HTAP数据库 PostgreSQL 场景与性能测试之 20 - (OLAP) 用户画像圈人场景 - 多个字段任意组合条件筛选与透视

2022-03-27 12:12:38

背景

PostgreSQL是一个历史悠久的数据库，历史可以追溯到1973年，最早由2014计算机图灵奖得主，关系数据库的鼻祖Michael_Stonebraker 操刀设计，PostgreSQL具备与Oracle类似的功能、性能、架构以及稳定性。

PostgreSQL社区的贡献者众多，来自全球各个行业，历经数年，PostgreSQL 每年发布一个大版本，以持久的生命力和稳定性著称。

2017年10月，PostgreSQL 推出10 版本，携带诸多惊天特性，目标是胜任OLAP和OLTP的HTAP混合场景的需求：

《最受开发者欢迎的HTAP数据库PostgreSQL 10特性》

1、多核并行增强

2、fdw 聚合下推

3、逻辑订阅

4、分区

5、金融级多副本

6、json、jsonb全文检索

7、还有插件化形式存在的特性，如向量计算、JIT、SQL图计算、SQL流计算、分布式并行计算、时序处理、基因测序、化学分析、图像分析等。

在各种应用场景中都可以看到PostgreSQL的应用：

PostgreSQL近年来的发展非常迅猛，从知名数据库评测网站dbranking的数据库评分趋势，可以看到PostgreSQL向上发展的趋势：

从每年PostgreSQL中国召开的社区会议，也能看到同样的趋势，参与的公司越来越多，分享的公司越来越多，分享的主题越来越丰富，横跨了传统企业、互联网、医疗、金融、国企、物流、电商、社交、车联网、共享XX、云、游戏、公共交通、航空、铁路、军工、培训、咨询服务等行业。

接下来的一系列文章，将给大家介绍PostgreSQL的各种应用场景以及对应的性能指标。

环境

环境部署方法参考：

《PostgreSQL 10 + PostGIS + Sharding(pg_pathman) + MySQL(fdw外部表) on ECS 部署指南(适合新用户)》

阿里云 ECS：56核，224G，1.5TB*2 SSD云盘。

操作系统：CentOS 7.4 x64

数据库版本：PostgreSQL 10

PS：ECS的CPU和IO性能相比物理机会打一定的折扣，可以按下降1倍性能来估算。跑物理主机可以按这里测试的性能乘以2来估算。

场景 - 用户画像圈人场景 - 多个字段任意组合条件筛选与透视 (OLAP)

1、背景

用户画像表有多个字段，表示不同类型的标签属性，在进行人群圈选时，需要对任意字段的组合条件进行条件筛选，并对人群结果进行透视。

PostgreSQL 有3种方法实现多个字段的任意组合过滤。

1、布隆过滤，支持任意字段组合的等值查询。

《PostgreSQL 9.6 黑科技 bloom 算法索引，一个索引支撑任意列组合查询》

2、多索引 bitmap scan

gin复合索引，或者多个b-tree单列索引，都可以实现bitmap scan。

当输入多个条件时，过滤、收敛到更少的数据块，顺序扫描+FILTER。

《PostgreSQL bitmapAnd, bitmapOr, bitmap index scan, bitmap heap scan》

3、GIN复合索引 bitmap scan

当输入多个条件时，过滤、收敛到更少的数据块，顺序扫描+FILTER。

《宝剑赠英雄 - 任意组合字段等效查询, 探探PostgreSQL多列展开式B树 (GIN)》

2、设计

1亿条记录，每条记录包含32个标签字段，每个字段的标签取值范围1万。另外包含3个属性字段用于透视。

3、准备测试表

do language plpgsql $$  
declare  
  sql text;  
begin  
  sql := 'create table t_multi_col (id int8, c1 int default random()*100, c2 int default random()*10, c3 int default random()*10, ';  
  for i in 4..35 loop  
    sql := sql||'c'||i||' int default random()*10000,';  
  end loop;  
  sql := rtrim(sql, ',');  
  sql := sql||')';  
  execute sql;  
end;  
$$;

4、准备测试函数(可选)

5、准备测试数据

insert into t_multi_col (id) select generate_series(1,100000000);

1、布隆索引

create extension bloom;  
  
do language plpgsql $$  
declare  
  sql text;  
begin  
  sql := 'create index idx_t_multi_col on t_multi_col using bloom (';  
  for i in 4..35 loop  
    sql := sql||'c'||i||',';  
  end loop;  
  sql := rtrim(sql, ',');  
  sql := sql||') with (length=80, ';  
  for i in 1..32 loop  
    sql := sql||'col'||i||'=2,';  
  end loop;  
  sql := rtrim(sql, ',');  
  sql := sql||')';  
  execute sql;  
end;  
$$;

2、GIN索引

create extension btree_gin;  
  
do language plpgsql $$  
declare  
  sql text;  
begin  
  sql := 'create index idx_t_multi_col_gin on t_multi_col using gin (';  
  for i in 4..35 loop  
    sql := sql||'c'||i||',';  
  end loop;  
  sql := rtrim(sql, ',');  
  sql := sql||')';  
  execute sql;  
end;  
$$;

6、准备测试脚本

vi test.sql  
  
\set a4 random(1,10000)  
\set a5 random(1,10000)  
\set a6 random(1,10000)  
\set a7 random(1,10000)  
\set a8 random(1,10000)  
\set a9 random(1,10000)  
select c1,c2,c3,count(*) from t_multi_col where c4=:a4 and c5=:a5 and c6=:a6 and c7=:a7 and c8=:a8 and c9=:a9 group by grouping sets ((c1),(c2),(c3));

7、测试

1、布隆索引，由于需要扫整个索引，耗时略高。500毫秒。

postgres=# explain (analyze,verbose,timing,costs,buffers) select c1,c2,c3,count(*) from t_multi_col where c4=3 and c5=2 and c6=1 and c7=4 and c8=5 and c9=6 and c10=1 and c11=1 and c12=1 group by grouping sets ((c1),(c2),(c3));  
                                                      QUERY PLAN  
-----------------------------------------------------------------------------------------------------------------------  
 HashAggregate  (cost=2985297.24..2985297.28 rows=3 width=20) (actual time=499.961..499.961 rows=0 loops=1)  
   Output: c1, c2, c3, count(*)  
   Hash Key: t_multi_col.c1  
   Hash Key: t_multi_col.c2  
   Hash Key: t_multi_col.c3  
   Buffers: shared hit=197418  
   ->  Bitmap Heap Scan on public.t_multi_col  (cost=2985296.00..2985297.23 rows=1 width=12) (actual time=499.958..499.958 rows=0 loops=1)  
         Output: id, c1, c2, c3, c4, c5, c6, c7, c8, c9, c10, c11, c12, c13, c14, c15, c16, c17, c18, c19, c20, c21, c22, c23, c24, c25, c26, c27, c28, c29, c30, c31, c32, c33, c34, c35  
         Recheck Cond: ((t_multi_col.c4 = 3) AND (t_multi_col.c5 = 2) AND (t_multi_col.c6 = 1) AND (t_multi_col.c7 = 4) AND (t_multi_col.c8 = 5) AND (t_multi_col.c9 = 6) AND (t_multi_col.c10 = 1) AND (t_multi_col.c11 = 1) AND (t_multi_col.c12 = 1))  
         Rows Removed by Index Recheck: 1339  
         Heap Blocks: exact=1339  
         Buffers: shared hit=197418  
         ->  Bitmap Index Scan on idx_t_multi_col  (cost=0.00..2985296.00 rows=1 width=0) (actual time=497.718..497.718 rows=1339 loops=1)  
               Index Cond: ((t_multi_col.c4 = 3) AND (t_multi_col.c5 = 2) AND (t_multi_col.c6 = 1) AND (t_multi_col.c7 = 4) AND (t_multi_col.c8 = 5) AND (t_multi_col.c9 = 6) AND (t_multi_col.c10 = 1) AND (t_multi_col.c11 = 1) AND (t_multi_col.c12 = 1))  
               Buffers: shared hit=196079  
 Planning time: 0.165 ms  
 Execution time: 500.025 ms  
(17 rows)

2、gin索引，精准定位，耗时2毫秒以内。

postgres=# explain (analyze,verbose,timing,costs,buffers) select c1,c2,c3,count(*) from t_multi_col where c4=3 and c5=2 and c6=1 and c7=4 and c8=5 and c9=6 and c10=1 and c11=1 and c12=1 group by grouping sets ((c1),(c2),(c3));  
                                  QUERY PLAN  
--------------------------------------------------------------------------------------------------------------------------------  
 HashAggregate  (cost=69.64..69.68 rows=3 width=20) (actual time=1.151..1.151 rows=0 loops=1)  
   Output: c1, c2, c3, count(*)  
   Hash Key: t_multi_col.c1  
   Hash Key: t_multi_col.c2  
   Hash Key: t_multi_col.c3  
   Buffers: shared hit=69  
   ->  Bitmap Heap Scan on public.t_multi_col  (cost=68.40..69.63 rows=1 width=12) (actual time=1.149..1.149 rows=0 loops=1)  
         Output: id, c1, c2, c3, c4, c5, c6, c7, c8, c9, c10, c11, c12, c13, c14, c15, c16, c17, c18, c19, c20, c21, c22, c23, c24, c25, c26, c27, c28, c29, c30, c31, c32, c33, c34, c35  
         Recheck Cond: ((t_multi_col.c4 = 3) AND (t_multi_col.c5 = 2) AND (t_multi_col.c6 = 1) AND (t_multi_col.c7 = 4) AND (t_multi_col.c8 = 5) AND (t_multi_col.c9 = 6) AND (t_multi_col.c10 = 1) AND (t_multi_col.c11 = 1) AND (t_multi_col.c12 = 1))  
         Buffers: shared hit=69  
         ->  Bitmap Index Scan on idx_t_multi_col_gin  (cost=0.00..68.40 rows=1 width=0) (actual time=1.146..1.146 rows=0 loops=1)  
               Index Cond: ((t_multi_col.c4 = 3) AND (t_multi_col.c5 = 2) AND (t_multi_col.c6 = 1) AND (t_multi_col.c7 = 4) AND (t_multi_col.c8 = 5) AND (t_multi_col.c9 = 6) AND (t_multi_col.c10 = 1) AND (t_multi_col.c11 = 1) AND (t_multi_col.c12 = 1))  
               Buffers: shared hit=69  
 Planning time: 0.263 ms  
 Execution time: 1.245 ms  
(15 rows)

压测

CONNECTS=56  
TIMES=300  
export PGHOST=$PGDATA  
export PGPORT=1999  
export PGUSER=postgres  
export PGPASSWORD=postgres  
export PGDATABASE=postgres  
  
pgbench -M prepared -n -r -f ./test.sql -P 5 -c $CONNECTS -j $CONNECTS -T $TIMES

8、测试结果

transaction type: ./test.sql  
scaling factor: 1  
query mode: prepared  
number of clients: 56  
number of threads: 56  
duration: 300 s  
number of transactions actually processed: 10740407  
latency average = 1.564 ms  
latency stddev = 0.561 ms  
tps = 35796.375710 (including connections establishing)  
tps = 35800.169989 (excluding connections establishing)  
script statistics:  
 - statement latencies in milliseconds:  
         0.002  \set a4 random(1,10000)  
         0.000  \set a5 random(1,10000)  
         0.000  \set a6 random(1,10000)  
         0.000  \set a7 random(1,10000)  
         0.000  \set a8 random(1,10000)  
         0.000  \set a9 random(1,10000)  
         1.562  select c1,c2,c3,count(*) from t_multi_col where c4=:a4 and c5=:a5 and c6=:a6 and c7=:a7 and c8=:a8 and c9=:a9 group by grouping sets ((c1),(c2),(c3));