标签
PostgreSQL , brin索引 , bloom filter , min max 分段
背景
BRIN索引是PG的一种块索引接口，存储指定连续数据块内被索引字段的元数据。
https://www.postgresql.org/docs/devel/static/brin.html
目前BRIN存储的元数据包括被索引字段在每个指定连续数据块区间的MIN,MAX值。所以对于比较分散的数据实际上效果是很差的，对于数据分布比较有时序属性的（或者说线性相关性很好）的字段，效果特别赞。
《HTAP数据库 PostgreSQL 场景与性能测试之 24 - (OLTP) 物联网 - 时序数据并发写入(含时序索引BRIN)》
《PostgreSQL BRIN索引的pages_per_range选项优化与内核代码优化思考》
《万亿级电商广告 - brin黑科技带你(最低成本)玩转毫秒级圈人(视觉挖掘姊妹篇) - 阿里云RDS PostgreSQL, HybridDB for PostgreSQL最佳实践》
《PostGIS空间索引(GiST、BRIN、R-Tree)选择、优化 - 阿里云RDS PostgreSQL最佳实践》
《自动选择正确索引访问接口(btree,hash,gin,gist,sp-gist,brin,bitmap...)的方法》
《PostgreSQL 并行写入堆表，如何保证时序线性存储 - BRIN索引优化》
《PostgreSQL 10.0 preview 功能增强 - BRIN 索引更新smooth化》
《PostgreSQL 聚集存储 与 BRIN索引 - 高并发行为、轨迹类大吞吐数据查询场景解说》
《PostgreSQL 物联网黑科技 - 瘦身几百倍的索引(BRIN index)》
《PostgreSQL 9.5 new feature - BRIN (block range index) index》
目前BRIN存在的可以改进的点：
当数据分布与HEAP存储的 线性相关性很差时，效果不好。如何改进呢？
多段MIN,MAX可能是一个非常有效果的改进方法，举个例子，我们有一个非常大的小区，有很多栋房子，然后每一栋房子我们保存了年龄最小和年龄最大的住户，比如说真实的分布是每栋楼都包含少部分是1-35岁，1个80岁的。
现在要找一位40岁的住户，如果是BRIN索引，会把所有的楼栋都返回给你原因是每栋楼的范围都是1-80岁。
如果使用多段存储，那么应该是1-35, 80。这样的话使用BRIN索引找40岁的住户直接返回0条记录。
1、现在PostgreSQL 11马上要提交的PATCH，就包含了multi min max的优化
https://commitfest.postgresql.org/17/1348/
2、第二个改进是引入了BRIN的BLOOM FILTER，我们知道BLOOM FILTER用少量的BIT位表示某被索引值是否存在，存在则设定这些BIT为1，如果对应的BITS不全为1，则说明没有这条记录。但是为了节约空间，BIT存在冲撞，例如某个值的BITS可能被其他一个或多个值的BITS覆盖。
那么就会出现一种情况，索引告诉你包含某个值，并不一定真的包含。但是索引告诉你不包含某个值，那就肯定不包含。
所以
select * from tbl where a=? and b=? and c=? or d=?
bloom会告诉你一个较大的结果集，然后再回HEAP表，使用FILTER过滤不满足条件的记录。
https://en.wikipedia.org/wiki/Bloom_filter
https://www.postgresql.org/docs/devel/static/bloom.html
目前使用bloom插件可以创建BLOOM索引，而PostgreSQL 11，会把这个功能加入BRIN索引接口中。
min max 分段
这个是POC里面的例子，可以看到使用分段MIN MAX后，BRIN索引的过滤性好了很多。
PATCH连接
https://commitfest.postgresql.org/17/1348/
To illustrate the improvement, consider this table:
create table a (val float8) with (fillfactor = 90);
insert into a select i::float from generate_series(1,10000000) s(i);
update a set val = 1 where random() < 0.01;
update a set val = 10000000 where random() < 0.01;
Which means the column 'val' is almost perfectly correlated with the
position in the table (which would be great for BRIN minmax indexes),
but then 1% of the values is set to 1 and 10.000.000. That means pretty
much every range will be [1,10000000], which makes this BRIN index
mostly useless, as illustrated by these explain plans:
create index on a using brin (val) with (pages_per_range = 16);
explain analyze select * from a where val = 100;
QUERY PLAN
--------------------------------------------------------------------
Bitmap Heap Scan on a (cost=54.01..10691.02 rows=8 width=8)
(actual time=5.901..785.520 rows=1 loops=1)
Recheck Cond: (val = '100'::double precision)
Rows Removed by Index Recheck: 9999999
Heap Blocks: lossy=49020
-> Bitmap Index Scan on a_val_idx
(cost=0.00..54.00 rows=3400 width=0)
(actual time=5.792..5.792 rows=490240 loops=1)
Index Cond: (val = '100'::double precision)
Planning time: 0.119 ms
Execution time: 785.583 ms
(8 rows)
explain analyze select * from a where val between 100 and 10000;
QUERY PLAN
------------------------------------------------------------------
Bitmap Heap Scan on a (cost=55.94..25132.00 rows=7728 width=8)
(actual time=5.939..858.125 rows=9695 loops=1)
Recheck Cond: ((val >= '100'::double precision) AND
(val <= '10000'::double precision))
Rows Removed by Index Recheck: 9990305
Heap Blocks: lossy=49020
-> Bitmap Index Scan on a_val_idx
(cost=0.00..54.01 rows=10200 width=0)
(actual time=5.831..5.831 rows=490240 loops=1)
Index Cond: ((val >= '100'::double precision) AND
(val <= '10000'::double precision))
Planning time: 0.139 ms
Execution time: 871.132 ms
(8 rows)
Obviously, the queries do scan the whole table and then eliminate most
of the rows in "Index Recheck". Decreasing pages_per_range does not
really make a measurable difference in this case - it eliminates maybe
10% of the rechecks, but most pages still have very wide minmax range.
With the patch, it looks about like this:
create index on a using brin (val float8_minmax_multi_ops)
with (pages_per_range = 16);
explain analyze select * from a where val = 100;
QUERY PLAN
-------------------------------------------------------------------
Bitmap Heap Scan on a (cost=830.01..11467.02 rows=8 width=8)
(actual time=7.772..8.533 rows=1 loops=1)
Recheck Cond: (val = '100'::double precision)
Rows Removed by Index Recheck: 3263
Heap Blocks: lossy=16
-> Bitmap Index Scan on a_val_idx
(cost=0.00..830.00 rows=3400 width=0)
(actual time=7.729..7.729 rows=160 loops=1)
Index Cond: (val = '100'::double precision)
Planning time: 0.124 ms
Execution time: 8.580 ms
(8 rows)
explain analyze select * from a where val between 100 and 10000;
QUERY PLAN
------------------------------------------------------------------
Bitmap Heap Scan on a (cost=831.94..25908.00 rows=7728 width=8)
(actual time=9.318..23.715 rows=9695 loops=1)
Recheck Cond: ((val >= '100'::double precision) AND
(val <= '10000'::double precision))
Rows Removed by Index Recheck: 3361
Heap Blocks: lossy=64
-> Bitmap Index Scan on a_val_idx
(cost=0.00..830.01 rows=10200 width=0)
(actual time=9.274..9.274 rows=640 loops=1)
Index Cond: ((val >= '100'::double precision) AND
(val <= '10000'::double precision))
Planning time: 0.138 ms
Execution time: 36.100 ms
(8 rows)
bloom filter
https://www.postgresql.org/docs/devel/static/bloom.html
