
5437 : : *
5438 : : * Since there is more than one way to make a joinrel for more than two
5439 : : * base relations, the results we get here could depend on which component
5440 : : * rel pair is provided. In theory we should get the same answers no matter
5441 : : * which pair is provided; in practice, since the selectivity estimation
5442 : : * routines don't handle all cases equally well, we might not. But there's
5443 : : * not much to be done about it. (Would it make sense to repeat the
5444 : : * calculations for each pair of input rels that's encountered, and somehow
5445 : : * average the results? Probably way more trouble than it's worth, and
5446 : : * anyway we must keep the rowcount estimate the same for all paths for the
5447 : : * joinrel.)
5448 : : *
5449 : : * We set only the rows field here. The reltarget field was already set by
5450 : : * build_joinrel_tlist, and baserestrictcost is not used for join rels.
5451 : : */
5452 : : void
7500 5453 : 128620 : set_joinrel_size_estimates(PlannerInfo *root, RelOptInfo *rel,
5454 : : RelOptInfo *outer_rel,
5455 : : RelOptInfo *inner_rel,
5456 : : SpecialJoinInfo *sjinfo,
5457 : : List *restrictlist)
5458 : : {
5073 5459 : 128620 : rel->rows = calc_joinrel_size_estimate(root,
5460 : : rel,
5461 : : outer_rel,
5462 : : inner_rel,
5463 : : outer_rel->rows,
5464 : : inner_rel->rows,
5465 : : sjinfo,
5466 : : restrictlist);
5467 : 128620 : }
5468 : :
5469 : : /*
5470 : : * get_parameterized_joinrel_size
5471 : : * Make a size estimate for a parameterized scan of a join relation.
5472 : : *
5473 : : * 'rel' is the joinrel under consideration.
5474 : : * 'outer_path', 'inner_path' are (probably also parameterized) Paths that
5475 : : * produce the relations being joined.
5476 : : * 'sjinfo' is any SpecialJoinInfo relevant to this join.
5477 : : * 'restrict_clauses' lists the join clauses that need to be applied at the
5478 : : * join node (including any movable clauses that were moved down to this join,
5479 : : * and not including any movable clauses that were pushed down into the
5480 : : * child paths).
5481 : : *
5482 : : * set_joinrel_size_estimates must have been applied already.
5483 : : */
5484 : : double
4990 5485 : 4838 : get_parameterized_joinrel_size(PlannerInfo *root, RelOptInfo *rel,
5486 : : Path *outer_path,
5487 : : Path *inner_path,
5488 : : SpecialJoinInfo *sjinfo,
5489 : : List *restrict_clauses)
5490 : : {
5491 : : double nrows;
5492 : :
5493 : : /*
5494 : : * Estimate the number of rows returned by the parameterized join as the
5495 : : * sizes of the input paths times the selectivity of the clauses that have
5496 : : * ended up at this join node.
5497 : : *
5498 : : * As with set_joinrel_size_estimates, the rowcount estimate could depend
5499 : : * on the pair of input paths provided, though ideally we'd get the same
5500 : : * estimate for any pair with the same parameterization.
5501 : : */
5502 : 4838 : nrows = calc_joinrel_size_estimate(root,
5503 : : rel,
5504 : : outer_path->parent,
5505 : : inner_path->parent,
5506 : : outer_path->rows,
5507 : : inner_path->rows,
5508 : : sjinfo,
5509 : : restrict_clauses);
5510 : : /* For safety, make sure result is not more than the base estimate */
5511 [ + + ]: 4838 : if (nrows > rel->rows)
5512 : 6 : nrows = rel->rows;
5513 : 4838 : return nrows;
5514 : : }
5515 : :
5516 : : /*
5517 : : * calc_joinrel_size_estimate
5518 : : * Workhorse for set_joinrel_size_estimates and
5519 : : * get_parameterized_joinrel_size.
5520 : : *
5521 : : * outer_rel/inner_rel are the relations being joined, but they should be
5522 : : * assumed to have sizes outer_rows/inner_rows; those numbers might be less
5523 : : * than what rel->rows says, when we are considering parameterized paths.
5524 : : */
5525 : : static double
5073 5526 : 133458 : calc_joinrel_size_estimate(PlannerInfo *root,
5527 : : RelOptInfo *joinrel,
5528 : : RelOptInfo *outer_rel,
5529 : : RelOptInfo *inner_rel,
5530 : : double outer_rows,
5531 : : double inner_rows,
5532 : : SpecialJoinInfo *sjinfo,
5533 : : List *restrictlist)
5534 : : {
6334 5535 : 133458 : JoinType jointype = sjinfo->jointype;
5536 : : Selectivity fkselec;
5537 : : Selectivity jselec;
5538 : : Selectivity pselec;
5539 : : double nrows;
5540 : :
5541 : : /*
5542 : : * Compute joinclause selectivity. Note that we are only considering
5543 : : * clauses that become restriction clauses at this join level; we are not
5544 : : * double-counting them because they were not considered in estimating the
5545 : : * sizes of the component rels.
5546 : : *
5547 : : * First, see whether any of the joinclauses can be matched to known FK
5548 : : * constraints. If so, drop those clauses from the restrictlist, and
5549 : : * instead estimate their selectivity using FK semantics. (We do this
5550 : : * without regard to whether said clauses are local or "pushed down".
5551 : : * Probably, an FK-matching clause could never be seen as pushed down at
5552 : : * an outer join, since it would be strict and hence would be grounds for
5553 : : * join strength reduction.) fkselec gets the net selectivity for
5554 : : * FK-matching clauses, or 1.0 if there are none.
5555 : : */
3469 5556 : 133458 : fkselec = get_foreign_key_join_selectivity(root,
5557 : : outer_rel->relids,
5558 : : inner_rel->relids,
5559 : : sjinfo,
5560 : : &restrictlist);
5561 : :
5562 : : /*
5563 : : * For an outer join, we have to distinguish the selectivity of the join's
5564 : : * own clauses (JOIN/ON conditions) from any clauses that were "pushed
5565 : : * down". For inner joins we just count them all as joinclauses.
5566 : : */
6977 5567 [ + + ]: 133458 : if (IS_OUTER_JOIN(jointype))
5568 : : {
5569 : 40663 : List *joinquals = NIL;
5570 : 40663 : List *pushedquals = NIL;
5571 : : ListCell *l;
5572 : :
5573 : : /* Grovel through the clauses to separate into two lists */
5574 [ + + + + : 91719 : foreach(l, restrictlist)
+ + ]
5575 : : {
3173 5576 : 51056 : RestrictInfo *rinfo = lfirst_node(RestrictInfo, l);
5577 : :
2798 5578 [ + + + + ]: 51056 : if (RINFO_IS_PUSHED_DOWN(rinfo, joinrel->relids))
6977 5579 : 2169 : pushedquals = lappend(pushedquals, rinfo);
5580 : : else
5581 : 48887 : joinquals = lappend(joinquals, rinfo);
5582 : : }
5583 : :
5584 : : /* Get the separate selectivities */
3480 5585 : 40663 : jselec = clauselist_selectivity(root,
5586 : : joinquals,
5587 : : 0,
5588 : : jointype,
5589 : : sjinfo);
6977 5590 : 40663 : pselec = clauselist_selectivity(root,
5591 : : pushedquals,
5592 : : 0,
5593 : : jointype,
5594 : : sjinfo);
5595 : :
5596 : : /* Avoid leaking a lot of ListCells */
5597 : 40663 : list_free(joinquals);
5598 : 40663 : list_free(pushedquals);
5599 : : }
5600 : : else
5601 : : {
3480 5602 : 92795 : jselec = clauselist_selectivity(root,
5603 : : restrictlist,
5604 : : 0,
5605 : : jointype,
5606 : : sjinfo);
6977 5607 : 92795 : pselec = 0.0; /* not used, keep compiler quiet */
5608 : : }
5609 : :
5610 : : /*
5611 : : * Basically, we multiply size of Cartesian product by selectivity.
5612 : : *
5613 : : * If we are doing an outer join, take that into account: the joinqual
5614 : : * selectivity has to be clamped using the knowledge that the output must
5615 : : * be at least as large as the non-nullable input. However, any
5616 : : * pushed-down quals are applied after the outer join, so their
5617 : : * selectivity applies fully.
5618 : : *
5619 : : * For JOIN_SEMI and JOIN_ANTI, the selectivity is defined as the fraction
5620 : : * of LHS rows that have matches, and we apply that straightforwardly.
5621 : : */
9070 5622 [ + + + + : 133458 : switch (jointype)
+ - ]
5623 : : {
5624 : 88677 : case JOIN_INNER:
3469 5625 : 88677 : nrows = outer_rows * inner_rows * fkselec * jselec;
5626 : : /* pselec not used */
9070 5627 : 88677 : break;
5628 : 37271 : case JOIN_LEFT:
3469 5629 : 37271 : nrows = outer_rows * inner_rows * fkselec * jselec;
5073 5630 [ + + ]: 37271 : if (nrows < outer_rows)
5631 : 15204 : nrows = outer_rows;
6977 5632 : 37271 : nrows *= pselec;
9070 5633 : 37271 : break;
5634 : 860 : case JOIN_FULL:
3469 5635 : 860 : nrows = outer_rows * inner_rows * fkselec * jselec;
5073 5636 [ + + ]: 860 : if (nrows < outer_rows)
5637 : 589 : nrows = outer_rows;
5638 [ + + ]: 860 : if (nrows < inner_rows)
5639 : 60 : nrows = inner_rows;
6977 5640 : 860 : nrows *= pselec;
9070 5641 : 860 : break;
6334 5642 : 4118 : case JOIN_SEMI:
3469 5643 : 4118 : nrows = outer_rows * fkselec * jselec;
5644 : : /* pselec not used */
8367 5645 : 4118 : break;
6334 5646 : 2532 : case JOIN_ANTI:
3469 5647 : 2532 : nrows = outer_rows * (1.0 - fkselec * jselec);
6334 5648 : 2532 : nrows *= pselec;
8367 5649 : 2532 : break;
9070 tgl@sss.pgh.pa.us 5650 :UBC 0 : default:
5651 : : /* other values not expected here */
8181 5652 [ # # ]: 0 : elog(ERROR, "unrecognized join type: %d", (int) jointype);
5653 : : nrows = 0; /* keep compiler quiet */
5654 : : break;
5655 : : }
5656 : :
5073 tgl@sss.pgh.pa.us 5657 :CBC 133458 : return clamp_row_est(nrows);
5658 : : }
5659 : :
5660 : : /*
5661 : : * get_foreign_key_join_selectivity
5662 : : * Estimate join selectivity for foreign-key-related clauses.
5663 : : *
5664 : : * Remove any clauses that can be matched to FK constraints from *restrictlist,
5665 : : * and return a substitute estimate of their selectivity. 1.0 is returned
5666 : : * when there are no such clauses.
5667 : : *
5668 : : * The reason for treating such clauses specially is that we can get better
5669 : : * estimates this way than by relying on clauselist_selectivity(), especially
5670 : : * for multi-column FKs where that function's assumption that the clauses are
5671 : : * independent falls down badly. But even with single-column FKs, we may be
5672 : : * able to get a better answer when the pg_statistic stats are missing or out
5673 : : * of date.
5674 : : */
5675 : : static Selectivity
3469 5676 : 133458 : get_foreign_key_join_selectivity(PlannerInfo *root,
5677 : : Relids outer_relids,
5678 : : Relids inner_relids,
5679 : : SpecialJoinInfo *sjinfo,
5680 : : List **restrictlist)
5681 : : {
5682 : 133458 : Selectivity fkselec = 1.0;
5683 : 133458 : JoinType jointype = sjinfo->jointype;
5684 : 133458 : List *worklist = *restrictlist;
5685 : : ListCell *lc;
5686 : :
5687 : : /* Consider each FK constraint that is known to match the query */
5688 [ + + + + : 134441 : foreach(lc, root->fkey_list)
+ + ]
5689 : : {
5690 : 983 : ForeignKeyOptInfo *fkinfo = (ForeignKeyOptInfo *) lfirst(lc);
5691 : : bool ref_is_outer;
5692 : : List *removedlist;
5693 : : ListCell *cell;
5694 : :
5695 : : /*
5696 : : * This FK is not relevant unless it connects a baserel on one side of
5697 : : * this join to a baserel on the other side.
5698 : : */
5699 [ + + + + ]: 1790 : if (bms_is_member(fkinfo->con_relid, outer_relids) &&
5700 : 807 : bms_is_member(fkinfo->ref_relid, inner_relids))
5701 : 720 : ref_is_outer = false;
5702 [ + + + + ]: 433 : else if (bms_is_member(fkinfo->ref_relid, outer_relids) &&
5703 : 170 : bms_is_member(fkinfo->con_relid, inner_relids))
5704 : 65 : ref_is_outer = true;
5705 : : else
5706 : 198 : continue;
5707 : :
5708 : : /*
5709 : : * If we're dealing with a semi/anti join, and the FK's referenced
5710 : : * relation is on the outside, then knowledge of the FK doesn't help
5711 : : * us figure out what we need to know (which is the fraction of outer
5712 : : * rows that have matches). On the other hand, if the referenced rel
5713 : : * is on the inside, then all outer rows must have matches in the
5714 : : * referenced table (ignoring nulls). But any restriction or join
5715 : : * clauses that filter that table will reduce the fraction of matches.
5716 : : * We can account for restriction clauses, but it's too hard to guess
5717 : : * how many table rows would get through a join that's inside the RHS.
5718 : : * Hence, if either case applies, punt and ignore the FK.
5719 : : */
3103 5720 [ + - + + : 785 : if ((jointype == JOIN_SEMI || jointype == JOIN_ANTI) &&
+ + ]
5721 [ - + ]: 524 : (ref_is_outer || bms_membership(inner_relids) != BMS_SINGLETON))
5722 : 6 : continue;
5723 : :
5724 : : /*
5725 : : * Modify the restrictlist by removing clauses that match the FK (and
5726 : : * putting them into removedlist instead). It seems unsafe to modify
5727 : : * the originally-passed List structure, so we make a shallow copy the
5728 : : * first time through.
5729 : : */
3469 5730 [ + + ]: 779 : if (worklist == *restrictlist)
5731 : 667 : worklist = list_copy(worklist);
5732 : :
5733 : 779 : removedlist = NIL;
2347 5734 [ + + + + : 1626 : foreach(cell, worklist)
+ + ]
5735 : : {
3469 5736 : 847 : RestrictInfo *rinfo = (RestrictInfo *) lfirst(cell);
5737 : 847 : bool remove_it = false;
5738 : : int i;
5739 : :
5740 : : /* Drop this clause if it matches any column of the FK */
5741 [ + + ]: 1070 : for (i = 0; i < fkinfo->nkeys; i++)
5742 : : {
5743 [ + + ]: 1055 : if (rinfo->parent_ec)
5744 : : {
5745 : : /*
5746 : : * EC-derived clauses can only match by EC. It is okay to
5747 : : * consider any clause derived from the same EC as
5748 : : * matching the FK: even if equivclass.c chose to generate
5749 : : * a clause equating some other pair of Vars, it could
5750 : : * have generated one equating the FK's Vars. So for
5751 : : * purposes of estimation, we can act as though it did so.
5752 : : *
5753 : : * Note: checking parent_ec is a bit of a cheat because
5754 : : * there are EC-derived clauses that don't have parent_ec
5755 : : * set; but such clauses must compare expressions that
5756 : : * aren't just Vars, so they cannot match the FK anyway.
5757 : : */
5758 [ + + ]: 152 : if (fkinfo->eclass[i] == rinfo->parent_ec)
5759 : : {
5760 : 149 : remove_it = true;
5761 : 149 : break;
5762 : : }
5763 : : }
5764 : : else
5765 : : {
5766 : : /*
5767 : : * Otherwise, see if rinfo was previously matched to FK as
5768 : : * a "loose" clause.
5769 : : */
5770 [ + + ]: 903 : if (list_member_ptr(fkinfo->rinfos[i], rinfo))
5771 : : {
5772 : 683 : remove_it = true;
5773 : 683 : break;
5774 : : }
5775 : : }
5776 : : }
5777 [ + + ]: 847 : if (remove_it)
5778 : : {
2347 5779 : 832 : worklist = foreach_delete_current(worklist, cell);
3469 5780 : 832 : removedlist = lappend(removedlist, rinfo);
5781 : : }
5782 : : }
5783 : :
5784 : : /*
5785 : : * If we failed to remove all the matching clauses we expected to
5786 : : * find, chicken out and ignore this FK; applying its selectivity
5787 : : * might result in double-counting. Put any clauses we did manage to
5788 : : * remove back into the worklist.
5789 : : *
5790 : : * Since the matching clauses are known not outerjoin-delayed, they
5791 : : * would normally have appeared in the initial joinclause list. If we
5792 : : * didn't find them, there are two possibilities:
5793 : : *
5794 : : * 1. If the FK match is based on an EC that is ec_has_const, it won't
5795 : : * have generated any join clauses at all. We discount such ECs while
5796 : : * checking to see if we have "all" the clauses. (Below, we'll adjust
5797 : : * the selectivity estimate for this case.)
5798 : : *
5799 : : * 2. The clauses were matched to some other FK in a previous
5800 : : * iteration of this loop, and thus removed from worklist. (A likely
5801 : : * case is that two FKs are matched to the same EC; there will be only
5802 : : * one EC-derived clause in the initial list, so the first FK will
5803 : : * consume it.) Applying both FKs' selectivity independently risks
5804 : : * underestimating the join size; in particular, this would undo one
5805 : : * of the main things that ECs were invented for, namely to avoid
5806 : : * double-counting the selectivity of redundant equality conditions.
5807 : : * Later we might think of a reasonable way to combine the estimates,
5808 : : * but for now, just punt, since this is a fairly uncommon situation.
5809 : : */
1876 5810 [ + + ]: 779 : if (removedlist == NIL ||
5811 : 636 : list_length(removedlist) !=
5812 [ - + ]: 636 : (fkinfo->nmatched_ec - fkinfo->nconst_ec + fkinfo->nmatched_ri))
5813 : : {
3469 5814 : 143 : worklist = list_concat(worklist, removedlist);
5815 : 143 : continue;
5816 : : }
5817 : :
5818 : : /*
5819 : : * Finally we get to the payoff: estimate selectivity using the
5820 : : * knowledge that each referencing row will match exactly one row in
5821 : : * the referenced table.
5822 : : *
5823 : : * XXX that's not true in the presence of nulls in the referencing
5824 : : * column(s), so in principle we should derate the estimate for those.
5825 : : * However (1) if there are any strict restriction clauses for the
5826 : : * referencing column(s) elsewhere in the query, derating here would
5827 : : * be double-counting the null fraction, and (2) it's not very clear
5828 : : * how to combine null fractions for multiple referencing columns. So
5829 : : * we do nothing for now about correcting for nulls.
5830 : : *
5831 : : * XXX another point here is that if either side of an FK constraint
5832 : : * is an inheritance parent, we estimate as though the constraint
5833 : : * covers all its children as well. This is not an unreasonable
5834 : : * assumption for a referencing table, ie the user probably applied
5835 : : * identical constraints to all child tables (though perhaps we ought
5836 : : * to check that). But it's not possible to have done that for a
5837 : : * referenced table. Fortunately, precisely because that doesn't
5838 : : * work, it is uncommon in practice to have an FK referencing a parent
5839 : : * table. So, at least for now, disregard inheritance here.
5840 : : */
3103 5841 [ + - + + ]: 636 : if (jointype == JOIN_SEMI || jointype == JOIN_ANTI)
3469 5842 : 412 : {
5843 : : /*
5844 : : * For JOIN_SEMI and JOIN_ANTI, we only get here when the FK's
5845 : : * referenced table is exactly the inside of the join. The join
5846 : : * selectivity is defined as the fraction of LHS rows that have
5847 : : * matches. The FK implies that every LHS row has a match *in the
5848 : : * referenced table*; but any restriction clauses on it will
5849 : : * reduce the number of matches. Hence we take the join
5850 : : * selectivity as equal to the selectivity of the table's
5851 : : * restriction clauses, which is rows / tuples; but we must guard
5852 : : * against tuples == 0.
5853 : : */
3103 5854 : 412 : RelOptInfo *ref_rel = find_base_rel(root, fkinfo->ref_relid);
5855 [ + + ]: 412 : double ref_tuples = Max(ref_rel->tuples, 1.0);
5856 : :
5857 : 412 : fkselec *= ref_rel->rows / ref_tuples;
5858 : : }
5859 : : else
5860 : : {
5861 : : /*
5862 : : * Otherwise, selectivity is exactly 1/referenced-table-size; but
5863 : : * guard against tuples == 0. Note we should use the raw table
5864 : : * tuple count, not any estimate of its filtered or joined size.
5865 : : */
3469 5866 : 224 : RelOptInfo *ref_rel = find_base_rel(root, fkinfo->ref_relid);
5867 [ + - ]: 224 : double ref_tuples = Max(ref_rel->tuples, 1.0);
5868 : :
5869 : 224 : fkselec *= 1.0 / ref_tuples;
5870 : : }
5871 : :
5872 : : /*
5873 : : * If any of the FK columns participated in ec_has_const ECs, then
5874 : : * equivclass.c will have generated "var = const" restrictions for
5875 : : * each side of the join, thus reducing the sizes of both input
5876 : : * relations. Taking the fkselec at face value would amount to
5877 : : * double-counting the selectivity of the constant restriction for the
5878 : : * referencing Var. Hence, look for the restriction clause(s) that
5879 : : * were applied to the referencing Var(s), and divide out their
5880 : : * selectivity to correct for this.
5881 : : */
1876 5882 [ + + ]: 636 : if (fkinfo->nconst_ec > 0)
5883 : : {
5884 [ + + ]: 12 : for (int i = 0; i < fkinfo->nkeys; i++)
5885 : : {
5886 : 9 : EquivalenceClass *ec = fkinfo->eclass[i];
5887 : :
5888 [ + - + + ]: 9 : if (ec && ec->ec_has_const)
5889 : : {
5890 : 3 : EquivalenceMember *em = fkinfo->fk_eclass_member[i];
257 amitlan@postgresql.o 5891 : 3 : RestrictInfo *rinfo = find_derived_clause_for_ec_member(root,
5892 : : ec,
5893 : : em);
5894 : :
1876 tgl@sss.pgh.pa.us 5895 [ + - ]: 3 : if (rinfo)
5896 : : {
5897 : : Selectivity s0;
5898 : :
5899 : 3 : s0 = clause_selectivity(root,
5900 : : (Node *) rinfo,
5901 : : 0,
5902 : : jointype,
5903 : : sjinfo);
5904 [ + - ]: 3 : if (s0 > 0)
5905 : 3 : fkselec /= s0;
5906 : : }
5907 : : }
5908 : : }
5909 : : }
5910 : : }
5911 : :
3469 5912 : 133458 : *restrictlist = worklist;
1876 5913 [ - + - + ]: 133458 : CLAMP_PROBABILITY(fkselec);
3469 5914 : 133458 : return fkselec;
5915 : : }
5916 : :
5917 : : /*
5918 : : * set_subquery_size_estimates
5919 : : * Set the size estimates for a base relation that is a subquery.
5920 : : *
5921 : : * The rel's targetlist and restrictinfo list must have been constructed
5922 : : * already, and the Paths for the subquery must have been completed.
5923 : : * We look at the subquery's PlannerInfo to extract data.
5924 : : *
5925 : : * We set the same fields as set_baserel_size_estimates.
5926 : : */
5927 : : void
5219 5928 : 19880 : set_subquery_size_estimates(PlannerInfo *root, RelOptInfo *rel)
5929 : : {
5930 : 19880 : PlannerInfo *subroot = rel->subroot;
5931 : : RelOptInfo *sub_final_rel;
5932 : : ListCell *lc;
5933 : :
5934 : : /* Should only be applied to base relations that are subqueries */
5507 5935 [ - + ]: 19880 : Assert(rel->relid > 0);
3009 andrew@dunslane.net 5936 [ + - - + ]: 19880 : Assert(planner_rt_fetch(rel->relid, root)->rtekind == RTE_SUBQUERY);
5937 : :
5938 : : /*
5939 : : * Copy raw number of output rows from subquery. All of its paths should
5940 : : * have the same output rowcount, so just look at cheapest-total.
5941 : : */
3572 tgl@sss.pgh.pa.us 5942 : 19880 : sub_final_rel = fetch_upper_rel(subroot, UPPERREL_FINAL, NULL);
5943 : 19880 : rel->tuples = sub_final_rel->cheapest_total_path->rows;
5944 : :
5945 : : /*
5946 : : * Compute per-output-column width estimates by examining the subquery's
5947 : : * targetlist. For any output that is a plain Var, get the width estimate
5948 : : * that was made while planning the subquery. Otherwise, we leave it to
5949 : : * set_rel_width to fill in a datatype-based default estimate.
5950 : : */
5507 5951 [ + + + + : 93976 : foreach(lc, subroot->parse->targetList)
+ + ]
5952 : : {
3173 5953 : 74096 : TargetEntry *te = lfirst_node(TargetEntry, lc);
5507 5954 : 74096 : Node *texpr = (Node *) te->expr;
5230 5955 : 74096 : int32 item_width = 0;
5956 : :
5957 : : /* junk columns aren't visible to upper query */
5507 5958 [ + + ]: 74096 : if (te->resjunk)
5959 : 3450 : continue;
5960 : :
5961 : : /*
5962 : : * The subquery could be an expansion of a view that's had columns
5963 : : * added to it since the current query was parsed, so that there are
5964 : : * non-junk tlist columns in it that don't correspond to any column
5965 : : * visible at our query level. Ignore such columns.
5966 : : */
4644 5967 [ + - - + ]: 70646 : if (te->resno < rel->min_attr || te->resno > rel->max_attr)
4644 tgl@sss.pgh.pa.us 5968 :UBC 0 : continue;
5969 : :
5970 : : /*
5971 : : * XXX This currently doesn't work for subqueries containing set
5972 : : * operations, because the Vars in their tlists are bogus references
5973 : : * to the first leaf subquery, which wouldn't give the right answer
5974 : : * even if we could still get to its PlannerInfo.
5975 : : *
5976 : : * Also, the subquery could be an appendrel for which all branches are
5977 : : * known empty due to constraint exclusion, in which case
5978 : : * set_append_rel_pathlist will have left the attr_widths set to zero.
5979 : : *
5980 : : * In either case, we just leave the width estimate zero until
5981 : : * set_rel_width fixes it.
5982 : : */
5507 tgl@sss.pgh.pa.us 5983 [ + + ]:CBC 70646 : if (IsA(texpr, Var) &&
5984 [ + + ]: 31680 : subroot->parse->setOperations == NULL)
5985 : : {
5365 bruce@momjian.us 5986 : 30721 : Var *var = (Var *) texpr;
5507 tgl@sss.pgh.pa.us 5987 : 30721 : RelOptInfo *subrel = find_base_rel(subroot, var->varno);
5988 : :
5989 : 30721 : item_width = subrel->attr_widths[var->varattno - subrel->min_attr];
5990 : : }
5991 : 70646 : rel->attr_widths[te->resno - rel->min_attr] = item_width;
5992 : : }
5993 : :
5994 : : /* Now estimate number of output rows, etc */
5995 : 19880 : set_baserel_size_estimates(root, rel);
5996 : 19880 : }
5997 : :
5998 : : /*
5999 : : * set_function_size_estimates
6000 : : * Set the size estimates for a base relation that is a function call.
6001 : : *
6002 : : * The rel's targetlist and restrictinfo list must have been constructed
6003 : : * already.
6004 : : *
6005 : : * We set the same fields as set_baserel_size_estimates.
6006 : : */
6007 : : void
7500 6008 : 25133 : set_function_size_estimates(PlannerInfo *root, RelOptInfo *rel)
6009 : : {
6010 : : RangeTblEntry *rte;
6011 : : ListCell *lc;
6012 : :
6013 : : /* Should only be applied to base relations that are functions */
8348 6014 [ - + ]: 25133 : Assert(rel->relid > 0);
6815 6015 [ + - ]: 25133 : rte = planner_rt_fetch(rel->relid, root);
7378 6016 [ - + ]: 25133 : Assert(rte->rtekind == RTE_FUNCTION);
6017 : :
6018 : : /*
6019 : : * Estimate number of rows the functions will return. The rowcount of the
6020 : : * node is that of the largest function result.
6021 : : */
4409 6022 : 25133 : rel->tuples = 0;
6023 [ + - + + : 50520 : foreach(lc, rte->functions)
+ + ]
6024 : : {
6025 : 25387 : RangeTblFunction *rtfunc = (RangeTblFunction *) lfirst(lc);
2503 6026 : 25387 : double ntup = expression_returns_set_rows(root, rtfunc->funcexpr);
6027 : :
4409 6028 [ + + ]: 25387 : if (ntup > rel->tuples)
6029 : 25145 : rel->tuples = ntup;
6030 : : }
6031 : :
6032 : : /* Now estimate number of output rows, etc */
8017 6033 : 25133 : set_baserel_size_estimates(root, rel);
8620 6034 : 25133 : }
6035 : :
6036 : : /*
6037 : : * set_function_size_estimates
6038 : : * Set the size estimates for a base relation that is a function call.
6039 : : *
6040 : : * The rel's targetlist and restrictinfo list must have been constructed
6041 : : * already.
6042 : : *
6043 : : * We set the same fields as set_tablefunc_size_estimates.
6044 : : */
6045 : : void
3206 alvherre@alvh.no-ip. 6046 : 311 : set_tablefunc_size_estimates(PlannerInfo *root, RelOptInfo *rel)
6047 : : {
6048 : : /* Should only be applied to base relations that are functions */
6049 [ - + ]: 311 : Assert(rel->relid > 0);
3009 andrew@dunslane.net 6050 [ + - - + ]: 311 : Assert(planner_rt_fetch(rel->relid, root)->rtekind == RTE_TABLEFUNC);
6051 : :
3206 alvherre@alvh.no-ip. 6052 : 311 : rel->tuples = 100;
6053 : :
6054 : : /* Now estimate number of output rows, etc */
6055 : 311 : set_baserel_size_estimates(root, rel);
6056 : 311 : }
6057 : :
6058 : : /*
6059 : : * set_values_size_estimates
6060 : : * Set the size estimates for a base relation that is a values list.
6061 : : *
6062 : : * The rel's targetlist and restrictinfo list must have been constructed
6063 : : * already.
6064 : : *
6065 : : * We set the same fields as set_baserel_size_estimates.
6066 : : */
6067 : : void
7077 mail@joeconway.com 6068 : 4236 : set_values_size_estimates(PlannerInfo *root, RelOptInfo *rel)
6069 : : {
6070 : : RangeTblEntry *rte;
6071 : :
6072 : : /* Should only be applied to base relations that are values lists */
6073 [ - + ]: 4236 : Assert(rel->relid > 0);
6815 tgl@sss.pgh.pa.us 6074 [ + - ]: 4236 : rte = planner_rt_fetch(rel->relid, root);
7077 mail@joeconway.com 6075 [ - + ]: 4236 : Assert(rte->rtekind == RTE_VALUES);
6076 : :
6077 : : /*
6078 : : * Estimate number of rows the values list will return. We know this
6079 : : * precisely based on the list length (well, barring set-returning
6080 : : * functions in list items, but that's a refinement not catered for
6081 : : * anywhere else either).
6082 : : */
6083 : 4236 : rel->tuples = list_length(rte->values_lists);
6084 : :
6085 : : /* Now estimate number of output rows, etc */
6086 : 4236 : set_baserel_size_estimates(root, rel);
6087 : 4236 : }
6088 : :
6089 : : /*
6090 : : * set_cte_size_estimates
6091 : : * Set the size estimates for a base relation that is a CTE reference.
6092 : : *
6093 : : * The rel's targetlist and restrictinfo list must have been constructed
6094 : : * already, and we need an estimate of the number of rows returned by the CTE
6095 : : * (if a regular CTE) or the non-recursive term (if a self-reference).
6096 : : *
6097 : : * We set the same fields as set_baserel_size_estimates.
6098 : : */
6099 : : void
3572 tgl@sss.pgh.pa.us 6100 : 2657 : set_cte_size_estimates(PlannerInfo *root, RelOptInfo *rel, double cte_rows)
6101 : : {
6102 : : RangeTblEntry *rte;
6103 : :
6104 : : /* Should only be applied to base relations that are CTE references */
6283 6105 [ - + ]: 2657 : Assert(rel->relid > 0);
6106 [ + - ]: 2657 : rte = planner_rt_fetch(rel->relid, root);
6107 [ - + ]: 2657 : Assert(rte->rtekind == RTE_CTE);
6108 : :
6109 [ + + ]: 2657 : if (rte->self_reference)
6110 : : {
6111 : : /*
6112 : : * In a self-reference, we assume the average worktable size is a
6113 : : * multiple of the nonrecursive term's size. The best multiplier will
6114 : : * vary depending on query "fan-out", so make its value adjustable.
6115 : : */
1364 6116 : 474 : rel->tuples = clamp_row_est(recursive_worktable_factor * cte_rows);
6117 : : }
6118 : : else
6119 : : {
6120 : : /* Otherwise just believe the CTE's rowcount estimate */
3572 6121 : 2183 : rel->tuples = cte_rows;
6122 : : }
6123 : :
6124 : : /* Now estimate number of output rows, etc */
6283 6125 : 2657 : set_baserel_size_estimates(root, rel);
6126 : 2657 : }
6127 : :
6128 : : /*
6129 : : * set_namedtuplestore_size_estimates
6130 : : * Set the size estimates for a base relation that is a tuplestore reference.
6131 : : *
6132 : : * The rel's targetlist and restrictinfo list must have been constructed
6133 : : * already.
6134 : : *
6135 : : * We set the same fields as set_baserel_size_estimates.
6136 : : */
6137 : : void
3183 kgrittn@postgresql.o 6138 : 241 : set_namedtuplestore_size_estimates(PlannerInfo *root, RelOptInfo *rel)
6139 : : {
6140 : : RangeTblEntry *rte;
6141 : :
6142 : : /* Should only be applied to base relations that are tuplestore references */
6143 [ - + ]: 241 : Assert(rel->relid > 0);
6144 [ + - ]: 241 : rte = planner_rt_fetch(rel->relid, root);
6145 [ - + ]: 241 : Assert(rte->rtekind == RTE_NAMEDTUPLESTORE);
6146 : :
6147 : : /*
6148 : : * Use the estimate provided by the code which is generating the named
6149 : : * tuplestore. In some cases, the actual number might be available; in
6150 : : * others the same plan will be re-used, so a "typical" value might be
6151 : : * estimated and used.
6152 : : */
6153 : 241 : rel->tuples = rte->enrtuples;
6154 [ - + ]: 241 : if (rel->tuples < 0)
3183 kgrittn@postgresql.o 6155 :UBC 0 : rel->tuples = 1000;
6156 : :
6157 : : /* Now estimate number of output rows, etc */
3183 kgrittn@postgresql.o 6158 :CBC 241 : set_baserel_size_estimates(root, rel);
6159 : 241 : }
6160 : :
6161 : : /*
6162 : : * set_result_size_estimates
6163 : : * Set the size estimates for an RTE_RESULT base relation
6164 : : *
6165 : : * The rel's targetlist and restrictinfo list must have been constructed
6166 : : * already.
6167 : : *
6168 : : * We set the same fields as set_baserel_size_estimates.
6169 : : */
6170 : : void
2515 tgl@sss.pgh.pa.us 6171 : 2143 : set_result_size_estimates(PlannerInfo *root, RelOptInfo *rel)
6172 : : {
6173 : : /* Should only be applied to RTE_RESULT base relations */
6174 [ - + ]: 2143 : Assert(rel->relid > 0);
6175 [ + - - + ]: 2143 : Assert(planner_rt_fetch(rel->relid, root)->rtekind == RTE_RESULT);
6176 : :
6177 : : /* RTE_RESULT always generates a single row, natively */
6178 : 2143 : rel->tuples = 1;
6179 : :
6180 : : /* Now estimate number of output rows, etc */
6181 : 2143 : set_baserel_size_estimates(root, rel);
6182 : 2143 : }
6183 : :
6184 : : /*
6185 : : * set_foreign_size_estimates
6186 : : * Set the size estimates for a base relation that is a foreign table.
6187 : : *
6188 : : * There is not a whole lot that we can do here; the foreign-data wrapper
6189 : : * is responsible for producing useful estimates. We can do a decent job
6190 : : * of estimating baserestrictcost, so we set that, and we also set up width
6191 : : * using what will be purely datatype-driven estimates from the targetlist.
6192 : : * There is no way to do anything sane with the rows value, so we just put
6193 : : * a default estimate and hope that the wrapper can improve on it. The
6194 : : * wrapper's GetForeignRelSize function will be called momentarily.
6195 : : *
6196 : : * The rel's targetlist and restrictinfo list must have been constructed
6197 : : * already.
6198 : : */
6199 : : void
5414 6200 : 1237 : set_foreign_size_estimates(PlannerInfo *root, RelOptInfo *rel)
6201 : : {
6202 : : /* Should only be applied to base relations */
6203 [ - + ]: 1237 : Assert(rel->relid > 0);
6204 : :
6205 : 1237 : rel->rows = 1000; /* entirely bogus default estimate */
6206 : :
6207 : 1237 : cost_qual_eval(&rel->baserestrictcost, rel->baserestrictinfo, root);
6208 : :
6209 : 1237 : set_rel_width(root, rel);
6210 : 1237 : }
6211 : :
6212 : :
6213 : : /*
6214 : : * set_rel_width
6215 : : * Set the estimated output width of a base relation.
6216 : : *
6217 : : * The estimated output width is the sum of the per-attribute width estimates
6218 : : * for the actually-referenced columns, plus any PHVs or other expressions
6219 : : * that have to be calculated at this relation. This is the amount of data
6220 : : * we'd need to pass upwards in case of a sort, hash, etc.
6221 : : *
6222 : : * This function also sets reltarget->cost, so it's a bit misnamed now.
6223 : : *
6224 : : * NB: this works best on plain relations because it prefers to look at
6225 : : * real Vars. For subqueries, set_subquery_size_estimates will already have
6226 : : * copied up whatever per-column estimates were made within the subquery,
6227 : : * and for other types of rels there isn't much we can do anyway. We fall
6228 : : * back on (fairly stupid) datatype-based width estimates if we can't get
6229 : : * any better number.
6230 : : *
6231 : : * The per-attribute width estimates are cached for possible re-use while
6232 : : * building join relations or post-scan/join pathtargets.
6233 : : */
6234 : : static void
7500 6235 : 266736 : set_rel_width(PlannerInfo *root, RelOptInfo *rel)
6236 : : {
6270 6237 [ + - ]: 266736 : Oid reloid = planner_rt_fetch(rel->relid, root)->relid;
729 6238 : 266736 : int64 tuple_width = 0;
5507 6239 : 266736 : bool have_wholerow_var = false;
6240 : : ListCell *lc;
6241 : :
6242 : : /* Vars are assumed to have cost zero, but other exprs do not */
3565 6243 : 266736 : rel->reltarget->cost.startup = 0;
6244 : 266736 : rel->reltarget->cost.per_tuple = 0;
6245 : :
6246 [ + + + + : 962813 : foreach(lc, rel->reltarget->exprs)
+ + ]
6247 : : {
6266 6248 : 696077 : Node *node = (Node *) lfirst(lc);
6249 : :
6250 : : /*
6251 : : * Ordinarily, a Var in a rel's targetlist must belong to that rel;
6252 : : * but there are corner cases involving LATERAL references where that
6253 : : * isn't so. If the Var has the wrong varno, fall through to the
6254 : : * generic case (it doesn't seem worth the trouble to be any smarter).
6255 : : */
4861 6256 [ + + ]: 696077 : if (IsA(node, Var) &&
6257 [ + + ]: 683860 : ((Var *) node)->varno == rel->relid)
7865 6258 : 183187 : {
6266 6259 : 683827 : Var *var = (Var *) node;
6260 : : int ndx;
6261 : : int32 item_width;
6262 : :
6263 [ - + ]: 683827 : Assert(var->varattno >= rel->min_attr);
6264 [ - + ]: 683827 : Assert(var->varattno <= rel->max_attr);
6265 : :
6266 : 683827 : ndx = var->varattno - rel->min_attr;
6267 : :
6268 : : /*
6269 : : * If it's a whole-row Var, we'll deal with it below after we have
6270 : : * already cached as many attr widths as possible.
6271 : : */
5507 6272 [ + + ]: 683827 : if (var->varattno == 0)
6273 : : {
6274 : 1529 : have_wholerow_var = true;
6275 : 1529 : continue;
6276 : : }
6277 : :
6278 : : /*
6279 : : * The width may have been cached already (especially if it's a
6280 : : * subquery), so don't duplicate effort.
6281 : : */
6266 6282 [ + + ]: 682298 : if (rel->attr_widths[ndx] > 0)
6283 : : {
6284 : 129975 : tuple_width += rel->attr_widths[ndx];
8207 6285 : 129975 : continue;
6286 : : }
6287 : :
6288 : : /* Try to get column width from statistics */
5507 6289 [ + + + + ]: 552323 : if (reloid != InvalidOid && var->varattno > 0)
6290 : : {
6266 6291 : 438792 : item_width = get_attavgwidth(reloid, var->varattno);
6292 [ + + ]: 438792 : if (item_width > 0)
6293 : : {
6294 : 369136 : rel->attr_widths[ndx] = item_width;
6295 : 369136 : tuple_width += item_width;
6296 : 369136 : continue;
6297 : : }
6298 : : }
6299 : :
6300 : : /*
6301 : : * Not a plain relation, or can't find statistics for it. Estimate
6302 : : * using just the type info.
6303 : : */
6304 : 183187 : item_width = get_typavgwidth(var->vartype, var->vartypmod);
6305 [ - + ]: 183187 : Assert(item_width > 0);
6306 : 183187 : rel->attr_widths[ndx] = item_width;
6307 : 183187 : tuple_width += item_width;
6308 : : }
6309 [ + + ]: 12250 : else if (IsA(node, PlaceHolderVar))
6310 : : {
6311 : : /*
6312 : : * We will need to evaluate the PHV's contained expression while
6313 : : * scanning this rel, so be sure to include it in reltarget->cost.
6314 : : */
6315 : 1007 : PlaceHolderVar *phv = (PlaceHolderVar *) node;
1218 6316 : 1007 : PlaceHolderInfo *phinfo = find_placeholder_info(root, phv);
6317 : : QualCost cost;
6318 : :
6266 6319 : 1007 : tuple_width += phinfo->ph_width;
3590 6320 : 1007 : cost_qual_eval_node(&cost, (Node *) phv->phexpr, root);
3565 6321 : 1007 : rel->reltarget->cost.startup += cost.startup;
6322 : 1007 : rel->reltarget->cost.per_tuple += cost.per_tuple;
6323 : : }
6324 : : else
6325 : : {
6326 : : /*
6327 : : * We could be looking at an expression pulled up from a subquery,
6328 : : * or a ROW() representing a whole-row child Var, etc. Do what we
6329 : : * can using the expression type information.
6330 : : */
6331 : : int32 item_width;
6332 : : QualCost cost;
6333 : :
6003 6334 : 11243 : item_width = get_typavgwidth(exprType(node), exprTypmod(node));
6335 [ - + ]: 11243 : Assert(item_width > 0);
6336 : 11243 : tuple_width += item_width;
6337 : : /* Not entirely clear if we need to account for cost, but do so */
3590 6338 : 11243 : cost_qual_eval_node(&cost, node, root);
3565 6339 : 11243 : rel->reltarget->cost.startup += cost.startup;
6340 : 11243 : rel->reltarget->cost.per_tuple += cost.per_tuple;
6341 : : }
6342 : : }
6343 : :
6344 : : /*
6345 : : * If we have a whole-row reference, estimate its width as the sum of
6346 : : * per-column widths plus heap tuple header overhead.
6347 : : */
5507 6348 [ + + ]: 266736 : if (have_wholerow_var)
6349 : : {
729 6350 : 1529 : int64 wholerow_width = MAXALIGN(SizeofHeapTupleHeader);
6351 : :
5507 6352 [ + + ]: 1529 : if (reloid != InvalidOid)
6353 : : {
6354 : : /* Real relation, so estimate true tuple width */
6355 : 1200 : wholerow_width += get_relation_data_width(reloid,
3101 6356 : 1200 : rel->attr_widths - rel->min_attr);
6357 : : }
6358 : : else
6359 : : {
6360 : : /* Do what we can with info for a phony rel */
6361 : : AttrNumber i;
6362 : :
5507 6363 [ + + ]: 897 : for (i = 1; i <= rel->max_attr; i++)
6364 : 568 : wholerow_width += rel->attr_widths[i - rel->min_attr];
6365 : : }
6366 : :
729 6367 : 1529 : rel->attr_widths[0 - rel->min_attr] = clamp_width_est(wholerow_width);
6368 : :
6369 : : /*
6370 : : * Include the whole-row Var as part of the output tuple. Yes, that
6371 : : * really is what happens at runtime.
6372 : : */
5507 6373 : 1529 : tuple_width += wholerow_width;
6374 : : }
6375 : :
729 6376 : 266736 : rel->reltarget->width = clamp_width_est(tuple_width);
10753 scrappy@hub.org 6377 : 266736 : }
6378 : :
6379 : : /*
6380 : : * set_pathtarget_cost_width
6381 : : * Set the estimated eval cost and output width of a PathTarget tlist.
6382 : : *
6383 : : * As a notational convenience, returns the same PathTarget pointer passed in.
6384 : : *
6385 : : * Most, though not quite all, uses of this function occur after we've run
6386 : : * set_rel_width() for base relations; so we can usually obtain cached width
6387 : : * estimates for Vars. If we can't, fall back on datatype-based width
6388 : : * estimates. Present early-planning uses of PathTargets don't need accurate
6389 : : * widths badly enough to justify going to the catalogs for better data.
6390 : : */
6391 : : PathTarget *
3572 tgl@sss.pgh.pa.us 6392 : 316943 : set_pathtarget_cost_width(PlannerInfo *root, PathTarget *target)
6393 : : {
729 6394 : 316943 : int64 tuple_width = 0;
6395 : : ListCell *lc;
6396 : :
6397 : : /* Vars are assumed to have cost zero, but other exprs do not */
3572 6398 : 316943 : target->cost.startup = 0;
6399 : 316943 : target->cost.per_tuple = 0;
6400 : :
6401 [ + + + + : 1110555 : foreach(lc, target->exprs)
+ + ]
6402 : : {
6403 : 793612 : Node *node = (Node *) lfirst(lc);
6404 : :
1003 drowley@postgresql.o 6405 : 793612 : tuple_width += get_expr_width(root, node);
6406 : :
6407 : : /* For non-Vars, account for evaluation cost */
6408 [ + + ]: 793612 : if (!IsA(node, Var))
6409 : : {
6410 : : QualCost cost;
6411 : :
3572 tgl@sss.pgh.pa.us 6412 : 335236 : cost_qual_eval_node(&cost, node, root);
6413 : 335236 : target->cost.startup += cost.startup;
6414 : 335236 : target->cost.per_tuple += cost.per_tuple;
6415 : : }
6416 : : }
6417 : :
729 6418 : 316943 : target->width = clamp_width_est(tuple_width);
6419 : :
3572 6420 : 316943 : return target;
6421 : : }
6422 : :
6423 : : /*
6424 : : * get_expr_width
6425 : : * Estimate the width of the given expr attempting to use the width
6426 : : * cached in a Var's owning RelOptInfo, else fallback on the type's
6427 : : * average width when unable to or when the given Node is not a Var.
6428 : : */
6429 : : static int32
1003 drowley@postgresql.o 6430 : 961800 : get_expr_width(PlannerInfo *root, const Node *expr)
6431 : : {
6432 : : int32 width;
6433 : :
6434 [ + + ]: 961800 : if (IsA(expr, Var))
6435 : : {
6436 : 620073 : const Var *var = (const Var *) expr;
6437 : :
6438 : : /* We should not see any upper-level Vars here */
6439 [ - + ]: 620073 : Assert(var->varlevelsup == 0);
6440 : :
6441 : : /* Try to get data from RelOptInfo cache */
6442 [ + + ]: 620073 : if (!IS_SPECIAL_VARNO(var->varno) &&
6443 [ + - ]: 617128 : var->varno < root->simple_rel_array_size)
6444 : : {
6445 : 617128 : RelOptInfo *rel = root->simple_rel_array[var->varno];
6446 : :
6447 [ + + ]: 617128 : if (rel != NULL &&
6448 [ + - ]: 608096 : var->varattno >= rel->min_attr &&
6449 [ + - ]: 608096 : var->varattno <= rel->max_attr)
6450 : : {
6451 : 608096 : int ndx = var->varattno - rel->min_attr;
6452 : :
6453 [ + + ]: 608096 : if (rel->attr_widths[ndx] > 0)
6454 : 591782 : return rel->attr_widths[ndx];
6455 : : }
6456 : : }
6457 : :
6458 : : /*
6459 : : * No cached data available, so estimate using just the type info.
6460 : : */
6461 : 28291 : width = get_typavgwidth(var->vartype, var->vartypmod);
6462 [ - + ]: 28291 : Assert(width > 0);
6463 : :
6464 : 28291 : return width;
6465 : : }
6466 : :
6467 : 341727 : width = get_typavgwidth(exprType(expr), exprTypmod(expr));
6468 [ - + ]: 341727 : Assert(width > 0);
6469 : 341727 : return width;
6470 : : }
6471 : :
6472 : : /*
6473 : : * relation_byte_size
6474 : : * Estimate the storage space in bytes for a given number of tuples
6475 : : * of a given width (size in bytes).
6476 : : */
6477 : : static double
9474 tgl@sss.pgh.pa.us 6478 : 2553542 : relation_byte_size(double tuples, int width)
6479 : : {
3952 6480 : 2553542 : return tuples * (MAXALIGN(width) + MAXALIGN(SizeofHeapTupleHeader));
6481 : : }
6482 : :
6483 : : /*
6484 : : * page_size
6485 : : * Returns an estimate of the number of pages covered by a given
6486 : : * number of tuples of a given width (size in bytes).
6487 : : */
6488 : : static double
9474 6489 : 4702 : page_size(double tuples, int width)
6490 : : {
6491 : 4702 : return ceil(relation_byte_size(tuples, width) / BLCKSZ);
6492 : : }
6493 : :
6494 : : /*
6495 : : * Estimate the fraction of the work that each worker will do given the
6496 : : * number of workers budgeted for the path.
6497 : : */
6498 : : static double
3260 rhaas@postgresql.org 6499 : 234442 : get_parallel_divisor(Path *path)
6500 : : {
6501 : 234442 : double parallel_divisor = path->parallel_workers;
6502 : :
6503 : : /*
6504 : : * Early experience with parallel query suggests that when there is only
6505 : : * one worker, the leader often makes a very substantial contribution to
6506 : : * executing the parallel portion of the plan, but as more workers are
6507 : : * added, it does less and less, because it's busy reading tuples from the
6508 : : * workers and doing whatever non-parallel post-processing is needed. By
6509 : : * the time we reach 4 workers, the leader no longer makes a meaningful
6510 : : * contribution. Thus, for now, estimate that the leader spends 30% of
6511 : : * its time servicing each worker, and the remainder executing the
6512 : : * parallel plan.
6513 : : */
2954 6514 [ + + ]: 234442 : if (parallel_leader_participation)
6515 : : {
6516 : : double leader_contribution;
6517 : :
6518 : 233791 : leader_contribution = 1.0 - (0.3 * path->parallel_workers);
6519 [ + + ]: 233791 : if (leader_contribution > 0)
6520 : 232501 : parallel_divisor += leader_contribution;
6521 : : }
6522 : :
3260 6523 : 234442 : return parallel_divisor;
6524 : : }
6525 : :
6526 : : /*
6527 : : * compute_bitmap_pages
6528 : : * Estimate number of pages fetched from heap in a bitmap heap scan.
6529 : : *
6530 : : * 'baserel' is the relation to be scanned
6531 : : * 'bitmapqual' is a tree of IndexPaths, BitmapAndPaths, and BitmapOrPaths
6532 : : * 'loop_count' is the number of repetitions of the indexscan to factor into
6533 : : * estimates of caching behavior
6534 : : *
6535 : : * If cost_p isn't NULL, the indexTotalCost estimate is returned in *cost_p.
6536 : : * If tuples_p isn't NULL, the tuples_fetched estimate is returned in *tuples_p.
6537 : : */
6538 : : double
730 tgl@sss.pgh.pa.us 6539 : 354776 : compute_bitmap_pages(PlannerInfo *root, RelOptInfo *baserel,
6540 : : Path *bitmapqual, double loop_count,
6541 : : Cost *cost_p, double *tuples_p)
6542 : : {
6543 : : Cost indexTotalCost;
6544 : : Selectivity indexSelectivity;
6545 : : double T;
6546 : : double pages_fetched;
6547 : : double tuples_fetched;
6548 : : double heap_pages;
6549 : : double maxentries;
6550 : :
6551 : : /*
6552 : : * Fetch total cost of obtaining the bitmap, as well as its total
6553 : : * selectivity.
6554 : : */
3246 rhaas@postgresql.org 6555 : 354776 : cost_bitmap_tree_node(bitmapqual, &indexTotalCost, &indexSelectivity);
6556 : :
6557 : : /*
6558 : : * Estimate number of main-table pages fetched.
6559 : : */
6560 : 354776 : tuples_fetched = clamp_row_est(indexSelectivity * baserel->tuples);
6561 : :
6562 [ + + ]: 354776 : T = (baserel->pages > 1) ? (double) baserel->pages : 1.0;
6563 : :
6564 : : /*
6565 : : * For a single scan, the number of heap pages that need to be fetched is
6566 : : * the same as the Mackert and Lohman formula for the case T <= b (ie, no
6567 : : * re-reads needed).
6568 : : */
2959 6569 : 354776 : pages_fetched = (2.0 * T * tuples_fetched) / (2.0 * T + tuples_fetched);
6570 : :
6571 : : /*
6572 : : * Calculate the number of pages fetched from the heap. Then based on
6573 : : * current work_mem estimate get the estimated maxentries in the bitmap.
6574 : : * (Note that we always do this calculation based on the number of pages
6575 : : * that would be fetched in a single iteration, even if loop_count > 1.
6576 : : * That's correct, because only that number of entries will be stored in
6577 : : * the bitmap at one time.)
6578 : : */
6579 [ + + ]: 354776 : heap_pages = Min(pages_fetched, baserel->pages);
320 tgl@sss.pgh.pa.us 6580 : 354776 : maxentries = tbm_calculate_entries(work_mem * (Size) 1024);
6581 : :
3246 rhaas@postgresql.org 6582 [ + + ]: 354776 : if (loop_count > 1)
6583 : : {
6584 : : /*
6585 : : * For repeated bitmap scans, scale up the number of tuples fetched in
6586 : : * the Mackert and Lohman formula by the number of scans, so that we
6587 : : * estimate the number of pages fetched by all the scans. Then
6588 : : * pro-rate for one scan.
6589 : : */
6590 : 74108 : pages_fetched = index_pages_fetched(tuples_fetched * loop_count,
6591 : : baserel->pages,
6592 : : get_indexpath_pages(bitmapqual),
6593 : : root);
6594 : 74108 : pages_fetched /= loop_count;
6595 : : }
6596 : :
6597 [ + + ]: 354776 : if (pages_fetched >= T)
6598 : 35836 : pages_fetched = T;
6599 : : else
6600 : 318940 : pages_fetched = ceil(pages_fetched);
6601 : :
2959 6602 [ + + ]: 354776 : if (maxentries < heap_pages)
6603 : : {
6604 : : double exact_pages;
6605 : : double lossy_pages;
6606 : :
6607 : : /*
6608 : : * Crude approximation of the number of lossy pages. Because of the
6609 : : * way tbm_lossify() is coded, the number of lossy pages increases
6610 : : * very sharply as soon as we run short of memory; this formula has
6611 : : * that property and seems to perform adequately in testing, but it's
6612 : : * possible we could do better somehow.
6613 : : */
6614 [ - + ]: 9 : lossy_pages = Max(0, heap_pages - maxentries / 2);
6615 : 9 : exact_pages = heap_pages - lossy_pages;
6616 : :
6617 : : /*
6618 : : * If there are lossy pages then recompute the number of tuples
6619 : : * processed by the bitmap heap node. We assume here that the chance
6620 : : * of a given tuple coming from an exact page is the same as the
6621 : : * chance that a given page is exact. This might not be true, but
6622 : : * it's not clear how we can do any better.
6623 : : */
6624 [ + - ]: 9 : if (lossy_pages > 0)
6625 : : tuples_fetched =
6626 : 9 : clamp_row_est(indexSelectivity *
6627 : 9 : (exact_pages / heap_pages) * baserel->tuples +
6628 : 9 : (lossy_pages / heap_pages) * baserel->tuples);
6629 : : }
6630 : :
730 tgl@sss.pgh.pa.us 6631 [ + + ]: 354776 : if (cost_p)
6632 : 280403 : *cost_p = indexTotalCost;
6633 [ + + ]: 354776 : if (tuples_p)
6634 : 280403 : *tuples_p = tuples_fetched;
6635 : :
3246 rhaas@postgresql.org 6636 : 354776 : return pages_fetched;
6637 : : }
6638 : :
6639 : : /*
6640 : : * compute_gather_rows
6641 : : * Estimate number of rows for gather (merge) nodes.
6642 : : *
6643 : : * In a parallel plan, each worker's row estimate is determined by dividing the
6644 : : * total number of rows by parallel_divisor, which accounts for the leader's
6645 : : * contribution in addition to the number of workers. Accordingly, when
6646 : : * estimating the number of rows for gather (merge) nodes, we multiply the rows
6647 : : * per worker by the same parallel_divisor to undo the division.
6648 : : */
6649 : : double
512 rguo@postgresql.org 6650 : 22178 : compute_gather_rows(Path *path)
6651 : : {
6652 [ - + ]: 22178 : Assert(path->parallel_workers > 0);
6653 : :
6654 : 22178 : return clamp_row_est(path->rows * get_parallel_divisor(path));
6655 : : }
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