我真正的问题与记录哪些大量反病毒产品同意给定样本是给定反病毒家族的成员有关。该数据库有数百万个样本，每个样本都有数十种反病毒产品投票。我想问一个问题，如“对于包含名称'XYZ'的恶意软件，哪个样本得票最多，哪些供应商投票支持？”得到如下结果：
"BadBadVirus"
V1 V2 V3 V4 V5 V6 V7
Sample 1 - 4 votes 1 0 1 0 0 1 1
Sample 2 - 5 votes 1 0 1 0 1 1 1
Sample 3 - 5 votes 1 0 1 0 1 1 1
total 14 3 3 2 3 3
可能会用来告诉我供应商2和供应商4或者不知道如何 检测这种恶意软件，或者将它们命名为不同的恶意软件。
我会尝试略微概括我的问题，同时希望不会破坏你帮助我的能力。假设我有五个选民（Alex，Bob，Carol，Dave，Ed）被要求查看五张照片（P1，P2，P3，P4，P5）并决定照片的“主要主题”是什么。对于我们的例子，我们只假设它们仅限于“猫”，“狗”或“马”。不是每个选民都对每件事都投票。
数据以这种形式存在于数据库中：
Photo, Voter, Decision
(1, 'Alex', 'Cat')
(1, 'Bob', 'Dog')
(1, 'Carol', 'Cat')
(1, 'Dave', 'Cat')
(1, 'Ed', 'Cat')
(2, 'Alex', 'Cat')
(2, 'Bob', 'Dog')
(2, 'Carol', 'Cat')
(2, 'Dave', 'Cat')
(2, 'Ed', 'Dog')
(3, 'Alex', 'Horse')
(3, 'Bob', 'Horse')
(3, 'Carol', 'Dog')
(3, 'Dave', 'Horse')
(3, 'Ed', 'Horse')
(4, 'Alex', 'Horse')
(4, 'Bob', 'Horse')
(4, 'Carol', 'Cat')
(4, 'Dave', 'Horse')
(4, 'Ed', 'Horse')
(5, 'Alex', 'Dog')
(5, 'Bob', 'Cat')
(5, 'Carol', 'Cat')
(5, 'Dave', 'Cat')
(5, 'Ed', 'Cat')
目标是，鉴于我们正在寻找一个照片主题，我们想知道有多少选民认为这是该照片的主要内容，但也列出了哪些选民认为。
Query for: "Cat"
Total Alex Bob Carol Dave Ed
1 - 4 1 0 1 1 1
2 - 3 1 0 1 1 0
3 - 0 0 0 0 0 0
4 - 1 0 0 1 0 0
5 - 4 0 1 1 1 1
------------------------------------
total 12 2 1 4 3 2
Query for: "Dog"
Total Alex Bob Carol Dave Ed
1 - 1 0 1 0 0 0
2 - 2 0 1 0 0 1
3 - 1 0 0 1 0 0
4 - 0 0 0 0 0 0
5 - 1 1 0 0 0 0
------------------------------------
total 5 1 2 1 0 1
我可以用我存储的格式处理数据吗？
我很难得到一个查询来做到这一点 - 虽然它很简单，可以将数据转储出去，然后编写一个程序来做到这一点，我真的希望能够在数据库中做到这一点，如果我能做到的话
感谢您的任何建议。
答案 0 :(得分：9)
create table vote (Photo integer, Voter text, Decision text);
insert into vote values
(1, 'Alex', 'Cat'),
(1, 'Bob', 'Dog'),
(1, 'Carol', 'Cat'),
(1, 'Dave', 'Cat'),
(1, 'Ed', 'Cat'),
(2, 'Alex', 'Cat'),
(2, 'Bob', 'Dog'),
(2, 'Carol', 'Cat'),
(2, 'Dave', 'Cat'),
(2, 'Ed', 'Dog'),
(3, 'Alex', 'Horse'),
(3, 'Bob', 'Horse'),
(3, 'Carol', 'Dog'),
(3, 'Dave', 'Horse'),
(3, 'Ed', 'Horse'),
(4, 'Alex', 'Horse'),
(4, 'Bob', 'Horse'),
(4, 'Carol', 'Cat'),
(4, 'Dave', 'Horse'),
(4, 'Ed', 'Horse'),
(5, 'Alex', 'Dog'),
(5, 'Bob', 'Cat'),
(5, 'Carol', 'Cat'),
(5, 'Dave', 'Cat'),
(5, 'Ed', 'Cat')
;
对猫的查询：
select photo,
alex + bob + carol + dave + ed as Total,
alex, bob, carol, dave, ed
from crosstab($$
select
photo, voter,
case decision when 'Cat' then 1 else 0 end
from vote
order by photo
$$,'
select distinct voter
from vote
order by voter
'
) as (
photo integer,
Alex integer,
Bob integer,
Carol integer,
Dave integer,
Ed integer
);
photo | total | alex | bob | carol | dave | ed
-------+-------+------+-----+-------+------+----
1 | 4 | 1 | 0 | 1 | 1 | 1
2 | 3 | 1 | 0 | 1 | 1 | 0
3 | 0 | 0 | 0 | 0 | 0 | 0
4 | 1 | 0 | 0 | 1 | 0 | 0
5 | 4 | 0 | 1 | 1 | 1 | 1
如果选民人数众多或未知，那么可以动态完成：
do $do$
declare
voter_list text;
r record;
begin
drop table if exists pivot;
voter_list := (
select string_agg(distinct voter, ' ' order by voter) from vote
);
execute(format('
create table pivot (
decision text,
photo integer,
Total integer,
%1$s
)', (replace(voter_list, ' ', ' integer, ') || ' integer')
));
for r in
select distinct decision from vote
loop
execute (format($f$
insert into pivot
select
%3$L as decision,
photo,
%1$s as Total,
%2$s
from crosstab($ct$
select
photo, voter,
case decision when %3$L then 1 else 0 end
from vote
order by photo
$ct$,$ct$
select distinct voter
from vote
order by voter
$ct$
) as (
photo integer,
%4$s
);$f$,
replace(voter_list, ' ', ' + '),
replace(voter_list, ' ', ', '),
r.decision,
replace(voter_list, ' ', ' integer, ') || ' integer'
));
end loop;
end; $do$;
上面的代码创建了表轴，并做出了所有决定：
select * from pivot where decision = 'Cat';
答案 1 :(得分：1)
您的愿望意味着转移一些数据（名称） 到列标题，即结果表的模式。 因为这是介于不方便和不可能之间的地方， 我建议只是在sql中对数据进行排序和求和， 并在数据库之外完成其余工作。
SELECT Photo, Voter
FROM data
WHERE Decision = '...'
ORDER BY Photo, Voter
和
SELECT Photo, COUNT(*) AS Total
FROM data
WHERE Decision = '...'
GROUP BY Photo
ORDER BY Photo;
答案 2 :(得分：1)
使用与Clodoaldo相同的样本数据（“创建表格投票...”）并使用plpythonu函数make_pivot_table（下面），您可以运行：
create temp table pivot_data on commit drop as
select * from vote where decision = 'Cat' union select photo, null, null from vote;
select * from make_pivot_table('{photo}', 'voter', 'decision', 'count', 'pivot_data',
'pivot_result', false);
select * from pivot_result order by photo;
make_pivot_table函数定义为：
-- make_pivot_table
-- python version 0.9
-- last edited 2015-08-11
create or replace function
make_pivot_table(row_headers text[], category_field text, value_field text,
value_action text, input_table text, output_table text, keep_result boolean)
returns void as
$$
# imports
from collections import defaultdict
import operator
import string
# constants
BATCH_SIZE = 100
VALID_ACTIONS = ('count', 'sum', 'min', 'max')
NULL_CATEGORY_NAME = 'NULL_CATEGORY'
TOTAL_COL = 'total'
# functions
def table_exists(tablename):
plan = plpy.prepare("""select table_schema, table_name from
information_schema.Tables where table_schema not in ('information_schema',
'pg_catalog') and table_name = $1""", ["text"])
rows = plpy.execute(plan, [input_table], 2)
return bool(rows)
def make_rowkey(row):
return tuple([row[header] for header in row_headers])
def quote_if_needed(value):
return plpy.quote_literal(value) if isinstance(value, basestring) else str(value)
# assumes None is never a value in the dct
def update_if(dct, key, new_value, op, result=True):
current_value = dct.get(key)
if current_value is None or op(value, current_value) == result:
dct[key] = new_value
def update_output_table(output_table, row_headers, colname, value):
pg_value = plpy.quote_literal(value) if isinstance(value, basestring) else value
sql = 'update %s set %s = %s where ' % (output_table, plpy.quote_ident(colname),
pg_value)
conditions = []
for index, row_header in enumerate(row_headers):
conditions.append('%s = %s' % (plpy.quote_ident(row_header),
quote_if_needed(rowkey[index])))
sql += ' and '.join(conditions)
plpy.execute(sql)
# -----------------
if not table_exists(input_table):
plpy.error('input_table %s dones not exist' % input_table)
if value_action not in VALID_ACTIONS:
plpy.error('%s is not a recognised action' % value_action)
# load the data into a dict
count_dict = defaultdict(int)
sum_dict = defaultdict(float)
total_dict = defaultdict(float)
min_dict = dict()
max_dict = dict()
categories_seen = set()
rowkeys_seen = set()
do_total = value_action in ('count', 'sum')
cursor = plpy.cursor('select * from %s' % plpy.quote_ident(input_table))
while True:
rows = cursor.fetch(BATCH_SIZE)
if not rows:
break
for row in rows:
rowkey = make_rowkey(row)
rowkeys_seen.add(rowkey)
category = row[category_field]
value = row[value_field]
dctkey = (rowkey, category)
# skip if value field is null
if value is None:
continue
categories_seen.add(category)
if value_action == 'count':
count_dict[dctkey] += 1
total_dict[rowkey] += 1
if value_action == 'sum':
sum_dict[dctkey] += value
total_dict[rowkey] += value
if value_action == 'min':
update_if(min_dict, dctkey, value, operator.lt)
if value_action == 'max':
update_if(max_dict, dctkey, value, operator.gt)
plpy.notice('seen %s summary rows and %s categories' % (len(rowkeys_seen),
len(categories_seen)))
# get the columns types
coltype_dict = dict()
input_type_sql = 'select * from %s where false' % plpy.quote_ident(input_table)
input_type_result = plpy.execute(input_type_sql)
for index, colname in enumerate(input_type_result.colnames()):
coltype_num = input_type_result.coltypes()[index]
coltype_sql = 'select typname from pg_type where oid = %s' % coltype_num
coltype = list(plpy.cursor(coltype_sql))[0]
plpy.notice('%s: %s' % (colname, coltype['typname']))
coltype_dict[colname] = coltype['typname']
plpy.execute('drop table if exists %s' % plpy.quote_ident(output_table))
sql_parts = []
if keep_result:
sql_parts.append('create table %s (' % plpy.quote_ident(output_table))
else:
sql_parts.append('create temp table %s (' % plpy.quote_ident(output_table))
cols = []
for row_header in row_headers:
cols.append('%s %s' % (plpy.quote_ident(row_header), coltype_dict[row_header]))
cat_type = 'bigint' if value_action == 'count' else coltype_dict[value_field]
for col in sorted(categories_seen):
if col is None:
cols.append('%s %s' % (plpy.quote_ident(NULL_CATEGORY_NAME), cat_type))
else:
cols.append('%s %s' % (plpy.quote_ident(col), cat_type))
if do_total:
cols.append('%s %s' % (TOTAL_COL, cat_type))
sql_parts.append(',\n'.join(cols))
if keep_result:
sql_parts.append(')')
else:
sql_parts.append(') on commit drop')
plpy.execute('\n'.join(sql_parts))
dict_map = {'count': count_dict, 'sum': sum_dict, 'min': min_dict, 'max': max_dict }
value_dict = dict_map[value_action]
for rowkey in rowkeys_seen:
sql = 'insert into %s values (' % plpy.quote_ident(output_table)
sql += ', '.join([quote_if_needed(part) for part in rowkey])
sql += ')'
plpy.execute(sql)
if do_total:
for rowkey, value in total_dict.iteritems():
update_output_table(output_table, row_headers, TOTAL_COL, value)
for (rowkey, category), value in value_dict.iteritems():
# put in cateogry value
colname = NULL_CATEGORY_NAME if category is None else category
update_output_table(output_table, row_headers, colname, value)
$$ language plpythonu