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ts_typanalyze.c

/*-------------------------------------------------------------------------
 *
 * ts_typanalyze.c
 *      functions for gathering statistics from tsvector columns
 *
 * Portions Copyright (c) 1996-2009, PostgreSQL Global Development Group
 *
 *
 * IDENTIFICATION
 *      $PostgreSQL: pgsql/src/backend/tsearch/ts_typanalyze.c,v 1.7.2.1 2010/05/30 21:59:09 tgl Exp $
 *
 *-------------------------------------------------------------------------
 */
#include "postgres.h"

#include "access/hash.h"
#include "catalog/pg_operator.h"
#include "commands/vacuum.h"
#include "tsearch/ts_type.h"
#include "utils/builtins.h"
#include "utils/hsearch.h"


/* A hash key for lexemes */
typedef struct
{
      char     *lexeme;             /* lexeme (not NULL terminated!) */
      int               length;                 /* its length in bytes */
} LexemeHashKey;

/* A hash table entry for the Lossy Counting algorithm */
typedef struct
{
      LexemeHashKey key;                  /* This is 'e' from the LC algorithm. */
      int               frequency;        /* This is 'f'. */
      int               delta;                  /* And this is 'delta'. */
} TrackItem;

static void compute_tsvector_stats(VacAttrStats *stats,
                                 AnalyzeAttrFetchFunc fetchfunc,
                                 int samplerows,
                                 double totalrows);
static void prune_lexemes_hashtable(HTAB *lexemes_tab, int b_current);
static uint32 lexeme_hash(const void *key, Size keysize);
static int  lexeme_match(const void *key1, const void *key2, Size keysize);
static int  lexeme_compare(const void *key1, const void *key2);
static int  trackitem_compare_frequencies_desc(const void *e1, const void *e2);
static int  trackitem_compare_lexemes(const void *e1, const void *e2);


/*
 *    ts_typanalyze -- a custom typanalyze function for tsvector columns
 */
Datum
ts_typanalyze(PG_FUNCTION_ARGS)
{
      VacAttrStats *stats = (VacAttrStats *) PG_GETARG_POINTER(0);
      Form_pg_attribute attr = stats->attr;

      /* If the attstattarget column is negative, use the default value */
      /* NB: it is okay to scribble on stats->attr since it's a copy */
      if (attr->attstattarget < 0)
            attr->attstattarget = default_statistics_target;

      stats->compute_stats = compute_tsvector_stats;
      /* see comment about the choice of minrows in commands/analyze.c */
      stats->minrows = 300 * attr->attstattarget;

      PG_RETURN_BOOL(true);
}

/*
 *    compute_tsvector_stats() -- compute statistics for a tsvector column
 *
 *    This functions computes statistics that are useful for determining @@
 *    operations' selectivity, along with the fraction of non-null rows and
 *    average width.
 *
 *    Instead of finding the most common values, as we do for most datatypes,
 *    we're looking for the most common lexemes. This is more useful, because
 *    there most probably won't be any two rows with the same tsvector and thus
 *    the notion of a MCV is a bit bogus with this datatype. With a list of the
 *    most common lexemes we can do a better job at figuring out @@ selectivity.
 *
 *    For the same reasons we assume that tsvector columns are unique when
 *    determining the number of distinct values.
 *
 *    The algorithm used is Lossy Counting, as proposed in the paper "Approximate
 *    frequency counts over data streams" by G. S. Manku and R. Motwani, in
 *    Proceedings of the 28th International Conference on Very Large Data Bases,
 *    Hong Kong, China, August 2002, section 4.2. The paper is available at
 *    http://www.vldb.org/conf/2002/S10P03.pdf
 *
 *    The Lossy Counting (aka LC) algorithm goes like this:
 *    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 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,
                                 AnalyzeAttrFetchFunc fetchfunc,
                                 int samplerows,
                                 double totalrows)
{
      int               num_mcelem;
      int               null_cnt = 0;
      double            total_width = 0;

      /* This is D from the LC algorithm. */
      HTAB     *lexemes_tab;
      HASHCTL           hash_ctl;
      HASH_SEQ_STATUS scan_status;

      /* This is the current bucket number from the LC algorithm */
      int               b_current;

      /* This is 'w' from the LC algorithm */
      int               bucket_width;
      int               vector_no,
                        lexeme_no;
      LexemeHashKey hash_key;
      TrackItem  *item;

      /*
       * 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 (num_mcelem + 10) / 0.007 as per the
       * comment above.
       */
      bucket_width = (num_mcelem + 10) * 1000 / 7;

      /*
       * Create the hashtable. It will be in local memory, so we don't need to
       * 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.keysize = sizeof(LexemeHashKey);
      hash_ctl.entrysize = sizeof(TrackItem);
      hash_ctl.hash = lexeme_hash;
      hash_ctl.match = lexeme_match;
      hash_ctl.hcxt = CurrentMemoryContext;
      lexemes_tab = hash_create("Analyzed lexemes table",
                                            bucket_width * 7,
                                            &hash_ctl,
                              HASH_ELEM | HASH_FUNCTION | HASH_COMPARE | HASH_CONTEXT);

      /* Initialize counters. */
      b_current = 1;
      lexeme_no = 0;

      /* Loop over the tsvectors. */
      for (vector_no = 0; vector_no < samplerows; vector_no++)
      {
            Datum       value;
            bool        isnull;
            TSVector    vector;
            WordEntry  *curentryptr;
            char     *lexemesptr;
            int               j;

            vacuum_delay_point();

            value = fetchfunc(stats, vector_no, &isnull);

            /*
             * Check for null/nonnull.
             */
            if (isnull)
            {
                  null_cnt++;
                  continue;
            }

            /*
             * Add up widths for average-width calculation.  Since it's a
             * tsvector, we know it's varlena.  As in the regular
             * compute_minimal_stats function, we use the toasted width for this
             * calculation.
             */
            total_width += VARSIZE_ANY(DatumGetPointer(value));

            /*
             * Now detoast the tsvector if needed.
             */
            vector = DatumGetTSVector(value);

            /*
             * We loop through the lexemes in the tsvector and add them to our
             * tracking hashtable.  Note: the hashtable entries will point into
             * the (detoasted) tsvector value, therefore we cannot free that
             * storage until we're done.
             */
            lexemesptr = STRPTR(vector);
            curentryptr = ARRPTR(vector);
            for (j = 0; j < vector->size; j++)
            {
                  bool        found;

                  /* Construct a hash key */
                  hash_key.lexeme = lexemesptr + curentryptr->pos;
                  hash_key.length = curentryptr->len;

                  /* Lookup current lexeme in hashtable, adding it if new */
                  item = (TrackItem *) hash_search(lexemes_tab,
                                                                   (const void *) &hash_key,
                                                                   HASH_ENTER, &found);

                  if (found)
                  {
                        /* The lexeme is already on the tracking list */
                        item->frequency++;
                  }
                  else
                  {
                        /* Initialize new tracking list element */
                        item->frequency = 1;
                        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)
                  {
                        prune_lexemes_hashtable(lexemes_tab, b_current);
                        b_current++;
                  }

                  /* Advance to the next WordEntry in the tsvector */
                  curentryptr++;
            }
      }

      /* We can only compute real stats if we found some non-null values. */
      if (null_cnt < samplerows)
      {
            int               nonnull_cnt = samplerows - null_cnt;
            int               i;
            TrackItem **sort_table;
            int               track_len;
            int               cutoff_freq;
            int               minfreq,
                              maxfreq;

            stats->stats_valid = true;
            /* Do the simple null-frac and average width stats */
            stats->stanullfrac = (double) null_cnt / (double) samplerows;
            stats->stawidth = total_width / (double) nonnull_cnt;

            /* Assume it's a unique column (see notes above) */
            stats->stadistinct = -1.0;

            /*
             * 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.
             */
            cutoff_freq = 9 * lexeme_no / bucket_width;

            i = hash_get_num_entries(lexemes_tab);          /* surely enough space */
            sort_table = (TrackItem **) palloc(sizeof(TrackItem *) * i);

            hash_seq_init(&scan_status, lexemes_tab);
            track_len = 0;
            minfreq = lexeme_no;
            maxfreq = 0;
            while ((item = (TrackItem *) hash_seq_search(&scan_status)) != NULL)
            {
                  if (item->frequency > cutoff_freq)
                  {
                        sort_table[track_len++] = item;
                        minfreq = Min(minfreq, item->frequency);
                        maxfreq = Max(maxfreq, item->frequency);
                  }
            }
            Assert(track_len <= i);

            /* 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);

            /*
             * 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)
            {
                  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;
            }
            else
                  num_mcelem = track_len;

            /* Generate MCELEM slot entry */
            if (num_mcelem > 0)
            {
                  MemoryContext old_context;
                  Datum    *mcelem_values;
                  float4         *mcelem_freqs;

                  /*
                   * We want to store statistics sorted on the lexeme value using
                   * first length, then byte-for-byte comparison. The reason for
                   * doing length comparison first is that we don't care about the
                   * ordering so long as it's consistent, and comparing lengths
                   * first gives us a chance to avoid a strncmp() call.
                   *
                   * This is different from what we do with scalar statistics --
                   * they get sorted on frequencies. The rationale is that we
                   * usually search through most common elements looking for a
                   * specific value, so we can grab its frequency.  When values are
                   * presorted we can employ binary search for that.    See
                   * ts_selfuncs.c for a real usage scenario.
                   */
                  qsort(sort_table, num_mcelem, sizeof(TrackItem *),
                          trackitem_compare_lexemes);

                  /* Must copy the target values into anl_context */
                  old_context = MemoryContextSwitchTo(stats->anl_context);

                  /*
                   * We sorted statistics on the lexeme value, but we want to be
                   * able to find out the minimal and maximal frequency without
                   * going through all the values.  We keep those two extra
                   * frequencies in two extra cells in mcelem_freqs.
                   */
                  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];

                        mcelem_values[i] =
                              PointerGetDatum(cstring_to_text_with_len(item->key.lexeme,
                                                                                      item->key.length));
                        mcelem_freqs[i] = (double) item->frequency / (double) nonnull_cnt;
                  }
                  mcelem_freqs[i++] = (double) minfreq / (double) nonnull_cnt;
                  mcelem_freqs[i] = (double) maxfreq / (double) nonnull_cnt;
                  MemoryContextSwitchTo(old_context);

                  stats->stakind[0] = STATISTIC_KIND_MCELEM;
                  stats->staop[0] = TextEqualOperator;
                  stats->stanumbers[0] = mcelem_freqs;
                  /* See above comment about two extra frequency fields */
                  stats->numnumbers[0] = num_mcelem + 2;
                  stats->stavalues[0] = mcelem_values;
                  stats->numvalues[0] = num_mcelem;
                  /* We are storing text values */
                  stats->statypid[0] = TEXTOID;
                  stats->statyplen[0] = -1;     /* typlen, -1 for varlena */
                  stats->statypbyval[0] = false;
                  stats->statypalign[0] = 'i';
            }
      }
      else
      {
            /* We found only nulls; assume the column is entirely null */
            stats->stats_valid = true;
            stats->stanullfrac = 1.0;
            stats->stawidth = 0;    /* "unknown" */
            stats->stadistinct = 0.0;           /* "unknown" */
      }

      /*
       * We don't need to bother cleaning up any of our temporary palloc's. The
       * hashtable should also go away, as it used a child memory context.
       */
}

/*
 *    A function to prune the D structure from the Lossy Counting algorithm.
 *    Consult compute_tsvector_stats() for wider explanation.
 */
static void
prune_lexemes_hashtable(HTAB *lexemes_tab, int b_current)
{
      HASH_SEQ_STATUS scan_status;
      TrackItem  *item;

      hash_seq_init(&scan_status, lexemes_tab);
      while ((item = (TrackItem *) hash_seq_search(&scan_status)) != NULL)
      {
            if (item->frequency + item->delta <= b_current)
            {
                  if (hash_search(lexemes_tab, (const void *) &item->key,
                                          HASH_REMOVE, NULL) == NULL)
                        elog(ERROR, "hash table corrupted");
            }
      }
}

/*
 * Hash functions for lexemes. They are strings, but not NULL terminated,
 * so we need a special hash function.
 */
static uint32
lexeme_hash(const void *key, Size keysize)
{
      const LexemeHashKey *l = (const LexemeHashKey *) key;

      return DatumGetUInt32(hash_any((const unsigned char *) l->lexeme,
                                                   l->length));
}

/*
 *    Matching function for lexemes, to be used in hashtable lookups.
 */
static int
lexeme_match(const void *key1, const void *key2, Size keysize)
{
      /* The keysize parameter is superfluous, the keys store their lengths */
      return lexeme_compare(key1, key2);
}

/*
 *    Comparison function for lexemes.
 */
static int
lexeme_compare(const void *key1, const void *key2)
{
      const LexemeHashKey *d1 = (const LexemeHashKey *) key1;
      const LexemeHashKey *d2 = (const LexemeHashKey *) key2;

      /* First, compare by length */
      if (d1->length > d2->length)
            return 1;
      else if (d1->length < d2->length)
            return -1;
      /* Lengths are equal, do a byte-by-byte comparison */
      return strncmp(d1->lexeme, d2->lexeme, d1->length);
}

/*
 *    qsort() comparator for sorting TrackItems on frequencies (descending sort)
 */
static int
trackitem_compare_frequencies_desc(const void *e1, const void *e2)
{
      const TrackItem *const * t1 = (const TrackItem *const *) e1;
      const TrackItem *const * t2 = (const TrackItem *const *) e2;

      return (*t2)->frequency - (*t1)->frequency;
}

/*
 *    qsort() comparator for sorting TrackItems on lexemes
 */
static int
trackitem_compare_lexemes(const void *e1, const void *e2)
{
      const TrackItem *const * t1 = (const TrackItem *const *) e1;
      const TrackItem *const * t2 = (const TrackItem *const *) e2;

      return lexeme_compare(&(*t1)->key, &(*t2)->key);
}

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