亲宝软件园·资讯

展开

postgresql通过索引优化查询速度操作

你不知道你所以是你 人气:0
这篇文章主要介绍了postgresql通过索引优化查询速度操作,具有很好的参考价值,希望对大家有所帮助。一起跟随小编过来看看吧

当数据量比较大的时候,提升查询效率就是需要去考虑的事情了。一个百万级别的表格,如果不做任何优化的话,即使是最简单的查询语句执行起来也是慢的让人难以接受;当然“优化”本身是一个比较复杂的工程,从设计表、字段到查询语句的写法都有很多讲究,这里只考虑索引的方式,且是最普通的索引;

下面的操作中对应数据库表w008_execrise_info(8000数据量), w008_wf02_info(4000数据量)

1 任务表数据

SELECT
 w.*
FROM
 w008_wf02_info w 
WHERE
 w.is_removed =0
 AND w.wfno = 'WF02' 
 AND EXISTS (
SELECT
 1 
FROM
 w008_execrise_info info
 JOIN w008_privilege_allocation P ON (
 info.subjecttyp = P.subjecttyp 
 AND info.gradetyp = P.gradetyp 
 AND P.loginname = w.create_by 
 AND P.verifyusers ='yixi_li'
 AND P.wftype = 20 
 ) 
WHERE
 info.is_removed =0 
 AND info.wfid = w.wfid 
 ) 
 
ORDER BY
 create_date DESC

执行结果:耗时在3秒左右(这个数据量,这个速度就比较慢了)

顺带说一句,这里把w.* 改成对应的字段也会稍微的提高一些查询速度(毕竟少了一步把*转成对应字段的操作),在标准的查询规范中也不会写成table.*这种方式的。

2 添加索引:

虽说索引可以提高查询速度,但是不代表加了索引就一定会加快查询速度,有时甚至会适得其反。

一般来说索引会加在where 后面的查询字段,尤其是关联字段上面,这里w008_execrise_info 表数据量最大,暂时针对这个表进行处理;w008_execrise_info 表涉及四个字段:subjecttyp,gradetyp,wfid和is_removed。

其中is_removed先不考虑,subjecttyp和gradetyp保存的是字典表的数据(数据内容比较少,类似10 20 30 40),wfid保存的是数字类型的字符串。

一个索引起到的效果还取决于这样一个条件,一般来说添加索引的字段的值"唯一性"越明显越好,在这里,subjecttyp和gradetyp包含大量的重复值,索引效果会“不明显”一些,而wfid 更像是“主键”,相对应的效果会好很多。

2.1 subjecttyp 和gradetyp添加索引

CREATE INDEX w008_execrise_info_gradetyp_index ON w008_execrise_info (gradetyp);

CREATE INDEX w008_execrise_info_subjecttyp_index ON w008_execrise_info (subjecttyp);

添加后执行时间大约2秒,快了一点点

2.2 对wfid添加索引

CREATE INDEX w008_execrise_info_wfid_index ON w008_execrise_info (wfid);

再次执行,0.2秒左右,快了很多

再补充一句,有很多情况下索引是不起作用的,比如 like后面跟的字段,还有条件语句or关联的字段,这种情况就是要考虑查询策略了。

3 查看当前表的索引内容;

select * from pg_indexes where tablename='w008_execrise_info';

select * from pg_statio_all_indexes where relname='w008_execrise_info';

4 删除索引

DROP INDEX indexName;

5 重置索引

对于一些经常改动的表,如果时间长了发现查询效率变慢,可以考虑重置一下索引;

因为如果表的内容被频繁的修改的话会产生许多类似'索引碎片'的东西,会导致查询索引本身的时间变长;

 REINDEX INDEX index_name;//重置单个索引
 REINDEX TABLE table_name;//重置整个表的索引
 REINDEX DATABASE db_name;//终止整个数据库的所以你

补充:PostgreSql查询优化之根据执行计划优化SQL

1、执行计划路径选择

postgresql查询规划过程中,查询请求的不同执行方案是通过建立不同的路径来表达的,在生成许多符合条件的路径之后,要从中选择出代价最小的路径(基于成本运算),把它转化为一个计划,传递给执行器执行,规划器的核心工作就是生成多条路径,然后从中找出最优的那一条。

1.1代价评估

评估路径优劣的依据是用系统表pg_statistic中的统计信息估算出来的不同路径的代价(cost),PostgreSQL估计计划成本的方式:基于统计信息估计计划中各个节点的成本。PostgreSQL会分析各个表来获取一个统计信息样本(这个操作通常是由autovacuum这个守护进程周期性的执行analyze,来收集这些统计信息,然后保存到pg_statistic和pg_class里面)。

1.2用于估算代价的参数postgresql.conf

# - Planner Cost Constants -
 
#seq_page_cost = 1.0  # measured on an arbitrary scale 顺序磁盘扫描时单个页面的开销
 
#random_page_cost = 4.0  # same scale as above 随机磁盘访问时单页面的读取开销
 
#cpu_tuple_cost = 0.01  # same scale as above cpu处理每一行的开销
 
#cpu_index_tuple_cost = 0.005 # same scale as above cpu处理每个索引行的开销
 
#cpu_operator_cost = 0.0025 # same scale as above cpu处理每个运算符或者函数调用的开销
 
#parallel_tuple_cost = 0.1 # same scale as above 计算并行处理的成本,如果成本高于非并行,则不会开启并行处理。
 
#parallel_setup_cost = 1000.0 # same scale as above
 
#min_parallel_relation_size = 8MB
 
#effective_cache_size = 4GB 再一次索引扫描中可用的文件系统内核缓冲区有效大小

也可以使用 show all的方式查看

1.3 路径的选择

--查看表信息

highgo=# \d t_jcxxgl_tjaj
 
  Table "db_jcxx.t_jcxxgl_tjaj"
 
 Column |  Type  | Modifiers --------------+--------------------------------+-----------
 
 c_bh | character(32)   | not null
 
 c_xzdm | character varying(300)  |
 
 c_jgid | character(32)   |
 
 c_ajbm | character(22)   |
 
...
 
Indexes:
 
 "t_jcxxgl_tjaj_pkey" PRIMARY KEY, btree (c_bh)
 
 "idx_ttjaj_cah" btree (c_ah)
 
 "idx_ttjaj_dslrq" btree (d_slrq)

首先更新统计信息vacuum analyze t_jcxxgl_tjaj,许多时候可能因为统计信息的不准确导致了不正常的执行计划--执行计划。

--执行计划,全表扫描

highgo=# explain (analyze,verbose,costs,buffers,timing)select c_bh,c_xzdm,c_jgid,c_ajbm from t_jcxxgl_tjaj where d_slrq >='2018-03-18';
 
       QUERY PLAN      ------------------------------------------------------------------------------------------------------------
 
 Seq Scan on db_jcxx.t_jcxxgl_tjaj (cost=0.00..9.76 rows=3 width=96) (actual time=1.031..1.055 rows=3 loops
 
=1)
 
 Output: c_bh, c_xzdm, c_jgid, c_ajbm
 
 Filter: (t_jcxxgl_tjaj.d_slrq >= '2018-03-18'::date)
 
 Rows Removed by Filter: 138
 
 Buffers: shared hit=8
 
 Planning time: 6.579 ms
 
 Execution time: 1.163 ms
 
(7 rows)

如上,d_slrq是有索引的,但是执行计划中并没有走索引,为什么呢?我们继续往下看。

--执行计划,关闭全表扫描

highgo=# set session enable_seqscan = off;
 
SET
 
highgo=# explain (analyze,verbose,costs,buffers,timing)select c_bh,c_xzdm,c_jgid,c_ajbm from t_jcxxgl_tjaj where d_slrq >='2018-03-18';
 
        QUERY PLAN        ------------------------------------------------------------------------------------------------------------
 
 Index Scan using idx_ttjaj_dslrq on db_jcxx.t_jcxxgl_tjaj (cost=0.14..13.90 rows=3 width=96) (actual time=0.012..0.026 rows=3 loops=1)
 
 Output: c_bh, c_xzdm, c_jgid, c_ajbm
 
 Index Cond: (t_jcxxgl_tjaj.d_slrq >= '2018-03-18'::date)
 
 Buffers: shared hit=4
 
 Planning time: 0.309 ms
 
 Execution time: 0.063 ms
 
(6 rows)

d_slrq上面有btree索引,但是查看执行计划并没有走索引,这是为什么呢?

代价计算:

一个路径的估算由三部分组成:启动代价(startup cost),总代价(totalcost),执行结果的排序方式(pathkeys)

代价估算公式:

总代价=启动代价+I/O代价+CPU代价(cost=S+P+W*T)

P:执行时要访问的页面数,反应磁盘的I/O次数

T:表示在执行时所要访问的元组数,反映了cpu开销

W:表示磁盘I/O代价和CPU开销建的权重因子

统计信息:

统计信息的其中一部分是每个表和索引中项的总数,以及每个表和索引占用的磁盘块数。这些信息保存在pg_class表的reltuples和relpages列中。我们可以这样查询相关信息:

​--查看统计信息

highgo=# select relpages,reltuples from pg_class where relname ='t_jcxxgl_tjaj';
 
 relpages | reltuples ----------+-----------
 
 8 | 141
 
(1 row)

total_cost = 1(seq_page_cost)*8(磁盘总页数)+0.01(cpu_tuple_cost)*141(表的总记录数)+0.0025(cpu_operation_cost)*141(表的总记录数)=9.7625

可以看到走索引的cost=13.90比全表扫描cost=9.76要大。所以上面没有关闭全表扫描的时候,根据成本代价,执行计划走的全表扫描。在表较小的情况下,全表扫描比索引扫描更有效, index scan 至少要发生两次I/O,一次是读取索引块,一次是读取数据块。

2、一个SQL优化实例

2.1慢SQL:

select c_ajbh, c_ah, c_cbfy, c_cbrxm, d_larq, d_jarq, n_dbjg, c_yqly from db_zxzhld.t_zhld_db dbxx join db_zxzhld.t_zhld_ajdbxx dbaj on dbxx.c_bh = dbaj.c_dbbh where dbxx.n_valid=1 and dbxx.n_state in (1,2,3) and dbxx.c_dbztbh='1003' and dbaj.c_zblx='1003' and dbaj.c_dbfy='0' and dbaj.c_gy = '2550' and c_ajbh in (select distinct c_ajbh from db_zxzhld.t_zhld_zbajxx where n_dbzt = 1 and c_zblx = '1003' and c_gy = '2550' ) order by d_larq asc, c_ajbh asc limit 15 offset 0;

慢sql耗时:7s

先过下这个sql是干什么的、首先dbxx和dbaj的一个join连接然后dbaj.c_ajbh要包含在zbaj表里面,做了个排序,取了15条记录、大概就这样。

Sql有个缺点就是我不知道查询的字段是从那个表里面取的、建议加上表别名.字段。

查看该sql的表的数据量:

  t_zhld_db :1311
  t_zhld_ajdbxx :341296
 t_zhld_zbajxx :1027619 

执行计划:

 Limit (cost=36328.67..36328.68 rows=1 width=107) (actual time=88957.677..88957.729 rows=15 loops=1)
 
 -> Sort (cost=36328.67..36328.68 rows=1 width=107) (actual time=88957.653..88957.672 rows=15 loops=1)
 
  Sort Key: dbaj.d_larq, dbaj.c_ajbh
 
  Sort Method: top-N heapsort Memory: 27kB
 
  -> Nested Loop Semi Join (cost=17099.76..36328.66 rows=1 width=107) (actual time=277.794..88932.662 rows=8605 loops=1)
 
  Join Filter: ((dbaj.c_ajbh)::text = (t_zhld_zbajxx.c_ajbh)::text)
 
  Rows Removed by Join Filter: 37018710
 
  -> Nested Loop (cost=0.00..19200.59 rows=1 width=107) (actual time=199.141..601.845 rows=8605 loops=1)
 
   Join Filter: (dbxx.c_bh = dbaj.c_dbbh)
 
   Rows Removed by Join Filter: 111865
 
   -> Seq Scan on t_zhld_ajdbxx dbaj (cost=0.00..19117.70 rows=219 width=140) (actual time=198.871..266.182 rows=8605 loops=1)
 
    Filter: ((n_valid = 1) AND ((c_zblx)::text = '1003'::text) AND ((c_dbfy)::text = '0'::text) AND ((c_gy)::text = '2550'::text))
 
    Rows Removed by Filter: 332691
 
   -> Materialize (cost=0.00..66.48 rows=5 width=33) (actual time=0.001..0.017 rows=14 loops=8605)
 
    -> Seq Scan on t_zhld_db dbxx (cost=0.00..66.45 rows=5 width=33) (actual time=0.044..0.722 rows=14 loops=1)
 
     Filter: ((n_valid = 1) AND ((c_dbztbh)::text = '1003'::text) AND (n_state = ANY ('{1,2,3}'::integer[])))
 
     Rows Removed by Filter: 1297
 
  -> Materialize (cost=17099.76..17117.46 rows=708 width=32) (actual time=0.006..4.890 rows=4303 loops=8605)
 
   -> HashAggregate (cost=17099.76..17106.84 rows=708 width=32) (actual time=44.011..54.924 rows=8605 loops=1)
 
    Group Key: t_zhld_zbajxx.c_ajbh
 
    -> Bitmap Heap Scan on t_zhld_zbajxx (cost=163.36..17097.99 rows=708 width=32) (actual time=5.218..30.278 rows=8605 loops=1)
 
     Recheck Cond: ((n_dbzt = 1) AND ((c_zblx)::text = '1003'::text))
 
     Filter: ((c_gy)::text = '2550'::text)
 
     Rows Removed by Filter: 21849
 
     Heap Blocks: exact=960
 
     -> Bitmap Index Scan on i_tzhldzbajxx_zblx_dbzt (cost=0.00..163.19 rows=5876 width=0) (actual time=5.011..5.011 rows=30458 loops=1)
 
     Index Cond: ((n_dbzt = 1) AND ((c_zblx)::text = '1003'::text))
 
 Planning time: 1.258 ms
 
 Execution time: 88958.029 ms

执行计划解读:

1:第27->21行,通过索引i_tzhldzbajxx_zblx_dbzt过滤表t_zhld_zbajxx的数据,然后根据过滤条件(c_gy)::text = '2550'::text过滤最终返回8605条数据

2:第17->15行,根据条件过滤t_zhld_db表的数据,最终返回了14条数据

3:第20->19行,对表t_zhld_zbajxx做group by的操作

4:第13->11行,全表扫描t_zhld_ajdbxx 最终返回了8605条数据

5:第08行,根据t_zhld_ajdbxx返回的8605条结果集作为驱动表和t_zhld_db的结果集(14条)做嵌套循环,t_zhld_db的结果集被循环了8605次。然后过滤掉了其中的111865条记录,那么最终将得到(8605*14-111865) = 8605

6:第07->05行,根据第08和18行返回的结果集最终做了Nested Loop Semi Join,第18行的4303条结果集被循环了8605次,(4303*8605-37018710)=8605

7: 第04->02行,对最终的8605条记录进行排序

8:第01行,limit最终获取15条记录

整个执行计划中耗时最长的地方在05行Nested Loop Semi Join,actual time=277.794..88932.662, 表db_zxzhld.t_zhld_db dbxx和db_zxzhld.t_zhld_ajdbxx均是全表扫描

2.2具体优化步骤

查看索引页并没有索引,创建c_ajbh,c_dbbh等逻辑外键的索引

drop index if exists I_T_ZHLD_AJDBXX_AJBH;
 
create index I_T_ZHLD_AJDBXX_AJBH on T_ZHLD_AJDBXX (c_ajbh);
 
commit;
 
drop index if exists I_T_ZHLD_AJDBXX_DBBH;
 
create index I_T_ZHLD_AJDBXX_DBBH on T_ZHLD_AJDBXX (c_dbbh);
 
commit;

创建d_larq,c_ajbh的排序索引:

drop index if exists I_T_ZHLD_AJDBXX_m6;create index I_T_ZHLD_AJDBXX_m6 on T_ZHLD_AJDBXX (c_zblx,c_dbfy,c_gy,d_larq asc,c_ajbh asc);
 
commit;
 
drop index if exists I_T_ZHLD_ZBAJXX_h3 ;
 
create index I_T_ZHLD_ZBAJXX_h3 on db_zxzhld.t_zhld_zbajxx (n_dbzt,c_zblx,c_gy,c_gy);
 
commit;

创建索引后执行计划有了改变,原来的dbaj表和dbxx表先做nestedloop变成了zbaj和dbaj表先做了nestedloop join,总的cost也从36328.68降到了12802.87,

执行计划

Limit (cost=12802.87..12802.87 rows=1 width=107) (actual time=4263.598..4263.648 rows=15 loops=1)
 
 -> Sort (cost=12802.87..12802.87 rows=1 width=107) (actual time=4263.592..4263.609 rows=15 loops=1)
 
 Sort Key: dbaj.d_larq, dbaj.c_ajbh
 
 Sort Method: top-N heapsort Memory: 27kB
 
 -> Nested Loop (cost=2516.05..12802.86 rows=1 width=107) (actual time=74.240..4239.723 rows=8605 loops=1)
 
  Join Filter: (dbaj.c_dbbh = dbxx.c_bh)
 
  Rows Removed by Join Filter: 111865
 
  -> Nested Loop (cost=2516.05..12736.34 rows=1 width=140) (actual time=74.083..327.974 rows=8605 loops=1)
 
   -> HashAggregate (cost=2515.62..2522.76 rows=714 width=32) (actual time=74.025..90.185 rows=8605 loops=1)
 
    Group Key: ("ANY_subquery".c_ajbh)::text
 
    -> Subquery Scan on "ANY_subquery" (cost=2499.56..2513.84 rows=714 width=32) (actual time=28.782..59.823 rows=8605 loops=1)
 
    -> HashAggregate (cost=2499.56..2506.70 rows=714 width=32) (actual time=28.778..39.968 rows=8605 loops=1)
 
     Group Key: zbaj.c_ajbh
 
     -> Index Scan using i_t_zhld_zbajxx_h3 on t_zhld_zbajxx zbaj (cost=0.42..2497.77 rows=715 width=32) (actual time=0.062..15.104 rows=8605 loops=1)
 
      Index Cond: ((n_dbzt = 1) AND ((c_zblx)::text = '1003'::text) AND ((c_gy)::text = '2550'::text))
 
   -> Index Scan using i_t_zhld_ajdbxx_ajbh on t_zhld_ajdbxx dbaj (cost=0.42..14.29 rows=1 width=140) (actual time=0.015..0.021 rows=1 loops=8605)
 
    Index Cond: ((c_ajbh)::text = ("ANY_subquery".c_ajbh)::text)
 
    Filter: (((c_zblx)::text = '1003'::text) AND ((c_dbfy)::text = '0'::text) AND ((c_gy)::text = '2550'::text))
 
    Rows Removed by Filter: 1
 
  -> Seq Scan on t_zhld_db dbxx (cost=0.00..66.45 rows=5 width=33) (actual time=0.015..0.430 rows=14 loops=8605)
 
   Filter: ((n_valid = 1) AND ((c_dbztbh)::text = '1003'::text) AND (n_state = ANY ('{1,2,3}'::integer[])))
 
   Rows Removed by Filter: 1298
 
Planning time: 1.075 ms
 
Execution time: 4263.803 ms

执行的时间还是要4s左右仍然不满足需求,并且没有使用上I_T_ZHLD_AJDBXX_m6这个索引。

2.3等价改写SQL(1)

等价改写:将排序条件加入db_zxzhld.t_zhld_ajdbxx让其先排序,再和t_zhld_db表连接。

修改后sql:

Select dbaj.c_ajbh, dbaj.c_ah, dbaj.c_cbfy, dbaj.c_cbrxm, dbaj.d_larq, dbaj.d_jarq, dbaj.n_dbjg, dbaj.c_yqly from (select * from db_zxzhld.t_zhld_db where n_valid=1 and n_state in (1,2,3) and c_dbztbh='1003' )dbxx
 
 join (select * from db_zxzhld.t_zhld_ajdbxx where n_valid=1 and c_zblx='1003'
 
 and c_dbfy='0' and c_gy = '2550' and
 
c_ajbh in (select distinct c_ajbh from db_zxzhld.t_zhld_zbajxx where n_dbzt = 1 and c_zblx = '1003' and c_gy = '2550' ) order by d_larq asc, c_ajbh asc)dbajon dbxx.c_bh = dbaj.c_dbbh
 
 limit 15 offset 0

再次查看执行计划:

Limit (cost=3223.92..3231.97 rows=1 width=107) (actual time=127.291..127.536 rows=15 loops=1)
 
 -> Nested Loop (cost=3223.92..3231.97 rows=1 width=107) (actual time=127.285..127.496 rows=15 loops=1)
 
 -> Sort (cost=3223.64..3223.65 rows=1 width=140) (actual time=127.210..127.225 rows=15 loops=1)
 
  Sort Key: t_zhld_ajdbxx.d_larq, t_zhld_ajdbxx.c_ajbh
 
  Sort Method: quicksort Memory: 2618kB
 
  -> Hash Semi Join (cost=2523.19..3223.63 rows=1 width=140) (actual time=55.913..107.265 rows=8605 loops=1)
 
   Hash Cond: ((t_zhld_ajdbxx.c_ajbh)::text = (t_zhld_zbajxx.c_ajbh)::text)
 
   -> Index Scan using i_t_zhld_ajdbxx_m6 on t_zhld_ajdbxx (cost=0.42..700.28 rows=219 width=140) (actual time=0.065..22.005 rows=8605 loops=1)
 
    Index Cond: (((c_zblx)::text = '1003'::text) AND ((c_dbfy)::text = '0'::text) AND ((c_gy)::text = '2550'::text))
 
   -> Hash (cost=2513.84..2513.84 rows=714 width=32) (actual time=55.802..55.802 rows=8605 loops=1)
 
    Buckets: 16384 (originally 1024) Batches: 1 (originally 1) Memory Usage: 675kB
 
    -> HashAggregate (cost=2499.56..2506.70 rows=714 width=32) (actual time=30.530..43.275 rows=8605 loops=1)
 
    Group Key: t_zhld_zbajxx.c_ajbh
 
    -> Index Scan using i_t_zhld_zbajxx_h3 on t_zhld_zbajxx (cost=0.42..2497.77 rows=715 width=32) (actual time=0.043..15.552 rows=8605 loops=1)
 
     Index Cond: ((n_dbzt = 1) AND ((c_zblx)::text = '1003'::text) AND ((c_gy)::text = '2550'::text))
 
 -> Index Scan using t_zhld_db_pkey on t_zhld_db (cost=0.28..8.30 rows=1 width=33) (actual time=0.009..0.011 rows=1 loops=15)
 
  Index Cond: (c_bh = t_zhld_ajdbxx.c_dbbh)
 
  Filter: (((c_dbztbh)::text = '1003'::text) AND (n_state = ANY ('{1,2,3}'::integer[])))
 
Planning time: 1.154 ms
 
Execution time: 127.734 ms

这一次可以看出,ajdbxx和zbajxx表做了hash semi join 消除了nestedloop,cost降到了3231.97。并且使用上了i_t_zhld_ajdbxx_m6子查询中in的结果集有一万多条数据。

继续尝试使用exists等价改写in,看能否有更好的结果

2.4等价改写SQL(2)

等价改写:将in替换为exists:

select c_ajbh, c_ah, c_cbfy, c_cbrxm, d_larq, d_jarq, n_dbjg, c_yqlyfrom (select c_bh from db_zxzhld.t_zhld_db where n_state in (1,2,3) and c_dbztbh='1003' )dbxx
 join (select c_ajbh, c_ah, c_cbfy, c_cbrxm, d_larq, d_jarq, n_dbjg, c_yqly,c_dbbh from db_zxzhld.t_zhld_ajdbxx ajdbxxwhere c_zblx='1003'
 and c_dbfy='0' and c_gy = '2550' and 
exists (select distinct c_ajbh from db_zxzhld.t_zhld_zbajxx zbajxx where ajdbxx.c_ajbh = zbajxx.c_ajbh and n_dbzt = 1 and c_zblx = '1003' and c_gy = '2550' ) order by d_larq asc, c_ajbh asc)dbajon dbxx.c_bh = dbaj.c_dbbh 
 limit 15 offset 0

再次查看执行计划:

Limit (cost=1.12..2547.17 rows=1 width=107) (actual time=0.140..0.727 rows=15 loops=1)
 
 -> Nested Loop (cost=1.12..2547.17 rows=1 width=107) (actual time=0.136..0.689 rows=15 loops=1)
 
 -> Nested Loop Semi Join (cost=0.85..2538.84 rows=1 width=140) (actual time=0.115..0.493 rows=15 loops=1)
 
  -> Index Scan using i_t_zhld_ajdbxx_m6 on t_zhld_ajdbxx t2 (cost=0.42..700.28 rows=219 width=140) (actual time=0.076..0.127 rows=15 loops=1)
 
   Index Cond: (((c_zblx)::text = '1003'::text) AND ((c_dbfy)::text = '0'::text) AND ((c_gy)::text = '2550'::text))
 
  -> Index Scan using i_t_zhld_zbajxx_c_ajbh on t_zhld_zbajxx t3 (cost=0.42..8.40 rows=1 width=32) (actual time=0.019..0.019 rows=1 loops=15)
 
   Index Cond: ((c_ajbh)::text = (t2.c_ajbh)::text)
 
   Filter: (((c_zblx)::text = '1003'::text) AND ((c_gy)::text = '2550'::text) AND (n_dbzt = 1))
 
 -> Index Scan using t_zhld_db_pkey on t_zhld_db (cost=0.28..8.30 rows=1 width=33) (actual time=0.007..0.008 rows=1 loops=15)
 
  Index Cond: (c_bh = t2.c_dbbh)
 
  Filter: (((c_dbztbh)::text = '1003'::text) AND (n_state = ANY ('{1,2,3}'::integer[])))
 
Planning time: 1.268 ms
 
Execution time: 0.859 ms

可以看出使用exist效果更好,最终cost 2547.17

(1).少了t_zhld_zbajxx表的group by操作:Sort Key: t_zhld_ajdbxx.d_larq, t_zhld_ajdbxx.c_ajbh。(这一步是因为使用了索引中的排序)

(2).少了分组的操作:Group Key: t_zhld_zbajxx.c_ajbh。

第(2)为什么这个查询消除了t_zhld_zbajxx表的group by操作呢?

原因是exists替换了distinct的功能,一旦满足条件则立刻返回。所以使用exists的时候子查询可以直接去掉distinct。

以上为个人经验,希望能给大家一个参考,也希望大家多多支持。如有错误或未考虑完全的地方,望不吝赐教。

加载全部内容

相关教程
猜你喜欢
用户评论