LCOV - code coverage report
Current view: top level - src/backend/tsearch - ts_typanalyze.c (source / functions) Hit Total Coverage
Test: PostgreSQL 13devel Lines: 121 131 92.4 %
Date: 2019-11-13 22:07:24 Functions: 8 8 100.0 %
Legend: Lines: hit not hit

          Line data    Source code
       1             : /*-------------------------------------------------------------------------
       2             :  *
       3             :  * ts_typanalyze.c
       4             :  *    functions for gathering statistics from tsvector columns
       5             :  *
       6             :  * Portions Copyright (c) 1996-2019, PostgreSQL Global Development Group
       7             :  *
       8             :  *
       9             :  * IDENTIFICATION
      10             :  *    src/backend/tsearch/ts_typanalyze.c
      11             :  *
      12             :  *-------------------------------------------------------------------------
      13             :  */
      14             : #include "postgres.h"
      15             : 
      16             : #include "catalog/pg_collation.h"
      17             : #include "catalog/pg_operator.h"
      18             : #include "commands/vacuum.h"
      19             : #include "tsearch/ts_type.h"
      20             : #include "utils/builtins.h"
      21             : #include "utils/hashutils.h"
      22             : 
      23             : 
      24             : /* A hash key for lexemes */
      25             : typedef struct
      26             : {
      27             :     char       *lexeme;         /* lexeme (not NULL terminated!) */
      28             :     int         length;         /* its length in bytes */
      29             : } LexemeHashKey;
      30             : 
      31             : /* A hash table entry for the Lossy Counting algorithm */
      32             : typedef struct
      33             : {
      34             :     LexemeHashKey key;          /* This is 'e' from the LC algorithm. */
      35             :     int         frequency;      /* This is 'f'. */
      36             :     int         delta;          /* And this is 'delta'. */
      37             : } TrackItem;
      38             : 
      39             : static void compute_tsvector_stats(VacAttrStats *stats,
      40             :                                    AnalyzeAttrFetchFunc fetchfunc,
      41             :                                    int samplerows,
      42             :                                    double totalrows);
      43             : static void prune_lexemes_hashtable(HTAB *lexemes_tab, int b_current);
      44             : static uint32 lexeme_hash(const void *key, Size keysize);
      45             : static int  lexeme_match(const void *key1, const void *key2, Size keysize);
      46             : static int  lexeme_compare(const void *key1, const void *key2);
      47             : static int  trackitem_compare_frequencies_desc(const void *e1, const void *e2);
      48             : static int  trackitem_compare_lexemes(const void *e1, const void *e2);
      49             : 
      50             : 
      51             : /*
      52             :  *  ts_typanalyze -- a custom typanalyze function for tsvector columns
      53             :  */
      54             : Datum
      55          10 : ts_typanalyze(PG_FUNCTION_ARGS)
      56             : {
      57          10 :     VacAttrStats *stats = (VacAttrStats *) PG_GETARG_POINTER(0);
      58          10 :     Form_pg_attribute attr = stats->attr;
      59             : 
      60             :     /* If the attstattarget column is negative, use the default value */
      61             :     /* NB: it is okay to scribble on stats->attr since it's a copy */
      62          10 :     if (attr->attstattarget < 0)
      63          10 :         attr->attstattarget = default_statistics_target;
      64             : 
      65          10 :     stats->compute_stats = compute_tsvector_stats;
      66             :     /* see comment about the choice of minrows in commands/analyze.c */
      67          10 :     stats->minrows = 300 * attr->attstattarget;
      68             : 
      69          10 :     PG_RETURN_BOOL(true);
      70             : }
      71             : 
      72             : /*
      73             :  *  compute_tsvector_stats() -- compute statistics for a tsvector column
      74             :  *
      75             :  *  This functions computes statistics that are useful for determining @@
      76             :  *  operations' selectivity, along with the fraction of non-null rows and
      77             :  *  average width.
      78             :  *
      79             :  *  Instead of finding the most common values, as we do for most datatypes,
      80             :  *  we're looking for the most common lexemes. This is more useful, because
      81             :  *  there most probably won't be any two rows with the same tsvector and thus
      82             :  *  the notion of a MCV is a bit bogus with this datatype. With a list of the
      83             :  *  most common lexemes we can do a better job at figuring out @@ selectivity.
      84             :  *
      85             :  *  For the same reasons we assume that tsvector columns are unique when
      86             :  *  determining the number of distinct values.
      87             :  *
      88             :  *  The algorithm used is Lossy Counting, as proposed in the paper "Approximate
      89             :  *  frequency counts over data streams" by G. S. Manku and R. Motwani, in
      90             :  *  Proceedings of the 28th International Conference on Very Large Data Bases,
      91             :  *  Hong Kong, China, August 2002, section 4.2. The paper is available at
      92             :  *  http://www.vldb.org/conf/2002/S10P03.pdf
      93             :  *
      94             :  *  The Lossy Counting (aka LC) algorithm goes like this:
      95             :  *  Let s be the threshold frequency for an item (the minimum frequency we
      96             :  *  are interested in) and epsilon the error margin for the frequency. Let D
      97             :  *  be a set of triples (e, f, delta), where e is an element value, f is that
      98             :  *  element's frequency (actually, its current occurrence count) and delta is
      99             :  *  the maximum error in f. We start with D empty and process the elements in
     100             :  *  batches of size w. (The batch size is also known as "bucket size" and is
     101             :  *  equal to 1/epsilon.) Let the current batch number be b_current, starting
     102             :  *  with 1. For each element e we either increment its f count, if it's
     103             :  *  already in D, or insert a new triple into D with values (e, 1, b_current
     104             :  *  - 1). After processing each batch we prune D, by removing from it all
     105             :  *  elements with f + delta <= b_current.  After the algorithm finishes we
     106             :  *  suppress all elements from D that do not satisfy f >= (s - epsilon) * N,
     107             :  *  where N is the total number of elements in the input.  We emit the
     108             :  *  remaining elements with estimated frequency f/N.  The LC paper proves
     109             :  *  that this algorithm finds all elements with true frequency at least s,
     110             :  *  and that no frequency is overestimated or is underestimated by more than
     111             :  *  epsilon.  Furthermore, given reasonable assumptions about the input
     112             :  *  distribution, the required table size is no more than about 7 times w.
     113             :  *
     114             :  *  We set s to be the estimated frequency of the K'th word in a natural
     115             :  *  language's frequency table, where K is the target number of entries in
     116             :  *  the MCELEM array plus an arbitrary constant, meant to reflect the fact
     117             :  *  that the most common words in any language would usually be stopwords
     118             :  *  so we will not actually see them in the input.  We assume that the
     119             :  *  distribution of word frequencies (including the stopwords) follows Zipf's
     120             :  *  law with an exponent of 1.
     121             :  *
     122             :  *  Assuming Zipfian distribution, the frequency of the K'th word is equal
     123             :  *  to 1/(K * H(W)) where H(n) is 1/2 + 1/3 + ... + 1/n and W is the number of
     124             :  *  words in the language.  Putting W as one million, we get roughly 0.07/K.
     125             :  *  Assuming top 10 words are stopwords gives s = 0.07/(K + 10).  We set
     126             :  *  epsilon = s/10, which gives bucket width w = (K + 10)/0.007 and
     127             :  *  maximum expected hashtable size of about 1000 * (K + 10).
     128             :  *
     129             :  *  Note: in the above discussion, s, epsilon, and f/N are in terms of a
     130             :  *  lexeme's frequency as a fraction of all lexemes seen in the input.
     131             :  *  However, what we actually want to store in the finished pg_statistic
     132             :  *  entry is each lexeme's frequency as a fraction of all rows that it occurs
     133             :  *  in.  Assuming that the input tsvectors are correctly constructed, no
     134             :  *  lexeme occurs more than once per tsvector, so the final count f is a
     135             :  *  correct estimate of the number of input tsvectors it occurs in, and we
     136             :  *  need only change the divisor from N to nonnull_cnt to get the number we
     137             :  *  want.
     138             :  */
     139             : static void
     140          10 : compute_tsvector_stats(VacAttrStats *stats,
     141             :                        AnalyzeAttrFetchFunc fetchfunc,
     142             :                        int samplerows,
     143             :                        double totalrows)
     144             : {
     145             :     int         num_mcelem;
     146          10 :     int         null_cnt = 0;
     147          10 :     double      total_width = 0;
     148             : 
     149             :     /* This is D from the LC algorithm. */
     150             :     HTAB       *lexemes_tab;
     151             :     HASHCTL     hash_ctl;
     152             :     HASH_SEQ_STATUS scan_status;
     153             : 
     154             :     /* This is the current bucket number from the LC algorithm */
     155             :     int         b_current;
     156             : 
     157             :     /* This is 'w' from the LC algorithm */
     158             :     int         bucket_width;
     159             :     int         vector_no,
     160             :                 lexeme_no;
     161             :     LexemeHashKey hash_key;
     162             :     TrackItem  *item;
     163             : 
     164             :     /*
     165             :      * We want statistics_target * 10 lexemes in the MCELEM array.  This
     166             :      * multiplier is pretty arbitrary, but is meant to reflect the fact that
     167             :      * the number of individual lexeme values tracked in pg_statistic ought to
     168             :      * be more than the number of values for a simple scalar column.
     169             :      */
     170          10 :     num_mcelem = stats->attr->attstattarget * 10;
     171             : 
     172             :     /*
     173             :      * We set bucket width equal to (num_mcelem + 10) / 0.007 as per the
     174             :      * comment above.
     175             :      */
     176          10 :     bucket_width = (num_mcelem + 10) * 1000 / 7;
     177             : 
     178             :     /*
     179             :      * Create the hashtable. It will be in local memory, so we don't need to
     180             :      * worry about overflowing the initial size. Also we don't need to pay any
     181             :      * attention to locking and memory management.
     182             :      */
     183          10 :     MemSet(&hash_ctl, 0, sizeof(hash_ctl));
     184          10 :     hash_ctl.keysize = sizeof(LexemeHashKey);
     185          10 :     hash_ctl.entrysize = sizeof(TrackItem);
     186          10 :     hash_ctl.hash = lexeme_hash;
     187          10 :     hash_ctl.match = lexeme_match;
     188          10 :     hash_ctl.hcxt = CurrentMemoryContext;
     189          10 :     lexemes_tab = hash_create("Analyzed lexemes table",
     190             :                               num_mcelem,
     191             :                               &hash_ctl,
     192             :                               HASH_ELEM | HASH_FUNCTION | HASH_COMPARE | HASH_CONTEXT);
     193             : 
     194             :     /* Initialize counters. */
     195          10 :     b_current = 1;
     196          10 :     lexeme_no = 0;
     197             : 
     198             :     /* Loop over the tsvectors. */
     199        4686 :     for (vector_no = 0; vector_no < samplerows; vector_no++)
     200             :     {
     201             :         Datum       value;
     202             :         bool        isnull;
     203             :         TSVector    vector;
     204             :         WordEntry  *curentryptr;
     205             :         char       *lexemesptr;
     206             :         int         j;
     207             : 
     208        4676 :         vacuum_delay_point();
     209             : 
     210        4676 :         value = fetchfunc(stats, vector_no, &isnull);
     211             : 
     212             :         /*
     213             :          * Check for null/nonnull.
     214             :          */
     215        4676 :         if (isnull)
     216             :         {
     217           0 :             null_cnt++;
     218           0 :             continue;
     219             :         }
     220             : 
     221             :         /*
     222             :          * Add up widths for average-width calculation.  Since it's a
     223             :          * tsvector, we know it's varlena.  As in the regular
     224             :          * compute_minimal_stats function, we use the toasted width for this
     225             :          * calculation.
     226             :          */
     227        4676 :         total_width += VARSIZE_ANY(DatumGetPointer(value));
     228             : 
     229             :         /*
     230             :          * Now detoast the tsvector if needed.
     231             :          */
     232        4676 :         vector = DatumGetTSVector(value);
     233             : 
     234             :         /*
     235             :          * We loop through the lexemes in the tsvector and add them to our
     236             :          * tracking hashtable.
     237             :          */
     238        4676 :         lexemesptr = STRPTR(vector);
     239        4676 :         curentryptr = ARRPTR(vector);
     240      268544 :         for (j = 0; j < vector->size; j++)
     241             :         {
     242             :             bool        found;
     243             : 
     244             :             /*
     245             :              * Construct a hash key.  The key points into the (detoasted)
     246             :              * tsvector value at this point, but if a new entry is created, we
     247             :              * make a copy of it.  This way we can free the tsvector value
     248             :              * once we've processed all its lexemes.
     249             :              */
     250      263868 :             hash_key.lexeme = lexemesptr + curentryptr->pos;
     251      263868 :             hash_key.length = curentryptr->len;
     252             : 
     253             :             /* Lookup current lexeme in hashtable, adding it if new */
     254      263868 :             item = (TrackItem *) hash_search(lexemes_tab,
     255             :                                              (const void *) &hash_key,
     256             :                                              HASH_ENTER, &found);
     257             : 
     258      263868 :             if (found)
     259             :             {
     260             :                 /* The lexeme is already on the tracking list */
     261      251456 :                 item->frequency++;
     262             :             }
     263             :             else
     264             :             {
     265             :                 /* Initialize new tracking list element */
     266       12412 :                 item->frequency = 1;
     267       12412 :                 item->delta = b_current - 1;
     268             : 
     269       12412 :                 item->key.lexeme = palloc(hash_key.length);
     270       12412 :                 memcpy(item->key.lexeme, hash_key.lexeme, hash_key.length);
     271             :             }
     272             : 
     273             :             /* lexeme_no is the number of elements processed (ie N) */
     274      263868 :             lexeme_no++;
     275             : 
     276             :             /* We prune the D structure after processing each bucket */
     277      263868 :             if (lexeme_no % bucket_width == 0)
     278             :             {
     279          12 :                 prune_lexemes_hashtable(lexemes_tab, b_current);
     280          12 :                 b_current++;
     281             :             }
     282             : 
     283             :             /* Advance to the next WordEntry in the tsvector */
     284      263868 :             curentryptr++;
     285             :         }
     286             : 
     287             :         /* If the vector was toasted, free the detoasted copy. */
     288        4676 :         if (TSVectorGetDatum(vector) != value)
     289         608 :             pfree(vector);
     290             :     }
     291             : 
     292             :     /* We can only compute real stats if we found some non-null values. */
     293          10 :     if (null_cnt < samplerows)
     294             :     {
     295          10 :         int         nonnull_cnt = samplerows - null_cnt;
     296             :         int         i;
     297             :         TrackItem **sort_table;
     298             :         int         track_len;
     299             :         int         cutoff_freq;
     300             :         int         minfreq,
     301             :                     maxfreq;
     302             : 
     303          10 :         stats->stats_valid = true;
     304             :         /* Do the simple null-frac and average width stats */
     305          10 :         stats->stanullfrac = (double) null_cnt / (double) samplerows;
     306          10 :         stats->stawidth = total_width / (double) nonnull_cnt;
     307             : 
     308             :         /* Assume it's a unique column (see notes above) */
     309          10 :         stats->stadistinct = -1.0 * (1.0 - stats->stanullfrac);
     310             : 
     311             :         /*
     312             :          * Construct an array of the interesting hashtable items, that is,
     313             :          * those meeting the cutoff frequency (s - epsilon)*N.  Also identify
     314             :          * the minimum and maximum frequencies among these items.
     315             :          *
     316             :          * Since epsilon = s/10 and bucket_width = 1/epsilon, the cutoff
     317             :          * frequency is 9*N / bucket_width.
     318             :          */
     319          10 :         cutoff_freq = 9 * lexeme_no / bucket_width;
     320             : 
     321          10 :         i = hash_get_num_entries(lexemes_tab);  /* surely enough space */
     322          10 :         sort_table = (TrackItem **) palloc(sizeof(TrackItem *) * i);
     323             : 
     324          10 :         hash_seq_init(&scan_status, lexemes_tab);
     325          10 :         track_len = 0;
     326          10 :         minfreq = lexeme_no;
     327          10 :         maxfreq = 0;
     328       10974 :         while ((item = (TrackItem *) hash_seq_search(&scan_status)) != NULL)
     329             :         {
     330       10954 :             if (item->frequency > cutoff_freq)
     331             :             {
     332        7808 :                 sort_table[track_len++] = item;
     333        7808 :                 minfreq = Min(minfreq, item->frequency);
     334        7808 :                 maxfreq = Max(maxfreq, item->frequency);
     335             :             }
     336             :         }
     337             :         Assert(track_len <= i);
     338             : 
     339             :         /* emit some statistics for debug purposes */
     340          10 :         elog(DEBUG3, "tsvector_stats: target # mces = %d, bucket width = %d, "
     341             :              "# lexemes = %d, hashtable size = %d, usable entries = %d",
     342             :              num_mcelem, bucket_width, lexeme_no, i, track_len);
     343             : 
     344             :         /*
     345             :          * If we obtained more lexemes than we really want, get rid of those
     346             :          * with least frequencies.  The easiest way is to qsort the array into
     347             :          * descending frequency order and truncate the array.
     348             :          */
     349          10 :         if (num_mcelem < track_len)
     350             :         {
     351          10 :             qsort(sort_table, track_len, sizeof(TrackItem *),
     352             :                   trackitem_compare_frequencies_desc);
     353             :             /* reset minfreq to the smallest frequency we're keeping */
     354          10 :             minfreq = sort_table[num_mcelem - 1]->frequency;
     355             :         }
     356             :         else
     357           0 :             num_mcelem = track_len;
     358             : 
     359             :         /* Generate MCELEM slot entry */
     360          10 :         if (num_mcelem > 0)
     361             :         {
     362             :             MemoryContext old_context;
     363             :             Datum      *mcelem_values;
     364             :             float4     *mcelem_freqs;
     365             : 
     366             :             /*
     367             :              * We want to store statistics sorted on the lexeme value using
     368             :              * first length, then byte-for-byte comparison. The reason for
     369             :              * doing length comparison first is that we don't care about the
     370             :              * ordering so long as it's consistent, and comparing lengths
     371             :              * first gives us a chance to avoid a strncmp() call.
     372             :              *
     373             :              * This is different from what we do with scalar statistics --
     374             :              * they get sorted on frequencies. The rationale is that we
     375             :              * usually search through most common elements looking for a
     376             :              * specific value, so we can grab its frequency.  When values are
     377             :              * presorted we can employ binary search for that.  See
     378             :              * ts_selfuncs.c for a real usage scenario.
     379             :              */
     380          10 :             qsort(sort_table, num_mcelem, sizeof(TrackItem *),
     381             :                   trackitem_compare_lexemes);
     382             : 
     383             :             /* Must copy the target values into anl_context */
     384          10 :             old_context = MemoryContextSwitchTo(stats->anl_context);
     385             : 
     386             :             /*
     387             :              * We sorted statistics on the lexeme value, but we want to be
     388             :              * able to find out the minimal and maximal frequency without
     389             :              * going through all the values.  We keep those two extra
     390             :              * frequencies in two extra cells in mcelem_freqs.
     391             :              *
     392             :              * (Note: the MCELEM statistics slot definition allows for a third
     393             :              * extra number containing the frequency of nulls, but we don't
     394             :              * create that for a tsvector column, since null elements aren't
     395             :              * possible.)
     396             :              */
     397          10 :             mcelem_values = (Datum *) palloc(num_mcelem * sizeof(Datum));
     398          10 :             mcelem_freqs = (float4 *) palloc((num_mcelem + 2) * sizeof(float4));
     399             : 
     400             :             /*
     401             :              * See comments above about use of nonnull_cnt as the divisor for
     402             :              * the final frequency estimates.
     403             :              */
     404        6230 :             for (i = 0; i < num_mcelem; i++)
     405             :             {
     406        6220 :                 TrackItem  *item = sort_table[i];
     407             : 
     408       12440 :                 mcelem_values[i] =
     409        6220 :                     PointerGetDatum(cstring_to_text_with_len(item->key.lexeme,
     410             :                                                              item->key.length));
     411        6220 :                 mcelem_freqs[i] = (double) item->frequency / (double) nonnull_cnt;
     412             :             }
     413          10 :             mcelem_freqs[i++] = (double) minfreq / (double) nonnull_cnt;
     414          10 :             mcelem_freqs[i] = (double) maxfreq / (double) nonnull_cnt;
     415          10 :             MemoryContextSwitchTo(old_context);
     416             : 
     417          10 :             stats->stakind[0] = STATISTIC_KIND_MCELEM;
     418          10 :             stats->staop[0] = TextEqualOperator;
     419          10 :             stats->stacoll[0] = DEFAULT_COLLATION_OID;
     420          10 :             stats->stanumbers[0] = mcelem_freqs;
     421             :             /* See above comment about two extra frequency fields */
     422          10 :             stats->numnumbers[0] = num_mcelem + 2;
     423          10 :             stats->stavalues[0] = mcelem_values;
     424          10 :             stats->numvalues[0] = num_mcelem;
     425             :             /* We are storing text values */
     426          10 :             stats->statypid[0] = TEXTOID;
     427          10 :             stats->statyplen[0] = -1;    /* typlen, -1 for varlena */
     428          10 :             stats->statypbyval[0] = false;
     429          10 :             stats->statypalign[0] = 'i';
     430             :         }
     431             :     }
     432             :     else
     433             :     {
     434             :         /* We found only nulls; assume the column is entirely null */
     435           0 :         stats->stats_valid = true;
     436           0 :         stats->stanullfrac = 1.0;
     437           0 :         stats->stawidth = 0; /* "unknown" */
     438           0 :         stats->stadistinct = 0.0;    /* "unknown" */
     439             :     }
     440             : 
     441             :     /*
     442             :      * We don't need to bother cleaning up any of our temporary palloc's. The
     443             :      * hashtable should also go away, as it used a child memory context.
     444             :      */
     445          10 : }
     446             : 
     447             : /*
     448             :  *  A function to prune the D structure from the Lossy Counting algorithm.
     449             :  *  Consult compute_tsvector_stats() for wider explanation.
     450             :  */
     451             : static void
     452          12 : prune_lexemes_hashtable(HTAB *lexemes_tab, int b_current)
     453             : {
     454             :     HASH_SEQ_STATUS scan_status;
     455             :     TrackItem  *item;
     456             : 
     457          12 :     hash_seq_init(&scan_status, lexemes_tab);
     458       11542 :     while ((item = (TrackItem *) hash_seq_search(&scan_status)) != NULL)
     459             :     {
     460       11518 :         if (item->frequency + item->delta <= b_current)
     461             :         {
     462        1458 :             char       *lexeme = item->key.lexeme;
     463             : 
     464        1458 :             if (hash_search(lexemes_tab, (const void *) &item->key,
     465             :                             HASH_REMOVE, NULL) == NULL)
     466           0 :                 elog(ERROR, "hash table corrupted");
     467        1458 :             pfree(lexeme);
     468             :         }
     469             :     }
     470          12 : }
     471             : 
     472             : /*
     473             :  * Hash functions for lexemes. They are strings, but not NULL terminated,
     474             :  * so we need a special hash function.
     475             :  */
     476             : static uint32
     477      265326 : lexeme_hash(const void *key, Size keysize)
     478             : {
     479      265326 :     const LexemeHashKey *l = (const LexemeHashKey *) key;
     480             : 
     481      265326 :     return DatumGetUInt32(hash_any((const unsigned char *) l->lexeme,
     482             :                                    l->length));
     483             : }
     484             : 
     485             : /*
     486             :  *  Matching function for lexemes, to be used in hashtable lookups.
     487             :  */
     488             : static int
     489      252914 : lexeme_match(const void *key1, const void *key2, Size keysize)
     490             : {
     491             :     /* The keysize parameter is superfluous, the keys store their lengths */
     492      252914 :     return lexeme_compare(key1, key2);
     493             : }
     494             : 
     495             : /*
     496             :  *  Comparison function for lexemes.
     497             :  */
     498             : static int
     499      316076 : lexeme_compare(const void *key1, const void *key2)
     500             : {
     501      316076 :     const LexemeHashKey *d1 = (const LexemeHashKey *) key1;
     502      316076 :     const LexemeHashKey *d2 = (const LexemeHashKey *) key2;
     503             : 
     504             :     /* First, compare by length */
     505      316076 :     if (d1->length > d2->length)
     506           0 :         return 1;
     507      316076 :     else if (d1->length < d2->length)
     508           0 :         return -1;
     509             :     /* Lengths are equal, do a byte-by-byte comparison */
     510      316076 :     return strncmp(d1->lexeme, d2->lexeme, d1->length);
     511             : }
     512             : 
     513             : /*
     514             :  *  qsort() comparator for sorting TrackItems on frequencies (descending sort)
     515             :  */
     516             : static int
     517       44802 : trackitem_compare_frequencies_desc(const void *e1, const void *e2)
     518             : {
     519       44802 :     const TrackItem *const *t1 = (const TrackItem *const *) e1;
     520       44802 :     const TrackItem *const *t2 = (const TrackItem *const *) e2;
     521             : 
     522       44802 :     return (*t2)->frequency - (*t1)->frequency;
     523             : }
     524             : 
     525             : /*
     526             :  *  qsort() comparator for sorting TrackItems on lexemes
     527             :  */
     528             : static int
     529       63162 : trackitem_compare_lexemes(const void *e1, const void *e2)
     530             : {
     531       63162 :     const TrackItem *const *t1 = (const TrackItem *const *) e1;
     532       63162 :     const TrackItem *const *t2 = (const TrackItem *const *) e2;
     533             : 
     534       63162 :     return lexeme_compare(&(*t1)->key, &(*t2)->key);
     535             : }

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