当表数据量越来越大时查询速度会下降，在表的条件字段上使用索引，快速定位到可能满足条件的记录，不需要遍历所有记录。
create table t(id int, info text); insert into t select generate_series(1,10000),‘lottu‘||generate_series(1,10000); create table t1 as select * from t; create table t2 as select * from t; create index ind_t2_id on t2(id);
lottu=# analyze t1;
ANALYZE
lottu=# analyze t2;
ANALYZE
# 没有索引
lottu=# explain (analyze,buffers,verbose) select * from t1 where id < 10;
QUERY PLAN
-----------------------------------------------------------------------------------------------------
Seq Scan on lottu.t1 (cost=0.00..180.00 rows=9 width=13) (actual time=0.073..5.650 rows=9 loops=1)
Output: id, info
Filter: (t1.id < 10)
Rows Removed by Filter: 9991
Buffers: shared hit=55
Planning time: 25.904 ms
Execution time: 5.741 ms
(7 rows)
# 有索引
lottu=# explain (analyze,verbose,buffers) select * from t2 where id < 10;
QUERY PLAN
---------------------------------------------------------------------------------------------------------------------
Index Scan using ind_t2_id on lottu.t2 (cost=0.29..8.44 rows=9 width=13) (actual time=0.008..0.014 rows=9 loops=1)
Output: id, info
Index Cond: (t2.id < 10)
Buffers: shared hit=3
Planning time: 0.400 ms
Execution time: 0.052 ms
(6 rows)
#在这个案例中：执行同一条SQL。t2有索引的执行数据是0.052 ms；t1没有索引的是：5.741 ms;
索引本身就是有序的。
#没有索引
lottu=# explain (analyze,verbose,buffers) select * from t1 where id > 2 order by id;
QUERY PLAN
-----------------------------------------------------------------------------------------------------------------
Sort (cost=844.31..869.31 rows=9999 width=13) (actual time=8.737..11.995 rows=9998 loops=1)
Output: id, info
Sort Key: t1.id
Sort Method: quicksort Memory: 853kB
Buffers: shared hit=55
-> Seq Scan on lottu.t1 (cost=0.00..180.00 rows=9999 width=13) (actual time=0.038..5.133 rows=9998 loops=1)
Output: id, info
Filter: (t1.id > 2)
Rows Removed by Filter: 2
Buffers: shared hit=55
Planning time: 0.116 ms
Execution time: 15.205 ms
(12 rows)
#有索引
lottu=# explain (analyze,verbose,buffers) select * from t2 where id > 2 order by id;
QUERY PLAN
-----------------------------------------------------------------------------------------------------------------------------
Index Scan using ind_t2_id on lottu.t2 (cost=0.29..353.27 rows=9999 width=13) (actual time=0.030..5.304 rows=9998 loops=1)
Output: id, info
Index Cond: (t2.id > 2)
Buffers: shared hit=84
Planning time: 0.295 ms
Execution time: 7.027 ms
(6 rows)
#在这个案例中：执行同一条SQL。
索引的扫描方式有3种
先查索引找到匹配记录的ctid，再通过ctid查堆表
先查索引找到匹配记录的ctid集合，把ctid通过bitmap做集合运算和排序后再查堆表
如果索引字段中包含了所有返回字段，对可见性映射 (vm)中全为可见的数据块，不查堆表直接返回索引中的值。
这里谈谈Indexscan扫描方式和Indexonlyscan扫描方式
对这两种扫描方式区别；借用oracle中索引扫描方式来讲；Indexscan扫描方式会产生回表读。根据上面解释来说；Indexscan扫描方式：查完索引之后还需要查表。 Indexonlyscan扫描方式只需要查索引。也就是说：Indexonlyscan扫描方式要优于Indexscan扫描方式？我们来看看
现有表t；在字段id上面建来ind_t_id索引
1. t表没有VM文件。
lottu=# \d+ t
Table "lottu.t"
Column | Type | Modifiers | Storage | Stats target | Description
--------+---------+-----------+----------+--------------+-------------
id | integer | | plain | |
info | text | | extended | |
Indexes:
"ind_t_id" btree (id)
lottu=# explain (analyze,buffers,verbose) select id from t where id < 10;
QUERY PLAN
-----------------------------------------------------------------------------------------------------------------------
Index Only Scan using ind_t_id on lottu.t (cost=0.29..8.44 rows=9 width=4) (actual time=0.009..0.015 rows=9 loops=1)
Output: id
Index Cond: (t.id < 10)
Heap Fetches: 9
Buffers: shared hit=3
Planning time: 0.177 ms
Execution time: 0.050 ms
(7 rows)
#人为更改执行计划
lottu=# set enable_indexonlyscan = off;
SET
lottu=# explain (analyze,buffers,verbose) select id from t where id < 10;
QUERY PLAN
------------------------------------------------------------------------------------------------------------------
Index Scan using ind_t_id on lottu.t (cost=0.29..8.44 rows=9 width=4) (actual time=0.008..0.014 rows=9 loops=1)
Output: id
Index Cond: (t.id < 10)
Buffers: shared hit=3
Planning time: 0.188 ms
Execution time: 0.050 ms
(6 rows)
# 可以发现两者几乎没有差异；唯一不同的是Indexonlyscan扫描方式存在扫描的Heap Fetches时间。 这个时间是不在Execution time里面的。
2. t表有VM文件
lottu=# delete from t where id >200 and id < 500;
DELETE 299
lottu=# vacuum t;
VACUUM
lottu=# analyze t;
ANALYZE
lottu=# explain (analyze,buffers,verbose) select id from t where id < 10;
QUERY PLAN
-----------------------------------------------------------------------------------------------------------------------
Index Only Scan using ind_t_id on lottu.t (cost=0.29..4.44 rows=9 width=4) (actual time=0.008..0.012 rows=9 loops=1)
Output: id
Index Cond: (t.id < 10)
Heap Fetches: 0
Buffers: shared hit=3
Planning time: 0.174 ms
Execution time: 0.048 ms
(7 rows)
lottu=# set enable_indexonlyscan = off;
SET
lottu=# explain (analyze,buffers,verbose) select id from t where id < 10;
QUERY PLAN
------------------------------------------------------------------------------------------------------------------
Index Scan using ind_t_id on lottu.t (cost=0.29..8.44 rows=9 width=4) (actual time=0.012..0.022 rows=9 loops=1)
Output: id
Index Cond: (t.id < 10)
Buffers: shared hit=3
Planning time: 0.179 ms
Execution time: 0.077 ms
(6 rows)
总结：
知识点1：
知识点2：
人为选择执行计划。可设置enable_xxx参数有
参考文献
PostgreSQL 支持索引类型有: B-tree, Hash, GiST, SP-GiST, GIN and BRIN。
创建索引语法：
lottu=# \h create index
Command: CREATE INDEX
Description: define a new index
Syntax:
CREATE [ UNIQUE ] INDEX [ CONCURRENTLY ] [ [ IF NOT EXISTS ] name ] ON table_name [ USING method ]
( { column_name | ( expression ) } [ COLLATE collation ] [ opclass ] [ ASC | DESC ] [ NULLS { FIRST | LAST } ] [, ...] )
[ WITH ( storage_parameter = value [, ... ] ) ]
[ TABLESPACE tablespace_name ]
[ WHERE predicate ]
接下来我们以t表为例。
1. 关键字【UNIQUE】
#创建唯一索引；主键就是一种唯一索引
CREATE UNIQUE INDEX ind_t_id_1 on t (id);
2. 关键字【CONCURRENTLY】
# 这是并发创建索引。跟oracle的online创建索引作用是一样的。创建索引过程中；不会阻塞表更新，插入，删除操作。当然创建的时间就会很漫长。
CREATE INDEX CONCURRENTLY ind_t_id_2 on t (id);
3. 关键字【IF NOT EXISTS】
#用该命令是用于确认索引名是否存在。若存在；也不会报错。
CREATE INDEX IF NOT EXISTS ind_t_id_3 on t (id);
4. 关键字【USING】
# 创建哪种类型的索引。 默认是B-tree。
CREATE INDEX ind_t_id_4 on t using btree (id);
5 关键字【[ ASC | DESC ] [ NULLS { FIRST | LAST]】
# 创建索引是采用降序还是升序。 若字段存在null值，是把null值放在前面还是最后：例如采用降序，null放在前面。
CREATE INDEX ind_t_id_5 on t (id desc nulls first)
6. 关键字【WITH ( storage_parameter = value)】
#索引的填充因子设为。例如创建索引的填充因子设为75
CREATE INDEX ind_t_id_6 on t (id) with (fillfactor = 75);
7. 关键字【TABLESPACE】
#是把索引创建在哪个表空间。
CREATE INDEX ind_t_id_7 on t (id) TABLESPACE tsp_lottu;
8. 关键字【WHERE】
#只在自己感兴趣的那部分数据上创建索引，而不是对每一行数据都创建索引，此种方式创建索引就需要使用WHERE条件了。
CREATE INDEX ind_t_id_8 on t (id) WHERE id < 1000;
修改索引语法
lottu=# \h alter index
Command: ALTER INDEX
Description: change the definition of an index
Syntax:
#把索引重新命名
ALTER INDEX [ IF EXISTS ] name RENAME TO new_name
#把索引迁移表空间
ALTER INDEX [ IF EXISTS ] name SET TABLESPACE tablespace_name
#把索引重设置填充因子
ALTER INDEX [ IF EXISTS ] name SET ( storage_parameter = value [, ... ] )
#把索引的填充因子设置为默认值
ALTER INDEX [ IF EXISTS ] name RESET ( storage_parameter [, ... ] )
#把表空间TSP1中索引迁移到新表空间
ALTER INDEX ALL IN TABLESPACE name [ OWNED BY role_name [, ... ] ]
SET TABLESPACE new_tablespace [ NOWAIT ]
删除索引语法
lottu=# \h drop index Command: DROP INDEX Description: remove an index Syntax: DROP INDEX [ CONCURRENTLY ] [ IF EXISTS ] name [, ...] [ CASCADE | RESTRICT ]
索引能带来加快对表中记录的查询，排序，以及唯一约束的作用。索引也是有代价
select pg_size_pretty(pg_relation_size(‘ind_t_id‘));
--通过pg_stat_user_indexes.idx_scan可检查利用索引进行扫描的次数；这样可以确认那些索引可以清理掉。 select idx_scan from pg_stat_user_indexes where indexrelname = ‘ind_t_id‘;
--如果一个表经过频繁更新之后，索引性能不好；需要重建索引。 lottu=# select pg_size_pretty(pg_relation_size(‘ind_t_id_1‘)); pg_size_pretty ---------------- 2200 kB (1 row) lottu=# delete from t where id > 1000; DELETE 99000 lottu=# analyze t; ANALYZE lottu=# select pg_size_pretty(pg_relation_size(‘ind_t_id_1‘)); pg_size_pretty ---------------- 2200 kB lottu=# insert into t select generate_series(2000,100000),‘lottu‘; INSERT 0 98001 lottu=# select pg_size_pretty(pg_relation_size(‘ind_t_id_1‘)); pg_size_pretty ---------------- 4336 kB (1 row) lottu=# vacuum full t; VACUUM lottu=# select pg_size_pretty(pg_relation_size(‘ind_t_id_1‘)); pg_size_pretty ---------------- 2176 kB 重建方法： 1. reindex：reindex不支持并行重建【CONCURRENTLY】;索引会锁表；会进行阻塞。 2. vacuum full; 对表进行重构；索引也会重建；同样也会锁表。 3. 创建一个新索引(索引名不同)；再删除旧索引。
原文：https://www.cnblogs.com/wangshaowei/p/9146683.html