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Remove useless "rough estimate" path from mcelem_array_contained_selec.
The code in this function that tried to cope with a missing count histogram was quite ineffective for anything except a perfectly flat distribution. Furthermore, since we were already punting for missing MCELEM slot, it's rather useless to sweat over missing DECHIST: there are no cases where ANALYZE will create the first but not the second. So just simplify the code by punting rather than pretending we can do something useful.
This commit is contained in:
Tom Lane
parent
4fb694aebc
commit
e2eed78910
@ -242,8 +242,7 @@ scalararraysel_containment(PlannerInfo *root,
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}
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/*
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* arraycontsel -- restriction selectivity for "arraycolumn @> const",
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* "arraycolumn && const" or "arraycolumn <@ const"
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* arraycontsel -- restriction selectivity for array @>, &&, <@ operators
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*/
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Datum
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arraycontsel(PG_FUNCTION_ARGS)
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@ -323,8 +322,7 @@ arraycontsel(PG_FUNCTION_ARGS)
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}
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/*
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* arraycontjoinsel -- join selectivity for "arraycolumn @> const",
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* "arraycolumn && const" or "arraycolumn <@ const"
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* arraycontjoinsel -- join selectivity for array @>, &&, <@ operators
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*/
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Datum
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arraycontjoinsel(PG_FUNCTION_ARGS)
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@ -744,6 +742,10 @@ mcelem_array_contained_selec(Datum *mcelem, int nmcelem,
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if (numbers == NULL || nnumbers != nmcelem + 3)
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return DEFAULT_CONTAIN_SEL;
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/* Can't do much without a count histogram, either */
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if (hist == NULL || nhist < 3)
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return DEFAULT_CONTAIN_SEL;
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/*
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* Grab some of the summary statistics that compute_array_stats() stores:
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* lowest frequency, frequency of null elements, and average distinct
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@ -751,11 +753,7 @@ mcelem_array_contained_selec(Datum *mcelem, int nmcelem,
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*/
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minfreq = numbers[nmcelem];
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nullelem_freq = numbers[nmcelem + 2];
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if (hist && nhist > 0)
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avg_count = hist[nhist - 1];
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else
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avg_count = 10.0f; /* default assumption */
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avg_count = hist[nhist - 1];
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/*
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* "rest" will be the sum of the frequencies of all elements not
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@ -853,83 +851,71 @@ mcelem_array_contained_selec(Datum *mcelem, int nmcelem,
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*/
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mult *= exp(-rest);
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/* Check we have nonempty distinct element count histogram */
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if (hist && nhist >= 3)
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{
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/*----------
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* Using the distinct element count histogram requires
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* O(unique_nitems * (nmcelem + unique_nitems))
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* operations. Beyond a certain computational cost threshold, it's
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* reasonable to sacrifice accuracy for decreased planning time.
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* We limit the number of operations to EFFORT * nmcelem; since
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* nmcelem is limited by the column's statistics target, the work
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* done is user-controllable.
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*
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* If the number of operations would be too large, we can reduce it
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* without losing all accuracy by reducing unique_nitems and
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* considering only the most-common elements of the constant array.
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* To make the results exactly match what we would have gotten with
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* only those elements to start with, we'd have to remove any
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* discarded elements' frequencies from "mult", but since this is only
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* an approximation anyway, we don't bother with that. Therefore it's
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* sufficient to qsort elem_selec[] and take the largest elements.
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* (They will no longer match up with the elements of array_data[],
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* but we don't care.)
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*----------
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*/
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/*----------
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* Using the distinct element count histogram requires
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* O(unique_nitems * (nmcelem + unique_nitems))
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* operations. Beyond a certain computational cost threshold, it's
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* reasonable to sacrifice accuracy for decreased planning time. We limit
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* the number of operations to EFFORT * nmcelem; since nmcelem is limited
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* by the column's statistics target, the work done is user-controllable.
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*
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* If the number of operations would be too large, we can reduce it
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* without losing all accuracy by reducing unique_nitems and considering
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* only the most-common elements of the constant array. To make the
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* results exactly match what we would have gotten with only those
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* elements to start with, we'd have to remove any discarded elements'
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* frequencies from "mult", but since this is only an approximation
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* anyway, we don't bother with that. Therefore it's sufficient to qsort
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* elem_selec[] and take the largest elements. (They will no longer match
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* up with the elements of array_data[], but we don't care.)
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*----------
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*/
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#define EFFORT 100
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if ((nmcelem + unique_nitems) > 0 &&
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unique_nitems > EFFORT * nmcelem / (nmcelem + unique_nitems))
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{
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/*
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* Use the quadratic formula to solve for largest allowable N;
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* we have A = 1, B = nmcelem, C = - EFFORT * nmcelem.
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*/
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double b = (double) nmcelem;
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int n;
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n = (int) ((sqrt(b * b + 4 * EFFORT * b) - b) / 2);
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/* Sort, then take just the first n elements */
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qsort(elem_selec, unique_nitems, sizeof(float),
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float_compare_desc);
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unique_nitems = n;
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}
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/*
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* Calculate probabilities of each distinct element count for both
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* mcelems and constant elements. At this point, assume independent
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* element occurrence.
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*/
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dist = calc_distr(elem_selec, unique_nitems, unique_nitems, 0.0f);
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mcelem_dist = calc_distr(numbers, nmcelem, unique_nitems, rest);
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/* ignore hist[nhist-1], which is the avg not a histogram member */
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hist_part = calc_hist(hist, nhist - 1, unique_nitems);
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selec = 0.0f;
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for (i = 0; i <= unique_nitems; i++)
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{
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/*
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* mult * dist[i] / mcelem_dist[i] gives us probability of qual
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* matching from assumption of independent element occurrence with
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* the condition that distinct element count = i.
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*/
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if (mcelem_dist[i] > 0)
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selec += hist_part[i] * mult * dist[i] / mcelem_dist[i];
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}
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pfree(dist);
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pfree(mcelem_dist);
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pfree(hist_part);
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}
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else
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if ((nmcelem + unique_nitems) > 0 &&
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unique_nitems > EFFORT * nmcelem / (nmcelem + unique_nitems))
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{
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/* We don't have histogram. Use a rough estimate. */
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selec = mult;
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/*
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* Use the quadratic formula to solve for largest allowable N. We
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* have A = 1, B = nmcelem, C = - EFFORT * nmcelem.
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*/
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double b = (double) nmcelem;
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int n;
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n = (int) ((sqrt(b * b + 4 * EFFORT * b) - b) / 2);
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/* Sort, then take just the first n elements */
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qsort(elem_selec, unique_nitems, sizeof(float),
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float_compare_desc);
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unique_nitems = n;
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}
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/*
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* Calculate probabilities of each distinct element count for both
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* mcelems and constant elements. At this point, assume independent
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* element occurrence.
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*/
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dist = calc_distr(elem_selec, unique_nitems, unique_nitems, 0.0f);
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mcelem_dist = calc_distr(numbers, nmcelem, unique_nitems, rest);
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/* ignore hist[nhist-1], which is the average not a histogram member */
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hist_part = calc_hist(hist, nhist - 1, unique_nitems);
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selec = 0.0f;
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for (i = 0; i <= unique_nitems; i++)
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{
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/*
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* mult * dist[i] / mcelem_dist[i] gives us probability of qual
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* matching from assumption of independent element occurrence with
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* the condition that distinct element count = i.
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*/
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if (mcelem_dist[i] > 0)
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selec += hist_part[i] * mult * dist[i] / mcelem_dist[i];
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}
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pfree(dist);
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pfree(mcelem_dist);
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pfree(hist_part);
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pfree(elem_selec);
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/* Take into account occurrence of NULL element. */
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