我有两个数据库，每个数据库都包含数据和“标签”（用表示）。在一个数据库中，标签与数据一起嵌入（存储在表中），而在另一个数据库中，标签存储在不同的表中，因此需要一个连接来访问标签。对于大多数查询，除了一个查询外，使用嵌入标签的变体速度更快。我想知道是否有人能告诉我为什么会这样，我自己对postgres，sql或数据库的细节不是很熟悉？我在下面列出了“解释-分析”输出。提前谢谢。
数据库由与这些位置点相关联的位置和用户组成，数据用\p字段标记。这两个数据库的区别在于，一个数据库中的标签与数据嵌入在同一个表中，另一个数据库中的标签存储在不同的表中，因此需要额外的联接。除此之外，两个查询做的事情完全相同。对于大多数查询，我们看到嵌入标签的方法速度更快，但是对于这个特定的查询，速度较慢，如果可能的话，我想知道原因。我不是真的在寻找一个详细的答案，我只是想知道是否有什么明显的原因导致一个查询计划比另一个慢。
外部标签查询：

SELECT firstname, lastname, latitude, longitude 
FROM locations INNER 
   JOIN users ON locations.userid = users.id 
   JOIN users_p users0x ON users.id = users0x.users_id 
   JOIN locations_p locations0x ON locations.id = locations0x.locations_id 
WHERE country = ? AND date_part('year', age(birthdate)) > 18 
    AND (date_part('year', to_date(timestamp, 'YYYY-MM-DD')) BETWEEN 2010 AND 2019) 
    AND (locations0x.longitude_p & '2') != 0 AND (users0x.lastname_p & '2') != 0 AND (users0x.firstname_p & '2') != 0 AND (users0x.birthdate_p & '2') != 0 AND (locations0x.timestamp_p & '2') != 0 
    AND (users0x.country_p & '2') != 0 AND (locations0x.latitude_p & '2') != 0

解释分析输出：

Nested Loop  (cost=0.42..10116.71 rows=12 width=22) (actual time=317.811..331.306 rows=954 loops=1)
 ->  Nested Loop  (cost=0.00..10051.48 rows=12 width=26) (actual time=317.793..328.701 rows=954 loops=1)
     Join Filter: (locations.userid = users0x.users_id)
     Rows Removed by Join Filter: 94446
     ->  Seq Scan on users_p users0x  (cost=0.00..4.00 rows=96 width=4) (actual time=0.007..0.051 rows=100 loops=1)
           Filter: (((country_p & 2) <> 0) AND ((birthdate_p & 2) <> 0) AND ((lastname_p & 2) <> 0) AND ((firstname_p & 2) <> 0))
     ->  Materialize  (cost=0.00..10030.23 rows=12 width=34) (actual time=0.000..3.209 rows=954 loops=100)
           ->  Nested Loop  (cost=0.00..10030.17 rows=12 width=34) (actual time=0.034..317.266 rows=954 loops=1)
                 Join Filter: (locations.userid = users.id)
                 Rows Removed by Join Filter: 98382
                 ->  Seq Scan on users  (cost=0.00..6.25 rows=1 width=18) (actual time=0.019..0.043 rows=2 loops=1)
                       Filter: ((country = 'Colombia'::text) AND (date_part('year'::text, age((('now'::cstring)::date)::timestamp with time zone, (to_date(birthdate, 'YYYY-MM-DD'::text))::timestamp with time zone)) > '18'::double precision))
                       Rows Removed by Filter: 98
                 ->  Seq Scan on locations  (cost=0.00..10008.30 rows=1250 width=16) (actual time=0.007..155.286 rows=49668 loops=2)
                       Filter: ((date_part('year'::text, (to_date("timestamp", 'YYYY-MM-DD'::text))::timestamp without time zone) >= '2010'::double precision) AND (date_part('year'::text, (to_date("timestamp", 'YYYY-MM-DD'::text))::timestamp without time zone) <= '2019'::double precision))
                       Rows Removed by Filter: 200332
 ->  Index Scan using locations_purpose_index on locations_p locations0x  (cost=0.42..5.43 rows=1 width=4) (actual time=0.002..0.002 rows=1 loops=954)
     Index Cond: (locations_id = locations.id)
     Filter: (((latitude_p & 2) <> 0) AND ((longitude_p & 2) <> 0) AND ((timestamp_p & 2) <> 0))
Planning time: 0.555 ms
Execution time: 331.449 ms

嵌入式标签查询：

SELECT firstname, lastname, latitude, longitude 
 FROM locations INNER JOIN users ON locations.userid = users.id 
 WHERE country = ? AND date_part('year', age(birthdate)) > 18 
    AND (date_part('year', to_date(timestamp, 'YYYY-MM-DD')) BETWEEN 2010 AND 2019) 
    AND (users.firstname_p & '2') != 0 
    AND (locations.timestamp_p & '2') != 0 
    AND (users.country_p & '2') != 0 AND (locations.longitude_p & '2') != 0 
    AND (locations.latitude_p & '2') != 0 
    AND (users.lastname_p & '2') != 0 AND (users.birthdate_p & '2') != 0

解释分析输出：

Nested Loop  (cost=0.00..13782.09 rows=12 width=22) (actual time=0.113..421.690 rows=954 loops=1)
 Join Filter: (locations.userid = users.id)
 Rows Removed by Join Filter: 98382
 ->  Seq Scan on users  (cost=0.00..8.25 rows=1 width=18) (actual time=0.062..0.087 rows=2 loops=1)
     Filter: ((country = 'Colombia'::text) AND ((country_p & 2) <> 0) AND ((birthdate_p & 2) <> 0) AND ((lastname_p & 2) <> 0) AND ((firstname_p & 2) <> 0) AND (date_part('year'::text, age((('now'::cstring)::date)::timestamp with time zone, (to_date(birthdate, 'YYYY-MM-DD'::text))::timestamp with time zone)) > '18'::double precision))
     Rows Removed by Filter: 98
 ->  Seq Scan on locations  (cost=0.00..13758.45 rows=1231 width=12) (actual time=0.018..207.065 rows=49668 loops=2)
     Filter: (((latitude_p & 2) <> 0) AND ((longitude_p & 2) <> 0) AND ((timestamp_p & 2) <> 0) AND (date_part('year'::text, (to_date("timestamp", 'YYYY-MM-DD'::text))::timestamp without time zone) >= '2010'::double precision) AND (date_part('year'::text, (to_date("timestamp", 'YYYY-MM-DD'::text))::timestamp without time zone) <= '2019'::double precision))
     Rows Removed by Filter: 200332
 Planning time: 0.811 ms
 Execution time: 421.820 ms