38.3. 物化视图

PostgreSQL里的物化视图像视图那样使用规则系统, 但是用类表的形式保存结果。

CREATE MATERIALIZED VIEW mymatview AS SELECT * FROM mytab;

和:

CREATE TABLE mymatview AS SELECT * FROM mytab;

之间最主要的区别是物化视图不能随后直接被更新,并且创建物化视图的查询就像视图的查询存储那样存储, 所以新数据可以用下面命令产生:

REFRESH MATERIALIZED VIEW mymatview;

PostgreSQL系统目录中有关物化视图的信息和表或视图的信息一样。 所以对于解析器,物化视图是一个关系,就像一个表或一个视图。当在查询中引用一个物化视图时, 数据直接从物化视图返回,就像从一个表返回;规则只是用来填充物化视图。

当访问存储在物化视图中的数据时,通常比直接访问底层表或通过一个视图更快, 数据并不总是当前的;然而有时不需要当前数据。考虑一个记录销售的表:

CREATE TABLE invoice (
    invoice_no    integer        PRIMARY KEY,
    seller_no     integer,       -- 销售人员的ID
    invoice_date  date,          -- 销售日期
    invoice_amt   numeric(13,2)  -- 销售数量
);

如果人们希望能够快速的图形化历史销售数据,他们可能想要汇总, 可能不关心当前未完成的数据:

CREATE MATERIALIZED VIEW sales_summary AS
  SELECT
      seller_no,
      invoice_date,
      sum(invoice_amt)::numeric(13,2) as sales_amt
    FROM invoice
    WHERE invoice_date < CURRENT_DATE
    GROUP BY
      seller_no,
      invoice_date
    ORDER BY
      seller_no,
      invoice_date;

CREATE UNIQUE INDEX sales_summary_seller
  ON sales_summary (seller_no, invoice_date);

物化视图可以用来在为销售人员创建的控制面板上显示图形。 可以使用下面的SQL语句在每天晚上更新统计数据:

REFRESH MATERIALIZED VIEW sales_summary;

物化视图的另一个用处是允许对远程系统中的数据快速访问,通过一个外部数据封装器。 下面是一个简单的使用file_fdw的例子,有计时, 但是因为这是使用的在本地系统的缓存,外部数据封装器到远程系统的性能可能更大。

CREATE EXTENSION file_fdw;
CREATE SERVER local_file FOREIGN DATA WRAPPER file_fdw;
CREATE FOREIGN TABLE words (word text NOT NULL)
  SERVER local_file
  OPTIONS (filename '/etc/dictionaries-common/words');
CREATE MATERIALIZED VIEW wrd AS SELECT * FROM words;
CREATE UNIQUE INDEX wrd_word ON wrd (word);
CREATE EXTENSION pg_trgm;
CREATE INDEX wrd_trgm ON wrd USING gist (word gist_trgm_ops);
VACUUM ANALYZE wrd;

现在让我们拼写检查一个单词。直接使用file_fdw

SELECT count(*) FROM words WHERE word = 'caterpiler';

 count 
-------
     0
(1 row)

计划是:

 Aggregate  (cost=4125.19..4125.20 rows=1 width=0) (actual time=26.013..26.014 rows=1 loops=1)
   ->  Foreign Scan on words  (cost=0.00..4124.70 rows=196 width=0) (actual time=26.011..26.011 rows=0 loops=1)
         Filter: (word = 'caterpiler'::text)
         Rows Removed by Filter: 99171
         Foreign File: /etc/dictionaries-common/words
         Foreign File Size: 938848
 Total runtime: 26.081 ms

如果使用物化视图,查询更快速:

 Aggregate  (cost=4.44..4.45 rows=1 width=0) (actual time=0.074..0.074 rows=1 loops=1)
   ->  Index Only Scan using wrd_word on wrd  (cost=0.42..4.44 rows=1 width=0) (actual time=0.071..0.071 rows=0 loops=1)
         Index Cond: (word = 'caterpiler'::text)
         Heap Fetches: 0
 Total runtime: 0.119 ms

无论哪种方式,这个词的拼写是错误的,所以我们看看我们想要的。还是使用file_fdw

SELECT word FROM words ORDER BY word <-> 'caterpiler' LIMIT 10;

     word     
---------------
 cater
 caterpillar
 Caterpillar
 caterpillars
 caterpillar's
 Caterpillar's
 caterer
 caterer's
 caters
 catered
(10 rows)
 Limit  (cost=2195.70..2195.72 rows=10 width=32) (actual time=218.904..218.906 rows=10 loops=1)
   ->  Sort  (cost=2195.70..2237.61 rows=16765 width=32) (actual time=218.902..218.904 rows=10 loops=1)
         Sort Key: ((word <-> 'caterpiler'::text))
         Sort Method: top-N heapsort  Memory: 25kB
         ->  Foreign Scan on words  (cost=0.00..1833.41 rows=16765 width=32) (actual time=0.046..200.965 rows=99171 loops=1)
               Foreign File: /etc/dictionaries-common/words
               Foreign File Size: 938848
 Total runtime: 218.966 ms

使用物化视图:

 Limit  (cost=0.28..1.02 rows=10 width=9) (actual time=24.916..25.079 rows=10 loops=1)
   ->  Index Scan using wrd_trgm on wrd  (cost=0.28..7383.70 rows=99171 width=9) (actual time=24.914..25.076 rows=10 loops=1)
         Order By: (word <-> 'caterpiler'::text)
 Total runtime: 25.884 ms

如果你能允许定期更新远程数据到本地数据库,会带来可观的性能优势。