Branch data 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/instrument.h"
261 : : #include "executor/nodeAgg.h"
262 : : #include "lib/hyperloglog.h"
263 : : #include "miscadmin.h"
264 : : #include "nodes/nodeFuncs.h"
265 : : #include "optimizer/optimizer.h"
266 : : #include "parser/parse_agg.h"
267 : : #include "parser/parse_coerce.h"
268 : : #include "port/pg_bitutils.h"
269 : : #include "utils/acl.h"
270 : : #include "utils/builtins.h"
271 : : #include "utils/datum.h"
272 : : #include "utils/expandeddatum.h"
273 : : #include "utils/injection_point.h"
274 : : #include "utils/logtape.h"
275 : : #include "utils/lsyscache.h"
276 : : #include "utils/memutils.h"
277 : : #include "utils/memutils_memorychunk.h"
278 : : #include "utils/syscache.h"
279 : : #include "utils/tuplesort.h"
280 : :
281 : : /*
282 : : * Control how many partitions are created when spilling HashAgg to
283 : : * disk.
284 : : *
285 : : * HASHAGG_PARTITION_FACTOR is multiplied by the estimated number of
286 : : * partitions needed such that each partition will fit in memory. The factor
287 : : * is set higher than one because there's not a high cost to having a few too
288 : : * many partitions, and it makes it less likely that a partition will need to
289 : : * be spilled recursively. Another benefit of having more, smaller partitions
290 : : * is that small hash tables may perform better than large ones due to memory
291 : : * caching effects.
292 : : *
293 : : * We also specify a min and max number of partitions per spill. Too few might
294 : : * mean a lot of wasted I/O from repeated spilling of the same tuples. Too
295 : : * many will result in lots of memory wasted buffering the spill files (which
296 : : * could instead be spent on a larger hash table).
297 : : */
298 : : #define HASHAGG_PARTITION_FACTOR 1.50
299 : : #define HASHAGG_MIN_PARTITIONS 4
300 : : #define HASHAGG_MAX_PARTITIONS 1024
301 : :
302 : : /*
303 : : * For reading from tapes, the buffer size must be a multiple of
304 : : * BLCKSZ. Larger values help when reading from multiple tapes concurrently,
305 : : * but that doesn't happen in HashAgg, so we simply use BLCKSZ. Writing to a
306 : : * tape always uses a buffer of size BLCKSZ.
307 : : */
308 : : #define HASHAGG_READ_BUFFER_SIZE BLCKSZ
309 : : #define HASHAGG_WRITE_BUFFER_SIZE BLCKSZ
310 : :
311 : : /*
312 : : * HyperLogLog is used for estimating the cardinality of the spilled tuples in
313 : : * a given partition. 5 bits corresponds to a size of about 32 bytes and a
314 : : * worst-case error of around 18%. That's effective enough to choose a
315 : : * reasonable number of partitions when recursing.
316 : : */
317 : : #define HASHAGG_HLL_BIT_WIDTH 5
318 : :
319 : : /*
320 : : * Assume the palloc overhead always uses sizeof(MemoryChunk) bytes.
321 : : */
322 : : #define CHUNKHDRSZ sizeof(MemoryChunk)
323 : :
324 : : /*
325 : : * Represents partitioned spill data for a single hashtable. Contains the
326 : : * necessary information to route tuples to the correct partition, and to
327 : : * transform the spilled data into new batches.
328 : : *
329 : : * The high bits are used for partition selection (when recursing, we ignore
330 : : * the bits that have already been used for partition selection at an earlier
331 : : * level).
332 : : */
333 : : typedef struct HashAggSpill
334 : : {
335 : : int npartitions; /* number of partitions */
336 : : LogicalTape **partitions; /* spill partition tapes */
337 : : int64 *ntuples; /* number of tuples in each partition */
338 : : uint32 mask; /* mask to find partition from hash value */
339 : : int shift; /* after masking, shift by this amount */
340 : : hyperLogLogState *hll_card; /* cardinality estimate for contents */
341 : : } HashAggSpill;
342 : :
343 : : /*
344 : : * Represents work to be done for one pass of hash aggregation (with only one
345 : : * grouping set).
346 : : *
347 : : * Also tracks the bits of the hash already used for partition selection by
348 : : * earlier iterations, so that this batch can use new bits. If all bits have
349 : : * already been used, no partitioning will be done (any spilled data will go
350 : : * to a single output tape).
351 : : */
352 : : typedef struct HashAggBatch
353 : : {
354 : : int setno; /* grouping set */
355 : : int used_bits; /* number of bits of hash already used */
356 : : LogicalTape *input_tape; /* input partition tape */
357 : : int64 input_tuples; /* number of tuples in this batch */
358 : : double input_card; /* estimated group cardinality */
359 : : } HashAggBatch;
360 : :
361 : : /* used to find referenced colnos */
362 : : typedef struct FindColsContext
363 : : {
364 : : bool is_aggref; /* is under an aggref */
365 : : Bitmapset *aggregated; /* column references under an aggref */
366 : : Bitmapset *unaggregated; /* other column references */
367 : : } FindColsContext;
368 : :
369 : : static void select_current_set(AggState *aggstate, int setno, bool is_hash);
370 : : static void initialize_phase(AggState *aggstate, int newphase);
371 : : static TupleTableSlot *fetch_input_tuple(AggState *aggstate);
372 : : static void initialize_aggregates(AggState *aggstate,
373 : : AggStatePerGroup *pergroups,
374 : : int numReset);
375 : : static void advance_transition_function(AggState *aggstate,
376 : : AggStatePerTrans pertrans,
377 : : AggStatePerGroup pergroupstate);
378 : : static void advance_aggregates(AggState *aggstate);
379 : : static void process_ordered_aggregate_single(AggState *aggstate,
380 : : AggStatePerTrans pertrans,
381 : : AggStatePerGroup pergroupstate);
382 : : static void process_ordered_aggregate_multi(AggState *aggstate,
383 : : AggStatePerTrans pertrans,
384 : : AggStatePerGroup pergroupstate);
385 : : static void finalize_aggregate(AggState *aggstate,
386 : : AggStatePerAgg peragg,
387 : : AggStatePerGroup pergroupstate,
388 : : Datum *resultVal, bool *resultIsNull);
389 : : static void finalize_partialaggregate(AggState *aggstate,
390 : : AggStatePerAgg peragg,
391 : : AggStatePerGroup pergroupstate,
392 : : Datum *resultVal, bool *resultIsNull);
393 : : static inline void prepare_hash_slot(AggStatePerHash perhash,
394 : : TupleTableSlot *inputslot,
395 : : TupleTableSlot *hashslot);
396 : : static void prepare_projection_slot(AggState *aggstate,
397 : : TupleTableSlot *slot,
398 : : int currentSet);
399 : : static void finalize_aggregates(AggState *aggstate,
400 : : AggStatePerAgg peraggs,
401 : : AggStatePerGroup pergroup);
402 : : static TupleTableSlot *project_aggregates(AggState *aggstate);
403 : : static void find_cols(AggState *aggstate, Bitmapset **aggregated,
404 : : Bitmapset **unaggregated);
405 : : static bool find_cols_walker(Node *node, FindColsContext *context);
406 : : static void build_hash_tables(AggState *aggstate);
407 : : static void build_hash_table(AggState *aggstate, int setno, double nbuckets);
408 : : static void hashagg_recompile_expressions(AggState *aggstate, bool minslot,
409 : : bool nullcheck);
410 : : static void hash_create_memory(AggState *aggstate);
411 : : static double hash_choose_num_buckets(double hashentrysize,
412 : : double ngroups, Size memory);
413 : : static int hash_choose_num_partitions(double input_groups,
414 : : double hashentrysize,
415 : : int used_bits,
416 : : int *log2_npartitions);
417 : : static void initialize_hash_entry(AggState *aggstate,
418 : : TupleHashTable hashtable,
419 : : TupleHashEntry entry);
420 : : static void lookup_hash_entries(AggState *aggstate);
421 : : static TupleTableSlot *agg_retrieve_direct(AggState *aggstate);
422 : : static void agg_fill_hash_table(AggState *aggstate);
423 : : static bool agg_refill_hash_table(AggState *aggstate);
424 : : static TupleTableSlot *agg_retrieve_hash_table(AggState *aggstate);
425 : : static TupleTableSlot *agg_retrieve_hash_table_in_memory(AggState *aggstate);
426 : : static void hash_agg_check_limits(AggState *aggstate);
427 : : static void hash_agg_enter_spill_mode(AggState *aggstate);
428 : : static void hash_agg_update_metrics(AggState *aggstate, bool from_tape,
429 : : int npartitions);
430 : : static void hashagg_finish_initial_spills(AggState *aggstate);
431 : : static void hashagg_reset_spill_state(AggState *aggstate);
432 : : static HashAggBatch *hashagg_batch_new(LogicalTape *input_tape, int setno,
433 : : int64 input_tuples, double input_card,
434 : : int used_bits);
435 : : static MinimalTuple hashagg_batch_read(HashAggBatch *batch, uint32 *hashp);
436 : : static void hashagg_spill_init(HashAggSpill *spill, LogicalTapeSet *tapeset,
437 : : int used_bits, double input_groups,
438 : : double hashentrysize);
439 : : static Size hashagg_spill_tuple(AggState *aggstate, HashAggSpill *spill,
440 : : TupleTableSlot *inputslot, uint32 hash);
441 : : static void hashagg_spill_finish(AggState *aggstate, HashAggSpill *spill,
442 : : int setno);
443 : : static Datum GetAggInitVal(Datum textInitVal, Oid transtype);
444 : : static void build_pertrans_for_aggref(AggStatePerTrans pertrans,
445 : : AggState *aggstate, EState *estate,
446 : : Aggref *aggref, Oid transfn_oid,
447 : : Oid aggtranstype, Oid aggserialfn,
448 : : Oid aggdeserialfn, Datum initValue,
449 : : bool initValueIsNull, Oid *inputTypes,
450 : : int numArguments);
451 : :
452 : :
453 : : /*
454 : : * Select the current grouping set; affects current_set and
455 : : * curaggcontext.
456 : : */
457 : : static void
458 : 5292688 : select_current_set(AggState *aggstate, int setno, bool is_hash)
459 : : {
460 : : /*
461 : : * When changing this, also adapt ExecAggPlainTransByVal() and
462 : : * ExecAggPlainTransByRef().
463 : : */
464 [ + + ]: 5292688 : if (is_hash)
465 : 4945848 : aggstate->curaggcontext = aggstate->hashcontext;
466 : : else
467 : 346840 : aggstate->curaggcontext = aggstate->aggcontexts[setno];
468 : :
469 : 5292688 : aggstate->current_set = setno;
470 : 5292688 : }
471 : :
472 : : /*
473 : : * Switch to phase "newphase", which must either be 0 or 1 (to reset) or
474 : : * current_phase + 1. Juggle the tuplesorts accordingly.
475 : : *
476 : : * Phase 0 is for hashing, which we currently handle last in the AGG_MIXED
477 : : * case, so when entering phase 0, all we need to do is drop open sorts.
478 : : */
479 : : static void
480 : 58255 : initialize_phase(AggState *aggstate, int newphase)
481 : : {
482 : : Assert(newphase <= 1 || newphase == aggstate->current_phase + 1);
483 : :
484 : : /*
485 : : * Whatever the previous state, we're now done with whatever input
486 : : * tuplesort was in use.
487 : : */
488 [ + + ]: 58255 : if (aggstate->sort_in)
489 : : {
490 : 28 : tuplesort_end(aggstate->sort_in);
491 : 28 : aggstate->sort_in = NULL;
492 : : }
493 : :
494 [ + + ]: 58255 : if (newphase <= 1)
495 : : {
496 : : /*
497 : : * Discard any existing output tuplesort.
498 : : */
499 [ + + ]: 58107 : if (aggstate->sort_out)
500 : : {
501 : 4 : tuplesort_end(aggstate->sort_out);
502 : 4 : aggstate->sort_out = NULL;
503 : : }
504 : : }
505 : : else
506 : : {
507 : : /*
508 : : * The old output tuplesort becomes the new input one, and this is the
509 : : * right time to actually sort it.
510 : : */
511 : 148 : aggstate->sort_in = aggstate->sort_out;
512 : 148 : aggstate->sort_out = NULL;
513 : : Assert(aggstate->sort_in);
514 : 148 : tuplesort_performsort(aggstate->sort_in);
515 : : }
516 : :
517 : : /*
518 : : * If this isn't the last phase, we need to sort appropriately for the
519 : : * next phase in sequence.
520 : : */
521 [ + + + + ]: 58255 : if (newphase > 0 && newphase < aggstate->numphases - 1)
522 : : {
523 : 196 : Sort *sortnode = aggstate->phases[newphase + 1].sortnode;
524 : 196 : PlanState *outerNode = outerPlanState(aggstate);
525 : 196 : TupleDesc tupDesc = ExecGetResultType(outerNode);
526 : :
527 : 196 : aggstate->sort_out = tuplesort_begin_heap(tupDesc,
528 : : sortnode->numCols,
529 : : sortnode->sortColIdx,
530 : : sortnode->sortOperators,
531 : : sortnode->collations,
532 : : sortnode->nullsFirst,
533 : : work_mem,
534 : : NULL, TUPLESORT_NONE);
535 : : }
536 : :
537 : 58255 : aggstate->current_phase = newphase;
538 : 58255 : aggstate->phase = &aggstate->phases[newphase];
539 : 58255 : }
540 : :
541 : : /*
542 : : * Fetch a tuple from either the outer plan (for phase 1) or from the sorter
543 : : * populated by the previous phase. Copy it to the sorter for the next phase
544 : : * if any.
545 : : *
546 : : * Callers cannot rely on memory for tuple in returned slot remaining valid
547 : : * past any subsequently fetched tuple.
548 : : */
549 : : static TupleTableSlot *
550 : 20291904 : fetch_input_tuple(AggState *aggstate)
551 : : {
552 : : TupleTableSlot *slot;
553 : :
554 [ + + ]: 20291904 : if (aggstate->sort_in)
555 : : {
556 : : /* make sure we check for interrupts in either path through here */
557 [ - + ]: 236620 : CHECK_FOR_INTERRUPTS();
558 [ + + ]: 236620 : if (!tuplesort_gettupleslot(aggstate->sort_in, true, false,
559 : : aggstate->sort_slot, NULL))
560 : 148 : return NULL;
561 : 236472 : slot = aggstate->sort_slot;
562 : : }
563 : : else
564 : 20055284 : slot = ExecProcNode(outerPlanState(aggstate));
565 : :
566 [ + + + + : 20291710 : if (!TupIsNull(slot) && aggstate->sort_out)
+ + ]
567 : 236472 : tuplesort_puttupleslot(aggstate->sort_out, slot);
568 : :
569 : 20291710 : return slot;
570 : : }
571 : :
572 : : /*
573 : : * (Re)Initialize an individual aggregate.
574 : : *
575 : : * This function handles only one grouping set, already set in
576 : : * aggstate->current_set.
577 : : *
578 : : * When called, CurrentMemoryContext should be the per-query context.
579 : : */
580 : : static void
581 : 728643 : initialize_aggregate(AggState *aggstate, AggStatePerTrans pertrans,
582 : : AggStatePerGroup pergroupstate)
583 : : {
584 : : /*
585 : : * Start a fresh sort operation for each DISTINCT/ORDER BY aggregate.
586 : : */
587 [ + + ]: 728643 : if (pertrans->aggsortrequired)
588 : : {
589 : : /*
590 : : * In case of rescan, maybe there could be an uncompleted sort
591 : : * operation? Clean it up if so.
592 : : */
593 [ - + ]: 35860 : if (pertrans->sortstates[aggstate->current_set])
594 : 0 : tuplesort_end(pertrans->sortstates[aggstate->current_set]);
595 : :
596 : :
597 : : /*
598 : : * We use a plain Datum sorter when there's a single input column;
599 : : * otherwise sort the full tuple. (See comments for
600 : : * process_ordered_aggregate_single.)
601 : : */
602 [ + + ]: 35860 : if (pertrans->numInputs == 1)
603 : : {
604 : 35804 : Form_pg_attribute attr = TupleDescAttr(pertrans->sortdesc, 0);
605 : :
606 : 35804 : pertrans->sortstates[aggstate->current_set] =
607 : 35804 : tuplesort_begin_datum(attr->atttypid,
608 : 35804 : pertrans->sortOperators[0],
609 : 35804 : pertrans->sortCollations[0],
610 : 35804 : pertrans->sortNullsFirst[0],
611 : : work_mem, NULL, TUPLESORT_NONE);
612 : : }
613 : : else
614 : 56 : pertrans->sortstates[aggstate->current_set] =
615 : 56 : tuplesort_begin_heap(pertrans->sortdesc,
616 : : pertrans->numSortCols,
617 : : pertrans->sortColIdx,
618 : : pertrans->sortOperators,
619 : : pertrans->sortCollations,
620 : : pertrans->sortNullsFirst,
621 : : work_mem, NULL, TUPLESORT_NONE);
622 : : }
623 : :
624 : : /*
625 : : * (Re)set transValue to the initial value.
626 : : *
627 : : * Note that when the initial value is pass-by-ref, we must copy it (into
628 : : * the aggcontext) since we will pfree the transValue later.
629 : : */
630 [ + + ]: 728643 : if (pertrans->initValueIsNull)
631 : 369389 : pergroupstate->transValue = pertrans->initValue;
632 : : else
633 : : {
634 : : MemoryContext oldContext;
635 : :
636 : 359254 : oldContext = MemoryContextSwitchTo(aggstate->curaggcontext->ecxt_per_tuple_memory);
637 : 718508 : pergroupstate->transValue = datumCopy(pertrans->initValue,
638 : 359254 : pertrans->transtypeByVal,
639 : 359254 : pertrans->transtypeLen);
640 : 359254 : MemoryContextSwitchTo(oldContext);
641 : : }
642 : 728643 : pergroupstate->transValueIsNull = pertrans->initValueIsNull;
643 : :
644 : : /*
645 : : * If the initial value for the transition state doesn't exist in the
646 : : * pg_aggregate table then we will let the first non-NULL value returned
647 : : * from the outer procNode become the initial value. (This is useful for
648 : : * aggregates like max() and min().) The noTransValue flag signals that we
649 : : * still need to do this.
650 : : */
651 : 728643 : pergroupstate->noTransValue = pertrans->initValueIsNull;
652 : 728643 : }
653 : :
654 : : /*
655 : : * Initialize all aggregate transition states for a new group of input values.
656 : : *
657 : : * If there are multiple grouping sets, we initialize only the first numReset
658 : : * of them (the grouping sets are ordered so that the most specific one, which
659 : : * is reset most often, is first). As a convenience, if numReset is 0, we
660 : : * reinitialize all sets.
661 : : *
662 : : * NB: This cannot be used for hash aggregates, as for those the grouping set
663 : : * number has to be specified from further up.
664 : : *
665 : : * When called, CurrentMemoryContext should be the per-query context.
666 : : */
667 : : static void
668 : 149158 : initialize_aggregates(AggState *aggstate,
669 : : AggStatePerGroup *pergroups,
670 : : int numReset)
671 : : {
672 : : int transno;
673 : 149158 : int numGroupingSets = Max(aggstate->phase->numsets, 1);
674 : 149158 : int setno = 0;
675 : 149158 : int numTrans = aggstate->numtrans;
676 : 149158 : AggStatePerTrans transstates = aggstate->pertrans;
677 : :
678 [ - + ]: 149158 : if (numReset == 0)
679 : 0 : numReset = numGroupingSets;
680 : :
681 [ + + ]: 307768 : for (setno = 0; setno < numReset; setno++)
682 : : {
683 : 158610 : AggStatePerGroup pergroup = pergroups[setno];
684 : :
685 : 158610 : select_current_set(aggstate, setno, false);
686 : :
687 [ + + ]: 467518 : for (transno = 0; transno < numTrans; transno++)
688 : : {
689 : 308908 : AggStatePerTrans pertrans = &transstates[transno];
690 : 308908 : AggStatePerGroup pergroupstate = &pergroup[transno];
691 : :
692 : 308908 : initialize_aggregate(aggstate, pertrans, pergroupstate);
693 : : }
694 : : }
695 : 149158 : }
696 : :
697 : : /*
698 : : * Given new input value(s), advance the transition function of one aggregate
699 : : * state within one grouping set only (already set in aggstate->current_set)
700 : : *
701 : : * The new values (and null flags) have been preloaded into argument positions
702 : : * 1 and up in pertrans->transfn_fcinfo, so that we needn't copy them again to
703 : : * pass to the transition function. We also expect that the static fields of
704 : : * the fcinfo are already initialized; that was done by ExecInitAgg().
705 : : *
706 : : * It doesn't matter which memory context this is called in.
707 : : */
708 : : static void
709 : 479809 : advance_transition_function(AggState *aggstate,
710 : : AggStatePerTrans pertrans,
711 : : AggStatePerGroup pergroupstate)
712 : : {
713 : 479809 : FunctionCallInfo fcinfo = pertrans->transfn_fcinfo;
714 : : MemoryContext oldContext;
715 : : Datum newVal;
716 : :
717 [ + + ]: 479809 : if (pertrans->transfn.fn_strict)
718 : : {
719 : : /*
720 : : * For a strict transfn, nothing happens when there's a NULL input; we
721 : : * just keep the prior transValue.
722 : : */
723 : 150000 : int numTransInputs = pertrans->numTransInputs;
724 : : int i;
725 : :
726 [ + + ]: 300000 : for (i = 1; i <= numTransInputs; i++)
727 : : {
728 [ - + ]: 150000 : if (fcinfo->args[i].isnull)
729 : 0 : return;
730 : : }
731 [ - + ]: 150000 : if (pergroupstate->noTransValue)
732 : : {
733 : : /*
734 : : * transValue has not been initialized. This is the first non-NULL
735 : : * input value. We use it as the initial value for transValue. (We
736 : : * already checked that the agg's input type is binary-compatible
737 : : * with its transtype, so straight copy here is OK.)
738 : : *
739 : : * We must copy the datum into aggcontext if it is pass-by-ref. We
740 : : * do not need to pfree the old transValue, since it's NULL.
741 : : */
742 : 0 : oldContext = MemoryContextSwitchTo(aggstate->curaggcontext->ecxt_per_tuple_memory);
743 : 0 : pergroupstate->transValue = datumCopy(fcinfo->args[1].value,
744 : 0 : pertrans->transtypeByVal,
745 : 0 : pertrans->transtypeLen);
746 : 0 : pergroupstate->transValueIsNull = false;
747 : 0 : pergroupstate->noTransValue = false;
748 : 0 : MemoryContextSwitchTo(oldContext);
749 : 0 : return;
750 : : }
751 [ - + ]: 150000 : if (pergroupstate->transValueIsNull)
752 : : {
753 : : /*
754 : : * Don't call a strict function with NULL inputs. Note it is
755 : : * possible to get here despite the above tests, if the transfn is
756 : : * strict *and* returned a NULL on a prior cycle. If that happens
757 : : * we will propagate the NULL all the way to the end.
758 : : */
759 : 0 : return;
760 : : }
761 : : }
762 : :
763 : : /* We run the transition functions in per-input-tuple memory context */
764 : 479809 : oldContext = MemoryContextSwitchTo(aggstate->tmpcontext->ecxt_per_tuple_memory);
765 : :
766 : : /* set up aggstate->curpertrans for AggGetAggref() */
767 : 479809 : aggstate->curpertrans = pertrans;
768 : :
769 : : /*
770 : : * OK to call the transition function
771 : : */
772 : 479809 : fcinfo->args[0].value = pergroupstate->transValue;
773 : 479809 : fcinfo->args[0].isnull = pergroupstate->transValueIsNull;
774 : 479809 : fcinfo->isnull = false; /* just in case transfn doesn't set it */
775 : :
776 : 479809 : newVal = FunctionCallInvoke(fcinfo);
777 : :
778 : 479809 : aggstate->curpertrans = NULL;
779 : :
780 : : /*
781 : : * If pass-by-ref datatype, must copy the new value into aggcontext and
782 : : * free the prior transValue. But if transfn returned a pointer to its
783 : : * first input, we don't need to do anything.
784 : : *
785 : : * It's safe to compare newVal with pergroup->transValue without regard
786 : : * for either being NULL, because ExecAggCopyTransValue takes care to set
787 : : * transValue to 0 when NULL. Otherwise we could end up accidentally not
788 : : * reparenting, when the transValue has the same numerical value as
789 : : * newValue, despite being NULL. This is a somewhat hot path, making it
790 : : * undesirable to instead solve this with another branch for the common
791 : : * case of the transition function returning its (modified) input
792 : : * argument.
793 : : */
794 [ - + - - ]: 479809 : if (!pertrans->transtypeByVal &&
795 : 0 : DatumGetPointer(newVal) != DatumGetPointer(pergroupstate->transValue))
796 : 0 : newVal = ExecAggCopyTransValue(aggstate, pertrans,
797 : 0 : newVal, fcinfo->isnull,
798 : : pergroupstate->transValue,
799 : 0 : pergroupstate->transValueIsNull);
800 : :
801 : 479809 : pergroupstate->transValue = newVal;
802 : 479809 : pergroupstate->transValueIsNull = fcinfo->isnull;
803 : :
804 : 479809 : MemoryContextSwitchTo(oldContext);
805 : : }
806 : :
807 : : /*
808 : : * Advance each aggregate transition state for one input tuple. The input
809 : : * tuple has been stored in tmpcontext->ecxt_outertuple, so that it is
810 : : * accessible to ExecEvalExpr.
811 : : *
812 : : * We have two sets of transition states to handle: one for sorted aggregation
813 : : * and one for hashed; we do them both here, to avoid multiple evaluation of
814 : : * the inputs.
815 : : *
816 : : * When called, CurrentMemoryContext should be the per-query context.
817 : : */
818 : : static void
819 : 20694041 : advance_aggregates(AggState *aggstate)
820 : : {
821 : 20694041 : ExecEvalExprNoReturnSwitchContext(aggstate->phase->evaltrans,
822 : : aggstate->tmpcontext);
823 : 20693989 : }
824 : :
825 : : /*
826 : : * Run the transition function for a DISTINCT or ORDER BY aggregate
827 : : * with only one input. This is called after we have completed
828 : : * entering all the input values into the sort object. We complete the
829 : : * sort, read out the values in sorted order, and run the transition
830 : : * function on each value (applying DISTINCT if appropriate).
831 : : *
832 : : * Note that the strictness of the transition function was checked when
833 : : * entering the values into the sort, so we don't check it again here;
834 : : * we just apply standard SQL DISTINCT logic.
835 : : *
836 : : * The one-input case is handled separately from the multi-input case
837 : : * for performance reasons: for single by-value inputs, such as the
838 : : * common case of count(distinct id), the tuplesort_getdatum code path
839 : : * is around 300% faster. (The speedup for by-reference types is less
840 : : * but still noticeable.)
841 : : *
842 : : * This function handles only one grouping set (already set in
843 : : * aggstate->current_set).
844 : : *
845 : : * When called, CurrentMemoryContext should be the per-query context.
846 : : */
847 : : static void
848 : 35804 : process_ordered_aggregate_single(AggState *aggstate,
849 : : AggStatePerTrans pertrans,
850 : : AggStatePerGroup pergroupstate)
851 : : {
852 : 35804 : Datum oldVal = (Datum) 0;
853 : 35804 : bool oldIsNull = true;
854 : 35804 : bool haveOldVal = false;
855 : 35804 : MemoryContext workcontext = aggstate->tmpcontext->ecxt_per_tuple_memory;
856 : : MemoryContext oldContext;
857 : 35804 : bool isDistinct = (pertrans->numDistinctCols > 0);
858 : 35804 : Datum newAbbrevVal = (Datum) 0;
859 : 35804 : Datum oldAbbrevVal = (Datum) 0;
860 : 35804 : FunctionCallInfo fcinfo = pertrans->transfn_fcinfo;
861 : : Datum *newVal;
862 : : bool *isNull;
863 : :
864 : : Assert(pertrans->numDistinctCols < 2);
865 : :
866 : 35804 : tuplesort_performsort(pertrans->sortstates[aggstate->current_set]);
867 : :
868 : : /* Load the column into argument 1 (arg 0 will be transition value) */
869 : 35804 : newVal = &fcinfo->args[1].value;
870 : 35804 : isNull = &fcinfo->args[1].isnull;
871 : :
872 : : /*
873 : : * Note: if input type is pass-by-ref, the datums returned by the sort are
874 : : * freshly palloc'd in the per-query context, so we must be careful to
875 : : * pfree them when they are no longer needed.
876 : : */
877 : :
878 [ + + ]: 595425 : while (tuplesort_getdatum(pertrans->sortstates[aggstate->current_set],
879 : : true, false, newVal, isNull, &newAbbrevVal))
880 : : {
881 : : /*
882 : : * Clear and select the working context for evaluation of the equality
883 : : * function and transition function.
884 : : */
885 : 559621 : MemoryContextReset(workcontext);
886 : 559621 : oldContext = MemoryContextSwitchTo(workcontext);
887 : :
888 : : /*
889 : : * If DISTINCT mode, and not distinct from prior, skip it.
890 : : */
891 [ + + + + ]: 559621 : if (isDistinct &&
892 [ - + ]: 203613 : haveOldVal &&
893 [ # # ]: 0 : ((oldIsNull && *isNull) ||
894 [ + - + - ]: 203613 : (!oldIsNull && !*isNull &&
895 [ + + + + ]: 399746 : oldAbbrevVal == newAbbrevVal &&
896 : 196133 : DatumGetBool(FunctionCall2Coll(&pertrans->equalfnOne,
897 : : pertrans->aggCollation,
898 : : oldVal, *newVal)))))
899 : : {
900 : 79956 : MemoryContextSwitchTo(oldContext);
901 : 79956 : continue;
902 : : }
903 : : else
904 : : {
905 : 479665 : advance_transition_function(aggstate, pertrans, pergroupstate);
906 : :
907 : 479665 : MemoryContextSwitchTo(oldContext);
908 : :
909 : : /*
910 : : * Forget the old value, if any, and remember the new one for
911 : : * subsequent equality checks.
912 : : */
913 [ + + ]: 479665 : if (!pertrans->inputtypeByVal)
914 : : {
915 [ + + ]: 350192 : if (!oldIsNull)
916 : 350072 : pfree(DatumGetPointer(oldVal));
917 [ + + ]: 350192 : if (!*isNull)
918 : 350152 : oldVal = datumCopy(*newVal, pertrans->inputtypeByVal,
919 : 350152 : pertrans->inputtypeLen);
920 : : }
921 : : else
922 : 129473 : oldVal = *newVal;
923 : 479665 : oldAbbrevVal = newAbbrevVal;
924 : 479665 : oldIsNull = *isNull;
925 : 479665 : haveOldVal = true;
926 : : }
927 : : }
928 : :
929 [ + + + + ]: 35804 : if (!oldIsNull && !pertrans->inputtypeByVal)
930 : 80 : pfree(DatumGetPointer(oldVal));
931 : :
932 : 35804 : tuplesort_end(pertrans->sortstates[aggstate->current_set]);
933 : 35804 : pertrans->sortstates[aggstate->current_set] = NULL;
934 : 35804 : }
935 : :
936 : : /*
937 : : * Run the transition function for a DISTINCT or ORDER BY aggregate
938 : : * with more than one input. This is called after we have completed
939 : : * entering all the input values into the sort object. We complete the
940 : : * sort, read out the values in sorted order, and run the transition
941 : : * function on each value (applying DISTINCT if appropriate).
942 : : *
943 : : * This function handles only one grouping set (already set in
944 : : * aggstate->current_set).
945 : : *
946 : : * When called, CurrentMemoryContext should be the per-query context.
947 : : */
948 : : static void
949 : 56 : process_ordered_aggregate_multi(AggState *aggstate,
950 : : AggStatePerTrans pertrans,
951 : : AggStatePerGroup pergroupstate)
952 : : {
953 : 56 : ExprContext *tmpcontext = aggstate->tmpcontext;
954 : 56 : FunctionCallInfo fcinfo = pertrans->transfn_fcinfo;
955 : 56 : TupleTableSlot *slot1 = pertrans->sortslot;
956 : 56 : TupleTableSlot *slot2 = pertrans->uniqslot;
957 : 56 : int numTransInputs = pertrans->numTransInputs;
958 : 56 : int numDistinctCols = pertrans->numDistinctCols;
959 : 56 : Datum newAbbrevVal = (Datum) 0;
960 : 56 : Datum oldAbbrevVal = (Datum) 0;
961 : 56 : bool haveOldValue = false;
962 : 56 : TupleTableSlot *save = aggstate->tmpcontext->ecxt_outertuple;
963 : : int i;
964 : :
965 : 56 : tuplesort_performsort(pertrans->sortstates[aggstate->current_set]);
966 : :
967 : 56 : ExecClearTuple(slot1);
968 [ - + ]: 56 : if (slot2)
969 : 0 : ExecClearTuple(slot2);
970 : :
971 [ + + ]: 200 : while (tuplesort_gettupleslot(pertrans->sortstates[aggstate->current_set],
972 : : true, true, slot1, &newAbbrevVal))
973 : : {
974 [ - + ]: 144 : CHECK_FOR_INTERRUPTS();
975 : :
976 : 144 : tmpcontext->ecxt_outertuple = slot1;
977 : 144 : tmpcontext->ecxt_innertuple = slot2;
978 : :
979 [ - + ]: 144 : if (numDistinctCols == 0 ||
980 [ # # ]: 0 : !haveOldValue ||
981 [ # # ]: 0 : newAbbrevVal != oldAbbrevVal ||
982 [ # # ]: 0 : !ExecQual(pertrans->equalfnMulti, tmpcontext))
983 : : {
984 : : /*
985 : : * Extract the first numTransInputs columns as datums to pass to
986 : : * the transfn.
987 : : */
988 : 144 : slot_getsomeattrs(slot1, numTransInputs);
989 : :
990 : : /* Load values into fcinfo */
991 : : /* Start from 1, since the 0th arg will be the transition value */
992 [ + + ]: 408 : for (i = 0; i < numTransInputs; i++)
993 : : {
994 : 264 : fcinfo->args[i + 1].value = slot1->tts_values[i];
995 : 264 : fcinfo->args[i + 1].isnull = slot1->tts_isnull[i];
996 : : }
997 : :
998 : 144 : advance_transition_function(aggstate, pertrans, pergroupstate);
999 : :
1000 [ - + ]: 144 : if (numDistinctCols > 0)
1001 : : {
1002 : : /* swap the slot pointers to retain the current tuple */
1003 : 0 : TupleTableSlot *tmpslot = slot2;
1004 : :
1005 : 0 : slot2 = slot1;
1006 : 0 : slot1 = tmpslot;
1007 : : /* avoid ExecQual() calls by reusing abbreviated keys */
1008 : 0 : oldAbbrevVal = newAbbrevVal;
1009 : 0 : haveOldValue = true;
1010 : : }
1011 : : }
1012 : :
1013 : : /* Reset context each time */
1014 : 144 : ResetExprContext(tmpcontext);
1015 : :
1016 : 144 : ExecClearTuple(slot1);
1017 : : }
1018 : :
1019 [ - + ]: 56 : if (slot2)
1020 : 0 : ExecClearTuple(slot2);
1021 : :
1022 : 56 : tuplesort_end(pertrans->sortstates[aggstate->current_set]);
1023 : 56 : pertrans->sortstates[aggstate->current_set] = NULL;
1024 : :
1025 : : /* restore previous slot, potentially in use for grouping sets */
1026 : 56 : tmpcontext->ecxt_outertuple = save;
1027 : 56 : }
1028 : :
1029 : : /*
1030 : : * Compute the final value of one aggregate.
1031 : : *
1032 : : * This function handles only one grouping set (already set in
1033 : : * aggstate->current_set).
1034 : : *
1035 : : * The finalfn will be run, and the result delivered, in the
1036 : : * output-tuple context; caller's CurrentMemoryContext does not matter.
1037 : : * (But note that in some cases, such as when there is no finalfn, the
1038 : : * result might be a pointer to or into the agg's transition value.)
1039 : : *
1040 : : * The finalfn uses the state as set in the transno. This also might be
1041 : : * being used by another aggregate function, so it's important that we do
1042 : : * nothing destructive here. Moreover, the aggregate's final value might
1043 : : * get used in multiple places, so we mustn't return a R/W expanded datum.
1044 : : */
1045 : : static void
1046 : 719277 : finalize_aggregate(AggState *aggstate,
1047 : : AggStatePerAgg peragg,
1048 : : AggStatePerGroup pergroupstate,
1049 : : Datum *resultVal, bool *resultIsNull)
1050 : : {
1051 : 719277 : LOCAL_FCINFO(fcinfo, FUNC_MAX_ARGS);
1052 : 719277 : bool anynull = false;
1053 : : MemoryContext oldContext;
1054 : : int i;
1055 : : ListCell *lc;
1056 : 719277 : AggStatePerTrans pertrans = &aggstate->pertrans[peragg->transno];
1057 : :
1058 : 719277 : oldContext = MemoryContextSwitchTo(aggstate->ss.ps.ps_ExprContext->ecxt_per_tuple_memory);
1059 : :
1060 : : /*
1061 : : * Evaluate any direct arguments. We do this even if there's no finalfn
1062 : : * (which is unlikely anyway), so that side-effects happen as expected.
1063 : : * The direct arguments go into arg positions 1 and up, leaving position 0
1064 : : * for the transition state value.
1065 : : */
1066 : 719277 : i = 1;
1067 [ + + + + : 719919 : foreach(lc, peragg->aggdirectargs)
+ + ]
1068 : : {
1069 : 642 : ExprState *expr = (ExprState *) lfirst(lc);
1070 : :
1071 : 642 : fcinfo->args[i].value = ExecEvalExpr(expr,
1072 : : aggstate->ss.ps.ps_ExprContext,
1073 : : &fcinfo->args[i].isnull);
1074 : 642 : anynull |= fcinfo->args[i].isnull;
1075 : 642 : i++;
1076 : : }
1077 : :
1078 : : /*
1079 : : * Apply the agg's finalfn if one is provided, else return transValue.
1080 : : */
1081 [ + + ]: 719277 : if (OidIsValid(peragg->finalfn_oid))
1082 : : {
1083 : 207080 : int numFinalArgs = peragg->numFinalArgs;
1084 : :
1085 : : /* set up aggstate->curperagg for AggGetAggref() */
1086 : 207080 : aggstate->curperagg = peragg;
1087 : :
1088 : 207080 : InitFunctionCallInfoData(*fcinfo, &peragg->finalfn,
1089 : : numFinalArgs,
1090 : : pertrans->aggCollation,
1091 : : (Node *) aggstate, NULL);
1092 : :
1093 : : /* Fill in the transition state value */
1094 : 207080 : fcinfo->args[0].value =
1095 [ + + + + ]: 207080 : MakeExpandedObjectReadOnly(pergroupstate->transValue,
1096 : : pergroupstate->transValueIsNull,
1097 : : pertrans->transtypeLen);
1098 : 207080 : fcinfo->args[0].isnull = pergroupstate->transValueIsNull;
1099 : 207080 : anynull |= pergroupstate->transValueIsNull;
1100 : :
1101 : : /* Fill any remaining argument positions with nulls */
1102 [ + + ]: 290217 : for (; i < numFinalArgs; i++)
1103 : : {
1104 : 83137 : fcinfo->args[i].value = (Datum) 0;
1105 : 83137 : fcinfo->args[i].isnull = true;
1106 : 83137 : anynull = true;
1107 : : }
1108 : :
1109 [ + + - + ]: 207080 : if (fcinfo->flinfo->fn_strict && anynull)
1110 : : {
1111 : : /* don't call a strict function with NULL inputs */
1112 : 0 : *resultVal = (Datum) 0;
1113 : 0 : *resultIsNull = true;
1114 : : }
1115 : : else
1116 : : {
1117 : : Datum result;
1118 : :
1119 : 207080 : result = FunctionCallInvoke(fcinfo);
1120 : 207072 : *resultIsNull = fcinfo->isnull;
1121 [ + + + + ]: 207072 : *resultVal = MakeExpandedObjectReadOnly(result,
1122 : : fcinfo->isnull,
1123 : : peragg->resulttypeLen);
1124 : : }
1125 : 207072 : aggstate->curperagg = NULL;
1126 : : }
1127 : : else
1128 : : {
1129 : 512197 : *resultVal =
1130 [ + + + + ]: 512197 : MakeExpandedObjectReadOnly(pergroupstate->transValue,
1131 : : pergroupstate->transValueIsNull,
1132 : : pertrans->transtypeLen);
1133 : 512197 : *resultIsNull = pergroupstate->transValueIsNull;
1134 : : }
1135 : :
1136 : 719269 : MemoryContextSwitchTo(oldContext);
1137 : 719269 : }
1138 : :
1139 : : /*
1140 : : * Compute the output value of one partial aggregate.
1141 : : *
1142 : : * The serialization function will be run, and the result delivered, in the
1143 : : * output-tuple context; caller's CurrentMemoryContext does not matter.
1144 : : */
1145 : : static void
1146 : 11856 : finalize_partialaggregate(AggState *aggstate,
1147 : : AggStatePerAgg peragg,
1148 : : AggStatePerGroup pergroupstate,
1149 : : Datum *resultVal, bool *resultIsNull)
1150 : : {
1151 : 11856 : AggStatePerTrans pertrans = &aggstate->pertrans[peragg->transno];
1152 : : MemoryContext oldContext;
1153 : :
1154 : 11856 : oldContext = MemoryContextSwitchTo(aggstate->ss.ps.ps_ExprContext->ecxt_per_tuple_memory);
1155 : :
1156 : : /*
1157 : : * serialfn_oid will be set if we must serialize the transvalue before
1158 : : * returning it
1159 : : */
1160 [ + + ]: 11856 : if (OidIsValid(pertrans->serialfn_oid))
1161 : : {
1162 : : /* Don't call a strict serialization function with NULL input. */
1163 [ + - + + ]: 444 : if (pertrans->serialfn.fn_strict && pergroupstate->transValueIsNull)
1164 : : {
1165 : 48 : *resultVal = (Datum) 0;
1166 : 48 : *resultIsNull = true;
1167 : : }
1168 : : else
1169 : : {
1170 : 396 : FunctionCallInfo fcinfo = pertrans->serialfn_fcinfo;
1171 : : Datum result;
1172 : :
1173 : 396 : fcinfo->args[0].value =
1174 [ + - + - ]: 396 : MakeExpandedObjectReadOnly(pergroupstate->transValue,
1175 : : pergroupstate->transValueIsNull,
1176 : : pertrans->transtypeLen);
1177 : 396 : fcinfo->args[0].isnull = pergroupstate->transValueIsNull;
1178 : 396 : fcinfo->isnull = false;
1179 : :
1180 : 396 : result = FunctionCallInvoke(fcinfo);
1181 : 396 : *resultIsNull = fcinfo->isnull;
1182 [ + - + - ]: 396 : *resultVal = MakeExpandedObjectReadOnly(result,
1183 : : fcinfo->isnull,
1184 : : peragg->resulttypeLen);
1185 : : }
1186 : : }
1187 : : else
1188 : : {
1189 : 11412 : *resultVal =
1190 [ + + + + ]: 11412 : MakeExpandedObjectReadOnly(pergroupstate->transValue,
1191 : : pergroupstate->transValueIsNull,
1192 : : pertrans->transtypeLen);
1193 : 11412 : *resultIsNull = pergroupstate->transValueIsNull;
1194 : : }
1195 : :
1196 : 11856 : MemoryContextSwitchTo(oldContext);
1197 : 11856 : }
1198 : :
1199 : : /*
1200 : : * Extract the attributes that make up the grouping key into the
1201 : : * hashslot. This is necessary to compute the hash or perform a lookup.
1202 : : */
1203 : : static inline void
1204 : 5604051 : prepare_hash_slot(AggStatePerHash perhash,
1205 : : TupleTableSlot *inputslot,
1206 : : TupleTableSlot *hashslot)
1207 : : {
1208 : : int i;
1209 : :
1210 : : /* transfer just the needed columns into hashslot */
1211 : 5604051 : slot_getsomeattrs(inputslot, perhash->largestGrpColIdx);
1212 : 5604051 : ExecClearTuple(hashslot);
1213 : :
1214 [ + + ]: 13820649 : for (i = 0; i < perhash->numhashGrpCols; i++)
1215 : : {
1216 : 8216598 : int varNumber = perhash->hashGrpColIdxInput[i] - 1;
1217 : :
1218 : 8216598 : hashslot->tts_values[i] = inputslot->tts_values[varNumber];
1219 : 8216598 : hashslot->tts_isnull[i] = inputslot->tts_isnull[varNumber];
1220 : : }
1221 : 5604051 : ExecStoreVirtualTuple(hashslot);
1222 : 5604051 : }
1223 : :
1224 : : /*
1225 : : * Prepare to finalize and project based on the specified representative tuple
1226 : : * slot and grouping set.
1227 : : *
1228 : : * In the specified tuple slot, force to null all attributes that should be
1229 : : * read as null in the context of the current grouping set. Also stash the
1230 : : * current group bitmap where GroupingExpr can get at it.
1231 : : *
1232 : : * This relies on three conditions:
1233 : : *
1234 : : * 1) Nothing is ever going to try and extract the whole tuple from this slot,
1235 : : * only reference it in evaluations, which will only access individual
1236 : : * attributes.
1237 : : *
1238 : : * 2) No system columns are going to need to be nulled. (If a system column is
1239 : : * referenced in a group clause, it is actually projected in the outer plan
1240 : : * tlist.)
1241 : : *
1242 : : * 3) Within a given phase, we never need to recover the value of an attribute
1243 : : * once it has been set to null.
1244 : : *
1245 : : * Poking into the slot this way is a bit ugly, but the consensus is that the
1246 : : * alternative was worse.
1247 : : */
1248 : : static void
1249 : 534296 : prepare_projection_slot(AggState *aggstate, TupleTableSlot *slot, int currentSet)
1250 : : {
1251 [ + + ]: 534296 : if (aggstate->phase->grouped_cols)
1252 : : {
1253 : 395504 : Bitmapset *grouped_cols = aggstate->phase->grouped_cols[currentSet];
1254 : :
1255 : 395504 : aggstate->grouped_cols = grouped_cols;
1256 : :
1257 [ + + ]: 395504 : if (TTS_EMPTY(slot))
1258 : : {
1259 : : /*
1260 : : * Force all values to be NULL if working on an empty input tuple
1261 : : * (i.e. an empty grouping set for which no input rows were
1262 : : * supplied).
1263 : : */
1264 : 40 : ExecStoreAllNullTuple(slot);
1265 : : }
1266 [ + + ]: 395464 : else if (aggstate->all_grouped_cols)
1267 : : {
1268 : : ListCell *lc;
1269 : :
1270 : : /* all_grouped_cols is arranged in desc order */
1271 : 395428 : slot_getsomeattrs(slot, linitial_int(aggstate->all_grouped_cols));
1272 : :
1273 [ + - + + : 1055393 : foreach(lc, aggstate->all_grouped_cols)
+ + ]
1274 : : {
1275 : 659965 : int attnum = lfirst_int(lc);
1276 : :
1277 [ + + ]: 659965 : if (!bms_is_member(attnum, grouped_cols))
1278 : 38708 : slot->tts_isnull[attnum - 1] = true;
1279 : : }
1280 : : }
1281 : : }
1282 : 534296 : }
1283 : :
1284 : : /*
1285 : : * Compute the final value of all aggregates for one group.
1286 : : *
1287 : : * This function handles only one grouping set at a time, which the caller must
1288 : : * have selected. It's also the caller's responsibility to adjust the supplied
1289 : : * pergroup parameter to point to the current set's transvalues.
1290 : : *
1291 : : * Results are stored in the output econtext aggvalues/aggnulls.
1292 : : */
1293 : : static void
1294 : 534296 : finalize_aggregates(AggState *aggstate,
1295 : : AggStatePerAgg peraggs,
1296 : : AggStatePerGroup pergroup)
1297 : : {
1298 : 534296 : ExprContext *econtext = aggstate->ss.ps.ps_ExprContext;
1299 : 534296 : Datum *aggvalues = econtext->ecxt_aggvalues;
1300 : 534296 : bool *aggnulls = econtext->ecxt_aggnulls;
1301 : : int aggno;
1302 : :
1303 : : /*
1304 : : * If there were any DISTINCT and/or ORDER BY aggregates, sort their
1305 : : * inputs and run the transition functions.
1306 : : */
1307 [ + + ]: 1265213 : for (int transno = 0; transno < aggstate->numtrans; transno++)
1308 : : {
1309 : 730917 : AggStatePerTrans pertrans = &aggstate->pertrans[transno];
1310 : : AggStatePerGroup pergroupstate;
1311 : :
1312 : 730917 : pergroupstate = &pergroup[transno];
1313 : :
1314 [ + + ]: 730917 : if (pertrans->aggsortrequired)
1315 : : {
1316 : : Assert(aggstate->aggstrategy != AGG_HASHED &&
1317 : : aggstate->aggstrategy != AGG_MIXED);
1318 : :
1319 [ + + ]: 35860 : if (pertrans->numInputs == 1)
1320 : 35804 : process_ordered_aggregate_single(aggstate,
1321 : : pertrans,
1322 : : pergroupstate);
1323 : : else
1324 : 56 : process_ordered_aggregate_multi(aggstate,
1325 : : pertrans,
1326 : : pergroupstate);
1327 : : }
1328 [ + + + + ]: 695057 : else if (pertrans->numDistinctCols > 0 && pertrans->haslast)
1329 : : {
1330 : 12241 : pertrans->haslast = false;
1331 : :
1332 [ + + ]: 12241 : if (pertrans->numDistinctCols == 1)
1333 : : {
1334 [ + + + + ]: 12177 : if (!pertrans->inputtypeByVal && !pertrans->lastisnull)
1335 : 174 : pfree(DatumGetPointer(pertrans->lastdatum));
1336 : :
1337 : 12177 : pertrans->lastisnull = false;
1338 : 12177 : pertrans->lastdatum = (Datum) 0;
1339 : : }
1340 : : else
1341 : 64 : ExecClearTuple(pertrans->uniqslot);
1342 : : }
1343 : : }
1344 : :
1345 : : /*
1346 : : * Run the final functions.
1347 : : */
1348 [ + + ]: 1265421 : for (aggno = 0; aggno < aggstate->numaggs; aggno++)
1349 : : {
1350 : 731133 : AggStatePerAgg peragg = &peraggs[aggno];
1351 : 731133 : int transno = peragg->transno;
1352 : : AggStatePerGroup pergroupstate;
1353 : :
1354 : 731133 : pergroupstate = &pergroup[transno];
1355 : :
1356 [ + + ]: 731133 : if (DO_AGGSPLIT_SKIPFINAL(aggstate->aggsplit))
1357 : 11856 : finalize_partialaggregate(aggstate, peragg, pergroupstate,
1358 : 11856 : &aggvalues[aggno], &aggnulls[aggno]);
1359 : : else
1360 : 719277 : finalize_aggregate(aggstate, peragg, pergroupstate,
1361 : 719277 : &aggvalues[aggno], &aggnulls[aggno]);
1362 : : }
1363 : 534288 : }
1364 : :
1365 : : /*
1366 : : * Project the result of a group (whose aggs have already been calculated by
1367 : : * finalize_aggregates). Returns the result slot, or NULL if no row is
1368 : : * projected (suppressed by qual).
1369 : : */
1370 : : static TupleTableSlot *
1371 : 534288 : project_aggregates(AggState *aggstate)
1372 : : {
1373 : 534288 : ExprContext *econtext = aggstate->ss.ps.ps_ExprContext;
1374 : :
1375 : : /*
1376 : : * Check the qual (HAVING clause); if the group does not match, ignore it.
1377 : : */
1378 [ + + ]: 534288 : if (ExecQual(aggstate->ss.ps.qual, econtext))
1379 : : {
1380 : : /*
1381 : : * Form and return projection tuple using the aggregate results and
1382 : : * the representative input tuple.
1383 : : */
1384 : 463272 : return ExecProject(aggstate->ss.ps.ps_ProjInfo);
1385 : : }
1386 : : else
1387 [ - + ]: 71016 : InstrCountFiltered1(aggstate, 1);
1388 : :
1389 : 71016 : return NULL;
1390 : : }
1391 : :
1392 : : /*
1393 : : * Find input-tuple columns that are needed, dividing them into
1394 : : * aggregated and unaggregated sets.
1395 : : */
1396 : : static void
1397 : 5100 : find_cols(AggState *aggstate, Bitmapset **aggregated, Bitmapset **unaggregated)
1398 : : {
1399 : 5100 : Agg *agg = (Agg *) aggstate->ss.ps.plan;
1400 : : FindColsContext context;
1401 : :
1402 : 5100 : context.is_aggref = false;
1403 : 5100 : context.aggregated = NULL;
1404 : 5100 : context.unaggregated = NULL;
1405 : :
1406 : : /* Examine tlist and quals */
1407 : 5100 : (void) find_cols_walker((Node *) agg->plan.targetlist, &context);
1408 : 5100 : (void) find_cols_walker((Node *) agg->plan.qual, &context);
1409 : :
1410 : : /* In some cases, grouping columns will not appear in the tlist */
1411 [ + + ]: 12595 : for (int i = 0; i < agg->numCols; i++)
1412 : 7495 : context.unaggregated = bms_add_member(context.unaggregated,
1413 : 7495 : agg->grpColIdx[i]);
1414 : :
1415 : 5100 : *aggregated = context.aggregated;
1416 : 5100 : *unaggregated = context.unaggregated;
1417 : 5100 : }
1418 : :
1419 : : static bool
1420 : 58037 : find_cols_walker(Node *node, FindColsContext *context)
1421 : : {
1422 [ + + ]: 58037 : if (node == NULL)
1423 : 10554 : return false;
1424 [ + + ]: 47483 : if (IsA(node, Var))
1425 : : {
1426 : 12935 : Var *var = (Var *) node;
1427 : :
1428 : : /* setrefs.c should have set the varno to OUTER_VAR */
1429 : : Assert(var->varno == OUTER_VAR);
1430 : : Assert(var->varlevelsup == 0);
1431 [ + + ]: 12935 : if (context->is_aggref)
1432 : 4054 : context->aggregated = bms_add_member(context->aggregated,
1433 : 4054 : var->varattno);
1434 : : else
1435 : 8881 : context->unaggregated = bms_add_member(context->unaggregated,
1436 : 8881 : var->varattno);
1437 : 12935 : return false;
1438 : : }
1439 [ + + ]: 34548 : if (IsA(node, Aggref))
1440 : : {
1441 : : Assert(!context->is_aggref);
1442 : 5852 : context->is_aggref = true;
1443 : 5852 : expression_tree_walker(node, find_cols_walker, context);
1444 : 5852 : context->is_aggref = false;
1445 : 5852 : return false;
1446 : : }
1447 : 28696 : return expression_tree_walker(node, find_cols_walker, context);
1448 : : }
1449 : :
1450 : : /*
1451 : : * (Re-)initialize the hash table(s) to empty.
1452 : : *
1453 : : * To implement hashed aggregation, we need a hashtable that stores a
1454 : : * representative tuple and an array of AggStatePerGroup structs for each
1455 : : * distinct set of GROUP BY column values. We compute the hash key from the
1456 : : * GROUP BY columns. The per-group data is allocated in initialize_hash_entry(),
1457 : : * for each entry.
1458 : : *
1459 : : * We have a separate hashtable and associated perhash data structure for each
1460 : : * grouping set for which we're doing hashing.
1461 : : *
1462 : : * The contents of the hash tables live in the aggstate's hash_tuplescxt
1463 : : * memory context (there is only one of these for all tables together, since
1464 : : * they are all reset at the same time).
1465 : : */
1466 : : static void
1467 : 11400 : build_hash_tables(AggState *aggstate)
1468 : : {
1469 : : int setno;
1470 : :
1471 [ + + ]: 23051 : for (setno = 0; setno < aggstate->num_hashes; ++setno)
1472 : : {
1473 : 11651 : AggStatePerHash perhash = &aggstate->perhash[setno];
1474 : : double nbuckets;
1475 : : Size memory;
1476 : :
1477 [ + + ]: 11651 : if (perhash->hashtable != NULL)
1478 : : {
1479 : 7758 : ResetTupleHashTable(perhash->hashtable);
1480 : 7758 : continue;
1481 : : }
1482 : :
1483 : 3893 : memory = aggstate->hash_mem_limit / aggstate->num_hashes;
1484 : :
1485 : : /* choose reasonable number of buckets per hashtable */
1486 : 3893 : nbuckets = hash_choose_num_buckets(aggstate->hashentrysize,
1487 : 3893 : perhash->aggnode->numGroups,
1488 : : memory);
1489 : :
1490 : : #ifdef USE_INJECTION_POINTS
1491 [ - + ]: 3893 : if (IS_INJECTION_POINT_ATTACHED("hash-aggregate-oversize-table"))
1492 : : {
1493 : 0 : nbuckets = memory / TupleHashEntrySize();
1494 : 0 : INJECTION_POINT_CACHED("hash-aggregate-oversize-table", NULL);
1495 : : }
1496 : : #endif
1497 : :
1498 : 3893 : build_hash_table(aggstate, setno, nbuckets);
1499 : : }
1500 : :
1501 : 11400 : aggstate->hash_ngroups_current = 0;
1502 : 11400 : }
1503 : :
1504 : : /*
1505 : : * Build a single hashtable for this grouping set.
1506 : : */
1507 : : static void
1508 : 3893 : build_hash_table(AggState *aggstate, int setno, double nbuckets)
1509 : : {
1510 : 3893 : AggStatePerHash perhash = &aggstate->perhash[setno];
1511 : 3893 : MemoryContext metacxt = aggstate->hash_metacxt;
1512 : 3893 : MemoryContext tuplescxt = aggstate->hash_tuplescxt;
1513 : 3893 : MemoryContext tmpcxt = aggstate->tmpcontext->ecxt_per_tuple_memory;
1514 : : Size additionalsize;
1515 : :
1516 : : Assert(aggstate->aggstrategy == AGG_HASHED ||
1517 : : aggstate->aggstrategy == AGG_MIXED);
1518 : :
1519 : : /*
1520 : : * Used to make sure initial hash table allocation does not exceed
1521 : : * hash_mem. Note that the estimate does not include space for
1522 : : * pass-by-reference transition data values, nor for the representative
1523 : : * tuple of each group.
1524 : : */
1525 : 3893 : additionalsize = aggstate->numtrans * sizeof(AggStatePerGroupData);
1526 : :
1527 : 7786 : perhash->hashtable = BuildTupleHashTable(&aggstate->ss.ps,
1528 : 3893 : perhash->hashslot->tts_tupleDescriptor,
1529 : 3893 : perhash->hashslot->tts_ops,
1530 : : perhash->numCols,
1531 : : perhash->hashGrpColIdxHash,
1532 : 3893 : perhash->eqfuncoids,
1533 : : perhash->hashfunctions,
1534 : 3893 : perhash->aggnode->grpCollations,
1535 : : nbuckets,
1536 : : additionalsize,
1537 : : metacxt,
1538 : : tuplescxt,
1539 : : tmpcxt,
1540 : 3893 : DO_AGGSPLIT_SKIPFINAL(aggstate->aggsplit));
1541 : 3893 : }
1542 : :
1543 : : /*
1544 : : * Compute columns that actually need to be stored in hashtable entries. The
1545 : : * incoming tuples from the child plan node will contain grouping columns,
1546 : : * other columns referenced in our targetlist and qual, columns used to
1547 : : * compute the aggregate functions, and perhaps just junk columns we don't use
1548 : : * at all. Only columns of the first two types need to be stored in the
1549 : : * hashtable, and getting rid of the others can make the table entries
1550 : : * significantly smaller. The hashtable only contains the relevant columns,
1551 : : * and is packed/unpacked in lookup_hash_entries() / agg_retrieve_hash_table()
1552 : : * into the format of the normal input descriptor.
1553 : : *
1554 : : * Additional columns, in addition to the columns grouped by, come from two
1555 : : * sources: Firstly functionally dependent columns that we don't need to group
1556 : : * by themselves, and secondly ctids for row-marks.
1557 : : *
1558 : : * To eliminate duplicates, we build a bitmapset of the needed columns, and
1559 : : * then build an array of the columns included in the hashtable. We might
1560 : : * still have duplicates if the passed-in grpColIdx has them, which can happen
1561 : : * in edge cases from semijoins/distinct; these can't always be removed,
1562 : : * because it's not certain that the duplicate cols will be using the same
1563 : : * hash function.
1564 : : *
1565 : : * Note that the array is preserved over ExecReScanAgg, so we allocate it in
1566 : : * the per-query context (unlike the hash table itself).
1567 : : */
1568 : : static void
1569 : 5100 : find_hash_columns(AggState *aggstate)
1570 : : {
1571 : : Bitmapset *base_colnos;
1572 : : Bitmapset *aggregated_colnos;
1573 : 5100 : TupleDesc scanDesc = aggstate->ss.ss_ScanTupleSlot->tts_tupleDescriptor;
1574 : 5100 : List *outerTlist = outerPlanState(aggstate)->plan->targetlist;
1575 : 5100 : int numHashes = aggstate->num_hashes;
1576 : 5100 : EState *estate = aggstate->ss.ps.state;
1577 : : int j;
1578 : :
1579 : : /* Find Vars that will be needed in tlist and qual */
1580 : 5100 : find_cols(aggstate, &aggregated_colnos, &base_colnos);
1581 : 5100 : aggstate->colnos_needed = bms_union(base_colnos, aggregated_colnos);
1582 : 5100 : aggstate->max_colno_needed = 0;
1583 : 5100 : aggstate->all_cols_needed = true;
1584 : :
1585 [ + + ]: 21198 : for (int i = 0; i < scanDesc->natts; i++)
1586 : : {
1587 : 16098 : int colno = i + 1;
1588 : :
1589 [ + + ]: 16098 : if (bms_is_member(colno, aggstate->colnos_needed))
1590 : 11067 : aggstate->max_colno_needed = colno;
1591 : : else
1592 : 5031 : aggstate->all_cols_needed = false;
1593 : : }
1594 : :
1595 [ + + ]: 10592 : for (j = 0; j < numHashes; ++j)
1596 : : {
1597 : 5492 : AggStatePerHash perhash = &aggstate->perhash[j];
1598 : 5492 : Bitmapset *colnos = bms_copy(base_colnos);
1599 : 5492 : AttrNumber *grpColIdx = perhash->aggnode->grpColIdx;
1600 : 5492 : List *hashTlist = NIL;
1601 : : TupleDesc hashDesc;
1602 : : int maxCols;
1603 : : int i;
1604 : :
1605 : 5492 : perhash->largestGrpColIdx = 0;
1606 : :
1607 : : /*
1608 : : * If we're doing grouping sets, then some Vars might be referenced in
1609 : : * tlist/qual for the benefit of other grouping sets, but not needed
1610 : : * when hashing; i.e. prepare_projection_slot will null them out, so
1611 : : * there'd be no point storing them. Use prepare_projection_slot's
1612 : : * logic to determine which.
1613 : : */
1614 [ + - ]: 5492 : if (aggstate->phases[0].grouped_cols)
1615 : : {
1616 : 5492 : Bitmapset *grouped_cols = aggstate->phases[0].grouped_cols[j];
1617 : : ListCell *lc;
1618 : :
1619 [ + + + + : 14510 : foreach(lc, aggstate->all_grouped_cols)
+ + ]
1620 : : {
1621 : 9018 : int attnum = lfirst_int(lc);
1622 : :
1623 [ + + ]: 9018 : if (!bms_is_member(attnum, grouped_cols))
1624 : 1060 : colnos = bms_del_member(colnos, attnum);
1625 : : }
1626 : : }
1627 : :
1628 : : /*
1629 : : * Compute maximum number of input columns accounting for possible
1630 : : * duplications in the grpColIdx array, which can happen in some edge
1631 : : * cases where HashAggregate was generated as part of a semijoin or a
1632 : : * DISTINCT.
1633 : : */
1634 : 5492 : maxCols = bms_num_members(colnos) + perhash->numCols;
1635 : :
1636 : 5492 : perhash->hashGrpColIdxInput =
1637 : 5492 : palloc(maxCols * sizeof(AttrNumber));
1638 : 5492 : perhash->hashGrpColIdxHash =
1639 : 5492 : palloc(perhash->numCols * sizeof(AttrNumber));
1640 : :
1641 : : /* Add all the grouping columns to colnos */
1642 [ + + ]: 13450 : for (i = 0; i < perhash->numCols; i++)
1643 : 7958 : colnos = bms_add_member(colnos, grpColIdx[i]);
1644 : :
1645 : : /*
1646 : : * First build mapping for columns directly hashed. These are the
1647 : : * first, because they'll be accessed when computing hash values and
1648 : : * comparing tuples for exact matches. We also build simple mapping
1649 : : * for execGrouping, so it knows where to find the to-be-hashed /
1650 : : * compared columns in the input.
1651 : : */
1652 [ + + ]: 13450 : for (i = 0; i < perhash->numCols; i++)
1653 : : {
1654 : 7958 : perhash->hashGrpColIdxInput[i] = grpColIdx[i];
1655 : 7958 : perhash->hashGrpColIdxHash[i] = i + 1;
1656 : 7958 : perhash->numhashGrpCols++;
1657 : : /* delete already mapped columns */
1658 : 7958 : colnos = bms_del_member(colnos, grpColIdx[i]);
1659 : : }
1660 : :
1661 : : /* and add the remaining columns */
1662 : 5492 : i = -1;
1663 [ + + ]: 6242 : while ((i = bms_next_member(colnos, i)) >= 0)
1664 : : {
1665 : 750 : perhash->hashGrpColIdxInput[perhash->numhashGrpCols] = i;
1666 : 750 : perhash->numhashGrpCols++;
1667 : : }
1668 : :
1669 : : /* and build a tuple descriptor for the hashtable */
1670 [ + + ]: 14200 : for (i = 0; i < perhash->numhashGrpCols; i++)
1671 : : {
1672 : 8708 : int varNumber = perhash->hashGrpColIdxInput[i] - 1;
1673 : :
1674 : 8708 : hashTlist = lappend(hashTlist, list_nth(outerTlist, varNumber));
1675 : 8708 : perhash->largestGrpColIdx =
1676 : 8708 : Max(varNumber + 1, perhash->largestGrpColIdx);
1677 : : }
1678 : :
1679 : 5492 : hashDesc = ExecTypeFromTL(hashTlist);
1680 : :
1681 : 5492 : execTuplesHashPrepare(perhash->numCols,
1682 : 5492 : perhash->aggnode->grpOperators,
1683 : : &perhash->eqfuncoids,
1684 : : &perhash->hashfunctions);
1685 : 5492 : perhash->hashslot =
1686 : 5492 : ExecAllocTableSlot(&estate->es_tupleTable, hashDesc,
1687 : : &TTSOpsMinimalTuple, 0);
1688 : :
1689 : 5492 : list_free(hashTlist);
1690 : 5492 : bms_free(colnos);
1691 : : }
1692 : :
1693 : 5100 : bms_free(base_colnos);
1694 : 5100 : }
1695 : :
1696 : : /*
1697 : : * Estimate per-hash-table-entry overhead.
1698 : : */
1699 : : Size
1700 : 34588 : hash_agg_entry_size(int numTrans, Size tupleWidth, Size transitionSpace)
1701 : : {
1702 : : Size tupleChunkSize;
1703 : : Size pergroupChunkSize;
1704 : : Size transitionChunkSize;
1705 : 34588 : Size tupleSize = (MAXALIGN(SizeofMinimalTupleHeader) +
1706 : : tupleWidth);
1707 : 34588 : Size pergroupSize = numTrans * sizeof(AggStatePerGroupData);
1708 : :
1709 : : /*
1710 : : * Entries use the Bump allocator, so the chunk sizes are the same as the
1711 : : * requested sizes.
1712 : : */
1713 : 34588 : tupleChunkSize = MAXALIGN(tupleSize);
1714 : 34588 : pergroupChunkSize = pergroupSize;
1715 : :
1716 : : /*
1717 : : * Transition values use AllocSet, which has a chunk header and also uses
1718 : : * power-of-two allocations.
1719 : : */
1720 [ + + ]: 34588 : if (transitionSpace > 0)
1721 : 4214 : transitionChunkSize = CHUNKHDRSZ + pg_nextpower2_size_t(transitionSpace);
1722 : : else
1723 : 30374 : transitionChunkSize = 0;
1724 : :
1725 : : return
1726 : 34588 : TupleHashEntrySize() +
1727 : 34588 : tupleChunkSize +
1728 : 34588 : pergroupChunkSize +
1729 : : transitionChunkSize;
1730 : : }
1731 : :
1732 : : /*
1733 : : * hashagg_recompile_expressions()
1734 : : *
1735 : : * Identifies the right phase, compiles the right expression given the
1736 : : * arguments, and then sets phase->evalfunc to that expression.
1737 : : *
1738 : : * Different versions of the compiled expression are needed depending on
1739 : : * whether hash aggregation has spilled or not, and whether it's reading from
1740 : : * the outer plan or a tape. Before spilling to disk, the expression reads
1741 : : * from the outer plan and does not need to perform a NULL check. After
1742 : : * HashAgg begins to spill, new groups will not be created in the hash table,
1743 : : * and the AggStatePerGroup array may be NULL; therefore we need to add a null
1744 : : * pointer check to the expression. Then, when reading spilled data from a
1745 : : * tape, we change the outer slot type to be a fixed minimal tuple slot.
1746 : : *
1747 : : * It would be wasteful to recompile every time, so cache the compiled
1748 : : * expressions in the AggStatePerPhase, and reuse when appropriate.
1749 : : */
1750 : : static void
1751 : 43411 : hashagg_recompile_expressions(AggState *aggstate, bool minslot, bool nullcheck)
1752 : : {
1753 : : AggStatePerPhase phase;
1754 : 43411 : int i = minslot ? 1 : 0;
1755 : 43411 : int j = nullcheck ? 1 : 0;
1756 : :
1757 : : Assert(aggstate->aggstrategy == AGG_HASHED ||
1758 : : aggstate->aggstrategy == AGG_MIXED);
1759 : :
1760 [ + + ]: 43411 : if (aggstate->aggstrategy == AGG_HASHED)
1761 : 8363 : phase = &aggstate->phases[0];
1762 : : else /* AGG_MIXED */
1763 : 35048 : phase = &aggstate->phases[1];
1764 : :
1765 [ + + ]: 43411 : if (phase->evaltrans_cache[i][j] == NULL)
1766 : : {
1767 : 64 : const TupleTableSlotOps *outerops = aggstate->ss.ps.outerops;
1768 : 64 : bool outerfixed = aggstate->ss.ps.outeropsfixed;
1769 : 64 : bool dohash = true;
1770 : 64 : bool dosort = false;
1771 : :
1772 : : /*
1773 : : * If minslot is true, that means we are processing a spilled batch
1774 : : * (inside agg_refill_hash_table()), and we must not advance the
1775 : : * sorted grouping sets.
1776 : : */
1777 [ + + + + ]: 64 : if (aggstate->aggstrategy == AGG_MIXED && !minslot)
1778 : 8 : dosort = true;
1779 : :
1780 : : /* temporarily change the outerops while compiling the expression */
1781 [ + + ]: 64 : if (minslot)
1782 : : {
1783 : 32 : aggstate->ss.ps.outerops = &TTSOpsMinimalTuple;
1784 : 32 : aggstate->ss.ps.outeropsfixed = true;
1785 : : }
1786 : :
1787 : 64 : phase->evaltrans_cache[i][j] = ExecBuildAggTrans(aggstate, phase,
1788 : : dosort, dohash,
1789 : : nullcheck);
1790 : :
1791 : : /* change back */
1792 : 64 : aggstate->ss.ps.outerops = outerops;
1793 : 64 : aggstate->ss.ps.outeropsfixed = outerfixed;
1794 : : }
1795 : :
1796 : 43411 : phase->evaltrans = phase->evaltrans_cache[i][j];
1797 : 43411 : }
1798 : :
1799 : : /*
1800 : : * Set limits that trigger spilling to avoid exceeding hash_mem. Consider the
1801 : : * number of partitions we expect to create (if we do spill).
1802 : : *
1803 : : * There are two limits: a memory limit, and also an ngroups limit. The
1804 : : * ngroups limit becomes important when we expect transition values to grow
1805 : : * substantially larger than the initial value.
1806 : : */
1807 : : void
1808 : 50201 : hash_agg_set_limits(double hashentrysize, double input_groups, int used_bits,
1809 : : Size *mem_limit, uint64 *ngroups_limit,
1810 : : int *num_partitions)
1811 : : {
1812 : : int npartitions;
1813 : : Size partition_mem;
1814 : 50201 : Size hash_mem_limit = get_hash_memory_limit();
1815 : :
1816 : : /* if not expected to spill, use all of hash_mem */
1817 [ + + ]: 50201 : if (input_groups * hashentrysize <= hash_mem_limit)
1818 : : {
1819 [ + + ]: 48566 : if (num_partitions != NULL)
1820 : 32085 : *num_partitions = 0;
1821 : 48566 : *mem_limit = hash_mem_limit;
1822 : 48566 : *ngroups_limit = hash_mem_limit / hashentrysize;
1823 : 48566 : return;
1824 : : }
1825 : :
1826 : : /*
1827 : : * Calculate expected memory requirements for spilling, which is the size
1828 : : * of the buffers needed for all the tapes that need to be open at once.
1829 : : * Then, subtract that from the memory available for holding hash tables.
1830 : : */
1831 : 1635 : npartitions = hash_choose_num_partitions(input_groups,
1832 : : hashentrysize,
1833 : : used_bits,
1834 : : NULL);
1835 [ + + ]: 1635 : if (num_partitions != NULL)
1836 : 79 : *num_partitions = npartitions;
1837 : :
1838 : 1635 : partition_mem =
1839 : 1635 : HASHAGG_READ_BUFFER_SIZE +
1840 : : HASHAGG_WRITE_BUFFER_SIZE * npartitions;
1841 : :
1842 : : /*
1843 : : * Don't set the limit below 3/4 of hash_mem. In that case, we are at the
1844 : : * minimum number of partitions, so we aren't going to dramatically exceed
1845 : : * work mem anyway.
1846 : : */
1847 [ - + ]: 1635 : if (hash_mem_limit > 4 * partition_mem)
1848 : 0 : *mem_limit = hash_mem_limit - partition_mem;
1849 : : else
1850 : 1635 : *mem_limit = hash_mem_limit * 0.75;
1851 : :
1852 [ + - ]: 1635 : if (*mem_limit > hashentrysize)
1853 : 1635 : *ngroups_limit = *mem_limit / hashentrysize;
1854 : : else
1855 : 0 : *ngroups_limit = 1;
1856 : : }
1857 : :
1858 : : /*
1859 : : * hash_agg_check_limits
1860 : : *
1861 : : * After adding a new group to the hash table, check whether we need to enter
1862 : : * spill mode. Allocations may happen without adding new groups (for instance,
1863 : : * if the transition state size grows), so this check is imperfect.
1864 : : */
1865 : : static void
1866 : 374410 : hash_agg_check_limits(AggState *aggstate)
1867 : : {
1868 : 374410 : uint64 ngroups = aggstate->hash_ngroups_current;
1869 : 374410 : Size meta_mem = MemoryContextMemAllocated(aggstate->hash_metacxt,
1870 : : true);
1871 : 374410 : Size entry_mem = MemoryContextMemAllocated(aggstate->hash_tuplescxt,
1872 : : true);
1873 : 374410 : Size tval_mem = MemoryContextMemAllocated(aggstate->hashcontext->ecxt_per_tuple_memory,
1874 : : true);
1875 : 374410 : Size total_mem = meta_mem + entry_mem + tval_mem;
1876 : 374410 : bool do_spill = false;
1877 : :
1878 : : #ifdef USE_INJECTION_POINTS
1879 [ + + ]: 374410 : if (ngroups >= 1000)
1880 : : {
1881 [ + + ]: 58757 : if (IS_INJECTION_POINT_ATTACHED("hash-aggregate-spill-1000"))
1882 : : {
1883 : 5 : do_spill = true;
1884 : 5 : INJECTION_POINT_CACHED("hash-aggregate-spill-1000", NULL);
1885 : : }
1886 : : }
1887 : : #endif
1888 : :
1889 : : /*
1890 : : * Don't spill unless there's at least one group in the hash table so we
1891 : : * can be sure to make progress even in edge cases.
1892 : : */
1893 [ + - ]: 374410 : if (aggstate->hash_ngroups_current > 0 &&
1894 [ + + ]: 374410 : (total_mem > aggstate->hash_mem_limit ||
1895 [ + + ]: 356782 : ngroups > aggstate->hash_ngroups_limit))
1896 : : {
1897 : 17651 : do_spill = true;
1898 : : }
1899 : :
1900 [ + + ]: 374410 : if (do_spill)
1901 : 17656 : hash_agg_enter_spill_mode(aggstate);
1902 : 374410 : }
1903 : :
1904 : : /*
1905 : : * Enter "spill mode", meaning that no new groups are added to any of the hash
1906 : : * tables. Tuples that would create a new group are instead spilled, and
1907 : : * processed later.
1908 : : */
1909 : : static void
1910 : 17656 : hash_agg_enter_spill_mode(AggState *aggstate)
1911 : : {
1912 : 17656 : INJECTION_POINT("hash-aggregate-enter-spill-mode", NULL);
1913 : 17656 : aggstate->hash_spill_mode = true;
1914 : 17656 : hashagg_recompile_expressions(aggstate, aggstate->table_filled, true);
1915 : :
1916 [ + + ]: 17656 : if (!aggstate->hash_ever_spilled)
1917 : : {
1918 : : Assert(aggstate->hash_tapeset == NULL);
1919 : : Assert(aggstate->hash_spills == NULL);
1920 : :
1921 : 44 : aggstate->hash_ever_spilled = true;
1922 : :
1923 : 44 : aggstate->hash_tapeset = LogicalTapeSetCreate(true, NULL, -1);
1924 : :
1925 : 44 : aggstate->hash_spills = palloc_array(HashAggSpill, aggstate->num_hashes);
1926 : :
1927 [ + + ]: 128 : for (int setno = 0; setno < aggstate->num_hashes; setno++)
1928 : : {
1929 : 84 : AggStatePerHash perhash = &aggstate->perhash[setno];
1930 : 84 : HashAggSpill *spill = &aggstate->hash_spills[setno];
1931 : :
1932 : 84 : hashagg_spill_init(spill, aggstate->hash_tapeset, 0,
1933 : 84 : perhash->aggnode->numGroups,
1934 : : aggstate->hashentrysize);
1935 : : }
1936 : : }
1937 : 17656 : }
1938 : :
1939 : : /*
1940 : : * Update metrics after filling the hash table.
1941 : : *
1942 : : * If reading from the outer plan, from_tape should be false; if reading from
1943 : : * another tape, from_tape should be true.
1944 : : */
1945 : : static void
1946 : 29265 : hash_agg_update_metrics(AggState *aggstate, bool from_tape, int npartitions)
1947 : : {
1948 : : Size meta_mem;
1949 : : Size entry_mem;
1950 : : Size hashkey_mem;
1951 : : Size buffer_mem;
1952 : : Size total_mem;
1953 : :
1954 [ + + ]: 29265 : if (aggstate->aggstrategy != AGG_MIXED &&
1955 [ - + ]: 11649 : aggstate->aggstrategy != AGG_HASHED)
1956 : 0 : return;
1957 : :
1958 : : /* memory for the hash table itself */
1959 : 29265 : meta_mem = MemoryContextMemAllocated(aggstate->hash_metacxt, true);
1960 : :
1961 : : /* memory for hash entries */
1962 : 29265 : entry_mem = MemoryContextMemAllocated(aggstate->hash_tuplescxt, true);
1963 : :
1964 : : /* memory for byref transition states */
1965 : 29265 : hashkey_mem = MemoryContextMemAllocated(aggstate->hashcontext->ecxt_per_tuple_memory, true);
1966 : :
1967 : : /* memory for read/write tape buffers, if spilled */
1968 : 29265 : buffer_mem = npartitions * HASHAGG_WRITE_BUFFER_SIZE;
1969 [ + + ]: 29265 : if (from_tape)
1970 : 18037 : buffer_mem += HASHAGG_READ_BUFFER_SIZE;
1971 : :
1972 : : /* update peak mem */
1973 : 29265 : total_mem = meta_mem + entry_mem + hashkey_mem + buffer_mem;
1974 [ + + ]: 29265 : if (total_mem > aggstate->hash_mem_peak)
1975 : 3546 : aggstate->hash_mem_peak = total_mem;
1976 : :
1977 : : /* update disk usage */
1978 [ + + ]: 29265 : if (aggstate->hash_tapeset != NULL)
1979 : : {
1980 : 18081 : uint64 disk_used = LogicalTapeSetBlocks(aggstate->hash_tapeset) * (BLCKSZ / 1024);
1981 : :
1982 [ + + ]: 18081 : if (aggstate->hash_disk_used < disk_used)
1983 : 37 : aggstate->hash_disk_used = disk_used;
1984 : : }
1985 : :
1986 : : /* update hashentrysize estimate based on contents */
1987 [ + + ]: 29265 : if (aggstate->hash_ngroups_current > 0)
1988 : : {
1989 : 28927 : aggstate->hashentrysize =
1990 : 28927 : TupleHashEntrySize() +
1991 : 28927 : (hashkey_mem / (double) aggstate->hash_ngroups_current);
1992 : : }
1993 : : }
1994 : :
1995 : : /*
1996 : : * Create memory contexts used for hash aggregation.
1997 : : */
1998 : : static void
1999 : 5100 : hash_create_memory(AggState *aggstate)
2000 : : {
2001 : 5100 : Size maxBlockSize = ALLOCSET_DEFAULT_MAXSIZE;
2002 : :
2003 : : /*
2004 : : * The hashcontext's per-tuple memory will be used for byref transition
2005 : : * values and returned by AggCheckCallContext().
2006 : : */
2007 : 5100 : aggstate->hashcontext = CreateWorkExprContext(aggstate->ss.ps.state);
2008 : :
2009 : : /*
2010 : : * The meta context will be used for the bucket array of
2011 : : * TupleHashEntryData (or arrays, in the case of grouping sets). As the
2012 : : * hash table grows, the bucket array will double in size and the old one
2013 : : * will be freed, so an AllocSet is appropriate. For large bucket arrays,
2014 : : * the large allocation path will be used, so it's not worth worrying
2015 : : * about wasting space due to power-of-two allocations.
2016 : : */
2017 : 5100 : aggstate->hash_metacxt = AllocSetContextCreate(aggstate->ss.ps.state->es_query_cxt,
2018 : : "HashAgg meta context",
2019 : : ALLOCSET_DEFAULT_SIZES);
2020 : :
2021 : : /*
2022 : : * The hash entries themselves, which include the grouping key
2023 : : * (firstTuple) and pergroup data, are stored in the table context. The
2024 : : * bump allocator can be used because the entries are not freed until the
2025 : : * entire hash table is reset. The bump allocator is faster for
2026 : : * allocations and avoids wasting space on the chunk header or
2027 : : * power-of-two allocations.
2028 : : *
2029 : : * Like CreateWorkExprContext(), use smaller sizings for smaller work_mem,
2030 : : * to avoid large jumps in memory usage.
2031 : : */
2032 : :
2033 : : /*
2034 : : * Like CreateWorkExprContext(), use smaller sizings for smaller work_mem,
2035 : : * to avoid large jumps in memory usage.
2036 : : */
2037 : 5100 : maxBlockSize = pg_prevpower2_size_t(work_mem * (Size) 1024 / 16);
2038 : :
2039 : : /* But no bigger than ALLOCSET_DEFAULT_MAXSIZE */
2040 : 5100 : maxBlockSize = Min(maxBlockSize, ALLOCSET_DEFAULT_MAXSIZE);
2041 : :
2042 : : /* and no smaller than ALLOCSET_DEFAULT_INITSIZE */
2043 : 5100 : maxBlockSize = Max(maxBlockSize, ALLOCSET_DEFAULT_INITSIZE);
2044 : :
2045 : 5100 : aggstate->hash_tuplescxt = BumpContextCreate(aggstate->ss.ps.state->es_query_cxt,
2046 : : "HashAgg hashed tuples",
2047 : : ALLOCSET_DEFAULT_MINSIZE,
2048 : : ALLOCSET_DEFAULT_INITSIZE,
2049 : : maxBlockSize);
2050 : :
2051 : 5100 : }
2052 : :
2053 : : /*
2054 : : * Choose a reasonable number of buckets for the initial hash table size.
2055 : : */
2056 : : static double
2057 : 3893 : hash_choose_num_buckets(double hashentrysize, double ngroups, Size memory)
2058 : : {
2059 : : double max_nbuckets;
2060 : 3893 : double nbuckets = ngroups;
2061 : :
2062 : 3893 : max_nbuckets = memory / hashentrysize;
2063 : :
2064 : : /*
2065 : : * Underestimating is better than overestimating. Too many buckets crowd
2066 : : * out space for group keys and transition state values.
2067 : : */
2068 : 3893 : max_nbuckets /= 2;
2069 : :
2070 [ + + ]: 3893 : if (nbuckets > max_nbuckets)
2071 : 52 : nbuckets = max_nbuckets;
2072 : :
2073 : : /*
2074 : : * BuildTupleHashTable will clamp any obviously-insane result, so we don't
2075 : : * need to be too careful here.
2076 : : */
2077 : 3893 : return nbuckets;
2078 : : }
2079 : :
2080 : : /*
2081 : : * Determine the number of partitions to create when spilling, which will
2082 : : * always be a power of two. If log2_npartitions is non-NULL, set
2083 : : * *log2_npartitions to the log2() of the number of partitions.
2084 : : */
2085 : : static int
2086 : 10067 : hash_choose_num_partitions(double input_groups, double hashentrysize,
2087 : : int used_bits, int *log2_npartitions)
2088 : : {
2089 : 10067 : Size hash_mem_limit = get_hash_memory_limit();
2090 : : double partition_limit;
2091 : : double mem_wanted;
2092 : : double dpartitions;
2093 : : int npartitions;
2094 : : int partition_bits;
2095 : :
2096 : : /*
2097 : : * Avoid creating so many partitions that the memory requirements of the
2098 : : * open partition files are greater than 1/4 of hash_mem.
2099 : : */
2100 : 10067 : partition_limit =
2101 : 10067 : (hash_mem_limit * 0.25 - HASHAGG_READ_BUFFER_SIZE) /
2102 : : HASHAGG_WRITE_BUFFER_SIZE;
2103 : :
2104 : 10067 : mem_wanted = HASHAGG_PARTITION_FACTOR * input_groups * hashentrysize;
2105 : :
2106 : : /* make enough partitions so that each one is likely to fit in memory */
2107 : 10067 : dpartitions = 1 + (mem_wanted / hash_mem_limit);
2108 : :
2109 [ + + ]: 10067 : if (dpartitions > partition_limit)
2110 : 10009 : dpartitions = partition_limit;
2111 : :
2112 [ + - ]: 10067 : if (dpartitions < HASHAGG_MIN_PARTITIONS)
2113 : 10067 : dpartitions = HASHAGG_MIN_PARTITIONS;
2114 [ - + ]: 10067 : if (dpartitions > HASHAGG_MAX_PARTITIONS)
2115 : 0 : dpartitions = HASHAGG_MAX_PARTITIONS;
2116 : :
2117 : : /* HASHAGG_MAX_PARTITIONS limit makes this safe */
2118 : 10067 : npartitions = (int) dpartitions;
2119 : :
2120 : : /* ceil(log2(npartitions)) */
2121 : 10067 : partition_bits = pg_ceil_log2_32(npartitions);
2122 : :
2123 : : /* make sure that we don't exhaust the hash bits */
2124 [ - + ]: 10067 : if (partition_bits + used_bits >= 32)
2125 : 0 : partition_bits = 32 - used_bits;
2126 : :
2127 [ + + ]: 10067 : if (log2_npartitions != NULL)
2128 : 8432 : *log2_npartitions = partition_bits;
2129 : :
2130 : : /* number of partitions will be a power of two */
2131 : 10067 : npartitions = 1 << partition_bits;
2132 : :
2133 : 10067 : return npartitions;
2134 : : }
2135 : :
2136 : : /*
2137 : : * Initialize a freshly-created TupleHashEntry.
2138 : : */
2139 : : static void
2140 : 374410 : initialize_hash_entry(AggState *aggstate, TupleHashTable hashtable,
2141 : : TupleHashEntry entry)
2142 : : {
2143 : : AggStatePerGroup pergroup;
2144 : : int transno;
2145 : :
2146 : 374410 : aggstate->hash_ngroups_current++;
2147 : 374410 : hash_agg_check_limits(aggstate);
2148 : :
2149 : : /* no need to allocate or initialize per-group state */
2150 [ + + ]: 374410 : if (aggstate->numtrans == 0)
2151 : 134278 : return;
2152 : :
2153 : 240132 : pergroup = (AggStatePerGroup) TupleHashEntryGetAdditional(hashtable, entry);
2154 : :
2155 : : /*
2156 : : * Initialize aggregates for new tuple group, lookup_hash_entries()
2157 : : * already has selected the relevant grouping set.
2158 : : */
2159 [ + + ]: 659867 : for (transno = 0; transno < aggstate->numtrans; transno++)
2160 : : {
2161 : 419735 : AggStatePerTrans pertrans = &aggstate->pertrans[transno];
2162 : 419735 : AggStatePerGroup pergroupstate = &pergroup[transno];
2163 : :
2164 : 419735 : initialize_aggregate(aggstate, pertrans, pergroupstate);
2165 : : }
2166 : : }
2167 : :
2168 : : /*
2169 : : * Look up hash entries for the current tuple in all hashed grouping sets.
2170 : : *
2171 : : * Some entries may be left NULL if we are in "spill mode". The same tuple
2172 : : * will belong to different groups for each grouping set, so may match a group
2173 : : * already in memory for one set and match a group not in memory for another
2174 : : * set. When in "spill mode", the tuple will be spilled for each grouping set
2175 : : * where it doesn't match a group in memory.
2176 : : *
2177 : : * NB: It's possible to spill the same tuple for several different grouping
2178 : : * sets. This may seem wasteful, but it's actually a trade-off: if we spill
2179 : : * the tuple multiple times for multiple grouping sets, it can be partitioned
2180 : : * for each grouping set, making the refilling of the hash table very
2181 : : * efficient.
2182 : : */
2183 : : static void
2184 : 4736575 : lookup_hash_entries(AggState *aggstate)
2185 : : {
2186 : 4736575 : AggStatePerGroup *pergroup = aggstate->hash_pergroup;
2187 : 4736575 : TupleTableSlot *outerslot = aggstate->tmpcontext->ecxt_outertuple;
2188 : : int setno;
2189 : :
2190 [ + + ]: 9562712 : for (setno = 0; setno < aggstate->num_hashes; setno++)
2191 : : {
2192 : 4826137 : AggStatePerHash perhash = &aggstate->perhash[setno];
2193 : 4826137 : TupleHashTable hashtable = perhash->hashtable;
2194 : 4826137 : TupleTableSlot *hashslot = perhash->hashslot;
2195 : : TupleHashEntry entry;
2196 : : uint32 hash;
2197 : 4826137 : bool isnew = false;
2198 : : bool *p_isnew;
2199 : :
2200 : : /* if hash table already spilled, don't create new entries */
2201 [ + + ]: 4826137 : p_isnew = aggstate->hash_spill_mode ? NULL : &isnew;
2202 : :
2203 : 4826137 : select_current_set(aggstate, setno, true);
2204 : 4826137 : prepare_hash_slot(perhash,
2205 : : outerslot,
2206 : : hashslot);
2207 : :
2208 : 4826137 : entry = LookupTupleHashEntry(hashtable, hashslot,
2209 : : p_isnew, &hash);
2210 : :
2211 [ + + ]: 4826137 : if (entry != NULL)
2212 : : {
2213 [ + + ]: 4368551 : if (isnew)
2214 : 243681 : initialize_hash_entry(aggstate, hashtable, entry);
2215 : 4368551 : pergroup[setno] = TupleHashEntryGetAdditional(hashtable, entry);
2216 : : }
2217 : : else
2218 : : {
2219 : 457586 : HashAggSpill *spill = &aggstate->hash_spills[setno];
2220 : 457586 : TupleTableSlot *slot = aggstate->tmpcontext->ecxt_outertuple;
2221 : :
2222 [ - + ]: 457586 : if (spill->partitions == NULL)
2223 : 0 : hashagg_spill_init(spill, aggstate->hash_tapeset, 0,
2224 : 0 : perhash->aggnode->numGroups,
2225 : : aggstate->hashentrysize);
2226 : :
2227 : 457586 : hashagg_spill_tuple(aggstate, spill, slot, hash);
2228 : 457586 : pergroup[setno] = NULL;
2229 : : }
2230 : : }
2231 : 4736575 : }
2232 : :
2233 : : /*
2234 : : * ExecAgg -
2235 : : *
2236 : : * ExecAgg receives tuples from its outer subplan and aggregates over
2237 : : * the appropriate attribute for each aggregate function use (Aggref
2238 : : * node) appearing in the targetlist or qual of the node. The number
2239 : : * of tuples to aggregate over depends on whether grouped or plain
2240 : : * aggregation is selected. In grouped aggregation, we produce a result
2241 : : * row for each group; in plain aggregation there's a single result row
2242 : : * for the whole query. In either case, the value of each aggregate is
2243 : : * stored in the expression context to be used when ExecProject evaluates
2244 : : * the result tuple.
2245 : : */
2246 : : static TupleTableSlot *
2247 : 516457 : ExecAgg(PlanState *pstate)
2248 : : {
2249 : 516457 : AggState *node = castNode(AggState, pstate);
2250 : 516457 : TupleTableSlot *result = NULL;
2251 : :
2252 [ + + ]: 516457 : CHECK_FOR_INTERRUPTS();
2253 : :
2254 [ + + ]: 516457 : if (!node->agg_done)
2255 : : {
2256 : : /* Dispatch based on strategy */
2257 [ + + + - ]: 475712 : switch (node->phase->aggstrategy)
2258 : : {
2259 : 305547 : case AGG_HASHED:
2260 [ + + ]: 305547 : if (!node->table_filled)
2261 : 11120 : agg_fill_hash_table(node);
2262 : : pg_fallthrough;
2263 : : case AGG_MIXED:
2264 : 323809 : result = agg_retrieve_hash_table(node);
2265 : 323809 : break;
2266 : 151903 : case AGG_PLAIN:
2267 : : case AGG_SORTED:
2268 : 151903 : result = agg_retrieve_direct(node);
2269 : 151789 : break;
2270 : : }
2271 : :
2272 [ + + + - ]: 475598 : if (!TupIsNull(result))
2273 : 463264 : return result;
2274 : : }
2275 : :
2276 : 53079 : return NULL;
2277 : : }
2278 : :
2279 : : /*
2280 : : * ExecAgg for non-hashed case
2281 : : */
2282 : : static TupleTableSlot *
2283 : 151903 : agg_retrieve_direct(AggState *aggstate)
2284 : : {
2285 : 151903 : Agg *node = aggstate->phase->aggnode;
2286 : : ExprContext *econtext;
2287 : : ExprContext *tmpcontext;
2288 : : AggStatePerAgg peragg;
2289 : : AggStatePerGroup *pergroups;
2290 : : TupleTableSlot *outerslot;
2291 : : TupleTableSlot *firstSlot;
2292 : : TupleTableSlot *result;
2293 : 151903 : bool hasGroupingSets = aggstate->phase->numsets > 0;
2294 : 151903 : int numGroupingSets = Max(aggstate->phase->numsets, 1);
2295 : : int currentSet;
2296 : : int nextSetSize;
2297 : : int numReset;
2298 : : int i;
2299 : :
2300 : : /*
2301 : : * get state info from node
2302 : : *
2303 : : * econtext is the per-output-tuple expression context
2304 : : *
2305 : : * tmpcontext is the per-input-tuple expression context
2306 : : */
2307 : 151903 : econtext = aggstate->ss.ps.ps_ExprContext;
2308 : 151903 : tmpcontext = aggstate->tmpcontext;
2309 : :
2310 : 151903 : peragg = aggstate->peragg;
2311 : 151903 : pergroups = aggstate->pergroups;
2312 : 151903 : firstSlot = aggstate->ss.ss_ScanTupleSlot;
2313 : :
2314 : : /*
2315 : : * We loop retrieving groups until we find one matching
2316 : : * aggstate->ss.ps.qual
2317 : : *
2318 : : * For grouping sets, we have the invariant that aggstate->projected_set
2319 : : * is either -1 (initial call) or the index (starting from 0) in
2320 : : * gset_lengths for the group we just completed (either by projecting a
2321 : : * row or by discarding it in the qual).
2322 : : */
2323 [ + + ]: 159305 : while (!aggstate->agg_done)
2324 : : {
2325 : : /*
2326 : : * Clear the per-output-tuple context for each group, as well as
2327 : : * aggcontext (which contains any pass-by-ref transvalues of the old
2328 : : * group). Some aggregate functions store working state in child
2329 : : * contexts; those now get reset automatically without us needing to
2330 : : * do anything special.
2331 : : *
2332 : : * We use ReScanExprContext not just ResetExprContext because we want
2333 : : * any registered shutdown callbacks to be called. That allows
2334 : : * aggregate functions to ensure they've cleaned up any non-memory
2335 : : * resources.
2336 : : */
2337 : 159168 : ReScanExprContext(econtext);
2338 : :
2339 : : /*
2340 : : * Determine how many grouping sets need to be reset at this boundary.
2341 : : */
2342 [ + + ]: 159168 : if (aggstate->projected_set >= 0 &&
2343 [ + + ]: 114935 : aggstate->projected_set < numGroupingSets)
2344 : 114923 : numReset = aggstate->projected_set + 1;
2345 : : else
2346 : 44245 : numReset = numGroupingSets;
2347 : :
2348 : : /*
2349 : : * numReset can change on a phase boundary, but that's OK; we want to
2350 : : * reset the contexts used in _this_ phase, and later, after possibly
2351 : : * changing phase, initialize the right number of aggregates for the
2352 : : * _new_ phase.
2353 : : */
2354 : :
2355 [ + + ]: 333185 : for (i = 0; i < numReset; i++)
2356 : : {
2357 : 174017 : ReScanExprContext(aggstate->aggcontexts[i]);
2358 : : }
2359 : :
2360 : : /*
2361 : : * Check if input is complete and there are no more groups to project
2362 : : * in this phase; move to next phase or mark as done.
2363 : : */
2364 [ + + ]: 159168 : if (aggstate->input_done == true &&
2365 [ + + ]: 1126 : aggstate->projected_set >= (numGroupingSets - 1))
2366 : : {
2367 [ + + ]: 576 : if (aggstate->current_phase < aggstate->numphases - 1)
2368 : : {
2369 : 148 : initialize_phase(aggstate, aggstate->current_phase + 1);
2370 : 148 : aggstate->input_done = false;
2371 : 148 : aggstate->projected_set = -1;
2372 : 148 : numGroupingSets = Max(aggstate->phase->numsets, 1);
2373 : 148 : node = aggstate->phase->aggnode;
2374 : 148 : numReset = numGroupingSets;
2375 : : }
2376 [ + + ]: 428 : else if (aggstate->aggstrategy == AGG_MIXED)
2377 : : {
2378 : : /*
2379 : : * Mixed mode; we've output all the grouped stuff and have
2380 : : * full hashtables, so switch to outputting those.
2381 : : */
2382 : 116 : initialize_phase(aggstate, 0);
2383 : 116 : aggstate->table_filled = true;
2384 : 116 : ResetTupleHashIterator(aggstate->perhash[0].hashtable,
2385 : : &aggstate->perhash[0].hashiter);
2386 : 116 : select_current_set(aggstate, 0, true);
2387 : 116 : return agg_retrieve_hash_table(aggstate);
2388 : : }
2389 : : else
2390 : : {
2391 : 312 : aggstate->agg_done = true;
2392 : 312 : break;
2393 : : }
2394 : : }
2395 : :
2396 : : /*
2397 : : * Get the number of columns in the next grouping set after the last
2398 : : * projected one (if any). This is the number of columns to compare to
2399 : : * see if we reached the boundary of that set too.
2400 : : */
2401 [ + + ]: 158740 : if (aggstate->projected_set >= 0 &&
2402 [ + + ]: 114359 : aggstate->projected_set < (numGroupingSets - 1))
2403 : 18254 : nextSetSize = aggstate->phase->gset_lengths[aggstate->projected_set + 1];
2404 : : else
2405 : 140486 : nextSetSize = 0;
2406 : :
2407 : : /*----------
2408 : : * If a subgroup for the current grouping set is present, project it.
2409 : : *
2410 : : * We have a new group if:
2411 : : * - we're out of input but haven't projected all grouping sets
2412 : : * (checked above)
2413 : : * OR
2414 : : * - we already projected a row that wasn't from the last grouping
2415 : : * set
2416 : : * AND
2417 : : * - the next grouping set has at least one grouping column (since
2418 : : * empty grouping sets project only once input is exhausted)
2419 : : * AND
2420 : : * - the previous and pending rows differ on the grouping columns
2421 : : * of the next grouping set
2422 : : *----------
2423 : : */
2424 : 158740 : tmpcontext->ecxt_innertuple = econtext->ecxt_outertuple;
2425 [ + + ]: 158740 : if (aggstate->input_done ||
2426 [ + + ]: 158190 : (node->aggstrategy != AGG_PLAIN &&
2427 [ + + ]: 115204 : aggstate->projected_set != -1 &&
2428 [ + + + + ]: 113809 : aggstate->projected_set < (numGroupingSets - 1) &&
2429 : 13294 : nextSetSize > 0 &&
2430 [ + + ]: 13294 : !ExecQualAndReset(aggstate->phase->eqfunctions[nextSetSize - 1],
2431 : : tmpcontext)))
2432 : : {
2433 : 9436 : aggstate->projected_set += 1;
2434 : :
2435 : : Assert(aggstate->projected_set < numGroupingSets);
2436 : 9436 : Assert(nextSetSize > 0 || aggstate->input_done);
2437 : : }
2438 : : else
2439 : : {
2440 : : /*
2441 : : * We no longer care what group we just projected, the next
2442 : : * projection will always be the first (or only) grouping set
2443 : : * (unless the input proves to be empty).
2444 : : */
2445 : 149304 : aggstate->projected_set = 0;
2446 : :
2447 : : /*
2448 : : * If we don't already have the first tuple of the new group,
2449 : : * fetch it from the outer plan.
2450 : : */
2451 [ + + ]: 149304 : if (aggstate->grp_firstTuple == NULL)
2452 : : {
2453 : 44381 : outerslot = fetch_input_tuple(aggstate);
2454 [ + + + + ]: 44351 : if (!TupIsNull(outerslot))
2455 : : {
2456 : : /*
2457 : : * Make a copy of the first input tuple; we will use this
2458 : : * for comparisons (in group mode) and for projection.
2459 : : */
2460 : 36748 : aggstate->grp_firstTuple = ExecCopySlotHeapTuple(outerslot);
2461 : : }
2462 : : else
2463 : : {
2464 : : /* outer plan produced no tuples at all */
2465 [ + + ]: 7603 : if (hasGroupingSets)
2466 : : {
2467 : : /*
2468 : : * If there was no input at all, we need to project
2469 : : * rows only if there are grouping sets of size 0.
2470 : : * Note that this implies that there can't be any
2471 : : * references to ungrouped Vars, which would otherwise
2472 : : * cause issues with the empty output slot.
2473 : : *
2474 : : * XXX: This is no longer true, we currently deal with
2475 : : * this in finalize_aggregates().
2476 : : */
2477 : 52 : aggstate->input_done = true;
2478 : :
2479 [ + + ]: 72 : while (aggstate->phase->gset_lengths[aggstate->projected_set] > 0)
2480 : : {
2481 : 32 : aggstate->projected_set += 1;
2482 [ + + ]: 32 : if (aggstate->projected_set >= numGroupingSets)
2483 : : {
2484 : : /*
2485 : : * We can't set agg_done here because we might
2486 : : * have more phases to do, even though the
2487 : : * input is empty. So we need to restart the
2488 : : * whole outer loop.
2489 : : */
2490 : 12 : break;
2491 : : }
2492 : : }
2493 : :
2494 [ + + ]: 52 : if (aggstate->projected_set >= numGroupingSets)
2495 : 12 : continue;
2496 : : }
2497 : : else
2498 : : {
2499 : 7551 : aggstate->agg_done = true;
2500 : : /* If we are grouping, we should produce no tuples too */
2501 [ + + ]: 7551 : if (node->aggstrategy != AGG_PLAIN)
2502 : 104 : return NULL;
2503 : : }
2504 : : }
2505 : : }
2506 : :
2507 : : /*
2508 : : * Initialize working state for a new input tuple group.
2509 : : */
2510 : 149158 : initialize_aggregates(aggstate, pergroups, numReset);
2511 : :
2512 [ + + ]: 149158 : if (aggstate->grp_firstTuple != NULL)
2513 : : {
2514 : : /*
2515 : : * Store the copied first input tuple in the tuple table slot
2516 : : * reserved for it. The tuple will be deleted when it is
2517 : : * cleared from the slot.
2518 : : */
2519 : 141671 : ExecForceStoreHeapTuple(aggstate->grp_firstTuple,
2520 : : firstSlot, true);
2521 : 141671 : aggstate->grp_firstTuple = NULL; /* don't keep two pointers */
2522 : :
2523 : : /* set up for first advance_aggregates call */
2524 : 141671 : tmpcontext->ecxt_outertuple = firstSlot;
2525 : :
2526 : : /*
2527 : : * Process each outer-plan tuple, and then fetch the next one,
2528 : : * until we exhaust the outer plan or cross a group boundary.
2529 : : */
2530 : : for (;;)
2531 : : {
2532 : : /*
2533 : : * During phase 1 only of a mixed agg, we need to update
2534 : : * hashtables as well in advance_aggregates.
2535 : : */
2536 [ + + ]: 15525078 : if (aggstate->aggstrategy == AGG_MIXED &&
2537 [ + - ]: 25198 : aggstate->current_phase == 1)
2538 : : {
2539 : 25198 : lookup_hash_entries(aggstate);
2540 : : }
2541 : :
2542 : : /* Advance the aggregates (or combine functions) */
2543 : 15525078 : advance_aggregates(aggstate);
2544 : :
2545 : : /* Reset per-input-tuple context after each tuple */
2546 : 15525026 : ResetExprContext(tmpcontext);
2547 : :
2548 : 15525026 : outerslot = fetch_input_tuple(aggstate);
2549 [ + + + + ]: 15525010 : if (TupIsNull(outerslot))
2550 : : {
2551 : : /* no more outer-plan tuples available */
2552 : :
2553 : : /* if we built hash tables, finalize any spills */
2554 [ + + ]: 36664 : if (aggstate->aggstrategy == AGG_MIXED &&
2555 [ + - ]: 108 : aggstate->current_phase == 1)
2556 : 108 : hashagg_finish_initial_spills(aggstate);
2557 : :
2558 [ + + ]: 36664 : if (hasGroupingSets)
2559 : : {
2560 : 524 : aggstate->input_done = true;
2561 : 524 : break;
2562 : : }
2563 : : else
2564 : : {
2565 : 36140 : aggstate->agg_done = true;
2566 : 36140 : break;
2567 : : }
2568 : : }
2569 : : /* set up for next advance_aggregates call */
2570 : 15488346 : tmpcontext->ecxt_outertuple = outerslot;
2571 : :
2572 : : /*
2573 : : * If we are grouping, check whether we've crossed a group
2574 : : * boundary.
2575 : : */
2576 [ + + + + ]: 15488346 : if (node->aggstrategy != AGG_PLAIN && node->numCols > 0)
2577 : : {
2578 : 1635834 : tmpcontext->ecxt_innertuple = firstSlot;
2579 [ + + ]: 1635834 : if (!ExecQual(aggstate->phase->eqfunctions[node->numCols - 1],
2580 : : tmpcontext))
2581 : : {
2582 : 104939 : aggstate->grp_firstTuple = ExecCopySlotHeapTuple(outerslot);
2583 : 104939 : break;
2584 : : }
2585 : : }
2586 : : }
2587 : : }
2588 : :
2589 : : /*
2590 : : * Use the representative input tuple for any references to
2591 : : * non-aggregated input columns in aggregate direct args, the node
2592 : : * qual, and the tlist. (If we are not grouping, and there are no
2593 : : * input rows at all, we will come here with an empty firstSlot
2594 : : * ... but if not grouping, there can't be any references to
2595 : : * non-aggregated input columns, so no problem.)
2596 : : */
2597 : 149090 : econtext->ecxt_outertuple = firstSlot;
2598 : : }
2599 : :
2600 : : Assert(aggstate->projected_set >= 0);
2601 : :
2602 : 158526 : currentSet = aggstate->projected_set;
2603 : :
2604 : 158526 : prepare_projection_slot(aggstate, econtext->ecxt_outertuple, currentSet);
2605 : :
2606 : 158526 : select_current_set(aggstate, currentSet, false);
2607 : :
2608 : 158526 : finalize_aggregates(aggstate,
2609 : : peragg,
2610 : 158526 : pergroups[currentSet]);
2611 : :
2612 : : /*
2613 : : * If there's no row to project right now, we must continue rather
2614 : : * than returning a null since there might be more groups.
2615 : : */
2616 : 158518 : result = project_aggregates(aggstate);
2617 [ + + ]: 158510 : if (result)
2618 : 151120 : return result;
2619 : : }
2620 : :
2621 : : /* No more groups */
2622 : 449 : return NULL;
2623 : : }
2624 : :
2625 : : /*
2626 : : * ExecAgg for hashed case: read input and build hash table
2627 : : */
2628 : : static void
2629 : 11120 : agg_fill_hash_table(AggState *aggstate)
2630 : : {
2631 : : TupleTableSlot *outerslot;
2632 : 11120 : ExprContext *tmpcontext = aggstate->tmpcontext;
2633 : :
2634 : : /*
2635 : : * Process each outer-plan tuple, and then fetch the next one, until we
2636 : : * exhaust the outer plan.
2637 : : */
2638 : : for (;;)
2639 : : {
2640 : 4722497 : outerslot = fetch_input_tuple(aggstate);
2641 [ + + + + ]: 4722497 : if (TupIsNull(outerslot))
2642 : : break;
2643 : :
2644 : : /* set up for lookup_hash_entries and advance_aggregates */
2645 : 4711377 : tmpcontext->ecxt_outertuple = outerslot;
2646 : :
2647 : : /* Find or build hashtable entries */
2648 : 4711377 : lookup_hash_entries(aggstate);
2649 : :
2650 : : /* Advance the aggregates (or combine functions) */
2651 : 4711377 : advance_aggregates(aggstate);
2652 : :
2653 : : /*
2654 : : * Reset per-input-tuple context after each tuple, but note that the
2655 : : * hash lookups do this too
2656 : : */
2657 : 4711377 : ResetExprContext(aggstate->tmpcontext);
2658 : : }
2659 : :
2660 : : /* finalize spills, if any */
2661 : 11120 : hashagg_finish_initial_spills(aggstate);
2662 : :
2663 : 11120 : aggstate->table_filled = true;
2664 : : /* Initialize to walk the first hash table */
2665 : 11120 : select_current_set(aggstate, 0, true);
2666 : 11120 : ResetTupleHashIterator(aggstate->perhash[0].hashtable,
2667 : : &aggstate->perhash[0].hashiter);
2668 : 11120 : }
2669 : :
2670 : : /*
2671 : : * If any data was spilled during hash aggregation, reset the hash table and
2672 : : * reprocess one batch of spilled data. After reprocessing a batch, the hash
2673 : : * table will again contain data, ready to be consumed by
2674 : : * agg_retrieve_hash_table_in_memory().
2675 : : *
2676 : : * Should only be called after all in memory hash table entries have been
2677 : : * finalized and emitted.
2678 : : *
2679 : : * Return false when input is exhausted and there's no more work to be done;
2680 : : * otherwise return true.
2681 : : */
2682 : : static bool
2683 : 29818 : agg_refill_hash_table(AggState *aggstate)
2684 : : {
2685 : : HashAggBatch *batch;
2686 : : AggStatePerHash perhash;
2687 : : HashAggSpill spill;
2688 : 29818 : LogicalTapeSet *tapeset = aggstate->hash_tapeset;
2689 : 29818 : bool spill_initialized = false;
2690 : :
2691 [ + + ]: 29818 : if (aggstate->hash_batches == NIL)
2692 : 11781 : return false;
2693 : :
2694 : : /* hash_batches is a stack, with the top item at the end of the list */
2695 : 18037 : batch = llast(aggstate->hash_batches);
2696 : 18037 : aggstate->hash_batches = list_delete_last(aggstate->hash_batches);
2697 : :
2698 : 18037 : hash_agg_set_limits(aggstate->hashentrysize, batch->input_card,
2699 : : batch->used_bits, &aggstate->hash_mem_limit,
2700 : : &aggstate->hash_ngroups_limit, NULL);
2701 : :
2702 : : /*
2703 : : * Each batch only processes one grouping set; set the rest to NULL so
2704 : : * that advance_aggregates() knows to ignore them. We don't touch
2705 : : * pergroups for sorted grouping sets here, because they will be needed if
2706 : : * we rescan later. The expressions for sorted grouping sets will not be
2707 : : * evaluated after we recompile anyway.
2708 : : */
2709 [ + - + - : 138434 : MemSet(aggstate->hash_pergroup, 0,
+ - + - +
+ ]
2710 : : sizeof(AggStatePerGroup) * aggstate->num_hashes);
2711 : :
2712 : : /* free memory and reset hash tables */
2713 : 18037 : ReScanExprContext(aggstate->hashcontext);
2714 [ + + ]: 138434 : for (int setno = 0; setno < aggstate->num_hashes; setno++)
2715 : 120397 : ResetTupleHashTable(aggstate->perhash[setno].hashtable);
2716 : :
2717 : 18037 : aggstate->hash_ngroups_current = 0;
2718 : :
2719 : : /*
2720 : : * In AGG_MIXED mode, hash aggregation happens in phase 1 and the output
2721 : : * happens in phase 0. So, we switch to phase 1 when processing a batch,
2722 : : * and back to phase 0 after the batch is done.
2723 : : */
2724 : : Assert(aggstate->current_phase == 0);
2725 [ + + ]: 18037 : if (aggstate->phase->aggstrategy == AGG_MIXED)
2726 : : {
2727 : 17508 : aggstate->current_phase = 1;
2728 : 17508 : aggstate->phase = &aggstate->phases[aggstate->current_phase];
2729 : : }
2730 : :
2731 : 18037 : select_current_set(aggstate, batch->setno, true);
2732 : :
2733 : 18037 : perhash = &aggstate->perhash[aggstate->current_set];
2734 : :
2735 : : /*
2736 : : * Spilled tuples are always read back as MinimalTuples, which may be
2737 : : * different from the outer plan, so recompile the aggregate expressions.
2738 : : *
2739 : : * We still need the NULL check, because we are only processing one
2740 : : * grouping set at a time and the rest will be NULL.
2741 : : */
2742 : 18037 : hashagg_recompile_expressions(aggstate, true, true);
2743 : :
2744 : 18037 : INJECTION_POINT("hash-aggregate-process-batch", NULL);
2745 : : for (;;)
2746 : 777914 : {
2747 : 795951 : TupleTableSlot *spillslot = aggstate->hash_spill_rslot;
2748 : 795951 : TupleTableSlot *hashslot = perhash->hashslot;
2749 : 795951 : TupleHashTable hashtable = perhash->hashtable;
2750 : : TupleHashEntry entry;
2751 : : MinimalTuple tuple;
2752 : : uint32 hash;
2753 : 795951 : bool isnew = false;
2754 [ + + ]: 795951 : bool *p_isnew = aggstate->hash_spill_mode ? NULL : &isnew;
2755 : :
2756 [ - + ]: 795951 : CHECK_FOR_INTERRUPTS();
2757 : :
2758 : 795951 : tuple = hashagg_batch_read(batch, &hash);
2759 [ + + ]: 795951 : if (tuple == NULL)
2760 : 18037 : break;
2761 : :
2762 : 777914 : ExecStoreMinimalTuple(tuple, spillslot, true);
2763 : 777914 : aggstate->tmpcontext->ecxt_outertuple = spillslot;
2764 : :
2765 : 777914 : prepare_hash_slot(perhash,
2766 : 777914 : aggstate->tmpcontext->ecxt_outertuple,
2767 : : hashslot);
2768 : 777914 : entry = LookupTupleHashEntryHash(hashtable, hashslot,
2769 : : p_isnew, hash);
2770 : :
2771 [ + + ]: 777914 : if (entry != NULL)
2772 : : {
2773 [ + + ]: 457586 : if (isnew)
2774 : 130729 : initialize_hash_entry(aggstate, hashtable, entry);
2775 : 457586 : aggstate->hash_pergroup[batch->setno] = TupleHashEntryGetAdditional(hashtable, entry);
2776 : 457586 : advance_aggregates(aggstate);
2777 : : }
2778 : : else
2779 : : {
2780 [ + + ]: 320328 : if (!spill_initialized)
2781 : : {
2782 : : /*
2783 : : * Avoid initializing the spill until we actually need it so
2784 : : * that we don't assign tapes that will never be used.
2785 : : */
2786 : 8348 : spill_initialized = true;
2787 : 8348 : hashagg_spill_init(&spill, tapeset, batch->used_bits,
2788 : : batch->input_card, aggstate->hashentrysize);
2789 : : }
2790 : : /* no memory for a new group, spill */
2791 : 320328 : hashagg_spill_tuple(aggstate, &spill, spillslot, hash);
2792 : :
2793 : 320328 : aggstate->hash_pergroup[batch->setno] = NULL;
2794 : : }
2795 : :
2796 : : /*
2797 : : * Reset per-input-tuple context after each tuple, but note that the
2798 : : * hash lookups do this too
2799 : : */
2800 : 777914 : ResetExprContext(aggstate->tmpcontext);
2801 : : }
2802 : :
2803 : 18037 : LogicalTapeClose(batch->input_tape);
2804 : :
2805 : : /* change back to phase 0 */
2806 : 18037 : aggstate->current_phase = 0;
2807 : 18037 : aggstate->phase = &aggstate->phases[aggstate->current_phase];
2808 : :
2809 [ + + ]: 18037 : if (spill_initialized)
2810 : : {
2811 : 8348 : hashagg_spill_finish(aggstate, &spill, batch->setno);
2812 : 8348 : hash_agg_update_metrics(aggstate, true, spill.npartitions);
2813 : : }
2814 : : else
2815 : 9689 : hash_agg_update_metrics(aggstate, true, 0);
2816 : :
2817 : 18037 : aggstate->hash_spill_mode = false;
2818 : :
2819 : : /* prepare to walk the first hash table */
2820 : 18037 : select_current_set(aggstate, batch->setno, true);
2821 : 18037 : ResetTupleHashIterator(aggstate->perhash[batch->setno].hashtable,
2822 : : &aggstate->perhash[batch->setno].hashiter);
2823 : :
2824 : 18037 : pfree(batch);
2825 : :
2826 : 18037 : return true;
2827 : : }
2828 : :
2829 : : /*
2830 : : * ExecAgg for hashed case: retrieving groups from hash table
2831 : : *
2832 : : * After exhausting in-memory tuples, also try refilling the hash table using
2833 : : * previously-spilled tuples. Only returns NULL after all in-memory and
2834 : : * spilled tuples are exhausted.
2835 : : */
2836 : : static TupleTableSlot *
2837 : 323925 : agg_retrieve_hash_table(AggState *aggstate)
2838 : : {
2839 : 323925 : TupleTableSlot *result = NULL;
2840 : :
2841 [ + + ]: 654106 : while (result == NULL)
2842 : : {
2843 : 341962 : result = agg_retrieve_hash_table_in_memory(aggstate);
2844 [ + + ]: 341962 : if (result == NULL)
2845 : : {
2846 [ + + ]: 29818 : if (!agg_refill_hash_table(aggstate))
2847 : : {
2848 : 11781 : aggstate->agg_done = true;
2849 : 11781 : break;
2850 : : }
2851 : : }
2852 : : }
2853 : :
2854 : 323925 : return result;
2855 : : }
2856 : :
2857 : : /*
2858 : : * Retrieve the groups from the in-memory hash tables without considering any
2859 : : * spilled tuples.
2860 : : */
2861 : : static TupleTableSlot *
2862 : 341962 : agg_retrieve_hash_table_in_memory(AggState *aggstate)
2863 : : {
2864 : : ExprContext *econtext;
2865 : : AggStatePerAgg peragg;
2866 : : AggStatePerGroup pergroup;
2867 : : TupleHashEntry entry;
2868 : : TupleTableSlot *firstSlot;
2869 : : TupleTableSlot *result;
2870 : : AggStatePerHash perhash;
2871 : :
2872 : : /*
2873 : : * get state info from node.
2874 : : *
2875 : : * econtext is the per-output-tuple expression context.
2876 : : */
2877 : 341962 : econtext = aggstate->ss.ps.ps_ExprContext;
2878 : 341962 : peragg = aggstate->peragg;
2879 : 341962 : firstSlot = aggstate->ss.ss_ScanTupleSlot;
2880 : :
2881 : : /*
2882 : : * Note that perhash (and therefore anything accessed through it) can
2883 : : * change inside the loop, as we change between grouping sets.
2884 : : */
2885 : 341962 : perhash = &aggstate->perhash[aggstate->current_set];
2886 : :
2887 : : /*
2888 : : * We loop retrieving groups until we find one satisfying
2889 : : * aggstate->ss.ps.qual
2890 : : */
2891 : : for (;;)
2892 : 130509 : {
2893 : 472471 : TupleTableSlot *hashslot = perhash->hashslot;
2894 : 472471 : TupleHashTable hashtable = perhash->hashtable;
2895 : : int i;
2896 : :
2897 [ - + ]: 472471 : CHECK_FOR_INTERRUPTS();
2898 : :
2899 : : /*
2900 : : * Find the next entry in the hash table
2901 : : */
2902 : 472471 : entry = ScanTupleHashTable(hashtable, &perhash->hashiter);
2903 [ + + ]: 472471 : if (entry == NULL)
2904 : : {
2905 : 96701 : int nextset = aggstate->current_set + 1;
2906 : :
2907 [ + + ]: 96701 : if (nextset < aggstate->num_hashes)
2908 : : {
2909 : : /*
2910 : : * Switch to next grouping set, reinitialize, and restart the
2911 : : * loop.
2912 : : */
2913 : 66883 : select_current_set(aggstate, nextset, true);
2914 : :
2915 : 66883 : perhash = &aggstate->perhash[aggstate->current_set];
2916 : :
2917 : 66883 : ResetTupleHashIterator(perhash->hashtable, &perhash->hashiter);
2918 : :
2919 : 66883 : continue;
2920 : : }
2921 : : else
2922 : : {
2923 : 29818 : return NULL;
2924 : : }
2925 : : }
2926 : :
2927 : : /*
2928 : : * Clear the per-output-tuple context for each group
2929 : : *
2930 : : * We intentionally don't use ReScanExprContext here; if any aggs have
2931 : : * registered shutdown callbacks, they mustn't be called yet, since we
2932 : : * might not be done with that agg.
2933 : : */
2934 : 375770 : ResetExprContext(econtext);
2935 : :
2936 : : /*
2937 : : * Transform representative tuple back into one with the right
2938 : : * columns.
2939 : : */
2940 : 375770 : ExecStoreMinimalTuple(TupleHashEntryGetTuple(entry), hashslot, false);
2941 : 375770 : slot_getallattrs(hashslot);
2942 : :
2943 : 375770 : ExecClearTuple(firstSlot);
2944 : 375770 : memset(firstSlot->tts_isnull, true,
2945 : 375770 : firstSlot->tts_tupleDescriptor->natts * sizeof(bool));
2946 : :
2947 [ + + ]: 966229 : for (i = 0; i < perhash->numhashGrpCols; i++)
2948 : : {
2949 : 590459 : int varNumber = perhash->hashGrpColIdxInput[i] - 1;
2950 : :
2951 : 590459 : firstSlot->tts_values[varNumber] = hashslot->tts_values[i];
2952 : 590459 : firstSlot->tts_isnull[varNumber] = hashslot->tts_isnull[i];
2953 : : }
2954 : 375770 : ExecStoreVirtualTuple(firstSlot);
2955 : :
2956 : 375770 : pergroup = (AggStatePerGroup) TupleHashEntryGetAdditional(hashtable, entry);
2957 : :
2958 : : /*
2959 : : * Use the representative input tuple for any references to
2960 : : * non-aggregated input columns in the qual and tlist.
2961 : : */
2962 : 375770 : econtext->ecxt_outertuple = firstSlot;
2963 : :
2964 : 375770 : prepare_projection_slot(aggstate,
2965 : : econtext->ecxt_outertuple,
2966 : : aggstate->current_set);
2967 : :
2968 : 375770 : finalize_aggregates(aggstate, peragg, pergroup);
2969 : :
2970 : 375770 : result = project_aggregates(aggstate);
2971 [ + + ]: 375770 : if (result)
2972 : 312144 : return result;
2973 : : }
2974 : :
2975 : : /* No more groups */
2976 : : return NULL;
2977 : : }
2978 : :
2979 : : /*
2980 : : * hashagg_spill_init
2981 : : *
2982 : : * Called after we determined that spilling is necessary. Chooses the number
2983 : : * of partitions to create, and initializes them.
2984 : : */
2985 : : static void
2986 : 8432 : hashagg_spill_init(HashAggSpill *spill, LogicalTapeSet *tapeset, int used_bits,
2987 : : double input_groups, double hashentrysize)
2988 : : {
2989 : : int npartitions;
2990 : : int partition_bits;
2991 : :
2992 : 8432 : npartitions = hash_choose_num_partitions(input_groups, hashentrysize,
2993 : : used_bits, &partition_bits);
2994 : :
2995 : : #ifdef USE_INJECTION_POINTS
2996 [ + + ]: 8432 : if (IS_INJECTION_POINT_ATTACHED("hash-aggregate-single-partition"))
2997 : : {
2998 : 5 : npartitions = 1;
2999 : 5 : partition_bits = 0;
3000 : 5 : INJECTION_POINT_CACHED("hash-aggregate-single-partition", NULL);
3001 : : }
3002 : : #endif
3003 : :
3004 : 8432 : spill->partitions = palloc0_array(LogicalTape *, npartitions);
3005 : 8432 : spill->ntuples = palloc0_array(int64, npartitions);
3006 : 8432 : spill->hll_card = palloc0_array(hyperLogLogState, npartitions);
3007 : :
3008 [ + + ]: 42145 : for (int i = 0; i < npartitions; i++)
3009 : 33713 : spill->partitions[i] = LogicalTapeCreate(tapeset);
3010 : :
3011 : 8432 : spill->shift = 32 - used_bits - partition_bits;
3012 [ + + ]: 8432 : if (spill->shift < 32)
3013 : 8427 : spill->mask = (npartitions - 1) << spill->shift;
3014 : : else
3015 : 5 : spill->mask = 0;
3016 : 8432 : spill->npartitions = npartitions;
3017 : :
3018 [ + + ]: 42145 : for (int i = 0; i < npartitions; i++)
3019 : 33713 : initHyperLogLog(&spill->hll_card[i], HASHAGG_HLL_BIT_WIDTH);
3020 : 8432 : }
3021 : :
3022 : : /*
3023 : : * hashagg_spill_tuple
3024 : : *
3025 : : * No room for new groups in the hash table. Save for later in the appropriate
3026 : : * partition.
3027 : : */
3028 : : static Size
3029 : 777914 : hashagg_spill_tuple(AggState *aggstate, HashAggSpill *spill,
3030 : : TupleTableSlot *inputslot, uint32 hash)
3031 : : {
3032 : : TupleTableSlot *spillslot;
3033 : : int partition;
3034 : : MinimalTuple tuple;
3035 : : LogicalTape *tape;
3036 : 777914 : int total_written = 0;
3037 : : bool shouldFree;
3038 : :
3039 : : Assert(spill->partitions != NULL);
3040 : :
3041 : : /* spill only attributes that we actually need */
3042 [ + + ]: 777914 : if (!aggstate->all_cols_needed)
3043 : : {
3044 : 61108 : spillslot = aggstate->hash_spill_wslot;
3045 : 61108 : slot_getsomeattrs(inputslot, aggstate->max_colno_needed);
3046 : 61108 : ExecClearTuple(spillslot);
3047 [ + + ]: 1024164 : for (int i = 0; i < spillslot->tts_tupleDescriptor->natts; i++)
3048 : : {
3049 [ + + ]: 963056 : if (bms_is_member(i + 1, aggstate->colnos_needed))
3050 : : {
3051 : 181228 : spillslot->tts_values[i] = inputslot->tts_values[i];
3052 : 181228 : spillslot->tts_isnull[i] = inputslot->tts_isnull[i];
3053 : : }
3054 : : else
3055 : 781828 : spillslot->tts_isnull[i] = true;
3056 : : }
3057 : 61108 : ExecStoreVirtualTuple(spillslot);
3058 : : }
3059 : : else
3060 : 716806 : spillslot = inputslot;
3061 : :
3062 : 777914 : tuple = ExecFetchSlotMinimalTuple(spillslot, &shouldFree);
3063 : :
3064 [ + + ]: 777914 : if (spill->shift < 32)
3065 : 767414 : partition = (hash & spill->mask) >> spill->shift;
3066 : : else
3067 : 10500 : partition = 0;
3068 : :
3069 : 777914 : spill->ntuples[partition]++;
3070 : :
3071 : : /*
3072 : : * All hash values destined for a given partition have some bits in
3073 : : * common, which causes bad HLL cardinality estimates. Hash the hash to
3074 : : * get a more uniform distribution.
3075 : : */
3076 : 777914 : addHyperLogLog(&spill->hll_card[partition], hash_bytes_uint32(hash));
3077 : :
3078 : 777914 : tape = spill->partitions[partition];
3079 : :
3080 : 777914 : LogicalTapeWrite(tape, &hash, sizeof(uint32));
3081 : 777914 : total_written += sizeof(uint32);
3082 : :
3083 : 777914 : LogicalTapeWrite(tape, tuple, tuple->t_len);
3084 : 777914 : total_written += tuple->t_len;
3085 : :
3086 [ + + ]: 777914 : if (shouldFree)
3087 : 480354 : pfree(tuple);
3088 : :
3089 : 777914 : return total_written;
3090 : : }
3091 : :
3092 : : /*
3093 : : * hashagg_batch_new
3094 : : *
3095 : : * Construct a HashAggBatch item, which represents one iteration of HashAgg to
3096 : : * be done.
3097 : : */
3098 : : static HashAggBatch *
3099 : 18037 : hashagg_batch_new(LogicalTape *input_tape, int setno,
3100 : : int64 input_tuples, double input_card, int used_bits)
3101 : : {
3102 : 18037 : HashAggBatch *batch = palloc0_object(HashAggBatch);
3103 : :
3104 : 18037 : batch->setno = setno;
3105 : 18037 : batch->used_bits = used_bits;
3106 : 18037 : batch->input_tape = input_tape;
3107 : 18037 : batch->input_tuples = input_tuples;
3108 : 18037 : batch->input_card = input_card;
3109 : :
3110 : 18037 : return batch;
3111 : : }
3112 : :
3113 : : /*
3114 : : * hashagg_batch_read
3115 : : * read the next tuple from a batch's tape. Return NULL if no more.
3116 : : */
3117 : : static MinimalTuple
3118 : 795951 : hashagg_batch_read(HashAggBatch *batch, uint32 *hashp)
3119 : : {
3120 : 795951 : LogicalTape *tape = batch->input_tape;
3121 : : MinimalTuple tuple;
3122 : : uint32 t_len;
3123 : : size_t nread;
3124 : : uint32 hash;
3125 : :
3126 : 795951 : nread = LogicalTapeRead(tape, &hash, sizeof(uint32));
3127 [ + + ]: 795951 : if (nread == 0)
3128 : 18037 : return NULL;
3129 [ - + ]: 777914 : if (nread != sizeof(uint32))
3130 [ # # ]: 0 : ereport(ERROR,
3131 : : (errcode_for_file_access(),
3132 : : errmsg_internal("unexpected EOF for tape %p: requested %zu bytes, read %zu bytes",
3133 : : tape, sizeof(uint32), nread)));
3134 [ + - ]: 777914 : if (hashp != NULL)
3135 : 777914 : *hashp = hash;
3136 : :
3137 : 777914 : nread = LogicalTapeRead(tape, &t_len, sizeof(t_len));
3138 [ - + ]: 777914 : if (nread != sizeof(uint32))
3139 [ # # ]: 0 : ereport(ERROR,
3140 : : (errcode_for_file_access(),
3141 : : errmsg_internal("unexpected EOF for tape %p: requested %zu bytes, read %zu bytes",
3142 : : tape, sizeof(uint32), nread)));
3143 : :
3144 : 777914 : tuple = (MinimalTuple) palloc(t_len);
3145 : 777914 : tuple->t_len = t_len;
3146 : :
3147 : 777914 : nread = LogicalTapeRead(tape,
3148 : : (char *) tuple + sizeof(uint32),
3149 : : t_len - sizeof(uint32));
3150 [ - + ]: 777914 : if (nread != t_len - sizeof(uint32))
3151 [ # # ]: 0 : ereport(ERROR,
3152 : : (errcode_for_file_access(),
3153 : : errmsg_internal("unexpected EOF for tape %p: requested %zu bytes, read %zu bytes",
3154 : : tape, t_len - sizeof(uint32), nread)));
3155 : :
3156 : 777914 : return tuple;
3157 : : }
3158 : :
3159 : : /*
3160 : : * hashagg_finish_initial_spills
3161 : : *
3162 : : * After a HashAggBatch has been processed, it may have spilled tuples to
3163 : : * disk. If so, turn the spilled partitions into new batches that must later
3164 : : * be executed.
3165 : : */
3166 : : static void
3167 : 11228 : hashagg_finish_initial_spills(AggState *aggstate)
3168 : : {
3169 : : int setno;
3170 : 11228 : int total_npartitions = 0;
3171 : :
3172 [ + + ]: 11228 : if (aggstate->hash_spills != NULL)
3173 : : {
3174 [ + + ]: 128 : for (setno = 0; setno < aggstate->num_hashes; setno++)
3175 : : {
3176 : 84 : HashAggSpill *spill = &aggstate->hash_spills[setno];
3177 : :
3178 : 84 : total_npartitions += spill->npartitions;
3179 : 84 : hashagg_spill_finish(aggstate, spill, setno);
3180 : : }
3181 : :
3182 : : /*
3183 : : * We're not processing tuples from outer plan any more; only
3184 : : * processing batches of spilled tuples. The initial spill structures
3185 : : * are no longer needed.
3186 : : */
3187 : 44 : pfree(aggstate->hash_spills);
3188 : 44 : aggstate->hash_spills = NULL;
3189 : : }
3190 : :
3191 : 11228 : hash_agg_update_metrics(aggstate, false, total_npartitions);
3192 : 11228 : aggstate->hash_spill_mode = false;
3193 : 11228 : }
3194 : :
3195 : : /*
3196 : : * hashagg_spill_finish
3197 : : *
3198 : : * Transform spill partitions into new batches.
3199 : : */
3200 : : static void
3201 : 8432 : hashagg_spill_finish(AggState *aggstate, HashAggSpill *spill, int setno)
3202 : : {
3203 : : int i;
3204 : 8432 : int used_bits = 32 - spill->shift;
3205 : :
3206 [ - + ]: 8432 : if (spill->npartitions == 0)
3207 : 0 : return; /* didn't spill */
3208 : :
3209 [ + + ]: 42145 : for (i = 0; i < spill->npartitions; i++)
3210 : : {
3211 : 33713 : LogicalTape *tape = spill->partitions[i];
3212 : : HashAggBatch *new_batch;
3213 : : double cardinality;
3214 : :
3215 : : /* if the partition is empty, don't create a new batch of work */
3216 [ + + ]: 33713 : if (spill->ntuples[i] == 0)
3217 : 15676 : continue;
3218 : :
3219 : 18037 : cardinality = estimateHyperLogLog(&spill->hll_card[i]);
3220 : 18037 : freeHyperLogLog(&spill->hll_card[i]);
3221 : :
3222 : : /* rewinding frees the buffer while not in use */
3223 : 18037 : LogicalTapeRewindForRead(tape, HASHAGG_READ_BUFFER_SIZE);
3224 : :
3225 : 18037 : new_batch = hashagg_batch_new(tape, setno,
3226 : 18037 : spill->ntuples[i], cardinality,
3227 : : used_bits);
3228 : 18037 : aggstate->hash_batches = lappend(aggstate->hash_batches, new_batch);
3229 : 18037 : aggstate->hash_batches_used++;
3230 : : }
3231 : :
3232 : 8432 : pfree(spill->ntuples);
3233 : 8432 : pfree(spill->hll_card);
3234 : 8432 : pfree(spill->partitions);
3235 : : }
3236 : :
3237 : : /*
3238 : : * Free resources related to a spilled HashAgg.
3239 : : */
3240 : : static void
3241 : 42216 : hashagg_reset_spill_state(AggState *aggstate)
3242 : : {
3243 : : /* free spills from initial pass */
3244 [ - + ]: 42216 : if (aggstate->hash_spills != NULL)
3245 : : {
3246 : : int setno;
3247 : :
3248 [ # # ]: 0 : for (setno = 0; setno < aggstate->num_hashes; setno++)
3249 : : {
3250 : 0 : HashAggSpill *spill = &aggstate->hash_spills[setno];
3251 : :
3252 : 0 : pfree(spill->ntuples);
3253 : 0 : pfree(spill->partitions);
3254 : : }
3255 : 0 : pfree(aggstate->hash_spills);
3256 : 0 : aggstate->hash_spills = NULL;
3257 : : }
3258 : :
3259 : : /* free batches */
3260 : 42216 : list_free_deep(aggstate->hash_batches);
3261 : 42216 : aggstate->hash_batches = NIL;
3262 : :
3263 : : /* close tape set */
3264 [ + + ]: 42216 : if (aggstate->hash_tapeset != NULL)
3265 : : {
3266 : 44 : LogicalTapeSetClose(aggstate->hash_tapeset);
3267 : 44 : aggstate->hash_tapeset = NULL;
3268 : : }
3269 : 42216 : }
3270 : :
3271 : :
3272 : : /* -----------------
3273 : : * ExecInitAgg
3274 : : *
3275 : : * Creates the run-time information for the agg node produced by the
3276 : : * planner and initializes its outer subtree.
3277 : : *
3278 : : * -----------------
3279 : : */
3280 : : AggState *
3281 : 34622 : ExecInitAgg(Agg *node, EState *estate, int eflags)
3282 : : {
3283 : : AggState *aggstate;
3284 : : AggStatePerAgg peraggs;
3285 : : AggStatePerTrans pertransstates;
3286 : : AggStatePerGroup *pergroups;
3287 : : Plan *outerPlan;
3288 : : ExprContext *econtext;
3289 : : TupleDesc scanDesc;
3290 : : int max_aggno;
3291 : : int max_transno;
3292 : : int numaggrefs;
3293 : : int numaggs;
3294 : : int numtrans;
3295 : : int phase;
3296 : : int phaseidx;
3297 : : ListCell *l;
3298 : 34622 : Bitmapset *all_grouped_cols = NULL;
3299 : 34622 : int numGroupingSets = 1;
3300 : : int numPhases;
3301 : : int numHashes;
3302 : 34622 : int i = 0;
3303 : 34622 : int j = 0;
3304 [ + + ]: 64326 : bool use_hashing = (node->aggstrategy == AGG_HASHED ||
3305 [ + + ]: 29704 : node->aggstrategy == AGG_MIXED);
3306 : :
3307 : : /* check for unsupported flags */
3308 : : Assert(!(eflags & (EXEC_FLAG_BACKWARD | EXEC_FLAG_MARK)));
3309 : :
3310 : : /*
3311 : : * create state structure
3312 : : */
3313 : 34622 : aggstate = makeNode(AggState);
3314 : 34622 : aggstate->ss.ps.plan = (Plan *) node;
3315 : 34622 : aggstate->ss.ps.state = estate;
3316 : 34622 : aggstate->ss.ps.ExecProcNode = ExecAgg;
3317 : :
3318 : 34622 : aggstate->aggs = NIL;
3319 : 34622 : aggstate->numaggs = 0;
3320 : 34622 : aggstate->numtrans = 0;
3321 : 34622 : aggstate->aggstrategy = node->aggstrategy;
3322 : 34622 : aggstate->aggsplit = node->aggsplit;
3323 : 34622 : aggstate->maxsets = 0;
3324 : 34622 : aggstate->projected_set = -1;
3325 : 34622 : aggstate->current_set = 0;
3326 : 34622 : aggstate->peragg = NULL;
3327 : 34622 : aggstate->pertrans = NULL;
3328 : 34622 : aggstate->curperagg = NULL;
3329 : 34622 : aggstate->curpertrans = NULL;
3330 : 34622 : aggstate->input_done = false;
3331 : 34622 : aggstate->agg_done = false;
3332 : 34622 : aggstate->pergroups = NULL;
3333 : 34622 : aggstate->grp_firstTuple = NULL;
3334 : 34622 : aggstate->sort_in = NULL;
3335 : 34622 : aggstate->sort_out = NULL;
3336 : :
3337 : : /*
3338 : : * phases[0] always exists, but is dummy in sorted/plain mode
3339 : : */
3340 [ + + ]: 34622 : numPhases = (use_hashing ? 1 : 2);
3341 : 34622 : numHashes = (use_hashing ? 1 : 0);
3342 : :
3343 : : /*
3344 : : * Calculate the maximum number of grouping sets in any phase; this
3345 : : * determines the size of some allocations. Also calculate the number of
3346 : : * phases, since all hashed/mixed nodes contribute to only a single phase.
3347 : : */
3348 [ + + ]: 34622 : if (node->groupingSets)
3349 : : {
3350 : 704 : numGroupingSets = list_length(node->groupingSets);
3351 : :
3352 [ + + + + : 1470 : foreach(l, node->chain)
+ + ]
3353 : : {
3354 : 766 : Agg *agg = lfirst(l);
3355 : :
3356 [ + + ]: 766 : numGroupingSets = Max(numGroupingSets,
3357 : : list_length(agg->groupingSets));
3358 : :
3359 : : /*
3360 : : * additional AGG_HASHED aggs become part of phase 0, but all
3361 : : * others add an extra phase.
3362 : : */
3363 [ + + ]: 766 : if (agg->aggstrategy != AGG_HASHED)
3364 : 374 : ++numPhases;
3365 : : else
3366 : 392 : ++numHashes;
3367 : : }
3368 : : }
3369 : :
3370 : 34622 : aggstate->maxsets = numGroupingSets;
3371 : 34622 : aggstate->numphases = numPhases;
3372 : :
3373 : 34622 : aggstate->aggcontexts = palloc0_array(ExprContext *, numGroupingSets);
3374 : :
3375 : : /*
3376 : : * Create expression contexts. We need three or more, one for
3377 : : * per-input-tuple processing, one for per-output-tuple processing, one
3378 : : * for all the hashtables, and one for each grouping set. The per-tuple
3379 : : * memory context of the per-grouping-set ExprContexts (aggcontexts)
3380 : : * replaces the standalone memory context formerly used to hold transition
3381 : : * values. We cheat a little by using ExecAssignExprContext() to build
3382 : : * all of them.
3383 : : *
3384 : : * NOTE: the details of what is stored in aggcontexts and what is stored
3385 : : * in the regular per-query memory context are driven by a simple
3386 : : * decision: we want to reset the aggcontext at group boundaries (if not
3387 : : * hashing) and in ExecReScanAgg to recover no-longer-wanted space.
3388 : : */
3389 : 34622 : ExecAssignExprContext(estate, &aggstate->ss.ps);
3390 : 34622 : aggstate->tmpcontext = aggstate->ss.ps.ps_ExprContext;
3391 : :
3392 [ + + ]: 69826 : for (i = 0; i < numGroupingSets; ++i)
3393 : : {
3394 : 35204 : ExecAssignExprContext(estate, &aggstate->ss.ps);
3395 : 35204 : aggstate->aggcontexts[i] = aggstate->ss.ps.ps_ExprContext;
3396 : : }
3397 : :
3398 [ + + ]: 34622 : if (use_hashing)
3399 : 5100 : hash_create_memory(aggstate);
3400 : :
3401 : 34622 : ExecAssignExprContext(estate, &aggstate->ss.ps);
3402 : :
3403 : : /*
3404 : : * Initialize child nodes.
3405 : : *
3406 : : * If we are doing a hashed aggregation then the child plan does not need
3407 : : * to handle REWIND efficiently; see ExecReScanAgg.
3408 : : */
3409 [ + + ]: 34622 : if (node->aggstrategy == AGG_HASHED)
3410 : 4918 : eflags &= ~EXEC_FLAG_REWIND;
3411 : 34622 : outerPlan = outerPlan(node);
3412 : 34622 : outerPlanState(aggstate) = ExecInitNode(outerPlan, estate, eflags);
3413 : :
3414 : : /*
3415 : : * initialize source tuple type.
3416 : : */
3417 : 34622 : aggstate->ss.ps.outerops =
3418 : 34622 : ExecGetResultSlotOps(outerPlanState(&aggstate->ss),
3419 : : &aggstate->ss.ps.outeropsfixed);
3420 : 34622 : aggstate->ss.ps.outeropsset = true;
3421 : :
3422 : 34622 : ExecCreateScanSlotFromOuterPlan(estate, &aggstate->ss,
3423 : : aggstate->ss.ps.outerops);
3424 : 34622 : scanDesc = aggstate->ss.ss_ScanTupleSlot->tts_tupleDescriptor;
3425 : :
3426 : : /*
3427 : : * If there are more than two phases (including a potential dummy phase
3428 : : * 0), input will be resorted using tuplesort. Need a slot for that.
3429 : : */
3430 [ + + ]: 34622 : if (numPhases > 2)
3431 : : {
3432 : 164 : aggstate->sort_slot = ExecInitExtraTupleSlot(estate, scanDesc,
3433 : : &TTSOpsMinimalTuple);
3434 : :
3435 : : /*
3436 : : * The output of the tuplesort, and the output from the outer child
3437 : : * might not use the same type of slot. In most cases the child will
3438 : : * be a Sort, and thus return a TTSOpsMinimalTuple type slot - but the
3439 : : * input can also be presorted due an index, in which case it could be
3440 : : * a different type of slot.
3441 : : *
3442 : : * XXX: For efficiency it would be good to instead/additionally
3443 : : * generate expressions with corresponding settings of outerops* for
3444 : : * the individual phases - deforming is often a bottleneck for
3445 : : * aggregations with lots of rows per group. If there's multiple
3446 : : * sorts, we know that all but the first use TTSOpsMinimalTuple (via
3447 : : * the nodeAgg.c internal tuplesort).
3448 : : */
3449 [ + - ]: 164 : if (aggstate->ss.ps.outeropsfixed &&
3450 [ + + ]: 164 : aggstate->ss.ps.outerops != &TTSOpsMinimalTuple)
3451 : 24 : aggstate->ss.ps.outeropsfixed = false;
3452 : : }
3453 : :
3454 : : /*
3455 : : * Initialize result type, slot and projection.
3456 : : */
3457 : 34622 : ExecInitResultTupleSlotTL(&aggstate->ss.ps, &TTSOpsVirtual);
3458 : 34622 : ExecAssignProjectionInfo(&aggstate->ss.ps, NULL);
3459 : :
3460 : : /*
3461 : : * initialize child expressions
3462 : : *
3463 : : * We expect the parser to have checked that no aggs contain other agg
3464 : : * calls in their arguments (and just to be sure, we verify it again while
3465 : : * initializing the plan node). This would make no sense under SQL
3466 : : * semantics, and it's forbidden by the spec. Because it is true, we
3467 : : * don't need to worry about evaluating the aggs in any particular order.
3468 : : *
3469 : : * Note: execExpr.c finds Aggrefs for us, and adds them to aggstate->aggs.
3470 : : * Aggrefs in the qual are found here; Aggrefs in the targetlist are found
3471 : : * during ExecAssignProjectionInfo, above.
3472 : : */
3473 : 34622 : aggstate->ss.ps.qual =
3474 : 34622 : ExecInitQual(node->plan.qual, (PlanState *) aggstate);
3475 : :
3476 : : /*
3477 : : * We should now have found all Aggrefs in the targetlist and quals.
3478 : : */
3479 : 34622 : numaggrefs = list_length(aggstate->aggs);
3480 : 34622 : max_aggno = -1;
3481 : 34622 : max_transno = -1;
3482 [ + + + + : 73304 : foreach(l, aggstate->aggs)
+ + ]
3483 : : {
3484 : 38682 : Aggref *aggref = (Aggref *) lfirst(l);
3485 : :
3486 : 38682 : max_aggno = Max(max_aggno, aggref->aggno);
3487 : 38682 : max_transno = Max(max_transno, aggref->aggtransno);
3488 : : }
3489 : 34622 : aggstate->numaggs = numaggs = max_aggno + 1;
3490 : 34622 : aggstate->numtrans = numtrans = max_transno + 1;
3491 : :
3492 : : /*
3493 : : * For each phase, prepare grouping set data and fmgr lookup data for
3494 : : * compare functions. Accumulate all_grouped_cols in passing.
3495 : : */
3496 : 34622 : aggstate->phases = palloc0_array(AggStatePerPhaseData, numPhases);
3497 : :
3498 : 34622 : aggstate->num_hashes = numHashes;
3499 [ + + ]: 34622 : if (numHashes)
3500 : : {
3501 : 5100 : aggstate->perhash = palloc0_array(AggStatePerHashData, numHashes);
3502 : 5100 : aggstate->phases[0].numsets = 0;
3503 : 5100 : aggstate->phases[0].gset_lengths = palloc_array(int, numHashes);
3504 : 5100 : aggstate->phases[0].grouped_cols = palloc_array(Bitmapset *, numHashes);
3505 : : }
3506 : :
3507 : 34622 : phase = 0;
3508 [ + + ]: 70010 : for (phaseidx = 0; phaseidx <= list_length(node->chain); ++phaseidx)
3509 : : {
3510 : : Agg *aggnode;
3511 : : Sort *sortnode;
3512 : :
3513 [ + + ]: 35388 : if (phaseidx > 0)
3514 : : {
3515 : 766 : aggnode = list_nth_node(Agg, node->chain, phaseidx - 1);
3516 : 766 : sortnode = castNode(Sort, outerPlan(aggnode));
3517 : : }
3518 : : else
3519 : : {
3520 : 34622 : aggnode = node;
3521 : 34622 : sortnode = NULL;
3522 : : }
3523 : :
3524 : : Assert(phase <= 1 || sortnode);
3525 : :
3526 [ + + ]: 35388 : if (aggnode->aggstrategy == AGG_HASHED
3527 [ + + ]: 30078 : || aggnode->aggstrategy == AGG_MIXED)
3528 : 5492 : {
3529 : 5492 : AggStatePerPhase phasedata = &aggstate->phases[0];
3530 : : AggStatePerHash perhash;
3531 : 5492 : Bitmapset *cols = NULL;
3532 : :
3533 : : Assert(phase == 0);
3534 : 5492 : i = phasedata->numsets++;
3535 : 5492 : perhash = &aggstate->perhash[i];
3536 : :
3537 : : /* phase 0 always points to the "real" Agg in the hash case */
3538 : 5492 : phasedata->aggnode = node;
3539 : 5492 : phasedata->aggstrategy = node->aggstrategy;
3540 : :
3541 : : /* but the actual Agg node representing this hash is saved here */
3542 : 5492 : perhash->aggnode = aggnode;
3543 : :
3544 : 5492 : phasedata->gset_lengths[i] = perhash->numCols = aggnode->numCols;
3545 : :
3546 [ + + ]: 13450 : for (j = 0; j < aggnode->numCols; ++j)
3547 : 7958 : cols = bms_add_member(cols, aggnode->grpColIdx[j]);
3548 : :
3549 : 5492 : phasedata->grouped_cols[i] = cols;
3550 : :
3551 : 5492 : all_grouped_cols = bms_add_members(all_grouped_cols, cols);
3552 : 5492 : continue;
3553 : : }
3554 : : else
3555 : : {
3556 : 29896 : AggStatePerPhase phasedata = &aggstate->phases[++phase];
3557 : : int num_sets;
3558 : :
3559 : 29896 : phasedata->numsets = num_sets = list_length(aggnode->groupingSets);
3560 : :
3561 [ + + ]: 29896 : if (num_sets)
3562 : : {
3563 : 750 : phasedata->gset_lengths = palloc(num_sets * sizeof(int));
3564 : 750 : phasedata->grouped_cols = palloc(num_sets * sizeof(Bitmapset *));
3565 : :
3566 : 750 : i = 0;
3567 [ + - + + : 2130 : foreach(l, aggnode->groupingSets)
+ + ]
3568 : : {
3569 : 1380 : int current_length = list_length(lfirst(l));
3570 : 1380 : Bitmapset *cols = NULL;
3571 : :
3572 : : /* planner forces this to be correct */
3573 [ + + ]: 2694 : for (j = 0; j < current_length; ++j)
3574 : 1314 : cols = bms_add_member(cols, aggnode->grpColIdx[j]);
3575 : :
3576 : 1380 : phasedata->grouped_cols[i] = cols;
3577 : 1380 : phasedata->gset_lengths[i] = current_length;
3578 : :
3579 : 1380 : ++i;
3580 : : }
3581 : :
3582 : 750 : all_grouped_cols = bms_add_members(all_grouped_cols,
3583 : 750 : phasedata->grouped_cols[0]);
3584 : : }
3585 : : else
3586 : : {
3587 : : Assert(phaseidx == 0);
3588 : :
3589 : 29146 : phasedata->gset_lengths = NULL;
3590 : 29146 : phasedata->grouped_cols = NULL;
3591 : : }
3592 : :
3593 : : /*
3594 : : * If we are grouping, precompute fmgr lookup data for inner loop.
3595 : : */
3596 [ + + ]: 29896 : if (aggnode->aggstrategy == AGG_SORTED)
3597 : : {
3598 : : /*
3599 : : * Build a separate function for each subset of columns that
3600 : : * need to be compared.
3601 : : */
3602 : 1927 : phasedata->eqfunctions = palloc0_array(ExprState *, aggnode->numCols);
3603 : :
3604 : : /* for each grouping set */
3605 [ + + ]: 3090 : for (int k = 0; k < phasedata->numsets; k++)
3606 : : {
3607 : 1163 : int length = phasedata->gset_lengths[k];
3608 : :
3609 : : /* nothing to do for empty grouping set */
3610 [ + + ]: 1163 : if (length == 0)
3611 : 233 : continue;
3612 : :
3613 : : /* if we already had one of this length, it'll do */
3614 [ + + ]: 930 : if (phasedata->eqfunctions[length - 1] != NULL)
3615 : 91 : continue;
3616 : :
3617 : 839 : phasedata->eqfunctions[length - 1] =
3618 : 839 : execTuplesMatchPrepare(scanDesc,
3619 : : length,
3620 : 839 : aggnode->grpColIdx,
3621 : 839 : aggnode->grpOperators,
3622 : 839 : aggnode->grpCollations,
3623 : : (PlanState *) aggstate);
3624 : : }
3625 : :
3626 : : /* and for all grouped columns, unless already computed */
3627 [ + + ]: 1927 : if (aggnode->numCols > 0 &&
3628 [ + + ]: 1826 : phasedata->eqfunctions[aggnode->numCols - 1] == NULL)
3629 : : {
3630 : 1245 : phasedata->eqfunctions[aggnode->numCols - 1] =
3631 : 1245 : execTuplesMatchPrepare(scanDesc,
3632 : : aggnode->numCols,
3633 : 1245 : aggnode->grpColIdx,
3634 : 1245 : aggnode->grpOperators,
3635 : 1245 : aggnode->grpCollations,
3636 : : (PlanState *) aggstate);
3637 : : }
3638 : : }
3639 : :
3640 : 29896 : phasedata->aggnode = aggnode;
3641 : 29896 : phasedata->aggstrategy = aggnode->aggstrategy;
3642 : 29896 : phasedata->sortnode = sortnode;
3643 : : }
3644 : : }
3645 : :
3646 : : /*
3647 : : * Convert all_grouped_cols to a descending-order list.
3648 : : */
3649 : 34622 : i = -1;
3650 [ + + ]: 43060 : while ((i = bms_next_member(all_grouped_cols, i)) >= 0)
3651 : 8438 : aggstate->all_grouped_cols = lcons_int(i, aggstate->all_grouped_cols);
3652 : :
3653 : : /*
3654 : : * Set up aggregate-result storage in the output expr context, and also
3655 : : * allocate my private per-agg working storage
3656 : : */
3657 : 34622 : econtext = aggstate->ss.ps.ps_ExprContext;
3658 : 34622 : econtext->ecxt_aggvalues = palloc0_array(Datum, numaggs);
3659 : 34622 : econtext->ecxt_aggnulls = palloc0_array(bool, numaggs);
3660 : :
3661 : 34622 : peraggs = palloc0_array(AggStatePerAggData, numaggs);
3662 : 34622 : pertransstates = palloc0_array(AggStatePerTransData, numtrans);
3663 : :
3664 : 34622 : aggstate->peragg = peraggs;
3665 : 34622 : aggstate->pertrans = pertransstates;
3666 : :
3667 : :
3668 : 34622 : aggstate->all_pergroups = palloc0_array(AggStatePerGroup, numGroupingSets + numHashes);
3669 : 34622 : pergroups = aggstate->all_pergroups;
3670 : :
3671 [ + + ]: 34622 : if (node->aggstrategy != AGG_HASHED)
3672 : : {
3673 [ + + ]: 59990 : for (i = 0; i < numGroupingSets; i++)
3674 : : {
3675 : 30286 : pergroups[i] = palloc0_array(AggStatePerGroupData, numaggs);
3676 : : }
3677 : :
3678 : 29704 : aggstate->pergroups = pergroups;
3679 : 29704 : pergroups += numGroupingSets;
3680 : : }
3681 : :
3682 : : /*
3683 : : * Hashing can only appear in the initial phase.
3684 : : */
3685 [ + + ]: 34622 : if (use_hashing)
3686 : : {
3687 : 5100 : Plan *outerplan = outerPlan(node);
3688 : 5100 : double totalGroups = 0;
3689 : :
3690 : 5100 : aggstate->hash_spill_rslot = ExecInitExtraTupleSlot(estate, scanDesc,
3691 : : &TTSOpsMinimalTuple);
3692 : 5100 : aggstate->hash_spill_wslot = ExecInitExtraTupleSlot(estate, scanDesc,
3693 : : &TTSOpsVirtual);
3694 : :
3695 : : /* this is an array of pointers, not structures */
3696 : 5100 : aggstate->hash_pergroup = pergroups;
3697 : :
3698 : 10200 : aggstate->hashentrysize = hash_agg_entry_size(aggstate->numtrans,
3699 : 5100 : outerplan->plan_width,
3700 : : node->transitionSpace);
3701 : :
3702 : : /*
3703 : : * Consider all of the grouping sets together when setting the limits
3704 : : * and estimating the number of partitions. This can be inaccurate
3705 : : * when there is more than one grouping set, but should still be
3706 : : * reasonable.
3707 : : */
3708 [ + + ]: 10592 : for (int k = 0; k < aggstate->num_hashes; k++)
3709 : 5492 : totalGroups += aggstate->perhash[k].aggnode->numGroups;
3710 : :
3711 : 5100 : hash_agg_set_limits(aggstate->hashentrysize, totalGroups, 0,
3712 : : &aggstate->hash_mem_limit,
3713 : : &aggstate->hash_ngroups_limit,
3714 : : &aggstate->hash_planned_partitions);
3715 : 5100 : find_hash_columns(aggstate);
3716 : :
3717 : : /* Skip massive memory allocation if we are just doing EXPLAIN */
3718 [ + + ]: 5100 : if (!(eflags & EXEC_FLAG_EXPLAIN_ONLY))
3719 : 3682 : build_hash_tables(aggstate);
3720 : :
3721 : 5100 : aggstate->table_filled = false;
3722 : :
3723 : : /* Initialize this to 1, meaning nothing spilled, yet */
3724 : 5100 : aggstate->hash_batches_used = 1;
3725 : : }
3726 : :
3727 : : /*
3728 : : * Initialize current phase-dependent values to initial phase. The initial
3729 : : * phase is 1 (first sort pass) for all strategies that use sorting (if
3730 : : * hashing is being done too, then phase 0 is processed last); but if only
3731 : : * hashing is being done, then phase 0 is all there is.
3732 : : */
3733 [ + + ]: 34622 : if (node->aggstrategy == AGG_HASHED)
3734 : : {
3735 : 4918 : aggstate->current_phase = 0;
3736 : 4918 : initialize_phase(aggstate, 0);
3737 : 4918 : select_current_set(aggstate, 0, true);
3738 : : }
3739 : : else
3740 : : {
3741 : 29704 : aggstate->current_phase = 1;
3742 : 29704 : initialize_phase(aggstate, 1);
3743 : 29704 : select_current_set(aggstate, 0, false);
3744 : : }
3745 : :
3746 : : /*
3747 : : * Perform lookups of aggregate function info, and initialize the
3748 : : * unchanging fields of the per-agg and per-trans data.
3749 : : */
3750 [ + + + + : 73300 : foreach(l, aggstate->aggs)
+ + ]
3751 : : {
3752 : 38682 : Aggref *aggref = lfirst(l);
3753 : : AggStatePerAgg peragg;
3754 : : AggStatePerTrans pertrans;
3755 : : Oid aggTransFnInputTypes[FUNC_MAX_ARGS];
3756 : : int numAggTransFnArgs;
3757 : : int numDirectArgs;
3758 : : HeapTuple aggTuple;
3759 : : Form_pg_aggregate aggform;
3760 : : AclResult aclresult;
3761 : : Oid finalfn_oid;
3762 : : Oid serialfn_oid,
3763 : : deserialfn_oid;
3764 : : Oid aggOwner;
3765 : : Expr *finalfnexpr;
3766 : : Oid aggtranstype;
3767 : :
3768 : : /* Planner should have assigned aggregate to correct level */
3769 : : Assert(aggref->agglevelsup == 0);
3770 : : /* ... and the split mode should match */
3771 : : Assert(aggref->aggsplit == aggstate->aggsplit);
3772 : :
3773 : 38682 : peragg = &peraggs[aggref->aggno];
3774 : :
3775 : : /* Check if we initialized the state for this aggregate already. */
3776 [ + + ]: 38682 : if (peragg->aggref != NULL)
3777 : 316 : continue;
3778 : :
3779 : 38366 : peragg->aggref = aggref;
3780 : 38366 : peragg->transno = aggref->aggtransno;
3781 : :
3782 : : /* Fetch the pg_aggregate row */
3783 : 38366 : aggTuple = SearchSysCache1(AGGFNOID,
3784 : : ObjectIdGetDatum(aggref->aggfnoid));
3785 [ - + ]: 38366 : if (!HeapTupleIsValid(aggTuple))
3786 [ # # ]: 0 : elog(ERROR, "cache lookup failed for aggregate %u",
3787 : : aggref->aggfnoid);
3788 : 38366 : aggform = (Form_pg_aggregate) GETSTRUCT(aggTuple);
3789 : :
3790 : : /* Check permission to call aggregate function */
3791 : 38366 : aclresult = object_aclcheck(ProcedureRelationId, aggref->aggfnoid, GetUserId(),
3792 : : ACL_EXECUTE);
3793 [ + + ]: 38366 : if (aclresult != ACLCHECK_OK)
3794 : 4 : aclcheck_error(aclresult, OBJECT_AGGREGATE,
3795 : 4 : get_func_name(aggref->aggfnoid));
3796 [ - + ]: 38362 : InvokeFunctionExecuteHook(aggref->aggfnoid);
3797 : :
3798 : : /* planner recorded transition state type in the Aggref itself */
3799 : 38362 : aggtranstype = aggref->aggtranstype;
3800 : : Assert(OidIsValid(aggtranstype));
3801 : :
3802 : : /* Final function only required if we're finalizing the aggregates */
3803 [ + + ]: 38362 : if (DO_AGGSPLIT_SKIPFINAL(aggstate->aggsplit))
3804 : 3714 : peragg->finalfn_oid = finalfn_oid = InvalidOid;
3805 : : else
3806 : 34648 : peragg->finalfn_oid = finalfn_oid = aggform->aggfinalfn;
3807 : :
3808 : 38362 : serialfn_oid = InvalidOid;
3809 : 38362 : deserialfn_oid = InvalidOid;
3810 : :
3811 : : /*
3812 : : * Check if serialization/deserialization is required. We only do it
3813 : : * for aggregates that have transtype INTERNAL.
3814 : : */
3815 [ + + ]: 38362 : if (aggtranstype == INTERNALOID)
3816 : : {
3817 : : /*
3818 : : * The planner should only have generated a serialize agg node if
3819 : : * every aggregate with an INTERNAL state has a serialization
3820 : : * function. Verify that.
3821 : : */
3822 [ + + ]: 15484 : if (DO_AGGSPLIT_SERIALIZE(aggstate->aggsplit))
3823 : : {
3824 : : /* serialization only valid when not running finalfn */
3825 : : Assert(DO_AGGSPLIT_SKIPFINAL(aggstate->aggsplit));
3826 : :
3827 [ - + ]: 224 : if (!OidIsValid(aggform->aggserialfn))
3828 [ # # ]: 0 : elog(ERROR, "serialfunc not provided for serialization aggregation");
3829 : 224 : serialfn_oid = aggform->aggserialfn;
3830 : : }
3831 : :
3832 : : /* Likewise for deserialization functions */
3833 [ + + ]: 15484 : if (DO_AGGSPLIT_DESERIALIZE(aggstate->aggsplit))
3834 : : {
3835 : : /* deserialization only valid when combining states */
3836 : : Assert(DO_AGGSPLIT_COMBINE(aggstate->aggsplit));
3837 : :
3838 [ - + ]: 80 : if (!OidIsValid(aggform->aggdeserialfn))
3839 [ # # ]: 0 : elog(ERROR, "deserialfunc not provided for deserialization aggregation");
3840 : 80 : deserialfn_oid = aggform->aggdeserialfn;
3841 : : }
3842 : : }
3843 : :
3844 : : /* Check that aggregate owner has permission to call component fns */
3845 : : {
3846 : : HeapTuple procTuple;
3847 : :
3848 : 38362 : procTuple = SearchSysCache1(PROCOID,
3849 : : ObjectIdGetDatum(aggref->aggfnoid));
3850 [ - + ]: 38362 : if (!HeapTupleIsValid(procTuple))
3851 [ # # ]: 0 : elog(ERROR, "cache lookup failed for function %u",
3852 : : aggref->aggfnoid);
3853 : 38362 : aggOwner = ((Form_pg_proc) GETSTRUCT(procTuple))->proowner;
3854 : 38362 : ReleaseSysCache(procTuple);
3855 : :
3856 [ + + ]: 38362 : if (OidIsValid(finalfn_oid))
3857 : : {
3858 : 16618 : aclresult = object_aclcheck(ProcedureRelationId, finalfn_oid, aggOwner,
3859 : : ACL_EXECUTE);
3860 [ - + ]: 16618 : if (aclresult != ACLCHECK_OK)
3861 : 0 : aclcheck_error(aclresult, OBJECT_FUNCTION,
3862 : 0 : get_func_name(finalfn_oid));
3863 [ - + ]: 16618 : InvokeFunctionExecuteHook(finalfn_oid);
3864 : : }
3865 [ + + ]: 38362 : if (OidIsValid(serialfn_oid))
3866 : : {
3867 : 224 : aclresult = object_aclcheck(ProcedureRelationId, serialfn_oid, aggOwner,
3868 : : ACL_EXECUTE);
3869 [ - + ]: 224 : if (aclresult != ACLCHECK_OK)
3870 : 0 : aclcheck_error(aclresult, OBJECT_FUNCTION,
3871 : 0 : get_func_name(serialfn_oid));
3872 [ - + ]: 224 : InvokeFunctionExecuteHook(serialfn_oid);
3873 : : }
3874 [ + + ]: 38362 : if (OidIsValid(deserialfn_oid))
3875 : : {
3876 : 80 : aclresult = object_aclcheck(ProcedureRelationId, deserialfn_oid, aggOwner,
3877 : : ACL_EXECUTE);
3878 [ - + ]: 80 : if (aclresult != ACLCHECK_OK)
3879 : 0 : aclcheck_error(aclresult, OBJECT_FUNCTION,
3880 : 0 : get_func_name(deserialfn_oid));
3881 [ - + ]: 80 : InvokeFunctionExecuteHook(deserialfn_oid);
3882 : : }
3883 : : }
3884 : :
3885 : : /*
3886 : : * Get actual datatypes of the (nominal) aggregate inputs. These
3887 : : * could be different from the agg's declared input types, when the
3888 : : * agg accepts ANY or a polymorphic type.
3889 : : */
3890 : 38362 : numAggTransFnArgs = get_aggregate_argtypes(aggref,
3891 : : aggTransFnInputTypes);
3892 : :
3893 : : /* Count the "direct" arguments, if any */
3894 : 38362 : numDirectArgs = list_length(aggref->aggdirectargs);
3895 : :
3896 : : /* Detect how many arguments to pass to the finalfn */
3897 [ + + ]: 38362 : if (aggform->aggfinalextra)
3898 : 10912 : peragg->numFinalArgs = numAggTransFnArgs + 1;
3899 : : else
3900 : 27450 : peragg->numFinalArgs = numDirectArgs + 1;
3901 : :
3902 : : /* Initialize any direct-argument expressions */
3903 : 38362 : peragg->aggdirectargs = ExecInitExprList(aggref->aggdirectargs,
3904 : : (PlanState *) aggstate);
3905 : :
3906 : : /*
3907 : : * build expression trees using actual argument & result types for the
3908 : : * finalfn, if it exists and is required.
3909 : : */
3910 [ + + ]: 38362 : if (OidIsValid(finalfn_oid))
3911 : : {
3912 : 16618 : build_aggregate_finalfn_expr(aggTransFnInputTypes,
3913 : : peragg->numFinalArgs,
3914 : : aggtranstype,
3915 : : aggref->aggtype,
3916 : : aggref->inputcollid,
3917 : : finalfn_oid,
3918 : : &finalfnexpr);
3919 : 16618 : fmgr_info(finalfn_oid, &peragg->finalfn);
3920 : 16618 : fmgr_info_set_expr((Node *) finalfnexpr, &peragg->finalfn);
3921 : : }
3922 : :
3923 : : /* get info about the output value's datatype */
3924 : 38362 : get_typlenbyval(aggref->aggtype,
3925 : : &peragg->resulttypeLen,
3926 : : &peragg->resulttypeByVal);
3927 : :
3928 : : /*
3929 : : * Build working state for invoking the transition function, if we
3930 : : * haven't done it already.
3931 : : */
3932 : 38362 : pertrans = &pertransstates[aggref->aggtransno];
3933 [ + + ]: 38362 : if (pertrans->aggref == NULL)
3934 : : {
3935 : : Datum textInitVal;
3936 : : Datum initValue;
3937 : : bool initValueIsNull;
3938 : : Oid transfn_oid;
3939 : :
3940 : : /*
3941 : : * If this aggregation is performing state combines, then instead
3942 : : * of using the transition function, we'll use the combine
3943 : : * function.
3944 : : */
3945 [ + + ]: 38146 : if (DO_AGGSPLIT_COMBINE(aggstate->aggsplit))
3946 : : {
3947 : 1494 : transfn_oid = aggform->aggcombinefn;
3948 : :
3949 : : /* If not set then the planner messed up */
3950 [ - + ]: 1494 : if (!OidIsValid(transfn_oid))
3951 [ # # ]: 0 : elog(ERROR, "combinefn not set for aggregate function");
3952 : : }
3953 : : else
3954 : 36652 : transfn_oid = aggform->aggtransfn;
3955 : :
3956 : 38146 : aclresult = object_aclcheck(ProcedureRelationId, transfn_oid, aggOwner, ACL_EXECUTE);
3957 [ - + ]: 38146 : if (aclresult != ACLCHECK_OK)
3958 : 0 : aclcheck_error(aclresult, OBJECT_FUNCTION,
3959 : 0 : get_func_name(transfn_oid));
3960 [ - + ]: 38146 : InvokeFunctionExecuteHook(transfn_oid);
3961 : :
3962 : : /*
3963 : : * initval is potentially null, so don't try to access it as a
3964 : : * struct field. Must do it the hard way with SysCacheGetAttr.
3965 : : */
3966 : 38146 : textInitVal = SysCacheGetAttr(AGGFNOID, aggTuple,
3967 : : Anum_pg_aggregate_agginitval,
3968 : : &initValueIsNull);
3969 [ + + ]: 38146 : if (initValueIsNull)
3970 : 21261 : initValue = (Datum) 0;
3971 : : else
3972 : 16885 : initValue = GetAggInitVal(textInitVal, aggtranstype);
3973 : :
3974 [ + + ]: 38146 : if (DO_AGGSPLIT_COMBINE(aggstate->aggsplit))
3975 : : {
3976 : 1494 : Oid combineFnInputTypes[] = {aggtranstype,
3977 : : aggtranstype};
3978 : :
3979 : : /*
3980 : : * When combining there's only one input, the to-be-combined
3981 : : * transition value. The transition value is not counted
3982 : : * here.
3983 : : */
3984 : 1494 : pertrans->numTransInputs = 1;
3985 : :
3986 : : /* aggcombinefn always has two arguments of aggtranstype */
3987 : 1494 : build_pertrans_for_aggref(pertrans, aggstate, estate,
3988 : : aggref, transfn_oid, aggtranstype,
3989 : : serialfn_oid, deserialfn_oid,
3990 : : initValue, initValueIsNull,
3991 : : combineFnInputTypes, 2);
3992 : :
3993 : : /*
3994 : : * Ensure that a combine function to combine INTERNAL states
3995 : : * is not strict. This should have been checked during CREATE
3996 : : * AGGREGATE, but the strict property could have been changed
3997 : : * since then.
3998 : : */
3999 [ + + - + ]: 1494 : if (pertrans->transfn.fn_strict && aggtranstype == INTERNALOID)
4000 [ # # ]: 0 : ereport(ERROR,
4001 : : (errcode(ERRCODE_INVALID_FUNCTION_DEFINITION),
4002 : : errmsg("combine function with transition type %s must not be declared STRICT",
4003 : : format_type_be(aggtranstype))));
4004 : : }
4005 : : else
4006 : : {
4007 : : /* Detect how many arguments to pass to the transfn */
4008 [ + + ]: 36652 : if (AGGKIND_IS_ORDERED_SET(aggref->aggkind))
4009 : 167 : pertrans->numTransInputs = list_length(aggref->args);
4010 : : else
4011 : 36485 : pertrans->numTransInputs = numAggTransFnArgs;
4012 : :
4013 : 36652 : build_pertrans_for_aggref(pertrans, aggstate, estate,
4014 : : aggref, transfn_oid, aggtranstype,
4015 : : serialfn_oid, deserialfn_oid,
4016 : : initValue, initValueIsNull,
4017 : : aggTransFnInputTypes,
4018 : : numAggTransFnArgs);
4019 : :
4020 : : /*
4021 : : * If the transfn is strict and the initval is NULL, make sure
4022 : : * input type and transtype are the same (or at least
4023 : : * binary-compatible), so that it's OK to use the first
4024 : : * aggregated input value as the initial transValue. This
4025 : : * should have been checked at agg definition time, but we
4026 : : * must check again in case the transfn's strictness property
4027 : : * has been changed.
4028 : : */
4029 [ + + + + ]: 36652 : if (pertrans->transfn.fn_strict && pertrans->initValueIsNull)
4030 : : {
4031 [ + - ]: 3061 : if (numAggTransFnArgs <= numDirectArgs ||
4032 [ - + ]: 3061 : !IsBinaryCoercible(aggTransFnInputTypes[numDirectArgs],
4033 : : aggtranstype))
4034 [ # # ]: 0 : ereport(ERROR,
4035 : : (errcode(ERRCODE_INVALID_FUNCTION_DEFINITION),
4036 : : errmsg("aggregate %u needs to have compatible input type and transition type",
4037 : : aggref->aggfnoid)));
4038 : : }
4039 : : }
4040 : : }
4041 : : else
4042 : 216 : pertrans->aggshared = true;
4043 : 38362 : ReleaseSysCache(aggTuple);
4044 : : }
4045 : :
4046 : : /*
4047 : : * Last, check whether any more aggregates got added onto the node while
4048 : : * we processed the expressions for the aggregate arguments (including not
4049 : : * only the regular arguments and FILTER expressions handled immediately
4050 : : * above, but any direct arguments we might've handled earlier). If so,
4051 : : * we have nested aggregate functions, which is semantically nonsensical,
4052 : : * so complain. (This should have been caught by the parser, so we don't
4053 : : * need to work hard on a helpful error message; but we defend against it
4054 : : * here anyway, just to be sure.)
4055 : : */
4056 [ - + ]: 34618 : if (numaggrefs != list_length(aggstate->aggs))
4057 [ # # ]: 0 : ereport(ERROR,
4058 : : (errcode(ERRCODE_GROUPING_ERROR),
4059 : : errmsg("aggregate function calls cannot be nested")));
4060 : :
4061 : : /*
4062 : : * Build expressions doing all the transition work at once. We build a
4063 : : * different one for each phase, as the number of transition function
4064 : : * invocation can differ between phases. Note this'll work both for
4065 : : * transition and combination functions (although there'll only be one
4066 : : * phase in the latter case).
4067 : : */
4068 [ + + ]: 99128 : for (phaseidx = 0; phaseidx < aggstate->numphases; phaseidx++)
4069 : : {
4070 : 64510 : AggStatePerPhase phase = &aggstate->phases[phaseidx];
4071 : 64510 : bool dohash = false;
4072 : 64510 : bool dosort = false;
4073 : :
4074 : : /* phase 0 doesn't necessarily exist */
4075 [ + + ]: 64510 : if (!phase->aggnode)
4076 : 29518 : continue;
4077 : :
4078 [ + + + + ]: 34992 : if (aggstate->aggstrategy == AGG_MIXED && phaseidx == 1)
4079 : : {
4080 : : /*
4081 : : * Phase one, and only phase one, in a mixed agg performs both
4082 : : * sorting and aggregation.
4083 : : */
4084 : 182 : dohash = true;
4085 : 182 : dosort = true;
4086 : : }
4087 [ + + + + ]: 34810 : else if (aggstate->aggstrategy == AGG_MIXED && phaseidx == 0)
4088 : : {
4089 : : /*
4090 : : * No need to compute a transition function for an AGG_MIXED phase
4091 : : * 0 - the contents of the hashtables will have been computed
4092 : : * during phase 1.
4093 : : */
4094 : 182 : continue;
4095 : : }
4096 [ + + ]: 34628 : else if (phase->aggstrategy == AGG_PLAIN ||
4097 [ + + ]: 6796 : phase->aggstrategy == AGG_SORTED)
4098 : : {
4099 : 29710 : dohash = false;
4100 : 29710 : dosort = true;
4101 : : }
4102 [ + - ]: 4918 : else if (phase->aggstrategy == AGG_HASHED)
4103 : : {
4104 : 4918 : dohash = true;
4105 : 4918 : dosort = false;
4106 : : }
4107 : : else
4108 : : Assert(false);
4109 : :
4110 : 34810 : phase->evaltrans = ExecBuildAggTrans(aggstate, phase, dosort, dohash,
4111 : : false);
4112 : :
4113 : : /* cache compiled expression for outer slot without NULL check */
4114 : 34810 : phase->evaltrans_cache[0][0] = phase->evaltrans;
4115 : : }
4116 : :
4117 : 34618 : return aggstate;
4118 : : }
4119 : :
4120 : : /*
4121 : : * Build the state needed to calculate a state value for an aggregate.
4122 : : *
4123 : : * This initializes all the fields in 'pertrans'. 'aggref' is the aggregate
4124 : : * to initialize the state for. 'transfn_oid', 'aggtranstype', and the rest
4125 : : * of the arguments could be calculated from 'aggref', but the caller has
4126 : : * calculated them already, so might as well pass them.
4127 : : *
4128 : : * 'transfn_oid' may be either the Oid of the aggtransfn or the aggcombinefn.
4129 : : */
4130 : : static void
4131 : 38146 : build_pertrans_for_aggref(AggStatePerTrans pertrans,
4132 : : AggState *aggstate, EState *estate,
4133 : : Aggref *aggref,
4134 : : Oid transfn_oid, Oid aggtranstype,
4135 : : Oid aggserialfn, Oid aggdeserialfn,
4136 : : Datum initValue, bool initValueIsNull,
4137 : : Oid *inputTypes, int numArguments)
4138 : : {
4139 : 38146 : int numGroupingSets = Max(aggstate->maxsets, 1);
4140 : : Expr *transfnexpr;
4141 : : int numTransArgs;
4142 : 38146 : Expr *serialfnexpr = NULL;
4143 : 38146 : Expr *deserialfnexpr = NULL;
4144 : : ListCell *lc;
4145 : : int numInputs;
4146 : : int numDirectArgs;
4147 : : List *sortlist;
4148 : : int numSortCols;
4149 : : int numDistinctCols;
4150 : : int i;
4151 : :
4152 : : /* Begin filling in the pertrans data */
4153 : 38146 : pertrans->aggref = aggref;
4154 : 38146 : pertrans->aggshared = false;
4155 : 38146 : pertrans->aggCollation = aggref->inputcollid;
4156 : 38146 : pertrans->transfn_oid = transfn_oid;
4157 : 38146 : pertrans->serialfn_oid = aggserialfn;
4158 : 38146 : pertrans->deserialfn_oid = aggdeserialfn;
4159 : 38146 : pertrans->initValue = initValue;
4160 : 38146 : pertrans->initValueIsNull = initValueIsNull;
4161 : :
4162 : : /* Count the "direct" arguments, if any */
4163 : 38146 : numDirectArgs = list_length(aggref->aggdirectargs);
4164 : :
4165 : : /* Count the number of aggregated input columns */
4166 : 38146 : pertrans->numInputs = numInputs = list_length(aggref->args);
4167 : :
4168 : 38146 : pertrans->aggtranstype = aggtranstype;
4169 : :
4170 : : /* account for the current transition state */
4171 : 38146 : numTransArgs = pertrans->numTransInputs + 1;
4172 : :
4173 : : /*
4174 : : * Set up infrastructure for calling the transfn. Note that invtransfn is
4175 : : * not needed here.
4176 : : */
4177 : 38146 : build_aggregate_transfn_expr(inputTypes,
4178 : : numArguments,
4179 : : numDirectArgs,
4180 : 38146 : aggref->aggvariadic,
4181 : : aggtranstype,
4182 : : aggref->inputcollid,
4183 : : transfn_oid,
4184 : : InvalidOid,
4185 : : &transfnexpr,
4186 : : NULL);
4187 : :
4188 : 38146 : fmgr_info(transfn_oid, &pertrans->transfn);
4189 : 38146 : fmgr_info_set_expr((Node *) transfnexpr, &pertrans->transfn);
4190 : :
4191 : 38146 : pertrans->transfn_fcinfo =
4192 : 38146 : (FunctionCallInfo) palloc(SizeForFunctionCallInfo(numTransArgs));
4193 : 38146 : InitFunctionCallInfoData(*pertrans->transfn_fcinfo,
4194 : : &pertrans->transfn,
4195 : : numTransArgs,
4196 : : pertrans->aggCollation,
4197 : : (Node *) aggstate, NULL);
4198 : :
4199 : : /* get info about the state value's datatype */
4200 : 38146 : get_typlenbyval(aggtranstype,
4201 : : &pertrans->transtypeLen,
4202 : : &pertrans->transtypeByVal);
4203 : :
4204 [ + + ]: 38146 : if (OidIsValid(aggserialfn))
4205 : : {
4206 : 224 : build_aggregate_serialfn_expr(aggserialfn,
4207 : : &serialfnexpr);
4208 : 224 : fmgr_info(aggserialfn, &pertrans->serialfn);
4209 : 224 : fmgr_info_set_expr((Node *) serialfnexpr, &pertrans->serialfn);
4210 : :
4211 : 224 : pertrans->serialfn_fcinfo =
4212 : 224 : (FunctionCallInfo) palloc(SizeForFunctionCallInfo(1));
4213 : 224 : InitFunctionCallInfoData(*pertrans->serialfn_fcinfo,
4214 : : &pertrans->serialfn,
4215 : : 1,
4216 : : InvalidOid,
4217 : : (Node *) aggstate, NULL);
4218 : : }
4219 : :
4220 [ + + ]: 38146 : if (OidIsValid(aggdeserialfn))
4221 : : {
4222 : 80 : build_aggregate_deserialfn_expr(aggdeserialfn,
4223 : : &deserialfnexpr);
4224 : 80 : fmgr_info(aggdeserialfn, &pertrans->deserialfn);
4225 : 80 : fmgr_info_set_expr((Node *) deserialfnexpr, &pertrans->deserialfn);
4226 : :
4227 : 80 : pertrans->deserialfn_fcinfo =
4228 : 80 : (FunctionCallInfo) palloc(SizeForFunctionCallInfo(2));
4229 : 80 : InitFunctionCallInfoData(*pertrans->deserialfn_fcinfo,
4230 : : &pertrans->deserialfn,
4231 : : 2,
4232 : : InvalidOid,
4233 : : (Node *) aggstate, NULL);
4234 : : }
4235 : :
4236 : : /*
4237 : : * If we're doing either DISTINCT or ORDER BY for a plain agg, then we
4238 : : * have a list of SortGroupClause nodes; fish out the data in them and
4239 : : * stick them into arrays. We ignore ORDER BY for an ordered-set agg,
4240 : : * however; the agg's transfn and finalfn are responsible for that.
4241 : : *
4242 : : * When the planner has set the aggpresorted flag, the input to the
4243 : : * aggregate is already correctly sorted. For ORDER BY aggregates we can
4244 : : * simply treat these as normal aggregates. For presorted DISTINCT
4245 : : * aggregates an extra step must be added to remove duplicate consecutive
4246 : : * inputs.
4247 : : *
4248 : : * Note that by construction, if there is a DISTINCT clause then the ORDER
4249 : : * BY clause is a prefix of it (see transformDistinctClause).
4250 : : */
4251 [ + + ]: 38146 : if (AGGKIND_IS_ORDERED_SET(aggref->aggkind))
4252 : : {
4253 : 167 : sortlist = NIL;
4254 : 167 : numSortCols = numDistinctCols = 0;
4255 : 167 : pertrans->aggsortrequired = false;
4256 : : }
4257 [ + + + + ]: 37979 : else if (aggref->aggpresorted && aggref->aggdistinct == NIL)
4258 : : {
4259 : 1267 : sortlist = NIL;
4260 : 1267 : numSortCols = numDistinctCols = 0;
4261 : 1267 : pertrans->aggsortrequired = false;
4262 : : }
4263 [ + + ]: 36712 : else if (aggref->aggdistinct)
4264 : : {
4265 : 394 : sortlist = aggref->aggdistinct;
4266 : 394 : numSortCols = numDistinctCols = list_length(sortlist);
4267 : : Assert(numSortCols >= list_length(aggref->aggorder));
4268 : 394 : pertrans->aggsortrequired = !aggref->aggpresorted;
4269 : : }
4270 : : else
4271 : : {
4272 : 36318 : sortlist = aggref->aggorder;
4273 : 36318 : numSortCols = list_length(sortlist);
4274 : 36318 : numDistinctCols = 0;
4275 : 36318 : pertrans->aggsortrequired = (numSortCols > 0);
4276 : : }
4277 : :
4278 : 38146 : pertrans->numSortCols = numSortCols;
4279 : 38146 : pertrans->numDistinctCols = numDistinctCols;
4280 : :
4281 : : /*
4282 : : * If we have either sorting or filtering to do, create a tupledesc and
4283 : : * slot corresponding to the aggregated inputs (including sort
4284 : : * expressions) of the agg.
4285 : : */
4286 [ + + + + ]: 38146 : if (numSortCols > 0 || aggref->aggfilter)
4287 : : {
4288 : 881 : pertrans->sortdesc = ExecTypeFromTL(aggref->args);
4289 : 881 : pertrans->sortslot =
4290 : 881 : ExecInitExtraTupleSlot(estate, pertrans->sortdesc,
4291 : : &TTSOpsMinimalTuple);
4292 : : }
4293 : :
4294 [ + + ]: 38146 : if (numSortCols > 0)
4295 : : {
4296 : : /*
4297 : : * We don't implement DISTINCT or ORDER BY aggs in the HASHED case
4298 : : * (yet)
4299 : : */
4300 : : Assert(aggstate->aggstrategy != AGG_HASHED && aggstate->aggstrategy != AGG_MIXED);
4301 : :
4302 : : /* ORDER BY aggregates are not supported with partial aggregation */
4303 : : Assert(!DO_AGGSPLIT_COMBINE(aggstate->aggsplit));
4304 : :
4305 : : /* If we have only one input, we need its len/byval info. */
4306 [ + + ]: 486 : if (numInputs == 1)
4307 : : {
4308 : 386 : get_typlenbyval(inputTypes[numDirectArgs],
4309 : : &pertrans->inputtypeLen,
4310 : : &pertrans->inputtypeByVal);
4311 : : }
4312 [ + + ]: 100 : else if (numDistinctCols > 0)
4313 : : {
4314 : : /* we will need an extra slot to store prior values */
4315 : 72 : pertrans->uniqslot =
4316 : 72 : ExecInitExtraTupleSlot(estate, pertrans->sortdesc,
4317 : : &TTSOpsMinimalTuple);
4318 : : }
4319 : :
4320 : : /* Extract the sort information for use later */
4321 : 486 : pertrans->sortColIdx =
4322 : 486 : (AttrNumber *) palloc(numSortCols * sizeof(AttrNumber));
4323 : 486 : pertrans->sortOperators =
4324 : 486 : (Oid *) palloc(numSortCols * sizeof(Oid));
4325 : 486 : pertrans->sortCollations =
4326 : 486 : (Oid *) palloc(numSortCols * sizeof(Oid));
4327 : 486 : pertrans->sortNullsFirst =
4328 : 486 : (bool *) palloc(numSortCols * sizeof(bool));
4329 : :
4330 : 486 : i = 0;
4331 [ + - + + : 1104 : foreach(lc, sortlist)
+ + ]
4332 : : {
4333 : 618 : SortGroupClause *sortcl = (SortGroupClause *) lfirst(lc);
4334 : 618 : TargetEntry *tle = get_sortgroupclause_tle(sortcl, aggref->args);
4335 : :
4336 : : /* the parser should have made sure of this */
4337 : : Assert(OidIsValid(sortcl->sortop));
4338 : :
4339 : 618 : pertrans->sortColIdx[i] = tle->resno;
4340 : 618 : pertrans->sortOperators[i] = sortcl->sortop;
4341 : 618 : pertrans->sortCollations[i] = exprCollation((Node *) tle->expr);
4342 : 618 : pertrans->sortNullsFirst[i] = sortcl->nulls_first;
4343 : 618 : i++;
4344 : : }
4345 : : Assert(i == numSortCols);
4346 : : }
4347 : :
4348 [ + + ]: 38146 : if (aggref->aggdistinct)
4349 : : {
4350 : : Oid *ops;
4351 : :
4352 : : Assert(numArguments > 0);
4353 : : Assert(list_length(aggref->aggdistinct) == numDistinctCols);
4354 : :
4355 : 394 : ops = palloc(numDistinctCols * sizeof(Oid));
4356 : :
4357 : 394 : i = 0;
4358 [ + - + + : 908 : foreach(lc, aggref->aggdistinct)
+ + ]
4359 : 514 : ops[i++] = ((SortGroupClause *) lfirst(lc))->eqop;
4360 : :
4361 : : /* lookup / build the necessary comparators */
4362 [ + + ]: 394 : if (numDistinctCols == 1)
4363 : 322 : fmgr_info(get_opcode(ops[0]), &pertrans->equalfnOne);
4364 : : else
4365 : 72 : pertrans->equalfnMulti =
4366 : 72 : execTuplesMatchPrepare(pertrans->sortdesc,
4367 : : numDistinctCols,
4368 : 72 : pertrans->sortColIdx,
4369 : : ops,
4370 : 72 : pertrans->sortCollations,
4371 : : &aggstate->ss.ps);
4372 : 394 : pfree(ops);
4373 : : }
4374 : :
4375 : 38146 : pertrans->sortstates = palloc0_array(Tuplesortstate *, numGroupingSets);
4376 : 38146 : }
4377 : :
4378 : :
4379 : : static Datum
4380 : 16885 : GetAggInitVal(Datum textInitVal, Oid transtype)
4381 : : {
4382 : : Oid typinput,
4383 : : typioparam;
4384 : : char *strInitVal;
4385 : : Datum initVal;
4386 : :
4387 : 16885 : getTypeInputInfo(transtype, &typinput, &typioparam);
4388 : 16885 : strInitVal = TextDatumGetCString(textInitVal);
4389 : 16885 : initVal = OidInputFunctionCall(typinput, strInitVal,
4390 : : typioparam, -1);
4391 : 16885 : pfree(strInitVal);
4392 : 16885 : return initVal;
4393 : : }
4394 : :
4395 : : void
4396 : 34498 : ExecEndAgg(AggState *node)
4397 : : {
4398 : : PlanState *outerPlan;
4399 : : int transno;
4400 : 34498 : int numGroupingSets = Max(node->maxsets, 1);
4401 : : int setno;
4402 : :
4403 : : /*
4404 : : * When ending a parallel worker, copy the statistics gathered by the
4405 : : * worker back into shared memory so that it can be picked up by the main
4406 : : * process to report in EXPLAIN ANALYZE.
4407 : : */
4408 [ + + + + ]: 34498 : if (node->shared_info && IsParallelWorker())
4409 : : {
4410 : : AggregateInstrumentation *si;
4411 : :
4412 : : Assert(ParallelWorkerNumber < node->shared_info->num_workers);
4413 : 111 : si = &node->shared_info->sinstrument[ParallelWorkerNumber];
4414 : 111 : si->hash_batches_used = node->hash_batches_used;
4415 : 111 : si->hash_disk_used = node->hash_disk_used;
4416 : 111 : si->hash_mem_peak = node->hash_mem_peak;
4417 : : }
4418 : :
4419 : : /* Make sure we have closed any open tuplesorts */
4420 : :
4421 [ + + ]: 34498 : if (node->sort_in)
4422 : 120 : tuplesort_end(node->sort_in);
4423 [ + + ]: 34498 : if (node->sort_out)
4424 : 44 : tuplesort_end(node->sort_out);
4425 : :
4426 : 34498 : hashagg_reset_spill_state(node);
4427 : :
4428 : : /* Release hash tables too */
4429 [ + + ]: 34498 : if (node->hash_metacxt != NULL)
4430 : : {
4431 : 5095 : MemoryContextDelete(node->hash_metacxt);
4432 : 5095 : node->hash_metacxt = NULL;
4433 : : }
4434 [ + + ]: 34498 : if (node->hash_tuplescxt != NULL)
4435 : : {
4436 : 5095 : MemoryContextDelete(node->hash_tuplescxt);
4437 : 5095 : node->hash_tuplescxt = NULL;
4438 : : }
4439 : :
4440 [ + + ]: 72522 : for (transno = 0; transno < node->numtrans; transno++)
4441 : : {
4442 : 38024 : AggStatePerTrans pertrans = &node->pertrans[transno];
4443 : :
4444 [ + + ]: 76756 : for (setno = 0; setno < numGroupingSets; setno++)
4445 : : {
4446 [ - + ]: 38732 : if (pertrans->sortstates[setno])
4447 : 0 : tuplesort_end(pertrans->sortstates[setno]);
4448 : : }
4449 : : }
4450 : :
4451 : : /* And ensure any agg shutdown callbacks have been called */
4452 [ + + ]: 69578 : for (setno = 0; setno < numGroupingSets; setno++)
4453 : 35080 : ReScanExprContext(node->aggcontexts[setno]);
4454 [ + + ]: 34498 : if (node->hashcontext)
4455 : 5095 : ReScanExprContext(node->hashcontext);
4456 : :
4457 : 34498 : outerPlan = outerPlanState(node);
4458 : 34498 : ExecEndNode(outerPlan);
4459 : 34498 : }
4460 : :
4461 : : void
4462 : 31756 : ExecReScanAgg(AggState *node)
4463 : : {
4464 : 31756 : ExprContext *econtext = node->ss.ps.ps_ExprContext;
4465 : 31756 : PlanState *outerPlan = outerPlanState(node);
4466 : 31756 : Agg *aggnode = (Agg *) node->ss.ps.plan;
4467 : : int transno;
4468 : 31756 : int numGroupingSets = Max(node->maxsets, 1);
4469 : : int setno;
4470 : :
4471 : 31756 : node->agg_done = false;
4472 : :
4473 [ + + ]: 31756 : if (node->aggstrategy == AGG_HASHED)
4474 : : {
4475 : : /*
4476 : : * In the hashed case, if we haven't yet built the hash table then we
4477 : : * can just return; nothing done yet, so nothing to undo. If subnode's
4478 : : * chgParam is not NULL then it will be re-scanned by ExecProcNode,
4479 : : * else no reason to re-scan it at all.
4480 : : */
4481 [ + + ]: 8387 : if (!node->table_filled)
4482 : 89 : return;
4483 : :
4484 : : /*
4485 : : * If we do have the hash table, and it never spilled, and the subplan
4486 : : * does not have any parameter changes, and none of our own parameter
4487 : : * changes affect input expressions of the aggregated functions, then
4488 : : * we can just rescan the existing hash table; no need to build it
4489 : : * again.
4490 : : */
4491 [ + + + - ]: 8298 : if (outerPlan->chgParam == NULL && !node->hash_ever_spilled &&
4492 [ + + ]: 616 : !bms_overlap(node->ss.ps.chgParam, aggnode->aggParams))
4493 : : {
4494 : 600 : ResetTupleHashIterator(node->perhash[0].hashtable,
4495 : : &node->perhash[0].hashiter);
4496 : 600 : select_current_set(node, 0, true);
4497 : 600 : return;
4498 : : }
4499 : : }
4500 : :
4501 : : /* Make sure we have closed any open tuplesorts */
4502 [ + + ]: 68172 : for (transno = 0; transno < node->numtrans; transno++)
4503 : : {
4504 [ + + ]: 74234 : for (setno = 0; setno < numGroupingSets; setno++)
4505 : : {
4506 : 37129 : AggStatePerTrans pertrans = &node->pertrans[transno];
4507 : :
4508 [ - + ]: 37129 : if (pertrans->sortstates[setno])
4509 : : {
4510 : 0 : tuplesort_end(pertrans->sortstates[setno]);
4511 : 0 : pertrans->sortstates[setno] = NULL;
4512 : : }
4513 : : }
4514 : : }
4515 : :
4516 : : /*
4517 : : * We don't need to ReScanExprContext the output tuple context here;
4518 : : * ExecReScan already did it. But we do need to reset our per-grouping-set
4519 : : * contexts, which may have transvalues stored in them. (We use rescan
4520 : : * rather than just reset because transfns may have registered callbacks
4521 : : * that need to be run now.) For the AGG_HASHED case, see below.
4522 : : */
4523 : :
4524 [ + + ]: 62158 : for (setno = 0; setno < numGroupingSets; setno++)
4525 : : {
4526 : 31091 : ReScanExprContext(node->aggcontexts[setno]);
4527 : : }
4528 : :
4529 : : /* Release first tuple of group, if we have made a copy */
4530 [ - + ]: 31067 : if (node->grp_firstTuple != NULL)
4531 : : {
4532 : 0 : heap_freetuple(node->grp_firstTuple);
4533 : 0 : node->grp_firstTuple = NULL;
4534 : : }
4535 : 31067 : ExecClearTuple(node->ss.ss_ScanTupleSlot);
4536 : :
4537 : : /* Forget current agg values */
4538 [ + - + - : 68172 : MemSet(econtext->ecxt_aggvalues, 0, sizeof(Datum) * node->numaggs);
+ - + - +
+ ]
4539 [ + - + + : 31067 : MemSet(econtext->ecxt_aggnulls, 0, sizeof(bool) * node->numaggs);
+ - + - -
+ ]
4540 : :
4541 : : /*
4542 : : * With AGG_HASHED/MIXED, the hash table is allocated in a sub-context of
4543 : : * the hashcontext. This used to be an issue, but now, resetting a context
4544 : : * automatically deletes sub-contexts too.
4545 : : */
4546 [ + + + + ]: 31067 : if (node->aggstrategy == AGG_HASHED || node->aggstrategy == AGG_MIXED)
4547 : : {
4548 : 7718 : hashagg_reset_spill_state(node);
4549 : :
4550 : 7718 : node->hash_ever_spilled = false;
4551 : 7718 : node->hash_spill_mode = false;
4552 : 7718 : node->hash_ngroups_current = 0;
4553 : :
4554 : 7718 : ReScanExprContext(node->hashcontext);
4555 : : /* Rebuild empty hash table(s) */
4556 : 7718 : build_hash_tables(node);
4557 : 7718 : node->table_filled = false;
4558 : : /* iterator will be reset when the table is filled */
4559 : :
4560 : 7718 : hashagg_recompile_expressions(node, false, false);
4561 : : }
4562 : :
4563 [ + + ]: 31067 : if (node->aggstrategy != AGG_HASHED)
4564 : : {
4565 : : /*
4566 : : * Reset the per-group state (in particular, mark transvalues null)
4567 : : */
4568 [ + + ]: 46762 : for (setno = 0; setno < numGroupingSets; setno++)
4569 : : {
4570 [ + - + - : 97587 : MemSet(node->pergroups[setno], 0,
+ - + - +
+ ]
4571 : : sizeof(AggStatePerGroupData) * node->numaggs);
4572 : : }
4573 : :
4574 : : /* reset to phase 1 */
4575 : 23369 : initialize_phase(node, 1);
4576 : :
4577 : 23369 : node->input_done = false;
4578 : 23369 : node->projected_set = -1;
4579 : : }
4580 : :
4581 [ + + ]: 31067 : if (outerPlan->chgParam == NULL)
4582 : 125 : ExecReScan(outerPlan);
4583 : : }
4584 : :
4585 : :
4586 : : /***********************************************************************
4587 : : * API exposed to aggregate functions
4588 : : ***********************************************************************/
4589 : :
4590 : :
4591 : : /*
4592 : : * AggCheckCallContext - test if a SQL function is being called as an aggregate
4593 : : *
4594 : : * The transition and/or final functions of an aggregate may want to verify
4595 : : * that they are being called as aggregates, rather than as plain SQL
4596 : : * functions. They should use this function to do so. The return value
4597 : : * is nonzero if being called as an aggregate, or zero if not. (Specific
4598 : : * nonzero values are AGG_CONTEXT_AGGREGATE or AGG_CONTEXT_WINDOW, but more
4599 : : * values could conceivably appear in future.)
4600 : : *
4601 : : * If aggcontext isn't NULL, the function also stores at *aggcontext the
4602 : : * identity of the memory context that aggregate transition values are being
4603 : : * stored in. Note that the same aggregate call site (flinfo) may be called
4604 : : * interleaved on different transition values in different contexts, so it's
4605 : : * not kosher to cache aggcontext under fn_extra. It is, however, kosher to
4606 : : * cache it in the transvalue itself (for internal-type transvalues).
4607 : : */
4608 : : int
4609 : 3553272 : AggCheckCallContext(FunctionCallInfo fcinfo, MemoryContext *aggcontext)
4610 : : {
4611 [ + + + + ]: 3553272 : if (fcinfo->context && IsA(fcinfo->context, AggState))
4612 : : {
4613 [ + + ]: 3545336 : if (aggcontext)
4614 : : {
4615 : 1766865 : AggState *aggstate = ((AggState *) fcinfo->context);
4616 : 1766865 : ExprContext *cxt = aggstate->curaggcontext;
4617 : :
4618 : 1766865 : *aggcontext = cxt->ecxt_per_tuple_memory;
4619 : : }
4620 : 3545336 : return AGG_CONTEXT_AGGREGATE;
4621 : : }
4622 [ + + + - ]: 7936 : if (fcinfo->context && IsA(fcinfo->context, WindowAggState))
4623 : : {
4624 [ + + ]: 6660 : if (aggcontext)
4625 : 540 : *aggcontext = ((WindowAggState *) fcinfo->context)->curaggcontext;
4626 : 6660 : return AGG_CONTEXT_WINDOW;
4627 : : }
4628 : :
4629 : : /* this is just to prevent "uninitialized variable" warnings */
4630 [ + + ]: 1276 : if (aggcontext)
4631 : 1236 : *aggcontext = NULL;
4632 : 1276 : return 0;
4633 : : }
4634 : :
4635 : : /*
4636 : : * AggGetAggref - allow an aggregate support function to get its Aggref
4637 : : *
4638 : : * If the function is being called as an aggregate support function,
4639 : : * return the Aggref node for the aggregate call. Otherwise, return NULL.
4640 : : *
4641 : : * Aggregates sharing the same inputs and transition functions can get
4642 : : * merged into a single transition calculation. If the transition function
4643 : : * calls AggGetAggref, it will get some one of the Aggrefs for which it is
4644 : : * executing. It must therefore not pay attention to the Aggref fields that
4645 : : * relate to the final function, as those are indeterminate. But if a final
4646 : : * function calls AggGetAggref, it will get a precise result.
4647 : : *
4648 : : * Note that if an aggregate is being used as a window function, this will
4649 : : * return NULL. We could provide a similar function to return the relevant
4650 : : * WindowFunc node in such cases, but it's not needed yet.
4651 : : */
4652 : : Aggref *
4653 : 163 : AggGetAggref(FunctionCallInfo fcinfo)
4654 : : {
4655 [ + - + - ]: 163 : if (fcinfo->context && IsA(fcinfo->context, AggState))
4656 : : {
4657 : 163 : AggState *aggstate = (AggState *) fcinfo->context;
4658 : : AggStatePerAgg curperagg;
4659 : : AggStatePerTrans curpertrans;
4660 : :
4661 : : /* check curperagg (valid when in a final function) */
4662 : 163 : curperagg = aggstate->curperagg;
4663 : :
4664 [ - + ]: 163 : if (curperagg)
4665 : 0 : return curperagg->aggref;
4666 : :
4667 : : /* check curpertrans (valid when in a transition function) */
4668 : 163 : curpertrans = aggstate->curpertrans;
4669 : :
4670 [ + - ]: 163 : if (curpertrans)
4671 : 163 : return curpertrans->aggref;
4672 : : }
4673 : 0 : return NULL;
4674 : : }
4675 : :
4676 : : /*
4677 : : * AggGetTempMemoryContext - fetch short-term memory context for aggregates
4678 : : *
4679 : : * This is useful in agg final functions; the context returned is one that
4680 : : * the final function can safely reset as desired. This isn't useful for
4681 : : * transition functions, since the context returned MAY (we don't promise)
4682 : : * be the same as the context those are called in.
4683 : : *
4684 : : * As above, this is currently not useful for aggs called as window functions.
4685 : : */
4686 : : MemoryContext
4687 : 0 : AggGetTempMemoryContext(FunctionCallInfo fcinfo)
4688 : : {
4689 [ # # # # ]: 0 : if (fcinfo->context && IsA(fcinfo->context, AggState))
4690 : : {
4691 : 0 : AggState *aggstate = (AggState *) fcinfo->context;
4692 : :
4693 : 0 : return aggstate->tmpcontext->ecxt_per_tuple_memory;
4694 : : }
4695 : 0 : return NULL;
4696 : : }
4697 : :
4698 : : /*
4699 : : * AggStateIsShared - find out whether transition state is shared
4700 : : *
4701 : : * If the function is being called as an aggregate support function,
4702 : : * return true if the aggregate's transition state is shared across
4703 : : * multiple aggregates, false if it is not.
4704 : : *
4705 : : * Returns true if not called as an aggregate support function.
4706 : : * This is intended as a conservative answer, ie "no you'd better not
4707 : : * scribble on your input". In particular, will return true if the
4708 : : * aggregate is being used as a window function, which is a scenario
4709 : : * in which changing the transition state is a bad idea. We might
4710 : : * want to refine the behavior for the window case in future.
4711 : : */
4712 : : bool
4713 : 163 : AggStateIsShared(FunctionCallInfo fcinfo)
4714 : : {
4715 [ + - + - ]: 163 : if (fcinfo->context && IsA(fcinfo->context, AggState))
4716 : : {
4717 : 163 : AggState *aggstate = (AggState *) fcinfo->context;
4718 : : AggStatePerAgg curperagg;
4719 : : AggStatePerTrans curpertrans;
4720 : :
4721 : : /* check curperagg (valid when in a final function) */
4722 : 163 : curperagg = aggstate->curperagg;
4723 : :
4724 [ - + ]: 163 : if (curperagg)
4725 : 0 : return aggstate->pertrans[curperagg->transno].aggshared;
4726 : :
4727 : : /* check curpertrans (valid when in a transition function) */
4728 : 163 : curpertrans = aggstate->curpertrans;
4729 : :
4730 [ + - ]: 163 : if (curpertrans)
4731 : 163 : return curpertrans->aggshared;
4732 : : }
4733 : 0 : return true;
4734 : : }
4735 : :
4736 : : /*
4737 : : * AggRegisterCallback - register a cleanup callback for an aggregate
4738 : : *
4739 : : * This is useful for aggs to register shutdown callbacks, which will ensure
4740 : : * that non-memory resources are freed. The callback will occur just before
4741 : : * the associated aggcontext (as returned by AggCheckCallContext) is reset,
4742 : : * either between groups or as a result of rescanning the query. The callback
4743 : : * will NOT be called on error paths. The typical use-case is for freeing of
4744 : : * tuplestores or tuplesorts maintained in aggcontext, or pins held by slots
4745 : : * created by the agg functions. (The callback will not be called until after
4746 : : * the result of the finalfn is no longer needed, so it's safe for the finalfn
4747 : : * to return data that will be freed by the callback.)
4748 : : *
4749 : : * As above, this is currently not useful for aggs called as window functions.
4750 : : */
4751 : : void
4752 : 433 : AggRegisterCallback(FunctionCallInfo fcinfo,
4753 : : ExprContextCallbackFunction func,
4754 : : Datum arg)
4755 : : {
4756 [ + - + - ]: 433 : if (fcinfo->context && IsA(fcinfo->context, AggState))
4757 : : {
4758 : 433 : AggState *aggstate = (AggState *) fcinfo->context;
4759 : 433 : ExprContext *cxt = aggstate->curaggcontext;
4760 : :
4761 : 433 : RegisterExprContextCallback(cxt, func, arg);
4762 : :
4763 : 433 : return;
4764 : : }
4765 [ # # ]: 0 : elog(ERROR, "aggregate function cannot register a callback in this context");
4766 : : }
4767 : :
4768 : :
4769 : : /* ----------------------------------------------------------------
4770 : : * Parallel Query Support
4771 : : * ----------------------------------------------------------------
4772 : : */
4773 : :
4774 : : /* ----------------------------------------------------------------
4775 : : * ExecAggEstimate
4776 : : *
4777 : : * Estimate space required to propagate aggregate statistics.
4778 : : * ----------------------------------------------------------------
4779 : : */
4780 : : void
4781 : 396 : ExecAggEstimate(AggState *node, ParallelContext *pcxt)
4782 : : {
4783 : : Size size;
4784 : :
4785 : : /* don't need this if not instrumenting or no workers */
4786 [ + + - + ]: 396 : if (!node->ss.ps.instrument || pcxt->nworkers == 0)
4787 : 328 : return;
4788 : :
4789 : 68 : size = mul_size(pcxt->nworkers, sizeof(AggregateInstrumentation));
4790 : 68 : size = add_size(size, offsetof(SharedAggInfo, sinstrument));
4791 : 68 : shm_toc_estimate_chunk(&pcxt->estimator, size);
4792 : 68 : shm_toc_estimate_keys(&pcxt->estimator, 1);
4793 : : }
4794 : :
4795 : : /* ----------------------------------------------------------------
4796 : : * ExecAggInitializeDSM
4797 : : *
4798 : : * Initialize DSM space for aggregate statistics.
4799 : : * ----------------------------------------------------------------
4800 : : */
4801 : : void
4802 : 396 : ExecAggInitializeDSM(AggState *node, ParallelContext *pcxt)
4803 : : {
4804 : : Size size;
4805 : :
4806 : : /* don't need this if not instrumenting or no workers */
4807 [ + + - + ]: 396 : if (!node->ss.ps.instrument || pcxt->nworkers == 0)
4808 : 328 : return;
4809 : :
4810 : 68 : size = offsetof(SharedAggInfo, sinstrument)
4811 : 68 : + pcxt->nworkers * sizeof(AggregateInstrumentation);
4812 : 68 : node->shared_info = shm_toc_allocate(pcxt->toc, size);
4813 : : /* ensure any unfilled slots will contain zeroes */
4814 : 68 : memset(node->shared_info, 0, size);
4815 : 68 : node->shared_info->num_workers = pcxt->nworkers;
4816 : 68 : shm_toc_insert(pcxt->toc, node->ss.ps.plan->plan_node_id,
4817 : 68 : node->shared_info);
4818 : : }
4819 : :
4820 : : /* ----------------------------------------------------------------
4821 : : * ExecAggInitializeWorker
4822 : : *
4823 : : * Attach worker to DSM space for aggregate statistics.
4824 : : * ----------------------------------------------------------------
4825 : : */
4826 : : void
4827 : 1116 : ExecAggInitializeWorker(AggState *node, ParallelWorkerContext *pwcxt)
4828 : : {
4829 : 1116 : node->shared_info =
4830 : 1116 : shm_toc_lookup(pwcxt->toc, node->ss.ps.plan->plan_node_id, true);
4831 : 1116 : }
4832 : :
4833 : : /* ----------------------------------------------------------------
4834 : : * ExecAggRetrieveInstrumentation
4835 : : *
4836 : : * Transfer aggregate statistics from DSM to private memory.
4837 : : * ----------------------------------------------------------------
4838 : : */
4839 : : void
4840 : 68 : ExecAggRetrieveInstrumentation(AggState *node)
4841 : : {
4842 : : Size size;
4843 : : SharedAggInfo *si;
4844 : :
4845 [ - + ]: 68 : if (node->shared_info == NULL)
4846 : 0 : return;
4847 : :
4848 : 68 : size = offsetof(SharedAggInfo, sinstrument)
4849 : 68 : + node->shared_info->num_workers * sizeof(AggregateInstrumentation);
4850 : 68 : si = palloc(size);
4851 : 68 : memcpy(si, node->shared_info, size);
4852 : 68 : node->shared_info = si;
4853 : : }
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