背景
PolarDB 的云原生存算分离架构, 具备低廉的数据存储、高效扩展弹性、高速多机并行计算能力、高速数据搜索和处理; PolarDB与计算算法结合, 将实现双剑合璧, 推动业务数据的价值产出, 将数据变成生产力.
本文将介绍使用 PolarDB 开源版高效率解决用户画像、实时精准营销类业务需求
测试环境为macOS+docker, PolarDB部署请参考下文:
原理
1、场景介绍:
用户画像通常被用于精准营销场景, 根据用户的行为分析并给用户打标签, 充分了解用户属性的诉求可以更好的实现供需连, 例如推送用户之所需, 根据市场需求进行备货等等.
数据分析时解决供需问题, 节省社会成本, 提升社会效率的有力手段.
2、难点:
- 标签多, 标签的多少动态增减, 需要大量DDL, 不适合大宽表
- 标签过滤需要全表扫描, 非常慢
- 标签组合(包含、不包含等等), 过滤效率低
- 每个用户的标签数量可能不一样, 不适合结构化存储
3、PolarDB如何解决这个问题:
- 画像存储: 采用数组, 解决了动态增减标签, 个性化标签的需求, 不涉及结构变更.
- GIN索引: 解决高效率组合搜索过滤问题
- fast update: 解决实时打标的效率问题. (后台异步merge gin index)
场景模拟和架构设计实践
1、创建模拟生成标签的函数
create or replace function gen_arr(normal int, hot int) returns int[] as $$
select array(select (100000*random())::int+500 from generate_series(1,$1)) || array(select (500*random())::int from generate_series(1,$2));
$$ language sql strict; 体现个性标签+热门标签, 个性标签10万个, 热门标签500个.
例如20个个性标签+10个热门标签, 组成了某个人的画像.
postgres=# select gen_arr(22,10);
gen_arr
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------
{84735,45437,35238,71110,22339,86790,89232,8340,851,50577,6600,53760,63854,95377,28505,12781,34180,56262,10835,53417,42865,67843,235,401,265,372,304,132,309,140,38,254}
(1 row) 2、创建测试表, 生产500万用户画像数据, 并创建gin索引
create table tbl (uid int8, tag int[]);
insert into tbl select uid, gen_arr(22,10) from generate_series(1,5000000) uid;
create index on tbl using gin (tag); 3、圈选用户测试, 使用GIN倒排索引.
postgres=# explain select count(*) from tbl where tag @> '{100}'::int[];
QUERY PLAN
--------------------------------------------------------------------------------------
Aggregate (cost=24422.02..24422.03 rows=1 width=8)
-> Bitmap Heap Scan on tbl (cost=210.25..24359.52 rows=25000 width=0)
Recheck Cond: (tag @> '{100}'::integer[])
-> Bitmap Index Scan on tbl_tag_idx (cost=0.00..204.00 rows=25000 width=0)
Index Cond: (tag @> '{100}'::integer[])
(5 rows) 圈选某个热门标签
postgres=# select count(*) from tbl where tag @> '{100}'::int[];
count
-------
99427
(1 row)
Time: 693.697 ms 圈选某些热门标签
postgres=# select count(*) from tbl where tag @> '{100,50}'::int[];
count
-------
1841
(1 row)
Time: 19.100 ms 圈选某个个性标签
postgres=# select count(*) from tbl where tag @> '{600}'::int[];
count
-------
1042
(1 row)
Time: 69.772 ms 圈选某些个性标签
postgres=# select count(*) from tbl where tag @> '{600,700}'::int[];
count
-------
0
(1 row)
Time: 11.029 ms
postgres=# select count(*) from tbl where tag @> '{600,680}'::int[];
count
-------
0
(1 row)
Time: 1.050 ms 圈选某个个性标签, 并排除某个热门标签
postgres=# explain select count(*) from tbl where tag @> '{600}'::int[] and not (tag @> '{60}'::int[]);
QUERY PLAN
--------------------------------------------------------------------------------------
Aggregate (cost=23537.17..23537.18 rows=1 width=8)
-> Bitmap Heap Scan on tbl (cost=200.64..23478.51 rows=23463 width=0)
Recheck Cond: (tag @> '{600}'::integer[])
Filter: (NOT (tag @> '{60}'::integer[]))
-> Bitmap Index Scan on tbl_tag_idx (cost=0.00..194.78 rows=23917 width=0)
Index Cond: (tag @> '{600}'::integer[])
(6 rows)
Time: 0.570 ms
postgres=# select count(*) from tbl where tag @> '{600}'::int[] and not (tag @> '{60}'::int[]);
count
-------
1018
(1 row)
Time: 3.715 ms 圈选某个热门标签, 并排除某个个性标签
postgres=# explain select count(*) from tbl where tag @> '{60}'::int[] and not (tag @> '{600}'::int[]);
QUERY PLAN
--------------------------------------------------------------------------------------
Aggregate (cost=109198.79..109198.80 rows=1 width=8)
-> Bitmap Heap Scan on tbl (cost=778.85..108962.84 rows=94380 width=0)
Recheck Cond: (tag @> '{60}'::integer[])
Filter: (NOT (tag @> '{600}'::integer[]))
-> Bitmap Index Scan on tbl_tag_idx (cost=0.00..755.26 rows=94834 width=0)
Index Cond: (tag @> '{60}'::integer[])
(6 rows)
Time: 1.714 ms
postgres=# select count(*) from tbl where tag @> '{60}'::int[] and not (tag @> '{600}'::int[]);
count
-------
98930
(1 row)
Time: 693.436 ms 在笔记本上, 性能已经起飞, 何况是高端机器?
