优化联合 SQL 查询
Optimizing an union SQL query
我有一个很大的查询,所有查询都包含我在我的应用程序中列出的供稿所需的相同类型。现在的问题是这个查询不是很快。我在想,如果我限制每个单独的工会,它可能会加快一点,但我不确定。
所以基本上我的问题是如何优化此查询以更快地执行?
SELECT DISTINCT
alert_id,
uniquekey,
type,
user_id,
date_added
FROM
(
( SELECT
r.alert_id,
r.alert_id AS uniquekey,
'reply' AS `type`,
r.user_id,
r.date_added
FROM
`reply` r
LEFT JOIN `alerts` a
ON r.alert_id = a.alert_content_id
WHERE
r.user_id = :id
AND a.hide = '0'
ORDER BY
date_added DESC )
UNION
( SELECT
r.alert_id,
r.alert_id AS uniquekey,
'replyfromfollowing' AS `type`,
r.user_id,
r.date_added
FROM
`reply` r
LEFT JOIN `alerts` a
ON r.alert_id = a.alert_content_id
WHERE
r.user_id IN( '$followingstring' )
AND a.hide = '0'
ORDER BY date_added DESC )
UNION
( SELECT
i.alert_id,
i.alert_id AS uniquekey,
'liked' AS `type`,
i.user_id,
i.date_added
FROM
`interactions` i
LEFT JOIN `alerts` a
ON i.alert_id = a.alert_content_id
WHERE
i.user_id = :id
AND a.hide = '0'
GROUP BY
alert_id
ORDER BY
date_added DESC )
UNION
( SELECT
i.alert_id,
i.alert_id AS uniquekey,
'likedfromfollowing' AS `type`,
i.user_id,
i.date_added
FROM
`interactions` i
LEFT JOIN `alerts` a
ON i.alert_id = a.alert_content_id
WHERE
i.user_id IN ( '$followingstring' )
AND a.hide = '0'
GROUP BY
alert_id
ORDER BY
date_added DESC )
UNION
( SELECT
alerts as alert_id,
alert_content_id AS uniquekey,
'following' AS `type`,
user_id,
date_added
FROM
alerts a
LEFT JOIN `alerts_content` ac
ON ac.id = a.alert_content_id
WHERE
a.user_id IN ( '$followingstring' )
AND ac.anoniem = '0'
AND a.hide = '0'
GROUP BY
alert_id
ORDER BY
date_added DESC )
) joined
GROUP BY
uniquekey
ORDER BY
date_added DESC
LIMIT
".(int)$start.",20"
table 结构
Reply table Structure:
id
user_id
alert_id
description
reply_on_alert
reply_on_reply
date_added
Interaction table Structure:
id
alert_id
action_id
reply_id
user_id
date_added
Alerts table structure(Yes i know BIG mistake naming `id` : `alerts`):
alerts
title
alert_content_id
user_id
cat
lat
lon
state
hide
date_added
alerts_content table structure:
id
alert_id
description
img
查询结果:
Array
(
[0] => Array
(
[alert_id] => 173404
[uniquekey] => 173404
[type] => reply
[user_id] => 2
[date_added] => 2015-06-01 16:34:16
)
[1] => Array
(
[alert_id] => 172174
[uniquekey] => 172174
[type] => replyfromfollowing
[user_id] => 1380
[date_added] => 2015-06-01 16:01:04
)
[2] => Array
(
[alert_id] => 171772
[uniquekey] => 171772
[type] => liked
[user_id] => 2
[date_added] => 2015-06-01 15:58:44
)
[3] => Array
(
[alert_id] => 149423
[uniquekey] => 149423
[type] => reply
[user_id] => 2
[date_added] => 2015-06-01 15:25:56
)
[4] => Array
(
[alert_id] => 164742
[uniquekey] => 164742
[type] => reply
[user_id] => 2
[date_added] => 2015-05-12 09:46:39
)
[5] => Array
(
[alert_id] => 163344
[uniquekey] => 163344
[type] => replyfromfollowing
[user_id] => 3
[date_added] => 2015-05-12 09:44:46
)
[6] => Array
(
[alert_id] => 164205
[uniquekey] => 164205
[type] => liked
[user_id] => 2
[date_added] => 2015-05-11 11:06:39
)
[7] => Array
(
[alert_id] => 160890
[uniquekey] => 160890
[type] => replyfromfollowing
[user_id] => 1380
[date_added] => 2015-05-08 14:29:34
)
[8] => Array
(
[alert_id] => 163002
[uniquekey] => 163002
[type] => replyfromfollowing
[user_id] => 1380
[date_added] => 2015-05-08 13:31:12
)
[9] => Array
(
[alert_id] => 159123
[uniquekey] => 159123
[type] => replyfromfollowing
[user_id] => 48
[date_added] => 2015-04-30 15:10:32
)
[10] => Array
(
[alert_id] => 150546
[uniquekey] => 150546
[type] => replyfromfollowing
[user_id] => 16
[date_added] => 2015-04-21 21:52:49
)
[11] => Array
(
[alert_id] => 149497
[uniquekey] => 149497
[type] => reply
[user_id] => 2
[date_added] => 2015-04-10 15:19:06
)
[12] => Array
(
[alert_id] => 141078
[uniquekey] => 141078
[type] => liked
[user_id] => 2
[date_added] => 2015-04-10 15:15:32
)
[13] => Array
(
[alert_id] => 125466
[uniquekey] => 125466
[type] => replyfromfollowing
[user_id] => 3
[date_added] => 2015-04-09 00:15:22
)
[14] => Array
(
[alert_id] => 134592
[uniquekey] => 134592
[type] => replyfromfollowing
[user_id] => 3
[date_added] => 2015-04-09 00:11:04
)
[15] => Array
(
[alert_id] => 124194
[uniquekey] => 124194
[type] => likedfromfollowing
[user_id] => 3
[date_added] => 2015-04-09 00:08:35
)
[16] => Array
(
[alert_id] => 128645
[uniquekey] => 128645
[type] => likedfromfollowing
[user_id] => 3
[date_added] => 2015-04-09 00:07:29
)
[17] => Array
(
[alert_id] => 144867
[uniquekey] => 144867
[type] => replyfromfollowing
[user_id] => 3
[date_added] => 2015-04-06 13:59:19
)
[18] => Array
(
[alert_id] => 133355
[uniquekey] => 133355
[type] => liked
[user_id] => 2
[date_added] => 2015-03-31 16:16:15
)
[19] => Array
(
[alert_id] => 141075
[uniquekey] => 141075
[type] => liked
[user_id] => 2
[date_added] => 2015-03-30 15:17:01
)
)
一些可能性,排名不分先后:
优化#1:
也在子查询中使用 LIMIT
。但是,由于您使用的是 OFFSET
,因此可能不太清楚如何操作。
在查询之前,计算$start+20 并将其放入,比如说$limit。然后使用 LIMIT $limit
进行内部查询。不,不要对它们使用 OFFSET
。这保证您从每个查询中获得足够的行以满足外部 OFFSET $start LIMIT 20
.
优化#2:
重组tables,这样你就不需要JOIN
到另一个table(alerts
)来确定是否显示记录。也就是说,hide
会阻止许多潜在的优化。在进一步建议之前,我们需要了解 LEFT
的必要性。 reply
等中是否有不在 alerts
中的行?如果没有,去掉 LEFT
并研究搜索 alerts with the
OFFSETand
LIMIT`,然后加入其他 4 个 table。
优化#3:
重组数据,使一个核心table,alerts
和另外4个table挂在上面。确保在这个新的 table.
中包含 this 查询所需的大部分(全部?)字段
优化#4:
当前结构需要在考虑 OFFSET
和 LIMIT
之前对 4 个 table 中的每一个进行完整扫描。这闻起来像 "pagination";是吗?为了优化 "pagination",目标是避免 table 扫描和 OFFSET
;相反,请记住您 "left off" 的位置,以便查询可以
WHERE ... AND x < $left_off
ORDER by x DESC
LIMIT 20
这应该可以只读取 20 行,而不是整个 table(s)。这将使查询 much 更快,尤其是对于后面的页面。 (更大的 OFFSET
需要更多时间`)
我在 my blog 中讨论分页优化。
我有一个很大的查询,所有查询都包含我在我的应用程序中列出的供稿所需的相同类型。现在的问题是这个查询不是很快。我在想,如果我限制每个单独的工会,它可能会加快一点,但我不确定。
所以基本上我的问题是如何优化此查询以更快地执行?
SELECT DISTINCT
alert_id,
uniquekey,
type,
user_id,
date_added
FROM
(
( SELECT
r.alert_id,
r.alert_id AS uniquekey,
'reply' AS `type`,
r.user_id,
r.date_added
FROM
`reply` r
LEFT JOIN `alerts` a
ON r.alert_id = a.alert_content_id
WHERE
r.user_id = :id
AND a.hide = '0'
ORDER BY
date_added DESC )
UNION
( SELECT
r.alert_id,
r.alert_id AS uniquekey,
'replyfromfollowing' AS `type`,
r.user_id,
r.date_added
FROM
`reply` r
LEFT JOIN `alerts` a
ON r.alert_id = a.alert_content_id
WHERE
r.user_id IN( '$followingstring' )
AND a.hide = '0'
ORDER BY date_added DESC )
UNION
( SELECT
i.alert_id,
i.alert_id AS uniquekey,
'liked' AS `type`,
i.user_id,
i.date_added
FROM
`interactions` i
LEFT JOIN `alerts` a
ON i.alert_id = a.alert_content_id
WHERE
i.user_id = :id
AND a.hide = '0'
GROUP BY
alert_id
ORDER BY
date_added DESC )
UNION
( SELECT
i.alert_id,
i.alert_id AS uniquekey,
'likedfromfollowing' AS `type`,
i.user_id,
i.date_added
FROM
`interactions` i
LEFT JOIN `alerts` a
ON i.alert_id = a.alert_content_id
WHERE
i.user_id IN ( '$followingstring' )
AND a.hide = '0'
GROUP BY
alert_id
ORDER BY
date_added DESC )
UNION
( SELECT
alerts as alert_id,
alert_content_id AS uniquekey,
'following' AS `type`,
user_id,
date_added
FROM
alerts a
LEFT JOIN `alerts_content` ac
ON ac.id = a.alert_content_id
WHERE
a.user_id IN ( '$followingstring' )
AND ac.anoniem = '0'
AND a.hide = '0'
GROUP BY
alert_id
ORDER BY
date_added DESC )
) joined
GROUP BY
uniquekey
ORDER BY
date_added DESC
LIMIT
".(int)$start.",20"
table 结构
Reply table Structure:
id
user_id
alert_id
description
reply_on_alert
reply_on_reply
date_added
Interaction table Structure:
id
alert_id
action_id
reply_id
user_id
date_added
Alerts table structure(Yes i know BIG mistake naming `id` : `alerts`):
alerts
title
alert_content_id
user_id
cat
lat
lon
state
hide
date_added
alerts_content table structure:
id
alert_id
description
img
查询结果:
Array
(
[0] => Array
(
[alert_id] => 173404
[uniquekey] => 173404
[type] => reply
[user_id] => 2
[date_added] => 2015-06-01 16:34:16
)
[1] => Array
(
[alert_id] => 172174
[uniquekey] => 172174
[type] => replyfromfollowing
[user_id] => 1380
[date_added] => 2015-06-01 16:01:04
)
[2] => Array
(
[alert_id] => 171772
[uniquekey] => 171772
[type] => liked
[user_id] => 2
[date_added] => 2015-06-01 15:58:44
)
[3] => Array
(
[alert_id] => 149423
[uniquekey] => 149423
[type] => reply
[user_id] => 2
[date_added] => 2015-06-01 15:25:56
)
[4] => Array
(
[alert_id] => 164742
[uniquekey] => 164742
[type] => reply
[user_id] => 2
[date_added] => 2015-05-12 09:46:39
)
[5] => Array
(
[alert_id] => 163344
[uniquekey] => 163344
[type] => replyfromfollowing
[user_id] => 3
[date_added] => 2015-05-12 09:44:46
)
[6] => Array
(
[alert_id] => 164205
[uniquekey] => 164205
[type] => liked
[user_id] => 2
[date_added] => 2015-05-11 11:06:39
)
[7] => Array
(
[alert_id] => 160890
[uniquekey] => 160890
[type] => replyfromfollowing
[user_id] => 1380
[date_added] => 2015-05-08 14:29:34
)
[8] => Array
(
[alert_id] => 163002
[uniquekey] => 163002
[type] => replyfromfollowing
[user_id] => 1380
[date_added] => 2015-05-08 13:31:12
)
[9] => Array
(
[alert_id] => 159123
[uniquekey] => 159123
[type] => replyfromfollowing
[user_id] => 48
[date_added] => 2015-04-30 15:10:32
)
[10] => Array
(
[alert_id] => 150546
[uniquekey] => 150546
[type] => replyfromfollowing
[user_id] => 16
[date_added] => 2015-04-21 21:52:49
)
[11] => Array
(
[alert_id] => 149497
[uniquekey] => 149497
[type] => reply
[user_id] => 2
[date_added] => 2015-04-10 15:19:06
)
[12] => Array
(
[alert_id] => 141078
[uniquekey] => 141078
[type] => liked
[user_id] => 2
[date_added] => 2015-04-10 15:15:32
)
[13] => Array
(
[alert_id] => 125466
[uniquekey] => 125466
[type] => replyfromfollowing
[user_id] => 3
[date_added] => 2015-04-09 00:15:22
)
[14] => Array
(
[alert_id] => 134592
[uniquekey] => 134592
[type] => replyfromfollowing
[user_id] => 3
[date_added] => 2015-04-09 00:11:04
)
[15] => Array
(
[alert_id] => 124194
[uniquekey] => 124194
[type] => likedfromfollowing
[user_id] => 3
[date_added] => 2015-04-09 00:08:35
)
[16] => Array
(
[alert_id] => 128645
[uniquekey] => 128645
[type] => likedfromfollowing
[user_id] => 3
[date_added] => 2015-04-09 00:07:29
)
[17] => Array
(
[alert_id] => 144867
[uniquekey] => 144867
[type] => replyfromfollowing
[user_id] => 3
[date_added] => 2015-04-06 13:59:19
)
[18] => Array
(
[alert_id] => 133355
[uniquekey] => 133355
[type] => liked
[user_id] => 2
[date_added] => 2015-03-31 16:16:15
)
[19] => Array
(
[alert_id] => 141075
[uniquekey] => 141075
[type] => liked
[user_id] => 2
[date_added] => 2015-03-30 15:17:01
)
)
一些可能性,排名不分先后:
优化#1:
也在子查询中使用 LIMIT
。但是,由于您使用的是 OFFSET
,因此可能不太清楚如何操作。
在查询之前,计算$start+20 并将其放入,比如说$limit。然后使用 LIMIT $limit
进行内部查询。不,不要对它们使用 OFFSET
。这保证您从每个查询中获得足够的行以满足外部 OFFSET $start LIMIT 20
.
优化#2:
重组tables,这样你就不需要JOIN
到另一个table(alerts
)来确定是否显示记录。也就是说,hide
会阻止许多潜在的优化。在进一步建议之前,我们需要了解 LEFT
的必要性。 reply
等中是否有不在 alerts
中的行?如果没有,去掉 LEFT
并研究搜索 alerts with the
OFFSETand
LIMIT`,然后加入其他 4 个 table。
优化#3:
重组数据,使一个核心table,alerts
和另外4个table挂在上面。确保在这个新的 table.
优化#4:
当前结构需要在考虑 OFFSET
和 LIMIT
之前对 4 个 table 中的每一个进行完整扫描。这闻起来像 "pagination";是吗?为了优化 "pagination",目标是避免 table 扫描和 OFFSET
;相反,请记住您 "left off" 的位置,以便查询可以
WHERE ... AND x < $left_off
ORDER by x DESC
LIMIT 20
这应该可以只读取 20 行,而不是整个 table(s)。这将使查询 much 更快,尤其是对于后面的页面。 (更大的 OFFSET
需要更多时间`)
我在 my blog 中讨论分页优化。