ts_typanalyze.c

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/*-------------------------------------------------------------------------
 *
 * ts_typanalyze.c
 *    functions for gathering statistics from tsvector columns
 *
 * Portions Copyright (c) 1996-2013, PostgreSQL Global Development Group
 *
 *
 * IDENTIFICATION
 *    src/backend/tsearch/ts_typanalyze.c
 *
 *-------------------------------------------------------------------------
 */
#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"


/* 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",
                              num_mcelem,
                              &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.
             *
             * (Note: the MCELEM statistics slot definition allows for a third
             * extra number containing the frequency of nulls, but we don't
             * create that for a tsvector column, since null elements aren't
             * possible.)
             */
            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);
}