Line data Source code
1 : /*-------------------------------------------------------------------------
2 : *
3 : * nodeAgg.c
4 : * Routines to handle aggregate nodes.
5 : *
6 : * ExecAgg normally evaluates each aggregate in the following steps:
7 : *
8 : * transvalue = initcond
9 : * foreach input_tuple do
10 : * transvalue = transfunc(transvalue, input_value(s))
11 : * result = finalfunc(transvalue, direct_argument(s))
12 : *
13 : * If a finalfunc is not supplied then the result is just the ending
14 : * value of transvalue.
15 : *
16 : * Other behaviors can be selected by the "aggsplit" mode, which exists
17 : * to support partial aggregation. It is possible to:
18 : * * Skip running the finalfunc, so that the output is always the
19 : * final transvalue state.
20 : * * Substitute the combinefunc for the transfunc, so that transvalue
21 : * states (propagated up from a child partial-aggregation step) are merged
22 : * rather than processing raw input rows. (The statements below about
23 : * the transfunc apply equally to the combinefunc, when it's selected.)
24 : * * Apply the serializefunc to the output values (this only makes sense
25 : * when skipping the finalfunc, since the serializefunc works on the
26 : * transvalue data type).
27 : * * Apply the deserializefunc to the input values (this only makes sense
28 : * when using the combinefunc, for similar reasons).
29 : * It is the planner's responsibility to connect up Agg nodes using these
30 : * alternate behaviors in a way that makes sense, with partial aggregation
31 : * results being fed to nodes that expect them.
32 : *
33 : * If a normal aggregate call specifies DISTINCT or ORDER BY, we sort the
34 : * input tuples and eliminate duplicates (if required) before performing
35 : * the above-depicted process. (However, we don't do that for ordered-set
36 : * aggregates; their "ORDER BY" inputs are ordinary aggregate arguments
37 : * so far as this module is concerned.) Note that partial aggregation
38 : * is not supported in these cases, since we couldn't ensure global
39 : * ordering or distinctness of the inputs.
40 : *
41 : * If transfunc is marked "strict" in pg_proc and initcond is NULL,
42 : * then the first non-NULL input_value is assigned directly to transvalue,
43 : * and transfunc isn't applied until the second non-NULL input_value.
44 : * The agg's first input type and transtype must be the same in this case!
45 : *
46 : * If transfunc is marked "strict" then NULL input_values are skipped,
47 : * keeping the previous transvalue. If transfunc is not strict then it
48 : * is called for every input tuple and must deal with NULL initcond
49 : * or NULL input_values for itself.
50 : *
51 : * If finalfunc is marked "strict" then it is not called when the
52 : * ending transvalue is NULL, instead a NULL result is created
53 : * automatically (this is just the usual handling of strict functions,
54 : * of course). A non-strict finalfunc can make its own choice of
55 : * what to return for a NULL ending transvalue.
56 : *
57 : * Ordered-set aggregates are treated specially in one other way: we
58 : * evaluate any "direct" arguments and pass them to the finalfunc along
59 : * with the transition value.
60 : *
61 : * A finalfunc can have additional arguments beyond the transvalue and
62 : * any "direct" arguments, corresponding to the input arguments of the
63 : * aggregate. These are always just passed as NULL. Such arguments may be
64 : * needed to allow resolution of a polymorphic aggregate's result type.
65 : *
66 : * We compute aggregate input expressions and run the transition functions
67 : * in a temporary econtext (aggstate->tmpcontext). This is reset at least
68 : * once per input tuple, so when the transvalue datatype is
69 : * pass-by-reference, we have to be careful to copy it into a longer-lived
70 : * memory context, and free the prior value to avoid memory leakage. We
71 : * store transvalues in another set of econtexts, aggstate->aggcontexts
72 : * (one per grouping set, see below), which are also used for the hashtable
73 : * structures in AGG_HASHED mode. These econtexts are rescanned, not just
74 : * reset, at group boundaries so that aggregate transition functions can
75 : * register shutdown callbacks via AggRegisterCallback.
76 : *
77 : * The node's regular econtext (aggstate->ss.ps.ps_ExprContext) is used to
78 : * run finalize functions and compute the output tuple; this context can be
79 : * reset once per output tuple.
80 : *
81 : * The executor's AggState node is passed as the fmgr "context" value in
82 : * all transfunc and finalfunc calls. It is not recommended that the
83 : * transition functions look at the AggState node directly, but they can
84 : * use AggCheckCallContext() to verify that they are being called by
85 : * nodeAgg.c (and not as ordinary SQL functions). The main reason a
86 : * transition function might want to know this is so that it can avoid
87 : * palloc'ing a fixed-size pass-by-ref transition value on every call:
88 : * it can instead just scribble on and return its left input. Ordinarily
89 : * it is completely forbidden for functions to modify pass-by-ref inputs,
90 : * but in the aggregate case we know the left input is either the initial
91 : * transition value or a previous function result, and in either case its
92 : * value need not be preserved. See int8inc() for an example. Notice that
93 : * the EEOP_AGG_PLAIN_TRANS step is coded to avoid a data copy step when
94 : * the previous transition value pointer is returned. It is also possible
95 : * to avoid repeated data copying when the transition value is an expanded
96 : * object: to do that, the transition function must take care to return
97 : * an expanded object that is in a child context of the memory context
98 : * returned by AggCheckCallContext(). Also, some transition functions want
99 : * to store working state in addition to the nominal transition value; they
100 : * can use the memory context returned by AggCheckCallContext() to do that.
101 : *
102 : * Note: AggCheckCallContext() is available as of PostgreSQL 9.0. The
103 : * AggState is available as context in earlier releases (back to 8.1),
104 : * but direct examination of the node is needed to use it before 9.0.
105 : *
106 : * As of 9.4, aggregate transition functions can also use AggGetAggref()
107 : * to get hold of the Aggref expression node for their aggregate call.
108 : * This is mainly intended for ordered-set aggregates, which are not
109 : * supported as window functions. (A regular aggregate function would
110 : * need some fallback logic to use this, since there's no Aggref node
111 : * for a window function.)
112 : *
113 : * Grouping sets:
114 : *
115 : * A list of grouping sets which is structurally equivalent to a ROLLUP
116 : * clause (e.g. (a,b,c), (a,b), (a)) can be processed in a single pass over
117 : * ordered data. We do this by keeping a separate set of transition values
118 : * for each grouping set being concurrently processed; for each input tuple
119 : * we update them all, and on group boundaries we reset those states
120 : * (starting at the front of the list) whose grouping values have changed
121 : * (the list of grouping sets is ordered from most specific to least
122 : * specific).
123 : *
124 : * Where more complex grouping sets are used, we break them down into
125 : * "phases", where each phase has a different sort order (except phase 0
126 : * which is reserved for hashing). During each phase but the last, the
127 : * input tuples are additionally stored in a tuplesort which is keyed to the
128 : * next phase's sort order; during each phase but the first, the input
129 : * tuples are drawn from the previously sorted data. (The sorting of the
130 : * data for the first phase is handled by the planner, as it might be
131 : * satisfied by underlying nodes.)
132 : *
133 : * Hashing can be mixed with sorted grouping. To do this, we have an
134 : * AGG_MIXED strategy that populates the hashtables during the first sorted
135 : * phase, and switches to reading them out after completing all sort phases.
136 : * We can also support AGG_HASHED with multiple hash tables and no sorting
137 : * at all.
138 : *
139 : * From the perspective of aggregate transition and final functions, the
140 : * only issue regarding grouping sets is this: a single call site (flinfo)
141 : * of an aggregate function may be used for updating several different
142 : * transition values in turn. So the function must not cache in the flinfo
143 : * anything which logically belongs as part of the transition value (most
144 : * importantly, the memory context in which the transition value exists).
145 : * The support API functions (AggCheckCallContext, AggRegisterCallback) are
146 : * sensitive to the grouping set for which the aggregate function is
147 : * currently being called.
148 : *
149 : * Plan structure:
150 : *
151 : * What we get from the planner is actually one "real" Agg node which is
152 : * part of the plan tree proper, but which optionally has an additional list
153 : * of Agg nodes hung off the side via the "chain" field. This is because an
154 : * Agg node happens to be a convenient representation of all the data we
155 : * need for grouping sets.
156 : *
157 : * For many purposes, we treat the "real" node as if it were just the first
158 : * node in the chain. The chain must be ordered such that hashed entries
159 : * come before sorted/plain entries; the real node is marked AGG_MIXED if
160 : * there are both types present (in which case the real node describes one
161 : * of the hashed groupings, other AGG_HASHED nodes may optionally follow in
162 : * the chain, followed in turn by AGG_SORTED or (one) AGG_PLAIN node). If
163 : * the real node is marked AGG_HASHED or AGG_SORTED, then all the chained
164 : * nodes must be of the same type; if it is AGG_PLAIN, there can be no
165 : * chained nodes.
166 : *
167 : * We collect all hashed nodes into a single "phase", numbered 0, and create
168 : * a sorted phase (numbered 1..n) for each AGG_SORTED or AGG_PLAIN node.
169 : * Phase 0 is allocated even if there are no hashes, but remains unused in
170 : * that case.
171 : *
172 : * AGG_HASHED nodes actually refer to only a single grouping set each,
173 : * because for each hashed grouping we need a separate grpColIdx and
174 : * numGroups estimate. AGG_SORTED nodes represent a "rollup", a list of
175 : * grouping sets that share a sort order. Each AGG_SORTED node other than
176 : * the first one has an associated Sort node which describes the sort order
177 : * to be used; the first sorted node takes its input from the outer subtree,
178 : * which the planner has already arranged to provide ordered data.
179 : *
180 : * Memory and ExprContext usage:
181 : *
182 : * Because we're accumulating aggregate values across input rows, we need to
183 : * use more memory contexts than just simple input/output tuple contexts.
184 : * In fact, for a rollup, we need a separate context for each grouping set
185 : * so that we can reset the inner (finer-grained) aggregates on their group
186 : * boundaries while continuing to accumulate values for outer
187 : * (coarser-grained) groupings. On top of this, we might be simultaneously
188 : * populating hashtables; however, we only need one context for all the
189 : * hashtables.
190 : *
191 : * So we create an array, aggcontexts, with an ExprContext for each grouping
192 : * set in the largest rollup that we're going to process, and use the
193 : * per-tuple memory context of those ExprContexts to store the aggregate
194 : * transition values. hashcontext is the single context created to support
195 : * all hash tables.
196 : *
197 : * Spilling To Disk
198 : *
199 : * When performing hash aggregation, if the hash table memory exceeds the
200 : * limit (see hash_agg_check_limits()), we enter "spill mode". In spill
201 : * mode, we advance the transition states only for groups already in the
202 : * hash table. For tuples that would need to create a new hash table
203 : * entries (and initialize new transition states), we instead spill them to
204 : * disk to be processed later. The tuples are spilled in a partitioned
205 : * manner, so that subsequent batches are smaller and less likely to exceed
206 : * hash_mem (if a batch does exceed hash_mem, it must be spilled
207 : * recursively).
208 : *
209 : * Spilled data is written to logical tapes. These provide better control
210 : * over memory usage, disk space, and the number of files than if we were
211 : * to use a BufFile for each spill. We don't know the number of tapes needed
212 : * at the start of the algorithm (because it can recurse), so a tape set is
213 : * allocated at the beginning, and individual tapes are created as needed.
214 : * As a particular tape is read, logtape.c recycles its disk space. When a
215 : * tape is read to completion, it is destroyed entirely.
216 : *
217 : * Tapes' buffers can take up substantial memory when many tapes are open at
218 : * once. We only need one tape open at a time in read mode (using a buffer
219 : * that's a multiple of BLCKSZ); but we need one tape open in write mode (each
220 : * requiring a buffer of size BLCKSZ) for each partition.
221 : *
222 : * Note that it's possible for transition states to start small but then
223 : * grow very large; for instance in the case of ARRAY_AGG. In such cases,
224 : * it's still possible to significantly exceed hash_mem. We try to avoid
225 : * this situation by estimating what will fit in the available memory, and
226 : * imposing a limit on the number of groups separately from the amount of
227 : * memory consumed.
228 : *
229 : * Transition / Combine function invocation:
230 : *
231 : * For performance reasons transition functions, including combine
232 : * functions, aren't invoked one-by-one from nodeAgg.c after computing
233 : * arguments using the expression evaluation engine. Instead
234 : * ExecBuildAggTrans() builds one large expression that does both argument
235 : * evaluation and transition function invocation. That avoids performance
236 : * issues due to repeated uses of expression evaluation, complications due
237 : * to filter expressions having to be evaluated early, and allows to JIT
238 : * the entire expression into one native function.
239 : *
240 : * Portions Copyright (c) 1996-2026, PostgreSQL Global Development Group
241 : * Portions Copyright (c) 1994, Regents of the University of California
242 : *
243 : * IDENTIFICATION
244 : * src/backend/executor/nodeAgg.c
245 : *
246 : *-------------------------------------------------------------------------
247 : */
248 :
249 : #include "postgres.h"
250 :
251 : #include "access/htup_details.h"
252 : #include "access/parallel.h"
253 : #include "catalog/objectaccess.h"
254 : #include "catalog/pg_aggregate.h"
255 : #include "catalog/pg_proc.h"
256 : #include "catalog/pg_type.h"
257 : #include "common/hashfn.h"
258 : #include "executor/execExpr.h"
259 : #include "executor/executor.h"
260 : #include "executor/nodeAgg.h"
261 : #include "lib/hyperloglog.h"
262 : #include "miscadmin.h"
263 : #include "nodes/nodeFuncs.h"
264 : #include "optimizer/optimizer.h"
265 : #include "parser/parse_agg.h"
266 : #include "parser/parse_coerce.h"
267 : #include "utils/acl.h"
268 : #include "utils/builtins.h"
269 : #include "utils/datum.h"
270 : #include "utils/expandeddatum.h"
271 : #include "utils/injection_point.h"
272 : #include "utils/logtape.h"
273 : #include "utils/lsyscache.h"
274 : #include "utils/memutils.h"
275 : #include "utils/memutils_memorychunk.h"
276 : #include "utils/syscache.h"
277 : #include "utils/tuplesort.h"
278 :
279 : /*
280 : * Control how many partitions are created when spilling HashAgg to
281 : * disk.
282 : *
283 : * HASHAGG_PARTITION_FACTOR is multiplied by the estimated number of
284 : * partitions needed such that each partition will fit in memory. The factor
285 : * is set higher than one because there's not a high cost to having a few too
286 : * many partitions, and it makes it less likely that a partition will need to
287 : * be spilled recursively. Another benefit of having more, smaller partitions
288 : * is that small hash tables may perform better than large ones due to memory
289 : * caching effects.
290 : *
291 : * We also specify a min and max number of partitions per spill. Too few might
292 : * mean a lot of wasted I/O from repeated spilling of the same tuples. Too
293 : * many will result in lots of memory wasted buffering the spill files (which
294 : * could instead be spent on a larger hash table).
295 : */
296 : #define HASHAGG_PARTITION_FACTOR 1.50
297 : #define HASHAGG_MIN_PARTITIONS 4
298 : #define HASHAGG_MAX_PARTITIONS 1024
299 :
300 : /*
301 : * For reading from tapes, the buffer size must be a multiple of
302 : * BLCKSZ. Larger values help when reading from multiple tapes concurrently,
303 : * but that doesn't happen in HashAgg, so we simply use BLCKSZ. Writing to a
304 : * tape always uses a buffer of size BLCKSZ.
305 : */
306 : #define HASHAGG_READ_BUFFER_SIZE BLCKSZ
307 : #define HASHAGG_WRITE_BUFFER_SIZE BLCKSZ
308 :
309 : /*
310 : * HyperLogLog is used for estimating the cardinality of the spilled tuples in
311 : * a given partition. 5 bits corresponds to a size of about 32 bytes and a
312 : * worst-case error of around 18%. That's effective enough to choose a
313 : * reasonable number of partitions when recursing.
314 : */
315 : #define HASHAGG_HLL_BIT_WIDTH 5
316 :
317 : /*
318 : * Assume the palloc overhead always uses sizeof(MemoryChunk) bytes.
319 : */
320 : #define CHUNKHDRSZ sizeof(MemoryChunk)
321 :
322 : /*
323 : * Represents partitioned spill data for a single hashtable. Contains the
324 : * necessary information to route tuples to the correct partition, and to
325 : * transform the spilled data into new batches.
326 : *
327 : * The high bits are used for partition selection (when recursing, we ignore
328 : * the bits that have already been used for partition selection at an earlier
329 : * level).
330 : */
331 : typedef struct HashAggSpill
332 : {
333 : int npartitions; /* number of partitions */
334 : LogicalTape **partitions; /* spill partition tapes */
335 : int64 *ntuples; /* number of tuples in each partition */
336 : uint32 mask; /* mask to find partition from hash value */
337 : int shift; /* after masking, shift by this amount */
338 : hyperLogLogState *hll_card; /* cardinality estimate for contents */
339 : } HashAggSpill;
340 :
341 : /*
342 : * Represents work to be done for one pass of hash aggregation (with only one
343 : * grouping set).
344 : *
345 : * Also tracks the bits of the hash already used for partition selection by
346 : * earlier iterations, so that this batch can use new bits. If all bits have
347 : * already been used, no partitioning will be done (any spilled data will go
348 : * to a single output tape).
349 : */
350 : typedef struct HashAggBatch
351 : {
352 : int setno; /* grouping set */
353 : int used_bits; /* number of bits of hash already used */
354 : LogicalTape *input_tape; /* input partition tape */
355 : int64 input_tuples; /* number of tuples in this batch */
356 : double input_card; /* estimated group cardinality */
357 : } HashAggBatch;
358 :
359 : /* used to find referenced colnos */
360 : typedef struct FindColsContext
361 : {
362 : bool is_aggref; /* is under an aggref */
363 : Bitmapset *aggregated; /* column references under an aggref */
364 : Bitmapset *unaggregated; /* other column references */
365 : } FindColsContext;
366 :
367 : static void select_current_set(AggState *aggstate, int setno, bool is_hash);
368 : static void initialize_phase(AggState *aggstate, int newphase);
369 : static TupleTableSlot *fetch_input_tuple(AggState *aggstate);
370 : static void initialize_aggregates(AggState *aggstate,
371 : AggStatePerGroup *pergroups,
372 : int numReset);
373 : static void advance_transition_function(AggState *aggstate,
374 : AggStatePerTrans pertrans,
375 : AggStatePerGroup pergroupstate);
376 : static void advance_aggregates(AggState *aggstate);
377 : static void process_ordered_aggregate_single(AggState *aggstate,
378 : AggStatePerTrans pertrans,
379 : AggStatePerGroup pergroupstate);
380 : static void process_ordered_aggregate_multi(AggState *aggstate,
381 : AggStatePerTrans pertrans,
382 : AggStatePerGroup pergroupstate);
383 : static void finalize_aggregate(AggState *aggstate,
384 : AggStatePerAgg peragg,
385 : AggStatePerGroup pergroupstate,
386 : Datum *resultVal, bool *resultIsNull);
387 : static void finalize_partialaggregate(AggState *aggstate,
388 : AggStatePerAgg peragg,
389 : AggStatePerGroup pergroupstate,
390 : Datum *resultVal, bool *resultIsNull);
391 : static inline void prepare_hash_slot(AggStatePerHash perhash,
392 : TupleTableSlot *inputslot,
393 : TupleTableSlot *hashslot);
394 : static void prepare_projection_slot(AggState *aggstate,
395 : TupleTableSlot *slot,
396 : int currentSet);
397 : static void finalize_aggregates(AggState *aggstate,
398 : AggStatePerAgg peraggs,
399 : AggStatePerGroup pergroup);
400 : static TupleTableSlot *project_aggregates(AggState *aggstate);
401 : static void find_cols(AggState *aggstate, Bitmapset **aggregated,
402 : Bitmapset **unaggregated);
403 : static bool find_cols_walker(Node *node, FindColsContext *context);
404 : static void build_hash_tables(AggState *aggstate);
405 : static void build_hash_table(AggState *aggstate, int setno, double nbuckets);
406 : static void hashagg_recompile_expressions(AggState *aggstate, bool minslot,
407 : bool nullcheck);
408 : static void hash_create_memory(AggState *aggstate);
409 : static double hash_choose_num_buckets(double hashentrysize,
410 : double ngroups, Size memory);
411 : static int hash_choose_num_partitions(double input_groups,
412 : double hashentrysize,
413 : int used_bits,
414 : int *log2_npartitions);
415 : static void initialize_hash_entry(AggState *aggstate,
416 : TupleHashTable hashtable,
417 : TupleHashEntry entry);
418 : static void lookup_hash_entries(AggState *aggstate);
419 : static TupleTableSlot *agg_retrieve_direct(AggState *aggstate);
420 : static void agg_fill_hash_table(AggState *aggstate);
421 : static bool agg_refill_hash_table(AggState *aggstate);
422 : static TupleTableSlot *agg_retrieve_hash_table(AggState *aggstate);
423 : static TupleTableSlot *agg_retrieve_hash_table_in_memory(AggState *aggstate);
424 : static void hash_agg_check_limits(AggState *aggstate);
425 : static void hash_agg_enter_spill_mode(AggState *aggstate);
426 : static void hash_agg_update_metrics(AggState *aggstate, bool from_tape,
427 : int npartitions);
428 : static void hashagg_finish_initial_spills(AggState *aggstate);
429 : static void hashagg_reset_spill_state(AggState *aggstate);
430 : static HashAggBatch *hashagg_batch_new(LogicalTape *input_tape, int setno,
431 : int64 input_tuples, double input_card,
432 : int used_bits);
433 : static MinimalTuple hashagg_batch_read(HashAggBatch *batch, uint32 *hashp);
434 : static void hashagg_spill_init(HashAggSpill *spill, LogicalTapeSet *tapeset,
435 : int used_bits, double input_groups,
436 : double hashentrysize);
437 : static Size hashagg_spill_tuple(AggState *aggstate, HashAggSpill *spill,
438 : TupleTableSlot *inputslot, uint32 hash);
439 : static void hashagg_spill_finish(AggState *aggstate, HashAggSpill *spill,
440 : int setno);
441 : static Datum GetAggInitVal(Datum textInitVal, Oid transtype);
442 : static void build_pertrans_for_aggref(AggStatePerTrans pertrans,
443 : AggState *aggstate, EState *estate,
444 : Aggref *aggref, Oid transfn_oid,
445 : Oid aggtranstype, Oid aggserialfn,
446 : Oid aggdeserialfn, Datum initValue,
447 : bool initValueIsNull, Oid *inputTypes,
448 : int numArguments);
449 :
450 :
451 : /*
452 : * Select the current grouping set; affects current_set and
453 : * curaggcontext.
454 : */
455 : static void
456 4099949 : select_current_set(AggState *aggstate, int setno, bool is_hash)
457 : {
458 : /*
459 : * When changing this, also adapt ExecAggPlainTransByVal() and
460 : * ExecAggPlainTransByRef().
461 : */
462 4099949 : if (is_hash)
463 3753865 : aggstate->curaggcontext = aggstate->hashcontext;
464 : else
465 346084 : aggstate->curaggcontext = aggstate->aggcontexts[setno];
466 :
467 4099949 : aggstate->current_set = setno;
468 4099949 : }
469 :
470 : /*
471 : * Switch to phase "newphase", which must either be 0 or 1 (to reset) or
472 : * current_phase + 1. Juggle the tuplesorts accordingly.
473 : *
474 : * Phase 0 is for hashing, which we currently handle last in the AGG_MIXED
475 : * case, so when entering phase 0, all we need to do is drop open sorts.
476 : */
477 : static void
478 48378 : initialize_phase(AggState *aggstate, int newphase)
479 : {
480 : Assert(newphase <= 1 || newphase == aggstate->current_phase + 1);
481 :
482 : /*
483 : * Whatever the previous state, we're now done with whatever input
484 : * tuplesort was in use.
485 : */
486 48378 : if (aggstate->sort_in)
487 : {
488 21 : tuplesort_end(aggstate->sort_in);
489 21 : aggstate->sort_in = NULL;
490 : }
491 :
492 48378 : if (newphase <= 1)
493 : {
494 : /*
495 : * Discard any existing output tuplesort.
496 : */
497 48267 : if (aggstate->sort_out)
498 : {
499 3 : tuplesort_end(aggstate->sort_out);
500 3 : aggstate->sort_out = NULL;
501 : }
502 : }
503 : else
504 : {
505 : /*
506 : * The old output tuplesort becomes the new input one, and this is the
507 : * right time to actually sort it.
508 : */
509 111 : aggstate->sort_in = aggstate->sort_out;
510 111 : aggstate->sort_out = NULL;
511 : Assert(aggstate->sort_in);
512 111 : tuplesort_performsort(aggstate->sort_in);
513 : }
514 :
515 : /*
516 : * If this isn't the last phase, we need to sort appropriately for the
517 : * next phase in sequence.
518 : */
519 48378 : if (newphase > 0 && newphase < aggstate->numphases - 1)
520 : {
521 147 : Sort *sortnode = aggstate->phases[newphase + 1].sortnode;
522 147 : PlanState *outerNode = outerPlanState(aggstate);
523 147 : TupleDesc tupDesc = ExecGetResultType(outerNode);
524 :
525 147 : aggstate->sort_out = tuplesort_begin_heap(tupDesc,
526 : sortnode->numCols,
527 : sortnode->sortColIdx,
528 : sortnode->sortOperators,
529 : sortnode->collations,
530 : sortnode->nullsFirst,
531 : work_mem,
532 : NULL, TUPLESORT_NONE);
533 : }
534 :
535 48378 : aggstate->current_phase = newphase;
536 48378 : aggstate->phase = &aggstate->phases[newphase];
537 48378 : }
538 :
539 : /*
540 : * Fetch a tuple from either the outer plan (for phase 1) or from the sorter
541 : * populated by the previous phase. Copy it to the sorter for the next phase
542 : * if any.
543 : *
544 : * Callers cannot rely on memory for tuple in returned slot remaining valid
545 : * past any subsequently fetched tuple.
546 : */
547 : static TupleTableSlot *
548 14731316 : fetch_input_tuple(AggState *aggstate)
549 : {
550 : TupleTableSlot *slot;
551 :
552 14731316 : if (aggstate->sort_in)
553 : {
554 : /* make sure we check for interrupts in either path through here */
555 177465 : CHECK_FOR_INTERRUPTS();
556 177465 : if (!tuplesort_gettupleslot(aggstate->sort_in, true, false,
557 : aggstate->sort_slot, NULL))
558 111 : return NULL;
559 177354 : slot = aggstate->sort_slot;
560 : }
561 : else
562 14553851 : slot = ExecProcNode(outerPlanState(aggstate));
563 :
564 14731160 : if (!TupIsNull(slot) && aggstate->sort_out)
565 177354 : tuplesort_puttupleslot(aggstate->sort_out, slot);
566 :
567 14731160 : return slot;
568 : }
569 :
570 : /*
571 : * (Re)Initialize an individual aggregate.
572 : *
573 : * This function handles only one grouping set, already set in
574 : * aggstate->current_set.
575 : *
576 : * When called, CurrentMemoryContext should be the per-query context.
577 : */
578 : static void
579 569826 : initialize_aggregate(AggState *aggstate, AggStatePerTrans pertrans,
580 : AggStatePerGroup pergroupstate)
581 : {
582 : /*
583 : * Start a fresh sort operation for each DISTINCT/ORDER BY aggregate.
584 : */
585 569826 : if (pertrans->aggsortrequired)
586 : {
587 : /*
588 : * In case of rescan, maybe there could be an uncompleted sort
589 : * operation? Clean it up if so.
590 : */
591 26921 : if (pertrans->sortstates[aggstate->current_set])
592 0 : tuplesort_end(pertrans->sortstates[aggstate->current_set]);
593 :
594 :
595 : /*
596 : * We use a plain Datum sorter when there's a single input column;
597 : * otherwise sort the full tuple. (See comments for
598 : * process_ordered_aggregate_single.)
599 : */
600 26921 : if (pertrans->numInputs == 1)
601 : {
602 26879 : Form_pg_attribute attr = TupleDescAttr(pertrans->sortdesc, 0);
603 :
604 26879 : pertrans->sortstates[aggstate->current_set] =
605 26879 : tuplesort_begin_datum(attr->atttypid,
606 26879 : pertrans->sortOperators[0],
607 26879 : pertrans->sortCollations[0],
608 26879 : pertrans->sortNullsFirst[0],
609 : work_mem, NULL, TUPLESORT_NONE);
610 : }
611 : else
612 42 : pertrans->sortstates[aggstate->current_set] =
613 42 : tuplesort_begin_heap(pertrans->sortdesc,
614 : pertrans->numSortCols,
615 : pertrans->sortColIdx,
616 : pertrans->sortOperators,
617 : pertrans->sortCollations,
618 : pertrans->sortNullsFirst,
619 : work_mem, NULL, TUPLESORT_NONE);
620 : }
621 :
622 : /*
623 : * (Re)set transValue to the initial value.
624 : *
625 : * Note that when the initial value is pass-by-ref, we must copy it (into
626 : * the aggcontext) since we will pfree the transValue later.
627 : */
628 569826 : if (pertrans->initValueIsNull)
629 299057 : pergroupstate->transValue = pertrans->initValue;
630 : else
631 : {
632 : MemoryContext oldContext;
633 :
634 270769 : oldContext = MemoryContextSwitchTo(aggstate->curaggcontext->ecxt_per_tuple_memory);
635 541538 : pergroupstate->transValue = datumCopy(pertrans->initValue,
636 270769 : pertrans->transtypeByVal,
637 270769 : pertrans->transtypeLen);
638 270769 : MemoryContextSwitchTo(oldContext);
639 : }
640 569826 : pergroupstate->transValueIsNull = pertrans->initValueIsNull;
641 :
642 : /*
643 : * If the initial value for the transition state doesn't exist in the
644 : * pg_aggregate table then we will let the first non-NULL value returned
645 : * from the outer procNode become the initial value. (This is useful for
646 : * aggregates like max() and min().) The noTransValue flag signals that we
647 : * still need to do this.
648 : */
649 569826 : pergroupstate->noTransValue = pertrans->initValueIsNull;
650 569826 : }
651 :
652 : /*
653 : * Initialize all aggregate transition states for a new group of input values.
654 : *
655 : * If there are multiple grouping sets, we initialize only the first numReset
656 : * of them (the grouping sets are ordered so that the most specific one, which
657 : * is reset most often, is first). As a convenience, if numReset is 0, we
658 : * reinitialize all sets.
659 : *
660 : * NB: This cannot be used for hash aggregates, as for those the grouping set
661 : * number has to be specified from further up.
662 : *
663 : * When called, CurrentMemoryContext should be the per-query context.
664 : */
665 : static void
666 153932 : initialize_aggregates(AggState *aggstate,
667 : AggStatePerGroup *pergroups,
668 : int numReset)
669 : {
670 : int transno;
671 153932 : int numGroupingSets = Max(aggstate->phase->numsets, 1);
672 153932 : int setno = 0;
673 153932 : int numTrans = aggstate->numtrans;
674 153932 : AggStatePerTrans transstates = aggstate->pertrans;
675 :
676 153932 : if (numReset == 0)
677 0 : numReset = numGroupingSets;
678 :
679 314951 : for (setno = 0; setno < numReset; setno++)
680 : {
681 161019 : AggStatePerGroup pergroup = pergroups[setno];
682 :
683 161019 : select_current_set(aggstate, setno, false);
684 :
685 498129 : for (transno = 0; transno < numTrans; transno++)
686 : {
687 337110 : AggStatePerTrans pertrans = &transstates[transno];
688 337110 : AggStatePerGroup pergroupstate = &pergroup[transno];
689 :
690 337110 : initialize_aggregate(aggstate, pertrans, pergroupstate);
691 : }
692 : }
693 153932 : }
694 :
695 : /*
696 : * Given new input value(s), advance the transition function of one aggregate
697 : * state within one grouping set only (already set in aggstate->current_set)
698 : *
699 : * The new values (and null flags) have been preloaded into argument positions
700 : * 1 and up in pertrans->transfn_fcinfo, so that we needn't copy them again to
701 : * pass to the transition function. We also expect that the static fields of
702 : * the fcinfo are already initialized; that was done by ExecInitAgg().
703 : *
704 : * It doesn't matter which memory context this is called in.
705 : */
706 : static void
707 362126 : advance_transition_function(AggState *aggstate,
708 : AggStatePerTrans pertrans,
709 : AggStatePerGroup pergroupstate)
710 : {
711 362126 : FunctionCallInfo fcinfo = pertrans->transfn_fcinfo;
712 : MemoryContext oldContext;
713 : Datum newVal;
714 :
715 362126 : if (pertrans->transfn.fn_strict)
716 : {
717 : /*
718 : * For a strict transfn, nothing happens when there's a NULL input; we
719 : * just keep the prior transValue.
720 : */
721 112500 : int numTransInputs = pertrans->numTransInputs;
722 : int i;
723 :
724 225000 : for (i = 1; i <= numTransInputs; i++)
725 : {
726 112500 : if (fcinfo->args[i].isnull)
727 0 : return;
728 : }
729 112500 : if (pergroupstate->noTransValue)
730 : {
731 : /*
732 : * transValue has not been initialized. This is the first non-NULL
733 : * input value. We use it as the initial value for transValue. (We
734 : * already checked that the agg's input type is binary-compatible
735 : * with its transtype, so straight copy here is OK.)
736 : *
737 : * We must copy the datum into aggcontext if it is pass-by-ref. We
738 : * do not need to pfree the old transValue, since it's NULL.
739 : */
740 0 : oldContext = MemoryContextSwitchTo(aggstate->curaggcontext->ecxt_per_tuple_memory);
741 0 : pergroupstate->transValue = datumCopy(fcinfo->args[1].value,
742 0 : pertrans->transtypeByVal,
743 0 : pertrans->transtypeLen);
744 0 : pergroupstate->transValueIsNull = false;
745 0 : pergroupstate->noTransValue = false;
746 0 : MemoryContextSwitchTo(oldContext);
747 0 : return;
748 : }
749 112500 : if (pergroupstate->transValueIsNull)
750 : {
751 : /*
752 : * Don't call a strict function with NULL inputs. Note it is
753 : * possible to get here despite the above tests, if the transfn is
754 : * strict *and* returned a NULL on a prior cycle. If that happens
755 : * we will propagate the NULL all the way to the end.
756 : */
757 0 : return;
758 : }
759 : }
760 :
761 : /* We run the transition functions in per-input-tuple memory context */
762 362126 : oldContext = MemoryContextSwitchTo(aggstate->tmpcontext->ecxt_per_tuple_memory);
763 :
764 : /* set up aggstate->curpertrans for AggGetAggref() */
765 362126 : aggstate->curpertrans = pertrans;
766 :
767 : /*
768 : * OK to call the transition function
769 : */
770 362126 : fcinfo->args[0].value = pergroupstate->transValue;
771 362126 : fcinfo->args[0].isnull = pergroupstate->transValueIsNull;
772 362126 : fcinfo->isnull = false; /* just in case transfn doesn't set it */
773 :
774 362126 : newVal = FunctionCallInvoke(fcinfo);
775 :
776 362126 : aggstate->curpertrans = NULL;
777 :
778 : /*
779 : * If pass-by-ref datatype, must copy the new value into aggcontext and
780 : * free the prior transValue. But if transfn returned a pointer to its
781 : * first input, we don't need to do anything.
782 : *
783 : * It's safe to compare newVal with pergroup->transValue without regard
784 : * for either being NULL, because ExecAggCopyTransValue takes care to set
785 : * transValue to 0 when NULL. Otherwise we could end up accidentally not
786 : * reparenting, when the transValue has the same numerical value as
787 : * newValue, despite being NULL. This is a somewhat hot path, making it
788 : * undesirable to instead solve this with another branch for the common
789 : * case of the transition function returning its (modified) input
790 : * argument.
791 : */
792 362126 : if (!pertrans->transtypeByVal &&
793 0 : DatumGetPointer(newVal) != DatumGetPointer(pergroupstate->transValue))
794 0 : newVal = ExecAggCopyTransValue(aggstate, pertrans,
795 0 : newVal, fcinfo->isnull,
796 : pergroupstate->transValue,
797 0 : pergroupstate->transValueIsNull);
798 :
799 362126 : pergroupstate->transValue = newVal;
800 362126 : pergroupstate->transValueIsNull = fcinfo->isnull;
801 :
802 362126 : MemoryContextSwitchTo(oldContext);
803 : }
804 :
805 : /*
806 : * Advance each aggregate transition state for one input tuple. The input
807 : * tuple has been stored in tmpcontext->ecxt_outertuple, so that it is
808 : * accessible to ExecEvalExpr.
809 : *
810 : * We have two sets of transition states to handle: one for sorted aggregation
811 : * and one for hashed; we do them both here, to avoid multiple evaluation of
812 : * the inputs.
813 : *
814 : * When called, CurrentMemoryContext should be the per-query context.
815 : */
816 : static void
817 15068305 : advance_aggregates(AggState *aggstate)
818 : {
819 15068305 : ExecEvalExprNoReturnSwitchContext(aggstate->phase->evaltrans,
820 : aggstate->tmpcontext);
821 15068266 : }
822 :
823 : /*
824 : * Run the transition function for a DISTINCT or ORDER BY aggregate
825 : * with only one input. This is called after we have completed
826 : * entering all the input values into the sort object. We complete the
827 : * sort, read out the values in sorted order, and run the transition
828 : * function on each value (applying DISTINCT if appropriate).
829 : *
830 : * Note that the strictness of the transition function was checked when
831 : * entering the values into the sort, so we don't check it again here;
832 : * we just apply standard SQL DISTINCT logic.
833 : *
834 : * The one-input case is handled separately from the multi-input case
835 : * for performance reasons: for single by-value inputs, such as the
836 : * common case of count(distinct id), the tuplesort_getdatum code path
837 : * is around 300% faster. (The speedup for by-reference types is less
838 : * but still noticeable.)
839 : *
840 : * This function handles only one grouping set (already set in
841 : * aggstate->current_set).
842 : *
843 : * When called, CurrentMemoryContext should be the per-query context.
844 : */
845 : static void
846 26879 : process_ordered_aggregate_single(AggState *aggstate,
847 : AggStatePerTrans pertrans,
848 : AggStatePerGroup pergroupstate)
849 : {
850 26879 : Datum oldVal = (Datum) 0;
851 26879 : bool oldIsNull = true;
852 26879 : bool haveOldVal = false;
853 26879 : MemoryContext workcontext = aggstate->tmpcontext->ecxt_per_tuple_memory;
854 : MemoryContext oldContext;
855 26879 : bool isDistinct = (pertrans->numDistinctCols > 0);
856 26879 : Datum newAbbrevVal = (Datum) 0;
857 26879 : Datum oldAbbrevVal = (Datum) 0;
858 26879 : FunctionCallInfo fcinfo = pertrans->transfn_fcinfo;
859 : Datum *newVal;
860 : bool *isNull;
861 :
862 : Assert(pertrans->numDistinctCols < 2);
863 :
864 26879 : tuplesort_performsort(pertrans->sortstates[aggstate->current_set]);
865 :
866 : /* Load the column into argument 1 (arg 0 will be transition value) */
867 26879 : newVal = &fcinfo->args[1].value;
868 26879 : isNull = &fcinfo->args[1].isnull;
869 :
870 : /*
871 : * Note: if input type is pass-by-ref, the datums returned by the sort are
872 : * freshly palloc'd in the per-query context, so we must be careful to
873 : * pfree them when they are no longer needed.
874 : */
875 :
876 449138 : while (tuplesort_getdatum(pertrans->sortstates[aggstate->current_set],
877 : true, false, newVal, isNull, &newAbbrevVal))
878 : {
879 : /*
880 : * Clear and select the working context for evaluation of the equality
881 : * function and transition function.
882 : */
883 422259 : MemoryContextReset(workcontext);
884 422259 : oldContext = MemoryContextSwitchTo(workcontext);
885 :
886 : /*
887 : * If DISTINCT mode, and not distinct from prior, skip it.
888 : */
889 422259 : if (isDistinct &&
890 155227 : haveOldVal &&
891 0 : ((oldIsNull && *isNull) ||
892 155227 : (!oldIsNull && !*isNull &&
893 302974 : oldAbbrevVal == newAbbrevVal &&
894 147747 : DatumGetBool(FunctionCall2Coll(&pertrans->equalfnOne,
895 : pertrans->aggCollation,
896 : oldVal, *newVal)))))
897 : {
898 60241 : MemoryContextSwitchTo(oldContext);
899 60241 : continue;
900 : }
901 : else
902 : {
903 362018 : advance_transition_function(aggstate, pertrans, pergroupstate);
904 :
905 362018 : MemoryContextSwitchTo(oldContext);
906 :
907 : /*
908 : * Forget the old value, if any, and remember the new one for
909 : * subsequent equality checks.
910 : */
911 362018 : if (!pertrans->inputtypeByVal)
912 : {
913 262644 : if (!oldIsNull)
914 262554 : pfree(DatumGetPointer(oldVal));
915 262644 : if (!*isNull)
916 262614 : oldVal = datumCopy(*newVal, pertrans->inputtypeByVal,
917 262614 : pertrans->inputtypeLen);
918 : }
919 : else
920 99374 : oldVal = *newVal;
921 362018 : oldAbbrevVal = newAbbrevVal;
922 362018 : oldIsNull = *isNull;
923 362018 : haveOldVal = true;
924 : }
925 : }
926 :
927 26879 : if (!oldIsNull && !pertrans->inputtypeByVal)
928 60 : pfree(DatumGetPointer(oldVal));
929 :
930 26879 : tuplesort_end(pertrans->sortstates[aggstate->current_set]);
931 26879 : pertrans->sortstates[aggstate->current_set] = NULL;
932 26879 : }
933 :
934 : /*
935 : * Run the transition function for a DISTINCT or ORDER BY aggregate
936 : * with more than one input. This is called after we have completed
937 : * entering all the input values into the sort object. We complete the
938 : * sort, read out the values in sorted order, and run the transition
939 : * function on each value (applying DISTINCT if appropriate).
940 : *
941 : * This function handles only one grouping set (already set in
942 : * aggstate->current_set).
943 : *
944 : * When called, CurrentMemoryContext should be the per-query context.
945 : */
946 : static void
947 42 : process_ordered_aggregate_multi(AggState *aggstate,
948 : AggStatePerTrans pertrans,
949 : AggStatePerGroup pergroupstate)
950 : {
951 42 : ExprContext *tmpcontext = aggstate->tmpcontext;
952 42 : FunctionCallInfo fcinfo = pertrans->transfn_fcinfo;
953 42 : TupleTableSlot *slot1 = pertrans->sortslot;
954 42 : TupleTableSlot *slot2 = pertrans->uniqslot;
955 42 : int numTransInputs = pertrans->numTransInputs;
956 42 : int numDistinctCols = pertrans->numDistinctCols;
957 42 : Datum newAbbrevVal = (Datum) 0;
958 42 : Datum oldAbbrevVal = (Datum) 0;
959 42 : bool haveOldValue = false;
960 42 : TupleTableSlot *save = aggstate->tmpcontext->ecxt_outertuple;
961 : int i;
962 :
963 42 : tuplesort_performsort(pertrans->sortstates[aggstate->current_set]);
964 :
965 42 : ExecClearTuple(slot1);
966 42 : if (slot2)
967 0 : ExecClearTuple(slot2);
968 :
969 150 : while (tuplesort_gettupleslot(pertrans->sortstates[aggstate->current_set],
970 : true, true, slot1, &newAbbrevVal))
971 : {
972 108 : CHECK_FOR_INTERRUPTS();
973 :
974 108 : tmpcontext->ecxt_outertuple = slot1;
975 108 : tmpcontext->ecxt_innertuple = slot2;
976 :
977 108 : if (numDistinctCols == 0 ||
978 0 : !haveOldValue ||
979 0 : newAbbrevVal != oldAbbrevVal ||
980 0 : !ExecQual(pertrans->equalfnMulti, tmpcontext))
981 : {
982 : /*
983 : * Extract the first numTransInputs columns as datums to pass to
984 : * the transfn.
985 : */
986 108 : slot_getsomeattrs(slot1, numTransInputs);
987 :
988 : /* Load values into fcinfo */
989 : /* Start from 1, since the 0th arg will be the transition value */
990 306 : for (i = 0; i < numTransInputs; i++)
991 : {
992 198 : fcinfo->args[i + 1].value = slot1->tts_values[i];
993 198 : fcinfo->args[i + 1].isnull = slot1->tts_isnull[i];
994 : }
995 :
996 108 : advance_transition_function(aggstate, pertrans, pergroupstate);
997 :
998 108 : if (numDistinctCols > 0)
999 : {
1000 : /* swap the slot pointers to retain the current tuple */
1001 0 : TupleTableSlot *tmpslot = slot2;
1002 :
1003 0 : slot2 = slot1;
1004 0 : slot1 = tmpslot;
1005 : /* avoid ExecQual() calls by reusing abbreviated keys */
1006 0 : oldAbbrevVal = newAbbrevVal;
1007 0 : haveOldValue = true;
1008 : }
1009 : }
1010 :
1011 : /* Reset context each time */
1012 108 : ResetExprContext(tmpcontext);
1013 :
1014 108 : ExecClearTuple(slot1);
1015 : }
1016 :
1017 42 : if (slot2)
1018 0 : ExecClearTuple(slot2);
1019 :
1020 42 : tuplesort_end(pertrans->sortstates[aggstate->current_set]);
1021 42 : pertrans->sortstates[aggstate->current_set] = NULL;
1022 :
1023 : /* restore previous slot, potentially in use for grouping sets */
1024 42 : tmpcontext->ecxt_outertuple = save;
1025 42 : }
1026 :
1027 : /*
1028 : * Compute the final value of one aggregate.
1029 : *
1030 : * This function handles only one grouping set (already set in
1031 : * aggstate->current_set).
1032 : *
1033 : * The finalfn will be run, and the result delivered, in the
1034 : * output-tuple context; caller's CurrentMemoryContext does not matter.
1035 : * (But note that in some cases, such as when there is no finalfn, the
1036 : * result might be a pointer to or into the agg's transition value.)
1037 : *
1038 : * The finalfn uses the state as set in the transno. This also might be
1039 : * being used by another aggregate function, so it's important that we do
1040 : * nothing destructive here. Moreover, the aggregate's final value might
1041 : * get used in multiple places, so we mustn't return a R/W expanded datum.
1042 : */
1043 : static void
1044 562728 : finalize_aggregate(AggState *aggstate,
1045 : AggStatePerAgg peragg,
1046 : AggStatePerGroup pergroupstate,
1047 : Datum *resultVal, bool *resultIsNull)
1048 : {
1049 562728 : LOCAL_FCINFO(fcinfo, FUNC_MAX_ARGS);
1050 562728 : bool anynull = false;
1051 : MemoryContext oldContext;
1052 : int i;
1053 : ListCell *lc;
1054 562728 : AggStatePerTrans pertrans = &aggstate->pertrans[peragg->transno];
1055 :
1056 562728 : oldContext = MemoryContextSwitchTo(aggstate->ss.ps.ps_ExprContext->ecxt_per_tuple_memory);
1057 :
1058 : /*
1059 : * Evaluate any direct arguments. We do this even if there's no finalfn
1060 : * (which is unlikely anyway), so that side-effects happen as expected.
1061 : * The direct arguments go into arg positions 1 and up, leaving position 0
1062 : * for the transition state value.
1063 : */
1064 562728 : i = 1;
1065 563215 : foreach(lc, peragg->aggdirectargs)
1066 : {
1067 487 : ExprState *expr = (ExprState *) lfirst(lc);
1068 :
1069 487 : fcinfo->args[i].value = ExecEvalExpr(expr,
1070 : aggstate->ss.ps.ps_ExprContext,
1071 : &fcinfo->args[i].isnull);
1072 487 : anynull |= fcinfo->args[i].isnull;
1073 487 : i++;
1074 : }
1075 :
1076 : /*
1077 : * Apply the agg's finalfn if one is provided, else return transValue.
1078 : */
1079 562728 : if (OidIsValid(peragg->finalfn_oid))
1080 : {
1081 169871 : int numFinalArgs = peragg->numFinalArgs;
1082 :
1083 : /* set up aggstate->curperagg for AggGetAggref() */
1084 169871 : aggstate->curperagg = peragg;
1085 :
1086 169871 : InitFunctionCallInfoData(*fcinfo, &peragg->finalfn,
1087 : numFinalArgs,
1088 : pertrans->aggCollation,
1089 : (Node *) aggstate, NULL);
1090 :
1091 : /* Fill in the transition state value */
1092 169871 : fcinfo->args[0].value =
1093 169871 : MakeExpandedObjectReadOnly(pergroupstate->transValue,
1094 : pergroupstate->transValueIsNull,
1095 : pertrans->transtypeLen);
1096 169871 : fcinfo->args[0].isnull = pergroupstate->transValueIsNull;
1097 169871 : anynull |= pergroupstate->transValueIsNull;
1098 :
1099 : /* Fill any remaining argument positions with nulls */
1100 246611 : for (; i < numFinalArgs; i++)
1101 : {
1102 76740 : fcinfo->args[i].value = (Datum) 0;
1103 76740 : fcinfo->args[i].isnull = true;
1104 76740 : anynull = true;
1105 : }
1106 :
1107 169871 : if (fcinfo->flinfo->fn_strict && anynull)
1108 : {
1109 : /* don't call a strict function with NULL inputs */
1110 0 : *resultVal = (Datum) 0;
1111 0 : *resultIsNull = true;
1112 : }
1113 : else
1114 : {
1115 : Datum result;
1116 :
1117 169871 : result = FunctionCallInvoke(fcinfo);
1118 169865 : *resultIsNull = fcinfo->isnull;
1119 169865 : *resultVal = MakeExpandedObjectReadOnly(result,
1120 : fcinfo->isnull,
1121 : peragg->resulttypeLen);
1122 : }
1123 169865 : aggstate->curperagg = NULL;
1124 : }
1125 : else
1126 : {
1127 392857 : *resultVal =
1128 392857 : MakeExpandedObjectReadOnly(pergroupstate->transValue,
1129 : pergroupstate->transValueIsNull,
1130 : pertrans->transtypeLen);
1131 392857 : *resultIsNull = pergroupstate->transValueIsNull;
1132 : }
1133 :
1134 562722 : MemoryContextSwitchTo(oldContext);
1135 562722 : }
1136 :
1137 : /*
1138 : * Compute the output value of one partial aggregate.
1139 : *
1140 : * The serialization function will be run, and the result delivered, in the
1141 : * output-tuple context; caller's CurrentMemoryContext does not matter.
1142 : */
1143 : static void
1144 8918 : finalize_partialaggregate(AggState *aggstate,
1145 : AggStatePerAgg peragg,
1146 : AggStatePerGroup pergroupstate,
1147 : Datum *resultVal, bool *resultIsNull)
1148 : {
1149 8918 : AggStatePerTrans pertrans = &aggstate->pertrans[peragg->transno];
1150 : MemoryContext oldContext;
1151 :
1152 8918 : oldContext = MemoryContextSwitchTo(aggstate->ss.ps.ps_ExprContext->ecxt_per_tuple_memory);
1153 :
1154 : /*
1155 : * serialfn_oid will be set if we must serialize the transvalue before
1156 : * returning it
1157 : */
1158 8918 : if (OidIsValid(pertrans->serialfn_oid))
1159 : {
1160 : /* Don't call a strict serialization function with NULL input. */
1161 253 : if (pertrans->serialfn.fn_strict && pergroupstate->transValueIsNull)
1162 : {
1163 38 : *resultVal = (Datum) 0;
1164 38 : *resultIsNull = true;
1165 : }
1166 : else
1167 : {
1168 215 : FunctionCallInfo fcinfo = pertrans->serialfn_fcinfo;
1169 : Datum result;
1170 :
1171 215 : fcinfo->args[0].value =
1172 215 : MakeExpandedObjectReadOnly(pergroupstate->transValue,
1173 : pergroupstate->transValueIsNull,
1174 : pertrans->transtypeLen);
1175 215 : fcinfo->args[0].isnull = pergroupstate->transValueIsNull;
1176 215 : fcinfo->isnull = false;
1177 :
1178 215 : result = FunctionCallInvoke(fcinfo);
1179 215 : *resultIsNull = fcinfo->isnull;
1180 215 : *resultVal = MakeExpandedObjectReadOnly(result,
1181 : fcinfo->isnull,
1182 : peragg->resulttypeLen);
1183 : }
1184 : }
1185 : else
1186 : {
1187 8665 : *resultVal =
1188 8665 : MakeExpandedObjectReadOnly(pergroupstate->transValue,
1189 : pergroupstate->transValueIsNull,
1190 : pertrans->transtypeLen);
1191 8665 : *resultIsNull = pergroupstate->transValueIsNull;
1192 : }
1193 :
1194 8918 : MemoryContextSwitchTo(oldContext);
1195 8918 : }
1196 :
1197 : /*
1198 : * Extract the attributes that make up the grouping key into the
1199 : * hashslot. This is necessary to compute the hash or perform a lookup.
1200 : */
1201 : static inline void
1202 4272418 : prepare_hash_slot(AggStatePerHash perhash,
1203 : TupleTableSlot *inputslot,
1204 : TupleTableSlot *hashslot)
1205 : {
1206 : int i;
1207 :
1208 : /* transfer just the needed columns into hashslot */
1209 4272418 : slot_getsomeattrs(inputslot, perhash->largestGrpColIdx);
1210 4272418 : ExecClearTuple(hashslot);
1211 :
1212 10518854 : for (i = 0; i < perhash->numhashGrpCols; i++)
1213 : {
1214 6246436 : int varNumber = perhash->hashGrpColIdxInput[i] - 1;
1215 :
1216 6246436 : hashslot->tts_values[i] = inputslot->tts_values[varNumber];
1217 6246436 : hashslot->tts_isnull[i] = inputslot->tts_isnull[varNumber];
1218 : }
1219 4272418 : ExecStoreVirtualTuple(hashslot);
1220 4272418 : }
1221 :
1222 : /*
1223 : * Prepare to finalize and project based on the specified representative tuple
1224 : * slot and grouping set.
1225 : *
1226 : * In the specified tuple slot, force to null all attributes that should be
1227 : * read as null in the context of the current grouping set. Also stash the
1228 : * current group bitmap where GroupingExpr can get at it.
1229 : *
1230 : * This relies on three conditions:
1231 : *
1232 : * 1) Nothing is ever going to try and extract the whole tuple from this slot,
1233 : * only reference it in evaluations, which will only access individual
1234 : * attributes.
1235 : *
1236 : * 2) No system columns are going to need to be nulled. (If a system column is
1237 : * referenced in a group clause, it is actually projected in the outer plan
1238 : * tlist.)
1239 : *
1240 : * 3) Within a given phase, we never need to recover the value of an attribute
1241 : * once it has been set to null.
1242 : *
1243 : * Poking into the slot this way is a bit ugly, but the consensus is that the
1244 : * alternative was worse.
1245 : */
1246 : static void
1247 427039 : prepare_projection_slot(AggState *aggstate, TupleTableSlot *slot, int currentSet)
1248 : {
1249 427039 : if (aggstate->phase->grouped_cols)
1250 : {
1251 280836 : Bitmapset *grouped_cols = aggstate->phase->grouped_cols[currentSet];
1252 :
1253 280836 : aggstate->grouped_cols = grouped_cols;
1254 :
1255 280836 : if (TTS_EMPTY(slot))
1256 : {
1257 : /*
1258 : * Force all values to be NULL if working on an empty input tuple
1259 : * (i.e. an empty grouping set for which no input rows were
1260 : * supplied).
1261 : */
1262 30 : ExecStoreAllNullTuple(slot);
1263 : }
1264 280806 : else if (aggstate->all_grouped_cols)
1265 : {
1266 : ListCell *lc;
1267 :
1268 : /* all_grouped_cols is arranged in desc order */
1269 280782 : slot_getsomeattrs(slot, linitial_int(aggstate->all_grouped_cols));
1270 :
1271 765018 : foreach(lc, aggstate->all_grouped_cols)
1272 : {
1273 484236 : int attnum = lfirst_int(lc);
1274 :
1275 484236 : if (!bms_is_member(attnum, grouped_cols))
1276 29005 : slot->tts_isnull[attnum - 1] = true;
1277 : }
1278 : }
1279 : }
1280 427039 : }
1281 :
1282 : /*
1283 : * Compute the final value of all aggregates for one group.
1284 : *
1285 : * This function handles only one grouping set at a time, which the caller must
1286 : * have selected. It's also the caller's responsibility to adjust the supplied
1287 : * pergroup parameter to point to the current set's transvalues.
1288 : *
1289 : * Results are stored in the output econtext aggvalues/aggnulls.
1290 : */
1291 : static void
1292 427039 : finalize_aggregates(AggState *aggstate,
1293 : AggStatePerAgg peraggs,
1294 : AggStatePerGroup pergroup)
1295 : {
1296 427039 : ExprContext *econtext = aggstate->ss.ps.ps_ExprContext;
1297 427039 : Datum *aggvalues = econtext->ecxt_aggvalues;
1298 427039 : bool *aggnulls = econtext->ecxt_aggnulls;
1299 : int aggno;
1300 :
1301 : /*
1302 : * If there were any DISTINCT and/or ORDER BY aggregates, sort their
1303 : * inputs and run the transition functions.
1304 : */
1305 998544 : for (int transno = 0; transno < aggstate->numtrans; transno++)
1306 : {
1307 571505 : AggStatePerTrans pertrans = &aggstate->pertrans[transno];
1308 : AggStatePerGroup pergroupstate;
1309 :
1310 571505 : pergroupstate = &pergroup[transno];
1311 :
1312 571505 : if (pertrans->aggsortrequired)
1313 : {
1314 : Assert(aggstate->aggstrategy != AGG_HASHED &&
1315 : aggstate->aggstrategy != AGG_MIXED);
1316 :
1317 26921 : if (pertrans->numInputs == 1)
1318 26879 : process_ordered_aggregate_single(aggstate,
1319 : pertrans,
1320 : pergroupstate);
1321 : else
1322 42 : process_ordered_aggregate_multi(aggstate,
1323 : pertrans,
1324 : pergroupstate);
1325 : }
1326 544584 : else if (pertrans->numDistinctCols > 0 && pertrans->haslast)
1327 : {
1328 9179 : pertrans->haslast = false;
1329 :
1330 9179 : if (pertrans->numDistinctCols == 1)
1331 : {
1332 9131 : if (!pertrans->inputtypeByVal && !pertrans->lastisnull)
1333 131 : pfree(DatumGetPointer(pertrans->lastdatum));
1334 :
1335 9131 : pertrans->lastisnull = false;
1336 9131 : pertrans->lastdatum = (Datum) 0;
1337 : }
1338 : else
1339 48 : ExecClearTuple(pertrans->uniqslot);
1340 : }
1341 : }
1342 :
1343 : /*
1344 : * Run the final functions.
1345 : */
1346 998679 : for (aggno = 0; aggno < aggstate->numaggs; aggno++)
1347 : {
1348 571646 : AggStatePerAgg peragg = &peraggs[aggno];
1349 571646 : int transno = peragg->transno;
1350 : AggStatePerGroup pergroupstate;
1351 :
1352 571646 : pergroupstate = &pergroup[transno];
1353 :
1354 571646 : if (DO_AGGSPLIT_SKIPFINAL(aggstate->aggsplit))
1355 8918 : finalize_partialaggregate(aggstate, peragg, pergroupstate,
1356 8918 : &aggvalues[aggno], &aggnulls[aggno]);
1357 : else
1358 562728 : finalize_aggregate(aggstate, peragg, pergroupstate,
1359 562728 : &aggvalues[aggno], &aggnulls[aggno]);
1360 : }
1361 427033 : }
1362 :
1363 : /*
1364 : * Project the result of a group (whose aggs have already been calculated by
1365 : * finalize_aggregates). Returns the result slot, or NULL if no row is
1366 : * projected (suppressed by qual).
1367 : */
1368 : static TupleTableSlot *
1369 427033 : project_aggregates(AggState *aggstate)
1370 : {
1371 427033 : ExprContext *econtext = aggstate->ss.ps.ps_ExprContext;
1372 :
1373 : /*
1374 : * Check the qual (HAVING clause); if the group does not match, ignore it.
1375 : */
1376 427033 : if (ExecQual(aggstate->ss.ps.qual, econtext))
1377 : {
1378 : /*
1379 : * Form and return projection tuple using the aggregate results and
1380 : * the representative input tuple.
1381 : */
1382 373789 : return ExecProject(aggstate->ss.ps.ps_ProjInfo);
1383 : }
1384 : else
1385 53244 : InstrCountFiltered1(aggstate, 1);
1386 :
1387 53244 : return NULL;
1388 : }
1389 :
1390 : /*
1391 : * Find input-tuple columns that are needed, dividing them into
1392 : * aggregated and unaggregated sets.
1393 : */
1394 : static void
1395 3580 : find_cols(AggState *aggstate, Bitmapset **aggregated, Bitmapset **unaggregated)
1396 : {
1397 3580 : Agg *agg = (Agg *) aggstate->ss.ps.plan;
1398 : FindColsContext context;
1399 :
1400 3580 : context.is_aggref = false;
1401 3580 : context.aggregated = NULL;
1402 3580 : context.unaggregated = NULL;
1403 :
1404 : /* Examine tlist and quals */
1405 3580 : (void) find_cols_walker((Node *) agg->plan.targetlist, &context);
1406 3580 : (void) find_cols_walker((Node *) agg->plan.qual, &context);
1407 :
1408 : /* In some cases, grouping columns will not appear in the tlist */
1409 9088 : for (int i = 0; i < agg->numCols; i++)
1410 5508 : context.unaggregated = bms_add_member(context.unaggregated,
1411 5508 : agg->grpColIdx[i]);
1412 :
1413 3580 : *aggregated = context.aggregated;
1414 3580 : *unaggregated = context.unaggregated;
1415 3580 : }
1416 :
1417 : static bool
1418 42339 : find_cols_walker(Node *node, FindColsContext *context)
1419 : {
1420 42339 : if (node == NULL)
1421 7644 : return false;
1422 34695 : if (IsA(node, Var))
1423 : {
1424 9565 : Var *var = (Var *) node;
1425 :
1426 : /* setrefs.c should have set the varno to OUTER_VAR */
1427 : Assert(var->varno == OUTER_VAR);
1428 : Assert(var->varlevelsup == 0);
1429 9565 : if (context->is_aggref)
1430 3051 : context->aggregated = bms_add_member(context->aggregated,
1431 3051 : var->varattno);
1432 : else
1433 6514 : context->unaggregated = bms_add_member(context->unaggregated,
1434 6514 : var->varattno);
1435 9565 : return false;
1436 : }
1437 25130 : if (IsA(node, Aggref))
1438 : {
1439 : Assert(!context->is_aggref);
1440 4314 : context->is_aggref = true;
1441 4314 : expression_tree_walker(node, find_cols_walker, context);
1442 4314 : context->is_aggref = false;
1443 4314 : return false;
1444 : }
1445 20816 : return expression_tree_walker(node, find_cols_walker, context);
1446 : }
1447 :
1448 : /*
1449 : * (Re-)initialize the hash table(s) to empty.
1450 : *
1451 : * To implement hashed aggregation, we need a hashtable that stores a
1452 : * representative tuple and an array of AggStatePerGroup structs for each
1453 : * distinct set of GROUP BY column values. We compute the hash key from the
1454 : * GROUP BY columns. The per-group data is allocated in initialize_hash_entry(),
1455 : * for each entry.
1456 : *
1457 : * We have a separate hashtable and associated perhash data structure for each
1458 : * grouping set for which we're doing hashing.
1459 : *
1460 : * The contents of the hash tables live in the aggstate's hash_tuplescxt
1461 : * memory context (there is only one of these for all tables together, since
1462 : * they are all reset at the same time).
1463 : */
1464 : static void
1465 8968 : build_hash_tables(AggState *aggstate)
1466 : {
1467 : int setno;
1468 :
1469 18114 : for (setno = 0; setno < aggstate->num_hashes; ++setno)
1470 : {
1471 9146 : AggStatePerHash perhash = &aggstate->perhash[setno];
1472 : double nbuckets;
1473 : Size memory;
1474 :
1475 9146 : if (perhash->hashtable != NULL)
1476 : {
1477 6359 : ResetTupleHashTable(perhash->hashtable);
1478 6359 : continue;
1479 : }
1480 :
1481 2787 : memory = aggstate->hash_mem_limit / aggstate->num_hashes;
1482 :
1483 : /* choose reasonable number of buckets per hashtable */
1484 2787 : nbuckets = hash_choose_num_buckets(aggstate->hashentrysize,
1485 2787 : perhash->aggnode->numGroups,
1486 : memory);
1487 :
1488 : #ifdef USE_INJECTION_POINTS
1489 2787 : if (IS_INJECTION_POINT_ATTACHED("hash-aggregate-oversize-table"))
1490 : {
1491 0 : nbuckets = memory / TupleHashEntrySize();
1492 0 : INJECTION_POINT_CACHED("hash-aggregate-oversize-table", NULL);
1493 : }
1494 : #endif
1495 :
1496 2787 : build_hash_table(aggstate, setno, nbuckets);
1497 : }
1498 :
1499 8968 : aggstate->hash_ngroups_current = 0;
1500 8968 : }
1501 :
1502 : /*
1503 : * Build a single hashtable for this grouping set.
1504 : */
1505 : static void
1506 2787 : build_hash_table(AggState *aggstate, int setno, double nbuckets)
1507 : {
1508 2787 : AggStatePerHash perhash = &aggstate->perhash[setno];
1509 2787 : MemoryContext metacxt = aggstate->hash_metacxt;
1510 2787 : MemoryContext tuplescxt = aggstate->hash_tuplescxt;
1511 2787 : MemoryContext tmpcxt = aggstate->tmpcontext->ecxt_per_tuple_memory;
1512 : Size additionalsize;
1513 :
1514 : Assert(aggstate->aggstrategy == AGG_HASHED ||
1515 : aggstate->aggstrategy == AGG_MIXED);
1516 :
1517 : /*
1518 : * Used to make sure initial hash table allocation does not exceed
1519 : * hash_mem. Note that the estimate does not include space for
1520 : * pass-by-reference transition data values, nor for the representative
1521 : * tuple of each group.
1522 : */
1523 2787 : additionalsize = aggstate->numtrans * sizeof(AggStatePerGroupData);
1524 :
1525 5574 : perhash->hashtable = BuildTupleHashTable(&aggstate->ss.ps,
1526 2787 : perhash->hashslot->tts_tupleDescriptor,
1527 2787 : perhash->hashslot->tts_ops,
1528 : perhash->numCols,
1529 : perhash->hashGrpColIdxHash,
1530 2787 : perhash->eqfuncoids,
1531 : perhash->hashfunctions,
1532 2787 : perhash->aggnode->grpCollations,
1533 : nbuckets,
1534 : additionalsize,
1535 : metacxt,
1536 : tuplescxt,
1537 : tmpcxt,
1538 2787 : DO_AGGSPLIT_SKIPFINAL(aggstate->aggsplit));
1539 2787 : }
1540 :
1541 : /*
1542 : * Compute columns that actually need to be stored in hashtable entries. The
1543 : * incoming tuples from the child plan node will contain grouping columns,
1544 : * other columns referenced in our targetlist and qual, columns used to
1545 : * compute the aggregate functions, and perhaps just junk columns we don't use
1546 : * at all. Only columns of the first two types need to be stored in the
1547 : * hashtable, and getting rid of the others can make the table entries
1548 : * significantly smaller. The hashtable only contains the relevant columns,
1549 : * and is packed/unpacked in lookup_hash_entries() / agg_retrieve_hash_table()
1550 : * into the format of the normal input descriptor.
1551 : *
1552 : * Additional columns, in addition to the columns grouped by, come from two
1553 : * sources: Firstly functionally dependent columns that we don't need to group
1554 : * by themselves, and secondly ctids for row-marks.
1555 : *
1556 : * To eliminate duplicates, we build a bitmapset of the needed columns, and
1557 : * then build an array of the columns included in the hashtable. We might
1558 : * still have duplicates if the passed-in grpColIdx has them, which can happen
1559 : * in edge cases from semijoins/distinct; these can't always be removed,
1560 : * because it's not certain that the duplicate cols will be using the same
1561 : * hash function.
1562 : *
1563 : * Note that the array is preserved over ExecReScanAgg, so we allocate it in
1564 : * the per-query context (unlike the hash table itself).
1565 : */
1566 : static void
1567 3580 : find_hash_columns(AggState *aggstate)
1568 : {
1569 : Bitmapset *base_colnos;
1570 : Bitmapset *aggregated_colnos;
1571 3580 : TupleDesc scanDesc = aggstate->ss.ss_ScanTupleSlot->tts_tupleDescriptor;
1572 3580 : List *outerTlist = outerPlanState(aggstate)->plan->targetlist;
1573 3580 : int numHashes = aggstate->num_hashes;
1574 3580 : EState *estate = aggstate->ss.ps.state;
1575 : int j;
1576 :
1577 : /* Find Vars that will be needed in tlist and qual */
1578 3580 : find_cols(aggstate, &aggregated_colnos, &base_colnos);
1579 3580 : aggstate->colnos_needed = bms_union(base_colnos, aggregated_colnos);
1580 3580 : aggstate->max_colno_needed = 0;
1581 3580 : aggstate->all_cols_needed = true;
1582 :
1583 15443 : for (int i = 0; i < scanDesc->natts; i++)
1584 : {
1585 11863 : int colno = i + 1;
1586 :
1587 11863 : if (bms_is_member(colno, aggstate->colnos_needed))
1588 8225 : aggstate->max_colno_needed = colno;
1589 : else
1590 3638 : aggstate->all_cols_needed = false;
1591 : }
1592 :
1593 7429 : for (j = 0; j < numHashes; ++j)
1594 : {
1595 3849 : AggStatePerHash perhash = &aggstate->perhash[j];
1596 3849 : Bitmapset *colnos = bms_copy(base_colnos);
1597 3849 : AttrNumber *grpColIdx = perhash->aggnode->grpColIdx;
1598 3849 : List *hashTlist = NIL;
1599 : TupleDesc hashDesc;
1600 : int maxCols;
1601 : int i;
1602 :
1603 3849 : perhash->largestGrpColIdx = 0;
1604 :
1605 : /*
1606 : * If we're doing grouping sets, then some Vars might be referenced in
1607 : * tlist/qual for the benefit of other grouping sets, but not needed
1608 : * when hashing; i.e. prepare_projection_slot will null them out, so
1609 : * there'd be no point storing them. Use prepare_projection_slot's
1610 : * logic to determine which.
1611 : */
1612 3849 : if (aggstate->phases[0].grouped_cols)
1613 : {
1614 3849 : Bitmapset *grouped_cols = aggstate->phases[0].grouped_cols[j];
1615 : ListCell *lc;
1616 :
1617 10418 : foreach(lc, aggstate->all_grouped_cols)
1618 : {
1619 6569 : int attnum = lfirst_int(lc);
1620 :
1621 6569 : if (!bms_is_member(attnum, grouped_cols))
1622 738 : colnos = bms_del_member(colnos, attnum);
1623 : }
1624 : }
1625 :
1626 : /*
1627 : * Compute maximum number of input columns accounting for possible
1628 : * duplications in the grpColIdx array, which can happen in some edge
1629 : * cases where HashAggregate was generated as part of a semijoin or a
1630 : * DISTINCT.
1631 : */
1632 3849 : maxCols = bms_num_members(colnos) + perhash->numCols;
1633 :
1634 3849 : perhash->hashGrpColIdxInput =
1635 3849 : palloc(maxCols * sizeof(AttrNumber));
1636 3849 : perhash->hashGrpColIdxHash =
1637 3849 : palloc(perhash->numCols * sizeof(AttrNumber));
1638 :
1639 : /* Add all the grouping columns to colnos */
1640 9680 : for (i = 0; i < perhash->numCols; i++)
1641 5831 : colnos = bms_add_member(colnos, grpColIdx[i]);
1642 :
1643 : /*
1644 : * First build mapping for columns directly hashed. These are the
1645 : * first, because they'll be accessed when computing hash values and
1646 : * comparing tuples for exact matches. We also build simple mapping
1647 : * for execGrouping, so it knows where to find the to-be-hashed /
1648 : * compared columns in the input.
1649 : */
1650 9680 : for (i = 0; i < perhash->numCols; i++)
1651 : {
1652 5831 : perhash->hashGrpColIdxInput[i] = grpColIdx[i];
1653 5831 : perhash->hashGrpColIdxHash[i] = i + 1;
1654 5831 : perhash->numhashGrpCols++;
1655 : /* delete already mapped columns */
1656 5831 : colnos = bms_del_member(colnos, grpColIdx[i]);
1657 : }
1658 :
1659 : /* and add the remaining columns */
1660 3849 : i = -1;
1661 4488 : while ((i = bms_next_member(colnos, i)) >= 0)
1662 : {
1663 639 : perhash->hashGrpColIdxInput[perhash->numhashGrpCols] = i;
1664 639 : perhash->numhashGrpCols++;
1665 : }
1666 :
1667 : /* and build a tuple descriptor for the hashtable */
1668 10319 : for (i = 0; i < perhash->numhashGrpCols; i++)
1669 : {
1670 6470 : int varNumber = perhash->hashGrpColIdxInput[i] - 1;
1671 :
1672 6470 : hashTlist = lappend(hashTlist, list_nth(outerTlist, varNumber));
1673 6470 : perhash->largestGrpColIdx =
1674 6470 : Max(varNumber + 1, perhash->largestGrpColIdx);
1675 : }
1676 :
1677 3849 : hashDesc = ExecTypeFromTL(hashTlist);
1678 :
1679 3849 : execTuplesHashPrepare(perhash->numCols,
1680 3849 : perhash->aggnode->grpOperators,
1681 : &perhash->eqfuncoids,
1682 : &perhash->hashfunctions);
1683 3849 : perhash->hashslot =
1684 3849 : ExecAllocTableSlot(&estate->es_tupleTable, hashDesc,
1685 : &TTSOpsMinimalTuple);
1686 :
1687 3849 : list_free(hashTlist);
1688 3849 : bms_free(colnos);
1689 : }
1690 :
1691 3580 : bms_free(base_colnos);
1692 3580 : }
1693 :
1694 : /*
1695 : * Estimate per-hash-table-entry overhead.
1696 : */
1697 : Size
1698 21447 : hash_agg_entry_size(int numTrans, Size tupleWidth, Size transitionSpace)
1699 : {
1700 : Size tupleChunkSize;
1701 : Size pergroupChunkSize;
1702 : Size transitionChunkSize;
1703 21447 : Size tupleSize = (MAXALIGN(SizeofMinimalTupleHeader) +
1704 : tupleWidth);
1705 21447 : Size pergroupSize = numTrans * sizeof(AggStatePerGroupData);
1706 :
1707 : /*
1708 : * Entries use the Bump allocator, so the chunk sizes are the same as the
1709 : * requested sizes.
1710 : */
1711 21447 : tupleChunkSize = MAXALIGN(tupleSize);
1712 21447 : pergroupChunkSize = pergroupSize;
1713 :
1714 : /*
1715 : * Transition values use AllocSet, which has a chunk header and also uses
1716 : * power-of-two allocations.
1717 : */
1718 21447 : if (transitionSpace > 0)
1719 2699 : transitionChunkSize = CHUNKHDRSZ + pg_nextpower2_size_t(transitionSpace);
1720 : else
1721 18748 : transitionChunkSize = 0;
1722 :
1723 : return
1724 21447 : TupleHashEntrySize() +
1725 21447 : tupleChunkSize +
1726 21447 : pergroupChunkSize +
1727 : transitionChunkSize;
1728 : }
1729 :
1730 : /*
1731 : * hashagg_recompile_expressions()
1732 : *
1733 : * Identifies the right phase, compiles the right expression given the
1734 : * arguments, and then sets phase->evalfunc to that expression.
1735 : *
1736 : * Different versions of the compiled expression are needed depending on
1737 : * whether hash aggregation has spilled or not, and whether it's reading from
1738 : * the outer plan or a tape. Before spilling to disk, the expression reads
1739 : * from the outer plan and does not need to perform a NULL check. After
1740 : * HashAgg begins to spill, new groups will not be created in the hash table,
1741 : * and the AggStatePerGroup array may be NULL; therefore we need to add a null
1742 : * pointer check to the expression. Then, when reading spilled data from a
1743 : * tape, we change the outer slot type to be a fixed minimal tuple slot.
1744 : *
1745 : * It would be wasteful to recompile every time, so cache the compiled
1746 : * expressions in the AggStatePerPhase, and reuse when appropriate.
1747 : */
1748 : static void
1749 33030 : hashagg_recompile_expressions(AggState *aggstate, bool minslot, bool nullcheck)
1750 : {
1751 : AggStatePerPhase phase;
1752 33030 : int i = minslot ? 1 : 0;
1753 33030 : int j = nullcheck ? 1 : 0;
1754 :
1755 : Assert(aggstate->aggstrategy == AGG_HASHED ||
1756 : aggstate->aggstrategy == AGG_MIXED);
1757 :
1758 33030 : if (aggstate->aggstrategy == AGG_HASHED)
1759 6744 : phase = &aggstate->phases[0];
1760 : else /* AGG_MIXED */
1761 26286 : phase = &aggstate->phases[1];
1762 :
1763 33030 : if (phase->evaltrans_cache[i][j] == NULL)
1764 : {
1765 44 : const TupleTableSlotOps *outerops = aggstate->ss.ps.outerops;
1766 44 : bool outerfixed = aggstate->ss.ps.outeropsfixed;
1767 44 : bool dohash = true;
1768 44 : bool dosort = false;
1769 :
1770 : /*
1771 : * If minslot is true, that means we are processing a spilled batch
1772 : * (inside agg_refill_hash_table()), and we must not advance the
1773 : * sorted grouping sets.
1774 : */
1775 44 : if (aggstate->aggstrategy == AGG_MIXED && !minslot)
1776 6 : dosort = true;
1777 :
1778 : /* temporarily change the outerops while compiling the expression */
1779 44 : if (minslot)
1780 : {
1781 22 : aggstate->ss.ps.outerops = &TTSOpsMinimalTuple;
1782 22 : aggstate->ss.ps.outeropsfixed = true;
1783 : }
1784 :
1785 44 : phase->evaltrans_cache[i][j] = ExecBuildAggTrans(aggstate, phase,
1786 : dosort, dohash,
1787 : nullcheck);
1788 :
1789 : /* change back */
1790 44 : aggstate->ss.ps.outerops = outerops;
1791 44 : aggstate->ss.ps.outeropsfixed = outerfixed;
1792 : }
1793 :
1794 33030 : phase->evaltrans = phase->evaltrans_cache[i][j];
1795 33030 : }
1796 :
1797 : /*
1798 : * Set limits that trigger spilling to avoid exceeding hash_mem. Consider the
1799 : * number of partitions we expect to create (if we do spill).
1800 : *
1801 : * There are two limits: a memory limit, and also an ngroups limit. The
1802 : * ngroups limit becomes important when we expect transition values to grow
1803 : * substantially larger than the initial value.
1804 : */
1805 : void
1806 33608 : hash_agg_set_limits(double hashentrysize, double input_groups, int used_bits,
1807 : Size *mem_limit, uint64 *ngroups_limit,
1808 : int *num_partitions)
1809 : {
1810 : int npartitions;
1811 : Size partition_mem;
1812 33608 : Size hash_mem_limit = get_hash_memory_limit();
1813 :
1814 : /* if not expected to spill, use all of hash_mem */
1815 33608 : if (input_groups * hashentrysize <= hash_mem_limit)
1816 : {
1817 32393 : if (num_partitions != NULL)
1818 20088 : *num_partitions = 0;
1819 32393 : *mem_limit = hash_mem_limit;
1820 32393 : *ngroups_limit = hash_mem_limit / hashentrysize;
1821 32393 : return;
1822 : }
1823 :
1824 : /*
1825 : * Calculate expected memory requirements for spilling, which is the size
1826 : * of the buffers needed for all the tapes that need to be open at once.
1827 : * Then, subtract that from the memory available for holding hash tables.
1828 : */
1829 1215 : npartitions = hash_choose_num_partitions(input_groups,
1830 : hashentrysize,
1831 : used_bits,
1832 : NULL);
1833 1215 : if (num_partitions != NULL)
1834 48 : *num_partitions = npartitions;
1835 :
1836 1215 : partition_mem =
1837 1215 : HASHAGG_READ_BUFFER_SIZE +
1838 : HASHAGG_WRITE_BUFFER_SIZE * npartitions;
1839 :
1840 : /*
1841 : * Don't set the limit below 3/4 of hash_mem. In that case, we are at the
1842 : * minimum number of partitions, so we aren't going to dramatically exceed
1843 : * work mem anyway.
1844 : */
1845 1215 : if (hash_mem_limit > 4 * partition_mem)
1846 0 : *mem_limit = hash_mem_limit - partition_mem;
1847 : else
1848 1215 : *mem_limit = hash_mem_limit * 0.75;
1849 :
1850 1215 : if (*mem_limit > hashentrysize)
1851 1215 : *ngroups_limit = *mem_limit / hashentrysize;
1852 : else
1853 0 : *ngroups_limit = 1;
1854 : }
1855 :
1856 : /*
1857 : * hash_agg_check_limits
1858 : *
1859 : * After adding a new group to the hash table, check whether we need to enter
1860 : * spill mode. Allocations may happen without adding new groups (for instance,
1861 : * if the transition state size grows), so this check is imperfect.
1862 : */
1863 : static void
1864 265111 : hash_agg_check_limits(AggState *aggstate)
1865 : {
1866 265111 : uint64 ngroups = aggstate->hash_ngroups_current;
1867 265111 : Size meta_mem = MemoryContextMemAllocated(aggstate->hash_metacxt,
1868 : true);
1869 265111 : Size entry_mem = MemoryContextMemAllocated(aggstate->hash_tuplescxt,
1870 : true);
1871 265111 : Size tval_mem = MemoryContextMemAllocated(aggstate->hashcontext->ecxt_per_tuple_memory,
1872 : true);
1873 265111 : Size total_mem = meta_mem + entry_mem + tval_mem;
1874 265111 : bool do_spill = false;
1875 :
1876 : #ifdef USE_INJECTION_POINTS
1877 265111 : if (ngroups >= 1000)
1878 : {
1879 47825 : if (IS_INJECTION_POINT_ATTACHED("hash-aggregate-spill-1000"))
1880 : {
1881 5 : do_spill = true;
1882 5 : INJECTION_POINT_CACHED("hash-aggregate-spill-1000", NULL);
1883 : }
1884 : }
1885 : #endif
1886 :
1887 : /*
1888 : * Don't spill unless there's at least one group in the hash table so we
1889 : * can be sure to make progress even in edge cases.
1890 : */
1891 265111 : if (aggstate->hash_ngroups_current > 0 &&
1892 265111 : (total_mem > aggstate->hash_mem_limit ||
1893 251905 : ngroups > aggstate->hash_ngroups_limit))
1894 : {
1895 13224 : do_spill = true;
1896 : }
1897 :
1898 265111 : if (do_spill)
1899 13229 : hash_agg_enter_spill_mode(aggstate);
1900 265111 : }
1901 :
1902 : /*
1903 : * Enter "spill mode", meaning that no new groups are added to any of the hash
1904 : * tables. Tuples that would create a new group are instead spilled, and
1905 : * processed later.
1906 : */
1907 : static void
1908 13229 : hash_agg_enter_spill_mode(AggState *aggstate)
1909 : {
1910 13229 : INJECTION_POINT("hash-aggregate-enter-spill-mode", NULL);
1911 13229 : aggstate->hash_spill_mode = true;
1912 13229 : hashagg_recompile_expressions(aggstate, aggstate->table_filled, true);
1913 :
1914 13229 : if (!aggstate->hash_ever_spilled)
1915 : {
1916 : Assert(aggstate->hash_tapeset == NULL);
1917 : Assert(aggstate->hash_spills == NULL);
1918 :
1919 31 : aggstate->hash_ever_spilled = true;
1920 :
1921 31 : aggstate->hash_tapeset = LogicalTapeSetCreate(true, NULL, -1);
1922 :
1923 31 : aggstate->hash_spills = palloc_array(HashAggSpill, aggstate->num_hashes);
1924 :
1925 92 : for (int setno = 0; setno < aggstate->num_hashes; setno++)
1926 : {
1927 61 : AggStatePerHash perhash = &aggstate->perhash[setno];
1928 61 : HashAggSpill *spill = &aggstate->hash_spills[setno];
1929 :
1930 61 : hashagg_spill_init(spill, aggstate->hash_tapeset, 0,
1931 61 : perhash->aggnode->numGroups,
1932 : aggstate->hashentrysize);
1933 : }
1934 : }
1935 13229 : }
1936 :
1937 : /*
1938 : * Update metrics after filling the hash table.
1939 : *
1940 : * If reading from the outer plan, from_tape should be false; if reading from
1941 : * another tape, from_tape should be true.
1942 : */
1943 : static void
1944 22312 : hash_agg_update_metrics(AggState *aggstate, bool from_tape, int npartitions)
1945 : {
1946 : Size meta_mem;
1947 : Size entry_mem;
1948 : Size hashkey_mem;
1949 : Size buffer_mem;
1950 : Size total_mem;
1951 :
1952 22312 : if (aggstate->aggstrategy != AGG_MIXED &&
1953 9101 : aggstate->aggstrategy != AGG_HASHED)
1954 0 : return;
1955 :
1956 : /* memory for the hash table itself */
1957 22312 : meta_mem = MemoryContextMemAllocated(aggstate->hash_metacxt, true);
1958 :
1959 : /* memory for hash entries */
1960 22312 : entry_mem = MemoryContextMemAllocated(aggstate->hash_tuplescxt, true);
1961 :
1962 : /* memory for byref transition states */
1963 22312 : hashkey_mem = MemoryContextMemAllocated(aggstate->hashcontext->ecxt_per_tuple_memory, true);
1964 :
1965 : /* memory for read/write tape buffers, if spilled */
1966 22312 : buffer_mem = npartitions * HASHAGG_WRITE_BUFFER_SIZE;
1967 22312 : if (from_tape)
1968 13472 : buffer_mem += HASHAGG_READ_BUFFER_SIZE;
1969 :
1970 : /* update peak mem */
1971 22312 : total_mem = meta_mem + entry_mem + hashkey_mem + buffer_mem;
1972 22312 : if (total_mem > aggstate->hash_mem_peak)
1973 2536 : aggstate->hash_mem_peak = total_mem;
1974 :
1975 : /* update disk usage */
1976 22312 : if (aggstate->hash_tapeset != NULL)
1977 : {
1978 13503 : uint64 disk_used = LogicalTapeSetBlocks(aggstate->hash_tapeset) * (BLCKSZ / 1024);
1979 :
1980 13503 : if (aggstate->hash_disk_used < disk_used)
1981 26 : aggstate->hash_disk_used = disk_used;
1982 : }
1983 :
1984 : /* update hashentrysize estimate based on contents */
1985 22312 : if (aggstate->hash_ngroups_current > 0)
1986 : {
1987 22045 : aggstate->hashentrysize =
1988 22045 : TupleHashEntrySize() +
1989 22045 : (hashkey_mem / (double) aggstate->hash_ngroups_current);
1990 : }
1991 : }
1992 :
1993 : /*
1994 : * Create memory contexts used for hash aggregation.
1995 : */
1996 : static void
1997 3580 : hash_create_memory(AggState *aggstate)
1998 : {
1999 3580 : Size maxBlockSize = ALLOCSET_DEFAULT_MAXSIZE;
2000 :
2001 : /*
2002 : * The hashcontext's per-tuple memory will be used for byref transition
2003 : * values and returned by AggCheckCallContext().
2004 : */
2005 3580 : aggstate->hashcontext = CreateWorkExprContext(aggstate->ss.ps.state);
2006 :
2007 : /*
2008 : * The meta context will be used for the bucket array of
2009 : * TupleHashEntryData (or arrays, in the case of grouping sets). As the
2010 : * hash table grows, the bucket array will double in size and the old one
2011 : * will be freed, so an AllocSet is appropriate. For large bucket arrays,
2012 : * the large allocation path will be used, so it's not worth worrying
2013 : * about wasting space due to power-of-two allocations.
2014 : */
2015 3580 : aggstate->hash_metacxt = AllocSetContextCreate(aggstate->ss.ps.state->es_query_cxt,
2016 : "HashAgg meta context",
2017 : ALLOCSET_DEFAULT_SIZES);
2018 :
2019 : /*
2020 : * The hash entries themselves, which include the grouping key
2021 : * (firstTuple) and pergroup data, are stored in the table context. The
2022 : * bump allocator can be used because the entries are not freed until the
2023 : * entire hash table is reset. The bump allocator is faster for
2024 : * allocations and avoids wasting space on the chunk header or
2025 : * power-of-two allocations.
2026 : *
2027 : * Like CreateWorkExprContext(), use smaller sizings for smaller work_mem,
2028 : * to avoid large jumps in memory usage.
2029 : */
2030 :
2031 : /*
2032 : * Like CreateWorkExprContext(), use smaller sizings for smaller work_mem,
2033 : * to avoid large jumps in memory usage.
2034 : */
2035 3580 : maxBlockSize = pg_prevpower2_size_t(work_mem * (Size) 1024 / 16);
2036 :
2037 : /* But no bigger than ALLOCSET_DEFAULT_MAXSIZE */
2038 3580 : maxBlockSize = Min(maxBlockSize, ALLOCSET_DEFAULT_MAXSIZE);
2039 :
2040 : /* and no smaller than ALLOCSET_DEFAULT_INITSIZE */
2041 3580 : maxBlockSize = Max(maxBlockSize, ALLOCSET_DEFAULT_INITSIZE);
2042 :
2043 3580 : aggstate->hash_tuplescxt = BumpContextCreate(aggstate->ss.ps.state->es_query_cxt,
2044 : "HashAgg hashed tuples",
2045 : ALLOCSET_DEFAULT_MINSIZE,
2046 : ALLOCSET_DEFAULT_INITSIZE,
2047 : maxBlockSize);
2048 :
2049 3580 : }
2050 :
2051 : /*
2052 : * Choose a reasonable number of buckets for the initial hash table size.
2053 : */
2054 : static double
2055 2787 : hash_choose_num_buckets(double hashentrysize, double ngroups, Size memory)
2056 : {
2057 : double max_nbuckets;
2058 2787 : double nbuckets = ngroups;
2059 :
2060 2787 : max_nbuckets = memory / hashentrysize;
2061 :
2062 : /*
2063 : * Underestimating is better than overestimating. Too many buckets crowd
2064 : * out space for group keys and transition state values.
2065 : */
2066 2787 : max_nbuckets /= 2;
2067 :
2068 2787 : if (nbuckets > max_nbuckets)
2069 36 : nbuckets = max_nbuckets;
2070 :
2071 : /*
2072 : * BuildTupleHashTable will clamp any obviously-insane result, so we don't
2073 : * need to be too careful here.
2074 : */
2075 2787 : return nbuckets;
2076 : }
2077 :
2078 : /*
2079 : * Determine the number of partitions to create when spilling, which will
2080 : * always be a power of two. If log2_npartitions is non-NULL, set
2081 : * *log2_npartitions to the log2() of the number of partitions.
2082 : */
2083 : static int
2084 7526 : hash_choose_num_partitions(double input_groups, double hashentrysize,
2085 : int used_bits, int *log2_npartitions)
2086 : {
2087 7526 : Size hash_mem_limit = get_hash_memory_limit();
2088 : double partition_limit;
2089 : double mem_wanted;
2090 : double dpartitions;
2091 : int npartitions;
2092 : int partition_bits;
2093 :
2094 : /*
2095 : * Avoid creating so many partitions that the memory requirements of the
2096 : * open partition files are greater than 1/4 of hash_mem.
2097 : */
2098 7526 : partition_limit =
2099 7526 : (hash_mem_limit * 0.25 - HASHAGG_READ_BUFFER_SIZE) /
2100 : HASHAGG_WRITE_BUFFER_SIZE;
2101 :
2102 7526 : mem_wanted = HASHAGG_PARTITION_FACTOR * input_groups * hashentrysize;
2103 :
2104 : /* make enough partitions so that each one is likely to fit in memory */
2105 7526 : dpartitions = 1 + (mem_wanted / hash_mem_limit);
2106 :
2107 7526 : if (dpartitions > partition_limit)
2108 7494 : dpartitions = partition_limit;
2109 :
2110 7526 : if (dpartitions < HASHAGG_MIN_PARTITIONS)
2111 7526 : dpartitions = HASHAGG_MIN_PARTITIONS;
2112 7526 : if (dpartitions > HASHAGG_MAX_PARTITIONS)
2113 0 : dpartitions = HASHAGG_MAX_PARTITIONS;
2114 :
2115 : /* HASHAGG_MAX_PARTITIONS limit makes this safe */
2116 7526 : npartitions = (int) dpartitions;
2117 :
2118 : /* ceil(log2(npartitions)) */
2119 7526 : partition_bits = pg_ceil_log2_32(npartitions);
2120 :
2121 : /* make sure that we don't exhaust the hash bits */
2122 7526 : if (partition_bits + used_bits >= 32)
2123 0 : partition_bits = 32 - used_bits;
2124 :
2125 7526 : if (log2_npartitions != NULL)
2126 6311 : *log2_npartitions = partition_bits;
2127 :
2128 : /* number of partitions will be a power of two */
2129 7526 : npartitions = 1 << partition_bits;
2130 :
2131 7526 : return npartitions;
2132 : }
2133 :
2134 : /*
2135 : * Initialize a freshly-created TupleHashEntry.
2136 : */
2137 : static void
2138 265111 : initialize_hash_entry(AggState *aggstate, TupleHashTable hashtable,
2139 : TupleHashEntry entry)
2140 : {
2141 : AggStatePerGroup pergroup;
2142 : int transno;
2143 :
2144 265111 : aggstate->hash_ngroups_current++;
2145 265111 : hash_agg_check_limits(aggstate);
2146 :
2147 : /* no need to allocate or initialize per-group state */
2148 265111 : if (aggstate->numtrans == 0)
2149 107166 : return;
2150 :
2151 157945 : pergroup = (AggStatePerGroup) TupleHashEntryGetAdditional(hashtable, entry);
2152 :
2153 : /*
2154 : * Initialize aggregates for new tuple group, lookup_hash_entries()
2155 : * already has selected the relevant grouping set.
2156 : */
2157 390661 : for (transno = 0; transno < aggstate->numtrans; transno++)
2158 : {
2159 232716 : AggStatePerTrans pertrans = &aggstate->pertrans[transno];
2160 232716 : AggStatePerGroup pergroupstate = &pergroup[transno];
2161 :
2162 232716 : initialize_aggregate(aggstate, pertrans, pergroupstate);
2163 : }
2164 : }
2165 :
2166 : /*
2167 : * Look up hash entries for the current tuple in all hashed grouping sets.
2168 : *
2169 : * Some entries may be left NULL if we are in "spill mode". The same tuple
2170 : * will belong to different groups for each grouping set, so may match a group
2171 : * already in memory for one set and match a group not in memory for another
2172 : * set. When in "spill mode", the tuple will be spilled for each grouping set
2173 : * where it doesn't match a group in memory.
2174 : *
2175 : * NB: It's possible to spill the same tuple for several different grouping
2176 : * sets. This may seem wasteful, but it's actually a trade-off: if we spill
2177 : * the tuple multiple times for multiple grouping sets, it can be partitioned
2178 : * for each grouping set, making the refilling of the hash table very
2179 : * efficient.
2180 : */
2181 : static void
2182 3596792 : lookup_hash_entries(AggState *aggstate)
2183 : {
2184 3596792 : AggStatePerGroup *pergroup = aggstate->hash_pergroup;
2185 3596792 : TupleTableSlot *outerslot = aggstate->tmpcontext->ecxt_outertuple;
2186 : int setno;
2187 :
2188 7260822 : for (setno = 0; setno < aggstate->num_hashes; setno++)
2189 : {
2190 3664030 : AggStatePerHash perhash = &aggstate->perhash[setno];
2191 3664030 : TupleHashTable hashtable = perhash->hashtable;
2192 3664030 : TupleTableSlot *hashslot = perhash->hashslot;
2193 : TupleHashEntry entry;
2194 : uint32 hash;
2195 3664030 : bool isnew = false;
2196 : bool *p_isnew;
2197 :
2198 : /* if hash table already spilled, don't create new entries */
2199 3664030 : p_isnew = aggstate->hash_spill_mode ? NULL : &isnew;
2200 :
2201 3664030 : select_current_set(aggstate, setno, true);
2202 3664030 : prepare_hash_slot(perhash,
2203 : outerslot,
2204 : hashslot);
2205 :
2206 3664030 : entry = LookupTupleHashEntry(hashtable, hashslot,
2207 : p_isnew, &hash);
2208 :
2209 3664030 : if (entry != NULL)
2210 : {
2211 3280412 : if (isnew)
2212 187271 : initialize_hash_entry(aggstate, hashtable, entry);
2213 3280412 : pergroup[setno] = TupleHashEntryGetAdditional(hashtable, entry);
2214 : }
2215 : else
2216 : {
2217 383618 : HashAggSpill *spill = &aggstate->hash_spills[setno];
2218 383618 : TupleTableSlot *slot = aggstate->tmpcontext->ecxt_outertuple;
2219 :
2220 383618 : if (spill->partitions == NULL)
2221 0 : hashagg_spill_init(spill, aggstate->hash_tapeset, 0,
2222 0 : perhash->aggnode->numGroups,
2223 : aggstate->hashentrysize);
2224 :
2225 383618 : hashagg_spill_tuple(aggstate, spill, slot, hash);
2226 383618 : pergroup[setno] = NULL;
2227 : }
2228 : }
2229 3596792 : }
2230 :
2231 : /*
2232 : * ExecAgg -
2233 : *
2234 : * ExecAgg receives tuples from its outer subplan and aggregates over
2235 : * the appropriate attribute for each aggregate function use (Aggref
2236 : * node) appearing in the targetlist or qual of the node. The number
2237 : * of tuples to aggregate over depends on whether grouped or plain
2238 : * aggregation is selected. In grouped aggregation, we produce a result
2239 : * row for each group; in plain aggregation there's a single result row
2240 : * for the whole query. In either case, the value of each aggregate is
2241 : * stored in the expression context to be used when ExecProject evaluates
2242 : * the result tuple.
2243 : */
2244 : static TupleTableSlot *
2245 418321 : ExecAgg(PlanState *pstate)
2246 : {
2247 418321 : AggState *node = castNode(AggState, pstate);
2248 418321 : TupleTableSlot *result = NULL;
2249 :
2250 418321 : CHECK_FOR_INTERRUPTS();
2251 :
2252 418321 : if (!node->agg_done)
2253 : {
2254 : /* Dispatch based on strategy */
2255 383547 : switch (node->phase->aggstrategy)
2256 : {
2257 243712 : case AGG_HASHED:
2258 243712 : if (!node->table_filled)
2259 8760 : agg_fill_hash_table(node);
2260 : pg_fallthrough;
2261 : case AGG_MIXED:
2262 257413 : result = agg_retrieve_hash_table(node);
2263 257413 : break;
2264 126134 : case AGG_PLAIN:
2265 : case AGG_SORTED:
2266 126134 : result = agg_retrieve_direct(node);
2267 126038 : break;
2268 : }
2269 :
2270 383451 : if (!TupIsNull(result))
2271 373783 : return result;
2272 : }
2273 :
2274 44442 : return NULL;
2275 : }
2276 :
2277 : /*
2278 : * ExecAgg for non-hashed case
2279 : */
2280 : static TupleTableSlot *
2281 126134 : agg_retrieve_direct(AggState *aggstate)
2282 : {
2283 126134 : Agg *node = aggstate->phase->aggnode;
2284 : ExprContext *econtext;
2285 : ExprContext *tmpcontext;
2286 : AggStatePerAgg peragg;
2287 : AggStatePerGroup *pergroups;
2288 : TupleTableSlot *outerslot;
2289 : TupleTableSlot *firstSlot;
2290 : TupleTableSlot *result;
2291 126134 : bool hasGroupingSets = aggstate->phase->numsets > 0;
2292 126134 : int numGroupingSets = Max(aggstate->phase->numsets, 1);
2293 : int currentSet;
2294 : int nextSetSize;
2295 : int numReset;
2296 : int i;
2297 :
2298 : /*
2299 : * get state info from node
2300 : *
2301 : * econtext is the per-output-tuple expression context
2302 : *
2303 : * tmpcontext is the per-input-tuple expression context
2304 : */
2305 126134 : econtext = aggstate->ss.ps.ps_ExprContext;
2306 126134 : tmpcontext = aggstate->tmpcontext;
2307 :
2308 126134 : peragg = aggstate->peragg;
2309 126134 : pergroups = aggstate->pergroups;
2310 126134 : firstSlot = aggstate->ss.ss_ScanTupleSlot;
2311 :
2312 : /*
2313 : * We loop retrieving groups until we find one matching
2314 : * aggstate->ss.ps.qual
2315 : *
2316 : * For grouping sets, we have the invariant that aggstate->projected_set
2317 : * is either -1 (initial call) or the index (starting from 0) in
2318 : * gset_lengths for the group we just completed (either by projecting a
2319 : * row or by discarding it in the qual).
2320 : */
2321 161550 : while (!aggstate->agg_done)
2322 : {
2323 : /*
2324 : * Clear the per-output-tuple context for each group, as well as
2325 : * aggcontext (which contains any pass-by-ref transvalues of the old
2326 : * group). Some aggregate functions store working state in child
2327 : * contexts; those now get reset automatically without us needing to
2328 : * do anything special.
2329 : *
2330 : * We use ReScanExprContext not just ResetExprContext because we want
2331 : * any registered shutdown callbacks to be called. That allows
2332 : * aggregate functions to ensure they've cleaned up any non-memory
2333 : * resources.
2334 : */
2335 161445 : ReScanExprContext(econtext);
2336 :
2337 : /*
2338 : * Determine how many grouping sets need to be reset at this boundary.
2339 : */
2340 161445 : if (aggstate->projected_set >= 0 &&
2341 123648 : aggstate->projected_set < numGroupingSets)
2342 123639 : numReset = aggstate->projected_set + 1;
2343 : else
2344 37806 : numReset = numGroupingSets;
2345 :
2346 : /*
2347 : * numReset can change on a phase boundary, but that's OK; we want to
2348 : * reset the contexts used in _this_ phase, and later, after possibly
2349 : * changing phase, initialize the right number of aggregates for the
2350 : * _new_ phase.
2351 : */
2352 :
2353 334029 : for (i = 0; i < numReset; i++)
2354 : {
2355 172584 : ReScanExprContext(aggstate->aggcontexts[i]);
2356 : }
2357 :
2358 : /*
2359 : * Check if input is complete and there are no more groups to project
2360 : * in this phase; move to next phase or mark as done.
2361 : */
2362 161445 : if (aggstate->input_done == true &&
2363 833 : aggstate->projected_set >= (numGroupingSets - 1))
2364 : {
2365 425 : if (aggstate->current_phase < aggstate->numphases - 1)
2366 : {
2367 111 : initialize_phase(aggstate, aggstate->current_phase + 1);
2368 111 : aggstate->input_done = false;
2369 111 : aggstate->projected_set = -1;
2370 111 : numGroupingSets = Max(aggstate->phase->numsets, 1);
2371 111 : node = aggstate->phase->aggnode;
2372 111 : numReset = numGroupingSets;
2373 : }
2374 314 : else if (aggstate->aggstrategy == AGG_MIXED)
2375 : {
2376 : /*
2377 : * Mixed mode; we've output all the grouped stuff and have
2378 : * full hashtables, so switch to outputting those.
2379 : */
2380 86 : initialize_phase(aggstate, 0);
2381 86 : aggstate->table_filled = true;
2382 86 : ResetTupleHashIterator(aggstate->perhash[0].hashtable,
2383 : &aggstate->perhash[0].hashiter);
2384 86 : select_current_set(aggstate, 0, true);
2385 86 : return agg_retrieve_hash_table(aggstate);
2386 : }
2387 : else
2388 : {
2389 228 : aggstate->agg_done = true;
2390 228 : break;
2391 : }
2392 : }
2393 :
2394 : /*
2395 : * Get the number of columns in the next grouping set after the last
2396 : * projected one (if any). This is the number of columns to compare to
2397 : * see if we reached the boundary of that set too.
2398 : */
2399 161131 : if (aggstate->projected_set >= 0 &&
2400 123223 : aggstate->projected_set < (numGroupingSets - 1))
2401 13647 : nextSetSize = aggstate->phase->gset_lengths[aggstate->projected_set + 1];
2402 : else
2403 147484 : nextSetSize = 0;
2404 :
2405 : /*----------
2406 : * If a subgroup for the current grouping set is present, project it.
2407 : *
2408 : * We have a new group if:
2409 : * - we're out of input but haven't projected all grouping sets
2410 : * (checked above)
2411 : * OR
2412 : * - we already projected a row that wasn't from the last grouping
2413 : * set
2414 : * AND
2415 : * - the next grouping set has at least one grouping column (since
2416 : * empty grouping sets project only once input is exhausted)
2417 : * AND
2418 : * - the previous and pending rows differ on the grouping columns
2419 : * of the next grouping set
2420 : *----------
2421 : */
2422 161131 : tmpcontext->ecxt_innertuple = econtext->ecxt_outertuple;
2423 161131 : if (aggstate->input_done ||
2424 160723 : (node->aggstrategy != AGG_PLAIN &&
2425 123850 : aggstate->projected_set != -1 &&
2426 122815 : aggstate->projected_set < (numGroupingSets - 1) &&
2427 9973 : nextSetSize > 0 &&
2428 9973 : !ExecQualAndReset(aggstate->phase->eqfunctions[nextSetSize - 1],
2429 : tmpcontext)))
2430 : {
2431 7075 : aggstate->projected_set += 1;
2432 :
2433 : Assert(aggstate->projected_set < numGroupingSets);
2434 7075 : Assert(nextSetSize > 0 || aggstate->input_done);
2435 : }
2436 : else
2437 : {
2438 : /*
2439 : * We no longer care what group we just projected, the next
2440 : * projection will always be the first (or only) grouping set
2441 : * (unless the input proves to be empty).
2442 : */
2443 154056 : aggstate->projected_set = 0;
2444 :
2445 : /*
2446 : * If we don't already have the first tuple of the new group,
2447 : * fetch it from the outer plan.
2448 : */
2449 154056 : if (aggstate->grp_firstTuple == NULL)
2450 : {
2451 37908 : outerslot = fetch_input_tuple(aggstate);
2452 37878 : if (!TupIsNull(outerslot))
2453 : {
2454 : /*
2455 : * Make a copy of the first input tuple; we will use this
2456 : * for comparisons (in group mode) and for projection.
2457 : */
2458 31204 : aggstate->grp_firstTuple = ExecCopySlotHeapTuple(outerslot);
2459 : }
2460 : else
2461 : {
2462 : /* outer plan produced no tuples at all */
2463 6674 : if (hasGroupingSets)
2464 : {
2465 : /*
2466 : * If there was no input at all, we need to project
2467 : * rows only if there are grouping sets of size 0.
2468 : * Note that this implies that there can't be any
2469 : * references to ungrouped Vars, which would otherwise
2470 : * cause issues with the empty output slot.
2471 : *
2472 : * XXX: This is no longer true, we currently deal with
2473 : * this in finalize_aggregates().
2474 : */
2475 39 : aggstate->input_done = true;
2476 :
2477 54 : while (aggstate->phase->gset_lengths[aggstate->projected_set] > 0)
2478 : {
2479 24 : aggstate->projected_set += 1;
2480 24 : if (aggstate->projected_set >= numGroupingSets)
2481 : {
2482 : /*
2483 : * We can't set agg_done here because we might
2484 : * have more phases to do, even though the
2485 : * input is empty. So we need to restart the
2486 : * whole outer loop.
2487 : */
2488 9 : break;
2489 : }
2490 : }
2491 :
2492 39 : if (aggstate->projected_set >= numGroupingSets)
2493 9 : continue;
2494 : }
2495 : else
2496 : {
2497 6635 : aggstate->agg_done = true;
2498 : /* If we are grouping, we should produce no tuples too */
2499 6635 : if (node->aggstrategy != AGG_PLAIN)
2500 85 : return NULL;
2501 : }
2502 : }
2503 : }
2504 :
2505 : /*
2506 : * Initialize working state for a new input tuple group.
2507 : */
2508 153932 : initialize_aggregates(aggstate, pergroups, numReset);
2509 :
2510 153932 : if (aggstate->grp_firstTuple != NULL)
2511 : {
2512 : /*
2513 : * Store the copied first input tuple in the tuple table slot
2514 : * reserved for it. The tuple will be deleted when it is
2515 : * cleared from the slot.
2516 : */
2517 147352 : ExecForceStoreHeapTuple(aggstate->grp_firstTuple,
2518 : firstSlot, true);
2519 147352 : aggstate->grp_firstTuple = NULL; /* don't keep two pointers */
2520 :
2521 : /* set up for first advance_aggregates call */
2522 147352 : tmpcontext->ecxt_outertuple = firstSlot;
2523 :
2524 : /*
2525 : * Process each outer-plan tuple, and then fetch the next one,
2526 : * until we exhaust the outer plan or cross a group boundary.
2527 : */
2528 : for (;;)
2529 : {
2530 : /*
2531 : * During phase 1 only of a mixed agg, we need to update
2532 : * hashtables as well in advance_aggregates.
2533 : */
2534 11106943 : if (aggstate->aggstrategy == AGG_MIXED &&
2535 19048 : aggstate->current_phase == 1)
2536 : {
2537 19048 : lookup_hash_entries(aggstate);
2538 : }
2539 :
2540 : /* Advance the aggregates (or combine functions) */
2541 11106943 : advance_aggregates(aggstate);
2542 :
2543 : /* Reset per-input-tuple context after each tuple */
2544 11106904 : ResetExprContext(tmpcontext);
2545 :
2546 11106904 : outerslot = fetch_input_tuple(aggstate);
2547 11106889 : if (TupIsNull(outerslot))
2548 : {
2549 : /* no more outer-plan tuples available */
2550 :
2551 : /* if we built hash tables, finalize any spills */
2552 31138 : if (aggstate->aggstrategy == AGG_MIXED &&
2553 80 : aggstate->current_phase == 1)
2554 80 : hashagg_finish_initial_spills(aggstate);
2555 :
2556 31138 : if (hasGroupingSets)
2557 : {
2558 386 : aggstate->input_done = true;
2559 386 : break;
2560 : }
2561 : else
2562 : {
2563 30752 : aggstate->agg_done = true;
2564 30752 : break;
2565 : }
2566 : }
2567 : /* set up for next advance_aggregates call */
2568 11075751 : tmpcontext->ecxt_outertuple = outerslot;
2569 :
2570 : /*
2571 : * If we are grouping, check whether we've crossed a group
2572 : * boundary.
2573 : */
2574 11075751 : if (node->aggstrategy != AGG_PLAIN && node->numCols > 0)
2575 : {
2576 1296600 : tmpcontext->ecxt_innertuple = firstSlot;
2577 1296600 : if (!ExecQual(aggstate->phase->eqfunctions[node->numCols - 1],
2578 : tmpcontext))
2579 : {
2580 116160 : aggstate->grp_firstTuple = ExecCopySlotHeapTuple(outerslot);
2581 116160 : break;
2582 : }
2583 : }
2584 : }
2585 : }
2586 :
2587 : /*
2588 : * Use the representative input tuple for any references to
2589 : * non-aggregated input columns in aggregate direct args, the node
2590 : * qual, and the tlist. (If we are not grouping, and there are no
2591 : * input rows at all, we will come here with an empty firstSlot
2592 : * ... but if not grouping, there can't be any references to
2593 : * non-aggregated input columns, so no problem.)
2594 : */
2595 153878 : econtext->ecxt_outertuple = firstSlot;
2596 : }
2597 :
2598 : Assert(aggstate->projected_set >= 0);
2599 :
2600 160953 : currentSet = aggstate->projected_set;
2601 :
2602 160953 : prepare_projection_slot(aggstate, econtext->ecxt_outertuple, currentSet);
2603 :
2604 160953 : select_current_set(aggstate, currentSet, false);
2605 :
2606 160953 : finalize_aggregates(aggstate,
2607 : peragg,
2608 160953 : pergroups[currentSet]);
2609 :
2610 : /*
2611 : * If there's no row to project right now, we must continue rather
2612 : * than returning a null since there might be more groups.
2613 : */
2614 160947 : result = project_aggregates(aggstate);
2615 160941 : if (result)
2616 125534 : return result;
2617 : }
2618 :
2619 : /* No more groups */
2620 333 : return NULL;
2621 : }
2622 :
2623 : /*
2624 : * ExecAgg for hashed case: read input and build hash table
2625 : */
2626 : static void
2627 8760 : agg_fill_hash_table(AggState *aggstate)
2628 : {
2629 : TupleTableSlot *outerslot;
2630 8760 : ExprContext *tmpcontext = aggstate->tmpcontext;
2631 :
2632 : /*
2633 : * Process each outer-plan tuple, and then fetch the next one, until we
2634 : * exhaust the outer plan.
2635 : */
2636 : for (;;)
2637 : {
2638 3586504 : outerslot = fetch_input_tuple(aggstate);
2639 3586504 : if (TupIsNull(outerslot))
2640 : break;
2641 :
2642 : /* set up for lookup_hash_entries and advance_aggregates */
2643 3577744 : tmpcontext->ecxt_outertuple = outerslot;
2644 :
2645 : /* Find or build hashtable entries */
2646 3577744 : lookup_hash_entries(aggstate);
2647 :
2648 : /* Advance the aggregates (or combine functions) */
2649 3577744 : advance_aggregates(aggstate);
2650 :
2651 : /*
2652 : * Reset per-input-tuple context after each tuple, but note that the
2653 : * hash lookups do this too
2654 : */
2655 3577744 : ResetExprContext(aggstate->tmpcontext);
2656 : }
2657 :
2658 : /* finalize spills, if any */
2659 8760 : hashagg_finish_initial_spills(aggstate);
2660 :
2661 8760 : aggstate->table_filled = true;
2662 : /* Initialize to walk the first hash table */
2663 8760 : select_current_set(aggstate, 0, true);
2664 8760 : ResetTupleHashIterator(aggstate->perhash[0].hashtable,
2665 : &aggstate->perhash[0].hashiter);
2666 8760 : }
2667 :
2668 : /*
2669 : * If any data was spilled during hash aggregation, reset the hash table and
2670 : * reprocess one batch of spilled data. After reprocessing a batch, the hash
2671 : * table will again contain data, ready to be consumed by
2672 : * agg_retrieve_hash_table_in_memory().
2673 : *
2674 : * Should only be called after all in memory hash table entries have been
2675 : * finalized and emitted.
2676 : *
2677 : * Return false when input is exhausted and there's no more work to be done;
2678 : * otherwise return true.
2679 : */
2680 : static bool
2681 22722 : agg_refill_hash_table(AggState *aggstate)
2682 : {
2683 : HashAggBatch *batch;
2684 : AggStatePerHash perhash;
2685 : HashAggSpill spill;
2686 22722 : LogicalTapeSet *tapeset = aggstate->hash_tapeset;
2687 22722 : bool spill_initialized = false;
2688 :
2689 22722 : if (aggstate->hash_batches == NIL)
2690 9250 : return false;
2691 :
2692 : /* hash_batches is a stack, with the top item at the end of the list */
2693 13472 : batch = llast(aggstate->hash_batches);
2694 13472 : aggstate->hash_batches = list_delete_last(aggstate->hash_batches);
2695 :
2696 13472 : hash_agg_set_limits(aggstate->hashentrysize, batch->input_card,
2697 : batch->used_bits, &aggstate->hash_mem_limit,
2698 : &aggstate->hash_ngroups_limit, NULL);
2699 :
2700 : /*
2701 : * Each batch only processes one grouping set; set the rest to NULL so
2702 : * that advance_aggregates() knows to ignore them. We don't touch
2703 : * pergroups for sorted grouping sets here, because they will be needed if
2704 : * we rescan later. The expressions for sorted grouping sets will not be
2705 : * evaluated after we recompile anyway.
2706 : */
2707 103714 : MemSet(aggstate->hash_pergroup, 0,
2708 : sizeof(AggStatePerGroup) * aggstate->num_hashes);
2709 :
2710 : /* free memory and reset hash tables */
2711 13472 : ReScanExprContext(aggstate->hashcontext);
2712 103714 : for (int setno = 0; setno < aggstate->num_hashes; setno++)
2713 90242 : ResetTupleHashTable(aggstate->perhash[setno].hashtable);
2714 :
2715 13472 : aggstate->hash_ngroups_current = 0;
2716 :
2717 : /*
2718 : * In AGG_MIXED mode, hash aggregation happens in phase 1 and the output
2719 : * happens in phase 0. So, we switch to phase 1 when processing a batch,
2720 : * and back to phase 0 after the batch is done.
2721 : */
2722 : Assert(aggstate->current_phase == 0);
2723 13472 : if (aggstate->phase->aggstrategy == AGG_MIXED)
2724 : {
2725 13131 : aggstate->current_phase = 1;
2726 13131 : aggstate->phase = &aggstate->phases[aggstate->current_phase];
2727 : }
2728 :
2729 13472 : select_current_set(aggstate, batch->setno, true);
2730 :
2731 13472 : perhash = &aggstate->perhash[aggstate->current_set];
2732 :
2733 : /*
2734 : * Spilled tuples are always read back as MinimalTuples, which may be
2735 : * different from the outer plan, so recompile the aggregate expressions.
2736 : *
2737 : * We still need the NULL check, because we are only processing one
2738 : * grouping set at a time and the rest will be NULL.
2739 : */
2740 13472 : hashagg_recompile_expressions(aggstate, true, true);
2741 :
2742 13472 : INJECTION_POINT("hash-aggregate-process-batch", NULL);
2743 : for (;;)
2744 608388 : {
2745 621860 : TupleTableSlot *spillslot = aggstate->hash_spill_rslot;
2746 621860 : TupleTableSlot *hashslot = perhash->hashslot;
2747 621860 : TupleHashTable hashtable = perhash->hashtable;
2748 : TupleHashEntry entry;
2749 : MinimalTuple tuple;
2750 : uint32 hash;
2751 621860 : bool isnew = false;
2752 621860 : bool *p_isnew = aggstate->hash_spill_mode ? NULL : &isnew;
2753 :
2754 621860 : CHECK_FOR_INTERRUPTS();
2755 :
2756 621860 : tuple = hashagg_batch_read(batch, &hash);
2757 621860 : if (tuple == NULL)
2758 13472 : break;
2759 :
2760 608388 : ExecStoreMinimalTuple(tuple, spillslot, true);
2761 608388 : aggstate->tmpcontext->ecxt_outertuple = spillslot;
2762 :
2763 608388 : prepare_hash_slot(perhash,
2764 608388 : aggstate->tmpcontext->ecxt_outertuple,
2765 : hashslot);
2766 608388 : entry = LookupTupleHashEntryHash(hashtable, hashslot,
2767 : p_isnew, hash);
2768 :
2769 608388 : if (entry != NULL)
2770 : {
2771 383618 : if (isnew)
2772 77840 : initialize_hash_entry(aggstate, hashtable, entry);
2773 383618 : aggstate->hash_pergroup[batch->setno] = TupleHashEntryGetAdditional(hashtable, entry);
2774 383618 : advance_aggregates(aggstate);
2775 : }
2776 : else
2777 : {
2778 224770 : if (!spill_initialized)
2779 : {
2780 : /*
2781 : * Avoid initializing the spill until we actually need it so
2782 : * that we don't assign tapes that will never be used.
2783 : */
2784 6250 : spill_initialized = true;
2785 6250 : hashagg_spill_init(&spill, tapeset, batch->used_bits,
2786 : batch->input_card, aggstate->hashentrysize);
2787 : }
2788 : /* no memory for a new group, spill */
2789 224770 : hashagg_spill_tuple(aggstate, &spill, spillslot, hash);
2790 :
2791 224770 : aggstate->hash_pergroup[batch->setno] = NULL;
2792 : }
2793 :
2794 : /*
2795 : * Reset per-input-tuple context after each tuple, but note that the
2796 : * hash lookups do this too
2797 : */
2798 608388 : ResetExprContext(aggstate->tmpcontext);
2799 : }
2800 :
2801 13472 : LogicalTapeClose(batch->input_tape);
2802 :
2803 : /* change back to phase 0 */
2804 13472 : aggstate->current_phase = 0;
2805 13472 : aggstate->phase = &aggstate->phases[aggstate->current_phase];
2806 :
2807 13472 : if (spill_initialized)
2808 : {
2809 6250 : hashagg_spill_finish(aggstate, &spill, batch->setno);
2810 6250 : hash_agg_update_metrics(aggstate, true, spill.npartitions);
2811 : }
2812 : else
2813 7222 : hash_agg_update_metrics(aggstate, true, 0);
2814 :
2815 13472 : aggstate->hash_spill_mode = false;
2816 :
2817 : /* prepare to walk the first hash table */
2818 13472 : select_current_set(aggstate, batch->setno, true);
2819 13472 : ResetTupleHashIterator(aggstate->perhash[batch->setno].hashtable,
2820 : &aggstate->perhash[batch->setno].hashiter);
2821 :
2822 13472 : pfree(batch);
2823 :
2824 13472 : return true;
2825 : }
2826 :
2827 : /*
2828 : * ExecAgg for hashed case: retrieving groups from hash table
2829 : *
2830 : * After exhausting in-memory tuples, also try refilling the hash table using
2831 : * previously-spilled tuples. Only returns NULL after all in-memory and
2832 : * spilled tuples are exhausted.
2833 : */
2834 : static TupleTableSlot *
2835 257499 : agg_retrieve_hash_table(AggState *aggstate)
2836 : {
2837 257499 : TupleTableSlot *result = NULL;
2838 :
2839 519220 : while (result == NULL)
2840 : {
2841 270971 : result = agg_retrieve_hash_table_in_memory(aggstate);
2842 270971 : if (result == NULL)
2843 : {
2844 22722 : if (!agg_refill_hash_table(aggstate))
2845 : {
2846 9250 : aggstate->agg_done = true;
2847 9250 : break;
2848 : }
2849 : }
2850 : }
2851 :
2852 257499 : return result;
2853 : }
2854 :
2855 : /*
2856 : * Retrieve the groups from the in-memory hash tables without considering any
2857 : * spilled tuples.
2858 : */
2859 : static TupleTableSlot *
2860 270971 : agg_retrieve_hash_table_in_memory(AggState *aggstate)
2861 : {
2862 : ExprContext *econtext;
2863 : AggStatePerAgg peragg;
2864 : AggStatePerGroup pergroup;
2865 : TupleHashEntry entry;
2866 : TupleTableSlot *firstSlot;
2867 : TupleTableSlot *result;
2868 : AggStatePerHash perhash;
2869 :
2870 : /*
2871 : * get state info from node.
2872 : *
2873 : * econtext is the per-output-tuple expression context.
2874 : */
2875 270971 : econtext = aggstate->ss.ps.ps_ExprContext;
2876 270971 : peragg = aggstate->peragg;
2877 270971 : firstSlot = aggstate->ss.ss_ScanTupleSlot;
2878 :
2879 : /*
2880 : * Note that perhash (and therefore anything accessed through it) can
2881 : * change inside the loop, as we change between grouping sets.
2882 : */
2883 270971 : perhash = &aggstate->perhash[aggstate->current_set];
2884 :
2885 : /*
2886 : * We loop retrieving groups until we find one satisfying
2887 : * aggstate->ss.ps.qual
2888 : */
2889 : for (;;)
2890 67989 : {
2891 338960 : TupleTableSlot *hashslot = perhash->hashslot;
2892 338960 : TupleHashTable hashtable = perhash->hashtable;
2893 : int i;
2894 :
2895 338960 : CHECK_FOR_INTERRUPTS();
2896 :
2897 : /*
2898 : * Find the next entry in the hash table
2899 : */
2900 338960 : entry = ScanTupleHashTable(hashtable, &perhash->hashiter);
2901 338960 : if (entry == NULL)
2902 : {
2903 72874 : int nextset = aggstate->current_set + 1;
2904 :
2905 72874 : if (nextset < aggstate->num_hashes)
2906 : {
2907 : /*
2908 : * Switch to next grouping set, reinitialize, and restart the
2909 : * loop.
2910 : */
2911 50152 : select_current_set(aggstate, nextset, true);
2912 :
2913 50152 : perhash = &aggstate->perhash[aggstate->current_set];
2914 :
2915 50152 : ResetTupleHashIterator(perhash->hashtable, &perhash->hashiter);
2916 :
2917 50152 : continue;
2918 : }
2919 : else
2920 : {
2921 22722 : return NULL;
2922 : }
2923 : }
2924 :
2925 : /*
2926 : * Clear the per-output-tuple context for each group
2927 : *
2928 : * We intentionally don't use ReScanExprContext here; if any aggs have
2929 : * registered shutdown callbacks, they mustn't be called yet, since we
2930 : * might not be done with that agg.
2931 : */
2932 266086 : ResetExprContext(econtext);
2933 :
2934 : /*
2935 : * Transform representative tuple back into one with the right
2936 : * columns.
2937 : */
2938 266086 : ExecStoreMinimalTuple(TupleHashEntryGetTuple(entry), hashslot, false);
2939 266086 : slot_getallattrs(hashslot);
2940 :
2941 266086 : ExecClearTuple(firstSlot);
2942 266086 : memset(firstSlot->tts_isnull, true,
2943 266086 : firstSlot->tts_tupleDescriptor->natts * sizeof(bool));
2944 :
2945 697929 : for (i = 0; i < perhash->numhashGrpCols; i++)
2946 : {
2947 431843 : int varNumber = perhash->hashGrpColIdxInput[i] - 1;
2948 :
2949 431843 : firstSlot->tts_values[varNumber] = hashslot->tts_values[i];
2950 431843 : firstSlot->tts_isnull[varNumber] = hashslot->tts_isnull[i];
2951 : }
2952 266086 : ExecStoreVirtualTuple(firstSlot);
2953 :
2954 266086 : pergroup = (AggStatePerGroup) TupleHashEntryGetAdditional(hashtable, entry);
2955 :
2956 : /*
2957 : * Use the representative input tuple for any references to
2958 : * non-aggregated input columns in the qual and tlist.
2959 : */
2960 266086 : econtext->ecxt_outertuple = firstSlot;
2961 :
2962 266086 : prepare_projection_slot(aggstate,
2963 : econtext->ecxt_outertuple,
2964 : aggstate->current_set);
2965 :
2966 266086 : finalize_aggregates(aggstate, peragg, pergroup);
2967 :
2968 266086 : result = project_aggregates(aggstate);
2969 266086 : if (result)
2970 248249 : return result;
2971 : }
2972 :
2973 : /* No more groups */
2974 : return NULL;
2975 : }
2976 :
2977 : /*
2978 : * hashagg_spill_init
2979 : *
2980 : * Called after we determined that spilling is necessary. Chooses the number
2981 : * of partitions to create, and initializes them.
2982 : */
2983 : static void
2984 6311 : hashagg_spill_init(HashAggSpill *spill, LogicalTapeSet *tapeset, int used_bits,
2985 : double input_groups, double hashentrysize)
2986 : {
2987 : int npartitions;
2988 : int partition_bits;
2989 :
2990 6311 : npartitions = hash_choose_num_partitions(input_groups, hashentrysize,
2991 : used_bits, &partition_bits);
2992 :
2993 : #ifdef USE_INJECTION_POINTS
2994 6311 : if (IS_INJECTION_POINT_ATTACHED("hash-aggregate-single-partition"))
2995 : {
2996 5 : npartitions = 1;
2997 5 : partition_bits = 0;
2998 5 : INJECTION_POINT_CACHED("hash-aggregate-single-partition", NULL);
2999 : }
3000 : #endif
3001 :
3002 6311 : spill->partitions = palloc0_array(LogicalTape *, npartitions);
3003 6311 : spill->ntuples = palloc0_array(int64, npartitions);
3004 6311 : spill->hll_card = palloc0_array(hyperLogLogState, npartitions);
3005 :
3006 31540 : for (int i = 0; i < npartitions; i++)
3007 25229 : spill->partitions[i] = LogicalTapeCreate(tapeset);
3008 :
3009 6311 : spill->shift = 32 - used_bits - partition_bits;
3010 6311 : if (spill->shift < 32)
3011 6306 : spill->mask = (npartitions - 1) << spill->shift;
3012 : else
3013 5 : spill->mask = 0;
3014 6311 : spill->npartitions = npartitions;
3015 :
3016 31540 : for (int i = 0; i < npartitions; i++)
3017 25229 : initHyperLogLog(&spill->hll_card[i], HASHAGG_HLL_BIT_WIDTH);
3018 6311 : }
3019 :
3020 : /*
3021 : * hashagg_spill_tuple
3022 : *
3023 : * No room for new groups in the hash table. Save for later in the appropriate
3024 : * partition.
3025 : */
3026 : static Size
3027 608388 : hashagg_spill_tuple(AggState *aggstate, HashAggSpill *spill,
3028 : TupleTableSlot *inputslot, uint32 hash)
3029 : {
3030 : TupleTableSlot *spillslot;
3031 : int partition;
3032 : MinimalTuple tuple;
3033 : LogicalTape *tape;
3034 608388 : int total_written = 0;
3035 : bool shouldFree;
3036 :
3037 : Assert(spill->partitions != NULL);
3038 :
3039 : /* spill only attributes that we actually need */
3040 608388 : if (!aggstate->all_cols_needed)
3041 : {
3042 786 : spillslot = aggstate->hash_spill_wslot;
3043 786 : slot_getsomeattrs(inputslot, aggstate->max_colno_needed);
3044 786 : ExecClearTuple(spillslot);
3045 2358 : for (int i = 0; i < spillslot->tts_tupleDescriptor->natts; i++)
3046 : {
3047 1572 : if (bms_is_member(i + 1, aggstate->colnos_needed))
3048 : {
3049 786 : spillslot->tts_values[i] = inputslot->tts_values[i];
3050 786 : spillslot->tts_isnull[i] = inputslot->tts_isnull[i];
3051 : }
3052 : else
3053 786 : spillslot->tts_isnull[i] = true;
3054 : }
3055 786 : ExecStoreVirtualTuple(spillslot);
3056 : }
3057 : else
3058 607602 : spillslot = inputslot;
3059 :
3060 608388 : tuple = ExecFetchSlotMinimalTuple(spillslot, &shouldFree);
3061 :
3062 608388 : if (spill->shift < 32)
3063 597888 : partition = (hash & spill->mask) >> spill->shift;
3064 : else
3065 10500 : partition = 0;
3066 :
3067 608388 : spill->ntuples[partition]++;
3068 :
3069 : /*
3070 : * All hash values destined for a given partition have some bits in
3071 : * common, which causes bad HLL cardinality estimates. Hash the hash to
3072 : * get a more uniform distribution.
3073 : */
3074 608388 : addHyperLogLog(&spill->hll_card[partition], hash_bytes_uint32(hash));
3075 :
3076 608388 : tape = spill->partitions[partition];
3077 :
3078 608388 : LogicalTapeWrite(tape, &hash, sizeof(uint32));
3079 608388 : total_written += sizeof(uint32);
3080 :
3081 608388 : LogicalTapeWrite(tape, tuple, tuple->t_len);
3082 608388 : total_written += tuple->t_len;
3083 :
3084 608388 : if (shouldFree)
3085 383618 : pfree(tuple);
3086 :
3087 608388 : return total_written;
3088 : }
3089 :
3090 : /*
3091 : * hashagg_batch_new
3092 : *
3093 : * Construct a HashAggBatch item, which represents one iteration of HashAgg to
3094 : * be done.
3095 : */
3096 : static HashAggBatch *
3097 13472 : hashagg_batch_new(LogicalTape *input_tape, int setno,
3098 : int64 input_tuples, double input_card, int used_bits)
3099 : {
3100 13472 : HashAggBatch *batch = palloc0_object(HashAggBatch);
3101 :
3102 13472 : batch->setno = setno;
3103 13472 : batch->used_bits = used_bits;
3104 13472 : batch->input_tape = input_tape;
3105 13472 : batch->input_tuples = input_tuples;
3106 13472 : batch->input_card = input_card;
3107 :
3108 13472 : return batch;
3109 : }
3110 :
3111 : /*
3112 : * hashagg_batch_read
3113 : * read the next tuple from a batch's tape. Return NULL if no more.
3114 : */
3115 : static MinimalTuple
3116 621860 : hashagg_batch_read(HashAggBatch *batch, uint32 *hashp)
3117 : {
3118 621860 : LogicalTape *tape = batch->input_tape;
3119 : MinimalTuple tuple;
3120 : uint32 t_len;
3121 : size_t nread;
3122 : uint32 hash;
3123 :
3124 621860 : nread = LogicalTapeRead(tape, &hash, sizeof(uint32));
3125 621860 : if (nread == 0)
3126 13472 : return NULL;
3127 608388 : if (nread != sizeof(uint32))
3128 0 : ereport(ERROR,
3129 : (errcode_for_file_access(),
3130 : errmsg_internal("unexpected EOF for tape %p: requested %zu bytes, read %zu bytes",
3131 : tape, sizeof(uint32), nread)));
3132 608388 : if (hashp != NULL)
3133 608388 : *hashp = hash;
3134 :
3135 608388 : nread = LogicalTapeRead(tape, &t_len, sizeof(t_len));
3136 608388 : if (nread != sizeof(uint32))
3137 0 : ereport(ERROR,
3138 : (errcode_for_file_access(),
3139 : errmsg_internal("unexpected EOF for tape %p: requested %zu bytes, read %zu bytes",
3140 : tape, sizeof(uint32), nread)));
3141 :
3142 608388 : tuple = (MinimalTuple) palloc(t_len);
3143 608388 : tuple->t_len = t_len;
3144 :
3145 608388 : nread = LogicalTapeRead(tape,
3146 : (char *) tuple + sizeof(uint32),
3147 : t_len - sizeof(uint32));
3148 608388 : if (nread != t_len - sizeof(uint32))
3149 0 : ereport(ERROR,
3150 : (errcode_for_file_access(),
3151 : errmsg_internal("unexpected EOF for tape %p: requested %zu bytes, read %zu bytes",
3152 : tape, t_len - sizeof(uint32), nread)));
3153 :
3154 608388 : return tuple;
3155 : }
3156 :
3157 : /*
3158 : * hashagg_finish_initial_spills
3159 : *
3160 : * After a HashAggBatch has been processed, it may have spilled tuples to
3161 : * disk. If so, turn the spilled partitions into new batches that must later
3162 : * be executed.
3163 : */
3164 : static void
3165 8840 : hashagg_finish_initial_spills(AggState *aggstate)
3166 : {
3167 : int setno;
3168 8840 : int total_npartitions = 0;
3169 :
3170 8840 : if (aggstate->hash_spills != NULL)
3171 : {
3172 92 : for (setno = 0; setno < aggstate->num_hashes; setno++)
3173 : {
3174 61 : HashAggSpill *spill = &aggstate->hash_spills[setno];
3175 :
3176 61 : total_npartitions += spill->npartitions;
3177 61 : hashagg_spill_finish(aggstate, spill, setno);
3178 : }
3179 :
3180 : /*
3181 : * We're not processing tuples from outer plan any more; only
3182 : * processing batches of spilled tuples. The initial spill structures
3183 : * are no longer needed.
3184 : */
3185 31 : pfree(aggstate->hash_spills);
3186 31 : aggstate->hash_spills = NULL;
3187 : }
3188 :
3189 8840 : hash_agg_update_metrics(aggstate, false, total_npartitions);
3190 8840 : aggstate->hash_spill_mode = false;
3191 8840 : }
3192 :
3193 : /*
3194 : * hashagg_spill_finish
3195 : *
3196 : * Transform spill partitions into new batches.
3197 : */
3198 : static void
3199 6311 : hashagg_spill_finish(AggState *aggstate, HashAggSpill *spill, int setno)
3200 : {
3201 : int i;
3202 6311 : int used_bits = 32 - spill->shift;
3203 :
3204 6311 : if (spill->npartitions == 0)
3205 0 : return; /* didn't spill */
3206 :
3207 31540 : for (i = 0; i < spill->npartitions; i++)
3208 : {
3209 25229 : LogicalTape *tape = spill->partitions[i];
3210 : HashAggBatch *new_batch;
3211 : double cardinality;
3212 :
3213 : /* if the partition is empty, don't create a new batch of work */
3214 25229 : if (spill->ntuples[i] == 0)
3215 11757 : continue;
3216 :
3217 13472 : cardinality = estimateHyperLogLog(&spill->hll_card[i]);
3218 13472 : freeHyperLogLog(&spill->hll_card[i]);
3219 :
3220 : /* rewinding frees the buffer while not in use */
3221 13472 : LogicalTapeRewindForRead(tape, HASHAGG_READ_BUFFER_SIZE);
3222 :
3223 13472 : new_batch = hashagg_batch_new(tape, setno,
3224 13472 : spill->ntuples[i], cardinality,
3225 : used_bits);
3226 13472 : aggstate->hash_batches = lappend(aggstate->hash_batches, new_batch);
3227 13472 : aggstate->hash_batches_used++;
3228 : }
3229 :
3230 6311 : pfree(spill->ntuples);
3231 6311 : pfree(spill->hll_card);
3232 6311 : pfree(spill->partitions);
3233 : }
3234 :
3235 : /*
3236 : * Free resources related to a spilled HashAgg.
3237 : */
3238 : static void
3239 33783 : hashagg_reset_spill_state(AggState *aggstate)
3240 : {
3241 : /* free spills from initial pass */
3242 33783 : if (aggstate->hash_spills != NULL)
3243 : {
3244 : int setno;
3245 :
3246 0 : for (setno = 0; setno < aggstate->num_hashes; setno++)
3247 : {
3248 0 : HashAggSpill *spill = &aggstate->hash_spills[setno];
3249 :
3250 0 : pfree(spill->ntuples);
3251 0 : pfree(spill->partitions);
3252 : }
3253 0 : pfree(aggstate->hash_spills);
3254 0 : aggstate->hash_spills = NULL;
3255 : }
3256 :
3257 : /* free batches */
3258 33783 : list_free_deep(aggstate->hash_batches);
3259 33783 : aggstate->hash_batches = NIL;
3260 :
3261 : /* close tape set */
3262 33783 : if (aggstate->hash_tapeset != NULL)
3263 : {
3264 31 : LogicalTapeSetClose(aggstate->hash_tapeset);
3265 31 : aggstate->hash_tapeset = NULL;
3266 : }
3267 33783 : }
3268 :
3269 :
3270 : /* -----------------
3271 : * ExecInitAgg
3272 : *
3273 : * Creates the run-time information for the agg node produced by the
3274 : * planner and initializes its outer subtree.
3275 : *
3276 : * -----------------
3277 : */
3278 : AggState *
3279 27559 : ExecInitAgg(Agg *node, EState *estate, int eflags)
3280 : {
3281 : AggState *aggstate;
3282 : AggStatePerAgg peraggs;
3283 : AggStatePerTrans pertransstates;
3284 : AggStatePerGroup *pergroups;
3285 : Plan *outerPlan;
3286 : ExprContext *econtext;
3287 : TupleDesc scanDesc;
3288 : int max_aggno;
3289 : int max_transno;
3290 : int numaggrefs;
3291 : int numaggs;
3292 : int numtrans;
3293 : int phase;
3294 : int phaseidx;
3295 : ListCell *l;
3296 27559 : Bitmapset *all_grouped_cols = NULL;
3297 27559 : int numGroupingSets = 1;
3298 : int numPhases;
3299 : int numHashes;
3300 27559 : int i = 0;
3301 27559 : int j = 0;
3302 51671 : bool use_hashing = (node->aggstrategy == AGG_HASHED ||
3303 24112 : node->aggstrategy == AGG_MIXED);
3304 :
3305 : /* check for unsupported flags */
3306 : Assert(!(eflags & (EXEC_FLAG_BACKWARD | EXEC_FLAG_MARK)));
3307 :
3308 : /*
3309 : * create state structure
3310 : */
3311 27559 : aggstate = makeNode(AggState);
3312 27559 : aggstate->ss.ps.plan = (Plan *) node;
3313 27559 : aggstate->ss.ps.state = estate;
3314 27559 : aggstate->ss.ps.ExecProcNode = ExecAgg;
3315 :
3316 27559 : aggstate->aggs = NIL;
3317 27559 : aggstate->numaggs = 0;
3318 27559 : aggstate->numtrans = 0;
3319 27559 : aggstate->aggstrategy = node->aggstrategy;
3320 27559 : aggstate->aggsplit = node->aggsplit;
3321 27559 : aggstate->maxsets = 0;
3322 27559 : aggstate->projected_set = -1;
3323 27559 : aggstate->current_set = 0;
3324 27559 : aggstate->peragg = NULL;
3325 27559 : aggstate->pertrans = NULL;
3326 27559 : aggstate->curperagg = NULL;
3327 27559 : aggstate->curpertrans = NULL;
3328 27559 : aggstate->input_done = false;
3329 27559 : aggstate->agg_done = false;
3330 27559 : aggstate->pergroups = NULL;
3331 27559 : aggstate->grp_firstTuple = NULL;
3332 27559 : aggstate->sort_in = NULL;
3333 27559 : aggstate->sort_out = NULL;
3334 :
3335 : /*
3336 : * phases[0] always exists, but is dummy in sorted/plain mode
3337 : */
3338 27559 : numPhases = (use_hashing ? 1 : 2);
3339 27559 : numHashes = (use_hashing ? 1 : 0);
3340 :
3341 : /*
3342 : * Calculate the maximum number of grouping sets in any phase; this
3343 : * determines the size of some allocations. Also calculate the number of
3344 : * phases, since all hashed/mixed nodes contribute to only a single phase.
3345 : */
3346 27559 : if (node->groupingSets)
3347 : {
3348 492 : numGroupingSets = list_length(node->groupingSets);
3349 :
3350 1038 : foreach(l, node->chain)
3351 : {
3352 546 : Agg *agg = lfirst(l);
3353 :
3354 546 : numGroupingSets = Max(numGroupingSets,
3355 : list_length(agg->groupingSets));
3356 :
3357 : /*
3358 : * additional AGG_HASHED aggs become part of phase 0, but all
3359 : * others add an extra phase.
3360 : */
3361 546 : if (agg->aggstrategy != AGG_HASHED)
3362 277 : ++numPhases;
3363 : else
3364 269 : ++numHashes;
3365 : }
3366 : }
3367 :
3368 27559 : aggstate->maxsets = numGroupingSets;
3369 27559 : aggstate->numphases = numPhases;
3370 :
3371 27559 : aggstate->aggcontexts = palloc0_array(ExprContext *, numGroupingSets);
3372 :
3373 : /*
3374 : * Create expression contexts. We need three or more, one for
3375 : * per-input-tuple processing, one for per-output-tuple processing, one
3376 : * for all the hashtables, and one for each grouping set. The per-tuple
3377 : * memory context of the per-grouping-set ExprContexts (aggcontexts)
3378 : * replaces the standalone memory context formerly used to hold transition
3379 : * values. We cheat a little by using ExecAssignExprContext() to build
3380 : * all of them.
3381 : *
3382 : * NOTE: the details of what is stored in aggcontexts and what is stored
3383 : * in the regular per-query memory context are driven by a simple
3384 : * decision: we want to reset the aggcontext at group boundaries (if not
3385 : * hashing) and in ExecReScanAgg to recover no-longer-wanted space.
3386 : */
3387 27559 : ExecAssignExprContext(estate, &aggstate->ss.ps);
3388 27559 : aggstate->tmpcontext = aggstate->ss.ps.ps_ExprContext;
3389 :
3390 55550 : for (i = 0; i < numGroupingSets; ++i)
3391 : {
3392 27991 : ExecAssignExprContext(estate, &aggstate->ss.ps);
3393 27991 : aggstate->aggcontexts[i] = aggstate->ss.ps.ps_ExprContext;
3394 : }
3395 :
3396 27559 : if (use_hashing)
3397 3580 : hash_create_memory(aggstate);
3398 :
3399 27559 : ExecAssignExprContext(estate, &aggstate->ss.ps);
3400 :
3401 : /*
3402 : * Initialize child nodes.
3403 : *
3404 : * If we are doing a hashed aggregation then the child plan does not need
3405 : * to handle REWIND efficiently; see ExecReScanAgg.
3406 : */
3407 27559 : if (node->aggstrategy == AGG_HASHED)
3408 3447 : eflags &= ~EXEC_FLAG_REWIND;
3409 27559 : outerPlan = outerPlan(node);
3410 27559 : outerPlanState(aggstate) = ExecInitNode(outerPlan, estate, eflags);
3411 :
3412 : /*
3413 : * initialize source tuple type.
3414 : */
3415 27559 : aggstate->ss.ps.outerops =
3416 27559 : ExecGetResultSlotOps(outerPlanState(&aggstate->ss),
3417 : &aggstate->ss.ps.outeropsfixed);
3418 27559 : aggstate->ss.ps.outeropsset = true;
3419 :
3420 27559 : ExecCreateScanSlotFromOuterPlan(estate, &aggstate->ss,
3421 : aggstate->ss.ps.outerops);
3422 27559 : scanDesc = aggstate->ss.ss_ScanTupleSlot->tts_tupleDescriptor;
3423 :
3424 : /*
3425 : * If there are more than two phases (including a potential dummy phase
3426 : * 0), input will be resorted using tuplesort. Need a slot for that.
3427 : */
3428 27559 : if (numPhases > 2)
3429 : {
3430 123 : aggstate->sort_slot = ExecInitExtraTupleSlot(estate, scanDesc,
3431 : &TTSOpsMinimalTuple);
3432 :
3433 : /*
3434 : * The output of the tuplesort, and the output from the outer child
3435 : * might not use the same type of slot. In most cases the child will
3436 : * be a Sort, and thus return a TTSOpsMinimalTuple type slot - but the
3437 : * input can also be presorted due an index, in which case it could be
3438 : * a different type of slot.
3439 : *
3440 : * XXX: For efficiency it would be good to instead/additionally
3441 : * generate expressions with corresponding settings of outerops* for
3442 : * the individual phases - deforming is often a bottleneck for
3443 : * aggregations with lots of rows per group. If there's multiple
3444 : * sorts, we know that all but the first use TTSOpsMinimalTuple (via
3445 : * the nodeAgg.c internal tuplesort).
3446 : */
3447 123 : if (aggstate->ss.ps.outeropsfixed &&
3448 123 : aggstate->ss.ps.outerops != &TTSOpsMinimalTuple)
3449 18 : aggstate->ss.ps.outeropsfixed = false;
3450 : }
3451 :
3452 : /*
3453 : * Initialize result type, slot and projection.
3454 : */
3455 27559 : ExecInitResultTupleSlotTL(&aggstate->ss.ps, &TTSOpsVirtual);
3456 27559 : ExecAssignProjectionInfo(&aggstate->ss.ps, NULL);
3457 :
3458 : /*
3459 : * initialize child expressions
3460 : *
3461 : * We expect the parser to have checked that no aggs contain other agg
3462 : * calls in their arguments (and just to be sure, we verify it again while
3463 : * initializing the plan node). This would make no sense under SQL
3464 : * semantics, and it's forbidden by the spec. Because it is true, we
3465 : * don't need to worry about evaluating the aggs in any particular order.
3466 : *
3467 : * Note: execExpr.c finds Aggrefs for us, and adds them to aggstate->aggs.
3468 : * Aggrefs in the qual are found here; Aggrefs in the targetlist are found
3469 : * during ExecAssignProjectionInfo, above.
3470 : */
3471 27559 : aggstate->ss.ps.qual =
3472 27559 : ExecInitQual(node->plan.qual, (PlanState *) aggstate);
3473 :
3474 : /*
3475 : * We should now have found all Aggrefs in the targetlist and quals.
3476 : */
3477 27559 : numaggrefs = list_length(aggstate->aggs);
3478 27559 : max_aggno = -1;
3479 27559 : max_transno = -1;
3480 58394 : foreach(l, aggstate->aggs)
3481 : {
3482 30835 : Aggref *aggref = (Aggref *) lfirst(l);
3483 :
3484 30835 : max_aggno = Max(max_aggno, aggref->aggno);
3485 30835 : max_transno = Max(max_transno, aggref->aggtransno);
3486 : }
3487 27559 : aggstate->numaggs = numaggs = max_aggno + 1;
3488 27559 : aggstate->numtrans = numtrans = max_transno + 1;
3489 :
3490 : /*
3491 : * For each phase, prepare grouping set data and fmgr lookup data for
3492 : * compare functions. Accumulate all_grouped_cols in passing.
3493 : */
3494 27559 : aggstate->phases = palloc0_array(AggStatePerPhaseData, numPhases);
3495 :
3496 27559 : aggstate->num_hashes = numHashes;
3497 27559 : if (numHashes)
3498 : {
3499 3580 : aggstate->perhash = palloc0_array(AggStatePerHashData, numHashes);
3500 3580 : aggstate->phases[0].numsets = 0;
3501 3580 : aggstate->phases[0].gset_lengths = palloc_array(int, numHashes);
3502 3580 : aggstate->phases[0].grouped_cols = palloc_array(Bitmapset *, numHashes);
3503 : }
3504 :
3505 27559 : phase = 0;
3506 55664 : for (phaseidx = 0; phaseidx <= list_length(node->chain); ++phaseidx)
3507 : {
3508 : Agg *aggnode;
3509 : Sort *sortnode;
3510 :
3511 28105 : if (phaseidx > 0)
3512 : {
3513 546 : aggnode = list_nth_node(Agg, node->chain, phaseidx - 1);
3514 546 : sortnode = castNode(Sort, outerPlan(aggnode));
3515 : }
3516 : else
3517 : {
3518 27559 : aggnode = node;
3519 27559 : sortnode = NULL;
3520 : }
3521 :
3522 : Assert(phase <= 1 || sortnode);
3523 :
3524 28105 : if (aggnode->aggstrategy == AGG_HASHED
3525 24389 : || aggnode->aggstrategy == AGG_MIXED)
3526 3849 : {
3527 3849 : AggStatePerPhase phasedata = &aggstate->phases[0];
3528 : AggStatePerHash perhash;
3529 3849 : Bitmapset *cols = NULL;
3530 :
3531 : Assert(phase == 0);
3532 3849 : i = phasedata->numsets++;
3533 3849 : perhash = &aggstate->perhash[i];
3534 :
3535 : /* phase 0 always points to the "real" Agg in the hash case */
3536 3849 : phasedata->aggnode = node;
3537 3849 : phasedata->aggstrategy = node->aggstrategy;
3538 :
3539 : /* but the actual Agg node representing this hash is saved here */
3540 3849 : perhash->aggnode = aggnode;
3541 :
3542 3849 : phasedata->gset_lengths[i] = perhash->numCols = aggnode->numCols;
3543 :
3544 9680 : for (j = 0; j < aggnode->numCols; ++j)
3545 5831 : cols = bms_add_member(cols, aggnode->grpColIdx[j]);
3546 :
3547 3849 : phasedata->grouped_cols[i] = cols;
3548 :
3549 3849 : all_grouped_cols = bms_add_members(all_grouped_cols, cols);
3550 3849 : continue;
3551 : }
3552 : else
3553 : {
3554 24256 : AggStatePerPhase phasedata = &aggstate->phases[++phase];
3555 : int num_sets;
3556 :
3557 24256 : phasedata->numsets = num_sets = list_length(aggnode->groupingSets);
3558 :
3559 24256 : if (num_sets)
3560 : {
3561 553 : phasedata->gset_lengths = palloc(num_sets * sizeof(int));
3562 553 : phasedata->grouped_cols = palloc(num_sets * sizeof(Bitmapset *));
3563 :
3564 553 : i = 0;
3565 1574 : foreach(l, aggnode->groupingSets)
3566 : {
3567 1021 : int current_length = list_length(lfirst(l));
3568 1021 : Bitmapset *cols = NULL;
3569 :
3570 : /* planner forces this to be correct */
3571 1991 : for (j = 0; j < current_length; ++j)
3572 970 : cols = bms_add_member(cols, aggnode->grpColIdx[j]);
3573 :
3574 1021 : phasedata->grouped_cols[i] = cols;
3575 1021 : phasedata->gset_lengths[i] = current_length;
3576 :
3577 1021 : ++i;
3578 : }
3579 :
3580 553 : all_grouped_cols = bms_add_members(all_grouped_cols,
3581 553 : phasedata->grouped_cols[0]);
3582 : }
3583 : else
3584 : {
3585 : Assert(phaseidx == 0);
3586 :
3587 23703 : phasedata->gset_lengths = NULL;
3588 23703 : phasedata->grouped_cols = NULL;
3589 : }
3590 :
3591 : /*
3592 : * If we are grouping, precompute fmgr lookup data for inner loop.
3593 : */
3594 24256 : if (aggnode->aggstrategy == AGG_SORTED)
3595 : {
3596 : /*
3597 : * Build a separate function for each subset of columns that
3598 : * need to be compared.
3599 : */
3600 1405 : phasedata->eqfunctions = palloc0_array(ExprState *, aggnode->numCols);
3601 :
3602 : /* for each grouping set */
3603 2261 : for (int k = 0; k < phasedata->numsets; k++)
3604 : {
3605 856 : int length = phasedata->gset_lengths[k];
3606 :
3607 : /* nothing to do for empty grouping set */
3608 856 : if (length == 0)
3609 169 : continue;
3610 :
3611 : /* if we already had one of this length, it'll do */
3612 687 : if (phasedata->eqfunctions[length - 1] != NULL)
3613 69 : continue;
3614 :
3615 618 : phasedata->eqfunctions[length - 1] =
3616 618 : execTuplesMatchPrepare(scanDesc,
3617 : length,
3618 618 : aggnode->grpColIdx,
3619 618 : aggnode->grpOperators,
3620 618 : aggnode->grpCollations,
3621 : (PlanState *) aggstate);
3622 : }
3623 :
3624 : /* and for all grouped columns, unless already computed */
3625 1405 : if (aggnode->numCols > 0 &&
3626 1347 : phasedata->eqfunctions[aggnode->numCols - 1] == NULL)
3627 : {
3628 923 : phasedata->eqfunctions[aggnode->numCols - 1] =
3629 923 : execTuplesMatchPrepare(scanDesc,
3630 : aggnode->numCols,
3631 923 : aggnode->grpColIdx,
3632 923 : aggnode->grpOperators,
3633 923 : aggnode->grpCollations,
3634 : (PlanState *) aggstate);
3635 : }
3636 : }
3637 :
3638 24256 : phasedata->aggnode = aggnode;
3639 24256 : phasedata->aggstrategy = aggnode->aggstrategy;
3640 24256 : phasedata->sortnode = sortnode;
3641 : }
3642 : }
3643 :
3644 : /*
3645 : * Convert all_grouped_cols to a descending-order list.
3646 : */
3647 27559 : i = -1;
3648 33730 : while ((i = bms_next_member(all_grouped_cols, i)) >= 0)
3649 6171 : aggstate->all_grouped_cols = lcons_int(i, aggstate->all_grouped_cols);
3650 :
3651 : /*
3652 : * Set up aggregate-result storage in the output expr context, and also
3653 : * allocate my private per-agg working storage
3654 : */
3655 27559 : econtext = aggstate->ss.ps.ps_ExprContext;
3656 27559 : econtext->ecxt_aggvalues = palloc0_array(Datum, numaggs);
3657 27559 : econtext->ecxt_aggnulls = palloc0_array(bool, numaggs);
3658 :
3659 27559 : peraggs = palloc0_array(AggStatePerAggData, numaggs);
3660 27559 : pertransstates = palloc0_array(AggStatePerTransData, numtrans);
3661 :
3662 27559 : aggstate->peragg = peraggs;
3663 27559 : aggstate->pertrans = pertransstates;
3664 :
3665 :
3666 27559 : aggstate->all_pergroups = palloc0_array(AggStatePerGroup, numGroupingSets + numHashes);
3667 27559 : pergroups = aggstate->all_pergroups;
3668 :
3669 27559 : if (node->aggstrategy != AGG_HASHED)
3670 : {
3671 48656 : for (i = 0; i < numGroupingSets; i++)
3672 : {
3673 24544 : pergroups[i] = palloc0_array(AggStatePerGroupData, numaggs);
3674 : }
3675 :
3676 24112 : aggstate->pergroups = pergroups;
3677 24112 : pergroups += numGroupingSets;
3678 : }
3679 :
3680 : /*
3681 : * Hashing can only appear in the initial phase.
3682 : */
3683 27559 : if (use_hashing)
3684 : {
3685 3580 : Plan *outerplan = outerPlan(node);
3686 3580 : double totalGroups = 0;
3687 :
3688 3580 : aggstate->hash_spill_rslot = ExecInitExtraTupleSlot(estate, scanDesc,
3689 : &TTSOpsMinimalTuple);
3690 3580 : aggstate->hash_spill_wslot = ExecInitExtraTupleSlot(estate, scanDesc,
3691 : &TTSOpsVirtual);
3692 :
3693 : /* this is an array of pointers, not structures */
3694 3580 : aggstate->hash_pergroup = pergroups;
3695 :
3696 7160 : aggstate->hashentrysize = hash_agg_entry_size(aggstate->numtrans,
3697 3580 : outerplan->plan_width,
3698 : node->transitionSpace);
3699 :
3700 : /*
3701 : * Consider all of the grouping sets together when setting the limits
3702 : * and estimating the number of partitions. This can be inaccurate
3703 : * when there is more than one grouping set, but should still be
3704 : * reasonable.
3705 : */
3706 7429 : for (int k = 0; k < aggstate->num_hashes; k++)
3707 3849 : totalGroups += aggstate->perhash[k].aggnode->numGroups;
3708 :
3709 3580 : hash_agg_set_limits(aggstate->hashentrysize, totalGroups, 0,
3710 : &aggstate->hash_mem_limit,
3711 : &aggstate->hash_ngroups_limit,
3712 : &aggstate->hash_planned_partitions);
3713 3580 : find_hash_columns(aggstate);
3714 :
3715 : /* Skip massive memory allocation if we are just doing EXPLAIN */
3716 3580 : if (!(eflags & EXEC_FLAG_EXPLAIN_ONLY))
3717 2639 : build_hash_tables(aggstate);
3718 :
3719 3580 : aggstate->table_filled = false;
3720 :
3721 : /* Initialize this to 1, meaning nothing spilled, yet */
3722 3580 : aggstate->hash_batches_used = 1;
3723 : }
3724 :
3725 : /*
3726 : * Initialize current phase-dependent values to initial phase. The initial
3727 : * phase is 1 (first sort pass) for all strategies that use sorting (if
3728 : * hashing is being done too, then phase 0 is processed last); but if only
3729 : * hashing is being done, then phase 0 is all there is.
3730 : */
3731 27559 : if (node->aggstrategy == AGG_HASHED)
3732 : {
3733 3447 : aggstate->current_phase = 0;
3734 3447 : initialize_phase(aggstate, 0);
3735 3447 : select_current_set(aggstate, 0, true);
3736 : }
3737 : else
3738 : {
3739 24112 : aggstate->current_phase = 1;
3740 24112 : initialize_phase(aggstate, 1);
3741 24112 : select_current_set(aggstate, 0, false);
3742 : }
3743 :
3744 : /*
3745 : * Perform lookups of aggregate function info, and initialize the
3746 : * unchanging fields of the per-agg and per-trans data.
3747 : */
3748 58391 : foreach(l, aggstate->aggs)
3749 : {
3750 30835 : Aggref *aggref = lfirst(l);
3751 : AggStatePerAgg peragg;
3752 : AggStatePerTrans pertrans;
3753 : Oid aggTransFnInputTypes[FUNC_MAX_ARGS];
3754 : int numAggTransFnArgs;
3755 : int numDirectArgs;
3756 : HeapTuple aggTuple;
3757 : Form_pg_aggregate aggform;
3758 : AclResult aclresult;
3759 : Oid finalfn_oid;
3760 : Oid serialfn_oid,
3761 : deserialfn_oid;
3762 : Oid aggOwner;
3763 : Expr *finalfnexpr;
3764 : Oid aggtranstype;
3765 :
3766 : /* Planner should have assigned aggregate to correct level */
3767 : Assert(aggref->agglevelsup == 0);
3768 : /* ... and the split mode should match */
3769 : Assert(aggref->aggsplit == aggstate->aggsplit);
3770 :
3771 30835 : peragg = &peraggs[aggref->aggno];
3772 :
3773 : /* Check if we initialized the state for this aggregate already. */
3774 30835 : if (peragg->aggref != NULL)
3775 244 : continue;
3776 :
3777 30591 : peragg->aggref = aggref;
3778 30591 : peragg->transno = aggref->aggtransno;
3779 :
3780 : /* Fetch the pg_aggregate row */
3781 30591 : aggTuple = SearchSysCache1(AGGFNOID,
3782 : ObjectIdGetDatum(aggref->aggfnoid));
3783 30591 : if (!HeapTupleIsValid(aggTuple))
3784 0 : elog(ERROR, "cache lookup failed for aggregate %u",
3785 : aggref->aggfnoid);
3786 30591 : aggform = (Form_pg_aggregate) GETSTRUCT(aggTuple);
3787 :
3788 : /* Check permission to call aggregate function */
3789 30591 : aclresult = object_aclcheck(ProcedureRelationId, aggref->aggfnoid, GetUserId(),
3790 : ACL_EXECUTE);
3791 30591 : if (aclresult != ACLCHECK_OK)
3792 3 : aclcheck_error(aclresult, OBJECT_AGGREGATE,
3793 3 : get_func_name(aggref->aggfnoid));
3794 30588 : InvokeFunctionExecuteHook(aggref->aggfnoid);
3795 :
3796 : /* planner recorded transition state type in the Aggref itself */
3797 30588 : aggtranstype = aggref->aggtranstype;
3798 : Assert(OidIsValid(aggtranstype));
3799 :
3800 : /* Final function only required if we're finalizing the aggregates */
3801 30588 : if (DO_AGGSPLIT_SKIPFINAL(aggstate->aggsplit))
3802 2787 : peragg->finalfn_oid = finalfn_oid = InvalidOid;
3803 : else
3804 27801 : peragg->finalfn_oid = finalfn_oid = aggform->aggfinalfn;
3805 :
3806 30588 : serialfn_oid = InvalidOid;
3807 30588 : deserialfn_oid = InvalidOid;
3808 :
3809 : /*
3810 : * Check if serialization/deserialization is required. We only do it
3811 : * for aggregates that have transtype INTERNAL.
3812 : */
3813 30588 : if (aggtranstype == INTERNALOID)
3814 : {
3815 : /*
3816 : * The planner should only have generated a serialize agg node if
3817 : * every aggregate with an INTERNAL state has a serialization
3818 : * function. Verify that.
3819 : */
3820 12096 : if (DO_AGGSPLIT_SERIALIZE(aggstate->aggsplit))
3821 : {
3822 : /* serialization only valid when not running finalfn */
3823 : Assert(DO_AGGSPLIT_SKIPFINAL(aggstate->aggsplit));
3824 :
3825 168 : if (!OidIsValid(aggform->aggserialfn))
3826 0 : elog(ERROR, "serialfunc not provided for serialization aggregation");
3827 168 : serialfn_oid = aggform->aggserialfn;
3828 : }
3829 :
3830 : /* Likewise for deserialization functions */
3831 12096 : if (DO_AGGSPLIT_DESERIALIZE(aggstate->aggsplit))
3832 : {
3833 : /* deserialization only valid when combining states */
3834 : Assert(DO_AGGSPLIT_COMBINE(aggstate->aggsplit));
3835 :
3836 60 : if (!OidIsValid(aggform->aggdeserialfn))
3837 0 : elog(ERROR, "deserialfunc not provided for deserialization aggregation");
3838 60 : deserialfn_oid = aggform->aggdeserialfn;
3839 : }
3840 : }
3841 :
3842 : /* Check that aggregate owner has permission to call component fns */
3843 : {
3844 : HeapTuple procTuple;
3845 :
3846 30588 : procTuple = SearchSysCache1(PROCOID,
3847 : ObjectIdGetDatum(aggref->aggfnoid));
3848 30588 : if (!HeapTupleIsValid(procTuple))
3849 0 : elog(ERROR, "cache lookup failed for function %u",
3850 : aggref->aggfnoid);
3851 30588 : aggOwner = ((Form_pg_proc) GETSTRUCT(procTuple))->proowner;
3852 30588 : ReleaseSysCache(procTuple);
3853 :
3854 30588 : if (OidIsValid(finalfn_oid))
3855 : {
3856 12907 : aclresult = object_aclcheck(ProcedureRelationId, finalfn_oid, aggOwner,
3857 : ACL_EXECUTE);
3858 12907 : if (aclresult != ACLCHECK_OK)
3859 0 : aclcheck_error(aclresult, OBJECT_FUNCTION,
3860 0 : get_func_name(finalfn_oid));
3861 12907 : InvokeFunctionExecuteHook(finalfn_oid);
3862 : }
3863 30588 : if (OidIsValid(serialfn_oid))
3864 : {
3865 168 : aclresult = object_aclcheck(ProcedureRelationId, serialfn_oid, aggOwner,
3866 : ACL_EXECUTE);
3867 168 : if (aclresult != ACLCHECK_OK)
3868 0 : aclcheck_error(aclresult, OBJECT_FUNCTION,
3869 0 : get_func_name(serialfn_oid));
3870 168 : InvokeFunctionExecuteHook(serialfn_oid);
3871 : }
3872 30588 : if (OidIsValid(deserialfn_oid))
3873 : {
3874 60 : aclresult = object_aclcheck(ProcedureRelationId, deserialfn_oid, aggOwner,
3875 : ACL_EXECUTE);
3876 60 : if (aclresult != ACLCHECK_OK)
3877 0 : aclcheck_error(aclresult, OBJECT_FUNCTION,
3878 0 : get_func_name(deserialfn_oid));
3879 60 : InvokeFunctionExecuteHook(deserialfn_oid);
3880 : }
3881 : }
3882 :
3883 : /*
3884 : * Get actual datatypes of the (nominal) aggregate inputs. These
3885 : * could be different from the agg's declared input types, when the
3886 : * agg accepts ANY or a polymorphic type.
3887 : */
3888 30588 : numAggTransFnArgs = get_aggregate_argtypes(aggref,
3889 : aggTransFnInputTypes);
3890 :
3891 : /* Count the "direct" arguments, if any */
3892 30588 : numDirectArgs = list_length(aggref->aggdirectargs);
3893 :
3894 : /* Detect how many arguments to pass to the finalfn */
3895 30588 : if (aggform->aggfinalextra)
3896 8856 : peragg->numFinalArgs = numAggTransFnArgs + 1;
3897 : else
3898 21732 : peragg->numFinalArgs = numDirectArgs + 1;
3899 :
3900 : /* Initialize any direct-argument expressions */
3901 30588 : peragg->aggdirectargs = ExecInitExprList(aggref->aggdirectargs,
3902 : (PlanState *) aggstate);
3903 :
3904 : /*
3905 : * build expression trees using actual argument & result types for the
3906 : * finalfn, if it exists and is required.
3907 : */
3908 30588 : if (OidIsValid(finalfn_oid))
3909 : {
3910 12907 : build_aggregate_finalfn_expr(aggTransFnInputTypes,
3911 : peragg->numFinalArgs,
3912 : aggtranstype,
3913 : aggref->aggtype,
3914 : aggref->inputcollid,
3915 : finalfn_oid,
3916 : &finalfnexpr);
3917 12907 : fmgr_info(finalfn_oid, &peragg->finalfn);
3918 12907 : fmgr_info_set_expr((Node *) finalfnexpr, &peragg->finalfn);
3919 : }
3920 :
3921 : /* get info about the output value's datatype */
3922 30588 : get_typlenbyval(aggref->aggtype,
3923 : &peragg->resulttypeLen,
3924 : &peragg->resulttypeByVal);
3925 :
3926 : /*
3927 : * Build working state for invoking the transition function, if we
3928 : * haven't done it already.
3929 : */
3930 30588 : pertrans = &pertransstates[aggref->aggtransno];
3931 30588 : if (pertrans->aggref == NULL)
3932 : {
3933 : Datum textInitVal;
3934 : Datum initValue;
3935 : bool initValueIsNull;
3936 : Oid transfn_oid;
3937 :
3938 : /*
3939 : * If this aggregation is performing state combines, then instead
3940 : * of using the transition function, we'll use the combine
3941 : * function.
3942 : */
3943 30447 : if (DO_AGGSPLIT_COMBINE(aggstate->aggsplit))
3944 : {
3945 1123 : transfn_oid = aggform->aggcombinefn;
3946 :
3947 : /* If not set then the planner messed up */
3948 1123 : if (!OidIsValid(transfn_oid))
3949 0 : elog(ERROR, "combinefn not set for aggregate function");
3950 : }
3951 : else
3952 29324 : transfn_oid = aggform->aggtransfn;
3953 :
3954 30447 : aclresult = object_aclcheck(ProcedureRelationId, transfn_oid, aggOwner, ACL_EXECUTE);
3955 30447 : if (aclresult != ACLCHECK_OK)
3956 0 : aclcheck_error(aclresult, OBJECT_FUNCTION,
3957 0 : get_func_name(transfn_oid));
3958 30447 : InvokeFunctionExecuteHook(transfn_oid);
3959 :
3960 : /*
3961 : * initval is potentially null, so don't try to access it as a
3962 : * struct field. Must do it the hard way with SysCacheGetAttr.
3963 : */
3964 30447 : textInitVal = SysCacheGetAttr(AGGFNOID, aggTuple,
3965 : Anum_pg_aggregate_agginitval,
3966 : &initValueIsNull);
3967 30447 : if (initValueIsNull)
3968 16751 : initValue = (Datum) 0;
3969 : else
3970 13696 : initValue = GetAggInitVal(textInitVal, aggtranstype);
3971 :
3972 30447 : if (DO_AGGSPLIT_COMBINE(aggstate->aggsplit))
3973 : {
3974 1123 : Oid combineFnInputTypes[] = {aggtranstype,
3975 : aggtranstype};
3976 :
3977 : /*
3978 : * When combining there's only one input, the to-be-combined
3979 : * transition value. The transition value is not counted
3980 : * here.
3981 : */
3982 1123 : pertrans->numTransInputs = 1;
3983 :
3984 : /* aggcombinefn always has two arguments of aggtranstype */
3985 1123 : build_pertrans_for_aggref(pertrans, aggstate, estate,
3986 : aggref, transfn_oid, aggtranstype,
3987 : serialfn_oid, deserialfn_oid,
3988 : initValue, initValueIsNull,
3989 : combineFnInputTypes, 2);
3990 :
3991 : /*
3992 : * Ensure that a combine function to combine INTERNAL states
3993 : * is not strict. This should have been checked during CREATE
3994 : * AGGREGATE, but the strict property could have been changed
3995 : * since then.
3996 : */
3997 1123 : if (pertrans->transfn.fn_strict && aggtranstype == INTERNALOID)
3998 0 : ereport(ERROR,
3999 : (errcode(ERRCODE_INVALID_FUNCTION_DEFINITION),
4000 : errmsg("combine function with transition type %s must not be declared STRICT",
4001 : format_type_be(aggtranstype))));
4002 : }
4003 : else
4004 : {
4005 : /* Detect how many arguments to pass to the transfn */
4006 29324 : if (AGGKIND_IS_ORDERED_SET(aggref->aggkind))
4007 126 : pertrans->numTransInputs = list_length(aggref->args);
4008 : else
4009 29198 : pertrans->numTransInputs = numAggTransFnArgs;
4010 :
4011 29324 : build_pertrans_for_aggref(pertrans, aggstate, estate,
4012 : aggref, transfn_oid, aggtranstype,
4013 : serialfn_oid, deserialfn_oid,
4014 : initValue, initValueIsNull,
4015 : aggTransFnInputTypes,
4016 : numAggTransFnArgs);
4017 :
4018 : /*
4019 : * If the transfn is strict and the initval is NULL, make sure
4020 : * input type and transtype are the same (or at least
4021 : * binary-compatible), so that it's OK to use the first
4022 : * aggregated input value as the initial transValue. This
4023 : * should have been checked at agg definition time, but we
4024 : * must check again in case the transfn's strictness property
4025 : * has been changed.
4026 : */
4027 29324 : if (pertrans->transfn.fn_strict && pertrans->initValueIsNull)
4028 : {
4029 2572 : if (numAggTransFnArgs <= numDirectArgs ||
4030 2572 : !IsBinaryCoercible(aggTransFnInputTypes[numDirectArgs],
4031 : aggtranstype))
4032 0 : ereport(ERROR,
4033 : (errcode(ERRCODE_INVALID_FUNCTION_DEFINITION),
4034 : errmsg("aggregate %u needs to have compatible input type and transition type",
4035 : aggref->aggfnoid)));
4036 : }
4037 : }
4038 : }
4039 : else
4040 141 : pertrans->aggshared = true;
4041 30588 : ReleaseSysCache(aggTuple);
4042 : }
4043 :
4044 : /*
4045 : * Last, check whether any more aggregates got added onto the node while
4046 : * we processed the expressions for the aggregate arguments (including not
4047 : * only the regular arguments and FILTER expressions handled immediately
4048 : * above, but any direct arguments we might've handled earlier). If so,
4049 : * we have nested aggregate functions, which is semantically nonsensical,
4050 : * so complain. (This should have been caught by the parser, so we don't
4051 : * need to work hard on a helpful error message; but we defend against it
4052 : * here anyway, just to be sure.)
4053 : */
4054 27556 : if (numaggrefs != list_length(aggstate->aggs))
4055 0 : ereport(ERROR,
4056 : (errcode(ERRCODE_GROUPING_ERROR),
4057 : errmsg("aggregate function calls cannot be nested")));
4058 :
4059 : /*
4060 : * Build expressions doing all the transition work at once. We build a
4061 : * different one for each phase, as the number of transition function
4062 : * invocation can differ between phases. Note this'll work both for
4063 : * transition and combination functions (although there'll only be one
4064 : * phase in the latter case).
4065 : */
4066 79365 : for (phaseidx = 0; phaseidx < aggstate->numphases; phaseidx++)
4067 : {
4068 51809 : AggStatePerPhase phase = &aggstate->phases[phaseidx];
4069 51809 : bool dohash = false;
4070 51809 : bool dosort = false;
4071 :
4072 : /* phase 0 doesn't necessarily exist */
4073 51809 : if (!phase->aggnode)
4074 23976 : continue;
4075 :
4076 27833 : if (aggstate->aggstrategy == AGG_MIXED && phaseidx == 1)
4077 : {
4078 : /*
4079 : * Phase one, and only phase one, in a mixed agg performs both
4080 : * sorting and aggregation.
4081 : */
4082 133 : dohash = true;
4083 133 : dosort = true;
4084 : }
4085 27700 : else if (aggstate->aggstrategy == AGG_MIXED && phaseidx == 0)
4086 : {
4087 : /*
4088 : * No need to compute a transition function for an AGG_MIXED phase
4089 : * 0 - the contents of the hashtables will have been computed
4090 : * during phase 1.
4091 : */
4092 133 : continue;
4093 : }
4094 27567 : else if (phase->aggstrategy == AGG_PLAIN ||
4095 4821 : phase->aggstrategy == AGG_SORTED)
4096 : {
4097 24120 : dohash = false;
4098 24120 : dosort = true;
4099 : }
4100 3447 : else if (phase->aggstrategy == AGG_HASHED)
4101 : {
4102 3447 : dohash = true;
4103 3447 : dosort = false;
4104 : }
4105 : else
4106 : Assert(false);
4107 :
4108 27700 : phase->evaltrans = ExecBuildAggTrans(aggstate, phase, dosort, dohash,
4109 : false);
4110 :
4111 : /* cache compiled expression for outer slot without NULL check */
4112 27700 : phase->evaltrans_cache[0][0] = phase->evaltrans;
4113 : }
4114 :
4115 27556 : return aggstate;
4116 : }
4117 :
4118 : /*
4119 : * Build the state needed to calculate a state value for an aggregate.
4120 : *
4121 : * This initializes all the fields in 'pertrans'. 'aggref' is the aggregate
4122 : * to initialize the state for. 'transfn_oid', 'aggtranstype', and the rest
4123 : * of the arguments could be calculated from 'aggref', but the caller has
4124 : * calculated them already, so might as well pass them.
4125 : *
4126 : * 'transfn_oid' may be either the Oid of the aggtransfn or the aggcombinefn.
4127 : */
4128 : static void
4129 30447 : build_pertrans_for_aggref(AggStatePerTrans pertrans,
4130 : AggState *aggstate, EState *estate,
4131 : Aggref *aggref,
4132 : Oid transfn_oid, Oid aggtranstype,
4133 : Oid aggserialfn, Oid aggdeserialfn,
4134 : Datum initValue, bool initValueIsNull,
4135 : Oid *inputTypes, int numArguments)
4136 : {
4137 30447 : int numGroupingSets = Max(aggstate->maxsets, 1);
4138 : Expr *transfnexpr;
4139 : int numTransArgs;
4140 30447 : Expr *serialfnexpr = NULL;
4141 30447 : Expr *deserialfnexpr = NULL;
4142 : ListCell *lc;
4143 : int numInputs;
4144 : int numDirectArgs;
4145 : List *sortlist;
4146 : int numSortCols;
4147 : int numDistinctCols;
4148 : int i;
4149 :
4150 : /* Begin filling in the pertrans data */
4151 30447 : pertrans->aggref = aggref;
4152 30447 : pertrans->aggshared = false;
4153 30447 : pertrans->aggCollation = aggref->inputcollid;
4154 30447 : pertrans->transfn_oid = transfn_oid;
4155 30447 : pertrans->serialfn_oid = aggserialfn;
4156 30447 : pertrans->deserialfn_oid = aggdeserialfn;
4157 30447 : pertrans->initValue = initValue;
4158 30447 : pertrans->initValueIsNull = initValueIsNull;
4159 :
4160 : /* Count the "direct" arguments, if any */
4161 30447 : numDirectArgs = list_length(aggref->aggdirectargs);
4162 :
4163 : /* Count the number of aggregated input columns */
4164 30447 : pertrans->numInputs = numInputs = list_length(aggref->args);
4165 :
4166 30447 : pertrans->aggtranstype = aggtranstype;
4167 :
4168 : /* account for the current transition state */
4169 30447 : numTransArgs = pertrans->numTransInputs + 1;
4170 :
4171 : /*
4172 : * Set up infrastructure for calling the transfn. Note that invtransfn is
4173 : * not needed here.
4174 : */
4175 30447 : build_aggregate_transfn_expr(inputTypes,
4176 : numArguments,
4177 : numDirectArgs,
4178 30447 : aggref->aggvariadic,
4179 : aggtranstype,
4180 : aggref->inputcollid,
4181 : transfn_oid,
4182 : InvalidOid,
4183 : &transfnexpr,
4184 : NULL);
4185 :
4186 30447 : fmgr_info(transfn_oid, &pertrans->transfn);
4187 30447 : fmgr_info_set_expr((Node *) transfnexpr, &pertrans->transfn);
4188 :
4189 30447 : pertrans->transfn_fcinfo =
4190 30447 : (FunctionCallInfo) palloc(SizeForFunctionCallInfo(numTransArgs));
4191 30447 : InitFunctionCallInfoData(*pertrans->transfn_fcinfo,
4192 : &pertrans->transfn,
4193 : numTransArgs,
4194 : pertrans->aggCollation,
4195 : (Node *) aggstate, NULL);
4196 :
4197 : /* get info about the state value's datatype */
4198 30447 : get_typlenbyval(aggtranstype,
4199 : &pertrans->transtypeLen,
4200 : &pertrans->transtypeByVal);
4201 :
4202 30447 : if (OidIsValid(aggserialfn))
4203 : {
4204 168 : build_aggregate_serialfn_expr(aggserialfn,
4205 : &serialfnexpr);
4206 168 : fmgr_info(aggserialfn, &pertrans->serialfn);
4207 168 : fmgr_info_set_expr((Node *) serialfnexpr, &pertrans->serialfn);
4208 :
4209 168 : pertrans->serialfn_fcinfo =
4210 168 : (FunctionCallInfo) palloc(SizeForFunctionCallInfo(1));
4211 168 : InitFunctionCallInfoData(*pertrans->serialfn_fcinfo,
4212 : &pertrans->serialfn,
4213 : 1,
4214 : InvalidOid,
4215 : (Node *) aggstate, NULL);
4216 : }
4217 :
4218 30447 : if (OidIsValid(aggdeserialfn))
4219 : {
4220 60 : build_aggregate_deserialfn_expr(aggdeserialfn,
4221 : &deserialfnexpr);
4222 60 : fmgr_info(aggdeserialfn, &pertrans->deserialfn);
4223 60 : fmgr_info_set_expr((Node *) deserialfnexpr, &pertrans->deserialfn);
4224 :
4225 60 : pertrans->deserialfn_fcinfo =
4226 60 : (FunctionCallInfo) palloc(SizeForFunctionCallInfo(2));
4227 60 : InitFunctionCallInfoData(*pertrans->deserialfn_fcinfo,
4228 : &pertrans->deserialfn,
4229 : 2,
4230 : InvalidOid,
4231 : (Node *) aggstate, NULL);
4232 : }
4233 :
4234 : /*
4235 : * If we're doing either DISTINCT or ORDER BY for a plain agg, then we
4236 : * have a list of SortGroupClause nodes; fish out the data in them and
4237 : * stick them into arrays. We ignore ORDER BY for an ordered-set agg,
4238 : * however; the agg's transfn and finalfn are responsible for that.
4239 : *
4240 : * When the planner has set the aggpresorted flag, the input to the
4241 : * aggregate is already correctly sorted. For ORDER BY aggregates we can
4242 : * simply treat these as normal aggregates. For presorted DISTINCT
4243 : * aggregates an extra step must be added to remove duplicate consecutive
4244 : * inputs.
4245 : *
4246 : * Note that by construction, if there is a DISTINCT clause then the ORDER
4247 : * BY clause is a prefix of it (see transformDistinctClause).
4248 : */
4249 30447 : if (AGGKIND_IS_ORDERED_SET(aggref->aggkind))
4250 : {
4251 126 : sortlist = NIL;
4252 126 : numSortCols = numDistinctCols = 0;
4253 126 : pertrans->aggsortrequired = false;
4254 : }
4255 30321 : else if (aggref->aggpresorted && aggref->aggdistinct == NIL)
4256 : {
4257 1245 : sortlist = NIL;
4258 1245 : numSortCols = numDistinctCols = 0;
4259 1245 : pertrans->aggsortrequired = false;
4260 : }
4261 29076 : else if (aggref->aggdistinct)
4262 : {
4263 294 : sortlist = aggref->aggdistinct;
4264 294 : numSortCols = numDistinctCols = list_length(sortlist);
4265 : Assert(numSortCols >= list_length(aggref->aggorder));
4266 294 : pertrans->aggsortrequired = !aggref->aggpresorted;
4267 : }
4268 : else
4269 : {
4270 28782 : sortlist = aggref->aggorder;
4271 28782 : numSortCols = list_length(sortlist);
4272 28782 : numDistinctCols = 0;
4273 28782 : pertrans->aggsortrequired = (numSortCols > 0);
4274 : }
4275 :
4276 30447 : pertrans->numSortCols = numSortCols;
4277 30447 : pertrans->numDistinctCols = numDistinctCols;
4278 :
4279 : /*
4280 : * If we have either sorting or filtering to do, create a tupledesc and
4281 : * slot corresponding to the aggregated inputs (including sort
4282 : * expressions) of the agg.
4283 : */
4284 30447 : if (numSortCols > 0 || aggref->aggfilter)
4285 : {
4286 718 : pertrans->sortdesc = ExecTypeFromTL(aggref->args);
4287 718 : pertrans->sortslot =
4288 718 : ExecInitExtraTupleSlot(estate, pertrans->sortdesc,
4289 : &TTSOpsMinimalTuple);
4290 : }
4291 :
4292 30447 : if (numSortCols > 0)
4293 : {
4294 : /*
4295 : * We don't implement DISTINCT or ORDER BY aggs in the HASHED case
4296 : * (yet)
4297 : */
4298 : Assert(aggstate->aggstrategy != AGG_HASHED && aggstate->aggstrategy != AGG_MIXED);
4299 :
4300 : /* ORDER BY aggregates are not supported with partial aggregation */
4301 : Assert(!DO_AGGSPLIT_COMBINE(aggstate->aggsplit));
4302 :
4303 : /* If we have only one input, we need its len/byval info. */
4304 363 : if (numInputs == 1)
4305 : {
4306 288 : get_typlenbyval(inputTypes[numDirectArgs],
4307 : &pertrans->inputtypeLen,
4308 : &pertrans->inputtypeByVal);
4309 : }
4310 75 : else if (numDistinctCols > 0)
4311 : {
4312 : /* we will need an extra slot to store prior values */
4313 54 : pertrans->uniqslot =
4314 54 : ExecInitExtraTupleSlot(estate, pertrans->sortdesc,
4315 : &TTSOpsMinimalTuple);
4316 : }
4317 :
4318 : /* Extract the sort information for use later */
4319 363 : pertrans->sortColIdx =
4320 363 : (AttrNumber *) palloc(numSortCols * sizeof(AttrNumber));
4321 363 : pertrans->sortOperators =
4322 363 : (Oid *) palloc(numSortCols * sizeof(Oid));
4323 363 : pertrans->sortCollations =
4324 363 : (Oid *) palloc(numSortCols * sizeof(Oid));
4325 363 : pertrans->sortNullsFirst =
4326 363 : (bool *) palloc(numSortCols * sizeof(bool));
4327 :
4328 363 : i = 0;
4329 825 : foreach(lc, sortlist)
4330 : {
4331 462 : SortGroupClause *sortcl = (SortGroupClause *) lfirst(lc);
4332 462 : TargetEntry *tle = get_sortgroupclause_tle(sortcl, aggref->args);
4333 :
4334 : /* the parser should have made sure of this */
4335 : Assert(OidIsValid(sortcl->sortop));
4336 :
4337 462 : pertrans->sortColIdx[i] = tle->resno;
4338 462 : pertrans->sortOperators[i] = sortcl->sortop;
4339 462 : pertrans->sortCollations[i] = exprCollation((Node *) tle->expr);
4340 462 : pertrans->sortNullsFirst[i] = sortcl->nulls_first;
4341 462 : i++;
4342 : }
4343 : Assert(i == numSortCols);
4344 : }
4345 :
4346 30447 : if (aggref->aggdistinct)
4347 : {
4348 : Oid *ops;
4349 :
4350 : Assert(numArguments > 0);
4351 : Assert(list_length(aggref->aggdistinct) == numDistinctCols);
4352 :
4353 294 : ops = palloc(numDistinctCols * sizeof(Oid));
4354 :
4355 294 : i = 0;
4356 678 : foreach(lc, aggref->aggdistinct)
4357 384 : ops[i++] = ((SortGroupClause *) lfirst(lc))->eqop;
4358 :
4359 : /* lookup / build the necessary comparators */
4360 294 : if (numDistinctCols == 1)
4361 240 : fmgr_info(get_opcode(ops[0]), &pertrans->equalfnOne);
4362 : else
4363 54 : pertrans->equalfnMulti =
4364 54 : execTuplesMatchPrepare(pertrans->sortdesc,
4365 : numDistinctCols,
4366 54 : pertrans->sortColIdx,
4367 : ops,
4368 54 : pertrans->sortCollations,
4369 : &aggstate->ss.ps);
4370 294 : pfree(ops);
4371 : }
4372 :
4373 30447 : pertrans->sortstates = palloc0_array(Tuplesortstate *, numGroupingSets);
4374 30447 : }
4375 :
4376 :
4377 : static Datum
4378 13696 : GetAggInitVal(Datum textInitVal, Oid transtype)
4379 : {
4380 : Oid typinput,
4381 : typioparam;
4382 : char *strInitVal;
4383 : Datum initVal;
4384 :
4385 13696 : getTypeInputInfo(transtype, &typinput, &typioparam);
4386 13696 : strInitVal = TextDatumGetCString(textInitVal);
4387 13696 : initVal = OidInputFunctionCall(typinput, strInitVal,
4388 : typioparam, -1);
4389 13696 : pfree(strInitVal);
4390 13696 : return initVal;
4391 : }
4392 :
4393 : void
4394 27454 : ExecEndAgg(AggState *node)
4395 : {
4396 : PlanState *outerPlan;
4397 : int transno;
4398 27454 : int numGroupingSets = Max(node->maxsets, 1);
4399 : int setno;
4400 :
4401 : /*
4402 : * When ending a parallel worker, copy the statistics gathered by the
4403 : * worker back into shared memory so that it can be picked up by the main
4404 : * process to report in EXPLAIN ANALYZE.
4405 : */
4406 27454 : if (node->shared_info && IsParallelWorker())
4407 : {
4408 : AggregateInstrumentation *si;
4409 :
4410 : Assert(ParallelWorkerNumber <= node->shared_info->num_workers);
4411 84 : si = &node->shared_info->sinstrument[ParallelWorkerNumber];
4412 84 : si->hash_batches_used = node->hash_batches_used;
4413 84 : si->hash_disk_used = node->hash_disk_used;
4414 84 : si->hash_mem_peak = node->hash_mem_peak;
4415 : }
4416 :
4417 : /* Make sure we have closed any open tuplesorts */
4418 :
4419 27454 : if (node->sort_in)
4420 90 : tuplesort_end(node->sort_in);
4421 27454 : if (node->sort_out)
4422 33 : tuplesort_end(node->sort_out);
4423 :
4424 27454 : hashagg_reset_spill_state(node);
4425 :
4426 : /* Release hash tables too */
4427 27454 : if (node->hash_metacxt != NULL)
4428 : {
4429 3576 : MemoryContextDelete(node->hash_metacxt);
4430 3576 : node->hash_metacxt = NULL;
4431 : }
4432 27454 : if (node->hash_tuplescxt != NULL)
4433 : {
4434 3576 : MemoryContextDelete(node->hash_tuplescxt);
4435 3576 : node->hash_tuplescxt = NULL;
4436 : }
4437 :
4438 57797 : for (transno = 0; transno < node->numtrans; transno++)
4439 : {
4440 30343 : AggStatePerTrans pertrans = &node->pertrans[transno];
4441 :
4442 61205 : for (setno = 0; setno < numGroupingSets; setno++)
4443 : {
4444 30862 : if (pertrans->sortstates[setno])
4445 0 : tuplesort_end(pertrans->sortstates[setno]);
4446 : }
4447 : }
4448 :
4449 : /* And ensure any agg shutdown callbacks have been called */
4450 55340 : for (setno = 0; setno < numGroupingSets; setno++)
4451 27886 : ReScanExprContext(node->aggcontexts[setno]);
4452 27454 : if (node->hashcontext)
4453 3576 : ReScanExprContext(node->hashcontext);
4454 :
4455 27454 : outerPlan = outerPlanState(node);
4456 27454 : ExecEndNode(outerPlan);
4457 27454 : }
4458 :
4459 : void
4460 27458 : ExecReScanAgg(AggState *node)
4461 : {
4462 27458 : ExprContext *econtext = node->ss.ps.ps_ExprContext;
4463 27458 : PlanState *outerPlan = outerPlanState(node);
4464 27458 : Agg *aggnode = (Agg *) node->ss.ps.plan;
4465 : int transno;
4466 27458 : int numGroupingSets = Max(node->maxsets, 1);
4467 : int setno;
4468 :
4469 27458 : node->agg_done = false;
4470 :
4471 27458 : if (node->aggstrategy == AGG_HASHED)
4472 : {
4473 : /*
4474 : * In the hashed case, if we haven't yet built the hash table then we
4475 : * can just return; nothing done yet, so nothing to undo. If subnode's
4476 : * chgParam is not NULL then it will be re-scanned by ExecProcNode,
4477 : * else no reason to re-scan it at all.
4478 : */
4479 6836 : if (!node->table_filled)
4480 76 : return;
4481 :
4482 : /*
4483 : * If we do have the hash table, and it never spilled, and the subplan
4484 : * does not have any parameter changes, and none of our own parameter
4485 : * changes affect input expressions of the aggregated functions, then
4486 : * we can just rescan the existing hash table; no need to build it
4487 : * again.
4488 : */
4489 6760 : if (outerPlan->chgParam == NULL && !node->hash_ever_spilled &&
4490 458 : !bms_overlap(node->ss.ps.chgParam, aggnode->aggParams))
4491 : {
4492 446 : ResetTupleHashIterator(node->perhash[0].hashtable,
4493 : &node->perhash[0].hashiter);
4494 446 : select_current_set(node, 0, true);
4495 446 : return;
4496 : }
4497 : }
4498 :
4499 : /* Make sure we have closed any open tuplesorts */
4500 61241 : for (transno = 0; transno < node->numtrans; transno++)
4501 : {
4502 68628 : for (setno = 0; setno < numGroupingSets; setno++)
4503 : {
4504 34323 : AggStatePerTrans pertrans = &node->pertrans[transno];
4505 :
4506 34323 : if (pertrans->sortstates[setno])
4507 : {
4508 0 : tuplesort_end(pertrans->sortstates[setno]);
4509 0 : pertrans->sortstates[setno] = NULL;
4510 : }
4511 : }
4512 : }
4513 :
4514 : /*
4515 : * We don't need to ReScanExprContext the output tuple context here;
4516 : * ExecReScan already did it. But we do need to reset our per-grouping-set
4517 : * contexts, which may have transvalues stored in them. (We use rescan
4518 : * rather than just reset because transfns may have registered callbacks
4519 : * that need to be run now.) For the AGG_HASHED case, see below.
4520 : */
4521 :
4522 53890 : for (setno = 0; setno < numGroupingSets; setno++)
4523 : {
4524 26954 : ReScanExprContext(node->aggcontexts[setno]);
4525 : }
4526 :
4527 : /* Release first tuple of group, if we have made a copy */
4528 26936 : if (node->grp_firstTuple != NULL)
4529 : {
4530 0 : heap_freetuple(node->grp_firstTuple);
4531 0 : node->grp_firstTuple = NULL;
4532 : }
4533 26936 : ExecClearTuple(node->ss.ss_ScanTupleSlot);
4534 :
4535 : /* Forget current agg values */
4536 61241 : MemSet(econtext->ecxt_aggvalues, 0, sizeof(Datum) * node->numaggs);
4537 26936 : MemSet(econtext->ecxt_aggnulls, 0, sizeof(bool) * node->numaggs);
4538 :
4539 : /*
4540 : * With AGG_HASHED/MIXED, the hash table is allocated in a sub-context of
4541 : * the hashcontext. This used to be an issue, but now, resetting a context
4542 : * automatically deletes sub-contexts too.
4543 : */
4544 26936 : if (node->aggstrategy == AGG_HASHED || node->aggstrategy == AGG_MIXED)
4545 : {
4546 6329 : hashagg_reset_spill_state(node);
4547 :
4548 6329 : node->hash_ever_spilled = false;
4549 6329 : node->hash_spill_mode = false;
4550 6329 : node->hash_ngroups_current = 0;
4551 :
4552 6329 : ReScanExprContext(node->hashcontext);
4553 : /* Rebuild empty hash table(s) */
4554 6329 : build_hash_tables(node);
4555 6329 : node->table_filled = false;
4556 : /* iterator will be reset when the table is filled */
4557 :
4558 6329 : hashagg_recompile_expressions(node, false, false);
4559 : }
4560 :
4561 26936 : if (node->aggstrategy != AGG_HASHED)
4562 : {
4563 : /*
4564 : * Reset the per-group state (in particular, mark transvalues null)
4565 : */
4566 41262 : for (setno = 0; setno < numGroupingSets; setno++)
4567 : {
4568 89238 : MemSet(node->pergroups[setno], 0,
4569 : sizeof(AggStatePerGroupData) * node->numaggs);
4570 : }
4571 :
4572 : /* reset to phase 1 */
4573 20622 : initialize_phase(node, 1);
4574 :
4575 20622 : node->input_done = false;
4576 20622 : node->projected_set = -1;
4577 : }
4578 :
4579 26936 : if (outerPlan->chgParam == NULL)
4580 94 : ExecReScan(outerPlan);
4581 : }
4582 :
4583 :
4584 : /***********************************************************************
4585 : * API exposed to aggregate functions
4586 : ***********************************************************************/
4587 :
4588 :
4589 : /*
4590 : * AggCheckCallContext - test if a SQL function is being called as an aggregate
4591 : *
4592 : * The transition and/or final functions of an aggregate may want to verify
4593 : * that they are being called as aggregates, rather than as plain SQL
4594 : * functions. They should use this function to do so. The return value
4595 : * is nonzero if being called as an aggregate, or zero if not. (Specific
4596 : * nonzero values are AGG_CONTEXT_AGGREGATE or AGG_CONTEXT_WINDOW, but more
4597 : * values could conceivably appear in future.)
4598 : *
4599 : * If aggcontext isn't NULL, the function also stores at *aggcontext the
4600 : * identity of the memory context that aggregate transition values are being
4601 : * stored in. Note that the same aggregate call site (flinfo) may be called
4602 : * interleaved on different transition values in different contexts, so it's
4603 : * not kosher to cache aggcontext under fn_extra. It is, however, kosher to
4604 : * cache it in the transvalue itself (for internal-type transvalues).
4605 : */
4606 : int
4607 2730852 : AggCheckCallContext(FunctionCallInfo fcinfo, MemoryContext *aggcontext)
4608 : {
4609 2730852 : if (fcinfo->context && IsA(fcinfo->context, AggState))
4610 : {
4611 2725099 : if (aggcontext)
4612 : {
4613 1386049 : AggState *aggstate = ((AggState *) fcinfo->context);
4614 1386049 : ExprContext *cxt = aggstate->curaggcontext;
4615 :
4616 1386049 : *aggcontext = cxt->ecxt_per_tuple_memory;
4617 : }
4618 2725099 : return AGG_CONTEXT_AGGREGATE;
4619 : }
4620 5753 : if (fcinfo->context && IsA(fcinfo->context, WindowAggState))
4621 : {
4622 4816 : if (aggcontext)
4623 415 : *aggcontext = ((WindowAggState *) fcinfo->context)->curaggcontext;
4624 4816 : return AGG_CONTEXT_WINDOW;
4625 : }
4626 :
4627 : /* this is just to prevent "uninitialized variable" warnings */
4628 937 : if (aggcontext)
4629 913 : *aggcontext = NULL;
4630 937 : return 0;
4631 : }
4632 :
4633 : /*
4634 : * AggGetAggref - allow an aggregate support function to get its Aggref
4635 : *
4636 : * If the function is being called as an aggregate support function,
4637 : * return the Aggref node for the aggregate call. Otherwise, return NULL.
4638 : *
4639 : * Aggregates sharing the same inputs and transition functions can get
4640 : * merged into a single transition calculation. If the transition function
4641 : * calls AggGetAggref, it will get some one of the Aggrefs for which it is
4642 : * executing. It must therefore not pay attention to the Aggref fields that
4643 : * relate to the final function, as those are indeterminate. But if a final
4644 : * function calls AggGetAggref, it will get a precise result.
4645 : *
4646 : * Note that if an aggregate is being used as a window function, this will
4647 : * return NULL. We could provide a similar function to return the relevant
4648 : * WindowFunc node in such cases, but it's not needed yet.
4649 : */
4650 : Aggref *
4651 123 : AggGetAggref(FunctionCallInfo fcinfo)
4652 : {
4653 123 : if (fcinfo->context && IsA(fcinfo->context, AggState))
4654 : {
4655 123 : AggState *aggstate = (AggState *) fcinfo->context;
4656 : AggStatePerAgg curperagg;
4657 : AggStatePerTrans curpertrans;
4658 :
4659 : /* check curperagg (valid when in a final function) */
4660 123 : curperagg = aggstate->curperagg;
4661 :
4662 123 : if (curperagg)
4663 0 : return curperagg->aggref;
4664 :
4665 : /* check curpertrans (valid when in a transition function) */
4666 123 : curpertrans = aggstate->curpertrans;
4667 :
4668 123 : if (curpertrans)
4669 123 : return curpertrans->aggref;
4670 : }
4671 0 : return NULL;
4672 : }
4673 :
4674 : /*
4675 : * AggGetTempMemoryContext - fetch short-term memory context for aggregates
4676 : *
4677 : * This is useful in agg final functions; the context returned is one that
4678 : * the final function can safely reset as desired. This isn't useful for
4679 : * transition functions, since the context returned MAY (we don't promise)
4680 : * be the same as the context those are called in.
4681 : *
4682 : * As above, this is currently not useful for aggs called as window functions.
4683 : */
4684 : MemoryContext
4685 0 : AggGetTempMemoryContext(FunctionCallInfo fcinfo)
4686 : {
4687 0 : if (fcinfo->context && IsA(fcinfo->context, AggState))
4688 : {
4689 0 : AggState *aggstate = (AggState *) fcinfo->context;
4690 :
4691 0 : return aggstate->tmpcontext->ecxt_per_tuple_memory;
4692 : }
4693 0 : return NULL;
4694 : }
4695 :
4696 : /*
4697 : * AggStateIsShared - find out whether transition state is shared
4698 : *
4699 : * If the function is being called as an aggregate support function,
4700 : * return true if the aggregate's transition state is shared across
4701 : * multiple aggregates, false if it is not.
4702 : *
4703 : * Returns true if not called as an aggregate support function.
4704 : * This is intended as a conservative answer, ie "no you'd better not
4705 : * scribble on your input". In particular, will return true if the
4706 : * aggregate is being used as a window function, which is a scenario
4707 : * in which changing the transition state is a bad idea. We might
4708 : * want to refine the behavior for the window case in future.
4709 : */
4710 : bool
4711 123 : AggStateIsShared(FunctionCallInfo fcinfo)
4712 : {
4713 123 : if (fcinfo->context && IsA(fcinfo->context, AggState))
4714 : {
4715 123 : AggState *aggstate = (AggState *) fcinfo->context;
4716 : AggStatePerAgg curperagg;
4717 : AggStatePerTrans curpertrans;
4718 :
4719 : /* check curperagg (valid when in a final function) */
4720 123 : curperagg = aggstate->curperagg;
4721 :
4722 123 : if (curperagg)
4723 0 : return aggstate->pertrans[curperagg->transno].aggshared;
4724 :
4725 : /* check curpertrans (valid when in a transition function) */
4726 123 : curpertrans = aggstate->curpertrans;
4727 :
4728 123 : if (curpertrans)
4729 123 : return curpertrans->aggshared;
4730 : }
4731 0 : return true;
4732 : }
4733 :
4734 : /*
4735 : * AggRegisterCallback - register a cleanup callback for an aggregate
4736 : *
4737 : * This is useful for aggs to register shutdown callbacks, which will ensure
4738 : * that non-memory resources are freed. The callback will occur just before
4739 : * the associated aggcontext (as returned by AggCheckCallContext) is reset,
4740 : * either between groups or as a result of rescanning the query. The callback
4741 : * will NOT be called on error paths. The typical use-case is for freeing of
4742 : * tuplestores or tuplesorts maintained in aggcontext, or pins held by slots
4743 : * created by the agg functions. (The callback will not be called until after
4744 : * the result of the finalfn is no longer needed, so it's safe for the finalfn
4745 : * to return data that will be freed by the callback.)
4746 : *
4747 : * As above, this is currently not useful for aggs called as window functions.
4748 : */
4749 : void
4750 330 : AggRegisterCallback(FunctionCallInfo fcinfo,
4751 : ExprContextCallbackFunction func,
4752 : Datum arg)
4753 : {
4754 330 : if (fcinfo->context && IsA(fcinfo->context, AggState))
4755 : {
4756 330 : AggState *aggstate = (AggState *) fcinfo->context;
4757 330 : ExprContext *cxt = aggstate->curaggcontext;
4758 :
4759 330 : RegisterExprContextCallback(cxt, func, arg);
4760 :
4761 330 : return;
4762 : }
4763 0 : elog(ERROR, "aggregate function cannot register a callback in this context");
4764 : }
4765 :
4766 :
4767 : /* ----------------------------------------------------------------
4768 : * Parallel Query Support
4769 : * ----------------------------------------------------------------
4770 : */
4771 :
4772 : /* ----------------------------------------------------------------
4773 : * ExecAggEstimate
4774 : *
4775 : * Estimate space required to propagate aggregate statistics.
4776 : * ----------------------------------------------------------------
4777 : */
4778 : void
4779 295 : ExecAggEstimate(AggState *node, ParallelContext *pcxt)
4780 : {
4781 : Size size;
4782 :
4783 : /* don't need this if not instrumenting or no workers */
4784 295 : if (!node->ss.ps.instrument || pcxt->nworkers == 0)
4785 244 : return;
4786 :
4787 51 : size = mul_size(pcxt->nworkers, sizeof(AggregateInstrumentation));
4788 51 : size = add_size(size, offsetof(SharedAggInfo, sinstrument));
4789 51 : shm_toc_estimate_chunk(&pcxt->estimator, size);
4790 51 : shm_toc_estimate_keys(&pcxt->estimator, 1);
4791 : }
4792 :
4793 : /* ----------------------------------------------------------------
4794 : * ExecAggInitializeDSM
4795 : *
4796 : * Initialize DSM space for aggregate statistics.
4797 : * ----------------------------------------------------------------
4798 : */
4799 : void
4800 295 : ExecAggInitializeDSM(AggState *node, ParallelContext *pcxt)
4801 : {
4802 : Size size;
4803 :
4804 : /* don't need this if not instrumenting or no workers */
4805 295 : if (!node->ss.ps.instrument || pcxt->nworkers == 0)
4806 244 : return;
4807 :
4808 51 : size = offsetof(SharedAggInfo, sinstrument)
4809 51 : + pcxt->nworkers * sizeof(AggregateInstrumentation);
4810 51 : node->shared_info = shm_toc_allocate(pcxt->toc, size);
4811 : /* ensure any unfilled slots will contain zeroes */
4812 51 : memset(node->shared_info, 0, size);
4813 51 : node->shared_info->num_workers = pcxt->nworkers;
4814 51 : shm_toc_insert(pcxt->toc, node->ss.ps.plan->plan_node_id,
4815 51 : node->shared_info);
4816 : }
4817 :
4818 : /* ----------------------------------------------------------------
4819 : * ExecAggInitializeWorker
4820 : *
4821 : * Attach worker to DSM space for aggregate statistics.
4822 : * ----------------------------------------------------------------
4823 : */
4824 : void
4825 834 : ExecAggInitializeWorker(AggState *node, ParallelWorkerContext *pwcxt)
4826 : {
4827 834 : node->shared_info =
4828 834 : shm_toc_lookup(pwcxt->toc, node->ss.ps.plan->plan_node_id, true);
4829 834 : }
4830 :
4831 : /* ----------------------------------------------------------------
4832 : * ExecAggRetrieveInstrumentation
4833 : *
4834 : * Transfer aggregate statistics from DSM to private memory.
4835 : * ----------------------------------------------------------------
4836 : */
4837 : void
4838 51 : ExecAggRetrieveInstrumentation(AggState *node)
4839 : {
4840 : Size size;
4841 : SharedAggInfo *si;
4842 :
4843 51 : if (node->shared_info == NULL)
4844 0 : return;
4845 :
4846 51 : size = offsetof(SharedAggInfo, sinstrument)
4847 51 : + node->shared_info->num_workers * sizeof(AggregateInstrumentation);
4848 51 : si = palloc(size);
4849 51 : memcpy(si, node->shared_info, size);
4850 51 : node->shared_info = si;
4851 : }
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