diff --git a/src/backend/utils/adt/array_selfuncs.c b/src/backend/utils/adt/array_selfuncs.c
index 3916de4bfb61df8309102a862a9d91e0302bac06..bc4ebd20749997ff3441b7824118dbde320b5524 100644
--- a/src/backend/utils/adt/array_selfuncs.c
+++ b/src/backend/utils/adt/array_selfuncs.c
@@ -242,8 +242,7 @@ scalararraysel_containment(PlannerInfo *root,
}
/*
- * arraycontsel -- restriction selectivity for "arraycolumn @> const",
- * "arraycolumn && const" or "arraycolumn <@ const"
+ * arraycontsel -- restriction selectivity for array @>, &&, <@ operators
*/
Datum
arraycontsel(PG_FUNCTION_ARGS)
@@ -323,8 +322,7 @@ arraycontsel(PG_FUNCTION_ARGS)
}
/*
- * arraycontjoinsel -- join selectivity for "arraycolumn @> const",
- * "arraycolumn && const" or "arraycolumn <@ const"
+ * arraycontjoinsel -- join selectivity for array @>, &&, <@ operators
*/
Datum
arraycontjoinsel(PG_FUNCTION_ARGS)
@@ -744,6 +742,10 @@ mcelem_array_contained_selec(Datum *mcelem, int nmcelem,
if (numbers == NULL || nnumbers != nmcelem + 3)
return DEFAULT_CONTAIN_SEL;
+ /* Can't do much without a count histogram, either */
+ if (hist == NULL || nhist < 3)
+ return DEFAULT_CONTAIN_SEL;
+
/*
* Grab some of the summary statistics that compute_array_stats() stores:
* lowest frequency, frequency of null elements, and average distinct
@@ -751,11 +753,7 @@ mcelem_array_contained_selec(Datum *mcelem, int nmcelem,
*/
minfreq = numbers[nmcelem];
nullelem_freq = numbers[nmcelem + 2];
-
- if (hist && nhist > 0)
- avg_count = hist[nhist - 1];
- else
- avg_count = 10.0f; /* default assumption */
+ avg_count = hist[nhist - 1];
/*
* "rest" will be the sum of the frequencies of all elements not
@@ -853,83 +851,71 @@ mcelem_array_contained_selec(Datum *mcelem, int nmcelem,
*/
mult *= exp(-rest);
- /* Check we have nonempty distinct element count histogram */
- if (hist && nhist >= 3)
- {
- /*----------
- * Using the distinct element count histogram requires
- * O(unique_nitems * (nmcelem + unique_nitems))
- * operations. Beyond a certain computational cost threshold, it's
- * reasonable to sacrifice accuracy for decreased planning time.
- * We limit the number of operations to EFFORT * nmcelem; since
- * nmcelem is limited by the column's statistics target, the work
- * done is user-controllable.
- *
- * If the number of operations would be too large, we can reduce it
- * without losing all accuracy by reducing unique_nitems and
- * considering only the most-common elements of the constant array.
- * To make the results exactly match what we would have gotten with
- * only those elements to start with, we'd have to remove any
- * discarded elements' frequencies from "mult", but since this is only
- * an approximation anyway, we don't bother with that. Therefore it's
- * sufficient to qsort elem_selec[] and take the largest elements.
- * (They will no longer match up with the elements of array_data[],
- * but we don't care.)
- *----------
- */
+ /*----------
+ * Using the distinct element count histogram requires
+ * O(unique_nitems * (nmcelem + unique_nitems))
+ * operations. Beyond a certain computational cost threshold, it's
+ * reasonable to sacrifice accuracy for decreased planning time. We limit
+ * the number of operations to EFFORT * nmcelem; since nmcelem is limited
+ * by the column's statistics target, the work done is user-controllable.
+ *
+ * If the number of operations would be too large, we can reduce it
+ * without losing all accuracy by reducing unique_nitems and considering
+ * only the most-common elements of the constant array. To make the
+ * results exactly match what we would have gotten with only those
+ * elements to start with, we'd have to remove any discarded elements'
+ * frequencies from "mult", but since this is only an approximation
+ * anyway, we don't bother with that. Therefore it's sufficient to qsort
+ * elem_selec[] and take the largest elements. (They will no longer match
+ * up with the elements of array_data[], but we don't care.)
+ *----------
+ */
#define EFFORT 100
- if ((nmcelem + unique_nitems) > 0 &&
- unique_nitems > EFFORT * nmcelem / (nmcelem + unique_nitems))
- {
- /*
- * Use the quadratic formula to solve for largest allowable N;
- * we have A = 1, B = nmcelem, C = - EFFORT * nmcelem.
- */
- double b = (double) nmcelem;
- int n;
-
- n = (int) ((sqrt(b * b + 4 * EFFORT * b) - b) / 2);
-
- /* Sort, then take just the first n elements */
- qsort(elem_selec, unique_nitems, sizeof(float),
- float_compare_desc);
- unique_nitems = n;
- }
-
+ if ((nmcelem + unique_nitems) > 0 &&
+ unique_nitems > EFFORT * nmcelem / (nmcelem + unique_nitems))
+ {
/*
- * Calculate probabilities of each distinct element count for both
- * mcelems and constant elements. At this point, assume independent
- * element occurrence.
+ * Use the quadratic formula to solve for largest allowable N. We
+ * have A = 1, B = nmcelem, C = - EFFORT * nmcelem.
*/
- dist = calc_distr(elem_selec, unique_nitems, unique_nitems, 0.0f);
- mcelem_dist = calc_distr(numbers, nmcelem, unique_nitems, rest);
+ double b = (double) nmcelem;
+ int n;
- /* ignore hist[nhist-1], which is the avg not a histogram member */
- hist_part = calc_hist(hist, nhist - 1, unique_nitems);
+ n = (int) ((sqrt(b * b + 4 * EFFORT * b) - b) / 2);
- selec = 0.0f;
- for (i = 0; i <= unique_nitems; i++)
- {
- /*
- * mult * dist[i] / mcelem_dist[i] gives us probability of qual
- * matching from assumption of independent element occurrence with
- * the condition that distinct element count = i.
- */
- if (mcelem_dist[i] > 0)
- selec += hist_part[i] * mult * dist[i] / mcelem_dist[i];
- }
-
- pfree(dist);
- pfree(mcelem_dist);
- pfree(hist_part);
+ /* Sort, then take just the first n elements */
+ qsort(elem_selec, unique_nitems, sizeof(float),
+ float_compare_desc);
+ unique_nitems = n;
}
- else
+
+ /*
+ * Calculate probabilities of each distinct element count for both
+ * mcelems and constant elements. At this point, assume independent
+ * element occurrence.
+ */
+ dist = calc_distr(elem_selec, unique_nitems, unique_nitems, 0.0f);
+ mcelem_dist = calc_distr(numbers, nmcelem, unique_nitems, rest);
+
+ /* ignore hist[nhist-1], which is the average not a histogram member */
+ hist_part = calc_hist(hist, nhist - 1, unique_nitems);
+
+ selec = 0.0f;
+ for (i = 0; i <= unique_nitems; i++)
{
- /* We don't have histogram. Use a rough estimate. */
- selec = mult;
+ /*
+ * mult * dist[i] / mcelem_dist[i] gives us probability of qual
+ * matching from assumption of independent element occurrence with
+ * the condition that distinct element count = i.
+ */
+ if (mcelem_dist[i] > 0)
+ selec += hist_part[i] * mult * dist[i] / mcelem_dist[i];
}
+ pfree(dist);
+ pfree(mcelem_dist);
+ pfree(hist_part);
pfree(elem_selec);
/* Take into account occurrence of NULL element. */