PostgreSQL bloom

bloom索引在其WITH子句中接受下列参数：

这是一个创建布鲁姆索引的例子：

CREATE INDEX bloomidx ON tbloom USING bloom (i1,i2,i3)
       WITH (length=80, col1=2, col2=2, col3=4);

该索引是用长度为 80 位的签名所创建，其中属性 i1 和 i2 被映射为 2 位，属性 i3 被映射为 4 位。我们可以省略length、col1和col2说明，因为它们都有默认值。

这里是布鲁姆索引定义和使用的更完整的例子，其中还与等效的 btree 做了对比。布鲁姆索引比 btree 索引更小，并且效率更高。

=# CREATE TABLE tbloom AS
   SELECT
     (random() * 1000000)::int as i1,
     (random() * 1000000)::int as i2,
     (random() * 1000000)::int as i3,
     (random() * 1000000)::int as i4,
     (random() * 1000000)::int as i5,
     (random() * 1000000)::int as i6
   FROM
  generate_series(1,10000000);
SELECT 10000000

在这个大表上的顺序扫描需要很长时间：

=# EXPLAIN ANALYZE SELECT * FROM tbloom WHERE i2 = 898732 AND i5 = 123451;
                                              QUERY PLAN                                              
-------------------------------------------------------------------​-----------------------------------
 Seq Scan on tbloom  (cost=0.00..2137.14 rows=3 width=24) (actual time=15.480..15.480 rows=0 loops=1)
   Filter: ((i2 = 898732) AND (i5 = 123451))
   Rows Removed by Filter: 100000
 Planning Time: 0.340 ms
 Execution Time: 15.501 ms
(5 rows)

即使定义了btree索引，结果将仍然是顺序扫描：

=# CREATE INDEX btreeidx ON tbloom (i1, i2, i3, i4, i5, i6);
CREATE INDEX
=# SELECT pg_size_pretty(pg_relation_size('btreeidx'));
 pg_size_pretty
----------------
 3976 kB
(1 row)
=# EXPLAIN ANALYZE SELECT * FROM tbloom WHERE i2 = 898732 AND i5 = 123451;
                                              QUERY PLAN                                              
-------------------------------------------------------------------​-----------------------------------
 Seq Scan on tbloom  (cost=0.00..2137.00 rows=2 width=24) (actual time=12.604..12.604 rows=0 loops=1)
   Filter: ((i2 = 898732) AND (i5 = 123451))
   Rows Removed by Filter: 100000
 Planning Time: 0.155 ms
 Execution Time: 12.617 ms
(5 rows)

在处理这类搜索时，bloom 比 btree 更好：

=# CREATE INDEX bloomidx ON tbloom USING bloom (i1, i2, i3, i4, i5, i6);
CREATE INDEX
=# SELECT pg_size_pretty(pg_relation_size('bloomidx'));
 pg_size_pretty
----------------
 1584 kB
(1 row)
=# EXPLAIN ANALYZE SELECT * FROM tbloom WHERE i2 = 898732 AND i5 = 123451;
                                                     QUERY PLAN                                                      
-------------------------------------------------------------------​--------------------------------------------------
 Bitmap Heap Scan on tbloom  (cost=1792.00..1799.69 rows=2 width=24) (actual time=0.384..0.384 rows=0 loops=1)
   Recheck Cond: ((i2 = 898732) AND (i5 = 123451))
   Rows Removed by Index Recheck: 26
   Heap Blocks: exact=26
   ->  Bitmap Index Scan on bloomidx  (cost=0.00..1792.00 rows=2 width=0) (actual time=0.350..0.350 rows=26 loops=1)
         Index Cond: ((i2 = 898732) AND (i5 = 123451))
 Planning Time: 0.122 ms
 Execution Time: 0.407 ms
(8 rows)

现在，btree 搜索的主要问题是，当搜索条件不约束前几个索引列时，btree 的效率不好。对于 btree 更好的策略是在每一列上创建一个独立的索引。那么规划器将选择这样的计划：

=# CREATE INDEX btreeidx1 ON tbloom (i1);
CREATE INDEX
=# CREATE INDEX btreeidx2 ON tbloom (i2);
CREATE INDEX
=# CREATE INDEX btreeidx3 ON tbloom (i3);
CREATE INDEX
=# CREATE INDEX btreeidx4 ON tbloom (i4);
CREATE INDEX
=# CREATE INDEX btreeidx5 ON tbloom (i5);
CREATE INDEX
=# CREATE INDEX btreeidx6 ON tbloom (i6);
CREATE INDEX
=# EXPLAIN ANALYZE SELECT * FROM tbloom WHERE i2 = 898732 AND i5 = 123451;
                                                        QUERY PLAN                                                         
-------------------------------------------------------------------​--------------------------------------------------------
 Bitmap Heap Scan on tbloom  (cost=24.34..32.03 rows=2 width=24) (actual time=0.032..0.033 rows=0 loops=1)
   Recheck Cond: ((i5 = 123451) AND (i2 = 898732))
   ->  BitmapAnd  (cost=24.34..24.34 rows=2 width=0) (actual time=0.029..0.030 rows=0 loops=1)
         ->  Bitmap Index Scan on btreeidx5  (cost=0.00..12.04 rows=500 width=0) (actual time=0.029..0.029 rows=0 loops=1)
               Index Cond: (i5 = 123451)
         ->  Bitmap Index Scan on btreeidx2  (cost=0.00..12.04 rows=500 width=0) (never executed)
               Index Cond: (i2 = 898732)
 Planning Time: 0.537 ms
 Execution Time: 0.064 ms
(9 rows)

尽管这个查询运行起来比在其中任一单个索引上都快，但是我们在索引尺寸上付出了代价。 每一个单列 btree 索引占用 2 MB，因此总的空间会超过 12 MB，这是bloom索引所使用的空间的 8 倍。

用于布鲁姆索引的操作符类只要一个用于被索引数据类型的哈希函数以及一个用于搜索的等值操作符。这个例子展示了用于text数据类型的操作符类定义：

CREATE OPERATOR CLASS text_ops
DEFAULT FOR TYPE text USING bloom AS
    OPERATOR    1   =(text, text),
    FUNCTION    1   hashtext(text);