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) 1996-2026, 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
8105 tgl@sss.pgh.pa.us 213 :CBC 6132122 : 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 : : */
1973 drowley@postgresql.o 221 [ + - - + ]: 6132122 : if (nrows > MAXIMUM_ROWCOUNT || isnan(nrows))
1973 drowley@postgresql.o 222 :UBC 0 : nrows = MAXIMUM_ROWCOUNT;
1973 drowley@postgresql.o 223 [ + + ]:CBC 6132122 : else if (nrows <= 1.0)
8105 tgl@sss.pgh.pa.us 224 : 1987729 : nrows = 1.0;
225 : : else
7633 226 : 4144393 : nrows = rint(nrows);
227 : :
8105 228 : 6132122 : 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
817 242 : 1102233 : 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 [ - + ]: 1102233 : if (tuple_width > MaxAllocSize)
817 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 : : */
817 tgl@sss.pgh.pa.us 255 [ - + ]:CBC 1102233 : Assert(tuple_width >= 0);
256 : :
257 : 1102233 : 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
7588 269 : 243651 : cost_seqscan(Path *path, PlannerInfo *root,
270 : : RelOptInfo *baserel, ParamPathInfo *param_info)
271 : : {
9525 272 : 243651 : 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;
46 rhaas@postgresql.org 278 :GNC 243651 : uint64 enable_mask = PGS_SEQSCAN;
279 : :
280 : : /* Should only be applied to base relations */
8436 tgl@sss.pgh.pa.us 281 [ - + ]:CBC 243651 : Assert(baserel->relid > 0);
8708 282 [ - + ]: 243651 : Assert(baserel->rtekind == RTE_RELATION);
283 : :
284 : : /* Mark the path with the correct row estimate */
5078 285 [ + + ]: 243651 : if (param_info)
286 : 420 : path->rows = param_info->ppi_rows;
287 : : else
288 : 243231 : path->rows = baserel->rows;
289 : :
290 : : /* fetch estimated page cost for tablespace containing table */
5913 rhaas@postgresql.org 291 : 243651 : get_tablespace_page_costs(baserel->reltablespace,
292 : : NULL,
293 : : &spc_seq_page_cost);
294 : :
295 : : /*
296 : : * disk costs
297 : : */
3707 298 : 243651 : disk_run_cost = spc_seq_page_cost * baserel->pages;
299 : :
300 : : /* CPU costs */
5078 tgl@sss.pgh.pa.us 301 : 243651 : get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
302 : :
303 : 243651 : startup_cost += qpqual_cost.startup;
304 : 243651 : cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple;
3707 rhaas@postgresql.org 305 : 243651 : cpu_run_cost = cpu_per_tuple * baserel->tuples;
306 : : /* tlist eval costs are paid per output row, not per tuple scanned */
3678 tgl@sss.pgh.pa.us 307 : 243651 : startup_cost += path->pathtarget->cost.startup;
308 : 243651 : cpu_run_cost += path->pathtarget->cost.per_tuple * path->rows;
309 : :
310 : : /* Adjust costing for parallelism, if used. */
3566 rhaas@postgresql.org 311 [ + + ]: 243651 : if (path->parallel_workers > 0)
312 : : {
3348 313 : 14596 : double parallel_divisor = get_parallel_divisor(path);
314 : :
315 : : /* The CPU cost is divided among all the workers. */
3707 316 : 14596 : cpu_run_cost /= parallel_divisor;
317 : :
318 : : /*
319 : : * It may be possible to amortize some of the I/O cost, but probably
320 : : * not very much, because most operating systems already do aggressive
321 : : * prefetching. For now, we assume that the disk run cost can't be
322 : : * amortized at all.
323 : : */
324 : :
325 : : /*
326 : : * In the case of a parallel plan, the row count needs to represent
327 : : * the number of tuples processed per worker.
328 : : */
3348 329 : 14596 : path->rows = clamp_row_est(path->rows / parallel_divisor);
330 : : }
331 : : else
46 rhaas@postgresql.org 332 :GNC 229055 : enable_mask |= PGS_CONSIDER_NONPARTIAL;
333 : :
334 : 243651 : path->disabled_nodes =
335 : 243651 : (baserel->pgs_mask & enable_mask) == enable_mask ? 0 : 1;
3957 simon@2ndQuadrant.co 336 :CBC 243651 : path->startup_cost = startup_cost;
3707 rhaas@postgresql.org 337 : 243651 : path->total_cost = startup_cost + cpu_run_cost + disk_run_cost;
3957 simon@2ndQuadrant.co 338 : 243651 : }
339 : :
340 : : /*
341 : : * cost_samplescan
342 : : * Determines and returns the cost of scanning a relation using sampling.
343 : : *
344 : : * 'baserel' is the relation to be scanned
345 : : * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
346 : : */
347 : : void
3886 tgl@sss.pgh.pa.us 348 : 153 : cost_samplescan(Path *path, PlannerInfo *root,
349 : : RelOptInfo *baserel, ParamPathInfo *param_info)
350 : : {
3957 simon@2ndQuadrant.co 351 : 153 : Cost startup_cost = 0;
352 : 153 : Cost run_cost = 0;
353 : : RangeTblEntry *rte;
354 : : TableSampleClause *tsc;
355 : : TsmRoutine *tsm;
356 : : double spc_seq_page_cost,
357 : : spc_random_page_cost,
358 : : spc_page_cost;
359 : : QualCost qpqual_cost;
360 : : Cost cpu_per_tuple;
46 rhaas@postgresql.org 361 :GNC 153 : uint64 enable_mask = 0;
362 : :
363 : : /* Should only be applied to base relations with tablesample clauses */
3957 simon@2ndQuadrant.co 364 [ - + ]:CBC 153 : Assert(baserel->relid > 0);
3886 tgl@sss.pgh.pa.us 365 [ + - ]: 153 : rte = planner_rt_fetch(baserel->relid, root);
366 [ - + ]: 153 : Assert(rte->rtekind == RTE_RELATION);
367 : 153 : tsc = rte->tablesample;
368 [ - + ]: 153 : Assert(tsc != NULL);
369 : 153 : tsm = GetTsmRoutine(tsc->tsmhandler);
370 : :
371 : : /* Mark the path with the correct row estimate */
372 [ + + ]: 153 : if (param_info)
373 : 36 : path->rows = param_info->ppi_rows;
374 : : else
3957 simon@2ndQuadrant.co 375 : 117 : path->rows = baserel->rows;
376 : :
377 : : /* fetch estimated page cost for tablespace containing table */
378 : 153 : get_tablespace_page_costs(baserel->reltablespace,
379 : : &spc_random_page_cost,
380 : : &spc_seq_page_cost);
381 : :
382 : : /* if NextSampleBlock is used, assume random access, else sequential */
3886 tgl@sss.pgh.pa.us 383 : 306 : spc_page_cost = (tsm->NextSampleBlock != NULL) ?
384 [ + + ]: 153 : spc_random_page_cost : spc_seq_page_cost;
385 : :
386 : : /*
387 : : * disk costs (recall that baserel->pages has already been set to the
388 : : * number of pages the sampling method will visit)
389 : : */
390 : 153 : run_cost += spc_page_cost * baserel->pages;
391 : :
392 : : /*
393 : : * CPU costs (recall that baserel->tuples has already been set to the
394 : : * number of tuples the sampling method will select). Note that we ignore
395 : : * execution cost of the TABLESAMPLE parameter expressions; they will be
396 : : * evaluated only once per scan, and in most usages they'll likely be
397 : : * simple constants anyway. We also don't charge anything for the
398 : : * calculations the sampling method might do internally.
399 : : */
400 : 153 : get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
401 : :
3957 simon@2ndQuadrant.co 402 : 153 : startup_cost += qpqual_cost.startup;
403 : 153 : cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple;
3886 tgl@sss.pgh.pa.us 404 : 153 : run_cost += cpu_per_tuple * baserel->tuples;
405 : : /* tlist eval costs are paid per output row, not per tuple scanned */
3678 406 : 153 : startup_cost += path->pathtarget->cost.startup;
407 : 153 : run_cost += path->pathtarget->cost.per_tuple * path->rows;
408 : :
46 rhaas@postgresql.org 409 [ + - ]:GNC 153 : if (path->parallel_workers == 0)
410 : 153 : enable_mask |= PGS_CONSIDER_NONPARTIAL;
411 : :
412 : 153 : path->disabled_nodes =
413 : 153 : (baserel->pgs_mask & enable_mask) == enable_mask ? 0 : 1;
9525 tgl@sss.pgh.pa.us 414 :CBC 153 : path->startup_cost = startup_cost;
415 : 153 : path->total_cost = startup_cost + run_cost;
10841 scrappy@hub.org 416 : 153 : }
417 : :
418 : : /*
419 : : * cost_gather
420 : : * Determines and returns the cost of gather path.
421 : : *
422 : : * 'rel' is the relation to be operated upon
423 : : * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
424 : : * 'rows' may be used to point to a row estimate; if non-NULL, it overrides
425 : : * both 'rel' and 'param_info'. This is useful when the path doesn't exactly
426 : : * correspond to any particular RelOptInfo.
427 : : */
428 : : void
3819 rhaas@postgresql.org 429 : 13545 : cost_gather(GatherPath *path, PlannerInfo *root,
430 : : RelOptInfo *rel, ParamPathInfo *param_info,
431 : : double *rows)
432 : : {
433 : 13545 : Cost startup_cost = 0;
434 : 13545 : Cost run_cost = 0;
435 : :
436 : : /* Mark the path with the correct row estimate */
3646 437 [ + + ]: 13545 : if (rows)
438 : 3486 : path->path.rows = *rows;
439 [ - + ]: 10059 : else if (param_info)
3819 rhaas@postgresql.org 440 :UBC 0 : path->path.rows = param_info->ppi_rows;
441 : : else
3819 rhaas@postgresql.org 442 :CBC 10059 : path->path.rows = rel->rows;
443 : :
444 : 13545 : startup_cost = path->subpath->startup_cost;
445 : :
446 : 13545 : run_cost = path->subpath->total_cost - path->subpath->startup_cost;
447 : :
448 : : /* Parallel setup and communication cost. */
449 : 13545 : startup_cost += parallel_setup_cost;
3777 450 : 13545 : run_cost += parallel_tuple_cost * path->path.rows;
451 : :
46 rhaas@postgresql.org 452 :GNC 13545 : path->path.disabled_nodes = path->subpath->disabled_nodes
453 : 13545 : + ((rel->pgs_mask & PGS_GATHER) != 0 ? 0 : 1);
3819 rhaas@postgresql.org 454 :CBC 13545 : path->path.startup_cost = startup_cost;
455 : 13545 : path->path.total_cost = (startup_cost + run_cost);
456 : 13545 : }
457 : :
458 : : /*
459 : : * cost_gather_merge
460 : : * Determines and returns the cost of gather merge path.
461 : : *
462 : : * GatherMerge merges several pre-sorted input streams, using a heap that at
463 : : * any given instant holds the next tuple from each stream. If there are N
464 : : * streams, we need about N*log2(N) tuple comparisons to construct the heap at
465 : : * startup, and then for each output tuple, about log2(N) comparisons to
466 : : * replace the top heap entry with the next tuple from the same stream.
467 : : */
468 : : void
3293 469 : 9745 : cost_gather_merge(GatherMergePath *path, PlannerInfo *root,
470 : : RelOptInfo *rel, ParamPathInfo *param_info,
471 : : int input_disabled_nodes,
472 : : Cost input_startup_cost, Cost input_total_cost,
473 : : double *rows)
474 : : {
475 : 9745 : Cost startup_cost = 0;
476 : 9745 : Cost run_cost = 0;
477 : : Cost comparison_cost;
478 : : double N;
479 : : double logN;
480 : :
481 : : /* Mark the path with the correct row estimate */
482 [ + + ]: 9745 : if (rows)
483 : 5794 : path->path.rows = *rows;
484 [ - + ]: 3951 : else if (param_info)
3293 rhaas@postgresql.org 485 :UBC 0 : path->path.rows = param_info->ppi_rows;
486 : : else
3293 rhaas@postgresql.org 487 :CBC 3951 : path->path.rows = rel->rows;
488 : :
489 : : /*
490 : : * Add one to the number of workers to account for the leader. This might
491 : : * be overgenerous since the leader will do less work than other workers
492 : : * in typical cases, but we'll go with it for now.
493 : : */
494 [ - + ]: 9745 : Assert(path->num_workers > 0);
495 : 9745 : N = (double) path->num_workers + 1;
496 : 9745 : logN = LOG2(N);
497 : :
498 : : /* Assumed cost per tuple comparison */
499 : 9745 : comparison_cost = 2.0 * cpu_operator_cost;
500 : :
501 : : /* Heap creation cost */
502 : 9745 : startup_cost += comparison_cost * N * logN;
503 : :
504 : : /* Per-tuple heap maintenance cost */
505 : 9745 : run_cost += path->path.rows * comparison_cost * logN;
506 : :
507 : : /* small cost for heap management, like cost_merge_append */
508 : 9745 : run_cost += cpu_operator_cost * path->path.rows;
509 : :
510 : : /*
511 : : * Parallel setup and communication cost. Since Gather Merge, unlike
512 : : * Gather, requires us to block until a tuple is available from every
513 : : * worker, we bump the IPC cost up a little bit as compared with Gather.
514 : : * For lack of a better idea, charge an extra 5%.
515 : : */
516 : 9745 : startup_cost += parallel_setup_cost;
517 : 9745 : run_cost += parallel_tuple_cost * path->path.rows * 1.05;
518 : :
46 rhaas@postgresql.org 519 :GNC 9745 : path->path.disabled_nodes = path->subpath->disabled_nodes
520 : 9745 : + ((rel->pgs_mask & PGS_GATHER_MERGE) != 0 ? 0 : 1);
3293 rhaas@postgresql.org 521 :CBC 9745 : path->path.startup_cost = startup_cost + input_startup_cost;
522 : 9745 : path->path.total_cost = (startup_cost + run_cost + input_total_cost);
523 : 9745 : }
524 : :
525 : : /*
526 : : * cost_index
527 : : * Determines and returns the cost of scanning a relation using an index.
528 : : *
529 : : * 'path' describes the indexscan under consideration, and is complete
530 : : * except for the fields to be set by this routine
531 : : * 'loop_count' is the number of repetitions of the indexscan to factor into
532 : : * estimates of caching behavior
533 : : *
534 : : * In addition to rows, startup_cost and total_cost, cost_index() sets the
535 : : * path's indextotalcost and indexselectivity fields. These values will be
536 : : * needed if the IndexPath is used in a BitmapIndexScan.
537 : : *
538 : : * NOTE: path->indexquals must contain only clauses usable as index
539 : : * restrictions. Any additional quals evaluated as qpquals may reduce the
540 : : * number of returned tuples, but they won't reduce the number of tuples
541 : : * we have to fetch from the table, so they don't reduce the scan cost.
542 : : */
543 : : void
3315 544 : 482590 : cost_index(IndexPath *path, PlannerInfo *root, double loop_count,
545 : : bool partial_path)
546 : : {
5195 tgl@sss.pgh.pa.us 547 : 482590 : IndexOptInfo *index = path->indexinfo;
7658 548 : 482590 : RelOptInfo *baserel = index->rel;
5195 549 : 482590 : bool indexonly = (path->path.pathtype == T_IndexOnlyScan);
550 : : amcostestimate_function amcostestimate;
551 : : List *qpquals;
9525 552 : 482590 : Cost startup_cost = 0;
553 : 482590 : Cost run_cost = 0;
3315 rhaas@postgresql.org 554 : 482590 : Cost cpu_run_cost = 0;
555 : : Cost indexStartupCost;
556 : : Cost indexTotalCost;
557 : : Selectivity indexSelectivity;
558 : : double indexCorrelation,
559 : : csquared;
560 : : double spc_seq_page_cost,
561 : : spc_random_page_cost;
562 : : Cost min_IO_cost,
563 : : max_IO_cost;
564 : : QualCost qpqual_cost;
565 : : Cost cpu_per_tuple;
566 : : double tuples_fetched;
567 : : double pages_fetched;
568 : : double rand_heap_pages;
569 : : double index_pages;
570 : : uint64 enable_mask;
571 : :
572 : : /* Should only be applied to base relations */
8708 tgl@sss.pgh.pa.us 573 [ + - - + ]: 482590 : Assert(IsA(baserel, RelOptInfo) &&
574 : : IsA(index, IndexOptInfo));
8436 575 [ - + ]: 482590 : Assert(baserel->relid > 0);
8708 576 [ - + ]: 482590 : Assert(baserel->rtekind == RTE_RELATION);
577 : :
578 : : /*
579 : : * Mark the path with the correct row estimate, and identify which quals
580 : : * will need to be enforced as qpquals. We need not check any quals that
581 : : * are implied by the index's predicate, so we can use indrestrictinfo not
582 : : * baserestrictinfo as the list of relevant restriction clauses for the
583 : : * rel.
584 : : */
5078 585 [ + + ]: 482590 : if (path->path.param_info)
586 : : {
587 : 96374 : path->path.rows = path->path.param_info->ppi_rows;
588 : : /* qpquals come from the rel's restriction clauses and ppi_clauses */
2591 589 : 96374 : qpquals = list_concat(extract_nonindex_conditions(path->indexinfo->indrestrictinfo,
590 : : path->indexclauses),
3189 591 : 96374 : extract_nonindex_conditions(path->path.param_info->ppi_clauses,
592 : : path->indexclauses));
593 : : }
594 : : else
595 : : {
5078 596 : 386216 : path->path.rows = baserel->rows;
597 : : /* qpquals come from just the rel's restriction clauses */
3636 598 : 386216 : qpquals = extract_nonindex_conditions(path->indexinfo->indrestrictinfo,
599 : : path->indexclauses);
600 : : }
601 : :
602 : : /* is this scan type disabled? */
46 rhaas@postgresql.org 603 [ + + ]:GNC 482590 : enable_mask = (indexonly ? PGS_INDEXONLYSCAN : PGS_INDEXSCAN)
604 [ + + ]: 482590 : | (partial_path ? 0 : PGS_CONSIDER_NONPARTIAL);
605 : 482590 : path->path.disabled_nodes =
606 : 482590 : (baserel->pgs_mask & enable_mask) == enable_mask ? 0 : 1;
607 : :
608 : : /*
609 : : * Call index-access-method-specific code to estimate the processing cost
610 : : * for scanning the index, as well as the selectivity of the index (ie,
611 : : * the fraction of main-table tuples we will have to retrieve) and its
612 : : * correlation to the main-table tuple order. We need a cast here because
613 : : * pathnodes.h uses a weak function type to avoid including amapi.h.
614 : : */
3710 tgl@sss.pgh.pa.us 615 :CBC 482590 : amcostestimate = (amcostestimate_function) index->amcostestimate;
616 : 482590 : amcostestimate(root, path, loop_count,
617 : : &indexStartupCost, &indexTotalCost,
618 : : &indexSelectivity, &indexCorrelation,
619 : : &index_pages);
620 : :
621 : : /*
622 : : * Save amcostestimate's results for possible use in bitmap scan planning.
623 : : * We don't bother to save indexStartupCost or indexCorrelation, because a
624 : : * bitmap scan doesn't care about either.
625 : : */
7633 626 : 482590 : path->indextotalcost = indexTotalCost;
627 : 482590 : path->indexselectivity = indexSelectivity;
628 : :
629 : : /* all costs for touching index itself included here */
9525 630 : 482590 : startup_cost += indexStartupCost;
631 : 482590 : run_cost += indexTotalCost - indexStartupCost;
632 : :
633 : : /* estimate number of main-table tuples fetched */
7222 634 : 482590 : tuples_fetched = clamp_row_est(indexSelectivity * baserel->tuples);
635 : :
636 : : /* fetch estimated page costs for tablespace containing table */
5913 rhaas@postgresql.org 637 : 482590 : get_tablespace_page_costs(baserel->reltablespace,
638 : : &spc_random_page_cost,
639 : : &spc_seq_page_cost);
640 : :
641 : : /*----------
642 : : * Estimate number of main-table pages fetched, and compute I/O cost.
643 : : *
644 : : * When the index ordering is uncorrelated with the table ordering,
645 : : * we use an approximation proposed by Mackert and Lohman (see
646 : : * index_pages_fetched() for details) to compute the number of pages
647 : : * fetched, and then charge spc_random_page_cost per page fetched.
648 : : *
649 : : * When the index ordering is exactly correlated with the table ordering
650 : : * (just after a CLUSTER, for example), the number of pages fetched should
651 : : * be exactly selectivity * table_size. What's more, all but the first
652 : : * will be sequential fetches, not the random fetches that occur in the
653 : : * uncorrelated case. So if the number of pages is more than 1, we
654 : : * ought to charge
655 : : * spc_random_page_cost + (pages_fetched - 1) * spc_seq_page_cost
656 : : * For partially-correlated indexes, we ought to charge somewhere between
657 : : * these two estimates. We currently interpolate linearly between the
658 : : * estimates based on the correlation squared (XXX is that appropriate?).
659 : : *
660 : : * If it's an index-only scan, then we will not need to fetch any heap
661 : : * pages for which the visibility map shows all tuples are visible.
662 : : * Hence, reduce the estimated number of heap fetches accordingly.
663 : : * We use the measured fraction of the entire heap that is all-visible,
664 : : * which might not be particularly relevant to the subset of the heap
665 : : * that this query will fetch; but it's not clear how to do better.
666 : : *----------
667 : : */
5161 tgl@sss.pgh.pa.us 668 [ + + ]: 482590 : if (loop_count > 1)
669 : : {
670 : : /*
671 : : * For repeated indexscans, the appropriate estimate for the
672 : : * uncorrelated case is to scale up the number of tuples fetched in
673 : : * the Mackert and Lohman formula by the number of scans, so that we
674 : : * estimate the number of pages fetched by all the scans; then
675 : : * pro-rate the costs for one scan. In this case we assume all the
676 : : * fetches are random accesses.
677 : : */
678 : 54871 : pages_fetched = index_pages_fetched(tuples_fetched * loop_count,
679 : : baserel->pages,
7117 680 : 54871 : (double) index->pages,
681 : : root);
682 : :
5272 683 [ + + ]: 54871 : if (indexonly)
5266 684 : 7038 : pages_fetched = ceil(pages_fetched * (1.0 - baserel->allvisfrac));
685 : :
3315 rhaas@postgresql.org 686 : 54871 : rand_heap_pages = pages_fetched;
687 : :
5161 tgl@sss.pgh.pa.us 688 : 54871 : max_IO_cost = (pages_fetched * spc_random_page_cost) / loop_count;
689 : :
690 : : /*
691 : : * In the perfectly correlated case, the number of pages touched by
692 : : * each scan is selectivity * table_size, and we can use the Mackert
693 : : * and Lohman formula at the page level to estimate how much work is
694 : : * saved by caching across scans. We still assume all the fetches are
695 : : * random, though, which is an overestimate that's hard to correct for
696 : : * without double-counting the cache effects. (But in most cases
697 : : * where such a plan is actually interesting, only one page would get
698 : : * fetched per scan anyway, so it shouldn't matter much.)
699 : : */
7030 700 : 54871 : pages_fetched = ceil(indexSelectivity * (double) baserel->pages);
701 : :
5161 702 : 54871 : pages_fetched = index_pages_fetched(pages_fetched * loop_count,
703 : : baserel->pages,
7030 704 : 54871 : (double) index->pages,
705 : : root);
706 : :
5272 707 [ + + ]: 54871 : if (indexonly)
5266 708 : 7038 : pages_fetched = ceil(pages_fetched * (1.0 - baserel->allvisfrac));
709 : :
5161 710 : 54871 : min_IO_cost = (pages_fetched * spc_random_page_cost) / loop_count;
711 : : }
712 : : else
713 : : {
714 : : /*
715 : : * Normal case: apply the Mackert and Lohman formula, and then
716 : : * interpolate between that and the correlation-derived result.
717 : : */
7222 718 : 427719 : pages_fetched = index_pages_fetched(tuples_fetched,
719 : : baserel->pages,
7117 720 : 427719 : (double) index->pages,
721 : : root);
722 : :
5272 723 [ + + ]: 427719 : if (indexonly)
5266 724 : 39531 : pages_fetched = ceil(pages_fetched * (1.0 - baserel->allvisfrac));
725 : :
3315 rhaas@postgresql.org 726 : 427719 : rand_heap_pages = pages_fetched;
727 : :
728 : : /* max_IO_cost is for the perfectly uncorrelated case (csquared=0) */
5913 729 : 427719 : max_IO_cost = pages_fetched * spc_random_page_cost;
730 : :
731 : : /* min_IO_cost is for the perfectly correlated case (csquared=1) */
7222 tgl@sss.pgh.pa.us 732 : 427719 : pages_fetched = ceil(indexSelectivity * (double) baserel->pages);
733 : :
5272 734 [ + + ]: 427719 : if (indexonly)
5266 735 : 39531 : pages_fetched = ceil(pages_fetched * (1.0 - baserel->allvisfrac));
736 : :
5264 737 [ + + ]: 427719 : if (pages_fetched > 0)
738 : : {
739 : 370306 : min_IO_cost = spc_random_page_cost;
740 [ + + ]: 370306 : if (pages_fetched > 1)
741 : 111496 : min_IO_cost += (pages_fetched - 1) * spc_seq_page_cost;
742 : : }
743 : : else
744 : 57413 : min_IO_cost = 0;
745 : : }
746 : :
3315 rhaas@postgresql.org 747 [ + + ]: 482590 : if (partial_path)
748 : : {
749 : : /*
750 : : * For index only scans compute workers based on number of index pages
751 : : * fetched; the number of heap pages we fetch might be so small as to
752 : : * effectively rule out parallelism, which we don't want to do.
753 : : */
3288 754 [ + + ]: 166272 : if (indexonly)
755 : 14603 : rand_heap_pages = -1;
756 : :
757 : : /*
758 : : * Estimate the number of parallel workers required to scan index. Use
759 : : * the number of heap pages computed considering heap fetches won't be
760 : : * sequential as for parallel scans the pages are accessed in random
761 : : * order.
762 : : */
3315 763 : 166272 : path->path.parallel_workers = compute_parallel_worker(baserel,
764 : : rand_heap_pages,
765 : : index_pages,
766 : : max_parallel_workers_per_gather);
767 : :
768 : : /*
769 : : * Fall out if workers can't be assigned for parallel scan, because in
770 : : * such a case this path will be rejected. So there is no benefit in
771 : : * doing extra computation.
772 : : */
773 [ + + ]: 166272 : if (path->path.parallel_workers <= 0)
774 : 161137 : return;
775 : :
776 : 5135 : path->path.parallel_aware = true;
777 : : }
778 : :
779 : : /*
780 : : * Now interpolate based on estimated index order correlation to get total
781 : : * disk I/O cost for main table accesses.
782 : : */
7030 tgl@sss.pgh.pa.us 783 : 321453 : csquared = indexCorrelation * indexCorrelation;
784 : :
785 : 321453 : run_cost += max_IO_cost + csquared * (min_IO_cost - max_IO_cost);
786 : :
787 : : /*
788 : : * Estimate CPU costs per tuple.
789 : : *
790 : : * What we want here is cpu_tuple_cost plus the evaluation costs of any
791 : : * qual clauses that we have to evaluate as qpquals.
792 : : */
4030 793 : 321453 : cost_qual_eval(&qpqual_cost, qpquals, root);
794 : :
5086 795 : 321453 : startup_cost += qpqual_cost.startup;
796 : 321453 : cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple;
797 : :
3315 rhaas@postgresql.org 798 : 321453 : cpu_run_cost += cpu_per_tuple * tuples_fetched;
799 : :
800 : : /* tlist eval costs are paid per output row, not per tuple scanned */
3678 tgl@sss.pgh.pa.us 801 : 321453 : startup_cost += path->path.pathtarget->cost.startup;
3315 rhaas@postgresql.org 802 : 321453 : cpu_run_cost += path->path.pathtarget->cost.per_tuple * path->path.rows;
803 : :
804 : : /* Adjust costing for parallelism, if used. */
805 [ + + ]: 321453 : if (path->path.parallel_workers > 0)
806 : : {
807 : 5135 : double parallel_divisor = get_parallel_divisor(&path->path);
808 : :
809 : 5135 : path->path.rows = clamp_row_est(path->path.rows / parallel_divisor);
810 : :
811 : : /* The CPU cost is divided among all the workers. */
812 : 5135 : cpu_run_cost /= parallel_divisor;
813 : : }
814 : :
815 : 321453 : run_cost += cpu_run_cost;
816 : :
7633 tgl@sss.pgh.pa.us 817 : 321453 : path->path.startup_cost = startup_cost;
818 : 321453 : path->path.total_cost = startup_cost + run_cost;
819 : : }
820 : :
821 : : /*
822 : : * extract_nonindex_conditions
823 : : *
824 : : * Given a list of quals to be enforced in an indexscan, extract the ones that
825 : : * will have to be applied as qpquals (ie, the index machinery won't handle
826 : : * them). Here we detect only whether a qual clause is directly redundant
827 : : * with some indexclause. If the index path is chosen for use, createplan.c
828 : : * will try a bit harder to get rid of redundant qual conditions; specifically
829 : : * it will see if quals can be proven to be implied by the indexquals. But
830 : : * it does not seem worth the cycles to try to factor that in at this stage,
831 : : * since we're only trying to estimate qual eval costs. Otherwise this must
832 : : * match the logic in create_indexscan_plan().
833 : : *
834 : : * qual_clauses, and the result, are lists of RestrictInfos.
835 : : * indexclauses is a list of IndexClauses.
836 : : */
837 : : static List *
2591 838 : 578964 : extract_nonindex_conditions(List *qual_clauses, List *indexclauses)
839 : : {
4030 840 : 578964 : List *result = NIL;
841 : : ListCell *lc;
842 : :
843 [ + + + + : 1201272 : foreach(lc, qual_clauses)
+ + ]
844 : : {
3261 845 : 622308 : RestrictInfo *rinfo = lfirst_node(RestrictInfo, lc);
846 : :
4030 847 [ + + ]: 622308 : if (rinfo->pseudoconstant)
848 : 2070 : continue; /* we may drop pseudoconstants here */
2591 849 [ + + ]: 620238 : if (is_redundant_with_indexclauses(rinfo, indexclauses))
850 : 358733 : continue; /* dup or derived from same EquivalenceClass */
851 : : /* ... skip the predicate proof attempt createplan.c will try ... */
4030 852 : 261505 : result = lappend(result, rinfo);
853 : : }
854 : 578964 : return result;
855 : : }
856 : :
857 : : /*
858 : : * index_pages_fetched
859 : : * Estimate the number of pages actually fetched after accounting for
860 : : * cache effects.
861 : : *
862 : : * We use an approximation proposed by Mackert and Lohman, "Index Scans
863 : : * Using a Finite LRU Buffer: A Validated I/O Model", ACM Transactions
864 : : * on Database Systems, Vol. 14, No. 3, September 1989, Pages 401-424.
865 : : * The Mackert and Lohman approximation is that the number of pages
866 : : * fetched is
867 : : * PF =
868 : : * min(2TNs/(2T+Ns), T) when T <= b
869 : : * 2TNs/(2T+Ns) when T > b and Ns <= 2Tb/(2T-b)
870 : : * b + (Ns - 2Tb/(2T-b))*(T-b)/T when T > b and Ns > 2Tb/(2T-b)
871 : : * where
872 : : * T = # pages in table
873 : : * N = # tuples in table
874 : : * s = selectivity = fraction of table to be scanned
875 : : * b = # buffer pages available (we include kernel space here)
876 : : *
877 : : * We assume that effective_cache_size is the total number of buffer pages
878 : : * available for the whole query, and pro-rate that space across all the
879 : : * tables in the query and the index currently under consideration. (This
880 : : * ignores space needed for other indexes used by the query, but since we
881 : : * don't know which indexes will get used, we can't estimate that very well;
882 : : * and in any case counting all the tables may well be an overestimate, since
883 : : * depending on the join plan not all the tables may be scanned concurrently.)
884 : : *
885 : : * The product Ns is the number of tuples fetched; we pass in that
886 : : * product rather than calculating it here. "pages" is the number of pages
887 : : * in the object under consideration (either an index or a table).
888 : : * "index_pages" is the amount to add to the total table space, which was
889 : : * computed for us by make_one_rel.
890 : : *
891 : : * Caller is expected to have ensured that tuples_fetched is greater than zero
892 : : * and rounded to integer (see clamp_row_est). The result will likewise be
893 : : * greater than zero and integral.
894 : : */
895 : : double
7222 896 : 686009 : index_pages_fetched(double tuples_fetched, BlockNumber pages,
897 : : double index_pages, PlannerInfo *root)
898 : : {
899 : : double pages_fetched;
900 : : double total_pages;
901 : : double T,
902 : : b;
903 : :
904 : : /* T is # pages in table, but don't allow it to be zero */
905 [ + + ]: 686009 : T = (pages > 1) ? (double) pages : 1.0;
906 : :
907 : : /* Compute number of pages assumed to be competing for cache space */
7117 908 : 686009 : total_pages = root->total_table_pages + index_pages;
909 [ + + ]: 686009 : total_pages = Max(total_pages, 1.0);
910 [ - + ]: 686009 : Assert(T <= total_pages);
911 : :
912 : : /* b is pro-rated share of effective_cache_size */
3189 913 : 686009 : b = (double) effective_cache_size * T / total_pages;
914 : :
915 : : /* force it positive and integral */
7222 916 [ - + ]: 686009 : if (b <= 1.0)
7222 tgl@sss.pgh.pa.us 917 :UBC 0 : b = 1.0;
918 : : else
7222 tgl@sss.pgh.pa.us 919 :CBC 686009 : b = ceil(b);
920 : :
921 : : /* This part is the Mackert and Lohman formula */
922 [ + - ]: 686009 : if (T <= b)
923 : : {
924 : 686009 : pages_fetched =
925 : 686009 : (2.0 * T * tuples_fetched) / (2.0 * T + tuples_fetched);
926 [ + + ]: 686009 : if (pages_fetched >= T)
927 : 403573 : pages_fetched = T;
928 : : else
929 : 282436 : pages_fetched = ceil(pages_fetched);
930 : : }
931 : : else
932 : : {
933 : : double lim;
934 : :
7222 tgl@sss.pgh.pa.us 935 :UBC 0 : lim = (2.0 * T * b) / (2.0 * T - b);
936 [ # # ]: 0 : if (tuples_fetched <= lim)
937 : : {
938 : 0 : pages_fetched =
939 : 0 : (2.0 * T * tuples_fetched) / (2.0 * T + tuples_fetched);
940 : : }
941 : : else
942 : : {
943 : 0 : pages_fetched =
944 : 0 : b + (tuples_fetched - lim) * (T - b) / T;
945 : : }
946 : 0 : pages_fetched = ceil(pages_fetched);
947 : : }
7222 tgl@sss.pgh.pa.us 948 :CBC 686009 : return pages_fetched;
949 : : }
950 : :
951 : : /*
952 : : * get_indexpath_pages
953 : : * Determine the total size of the indexes used in a bitmap index path.
954 : : *
955 : : * Note: if the same index is used more than once in a bitmap tree, we will
956 : : * count it multiple times, which perhaps is the wrong thing ... but it's
957 : : * not completely clear, and detecting duplicates is difficult, so ignore it
958 : : * for now.
959 : : */
960 : : static double
7117 961 : 120669 : get_indexpath_pages(Path *bitmapqual)
962 : : {
963 : 120669 : double result = 0;
964 : : ListCell *l;
965 : :
966 [ + + ]: 120669 : if (IsA(bitmapqual, BitmapAndPath))
967 : : {
968 : 14835 : BitmapAndPath *apath = (BitmapAndPath *) bitmapqual;
969 : :
970 [ + - + + : 44505 : foreach(l, apath->bitmapquals)
+ + ]
971 : : {
972 : 29670 : result += get_indexpath_pages((Path *) lfirst(l));
973 : : }
974 : : }
975 [ + + ]: 105834 : else if (IsA(bitmapqual, BitmapOrPath))
976 : : {
977 : 35 : BitmapOrPath *opath = (BitmapOrPath *) bitmapqual;
978 : :
979 [ + - + + : 111 : foreach(l, opath->bitmapquals)
+ + ]
980 : : {
981 : 76 : result += get_indexpath_pages((Path *) lfirst(l));
982 : : }
983 : : }
984 [ + - ]: 105799 : else if (IsA(bitmapqual, IndexPath))
985 : : {
986 : 105799 : IndexPath *ipath = (IndexPath *) bitmapqual;
987 : :
988 : 105799 : result = (double) ipath->indexinfo->pages;
989 : : }
990 : : else
7117 tgl@sss.pgh.pa.us 991 [ # # ]:UBC 0 : elog(ERROR, "unrecognized node type: %d", nodeTag(bitmapqual));
992 : :
7117 tgl@sss.pgh.pa.us 993 :CBC 120669 : return result;
994 : : }
995 : :
996 : : /*
997 : : * cost_bitmap_heap_scan
998 : : * Determines and returns the cost of scanning a relation using a bitmap
999 : : * index-then-heap plan.
1000 : : *
1001 : : * 'baserel' is the relation to be scanned
1002 : : * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
1003 : : * 'bitmapqual' is a tree of IndexPaths, BitmapAndPaths, and BitmapOrPaths
1004 : : * 'loop_count' is the number of repetitions of the indexscan to factor into
1005 : : * estimates of caching behavior
1006 : : *
1007 : : * Note: the component IndexPaths in bitmapqual should have been costed
1008 : : * using the same loop_count.
1009 : : */
1010 : : void
7588 1011 : 327617 : cost_bitmap_heap_scan(Path *path, PlannerInfo *root, RelOptInfo *baserel,
1012 : : ParamPathInfo *param_info,
1013 : : Path *bitmapqual, double loop_count)
1014 : : {
7635 1015 : 327617 : Cost startup_cost = 0;
1016 : 327617 : Cost run_cost = 0;
1017 : : Cost indexTotalCost;
1018 : : QualCost qpqual_cost;
1019 : : Cost cpu_per_tuple;
1020 : : Cost cost_per_page;
1021 : : Cost cpu_run_cost;
1022 : : double tuples_fetched;
1023 : : double pages_fetched;
1024 : : double spc_seq_page_cost,
1025 : : spc_random_page_cost;
1026 : : double T;
46 rhaas@postgresql.org 1027 :GNC 327617 : uint64 enable_mask = PGS_BITMAPSCAN;
1028 : :
1029 : : /* Should only be applied to base relations */
7635 tgl@sss.pgh.pa.us 1030 [ - + ]:CBC 327617 : Assert(IsA(baserel, RelOptInfo));
1031 [ - + ]: 327617 : Assert(baserel->relid > 0);
1032 [ - + ]: 327617 : Assert(baserel->rtekind == RTE_RELATION);
1033 : :
1034 : : /* Mark the path with the correct row estimate */
5078 1035 [ + + ]: 327617 : if (param_info)
1036 : 146018 : path->rows = param_info->ppi_rows;
1037 : : else
5161 1038 : 181599 : path->rows = baserel->rows;
1039 : :
3334 rhaas@postgresql.org 1040 : 327617 : pages_fetched = compute_bitmap_pages(root, baserel, bitmapqual,
1041 : : loop_count, &indexTotalCost,
1042 : : &tuples_fetched);
1043 : :
7633 tgl@sss.pgh.pa.us 1044 : 327617 : startup_cost += indexTotalCost;
3334 rhaas@postgresql.org 1045 [ + + ]: 327617 : T = (baserel->pages > 1) ? (double) baserel->pages : 1.0;
1046 : :
1047 : : /* Fetch estimated page costs for tablespace containing table. */
5913 1048 : 327617 : get_tablespace_page_costs(baserel->reltablespace,
1049 : : &spc_random_page_cost,
1050 : : &spc_seq_page_cost);
1051 : :
1052 : : /*
1053 : : * For small numbers of pages we should charge spc_random_page_cost
1054 : : * apiece, while if nearly all the table's pages are being read, it's more
1055 : : * appropriate to charge spc_seq_page_cost apiece. The effect is
1056 : : * nonlinear, too. For lack of a better idea, interpolate like this to
1057 : : * determine the cost per page.
1058 : : */
7632 tgl@sss.pgh.pa.us 1059 [ + + ]: 327617 : if (pages_fetched >= 2.0)
5913 rhaas@postgresql.org 1060 : 64289 : cost_per_page = spc_random_page_cost -
1061 : 64289 : (spc_random_page_cost - spc_seq_page_cost)
1062 : 64289 : * sqrt(pages_fetched / T);
1063 : : else
1064 : 263328 : cost_per_page = spc_random_page_cost;
1065 : :
7633 tgl@sss.pgh.pa.us 1066 : 327617 : run_cost += pages_fetched * cost_per_page;
1067 : :
1068 : : /*
1069 : : * Estimate CPU costs per tuple.
1070 : : *
1071 : : * Often the indexquals don't need to be rechecked at each tuple ... but
1072 : : * not always, especially not if there are enough tuples involved that the
1073 : : * bitmaps become lossy. For the moment, just assume they will be
1074 : : * rechecked always. This means we charge the full freight for all the
1075 : : * scan clauses.
1076 : : */
5078 1077 : 327617 : get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
1078 : :
1079 : 327617 : startup_cost += qpqual_cost.startup;
1080 : 327617 : cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple;
3294 rhaas@postgresql.org 1081 : 327617 : cpu_run_cost = cpu_per_tuple * tuples_fetched;
1082 : :
1083 : : /* Adjust costing for parallelism, if used. */
1084 [ + + ]: 327617 : if (path->parallel_workers > 0)
1085 : : {
1086 : 2473 : double parallel_divisor = get_parallel_divisor(path);
1087 : :
1088 : : /* The CPU cost is divided among all the workers. */
1089 : 2473 : cpu_run_cost /= parallel_divisor;
1090 : :
1091 : 2473 : path->rows = clamp_row_est(path->rows / parallel_divisor);
1092 : : }
1093 : : else
46 rhaas@postgresql.org 1094 :GNC 325144 : enable_mask |= PGS_CONSIDER_NONPARTIAL;
1095 : :
1096 : :
3294 rhaas@postgresql.org 1097 :CBC 327617 : run_cost += cpu_run_cost;
1098 : :
1099 : : /* tlist eval costs are paid per output row, not per tuple scanned */
3678 tgl@sss.pgh.pa.us 1100 : 327617 : startup_cost += path->pathtarget->cost.startup;
1101 : 327617 : run_cost += path->pathtarget->cost.per_tuple * path->rows;
1102 : :
46 rhaas@postgresql.org 1103 :GNC 327617 : path->disabled_nodes =
1104 : 327617 : (baserel->pgs_mask & enable_mask) == enable_mask ? 0 : 1;
7635 tgl@sss.pgh.pa.us 1105 :CBC 327617 : path->startup_cost = startup_cost;
1106 : 327617 : path->total_cost = startup_cost + run_cost;
1107 : 327617 : }
1108 : :
1109 : : /*
1110 : : * cost_bitmap_tree_node
1111 : : * Extract cost and selectivity from a bitmap tree node (index/and/or)
1112 : : */
1113 : : void
7633 1114 : 611331 : cost_bitmap_tree_node(Path *path, Cost *cost, Selectivity *selec)
1115 : : {
1116 [ + + ]: 611331 : if (IsA(path, IndexPath))
1117 : : {
1118 : 577673 : *cost = ((IndexPath *) path)->indextotalcost;
1119 : 577673 : *selec = ((IndexPath *) path)->indexselectivity;
1120 : :
1121 : : /*
1122 : : * Charge a small amount per retrieved tuple to reflect the costs of
1123 : : * manipulating the bitmap. This is mostly to make sure that a bitmap
1124 : : * scan doesn't look to be the same cost as an indexscan to retrieve a
1125 : : * single tuple.
1126 : : */
5161 1127 : 577673 : *cost += 0.1 * cpu_operator_cost * path->rows;
1128 : : }
7633 1129 [ + + ]: 33658 : else if (IsA(path, BitmapAndPath))
1130 : : {
1131 : 30632 : *cost = path->total_cost;
1132 : 30632 : *selec = ((BitmapAndPath *) path)->bitmapselectivity;
1133 : : }
1134 [ + - ]: 3026 : else if (IsA(path, BitmapOrPath))
1135 : : {
1136 : 3026 : *cost = path->total_cost;
1137 : 3026 : *selec = ((BitmapOrPath *) path)->bitmapselectivity;
1138 : : }
1139 : : else
1140 : : {
7633 tgl@sss.pgh.pa.us 1141 [ # # ]:UBC 0 : elog(ERROR, "unrecognized node type: %d", nodeTag(path));
1142 : : *cost = *selec = 0; /* keep compiler quiet */
1143 : : }
7633 tgl@sss.pgh.pa.us 1144 :CBC 611331 : }
1145 : :
1146 : : /*
1147 : : * cost_bitmap_and_node
1148 : : * Estimate the cost of a BitmapAnd node
1149 : : *
1150 : : * Note that this considers only the costs of index scanning and bitmap
1151 : : * creation, not the eventual heap access. In that sense the object isn't
1152 : : * truly a Path, but it has enough path-like properties (costs in particular)
1153 : : * to warrant treating it as one. We don't bother to set the path rows field,
1154 : : * however.
1155 : : */
1156 : : void
7588 1157 : 30525 : cost_bitmap_and_node(BitmapAndPath *path, PlannerInfo *root)
1158 : : {
1159 : : Cost totalCost;
1160 : : Selectivity selec;
1161 : : ListCell *l;
1162 : :
1163 : : /*
1164 : : * We estimate AND selectivity on the assumption that the inputs are
1165 : : * independent. This is probably often wrong, but we don't have the info
1166 : : * to do better.
1167 : : *
1168 : : * The runtime cost of the BitmapAnd itself is estimated at 100x
1169 : : * cpu_operator_cost for each tbm_intersect needed. Probably too small,
1170 : : * definitely too simplistic?
1171 : : */
7633 1172 : 30525 : totalCost = 0.0;
1173 : 30525 : selec = 1.0;
1174 [ + - + + : 91575 : foreach(l, path->bitmapquals)
+ + ]
1175 : : {
7456 bruce@momjian.us 1176 : 61050 : Path *subpath = (Path *) lfirst(l);
1177 : : Cost subCost;
1178 : : Selectivity subselec;
1179 : :
7633 tgl@sss.pgh.pa.us 1180 : 61050 : cost_bitmap_tree_node(subpath, &subCost, &subselec);
1181 : :
1182 : 61050 : selec *= subselec;
1183 : :
1184 : 61050 : totalCost += subCost;
1185 [ + + ]: 61050 : if (l != list_head(path->bitmapquals))
1186 : 30525 : totalCost += 100.0 * cpu_operator_cost;
1187 : : }
1188 : 30525 : path->bitmapselectivity = selec;
5161 1189 : 30525 : path->path.rows = 0; /* per above, not used */
571 rhaas@postgresql.org 1190 : 30525 : path->path.disabled_nodes = 0;
7633 tgl@sss.pgh.pa.us 1191 : 30525 : path->path.startup_cost = totalCost;
1192 : 30525 : path->path.total_cost = totalCost;
1193 : 30525 : }
1194 : :
1195 : : /*
1196 : : * cost_bitmap_or_node
1197 : : * Estimate the cost of a BitmapOr node
1198 : : *
1199 : : * See comments for cost_bitmap_and_node.
1200 : : */
1201 : : void
7588 1202 : 1116 : cost_bitmap_or_node(BitmapOrPath *path, PlannerInfo *root)
1203 : : {
1204 : : Cost totalCost;
1205 : : Selectivity selec;
1206 : : ListCell *l;
1207 : :
1208 : : /*
1209 : : * We estimate OR selectivity on the assumption that the inputs are
1210 : : * non-overlapping, since that's often the case in "x IN (list)" type
1211 : : * situations. Of course, we clamp to 1.0 at the end.
1212 : : *
1213 : : * The runtime cost of the BitmapOr itself is estimated at 100x
1214 : : * cpu_operator_cost for each tbm_union needed. Probably too small,
1215 : : * definitely too simplistic? We are aware that the tbm_unions are
1216 : : * optimized out when the inputs are BitmapIndexScans.
1217 : : */
7633 1218 : 1116 : totalCost = 0.0;
1219 : 1116 : selec = 0.0;
1220 [ + - + + : 2653 : foreach(l, path->bitmapquals)
+ + ]
1221 : : {
7456 bruce@momjian.us 1222 : 1537 : Path *subpath = (Path *) lfirst(l);
1223 : : Cost subCost;
1224 : : Selectivity subselec;
1225 : :
7633 tgl@sss.pgh.pa.us 1226 : 1537 : cost_bitmap_tree_node(subpath, &subCost, &subselec);
1227 : :
1228 : 1537 : selec += subselec;
1229 : :
1230 : 1537 : totalCost += subCost;
1231 [ + + ]: 1537 : if (l != list_head(path->bitmapquals) &&
1232 [ - + ]: 421 : !IsA(subpath, IndexPath))
7633 tgl@sss.pgh.pa.us 1233 :LBC (3) : totalCost += 100.0 * cpu_operator_cost;
1234 : : }
7633 tgl@sss.pgh.pa.us 1235 [ + - ]:CBC 1116 : path->bitmapselectivity = Min(selec, 1.0);
5161 1236 : 1116 : path->path.rows = 0; /* per above, not used */
7633 1237 : 1116 : path->path.startup_cost = totalCost;
1238 : 1116 : path->path.total_cost = totalCost;
1239 : 1116 : }
1240 : :
1241 : : /*
1242 : : * cost_tidscan
1243 : : * Determines and returns the cost of scanning a relation using TIDs.
1244 : : *
1245 : : * 'baserel' is the relation to be scanned
1246 : : * 'tidquals' is the list of TID-checkable quals
1247 : : * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
1248 : : */
1249 : : void
7588 1250 : 446 : cost_tidscan(Path *path, PlannerInfo *root,
1251 : : RelOptInfo *baserel, List *tidquals, ParamPathInfo *param_info)
1252 : : {
9525 1253 : 446 : Cost startup_cost = 0;
1254 : 446 : Cost run_cost = 0;
1255 : : QualCost qpqual_cost;
1256 : : Cost cpu_per_tuple;
1257 : : QualCost tid_qual_cost;
1258 : : double ntuples;
1259 : : ListCell *l;
1260 : : double spc_random_page_cost;
46 rhaas@postgresql.org 1261 :GNC 446 : uint64 enable_mask = 0;
1262 : :
1263 : : /* Should only be applied to base relations */
8436 tgl@sss.pgh.pa.us 1264 [ - + ]:CBC 446 : Assert(baserel->relid > 0);
8708 1265 [ - + ]: 446 : Assert(baserel->rtekind == RTE_RELATION);
571 rhaas@postgresql.org 1266 [ - + ]: 446 : Assert(tidquals != NIL);
1267 : :
1268 : : /* Mark the path with the correct row estimate */
4949 tgl@sss.pgh.pa.us 1269 [ + + ]: 446 : if (param_info)
1270 : 72 : path->rows = param_info->ppi_rows;
1271 : : else
1272 : 374 : path->rows = baserel->rows;
1273 : :
1274 : : /* Count how many tuples we expect to retrieve */
7414 1275 : 446 : ntuples = 0;
1276 [ + - + + : 905 : foreach(l, tidquals)
+ + ]
1277 : : {
2632 1278 : 459 : RestrictInfo *rinfo = lfirst_node(RestrictInfo, l);
1279 : 459 : Expr *qual = rinfo->clause;
1280 : :
1281 : : /*
1282 : : * We must use a TID scan for CurrentOfExpr; in any other case, we
1283 : : * should be generating a TID scan only if TID scans are allowed.
1284 : : * Also, if CurrentOfExpr is the qual, there should be only one.
1285 : : */
46 rhaas@postgresql.org 1286 [ - + - - ]:GNC 459 : Assert((baserel->pgs_mask & PGS_TIDSCAN) != 0 || IsA(qual, CurrentOfExpr));
571 rhaas@postgresql.org 1287 [ + + - + ]:CBC 459 : Assert(list_length(tidquals) == 1 || !IsA(qual, CurrentOfExpr));
1288 : :
2632 tgl@sss.pgh.pa.us 1289 [ + + ]: 459 : if (IsA(qual, ScalarArrayOpExpr))
1290 : : {
1291 : : /* Each element of the array yields 1 tuple */
1292 : 25 : ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) qual;
7102 bruce@momjian.us 1293 : 25 : Node *arraynode = (Node *) lsecond(saop->args);
1294 : :
801 tgl@sss.pgh.pa.us 1295 : 25 : ntuples += estimate_array_length(root, arraynode);
1296 : : }
2632 1297 [ + + ]: 434 : else if (IsA(qual, CurrentOfExpr))
1298 : : {
1299 : : /* CURRENT OF yields 1 tuple */
6717 1300 : 202 : ntuples++;
1301 : : }
1302 : : else
1303 : : {
1304 : : /* It's just CTID = something, count 1 tuple */
7414 1305 : 232 : ntuples++;
1306 : : }
1307 : : }
1308 : :
1309 : : /*
1310 : : * The TID qual expressions will be computed once, any other baserestrict
1311 : : * quals once per retrieved tuple.
1312 : : */
6852 1313 : 446 : cost_qual_eval(&tid_qual_cost, tidquals, root);
1314 : :
1315 : : /* fetch estimated page cost for tablespace containing table */
5913 rhaas@postgresql.org 1316 : 446 : get_tablespace_page_costs(baserel->reltablespace,
1317 : : &spc_random_page_cost,
1318 : : NULL);
1319 : :
1320 : : /* disk costs --- assume each tuple on a different page */
1321 : 446 : run_cost += spc_random_page_cost * ntuples;
1322 : :
1323 : : /* Add scanning CPU costs */
4949 tgl@sss.pgh.pa.us 1324 : 446 : get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
1325 : :
1326 : : /* XXX currently we assume TID quals are a subset of qpquals */
1327 : 446 : startup_cost += qpqual_cost.startup + tid_qual_cost.per_tuple;
1328 : 446 : cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple -
6852 1329 : 446 : tid_qual_cost.per_tuple;
9525 1330 : 446 : run_cost += cpu_per_tuple * ntuples;
1331 : :
1332 : : /* tlist eval costs are paid per output row, not per tuple scanned */
3678 1333 : 446 : startup_cost += path->pathtarget->cost.startup;
1334 : 446 : run_cost += path->pathtarget->cost.per_tuple * path->rows;
1335 : :
1336 : : /*
1337 : : * There are assertions above verifying that we only reach this function
1338 : : * either when baserel->pgs_mask includes PGS_TIDSCAN or when the TID scan
1339 : : * is the only legal path, so we only need to consider the effects of
1340 : : * PGS_CONSIDER_NONPARTIAL here.
1341 : : */
46 rhaas@postgresql.org 1342 [ + - ]:GNC 446 : if (path->parallel_workers == 0)
1343 : 446 : enable_mask |= PGS_CONSIDER_NONPARTIAL;
1344 : 446 : path->disabled_nodes =
1345 : 446 : (baserel->pgs_mask & enable_mask) != enable_mask ? 1 : 0;
9525 tgl@sss.pgh.pa.us 1346 :CBC 446 : path->startup_cost = startup_cost;
1347 : 446 : path->total_cost = startup_cost + run_cost;
9609 bruce@momjian.us 1348 : 446 : }
1349 : :
1350 : : /*
1351 : : * cost_tidrangescan
1352 : : * Determines and sets the costs of scanning a relation using a range of
1353 : : * TIDs for 'path'
1354 : : *
1355 : : * 'baserel' is the relation to be scanned
1356 : : * 'tidrangequals' is the list of TID-checkable range quals
1357 : : * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
1358 : : */
1359 : : void
1842 drowley@postgresql.o 1360 : 1029 : cost_tidrangescan(Path *path, PlannerInfo *root,
1361 : : RelOptInfo *baserel, List *tidrangequals,
1362 : : ParamPathInfo *param_info)
1363 : : {
1364 : : Selectivity selectivity;
1365 : : double pages;
1366 : : Cost startup_cost;
1367 : : Cost cpu_run_cost;
1368 : : Cost disk_run_cost;
1369 : : QualCost qpqual_cost;
1370 : : Cost cpu_per_tuple;
1371 : : QualCost tid_qual_cost;
1372 : : double ntuples;
1373 : : double nseqpages;
1374 : : double spc_random_page_cost;
1375 : : double spc_seq_page_cost;
46 rhaas@postgresql.org 1376 :GNC 1029 : uint64 enable_mask = PGS_TIDSCAN;
1377 : :
1378 : : /* Should only be applied to base relations */
1842 drowley@postgresql.o 1379 [ - + ]:CBC 1029 : Assert(baserel->relid > 0);
1380 [ - + ]: 1029 : Assert(baserel->rtekind == RTE_RELATION);
1381 : :
1382 : : /* Mark the path with the correct row estimate */
1383 [ - + ]: 1029 : if (param_info)
1842 drowley@postgresql.o 1384 :UBC 0 : path->rows = param_info->ppi_rows;
1385 : : else
1842 drowley@postgresql.o 1386 :CBC 1029 : path->rows = baserel->rows;
1387 : :
1388 : : /* Count how many tuples and pages we expect to scan */
1389 : 1029 : selectivity = clauselist_selectivity(root, tidrangequals, baserel->relid,
1390 : : JOIN_INNER, NULL);
1391 : 1029 : pages = ceil(selectivity * baserel->pages);
1392 : :
1393 [ + + ]: 1029 : if (pages <= 0.0)
1394 : 21 : pages = 1.0;
1395 : :
1396 : : /*
1397 : : * The first page in a range requires a random seek, but each subsequent
1398 : : * page is just a normal sequential page read. NOTE: it's desirable for
1399 : : * TID Range Scans to cost more than the equivalent Sequential Scans,
1400 : : * because Seq Scans have some performance advantages such as scan
1401 : : * synchronization, and we'd prefer one of them to be picked unless a TID
1402 : : * Range Scan really is better.
1403 : : */
1404 : 1029 : ntuples = selectivity * baserel->tuples;
1405 : 1029 : nseqpages = pages - 1.0;
1406 : :
1407 : : /*
1408 : : * The TID qual expressions will be computed once, any other baserestrict
1409 : : * quals once per retrieved tuple.
1410 : : */
1411 : 1029 : cost_qual_eval(&tid_qual_cost, tidrangequals, root);
1412 : :
1413 : : /* fetch estimated page cost for tablespace containing table */
1414 : 1029 : get_tablespace_page_costs(baserel->reltablespace,
1415 : : &spc_random_page_cost,
1416 : : &spc_seq_page_cost);
1417 : :
1418 : : /* disk costs; 1 random page and the remainder as seq pages */
108 drowley@postgresql.o 1419 :GNC 1029 : disk_run_cost = spc_random_page_cost + spc_seq_page_cost * nseqpages;
1420 : :
1421 : : /* Add scanning CPU costs */
1842 drowley@postgresql.o 1422 :CBC 1029 : get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
1423 : :
1424 : : /*
1425 : : * XXX currently we assume TID quals are a subset of qpquals at this
1426 : : * point; they will be removed (if possible) when we create the plan, so
1427 : : * we subtract their cost from the total qpqual cost. (If the TID quals
1428 : : * can't be removed, this is a mistake and we're going to underestimate
1429 : : * the CPU cost a bit.)
1430 : : */
108 drowley@postgresql.o 1431 :GNC 1029 : startup_cost = qpqual_cost.startup + tid_qual_cost.per_tuple;
1842 drowley@postgresql.o 1432 :CBC 1029 : cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple -
1433 : 1029 : tid_qual_cost.per_tuple;
108 drowley@postgresql.o 1434 :GNC 1029 : cpu_run_cost = cpu_per_tuple * ntuples;
1435 : :
1436 : : /* tlist eval costs are paid per output row, not per tuple scanned */
1842 drowley@postgresql.o 1437 :CBC 1029 : startup_cost += path->pathtarget->cost.startup;
108 drowley@postgresql.o 1438 :GNC 1029 : cpu_run_cost += path->pathtarget->cost.per_tuple * path->rows;
1439 : :
1440 : : /* Adjust costing for parallelism, if used. */
1441 [ + + ]: 1029 : if (path->parallel_workers > 0)
1442 : : {
1443 : 24 : double parallel_divisor = get_parallel_divisor(path);
1444 : :
1445 : : /* The CPU cost is divided among all the workers. */
1446 : 24 : cpu_run_cost /= parallel_divisor;
1447 : :
1448 : : /*
1449 : : * In the case of a parallel plan, the row count needs to represent
1450 : : * the number of tuples processed per worker.
1451 : : */
1452 : 24 : path->rows = clamp_row_est(path->rows / parallel_divisor);
1453 : : }
1454 : :
1455 : : /*
1456 : : * We should not generate this path type when PGS_TIDSCAN is unset, but we
1457 : : * might need to disable this path due to PGS_CONSIDER_NONPARTIAL.
1458 : : */
46 rhaas@postgresql.org 1459 [ - + ]: 1029 : Assert((baserel->pgs_mask & PGS_TIDSCAN) != 0);
1460 [ + + ]: 1029 : if (path->parallel_workers == 0)
1461 : 1005 : enable_mask |= PGS_CONSIDER_NONPARTIAL;
1462 : 1029 : path->disabled_nodes =
1463 : 1029 : (baserel->pgs_mask & enable_mask) != enable_mask ? 1 : 0;
1842 drowley@postgresql.o 1464 :CBC 1029 : path->startup_cost = startup_cost;
108 drowley@postgresql.o 1465 :GNC 1029 : path->total_cost = startup_cost + cpu_run_cost + disk_run_cost;
1842 drowley@postgresql.o 1466 :CBC 1029 : }
1467 : :
1468 : : /*
1469 : : * cost_subqueryscan
1470 : : * Determines and returns the cost of scanning a subquery RTE.
1471 : : *
1472 : : * 'baserel' is the relation to be scanned
1473 : : * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
1474 : : * 'trivial_pathtarget' is true if the pathtarget is believed to be trivial.
1475 : : */
1476 : : void
3660 tgl@sss.pgh.pa.us 1477 : 33647 : cost_subqueryscan(SubqueryScanPath *path, PlannerInfo *root,
1478 : : RelOptInfo *baserel, ParamPathInfo *param_info,
1479 : : bool trivial_pathtarget)
1480 : : {
1481 : : Cost startup_cost;
1482 : : Cost run_cost;
1483 : : List *qpquals;
1484 : : QualCost qpqual_cost;
1485 : : Cost cpu_per_tuple;
46 rhaas@postgresql.org 1486 :GNC 33647 : uint64 enable_mask = 0;
1487 : :
1488 : : /* Should only be applied to base relations that are subqueries */
8280 tgl@sss.pgh.pa.us 1489 [ - + ]:CBC 33647 : Assert(baserel->relid > 0);
1490 [ - + ]: 33647 : Assert(baserel->rtekind == RTE_SUBQUERY);
1491 : :
1492 : : /*
1493 : : * We compute the rowcount estimate as the subplan's estimate times the
1494 : : * selectivity of relevant restriction clauses. In simple cases this will
1495 : : * come out the same as baserel->rows; but when dealing with parallelized
1496 : : * paths we must do it like this to get the right answer.
1497 : : */
5078 1498 [ + + ]: 33647 : if (param_info)
1411 1499 : 663 : qpquals = list_concat_copy(param_info->ppi_clauses,
1500 : 663 : baserel->baserestrictinfo);
1501 : : else
1502 : 32984 : qpquals = baserel->baserestrictinfo;
1503 : :
1504 : 33647 : path->path.rows = clamp_row_est(path->subpath->rows *
1505 : 33647 : clauselist_selectivity(root,
1506 : : qpquals,
1507 : : 0,
1508 : : JOIN_INNER,
1509 : : NULL));
1510 : :
1511 : : /*
1512 : : * Cost of path is cost of evaluating the subplan, plus cost of evaluating
1513 : : * any restriction clauses and tlist that will be attached to the
1514 : : * SubqueryScan node, plus cpu_tuple_cost to account for selection and
1515 : : * projection overhead.
1516 : : */
46 rhaas@postgresql.org 1517 [ + + ]:GNC 33647 : if (path->path.parallel_workers == 0)
1518 : 33614 : enable_mask |= PGS_CONSIDER_NONPARTIAL;
1519 : 33647 : path->path.disabled_nodes = path->subpath->disabled_nodes
1520 : 33647 : + (((baserel->pgs_mask & enable_mask) != enable_mask) ? 1 : 0);
3660 tgl@sss.pgh.pa.us 1521 :CBC 33647 : path->path.startup_cost = path->subpath->startup_cost;
1522 : 33647 : path->path.total_cost = path->subpath->total_cost;
1523 : :
1524 : : /*
1525 : : * However, if there are no relevant restriction clauses and the
1526 : : * pathtarget is trivial, then we expect that setrefs.c will optimize away
1527 : : * the SubqueryScan plan node altogether, so we should just make its cost
1528 : : * and rowcount equal to the input path's.
1529 : : *
1530 : : * Note: there are some edge cases where createplan.c will apply a
1531 : : * different targetlist to the SubqueryScan node, thus falsifying our
1532 : : * current estimate of whether the target is trivial, and making the cost
1533 : : * estimate (though not the rowcount) wrong. It does not seem worth the
1534 : : * extra complication to try to account for that exactly, especially since
1535 : : * that behavior falsifies other cost estimates as well.
1536 : : */
1335 1537 [ + + + + ]: 33647 : if (qpquals == NIL && trivial_pathtarget)
1538 : 14243 : return;
1539 : :
5078 1540 : 19404 : get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
1541 : :
1542 : 19404 : startup_cost = qpqual_cost.startup;
1543 : 19404 : cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple;
1411 1544 : 19404 : run_cost = cpu_per_tuple * path->subpath->rows;
1545 : :
1546 : : /* tlist eval costs are paid per output row, not per tuple scanned */
3660 1547 : 19404 : startup_cost += path->path.pathtarget->cost.startup;
1548 : 19404 : run_cost += path->path.pathtarget->cost.per_tuple * path->path.rows;
1549 : :
1550 : 19404 : path->path.startup_cost += startup_cost;
1551 : 19404 : path->path.total_cost += startup_cost + run_cost;
1552 : : }
1553 : :
1554 : : /*
1555 : : * cost_functionscan
1556 : : * Determines and returns the cost of scanning a function RTE.
1557 : : *
1558 : : * 'baserel' is the relation to be scanned
1559 : : * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
1560 : : */
1561 : : void
4968 1562 : 27918 : cost_functionscan(Path *path, PlannerInfo *root,
1563 : : RelOptInfo *baserel, ParamPathInfo *param_info)
1564 : : {
8708 1565 : 27918 : Cost startup_cost = 0;
1566 : 27918 : Cost run_cost = 0;
1567 : : QualCost qpqual_cost;
1568 : : Cost cpu_per_tuple;
1569 : : RangeTblEntry *rte;
1570 : : QualCost exprcost;
46 rhaas@postgresql.org 1571 :GNC 27918 : uint64 enable_mask = 0;
1572 : :
1573 : : /* Should only be applied to base relations that are functions */
8436 tgl@sss.pgh.pa.us 1574 [ - + ]:CBC 27918 : Assert(baserel->relid > 0);
6903 1575 [ + - ]: 27918 : rte = planner_rt_fetch(baserel->relid, root);
6992 1576 [ - + ]: 27918 : Assert(rte->rtekind == RTE_FUNCTION);
1577 : :
1578 : : /* Mark the path with the correct row estimate */
4968 1579 [ + + ]: 27918 : if (param_info)
1580 : 4520 : path->rows = param_info->ppi_rows;
1581 : : else
1582 : 23398 : path->rows = baserel->rows;
1583 : :
1584 : : /*
1585 : : * Estimate costs of executing the function expression(s).
1586 : : *
1587 : : * Currently, nodeFunctionscan.c always executes the functions to
1588 : : * completion before returning any rows, and caches the results in a
1589 : : * tuplestore. So the function eval cost is all startup cost, and per-row
1590 : : * costs are minimal.
1591 : : *
1592 : : * XXX in principle we ought to charge tuplestore spill costs if the
1593 : : * number of rows is large. However, given how phony our rowcount
1594 : : * estimates for functions tend to be, there's not a lot of point in that
1595 : : * refinement right now.
1596 : : */
4497 1597 : 27918 : cost_qual_eval_node(&exprcost, (Node *) rte->functions, root);
1598 : :
6028 1599 : 27918 : startup_cost += exprcost.startup + exprcost.per_tuple;
1600 : :
1601 : : /* Add scanning CPU costs */
4968 1602 : 27918 : get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
1603 : :
1604 : 27918 : startup_cost += qpqual_cost.startup;
1605 : 27918 : cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple;
8708 1606 : 27918 : run_cost += cpu_per_tuple * baserel->tuples;
1607 : :
1608 : : /* tlist eval costs are paid per output row, not per tuple scanned */
3678 1609 : 27918 : startup_cost += path->pathtarget->cost.startup;
1610 : 27918 : run_cost += path->pathtarget->cost.per_tuple * path->rows;
1611 : :
46 rhaas@postgresql.org 1612 [ + - ]:GNC 27918 : if (path->parallel_workers == 0)
1613 : 27918 : enable_mask |= PGS_CONSIDER_NONPARTIAL;
1614 : 27918 : path->disabled_nodes =
1615 : 27918 : (baserel->pgs_mask & enable_mask) != enable_mask ? 1 : 0;
8708 tgl@sss.pgh.pa.us 1616 :CBC 27918 : path->startup_cost = startup_cost;
1617 : 27918 : path->total_cost = startup_cost + run_cost;
1618 : 27918 : }
1619 : :
1620 : : /*
1621 : : * cost_tablefuncscan
1622 : : * Determines and returns the cost of scanning a table function.
1623 : : *
1624 : : * 'baserel' is the relation to be scanned
1625 : : * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
1626 : : */
1627 : : void
3294 alvherre@alvh.no-ip. 1628 : 311 : cost_tablefuncscan(Path *path, PlannerInfo *root,
1629 : : RelOptInfo *baserel, ParamPathInfo *param_info)
1630 : : {
1631 : 311 : Cost startup_cost = 0;
1632 : 311 : Cost run_cost = 0;
1633 : : QualCost qpqual_cost;
1634 : : Cost cpu_per_tuple;
1635 : : RangeTblEntry *rte;
1636 : : QualCost exprcost;
46 rhaas@postgresql.org 1637 :GNC 311 : uint64 enable_mask = 0;
1638 : :
1639 : : /* Should only be applied to base relations that are functions */
3294 alvherre@alvh.no-ip. 1640 [ - + ]:CBC 311 : Assert(baserel->relid > 0);
1641 [ + - ]: 311 : rte = planner_rt_fetch(baserel->relid, root);
1642 [ - + ]: 311 : Assert(rte->rtekind == RTE_TABLEFUNC);
1643 : :
1644 : : /* Mark the path with the correct row estimate */
1645 [ + + ]: 311 : if (param_info)
1646 : 117 : path->rows = param_info->ppi_rows;
1647 : : else
1648 : 194 : path->rows = baserel->rows;
1649 : :
1650 : : /*
1651 : : * Estimate costs of executing the table func expression(s).
1652 : : *
1653 : : * XXX in principle we ought to charge tuplestore spill costs if the
1654 : : * number of rows is large. However, given how phony our rowcount
1655 : : * estimates for tablefuncs tend to be, there's not a lot of point in that
1656 : : * refinement right now.
1657 : : */
1658 : 311 : cost_qual_eval_node(&exprcost, (Node *) rte->tablefunc, root);
1659 : :
1660 : 311 : startup_cost += exprcost.startup + exprcost.per_tuple;
1661 : :
1662 : : /* Add scanning CPU costs */
1663 : 311 : get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
1664 : :
1665 : 311 : startup_cost += qpqual_cost.startup;
1666 : 311 : cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple;
1667 : 311 : run_cost += cpu_per_tuple * baserel->tuples;
1668 : :
1669 : : /* tlist eval costs are paid per output row, not per tuple scanned */
1670 : 311 : startup_cost += path->pathtarget->cost.startup;
1671 : 311 : run_cost += path->pathtarget->cost.per_tuple * path->rows;
1672 : :
46 rhaas@postgresql.org 1673 [ + - ]:GNC 311 : if (path->parallel_workers == 0)
1674 : 311 : enable_mask |= PGS_CONSIDER_NONPARTIAL;
1675 : 311 : path->disabled_nodes =
1676 : 311 : (baserel->pgs_mask & enable_mask) != enable_mask ? 1 : 0;
3294 alvherre@alvh.no-ip. 1677 :CBC 311 : path->startup_cost = startup_cost;
1678 : 311 : path->total_cost = startup_cost + run_cost;
1679 : 311 : }
1680 : :
1681 : : /*
1682 : : * cost_valuesscan
1683 : : * Determines and returns the cost of scanning a VALUES RTE.
1684 : : *
1685 : : * 'baserel' is the relation to be scanned
1686 : : * 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
1687 : : */
1688 : : void
4963 tgl@sss.pgh.pa.us 1689 : 4326 : cost_valuesscan(Path *path, PlannerInfo *root,
1690 : : RelOptInfo *baserel, ParamPathInfo *param_info)
1691 : : {
7165 mail@joeconway.com 1692 : 4326 : Cost startup_cost = 0;
1693 : 4326 : Cost run_cost = 0;
1694 : : QualCost qpqual_cost;
1695 : : Cost cpu_per_tuple;
46 rhaas@postgresql.org 1696 :GNC 4326 : uint64 enable_mask = 0;
1697 : :
1698 : : /* Should only be applied to base relations that are values lists */
7165 mail@joeconway.com 1699 [ - + ]:CBC 4326 : Assert(baserel->relid > 0);
1700 [ - + ]: 4326 : Assert(baserel->rtekind == RTE_VALUES);
1701 : :
1702 : : /* Mark the path with the correct row estimate */
4963 tgl@sss.pgh.pa.us 1703 [ + + ]: 4326 : if (param_info)
1704 : 33 : path->rows = param_info->ppi_rows;
1705 : : else
1706 : 4293 : path->rows = baserel->rows;
1707 : :
1708 : : /*
1709 : : * For now, estimate list evaluation cost at one operator eval per list
1710 : : * (probably pretty bogus, but is it worth being smarter?)
1711 : : */
7165 mail@joeconway.com 1712 : 4326 : cpu_per_tuple = cpu_operator_cost;
1713 : :
1714 : : /* Add scanning CPU costs */
4963 tgl@sss.pgh.pa.us 1715 : 4326 : get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
1716 : :
1717 : 4326 : startup_cost += qpqual_cost.startup;
1718 : 4326 : cpu_per_tuple += cpu_tuple_cost + qpqual_cost.per_tuple;
7165 mail@joeconway.com 1719 : 4326 : run_cost += cpu_per_tuple * baserel->tuples;
1720 : :
1721 : : /* tlist eval costs are paid per output row, not per tuple scanned */
3678 tgl@sss.pgh.pa.us 1722 : 4326 : startup_cost += path->pathtarget->cost.startup;
1723 : 4326 : run_cost += path->pathtarget->cost.per_tuple * path->rows;
1724 : :
46 rhaas@postgresql.org 1725 [ + - ]:GNC 4326 : if (path->parallel_workers == 0)
1726 : 4326 : enable_mask |= PGS_CONSIDER_NONPARTIAL;
1727 : 4326 : path->disabled_nodes =
1728 : 4326 : (baserel->pgs_mask & enable_mask) != enable_mask ? 1 : 0;
7165 mail@joeconway.com 1729 :CBC 4326 : path->startup_cost = startup_cost;
1730 : 4326 : path->total_cost = startup_cost + run_cost;
1731 : 4326 : }
1732 : :
1733 : : /*
1734 : : * cost_ctescan
1735 : : * Determines and returns the cost of scanning a CTE RTE.
1736 : : *
1737 : : * Note: this is used for both self-reference and regular CTEs; the
1738 : : * possible cost differences are below the threshold of what we could
1739 : : * estimate accurately anyway. Note that the costs of evaluating the
1740 : : * referenced CTE query are added into the final plan as initplan costs,
1741 : : * and should NOT be counted here.
1742 : : */
1743 : : void
4949 tgl@sss.pgh.pa.us 1744 : 2915 : cost_ctescan(Path *path, PlannerInfo *root,
1745 : : RelOptInfo *baserel, ParamPathInfo *param_info)
1746 : : {
6371 1747 : 2915 : Cost startup_cost = 0;
1748 : 2915 : Cost run_cost = 0;
1749 : : QualCost qpqual_cost;
1750 : : Cost cpu_per_tuple;
46 rhaas@postgresql.org 1751 :GNC 2915 : uint64 enable_mask = 0;
1752 : :
1753 : : /* Should only be applied to base relations that are CTEs */
6371 tgl@sss.pgh.pa.us 1754 [ - + ]:CBC 2915 : Assert(baserel->relid > 0);
1755 [ - + ]: 2915 : Assert(baserel->rtekind == RTE_CTE);
1756 : :
1757 : : /* Mark the path with the correct row estimate */
4949 1758 [ - + ]: 2915 : if (param_info)
4949 tgl@sss.pgh.pa.us 1759 :UBC 0 : path->rows = param_info->ppi_rows;
1760 : : else
4949 tgl@sss.pgh.pa.us 1761 :CBC 2915 : path->rows = baserel->rows;
1762 : :
1763 : : /* Charge one CPU tuple cost per row for tuplestore manipulation */
6371 1764 : 2915 : cpu_per_tuple = cpu_tuple_cost;
1765 : :
1766 : : /* Add scanning CPU costs */
4949 1767 : 2915 : get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
1768 : :
1769 : 2915 : startup_cost += qpqual_cost.startup;
1770 : 2915 : cpu_per_tuple += cpu_tuple_cost + qpqual_cost.per_tuple;
6371 1771 : 2915 : run_cost += cpu_per_tuple * baserel->tuples;
1772 : :
1773 : : /* tlist eval costs are paid per output row, not per tuple scanned */
3678 1774 : 2915 : startup_cost += path->pathtarget->cost.startup;
1775 : 2915 : run_cost += path->pathtarget->cost.per_tuple * path->rows;
1776 : :
46 rhaas@postgresql.org 1777 [ + - ]:GNC 2915 : if (path->parallel_workers == 0)
1778 : 2915 : enable_mask |= PGS_CONSIDER_NONPARTIAL;
1779 : 2915 : path->disabled_nodes =
1780 : 2915 : (baserel->pgs_mask & enable_mask) != enable_mask ? 1 : 0;
6371 tgl@sss.pgh.pa.us 1781 :CBC 2915 : path->startup_cost = startup_cost;
1782 : 2915 : path->total_cost = startup_cost + run_cost;
1783 : 2915 : }
1784 : :
1785 : : /*
1786 : : * cost_namedtuplestorescan
1787 : : * Determines and returns the cost of scanning a named tuplestore.
1788 : : */
1789 : : void
3271 kgrittn@postgresql.o 1790 : 241 : cost_namedtuplestorescan(Path *path, PlannerInfo *root,
1791 : : RelOptInfo *baserel, ParamPathInfo *param_info)
1792 : : {
1793 : 241 : Cost startup_cost = 0;
1794 : 241 : Cost run_cost = 0;
1795 : : QualCost qpqual_cost;
1796 : : Cost cpu_per_tuple;
46 rhaas@postgresql.org 1797 :GNC 241 : uint64 enable_mask = 0;
1798 : :
1799 : : /* Should only be applied to base relations that are Tuplestores */
3271 kgrittn@postgresql.o 1800 [ - + ]:CBC 241 : Assert(baserel->relid > 0);
1801 [ - + ]: 241 : Assert(baserel->rtekind == RTE_NAMEDTUPLESTORE);
1802 : :
1803 : : /* Mark the path with the correct row estimate */
1804 [ - + ]: 241 : if (param_info)
3271 kgrittn@postgresql.o 1805 :UBC 0 : path->rows = param_info->ppi_rows;
1806 : : else
3271 kgrittn@postgresql.o 1807 :CBC 241 : path->rows = baserel->rows;
1808 : :
1809 : : /* Charge one CPU tuple cost per row for tuplestore manipulation */
1810 : 241 : cpu_per_tuple = cpu_tuple_cost;
1811 : :
1812 : : /* Add scanning CPU costs */
1813 : 241 : get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
1814 : :
1815 : 241 : startup_cost += qpqual_cost.startup;
1816 : 241 : cpu_per_tuple += cpu_tuple_cost + qpqual_cost.per_tuple;
1817 : 241 : run_cost += cpu_per_tuple * baserel->tuples;
1818 : :
46 rhaas@postgresql.org 1819 [ + - ]:GNC 241 : if (path->parallel_workers == 0)
1820 : 241 : enable_mask |= PGS_CONSIDER_NONPARTIAL;
1821 : 241 : path->disabled_nodes =
1822 : 241 : (baserel->pgs_mask & enable_mask) != enable_mask ? 1 : 0;
3271 kgrittn@postgresql.o 1823 :CBC 241 : path->startup_cost = startup_cost;
1824 : 241 : path->total_cost = startup_cost + run_cost;
1825 : 241 : }
1826 : :
1827 : : /*
1828 : : * cost_resultscan
1829 : : * Determines and returns the cost of scanning an RTE_RESULT relation.
1830 : : */
1831 : : void
2603 tgl@sss.pgh.pa.us 1832 : 2202 : cost_resultscan(Path *path, PlannerInfo *root,
1833 : : RelOptInfo *baserel, ParamPathInfo *param_info)
1834 : : {
1835 : 2202 : Cost startup_cost = 0;
1836 : 2202 : Cost run_cost = 0;
1837 : : QualCost qpqual_cost;
1838 : : Cost cpu_per_tuple;
46 rhaas@postgresql.org 1839 :GNC 2202 : uint64 enable_mask = 0;
1840 : :
1841 : : /* Should only be applied to RTE_RESULT base relations */
2603 tgl@sss.pgh.pa.us 1842 [ - + ]:CBC 2202 : Assert(baserel->relid > 0);
1843 [ - + ]: 2202 : Assert(baserel->rtekind == RTE_RESULT);
1844 : :
1845 : : /* Mark the path with the correct row estimate */
1846 [ + + ]: 2202 : if (param_info)
1847 : 90 : path->rows = param_info->ppi_rows;
1848 : : else
1849 : 2112 : path->rows = baserel->rows;
1850 : :
1851 : : /* We charge qual cost plus cpu_tuple_cost */
1852 : 2202 : get_restriction_qual_cost(root, baserel, param_info, &qpqual_cost);
1853 : :
1854 : 2202 : startup_cost += qpqual_cost.startup;
1855 : 2202 : cpu_per_tuple = cpu_tuple_cost + qpqual_cost.per_tuple;
1856 : 2202 : run_cost += cpu_per_tuple * baserel->tuples;
1857 : :
46 rhaas@postgresql.org 1858 [ + - ]:GNC 2202 : if (path->parallel_workers == 0)
1859 : 2202 : enable_mask |= PGS_CONSIDER_NONPARTIAL;
1860 : 2202 : path->disabled_nodes =
1861 : 2202 : (baserel->pgs_mask & enable_mask) != enable_mask ? 1 : 0;
2603 tgl@sss.pgh.pa.us 1862 :CBC 2202 : path->startup_cost = startup_cost;
1863 : 2202 : path->total_cost = startup_cost + run_cost;
1864 : 2202 : }
1865 : :
1866 : : /*
1867 : : * cost_recursive_union
1868 : : * Determines and returns the cost of performing a recursive union,
1869 : : * and also the estimated output size.
1870 : : *
1871 : : * We are given Paths for the nonrecursive and recursive terms.
1872 : : */
1873 : : void
3660 1874 : 540 : cost_recursive_union(Path *runion, Path *nrterm, Path *rterm)
1875 : : {
1876 : : Cost startup_cost;
1877 : : Cost total_cost;
1878 : : double total_rows;
46 rhaas@postgresql.org 1879 :GNC 540 : uint64 enable_mask = 0;
1880 : :
1881 : : /* We probably have decent estimates for the non-recursive term */
6371 tgl@sss.pgh.pa.us 1882 :CBC 540 : startup_cost = nrterm->startup_cost;
1883 : 540 : total_cost = nrterm->total_cost;
3660 1884 : 540 : total_rows = nrterm->rows;
1885 : :
1886 : : /*
1887 : : * We arbitrarily assume that about 10 recursive iterations will be
1888 : : * needed, and that we've managed to get a good fix on the cost and output
1889 : : * size of each one of them. These are mighty shaky assumptions but it's
1890 : : * hard to see how to do better.
1891 : : */
6371 1892 : 540 : total_cost += 10 * rterm->total_cost;
3660 1893 : 540 : total_rows += 10 * rterm->rows;
1894 : :
1895 : : /*
1896 : : * Also charge cpu_tuple_cost per row to account for the costs of
1897 : : * manipulating the tuplestores. (We don't worry about possible
1898 : : * spill-to-disk costs.)
1899 : : */
6371 1900 : 540 : total_cost += cpu_tuple_cost * total_rows;
1901 : :
46 rhaas@postgresql.org 1902 [ + - ]:GNC 540 : if (runion->parallel_workers == 0)
1903 : 540 : enable_mask |= PGS_CONSIDER_NONPARTIAL;
1904 : 540 : runion->disabled_nodes =
1905 : 540 : (runion->parent->pgs_mask & enable_mask) != enable_mask ? 1 : 0;
6371 tgl@sss.pgh.pa.us 1906 :CBC 540 : runion->startup_cost = startup_cost;
1907 : 540 : runion->total_cost = total_cost;
3660 1908 : 540 : runion->rows = total_rows;
1909 : 540 : runion->pathtarget->width = Max(nrterm->pathtarget->width,
1910 : : rterm->pathtarget->width);
6371 1911 : 540 : }
1912 : :
1913 : : /*
1914 : : * cost_tuplesort
1915 : : * Determines and returns the cost of sorting a relation using tuplesort,
1916 : : * not including the cost of reading the input data.
1917 : : *
1918 : : * If the total volume of data to sort is less than sort_mem, we will do
1919 : : * an in-memory sort, which requires no I/O and about t*log2(t) tuple
1920 : : * comparisons for t tuples.
1921 : : *
1922 : : * If the total volume exceeds sort_mem, we switch to a tape-style merge
1923 : : * algorithm. There will still be about t*log2(t) tuple comparisons in
1924 : : * total, but we will also need to write and read each tuple once per
1925 : : * merge pass. We expect about ceil(logM(r)) merge passes where r is the
1926 : : * number of initial runs formed and M is the merge order used by tuplesort.c.
1927 : : * Since the average initial run should be about sort_mem, we have
1928 : : * disk traffic = 2 * relsize * ceil(logM(p / sort_mem))
1929 : : * cpu = comparison_cost * t * log2(t)
1930 : : *
1931 : : * If the sort is bounded (i.e., only the first k result tuples are needed)
1932 : : * and k tuples can fit into sort_mem, we use a heap method that keeps only
1933 : : * k tuples in the heap; this will require about t*log2(k) tuple comparisons.
1934 : : *
1935 : : * The disk traffic is assumed to be 3/4ths sequential and 1/4th random
1936 : : * accesses (XXX can't we refine that guess?)
1937 : : *
1938 : : * By default, we charge two operator evals per tuple comparison, which should
1939 : : * be in the right ballpark in most cases. The caller can tweak this by
1940 : : * specifying nonzero comparison_cost; typically that's used for any extra
1941 : : * work that has to be done to prepare the inputs to the comparison operators.
1942 : : *
1943 : : * 'tuples' is the number of tuples in the relation
1944 : : * 'width' is the average tuple width in bytes
1945 : : * 'comparison_cost' is the extra cost per comparison, if any
1946 : : * 'sort_mem' is the number of kilobytes of work memory allowed for the sort
1947 : : * 'limit_tuples' is the bound on the number of output tuples; -1 if no bound
1948 : : */
1949 : : static void
1259 1950 : 1243866 : cost_tuplesort(Cost *startup_cost, Cost *run_cost,
1951 : : double tuples, int width,
1952 : : Cost comparison_cost, int sort_mem,
1953 : : double limit_tuples)
1954 : : {
6890 1955 : 1243866 : double input_bytes = relation_byte_size(tuples, width);
1956 : : double output_bytes;
1957 : : double output_tuples;
408 1958 : 1243866 : int64 sort_mem_bytes = sort_mem * (int64) 1024;
1959 : :
1960 : : /*
1961 : : * We want to be sure the cost of a sort is never estimated as zero, even
1962 : : * if passed-in tuple count is zero. Besides, mustn't do log(0)...
1963 : : */
9562 1964 [ + + ]: 1243866 : if (tuples < 2.0)
1965 : 348943 : tuples = 2.0;
1966 : :
1967 : : /* Include the default cost-per-comparison */
1259 1968 : 1243866 : comparison_cost += 2.0 * cpu_operator_cost;
1969 : :
1970 : : /* Do we have a useful LIMIT? */
6890 1971 [ + + + + ]: 1243866 : if (limit_tuples > 0 && limit_tuples < tuples)
1972 : : {
1973 : 963 : output_tuples = limit_tuples;
1974 : 963 : output_bytes = relation_byte_size(output_tuples, width);
1975 : : }
1976 : : else
1977 : : {
1978 : 1242903 : output_tuples = tuples;
1979 : 1242903 : output_bytes = input_bytes;
1980 : : }
1981 : :
5638 1982 [ + + ]: 1243866 : if (output_bytes > sort_mem_bytes)
1983 : : {
1984 : : /*
1985 : : * We'll have to use a disk-based sort of all the tuples
1986 : : */
6890 1987 : 11766 : double npages = ceil(input_bytes / BLCKSZ);
3628 rhaas@postgresql.org 1988 : 11766 : double nruns = input_bytes / sort_mem_bytes;
5638 tgl@sss.pgh.pa.us 1989 : 11766 : double mergeorder = tuplesort_merge_order(sort_mem_bytes);
1990 : : double log_runs;
1991 : : double npageaccesses;
1992 : :
1993 : : /*
1994 : : * CPU costs
1995 : : *
1996 : : * Assume about N log2 N comparisons
1997 : : */
1259 1998 : 11766 : *startup_cost = comparison_cost * tuples * LOG2(tuples);
1999 : :
2000 : : /* Disk costs */
2001 : :
2002 : : /* Compute logM(r) as log(r) / log(M) */
7329 2003 [ + + ]: 11766 : if (nruns > mergeorder)
2004 : 2653 : log_runs = ceil(log(nruns) / log(mergeorder));
2005 : : else
9562 2006 : 9113 : log_runs = 1.0;
9525 2007 : 11766 : npageaccesses = 2.0 * npages * log_runs;
2008 : : /* Assume 3/4ths of accesses are sequential, 1/4th are not */
2169 tomas.vondra@postgre 2009 : 11766 : *startup_cost += npageaccesses *
7223 tgl@sss.pgh.pa.us 2010 : 11766 : (seq_page_cost * 0.75 + random_page_cost * 0.25);
2011 : : }
5638 2012 [ + + - + ]: 1232100 : else if (tuples > 2 * output_tuples || input_bytes > sort_mem_bytes)
2013 : : {
2014 : : /*
2015 : : * We'll use a bounded heap-sort keeping just K tuples in memory, for
2016 : : * a total number of tuple comparisons of N log2 K; but the constant
2017 : : * factor is a bit higher than for quicksort. Tweak it so that the
2018 : : * cost curve is continuous at the crossover point.
2019 : : */
1259 2020 : 668 : *startup_cost = comparison_cost * tuples * LOG2(2.0 * output_tuples);
2021 : : }
2022 : : else
2023 : : {
2024 : : /* We'll use plain quicksort on all the input tuples */
2025 : 1231432 : *startup_cost = comparison_cost * tuples * LOG2(tuples);
2026 : : }
2027 : :
2028 : : /*
2029 : : * Also charge a small amount (arbitrarily set equal to operator cost) per
2030 : : * extracted tuple. We don't charge cpu_tuple_cost because a Sort node
2031 : : * doesn't do qual-checking or projection, so it has less overhead than
2032 : : * most plan nodes. Note it's correct to use tuples not output_tuples
2033 : : * here --- the upper LIMIT will pro-rate the run cost so we'd be double
2034 : : * counting the LIMIT otherwise.
2035 : : */
2169 tomas.vondra@postgre 2036 : 1243866 : *run_cost = cpu_operator_cost * tuples;
2037 : 1243866 : }
2038 : :
2039 : : /*
2040 : : * cost_incremental_sort
2041 : : * Determines and returns the cost of sorting a relation incrementally, when
2042 : : * the input path is presorted by a prefix of the pathkeys.
2043 : : *
2044 : : * 'presorted_keys' is the number of leading pathkeys by which the input path
2045 : : * is sorted.
2046 : : *
2047 : : * We estimate the number of groups into which the relation is divided by the
2048 : : * leading pathkeys, and then calculate the cost of sorting a single group
2049 : : * with tuplesort using cost_tuplesort().
2050 : : */
2051 : : void
2052 : 6592 : cost_incremental_sort(Path *path,
2053 : : PlannerInfo *root, List *pathkeys, int presorted_keys,
2054 : : int input_disabled_nodes,
2055 : : Cost input_startup_cost, Cost input_total_cost,
2056 : : double input_tuples, int width, Cost comparison_cost, int sort_mem,
2057 : : double limit_tuples)
2058 : : {
2059 : : Cost startup_cost,
2060 : : run_cost,
2061 : 6592 : input_run_cost = input_total_cost - input_startup_cost;
2062 : : double group_tuples,
2063 : : input_groups;
2064 : : Cost group_startup_cost,
2065 : : group_run_cost,
2066 : : group_input_run_cost;
2067 : 6592 : List *presortedExprs = NIL;
2068 : : ListCell *l;
2152 2069 : 6592 : bool unknown_varno = false;
2070 : :
1185 drowley@postgresql.o 2071 [ + - - + ]: 6592 : Assert(presorted_keys > 0 && presorted_keys < list_length(pathkeys));
2072 : :
2073 : : /*
2074 : : * We want to be sure the cost of a sort is never estimated as zero, even
2075 : : * if passed-in tuple count is zero. Besides, mustn't do log(0)...
2076 : : */
2169 tomas.vondra@postgre 2077 [ + + ]: 6592 : if (input_tuples < 2.0)
2078 : 3579 : input_tuples = 2.0;
2079 : :
2080 : : /* Default estimate of number of groups, capped to one group per row. */
2152 2081 [ + + ]: 6592 : input_groups = Min(input_tuples, DEFAULT_NUM_DISTINCT);
2082 : :
2083 : : /*
2084 : : * Extract presorted keys as list of expressions.
2085 : : *
2086 : : * We need to be careful about Vars containing "varno 0" which might have
2087 : : * been introduced by generate_append_tlist, which would confuse
2088 : : * estimate_num_groups (in fact it'd fail for such expressions). See
2089 : : * recurse_set_operations which has to deal with the same issue.
2090 : : *
2091 : : * Unlike recurse_set_operations we can't access the original target list
2092 : : * here, and even if we could it's not very clear how useful would that be
2093 : : * for a set operation combining multiple tables. So we simply detect if
2094 : : * there are any expressions with "varno 0" and use the default
2095 : : * DEFAULT_NUM_DISTINCT in that case.
2096 : : *
2097 : : * We might also use either 1.0 (a single group) or input_tuples (each row
2098 : : * being a separate group), pretty much the worst and best case for
2099 : : * incremental sort. But those are extreme cases and using something in
2100 : : * between seems reasonable. Furthermore, generate_append_tlist is used
2101 : : * for set operations, which are likely to produce mostly unique output
2102 : : * anyway - from that standpoint the DEFAULT_NUM_DISTINCT is defensive
2103 : : * while maintaining lower startup cost.
2104 : : */
2169 2105 [ + - + - : 6640 : foreach(l, pathkeys)
+ - ]
2106 : : {
2107 : 6640 : PathKey *key = (PathKey *) lfirst(l);
2108 : 6640 : EquivalenceMember *member = (EquivalenceMember *)
1031 tgl@sss.pgh.pa.us 2109 : 6640 : linitial(key->pk_eclass->ec_members);
2110 : :
2111 : : /*
2112 : : * Check if the expression contains Var with "varno 0" so that we
2113 : : * don't call estimate_num_groups in that case.
2114 : : */
1879 2115 [ + + ]: 6640 : if (bms_is_member(0, pull_varnos(root, (Node *) member->em_expr)))
2116 : : {
2152 tomas.vondra@postgre 2117 : 5 : unknown_varno = true;
2118 : 5 : break;
2119 : : }
2120 : :
2121 : : /* expression not containing any Vars with "varno 0" */
2169 2122 : 6635 : presortedExprs = lappend(presortedExprs, member->em_expr);
2123 : :
1185 drowley@postgresql.o 2124 [ + + ]: 6635 : if (foreach_current_index(l) + 1 >= presorted_keys)
2169 tomas.vondra@postgre 2125 : 6587 : break;
2126 : : }
2127 : :
2128 : : /* Estimate the number of groups with equal presorted keys. */
2152 2129 [ + + ]: 6592 : if (!unknown_varno)
1811 drowley@postgresql.o 2130 : 6587 : input_groups = estimate_num_groups(root, presortedExprs, input_tuples,
2131 : : NULL, NULL);
2132 : :
2169 tomas.vondra@postgre 2133 : 6592 : group_tuples = input_tuples / input_groups;
2134 : 6592 : group_input_run_cost = input_run_cost / input_groups;
2135 : :
2136 : : /*
2137 : : * Estimate the average cost of sorting of one group where presorted keys
2138 : : * are equal.
2139 : : */
1259 tgl@sss.pgh.pa.us 2140 : 6592 : cost_tuplesort(&group_startup_cost, &group_run_cost,
2141 : : group_tuples, width, comparison_cost, sort_mem,
2142 : : limit_tuples);
2143 : :
2144 : : /*
2145 : : * Startup cost of incremental sort is the startup cost of its first group
2146 : : * plus the cost of its input.
2147 : : */
1185 drowley@postgresql.o 2148 : 6592 : startup_cost = group_startup_cost + input_startup_cost +
2149 : : group_input_run_cost;
2150 : :
2151 : : /*
2152 : : * After we started producing tuples from the first group, the cost of
2153 : : * producing all the tuples is given by the cost to finish processing this
2154 : : * group, plus the total cost to process the remaining groups, plus the
2155 : : * remaining cost of input.
2156 : : */
2157 : 6592 : run_cost = group_run_cost + (group_run_cost + group_startup_cost) *
2158 : 6592 : (input_groups - 1) + group_input_run_cost * (input_groups - 1);
2159 : :
2160 : : /*
2161 : : * Incremental sort adds some overhead by itself. Firstly, it has to
2162 : : * detect the sort groups. This is roughly equal to one extra copy and
2163 : : * comparison per tuple.
2164 : : */
2169 tomas.vondra@postgre 2165 : 6592 : run_cost += (cpu_tuple_cost + comparison_cost) * input_tuples;
2166 : :
2167 : : /*
2168 : : * Additionally, we charge double cpu_tuple_cost for each input group to
2169 : : * account for the tuplesort_reset that's performed after each group.
2170 : : */
2171 : 6592 : run_cost += 2.0 * cpu_tuple_cost * input_groups;
2172 : :
2173 : 6592 : path->rows = input_tuples;
2174 : :
2175 : : /*
2176 : : * We should not generate these paths when enable_incremental_sort=false.
2177 : : * We can ignore PGS_CONSIDER_NONPARTIAL here, because if it's relevant,
2178 : : * it will have already affected the input path.
2179 : : */
571 rhaas@postgresql.org 2180 [ - + ]: 6592 : Assert(enable_incremental_sort);
2181 : 6592 : path->disabled_nodes = input_disabled_nodes;
2182 : :
2169 tomas.vondra@postgre 2183 : 6592 : path->startup_cost = startup_cost;
2184 : 6592 : path->total_cost = startup_cost + run_cost;
2185 : 6592 : }
2186 : :
2187 : : /*
2188 : : * cost_sort
2189 : : * Determines and returns the cost of sorting a relation, including
2190 : : * the cost of reading the input data.
2191 : : *
2192 : : * NOTE: some callers currently pass NIL for pathkeys because they
2193 : : * can't conveniently supply the sort keys. Since this routine doesn't
2194 : : * currently do anything with pathkeys anyway, that doesn't matter...
2195 : : * but if it ever does, it should react gracefully to lack of key data.
2196 : : * (Actually, the thing we'd most likely be interested in is just the number
2197 : : * of sort keys, which all callers *could* supply.)
2198 : : */
2199 : : void
2200 : 1237274 : cost_sort(Path *path, PlannerInfo *root,
2201 : : List *pathkeys, int input_disabled_nodes,
2202 : : Cost input_cost, double tuples, int width,
2203 : : Cost comparison_cost, int sort_mem,
2204 : : double limit_tuples)
2205 : :
2206 : : {
2207 : : Cost startup_cost;
2208 : : Cost run_cost;
2209 : :
1259 tgl@sss.pgh.pa.us 2210 : 1237274 : cost_tuplesort(&startup_cost, &run_cost,
2211 : : tuples, width,
2212 : : comparison_cost, sort_mem,
2213 : : limit_tuples);
2214 : :
2169 tomas.vondra@postgre 2215 : 1237274 : startup_cost += input_cost;
2216 : :
2217 : : /*
2218 : : * We can ignore PGS_CONSIDER_NONPARTIAL here, because if it's relevant,
2219 : : * it will have already affected the input path.
2220 : : */
2221 : 1237274 : path->rows = tuples;
571 rhaas@postgresql.org 2222 : 1237274 : path->disabled_nodes = input_disabled_nodes + (enable_sort ? 0 : 1);
9525 tgl@sss.pgh.pa.us 2223 : 1237274 : path->startup_cost = startup_cost;
2224 : 1237274 : path->total_cost = startup_cost + run_cost;
10841 scrappy@hub.org 2225 : 1237274 : }
2226 : :
2227 : : /*
2228 : : * append_nonpartial_cost
2229 : : * Estimate the cost of the non-partial paths in a Parallel Append.
2230 : : * The non-partial paths are assumed to be the first "numpaths" paths
2231 : : * from the subpaths list, and to be in order of decreasing cost.
2232 : : */
2233 : : static Cost
3022 rhaas@postgresql.org 2234 : 13163 : append_nonpartial_cost(List *subpaths, int numpaths, int parallel_workers)
2235 : : {
2236 : : Cost *costarr;
2237 : : int arrlen;
2238 : : ListCell *l;
2239 : : ListCell *cell;
2240 : : int path_index;
2241 : : int min_index;
2242 : : int max_index;
2243 : :
2244 [ + + ]: 13163 : if (numpaths == 0)
2245 : 10549 : return 0;
2246 : :
2247 : : /*
2248 : : * Array length is number of workers or number of relevant paths,
2249 : : * whichever is less.
2250 : : */
2251 : 2614 : arrlen = Min(parallel_workers, numpaths);
95 michael@paquier.xyz 2252 :GNC 2614 : costarr = palloc_array(Cost, arrlen);
2253 : :
2254 : : /* The first few paths will each be claimed by a different worker. */
3022 rhaas@postgresql.org 2255 :CBC 2614 : path_index = 0;
2256 [ + - + + : 7605 : foreach(cell, subpaths)
+ + ]
2257 : : {
2258 : 5714 : Path *subpath = (Path *) lfirst(cell);
2259 : :
2260 [ + + ]: 5714 : if (path_index == arrlen)
2261 : 723 : break;
2262 : 4991 : costarr[path_index++] = subpath->total_cost;
2263 : : }
2264 : :
2265 : : /*
2266 : : * Since subpaths are sorted by decreasing cost, the last one will have
2267 : : * the minimum cost.
2268 : : */
2269 : 2614 : min_index = arrlen - 1;
2270 : :
2271 : : /*
2272 : : * For each of the remaining subpaths, add its cost to the array element
2273 : : * with minimum cost.
2274 : : */
2435 tgl@sss.pgh.pa.us 2275 [ + - + + : 5782 : for_each_cell(l, subpaths, cell)
+ + ]
2276 : : {
3022 rhaas@postgresql.org 2277 : 3441 : Path *subpath = (Path *) lfirst(l);
2278 : :
2279 : : /* Consider only the non-partial paths */
2280 [ + + ]: 3441 : if (path_index++ == numpaths)
2281 : 273 : break;
2282 : :
2283 : 3168 : costarr[min_index] += subpath->total_cost;
2284 : :
2285 : : /* Update the new min cost array index */
1299 drowley@postgresql.o 2286 : 3168 : min_index = 0;
2287 [ + + ]: 9522 : for (int i = 0; i < arrlen; i++)
2288 : : {
3022 rhaas@postgresql.org 2289 [ + + ]: 6354 : if (costarr[i] < costarr[min_index])
2290 : 843 : min_index = i;
2291 : : }
2292 : : }
2293 : :
2294 : : /* Return the highest cost from the array */
1299 drowley@postgresql.o 2295 : 2614 : max_index = 0;
2296 [ + + ]: 7605 : for (int i = 0; i < arrlen; i++)
2297 : : {
3022 rhaas@postgresql.org 2298 [ + + ]: 4991 : if (costarr[i] > costarr[max_index])
2299 : 354 : max_index = i;
2300 : : }
2301 : :
2302 : 2614 : return costarr[max_index];
2303 : : }
2304 : :
2305 : : /*
2306 : : * cost_append
2307 : : * Determines and returns the cost of an Append node.
2308 : : */
2309 : : void
250 rguo@postgresql.org 2310 :GNC 35940 : cost_append(AppendPath *apath, PlannerInfo *root)
2311 : : {
46 rhaas@postgresql.org 2312 : 35940 : RelOptInfo *rel = apath->path.parent;
2313 : : ListCell *l;
2314 : 35940 : uint64 enable_mask = PGS_APPEND;
2315 : :
2316 [ + + ]: 35940 : if (apath->path.parallel_workers == 0)
2317 : 22753 : enable_mask |= PGS_CONSIDER_NONPARTIAL;
2318 : :
2319 : 35940 : apath->path.disabled_nodes =
2320 : 35940 : (rel->pgs_mask & enable_mask) == enable_mask ? 0 : 1;
3022 rhaas@postgresql.org 2321 :CBC 35940 : apath->path.startup_cost = 0;
2322 : 35940 : apath->path.total_cost = 0;
2536 tgl@sss.pgh.pa.us 2323 : 35940 : apath->path.rows = 0;
2324 : :
3022 rhaas@postgresql.org 2325 [ + + ]: 35940 : if (apath->subpaths == NIL)
2326 : 1065 : return;
2327 : :
2328 [ + + ]: 34875 : if (!apath->path.parallel_aware)
2329 : : {
2536 tgl@sss.pgh.pa.us 2330 : 21712 : List *pathkeys = apath->path.pathkeys;
2331 : :
2332 [ + + ]: 21712 : if (pathkeys == NIL)
2333 : : {
1257 drowley@postgresql.o 2334 : 20608 : Path *firstsubpath = (Path *) linitial(apath->subpaths);
2335 : :
2336 : : /*
2337 : : * For an unordered, non-parallel-aware Append we take the startup
2338 : : * cost as the startup cost of the first subpath.
2339 : : */
2340 : 20608 : apath->path.startup_cost = firstsubpath->startup_cost;
2341 : :
2342 : : /*
2343 : : * Compute rows, number of disabled nodes, and total cost as sums
2344 : : * of underlying subplan values.
2345 : : */
2536 tgl@sss.pgh.pa.us 2346 [ + - + + : 81311 : foreach(l, apath->subpaths)
+ + ]
2347 : : {
2348 : 60703 : Path *subpath = (Path *) lfirst(l);
2349 : :
2350 : 60703 : apath->path.rows += subpath->rows;
571 rhaas@postgresql.org 2351 : 60703 : apath->path.disabled_nodes += subpath->disabled_nodes;
2536 tgl@sss.pgh.pa.us 2352 : 60703 : apath->path.total_cost += subpath->total_cost;
2353 : : }
2354 : : }
2355 : : else
2356 : : {
2357 : : /*
2358 : : * For an ordered, non-parallel-aware Append we take the startup
2359 : : * cost as the sum of the subpath startup costs. This ensures
2360 : : * that we don't underestimate the startup cost when a query's
2361 : : * LIMIT is such that several of the children have to be run to
2362 : : * satisfy it. This might be overkill --- another plausible hack
2363 : : * would be to take the Append's startup cost as the maximum of
2364 : : * the child startup costs. But we don't want to risk believing
2365 : : * that an ORDER BY LIMIT query can be satisfied at small cost
2366 : : * when the first child has small startup cost but later ones
2367 : : * don't. (If we had the ability to deal with nonlinear cost
2368 : : * interpolation for partial retrievals, we would not need to be
2369 : : * so conservative about this.)
2370 : : *
2371 : : * This case is also different from the above in that we have to
2372 : : * account for possibly injecting sorts into subpaths that aren't
2373 : : * natively ordered.
2374 : : */
2375 [ + - + + : 4297 : foreach(l, apath->subpaths)
+ + ]
2376 : : {
2377 : 3193 : Path *subpath = (Path *) lfirst(l);
2378 : : int presorted_keys;
2379 : : Path sort_path; /* dummy for result of
2380 : : * cost_sort/cost_incremental_sort */
2381 : :
250 rguo@postgresql.org 2382 [ + + ]:GNC 3193 : if (!pathkeys_count_contained_in(pathkeys, subpath->pathkeys,
2383 : : &presorted_keys))
2384 : : {
2385 : : /*
2386 : : * We'll need to insert a Sort node, so include costs for
2387 : : * that. We choose to use incremental sort if it is
2388 : : * enabled and there are presorted keys; otherwise we use
2389 : : * full sort.
2390 : : *
2391 : : * We can use the parent's LIMIT if any, since we
2392 : : * certainly won't pull more than that many tuples from
2393 : : * any child.
2394 : : */
2395 [ + - + + ]: 22 : if (enable_incremental_sort && presorted_keys > 0)
2396 : : {
2397 : 6 : cost_incremental_sort(&sort_path,
2398 : : root,
2399 : : pathkeys,
2400 : : presorted_keys,
2401 : : subpath->disabled_nodes,
2402 : : subpath->startup_cost,
2403 : : subpath->total_cost,
2404 : : subpath->rows,
2405 : 6 : subpath->pathtarget->width,
2406 : : 0.0,
2407 : : work_mem,
2408 : : apath->limit_tuples);
2409 : : }
2410 : : else
2411 : : {
2412 : 16 : cost_sort(&sort_path,
2413 : : root,
2414 : : pathkeys,
2415 : : subpath->disabled_nodes,
2416 : : subpath->total_cost,
2417 : : subpath->rows,
2418 : 16 : subpath->pathtarget->width,
2419 : : 0.0,
2420 : : work_mem,
2421 : : apath->limit_tuples);
2422 : : }
2423 : :
2536 tgl@sss.pgh.pa.us 2424 :CBC 22 : subpath = &sort_path;
2425 : : }
2426 : :
2427 : 3193 : apath->path.rows += subpath->rows;
571 rhaas@postgresql.org 2428 : 3193 : apath->path.disabled_nodes += subpath->disabled_nodes;
2536 tgl@sss.pgh.pa.us 2429 : 3193 : apath->path.startup_cost += subpath->startup_cost;
2430 : 3193 : apath->path.total_cost += subpath->total_cost;
2431 : : }
2432 : : }
2433 : : }
2434 : : else /* parallel-aware */
2435 : : {
3022 rhaas@postgresql.org 2436 : 13163 : int i = 0;
2437 : 13163 : double parallel_divisor = get_parallel_divisor(&apath->path);
2438 : :
2439 : : /* Parallel-aware Append never produces ordered output. */
2536 tgl@sss.pgh.pa.us 2440 [ - + ]: 13163 : Assert(apath->path.pathkeys == NIL);
2441 : :
2442 : : /* Calculate startup cost. */
3022 rhaas@postgresql.org 2443 [ + - + + : 52907 : foreach(l, apath->subpaths)
+ + ]
2444 : : {
2445 : 39744 : Path *subpath = (Path *) lfirst(l);
2446 : :
2447 : : /*
2448 : : * Append will start returning tuples when the child node having
2449 : : * lowest startup cost is done setting up. We consider only the
2450 : : * first few subplans that immediately get a worker assigned.
2451 : : */
2452 [ + + ]: 39744 : if (i == 0)
2453 : 13163 : apath->path.startup_cost = subpath->startup_cost;
2454 [ + + ]: 26581 : else if (i < apath->path.parallel_workers)
2455 [ + + ]: 12875 : apath->path.startup_cost = Min(apath->path.startup_cost,
2456 : : subpath->startup_cost);
2457 : :
2458 : : /*
2459 : : * Apply parallel divisor to subpaths. Scale the number of rows
2460 : : * for each partial subpath based on the ratio of the parallel
2461 : : * divisor originally used for the subpath to the one we adopted.
2462 : : * Also add the cost of partial paths to the total cost, but
2463 : : * ignore non-partial paths for now.
2464 : : */
2465 [ + + ]: 39744 : if (i < apath->first_partial_path)
2466 : 8159 : apath->path.rows += subpath->rows / parallel_divisor;
2467 : : else
2468 : : {
2469 : : double subpath_parallel_divisor;
2470 : :
2992 2471 : 31585 : subpath_parallel_divisor = get_parallel_divisor(subpath);
2472 : 31585 : apath->path.rows += subpath->rows * (subpath_parallel_divisor /
2473 : : parallel_divisor);
3022 2474 : 31585 : apath->path.total_cost += subpath->total_cost;
2475 : : }
2476 : :
571 2477 : 39744 : apath->path.disabled_nodes += subpath->disabled_nodes;
2992 2478 : 39744 : apath->path.rows = clamp_row_est(apath->path.rows);
2479 : :
3022 2480 : 39744 : i++;
2481 : : }
2482 : :
2483 : : /* Add cost for non-partial subpaths. */
2484 : 13163 : apath->path.total_cost +=
2485 : 13163 : append_nonpartial_cost(apath->subpaths,
2486 : : apath->first_partial_path,
2487 : : apath->path.parallel_workers);
2488 : : }
2489 : :
2490 : : /*
2491 : : * Although Append does not do any selection or projection, it's not free;
2492 : : * add a small per-tuple overhead.
2493 : : */
2944 2494 : 34875 : apath->path.total_cost +=
2495 : 34875 : cpu_tuple_cost * APPEND_CPU_COST_MULTIPLIER * apath->path.rows;
2496 : : }
2497 : :
2498 : : /*
2499 : : * cost_merge_append
2500 : : * Determines and returns the cost of a MergeAppend node.
2501 : : *
2502 : : * MergeAppend merges several pre-sorted input streams, using a heap that
2503 : : * at any given instant holds the next tuple from each stream. If there
2504 : : * are N streams, we need about N*log2(N) tuple comparisons to construct
2505 : : * the heap at startup, and then for each output tuple, about log2(N)
2506 : : * comparisons to replace the top entry.
2507 : : *
2508 : : * (The effective value of N will drop once some of the input streams are
2509 : : * exhausted, but it seems unlikely to be worth trying to account for that.)
2510 : : *
2511 : : * The heap is never spilled to disk, since we assume N is not very large.
2512 : : * So this is much simpler than cost_sort.
2513 : : *
2514 : : * As in cost_sort, we charge two operator evals per tuple comparison.
2515 : : *
2516 : : * 'pathkeys' is a list of sort keys
2517 : : * 'n_streams' is the number of input streams
2518 : : * 'input_disabled_nodes' is the sum of the input streams' disabled node counts
2519 : : * 'input_startup_cost' is the sum of the input streams' startup costs
2520 : : * 'input_total_cost' is the sum of the input streams' total costs
2521 : : * 'tuples' is the number of tuples in all the streams
2522 : : */
2523 : : void
5631 tgl@sss.pgh.pa.us 2524 : 4970 : cost_merge_append(Path *path, PlannerInfo *root,
2525 : : List *pathkeys, int n_streams,
2526 : : int input_disabled_nodes,
2527 : : Cost input_startup_cost, Cost input_total_cost,
2528 : : double tuples)
2529 : : {
46 rhaas@postgresql.org 2530 :GNC 4970 : RelOptInfo *rel = path->parent;
5631 tgl@sss.pgh.pa.us 2531 :CBC 4970 : Cost startup_cost = 0;
2532 : 4970 : Cost run_cost = 0;
2533 : : Cost comparison_cost;
2534 : : double N;
2535 : : double logN;
46 rhaas@postgresql.org 2536 :GNC 4970 : uint64 enable_mask = PGS_MERGE_APPEND;
2537 : :
2538 [ + - ]: 4970 : if (path->parallel_workers == 0)
2539 : 4970 : enable_mask |= PGS_CONSIDER_NONPARTIAL;
2540 : :
2541 : : /*
2542 : : * Avoid log(0)...
2543 : : */
5631 tgl@sss.pgh.pa.us 2544 [ + - ]:CBC 4970 : N = (n_streams < 2) ? 2.0 : (double) n_streams;
2545 : 4970 : logN = LOG2(N);
2546 : :
2547 : : /* Assumed cost per tuple comparison */
2548 : 4970 : comparison_cost = 2.0 * cpu_operator_cost;
2549 : :
2550 : : /* Heap creation cost */
2551 : 4970 : startup_cost += comparison_cost * N * logN;
2552 : :
2553 : : /* Per-tuple heap maintenance cost */
3417 2554 : 4970 : run_cost += tuples * comparison_cost * logN;
2555 : :
2556 : : /*
2557 : : * Although MergeAppend does not do any selection or projection, it's not
2558 : : * free; add a small per-tuple overhead.
2559 : : */
2944 rhaas@postgresql.org 2560 : 4970 : run_cost += cpu_tuple_cost * APPEND_CPU_COST_MULTIPLIER * tuples;
2561 : :
46 rhaas@postgresql.org 2562 :GNC 4970 : path->disabled_nodes =
2563 : 4970 : (rel->pgs_mask & enable_mask) == enable_mask ? 0 : 1;
2564 : 4970 : path->disabled_nodes += input_disabled_nodes;
5631 tgl@sss.pgh.pa.us 2565 :CBC 4970 : path->startup_cost = startup_cost + input_startup_cost;
2566 : 4970 : path->total_cost = startup_cost + run_cost + input_total_cost;
2567 : 4970 : }
2568 : :
2569 : : /*
2570 : : * cost_material
2571 : : * Determines and returns the cost of materializing a relation, including
2572 : : * the cost of reading the input data.
2573 : : *
2574 : : * If the total volume of data to materialize exceeds work_mem, we will need
2575 : : * to write it to disk, so the cost is much higher in that case.
2576 : : *
2577 : : * Note that here we are estimating the costs for the first scan of the
2578 : : * relation, so the materialization is all overhead --- any savings will
2579 : : * occur only on rescan, which is estimated in cost_rescan.
2580 : : */
2581 : : void
8506 2582 : 392535 : cost_material(Path *path,
2583 : : bool enabled, int input_disabled_nodes,
2584 : : Cost input_startup_cost, Cost input_total_cost,
2585 : : double tuples, int width)
2586 : : {
6028 2587 : 392535 : Cost startup_cost = input_startup_cost;
2588 : 392535 : Cost run_cost = input_total_cost - input_startup_cost;
8506 2589 : 392535 : double nbytes = relation_byte_size(tuples, width);
408 2590 : 392535 : double work_mem_bytes = work_mem * (Size) 1024;
2591 : :
5161 2592 : 392535 : path->rows = tuples;
2593 : :
2594 : : /*
2595 : : * Whether spilling or not, charge 2x cpu_operator_cost per tuple to
2596 : : * reflect bookkeeping overhead. (This rate must be more than what
2597 : : * cost_rescan charges for materialize, ie, cpu_operator_cost per tuple;
2598 : : * if it is exactly the same then there will be a cost tie between
2599 : : * nestloop with A outer, materialized B inner and nestloop with B outer,
2600 : : * materialized A inner. The extra cost ensures we'll prefer
2601 : : * materializing the smaller rel.) Note that this is normally a good deal
2602 : : * less than cpu_tuple_cost; which is OK because a Material plan node
2603 : : * doesn't do qual-checking or projection, so it's got less overhead than
2604 : : * most plan nodes.
2605 : : */
5868 2606 : 392535 : run_cost += 2 * cpu_operator_cost * tuples;
2607 : :
2608 : : /*
2609 : : * If we will spill to disk, charge at the rate of seq_page_cost per page.
2610 : : * This cost is assumed to be evenly spread through the plan run phase,
2611 : : * which isn't exactly accurate but our cost model doesn't allow for
2612 : : * nonuniform costs within the run phase.
2613 : : */
8076 2614 [ + + ]: 392535 : if (nbytes > work_mem_bytes)
2615 : : {
8506 2616 : 3060 : double npages = ceil(nbytes / BLCKSZ);
2617 : :
7223 2618 : 3060 : run_cost += seq_page_cost * npages;
2619 : : }
2620 : :
46 rhaas@postgresql.org 2621 :GNC 392535 : path->disabled_nodes = input_disabled_nodes + (enabled ? 0 : 1);
8506 tgl@sss.pgh.pa.us 2622 :CBC 392535 : path->startup_cost = startup_cost;
2623 : 392535 : path->total_cost = startup_cost + run_cost;
2624 : 392535 : }
2625 : :
2626 : : /*
2627 : : * cost_memoize_rescan
2628 : : * Determines the estimated cost of rescanning a Memoize node.
2629 : : *
2630 : : * In order to estimate this, we must gain knowledge of how often we expect to
2631 : : * be called and how many distinct sets of parameters we are likely to be
2632 : : * called with. If we expect a good cache hit ratio, then we can set our
2633 : : * costs to account for that hit ratio, plus a little bit of cost for the
2634 : : * caching itself. Caching will not work out well if we expect to be called
2635 : : * with too many distinct parameter values. The worst-case here is that we
2636 : : * never see any parameter value twice, in which case we'd never get a cache
2637 : : * hit and caching would be a complete waste of effort.
2638 : : */
2639 : : static void
1705 drowley@postgresql.o 2640 : 175512 : cost_memoize_rescan(PlannerInfo *root, MemoizePath *mpath,
2641 : : Cost *rescan_startup_cost, Cost *rescan_total_cost)
2642 : : {
2643 : : EstimationInfo estinfo;
2644 : : ListCell *lc;
2645 : 175512 : Cost input_startup_cost = mpath->subpath->startup_cost;
2646 : 175512 : Cost input_total_cost = mpath->subpath->total_cost;
2647 : 175512 : double tuples = mpath->subpath->rows;
229 drowley@postgresql.o 2648 :GNC 175512 : Cardinality est_calls = mpath->est_calls;
1705 drowley@postgresql.o 2649 :CBC 175512 : int width = mpath->subpath->pathtarget->width;
2650 : :
2651 : : double hash_mem_bytes;
2652 : : double est_entry_bytes;
2653 : : Cardinality est_cache_entries;
2654 : : Cardinality ndistinct;
2655 : : double evict_ratio;
2656 : : double hit_ratio;
2657 : : Cost startup_cost;
2658 : : Cost total_cost;
2659 : :
2660 : : /* available cache space */
1694 tgl@sss.pgh.pa.us 2661 : 175512 : hash_mem_bytes = get_hash_memory_limit();
2662 : :
2663 : : /*
2664 : : * Set the number of bytes each cache entry should consume in the cache.
2665 : : * To provide us with better estimations on how many cache entries we can
2666 : : * store at once, we make a call to the executor here to ask it what
2667 : : * memory overheads there are for a single cache entry.
2668 : : */
1808 drowley@postgresql.o 2669 : 175512 : est_entry_bytes = relation_byte_size(tuples, width) +
2670 : 175512 : ExecEstimateCacheEntryOverheadBytes(tuples);
2671 : :
2672 : : /* include the estimated width for the cache keys */
1091 2673 [ + - + + : 375694 : foreach(lc, mpath->param_exprs)
+ + ]
2674 : 200182 : est_entry_bytes += get_expr_width(root, (Node *) lfirst(lc));
2675 : :
2676 : : /* estimate on the upper limit of cache entries we can hold at once */
1808 2677 : 175512 : est_cache_entries = floor(hash_mem_bytes / est_entry_bytes);
2678 : :
2679 : : /* estimate on the distinct number of parameter values */
229 drowley@postgresql.o 2680 :GNC 175512 : ndistinct = estimate_num_groups(root, mpath->param_exprs, est_calls, NULL,
2681 : : &estinfo);
2682 : :
2683 : : /*
2684 : : * When the estimation fell back on using a default value, it's a bit too
2685 : : * risky to assume that it's ok to use a Memoize node. The use of a
2686 : : * default could cause us to use a Memoize node when it's really
2687 : : * inappropriate to do so. If we see that this has been done, then we'll
2688 : : * assume that every call will have unique parameters, which will almost
2689 : : * certainly mean a MemoizePath will never survive add_path().
2690 : : */
1808 drowley@postgresql.o 2691 [ + + ]:CBC 175512 : if ((estinfo.flags & SELFLAG_USED_DEFAULT) != 0)
229 drowley@postgresql.o 2692 :GNC 8614 : ndistinct = est_calls;
2693 : :
2694 : : /* Remember the ndistinct estimate for EXPLAIN */
2695 : 175512 : mpath->est_unique_keys = ndistinct;
2696 : :
2697 : : /*
2698 : : * Since we've already estimated the maximum number of entries we can
2699 : : * store at once and know the estimated number of distinct values we'll be
2700 : : * called with, we'll take this opportunity to set the path's est_entries.
2701 : : * This will ultimately determine the hash table size that the executor
2702 : : * will use. If we leave this at zero, the executor will just choose the
2703 : : * size itself. Really this is not the right place to do this, but it's
2704 : : * convenient since everything is already calculated.
2705 : : */
1705 drowley@postgresql.o 2706 [ + + + - :CBC 175512 : mpath->est_entries = Min(Min(ndistinct, est_cache_entries),
+ + ]
2707 : : PG_UINT32_MAX);
2708 : :
2709 : : /*
2710 : : * When the number of distinct parameter values is above the amount we can
2711 : : * store in the cache, then we'll have to evict some entries from the
2712 : : * cache. This is not free. Here we estimate how often we'll incur the
2713 : : * cost of that eviction.
2714 : : */
1808 2715 [ + + ]: 175512 : evict_ratio = 1.0 - Min(est_cache_entries, ndistinct) / ndistinct;
2716 : :
2717 : : /*
2718 : : * In order to estimate how costly a single scan will be, we need to
2719 : : * attempt to estimate what the cache hit ratio will be. To do that we
2720 : : * must look at how many scans are estimated in total for this node and
2721 : : * how many of those scans we expect to get a cache hit.
2722 : : */
229 drowley@postgresql.o 2723 :GNC 351024 : hit_ratio = ((est_calls - ndistinct) / est_calls) *
1089 drowley@postgresql.o 2724 [ + + ]:CBC 175512 : (est_cache_entries / Max(ndistinct, est_cache_entries));
2725 : :
2726 : : /* Remember the hit ratio estimate for EXPLAIN */
229 drowley@postgresql.o 2727 :GNC 175512 : mpath->est_hit_ratio = hit_ratio;
2728 : :
1089 drowley@postgresql.o 2729 [ + - - + ]:CBC 175512 : Assert(hit_ratio >= 0 && hit_ratio <= 1.0);
2730 : :
2731 : : /*
2732 : : * Set the total_cost accounting for the expected cache hit ratio. We
2733 : : * also add on a cpu_operator_cost to account for a cache lookup. This
2734 : : * will happen regardless of whether it's a cache hit or not.
2735 : : */
1808 2736 : 175512 : total_cost = input_total_cost * (1.0 - hit_ratio) + cpu_operator_cost;
2737 : :
2738 : : /* Now adjust the total cost to account for cache evictions */
2739 : :
2740 : : /* Charge a cpu_tuple_cost for evicting the actual cache entry */
2741 : 175512 : total_cost += cpu_tuple_cost * evict_ratio;
2742 : :
2743 : : /*
2744 : : * Charge a 10th of cpu_operator_cost to evict every tuple in that entry.
2745 : : * The per-tuple eviction is really just a pfree, so charging a whole
2746 : : * cpu_operator_cost seems a little excessive.
2747 : : */
2748 : 175512 : total_cost += cpu_operator_cost / 10.0 * evict_ratio * tuples;
2749 : :
2750 : : /*
2751 : : * Now adjust for storing things in the cache, since that's not free
2752 : : * either. Everything must go in the cache. We don't proportion this
2753 : : * over any ratio, just apply it once for the scan. We charge a
2754 : : * cpu_tuple_cost for the creation of the cache entry and also a
2755 : : * cpu_operator_cost for each tuple we expect to cache.
2756 : : */
2757 : 175512 : total_cost += cpu_tuple_cost + cpu_operator_cost * tuples;
2758 : :
2759 : : /*
2760 : : * Getting the first row must be also be proportioned according to the
2761 : : * expected cache hit ratio.
2762 : : */
2763 : 175512 : startup_cost = input_startup_cost * (1.0 - hit_ratio);
2764 : :
2765 : : /*
2766 : : * Additionally we charge a cpu_tuple_cost to account for cache lookups,
2767 : : * which we'll do regardless of whether it was a cache hit or not.
2768 : : */
2769 : 175512 : startup_cost += cpu_tuple_cost;
2770 : :
2771 : 175512 : *rescan_startup_cost = startup_cost;
2772 : 175512 : *rescan_total_cost = total_cost;
2773 : 175512 : }
2774 : :
2775 : : /*
2776 : : * cost_agg
2777 : : * Determines and returns the cost of performing an Agg plan node,
2778 : : * including the cost of its input.
2779 : : *
2780 : : * aggcosts can be NULL when there are no actual aggregate functions (i.e.,
2781 : : * we are using a hashed Agg node just to do grouping).
2782 : : *
2783 : : * Note: when aggstrategy == AGG_SORTED, caller must ensure that input costs
2784 : : * are for appropriately-sorted input.
2785 : : */
2786 : : void
7588 tgl@sss.pgh.pa.us 2787 : 48385 : cost_agg(Path *path, PlannerInfo *root,
2788 : : AggStrategy aggstrategy, const AggClauseCosts *aggcosts,
2789 : : int numGroupCols, double numGroups,
2790 : : List *quals,
2791 : : int disabled_nodes,
2792 : : Cost input_startup_cost, Cost input_total_cost,
2793 : : double input_tuples, double input_width)
2794 : : {
2795 : : double output_tuples;
2796 : : Cost startup_cost;
2797 : : Cost total_cost;
360 peter@eisentraut.org 2798 : 48385 : const AggClauseCosts dummy_aggcosts = {0};
2799 : :
2800 : : /* Use all-zero per-aggregate costs if NULL is passed */
5439 tgl@sss.pgh.pa.us 2801 [ + + ]: 48385 : if (aggcosts == NULL)
2802 : : {
2803 [ - + ]: 9782 : Assert(aggstrategy == AGG_HASHED);
2804 : 9782 : aggcosts = &dummy_aggcosts;
2805 : : }
2806 : :
2807 : : /*
2808 : : * The transCost.per_tuple component of aggcosts should be charged once
2809 : : * per input tuple, corresponding to the costs of evaluating the aggregate
2810 : : * transfns and their input expressions. The finalCost.per_tuple component
2811 : : * is charged once per output tuple, corresponding to the costs of
2812 : : * evaluating the finalfns. Startup costs are of course charged but once.
2813 : : *
2814 : : * If we are grouping, we charge an additional cpu_operator_cost per
2815 : : * grouping column per input tuple for grouping comparisons.
2816 : : *
2817 : : * We will produce a single output tuple if not grouping, and a tuple per
2818 : : * group otherwise. We charge cpu_tuple_cost for each output tuple.
2819 : : *
2820 : : * Note: in this cost model, AGG_SORTED and AGG_HASHED have exactly the
2821 : : * same total CPU cost, but AGG_SORTED has lower startup cost. If the
2822 : : * input path is already sorted appropriately, AGG_SORTED should be
2823 : : * preferred (since it has no risk of memory overflow). This will happen
2824 : : * as long as the computed total costs are indeed exactly equal --- but if
2825 : : * there's roundoff error we might do the wrong thing. So be sure that
2826 : : * the computations below form the same intermediate values in the same
2827 : : * order.
2828 : : */
8515 2829 [ + + ]: 48385 : if (aggstrategy == AGG_PLAIN)
2830 : : {
2831 : 23022 : startup_cost = input_total_cost;
5439 2832 : 23022 : startup_cost += aggcosts->transCost.startup;
2833 : 23022 : startup_cost += aggcosts->transCost.per_tuple * input_tuples;
2591 2834 : 23022 : startup_cost += aggcosts->finalCost.startup;
2835 : 23022 : startup_cost += aggcosts->finalCost.per_tuple;
2836 : : /* we aren't grouping */
7505 2837 : 23022 : total_cost = startup_cost + cpu_tuple_cost;
5161 2838 : 23022 : output_tuples = 1;
2839 : : }
3275 rhodiumtoad@postgres 2840 [ + + + + ]: 25363 : else if (aggstrategy == AGG_SORTED || aggstrategy == AGG_MIXED)
2841 : : {
2842 : : /* Here we are able to deliver output on-the-fly */
8515 tgl@sss.pgh.pa.us 2843 : 9216 : startup_cost = input_startup_cost;
2844 : 9216 : total_cost = input_total_cost;
3275 rhodiumtoad@postgres 2845 [ + + + + ]: 9216 : if (aggstrategy == AGG_MIXED && !enable_hashagg)
571 rhaas@postgresql.org 2846 : 276 : ++disabled_nodes;
2847 : : /* calcs phrased this way to match HASHED case, see note above */
5439 tgl@sss.pgh.pa.us 2848 : 9216 : total_cost += aggcosts->transCost.startup;
2849 : 9216 : total_cost += aggcosts->transCost.per_tuple * input_tuples;
2850 : 9216 : total_cost += (cpu_operator_cost * numGroupCols) * input_tuples;
2591 2851 : 9216 : total_cost += aggcosts->finalCost.startup;
2852 : 9216 : total_cost += aggcosts->finalCost.per_tuple * numGroups;
7505 2853 : 9216 : total_cost += cpu_tuple_cost * numGroups;
5161 2854 : 9216 : output_tuples = numGroups;
2855 : : }
2856 : : else
2857 : : {
2858 : : /* must be AGG_HASHED */
8515 2859 : 16147 : startup_cost = input_total_cost;
3646 rhaas@postgresql.org 2860 [ + + ]: 16147 : if (!enable_hashagg)
571 2861 : 945 : ++disabled_nodes;
5439 tgl@sss.pgh.pa.us 2862 : 16147 : startup_cost += aggcosts->transCost.startup;
2863 : 16147 : startup_cost += aggcosts->transCost.per_tuple * input_tuples;
2864 : : /* cost of computing hash value */
2865 : 16147 : startup_cost += (cpu_operator_cost * numGroupCols) * input_tuples;
2591 2866 : 16147 : startup_cost += aggcosts->finalCost.startup;
2867 : :
8515 2868 : 16147 : total_cost = startup_cost;
2591 2869 : 16147 : total_cost += aggcosts->finalCost.per_tuple * numGroups;
2870 : : /* cost of retrieving from hash table */
7505 2871 : 16147 : total_cost += cpu_tuple_cost * numGroups;
5161 2872 : 16147 : output_tuples = numGroups;
2873 : : }
2874 : :
2875 : : /*
2876 : : * Add the disk costs of hash aggregation that spills to disk.
2877 : : *
2878 : : * Groups that go into the hash table stay in memory until finalized, so
2879 : : * spilling and reprocessing tuples doesn't incur additional invocations
2880 : : * of transCost or finalCost. Furthermore, the computed hash value is
2881 : : * stored with the spilled tuples, so we don't incur extra invocations of
2882 : : * the hash function.
2883 : : *
2884 : : * Hash Agg begins returning tuples after the first batch is complete.
2885 : : * Accrue writes (spilled tuples) to startup_cost and to total_cost;
2886 : : * accrue reads only to total_cost.
2887 : : */
2188 jdavis@postgresql.or 2888 [ + + + + ]: 48385 : if (aggstrategy == AGG_HASHED || aggstrategy == AGG_MIXED)
2889 : : {
2890 : : double pages;
2131 tgl@sss.pgh.pa.us 2891 : 16673 : double pages_written = 0.0;
2892 : 16673 : double pages_read = 0.0;
2893 : : double spill_cost;
2894 : : double hashentrysize;
2895 : : double nbatches;
2896 : : Size mem_limit;
2897 : : uint64 ngroups_limit;
2898 : : int num_partitions;
2899 : : int depth;
2900 : :
2901 : : /*
2902 : : * Estimate number of batches based on the computed limits. If less
2903 : : * than or equal to one, all groups are expected to fit in memory;
2904 : : * otherwise we expect to spill.
2905 : : */
1937 heikki.linnakangas@i 2906 : 16673 : hashentrysize = hash_agg_entry_size(list_length(root->aggtransinfos),
2907 : : input_width,
2131 tgl@sss.pgh.pa.us 2908 : 16673 : aggcosts->transitionSpace);
2188 jdavis@postgresql.or 2909 : 16673 : hash_agg_set_limits(hashentrysize, numGroups, 0, &mem_limit,
2910 : : &ngroups_limit, &num_partitions);
2911 : :
2131 tgl@sss.pgh.pa.us 2912 [ - + ]: 16673 : nbatches = Max((numGroups * hashentrysize) / mem_limit,
2913 : : numGroups / ngroups_limit);
2914 : :
2178 jdavis@postgresql.or 2915 [ + + ]: 16673 : nbatches = Max(ceil(nbatches), 1.0);
2916 : 16673 : num_partitions = Max(num_partitions, 2);
2917 : :
2918 : : /*
2919 : : * The number of partitions can change at different levels of
2920 : : * recursion; but for the purposes of this calculation assume it stays
2921 : : * constant.
2922 : : */
2131 tgl@sss.pgh.pa.us 2923 : 16673 : depth = ceil(log(nbatches) / log(num_partitions));
2924 : :
2925 : : /*
2926 : : * Estimate number of pages read and written. For each level of
2927 : : * recursion, a tuple must be written and then later read.
2928 : : */
2178 jdavis@postgresql.or 2929 : 16673 : pages = relation_byte_size(input_tuples, input_width) / BLCKSZ;
2930 : 16673 : pages_written = pages_read = pages * depth;
2931 : :
2932 : : /*
2933 : : * HashAgg has somewhat worse IO behavior than Sort on typical
2934 : : * hardware/OS combinations. Account for this with a generic penalty.
2935 : : */
2015 2936 : 16673 : pages_read *= 2.0;
2937 : 16673 : pages_written *= 2.0;
2938 : :
2188 2939 : 16673 : startup_cost += pages_written * random_page_cost;
2940 : 16673 : total_cost += pages_written * random_page_cost;
2941 : 16673 : total_cost += pages_read * seq_page_cost;
2942 : :
2943 : : /* account for CPU cost of spilling a tuple and reading it back */
2015 2944 : 16673 : spill_cost = depth * input_tuples * 2.0 * cpu_tuple_cost;
2945 : 16673 : startup_cost += spill_cost;
2946 : 16673 : total_cost += spill_cost;
2947 : : }
2948 : :
2949 : : /*
2950 : : * If there are quals (HAVING quals), account for their cost and
2951 : : * selectivity.
2952 : : */
3055 tgl@sss.pgh.pa.us 2953 [ + + ]: 48385 : if (quals)
2954 : : {
2955 : : QualCost qual_cost;
2956 : :
2957 : 2328 : cost_qual_eval(&qual_cost, quals, root);
2958 : 2328 : startup_cost += qual_cost.startup;
2959 : 2328 : total_cost += qual_cost.startup + output_tuples * qual_cost.per_tuple;
2960 : :
2961 : 2328 : output_tuples = clamp_row_est(output_tuples *
2962 : 2328 : clauselist_selectivity(root,
2963 : : quals,
2964 : : 0,
2965 : : JOIN_INNER,
2966 : : NULL));
2967 : : }
2968 : :
5161 2969 : 48385 : path->rows = output_tuples;
571 rhaas@postgresql.org 2970 : 48385 : path->disabled_nodes = disabled_nodes;
8515 tgl@sss.pgh.pa.us 2971 : 48385 : path->startup_cost = startup_cost;
2972 : 48385 : path->total_cost = total_cost;
2973 : 48385 : }
2974 : :
2975 : : /*
2976 : : * get_windowclause_startup_tuples
2977 : : * Estimate how many tuples we'll need to fetch from a WindowAgg's
2978 : : * subnode before we can output the first WindowAgg tuple.
2979 : : *
2980 : : * How many tuples need to be read depends on the WindowClause. For example,
2981 : : * a WindowClause with no PARTITION BY and no ORDER BY requires that all
2982 : : * subnode tuples are read and aggregated before the WindowAgg can output
2983 : : * anything. If there's a PARTITION BY, then we only need to look at tuples
2984 : : * in the first partition. Here we attempt to estimate just how many
2985 : : * 'input_tuples' the WindowAgg will need to read for the given WindowClause
2986 : : * before the first tuple can be output.
2987 : : */
2988 : : static double
954 drowley@postgresql.o 2989 : 1538 : get_windowclause_startup_tuples(PlannerInfo *root, WindowClause *wc,
2990 : : double input_tuples)
2991 : : {
2992 : 1538 : int frameOptions = wc->frameOptions;
2993 : : double partition_tuples;
2994 : : double return_tuples;
2995 : : double peer_tuples;
2996 : :
2997 : : /*
2998 : : * First, figure out how many partitions there are likely to be and set
2999 : : * partition_tuples according to that estimate.
3000 : : */
3001 [ + + ]: 1538 : if (wc->partitionClause != NIL)
3002 : : {
3003 : : double num_partitions;
3004 : 367 : List *partexprs = get_sortgrouplist_exprs(wc->partitionClause,
3005 : 367 : root->parse->targetList);
3006 : :
3007 : 367 : num_partitions = estimate_num_groups(root, partexprs, input_tuples,
3008 : : NULL, NULL);
3009 : 367 : list_free(partexprs);
3010 : :
3011 : 367 : partition_tuples = input_tuples / num_partitions;
3012 : : }
3013 : : else
3014 : : {
3015 : : /* all tuples belong to the same partition */
3016 : 1171 : partition_tuples = input_tuples;
3017 : : }
3018 : :
3019 : : /* estimate the number of tuples in each peer group */
3020 [ + + ]: 1538 : if (wc->orderClause != NIL)
3021 : : {
3022 : : double num_groups;
3023 : : List *orderexprs;
3024 : :
3025 : 1185 : orderexprs = get_sortgrouplist_exprs(wc->orderClause,
3026 : 1185 : root->parse->targetList);
3027 : :
3028 : : /* estimate out how many peer groups there are in the partition */
3029 : 1185 : num_groups = estimate_num_groups(root, orderexprs,
3030 : : partition_tuples, NULL,
3031 : : NULL);
3032 : 1185 : list_free(orderexprs);
3033 : 1185 : peer_tuples = partition_tuples / num_groups;
3034 : : }
3035 : : else
3036 : : {
3037 : : /* no ORDER BY so only 1 tuple belongs in each peer group */
3038 : 353 : peer_tuples = 1.0;
3039 : : }
3040 : :
3041 [ + + ]: 1538 : if (frameOptions & FRAMEOPTION_END_UNBOUNDED_FOLLOWING)
3042 : : {
3043 : : /* include all partition rows */
3044 : 182 : return_tuples = partition_tuples;
3045 : : }
3046 [ + + ]: 1356 : else if (frameOptions & FRAMEOPTION_END_CURRENT_ROW)
3047 : : {
3048 [ + + ]: 837 : if (frameOptions & FRAMEOPTION_ROWS)
3049 : : {
3050 : : /* just count the current row */
3051 : 361 : return_tuples = 1.0;
3052 : : }
3053 [ + - ]: 476 : else if (frameOptions & (FRAMEOPTION_RANGE | FRAMEOPTION_GROUPS))
3054 : : {
3055 : : /*
3056 : : * When in RANGE/GROUPS mode, it's more complex. If there's no
3057 : : * ORDER BY, then all rows in the partition are peers, otherwise
3058 : : * we'll need to read the first group of peers.
3059 : : */
3060 [ + + ]: 476 : if (wc->orderClause == NIL)
3061 : 213 : return_tuples = partition_tuples;
3062 : : else
3063 : 263 : return_tuples = peer_tuples;
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 : : */
954 drowley@postgresql.o 3071 :UBC 0 : Assert(false);
3072 : : return_tuples = 1.0;
3073 : : }
3074 : : }
954 drowley@postgresql.o 3075 [ + + ]:CBC 519 : else if (frameOptions & FRAMEOPTION_END_OFFSET_PRECEDING)
3076 : : {
3077 : : /*
3078 : : * BETWEEN ... AND N PRECEDING will only need to read the WindowAgg's
3079 : : * subnode after N ROWS/RANGES/GROUPS. N can be 0, but not negative,
3080 : : * so we'll just assume only the current row needs to be read to fetch
3081 : : * the first WindowAgg row.
3082 : : */
3083 : 54 : return_tuples = 1.0;
3084 : : }
3085 [ + - ]: 465 : else if (frameOptions & FRAMEOPTION_END_OFFSET_FOLLOWING)
3086 : : {
3087 : 465 : Const *endOffset = (Const *) wc->endOffset;
3088 : : double end_offset_value;
3089 : :
3090 : : /* try and figure out the value specified in the endOffset. */
3091 [ + - ]: 465 : if (IsA(endOffset, Const))
3092 : : {
3093 [ - + ]: 465 : if (endOffset->constisnull)
3094 : : {
3095 : : /*
3096 : : * NULLs are not allowed, but currently, there's no code to
3097 : : * error out if there's a NULL Const. We'll only discover
3098 : : * this during execution. For now, just pretend everything is
3099 : : * fine and assume that just the first row/range/group will be
3100 : : * needed.
3101 : : */
954 drowley@postgresql.o 3102 :UBC 0 : end_offset_value = 1.0;
3103 : : }
3104 : : else
3105 : : {
954 drowley@postgresql.o 3106 [ + + + + ]:CBC 465 : switch (endOffset->consttype)
3107 : : {
3108 : 12 : case INT2OID:
3109 : 12 : end_offset_value =
3110 : 12 : (double) DatumGetInt16(endOffset->constvalue);
3111 : 12 : break;
3112 : 66 : case INT4OID:
3113 : 66 : end_offset_value =
3114 : 66 : (double) DatumGetInt32(endOffset->constvalue);
3115 : 66 : break;
3116 : 216 : case INT8OID:
3117 : 216 : end_offset_value =
3118 : 216 : (double) DatumGetInt64(endOffset->constvalue);
3119 : 216 : break;
3120 : 171 : default:
3121 : 171 : end_offset_value =
3122 : 171 : partition_tuples / peer_tuples *
3123 : : DEFAULT_INEQ_SEL;
3124 : 171 : break;
3125 : : }
3126 : : }
3127 : : }
3128 : : else
3129 : : {
3130 : : /*
3131 : : * When the end bound is not a Const, we'll just need to guess. We
3132 : : * just make use of DEFAULT_INEQ_SEL.
3133 : : */
954 drowley@postgresql.o 3134 :UBC 0 : end_offset_value =
3135 : 0 : partition_tuples / peer_tuples * DEFAULT_INEQ_SEL;
3136 : : }
3137 : :
954 drowley@postgresql.o 3138 [ + + ]:CBC 465 : if (frameOptions & FRAMEOPTION_ROWS)
3139 : : {
3140 : : /* include the N FOLLOWING and the current row */
3141 : 135 : return_tuples = end_offset_value + 1.0;
3142 : : }
3143 [ + - ]: 330 : else if (frameOptions & (FRAMEOPTION_RANGE | FRAMEOPTION_GROUPS))
3144 : : {
3145 : : /* include N FOLLOWING ranges/group and the initial range/group */
3146 : 330 : return_tuples = peer_tuples * (end_offset_value + 1.0);
3147 : : }
3148 : : else
3149 : : {
3150 : : /*
3151 : : * Something new we don't support yet? This needs attention.
3152 : : * We'll just return 1.0 in the meantime.
3153 : : */
954 drowley@postgresql.o 3154 :UBC 0 : Assert(false);
3155 : : return_tuples = 1.0;
3156 : : }
3157 : : }
3158 : : else
3159 : : {
3160 : : /*
3161 : : * Something new we don't support yet? This needs attention. We'll
3162 : : * just return 1.0 in the meantime.
3163 : : */
3164 : 0 : Assert(false);
3165 : : return_tuples = 1.0;
3166 : : }
3167 : :
954 drowley@postgresql.o 3168 [ + + + + ]:CBC 1538 : if (wc->partitionClause != NIL || wc->orderClause != NIL)
3169 : : {
3170 : : /*
3171 : : * Cap the return value to the estimated partition tuples and account
3172 : : * for the extra tuple WindowAgg will need to read to confirm the next
3173 : : * tuple does not belong to the same partition or peer group.
3174 : : */
3175 [ + + ]: 1291 : return_tuples = Min(return_tuples + 1.0, partition_tuples);
3176 : : }
3177 : : else
3178 : : {
3179 : : /*
3180 : : * Cap the return value so it's never higher than the expected tuples
3181 : : * in the partition.
3182 : : */
3183 [ + + ]: 247 : return_tuples = Min(return_tuples, partition_tuples);
3184 : : }
3185 : :
3186 : : /*
3187 : : * We needn't worry about any EXCLUDE options as those only exclude rows
3188 : : * from being aggregated, not from being read from the WindowAgg's
3189 : : * subnode.
3190 : : */
3191 : :
3192 : 1538 : return clamp_row_est(return_tuples);
3193 : : }
3194 : :
3195 : : /*
3196 : : * cost_windowagg
3197 : : * Determines and returns the cost of performing a WindowAgg plan node,
3198 : : * including the cost of its input.
3199 : : *
3200 : : * Input is assumed already properly sorted.
3201 : : */
3202 : : void
6286 tgl@sss.pgh.pa.us 3203 : 1538 : cost_windowagg(Path *path, PlannerInfo *root,
3204 : : List *windowFuncs, WindowClause *winclause,
3205 : : int input_disabled_nodes,
3206 : : Cost input_startup_cost, Cost input_total_cost,
3207 : : double input_tuples)
3208 : : {
3209 : : Cost startup_cost;
3210 : : Cost total_cost;
3211 : : double startup_tuples;
3212 : : int numPartCols;
3213 : : int numOrderCols;
3214 : : ListCell *lc;
3215 : :
954 drowley@postgresql.o 3216 : 1538 : numPartCols = list_length(winclause->partitionClause);
3217 : 1538 : numOrderCols = list_length(winclause->orderClause);
3218 : :
6286 tgl@sss.pgh.pa.us 3219 : 1538 : startup_cost = input_startup_cost;
3220 : 1538 : total_cost = input_total_cost;
3221 : :
3222 : : /*
3223 : : * Window functions are assumed to cost their stated execution cost, plus
3224 : : * the cost of evaluating their input expressions, per tuple. Since they
3225 : : * may in fact evaluate their inputs at multiple rows during each cycle,
3226 : : * this could be a drastic underestimate; but without a way to know how
3227 : : * many rows the window function will fetch, it's hard to do better. In
3228 : : * any case, it's a good estimate for all the built-in window functions,
3229 : : * so we'll just do this for now.
3230 : : */
5439 3231 [ + - + + : 3517 : foreach(lc, windowFuncs)
+ + ]
3232 : : {
3261 3233 : 1979 : WindowFunc *wfunc = lfirst_node(WindowFunc, lc);
3234 : : Cost wfunccost;
3235 : : QualCost argcosts;
3236 : :
2591 3237 : 1979 : argcosts.startup = argcosts.per_tuple = 0;
3238 : 1979 : add_function_cost(root, wfunc->winfnoid, (Node *) wfunc,
3239 : : &argcosts);
3240 : 1979 : startup_cost += argcosts.startup;
3241 : 1979 : wfunccost = argcosts.per_tuple;
3242 : :
3243 : : /* also add the input expressions' cost to per-input-row costs */
5439 3244 : 1979 : cost_qual_eval_node(&argcosts, (Node *) wfunc->args, root);
3245 : 1979 : startup_cost += argcosts.startup;
3246 : 1979 : wfunccost += argcosts.per_tuple;
3247 : :
3248 : : /*
3249 : : * Add the filter's cost to per-input-row costs. XXX We should reduce
3250 : : * input expression costs according to filter selectivity.
3251 : : */
4625 noah@leadboat.com 3252 : 1979 : cost_qual_eval_node(&argcosts, (Node *) wfunc->aggfilter, root);
3253 : 1979 : startup_cost += argcosts.startup;
3254 : 1979 : wfunccost += argcosts.per_tuple;
3255 : :
5439 tgl@sss.pgh.pa.us 3256 : 1979 : total_cost += wfunccost * input_tuples;
3257 : : }
3258 : :
3259 : : /*
3260 : : * We also charge cpu_operator_cost per grouping column per tuple for
3261 : : * grouping comparisons, plus cpu_tuple_cost per tuple for general
3262 : : * overhead.
3263 : : *
3264 : : * XXX this neglects costs of spooling the data to disk when it overflows
3265 : : * work_mem. Sooner or later that should get accounted for.
3266 : : */
3267 : 1538 : total_cost += cpu_operator_cost * (numPartCols + numOrderCols) * input_tuples;
6286 3268 : 1538 : total_cost += cpu_tuple_cost * input_tuples;
3269 : :
5161 3270 : 1538 : path->rows = input_tuples;
571 rhaas@postgresql.org 3271 : 1538 : path->disabled_nodes = input_disabled_nodes;
6286 tgl@sss.pgh.pa.us 3272 : 1538 : path->startup_cost = startup_cost;
3273 : 1538 : path->total_cost = total_cost;
3274 : :
3275 : : /*
3276 : : * Also, take into account how many tuples we need to read from the
3277 : : * subnode in order to produce the first tuple from the WindowAgg. To do
3278 : : * this we proportion the run cost (total cost not including startup cost)
3279 : : * over the estimated startup tuples. We already included the startup
3280 : : * cost of the subnode, so we only need to do this when the estimated
3281 : : * startup tuples is above 1.0.
3282 : : */
954 drowley@postgresql.o 3283 : 1538 : startup_tuples = get_windowclause_startup_tuples(root, winclause,
3284 : : input_tuples);
3285 : :
3286 [ + + ]: 1538 : if (startup_tuples > 1.0)
3287 : 1284 : path->startup_cost += (total_cost - startup_cost) / input_tuples *
3288 : 1284 : (startup_tuples - 1.0);
6286 tgl@sss.pgh.pa.us 3289 : 1538 : }
3290 : :
3291 : : /*
3292 : : * cost_group
3293 : : * Determines and returns the cost of performing a Group plan node,
3294 : : * including the cost of its input.
3295 : : *
3296 : : * Note: caller must ensure that input costs are for appropriately-sorted
3297 : : * input.
3298 : : */
3299 : : void
7588 3300 : 625 : cost_group(Path *path, PlannerInfo *root,
3301 : : int numGroupCols, double numGroups,
3302 : : List *quals,
3303 : : int input_disabled_nodes,
3304 : : Cost input_startup_cost, Cost input_total_cost,
3305 : : double input_tuples)
3306 : : {
3307 : : double output_tuples;
3308 : : Cost startup_cost;
3309 : : Cost total_cost;
3310 : :
3055 3311 : 625 : output_tuples = numGroups;
8515 3312 : 625 : startup_cost = input_startup_cost;
3313 : 625 : total_cost = input_total_cost;
3314 : :
3315 : : /*
3316 : : * Charge one cpu_operator_cost per comparison per input tuple. We assume
3317 : : * all columns get compared at most of the tuples.
3318 : : */
3319 : 625 : total_cost += cpu_operator_cost * input_tuples * numGroupCols;
3320 : :
3321 : : /*
3322 : : * If there are quals (HAVING quals), account for their cost and
3323 : : * selectivity.
3324 : : */
3055 3325 [ - + ]: 625 : if (quals)
3326 : : {
3327 : : QualCost qual_cost;
3328 : :
3055 tgl@sss.pgh.pa.us 3329 :UBC 0 : cost_qual_eval(&qual_cost, quals, root);
3330 : 0 : startup_cost += qual_cost.startup;
3331 : 0 : total_cost += qual_cost.startup + output_tuples * qual_cost.per_tuple;
3332 : :
3333 : 0 : output_tuples = clamp_row_est(output_tuples *
3334 : 0 : clauselist_selectivity(root,
3335 : : quals,
3336 : : 0,
3337 : : JOIN_INNER,
3338 : : NULL));
3339 : : }
3340 : :
3055 tgl@sss.pgh.pa.us 3341 :CBC 625 : path->rows = output_tuples;
571 rhaas@postgresql.org 3342 : 625 : path->disabled_nodes = input_disabled_nodes;
8515 tgl@sss.pgh.pa.us 3343 : 625 : path->startup_cost = startup_cost;
3344 : 625 : path->total_cost = total_cost;
3345 : 625 : }
3346 : :
3347 : : /*
3348 : : * initial_cost_nestloop
3349 : : * Preliminary estimate of the cost of a nestloop join path.
3350 : : *
3351 : : * This must quickly produce lower-bound estimates of the path's startup and
3352 : : * total costs. If we are unable to eliminate the proposed path from
3353 : : * consideration using the lower bounds, final_cost_nestloop will be called
3354 : : * to obtain the final estimates.
3355 : : *
3356 : : * The exact division of labor between this function and final_cost_nestloop
3357 : : * is private to them, and represents a tradeoff between speed of the initial
3358 : : * estimate and getting a tight lower bound. We choose to not examine the
3359 : : * join quals here, since that's by far the most expensive part of the
3360 : : * calculations. The end result is that CPU-cost considerations must be
3361 : : * left for the second phase; and for SEMI/ANTI joins, we must also postpone
3362 : : * incorporation of the inner path's run cost.
3363 : : *
3364 : : * 'workspace' is to be filled with startup_cost, total_cost, and perhaps
3365 : : * other data to be used by final_cost_nestloop
3366 : : * 'jointype' is the type of join to be performed
3367 : : * 'outer_path' is the outer input to the join
3368 : : * 'inner_path' is the inner input to the join
3369 : : * 'extra' contains miscellaneous information about the join
3370 : : */
3371 : : void
5161 3372 : 1984886 : initial_cost_nestloop(PlannerInfo *root, JoinCostWorkspace *workspace,
3373 : : JoinType jointype, uint64 enable_mask,
3374 : : Path *outer_path, Path *inner_path,
3375 : : JoinPathExtraData *extra)
3376 : : {
3377 : : int disabled_nodes;
9525 3378 : 1984886 : Cost startup_cost = 0;
3379 : 1984886 : Cost run_cost = 0;
5161 3380 : 1984886 : double outer_path_rows = outer_path->rows;
3381 : : Cost inner_rescan_start_cost;
3382 : : Cost inner_rescan_total_cost;
3383 : : Cost inner_run_cost;
3384 : : Cost inner_rescan_run_cost;
3385 : :
3386 : : /* Count up disabled nodes. */
46 rhaas@postgresql.org 3387 :GNC 1984886 : disabled_nodes = (extra->pgs_mask & enable_mask) == enable_mask ? 0 : 1;
571 rhaas@postgresql.org 3388 :CBC 1984886 : disabled_nodes += inner_path->disabled_nodes;
3389 : 1984886 : disabled_nodes += outer_path->disabled_nodes;
3390 : :
3391 : : /* estimate costs to rescan the inner relation */
6028 tgl@sss.pgh.pa.us 3392 : 1984886 : cost_rescan(root, inner_path,
3393 : : &inner_rescan_start_cost,
3394 : : &inner_rescan_total_cost);
3395 : :
3396 : : /* cost of source data */
3397 : :
3398 : : /*
3399 : : * NOTE: clearly, we must pay both outer and inner paths' startup_cost
3400 : : * before we can start returning tuples, so the join's startup cost is
3401 : : * their sum. We'll also pay the inner path's rescan startup cost
3402 : : * multiple times.
3403 : : */
9525 3404 : 1984886 : startup_cost += outer_path->startup_cost + inner_path->startup_cost;
3405 : 1984886 : run_cost += outer_path->total_cost - outer_path->startup_cost;
6028 3406 [ + + ]: 1984886 : if (outer_path_rows > 1)
3407 : 1431123 : run_cost += (outer_path_rows - 1) * inner_rescan_start_cost;
3408 : :
6154 3409 : 1984886 : inner_run_cost = inner_path->total_cost - inner_path->startup_cost;
6028 3410 : 1984886 : inner_rescan_run_cost = inner_rescan_total_cost - inner_rescan_start_cost;
3411 : :
3264 3412 [ + + + + ]: 1984886 : if (jointype == JOIN_SEMI || jointype == JOIN_ANTI ||
3413 [ + + ]: 1940210 : extra->inner_unique)
3414 : : {
3415 : : /*
3416 : : * With a SEMI or ANTI join, or if the innerrel is known unique, the
3417 : : * executor will stop after the first match.
3418 : : *
3419 : : * Getting decent estimates requires inspection of the join quals,
3420 : : * which we choose to postpone to final_cost_nestloop.
3421 : : */
3422 : :
3423 : : /* Save private data for final_cost_nestloop */
3938 3424 : 845423 : workspace->inner_run_cost = inner_run_cost;
3425 : 845423 : workspace->inner_rescan_run_cost = inner_rescan_run_cost;
3426 : : }
3427 : : else
3428 : : {
3429 : : /* Normal case; we'll scan whole input rel for each outer row */
5161 3430 : 1139463 : run_cost += inner_run_cost;
3431 [ + + ]: 1139463 : if (outer_path_rows > 1)
3432 : 870392 : run_cost += (outer_path_rows - 1) * inner_rescan_run_cost;
3433 : : }
3434 : :
3435 : : /* CPU costs left for later */
3436 : :
3437 : : /* Public result fields */
571 rhaas@postgresql.org 3438 : 1984886 : workspace->disabled_nodes = disabled_nodes;
5161 tgl@sss.pgh.pa.us 3439 : 1984886 : workspace->startup_cost = startup_cost;
3440 : 1984886 : workspace->total_cost = startup_cost + run_cost;
3441 : : /* Save private data for final_cost_nestloop */
3442 : 1984886 : workspace->run_cost = run_cost;
3443 : 1984886 : }
3444 : :
3445 : : /*
3446 : : * final_cost_nestloop
3447 : : * Final estimate of the cost and result size of a nestloop join path.
3448 : : *
3449 : : * 'path' is already filled in except for the rows and cost fields
3450 : : * 'workspace' is the result from initial_cost_nestloop
3451 : : * 'extra' contains miscellaneous information about the join
3452 : : */
3453 : : void
3454 : 885969 : final_cost_nestloop(PlannerInfo *root, NestPath *path,
3455 : : JoinCostWorkspace *workspace,
3456 : : JoinPathExtraData *extra)
3457 : : {
1680 peter@eisentraut.org 3458 : 885969 : Path *outer_path = path->jpath.outerjoinpath;
3459 : 885969 : Path *inner_path = path->jpath.innerjoinpath;
5161 tgl@sss.pgh.pa.us 3460 : 885969 : double outer_path_rows = outer_path->rows;
3461 : 885969 : double inner_path_rows = inner_path->rows;
3462 : 885969 : Cost startup_cost = workspace->startup_cost;
3463 : 885969 : Cost run_cost = workspace->run_cost;
3464 : : Cost cpu_per_tuple;
3465 : : QualCost restrict_qual_cost;
3466 : : double ntuples;
3467 : :
3468 : : /* Set the number of disabled nodes. */
571 rhaas@postgresql.org 3469 : 885969 : path->jpath.path.disabled_nodes = workspace->disabled_nodes;
3470 : :
3471 : : /* Protect some assumptions below that rowcounts aren't zero */
1973 drowley@postgresql.o 3472 [ - + ]: 885969 : if (outer_path_rows <= 0)
3641 tgl@sss.pgh.pa.us 3473 :UBC 0 : outer_path_rows = 1;
1973 drowley@postgresql.o 3474 [ + + ]:CBC 885969 : if (inner_path_rows <= 0)
3641 tgl@sss.pgh.pa.us 3475 : 363 : inner_path_rows = 1;
3476 : : /* Mark the path with the correct row estimate */
1680 peter@eisentraut.org 3477 [ + + ]: 885969 : if (path->jpath.path.param_info)
3478 : 21811 : path->jpath.path.rows = path->jpath.path.param_info->ppi_rows;
3479 : : else
3480 : 864158 : path->jpath.path.rows = path->jpath.path.parent->rows;
3481 : :
3482 : : /* For partial paths, scale row estimate. */
3483 [ + + ]: 885969 : if (path->jpath.path.parallel_workers > 0)
3484 : : {
3485 : 21378 : double parallel_divisor = get_parallel_divisor(&path->jpath.path);
3486 : :
3487 : 21378 : path->jpath.path.rows =
3488 : 21378 : clamp_row_est(path->jpath.path.rows / parallel_divisor);
3489 : : }
3490 : :
3491 : : /* cost of inner-relation source data (we already dealt with outer rel) */
3492 : :
3493 [ + + + + ]: 885969 : if (path->jpath.jointype == JOIN_SEMI || path->jpath.jointype == JOIN_ANTI ||
3264 tgl@sss.pgh.pa.us 3494 [ + + ]: 856059 : extra->inner_unique)
5161 3495 : 571972 : {
3496 : : /*
3497 : : * With a SEMI or ANTI join, or if the innerrel is known unique, the
3498 : : * executor will stop after the first match.
3499 : : */
3938 3500 : 571972 : Cost inner_run_cost = workspace->inner_run_cost;
3501 : 571972 : Cost inner_rescan_run_cost = workspace->inner_rescan_run_cost;
3502 : : double outer_matched_rows;
3503 : : double outer_unmatched_rows;
3504 : : Selectivity inner_scan_frac;
3505 : :
3506 : : /*
3507 : : * For an outer-rel row that has at least one match, we can expect the
3508 : : * inner scan to stop after a fraction 1/(match_count+1) of the inner
3509 : : * rows, if the matches are evenly distributed. Since they probably
3510 : : * aren't quite evenly distributed, we apply a fuzz factor of 2.0 to
3511 : : * that fraction. (If we used a larger fuzz factor, we'd have to
3512 : : * clamp inner_scan_frac to at most 1.0; but since match_count is at
3513 : : * least 1, no such clamp is needed now.)
3514 : : */
3264 3515 : 571972 : outer_matched_rows = rint(outer_path_rows * extra->semifactors.outer_match_frac);
3207 3516 : 571972 : outer_unmatched_rows = outer_path_rows - outer_matched_rows;
3264 3517 : 571972 : inner_scan_frac = 2.0 / (extra->semifactors.match_count + 1.0);
3518 : :
3519 : : /*
3520 : : * Compute number of tuples processed (not number emitted!). First,
3521 : : * account for successfully-matched outer rows.
3522 : : */
6154 3523 : 571972 : ntuples = outer_matched_rows * inner_path_rows * inner_scan_frac;
3524 : :
3525 : : /*
3526 : : * Now we need to estimate the actual costs of scanning the inner
3527 : : * relation, which may be quite a bit less than N times inner_run_cost
3528 : : * due to early scan stops. We consider two cases. If the inner path
3529 : : * is an indexscan using all the joinquals as indexquals, then an
3530 : : * unmatched outer row results in an indexscan returning no rows,
3531 : : * which is probably quite cheap. Otherwise, the executor will have
3532 : : * to scan the whole inner rel for an unmatched row; not so cheap.
3533 : : */
5078 3534 [ + + ]: 571972 : if (has_indexed_join_quals(path))
3535 : : {
3536 : : /*
3537 : : * Successfully-matched outer rows will only require scanning
3538 : : * inner_scan_frac of the inner relation. In this case, we don't
3539 : : * need to charge the full inner_run_cost even when that's more
3540 : : * than inner_rescan_run_cost, because we can assume that none of
3541 : : * the inner scans ever scan the whole inner relation. So it's
3542 : : * okay to assume that all the inner scan executions can be
3543 : : * fractions of the full cost, even if materialization is reducing
3544 : : * the rescan cost. At this writing, it's impossible to get here
3545 : : * for a materialized inner scan, so inner_run_cost and
3546 : : * inner_rescan_run_cost will be the same anyway; but just in
3547 : : * case, use inner_run_cost for the first matched tuple and
3548 : : * inner_rescan_run_cost for additional ones.
3549 : : */
3938 3550 : 90250 : run_cost += inner_run_cost * inner_scan_frac;
3551 [ + + ]: 90250 : if (outer_matched_rows > 1)
3552 : 12767 : run_cost += (outer_matched_rows - 1) * inner_rescan_run_cost * inner_scan_frac;
3553 : :
3554 : : /*
3555 : : * Add the cost of inner-scan executions for unmatched outer rows.
3556 : : * We estimate this as the same cost as returning the first tuple
3557 : : * of a nonempty scan. We consider that these are all rescans,
3558 : : * since we used inner_run_cost once already.
3559 : : */
3207 3560 : 90250 : run_cost += outer_unmatched_rows *
6028 3561 : 90250 : inner_rescan_run_cost / inner_path_rows;
3562 : :
3563 : : /*
3564 : : * We won't be evaluating any quals at all for unmatched rows, so
3565 : : * don't add them to ntuples.
3566 : : */
3567 : : }
3568 : : else
3569 : : {
3570 : : /*
3571 : : * Here, a complicating factor is that rescans may be cheaper than
3572 : : * first scans. If we never scan all the way to the end of the
3573 : : * inner rel, it might be (depending on the plan type) that we'd
3574 : : * never pay the whole inner first-scan run cost. However it is
3575 : : * difficult to estimate whether that will happen (and it could
3576 : : * not happen if there are any unmatched outer rows!), so be
3577 : : * conservative and always charge the whole first-scan cost once.
3578 : : * We consider this charge to correspond to the first unmatched
3579 : : * outer row, unless there isn't one in our estimate, in which
3580 : : * case blame it on the first matched row.
3581 : : */
3582 : :
3583 : : /* First, count all unmatched join tuples as being processed */
3207 3584 : 481722 : ntuples += outer_unmatched_rows * inner_path_rows;
3585 : :
3586 : : /* Now add the forced full scan, and decrement appropriate count */
3938 3587 : 481722 : run_cost += inner_run_cost;
3207 3588 [ + + ]: 481722 : if (outer_unmatched_rows >= 1)
3589 : 460991 : outer_unmatched_rows -= 1;
3590 : : else
3591 : 20731 : outer_matched_rows -= 1;
3592 : :
3593 : : /* Add inner run cost for additional outer tuples having matches */
3594 [ + + ]: 481722 : if (outer_matched_rows > 0)
3595 : 163594 : run_cost += outer_matched_rows * inner_rescan_run_cost * inner_scan_frac;
3596 : :
3597 : : /* Add inner run cost for additional unmatched outer tuples */
3598 [ + + ]: 481722 : if (outer_unmatched_rows > 0)
3599 : 303972 : run_cost += outer_unmatched_rows * inner_rescan_run_cost;
3600 : : }
3601 : : }
3602 : : else
3603 : : {
3604 : : /* Normal-case source costs were included in preliminary estimate */
3605 : :
3606 : : /* Compute number of tuples processed (not number emitted!) */
6154 3607 : 313997 : ntuples = outer_path_rows * inner_path_rows;
3608 : : }
3609 : :
3610 : : /* CPU costs */
1680 peter@eisentraut.org 3611 : 885969 : cost_qual_eval(&restrict_qual_cost, path->jpath.joinrestrictinfo, root);
8463 tgl@sss.pgh.pa.us 3612 : 885969 : startup_cost += restrict_qual_cost.startup;
3613 : 885969 : cpu_per_tuple = cpu_tuple_cost + restrict_qual_cost.per_tuple;
9525 3614 : 885969 : run_cost += cpu_per_tuple * ntuples;
3615 : :
3616 : : /* tlist eval costs are paid per output row, not per tuple scanned */
1680 peter@eisentraut.org 3617 : 885969 : startup_cost += path->jpath.path.pathtarget->cost.startup;
3618 : 885969 : run_cost += path->jpath.path.pathtarget->cost.per_tuple * path->jpath.path.rows;
3619 : :
3620 : 885969 : path->jpath.path.startup_cost = startup_cost;
3621 : 885969 : path->jpath.path.total_cost = startup_cost + run_cost;
10841 scrappy@hub.org 3622 : 885969 : }
3623 : :
3624 : : /*
3625 : : * initial_cost_mergejoin
3626 : : * Preliminary estimate of the cost of a mergejoin path.
3627 : : *
3628 : : * This must quickly produce lower-bound estimates of the path's startup and
3629 : : * total costs. If we are unable to eliminate the proposed path from
3630 : : * consideration using the lower bounds, final_cost_mergejoin will be called
3631 : : * to obtain the final estimates.
3632 : : *
3633 : : * The exact division of labor between this function and final_cost_mergejoin
3634 : : * is private to them, and represents a tradeoff between speed of the initial
3635 : : * estimate and getting a tight lower bound. We choose to not examine the
3636 : : * join quals here, except for obtaining the scan selectivity estimate which
3637 : : * is really essential (but fortunately, use of caching keeps the cost of
3638 : : * getting that down to something reasonable).
3639 : : * We also assume that cost_sort/cost_incremental_sort is cheap enough to use
3640 : : * here.
3641 : : *
3642 : : * 'workspace' is to be filled with startup_cost, total_cost, and perhaps
3643 : : * other data to be used by final_cost_mergejoin
3644 : : * 'jointype' is the type of join to be performed
3645 : : * 'mergeclauses' is the list of joinclauses to be used as merge clauses
3646 : : * 'outer_path' is the outer input to the join
3647 : : * 'inner_path' is the inner input to the join
3648 : : * 'outersortkeys' is the list of sort keys for the outer path
3649 : : * 'innersortkeys' is the list of sort keys for the inner path
3650 : : * 'outer_presorted_keys' is the number of presorted keys of the outer path
3651 : : * 'extra' contains miscellaneous information about the join
3652 : : *
3653 : : * Note: outersortkeys and innersortkeys should be NIL if no explicit
3654 : : * sort is needed because the respective source path is already ordered.
3655 : : */
3656 : : void
5161 tgl@sss.pgh.pa.us 3657 : 910499 : initial_cost_mergejoin(PlannerInfo *root, JoinCostWorkspace *workspace,
3658 : : JoinType jointype,
3659 : : List *mergeclauses,
3660 : : Path *outer_path, Path *inner_path,
3661 : : List *outersortkeys, List *innersortkeys,
3662 : : int outer_presorted_keys,
3663 : : JoinPathExtraData *extra)
3664 : : {
3665 : : int disabled_nodes;
9525 3666 : 910499 : Cost startup_cost = 0;
3667 : 910499 : Cost run_cost = 0;
5161 3668 : 910499 : double outer_path_rows = outer_path->rows;
3669 : 910499 : double inner_path_rows = inner_path->rows;
3670 : : Cost inner_run_cost;
3671 : : double outer_rows,
3672 : : inner_rows,
3673 : : outer_skip_rows,
3674 : : inner_skip_rows;
3675 : : Selectivity outerstartsel,
3676 : : outerendsel,
3677 : : innerstartsel,
3678 : : innerendsel;
3679 : : Path sort_path; /* dummy for result of
3680 : : * cost_sort/cost_incremental_sort */
3681 : :
3682 : : /* Protect some assumptions below that rowcounts aren't zero */
1973 drowley@postgresql.o 3683 [ + + ]: 910499 : if (outer_path_rows <= 0)
6565 tgl@sss.pgh.pa.us 3684 : 48 : outer_path_rows = 1;
1973 drowley@postgresql.o 3685 [ + + ]: 910499 : if (inner_path_rows <= 0)
6565 tgl@sss.pgh.pa.us 3686 : 63 : inner_path_rows = 1;
3687 : :
3688 : : /*
3689 : : * A merge join will stop as soon as it exhausts either input stream
3690 : : * (unless it's an outer join, in which case the outer side has to be
3691 : : * scanned all the way anyway). Estimate fraction of the left and right
3692 : : * inputs that will actually need to be scanned. Likewise, we can
3693 : : * estimate the number of rows that will be skipped before the first join
3694 : : * pair is found, which should be factored into startup cost. We use only
3695 : : * the first (most significant) merge clause for this purpose. Since
3696 : : * mergejoinscansel() is a fairly expensive computation, we cache the
3697 : : * results in the merge clause RestrictInfo.
3698 : : */
5161 3699 [ + + + + ]: 910499 : if (mergeclauses && jointype != JOIN_FULL)
8780 3700 : 907322 : {
6994 3701 : 907322 : RestrictInfo *firstclause = (RestrictInfo *) linitial(mergeclauses);
3702 : : List *opathkeys;
3703 : : List *ipathkeys;
3704 : : PathKey *opathkey;
3705 : : PathKey *ipathkey;
3706 : : MergeScanSelCache *cache;
3707 : :
3708 : : /* Get the input pathkeys to determine the sort-order details */
3709 [ + + ]: 907322 : opathkeys = outersortkeys ? outersortkeys : outer_path->pathkeys;
3710 [ + + ]: 907322 : ipathkeys = innersortkeys ? innersortkeys : inner_path->pathkeys;
3711 [ - + ]: 907322 : Assert(opathkeys);
3712 [ - + ]: 907322 : Assert(ipathkeys);
3713 : 907322 : opathkey = (PathKey *) linitial(opathkeys);
3714 : 907322 : ipathkey = (PathKey *) linitial(ipathkeys);
3715 : : /* debugging check */
3716 [ + - ]: 907322 : if (opathkey->pk_opfamily != ipathkey->pk_opfamily ||
5475 3717 [ + - ]: 907322 : opathkey->pk_eclass->ec_collation != ipathkey->pk_eclass->ec_collation ||
345 peter@eisentraut.org 3718 [ + - ]: 907322 : opathkey->pk_cmptype != ipathkey->pk_cmptype ||
6994 tgl@sss.pgh.pa.us 3719 [ - + ]: 907322 : opathkey->pk_nulls_first != ipathkey->pk_nulls_first)
6994 tgl@sss.pgh.pa.us 3720 [ # # ]:UBC 0 : elog(ERROR, "left and right pathkeys do not match in mergejoin");
3721 : :
3722 : : /* Get the selectivity with caching */
6992 tgl@sss.pgh.pa.us 3723 :CBC 907322 : cache = cached_scansel(root, firstclause, opathkey);
3724 : :
6994 3725 [ + + ]: 907322 : if (bms_is_subset(firstclause->left_relids,
3726 : 907322 : outer_path->parent->relids))
3727 : : {
3728 : : /* left side of clause is outer */
6672 3729 : 471803 : outerstartsel = cache->leftstartsel;
3730 : 471803 : outerendsel = cache->leftendsel;
3731 : 471803 : innerstartsel = cache->rightstartsel;
3732 : 471803 : innerendsel = cache->rightendsel;
3733 : : }
3734 : : else
3735 : : {
3736 : : /* left side of clause is inner */
3737 : 435519 : outerstartsel = cache->rightstartsel;
3738 : 435519 : outerendsel = cache->rightendsel;
3739 : 435519 : innerstartsel = cache->leftstartsel;
3740 : 435519 : innerendsel = cache->leftendsel;
3741 : : }
5161 3742 [ + + + + ]: 907322 : if (jointype == JOIN_LEFT ||
3743 : : jointype == JOIN_ANTI)
3744 : : {
6672 3745 : 125220 : outerstartsel = 0.0;
3746 : 125220 : outerendsel = 1.0;
3747 : : }
1075 3748 [ + + + + ]: 782102 : else if (jointype == JOIN_RIGHT ||
3749 : : jointype == JOIN_RIGHT_ANTI)
3750 : : {
6672 3751 : 119685 : innerstartsel = 0.0;
3752 : 119685 : innerendsel = 1.0;
3753 : : }
3754 : : }
3755 : : else
3756 : : {
3757 : : /* cope with clauseless or full mergejoin */
3758 : 3177 : outerstartsel = innerstartsel = 0.0;
3759 : 3177 : outerendsel = innerendsel = 1.0;
3760 : : }
3761 : :
3762 : : /*
3763 : : * Convert selectivities to row counts. We force outer_rows and
3764 : : * inner_rows to be at least 1, but the skip_rows estimates can be zero.
3765 : : */
3766 : 910499 : outer_skip_rows = rint(outer_path_rows * outerstartsel);
3767 : 910499 : inner_skip_rows = rint(inner_path_rows * innerstartsel);
3768 : 910499 : outer_rows = clamp_row_est(outer_path_rows * outerendsel);
3769 : 910499 : inner_rows = clamp_row_est(inner_path_rows * innerendsel);
3770 : :
3771 [ - + ]: 910499 : Assert(outer_skip_rows <= outer_rows);
3772 [ - + ]: 910499 : Assert(inner_skip_rows <= inner_rows);
3773 : :
3774 : : /*
3775 : : * Readjust scan selectivities to account for above rounding. This is
3776 : : * normally an insignificant effect, but when there are only a few rows in
3777 : : * the inputs, failing to do this makes for a large percentage error.
3778 : : */
3779 : 910499 : outerstartsel = outer_skip_rows / outer_path_rows;
3780 : 910499 : innerstartsel = inner_skip_rows / inner_path_rows;
3781 : 910499 : outerendsel = outer_rows / outer_path_rows;
3782 : 910499 : innerendsel = inner_rows / inner_path_rows;
3783 : :
5189 3784 [ - + ]: 910499 : Assert(outerstartsel <= outerendsel);
3785 [ - + ]: 910499 : Assert(innerstartsel <= innerendsel);
3786 : :
3787 : : /*
3788 : : * We don't decide whether to materialize the inner path until we get to
3789 : : * final_cost_mergejoin(), so we don't know whether to check the pgs_mask
3790 : : * against PGS_MERGEJOIN_PLAIN or PGS_MERGEJOIN_MATERIALIZE. Instead, we
3791 : : * just account for any child nodes here and assume that this node is not
3792 : : * itself disabled; we can sort out the details in final_cost_mergejoin().
3793 : : *
3794 : : * (We could be more precise here by setting disabled_nodes to 1 at this
3795 : : * stage if both PGS_MERGEJOIN_PLAIN and PGS_MERGEJOIN_MATERIALIZE are
3796 : : * disabled, but that seems to against the idea of making this function
3797 : : * produce a quick, optimistic approximation of the final cost.)
3798 : : */
46 rhaas@postgresql.org 3799 :GNC 910499 : disabled_nodes = 0;
3800 : :
3801 : : /* cost of source data */
3802 : :
9525 tgl@sss.pgh.pa.us 3803 [ + + ]:CBC 910499 : if (outersortkeys) /* do we need to sort outer? */
3804 : : {
3805 : : /*
3806 : : * We can assert that the outer path is not already ordered
3807 : : * appropriately for the mergejoin; otherwise, outersortkeys would
3808 : : * have been set to NIL.
3809 : : */
311 rguo@postgresql.org 3810 [ - + ]: 448802 : Assert(!pathkeys_contained_in(outersortkeys, outer_path->pathkeys));
3811 : :
3812 : : /*
3813 : : * We choose to use incremental sort if it is enabled and there are
3814 : : * presorted keys; otherwise we use full sort.
3815 : : */
3816 [ + + + + ]: 448802 : if (enable_incremental_sort && outer_presorted_keys > 0)
3817 : : {
3818 : 861 : cost_incremental_sort(&sort_path,
3819 : : root,
3820 : : outersortkeys,
3821 : : outer_presorted_keys,
3822 : : outer_path->disabled_nodes,
3823 : : outer_path->startup_cost,
3824 : : outer_path->total_cost,
3825 : : outer_path_rows,
3826 : 861 : outer_path->pathtarget->width,
3827 : : 0.0,
3828 : : work_mem,
3829 : : -1.0);
3830 : : }
3831 : : else
3832 : : {
522 3833 : 447941 : cost_sort(&sort_path,
3834 : : root,
3835 : : outersortkeys,
3836 : : outer_path->disabled_nodes,
3837 : : outer_path->total_cost,
3838 : : outer_path_rows,
3839 : 447941 : outer_path->pathtarget->width,
3840 : : 0.0,
3841 : : work_mem,
3842 : : -1.0);
3843 : : }
3844 : :
571 rhaas@postgresql.org 3845 : 448802 : disabled_nodes += sort_path.disabled_nodes;
9525 tgl@sss.pgh.pa.us 3846 : 448802 : startup_cost += sort_path.startup_cost;
6672 3847 : 448802 : startup_cost += (sort_path.total_cost - sort_path.startup_cost)
3848 : 448802 : * outerstartsel;
8780 3849 : 448802 : run_cost += (sort_path.total_cost - sort_path.startup_cost)
6672 3850 : 448802 : * (outerendsel - outerstartsel);
3851 : : }
3852 : : else
3853 : : {
571 rhaas@postgresql.org 3854 : 461697 : disabled_nodes += outer_path->disabled_nodes;
9525 tgl@sss.pgh.pa.us 3855 : 461697 : startup_cost += outer_path->startup_cost;
6672 3856 : 461697 : startup_cost += (outer_path->total_cost - outer_path->startup_cost)
3857 : 461697 : * outerstartsel;
8780 3858 : 461697 : run_cost += (outer_path->total_cost - outer_path->startup_cost)
6672 3859 : 461697 : * (outerendsel - outerstartsel);
3860 : : }
3861 : :
9525 3862 [ + + ]: 910499 : if (innersortkeys) /* do we need to sort inner? */
3863 : : {
3864 : : /*
3865 : : * We can assert that the inner path is not already ordered
3866 : : * appropriately for the mergejoin; otherwise, innersortkeys would
3867 : : * have been set to NIL.
3868 : : */
311 rguo@postgresql.org 3869 [ - + ]: 720274 : Assert(!pathkeys_contained_in(innersortkeys, inner_path->pathkeys));
3870 : :
3871 : : /*
3872 : : * We do not consider incremental sort for inner path, because
3873 : : * incremental sort does not support mark/restore.
3874 : : */
3875 : :
9525 tgl@sss.pgh.pa.us 3876 : 720274 : cost_sort(&sort_path,
3877 : : root,
3878 : : innersortkeys,
3879 : : inner_path->disabled_nodes,
3880 : : inner_path->total_cost,
3881 : : inner_path_rows,
3678 3882 : 720274 : inner_path->pathtarget->width,
3883 : : 0.0,
3884 : : work_mem,
3885 : : -1.0);
571 rhaas@postgresql.org 3886 : 720274 : disabled_nodes += sort_path.disabled_nodes;
9525 tgl@sss.pgh.pa.us 3887 : 720274 : startup_cost += sort_path.startup_cost;
6672 3888 : 720274 : startup_cost += (sort_path.total_cost - sort_path.startup_cost)
5964 3889 : 720274 : * innerstartsel;
3890 : 720274 : inner_run_cost = (sort_path.total_cost - sort_path.startup_cost)
3891 : 720274 : * (innerendsel - innerstartsel);
3892 : : }
3893 : : else
3894 : : {
571 rhaas@postgresql.org 3895 : 190225 : disabled_nodes += inner_path->disabled_nodes;
9525 tgl@sss.pgh.pa.us 3896 : 190225 : startup_cost += inner_path->startup_cost;
6672 3897 : 190225 : startup_cost += (inner_path->total_cost - inner_path->startup_cost)
5964 3898 : 190225 : * innerstartsel;
3899 : 190225 : inner_run_cost = (inner_path->total_cost - inner_path->startup_cost)
3900 : 190225 : * (innerendsel - innerstartsel);
3901 : : }
3902 : :
3903 : : /*
3904 : : * We can't yet determine whether rescanning occurs, or whether
3905 : : * materialization of the inner input should be done. The minimum
3906 : : * possible inner input cost, regardless of rescan and materialization
3907 : : * considerations, is inner_run_cost. We include that in
3908 : : * workspace->total_cost, but not yet in run_cost.
3909 : : */
3910 : :
3911 : : /* CPU costs left for later */
3912 : :
3913 : : /* Public result fields */
571 rhaas@postgresql.org 3914 : 910499 : workspace->disabled_nodes = disabled_nodes;
5161 tgl@sss.pgh.pa.us 3915 : 910499 : workspace->startup_cost = startup_cost;
3916 : 910499 : workspace->total_cost = startup_cost + run_cost + inner_run_cost;
3917 : : /* Save private data for final_cost_mergejoin */
3918 : 910499 : workspace->run_cost = run_cost;
3919 : 910499 : workspace->inner_run_cost = inner_run_cost;
3920 : 910499 : workspace->outer_rows = outer_rows;
3921 : 910499 : workspace->inner_rows = inner_rows;
3922 : 910499 : workspace->outer_skip_rows = outer_skip_rows;
3923 : 910499 : workspace->inner_skip_rows = inner_skip_rows;
3924 : 910499 : }
3925 : :
3926 : : /*
3927 : : * final_cost_mergejoin
3928 : : * Final estimate of the cost and result size of a mergejoin path.
3929 : : *
3930 : : * Unlike other costsize functions, this routine makes two actual decisions:
3931 : : * whether the executor will need to do mark/restore, and whether we should
3932 : : * materialize the inner path. It would be logically cleaner to build
3933 : : * separate paths testing these alternatives, but that would require repeating
3934 : : * most of the cost calculations, which are not all that cheap. Since the
3935 : : * choice will not affect output pathkeys or startup cost, only total cost,
3936 : : * there is no possibility of wanting to keep more than one path. So it seems
3937 : : * best to make the decisions here and record them in the path's
3938 : : * skip_mark_restore and materialize_inner fields.
3939 : : *
3940 : : * Mark/restore overhead is usually required, but can be skipped if we know
3941 : : * that the executor need find only one match per outer tuple, and that the
3942 : : * mergeclauses are sufficient to identify a match.
3943 : : *
3944 : : * We materialize the inner path if we need mark/restore and either the inner
3945 : : * path can't support mark/restore, or it's cheaper to use an interposed
3946 : : * Material node to handle mark/restore.
3947 : : *
3948 : : * 'path' is already filled in except for the rows and cost fields and
3949 : : * skip_mark_restore and materialize_inner
3950 : : * 'workspace' is the result from initial_cost_mergejoin
3951 : : * 'extra' contains miscellaneous information about the join
3952 : : */
3953 : : void
3954 : 261405 : final_cost_mergejoin(PlannerInfo *root, MergePath *path,
3955 : : JoinCostWorkspace *workspace,
3956 : : JoinPathExtraData *extra)
3957 : : {
3958 : 261405 : Path *outer_path = path->jpath.outerjoinpath;
3959 : 261405 : Path *inner_path = path->jpath.innerjoinpath;
3960 : 261405 : double inner_path_rows = inner_path->rows;
3961 : 261405 : List *mergeclauses = path->path_mergeclauses;
3962 : 261405 : List *innersortkeys = path->innersortkeys;
3963 : 261405 : Cost startup_cost = workspace->startup_cost;
3964 : 261405 : Cost run_cost = workspace->run_cost;
3965 : 261405 : Cost inner_run_cost = workspace->inner_run_cost;
3966 : 261405 : double outer_rows = workspace->outer_rows;
3967 : 261405 : double inner_rows = workspace->inner_rows;
3968 : 261405 : double outer_skip_rows = workspace->outer_skip_rows;
3969 : 261405 : double inner_skip_rows = workspace->inner_skip_rows;
3970 : : Cost cpu_per_tuple,
3971 : : bare_inner_cost,
3972 : : mat_inner_cost;
3973 : : QualCost merge_qual_cost;
3974 : : QualCost qp_qual_cost;
3975 : : double mergejointuples,
3976 : : rescannedtuples;
3977 : : double rescanratio;
46 rhaas@postgresql.org 3978 :GNC 261405 : uint64 enable_mask = 0;
3979 : :
3980 : : /* Protect some assumptions below that rowcounts aren't zero */
1973 drowley@postgresql.o 3981 [ + + ]:CBC 261405 : if (inner_path_rows <= 0)
5161 tgl@sss.pgh.pa.us 3982 : 45 : inner_path_rows = 1;
3983 : :
3984 : : /* Mark the path with the correct row estimate */
5078 3985 [ + + ]: 261405 : if (path->jpath.path.param_info)
3986 : 1300 : path->jpath.path.rows = path->jpath.path.param_info->ppi_rows;
3987 : : else
3988 : 260105 : path->jpath.path.rows = path->jpath.path.parent->rows;
3989 : :
3990 : : /* For partial paths, scale row estimate. */
3348 rhaas@postgresql.org 3991 [ + + ]: 261405 : if (path->jpath.path.parallel_workers > 0)
3992 : : {
3224 bruce@momjian.us 3993 : 32796 : double parallel_divisor = get_parallel_divisor(&path->jpath.path);
3994 : :
3287 rhaas@postgresql.org 3995 : 32796 : path->jpath.path.rows =
3996 : 32796 : clamp_row_est(path->jpath.path.rows / parallel_divisor);
3997 : : }
3998 : :
3999 : : /*
4000 : : * Compute cost of the mergequals and qpquals (other restriction clauses)
4001 : : * separately.
4002 : : */
5161 tgl@sss.pgh.pa.us 4003 : 261405 : cost_qual_eval(&merge_qual_cost, mergeclauses, root);
4004 : 261405 : cost_qual_eval(&qp_qual_cost, path->jpath.joinrestrictinfo, root);
4005 : 261405 : qp_qual_cost.startup -= merge_qual_cost.startup;
4006 : 261405 : qp_qual_cost.per_tuple -= merge_qual_cost.per_tuple;
4007 : :
4008 : : /*
4009 : : * With a SEMI or ANTI join, or if the innerrel is known unique, the
4010 : : * executor will stop scanning for matches after the first match. When
4011 : : * all the joinclauses are merge clauses, this means we don't ever need to
4012 : : * back up the merge, and so we can skip mark/restore overhead.
4013 : : */
3264 4014 [ + + ]: 261405 : if ((path->jpath.jointype == JOIN_SEMI ||
4015 [ + + ]: 257753 : path->jpath.jointype == JOIN_ANTI ||
4016 [ + + + + ]: 348409 : extra->inner_unique) &&
4017 : 96141 : (list_length(path->jpath.joinrestrictinfo) ==
4018 : 96141 : list_length(path->path_mergeclauses)))
4019 : 81296 : path->skip_mark_restore = true;
4020 : : else
4021 : 180109 : path->skip_mark_restore = false;
4022 : :
4023 : : /*
4024 : : * Get approx # tuples passing the mergequals. We use approx_tuple_count
4025 : : * here because we need an estimate done with JOIN_INNER semantics.
4026 : : */
5161 4027 : 261405 : mergejointuples = approx_tuple_count(root, &path->jpath, mergeclauses);
4028 : :
4029 : : /*
4030 : : * When there are equal merge keys in the outer relation, the mergejoin
4031 : : * must rescan any matching tuples in the inner relation. This means
4032 : : * re-fetching inner tuples; we have to estimate how often that happens.
4033 : : *
4034 : : * For regular inner and outer joins, the number of re-fetches can be
4035 : : * estimated approximately as size of merge join output minus size of
4036 : : * inner relation. Assume that the distinct key values are 1, 2, ..., and
4037 : : * denote the number of values of each key in the outer relation as m1,
4038 : : * m2, ...; in the inner relation, n1, n2, ... Then we have
4039 : : *
4040 : : * size of join = m1 * n1 + m2 * n2 + ...
4041 : : *
4042 : : * number of rescanned tuples = (m1 - 1) * n1 + (m2 - 1) * n2 + ... = m1 *
4043 : : * n1 + m2 * n2 + ... - (n1 + n2 + ...) = size of join - size of inner
4044 : : * relation
4045 : : *
4046 : : * This equation works correctly for outer tuples having no inner match
4047 : : * (nk = 0), but not for inner tuples having no outer match (mk = 0); we
4048 : : * are effectively subtracting those from the number of rescanned tuples,
4049 : : * when we should not. Can we do better without expensive selectivity
4050 : : * computations?
4051 : : *
4052 : : * The whole issue is moot if we know we don't need to mark/restore at
4053 : : * all, or if we are working from a unique-ified outer input.
4054 : : */
208 rguo@postgresql.org 4055 [ + + ]:GNC 261405 : if (path->skip_mark_restore ||
4056 [ + + + + : 180109 : RELATION_WAS_MADE_UNIQUE(outer_path->parent, extra->sjinfo,
+ + ]
4057 : : path->jpath.jointype))
5161 tgl@sss.pgh.pa.us 4058 :CBC 83928 : rescannedtuples = 0;
4059 : : else
4060 : : {
4061 : 177477 : rescannedtuples = mergejointuples - inner_path_rows;
4062 : : /* Must clamp because of possible underestimate */
4063 [ + + ]: 177477 : if (rescannedtuples < 0)
4064 : 47624 : rescannedtuples = 0;
4065 : : }
4066 : :
4067 : : /*
4068 : : * We'll inflate various costs this much to account for rescanning. Note
4069 : : * that this is to be multiplied by something involving inner_rows, or
4070 : : * another number related to the portion of the inner rel we'll scan.
4071 : : */
2644 4072 : 261405 : rescanratio = 1.0 + (rescannedtuples / inner_rows);
4073 : :
4074 : : /*
4075 : : * Decide whether we want to materialize the inner input to shield it from
4076 : : * mark/restore and performing re-fetches. Our cost model for regular
4077 : : * re-fetches is that a re-fetch costs the same as an original fetch,
4078 : : * which is probably an overestimate; but on the other hand we ignore the
4079 : : * bookkeeping costs of mark/restore. Not clear if it's worth developing
4080 : : * a more refined model. So we just need to inflate the inner run cost by
4081 : : * rescanratio.
4082 : : */
5964 4083 : 261405 : bare_inner_cost = inner_run_cost * rescanratio;
4084 : :
4085 : : /*
4086 : : * When we interpose a Material node the re-fetch cost is assumed to be
4087 : : * just cpu_operator_cost per tuple, independently of the underlying
4088 : : * plan's cost; and we charge an extra cpu_operator_cost per original
4089 : : * fetch as well. Note that we're assuming the materialize node will
4090 : : * never spill to disk, since it only has to remember tuples back to the
4091 : : * last mark. (If there are a huge number of duplicates, our other cost
4092 : : * factors will make the path so expensive that it probably won't get
4093 : : * chosen anyway.) So we don't use cost_rescan here.
4094 : : *
4095 : : * Note: keep this estimate in sync with create_mergejoin_plan's labeling
4096 : : * of the generated Material node.
4097 : : */
4098 : 261405 : mat_inner_cost = inner_run_cost +
2644 4099 : 261405 : cpu_operator_cost * inner_rows * rescanratio;
4100 : :
4101 : : /*
4102 : : * If we don't need mark/restore at all, we don't need materialization.
4103 : : */
3264 4104 [ + + ]: 261405 : if (path->skip_mark_restore)
4105 : 81296 : path->materialize_inner = false;
4106 : :
4107 : : /*
4108 : : * If merge joins with materialization are enabled, then choose
4109 : : * materialization if either (a) it looks cheaper or (b) merge joins
4110 : : * without materialization are disabled.
4111 : : */
46 rhaas@postgresql.org 4112 [ + + + + ]:GNC 180109 : else if ((extra->pgs_mask & PGS_MERGEJOIN_MATERIALIZE) != 0 &&
4113 : 178224 : (mat_inner_cost < bare_inner_cost ||
4114 [ + + ]: 178224 : (extra->pgs_mask & PGS_MERGEJOIN_PLAIN) == 0))
5964 tgl@sss.pgh.pa.us 4115 :CBC 1849 : path->materialize_inner = true;
4116 : :
4117 : : /*
4118 : : * Regardless of what plan shapes are enabled and what the costs seem to
4119 : : * be, we *must* materialize it if the inner path is to be used directly
4120 : : * (without sorting) and it doesn't support mark/restore. Planner failure
4121 : : * is not an option!
4122 : : *
4123 : : * Since the inner side must be ordered, and only Sorts and IndexScans can
4124 : : * create order to begin with, and they both support mark/restore, you
4125 : : * might think there's no problem --- but you'd be wrong. Nestloop and
4126 : : * merge joins can *preserve* the order of their inputs, so they can be
4127 : : * selected as the input of a mergejoin, and they don't support
4128 : : * mark/restore at present.
4129 : : */
4130 [ + + ]: 178260 : else if (innersortkeys == NIL &&
4146 rhaas@postgresql.org 4131 [ + + ]: 4798 : !ExecSupportsMarkRestore(inner_path))
5964 tgl@sss.pgh.pa.us 4132 : 963 : path->materialize_inner = true;
4133 : :
4134 : : /*
4135 : : * Also, force materializing if the inner path is to be sorted and the
4136 : : * sort is expected to spill to disk. This is because the final merge
4137 : : * pass can be done on-the-fly if it doesn't have to support mark/restore.
4138 : : * We don't try to adjust the cost estimates for this consideration,
4139 : : * though.
4140 : : *
4141 : : * Since materialization is a performance optimization in this case,
4142 : : * rather than necessary for correctness, we skip it if materialization is
4143 : : * switched off.
4144 : : */
46 rhaas@postgresql.org 4145 [ + + + + ]:GNC 177297 : else if ((extra->pgs_mask & PGS_MERGEJOIN_MATERIALIZE) != 0 &&
4146 : 173428 : innersortkeys != NIL &&
3678 tgl@sss.pgh.pa.us 4147 :CBC 173428 : relation_byte_size(inner_path_rows,
4148 : 173428 : inner_path->pathtarget->width) >
408 4149 [ + + ]: 173428 : work_mem * (Size) 1024)
5964 4150 : 142 : path->materialize_inner = true;
4151 : : else
4152 : 177155 : path->materialize_inner = false;
4153 : :
4154 : : /* Get the number of disabled nodes, not yet including this one. */
46 rhaas@postgresql.org 4155 :GNC 261405 : path->jpath.path.disabled_nodes = workspace->disabled_nodes;
4156 : :
4157 : : /*
4158 : : * Charge the right incremental cost for the chosen case, and update
4159 : : * enable_mask as appropriate.
4160 : : */
5964 tgl@sss.pgh.pa.us 4161 [ + + ]:CBC 261405 : if (path->materialize_inner)
4162 : : {
4163 : 2954 : run_cost += mat_inner_cost;
46 rhaas@postgresql.org 4164 :GNC 2954 : enable_mask |= PGS_MERGEJOIN_MATERIALIZE;
4165 : : }
4166 : : else
4167 : : {
5964 tgl@sss.pgh.pa.us 4168 :CBC 258451 : run_cost += bare_inner_cost;
46 rhaas@postgresql.org 4169 :GNC 258451 : enable_mask |= PGS_MERGEJOIN_PLAIN;
4170 : : }
4171 : :
4172 : : /* Incremental count of disabled nodes if this node is disabled. */
4173 [ + + ]: 261405 : if (path->jpath.path.parallel_workers == 0)
4174 : 228609 : enable_mask |= PGS_CONSIDER_NONPARTIAL;
4175 [ + + ]: 261405 : if ((extra->pgs_mask & enable_mask) != enable_mask)
4176 : 33 : ++path->jpath.path.disabled_nodes;
4177 : :
4178 : : /* CPU costs */
4179 : :
4180 : : /*
4181 : : * The number of tuple comparisons needed is approximately number of outer
4182 : : * rows plus number of inner rows plus number of rescanned tuples (can we
4183 : : * refine this?). At each one, we need to evaluate the mergejoin quals.
4184 : : */
8448 tgl@sss.pgh.pa.us 4185 :CBC 261405 : startup_cost += merge_qual_cost.startup;
6672 4186 : 261405 : startup_cost += merge_qual_cost.per_tuple *
4187 : 261405 : (outer_skip_rows + inner_skip_rows * rescanratio);
8448 4188 : 261405 : run_cost += merge_qual_cost.per_tuple *
6672 4189 : 261405 : ((outer_rows - outer_skip_rows) +
4190 : 261405 : (inner_rows - inner_skip_rows) * rescanratio);
4191 : :
4192 : : /*
4193 : : * For each tuple that gets through the mergejoin proper, we charge
4194 : : * cpu_tuple_cost plus the cost of evaluating additional restriction
4195 : : * clauses that are to be applied at the join. (This is pessimistic since
4196 : : * not all of the quals may get evaluated at each tuple.)
4197 : : *
4198 : : * Note: we could adjust for SEMI/ANTI joins skipping some qual
4199 : : * evaluations here, but it's probably not worth the trouble.
4200 : : */
8448 4201 : 261405 : startup_cost += qp_qual_cost.startup;
4202 : 261405 : cpu_per_tuple = cpu_tuple_cost + qp_qual_cost.per_tuple;
6420 4203 : 261405 : run_cost += cpu_per_tuple * mergejointuples;
4204 : :
4205 : : /* tlist eval costs are paid per output row, not per tuple scanned */
3678 4206 : 261405 : startup_cost += path->jpath.path.pathtarget->cost.startup;
4207 : 261405 : run_cost += path->jpath.path.pathtarget->cost.per_tuple * path->jpath.path.rows;
4208 : :
8448 4209 : 261405 : path->jpath.path.startup_cost = startup_cost;
4210 : 261405 : path->jpath.path.total_cost = startup_cost + run_cost;
10841 scrappy@hub.org 4211 : 261405 : }
4212 : :
4213 : : /*
4214 : : * run mergejoinscansel() with caching
4215 : : */
4216 : : static MergeScanSelCache *
6695 bruce@momjian.us 4217 : 907322 : cached_scansel(PlannerInfo *root, RestrictInfo *rinfo, PathKey *pathkey)
4218 : : {
4219 : : MergeScanSelCache *cache;
4220 : : ListCell *lc;
4221 : : Selectivity leftstartsel,
4222 : : leftendsel,
4223 : : rightstartsel,
4224 : : rightendsel;
4225 : : MemoryContext oldcontext;
4226 : :
4227 : : /* Do we have this result already? */
6992 tgl@sss.pgh.pa.us 4228 [ + + + + : 907325 : foreach(lc, rinfo->scansel_cache)
+ + ]
4229 : : {
4230 : 825094 : cache = (MergeScanSelCache *) lfirst(lc);
4231 [ + - ]: 825094 : if (cache->opfamily == pathkey->pk_opfamily &&
5475 4232 [ + - ]: 825094 : cache->collation == pathkey->pk_eclass->ec_collation &&
345 peter@eisentraut.org 4233 [ + + ]: 825094 : cache->cmptype == pathkey->pk_cmptype &&
6992 tgl@sss.pgh.pa.us 4234 [ + - ]: 825091 : cache->nulls_first == pathkey->pk_nulls_first)
4235 : 825091 : return cache;
4236 : : }
4237 : :
4238 : : /* Nope, do the computation */
4239 : 82231 : mergejoinscansel(root,
4240 : 82231 : (Node *) rinfo->clause,
4241 : : pathkey->pk_opfamily,
4242 : : pathkey->pk_cmptype,
4243 : 82231 : pathkey->pk_nulls_first,
4244 : : &leftstartsel,
4245 : : &leftendsel,
4246 : : &rightstartsel,
4247 : : &rightendsel);
4248 : :
4249 : : /* Cache the result in suitably long-lived workspace */
4250 : 82231 : oldcontext = MemoryContextSwitchTo(root->planner_cxt);
4251 : :
95 michael@paquier.xyz 4252 :GNC 82231 : cache = palloc_object(MergeScanSelCache);
6992 tgl@sss.pgh.pa.us 4253 :CBC 82231 : cache->opfamily = pathkey->pk_opfamily;
5475 4254 : 82231 : cache->collation = pathkey->pk_eclass->ec_collation;
345 peter@eisentraut.org 4255 : 82231 : cache->cmptype = pathkey->pk_cmptype;
6992 tgl@sss.pgh.pa.us 4256 : 82231 : cache->nulls_first = pathkey->pk_nulls_first;
6672 4257 : 82231 : cache->leftstartsel = leftstartsel;
4258 : 82231 : cache->leftendsel = leftendsel;
4259 : 82231 : cache->rightstartsel = rightstartsel;
4260 : 82231 : cache->rightendsel = rightendsel;
4261 : :
6992 4262 : 82231 : rinfo->scansel_cache = lappend(rinfo->scansel_cache, cache);
4263 : :
4264 : 82231 : MemoryContextSwitchTo(oldcontext);
4265 : :
4266 : 82231 : return cache;
4267 : : }
4268 : :
4269 : : /*
4270 : : * initial_cost_hashjoin
4271 : : * Preliminary estimate of the cost of a hashjoin path.
4272 : : *
4273 : : * This must quickly produce lower-bound estimates of the path's startup and
4274 : : * total costs. If we are unable to eliminate the proposed path from
4275 : : * consideration using the lower bounds, final_cost_hashjoin will be called
4276 : : * to obtain the final estimates.
4277 : : *
4278 : : * The exact division of labor between this function and final_cost_hashjoin
4279 : : * is private to them, and represents a tradeoff between speed of the initial
4280 : : * estimate and getting a tight lower bound. We choose to not examine the
4281 : : * join quals here (other than by counting the number of hash clauses),
4282 : : * so we can't do much with CPU costs. We do assume that
4283 : : * ExecChooseHashTableSize is cheap enough to use here.
4284 : : *
4285 : : * 'workspace' is to be filled with startup_cost, total_cost, and perhaps
4286 : : * other data to be used by final_cost_hashjoin
4287 : : * 'jointype' is the type of join to be performed
4288 : : * 'hashclauses' is the list of joinclauses to be used as hash clauses
4289 : : * 'outer_path' is the outer input to the join
4290 : : * 'inner_path' is the inner input to the join
4291 : : * 'extra' contains miscellaneous information about the join
4292 : : * 'parallel_hash' indicates that inner_path is partial and that a shared
4293 : : * hash table will be built in parallel
4294 : : */
4295 : : void
5161 4296 : 510567 : initial_cost_hashjoin(PlannerInfo *root, JoinCostWorkspace *workspace,
4297 : : JoinType jointype,
4298 : : List *hashclauses,
4299 : : Path *outer_path, Path *inner_path,
4300 : : JoinPathExtraData *extra,
4301 : : bool parallel_hash)
4302 : : {
4303 : : int disabled_nodes;
9525 4304 : 510567 : Cost startup_cost = 0;
4305 : 510567 : Cost run_cost = 0;
5161 4306 : 510567 : double outer_path_rows = outer_path->rows;
4307 : 510567 : double inner_path_rows = inner_path->rows;
3007 andres@anarazel.de 4308 : 510567 : double inner_path_rows_total = inner_path_rows;
7959 neilc@samurai.com 4309 : 510567 : int num_hashclauses = list_length(hashclauses);
4310 : : int numbuckets;
4311 : : int numbatches;
4312 : : int num_skew_mcvs;
4313 : : size_t space_allowed; /* unused */
46 rhaas@postgresql.org 4314 :GNC 510567 : uint64 enable_mask = PGS_HASHJOIN;
4315 : :
4316 [ + + ]: 510567 : if (outer_path->parallel_workers == 0)
4317 : 436599 : enable_mask |= PGS_CONSIDER_NONPARTIAL;
4318 : :
4319 : : /* Count up disabled nodes. */
4320 : 510567 : disabled_nodes = (extra->pgs_mask & enable_mask) == enable_mask ? 0 : 1;
571 rhaas@postgresql.org 4321 :CBC 510567 : disabled_nodes += inner_path->disabled_nodes;
4322 : 510567 : disabled_nodes += outer_path->disabled_nodes;
4323 : :
4324 : : /* cost of source data */
9525 tgl@sss.pgh.pa.us 4325 : 510567 : startup_cost += outer_path->startup_cost;
4326 : 510567 : run_cost += outer_path->total_cost - outer_path->startup_cost;
4327 : 510567 : startup_cost += inner_path->total_cost;
4328 : :
4329 : : /*
4330 : : * Cost of computing hash function: must do it once per input tuple. We
4331 : : * charge one cpu_operator_cost for each column's hash function. Also,
4332 : : * tack on one cpu_tuple_cost per inner row, to model the costs of
4333 : : * inserting the row into the hashtable.
4334 : : *
4335 : : * XXX when a hashclause is more complex than a single operator, we really
4336 : : * should charge the extra eval costs of the left or right side, as
4337 : : * appropriate, here. This seems more work than it's worth at the moment.
4338 : : */
7006 4339 : 510567 : startup_cost += (cpu_operator_cost * num_hashclauses + cpu_tuple_cost)
4340 : 510567 : * inner_path_rows;
8448 4341 : 510567 : run_cost += cpu_operator_cost * num_hashclauses * outer_path_rows;
4342 : :
4343 : : /*
4344 : : * If this is a parallel hash build, then the value we have for
4345 : : * inner_rows_total currently refers only to the rows returned by each
4346 : : * participant. For shared hash table size estimation, we need the total
4347 : : * number, so we need to undo the division.
4348 : : */
3007 andres@anarazel.de 4349 [ + + ]: 510567 : if (parallel_hash)
4350 : 37621 : inner_path_rows_total *= get_parallel_divisor(inner_path);
4351 : :
4352 : : /*
4353 : : * Get hash table size that executor would use for inner relation.
4354 : : *
4355 : : * XXX for the moment, always assume that skew optimization will be
4356 : : * performed. As long as SKEW_HASH_MEM_PERCENT is small, it's not worth
4357 : : * trying to determine that for sure.
4358 : : *
4359 : : * XXX at some point it might be interesting to try to account for skew
4360 : : * optimization in the cost estimate, but for now, we don't.
4361 : : */
4362 : 510567 : ExecChooseHashTableSize(inner_path_rows_total,
3678 tgl@sss.pgh.pa.us 4363 : 510567 : inner_path->pathtarget->width,
4364 : : true, /* useskew */
4365 : : parallel_hash, /* try_combined_hash_mem */
4366 : : outer_path->parallel_workers,
4367 : : &space_allowed,
4368 : : &numbuckets,
4369 : : &numbatches,
4370 : : &num_skew_mcvs);
4371 : :
4372 : : /*
4373 : : * If inner relation is too big then we will need to "batch" the join,
4374 : : * which implies writing and reading most of the tuples to disk an extra
4375 : : * time. Charge seq_page_cost per page, since the I/O should be nice and
4376 : : * sequential. Writing the inner rel counts as startup cost, all the rest
4377 : : * as run cost.
4378 : : */
5161 4379 [ + + ]: 510567 : if (numbatches > 1)
4380 : : {
4381 : 2875 : double outerpages = page_size(outer_path_rows,
3678 4382 : 2875 : outer_path->pathtarget->width);
5161 4383 : 2875 : double innerpages = page_size(inner_path_rows,
3678 4384 : 2875 : inner_path->pathtarget->width);
4385 : :
5161 4386 : 2875 : startup_cost += seq_page_cost * innerpages;
4387 : 2875 : run_cost += seq_page_cost * (innerpages + 2 * outerpages);
4388 : : }
4389 : :
4390 : : /* CPU costs left for later */
4391 : :
4392 : : /* Public result fields */
571 rhaas@postgresql.org 4393 : 510567 : workspace->disabled_nodes = disabled_nodes;
5161 tgl@sss.pgh.pa.us 4394 : 510567 : workspace->startup_cost = startup_cost;
4395 : 510567 : workspace->total_cost = startup_cost + run_cost;
4396 : : /* Save private data for final_cost_hashjoin */
4397 : 510567 : workspace->run_cost = run_cost;
4398 : 510567 : workspace->numbuckets = numbuckets;
4399 : 510567 : workspace->numbatches = numbatches;
3007 andres@anarazel.de 4400 : 510567 : workspace->inner_rows_total = inner_path_rows_total;
5161 tgl@sss.pgh.pa.us 4401 : 510567 : }
4402 : :
4403 : : /*
4404 : : * final_cost_hashjoin
4405 : : * Final estimate of the cost and result size of a hashjoin path.
4406 : : *
4407 : : * Note: the numbatches estimate is also saved into 'path' for use later
4408 : : *
4409 : : * 'path' is already filled in except for the rows and cost fields and
4410 : : * num_batches
4411 : : * 'workspace' is the result from initial_cost_hashjoin
4412 : : * 'extra' contains miscellaneous information about the join
4413 : : */
4414 : : void
4415 : 254716 : final_cost_hashjoin(PlannerInfo *root, HashPath *path,
4416 : : JoinCostWorkspace *workspace,
4417 : : JoinPathExtraData *extra)
4418 : : {
4419 : 254716 : Path *outer_path = path->jpath.outerjoinpath;
4420 : 254716 : Path *inner_path = path->jpath.innerjoinpath;
4421 : 254716 : double outer_path_rows = outer_path->rows;
4422 : 254716 : double inner_path_rows = inner_path->rows;
3007 andres@anarazel.de 4423 : 254716 : double inner_path_rows_total = workspace->inner_rows_total;
5161 tgl@sss.pgh.pa.us 4424 : 254716 : List *hashclauses = path->path_hashclauses;
4425 : 254716 : Cost startup_cost = workspace->startup_cost;
4426 : 254716 : Cost run_cost = workspace->run_cost;
4427 : 254716 : int numbuckets = workspace->numbuckets;
4428 : 254716 : int numbatches = workspace->numbatches;
4429 : : Cost cpu_per_tuple;
4430 : : QualCost hash_qual_cost;
4431 : : QualCost qp_qual_cost;
4432 : : double hashjointuples;
4433 : : double virtualbuckets;
4434 : : Selectivity innerbucketsize;
4435 : : Selectivity innermcvfreq;
4436 : : ListCell *hcl;
4437 : :
4438 : : /* Set the number of disabled nodes. */
571 rhaas@postgresql.org 4439 : 254716 : path->jpath.path.disabled_nodes = workspace->disabled_nodes;
4440 : :
4441 : : /* Mark the path with the correct row estimate */
5078 tgl@sss.pgh.pa.us 4442 [ + + ]: 254716 : if (path->jpath.path.param_info)
4443 : 2667 : path->jpath.path.rows = path->jpath.path.param_info->ppi_rows;
4444 : : else
4445 : 252049 : path->jpath.path.rows = path->jpath.path.parent->rows;
4446 : :
4447 : : /* For partial paths, scale row estimate. */
3348 rhaas@postgresql.org 4448 [ + + ]: 254716 : if (path->jpath.path.parallel_workers > 0)
4449 : : {
3224 bruce@momjian.us 4450 : 52949 : double parallel_divisor = get_parallel_divisor(&path->jpath.path);
4451 : :
3287 rhaas@postgresql.org 4452 : 52949 : path->jpath.path.rows =
4453 : 52949 : clamp_row_est(path->jpath.path.rows / parallel_divisor);
4454 : : }
4455 : :
4456 : : /* mark the path with estimated # of batches */
6198 tgl@sss.pgh.pa.us 4457 : 254716 : path->num_batches = numbatches;
4458 : :
4459 : : /* store the total number of tuples (sum of partial row estimates) */
3007 andres@anarazel.de 4460 : 254716 : path->inner_rows_total = inner_path_rows_total;
4461 : :
4462 : : /* and compute the number of "virtual" buckets in the whole join */
3189 tgl@sss.pgh.pa.us 4463 : 254716 : virtualbuckets = (double) numbuckets * (double) numbatches;
4464 : :
4465 : : /*
4466 : : * Determine bucketsize fraction and MCV frequency for the inner relation.
4467 : : * We use the smallest bucketsize or MCV frequency estimated for any
4468 : : * individual hashclause; this is undoubtedly conservative.
4469 : : *
4470 : : * BUT: if inner relation has been unique-ified, we can assume it's good
4471 : : * for hashing. This is important both because it's the right answer, and
4472 : : * because we avoid contaminating the cache with a value that's wrong for
4473 : : * non-unique-ified paths.
4474 : : */
208 rguo@postgresql.org 4475 [ + + + + :GNC 254716 : if (RELATION_WAS_MADE_UNIQUE(inner_path->parent, extra->sjinfo,
+ + ]
4476 : : path->jpath.jointype))
4477 : : {
8447 tgl@sss.pgh.pa.us 4478 :CBC 2360 : innerbucketsize = 1.0 / virtualbuckets;
76 tgl@sss.pgh.pa.us 4479 :GNC 2360 : innermcvfreq = 1.0 / inner_path_rows_total;
4480 : : }
4481 : : else
4482 : : {
4483 : : List *otherclauses;
4484 : :
8447 tgl@sss.pgh.pa.us 4485 :CBC 252356 : innerbucketsize = 1.0;
3134 4486 : 252356 : innermcvfreq = 1.0;
4487 : :
4488 : : /* At first, try to estimate bucket size using extended statistics. */
370 akorotkov@postgresql 4489 : 252356 : otherclauses = estimate_multivariate_bucketsize(root,
4490 : : inner_path->parent,
4491 : : hashclauses,
4492 : : &innerbucketsize);
4493 : :
4494 : : /* Pass through the remaining clauses */
4495 [ + + + + : 525358 : foreach(hcl, otherclauses)
+ + ]
4496 : : {
3261 tgl@sss.pgh.pa.us 4497 : 273002 : RestrictInfo *restrictinfo = lfirst_node(RestrictInfo, hcl);
4498 : : Selectivity thisbucketsize;
4499 : : Selectivity thismcvfreq;
4500 : :
4501 : : /*
4502 : : * First we have to figure out which side of the hashjoin clause
4503 : : * is the inner side.
4504 : : *
4505 : : * Since we tend to visit the same clauses over and over when
4506 : : * planning a large query, we cache the bucket stats estimates in
4507 : : * the RestrictInfo node to avoid repeated lookups of statistics.
4508 : : */
8436 4509 [ + + ]: 273002 : if (bms_is_subset(restrictinfo->right_relids,
4510 : 273002 : inner_path->parent->relids))
4511 : : {
4512 : : /* righthand side is inner */
8447 4513 : 141590 : thisbucketsize = restrictinfo->right_bucketsize;
4514 [ + + ]: 141590 : if (thisbucketsize < 0)
4515 : : {
4516 : : /* not cached yet */
3134 4517 : 62705 : estimate_hash_bucket_stats(root,
4518 : 62705 : get_rightop(restrictinfo->clause),
4519 : : virtualbuckets,
4520 : : &restrictinfo->right_mcvfreq,
4521 : : &restrictinfo->right_bucketsize);
4522 : 62705 : thisbucketsize = restrictinfo->right_bucketsize;
4523 : : }
4524 : 141590 : thismcvfreq = restrictinfo->right_mcvfreq;
4525 : : }
4526 : : else
4527 : : {
8436 4528 [ - + ]: 131412 : Assert(bms_is_subset(restrictinfo->left_relids,
4529 : : inner_path->parent->relids));
4530 : : /* lefthand side is inner */
8447 4531 : 131412 : thisbucketsize = restrictinfo->left_bucketsize;
4532 [ + + ]: 131412 : if (thisbucketsize < 0)
4533 : : {
4534 : : /* not cached yet */
3134 4535 : 53279 : estimate_hash_bucket_stats(root,
4536 : 53279 : get_leftop(restrictinfo->clause),
4537 : : virtualbuckets,
4538 : : &restrictinfo->left_mcvfreq,
4539 : : &restrictinfo->left_bucketsize);
4540 : 53279 : thisbucketsize = restrictinfo->left_bucketsize;
4541 : : }
4542 : 131412 : thismcvfreq = restrictinfo->left_mcvfreq;
4543 : : }
4544 : :
8447 4545 [ + + ]: 273002 : if (innerbucketsize > thisbucketsize)
4546 : 205945 : innerbucketsize = thisbucketsize;
4547 : : /* Disregard zero for MCV freq, it means we have no data */
76 tgl@sss.pgh.pa.us 4548 [ + + + + ]:GNC 273002 : if (thismcvfreq > 0.0 && innermcvfreq > thismcvfreq)
3134 tgl@sss.pgh.pa.us 4549 :CBC 207278 : innermcvfreq = thismcvfreq;
4550 : : }
4551 : : }
4552 : :
4553 : : /*
4554 : : * If the bucket holding the inner MCV would exceed hash_mem, we don't
4555 : : * want to hash unless there is really no other alternative, so apply
4556 : : * disable_cost. (The executor normally copes with excessive memory usage
4557 : : * by splitting batches, but obviously it cannot separate equal values
4558 : : * that way, so it will be unable to drive the batch size below hash_mem
4559 : : * when this is true.)
4560 : : */
4561 : 254716 : if (relation_byte_size(clamp_row_est(inner_path_rows * innermcvfreq),
1694 4562 [ + + ]: 509432 : inner_path->pathtarget->width) > get_hash_memory_limit())
3134 4563 : 61 : startup_cost += disable_cost;
4564 : :
4565 : : /*
4566 : : * Compute cost of the hashquals and qpquals (other restriction clauses)
4567 : : * separately.
4568 : : */
5161 4569 : 254716 : cost_qual_eval(&hash_qual_cost, hashclauses, root);
4570 : 254716 : cost_qual_eval(&qp_qual_cost, path->jpath.joinrestrictinfo, root);
4571 : 254716 : qp_qual_cost.startup -= hash_qual_cost.startup;
4572 : 254716 : qp_qual_cost.per_tuple -= hash_qual_cost.per_tuple;
4573 : :
4574 : : /* CPU costs */
4575 : :
3264 4576 [ + + ]: 254716 : if (path->jpath.jointype == JOIN_SEMI ||
4577 [ + + ]: 251527 : path->jpath.jointype == JOIN_ANTI ||
4578 [ + + ]: 247418 : extra->inner_unique)
6154 4579 : 77113 : {
4580 : : double outer_matched_rows;
4581 : : Selectivity inner_scan_frac;
4582 : :
4583 : : /*
4584 : : * With a SEMI or ANTI join, or if the innerrel is known unique, the
4585 : : * executor will stop after the first match.
4586 : : *
4587 : : * For an outer-rel row that has at least one match, we can expect the
4588 : : * bucket scan to stop after a fraction 1/(match_count+1) of the
4589 : : * bucket's rows, if the matches are evenly distributed. Since they
4590 : : * probably aren't quite evenly distributed, we apply a fuzz factor of
4591 : : * 2.0 to that fraction. (If we used a larger fuzz factor, we'd have
4592 : : * to clamp inner_scan_frac to at most 1.0; but since match_count is
4593 : : * at least 1, no such clamp is needed now.)
4594 : : */
3264 4595 : 77113 : outer_matched_rows = rint(outer_path_rows * extra->semifactors.outer_match_frac);
4596 : 77113 : inner_scan_frac = 2.0 / (extra->semifactors.match_count + 1.0);
4597 : :
6154 4598 : 77113 : startup_cost += hash_qual_cost.startup;
4599 : 154226 : run_cost += hash_qual_cost.per_tuple * outer_matched_rows *
4600 : 77113 : clamp_row_est(inner_path_rows * innerbucketsize * inner_scan_frac) * 0.5;
4601 : :
4602 : : /*
4603 : : * For unmatched outer-rel rows, the picture is quite a lot different.
4604 : : * In the first place, there is no reason to assume that these rows
4605 : : * preferentially hit heavily-populated buckets; instead assume they
4606 : : * are uncorrelated with the inner distribution and so they see an
4607 : : * average bucket size of inner_path_rows / virtualbuckets. In the
4608 : : * second place, it seems likely that they will have few if any exact
4609 : : * hash-code matches and so very few of the tuples in the bucket will
4610 : : * actually require eval of the hash quals. We don't have any good
4611 : : * way to estimate how many will, but for the moment assume that the
4612 : : * effective cost per bucket entry is one-tenth what it is for
4613 : : * matchable tuples.
4614 : : */
4615 : 154226 : run_cost += hash_qual_cost.per_tuple *
4616 : 154226 : (outer_path_rows - outer_matched_rows) *
4617 : 77113 : clamp_row_est(inner_path_rows / virtualbuckets) * 0.05;
4618 : :
4619 : : /* Get # of tuples that will pass the basic join */
2801 4620 [ + + ]: 77113 : if (path->jpath.jointype == JOIN_ANTI)
6154 4621 : 4109 : hashjointuples = outer_path_rows - outer_matched_rows;
4622 : : else
2801 4623 : 73004 : hashjointuples = outer_matched_rows;
4624 : : }
4625 : : else
4626 : : {
4627 : : /*
4628 : : * The number of tuple comparisons needed is the number of outer
4629 : : * tuples times the typical number of tuples in a hash bucket, which
4630 : : * is the inner relation size times its bucketsize fraction. At each
4631 : : * one, we need to evaluate the hashjoin quals. But actually,
4632 : : * charging the full qual eval cost at each tuple is pessimistic,
4633 : : * since we don't evaluate the quals unless the hash values match
4634 : : * exactly. For lack of a better idea, halve the cost estimate to
4635 : : * allow for that.
4636 : : */
6154 4637 : 177603 : startup_cost += hash_qual_cost.startup;
4638 : 355206 : run_cost += hash_qual_cost.per_tuple * outer_path_rows *
4639 : 177603 : clamp_row_est(inner_path_rows * innerbucketsize) * 0.5;
4640 : :
4641 : : /*
4642 : : * Get approx # tuples passing the hashquals. We use
4643 : : * approx_tuple_count here because we need an estimate done with
4644 : : * JOIN_INNER semantics.
4645 : : */
4646 : 177603 : hashjointuples = approx_tuple_count(root, &path->jpath, hashclauses);
4647 : : }
4648 : :
4649 : : /*
4650 : : * For each tuple that gets through the hashjoin proper, we charge
4651 : : * cpu_tuple_cost plus the cost of evaluating additional restriction
4652 : : * clauses that are to be applied at the join. (This is pessimistic since
4653 : : * not all of the quals may get evaluated at each tuple.)
4654 : : */
8448 4655 : 254716 : startup_cost += qp_qual_cost.startup;
4656 : 254716 : cpu_per_tuple = cpu_tuple_cost + qp_qual_cost.per_tuple;
6420 4657 : 254716 : run_cost += cpu_per_tuple * hashjointuples;
4658 : :
4659 : : /* tlist eval costs are paid per output row, not per tuple scanned */
3678 4660 : 254716 : startup_cost += path->jpath.path.pathtarget->cost.startup;
4661 : 254716 : run_cost += path->jpath.path.pathtarget->cost.per_tuple * path->jpath.path.rows;
4662 : :
8448 4663 : 254716 : path->jpath.path.startup_cost = startup_cost;
4664 : 254716 : path->jpath.path.total_cost = startup_cost + run_cost;
9525 4665 : 254716 : }
4666 : :
4667 : :
4668 : : /*
4669 : : * cost_subplan
4670 : : * Figure the costs for a SubPlan (or initplan).
4671 : : *
4672 : : * Note: we could dig the subplan's Plan out of the root list, but in practice
4673 : : * all callers have it handy already, so we make them pass it.
4674 : : */
4675 : : void
6414 4676 : 24684 : cost_subplan(PlannerInfo *root, SubPlan *subplan, Plan *plan)
4677 : : {
4678 : : QualCost sp_cost;
4679 : :
4680 : : /*
4681 : : * Figure any cost for evaluating the testexpr.
4682 : : *
4683 : : * Usually, SubPlan nodes are built very early, before we have constructed
4684 : : * any RelOptInfos for the parent query level, which means the parent root
4685 : : * does not yet contain enough information to safely consult statistics.
4686 : : * Therefore, we pass root as NULL here. cost_qual_eval() is already
4687 : : * well-equipped to handle a NULL root.
4688 : : *
4689 : : * One exception is SubPlan nodes built for the initplans of MIN/MAX
4690 : : * aggregates from indexes (cf. SS_make_initplan_from_plan). In this
4691 : : * case, having a NULL root is safe because testexpr will be NULL.
4692 : : * Besides, an initplan will by definition not consult anything from the
4693 : : * parent plan.
4694 : : */
4695 : 24684 : cost_qual_eval(&sp_cost,
4696 : 24684 : make_ands_implicit((Expr *) subplan->testexpr),
4697 : : NULL);
4698 : :
4699 [ + + ]: 24684 : if (subplan->useHashTable)
4700 : : {
4701 : : /*
4702 : : * If we are using a hash table for the subquery outputs, then the
4703 : : * cost of evaluating the query is a one-time cost. We charge one
4704 : : * cpu_operator_cost per tuple for the work of loading the hashtable,
4705 : : * too.
4706 : : */
4707 : 1157 : sp_cost.startup += plan->total_cost +
4708 : 1157 : cpu_operator_cost * plan->plan_rows;
4709 : :
4710 : : /*
4711 : : * The per-tuple costs include the cost of evaluating the lefthand
4712 : : * expressions, plus the cost of probing the hashtable. We already
4713 : : * accounted for the lefthand expressions as part of the testexpr, and
4714 : : * will also have counted one cpu_operator_cost for each comparison
4715 : : * operator. That is probably too low for the probing cost, but it's
4716 : : * hard to make a better estimate, so live with it for now.
4717 : : */
4718 : : }
4719 : : else
4720 : : {
4721 : : /*
4722 : : * Otherwise we will be rescanning the subplan output on each
4723 : : * evaluation. We need to estimate how much of the output we will
4724 : : * actually need to scan. NOTE: this logic should agree with the
4725 : : * tuple_fraction estimates used by make_subplan() in
4726 : : * plan/subselect.c.
4727 : : */
4728 : 23527 : Cost plan_run_cost = plan->total_cost - plan->startup_cost;
4729 : :
4730 [ + + ]: 23527 : if (subplan->subLinkType == EXISTS_SUBLINK)
4731 : : {
4732 : : /* we only need to fetch 1 tuple; clamp to avoid zero divide */
3641 4733 : 1414 : sp_cost.per_tuple += plan_run_cost / clamp_row_est(plan->plan_rows);
4734 : : }
6414 4735 [ + + ]: 22113 : else if (subplan->subLinkType == ALL_SUBLINK ||
4736 [ + + ]: 22104 : subplan->subLinkType == ANY_SUBLINK)
4737 : : {
4738 : : /* assume we need 50% of the tuples */
4739 : 79 : sp_cost.per_tuple += 0.50 * plan_run_cost;
4740 : : /* also charge a cpu_operator_cost per row examined */
4741 : 79 : sp_cost.per_tuple += 0.50 * plan->plan_rows * cpu_operator_cost;
4742 : : }
4743 : : else
4744 : : {
4745 : : /* assume we need all tuples */
4746 : 22034 : sp_cost.per_tuple += plan_run_cost;
4747 : : }
4748 : :
4749 : : /*
4750 : : * Also account for subplan's startup cost. If the subplan is
4751 : : * uncorrelated or undirect correlated, AND its topmost node is one
4752 : : * that materializes its output, assume that we'll only need to pay
4753 : : * its startup cost once; otherwise assume we pay the startup cost
4754 : : * every time.
4755 : : */
4756 [ + + + + ]: 31273 : if (subplan->parParam == NIL &&
6028 4757 : 7746 : ExecMaterializesOutput(nodeTag(plan)))
6414 4758 : 599 : sp_cost.startup += plan->startup_cost;
4759 : : else
4760 : 22928 : sp_cost.per_tuple += plan->startup_cost;
4761 : : }
4762 : :
4763 : 24684 : subplan->startup_cost = sp_cost.startup;
4764 : 24684 : subplan->per_call_cost = sp_cost.per_tuple;
4765 : 24684 : }
4766 : :
4767 : :
4768 : : /*
4769 : : * cost_rescan
4770 : : * Given a finished Path, estimate the costs of rescanning it after
4771 : : * having done so the first time. For some Path types a rescan is
4772 : : * cheaper than an original scan (if no parameters change), and this
4773 : : * function embodies knowledge about that. The default is to return
4774 : : * the same costs stored in the Path. (Note that the cost estimates
4775 : : * actually stored in Paths are always for first scans.)
4776 : : *
4777 : : * This function is not currently intended to model effects such as rescans
4778 : : * being cheaper due to disk block caching; what we are concerned with is
4779 : : * plan types wherein the executor caches results explicitly, or doesn't
4780 : : * redo startup calculations, etc.
4781 : : */
4782 : : static void
6028 4783 : 1984886 : cost_rescan(PlannerInfo *root, Path *path,
4784 : : Cost *rescan_startup_cost, /* output parameters */
4785 : : Cost *rescan_total_cost)
4786 : : {
4787 [ + + + + : 1984886 : switch (path->pathtype)
+ + ]
4788 : : {
4789 : 32558 : case T_FunctionScan:
4790 : :
4791 : : /*
4792 : : * Currently, nodeFunctionscan.c always executes the function to
4793 : : * completion before returning any rows, and caches the results in
4794 : : * a tuplestore. So the function eval cost is all startup cost
4795 : : * and isn't paid over again on rescans. However, all run costs
4796 : : * will be paid over again.
4797 : : */
4798 : 32558 : *rescan_startup_cost = 0;
4799 : 32558 : *rescan_total_cost = path->total_cost - path->startup_cost;
4800 : 32558 : break;
4801 : 78919 : case T_HashJoin:
4802 : :
4803 : : /*
4804 : : * If it's a single-batch join, we don't need to rebuild the hash
4805 : : * table during a rescan.
4806 : : */
3518 4807 [ + - ]: 78919 : if (((HashPath *) path)->num_batches == 1)
4808 : : {
4809 : : /* Startup cost is exactly the cost of hash table building */
4810 : 78919 : *rescan_startup_cost = 0;
4811 : 78919 : *rescan_total_cost = path->total_cost - path->startup_cost;
4812 : : }
4813 : : else
4814 : : {
4815 : : /* Otherwise, no special treatment */
3518 tgl@sss.pgh.pa.us 4816 :UBC 0 : *rescan_startup_cost = path->startup_cost;
4817 : 0 : *rescan_total_cost = path->total_cost;
4818 : : }
6028 tgl@sss.pgh.pa.us 4819 :CBC 78919 : break;
4820 : 5033 : case T_CteScan:
4821 : : case T_WorkTableScan:
4822 : : {
4823 : : /*
4824 : : * These plan types materialize their final result in a
4825 : : * tuplestore or tuplesort object. So the rescan cost is only
4826 : : * cpu_tuple_cost per tuple, unless the result is large enough
4827 : : * to spill to disk.
4828 : : */
5161 4829 : 5033 : Cost run_cost = cpu_tuple_cost * path->rows;
4830 : 5033 : double nbytes = relation_byte_size(path->rows,
3189 4831 : 5033 : path->pathtarget->width);
408 4832 : 5033 : double work_mem_bytes = work_mem * (Size) 1024;
4833 : :
6028 4834 [ + + ]: 5033 : if (nbytes > work_mem_bytes)
4835 : : {
4836 : : /* It will spill, so account for re-read cost */
4837 : 184 : double npages = ceil(nbytes / BLCKSZ);
4838 : :
4839 : 184 : run_cost += seq_page_cost * npages;
4840 : : }
4841 : 5033 : *rescan_startup_cost = 0;
4842 : 5033 : *rescan_total_cost = run_cost;
4843 : : }
4844 : 5033 : break;
5868 4845 : 707983 : case T_Material:
4846 : : case T_Sort:
4847 : : {
4848 : : /*
4849 : : * These plan types not only materialize their results, but do
4850 : : * not implement qual filtering or projection. So they are
4851 : : * even cheaper to rescan than the ones above. We charge only
4852 : : * cpu_operator_cost per tuple. (Note: keep that in sync with
4853 : : * the run_cost charge in cost_sort, and also see comments in
4854 : : * cost_material before you change it.)
4855 : : */
5161 4856 : 707983 : Cost run_cost = cpu_operator_cost * path->rows;
4857 : 707983 : double nbytes = relation_byte_size(path->rows,
3189 4858 : 707983 : path->pathtarget->width);
408 4859 : 707983 : double work_mem_bytes = work_mem * (Size) 1024;
4860 : :
5868 4861 [ + + ]: 707983 : if (nbytes > work_mem_bytes)
4862 : : {
4863 : : /* It will spill, so account for re-read cost */
4864 : 6390 : double npages = ceil(nbytes / BLCKSZ);
4865 : :
4866 : 6390 : run_cost += seq_page_cost * npages;
4867 : : }
4868 : 707983 : *rescan_startup_cost = 0;
4869 : 707983 : *rescan_total_cost = run_cost;
4870 : : }
4871 : 707983 : break;
1705 drowley@postgresql.o 4872 : 175512 : case T_Memoize:
4873 : : /* All the hard work is done by cost_memoize_rescan */
4874 : 175512 : cost_memoize_rescan(root, (MemoizePath *) path,
4875 : : rescan_startup_cost, rescan_total_cost);
1808 4876 : 175512 : break;
6028 tgl@sss.pgh.pa.us 4877 : 984881 : default:
4878 : 984881 : *rescan_startup_cost = path->startup_cost;
4879 : 984881 : *rescan_total_cost = path->total_cost;
4880 : 984881 : break;
4881 : : }
4882 : 1984886 : }
4883 : :
4884 : :
4885 : : /*
4886 : : * cost_qual_eval
4887 : : * Estimate the CPU costs of evaluating a WHERE clause.
4888 : : * The input can be either an implicitly-ANDed list of boolean
4889 : : * expressions, or a list of RestrictInfo nodes. (The latter is
4890 : : * preferred since it allows caching of the results.)
4891 : : * The result includes both a one-time (startup) component,
4892 : : * and a per-evaluation component.
4893 : : *
4894 : : * Note: in some code paths root can be passed as NULL, resulting in
4895 : : * slightly worse estimates.
4896 : : */
4897 : : void
6961 4898 : 2715065 : cost_qual_eval(QualCost *cost, List *quals, PlannerInfo *root)
4899 : : {
4900 : : cost_qual_eval_context context;
4901 : : ListCell *l;
4902 : :
4903 : 2715065 : context.root = root;
4904 : 2715065 : context.total.startup = 0;
4905 : 2715065 : context.total.per_tuple = 0;
4906 : :
4907 : : /* We don't charge any cost for the implicit ANDing at top level ... */
4908 : :
9224 4909 [ + + + + : 5206394 : foreach(l, quals)
+ + ]
4910 : : {
9124 bruce@momjian.us 4911 : 2491329 : Node *qual = (Node *) lfirst(l);
4912 : :
6961 tgl@sss.pgh.pa.us 4913 : 2491329 : cost_qual_eval_walker(qual, &context);
4914 : : }
4915 : :
4916 : 2715065 : *cost = context.total;
9525 4917 : 2715065 : }
4918 : :
4919 : : /*
4920 : : * cost_qual_eval_node
4921 : : * As above, for a single RestrictInfo or expression.
4922 : : */
4923 : : void
6961 4924 : 1041633 : cost_qual_eval_node(QualCost *cost, Node *qual, PlannerInfo *root)
4925 : : {
4926 : : cost_qual_eval_context context;
4927 : :
4928 : 1041633 : context.root = root;
4929 : 1041633 : context.total.startup = 0;
4930 : 1041633 : context.total.per_tuple = 0;
4931 : :
4932 : 1041633 : cost_qual_eval_walker(qual, &context);
4933 : :
4934 : 1041633 : *cost = context.total;
6992 4935 : 1041633 : }
4936 : :
4937 : : static bool
6695 bruce@momjian.us 4938 : 5499440 : cost_qual_eval_walker(Node *node, cost_qual_eval_context *context)
4939 : : {
9525 tgl@sss.pgh.pa.us 4940 [ + + ]: 5499440 : if (node == NULL)
4941 : 53202 : return false;
4942 : :
4943 : : /*
4944 : : * RestrictInfo nodes contain an eval_cost field reserved for this
4945 : : * routine's use, so that it's not necessary to evaluate the qual clause's
4946 : : * cost more than once. If the clause's cost hasn't been computed yet,
4947 : : * the field's startup value will contain -1.
4948 : : */
6992 4949 [ + + ]: 5446238 : if (IsA(node, RestrictInfo))
4950 : : {
4951 : 2604117 : RestrictInfo *rinfo = (RestrictInfo *) node;
4952 : :
4953 [ + + ]: 2604117 : if (rinfo->eval_cost.startup < 0)
4954 : : {
4955 : : cost_qual_eval_context locContext;
4956 : :
6961 4957 : 341260 : locContext.root = context->root;
4958 : 341260 : locContext.total.startup = 0;
4959 : 341260 : locContext.total.per_tuple = 0;
4960 : :
4961 : : /*
4962 : : * For an OR clause, recurse into the marked-up tree so that we
4963 : : * set the eval_cost for contained RestrictInfos too.
4964 : : */
6992 4965 [ + + ]: 341260 : if (rinfo->orclause)
6961 4966 : 6390 : cost_qual_eval_walker((Node *) rinfo->orclause, &locContext);
4967 : : else
4968 : 334870 : cost_qual_eval_walker((Node *) rinfo->clause, &locContext);
4969 : :
4970 : : /*
4971 : : * If the RestrictInfo is marked pseudoconstant, it will be tested
4972 : : * only once, so treat its cost as all startup cost.
4973 : : */
6992 4974 [ + + ]: 341260 : if (rinfo->pseudoconstant)
4975 : : {
4976 : : /* count one execution during startup */
6961 4977 : 5191 : locContext.total.startup += locContext.total.per_tuple;
4978 : 5191 : locContext.total.per_tuple = 0;
4979 : : }
4980 : 341260 : rinfo->eval_cost = locContext.total;
4981 : : }
4982 : 2604117 : context->total.startup += rinfo->eval_cost.startup;
4983 : 2604117 : context->total.per_tuple += rinfo->eval_cost.per_tuple;
4984 : : /* do NOT recurse into children */
6992 4985 : 2604117 : return false;
4986 : : }
4987 : :
4988 : : /*
4989 : : * For each operator or function node in the given tree, we charge the
4990 : : * estimated execution cost given by pg_proc.procost (remember to multiply
4991 : : * this by cpu_operator_cost).
4992 : : *
4993 : : * Vars and Consts are charged zero, and so are boolean operators (AND,
4994 : : * OR, NOT). Simplistic, but a lot better than no model at all.
4995 : : *
4996 : : * Should we try to account for the possibility of short-circuit
4997 : : * evaluation of AND/OR? Probably *not*, because that would make the
4998 : : * results depend on the clause ordering, and we are not in any position
4999 : : * to expect that the current ordering of the clauses is the one that's
5000 : : * going to end up being used. The above per-RestrictInfo caching would
5001 : : * not mix well with trying to re-order clauses anyway.
5002 : : *
5003 : : * Another issue that is entirely ignored here is that if a set-returning
5004 : : * function is below top level in the tree, the functions/operators above
5005 : : * it will need to be evaluated multiple times. In practical use, such
5006 : : * cases arise so seldom as to not be worth the added complexity needed;
5007 : : * moreover, since our rowcount estimates for functions tend to be pretty
5008 : : * phony, the results would also be pretty phony.
5009 : : */
5010 [ + + ]: 2842121 : if (IsA(node, FuncExpr))
5011 : : {
2591 5012 : 184423 : add_function_cost(context->root, ((FuncExpr *) node)->funcid, node,
5013 : : &context->total);
5014 : : }
6992 5015 [ + + ]: 2657698 : else if (IsA(node, OpExpr) ||
5016 [ + + ]: 2285408 : IsA(node, DistinctExpr) ||
5017 [ + + ]: 2284716 : IsA(node, NullIfExpr))
5018 : : {
5019 : : /* rely on struct equivalence to treat these all alike */
5020 : 373174 : set_opfuncid((OpExpr *) node);
2591 5021 : 373174 : add_function_cost(context->root, ((OpExpr *) node)->opfuncid, node,
5022 : : &context->total);
5023 : : }
8295 5024 [ + + ]: 2284524 : else if (IsA(node, ScalarArrayOpExpr))
5025 : : {
7414 5026 : 23735 : ScalarArrayOpExpr *saop = (ScalarArrayOpExpr *) node;
7102 bruce@momjian.us 5027 : 23735 : Node *arraynode = (Node *) lsecond(saop->args);
5028 : : QualCost sacosts;
5029 : : QualCost hcosts;
801 tgl@sss.pgh.pa.us 5030 : 23735 : double estarraylen = estimate_array_length(context->root, arraynode);
5031 : :
6992 5032 : 23735 : set_sa_opfuncid(saop);
2591 5033 : 23735 : sacosts.startup = sacosts.per_tuple = 0;
5034 : 23735 : add_function_cost(context->root, saop->opfuncid, NULL,
5035 : : &sacosts);
5036 : :
1802 drowley@postgresql.o 5037 [ + + ]: 23735 : if (OidIsValid(saop->hashfuncid))
5038 : : {
5039 : : /* Handle costs for hashed ScalarArrayOpExpr */
5040 : 218 : hcosts.startup = hcosts.per_tuple = 0;
5041 : :
5042 : 218 : add_function_cost(context->root, saop->hashfuncid, NULL, &hcosts);
5043 : 218 : context->total.startup += sacosts.startup + hcosts.startup;
5044 : :
5045 : : /* Estimate the cost of building the hashtable. */
5046 : 218 : context->total.startup += estarraylen * hcosts.per_tuple;
5047 : :
5048 : : /*
5049 : : * XXX should we charge a little bit for sacosts.per_tuple when
5050 : : * building the table, or is it ok to assume there will be zero
5051 : : * hash collision?
5052 : : */
5053 : :
5054 : : /*
5055 : : * Charge for hashtable lookups. Charge a single hash and a
5056 : : * single comparison.
5057 : : */
5058 : 218 : context->total.per_tuple += hcosts.per_tuple + sacosts.per_tuple;
5059 : : }
5060 : : else
5061 : : {
5062 : : /*
5063 : : * Estimate that the operator will be applied to about half of the
5064 : : * array elements before the answer is determined.
5065 : : */
5066 : 23517 : context->total.startup += sacosts.startup;
5067 : 47034 : context->total.per_tuple += sacosts.per_tuple *
801 tgl@sss.pgh.pa.us 5068 : 23517 : estimate_array_length(context->root, arraynode) * 0.5;
5069 : : }
5070 : : }
5439 5071 [ + + ]: 2260789 : else if (IsA(node, Aggref) ||
5072 [ + + ]: 2223731 : IsA(node, WindowFunc))
5073 : : {
5074 : : /*
5075 : : * Aggref and WindowFunc nodes are (and should be) treated like Vars,
5076 : : * ie, zero execution cost in the current model, because they behave
5077 : : * essentially like Vars at execution. We disregard the costs of
5078 : : * their input expressions for the same reason. The actual execution
5079 : : * costs of the aggregate/window functions and their arguments have to
5080 : : * be factored into plan-node-specific costing of the Agg or WindowAgg
5081 : : * plan node.
5082 : : */
5083 : 39097 : return false; /* don't recurse into children */
5084 : : }
1455 5085 [ + + ]: 2221692 : else if (IsA(node, GroupingFunc))
5086 : : {
5087 : : /* Treat this as having cost 1 */
5088 : 218 : context->total.per_tuple += cpu_operator_cost;
5089 : 218 : return false; /* don't recurse into children */
5090 : : }
6858 5091 [ + + ]: 2221474 : else if (IsA(node, CoerceViaIO))
5092 : : {
5093 : 13255 : CoerceViaIO *iocoerce = (CoerceViaIO *) node;
5094 : : Oid iofunc;
5095 : : Oid typioparam;
5096 : : bool typisvarlena;
5097 : :
5098 : : /* check the result type's input function */
5099 : 13255 : getTypeInputInfo(iocoerce->resulttype,
5100 : : &iofunc, &typioparam);
2591 5101 : 13255 : add_function_cost(context->root, iofunc, NULL,
5102 : : &context->total);
5103 : : /* check the input type's output function */
6858 5104 : 13255 : getTypeOutputInfo(exprType((Node *) iocoerce->arg),
5105 : : &iofunc, &typisvarlena);
2591 5106 : 13255 : add_function_cost(context->root, iofunc, NULL,
5107 : : &context->total);
5108 : : }
6928 5109 [ + + ]: 2208219 : else if (IsA(node, ArrayCoerceExpr))
5110 : : {
5111 : 2779 : ArrayCoerceExpr *acoerce = (ArrayCoerceExpr *) node;
5112 : : QualCost perelemcost;
5113 : :
3088 5114 : 2779 : cost_qual_eval_node(&perelemcost, (Node *) acoerce->elemexpr,
5115 : : context->root);
5116 : 2779 : context->total.startup += perelemcost.startup;
5117 [ + + ]: 2779 : if (perelemcost.per_tuple > 0)
5118 : 33 : context->total.per_tuple += perelemcost.per_tuple *
801 5119 : 33 : estimate_array_length(context->root, (Node *) acoerce->arg);
5120 : : }
7382 5121 [ + + ]: 2205440 : else if (IsA(node, RowCompareExpr))
5122 : : {
5123 : : /* Conservatively assume we will check all the columns */
5124 : 129 : RowCompareExpr *rcexpr = (RowCompareExpr *) node;
5125 : : ListCell *lc;
5126 : :
6992 5127 [ + - + + : 414 : foreach(lc, rcexpr->opnos)
+ + ]
5128 : : {
6695 bruce@momjian.us 5129 : 285 : Oid opid = lfirst_oid(lc);
5130 : :
2591 tgl@sss.pgh.pa.us 5131 : 285 : add_function_cost(context->root, get_opcode(opid), NULL,
5132 : : &context->total);
5133 : : }
5134 : : }
3166 5135 [ + + ]: 2205311 : else if (IsA(node, MinMaxExpr) ||
1033 michael@paquier.xyz 5136 [ + + ]: 2205172 : IsA(node, SQLValueFunction) ||
3166 tgl@sss.pgh.pa.us 5137 [ + + ]: 2202651 : IsA(node, XmlExpr) ||
5138 [ + + ]: 2202300 : IsA(node, CoerceToDomain) ||
724 amitlan@postgresql.o 5139 [ + + ]: 2197371 : IsA(node, NextValueExpr) ||
5140 [ + + ]: 2197172 : IsA(node, JsonExpr))
5141 : : {
5142 : : /* Treat all these as having cost 1 */
3166 tgl@sss.pgh.pa.us 5143 : 9421 : context->total.per_tuple += cpu_operator_cost;
5144 : : }
8463 5145 [ - + ]: 2195890 : else if (IsA(node, SubLink))
5146 : : {
5147 : : /* This routine should not be applied to un-planned expressions */
8269 tgl@sss.pgh.pa.us 5148 [ # # ]:UBC 0 : elog(ERROR, "cannot handle unplanned sub-select");
5149 : : }
8492 tgl@sss.pgh.pa.us 5150 [ + + ]:CBC 2195890 : else if (IsA(node, SubPlan))
5151 : : {
5152 : : /*
5153 : : * A subplan node in an expression typically indicates that the
5154 : : * subplan will be executed on each evaluation, so charge accordingly.
5155 : : * (Sub-selects that can be executed as InitPlans have already been
5156 : : * removed from the expression.)
5157 : : */
8259 bruce@momjian.us 5158 : 24056 : SubPlan *subplan = (SubPlan *) node;
5159 : :
6414 tgl@sss.pgh.pa.us 5160 : 24056 : context->total.startup += subplan->startup_cost;
5161 : 24056 : context->total.per_tuple += subplan->per_call_cost;
5162 : :
5163 : : /*
5164 : : * We don't want to recurse into the testexpr, because it was already
5165 : : * counted in the SubPlan node's costs. So we're done.
5166 : : */
5167 : 24056 : return false;
5168 : : }
5169 [ + + ]: 2171834 : else if (IsA(node, AlternativeSubPlan))
5170 : : {
5171 : : /*
5172 : : * Arbitrarily use the first alternative plan for costing. (We should
5173 : : * certainly only include one alternative, and we don't yet have
5174 : : * enough information to know which one the executor is most likely to
5175 : : * use.)
5176 : : */
5177 : 1002 : AlternativeSubPlan *asplan = (AlternativeSubPlan *) node;
5178 : :
5179 : 1002 : return cost_qual_eval_walker((Node *) linitial(asplan->subplans),
5180 : : context);
5181 : : }
3678 5182 [ + + ]: 2170832 : else if (IsA(node, PlaceHolderVar))
5183 : : {
5184 : : /*
5185 : : * A PlaceHolderVar should be given cost zero when considering general
5186 : : * expression evaluation costs. The expense of doing the contained
5187 : : * expression is charged as part of the tlist eval costs of the scan
5188 : : * or join where the PHV is first computed (see set_rel_width and
5189 : : * add_placeholders_to_joinrel). If we charged it again here, we'd be
5190 : : * double-counting the cost for each level of plan that the PHV
5191 : : * bubbles up through. Hence, return without recursing into the
5192 : : * phexpr.
5193 : : */
5194 : 2882 : return false;
5195 : : }
5196 : :
5197 : : /* recurse into children */
472 peter@eisentraut.org 5198 : 2774866 : return expression_tree_walker(node, cost_qual_eval_walker, context);
5199 : : }
5200 : :
5201 : : /*
5202 : : * get_restriction_qual_cost
5203 : : * Compute evaluation costs of a baserel's restriction quals, plus any
5204 : : * movable join quals that have been pushed down to the scan.
5205 : : * Results are returned into *qpqual_cost.
5206 : : *
5207 : : * This is a convenience subroutine that works for seqscans and other cases
5208 : : * where all the given quals will be evaluated the hard way. It's not useful
5209 : : * for cost_index(), for example, where the index machinery takes care of
5210 : : * some of the quals. We assume baserestrictcost was previously set by
5211 : : * set_baserel_size_estimates().
5212 : : */
5213 : : static void
5078 tgl@sss.pgh.pa.us 5214 : 630213 : get_restriction_qual_cost(PlannerInfo *root, RelOptInfo *baserel,
5215 : : ParamPathInfo *param_info,
5216 : : QualCost *qpqual_cost)
5217 : : {
5218 [ + + ]: 630213 : if (param_info)
5219 : : {
5220 : : /* Include costs of pushed-down clauses */
5221 : 151761 : cost_qual_eval(qpqual_cost, param_info->ppi_clauses, root);
5222 : :
5223 : 151761 : qpqual_cost->startup += baserel->baserestrictcost.startup;
5224 : 151761 : qpqual_cost->per_tuple += baserel->baserestrictcost.per_tuple;
5225 : : }
5226 : : else
5227 : 478452 : *qpqual_cost = baserel->baserestrictcost;
5228 : 630213 : }
5229 : :
5230 : :
5231 : : /*
5232 : : * compute_semi_anti_join_factors
5233 : : * Estimate how much of the inner input a SEMI, ANTI, or inner_unique join
5234 : : * can be expected to scan.
5235 : : *
5236 : : * In a hash or nestloop SEMI/ANTI join, the executor will stop scanning
5237 : : * inner rows as soon as it finds a match to the current outer row.
5238 : : * The same happens if we have detected the inner rel is unique.
5239 : : * We should therefore adjust some of the cost components for this effect.
5240 : : * This function computes some estimates needed for these adjustments.
5241 : : * These estimates will be the same regardless of the particular paths used
5242 : : * for the outer and inner relation, so we compute these once and then pass
5243 : : * them to all the join cost estimation functions.
5244 : : *
5245 : : * Input parameters:
5246 : : * joinrel: join relation under consideration
5247 : : * outerrel: outer relation under consideration
5248 : : * innerrel: inner relation under consideration
5249 : : * jointype: if not JOIN_SEMI or JOIN_ANTI, we assume it's inner_unique
5250 : : * sjinfo: SpecialJoinInfo relevant to this join
5251 : : * restrictlist: join quals
5252 : : * Output parameters:
5253 : : * *semifactors is filled in (see pathnodes.h for field definitions)
5254 : : */
5255 : : void
5161 5256 : 134190 : compute_semi_anti_join_factors(PlannerInfo *root,
5257 : : RelOptInfo *joinrel,
5258 : : RelOptInfo *outerrel,
5259 : : RelOptInfo *innerrel,
5260 : : JoinType jointype,
5261 : : SpecialJoinInfo *sjinfo,
5262 : : List *restrictlist,
5263 : : SemiAntiJoinFactors *semifactors)
5264 : : {
5265 : : Selectivity jselec;
5266 : : Selectivity nselec;
5267 : : Selectivity avgmatch;
5268 : : SpecialJoinInfo norm_sjinfo;
5269 : : List *joinquals;
5270 : : ListCell *l;
5271 : :
5272 : : /*
5273 : : * In an ANTI join, we must ignore clauses that are "pushed down", since
5274 : : * those won't affect the match logic. In a SEMI join, we do not
5275 : : * distinguish joinquals from "pushed down" quals, so just use the whole
5276 : : * restrictinfo list. For other outer join types, we should consider only
5277 : : * non-pushed-down quals, so that this devolves to an IS_OUTER_JOIN check.
5278 : : */
3264 5279 [ + + ]: 134190 : if (IS_OUTER_JOIN(jointype))
5280 : : {
6154 5281 : 45899 : joinquals = NIL;
5161 5282 [ + + + + : 103801 : foreach(l, restrictlist)
+ + ]
5283 : : {
3261 5284 : 57902 : RestrictInfo *rinfo = lfirst_node(RestrictInfo, l);
5285 : :
2886 5286 [ + + + - ]: 57902 : if (!RINFO_IS_PUSHED_DOWN(rinfo, joinrel->relids))
6154 5287 : 51684 : joinquals = lappend(joinquals, rinfo);
5288 : : }
5289 : : }
5290 : : else
5161 5291 : 88291 : joinquals = restrictlist;
5292 : :
5293 : : /*
5294 : : * Get the JOIN_SEMI or JOIN_ANTI selectivity of the join clauses.
5295 : : */
6154 5296 [ + + ]: 134190 : jselec = clauselist_selectivity(root,
5297 : : joinquals,
5298 : : 0,
5299 : : (jointype == JOIN_ANTI) ? JOIN_ANTI : JOIN_SEMI,
5300 : : sjinfo);
5301 : :
5302 : : /*
5303 : : * Also get the normal inner-join selectivity of the join clauses.
5304 : : */
720 amitlan@postgresql.o 5305 : 134190 : init_dummy_sjinfo(&norm_sjinfo, outerrel->relids, innerrel->relids);
5306 : :
6154 tgl@sss.pgh.pa.us 5307 : 134190 : nselec = clauselist_selectivity(root,
5308 : : joinquals,
5309 : : 0,
5310 : : JOIN_INNER,
5311 : : &norm_sjinfo);
5312 : :
5313 : : /* Avoid leaking a lot of ListCells */
3264 5314 [ + + ]: 134190 : if (IS_OUTER_JOIN(jointype))
6154 5315 : 45899 : list_free(joinquals);
5316 : :
5317 : : /*
5318 : : * jselec can be interpreted as the fraction of outer-rel rows that have
5319 : : * any matches (this is true for both SEMI and ANTI cases). And nselec is
5320 : : * the fraction of the Cartesian product that matches. So, the average
5321 : : * number of matches for each outer-rel row that has at least one match is
5322 : : * nselec * inner_rows / jselec.
5323 : : *
5324 : : * Note: it is correct to use the inner rel's "rows" count here, even
5325 : : * though we might later be considering a parameterized inner path with
5326 : : * fewer rows. This is because we have included all the join clauses in
5327 : : * the selectivity estimate.
5328 : : */
5329 [ + + ]: 134190 : if (jselec > 0) /* protect against zero divide */
5330 : : {
5161 5331 : 133920 : avgmatch = nselec * innerrel->rows / jselec;
5332 : : /* Clamp to sane range */
6154 5333 [ + + ]: 133920 : avgmatch = Max(1.0, avgmatch);
5334 : : }
5335 : : else
5336 : 270 : avgmatch = 1.0;
5337 : :
5161 5338 : 134190 : semifactors->outer_match_frac = jselec;
5339 : 134190 : semifactors->match_count = avgmatch;
5340 : 134190 : }
5341 : :
5342 : : /*
5343 : : * has_indexed_join_quals
5344 : : * Check whether all the joinquals of a nestloop join are used as
5345 : : * inner index quals.
5346 : : *
5347 : : * If the inner path of a SEMI/ANTI join is an indexscan (including bitmap
5348 : : * indexscan) that uses all the joinquals as indexquals, we can assume that an
5349 : : * unmatched outer tuple is cheap to process, whereas otherwise it's probably
5350 : : * expensive.
5351 : : */
5352 : : static bool
1680 peter@eisentraut.org 5353 : 571972 : has_indexed_join_quals(NestPath *path)
5354 : : {
5355 : 571972 : JoinPath *joinpath = &path->jpath;
5078 tgl@sss.pgh.pa.us 5356 : 571972 : Relids joinrelids = joinpath->path.parent->relids;
5357 : 571972 : Path *innerpath = joinpath->innerjoinpath;
5358 : : List *indexclauses;
5359 : : bool found_one;
5360 : : ListCell *lc;
5361 : :
5362 : : /* If join still has quals to evaluate, it's not fast */
5363 [ + + ]: 571972 : if (joinpath->joinrestrictinfo != NIL)
5364 : 412710 : return false;
5365 : : /* Nor if the inner path isn't parameterized at all */
5366 [ + + ]: 159262 : if (innerpath->param_info == NULL)
5367 : 1650 : return false;
5368 : :
5369 : : /* Find the indexclauses list for the inner scan */
5370 [ + + + ]: 157612 : switch (innerpath->pathtype)
5371 : : {
5372 : 96006 : case T_IndexScan:
5373 : : case T_IndexOnlyScan:
5374 : 96006 : indexclauses = ((IndexPath *) innerpath)->indexclauses;
5375 : 96006 : break;
5376 : 147 : case T_BitmapHeapScan:
5377 : : {
5378 : : /* Accept only a simple bitmap scan, not AND/OR cases */
5026 bruce@momjian.us 5379 : 147 : Path *bmqual = ((BitmapHeapPath *) innerpath)->bitmapqual;
5380 : :
5381 [ + + ]: 147 : if (IsA(bmqual, IndexPath))
5382 : 123 : indexclauses = ((IndexPath *) bmqual)->indexclauses;
5383 : : else
5384 : 24 : return false;
5385 : 123 : break;
5386 : : }
5078 tgl@sss.pgh.pa.us 5387 : 61459 : default:
5388 : :
5389 : : /*
5390 : : * If it's not a simple indexscan, it probably doesn't run quickly
5391 : : * for zero rows out, even if it's a parameterized path using all
5392 : : * the joinquals.
5393 : : */
5161 5394 : 61459 : return false;
5395 : : }
5396 : :
5397 : : /*
5398 : : * Examine the inner path's param clauses. Any that are from the outer
5399 : : * path must be found in the indexclauses list, either exactly or in an
5400 : : * equivalent form generated by equivclass.c. Also, we must find at least
5401 : : * one such clause, else it's a clauseless join which isn't fast.
5402 : : */
5078 5403 : 96129 : found_one = false;
5404 [ + - + + : 189236 : foreach(lc, innerpath->param_info->ppi_clauses)
+ + ]
5405 : : {
5406 : 98710 : RestrictInfo *rinfo = (RestrictInfo *) lfirst(lc);
5407 : :
5408 [ + + ]: 98710 : if (join_clause_is_movable_into(rinfo,
5409 : 98710 : innerpath->parent->relids,
5410 : : joinrelids))
5411 : : {
2591 5412 [ + + ]: 98434 : if (!is_redundant_with_indexclauses(rinfo, indexclauses))
5078 5413 : 5603 : return false;
5414 : 92831 : found_one = true;
5415 : : }
5416 : : }
5417 : 90526 : return found_one;
5418 : : }
5419 : :
5420 : :
5421 : : /*
5422 : : * approx_tuple_count
5423 : : * Quick-and-dirty estimation of the number of join rows passing
5424 : : * a set of qual conditions.
5425 : : *
5426 : : * The quals can be either an implicitly-ANDed list of boolean expressions,
5427 : : * or a list of RestrictInfo nodes (typically the latter).
5428 : : *
5429 : : * We intentionally compute the selectivity under JOIN_INNER rules, even
5430 : : * if it's some type of outer join. This is appropriate because we are
5431 : : * trying to figure out how many tuples pass the initial merge or hash
5432 : : * join step.
5433 : : *
5434 : : * This is quick-and-dirty because we bypass clauselist_selectivity, and
5435 : : * simply multiply the independent clause selectivities together. Now
5436 : : * clauselist_selectivity often can't do any better than that anyhow, but
5437 : : * for some situations (such as range constraints) it is smarter. However,
5438 : : * we can't effectively cache the results of clauselist_selectivity, whereas
5439 : : * the individual clause selectivities can be and are cached.
5440 : : *
5441 : : * Since we are only using the results to estimate how many potential
5442 : : * output tuples are generated and passed through qpqual checking, it
5443 : : * seems OK to live with the approximation.
5444 : : */
5445 : : static double
6246 5446 : 439008 : approx_tuple_count(PlannerInfo *root, JoinPath *path, List *quals)
5447 : : {
5448 : : double tuples;
5161 5449 : 439008 : double outer_tuples = path->outerjoinpath->rows;
5450 : 439008 : double inner_tuples = path->innerjoinpath->rows;
5451 : : SpecialJoinInfo sjinfo;
6420 5452 : 439008 : Selectivity selec = 1.0;
5453 : : ListCell *l;
5454 : :
5455 : : /*
5456 : : * Make up a SpecialJoinInfo for JOIN_INNER semantics.
5457 : : */
720 amitlan@postgresql.o 5458 : 439008 : init_dummy_sjinfo(&sjinfo, path->outerjoinpath->parent->relids,
5459 : 439008 : path->innerjoinpath->parent->relids);
5460 : :
5461 : : /* Get the approximate selectivity */
9049 tgl@sss.pgh.pa.us 5462 [ + + + + : 931292 : foreach(l, quals)
+ + ]
5463 : : {
5464 : 492284 : Node *qual = (Node *) lfirst(l);
5465 : :
5466 : : /* Note that clause_selectivity will be able to cache its result */
3265 simon@2ndQuadrant.co 5467 : 492284 : selec *= clause_selectivity(root, qual, 0, JOIN_INNER, &sjinfo);
5468 : : }
5469 : :
5470 : : /* Apply it to the input relation sizes */
6246 tgl@sss.pgh.pa.us 5471 : 439008 : tuples = selec * outer_tuples * inner_tuples;
5472 : :
6420 5473 : 439008 : return clamp_row_est(tuples);
5474 : : }
5475 : :
5476 : :
5477 : : /*
5478 : : * set_baserel_size_estimates
5479 : : * Set the size estimates for the given base relation.
5480 : : *
5481 : : * The rel's targetlist and restrictinfo list must have been constructed
5482 : : * already, and rel->tuples must be set.
5483 : : *
5484 : : * We set the following fields of the rel node:
5485 : : * rows: the estimated number of output tuples (after applying
5486 : : * restriction clauses).
5487 : : * width: the estimated average output tuple width in bytes.
5488 : : * baserestrictcost: estimated cost of evaluating baserestrictinfo clauses.
5489 : : */
5490 : : void
7588 5491 : 290007 : set_baserel_size_estimates(PlannerInfo *root, RelOptInfo *rel)
5492 : : {
5493 : : double nrows;
5494 : :
5495 : : /* Should only be applied to base relations */
8436 5496 [ - + ]: 290007 : Assert(rel->relid > 0);
5497 : :
8105 5498 : 579999 : nrows = rel->tuples *
8106 5499 : 290007 : clauselist_selectivity(root,
5500 : : rel->baserestrictinfo,
5501 : : 0,
5502 : : JOIN_INNER,
5503 : : NULL);
5504 : :
8105 5505 : 289992 : rel->rows = clamp_row_est(nrows);
5506 : :
6961 5507 : 289992 : cost_qual_eval(&rel->baserestrictcost, rel->baserestrictinfo, root);
5508 : :
9562 5509 : 289992 : set_rel_width(root, rel);
10841 scrappy@hub.org 5510 : 289992 : }
5511 : :
5512 : : /*
5513 : : * get_parameterized_baserel_size
5514 : : * Make a size estimate for a parameterized scan of a base relation.
5515 : : *
5516 : : * 'param_clauses' lists the additional join clauses to be used.
5517 : : *
5518 : : * set_baserel_size_estimates must have been applied already.
5519 : : */
5520 : : double
5078 tgl@sss.pgh.pa.us 5521 : 96131 : get_parameterized_baserel_size(PlannerInfo *root, RelOptInfo *rel,
5522 : : List *param_clauses)
5523 : : {
5524 : : List *allclauses;
5525 : : double nrows;
5526 : :
5527 : : /*
5528 : : * Estimate the number of rows returned by the parameterized scan, knowing
5529 : : * that it will apply all the extra join clauses as well as the rel's own
5530 : : * restriction clauses. Note that we force the clauses to be treated as
5531 : : * non-join clauses during selectivity estimation.
5532 : : */
2407 5533 : 96131 : allclauses = list_concat_copy(param_clauses, rel->baserestrictinfo);
5078 5534 : 192262 : nrows = rel->tuples *
5535 : 96131 : clauselist_selectivity(root,
5536 : : allclauses,
3189 5537 : 96131 : rel->relid, /* do not use 0! */
5538 : : JOIN_INNER,
5539 : : NULL);
5078 5540 : 96131 : nrows = clamp_row_est(nrows);
5541 : : /* For safety, make sure result is not more than the base estimate */
5542 [ - + ]: 96131 : if (nrows > rel->rows)
5078 tgl@sss.pgh.pa.us 5543 :UBC 0 : nrows = rel->rows;
5078 tgl@sss.pgh.pa.us 5544 :CBC 96131 : return nrows;
5545 : : }
5546 : :
5547 : : /*
5548 : : * set_joinrel_size_estimates
5549 : : * Set the size estimates for the given join relation.
5550 : : *
5551 : : * The rel's targetlist must have been constructed already, and a
5552 : : * restriction clause list that matches the given component rels must
5553 : : * be provided.
5554 : : *
5555 : : * Since there is more than one way to make a joinrel for more than two
5556 : : * base relations, the results we get here could depend on which component
5557 : : * rel pair is provided. In theory we should get the same answers no matter
5558 : : * which pair is provided; in practice, since the selectivity estimation
5559 : : * routines don't handle all cases equally well, we might not. But there's
5560 : : * not much to be done about it. (Would it make sense to repeat the
5561 : : * calculations for each pair of input rels that's encountered, and somehow
5562 : : * average the results? Probably way more trouble than it's worth, and
5563 : : * anyway we must keep the rowcount estimate the same for all paths for the
5564 : : * joinrel.)
5565 : : *
5566 : : * We set only the rows field here. The reltarget field was already set by
5567 : : * build_joinrel_tlist, and baserestrictcost is not used for join rels.
5568 : : */
5569 : : void
7588 5570 : 147408 : set_joinrel_size_estimates(PlannerInfo *root, RelOptInfo *rel,
5571 : : RelOptInfo *outer_rel,
5572 : : RelOptInfo *inner_rel,
5573 : : SpecialJoinInfo *sjinfo,
5574 : : List *restrictlist)
5575 : : {
5161 5576 : 147408 : rel->rows = calc_joinrel_size_estimate(root,
5577 : : rel,
5578 : : outer_rel,
5579 : : inner_rel,
5580 : : outer_rel->rows,
5581 : : inner_rel->rows,
5582 : : sjinfo,
5583 : : restrictlist);
5584 : 147408 : }
5585 : :
5586 : : /*
5587 : : * get_parameterized_joinrel_size
5588 : : * Make a size estimate for a parameterized scan of a join relation.
5589 : : *
5590 : : * 'rel' is the joinrel under consideration.
5591 : : * 'outer_path', 'inner_path' are (probably also parameterized) Paths that
5592 : : * produce the relations being joined.
5593 : : * 'sjinfo' is any SpecialJoinInfo relevant to this join.
5594 : : * 'restrict_clauses' lists the join clauses that need to be applied at the
5595 : : * join node (including any movable clauses that were moved down to this join,
5596 : : * and not including any movable clauses that were pushed down into the
5597 : : * child paths).
5598 : : *
5599 : : * set_joinrel_size_estimates must have been applied already.
5600 : : */
5601 : : double
5078 5602 : 6728 : get_parameterized_joinrel_size(PlannerInfo *root, RelOptInfo *rel,
5603 : : Path *outer_path,
5604 : : Path *inner_path,
5605 : : SpecialJoinInfo *sjinfo,
5606 : : List *restrict_clauses)
5607 : : {
5608 : : double nrows;
5609 : :
5610 : : /*
5611 : : * Estimate the number of rows returned by the parameterized join as the
5612 : : * sizes of the input paths times the selectivity of the clauses that have
5613 : : * ended up at this join node.
5614 : : *
5615 : : * As with set_joinrel_size_estimates, the rowcount estimate could depend
5616 : : * on the pair of input paths provided, though ideally we'd get the same
5617 : : * estimate for any pair with the same parameterization.
5618 : : */
5619 : 6728 : nrows = calc_joinrel_size_estimate(root,
5620 : : rel,
5621 : : outer_path->parent,
5622 : : inner_path->parent,
5623 : : outer_path->rows,
5624 : : inner_path->rows,
5625 : : sjinfo,
5626 : : restrict_clauses);
5627 : : /* For safety, make sure result is not more than the base estimate */
5628 [ + + ]: 6728 : if (nrows > rel->rows)
5629 : 314 : nrows = rel->rows;
5630 : 6728 : return nrows;
5631 : : }
5632 : :
5633 : : /*
5634 : : * calc_joinrel_size_estimate
5635 : : * Workhorse for set_joinrel_size_estimates and
5636 : : * get_parameterized_joinrel_size.
5637 : : *
5638 : : * outer_rel/inner_rel are the relations being joined, but they should be
5639 : : * assumed to have sizes outer_rows/inner_rows; those numbers might be less
5640 : : * than what rel->rows says, when we are considering parameterized paths.
5641 : : */
5642 : : static double
5161 5643 : 154136 : calc_joinrel_size_estimate(PlannerInfo *root,
5644 : : RelOptInfo *joinrel,
5645 : : RelOptInfo *outer_rel,
5646 : : RelOptInfo *inner_rel,
5647 : : double outer_rows,
5648 : : double inner_rows,
5649 : : SpecialJoinInfo *sjinfo,
5650 : : List *restrictlist)
5651 : : {
6422 5652 : 154136 : JoinType jointype = sjinfo->jointype;
5653 : : Selectivity fkselec;
5654 : : Selectivity jselec;
5655 : : Selectivity pselec;
5656 : : double nrows;
5657 : :
5658 : : /*
5659 : : * Compute joinclause selectivity. Note that we are only considering
5660 : : * clauses that become restriction clauses at this join level; we are not
5661 : : * double-counting them because they were not considered in estimating the
5662 : : * sizes of the component rels.
5663 : : *
5664 : : * First, see whether any of the joinclauses can be matched to known FK
5665 : : * constraints. If so, drop those clauses from the restrictlist, and
5666 : : * instead estimate their selectivity using FK semantics. (We do this
5667 : : * without regard to whether said clauses are local or "pushed down".
5668 : : * Probably, an FK-matching clause could never be seen as pushed down at
5669 : : * an outer join, since it would be strict and hence would be grounds for
5670 : : * join strength reduction.) fkselec gets the net selectivity for
5671 : : * FK-matching clauses, or 1.0 if there are none.
5672 : : */
3557 5673 : 154136 : fkselec = get_foreign_key_join_selectivity(root,
5674 : : outer_rel->relids,
5675 : : inner_rel->relids,
5676 : : sjinfo,
5677 : : &restrictlist);
5678 : :
5679 : : /*
5680 : : * For an outer join, we have to distinguish the selectivity of the join's
5681 : : * own clauses (JOIN/ON conditions) from any clauses that were "pushed
5682 : : * down". For inner joins we just count them all as joinclauses.
5683 : : */
7065 5684 [ + + ]: 154136 : if (IS_OUTER_JOIN(jointype))
5685 : : {
5686 : 47829 : List *joinquals = NIL;
5687 : 47829 : List *pushedquals = NIL;
5688 : : ListCell *l;
5689 : :
5690 : : /* Grovel through the clauses to separate into two lists */
5691 [ + + + + : 109940 : foreach(l, restrictlist)
+ + ]
5692 : : {
3261 5693 : 62111 : RestrictInfo *rinfo = lfirst_node(RestrictInfo, l);
5694 : :
2886 5695 [ + + + + ]: 62111 : if (RINFO_IS_PUSHED_DOWN(rinfo, joinrel->relids))
7065 5696 : 4053 : pushedquals = lappend(pushedquals, rinfo);
5697 : : else
5698 : 58058 : joinquals = lappend(joinquals, rinfo);
5699 : : }
5700 : :
5701 : : /* Get the separate selectivities */
3568 5702 : 47829 : jselec = clauselist_selectivity(root,
5703 : : joinquals,
5704 : : 0,
5705 : : jointype,
5706 : : sjinfo);
7065 5707 : 47829 : pselec = clauselist_selectivity(root,
5708 : : pushedquals,
5709 : : 0,
5710 : : jointype,
5711 : : sjinfo);
5712 : :
5713 : : /* Avoid leaking a lot of ListCells */
5714 : 47829 : list_free(joinquals);
5715 : 47829 : list_free(pushedquals);
5716 : : }
5717 : : else
5718 : : {
3568 5719 : 106307 : jselec = clauselist_selectivity(root,
5720 : : restrictlist,
5721 : : 0,
5722 : : jointype,
5723 : : sjinfo);
7065 5724 : 106307 : pselec = 0.0; /* not used, keep compiler quiet */
5725 : : }
5726 : :
5727 : : /*
5728 : : * Basically, we multiply size of Cartesian product by selectivity.
5729 : : *
5730 : : * If we are doing an outer join, take that into account: the joinqual
5731 : : * selectivity has to be clamped using the knowledge that the output must
5732 : : * be at least as large as the non-nullable input. However, any
5733 : : * pushed-down quals are applied after the outer join, so their
5734 : : * selectivity applies fully.
5735 : : *
5736 : : * For JOIN_SEMI and JOIN_ANTI, the selectivity is defined as the fraction
5737 : : * of LHS rows that have matches, and we apply that straightforwardly.
5738 : : */
9158 5739 [ + + + + : 154136 : switch (jointype)
+ - ]
5740 : : {
5741 : 102163 : case JOIN_INNER:
3557 5742 : 102163 : nrows = outer_rows * inner_rows * fkselec * jselec;
5743 : : /* pselec not used */
9158 5744 : 102163 : break;
5745 : 42550 : case JOIN_LEFT:
3557 5746 : 42550 : nrows = outer_rows * inner_rows * fkselec * jselec;
5161 5747 [ + + ]: 42550 : if (nrows < outer_rows)
5748 : 19408 : nrows = outer_rows;
7065 5749 : 42550 : nrows *= pselec;
9158 5750 : 42550 : break;
5751 : 876 : case JOIN_FULL:
3557 5752 : 876 : nrows = outer_rows * inner_rows * fkselec * jselec;
5161 5753 [ + + ]: 876 : if (nrows < outer_rows)
5754 : 596 : nrows = outer_rows;
5755 [ + + ]: 876 : if (nrows < inner_rows)
5756 : 66 : nrows = inner_rows;
7065 5757 : 876 : nrows *= pselec;
9158 5758 : 876 : break;
6422 5759 : 4144 : case JOIN_SEMI:
3557 5760 : 4144 : nrows = outer_rows * fkselec * jselec;
5761 : : /* pselec not used */
8455 5762 : 4144 : break;
6422 5763 : 4403 : case JOIN_ANTI:
3557 5764 : 4403 : nrows = outer_rows * (1.0 - fkselec * jselec);
6422 5765 : 4403 : nrows *= pselec;
8455 5766 : 4403 : break;
9158 tgl@sss.pgh.pa.us 5767 :UBC 0 : default:
5768 : : /* other values not expected here */
8269 5769 [ # # ]: 0 : elog(ERROR, "unrecognized join type: %d", (int) jointype);
5770 : : nrows = 0; /* keep compiler quiet */
5771 : : break;
5772 : : }
5773 : :
5161 tgl@sss.pgh.pa.us 5774 :CBC 154136 : return clamp_row_est(nrows);
5775 : : }
5776 : :
5777 : : /*
5778 : : * get_foreign_key_join_selectivity
5779 : : * Estimate join selectivity for foreign-key-related clauses.
5780 : : *
5781 : : * Remove any clauses that can be matched to FK constraints from *restrictlist,
5782 : : * and return a substitute estimate of their selectivity. 1.0 is returned
5783 : : * when there are no such clauses.
5784 : : *
5785 : : * The reason for treating such clauses specially is that we can get better
5786 : : * estimates this way than by relying on clauselist_selectivity(), especially
5787 : : * for multi-column FKs where that function's assumption that the clauses are
5788 : : * independent falls down badly. But even with single-column FKs, we may be
5789 : : * able to get a better answer when the pg_statistic stats are missing or out
5790 : : * of date.
5791 : : */
5792 : : static Selectivity
3557 5793 : 154136 : get_foreign_key_join_selectivity(PlannerInfo *root,
5794 : : Relids outer_relids,
5795 : : Relids inner_relids,
5796 : : SpecialJoinInfo *sjinfo,
5797 : : List **restrictlist)
5798 : : {
5799 : 154136 : Selectivity fkselec = 1.0;
5800 : 154136 : JoinType jointype = sjinfo->jointype;
5801 : 154136 : List *worklist = *restrictlist;
5802 : : ListCell *lc;
5803 : :
5804 : : /* Consider each FK constraint that is known to match the query */
5805 [ + + + + : 155229 : foreach(lc, root->fkey_list)
+ + ]
5806 : : {
5807 : 1093 : ForeignKeyOptInfo *fkinfo = (ForeignKeyOptInfo *) lfirst(lc);
5808 : : bool ref_is_outer;
5809 : : List *removedlist;
5810 : : ListCell *cell;
5811 : :
5812 : : /*
5813 : : * This FK is not relevant unless it connects a baserel on one side of
5814 : : * this join to a baserel on the other side.
5815 : : */
5816 [ + + + + ]: 1990 : if (bms_is_member(fkinfo->con_relid, outer_relids) &&
5817 : 897 : bms_is_member(fkinfo->ref_relid, inner_relids))
5818 : 780 : ref_is_outer = false;
5819 [ + + + + ]: 503 : else if (bms_is_member(fkinfo->ref_relid, outer_relids) &&
5820 : 190 : bms_is_member(fkinfo->con_relid, inner_relids))
5821 : 65 : ref_is_outer = true;
5822 : : else
5823 : 248 : continue;
5824 : :
5825 : : /*
5826 : : * If we're dealing with a semi/anti join, and the FK's referenced
5827 : : * relation is on the outside, then knowledge of the FK doesn't help
5828 : : * us figure out what we need to know (which is the fraction of outer
5829 : : * rows that have matches). On the other hand, if the referenced rel
5830 : : * is on the inside, then all outer rows must have matches in the
5831 : : * referenced table (ignoring nulls). But any restriction or join
5832 : : * clauses that filter that table will reduce the fraction of matches.
5833 : : * We can account for restriction clauses, but it's too hard to guess
5834 : : * how many table rows would get through a join that's inside the RHS.
5835 : : * Hence, if either case applies, punt and ignore the FK.
5836 : : */
3191 5837 [ + - + + : 845 : if ((jointype == JOIN_SEMI || jointype == JOIN_ANTI) &&
+ + ]
5838 [ - + ]: 524 : (ref_is_outer || bms_membership(inner_relids) != BMS_SINGLETON))
5839 : 6 : continue;
5840 : :
5841 : : /*
5842 : : * Modify the restrictlist by removing clauses that match the FK (and
5843 : : * putting them into removedlist instead). It seems unsafe to modify
5844 : : * the originally-passed List structure, so we make a shallow copy the
5845 : : * first time through.
5846 : : */
3557 5847 [ + + ]: 839 : if (worklist == *restrictlist)
5848 : 727 : worklist = list_copy(worklist);
5849 : :
5850 : 839 : removedlist = NIL;
2435 5851 [ + + + + : 1746 : foreach(cell, worklist)
+ + ]
5852 : : {
3557 5853 : 907 : RestrictInfo *rinfo = (RestrictInfo *) lfirst(cell);
5854 : 907 : bool remove_it = false;
5855 : : int i;
5856 : :
5857 : : /* Drop this clause if it matches any column of the FK */
5858 [ + + ]: 1130 : for (i = 0; i < fkinfo->nkeys; i++)
5859 : : {
5860 [ + + ]: 1115 : if (rinfo->parent_ec)
5861 : : {
5862 : : /*
5863 : : * EC-derived clauses can only match by EC. It is okay to
5864 : : * consider any clause derived from the same EC as
5865 : : * matching the FK: even if equivclass.c chose to generate
5866 : : * a clause equating some other pair of Vars, it could
5867 : : * have generated one equating the FK's Vars. So for
5868 : : * purposes of estimation, we can act as though it did so.
5869 : : *
5870 : : * Note: checking parent_ec is a bit of a cheat because
5871 : : * there are EC-derived clauses that don't have parent_ec
5872 : : * set; but such clauses must compare expressions that
5873 : : * aren't just Vars, so they cannot match the FK anyway.
5874 : : */
5875 [ + + ]: 212 : if (fkinfo->eclass[i] == rinfo->parent_ec)
5876 : : {
5877 : 209 : remove_it = true;
5878 : 209 : break;
5879 : : }
5880 : : }
5881 : : else
5882 : : {
5883 : : /*
5884 : : * Otherwise, see if rinfo was previously matched to FK as
5885 : : * a "loose" clause.
5886 : : */
5887 [ + + ]: 903 : if (list_member_ptr(fkinfo->rinfos[i], rinfo))
5888 : : {
5889 : 683 : remove_it = true;
5890 : 683 : break;
5891 : : }
5892 : : }
5893 : : }
5894 [ + + ]: 907 : if (remove_it)
5895 : : {
2435 5896 : 892 : worklist = foreach_delete_current(worklist, cell);
3557 5897 : 892 : removedlist = lappend(removedlist, rinfo);
5898 : : }
5899 : : }
5900 : :
5901 : : /*
5902 : : * If we failed to remove all the matching clauses we expected to
5903 : : * find, chicken out and ignore this FK; applying its selectivity
5904 : : * might result in double-counting. Put any clauses we did manage to
5905 : : * remove back into the worklist.
5906 : : *
5907 : : * Since the matching clauses are known not outerjoin-delayed, they
5908 : : * would normally have appeared in the initial joinclause list. If we
5909 : : * didn't find them, there are two possibilities:
5910 : : *
5911 : : * 1. If the FK match is based on an EC that is ec_has_const, it won't
5912 : : * have generated any join clauses at all. We discount such ECs while
5913 : : * checking to see if we have "all" the clauses. (Below, we'll adjust
5914 : : * the selectivity estimate for this case.)
5915 : : *
5916 : : * 2. The clauses were matched to some other FK in a previous
5917 : : * iteration of this loop, and thus removed from worklist. (A likely
5918 : : * case is that two FKs are matched to the same EC; there will be only
5919 : : * one EC-derived clause in the initial list, so the first FK will
5920 : : * consume it.) Applying both FKs' selectivity independently risks
5921 : : * underestimating the join size; in particular, this would undo one
5922 : : * of the main things that ECs were invented for, namely to avoid
5923 : : * double-counting the selectivity of redundant equality conditions.
5924 : : * Later we might think of a reasonable way to combine the estimates,
5925 : : * but for now, just punt, since this is a fairly uncommon situation.
5926 : : */
1964 5927 [ + + ]: 839 : if (removedlist == NIL ||
5928 : 696 : list_length(removedlist) !=
5929 [ - + ]: 696 : (fkinfo->nmatched_ec - fkinfo->nconst_ec + fkinfo->nmatched_ri))
5930 : : {
3557 5931 : 143 : worklist = list_concat(worklist, removedlist);
5932 : 143 : continue;
5933 : : }
5934 : :
5935 : : /*
5936 : : * Finally we get to the payoff: estimate selectivity using the
5937 : : * knowledge that each referencing row will match exactly one row in
5938 : : * the referenced table.
5939 : : *
5940 : : * XXX that's not true in the presence of nulls in the referencing
5941 : : * column(s), so in principle we should derate the estimate for those.
5942 : : * However (1) if there are any strict restriction clauses for the
5943 : : * referencing column(s) elsewhere in the query, derating here would
5944 : : * be double-counting the null fraction, and (2) it's not very clear
5945 : : * how to combine null fractions for multiple referencing columns. So
5946 : : * we do nothing for now about correcting for nulls.
5947 : : *
5948 : : * XXX another point here is that if either side of an FK constraint
5949 : : * is an inheritance parent, we estimate as though the constraint
5950 : : * covers all its children as well. This is not an unreasonable
5951 : : * assumption for a referencing table, ie the user probably applied
5952 : : * identical constraints to all child tables (though perhaps we ought
5953 : : * to check that). But it's not possible to have done that for a
5954 : : * referenced table. Fortunately, precisely because that doesn't
5955 : : * work, it is uncommon in practice to have an FK referencing a parent
5956 : : * table. So, at least for now, disregard inheritance here.
5957 : : */
3191 5958 [ + - + + ]: 696 : if (jointype == JOIN_SEMI || jointype == JOIN_ANTI)
3557 5959 : 412 : {
5960 : : /*
5961 : : * For JOIN_SEMI and JOIN_ANTI, we only get here when the FK's
5962 : : * referenced table is exactly the inside of the join. The join
5963 : : * selectivity is defined as the fraction of LHS rows that have
5964 : : * matches. The FK implies that every LHS row has a match *in the
5965 : : * referenced table*; but any restriction clauses on it will
5966 : : * reduce the number of matches. Hence we take the join
5967 : : * selectivity as equal to the selectivity of the table's
5968 : : * restriction clauses, which is rows / tuples; but we must guard
5969 : : * against tuples == 0.
5970 : : */
3191 5971 : 412 : RelOptInfo *ref_rel = find_base_rel(root, fkinfo->ref_relid);
5972 [ + + ]: 412 : double ref_tuples = Max(ref_rel->tuples, 1.0);
5973 : :
5974 : 412 : fkselec *= ref_rel->rows / ref_tuples;
5975 : : }
5976 : : else
5977 : : {
5978 : : /*
5979 : : * Otherwise, selectivity is exactly 1/referenced-table-size; but
5980 : : * guard against tuples == 0. Note we should use the raw table
5981 : : * tuple count, not any estimate of its filtered or joined size.
5982 : : */
3557 5983 : 284 : RelOptInfo *ref_rel = find_base_rel(root, fkinfo->ref_relid);
5984 [ + - ]: 284 : double ref_tuples = Max(ref_rel->tuples, 1.0);
5985 : :
5986 : 284 : fkselec *= 1.0 / ref_tuples;
5987 : : }
5988 : :
5989 : : /*
5990 : : * If any of the FK columns participated in ec_has_const ECs, then
5991 : : * equivclass.c will have generated "var = const" restrictions for
5992 : : * each side of the join, thus reducing the sizes of both input
5993 : : * relations. Taking the fkselec at face value would amount to
5994 : : * double-counting the selectivity of the constant restriction for the
5995 : : * referencing Var. Hence, look for the restriction clause(s) that
5996 : : * were applied to the referencing Var(s), and divide out their
5997 : : * selectivity to correct for this.
5998 : : */
1964 5999 [ + + ]: 696 : if (fkinfo->nconst_ec > 0)
6000 : : {
6001 [ + + ]: 12 : for (int i = 0; i < fkinfo->nkeys; i++)
6002 : : {
6003 : 9 : EquivalenceClass *ec = fkinfo->eclass[i];
6004 : :
6005 [ + - + + ]: 9 : if (ec && ec->ec_has_const)
6006 : : {
6007 : 3 : EquivalenceMember *em = fkinfo->fk_eclass_member[i];
345 amitlan@postgresql.o 6008 : 3 : RestrictInfo *rinfo = find_derived_clause_for_ec_member(root,
6009 : : ec,
6010 : : em);
6011 : :
1964 tgl@sss.pgh.pa.us 6012 [ + - ]: 3 : if (rinfo)
6013 : : {
6014 : : Selectivity s0;
6015 : :
6016 : 3 : s0 = clause_selectivity(root,
6017 : : (Node *) rinfo,
6018 : : 0,
6019 : : jointype,
6020 : : sjinfo);
6021 [ + - ]: 3 : if (s0 > 0)
6022 : 3 : fkselec /= s0;
6023 : : }
6024 : : }
6025 : : }
6026 : : }
6027 : : }
6028 : :
3557 6029 : 154136 : *restrictlist = worklist;
1964 6030 [ - + - + ]: 154136 : CLAMP_PROBABILITY(fkselec);
3557 6031 : 154136 : return fkselec;
6032 : : }
6033 : :
6034 : : /*
6035 : : * set_subquery_size_estimates
6036 : : * Set the size estimates for a base relation that is a subquery.
6037 : : *
6038 : : * The rel's targetlist and restrictinfo list must have been constructed
6039 : : * already, and the Paths for the subquery must have been completed.
6040 : : * We look at the subquery's PlannerInfo to extract data.
6041 : : *
6042 : : * We set the same fields as set_baserel_size_estimates.
6043 : : */
6044 : : void
5307 6045 : 22041 : set_subquery_size_estimates(PlannerInfo *root, RelOptInfo *rel)
6046 : : {
6047 : 22041 : PlannerInfo *subroot = rel->subroot;
6048 : : RelOptInfo *sub_final_rel;
6049 : : ListCell *lc;
6050 : :
6051 : : /* Should only be applied to base relations that are subqueries */
5595 6052 [ - + ]: 22041 : Assert(rel->relid > 0);
3097 andrew@dunslane.net 6053 [ + - - + ]: 22041 : Assert(planner_rt_fetch(rel->relid, root)->rtekind == RTE_SUBQUERY);
6054 : :
6055 : : /*
6056 : : * Copy raw number of output rows from subquery. All of its paths should
6057 : : * have the same output rowcount, so just look at cheapest-total.
6058 : : */
3660 tgl@sss.pgh.pa.us 6059 : 22041 : sub_final_rel = fetch_upper_rel(subroot, UPPERREL_FINAL, NULL);
6060 : 22041 : rel->tuples = sub_final_rel->cheapest_total_path->rows;
6061 : :
6062 : : /*
6063 : : * Compute per-output-column width estimates by examining the subquery's
6064 : : * targetlist. For any output that is a plain Var, get the width estimate
6065 : : * that was made while planning the subquery. Otherwise, we leave it to
6066 : : * set_rel_width to fill in a datatype-based default estimate.
6067 : : */
5595 6068 [ + + + + : 113670 : foreach(lc, subroot->parse->targetList)
+ + ]
6069 : : {
3261 6070 : 91629 : TargetEntry *te = lfirst_node(TargetEntry, lc);
5595 6071 : 91629 : Node *texpr = (Node *) te->expr;
5318 6072 : 91629 : int32 item_width = 0;
6073 : :
6074 : : /* junk columns aren't visible to upper query */
5595 6075 [ + + ]: 91629 : if (te->resjunk)
6076 : 3456 : continue;
6077 : :
6078 : : /*
6079 : : * The subquery could be an expansion of a view that's had columns
6080 : : * added to it since the current query was parsed, so that there are
6081 : : * non-junk tlist columns in it that don't correspond to any column
6082 : : * visible at our query level. Ignore such columns.
6083 : : */
4732 6084 [ + - - + ]: 88173 : if (te->resno < rel->min_attr || te->resno > rel->max_attr)
4732 tgl@sss.pgh.pa.us 6085 :UBC 0 : continue;
6086 : :
6087 : : /*
6088 : : * XXX This currently doesn't work for subqueries containing set
6089 : : * operations, because the Vars in their tlists are bogus references
6090 : : * to the first leaf subquery, which wouldn't give the right answer
6091 : : * even if we could still get to its PlannerInfo.
6092 : : *
6093 : : * Also, the subquery could be an appendrel for which all branches are
6094 : : * known empty due to constraint exclusion, in which case
6095 : : * set_append_rel_pathlist will have left the attr_widths set to zero.
6096 : : *
6097 : : * In either case, we just leave the width estimate zero until
6098 : : * set_rel_width fixes it.
6099 : : */
5595 tgl@sss.pgh.pa.us 6100 [ + + ]:CBC 88173 : if (IsA(texpr, Var) &&
6101 [ + + ]: 39516 : subroot->parse->setOperations == NULL)
6102 : : {
5453 bruce@momjian.us 6103 : 37964 : Var *var = (Var *) texpr;
5595 tgl@sss.pgh.pa.us 6104 : 37964 : RelOptInfo *subrel = find_base_rel(subroot, var->varno);
6105 : :
6106 : 37964 : item_width = subrel->attr_widths[var->varattno - subrel->min_attr];
6107 : : }
6108 : 88173 : rel->attr_widths[te->resno - rel->min_attr] = item_width;
6109 : : }
6110 : :
6111 : : /* Now estimate number of output rows, etc */
6112 : 22041 : set_baserel_size_estimates(root, rel);
6113 : 22041 : }
6114 : :
6115 : : /*
6116 : : * set_function_size_estimates
6117 : : * Set the size estimates for a base relation that is a function call.
6118 : : *
6119 : : * The rel's targetlist and restrictinfo list must have been constructed
6120 : : * already.
6121 : : *
6122 : : * We set the same fields as set_baserel_size_estimates.
6123 : : */
6124 : : void
7588 6125 : 27918 : set_function_size_estimates(PlannerInfo *root, RelOptInfo *rel)
6126 : : {
6127 : : RangeTblEntry *rte;
6128 : : ListCell *lc;
6129 : :
6130 : : /* Should only be applied to base relations that are functions */
8436 6131 [ - + ]: 27918 : Assert(rel->relid > 0);
6903 6132 [ + - ]: 27918 : rte = planner_rt_fetch(rel->relid, root);
7466 6133 [ - + ]: 27918 : Assert(rte->rtekind == RTE_FUNCTION);
6134 : :
6135 : : /*
6136 : : * Estimate number of rows the functions will return. The rowcount of the
6137 : : * node is that of the largest function result.
6138 : : */
4497 6139 : 27918 : rel->tuples = 0;
6140 [ + - + + : 56091 : foreach(lc, rte->functions)
+ + ]
6141 : : {
6142 : 28173 : RangeTblFunction *rtfunc = (RangeTblFunction *) lfirst(lc);
2591 6143 : 28173 : double ntup = expression_returns_set_rows(root, rtfunc->funcexpr);
6144 : :
4497 6145 [ + + ]: 28173 : if (ntup > rel->tuples)
6146 : 27930 : rel->tuples = ntup;
6147 : : }
6148 : :
6149 : : /* Now estimate number of output rows, etc */
8105 6150 : 27918 : set_baserel_size_estimates(root, rel);
8708 6151 : 27918 : }
6152 : :
6153 : : /*
6154 : : * set_function_size_estimates
6155 : : * Set the size estimates for a base relation that is a function call.
6156 : : *
6157 : : * The rel's targetlist and restrictinfo list must have been constructed
6158 : : * already.
6159 : : *
6160 : : * We set the same fields as set_tablefunc_size_estimates.
6161 : : */
6162 : : void
3294 alvherre@alvh.no-ip. 6163 : 311 : set_tablefunc_size_estimates(PlannerInfo *root, RelOptInfo *rel)
6164 : : {
6165 : : /* Should only be applied to base relations that are functions */
6166 [ - + ]: 311 : Assert(rel->relid > 0);
3097 andrew@dunslane.net 6167 [ + - - + ]: 311 : Assert(planner_rt_fetch(rel->relid, root)->rtekind == RTE_TABLEFUNC);
6168 : :
3294 alvherre@alvh.no-ip. 6169 : 311 : rel->tuples = 100;
6170 : :
6171 : : /* Now estimate number of output rows, etc */
6172 : 311 : set_baserel_size_estimates(root, rel);
6173 : 311 : }
6174 : :
6175 : : /*
6176 : : * set_values_size_estimates
6177 : : * Set the size estimates for a base relation that is a values list.
6178 : : *
6179 : : * The rel's targetlist and restrictinfo list must have been constructed
6180 : : * already.
6181 : : *
6182 : : * We set the same fields as set_baserel_size_estimates.
6183 : : */
6184 : : void
7165 mail@joeconway.com 6185 : 4326 : set_values_size_estimates(PlannerInfo *root, RelOptInfo *rel)
6186 : : {
6187 : : RangeTblEntry *rte;
6188 : :
6189 : : /* Should only be applied to base relations that are values lists */
6190 [ - + ]: 4326 : Assert(rel->relid > 0);
6903 tgl@sss.pgh.pa.us 6191 [ + - ]: 4326 : rte = planner_rt_fetch(rel->relid, root);
7165 mail@joeconway.com 6192 [ - + ]: 4326 : Assert(rte->rtekind == RTE_VALUES);
6193 : :
6194 : : /*
6195 : : * Estimate number of rows the values list will return. We know this
6196 : : * precisely based on the list length (well, barring set-returning
6197 : : * functions in list items, but that's a refinement not catered for
6198 : : * anywhere else either).
6199 : : */
6200 : 4326 : rel->tuples = list_length(rte->values_lists);
6201 : :
6202 : : /* Now estimate number of output rows, etc */
6203 : 4326 : set_baserel_size_estimates(root, rel);
6204 : 4326 : }
6205 : :
6206 : : /*
6207 : : * set_cte_size_estimates
6208 : : * Set the size estimates for a base relation that is a CTE reference.
6209 : : *
6210 : : * The rel's targetlist and restrictinfo list must have been constructed
6211 : : * already, and we need an estimate of the number of rows returned by the CTE
6212 : : * (if a regular CTE) or the non-recursive term (if a self-reference).
6213 : : *
6214 : : * We set the same fields as set_baserel_size_estimates.
6215 : : */
6216 : : void
3660 tgl@sss.pgh.pa.us 6217 : 2915 : set_cte_size_estimates(PlannerInfo *root, RelOptInfo *rel, double cte_rows)
6218 : : {
6219 : : RangeTblEntry *rte;
6220 : :
6221 : : /* Should only be applied to base relations that are CTE references */
6371 6222 [ - + ]: 2915 : Assert(rel->relid > 0);
6223 [ + - ]: 2915 : rte = planner_rt_fetch(rel->relid, root);
6224 [ - + ]: 2915 : Assert(rte->rtekind == RTE_CTE);
6225 : :
6226 [ + + ]: 2915 : if (rte->self_reference)
6227 : : {
6228 : : /*
6229 : : * In a self-reference, we assume the average worktable size is a
6230 : : * multiple of the nonrecursive term's size. The best multiplier will
6231 : : * vary depending on query "fan-out", so make its value adjustable.
6232 : : */
1452 6233 : 543 : rel->tuples = clamp_row_est(recursive_worktable_factor * cte_rows);
6234 : : }
6235 : : else
6236 : : {
6237 : : /* Otherwise just believe the CTE's rowcount estimate */
3660 6238 : 2372 : rel->tuples = cte_rows;
6239 : : }
6240 : :
6241 : : /* Now estimate number of output rows, etc */
6371 6242 : 2915 : set_baserel_size_estimates(root, rel);
6243 : 2915 : }
6244 : :
6245 : : /*
6246 : : * set_namedtuplestore_size_estimates
6247 : : * Set the size estimates for a base relation that is a tuplestore reference.
6248 : : *
6249 : : * The rel's targetlist and restrictinfo list must have been constructed
6250 : : * already.
6251 : : *
6252 : : * We set the same fields as set_baserel_size_estimates.
6253 : : */
6254 : : void
3271 kgrittn@postgresql.o 6255 : 241 : set_namedtuplestore_size_estimates(PlannerInfo *root, RelOptInfo *rel)
6256 : : {
6257 : : RangeTblEntry *rte;
6258 : :
6259 : : /* Should only be applied to base relations that are tuplestore references */
6260 [ - + ]: 241 : Assert(rel->relid > 0);
6261 [ + - ]: 241 : rte = planner_rt_fetch(rel->relid, root);
6262 [ - + ]: 241 : Assert(rte->rtekind == RTE_NAMEDTUPLESTORE);
6263 : :
6264 : : /*
6265 : : * Use the estimate provided by the code which is generating the named
6266 : : * tuplestore. In some cases, the actual number might be available; in
6267 : : * others the same plan will be re-used, so a "typical" value might be
6268 : : * estimated and used.
6269 : : */
6270 : 241 : rel->tuples = rte->enrtuples;
6271 [ - + ]: 241 : if (rel->tuples < 0)
3271 kgrittn@postgresql.o 6272 :UBC 0 : rel->tuples = 1000;
6273 : :
6274 : : /* Now estimate number of output rows, etc */
3271 kgrittn@postgresql.o 6275 :CBC 241 : set_baserel_size_estimates(root, rel);
6276 : 241 : }
6277 : :
6278 : : /*
6279 : : * set_result_size_estimates
6280 : : * Set the size estimates for an RTE_RESULT base relation
6281 : : *
6282 : : * The rel's targetlist and restrictinfo list must have been constructed
6283 : : * already.
6284 : : *
6285 : : * We set the same fields as set_baserel_size_estimates.
6286 : : */
6287 : : void
2603 tgl@sss.pgh.pa.us 6288 : 2172 : set_result_size_estimates(PlannerInfo *root, RelOptInfo *rel)
6289 : : {
6290 : : /* Should only be applied to RTE_RESULT base relations */
6291 [ - + ]: 2172 : Assert(rel->relid > 0);
6292 [ + - - + ]: 2172 : Assert(planner_rt_fetch(rel->relid, root)->rtekind == RTE_RESULT);
6293 : :
6294 : : /* RTE_RESULT always generates a single row, natively */
6295 : 2172 : rel->tuples = 1;
6296 : :
6297 : : /* Now estimate number of output rows, etc */
6298 : 2172 : set_baserel_size_estimates(root, rel);
6299 : 2172 : }
6300 : :
6301 : : /*
6302 : : * set_foreign_size_estimates
6303 : : * Set the size estimates for a base relation that is a foreign table.
6304 : : *
6305 : : * There is not a whole lot that we can do here; the foreign-data wrapper
6306 : : * is responsible for producing useful estimates. We can do a decent job
6307 : : * of estimating baserestrictcost, so we set that, and we also set up width
6308 : : * using what will be purely datatype-driven estimates from the targetlist.
6309 : : * There is no way to do anything sane with the rows value, so we just put
6310 : : * a default estimate and hope that the wrapper can improve on it. The
6311 : : * wrapper's GetForeignRelSize function will be called momentarily.
6312 : : *
6313 : : * The rel's targetlist and restrictinfo list must have been constructed
6314 : : * already.
6315 : : */
6316 : : void
5502 6317 : 1241 : set_foreign_size_estimates(PlannerInfo *root, RelOptInfo *rel)
6318 : : {
6319 : : /* Should only be applied to base relations */
6320 [ - + ]: 1241 : Assert(rel->relid > 0);
6321 : :
6322 : 1241 : rel->rows = 1000; /* entirely bogus default estimate */
6323 : :
6324 : 1241 : cost_qual_eval(&rel->baserestrictcost, rel->baserestrictinfo, root);
6325 : :
6326 : 1241 : set_rel_width(root, rel);
6327 : 1241 : }
6328 : :
6329 : :
6330 : : /*
6331 : : * set_rel_width
6332 : : * Set the estimated output width of a base relation.
6333 : : *
6334 : : * The estimated output width is the sum of the per-attribute width estimates
6335 : : * for the actually-referenced columns, plus any PHVs or other expressions
6336 : : * that have to be calculated at this relation. This is the amount of data
6337 : : * we'd need to pass upwards in case of a sort, hash, etc.
6338 : : *
6339 : : * This function also sets reltarget->cost, so it's a bit misnamed now.
6340 : : *
6341 : : * NB: this works best on plain relations because it prefers to look at
6342 : : * real Vars. For subqueries, set_subquery_size_estimates will already have
6343 : : * copied up whatever per-column estimates were made within the subquery,
6344 : : * and for other types of rels there isn't much we can do anyway. We fall
6345 : : * back on (fairly stupid) datatype-based width estimates if we can't get
6346 : : * any better number.
6347 : : *
6348 : : * The per-attribute width estimates are cached for possible re-use while
6349 : : * building join relations or post-scan/join pathtargets.
6350 : : */
6351 : : static void
7588 6352 : 291233 : set_rel_width(PlannerInfo *root, RelOptInfo *rel)
6353 : : {
6358 6354 [ + - ]: 291233 : Oid reloid = planner_rt_fetch(rel->relid, root)->relid;
817 6355 : 291233 : int64 tuple_width = 0;
5595 6356 : 291233 : bool have_wholerow_var = false;
6357 : : ListCell *lc;
6358 : :
6359 : : /* Vars are assumed to have cost zero, but other exprs do not */
3653 6360 : 291233 : rel->reltarget->cost.startup = 0;
6361 : 291233 : rel->reltarget->cost.per_tuple = 0;
6362 : :
6363 [ + + + + : 1062255 : foreach(lc, rel->reltarget->exprs)
+ + ]
6364 : : {
6354 6365 : 771022 : Node *node = (Node *) lfirst(lc);
6366 : :
6367 : : /*
6368 : : * Ordinarily, a Var in a rel's targetlist must belong to that rel;
6369 : : * but there are corner cases involving LATERAL references where that
6370 : : * isn't so. If the Var has the wrong varno, fall through to the
6371 : : * generic case (it doesn't seem worth the trouble to be any smarter).
6372 : : */
4949 6373 [ + + ]: 771022 : if (IsA(node, Var) &&
6374 [ + + ]: 758500 : ((Var *) node)->varno == rel->relid)
7953 6375 : 211471 : {
6354 6376 : 758455 : Var *var = (Var *) node;
6377 : : int ndx;
6378 : : int32 item_width;
6379 : :
6380 [ - + ]: 758455 : Assert(var->varattno >= rel->min_attr);
6381 [ - + ]: 758455 : Assert(var->varattno <= rel->max_attr);
6382 : :
6383 : 758455 : ndx = var->varattno - rel->min_attr;
6384 : :
6385 : : /*
6386 : : * If it's a whole-row Var, we'll deal with it below after we have
6387 : : * already cached as many attr widths as possible.
6388 : : */
5595 6389 [ + + ]: 758455 : if (var->varattno == 0)
6390 : : {
6391 : 1535 : have_wholerow_var = true;
6392 : 1535 : continue;
6393 : : }
6394 : :
6395 : : /*
6396 : : * The width may have been cached already (especially if it's a
6397 : : * subquery), so don't duplicate effort.
6398 : : */
6354 6399 [ + + ]: 756920 : if (rel->attr_widths[ndx] > 0)
6400 : : {
6401 : 143757 : tuple_width += rel->attr_widths[ndx];
8295 6402 : 143757 : continue;
6403 : : }
6404 : :
6405 : : /* Try to get column width from statistics */
5595 6406 [ + + + + ]: 613163 : if (reloid != InvalidOid && var->varattno > 0)
6407 : : {
6354 6408 : 488733 : item_width = get_attavgwidth(reloid, var->varattno);
6409 [ + + ]: 488733 : if (item_width > 0)
6410 : : {
6411 : 401692 : rel->attr_widths[ndx] = item_width;
6412 : 401692 : tuple_width += item_width;
6413 : 401692 : continue;
6414 : : }
6415 : : }
6416 : :
6417 : : /*
6418 : : * Not a plain relation, or can't find statistics for it. Estimate
6419 : : * using just the type info.
6420 : : */
6421 : 211471 : item_width = get_typavgwidth(var->vartype, var->vartypmod);
6422 [ - + ]: 211471 : Assert(item_width > 0);
6423 : 211471 : rel->attr_widths[ndx] = item_width;
6424 : 211471 : tuple_width += item_width;
6425 : : }
6426 [ + + ]: 12567 : else if (IsA(node, PlaceHolderVar))
6427 : : {
6428 : : /*
6429 : : * We will need to evaluate the PHV's contained expression while
6430 : : * scanning this rel, so be sure to include it in reltarget->cost.
6431 : : */
6432 : 1088 : PlaceHolderVar *phv = (PlaceHolderVar *) node;
1306 6433 : 1088 : PlaceHolderInfo *phinfo = find_placeholder_info(root, phv);
6434 : : QualCost cost;
6435 : :
6354 6436 : 1088 : tuple_width += phinfo->ph_width;
3678 6437 : 1088 : cost_qual_eval_node(&cost, (Node *) phv->phexpr, root);
3653 6438 : 1088 : rel->reltarget->cost.startup += cost.startup;
6439 : 1088 : rel->reltarget->cost.per_tuple += cost.per_tuple;
6440 : : }
6441 : : else
6442 : : {
6443 : : /*
6444 : : * We could be looking at an expression pulled up from a subquery,
6445 : : * or a ROW() representing a whole-row child Var, etc. Do what we
6446 : : * can using the expression type information.
6447 : : */
6448 : : int32 item_width;
6449 : : QualCost cost;
6450 : :
6091 6451 : 11479 : item_width = get_typavgwidth(exprType(node), exprTypmod(node));
6452 [ - + ]: 11479 : Assert(item_width > 0);
6453 : 11479 : tuple_width += item_width;
6454 : : /* Not entirely clear if we need to account for cost, but do so */
3678 6455 : 11479 : cost_qual_eval_node(&cost, node, root);
3653 6456 : 11479 : rel->reltarget->cost.startup += cost.startup;
6457 : 11479 : rel->reltarget->cost.per_tuple += cost.per_tuple;
6458 : : }
6459 : : }
6460 : :
6461 : : /*
6462 : : * If we have a whole-row reference, estimate its width as the sum of
6463 : : * per-column widths plus heap tuple header overhead.
6464 : : */
5595 6465 [ + + ]: 291233 : if (have_wholerow_var)
6466 : : {
817 6467 : 1535 : int64 wholerow_width = MAXALIGN(SizeofHeapTupleHeader);
6468 : :
5595 6469 [ + + ]: 1535 : if (reloid != InvalidOid)
6470 : : {
6471 : : /* Real relation, so estimate true tuple width */
6472 : 1200 : wholerow_width += get_relation_data_width(reloid,
3189 6473 : 1200 : rel->attr_widths - rel->min_attr);
6474 : : }
6475 : : else
6476 : : {
6477 : : /* Do what we can with info for a phony rel */
6478 : : AttrNumber i;
6479 : :
5595 6480 [ + + ]: 910 : for (i = 1; i <= rel->max_attr; i++)
6481 : 575 : wholerow_width += rel->attr_widths[i - rel->min_attr];
6482 : : }
6483 : :
817 6484 : 1535 : rel->attr_widths[0 - rel->min_attr] = clamp_width_est(wholerow_width);
6485 : :
6486 : : /*
6487 : : * Include the whole-row Var as part of the output tuple. Yes, that
6488 : : * really is what happens at runtime.
6489 : : */
5595 6490 : 1535 : tuple_width += wholerow_width;
6491 : : }
6492 : :
817 6493 : 291233 : rel->reltarget->width = clamp_width_est(tuple_width);
10841 scrappy@hub.org 6494 : 291233 : }
6495 : :
6496 : : /*
6497 : : * set_pathtarget_cost_width
6498 : : * Set the estimated eval cost and output width of a PathTarget tlist.
6499 : : *
6500 : : * As a notational convenience, returns the same PathTarget pointer passed in.
6501 : : *
6502 : : * Most, though not quite all, uses of this function occur after we've run
6503 : : * set_rel_width() for base relations; so we can usually obtain cached width
6504 : : * estimates for Vars. If we can't, fall back on datatype-based width
6505 : : * estimates. Present early-planning uses of PathTargets don't need accurate
6506 : : * widths badly enough to justify going to the catalogs for better data.
6507 : : */
6508 : : PathTarget *
3660 tgl@sss.pgh.pa.us 6509 : 335754 : set_pathtarget_cost_width(PlannerInfo *root, PathTarget *target)
6510 : : {
817 6511 : 335754 : int64 tuple_width = 0;
6512 : : ListCell *lc;
6513 : :
6514 : : /* Vars are assumed to have cost zero, but other exprs do not */
3660 6515 : 335754 : target->cost.startup = 0;
6516 : 335754 : target->cost.per_tuple = 0;
6517 : :
6518 [ + + + + : 1196924 : foreach(lc, target->exprs)
+ + ]
6519 : : {
6520 : 861170 : Node *node = (Node *) lfirst(lc);
6521 : :
1091 drowley@postgresql.o 6522 : 861170 : tuple_width += get_expr_width(root, node);
6523 : :
6524 : : /* For non-Vars, account for evaluation cost */
6525 [ + + ]: 861170 : if (!IsA(node, Var))
6526 : : {
6527 : : QualCost cost;
6528 : :
3660 tgl@sss.pgh.pa.us 6529 : 356004 : cost_qual_eval_node(&cost, node, root);
6530 : 356004 : target->cost.startup += cost.startup;
6531 : 356004 : target->cost.per_tuple += cost.per_tuple;
6532 : : }
6533 : : }
6534 : :
817 6535 : 335754 : target->width = clamp_width_est(tuple_width);
6536 : :
3660 6537 : 335754 : return target;
6538 : : }
6539 : :
6540 : : /*
6541 : : * get_expr_width
6542 : : * Estimate the width of the given expr attempting to use the width
6543 : : * cached in a Var's owning RelOptInfo, else fallback on the type's
6544 : : * average width when unable to or when the given Node is not a Var.
6545 : : */
6546 : : static int32
1091 drowley@postgresql.o 6547 : 1061352 : get_expr_width(PlannerInfo *root, const Node *expr)
6548 : : {
6549 : : int32 width;
6550 : :
6551 [ + + ]: 1061352 : if (IsA(expr, Var))
6552 : : {
6553 : 698339 : const Var *var = (const Var *) expr;
6554 : :
6555 : : /* We should not see any upper-level Vars here */
6556 [ - + ]: 698339 : Assert(var->varlevelsup == 0);
6557 : :
6558 : : /* Try to get data from RelOptInfo cache */
6559 [ + + ]: 698339 : if (!IS_SPECIAL_VARNO(var->varno) &&
6560 [ + - ]: 695360 : var->varno < root->simple_rel_array_size)
6561 : : {
6562 : 695360 : RelOptInfo *rel = root->simple_rel_array[var->varno];
6563 : :
6564 [ + + ]: 695360 : if (rel != NULL &&
6565 [ + - ]: 685519 : var->varattno >= rel->min_attr &&
6566 [ + - ]: 685519 : var->varattno <= rel->max_attr)
6567 : : {
6568 : 685519 : int ndx = var->varattno - rel->min_attr;
6569 : :
6570 [ + + ]: 685519 : if (rel->attr_widths[ndx] > 0)
6571 : 667352 : return rel->attr_widths[ndx];
6572 : : }
6573 : : }
6574 : :
6575 : : /*
6576 : : * No cached data available, so estimate using just the type info.
6577 : : */
6578 : 30987 : width = get_typavgwidth(var->vartype, var->vartypmod);
6579 [ - + ]: 30987 : Assert(width > 0);
6580 : :
6581 : 30987 : return width;
6582 : : }
6583 : :
6584 : 363013 : width = get_typavgwidth(exprType(expr), exprTypmod(expr));
6585 [ - + ]: 363013 : Assert(width > 0);
6586 : 363013 : return width;
6587 : : }
6588 : :
6589 : : /*
6590 : : * relation_byte_size
6591 : : * Estimate the storage space in bytes for a given number of tuples
6592 : : * of a given width (size in bytes).
6593 : : */
6594 : : static double
9562 tgl@sss.pgh.pa.us 6595 : 2976459 : relation_byte_size(double tuples, int width)
6596 : : {
4040 6597 : 2976459 : return tuples * (MAXALIGN(width) + MAXALIGN(SizeofHeapTupleHeader));
6598 : : }
6599 : :
6600 : : /*
6601 : : * page_size
6602 : : * Returns an estimate of the number of pages covered by a given
6603 : : * number of tuples of a given width (size in bytes).
6604 : : */
6605 : : static double
9562 6606 : 5750 : page_size(double tuples, int width)
6607 : : {
6608 : 5750 : return ceil(relation_byte_size(tuples, width) / BLCKSZ);
6609 : : }
6610 : :
6611 : : /*
6612 : : * Estimate the fraction of the work that each worker will do given the
6613 : : * number of workers budgeted for the path.
6614 : : */
6615 : : static double
3348 rhaas@postgresql.org 6616 : 235007 : get_parallel_divisor(Path *path)
6617 : : {
6618 : 235007 : double parallel_divisor = path->parallel_workers;
6619 : :
6620 : : /*
6621 : : * Early experience with parallel query suggests that when there is only
6622 : : * one worker, the leader often makes a very substantial contribution to
6623 : : * executing the parallel portion of the plan, but as more workers are
6624 : : * added, it does less and less, because it's busy reading tuples from the
6625 : : * workers and doing whatever non-parallel post-processing is needed. By
6626 : : * the time we reach 4 workers, the leader no longer makes a meaningful
6627 : : * contribution. Thus, for now, estimate that the leader spends 30% of
6628 : : * its time servicing each worker, and the remainder executing the
6629 : : * parallel plan.
6630 : : */
3042 6631 [ + + ]: 235007 : if (parallel_leader_participation)
6632 : : {
6633 : : double leader_contribution;
6634 : :
6635 : 234392 : leader_contribution = 1.0 - (0.3 * path->parallel_workers);
6636 [ + + ]: 234392 : if (leader_contribution > 0)
6637 : 233102 : parallel_divisor += leader_contribution;
6638 : : }
6639 : :
3348 6640 : 235007 : return parallel_divisor;
6641 : : }
6642 : :
6643 : : /*
6644 : : * compute_bitmap_pages
6645 : : * Estimate number of pages fetched from heap in a bitmap heap scan.
6646 : : *
6647 : : * 'baserel' is the relation to be scanned
6648 : : * 'bitmapqual' is a tree of IndexPaths, BitmapAndPaths, and BitmapOrPaths
6649 : : * 'loop_count' is the number of repetitions of the indexscan to factor into
6650 : : * estimates of caching behavior
6651 : : *
6652 : : * If cost_p isn't NULL, the indexTotalCost estimate is returned in *cost_p.
6653 : : * If tuples_p isn't NULL, the tuples_fetched estimate is returned in *tuples_p.
6654 : : */
6655 : : double
818 tgl@sss.pgh.pa.us 6656 : 410180 : compute_bitmap_pages(PlannerInfo *root, RelOptInfo *baserel,
6657 : : Path *bitmapqual, double loop_count,
6658 : : Cost *cost_p, double *tuples_p)
6659 : : {
6660 : : Cost indexTotalCost;
6661 : : Selectivity indexSelectivity;
6662 : : double T;
6663 : : double pages_fetched;
6664 : : double tuples_fetched;
6665 : : double heap_pages;
6666 : : double maxentries;
6667 : :
6668 : : /*
6669 : : * Fetch total cost of obtaining the bitmap, as well as its total
6670 : : * selectivity.
6671 : : */
3334 rhaas@postgresql.org 6672 : 410180 : cost_bitmap_tree_node(bitmapqual, &indexTotalCost, &indexSelectivity);
6673 : :
6674 : : /*
6675 : : * Estimate number of main-table pages fetched.
6676 : : */
6677 : 410180 : tuples_fetched = clamp_row_est(indexSelectivity * baserel->tuples);
6678 : :
6679 [ + + ]: 410180 : T = (baserel->pages > 1) ? (double) baserel->pages : 1.0;
6680 : :
6681 : : /*
6682 : : * For a single scan, the number of heap pages that need to be fetched is
6683 : : * the same as the Mackert and Lohman formula for the case T <= b (ie, no
6684 : : * re-reads needed).
6685 : : */
3047 6686 : 410180 : pages_fetched = (2.0 * T * tuples_fetched) / (2.0 * T + tuples_fetched);
6687 : :
6688 : : /*
6689 : : * Calculate the number of pages fetched from the heap. Then based on
6690 : : * current work_mem estimate get the estimated maxentries in the bitmap.
6691 : : * (Note that we always do this calculation based on the number of pages
6692 : : * that would be fetched in a single iteration, even if loop_count > 1.
6693 : : * That's correct, because only that number of entries will be stored in
6694 : : * the bitmap at one time.)
6695 : : */
6696 [ + + ]: 410180 : heap_pages = Min(pages_fetched, baserel->pages);
408 tgl@sss.pgh.pa.us 6697 : 410180 : maxentries = tbm_calculate_entries(work_mem * (Size) 1024);
6698 : :
3334 rhaas@postgresql.org 6699 [ + + ]: 410180 : if (loop_count > 1)
6700 : : {
6701 : : /*
6702 : : * For repeated bitmap scans, scale up the number of tuples fetched in
6703 : : * the Mackert and Lohman formula by the number of scans, so that we
6704 : : * estimate the number of pages fetched by all the scans. Then
6705 : : * pro-rate for one scan.
6706 : : */
6707 : 90923 : pages_fetched = index_pages_fetched(tuples_fetched * loop_count,
6708 : : baserel->pages,
6709 : : get_indexpath_pages(bitmapqual),
6710 : : root);
6711 : 90923 : pages_fetched /= loop_count;
6712 : : }
6713 : :
6714 [ + + ]: 410180 : if (pages_fetched >= T)
6715 : 39728 : pages_fetched = T;
6716 : : else
6717 : 370452 : pages_fetched = ceil(pages_fetched);
6718 : :
3047 6719 [ + + ]: 410180 : if (maxentries < heap_pages)
6720 : : {
6721 : : double exact_pages;
6722 : : double lossy_pages;
6723 : :
6724 : : /*
6725 : : * Crude approximation of the number of lossy pages. Because of the
6726 : : * way tbm_lossify() is coded, the number of lossy pages increases
6727 : : * very sharply as soon as we run short of memory; this formula has
6728 : : * that property and seems to perform adequately in testing, but it's
6729 : : * possible we could do better somehow.
6730 : : */
6731 [ - + ]: 9 : lossy_pages = Max(0, heap_pages - maxentries / 2);
6732 : 9 : exact_pages = heap_pages - lossy_pages;
6733 : :
6734 : : /*
6735 : : * If there are lossy pages then recompute the number of tuples
6736 : : * processed by the bitmap heap node. We assume here that the chance
6737 : : * of a given tuple coming from an exact page is the same as the
6738 : : * chance that a given page is exact. This might not be true, but
6739 : : * it's not clear how we can do any better.
6740 : : */
6741 [ + - ]: 9 : if (lossy_pages > 0)
6742 : : tuples_fetched =
6743 : 9 : clamp_row_est(indexSelectivity *
6744 : 9 : (exact_pages / heap_pages) * baserel->tuples +
6745 : 9 : (lossy_pages / heap_pages) * baserel->tuples);
6746 : : }
6747 : :
818 tgl@sss.pgh.pa.us 6748 [ + + ]: 410180 : if (cost_p)
6749 : 327617 : *cost_p = indexTotalCost;
6750 [ + + ]: 410180 : if (tuples_p)
6751 : 327617 : *tuples_p = tuples_fetched;
6752 : :
3334 rhaas@postgresql.org 6753 : 410180 : return pages_fetched;
6754 : : }
6755 : :
6756 : : /*
6757 : : * compute_gather_rows
6758 : : * Estimate number of rows for gather (merge) nodes.
6759 : : *
6760 : : * In a parallel plan, each worker's row estimate is determined by dividing the
6761 : : * total number of rows by parallel_divisor, which accounts for the leader's
6762 : : * contribution in addition to the number of workers. Accordingly, when
6763 : : * estimating the number of rows for gather (merge) nodes, we multiply the rows
6764 : : * per worker by the same parallel_divisor to undo the division.
6765 : : */
6766 : : double
600 rguo@postgresql.org 6767 : 23287 : compute_gather_rows(Path *path)
6768 : : {
6769 [ - + ]: 23287 : Assert(path->parallel_workers > 0);
6770 : :
6771 : 23287 : return clamp_row_est(path->rows * get_parallel_divisor(path));
6772 : : }
|
