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pgindent run for 9.4

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This includes removing tabs after periods in C comments, which was
applied to back branches, so this change should not effect backpatching.
This commit is contained in:
Bruce Momjian
2014-05-06 12:12:18 -04:00
parent fb85cd4320
commit 0a78320057
854 changed files with 7848 additions and 7368 deletions
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14
src/backend/utils/adt/array_typanalyze.c
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@@ -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