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partial_stats
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partial_stats
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# LogicTest: local
# Tests that verify we can create and use partial table statistics
# USING EXTREMES correctly.
# Verify that we can use partial statistics for a simple
# table that has small amount of values added to the end
# of the latest full statistic.
# We store multiple full stats to ensure that merged stats
# work with forecasted statistics as well.
let $forecastsEnabledPrev
SHOW CLUSTER SETTING sql.stats.forecasts.enabled
# First: enable statistics forecasts.
statement ok
SET CLUSTER SETTING sql.stats.forecasts.enabled = true
statement ok
CREATE TABLE g (b INT PRIMARY KEY)
statement ok
ALTER TABLE g INJECT STATISTICS '[
{
"avg_size": 1,
"columns": [
"b"
],
"created_at": "1988-08-05 00:00:00.000000",
"distinct_count": 3,
"histo_buckets": [
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "0"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "1"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "2"
}
],
"histo_col_type": "INT8",
"histo_version": 2,
"name": "__auto__",
"null_count": 0,
"row_count": 3,
"statistics_id": 1
},
{
"avg_size": 1,
"columns": [
"b"
],
"created_at": "1988-08-07 00:00:00.000000",
"distinct_count": 9,
"histo_buckets": [
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "0"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "1"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "2"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "3"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "4"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "5"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "6"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "7"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "8"
}
],
"histo_col_type": "INT8",
"histo_version": 2,
"name": "__auto__",
"null_count": 0,
"row_count": 9
},
{
"avg_size": 2,
"columns": [
"b"
],
"created_at": "1988-08-08 00:00:00.000000",
"distinct_count": 3,
"histo_buckets": [
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "9"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "10"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "11"
}
],
"histo_col_type": "INT8",
"histo_version": 2,
"name": "partial",
"null_count": 0,
"partial_predicate": "(b < 0:::INT8) OR ((b > 8:::INT8) OR (b IS NULL))",
"row_count": 3
}
]'
# Here and below, we retrieve the id of the statistic (which is the
# histogram_id) to ensure that we can mark our partial injected statistic(s) as
# being collected from the injected full table statistics, for the purposes of
# testing. This is because our merge function verifies that a partial statistic
# is merging with the full statistic it has been collected from. Since the
# statistic id is generated when a statistic is added to
# system.table_statistics, and since we don't store it in the JSON statistic
# object, we need to set it manually here.
let $stat_id_simple
SELECT "histogram_id" FROM [SHOW STATISTICS FOR TABLE g] WHERE created = '1988-08-07 00:00:00.000000'
statement ok
UPDATE system.table_statistics SET "fullStatisticID" = '$stat_id_simple' WHERE name = 'partial'
query TTTIIII
SELECT statistics_name, column_names, created, row_count, distinct_count, null_count, avg_size
FROM [SHOW STATISTICS FOR TABLE g WITH MERGE]
ORDER BY created
----
__auto__ {b} 1988-08-05 00:00:00 +0000 UTC 3 3 0 1
__auto__ {b} 1988-08-07 00:00:00 +0000 UTC 9 9 0 1
partial {b} 1988-08-08 00:00:00 +0000 UTC 3 3 0 2
__merged__ {b} 1988-08-08 00:00:00 +0000 UTC 12 12 0 1
query T
SELECT jsonb_pretty(stat)
FROM (
SELECT jsonb_array_elements(statistics) AS stat FROM [SHOW STATISTICS USING JSON FOR TABLE g WITH MERGE]
)
WHERE stat->>'name' = '__merged__';
----
{
"avg_size": 1,
"columns": [
"b"
],
"created_at": "1988-08-08 00:00:00",
"distinct_count": 12,
"histo_buckets": [
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "0"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "1"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "2"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "3"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "4"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "5"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "6"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "7"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "8"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "9"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "10"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "11"
}
],
"histo_col_type": "INT8",
"histo_version": 3,
"name": "__merged__",
"null_count": 0,
"row_count": 12
}
# The new merged statistic raises the number of full statistics to greater than
# the minimum threshold required to create forecasts, so the optimizer uses a
# forecasted statistic here of which the merged statistic is a part.
query T
EXPLAIN (OPT, VERBOSE) SELECT * FROM g WHERE b >= 10
----
scan g
├── columns: b:1
├── constraint: /1: [/10 - ]
├── stats: [rows=7, distinct(1)=6, null(1)=0]
│ histogram(1)= 0 2 0 1 0 1 0 1 0 1 0 1
│ <--- 11 --- 12 --- 13 --- 14 --- 15 --- 17
├── cost: 25.09
├── key: (1)
└── distribution: test
# Since merged stats can be used for forecasting, verify
# that merging and forecasting can work together.
query TTTIIII
SELECT statistics_name, column_names, created, row_count, distinct_count, null_count, avg_size
FROM [SHOW STATISTICS FOR TABLE g WITH MERGE, FORECAST]
ORDER BY created
----
__auto__ {b} 1988-08-05 00:00:00 +0000 UTC 3 3 0 1
__auto__ {b} 1988-08-07 00:00:00 +0000 UTC 9 9 0 1
partial {b} 1988-08-08 00:00:00 +0000 UTC 3 3 0 2
__merged__ {b} 1988-08-08 00:00:00 +0000 UTC 12 12 0 1
__forecast__ {b} 1988-08-10 00:00:00 +0000 UTC 18 18 0 1
query T
SELECT jsonb_pretty(stat)
FROM (
SELECT jsonb_array_elements(statistics) AS stat FROM [SHOW STATISTICS USING JSON FOR TABLE g WITH MERGE, FORECAST]
)
WHERE stat->>'name' = '__forecast__';
----
{
"avg_size": 1,
"columns": [
"b"
],
"created_at": "1988-08-10 00:00:00",
"distinct_count": 18,
"histo_buckets": [
{
"distinct_range": 0,
"num_eq": 2,
"num_range": 0,
"upper_bound": "0"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "1"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "2"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "3"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "4"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "5"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "6"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "7"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "8"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "9"
},
{
"distinct_range": 0,
"num_eq": 0,
"num_range": 0,
"upper_bound": "10"
},
{
"distinct_range": 0,
"num_eq": 2,
"num_range": 0,
"upper_bound": "11"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "12"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "13"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "14"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "15"
},
{
"distinct_range": 0,
"num_eq": 0,
"num_range": 0,
"upper_bound": "16"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "17"
}
],
"histo_col_type": "INT8",
"histo_version": 3,
"name": "__forecast__",
"null_count": 0,
"row_count": 18
}
# Verify that when the partial histogram is empty
# the returned statistic is the latest full statistic
# renamed to __merged__ with the created_at time
# updated to that of the partial statistic.
statement ok
CREATE TABLE h (h INT PRIMARY KEY)
statement ok
ALTER TABLE h INJECT STATISTICS '
[
{
"avg_size": 1,
"columns": [
"h"
],
"created_at": "2022-12-07 17:43:58.649416",
"distinct_count": 4,
"histo_buckets": [
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "10"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "20"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "30"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "40"
}
],
"histo_col_type": "INT8",
"histo_version": 2,
"name": "full",
"null_count": 0,
"row_count": 4
},
{
"avg_size": 0,
"columns": [
"h"
],
"created_at": "2022-12-07 17:46:22.436487",
"distinct_count": 0,
"histo_col_type": "INT8",
"histo_version": 2,
"name": "partial",
"null_count": 0,
"partial_predicate": "(h < 10:::INT8) OR ((h > 40:::INT8) OR (h IS NULL))",
"row_count": 0
}
]'
let $stat_id_empty
SELECT "histogram_id" FROM [SHOW STATISTICS FOR TABLE h] WHERE statistics_name = 'full'
statement ok
UPDATE system.table_statistics SET "fullStatisticID" = '$stat_id_empty' WHERE name = 'partial'
query TTTIIII
SELECT statistics_name, column_names, created, row_count, distinct_count, null_count, avg_size
FROM [SHOW STATISTICS FOR TABLE h WITH MERGE]
ORDER BY created
----
full {h} 2022-12-07 17:43:58.649416 +0000 UTC 4 4 0 1
partial {h} 2022-12-07 17:46:22.436487 +0000 UTC 0 0 0 0
__merged__ {h} 2022-12-07 17:46:22.436487 +0000 UTC 4 4 0 1
query T
SELECT jsonb_pretty(stat)
FROM (
SELECT jsonb_array_elements(statistics) AS stat FROM [SHOW STATISTICS USING JSON FOR TABLE h WITH MERGE]
)
WHERE stat->>'name' = '__merged__';
----
{
"avg_size": 1,
"columns": [
"h"
],
"created_at": "2022-12-07 17:46:22.436487",
"distinct_count": 4,
"histo_buckets": [
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "10"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "20"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "30"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "40"
}
],
"histo_col_type": "INT8",
"histo_version": 2,
"name": "__merged__",
"null_count": 0,
"row_count": 4
}
# Verify partial statistics collection when we add
# values to the start and end of our column.
statement ok
CREATE TABLE i (i INT PRIMARY KEY)
statement ok
ALTER TABLE i INJECT STATISTICS '[
{
"avg_size": 1,
"columns": [
"i"
],
"created_at": "1988-08-05 00:00:00.000000",
"distinct_count": 3,
"histo_buckets": [
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "0"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "1"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "2"
}
],
"histo_col_type": "INT8",
"histo_version": 2,
"name": "full_0",
"null_count": 0,
"row_count": 3
},
{
"avg_size": 2,
"columns": [
"i"
],
"created_at": "2022-12-07 19:57:07.781368",
"distinct_count": 6,
"histo_buckets": [
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "-3"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "-2"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "-1"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "3"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "4"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "5"
}
],
"histo_col_type": "INT8",
"histo_version": 2,
"name": "sp",
"null_count": 0,
"partial_predicate": "(i < 0:::INT8) OR ((i > 3:::INT8) OR (i IS NULL))",
"row_count": 6
}
]'
let $stat_id_start_and_end
SELECT "histogram_id" FROM [SHOW STATISTICS FOR TABLE i] WHERE statistics_name = 'full_0'
statement ok
UPDATE system.table_statistics SET "fullStatisticID" = '$stat_id_start_and_end' WHERE name = 'sp'
query TTTIIII
SELECT statistics_name, column_names, created, row_count, distinct_count, null_count, avg_size
FROM [SHOW STATISTICS FOR TABLE i WITH MERGE]
ORDER BY created
----
full_0 {i} 1988-08-05 00:00:00 +0000 UTC 3 3 0 1
sp {i} 2022-12-07 19:57:07.781368 +0000 UTC 6 6 0 2
__merged__ {i} 2022-12-07 19:57:07.781368 +0000 UTC 9 9 0 1
query T
SELECT jsonb_pretty(stat)
FROM (
SELECT jsonb_array_elements(statistics) AS stat FROM [SHOW STATISTICS USING JSON FOR TABLE i WITH MERGE]
)
WHERE stat->>'name' = '__merged__';
----
{
"avg_size": 1,
"columns": [
"i"
],
"created_at": "2022-12-07 19:57:07.781368",
"distinct_count": 9,
"histo_buckets": [
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "-3"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "-2"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "-1"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "0"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "1"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "2"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "3"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "4"
},
{
"distinct_range": 0,
"num_eq": 1,
"num_range": 0,
"upper_bound": "5"
}
],
"histo_col_type": "INT8",
"histo_version": 3,
"name": "__merged__",
"null_count": 0,
"row_count": 9
}
statement ok
SET optimizer_use_merged_partial_statistics = on
query T
EXPLAIN (OPT, VERBOSE) SELECT * FROM i WHERE i = 6 OR i <= -1
----
scan i
├── columns: i:1
├── constraint: /1
│ ├── [ - /-1]
│ └── [/6 - /6]
├── stats: [rows=3, distinct(1)=3, null(1)=0]
│ histogram(1)= 0 1 0 1 0 1 0 0
│ <--- -3 --- -2 --- -1 --- 5
├── cost: 25.05
├── key: (1)
└── distribution: test
# Verify that we can merge and use partial statistics
# for columns with non-zero NumRange and DistinctRange
# buckets, and a non-zero null count, both when the
# bucket sizes of each side are the same and when
# they are different.
statement ok
CREATE TABLE ab (a INT, b INT, INDEX (a, b));
statement ok
ALTER TABLE ab INJECT STATISTICS
'[
{
"avg_size":3,
"columns":[
"b"
],
"created_at":"2022-12-12 15:22:21.988179",
"distinct_count":26,
"histo_buckets":[
{
"distinct_range":0,
"num_eq":1,
"num_range":0,
"upper_bound":"11"
},
{
"distinct_range":3,
"num_eq":1,
"num_range":3,
"upper_bound":"15"
},
{
"distinct_range":4,
"num_eq":1,
"num_range":4,
"upper_bound":"20"
},
{
"distinct_range":4,
"num_eq":1,
"num_range":4,
"upper_bound":"25"
},
{
"distinct_range":4,
"num_eq":1,
"num_range":4,
"upper_bound":"30"
},
{
"distinct_range":4,
"num_eq":1,
"num_range":4,
"upper_bound":"35"
}
],
"histo_col_type":"INT8",
"histo_version":2,
"name":"full_b",
"null_count":3,
"row_count":28
},
{
"avg_size":3,
"columns":[
"b"
],
"created_at":"2022-12-13 15:22:21.988179",
"distinct_count":32,
"histo_buckets":[
{
"distinct_range":0,
"num_eq":1,
"num_range":0,
"upper_bound":"0"
},
{
"distinct_range":4,
"num_eq":1,
"num_range":4,
"upper_bound":"5"
},
{
"distinct_range":4,
"num_eq":1,
"num_range":4,
"upper_bound":"10"
},
{
"distinct_range":0,
"num_eq":1,
"num_range":0,
"upper_bound":"36"
},
{
"distinct_range":3,
"num_eq":1,
"num_range":3,
"upper_bound":"40"
},
{
"distinct_range":4,
"num_eq":1,
"num_range":4,
"upper_bound":"45"
},
{
"distinct_range":4,
"num_eq":1,
"num_range":4,
"upper_bound":"50"
},
{
"distinct_range":4,
"num_eq":1,
"num_range":4,
"upper_bound":"55"
}
],
"histo_col_type":"INT8",
"histo_version":2,
"name":"partial_b",
"null_count":5,
"row_count":36,
"partial_predicate": "(b < 15:::INT8) OR ((b > 35:::INT8) OR (b IS NULL))"
},
{
"avg_size":3,
"columns":[
"a"
],
"created_at":"2022-12-12 15:22:21.988179",
"distinct_count":26,
"histo_buckets":[
{
"distinct_range":0,
"num_eq":1,
"num_range":0,
"upper_bound":"11"
},
{
"distinct_range":3,
"num_eq":1,
"num_range":3,
"upper_bound":"15"
},
{
"distinct_range":4,
"num_eq":1,
"num_range":4,
"upper_bound":"20"
},
{
"distinct_range":4,
"num_eq":1,
"num_range":4,
"upper_bound":"25"
},
{
"distinct_range":4,
"num_eq":1,
"num_range":4,
"upper_bound":"30"
},
{
"distinct_range":4,
"num_eq":1,
"num_range":4,
"upper_bound":"35"
}
],
"histo_col_type":"INT8",
"histo_version":2,
"name":"full_a",
"null_count":3,
"row_count":28
},
{
"avg_size":3,
"columns":[
"a"
],
"created_at":"2022-12-14 15:22:21.988179",
"distinct_count":28,
"histo_buckets":[
{
"distinct_range":0,
"num_eq":1,
"num_range":0,
"upper_bound":"0"
},
{
"distinct_range":9,
"num_eq":1,
"num_range":9,
"upper_bound":"10"
},
{
"distinct_range":0,
"num_eq":1,
"num_range":0,
"upper_bound":"36"
},
{
"distinct_range":2,
"num_eq":1,
"num_range":3,
"upper_bound":"40"
},
{
"distinct_range":3,
"num_eq":1,
"num_range":4,
"upper_bound":"44"
},
{
"distinct_range":3,
"num_eq":1,
"num_range":4,
"upper_bound":"48"
},
{
"distinct_range":3,