AntDB中bloom索引试用

bloom索引时PG9.6新增的索引类型，支持任意列的组合查询。下面将在AntDB中简单地试用。

建立表，插入数据。

create table tt (

  i1 int4,

  i2 int4,

  i3 int4,

  i4 int4,

  i5 int4,

  i6 int4

);

插入数据：

insert into tt (i1,i2,i3,i4,i5,i6) 

select 

  random()*1000000,

  random()*1000000,

  random()*1000000,

  random()*1000000,

  random()*1000000,

  random()*1000000

from generate_series(1,2000000);

比较：

当没有索引时，根据 i2， 耗时601.534 ms。

根据i2和i3查询，耗时592.506 ms。

postgres=# explain (verbose, analyze) select * from tt where i2 = 345678;

QUERY PLAN

--------------------------------------------------------------------------------------------------------------------------

Data Node Scan on "__REMOTE_FQS_QUERY__" (cost=0.00..0.00 rows=0 width=0) (actual time=300.605..601.462 rows=2 loops=1)

Output: tt.i1, tt.i2, tt.i3, tt.i4, tt.i5, tt.i6

Primary node/s: dm0

Node/s: dm0, dm1

Remote query: SELECT i1, i2, i3, i4, i5, i6 FROM public.tt tt WHERE (i2 = 345678)

Planning time: 0.200 ms

Execution time: 601.534 ms

(7 rows)

 

postgres=# explain (verbose, analyze) select * from tt where i2 = 345678 and i3 = 45678;

QUERY PLAN

--------------------------------------------------------------------------------------------------------------------------

Data Node Scan on "__REMOTE_FQS_QUERY__" (cost=0.00..0.00 rows=0 width=0) (actual time=592.444..592.444 rows=0 loops=1)

Output: tt.i1, tt.i2, tt.i3, tt.i4, tt.i5, tt.i6

Primary node/s: dm0

Node/s: dm0, dm1

Remote query: SELECT i1, i2, i3, i4, i5, i6 FROM public.tt tt WHERE ((i2 = 345678) AND (i3 = 45678))

Planning time: 0.206 ms

Execution time: 592.506 ms

(7 rows)

 

当建立bloom索引后，根据i2查询耗时121.513 ms。

根据i2和i3，查询耗时70.326 ms。

create extension bloom;

create index bloom_id on tt using bloom (i1,i2,i3,i4,i5,i6);

postgres=# explain (verbose, analyze) select * from tt where i2 = 345678 and i3 = 45678;

QUERY PLAN

------------------------------------------------------------------------------------------------------------------------

Data Node Scan on "__REMOTE_FQS_QUERY__" (cost=0.00..0.00 rows=0 width=0) (actual time=70.252..70.252 rows=0 loops=1)

Output: tt.i1, tt.i2, tt.i3, tt.i4, tt.i5, tt.i6

Primary node/s: dm0

Node/s: dm0, dm1

Remote query: SELECT i1, i2, i3, i4, i5, i6 FROM public.tt tt WHERE ((i2 = 345678) AND (i3 = 45678))

Planning time: 0.802 ms

Execution time: 70.326 ms

(7 rows)

 

postgres=# explain (verbose, analyze) select * from tt where i2 = 345678;

QUERY PLAN

-------------------------------------------------------------------------------------------------------------------------

Data Node Scan on "__REMOTE_FQS_QUERY__" (cost=0.00..0.00 rows=0 width=0) (actual time=59.891..121.443 rows=2 loops=1)

Output: tt.i1, tt.i2, tt.i3, tt.i4, tt.i5, tt.i6

Primary node/s: dm0

Node/s: dm0, dm1

Remote query: SELECT i1, i2, i3, i4, i5, i6 FROM public.tt tt WHERE (i2 = 345678)

Planning time: 0.203 ms

Execution time: 121.513 ms

(7 rows)

 

删除bloom index，建立btree类型的index，根据i2和i3查询耗时3.594ms

postgres=# drop index bloom_id;

DROP INDEX

postgres=# create index btree_id on tt using btree(i2, i3);

CREATE INDEX

postgres=# explain (verbose, analyze) select * from tt where i2 = 345678 and i3 = 45678;

QUERY PLAN

----------------------------------------------------------------------------------------------------------------------

Data Node Scan on "__REMOTE_FQS_QUERY__" (cost=0.00..0.00 rows=0 width=0) (actual time=3.531..3.531 rows=0 loops=1)

Output: tt.i1, tt.i2, tt.i3, tt.i4, tt.i5, tt.i6

Primary node/s: dm0

Node/s: dm0, dm1

Remote query: SELECT i1, i2, i3, i4, i5, i6 FROM public.tt tt WHERE ((i2 = 345678) AND (i3 = 45678))

Planning time: 0.214 ms

Execution time: 3.594 ms

(7 rows)

    对于多列索引，btree类型的索引效率比bloom要高许多，可是须要建立不一样的多列组合索引，索引过多，维护起来不方便。

    当表中列较多时，能够建立bloom类型的索引，能够适当提升查询效率，虽然效率不是最高的，可是免去了在各个列上建立索引的麻烦。

 

参考：

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