| /*------------------------------------------------------------------------- |
| * |
| * ts_selfuncs.c |
| * Selectivity estimation functions for text search operators. |
| * |
| * Portions Copyright (c) 1996-2023, PostgreSQL Global Development Group |
| * |
| * |
| * IDENTIFICATION |
| * src/backend/tsearch/ts_selfuncs.c |
| * |
| *------------------------------------------------------------------------- |
| */ |
| #include "postgres.h" |
| |
| #include "access/htup_details.h" |
| #include "catalog/pg_statistic.h" |
| #include "catalog/pg_type.h" |
| #include "miscadmin.h" |
| #include "nodes/nodes.h" |
| #include "tsearch/ts_type.h" |
| #include "utils/builtins.h" |
| #include "utils/lsyscache.h" |
| #include "utils/selfuncs.h" |
| #include "utils/syscache.h" |
| |
| |
| /* |
| * The default text search selectivity is chosen to be small enough to |
| * encourage indexscans for typical table densities. See selfuncs.h and |
| * DEFAULT_EQ_SEL for details. |
| */ |
| #define DEFAULT_TS_MATCH_SEL 0.005 |
| |
| /* lookup table type for binary searching through MCELEMs */ |
| typedef struct |
| { |
| text *element; |
| float4 frequency; |
| } TextFreq; |
| |
| /* type of keys for bsearch'ing through an array of TextFreqs */ |
| typedef struct |
| { |
| char *lexeme; |
| int length; |
| } LexemeKey; |
| |
| static Selectivity tsquerysel(VariableStatData *vardata, Datum constval); |
| static Selectivity mcelem_tsquery_selec(TSQuery query, |
| Datum *mcelem, int nmcelem, |
| float4 *numbers, int nnumbers); |
| static Selectivity tsquery_opr_selec(QueryItem *item, char *operand, |
| TextFreq *lookup, int length, float4 minfreq); |
| static int compare_lexeme_textfreq(const void *e1, const void *e2); |
| |
| #define tsquery_opr_selec_no_stats(query) \ |
| tsquery_opr_selec(GETQUERY(query), GETOPERAND(query), NULL, 0, 0) |
| |
| |
| /* |
| * tsmatchsel -- Selectivity of "@@" |
| * |
| * restriction selectivity function for tsvector @@ tsquery and |
| * tsquery @@ tsvector |
| */ |
| Datum |
| tsmatchsel(PG_FUNCTION_ARGS) |
| { |
| PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0); |
| |
| #ifdef NOT_USED |
| Oid operator = PG_GETARG_OID(1); |
| #endif |
| List *args = (List *) PG_GETARG_POINTER(2); |
| int varRelid = PG_GETARG_INT32(3); |
| VariableStatData vardata; |
| Node *other; |
| bool varonleft; |
| Selectivity selec; |
| |
| /* |
| * If expression is not variable = something or something = variable, then |
| * punt and return a default estimate. |
| */ |
| if (!get_restriction_variable(root, args, varRelid, |
| &vardata, &other, &varonleft)) |
| PG_RETURN_FLOAT8(DEFAULT_TS_MATCH_SEL); |
| |
| /* |
| * Can't do anything useful if the something is not a constant, either. |
| */ |
| if (!IsA(other, Const)) |
| { |
| ReleaseVariableStats(vardata); |
| PG_RETURN_FLOAT8(DEFAULT_TS_MATCH_SEL); |
| } |
| |
| /* |
| * The "@@" operator is strict, so we can cope with NULL right away |
| */ |
| if (((Const *) other)->constisnull) |
| { |
| ReleaseVariableStats(vardata); |
| PG_RETURN_FLOAT8(0.0); |
| } |
| |
| /* |
| * OK, there's a Var and a Const we're dealing with here. We need the |
| * Const to be a TSQuery, else we can't do anything useful. We have to |
| * check this because the Var might be the TSQuery not the TSVector. |
| */ |
| if (((Const *) other)->consttype == TSQUERYOID) |
| { |
| /* tsvector @@ tsquery or the other way around */ |
| Assert(vardata.vartype == TSVECTOROID); |
| |
| selec = tsquerysel(&vardata, ((Const *) other)->constvalue); |
| } |
| else |
| { |
| /* If we can't see the query structure, must punt */ |
| selec = DEFAULT_TS_MATCH_SEL; |
| } |
| |
| ReleaseVariableStats(vardata); |
| |
| CLAMP_PROBABILITY(selec); |
| |
| PG_RETURN_FLOAT8((float8) selec); |
| } |
| |
| |
| /* |
| * tsmatchjoinsel -- join selectivity of "@@" |
| * |
| * join selectivity function for tsvector @@ tsquery and tsquery @@ tsvector |
| */ |
| Datum |
| tsmatchjoinsel(PG_FUNCTION_ARGS) |
| { |
| /* for the moment we just punt */ |
| PG_RETURN_FLOAT8(DEFAULT_TS_MATCH_SEL); |
| } |
| |
| |
| /* |
| * @@ selectivity for tsvector var vs tsquery constant |
| */ |
| static Selectivity |
| tsquerysel(VariableStatData *vardata, Datum constval) |
| { |
| Selectivity selec; |
| TSQuery query; |
| |
| /* The caller made sure the const is a TSQuery, so get it now */ |
| query = DatumGetTSQuery(constval); |
| |
| /* Empty query matches nothing */ |
| if (query->size == 0) |
| return (Selectivity) 0.0; |
| |
| if (HeapTupleIsValid(vardata->statsTuple)) |
| { |
| Form_pg_statistic stats; |
| AttStatsSlot sslot; |
| |
| stats = (Form_pg_statistic) GETSTRUCT(vardata->statsTuple); |
| |
| /* MCELEM will be an array of TEXT elements for a tsvector column */ |
| if (get_attstatsslot(&sslot, vardata->statsTuple, |
| STATISTIC_KIND_MCELEM, InvalidOid, |
| ATTSTATSSLOT_VALUES | ATTSTATSSLOT_NUMBERS)) |
| { |
| /* |
| * There is a most-common-elements slot for the tsvector Var, so |
| * use that. |
| */ |
| selec = mcelem_tsquery_selec(query, sslot.values, sslot.nvalues, |
| sslot.numbers, sslot.nnumbers); |
| free_attstatsslot(&sslot); |
| } |
| else |
| { |
| /* No most-common-elements info, so do without */ |
| selec = tsquery_opr_selec_no_stats(query); |
| } |
| |
| /* |
| * MCE stats count only non-null rows, so adjust for null rows. |
| */ |
| selec *= (1.0 - stats->stanullfrac); |
| } |
| else |
| { |
| /* No stats at all, so do without */ |
| selec = tsquery_opr_selec_no_stats(query); |
| /* we assume no nulls here, so no stanullfrac correction */ |
| } |
| |
| return selec; |
| } |
| |
| /* |
| * Extract data from the pg_statistic arrays into useful format. |
| */ |
| static Selectivity |
| mcelem_tsquery_selec(TSQuery query, Datum *mcelem, int nmcelem, |
| float4 *numbers, int nnumbers) |
| { |
| float4 minfreq; |
| TextFreq *lookup; |
| Selectivity selec; |
| int i; |
| |
| /* |
| * There should be two more Numbers than Values, because the last two |
| * cells are taken for minimal and maximal frequency. Punt if not. |
| * |
| * (Note: the MCELEM statistics slot definition allows for a third extra |
| * number containing the frequency of nulls, but we're not expecting that |
| * to appear for a tsvector column.) |
| */ |
| if (nnumbers != nmcelem + 2) |
| return tsquery_opr_selec_no_stats(query); |
| |
| /* |
| * Transpose the data into a single array so we can use bsearch(). |
| */ |
| lookup = (TextFreq *) palloc(sizeof(TextFreq) * nmcelem); |
| for (i = 0; i < nmcelem; i++) |
| { |
| /* |
| * The text Datums came from an array, so it cannot be compressed or |
| * stored out-of-line -- it's safe to use VARSIZE_ANY*. |
| */ |
| Assert(!VARATT_IS_COMPRESSED(mcelem[i]) && !VARATT_IS_EXTERNAL(mcelem[i])); |
| lookup[i].element = (text *) DatumGetPointer(mcelem[i]); |
| lookup[i].frequency = numbers[i]; |
| } |
| |
| /* |
| * Grab the lowest frequency. compute_tsvector_stats() stored it for us in |
| * the one before the last cell of the Numbers array. See ts_typanalyze.c |
| */ |
| minfreq = numbers[nnumbers - 2]; |
| |
| selec = tsquery_opr_selec(GETQUERY(query), GETOPERAND(query), lookup, |
| nmcelem, minfreq); |
| |
| pfree(lookup); |
| |
| return selec; |
| } |
| |
| /* |
| * Traverse the tsquery in preorder, calculating selectivity as: |
| * |
| * selec(left_oper) * selec(right_oper) in AND & PHRASE nodes, |
| * |
| * selec(left_oper) + selec(right_oper) - |
| * selec(left_oper) * selec(right_oper) in OR nodes, |
| * |
| * 1 - select(oper) in NOT nodes |
| * |
| * histogram-based estimation in prefix VAL nodes |
| * |
| * freq[val] in exact VAL nodes, if the value is in MCELEM |
| * min(freq[MCELEM]) / 2 in VAL nodes, if it is not |
| * |
| * The MCELEM array is already sorted (see ts_typanalyze.c), so we can use |
| * binary search for determining freq[MCELEM]. |
| * |
| * If we don't have stats for the tsvector, we still use this logic, |
| * except we use default estimates for VAL nodes. This case is signaled |
| * by lookup == NULL. |
| */ |
| static Selectivity |
| tsquery_opr_selec(QueryItem *item, char *operand, |
| TextFreq *lookup, int length, float4 minfreq) |
| { |
| Selectivity selec; |
| |
| /* since this function recurses, it could be driven to stack overflow */ |
| check_stack_depth(); |
| |
| if (item->type == QI_VAL) |
| { |
| QueryOperand *oper = (QueryOperand *) item; |
| LexemeKey key; |
| |
| /* |
| * Prepare the key for bsearch(). |
| */ |
| key.lexeme = operand + oper->distance; |
| key.length = oper->length; |
| |
| if (oper->prefix) |
| { |
| /* Prefix match, ie the query item is lexeme:* */ |
| Selectivity matched, |
| allmces; |
| int i, |
| n_matched; |
| |
| /* |
| * Our strategy is to scan through the MCELEM list and combine the |
| * frequencies of the ones that match the prefix. We then |
| * extrapolate the fraction of matching MCELEMs to the remaining |
| * rows, assuming that the MCELEMs are representative of the whole |
| * lexeme population in this respect. (Compare |
| * histogram_selectivity().) Note that these are most common |
| * elements not most common values, so they're not mutually |
| * exclusive. We treat occurrences as independent events. |
| * |
| * This is only a good plan if we have a pretty fair number of |
| * MCELEMs available; we set the threshold at 100. If no stats or |
| * insufficient stats, arbitrarily use DEFAULT_TS_MATCH_SEL*4. |
| */ |
| if (lookup == NULL || length < 100) |
| return (Selectivity) (DEFAULT_TS_MATCH_SEL * 4); |
| |
| matched = allmces = 0; |
| n_matched = 0; |
| for (i = 0; i < length; i++) |
| { |
| TextFreq *t = lookup + i; |
| int tlen = VARSIZE_ANY_EXHDR(t->element); |
| |
| if (tlen >= key.length && |
| strncmp(key.lexeme, VARDATA_ANY(t->element), |
| key.length) == 0) |
| { |
| matched += t->frequency - matched * t->frequency; |
| n_matched++; |
| } |
| allmces += t->frequency - allmces * t->frequency; |
| } |
| |
| /* Clamp to ensure sanity in the face of roundoff error */ |
| CLAMP_PROBABILITY(matched); |
| CLAMP_PROBABILITY(allmces); |
| |
| selec = matched + (1.0 - allmces) * ((double) n_matched / length); |
| |
| /* |
| * In any case, never believe that a prefix match has selectivity |
| * less than we would assign for a non-MCELEM lexeme. This |
| * preserves the property that "word:*" should be estimated to |
| * match at least as many rows as "word" would be. |
| */ |
| selec = Max(Min(DEFAULT_TS_MATCH_SEL, minfreq / 2), selec); |
| } |
| else |
| { |
| /* Regular exact lexeme match */ |
| TextFreq *searchres; |
| |
| /* If no stats for the variable, use DEFAULT_TS_MATCH_SEL */ |
| if (lookup == NULL) |
| return (Selectivity) DEFAULT_TS_MATCH_SEL; |
| |
| searchres = (TextFreq *) bsearch(&key, lookup, length, |
| sizeof(TextFreq), |
| compare_lexeme_textfreq); |
| |
| if (searchres) |
| { |
| /* |
| * The element is in MCELEM. Return precise selectivity (or |
| * at least as precise as ANALYZE could find out). |
| */ |
| selec = searchres->frequency; |
| } |
| else |
| { |
| /* |
| * The element is not in MCELEM. Punt, but assume that the |
| * selectivity cannot be more than minfreq / 2. |
| */ |
| selec = Min(DEFAULT_TS_MATCH_SEL, minfreq / 2); |
| } |
| } |
| } |
| else |
| { |
| /* Current TSQuery node is an operator */ |
| Selectivity s1, |
| s2; |
| |
| switch (item->qoperator.oper) |
| { |
| case OP_NOT: |
| selec = 1.0 - tsquery_opr_selec(item + 1, operand, |
| lookup, length, minfreq); |
| break; |
| |
| case OP_PHRASE: |
| case OP_AND: |
| s1 = tsquery_opr_selec(item + 1, operand, |
| lookup, length, minfreq); |
| s2 = tsquery_opr_selec(item + item->qoperator.left, operand, |
| lookup, length, minfreq); |
| selec = s1 * s2; |
| break; |
| |
| case OP_OR: |
| s1 = tsquery_opr_selec(item + 1, operand, |
| lookup, length, minfreq); |
| s2 = tsquery_opr_selec(item + item->qoperator.left, operand, |
| lookup, length, minfreq); |
| selec = s1 + s2 - s1 * s2; |
| break; |
| |
| default: |
| elog(ERROR, "unrecognized operator: %d", item->qoperator.oper); |
| selec = 0; /* keep compiler quiet */ |
| break; |
| } |
| } |
| |
| /* Clamp intermediate results to stay sane despite roundoff error */ |
| CLAMP_PROBABILITY(selec); |
| |
| return selec; |
| } |
| |
| /* |
| * bsearch() comparator for a lexeme (non-NULL terminated string with length) |
| * and a TextFreq. Use length, then byte-for-byte comparison, because that's |
| * how ANALYZE code sorted data before storing it in a statistic tuple. |
| * See ts_typanalyze.c for details. |
| */ |
| static int |
| compare_lexeme_textfreq(const void *e1, const void *e2) |
| { |
| const LexemeKey *key = (const LexemeKey *) e1; |
| const TextFreq *t = (const TextFreq *) e2; |
| int len1, |
| len2; |
| |
| len1 = key->length; |
| len2 = VARSIZE_ANY_EXHDR(t->element); |
| |
| /* Compare lengths first, possibly avoiding a strncmp call */ |
| if (len1 > len2) |
| return 1; |
| else if (len1 < len2) |
| return -1; |
| |
| /* Fall back on byte-for-byte comparison */ |
| return strncmp(key->lexeme, VARDATA_ANY(t->element), len1); |
| } |