Mysql 你问我的问题中的数据？给出一个简短的示例，即使它稍微扩展了我的示例作为指导。@DRapp——假设您有一个民意测验（1，“性别？”），有两个选项（1，“男性”；2，“女性”）。假设50%的用户回答1，50%的用户回答2。现在假设你有另一个民意调查（2，“你喜
Mysql 你问我的问题中的数据？给出一个简短的示例，即使它稍微扩展了我的示例作为指导。@DRapp——假设您有一个民意测验（1，“性别？”），有两个选项（1，“男性”；2，“女性”）。假设50%的用户回答1，50%的用户回答2。现在假设你有另一个民意调查（2，“你喜,mysql,query-optimization,Mysql,Query Optimization,你问我的问题中的数据？给出一个简短的示例，即使它稍微扩展了我的示例作为指导。@DRapp——假设您有一个民意测验（1，“性别？”），有两个选项（1，“男性”；2，“女性”）。假设50%的用户回答1，50%的用户回答2。现在假设你有另一个民意调查（2，“你喜欢足球吗？”），有两个选项（3，“是”；4，“否”）。现在，假设60%的用户回答3。但是在男性（回答1的人）中，75%回答3。偏差为15（75-60）。因此，基本上，给定一个特定的选项ID（在我上面的查询中，它是选项122），我们希望得到一个所
你问我的问题中的数据？给出一个简短的示例，即使它稍微扩展了我的示例作为指导。@DRapp——假设您有一个民意测验（1，“性别？”），有两个选项（1，“男性”；2，“女性”）。假设50%的用户回答1，50%的用户回答2。现在假设你有另一个民意调查（2，“你喜欢足球吗？”），有两个选项（3，“是”；4，“否”）。现在，假设60%的用户回答3。但是在男性（回答1的人）中，75%回答3。偏差为15（75-60）。因此，基本上，给定一个特定的选项ID（在我上面的查询中，它是选项122），我们希望得到一个所有其他选项ID的列表，按偏差排序，这是衡量两个选项之间关联程度的一个指标。我能够使用您的查询，只需很少修改，为了精确地产生我需要的结果，执行时间从大约一分钟减少到大约一秒钟。唯一真正重要的变化是：“v.target_投票/t.target_投票作为分组百分比，v.all_投票/t.all_投票作为总百分比”
(SELECT p_id as poll_id, o_id AS option_id, description, optCount AS option_count, subgroup_percent, total_percent, ABS(total_percent - subgroup_percent) AS deviation
FROM(
SELECT poll_id AS p_id,
option_id AS o_id,
(SELECT description FROM `option` WHERE id = o_id) AS description,
COUNT(*) AS optCount,
(SELECT COUNT(*) FROM response INNER JOIN user_ids_122 ON response.user_id = user_ids_122.user_id WHERE option_id = o_id ) /
(SELECT COUNT(*) FROM response INNER JOIN user_ids_122 ON response.user_id = user_ids_122.user_id WHERE poll_id = p_id) AS subgroup_percent,
(SELECT COUNT(*) FROM response WHERE option_id = o_id) /
(SELECT COUNT(*) FROM response WHERE poll_id = p_id) AS total_percent
FROM response
INNER JOIN user_ids_122 ON response.user_id = user_ids_122.user_id
WHERE poll_id < '61'
GROUP BY option_id DESC
) AS derived_table_122
)
ORDER BY deviation DESC, option_count DESC
1 PRIMARY <derived2> ALL NULL NULL NULL NULL 121 Using filesort
2 DERIVED user_ids_122 ALL NULL NULL NULL NULL 74 Using temporary; Using filesort
2 DERIVED response ref poll_id,user_id user_id 4 correlated.user_ids_122.user_id 780 Using where
7 DEPENDENT SUBQUERY response ref poll_id poll_id 4 func 7800 Using index
6 DEPENDENT SUBQUERY response ref option_id option_id 4 func 7800 Using index
5 DEPENDENT SUBQUERY user_ids_122 ALL NULL NULL NULL NULL 74
5 DEPENDENT SUBQUERY response ref poll_id,user_id poll_id 4 func 7800 Using where
4 DEPENDENT SUBQUERY user_ids_122 ALL NULL NULL NULL NULL 74
4 DEPENDENT SUBQUERY response ref user_id,option_id user_id 4 correlated.user_ids_122.user_id 780 Using where
3 DEPENDENT SUBQUERY option eq_ref PRIMARY PRIMARY 4 func 1
id (INT) poll_id (INT) user_id (INT) option_id (INT) created (DATETIME)
7 7 1 14 2011-03-17 09:25:10
id (INT) poll_id (INT) text (TEXT) description (TEXT)
14 7 No people who dislike country music
id (INT) email (TEXT) created (DATETIME)
1 user@example.com 2011-02-15 11:16:03
-- Compute the average you're looking for.
select ..., agg1, agg2, avg(...)
from (
-- Use max() to merge the retrieved aggregates as individual rows.
-- (This will be faster than joins if you're dealing with tons of rows.)
select ..., max(agg1) as agg1, max(agg2) as agg2, ...
from (
-- Compute individual aggregates without nested loops.
select ..., count(*) as agg1, null as agg2, ...
from ...
where ...
group by ...
union all
select ..., null as agg1, count(*) as agg2, ...
from ...
where ...
group by ...
union all
...
) as aggs
group by ...
) as rows
group by ...
(SELECT COUNT(*) FROM response WHERE option_id = o_id) /
(SELECT COUNT(*) FROM response WHERE poll_id = p_id) as total_percent
SELECT [fields you need],
MAX(total_reponses_by_option_id) / MAX(total_reponses_by_option_id) as total_percent
FROM (
SELECT [fields you need],
COUNT(*) as total_reponses_by_option_id,
NULL as total_reponses_by_poll_id
FROM response
[join/where as needed]
GROUP BY [fields you need]
UNION ALL
SELECT [fields you need],
NULL as total_reponses_by_option_id,
COUNT(*) as total_reponses_by_poll_id
FROM response
[join/where as needed]
GROUP BY [fields you need]
) as agg
GROUP BY [fields you need];
(Result from PreQuery 1 on just the poll counts)
Poll Count
1 50
2 30
(Result from PreQuery 2 on poll AND Option)
Poll Option Count
1 1 30
1 2 12
1 3 5
1 4 3
2 5 8
2 6 12
2 7 10
Final join should have
Poll Option Description PerPollAndOption SubGroup_Percent PerPollResponse
1 1 Descrip 1 30 .60 50
1 2 Descrip 2 12 .24 50
1 3 Descrip 3 5 .10 50
1 4 Descrip 4 3 .06 50
2 5 Descrip 5 8 .27 30
2 6 Descrip 6 12 .40 30
2 7 Descrip 7 10 .33 30
SELECT
ByPoll.Poll_ID,
ByPollOption.Option_ID,
Option.Description,
ByPollOption.PerPollAndOption,
ByPollOption.PerPollAndOption / ByPoll.PerPollResponse as SubGroup_Percent,
ByPoll.PerPollResponse
FROM
( select
Poll_ID,
COUNT(*) as PerPollResponse
from
Response
where
Poll_ID < '61'
group by
Poll_ID ) ByPoll
JOIN ( select r.Poll_ID,
r.Option_ID,
COUNT(*) as PerPollAndOption
from
Responses r
join option o
ON r.Option_ID = o.id
where
Poll_ID < '61'
group by
r.Poll_ID,
r.Option_ID ) ByPollOption
ON ByPoll.Poll_ID = ByPollOption.Poll_ID
JOIN OPTION
ON ByPollOption.Option_ID = Option.ID
WITH
-- users of interest : target group
uids AS (
SELECT DISTINCT user_id
FROM options
JOIN responses USING (option_id)
WHERE poll_id=22
),
-- votes of everyone and target group
votes AS (
SELECT poll_id, option_id, sum(all_votes) AS all_votes, sum(target_votes) AS target_votes
FROM (
SELECT option_id, count(*) AS all_votes, count(uids.user_id) AS target_votes
FROM responses
LEFT JOIN uids USING (user_id)
GROUP BY option_id
) v
JOIN options USING (option_id)
GROUP BY poll_id, option_id
),
-- totals for all polls (reuse previous result)
totals AS (
SELECT poll_id, sum(all_votes) AS all_votes, sum(target_votes) AS target_votes
FROM votes
GROUP BY poll_id
),
poll_options AS (
SELECT poll_id, count(*) AS poll_option_count
FROM options
GROUP BY poll_id
)
-- reuse previous tables to get some stats
SELECT *, ABS(total_percent - subgroup_percent) AS deviation
FROM (
SELECT
poll_id,
option_id,
v.target_votes / v.all_votes AS subgroup_percent,
t.target_votes / t.all_votes AS total_percent,
poll_option_count
FROM votes v
JOIN totals t USING (poll_id)
JOIN poll_options po USING (poll_id)
) AS foo
ORDER BY deviation DESC, poll_option_count DESC;
QUERY PLAN
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Sort (cost=14910.46..14910.56 rows=40 width=144) (actual time=299.844..299.862 rows=200 loops=1)
Sort Key: (abs(((t.target_votes / t.all_votes) - (v.target_votes / v.all_votes)))), po.poll_option_count
Sort Method: quicksort Memory: 52kB
CTE uids
-> HashAggregate (cost=1801.43..1850.52 rows=4909 width=4) (actual time=3.935..4.793 rows=4860 loops=1)
-> Nested Loop (cost=0.00..1789.16 rows=4909 width=4) (actual time=0.029..2.555 rows=4860 loops=1)
-> Seq Scan on options (cost=0.00..3.50 rows=5 width=4) (actual time=0.008..0.032 rows=5 loops=1)
Filter: (poll_id = 22)
-> Index Scan using responses_option_id_key on responses (cost=0.00..344.86 rows=982 width=8) (actual time=0.012..0.298 rows=972 loops=5)
Index Cond: (public.responses.option_id = public.options.option_id)
CTE votes
-> HashAggregate (cost=13029.43..13032.43 rows=200 width=24) (actual time=298.255..298.317 rows=200 loops=1)
-> Hash Join (cost=13019.68..13027.43 rows=200 width=24) (actual time=297.953..298.103 rows=200 loops=1)
Hash Cond: (public.responses.option_id = public.options.option_id)
-> HashAggregate (cost=13014.18..13017.18 rows=200 width=8) (actual time=297.839..297.879 rows=200 loops=1)
-> Merge Left Join (cost=399.13..11541.43 rows=196366 width=8) (actual time=9.301..230.467 rows=196366 loops=1)
Merge Cond: (public.responses.user_id = uids.user_id)
-> Index Scan using responses_pkey on responses (cost=0.00..8585.75 rows=196366 width=8) (actual time=0.015..121.971 rows=196366 loops=1)
-> Sort (cost=399.13..411.40 rows=4909 width=4) (actual time=9.281..22.044 rows=137645 loops=1)
Sort Key: uids.user_id
Sort Method: quicksort Memory: 420kB
-> CTE Scan on uids (cost=0.00..98.18 rows=4909 width=4) (actual time=3.937..6.549 rows=4860 loops=1)
-> Hash (cost=3.00..3.00 rows=200 width=8) (actual time=0.095..0.095 rows=200 loops=1)
-> Seq Scan on options (cost=0.00..3.00 rows=200 width=8) (actual time=0.007..0.043 rows=200 loops=1)
CTE totals
-> HashAggregate (cost=5.50..8.50 rows=200 width=68) (actual time=298.629..298.640 rows=40 loops=1)
-> CTE Scan on votes (cost=0.00..4.00 rows=200 width=68) (actual time=298.257..298.425 rows=200 loops=1)
CTE poll_options
-> HashAggregate (cost=4.00..4.50 rows=40 width=4) (actual time=0.091..0.101 rows=40 loops=1)
-> Seq Scan on options (cost=0.00..3.00 rows=200 width=4) (actual time=0.005..0.020 rows=200 loops=1)
-> Hash Join (cost=6.95..13.45 rows=40 width=144) (actual time=298.994..299.554 rows=200 loops=1)
Hash Cond: (t.poll_id = v.poll_id)
-> CTE Scan on totals t (cost=0.00..4.00 rows=200 width=68) (actual time=298.632..298.669 rows=40 loops=1)
-> Hash (cost=6.45..6.45 rows=40 width=84) (actual time=0.335..0.335 rows=200 loops=1)
-> Hash Join (cost=1.30..6.45 rows=40 width=84) (actual time=0.140..0.263 rows=200 loops=1)
Hash Cond: (v.poll_id = po.poll_id)
-> CTE Scan on votes v (cost=0.00..4.00 rows=200 width=72) (actual time=0.001..0.030 rows=200 loops=1)
-> Hash (cost=0.80..0.80 rows=40 width=12) (actual time=0.130..0.130 rows=40 loops=1)
-> CTE Scan on poll_options po (cost=0.00..0.80 rows=40 width=12) (actual time=0.093..0.119 rows=40 loops=1)
Total runtime: 300.132 ms