1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
|
# https://www.periscopedata.com/blog/medians-in-sql.html
module Gitlab
module Database
module Median
def median_datetime(arel_table, query_so_far, column_sym)
case ActiveRecord::Base.connection.adapter_name
when 'PostgreSQL'
pg_median_datetime(arel_table, query_so_far, column_sym)
when 'Mysql2'
mysql_median_datetime(arel_table, query_so_far, column_sym)
else
raise NotImplementedError, "We haven't implemented a database median strategy for your database type."
end
end
def mysql_median_datetime(arel_table, query_so_far, column_sym)
query = arel_table.
from(arel_table.project(Arel.sql('*')).order(arel_table[column_sym]).as(arel_table.table_name)).
project(average([arel_table[column_sym]], 'median')).
where(Arel::Nodes::Between.new(Arel.sql("(select @row_id := @row_id + 1)"),
Arel::Nodes::And.new([Arel.sql('@ct/2.0'),
Arel.sql('@ct/2.0 + 1')]))).
# Disallow negative values
where(arel_table[column_sym].gteq(0))
[Arel.sql("CREATE TEMPORARY TABLE IF NOT EXISTS #{query_so_far.to_sql}"),
Arel.sql("set @ct := (select count(1) from #{arel_table.table_name});"),
Arel.sql("set @row_id := 0;"),
query,
Arel.sql("DROP TEMPORARY TABLE IF EXISTS #{arel_table.table_name};")]
end
def pg_median_datetime(arel_table, query_so_far, column_sym)
# Create a CTE with the column we're operating on, row number (after sorting by the column
# we're operating on), and count of the table we're operating on (duplicated across) all rows
# of the CTE. For example, if we're looking to find the median of the `projects.star_count`
# column, the CTE might look like this:
#
# star_count | row_id | ct
# ------------+--------+----
# 5 | 1 | 3
# 9 | 2 | 3
# 15 | 3 | 3
cte_table = Arel::Table.new("ordered_records")
cte = Arel::Nodes::As.new(cte_table,
arel_table.
project(arel_table[column_sym].as(column_sym.to_s),
Arel::Nodes::Over.new(Arel::Nodes::NamedFunction.new("row_number", []),
Arel::Nodes::Window.new.order(arel_table[column_sym])).as('row_id'),
arel_table.project("COUNT(1)").as('ct')).
# Disallow negative values
where(arel_table[column_sym].gteq(zero_interval)))
# From the CTE, select either the middle row or the middle two rows (this is accomplished
# by 'where cte.row_id between cte.ct / 2.0 AND cte.ct / 2.0 + 1'). Find the average of the
# selected rows, and this is the median value.
cte_table.project(average([extract_epoch(cte_table[column_sym])], "median")).
where(Arel::Nodes::Between.new(cte_table[:row_id],
Arel::Nodes::And.new([(cte_table[:ct] / Arel.sql('2.0')),
(cte_table[:ct] / Arel.sql('2.0') + 1)]))).
with(query_so_far, cte)
end
private
def average(args, as)
Arel::Nodes::NamedFunction.new("AVG", args, as)
end
def extract_epoch(arel_attribute)
Arel.sql(%Q{EXTRACT(EPOCH FROM "#{arel_attribute.relation.name}"."#{arel_attribute.name}")})
end
# Need to cast '0' to an INTERVAL before we can check if the interval is positive
def zero_interval
Arel::Nodes::NamedFunction.new("CAST", [Arel.sql("'0' AS INTERVAL")])
end
end
end
end
|
