On 09.12.2024 16:10, Ilia Evdokimov wrote:
Hi hackers,
The repeated use of the number 300 in the ANALYZE-related code creates
redundancy and relies on scattered, sometimes unclear, comments to
explain its purpose. This can make the code harder to understand,
especially for new contributors who might not immediately understand
its significance. To address this, I propose introducing a macro
STATS_MIN_ROWS to represent this value and consolidating its
explanation in a single place, making the code more consistent and
readable.
--
Best regards,
Ilia Evdokimov,
Tantor Labs LLC.
Hi everyone,
Currently, the value 300 is used as the basis for determining the number
of rows sampled during ANALYZE, both for single-column and extended
statistics. While this value has a well-established rationale for
single-column statistics, its suitability for extended statistics
remains uncertain, as no specific research has confirmed that this is an
optimal choice for them. To better reflect this distinction, I propose
introducing two macros: STATS_MIN_ROWS for single-column statistics and
EXT_STATS_MIN_ROWS for extended statistics.
This change separates the concerns of single-column and extended
statistics sampling, making the code more explicit and easier to adapt
if future research suggests a different approach for extended
statistics. The values remain the same for now, but the introduction of
distinct macros improves clarity and prepares the codebase for potential
refinements.
Does this seem like a reasonable approach to handling these differences?
--
Best regards,
Ilia Evdokimov,
Tantor Labs LLC.
From 164535616040447207c36ea47c52b206f4d77dcf Mon Sep 17 00:00:00 2001
From: Ilia Evdokimov <ilya.evdoki...@tantorlabs.ru>
Date: Tue, 10 Dec 2024 16:25:29 +0300
Subject: [PATCH v2] Define macros for minimum rows of stats in ANALYZE
This introduces two macros, STATS_MIN_ROWS and EXT_STATS_MIN_ROWS,
to represent the default minimum number of rows sampled in ANALYZE.
STATS_MIN_ROWS is used for single-column statistics,
while EXT_STATS_MIN_ROWS is intended for extended statistics.
Both macros replace the hardcoded value of 300, improving clarity.
---
src/backend/commands/analyze.c | 31 +++++--------------
src/backend/statistics/extended_stats.c | 2 +-
src/backend/tsearch/ts_typanalyze.c | 4 +--
src/backend/utils/adt/rangetypes_typanalyze.c | 7 ++---
.../statistics/extended_stats_internal.h | 11 +++++++
src/include/statistics/statistics.h | 23 ++++++++++++++
6 files changed, 47 insertions(+), 31 deletions(-)
diff --git a/src/backend/commands/analyze.c b/src/backend/commands/analyze.c
index 9a56de2282..e358b0d828 100644
--- a/src/backend/commands/analyze.c
+++ b/src/backend/commands/analyze.c
@@ -1897,42 +1897,25 @@ std_typanalyze(VacAttrStats *stats)
{
/* Seems to be a scalar datatype */
stats->compute_stats = compute_scalar_stats;
- /*--------------------
- * The following choice of minrows is based on the paper
- * "Random sampling for histogram construction: how much is enough?"
- * by Surajit Chaudhuri, Rajeev Motwani and Vivek Narasayya, in
- * Proceedings of ACM SIGMOD International Conference on Management
- * of Data, 1998, Pages 436-447. Their Corollary 1 to Theorem 5
- * says that for table size n, histogram size k, maximum relative
- * error in bin size f, and error probability gamma, the minimum
- * random sample size is
- * r = 4 * k * ln(2*n/gamma) / f^2
- * Taking f = 0.5, gamma = 0.01, n = 10^6 rows, we obtain
- * r = 305.82 * k
- * Note that because of the log function, the dependence on n is
- * quite weak; even at n = 10^12, a 300*k sample gives <= 0.66
- * bin size error with probability 0.99. So there's no real need to
- * scale for n, which is a good thing because we don't necessarily
- * know it at this point.
- *--------------------
- */
- stats->minrows = 300 * stats->attstattarget;
}
else if (OidIsValid(eqopr))
{
/* We can still recognize distinct values */
stats->compute_stats = compute_distinct_stats;
- /* Might as well use the same minrows as above */
- stats->minrows = 300 * stats->attstattarget;
}
else
{
/* Can't do much but the trivial stuff */
stats->compute_stats = compute_trivial_stats;
- /* Might as well use the same minrows as above */
- stats->minrows = 300 * stats->attstattarget;
}
+ /*
+ * For the scalar types, STATS_MIN_ROWS is derived from research on
+ * optimal sample sizes for histograms. For other cases,
+ * we assume the same value.
+ */
+ stats->minrows = (STATS_MIN_ROWS * stats->attstattarget);
+
return true;
}
diff --git a/src/backend/statistics/extended_stats.c b/src/backend/statistics/extended_stats.c
index 99fdf208db..8cd024b5ce 100644
--- a/src/backend/statistics/extended_stats.c
+++ b/src/backend/statistics/extended_stats.c
@@ -320,7 +320,7 @@ ComputeExtStatisticsRows(Relation onerel,
MemoryContextDelete(cxt);
/* compute sample size based on the statistics target */
- return (300 * result);
+ return (EXT_STATS_MIN_ROWS * result);
}
/*
diff --git a/src/backend/tsearch/ts_typanalyze.c b/src/backend/tsearch/ts_typanalyze.c
index ccafe42729..befd90c9d5 100644
--- a/src/backend/tsearch/ts_typanalyze.c
+++ b/src/backend/tsearch/ts_typanalyze.c
@@ -17,6 +17,7 @@
#include "catalog/pg_operator.h"
#include "commands/vacuum.h"
#include "common/hashfn.h"
+#include "statistics/statistics.h"
#include "tsearch/ts_type.h"
#include "utils/builtins.h"
#include "varatt.h"
@@ -64,8 +65,7 @@ ts_typanalyze(PG_FUNCTION_ARGS)
stats->attstattarget = default_statistics_target;
stats->compute_stats = compute_tsvector_stats;
- /* see comment about the choice of minrows in commands/analyze.c */
- stats->minrows = 300 * stats->attstattarget;
+ stats->minrows = (STATS_MIN_ROWS * stats->attstattarget);
PG_RETURN_BOOL(true);
}
diff --git a/src/backend/utils/adt/rangetypes_typanalyze.c b/src/backend/utils/adt/rangetypes_typanalyze.c
index 3773f98115..7fcac5621c 100644
--- a/src/backend/utils/adt/rangetypes_typanalyze.c
+++ b/src/backend/utils/adt/rangetypes_typanalyze.c
@@ -26,6 +26,7 @@
#include "catalog/pg_operator.h"
#include "commands/vacuum.h"
+#include "statistics/statistics.h"
#include "utils/float.h"
#include "utils/fmgrprotos.h"
#include "utils/lsyscache.h"
@@ -56,8 +57,7 @@ range_typanalyze(PG_FUNCTION_ARGS)
stats->compute_stats = compute_range_stats;
stats->extra_data = typcache;
- /* same as in std_typanalyze */
- stats->minrows = 300 * stats->attstattarget;
+ stats->minrows = (STATS_MIN_ROWS * stats->attstattarget);
PG_RETURN_BOOL(true);
}
@@ -82,8 +82,7 @@ multirange_typanalyze(PG_FUNCTION_ARGS)
stats->compute_stats = compute_range_stats;
stats->extra_data = typcache;
- /* same as in std_typanalyze */
- stats->minrows = 300 * stats->attstattarget;
+ stats->minrows = (STATS_MIN_ROWS * stats->attstattarget);
PG_RETURN_BOOL(true);
}
diff --git a/src/include/statistics/extended_stats_internal.h b/src/include/statistics/extended_stats_internal.h
index 8eed9b338d..69c7fcca02 100644
--- a/src/include/statistics/extended_stats_internal.h
+++ b/src/include/statistics/extended_stats_internal.h
@@ -17,6 +17,17 @@
#include "statistics/statistics.h"
#include "utils/sortsupport.h"
+/* Minimum of rows wanted for extended stats
+ *
+ * Based on research, a fixed size proportional to statistics_target
+ * (300 * statistics_target) is generally sufficient for accurate single-column
+ * histograms and MCV lists. Its suitability for extended statistics.
+ *
+ * XXX While this value works reasonably well for individual columns, its
+ * suitability for extended statistics is still an open question.
+ */
+#define EXT_STATS_MIN_ROWS 300
+
typedef struct
{
Oid eqopr; /* '=' operator for datatype, if any */
diff --git a/src/include/statistics/statistics.h b/src/include/statistics/statistics.h
index 7f2bf18716..f578f7fd1d 100644
--- a/src/include/statistics/statistics.h
+++ b/src/include/statistics/statistics.h
@@ -22,6 +22,29 @@
#define STATS_NDISTINCT_MAGIC 0xA352BFA4 /* struct identifier */
#define STATS_NDISTINCT_TYPE_BASIC 1 /* struct version */
+/*--------------------
+ * Minimum of rows wanted for stats
+ *
+ * The following choice of minrows is based on the paper
+ * "Random sampling for histogram construction: how much is enough?"
+ * by Surajit Chaudhuri, Rajeev Motwani and Vivek Narasayya, in
+ * Proceedings of ACM SIGMOD International Conference on Management
+ * of Data, 1998, Pages 436-447. Their Corollary 1 to Theorem 5
+ * says that for table size n, histogram size k, maximum relative
+ * error in bin size f, and error probability gamma, the minimum
+ * random sample size is
+ * r = 4 * k * ln(2*n/gamma) / f^2
+ * Taking f = 0.5, gamma = 0.01, n = 10^6 rows, we obtain
+ * r = 305.82 * k
+ * Note that because of the log function, the dependence on n is
+ * quite weak; even at n = 10^12, a 300*k sample gives <= 0.66
+ * bin size error with probability 0.99. So there's no real need to
+ * scale for n, which is a good thing because we don't necessarily
+ * know it at this point.
+ *--------------------
+ */
+#define STATS_MIN_ROWS 300
+
/* MVNDistinctItem represents a single combination of columns */
typedef struct MVNDistinctItem
{
--
2.34.1
