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Separate block sampling functions

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Refactoring ahead of tablesample patch

Requested and reviewed by Michael Paquier

Petr Jelinek
This commit is contained in:
Simon Riggs
2015-05-15 04:02:54 +02:00
parent 5a3022fde0
commit 83e176ec18
7 changed files with 287 additions and 232 deletions
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225
src/backend/commands/analyze.c
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@ -50,23 +50,13 @@
#include "utils/lsyscache.h"
#include "utils/memutils.h"
#include "utils/pg_rusage.h"
#include "utils/sampling.h"
#include "utils/sortsupport.h"
#include "utils/syscache.h"
#include "utils/timestamp.h"
#include "utils/tqual.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
{
@ -89,10 +79,6 @@ static void do_analyze_rel(Relation onerel, int options,
VacuumParams *params, List *va_cols,
AcquireSampleRowsFunc acquirefunc, BlockNumber relpages,
bool inh, bool in_outer_xact, int elevel);
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,
@ -950,94 +936,6 @@ examine_attribute(Relation onerel, int attnum, Node *index_expr)
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 an anl_random_fract() call for each block
* number. But we can reduce this to one anl_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 = anl_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
*
@ -1084,7 +982,7 @@ acquire_sample_rows(Relation onerel, int elevel,
BlockNumber totalblocks;
TransactionId OldestXmin;
BlockSamplerData bs;
double rstate;
ReservoirStateData rstate;
Assert(targrows > 0);
@ -1094,9 +992,9 @@ acquire_sample_rows(Relation onerel, int elevel,
OldestXmin = GetOldestXmin(onerel, true);
/* Prepare for sampling block numbers */
BlockSampler_Init(&bs, totalblocks, targrows);
BlockSampler_Init(&bs, totalblocks, targrows, random());
/* Prepare for sampling rows */
rstate = anl_init_selection_state(targrows);
reservoir_init_selection_state(&rstate, targrows);
/* Outer loop over blocks to sample */
while (BlockSampler_HasMore(&bs))
@ -1244,8 +1142,7 @@ acquire_sample_rows(Relation onerel, int elevel,
* t.
*/
if (rowstoskip < 0)
rowstoskip = anl_get_next_S(samplerows, targrows,
&rstate);
rowstoskip = reservoir_get_next_S(&rstate, samplerows, targrows);
if (rowstoskip <= 0)
{
@ -1253,7 +1150,7 @@ acquire_sample_rows(Relation onerel, int elevel,
* Found a suitable tuple, so save it, replacing one
* old tuple at random
*/
int k = (int) (targrows * anl_random_fract());
int k = (int) (targrows * sampler_random_fract());
Assert(k >= 0 && k < targrows);
heap_freetuple(rows[k]);
@ -1312,116 +1209,6 @@ acquire_sample_rows(Relation onerel, int elevel,
return numrows;
}
/* Select a random value R uniformly distributed in (0 - 1) */
double
anl_random_fract(void)
{
return ((double) random() + 1) / ((double) MAX_RANDOM_VALUE + 2);
}
/*
* 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. 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.
*
* anl_init_selection_state computes the initial W value.
*
* Given that we've already read t records (t >= n), anl_get_next_S
* determines the number of records to skip before the next record is
* processed.
*/
double
anl_init_selection_state(int n)
{
/* Initial value of W (for use when Algorithm Z is first applied) */
return exp(-log(anl_random_fract()) / n);
}
double
anl_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))
{
/* Process records using Algorithm X until t is large enough */
double V,
quot;
V = anl_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;
}
}
else
{
/* Now apply Algorithm Z */
double W = *stateptr;
double term = t - (double) n + 1;
for (;;)
{
double numer,
numer_lim,
denom;
double U,
X,
lhs,
rhs,
y,
tmp;
/* Generate U and X */
U = anl_random_fract();
X = t * (W - 1.0);
S = floor(X); /* S is tentatively set to floor(X) */
/* Test if U <= h(S)/cg(X) in the manner of (6.3) */
tmp = (t + 1) / term;
lhs = exp(log(((U * tmp * tmp) * (term + S)) / (t + X)) / n);
rhs = (((t + X) / (term + S)) * term) / t;
if (lhs <= rhs)
{
W = rhs / lhs;
break;
}
/* Test if U <= f(S)/cg(X) */
y = (((U * (t + 1)) / term) * (t + S + 1)) / (t + X);
if ((double) n < S)
{
denom = t;
numer_lim = term + S;
}
else
{
denom = t - (double) n + S;
numer_lim = t + 1;
}
for (numer = t + S; numer >= numer_lim; numer -= 1)
{
y *= numer / denom;
denom -= 1;
}
W = exp(-log(anl_random_fract()) / n); /* Generate W in advance */
if (exp(log(y) / n) <= (t + X) / t)
break;
}
*stateptr = W;
}
return S;
}
/*
* qsort comparator for sorting rows[] array
*/