*
*
* IDENTIFICATION
- * $PostgreSQL: pgsql/src/backend/commands/analyze.c,v 1.71 2004/05/08 19:09:24 tgl Exp $
+ * $PostgreSQL: pgsql/src/backend/commands/analyze.c,v 1.72 2004/05/23 21:24:02 tgl Exp $
*
*-------------------------------------------------------------------------
*/
#include "utils/tuplesort.h"
+/* Data structure for Algorithm S from Knuth 3.4.2 */
+typedef struct
+{
+ BlockNumber N; /* number of blocks, known in advance */
+ int n; /* desired sample size */
+ BlockNumber t; /* current block number */
+ int m; /* blocks selected so far */
+} BlockSamplerData;
+typedef BlockSamplerData *BlockSampler;
+
/* Per-index data for ANALYZE */
typedef struct AnlIndexData
{
static MemoryContext anl_context = NULL;
+static void BlockSampler_Init(BlockSampler bs, BlockNumber nblocks,
+ int samplesize);
+static bool BlockSampler_HasMore(BlockSampler bs);
+static BlockNumber BlockSampler_Next(BlockSampler bs);
static void compute_index_stats(Relation onerel, double totalrows,
AnlIndexData *indexdata, int nindexes,
HeapTuple *rows, int numrows,
int targrows, double *totalrows);
static double random_fract(void);
static double init_selection_state(int n);
-static double select_next_random_record(double t, int n, double *stateptr);
+static double get_next_S(double t, int n, double *stateptr);
static int compare_rows(const void *a, const void *b);
static void update_attstats(Oid relid, int natts, VacAttrStats **vacattrstats);
static Datum std_fetch_func(VacAttrStatsP stats, int rownum, bool *isNull);
return stats;
}
+/*
+ * BlockSampler_Init -- prepare for random sampling of blocknumbers
+ *
+ * BlockSampler is used for stage one of our new two-stage tuple
+ * sampling mechanism as discussed on pgsql-hackers 2004-04-02 (subject
+ * "Large DB"). It selects a random sample of samplesize blocks out of
+ * the nblocks blocks in the table. If the table has less than
+ * samplesize blocks, all blocks are selected.
+ *
+ * Since we know the total number of blocks in advance, we can use the
+ * straightforward Algorithm S from Knuth 3.4.2, rather than Vitter's
+ * algorithm.
+ */
+static void
+BlockSampler_Init(BlockSampler bs, BlockNumber nblocks, int samplesize)
+{
+ bs->N = nblocks; /* measured table size */
+ /*
+ * If we decide to reduce samplesize for tables that have less or
+ * not much more than samplesize blocks, here is the place to do
+ * it.
+ */
+ bs->n = samplesize;
+ bs->t = 0; /* blocks scanned so far */
+ bs->m = 0; /* blocks selected so far */
+}
+
+static bool
+BlockSampler_HasMore(BlockSampler bs)
+{
+ return (bs->t < bs->N) && (bs->m < bs->n);
+}
+
+static BlockNumber
+BlockSampler_Next(BlockSampler bs)
+{
+ BlockNumber K = bs->N - bs->t; /* remaining blocks */
+ int k = bs->n - bs->m; /* blocks still to sample */
+ double p; /* probability to skip block */
+ double V; /* random */
+
+ Assert(BlockSampler_HasMore(bs)); /* hence K > 0 and k > 0 */
+
+ if ((BlockNumber) k >= K)
+ {
+ /* need all the rest */
+ bs->m++;
+ return bs->t++;
+ }
+
+ /*----------
+ * It is not obvious that this code matches Knuth's Algorithm S.
+ * Knuth says to skip the current block with probability 1 - k/K.
+ * If we are to skip, we should advance t (hence decrease K), and
+ * repeat the same probabilistic test for the next block. The naive
+ * implementation thus requires a random_fract() call for each block
+ * number. But we can reduce this to one random_fract() call per
+ * selected block, by noting that each time the while-test succeeds,
+ * we can reinterpret V as a uniform random number in the range 0 to p.
+ * Therefore, instead of choosing a new V, we just adjust p to be
+ * the appropriate fraction of its former value, and our next loop
+ * makes the appropriate probabilistic test.
+ *
+ * We have initially K > k > 0. If the loop reduces K to equal k,
+ * the next while-test must fail since p will become exactly zero
+ * (we assume there will not be roundoff error in the division).
+ * (Note: Knuth suggests a "<=" loop condition, but we use "<" just
+ * to be doubly sure about roundoff error.) Therefore K cannot become
+ * less than k, which means that we cannot fail to select enough blocks.
+ *----------
+ */
+ V = random_fract();
+ p = 1.0 - (double) k / (double) K;
+ while (V < p)
+ {
+ /* skip */
+ bs->t++;
+ K--; /* keep K == N - t */
+
+ /* adjust p to be new cutoff point in reduced range */
+ p *= 1.0 - (double) k / (double) K;
+ }
+
+ /* select */
+ bs->m++;
+ return bs->t++;
+}
+
/*
* acquire_sample_rows -- acquire a random sample of rows from the table
*
- * Up to targrows rows are collected (if there are fewer than that many
- * rows in the table, all rows are collected). When the table is larger
- * than targrows, a truly random sample is collected: every row has an
- * equal chance of ending up in the final sample.
+ * As of May 2004 we use a new two-stage method: Stage one selects up
+ * to targrows random blocks (or all blocks, if there aren't so many).
+ * Stage two scans these blocks and uses the Vitter algorithm to create
+ * a random sample of targrows rows (or less, if there are less in the
+ * sample of blocks). The two stages are executed simultaneously: each
+ * block is processed as soon as stage one returns its number and while
+ * the rows are read stage two controls which ones are to be inserted
+ * into the sample.
+ *
+ * Although every row has an equal chance of ending up in the final
+ * sample, this sampling method is not perfect: not every possible
+ * sample has an equal chance of being selected. For large relations
+ * the number of different blocks represented by the sample tends to be
+ * too small. We can live with that for now. Improvements are welcome.
*
* We also estimate the total number of rows in the table, and return that
- * into *totalrows.
+ * into *totalrows. An important property of this sampling method is that
+ * because we do look at a statistically unbiased set of blocks, we should
+ * get an unbiased estimate of the average number of live rows per block.
+ * The previous sampling method put too much credence in the row density near
+ * the start of the table.
*
* The returned list of tuples is in order by physical position in the table.
* (We will rely on this later to derive correlation estimates.)
acquire_sample_rows(Relation onerel, HeapTuple *rows, int targrows,
double *totalrows)
{
- int numrows = 0;
- HeapScanDesc scan;
+ int numrows = 0; /* # rows collected */
+ double liverows = 0; /* # rows seen */
+ double deadrows = 0;
+ double rowstoskip = -1; /* -1 means not set yet */
BlockNumber totalblocks;
- HeapTuple tuple;
- ItemPointer lasttuple;
- BlockNumber lastblock,
- estblock;
- OffsetNumber lastoffset;
- int numest;
- double tuplesperpage;
- double t;
+ BlockSamplerData bs;
double rstate;
Assert(targrows > 1);
- /*
- * Do a simple linear scan until we reach the target number of rows.
- */
- scan = heap_beginscan(onerel, SnapshotNow, 0, NULL);
- totalblocks = scan->rs_nblocks; /* grab current relation size */
- while ((tuple = heap_getnext(scan, ForwardScanDirection)) != NULL)
- {
- rows[numrows++] = heap_copytuple(tuple);
- if (numrows >= targrows)
- break;
- vacuum_delay_point();
- }
- heap_endscan(scan);
-
- /*
- * If we ran out of tuples then we're done, no matter how few we
- * collected. No sort is needed, since they're already in order.
- */
- if (!HeapTupleIsValid(tuple))
- {
- *totalrows = (double) numrows;
-
- ereport(elevel,
- (errmsg("\"%s\": %u pages, %d rows sampled, %.0f estimated total rows",
- RelationGetRelationName(onerel),
- totalblocks, numrows, *totalrows)));
-
- return numrows;
- }
-
- /*
- * Otherwise, start replacing tuples in the sample until we reach the
- * end of the relation. This algorithm is from Jeff Vitter's paper
- * (see full citation below). It works by repeatedly computing the
- * number of the next tuple we want to fetch, which will replace a
- * randomly chosen element of the reservoir (current set of tuples).
- * At all times the reservoir is a true random sample of the tuples
- * we've passed over so far, so when we fall off the end of the
- * relation we're done.
- *
- * A slight difficulty is that since we don't want to fetch tuples or
- * even pages that we skip over, it's not possible to fetch *exactly*
- * the N'th tuple at each step --- we don't know how many valid tuples
- * are on the skipped pages. We handle this by assuming that the
- * average number of valid tuples/page on the pages already scanned
- * over holds good for the rest of the relation as well; this lets us
- * estimate which page the next tuple should be on and its position in
- * the page. Then we fetch the first valid tuple at or after that
- * position, being careful not to use the same tuple twice. This
- * approach should still give a good random sample, although it's not
- * perfect.
- */
- lasttuple = &(rows[numrows - 1]->t_self);
- lastblock = ItemPointerGetBlockNumber(lasttuple);
- lastoffset = ItemPointerGetOffsetNumber(lasttuple);
-
- /*
- * If possible, estimate tuples/page using only completely-scanned
- * pages.
- */
- for (numest = numrows; numest > 0; numest--)
- {
- if (ItemPointerGetBlockNumber(&(rows[numest - 1]->t_self)) != lastblock)
- break;
- }
- if (numest == 0)
- {
- numest = numrows; /* don't have a full page? */
- estblock = lastblock + 1;
- }
- else
- estblock = lastblock;
- tuplesperpage = (double) numest / (double) estblock;
+ totalblocks = RelationGetNumberOfBlocks(onerel);
- t = (double) numrows; /* t is the # of records processed so far */
+ /* Prepare for sampling block numbers */
+ BlockSampler_Init(&bs, totalblocks, targrows);
+ /* Prepare for sampling rows */
rstate = init_selection_state(targrows);
- for (;;)
+
+ /* Outer loop over blocks to sample */
+ while (BlockSampler_HasMore(&bs))
{
- double targpos;
- BlockNumber targblock;
+ BlockNumber targblock = BlockSampler_Next(&bs);
Buffer targbuffer;
Page targpage;
OffsetNumber targoffset,
vacuum_delay_point();
- t = select_next_random_record(t, targrows, &rstate);
- /* Try to read the t'th record in the table */
- targpos = t / tuplesperpage;
- targblock = (BlockNumber) targpos;
- targoffset = ((int) ((targpos - targblock) * tuplesperpage)) +
- FirstOffsetNumber;
- /* Make sure we are past the last selected record */
- if (targblock <= lastblock)
- {
- targblock = lastblock;
- if (targoffset <= lastoffset)
- targoffset = lastoffset + 1;
- }
- /* Loop to find first valid record at or after given position */
-pageloop:;
-
- /*
- * Have we fallen off the end of the relation?
- */
- if (targblock >= totalblocks)
- break;
-
/*
* We must maintain a pin on the target page's buffer to ensure
* that the maxoffset value stays good (else concurrent VACUUM
maxoffset = PageGetMaxOffsetNumber(targpage);
LockBuffer(targbuffer, BUFFER_LOCK_UNLOCK);
- for (;;)
+ /* Inner loop over all tuples on the selected page */
+ for (targoffset = FirstOffsetNumber; targoffset <= maxoffset; targoffset++)
{
HeapTupleData targtuple;
Buffer tupbuffer;
- if (targoffset > maxoffset)
- {
- /* Fell off end of this page, try next */
- ReleaseBuffer(targbuffer);
- targblock++;
- targoffset = FirstOffsetNumber;
- goto pageloop;
- }
ItemPointerSet(&targtuple.t_self, targblock, targoffset);
if (heap_fetch(onerel, SnapshotNow, &targtuple, &tupbuffer,
false, NULL))
{
/*
- * Found a suitable tuple, so save it, replacing one old
- * tuple at random
+ * The first targrows live rows are simply copied into the
+ * reservoir.
+ * Then we start replacing tuples in the sample until
+ * we reach the end of the relation. This algorithm is
+ * from Jeff Vitter's paper (see full citation below).
+ * It works by repeatedly computing the number of tuples
+ * to skip before selecting a tuple, which replaces a
+ * randomly chosen element of the reservoir (current
+ * set of tuples). At all times the reservoir is a true
+ * random sample of the tuples we've passed over so far,
+ * so when we fall off the end of the relation we're done.
*/
- int k = (int) (targrows * random_fract());
+ if (numrows < targrows)
+ rows[numrows++] = heap_copytuple(&targtuple);
+ else
+ {
+ /*
+ * t in Vitter's paper is the number of records already
+ * processed. If we need to compute a new S value, we
+ * must use the not-yet-incremented value of liverows
+ * as t.
+ */
+ if (rowstoskip < 0)
+ rowstoskip = get_next_S(liverows, targrows, &rstate);
+
+ if (rowstoskip <= 0)
+ {
+ /*
+ * Found a suitable tuple, so save it,
+ * replacing one old tuple at random
+ */
+ int k = (int) (targrows * random_fract());
- Assert(k >= 0 && k < targrows);
- heap_freetuple(rows[k]);
- rows[k] = heap_copytuple(&targtuple);
- /* this releases the second pin acquired by heap_fetch: */
+ Assert(k >= 0 && k < targrows);
+ heap_freetuple(rows[k]);
+ rows[k] = heap_copytuple(&targtuple);
+ }
+
+ rowstoskip -= 1;
+ }
+
+ /* must release the extra pin acquired by heap_fetch */
ReleaseBuffer(tupbuffer);
- /* this releases the initial pin: */
- ReleaseBuffer(targbuffer);
- lastblock = targblock;
- lastoffset = targoffset;
- break;
+
+ liverows += 1;
+ }
+ else
+ {
+ /*
+ * Count dead rows, but not empty slots. This information is
+ * currently not used, but it seems likely we'll want it
+ * someday.
+ */
+ if (targtuple.t_data != NULL)
+ deadrows += 1;
}
- /* this tuple is dead, so advance to next one on same page */
- targoffset++;
}
+
+ /* Now release the initial pin on the page */
+ ReleaseBuffer(targbuffer);
}
/*
- * Now we need to sort the collected tuples by position (itempointer).
+ * If we didn't find as many tuples as we wanted then we're done.
+ * No sort is needed, since they're already in order.
+ *
+ * Otherwise we need to sort the collected tuples by position
+ * (itempointer). It's not worth worrying about corner cases
+ * where the tuples are already sorted.
*/
- qsort((void *) rows, numrows, sizeof(HeapTuple), compare_rows);
+ if (numrows == targrows)
+ qsort((void *) rows, numrows, sizeof(HeapTuple), compare_rows);
/*
- * Estimate total number of valid rows in relation.
+ * Estimate total number of live rows in relation.
*/
- *totalrows = floor((double) totalblocks * tuplesperpage + 0.5);
+ if (bs.m > 0)
+ *totalrows = floor((liverows * totalblocks) / bs.m + 0.5);
+ else
+ *totalrows = 0.0;
/*
* Emit some interesting relation info
*/
ereport(elevel,
- (errmsg("\"%s\": %u pages, %d rows sampled, %.0f estimated total rows",
+ (errmsg("\"%s\": scanned %d of %u pages, "
+ "containing %.0f live rows and %.0f dead rows; "
+ "%d rows in sample, %.0f estimated total rows",
RelationGetRelationName(onerel),
- totalblocks, numrows, *totalrows)));
+ bs.m, totalblocks,
+ liverows, deadrows,
+ numrows, *totalrows)));
return numrows;
}
/*
* These two routines embody Algorithm Z from "Random sampling with a
* reservoir" by Jeffrey S. Vitter, in ACM Trans. Math. Softw. 11, 1
- * (Mar. 1985), Pages 37-57. While Vitter describes his algorithm in terms
- * of the count S of records to skip before processing another record,
- * it is convenient to work primarily with t, the index (counting from 1)
- * of the last record processed and next record to process. The only extra
- * state needed between calls is W, a random state variable.
- *
- * Note: the original algorithm defines t, S, numer, and denom as integers.
- * Here we express them as doubles to avoid overflow if the number of rows
- * in the table exceeds INT_MAX. The algorithm should work as long as the
- * row count does not become so large that it is not represented accurately
- * in a double (on IEEE-math machines this would be around 2^52 rows).
+ * (Mar. 1985), Pages 37-57. Vitter describes his algorithm in terms
+ * of the count S of records to skip before processing another record.
+ * It is computed primarily based on t, the number of records already read.
+ * The only extra state needed between calls is W, a random state variable.
*
* init_selection_state computes the initial W value.
*
- * Given that we've already processed t records (t >= n),
- * select_next_random_record determines the number of the next record to
- * process.
+ * Given that we've already read t records (t >= n), get_next_S
+ * determines the number of records to skip before the next record is
+ * processed.
*/
static double
init_selection_state(int n)
}
static double
-select_next_random_record(double t, int n, double *stateptr)
+get_next_S(double t, int n, double *stateptr)
{
+ double S;
+
/* The magic constant here is T from Vitter's paper */
if (t <= (22.0 * n))
{
quot;
V = random_fract(); /* Generate V */
+ S = 0;
t += 1;
+ /* Note: "num" in Vitter's code is always equal to t - n */
quot = (t - (double) n) / t;
/* Find min S satisfying (4.1) */
while (quot > V)
{
+ S += 1;
t += 1;
quot *= (t - (double) n) / t;
}
/* Now apply Algorithm Z */
double W = *stateptr;
double term = t - (double) n + 1;
- double S;
for (;;)
{
if (exp(log(y) / n) <= (t + X) / t)
break;
}
- t += S + 1;
*stateptr = W;
}
- return t;
+ return S;
}
/*
projects / postgresql.git / commitdiff