diff options
Diffstat (limited to 'src/backend/utils/adt/array_typanalyze.c')
| -rw-r--r-- | src/backend/utils/adt/array_typanalyze.c | 14 |
1 files changed, 7 insertions, 7 deletions
diff --git a/src/backend/utils/adt/array_typanalyze.c b/src/backend/utils/adt/array_typanalyze.c index 70aba1b5d8d..4d7e9c311fb 100644 --- a/src/backend/utils/adt/array_typanalyze.c +++ b/src/backend/utils/adt/array_typanalyze.c @@ -160,13 +160,13 @@ array_typanalyze(PG_FUNCTION_ARGS) * compute_array_stats() -- compute statistics for a array column * * This function computes statistics useful for determining selectivity of - * the array operators <@, &&, and @>. It is invoked by ANALYZE via the + * the array operators <@, &&, and @>. It is invoked by ANALYZE via the * compute_stats hook after sample rows have been collected. * * We also invoke the standard compute_stats function, which will compute * "scalar" statistics relevant to the btree-style array comparison operators. * However, exact duplicates of an entire array may be rare despite many - * arrays sharing individual elements. This especially afflicts long arrays, + * arrays sharing individual elements. This especially afflicts long arrays, * which are also liable to lack all scalar statistics due to the low * WIDTH_THRESHOLD used in analyze.c. So, in addition to the standard stats, * we find the most common array elements and compute a histogram of distinct @@ -201,7 +201,7 @@ array_typanalyze(PG_FUNCTION_ARGS) * In the absence of a principled basis for other particular values, we * follow ts_typanalyze() and use parameters s = 0.07/K, epsilon = s/10. * But we leave out the correction for stopwords, which do not apply to - * arrays. These parameters give bucket width w = K/0.007 and maximum + * arrays. These parameters give bucket width w = K/0.007 and maximum * expected hashtable size of about 1000 * K. * * Elements may repeat within an array. Since duplicates do not change the @@ -463,7 +463,7 @@ compute_array_stats(VacAttrStats *stats, AnalyzeAttrFetchFunc fetchfunc, /* * Construct an array of the interesting hashtable items, that is, - * those meeting the cutoff frequency (s - epsilon)*N. Also identify + * those meeting the cutoff frequency (s - epsilon)*N. Also identify * the minimum and maximum frequencies among these items. * * Since epsilon = s/10 and bucket_width = 1/epsilon, the cutoff @@ -498,7 +498,7 @@ compute_array_stats(VacAttrStats *stats, AnalyzeAttrFetchFunc fetchfunc, /* * If we obtained more elements than we really want, get rid of those - * with least frequencies. The easiest way is to qsort the array into + * with least frequencies. The easiest way is to qsort the array into * descending frequency order and truncate the array. */ if (num_mcelem < track_len) @@ -532,7 +532,7 @@ compute_array_stats(VacAttrStats *stats, AnalyzeAttrFetchFunc fetchfunc, /* * We sorted statistics on the element value, but we want to be * able to find the minimal and maximal frequencies without going - * through all the values. We also want the frequency of null + * through all the values. We also want the frequency of null * elements. Store these three values at the end of mcelem_freqs. */ mcelem_values = (Datum *) palloc(num_mcelem * sizeof(Datum)); @@ -623,7 +623,7 @@ compute_array_stats(VacAttrStats *stats, AnalyzeAttrFetchFunc fetchfunc, * (compare the histogram-making loop in compute_scalar_stats()). * But instead of that we have the sorted_count_items[] array, * which holds unique DEC values with their frequencies (that is, - * a run-length-compressed version of the full array). So we + * a run-length-compressed version of the full array). So we * control advancing through sorted_count_items[] with the * variable "frac", which is defined as (x - y) * (num_hist - 1), * where x is the index in the notional DECs array corresponding |