*
*
* IDENTIFICATION
- * $PostgreSQL: pgsql/src/backend/tsearch/ts_typanalyze.c,v 1.7 2009/06/11 14:49:03 momjian Exp $
+ * $PostgreSQL: pgsql/src/backend/tsearch/ts_typanalyze.c,v 1.7.2.1 2010/05/30 21:59:09 tgl Exp $
*
*-------------------------------------------------------------------------
*/
* http://www.vldb.org/conf/2002/S10P03.pdf
*
* The Lossy Counting (aka LC) algorithm goes like this:
- * Let D be a set of triples (e, f, d), where e is an element value, f is
- * that element's frequency (occurrence count) and d is the maximum error in
- * f. We start with D empty and process the elements in batches of size
- * w. (The batch size is also known as "bucket size".) Let the current batch
- * number be b_current, starting with 1. For each element e we either
- * increment its f count, if it's already in D, or insert a new triple into D
- * with values (e, 1, b_current - 1). After processing each batch we prune D,
- * by removing from it all elements with f + d <= b_current. Finally, we
- * gather elements with largest f. The LC paper proves error bounds on f
- * dependent on the batch size w, and shows that the required table size
- * is no more than a few times w.
+ * Let s be the threshold frequency for an item (the minimum frequency we
+ * are interested in) and epsilon the error margin for the frequency. Let D
+ * be a set of triples (e, f, delta), where e is an element value, f is that
+ * element's frequency (actually, its current occurrence count) and delta is
+ * the maximum error in f. We start with D empty and process the elements in
+ * batches of size w. (The batch size is also known as "bucket size" and is
+ * equal to 1/epsilon.) Let the current batch number be b_current, starting
+ * with 1. For each element e we either increment its f count, if it's
+ * already in D, or insert a new triple into D with values (e, 1, b_current
+ * - 1). After processing each batch we prune D, by removing from it all
+ * elements with f + delta <= b_current. After the algorithm finishes we
+ * suppress all elements from D that do not satisfy f >= (s - epsilon) * N,
+ * where N is the total number of elements in the input. We emit the
+ * remaining elements with estimated frequency f/N. The LC paper proves
+ * that this algorithm finds all elements with true frequency at least s,
+ * and that no frequency is overestimated or is underestimated by more than
+ * epsilon. Furthermore, given reasonable assumptions about the input
+ * distribution, the required table size is no more than about 7 times w.
*
- * We use a hashtable for the D structure and a bucket width of
- * statistics_target * 10, where 10 is an arbitrarily chosen constant,
- * meant to approximate the number of lexemes in a single tsvector.
+ * We set s to be the estimated frequency of the K'th word in a natural
+ * language's frequency table, where K is the target number of entries in
+ * the MCELEM array plus an arbitrary constant, meant to reflect the fact
+ * that the most common words in any language would usually be stopwords
+ * so we will not actually see them in the input. We assume that the
+ * distribution of word frequencies (including the stopwords) follows Zipf's
+ * law with an exponent of 1.
+ *
+ * Assuming Zipfian distribution, the frequency of the K'th word is equal
+ * to 1/(K * H(W)) where H(n) is 1/2 + 1/3 + ... + 1/n and W is the number of
+ * words in the language. Putting W as one million, we get roughly 0.07/K.
+ * Assuming top 10 words are stopwords gives s = 0.07/(K + 10). We set
+ * epsilon = s/10, which gives bucket width w = (K + 10)/0.007 and
+ * maximum expected hashtable size of about 1000 * (K + 10).
+ *
+ * Note: in the above discussion, s, epsilon, and f/N are in terms of a
+ * lexeme's frequency as a fraction of all lexemes seen in the input.
+ * However, what we actually want to store in the finished pg_statistic
+ * entry is each lexeme's frequency as a fraction of all rows that it occurs
+ * in. Assuming that the input tsvectors are correctly constructed, no
+ * lexeme occurs more than once per tsvector, so the final count f is a
+ * correct estimate of the number of input tsvectors it occurs in, and we
+ * need only change the divisor from N to nonnull_cnt to get the number we
+ * want.
*/
static void
compute_tsvector_stats(VacAttrStats *stats,
LexemeHashKey hash_key;
TrackItem *item;
- /* We want statistics_target * 10 lexemes in the MCELEM array */
+ /*
+ * We want statistics_target * 10 lexemes in the MCELEM array. This
+ * multiplier is pretty arbitrary, but is meant to reflect the fact that
+ * the number of individual lexeme values tracked in pg_statistic ought
+ * to be more than the number of values for a simple scalar column.
+ */
num_mcelem = stats->attr->attstattarget * 10;
/*
- * We set bucket width equal to the target number of result lexemes. This
- * is probably about right but perhaps might need to be scaled up or down
- * a bit?
+ * We set bucket width equal to (num_mcelem + 10) / 0.007 as per the
+ * comment above.
*/
- bucket_width = num_mcelem;
+ bucket_width = (num_mcelem + 10) * 1000 / 7;
/*
* Create the hashtable. It will be in local memory, so we don't need to
- * worry about initial size too much. Also we don't need to pay any
+ * worry about overflowing the initial size. Also we don't need to pay any
* attention to locking and memory management.
*/
MemSet(&hash_ctl, 0, sizeof(hash_ctl));
hash_ctl.match = lexeme_match;
hash_ctl.hcxt = CurrentMemoryContext;
lexemes_tab = hash_create("Analyzed lexemes table",
- bucket_width * 4,
+ bucket_width * 7,
&hash_ctl,
HASH_ELEM | HASH_FUNCTION | HASH_COMPARE | HASH_CONTEXT);
/* Initialize counters. */
b_current = 1;
- lexeme_no = 1;
+ lexeme_no = 0;
/* Loop over the tsvectors. */
for (vector_no = 0; vector_no < samplerows; vector_no++)
item->delta = b_current - 1;
}
+ /* lexeme_no is the number of elements processed (ie N) */
+ lexeme_no++;
+
/* We prune the D structure after processing each bucket */
if (lexeme_no % bucket_width == 0)
{
}
/* Advance to the next WordEntry in the tsvector */
- lexeme_no++;
curentryptr++;
}
}
int i;
TrackItem **sort_table;
int track_len;
+ int cutoff_freq;
int minfreq,
maxfreq;
stats->stadistinct = -1.0;
/*
- * Determine the top-N lexemes by simply copying pointers from the
- * hashtable into an array and applying qsort()
+ * Construct an array of the interesting hashtable items, that is,
+ * 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
+ * frequency is 9*N / bucket_width.
*/
- track_len = hash_get_num_entries(lexemes_tab);
+ cutoff_freq = 9 * lexeme_no / bucket_width;
- sort_table = (TrackItem **) palloc(sizeof(TrackItem *) * track_len);
+ i = hash_get_num_entries(lexemes_tab); /* surely enough space */
+ sort_table = (TrackItem **) palloc(sizeof(TrackItem *) * i);
hash_seq_init(&scan_status, lexemes_tab);
- i = 0;
+ track_len = 0;
+ minfreq = lexeme_no;
+ maxfreq = 0;
while ((item = (TrackItem *) hash_seq_search(&scan_status)) != NULL)
{
- sort_table[i++] = item;
+ if (item->frequency > cutoff_freq)
+ {
+ sort_table[track_len++] = item;
+ minfreq = Min(minfreq, item->frequency);
+ maxfreq = Max(maxfreq, item->frequency);
+ }
}
- Assert(i == track_len);
+ Assert(track_len <= i);
- qsort(sort_table, track_len, sizeof(TrackItem *),
- trackitem_compare_frequencies_desc);
+ /* emit some statistics for debug purposes */
+ elog(DEBUG3, "tsvector_stats: target # mces = %d, bucket width = %d, "
+ "# lexemes = %d, hashtable size = %d, usable entries = %d",
+ num_mcelem, bucket_width, lexeme_no, i, track_len);
- /* Suppress any single-occurrence items */
- while (track_len > 0)
+ /*
+ * If we obtained more lexemes than we really want, get rid of
+ * those with least frequencies. The easiest way is to qsort the
+ * array into descending frequency order and truncate the array.
+ */
+ if (num_mcelem < track_len)
{
- if (sort_table[track_len - 1]->frequency > 1)
- break;
- track_len--;
+ qsort(sort_table, track_len, sizeof(TrackItem *),
+ trackitem_compare_frequencies_desc);
+ /* reset minfreq to the smallest frequency we're keeping */
+ minfreq = sort_table[num_mcelem - 1]->frequency;
}
-
- /* Determine the number of most common lexemes to be stored */
- if (num_mcelem > track_len)
+ else
num_mcelem = track_len;
/* Generate MCELEM slot entry */
Datum *mcelem_values;
float4 *mcelem_freqs;
- /* Grab the minimal and maximal frequencies that will get stored */
- minfreq = sort_table[num_mcelem - 1]->frequency;
- maxfreq = sort_table[0]->frequency;
-
/*
* We want to store statistics sorted on the lexeme value using
* first length, then byte-for-byte comparison. The reason for
mcelem_values = (Datum *) palloc(num_mcelem * sizeof(Datum));
mcelem_freqs = (float4 *) palloc((num_mcelem + 2) * sizeof(float4));
+ /*
+ * See comments above about use of nonnull_cnt as the divisor
+ * for the final frequency estimates.
+ */
for (i = 0; i < num_mcelem; i++)
{
TrackItem *item = sort_table[i];
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