标签
PostgreSQL , HTAP , OLTP , OLAP , 场景与性能测试
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 - 数据量与性能的线性关系(10亿+无衰减), 暨单表多大需要分区 (OLTP)
1、背景
这个测试回答用户一个问题，PostgreSQL在单表可以管理多大的数据量性能不会衰减。
单表多大需要分区。
单表记录数在不同的级别（如千万、亿、十亿、百亿），查询，更新，写入吞吐 分别是什么样的性能。
2、设计
单表记录数：千万、亿、十亿。
分别测试写入吞吐、查询tps，更新TPS。
3、准备测试表
create unlogged table test(id int primary key, info text, crt_time timestamp);
create unlogged table test1(id int primary key, info text, crt_time timestamp);
create unlogged table test3(id int primary key, info text, crt_time timestamp);
4、准备测试函数(可选)
5、准备测试数据
postgres=# insert into test select generate_series(1,10000000), 'test', now();
INSERT 0 10000000
Time: 17197.822 ms (00:17.198)
postgres=# insert into test1 select generate_series(1,100000000), 'test', now();
INSERT 0 100000000
Time: 187844.576 ms (03:07.845)
for ((i=1;i<=100;i++)) ; do psql -c "insert into test3 select generate_series(($i-1)*10000000+1, $i*10000000),'test', now();" & done
写入10亿 耗时 615秒
6、准备测试脚本
1、查询测试
-- 1000万
vi test.sql
\set id random(1,10000000)
select * from test where id=:id;
-- 1亿
vi test1.sql
\set id random(1,100000000)
select * from test1 where id=:id;
-- 10亿
vi test3.sql
\set id random(1,1000000000)
select * from test3 where id=:id;
2、更新测试
-- 1000万
vi test.sql
\set id random(1,10000000)
update test set crt_time=now() where id=:id;
-- 1亿
vi test1.sql
\set id random(1,100000000)
update test1 set crt_time=now() where id=:id;
-- 10亿
-- 第三个CASE，虽然记录数10亿，但是频繁被更新的数据假设在1亿内。测试时被访问的数据依旧在10亿的范围。
vi test3.sql
\set id random(1,100000000)
\set id1 random(1,1000000000)
with tmp as (select * from test3 where id=:id1)
update test3 set crt_time=now() where id=:id ;
7、测试
测试脚本
CONNECTS=48
TIMES=120
export PGHOST=$PGDATA
export PGPORT=1921
export PGUSER=postgres
export PGPASSWORD=postgres
export PGDATABASE=postgres
pgbench -M prepared -n -r -P 5 -f ./test.sql -c $CONNECTS -j $CONNECTS -T $TIMES
pgbench -M prepared -n -r -P 5 -f ./test1.sql -c $CONNECTS -j $CONNECTS -T $TIMES
pgbench -M prepared -n -r -P 5 -f ./test3.sql -c $CONNECTS -j $CONNECTS -T $TIMES
8、测试结果
1、查询测试TPS
pgbench -M prepared -n -r -P 1 -f ./test.sql -c 48 -j 48 -T 120
transaction type: ./test.sql
scaling factor: 1
query mode: prepared
number of clients: 48
number of threads: 48
duration: 120 s
number of transactions actually processed: 80378275
latency average = 0.072 ms
latency stddev = 0.012 ms
tps = 669810.772760 (including connections establishing)
tps = 669876.004400 (excluding connections establishing)
script statistics:
- statement latencies in milliseconds:
0.001 \set id random(1,10000000)
0.071 select * from test where id=:id;
pgbench -M prepared -n -r -P 1 -f ./test1.sql -c 48 -j 48 -T 120
transaction type: ./test1.sql
scaling factor: 1
query mode: prepared
number of clients: 48
number of threads: 48
duration: 120 s
number of transactions actually processed: 76078076
latency average = 0.076 ms
latency stddev = 0.010 ms
tps = 633977.716555 (including connections establishing)
tps = 634041.588175 (excluding connections establishing)
script statistics:
- statement latencies in milliseconds:
0.001 \set id random(1,100000000)
0.074 select * from test1 where id=:id;
pgbench -M prepared -n -r -P 1 -f ./test3.sql -c 48 -j 48 -T 120
transaction type: ./test3.sql
scaling factor: 1
query mode: prepared
number of clients: 48
number of threads: 48
duration: 120 s
number of transactions actually processed: 72746181
latency average = 0.079 ms
latency stddev = 0.019 ms
tps = 606203.459638 (including connections establishing)
tps = 606259.356671 (excluding connections establishing)
script statistics:
- statement latencies in milliseconds:
0.001 \set id random(1,1000000000)
0.078 select * from test3 where id=:id;
2、更新测试TPS
pgbench -M prepared -n -r -P 1 -f ./test.sql -c 48 -j 48 -T 120
transaction type: ./test1.sql
scaling factor: 1
query mode: prepared
number of clients: 48
number of threads: 48
duration: 120 s
number of transactions actually processed: 27703709
latency average = 0.208 ms
latency stddev = 0.126 ms
tps = 230828.616797 (including connections establishing)
tps = 230853.344303 (excluding connections establishing)
script statistics:
- statement latencies in milliseconds:
0.002 \set id random(1,100000000)
0.207 update test1 set crt_time=now() where id=:id;
pgbench -M prepared -n -r -P 1 -f ./test1.sql -c 48 -j 48 -T 120
transaction type: ./test1.sql
scaling factor: 1
query mode: prepared
number of clients: 48
number of threads: 48
duration: 120 s
number of transactions actually processed: 29387603
latency average = 0.196 ms
latency stddev = 0.110 ms
tps = 244891.957430 (including connections establishing)
tps = 244917.399306 (excluding connections establishing)
script statistics:
- statement latencies in milliseconds:
0.001 \set id random(1,100000000)
0.195 update test1 set crt_time=now() where id=:id;
pgbench -M prepared -n -r -P 1 -f ./test3.sql -c 48 -j 48 -T 120
transaction type: ./test3.sql
scaling factor: 1
query mode: prepared
number of clients: 48
number of threads: 48
duration: 120 s
number of transactions actually processed: 28026501
latency average = 0.205 ms
latency stddev = 0.110 ms
tps = 233533.801692 (including connections establishing)
tps = 233554.689137 (excluding connections establishing)
script statistics:
- statement latencies in milliseconds:
0.002 \set id random(1,100000000)
0.001 \set id1 random(1,1000000000)
0.203 with tmp as (select * from test3 where id=:id1)
索引深度的差别：
postgres=# explain (analyze,verbose,timing,costs,buffers) select * from test where id=1;
QUERY PLAN
------------------------------------------------------------------------------------------------------------------------
Index Scan using test_pkey on public.test (cost=0.43..2.85 rows=1 width=44) (actual time=0.074..0.075 rows=1 loops=1)
Output: id, info, crt_time
Index Cond: (test.id = 1)
Buffers: shared read=4
Planning time: 0.215 ms
Execution time: 0.102 ms
(6 rows)
postgres=# explain (analyze,verbose,timing,costs,buffers) select * from test1 where id=1;
QUERY PLAN
--------------------------------------------------------------------------------------------------------------------------
Index Scan using test1_pkey on public.test1 (cost=0.57..2.98 rows=1 width=44) (actual time=0.094..0.094 rows=1 loops=1)
Output: id, info, crt_time
Index Cond: (test1.id = 1)
Buffers: shared read=5
Planning time: 0.217 ms
Execution time: 0.119 ms
(6 rows)
postgres=# explain (analyze,verbose,timing,costs,buffers) select * from test3 where id=1;
QUERY PLAN
--------------------------------------------------------------------------------------------------------------------------
Index Scan using test3_pkey on public.test3 (cost=0.57..2.99 rows=1 width=44) (actual time=0.054..0.055 rows=1 loops=1)
Output: id, info, crt_time
Index Cond: (test3.id = 1)
Buffers: shared hit=5
Planning time: 0.413 ms
Execution time: 0.080 ms
(6 rows)
性能小结
| 数据量 | 写入吞吐 | 查询tps | 更新tps |
|---|---|---|---|
| 1000万 | 58万行/s | 67万 | 23.1万 |
| 1亿 | 53.2万行/s | 63.4万 | 24.5万 |
| 10亿 | 162.6万行/s | 60.6万 | 23.4万 |
表分区建议
单表多大需要分区？
1、非常频繁更新的表（考虑到autovacuum的速度）
2亿
指表中频繁被更新的记录数在2亿以内，表本身的记录数可以更多。
2、更新、删除不频繁或毫无的表（考虑到设计rewrite的DDL，建索引，逻辑备份等的速度）
20亿(还需要考虑单行大小，直接影响DDL rewrite table的开销)
参考
《PostgreSQL、Greenplum 应用案例宝典《如来神掌》 - 目录》
